- Nov 2023
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Referee #1
Evidence, reproducibility and clarity
The author investigated the role of the stress sensor pathway in the mechanism of tumor cell survival<br /> They identified a long noncoding RNA as JUNI that regulates antagonizing MAP phosphatase and favors the JUN transcription. JUNI correlated with the survival of several cancer histotypes, particularly in RCC, as a highly specific and correlated prognosis.
The abstract although not always required from the journal should be divided into methods used to reach the main findings and clear presentation of results it is unclear whether JUNI is a positive or negative regulator of JUI. When the author indicates that JUNI antagonizes MAP PHOSPHATASE is not correct the term antagonism is related to receptors but the authors did not show any receptor. Correlated with prognosis ( negative or positive ) Statistical value should be reported in the abstract. Generally, Jun oncogene correlated with poor overall survival while the table indicates promote survival so good prognosis?
Major comments
The introduction contains the main information to follow the role of JUN and renal carcinoma<br /> However, should be improved with background on the key role of stress genes in the pro-survival pathway of tumors during progression and hypoxia condition. Too many references on long noncoding compared to the JUN complex with AP-1 and transformation.<br /> Results In Figure 1 the authors showed expression levels of JUNI and JUN that are clearly different after UV stimuli they demonstrate that are both regulated by UV but the amount and the time are different the author should comment on these data if they want to study the regulative mechanism figure 1 F the cellular distribution of JUNI which is the rational of this experiment to provide that is into nucleus while normally is into the cytoplasm? What adds this experiment?<br /> In Figure 2 the authors provided that the kinase pathway is important for Jun regulation but the effect on JUNI a Luciferase assay needs to be provided<br /> In Figure 3 for Migration assay is necessary to see cells on the other side of the filter by staining not a graphical representation the experiment on kinase does not add any data to what is already known on jun probably should be shifted in Figure 6. Table 1 is cited two times once in the context of Figure 3 and then in Figure 6 indicating that the authors go forward and back on their experimental design<br /> in figure 4 the apoptotic cells are not clearly visible a specific staining marker is necessary to provide the phenomenon additionally XTT assay should be reported as the percentage of survival cells not staining incorporated compared to untreated cells over time.<br /> The data on prognosis and correlation of gene expression are not clearly presented and discussed
Significance
The authors identified a long noncoding RNA as JUNI that regulates antagonizing MAP phosphatase and favors the JUN transcription. JUNI correlated with survival of several cancer histotypes In particular in RCC as a highly specific and correlated prognosis.
The data are not presented with a good rationale often the authors go forward and back on the experimental design. The data are not presented in the best way some data are shown as bar graph but need to be supported by cell staining of transwell staining and standard plot for survival rate The western blot need to be quantified
In general, the experimental design does not match the rational
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Reply to the reviewers
_We have underlined the important points in the reviewer's comments. All responses have been read and authorized by all authors of this manuscript. Authors would like to thank the reviewers and the editor for their valuable time. We believe that the comments and suggestions from both reviewers will significantly improve SMorph and the manuscript. _
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
First of all, I want to apologize the authors and editor for my delay. Secondly, for clarity, I want to disclose that I am the author of the Fiji's 'Sholl Analysis' plugin, that the authors cite extensively (Ferreira et al, Nat Methods, 2014).
In this study, Sethi et al introduce a software tool - SMorph - for bulk morphometric analysis of neurons and glia (astrocytes and microglia), based on the Sholl technique. The authors compare it to the state-of-the-art in a series of validation experiments (stab wound injury), to conclude that it is 1000 times faster that existing tools. Empowered by the tool, the authors show that chronic administration of a tricyclic antidepressant (DMI) leads to structural changes of astrocytes in the mouse hippocampus. The paper is well written, the description of the tool is clear, and the authors make all of the source code available, as well as most of the imagery analyzed in the manuscript. The latter on its own, makes me really appreciative of the authors work.
We thank reviewer #1 for their careful reading of the manuscript and their comments.
**Major comments:**
A major strength of SMorph is that it leverages the Python ecosystem, which allow the authors take advantage of powerful python packages such as sklearn, without the need for external packages or tools. However, I have strong criticisms for the claims that are made in terms of speed and broad-applicability of the software, including PCA.
Speed:
The 1000x speed gains, assumes - for the most part -- that the processing in Fiji cannot be automated. This is false. I read the source code of SMorph, and with exception of the PCA analysis, all aspects of SMorph can be automated in Fiji, using any of Fiji's scripting languages to make direct calls to the Fiji and
Sholl Analysis
plugin APIs (See https://javadoc.scijava.org/) . Now, perhaps the authors do not have experience with ImageJ scripting, or perhaps we Fiji developers failed to provide clear tutorials and examples on how to do so. Or perhaps, there is something inherently cumbersome with Fiji scripting that makes this hard (e.g., there is a current limitation with the ImageJ2 version of 'Sholl Analysis' that does not make it macro recordable). It such limitations do exist, it is perfectly fine to mention them, but do contact us at https://forum.image.sc, if something is unclear. We do strive to make our work as re-usable as possible. Unfortunately our own research does not always allow us the time required to do so. Case in point, our scripting examples (e.g., https://github.com/tferr/ASA/blob/master/scripting-examples/3D_Analysis_ImageStack.py; https://github.com/tferr/ASA/blob/master/scripting-examples/3D_Analysis_ImageStack.py) are not well advertised. That being said, I am still surprised that in their side-by-side comparisons the authors were not able to automate more the processing steps (e.g., the ImageJ1 version of 'Sholl Analysis' remains fully functional and is macro recordable). If I misunderstood what was done, please provide the ImageJ macros you used. Also, I wanted to mention that i) semi-manual tracing with Simple Neurite Tracer (now "SNT"), can also be scripted (see https://doi.org/10.1101/2020.07.13.179325); and that ii) Fiji commands and plugins can also be called in native python using pyimagej (https://pypi.org/project/pyimagej/), see e.g., https://github.com/morphonets/SNT/tree/master/notebooks#snt-notebooks). Arguably, the fact that SMorph handles blob detection and skeletonization-based metrics directly is more advantageous from a user point of view. In Fiji, blob detection, skeletonization and Strahler analysis (https://imagej.net/Strahler_Analysis) of the skeleton are handled by different plugins. However, those are also fully scriptable, and interoperate well. The point that topographic skeletonization in Fiji can originate loops is valid, however the authors should know that such cycles can be detected and pruned programmatically using e.g., pixel intensities (see https://imagej.net/AnalyzeSkeleton.html#Loop_detection_and_pruning and the original publication (https://pubmed.ncbi.nlm.nih.gov/20232465/)We completely agree with the reviewer’s assertion that most parts of the functionality of SMorph can be automated within imageJ as well, and in such comparison, the speed gains with SMorph will not be >1000X.
However, automating the analysis in imageJ is beyond the scope of the present manuscript. In fact, imageJ analysis comparison was not a part of our original manuscript at all. Upon presubmission inquiry to one of the affiliate journals of Review Commons, we were specifically asked to include a side-by-side comparison with “already available” methods. So, we decided to use ImageJ as it is, and automation, if any, was limited to simple macros to run a series of commands sequentially on batches of images. Although it is true that this analysis could be done much more efficiently with additional scripting, it would not have met the definition of “already available” tools. The imageJ analysis was performed in a way an average biologist with no programming experience would perform it, since that group will find SMorph most useful. In no way do we intend to imply that imageJ analysis can’t be made more efficient and automated. Perhaps it was not clear from the way the text was framed in the initial version of the manuscript. We will add additional text to make this point clearer.
On a side-note, in response to reviewer #2’s comments, we will perform the speed comparison on a per-image basis, so the speed gain (1080X) may change a little in the new comparison.
Broad applicability:
In our work, we made a significant effort to ensure that automated Sholl could be performed on any cell type: e.g., By supporting 2D and 3D images, by allowing repeated measures at each sampled distance, and by improving curve fitting. For linear profiles, we implemented the ability to perform polynomial fits of arbitrary degree, and implemented heuristics for 'best degree' determination. For normalized profiles, we implemented several normalizers, and alternatives for determining regression coefficients. We did not tackle segmentation of images directly (we did provide some accompanying scripts to aid users, see e.g. https://imagej.net/BAR) because in our case that is handled directly by ImageJ and Fiji's large collection of plugins. However, in SMorph, several of these parameters are hard-wired in the code. They may be suitable to the analyzed images, but they can be hardly generalized to other datasets. In detail: In terms of segmentation, SMorph is restricted to 2D images, scales data to a fixed 98 percentile, and uses a fixed auto-threshold method (Otsu). These settings are tethered to the authors imagery. They will give ill results for someone else using a different imaging setup, or staining method. In terms of curve fitting, the polynomial regression seems to be fixed at a 3rd order polynomial, which will not be suitable to different cell types (not even to all cells of 'radial morphology').
We have indeed hard-coded the parameters that the reviewer mentions, and we agree that we can perhaps give all options to the end-users to choose from. The decision was made to hard-code the parameters so that SMorph becomes very easy and minimalistic to use for the end-users. But the reviewer is right to point out that this may compromise the broad applicability and accuracy. We will update the code in the revised version of the manuscript to give the users control over choosing these parameters.
PCA:
The idea of making PCA analysis of Sholl-based morphometry accessible to a broader user base has merit and is welcomed. However, it has to be done carefully in a self-critic manner as opposed to a black-box solution. E.g., in the text it is mentioned that 2 principal components are used, in the tutorial notebook, 3. Why not provide intuitive scree plots that empower users with the ability to criticize choice? Also, it would be useful for users to understand which metrics correlate with each other, and their variable weights.
Reviewer #1’s suggestions would indeed make the PCA analysis more useful to the users. In the revised version of the code, we will provide additional data/plots to the user for making an informed choice of the significant principal components e.g. the elbow method, Ogive or Pareto plots, variable weights of different features in the principal components and correlation/covariance matrices.
When we showcased the utility of PCA to distinguish closely related morphology groups (as in Type-1 and Type-2 PV neurons), we had been unable to base the distinction on individual metrics, at least not in a robust manner (see Fig. S4 in Ferreira et al, 2014). A minor conundrum of the paper, is that it does not directly highlight the advantages of "analyzes in a multidimensional space". The differences between groups in the stab wound and DMI assays are such, that PCA is hardly needed: I.e., the differences depicted Fig2F,G are already significant, and already convey changes in "size and branch complexity" (as per PC1). The same argument applies to Fig. 5. The paper would profit from having this discussed.
PCA data indeed is not required to make any of the inferences we make in the paper and is superfluous. However, as mentioned in the discussion section of this manuscript, the low-dimensional PCA data can be used in future for other applications, e.g to cluster the astrocytes into morphometrically-defined subpopulations. SMorph can be further developed to perform real-time classification of these cells into morphometric clusters, which will allow the researchers to investigate clusters-specific gene expression, electrophysiology etc. Preliminary results from our lab do suggest that such clusters are differentially altered by stress and antidepressant treatments. However, these results are preliminary and are a part of a long-term future study. The data is really premature to publish at this stage, since it will require a lot of experimentation to show that these astrocyte subpopulations are indeed physiologically and functionally different. Nevertheless, we think that the utility of SMorph for such analyses may help others to come up with additional innovative ways to use the PCA data. Hence, we do believe that the community will benefit from the current release of SMorph having PCA. PCA data was shown in the figures just to demonstrate the functionality of SMorph. We will add additional text to make these points clearer.
Other:
- All metrics and parameters should be expressed in physical units (e.g.," radii increasing by 3 pixels", axes in Figure 2, 3, 5, S2) so that readers can directly interpret them.
In the revised manuscript, we will convert all units into actual physical distances.
- The paper would profit from the insights provided by Bird & Cuntz (https://pubmed.ncbi.nlm.nih.gov/31167149/)
We thank the reviewer for suggesting this paper. We will include this in the discussion of the manuscript.
**Minor comments:**
- Usage of RGB images (8-bit per channel) seems hardly justifiable. Aren't you loosing dynamic range of GFAP signal?
We agree that we could have captured the images at a higher dynamic range. However, for the changes we observe between treatment groups using GFAP immunoreactivity signal as presented in the manuscript, we do not see an advantage of using higher dynamic range. However, as the reviewer rightly pointed out, under certain conditions, imaging using a higher dynamic range may help and hence, we will include this recommendation in the materials and methods section.
- Please explain how MaxAbsScaler "prevents sub-optimal results"
Since morphometric features extracted from cell images either have different units or are scalar, we had to perform normalization before PCA. We will add further explanation in the methods section of the manuscript.
- The fact that automated batch processing can stall on a single bad 'contrast ratio' image seems rather cumbersome to deal with
This problem has been resolved in the current version of SMorph, which will be uploaded with the revised version of the manuscript.
- Please add a license to https://github.com/parulsethi/SMorph/. Without it, other projects may shy away from using SMorph
We will add a ____GPLv3 license
- "mounted on stereotax" should be "mounted on a stereotaxis device"?
We will make this change
- Ensure Schoenen is capitalized
We will make this change
Reviewer #1 (Significance (Required)):
I find the Desipramine results interesting. However, given the existing claims that DMI can modulate LTP, I regret that the authors did not look at structural modifications in hippocampal neurons (e.g., by performing the experiments in Thy1-M-eGFP animals). I understand, that doing so at this point would be a large undertaking.
Another manuscript from our lab__1, as well as work from other labs have shown that stress causes significant degenerative changes in hippocampal astrocytes__2,3__. In the light of these observations, we do believe that our observation of chronic antidepressant treatment inducing structural plasticity in astrocytes is significant. Structural alterations in neurons after DMI treatment are of interest. But in our experience, we have not seen gross morphological (dendritic arborization) changes in hippocampal neurons as a result of antidepressant drug treatments. Such changes are restricted to spine morphology and axonal varicosities, which is beyond the capabilities of SMorph. __
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
This paper addresses the challenge of automatic Sholl analysis of large dataset of multiple cell types such as neurons, astrocytes and microglia. The developed approach should improve the speed of morphology analysis compared to the state of the art without compromising on the accuracy. The authors present an interesting application of their tool to the morphological analysis of astrocytes following chronic antidepressant treatment. The paper is well written, and the tool presented could be beneficial for different applications and context. However, some major aspects should be addressed by the author concerning the description of the algorithms used and the quantification of the results.
We thank reviewer #2 for their careful reading of the paper and their comments.
**Major comments/Questions:**
- In the Results and/or Methods sections, the author should better describe how their approach is different from state-of-the-art approaches in terms of algorithms used and how these difference impacts on the speed and accuracy of the analysis.
We will add these descriptions in the methods section in response to this comment as well as some comments from reviewer #1.
Imaging was performed on a Zeiss LSM 880 airyscan confocal microscope. Is this method robust to other types of imaging techniques, other microscopes, variable levels of signal-to-noise? This should be tested and quantified.
We will demonstrate the results obtained from images taken using different microscopes and imaging techniques, and quantify the outcome.
Manual cropping of the cells with ImageJ was used. However, in the methods section, the authors mention that other machine learning tools could be used for this task. Why were these tools not implemented in this paper in order to propose a fully automated analysis approach in combination with SMorph?
We have tried both the machine learning tools cited in this paper (one for DAB images and other for confocal images). However, in our experience, we do not get robust performance from these tools with our datasets, and these tools will perhaps need more optimization for broad applicability. We are developing an auto-cropping tool in-house, but that is beyond the scope of the current study. Another point is that these tools are tailor-made for astrocytes, and their integration into SMorph will restrict its applicability to just one cell type.
In the methods section you state that cropped cells need to have a good contrast ratio for automated batch processing. Could you define what a good contrast ratio is and characterize the performance of your approach for different contrast ratio?
In the revised manuscript, we will compare the images taken from multiple microscopes and quantify the outcome. We will change the text accordingly. As such, the comment on rejected cells referred to really poor quality images. In the revised manuscript, we will make specific recommendations on imaging parameters so that this should not be an issue at all.
It is mentioned that the analysis routine can be interupted by a cell with lower contrast ratio. This is a major drawback of the approach (but I think that it could be easily improved), as such interruptions may not be= practicable for many applications that need to rely on automated processing.
We have already rectified this problem and the updated version of SMorph will be uploaded with the revised manuscript.
Also, you should precise how the contrast ratio should be enhanced without modifying raw data in order to be processed with your approach. You suggest removing cells with lower contrast ratio from the analysis, but can this impact on the findings especially if some treatments impact on the detected fluorescence signal? Can you propose ways to improve the robustness of your approach to variable signal ratios?
It is indeed possible that removing cells from analysis, may in certain cases, affect the results. To rectify this, we are testing the method on images obtained from different microscopes and under different imaging conditions. From these analyses, we will deduce minimum recommendations for imaging conditions so that images don’t have to be edited/altogether removed from analysis for the software to work. In the materials and methods section, we will add these recommendations to the users on the optimal range of imaging parameters. This way, rejection/modification of images should not be an issue.
In the Results section, you describe the time necessary to perform different analysis. However, giving a total time in hours is not very informative as this will likely vary a lot depending on the size of the dataset, complexity of the images, etc. You should compare the average time per image for both methods and types of analysis.
We compared the total time required for the entire dataset, since SMorph is meant for batch-processing all the images at once. However, we can change the comparisons to time taken per image. We can divide the total time taken by SMorph by the number of images analysed. However, in our opinion, the time taken to initiate SMorph will make these comparisons inaccurate.
You state that for the number of branch point, the lower value of the measured slope when comparing SMorph and ImageJ was related to a constant overestimation of this parameter with ImageJ. How was this quantified? I think you should stress out more the comparison of both approaches with the manually annotated dataset.
In the revised version of this manuscript, we will include some examples of skeletonized images that overestimate the number of forks. We have observed this to be a recurring problem with the skeletonization tools we have tried in imageJ. This can be rectified in imageJ itself as pointed out by reviewer #1. However, that’s beyond the scope of the present study and will not fit the definition of comparison with “already available” methods.
How can you explain the differences in the 2D-projected Area, total skeleton length and convex hull between SMorph and ImageJ, which all show a slope around 0.83? Can you quantify the performance of both methods by comparing them with your manually annotated dataset?
In the revised version, we will include the correlation data between completely manual and SMorph comparisons. We will discuss these comparisons further in the manuscript and make specific conclusions about the accuracy.
In the introduction and discussion, you mention that you present a method that works on neurons, astrocytes and microglia. However, I don't see in the paper the comparison between the accuracy for all these cell types as you seem to have analyzed only the morphology of astrocytes.
In the revised manuscript, we will include the Sholl analysis comparison (imageJ vs SMorph) from images of neurons and microglia.
You mention that your method is quite sensitive to variation in contrast ratio. You should quantify the contrast ratio throughout the experiments and ensure that this is not biasing the SMorph analysis for some of the treatments.
We thank both reviewers for highlighting this issue in the initial version of SMorph. As mentioned in our response to point #6, we will perform additional analyses to make specific recommendations to the end users regarding imaging parameters so that SMorph can work on images as they are. As such, our comments on contrast ratio applied only to very poor quality images. If images are acquired conforming to the imaging parameters we will recommend in the revised manuscript, images can be analysed without any issues.
**Minor Points :**
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Precise the exact inclusion and exclusion criteria for Soma detection and rephrase: "The high-intensity blobs were detected as a position of soma..." & "Boundary blobs coming from adjacent cells...".
We will add a complete explanation of blob detection and the exclusion criterion in the methods section.
Throughout the text, make sure to always refer to an analysis time per image or per cell and not only include absolute duration values without reference to the task at hand (e.g. in the discussion : SMorph took 40 second to complete the analysis... please state to which analysis you are exactly referring to and if applicable if it varies from cell to cell).
We will change all comparisons to time taken per cell. Text will be added to mention which datasets were used when any claims of speed are made.
When you state in the discussion that "Although some methods do allow Sholl analysis without manual neurite tracing, they still work on one cell at a time", please precise if the only aspect that is missing from this type of analysis is batch processing (looping through the data) or if there is a major obstacle to automate this technique. This is important a SMorph does proceed with the analysis one cell at a time but can work in a loop/batch.
We will elaborate further on our assertion regarding the challenges of using imageJ plugins for sholl analysis in large batches of cells.
Reviewer #2 (Significance (Required)):
This tool could very useful to researchers in the field of cellular neuroscience working with high-throughput analysis of microscopy data. The authors show some interesting improvements over existing methods. An improved quantitative characterization of the robustness of their approach would be of great importance to ensure the significance of this tool to a large community of researchers using different types of microscopes or studying different cell types.
My expertise is in the field of optical microscopy and high-throughput (automated) image analysis for neuroscience. My expertise to evaluate the biological findings in this study is very limited.
We thank reviewer #2 for their careful reading of the manuscript and their insightful comments. Growing evidence (clinical and preclinical) shows a significant reduction in astrocyte density in key limbic brain regions as a result of depression. We believe that the structural plasticity induced by chronic antidepressant treatment, as demonstrated in this manuscript, is an interesting novel plasticity mechanism that can negate deleterious effects of stress on astrocytes.
The improvements suggested by both reviewers will help us to greatly improve SMorph in the revised version of this manuscript.
References:
- Virmani, G., D’almeida, P., Nandi, A. & Marathe, S. Subfield-specific Effects of Chronic Mild Unpredictable Stress on Hippocampal Astrocytes. doi:10.1101/2020.02.07.938472.
- Czéh, B., Simon, M., Schmelting, B., Hiemke, C. & Fuchs, E. Astroglial plasticity in the hippocampus is affected by chronic psychosocial stress and concomitant fluoxetine treatment. Neuropsychopharmacology 31, 1616–1626 (2006).
- Musholt, K. et al. Neonatal separation stress reduces glial fibrillary acidic protein- and S100beta-immunoreactive astrocytes in the rat medial precentral cortex. Dev. Neurobiol. 69, 203–211 (2009).
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
**Summary:**
-The authors have carried out an extensive survey of dorso-ventral axis determination in the cricket Gryllus bimaculatus. They did this through analysing and knocking down key components of the two main pathways involved in D/V patterning, the toll pathway and BMP signalling. This analysis was placed in a comparative context, looking at published data on four other insect species, with the aim of contributing to our understanding of the evolution of D/V patterning.
-The authors find significant similarities between D/V patterning in Gryllus and in Drosophila - These similarities are both in the relative contributions of toll and BMP to D/V polarization and in the early ovarian activation of the toll pathway. Despite these similarities, a closer analyses of the molecular interactions uncovers some significant differences, most notably, the absence of several key modulators of BMP activity. These results lead the authors to conclude that the similarities in D/V patterning between Gryllus and Drosophila are due to convergence and not due to the conservation in Drosophila of an ancestral patterning mechanism that has been lost in almost all other lineages studied.
**Major comments:**
•All in all this is an excellent paper. There is a huge amount of data in here, and everything is done very meticulously and carefully. There is a good balance between mostly descriptive work (gene expression patterns, cell movements in WT embryos) and experimental work. I could find no obvious flaws with any of the results or methods, and I think the authors have made a convincing case to support their conclusions, without being too dogmatic.
•I don't see a need for any additional experiments beyond what the authors have done. They have covered all relevant aspects of D/V patterning, and make a convincing case with the data they have.
**Minor comments:**
The few comments I have are very minor and technical:
•Missing taxonomic names (families) in Fig. 1
•Missing label in Fig. 6 Panel A.
•Punctuation could be improved. There are several instances of missing commas, and other places with unnecessary commas.
*Reviewer #1 (Significance (Required)):
•The manuscript represents an admirable amount of work. One can say that in a single paper, the authors have provided nearly as much information about Gryllus D/V patterning as is available for other "second-order" insect model species such as Oncopeltus or Nasonia. A such, it provides an additional major phylogenetic anchor point for understanding the evolution of early patterning.
•In terms of significance to advancing our knowledge, the data in the manuscript is, as stated above, an anchor point. It does not on its own provide any major novel insight, but fits into an ever-expanding body of comparative knowledge, whose importance is greater than the sum of its parts. Perhaps the most interesting conclusion, is indeed the one the authors have chosen as the selling-point of their paper, the fact that there is functional convergence in certain aspects of D/V patterning between two widely diverged insect species, with very different oogenesis and early development. This is again, not a major advance on its own, but an important additional piece of the comparative picture of early insect development.
•This paper will be of significant interest to the research community of comparative insect development (the community to which this reviewer belongs). It will also be of interest to those interested in examples of convergence at the functional and molecular level, to those interested in the evolution of gene families and to those interested specifically in the signalling pathways discussed (even in a non-comparative context).*
Response
We thank the reviewer for the very positive response to our paper.
We added missing taxonomic names and labels in Figure 6A and improved the punctuation throughout the manuscript.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
In this paper Pechmann and colleagues investigate the molecular mechanisms of dorso-ventral patterning in Gryllus bimaculatus. As a basis for their study they carry out thorough RNAseq analyses of various embryonic stages. Gryllus is a member of the hemimetabolous insects and therefore of interest for comparison with holometabolous insects such as Drosophila, Tribolium and Nasonia. Previous work has shown that there are significant differences in the use of Toll and Sog in establishing the dorso-ventral gradient of BMP signaling among Drosophila and Nasonia. Pechmann et al find that in Gryllus Toll has a similar role as in Drosophila and is regulated via Pipe, so far only found in Drosophila. Furthermore, they show by RNAi knockdown studies that loss of BMP signaling has little impact on the differentiation of mesoderm in Gryllus, like in Drosophila, hence, BMP signaling has largely a role in dorsal fates. Ventral fates are under direct control of the Toll gradient. Surprisingly, they also find that the key antagonist of BMP signaling and shuttle for BMPs, Sog, has been lost in Ensifera, the lineage leading to Gryllus.
This is a thorough and detailed study involving a series of functional experiments, which highlights the flexibility and evolvability of GRN of the dorso-ventral body axis formation in insects. The major finding that Gryllus is more similar to Drosophila than is Nasonia and Tribolium is interesting and even somewhat unexpected, since Drosophila is often regarded as the derived odd ball. The authors discuss two obvious explanations: the situation found in Gryllus and Drosophila reflects the ancestral condition, or, alternatively, it is the result of convergent evolution. They tend to favor the latter hypothesis. This study is an important advancement to our understanding, as it shows the constraints and the evolvability of a key patterning system to establish a body axis.
Even though the authors show nicely that Toll signaling is required to establish the BMP signaling gradient, the loss of Sog in Gryllus leaves the question unanswered how the long range BMP gradient and its shape is established. In Drosophila and vertebrates, Sog/Chordin acts both as an antagonist close to its source and as a shuttling factor, promoting BMP signaling at a distance, which is crucially important for the long range and the shape of the BMP signaling gradient. It would be desirable to test the function of other potential BMP antagonists (follistatin, gremlin, noggin) or competing BMPs (BMP3, ADAMP) in this context.
As a minor suggestion, I would recommend to summarize the findings in a synthetic picture depicting the evolutionary scenarios of the two hypotheses.
Reviewer #2 (Significance (Required)):
This study is an important advancement to our understanding, as it shows the constraints and the evolvability of a key patterning system to establish a body axis.
Response
We thank the reviewer for the very positive response to our paper.
As the reviewer suggested we added a schematic representation (Figure 11) depicting the two scenarios, which explain the evolution of DV patterning.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
**Summary**
This manuscript continues a series of beautiful papers from Roth, Pechman, Lynch and colleagues analysing D/V patterning in a range of insects. The work started with Drosophila and has extended to other holometabolous and now hemimetabolous insect species.
This paper is in many ways one of the most remarkable of the series, for it shows that the mechanisms of D/V patterning in the cricket Gryllus are, in several striking respects, very similar to those known from Drosophila - much more so than in some of the other insects studied to date, even though Gryllus is phylogenetically the most distant from Drosophila.
Specifically, the authors present compelling data to show that the roles of Toll and dpp, as inferred from their knockdown phenotypes, are remarkably similar in Gryllus and Drosophila. This is very different from the consequences of toll and dpp knockdown in the hemipteran Oncopeltus, a species which almost certainly shares a more recent common ancestor with Drosophila.
The discussion, after summarising the results, addresses the interpretation of this surprising observation. The authors favour the hypothesis that the similarity between Drosophila and Gryllus is the result of convergence in the roles and regulation of Toll and dpp signalling, rather than an ancestral trait that has been lost to a greater or lesser extent in Oncopeltus, and in the two other insects previously studied. The argument for this interpretation is carefully made, on the basis of a thorough knowledge of the comparative embryological literature (including highly relevant recent work).
**Major comments**
The work depends on an analysis of candidate genes, not de novo functional searches. However, it builds on the well established understanding of the relevant genetic machinery in Drosophila, and on extensive knowledge of the genome and transcriptome of Gryllus, a dataset that has been substantially extended by new work reported in this paper, on ovary and embryonic transcriptomes. These data are sufficiently complete to give confidence that all orthologues of most of the known candidate genes have been identified, and to highlight the apparent absence from the Gryllus genome of any sog/chordin orthologue - a key dpp inhibitor widely involved in D/v patterning.
The embryology is beautifully described. The early stages of these very yolky eggs are not easy to handle, but the stainings reported here are almost all of high quality, as are the movies of live development using a nuclear GFP marked line.
The gene knockdowns appear to have been carried out carefully with due regard for the potential biases caused by sterility following parental RNAi. Phenotypes have been documented effectively by the expression of marker genes in fixed embryos, and by live imaging of development in knockdown embryos. Tables in the supplementary data show that sufficient numbers have been obtained. The work is carefully interpreted, and where inferences are less than certain, they are carefully phrased.
I find the results convincing, and therefore accept the conclusion of fundamental similarity between the roles of Toll and dpp in Drosophila and Gryllus.
Time will tell whether or not the authors favoured interpretation of these data as convergent is correct, but I certainly believe that the argument as here presented in the discussion is appropriate for publication in its current form. The abstract is, appropriately, more non-committal than the discussion itself on the interpretation of these results.
The paper is well written.
**Minor points**
Videos - please state orientation of the embryos, especially in videos 2 &4
Page 23 bottom "The early dorsal-to-ventral gradient of pMad (Figure 5AB) indicates that BMP signalling plays an important role ...." suggests would be better than indicates here, until functional data is considered.
Reviewer #3 (Significance (Required)):
The gene networks mediating patterning of the D/V body axis are related across the whole range of animals, with in particular the involvement of TGFb/dpp signalling being almost universal in this process. However, there are a great many variations on this theme. Even within the insects, the mechanisms that have been described for establishing localised TGFb and Toll signalling span the range from self organisation to effective maternal prelocalisation. This has made the GRN underlying D/V patterning a key model for studies of the evolution of gene regulatory networks.
This paper adds an interesting and important twist to the story. It is certainly not the result that any of us would have expected, based on prior published work from Oncopeltus.
If indeed it does turn out to be a case of convergence, a more detailed mechanistic analysis of that convergence will provide considerable insight into the reproducibility of evolution.
Other published work: There is no comparable work on D/V patterning in any other polyneopteran insect, to my knowledge.
Audience: Insect developmental biologists, evolutionary developmental biologists and others interested in the evolution of gene regulatory networks.
My expertise: Arthropod embryology, axial patterning, evolutionary developmental biology.
I have not reviewed in detail the presentation of the transcriptomic data and the phylogenetic analysis of gene sequences as presented in the supplementary info.
Response
We thank the reviewer for the very positive response to our paper.
We made the small textual corrections suggested by the reviewer.
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Reply to the reviewers
Overall, we were pleased that the reviewers found our study carefully designed and interesting. We have addressed their comments below.
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
The manuscript by Kern, et al., demonstrates that phagocytosis in macrophages is regulated in part by the intermolecular distance of phagocytosis-promoting receptors engaging phagocytic targets. Cells expressing chimeric receptors containing cytosolic domains of Fc receptors (FcR) and defined ligand-binding DNA domains were used to drive phagocytosis of opsonized glass beads coated with complementary DNA ligands of defined spacing and number. These so-called origami ligands allowed manipulation of receptor spacing following engagement, which allowed the demonstration that tight spacing of ligands (7 nm or 3.5 nm) optimized signaling for phagocytosis. The study is carefully performed and convincing. I have a few technical concerns and minor suggestions.
- __ It is assumed that the origami preparations were entirely uniform. How much variation was there? Is that supported by TIRF microscopy of origami preparations? Was the TIRF microscopy calibrated for uniformity of fluorescence (ie., shade correction)?__ Our laboratory, Dong et al., has extensively characterized the origami uniformity and robustness of these exact pegboards. This paper was just posted on bioRxiv (Dong et. al, 2021). We have also cited this paper in our revised manuscript in reference to the characterization of the DNA origami (Line 117).
We did not use any shade correction. Instead we only collected data from a central ROI in our TIRF field. To check for uniformity of illumination, we plotted the origami pegboard fluorescent intensity along the x and y axis. We observed very modest drop off in signal - the average signal intensity of origamis within 100 pixels of the edge is 76 ± 6% the intensity of origamis in a 100 pixel square in the center of the ROI. Fitting this data with a Gaussian model resulted in very poor R values. While this may account for some of the variation in signal intensity at individual points, we expect the normalized averages of each condition to be unaffected. We have amended the methods to describe this strategy (Lines 851-854).
(Image could not be uploaded)
__ Likewise, how much variation was there in the expression of the chimeric receptors? Large variation in receptor numbers per cell could significantly alter the quantitative studies. Aside from the flow sorting for cells expressing two different molecules, how were cells selected for analysis?__
We thank the reviewer for bringing up this point. We confirmed comparable receptor expression levels at the cell cortex of the DNA CAR-𝛾 and the DNA CAR-adhesion used throughout the paper. We also have confirmed that receptor levels at the cell cortex were similar for the large DNA CAR constructs used in Figure 6C-D. This data is now included in Figures S5 and S7. We have also altered the text to include this (lines 169-172):
Expression of the various DNA CARs at the cell cortex was comparable, and engulfment of beads functionalized with both the 4T and the 4S origami platforms was dependent on the Fc𝛾R signaling domain (Figure S5).
When quantifying bead engulfment, cells were selected for analysis based on a threshold of GFP fluorescence, which was held constant throughout analysis for each individual experiment. We have amended the “Quantification of engulfment” methods section to convey this (lines 921-923).
__ The scale of the origami relative to the cells is difficult to discern in Figures 2C and D. Additional text would be helpful to indicate, for example, that the spots on the Fig. 2D inset indicate entire origami rather than ligand spots on individual origami particles.__
Thank you for pointing this out, we see how the legend was unclear and have corrected it (lines 453-454), including specifically noting “Each diffraction limited magenta spot represents an origami pegboard.” We have also outlined the cell boundary in yellow to make the cell size more clear.
__ Figure 5 legend, line 482: How was macrophage membrane visualized for these measurements?__
We have added the following clarification (line 535-536): “The macrophage membrane was visualized using the DNA CAR𝛾, which was present throughout the cell cortex.”
__ line 265: "our data suggest that there may be a local density-dependent trigger for receptor phosphorylation and downstream signaling". This threshold-dependent trigger response was also indicated in the study of Zhang, et al. 2010. PNAS.__
The Zhang et al. study was influential in our study design, and we wish to give the appropriate credit. Zhang et al. found that a sufficient amount of IgG is necessary to activate late (but not early) steps in the phagocytic signaling pathway. In contrast, our study addresses IgG concentration in small nanoclusters. We find that this nanoscale density affects receptor phosphorylation. Thus, we think these two studies are distinct and complementary.
Lines 283-287 now read:
While this model has largely fallen out of favor, more recent studies have found that a critical IgG threshold is needed to activate the final stages of phagocytosis (Zhang et al., 2010). Our data suggest that there may also be a nanoscale density-dependent trigger for receptor phosphorylation and downstream signaling.
__ line 55: Rephrase, “we found that a minimum threshold of 8 ligands per cluster maximized FcgR-driven engulfment.” It is difficult to picture how a minimum threshold maximizes something.__
We now state “we found that 8 or more ligands per cluster maximized FcgR-driven engulfment.”
__ line 184: Rephrase, "we created... pegboards with very high-affinity DNA ligands that are predicted not to dissociate on a time scale of >7 hr". Remove "not".__
Thank you for pointing this out, it is now correct.
Reviewer #1 (Significance (Required)):
This study provides a significant advance in understanding about the molecular mechanisms of signaling for particle ingestion by phagocytosis.
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Reviewer #2 (Evidence, reproducibility and clarity (Required)):
The manuscript on “Tight nanoscale clustering of Fcg-receptors using DNA origami promotes phagocytosis" studies how clustering and nanoscale spacing of ligand molecules for a chimeric Fcg-receptors influence the phagocytosis of functionalized silicon beads by macrophage cell lines. The basis of this study is the design of a chimeric Fc-receptor (DNA-CARg) comprising an extracellular SNAP-tag domain that can be loaded with single-stranded (ss) DNA, the transmembrane part of CD86 and the cytosolic part of the Fc-receptor g-chain containing an immunoreceptor tyrosine-based activation motif (ITAM) as well as a C-terminal green fluorescent protein (GFP). As control the authors used a similar designed DNA-CAR that is lacking the intracellular ITAM-containing FCg tail. The chosen target for this chimeric DNA-CAR, are silicon beads covered by a lipid bilayer that contains biotin-labelled lipids that, via Neutravidin, can be loaded with a biotinylated DNA origami pegboard displaying complimentary ss-DNA as ligand for the DNA-CAR. The DNA origami pegboard contains four ATTO647N fluorescence for visualization and the ssDNA ligand in different quantities and spacing. Using these principles, the authors study how ligand affinity, concentration and spacing influence the activation of the DNA-CARg and the engulfment of the loaded beads.
The authors show that bead engulfment is increased between 2 till 8 ssDNA ligands on the pegboard. After this, ligand numbers do not play a role anymore in the engulfment. They then study the role of the ligand spacing using pegboards that either contain 4 single strand DNA ligands in close (7nm/3,5nm) proximity or a more spaced version using 21/17,5 nm or 35/38,5 nm. The authors find that the bead engulfment is maximally and positively affected by the close spacing of the ssDNA ligands. In their final experiments the authors vary the design of the DNA-CARs by tetramerization of the ITAM-containing Fcg-signaling subunit. In their discussion the authors mention different possibilities for the effect of spacing on the engulfment process.
I think that, in general, this is an interesting study. However, it has some caveats and open issues that should be clarified before its publication.
**Major comments**
- __ As a general comment, it is somewhat a pity that the authors did not use the endogenous FcR as a control. It would have been quite easy for the authors to place the SNAP-tag domain on the Fcg extracellular domain which would allow to do all their experiments in parallel, not only with the DNA-CAR, but also with a DNA-containing wild type receptor. Such a control would be important because, by using a CD86 transmembrane domain, the authors do not know whether the nanoscale localization of their chimeric receptors is reflecting that of the endogenous Fcg receptor.__
We agree with the reviewer completely. We have repeated experiments shown in Figure 4A with a DNA-CAR containing the Fc𝛾 transmembrane domain instead of CD86 as the reviewer suggests. We also included a DNA-CAR version of the Fc𝛾R1 alpha chain, although this construct was not expressed as well as the others. These data are now included in Figure S5, and referenced in lines 167-168.
__ An important issue that is discussed by the authors but not addressed in this manuscript is whether the different amount and spacing of the ligand is only impacting on signaling or also on the mechanical stress of the cells. Indeed, mechanical stress on the cytoskeleton arrangement could influence the engulfment process. For this, it would be very important to test that the different bead engulfment, for example, those shown in Fig. 4, is strictly dependent on signaling kinases. The authors should repeat the experiment of Fig. 4 a and b in the presence or absence of kinase inhibitors such as the Syk inhibitor R406 or the Src inhibitor PP2 to show whether the different phase of engulfment is dependent on the signaling function of these kinases. This crucial experiment is clearly missing from their study.__
We agree this is an interesting point. We find that ligand spacing affects receptor phosphorylation; however this does not preclude effects on downstream aspects of the signaling pathway. We will clarify this by adding the following comment to the manuscript (line 299-301):
While our data pinpoints a role for ligand spacing in regulating receptor phosphorylation, it is possible that later steps in the phagocytic signaling pathway are also directly affected by ligand spacing.
The DNA-CAR-adhesion in Figure 1 strongly suggests that intracellular signaling is essential for phagocytosis. We have now included additional controls using this construct as detailed in our response to point 3 below. Unfortunately, Src and Syk inhibitors or knockout abrogate Fc𝛾R mediated phagocytosis (for example, PMIDs 11698501, 9632805, 12176909, 15136586) and thus would eliminate phagocytosis in both the 4T and 4S conditions. This precludes analysis of downstream steps in the phagocytic signaling pathway.
__ Another problem of this study is that the authors show in Fig. 1A the control DNA-CAR-adhesion but then hardly use it in their study. For example, the crucial experiments shown in Fig. 4 should be conducted in parallel with DNA-CAR-adhesion expressing macrophage cells. This study could provide another indication whether or not ITAM signaling is important for the engulfment process.__
We have added this control. It is now included in Figure S5 and S7. Figure 3D also shows that the DNA-CAR-adhesion combined with the 4T origami pegboards does not activate phagocytosis and we have amended the text to make this more clear (line 152).
__ Another important aspect is how the concentration of the loaded origami pegboard is influencing the engulfment process. In particular, it would be interesting to show the padlocks with different spacings such as the 4T closed spacing versus 4s large spacing show a different dependency on the concentration of this padlock loading on the beads. This would be another important experiment to add to their study.__
We agree that this is an interesting question. We suspect that at a very high origami density, 4S signaling would improve, and potentially approach the 4T. However, we are currently coating the beads in saturating levels of origami pegboards. Thus we cannot increase origami pegboard density and address this directly.
**Minor comments:**
- __ The definition of the ITAM is Immunoreceptor Tyrosine-based Activation Motif and not "Immune Tyrosine Activation Motif" as stated by the authors.__ We have corrected this.
__ The authors discuss that it is the segregation of the inhibitory phosphatase CD45 from the clustered Fc receptors is the major mechanism explaining their finding that 4T closed spacing is more effective than 4s large spacing. With the event of the CRISPR/Cas9 technology it is trivial to delete the CD45 gene in the genome of the RAW264.7 macrophage cell line used in this study and I am puzzled why they author are not conducting such a simple but for their study very important experiment (it takes only 1-2 month to get the results).__
This experiment may be informative but we have two concerns about its feasibility. First, CD45 is a phosphatase with many different roles in macrophage biology, including activating Src family kinases by dephosphorylating inhibitory phosphorylation sites (PMID 8175795, 18249142, 12414720). Second, CD45 is not the only bulky phosphatase segregated from receptor nanoclusters. For example, CD148 is also excluded from the phagocytic synapse (PMID 21525931). CD45 and CD148 double knockout macrophages show hyperphosphorylation of the inhibitory tyrosine on Src family kinases, severe inhibition of phagocytosis, and an overall decrease in tyrosine phosphorylation (PMID 18249142). CD45 knockout alone showed mild phenotypes in macrophages. We anticipate that knocking out CD45 alone would have little effect, and knocking out both of these phosphatases would preclude analysis of phagocytosis. Because of our feasibility concerns and the lengthy timeline for this experiment, we believe this is outside of the scope of our study.
In our discussion, we simplistically described our possible models in terms of CD45 exclusion, as the mechanisms of CD45 exclusion have been well characterized. This was an error and we have amended our discussion to read (lines 335-343):
As an alternative model, a denser cluster of ligated receptors may enhance the steric exclusion of the bulky transmembrane proteins like the phosphatases CD45 and CD148 (Bakalar et al., 2018; Goodridge et al., 2012; Zhu, Brdicka, Katsumoto, Lin, & Weiss, 2008).
Reviewer #2 (Significance (Required)):
The innovative part of this study is the combination of SNAP-tag attached, chimeric Fc-receptor with the DNA origami pegboard technology to address important open question on receptor function.
**Referees cross-commenting**
I find most of my three reviewing colleagues reasonable
I also agrée to Reviewer #1 comments 2
Likewise, how much variation was there in the expression of the chimeric receptors? Large variation in receptor numbers per cell could significantly alter the quantitative studies. Aside from the flow sorting for cells expressing two different molecules, how were cells selected for analysis?
But I want to add it is not only the amount of receptors but ils the nanoscale location that is key to receptor function
We have ensured that all receptors are trafficked to the cell surface. We have also measured their intensity at the cell cortex as discussed in response to Reviewer 1.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
This is a very nicely done synthetic biology/biophysics study on the effect of ligands spacing on phagocytosis. They use a DNA based recognition system that the group has previously use to investigate T cell signaling, but express the SNAP tag linked transmembrane receptor in a macrophage cell line and present the ligands using DNA origami mats to control the number and spacing of complementary ligands that are designed to be in the typical range for low or high affinity FcR, a receptor that can trigger phagocytosis. The study offers some very nice quantitative data sets that will be of immediate interest to groups working in this area and, in the future, for design of synthetic receptors for immunotherapy applications. Other groups are working on similar platform for TCR. I don't feel there is any need for more experiments, but I have some questions and suggestions. Answering and considering these could clarify the new biological knowledge gained.
We thank the reviewer for their support of our manuscript. Given the reviewer’s statement that no new experiments are required, we have answered their questions to the best of our ability given the current data. Should the editor decide that any of these topics require experimental data to enhance the significance of the paper, we are happy to discuss new experiments.
Reviewer #3 (Significance (Required)):
I think the significance would be increased by addressing these questions, that would help understand how the synthesis system described related to other system directed as similar questions and more natural settings.
- __ The densities of the freely mobile DNA ligands required to trigger phagocytosis is quite high. Was the length of the DNA duplexes optimized? The entire complex for both the intermediate and high affinity duplexes seems quite short, perhaps The extracellular domain of the DNA-CAR (SNAP tag and ssDNA strand) are approximately 10 nm (PMID 28340336). The biotinylated ligand ssDNA is attached to the bilayer via neutravidin, resulting in a predicted 14 nm intermembrane spacing. The endogenous IgG FcR complex is 11.5 nm. Bakalar et al (PMID 29958103) tested the effect of antigen height on phagocytosis and found that the shortest intermembrane distance tested (approximately 15 nm) was the most effective. As the reviewer notes, the optimal distance between macrophage and target may be larger than our DNA-CAR. However we think the intermembrane spacing in our system is within the biologically relevant range.
We saw robust phagocytosis at 300 molecules/micron of ssDNA, which is similar to the IgG density used on supported lipid bilayer-coated beads in other phagocytosis studies (PMID 29958103, 32768386). As the reviewer noticed, this is significantly higher than ligand density necessary to activate T cells (PMID 28340336). We have added a comment on ligand density to lines 96-97.
__ Are the origami mats generally laterally mobile on the bilayers. If so, what is the diffusion coefficient? Can one detect the mats accumulating in the initial interface between the bead and cell, particularly in cased where there is no phagocytosis? Would immobility of the mats make them more efficient at mediating phagocytosis compared to the monodispersed ligands, which I assume are highly mobile and might even be "slippery".__
We have confirmed that our bead protocol generally produces mobile bilayers, where his-tagged proteins can freely diffuse to the cell-bead interface (see accumulation of a his-tagged FRB binding to a transmembrane FKBP receptor at the cell-bead synapse below). We can qualitatively say that the origamis appear mobile on a planar lipid bilayer (see Dong et. al 2021 and images below). Directly measuring the diffusion coefficient on the beads is extremely difficult because the beads themselves are mobile (both diffusing and rotating), and cannot be imaged via TIRF. We do not see much accumulation of the origami at cell-bead synapses. This could reflect lower mobility of the origamis, or could be because the relative enrichment of origamis is difficult to detect over the signal from unligated origamis.
Overall, we expect the origami pegboards (tethered by 12 neutravidins) are less mobile than single strand DNA (tethered by a single neutravidin, supported by qualitative images below). We are uncertain whether this promotes phagocytosis. At least one study suggests that increased IgG mobility promotes phagocytosis (PMID 25771017). However, the zipper model would suggest that tethered ligands may provide a better foothold for the macrophage as it zippers the phagosome closed (PMID 14732161). Hypothetically, ligand mobility could affect signaling in two ways - first by promoting nanocluster formation, and second by serving as a stable platform for signaling as the phagosome closes. Since our system has pre-formed nanoclusters, the effect of ligand mobility may be quite different than in the endogenous setting.
(Image could not be uploaded)
In the above images, a 10xHis-FRB labeled with AlexaFluor647 was conjugated to Ni-chelating lipids in the bead supported lipid bilayer. The macrophages express a synthetic receptor containing an extracellular FKBP and an intracellular GFP. Upon addition of rapamycin, FRB and FKBP form a high affinity dimer, and FRB accumulates at the bead-macrophage contact sites.
(Image could not be uploaded)
In the above images, single molecules were imaged for 3 sec. The tracks of each molecule are depicted by lines, colored to distinguish between individual molecules. The scale bar represents 5 microns in both panels.
__ Breaking down the analysis into initiation and completion is interesting. When using the non-signalling adhesion constructs, would they get to the initiation stage or would that attachment be less extensive than the initiation phase.__
This is an interesting question. While we did not include the DNA-CAR-adhesion in our kinetic experiments, we have now quantified the frequency of cups that would match our ‘initiation’ criteria in 3 representative data sets where macrophages were fixed after 45 minutes of interaction with origami pegboard-coated beads. We found that an average of 16/125 of 4T beads touching DNA-CAR-adhesion macrophages met the ‘initiation’ criteria and an average of 2/125 were eaten (14% total). In comparison, we examined 4T beads touching DNA CAR𝛾 macrophages and found that on average 23/125 met the ‘initiation’ criteria, and 45/125 were already engulfed (54%). This suggests that the DNA-CAR-adhesion alone may induce enough interaction to meet our initiation criteria, but without active signaling from the FcR this extensive interaction is rare. We have added this data in a new Figure S6 and commented on this in lines 213-215.
__ It would be interesting to put these results in perspective of earier work on spacing with planar nanoarrays, although these can't be applied to beads. For integrin mediated adhesion there was a very distinct threshold for RGD ligand spacing that could be related to the size of some integrin-cytoskeletal linkers (PMID: 15067875). On the other hand, T cell activation seemed more continuous with changes in spacing over a wide range with no discrete threshold (PMID: 24117051, 24125583) unless the spacing was increased to allow access to CD45, in which case a more discrete threshold was generated (PMID: 29713075). The results here for phagocytosis with the very small ligands that would likely exclude CD45 seems to be more of a continuum without a discrete threshold, although high densities of ligand are needed. This issue of continuous sensing vs sharp threshold is biologically interesting so would be good assess this by as consistent standards are possible across systems.__
We agree that this is an interesting body of literature worth adding to our discussion. We have added a paragraph that puts our study in the context of prior work on related systems, including these nanolithography studies (Line 364-382):
How does the spacing requirements for Fc𝛾R nanoclusters compare to other signaling systems? Engineered multivalent Fc oligomers revealed that IgE ligand geometry alters Fcε receptor signaling in mast cells (Sil, Lee, Luo, Holowka, & Baird, 2007). DNA origami nanoparticles and planar nanolithography arrays have previously examined optimal inter-ligand distance for the T cell receptor, B cell receptor, NK cell receptor CD16, death receptor Fas, and integrins (Arnold et al., 2004; Berger et al., 2020; Cai et al., 2018; Deeg et al., 2013; Delcassian et al., 2013; Dong et al., 2021; Veneziano et al., 2020). Some systems, like integrin-mediated cell adhesion, appear to have very discrete threshold requirements for ligand spacing while others, like T cell activation, appear to continuously improve with reduced intermolecular spacing (Arnold et al., 2004; Cai et al., 2018). Our system may be more similar to the continuous improvement observed in T cell activation, as our most spaced ligands (36.5 nm) are capable of activating some phagocytosis, albeit not as potently as the 4T. Interestingly, as the intermembrane distance between T cell and target increases, the requirement for tight ligand spacing becomes more stringent (Cai et al., 2018). This suggests that IgG bound to tall antigens may be more dependent on tight nanocluster spacing than short antigens. Planar arrays have also been used to vary inter-cluster spacing, in addition to inter-ligand spacing (Cai et al., 2018; Freeman et al., 2016). Examining the optimal inter-cluster spacing during phagosome closure may be an interesting direction for future studies.
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Additional experiments performed in revision
In addition to these reviewer comments, we have added additional controls validating the DNA-CAR-4x𝛾 used in Figure 6c,d. We compared the DNA-CAR-4x𝛾 to versions of the DNA-CAR-1x𝛾-3x𝛥ITAM construct with the functional ITAM in the second and fourth positions (see the schematics now included Figure S7). We found that four individual receptors with a single ITAM each were able to induce phagocytosis regardless of which position the ITAM was in. However the DNA-CAR-4x𝛾 construct, which also contains 4 ITAMs, was not. This further validates the experiment presented in 6c,d. We also fixed minor errors we discovered in the presentation of data for Figures 1C and S1A.
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Reply to the reviewers
__Reviewer #1 __ __1. One key citation missing from the current manuscript is from Hwang et al. 2014 (PMID 25288734). This study has already described that the isp-1 mutant strain survives longer during P. aeruginosa infection. This citation also describes that the gene expression profile of isp-1 mutants animals includes a considerable number of pathogen-responsive genes that are similarly induced during infection. While the current manuscript does go into the mechanism of this resistance with more detail, they should amend the language to more appropriately reflect previous work, notably the above reference.
__
We apologize for the oversight and have added the suggested citation. Hwang et al. show that isp-1 worms have increased resistance to bacterial pathogens that is dependent on HIF-1/HIF1 and AAK-2/AMPK. In future work, it will be interesting to examine whether HIF-1 and AAK-2 act in concert with, or independently of, ATFS-1 and the p38-mediated innate immune signaling pathway to mediate pathogen resistance and longevity in isp-1 worms. We will add these points to our discussion.
__2. The authors suggest that ROS activation of the p38 MAPK pathway is likely not the mechanism that explains the resistance of long-lived mitochondrial mutant animals due to their reduced food intake. However, is ROS production nonetheless involved? Does antioxidant treatment suppress the increased resistance during infection of isp-1 and/or nuo-6 mutant animals?
__
To address this question, we will treat wild-type, isp-1 and nuo-6 worms with antioxidant and then measure resistance to bacterial pathogens using the P. aeruginosa strain PA14 slow kill assay. For the antioxidant treatment, we will use 10 mM Vitamin C as we have previously shown that this concentration is effective at reducing ROS in isp-1 worms to decrease isp-1 lifespan (Van Raamsdonk and Hekimi 2012, PNAS). Although antioxidant treatment can have pleiotropic effects, if this decreases survival of bacterial pathogen exposure, it will suggest that the elevated ROS production in isp-1 and nuo-6 worms may contribute to their enhanced bacterial pathogen resistance.
__3. (line 278-282): the authors should elaborate on how the p38 MAPK pathway plays a permissive role. It is intriguing that ATFS-1 and ATF-7 are both bZIP transcription factors that could theoretically heterodimerize and that they share common immune gene targets. The authors do indicate that the binding sites for ATFS-1 and ATF-7 are very different and are likely acting distinctly but some speculation would nonetheless strengthen this statement.
__
While ATFS-1 and ATF-7 were shown to bind to the promoter regions of the same innate immunity genes, the apparent consensus binding sites are different suggesting that they bind to different regions of the promoter. One way in which the p38 MAPK pathway may be playing a permissive role is that ATF-7 binding and relief from its repressor activity is required for any transcription of p38-mediated innate immunity target genes to occur. This is consistent with our data showing that disruption of nsy-1, sek-1, pmk-1 or atf-7 decreases the expression of innate immunity genes in wild-type worms. In contrast, it may be that the role of ATFS-1 is for enhanced expression of innate immunity genes such that when ATFS-1 is bound to the promoter region, or perhaps enhancer elements, the baseline expression of innate immunity genes that results from the binding of ATF-7 is increased. This idea is supported by our data showing that disruption of atfs-1 does not affect the expression of innate immunity genes in wild-type worms but prevents nuo-6 mutants from having increased expression. We will update our manuscript to include these points.
__4. The authors suggest that reduced food consumption of nuo-6 and isp-1 animals may suppress ROS-induced activation of the p38 innate immune pathway. It is intriguing that dietary restriction was previously shown to increase resistance to infection, presumably through p38-independent mechanisms (PMID 30905669). It would be interesting to measure host survival of nuo-6 and isp-1 mutant animals that are dietary-restricted to see if the enhanced survival rates conferred by mitochondrial stress and DR are additive or not.
__
According to this suggestion, we will compare the bacterial pathogen resistance of wild-type, isp-1 and nuo-6 worms that have undergone dietary restriction to the same strains under ad libitum conditions. This will determine the extent to which their enhancement of pathogen resistance might be additive.
__5. Figure 2: It is intriguing that loss of p38 signaling appears to have different effects in nuo-6 versus isp-1 animals. Specifically, loss of p38 signaling in isp-1 mutants renders them more sensitive to infection than wild-type, whereas it generally suppresses survival rates back to wild-type levels in the nuo-6 mutant background. Even within the nuo-6 mutant group, loss of SEK-1 has more dramatic effects on nuo-6 mutant animals than does loss of NSY-1, PMK-1 or ATF-7(gf). This is despite the fact that the nsy-1, sek-1, and pmk-1 alleles that are used in this study are all reported to be null. Can the authors speculate on these differences?
__
While the isp-1 and nuo-6 mutations both alter mitochondrial function, they affect different components of the electron transport chain. isp-1 mutations affect Complex III (Feng et al. 2001, Dev. Cell), while nuo-6 mutations affect Complex I (Yang and Hekimi 2010, Aging Cell). Although these mutants both have increased lifespan and a similar slowing of physiologic rates, it is not uncommon to observe differences between these mutants. For example, while treatment with the antioxidant NAC completely reverts nuo-6 lifespan to wild-type, it only partially reduces isp-1 lifespan (Yang and Hekimi 2010, PLoS Biology), suggesting that nuo-6 lifespan may be more dependent on ROS than isp-1. We have recently shown that deletion of atfs-1 reduces nuo-6 lifespan, but completely prevents isp-1 worms from developing to adulthood (Wu et al. 2018, BMC Biology), suggesting that isp-1 worms are more dependent on ATFS-1 than nuo-6 worms. Disruption of sek-1 has a greater impact on pathogen resistance than nsy-1 and pmk-1 because SEK-1 is absolutely required for innate immune signaling, while some partial redundancy exists for NSY-1 and PMK-1. We will add these points to our manuscript.
__6. One of the main conclusions from this study is that ATFS-1 likely binds directly to innate immune genes that are in common with ATF-7. Since this is such a pivotal finding, the authors should validate some candidate genes from the referenced ChIP seq datasets using ChIP qPCR. Also, are there predicted ATFS-1 binding sites (PMID 25773600) in these promoters?
__
Our data shows that activation of ATFS-1 increases the expression of innate immunity genes without increasing activation of p38. The simplest explanation for this observation is that ATFS-1 can upregulate the same innate immunity genes as ATF-7. Accordingly, we hypothesized that ATFS-1 and ATF-7 can bind to the same promoter. Fortunately, two previous ChIP-Seq studies, from well-established laboratories who have extensive experience studying ATFS-1 and ATF-7, had already determined which genes are bound by these two transcription factors (Nargund et al. 2015, Molecular Cell; Fletcher et al. 2019, PLoS Genetics). Comparing the results of these two published studies confirmed our hypothesis by demonstrating that the same innate immunity genes are bound by both ATF-7 and ATFS-1 in vivo. In order to provide additional support for the conclusion that ATFS-1 and ATF-7 can bind to the same genes, we will examine the genetic sequence of innate immunity genes that were shown to be bound by both ATFS-1 and ATF-7 in the published ChIP-seq studies to identify predicted binding sites for ATFS-1 and ATF-7, while noting that the ATFS-1-associated sequence is an enriched motif and not an established binding site. If we are able to identify the predicted binding sites for these two transcription factors in the same gene, it will provide further support for the conclusion that these transcription factors can both bind to the same innate immunity genes.
__Reviewer #2:
(1) The authors state that the p38 MAPK PMK-1 is not activated in the long-lived mitochondrial mutants. However, it might be better to state that there is "no enhanced activation" of PMK-1, since they clearly show in nuo-6 and isp-1 mutants the presence of phosphorylated PMK-1 (Fig. 4A), which would indicate an activated form of PMK-1 in these mutants.__
According to this suggestion, we will change the text to indicate that there is no enhanced activation of PMK-1 in nuo-6 and isp-1 worms.
__(2) Are the food-intake behaviors of all mutants in liquid culture (Fig. 4B-F) the same as their food-intake behaviors on solid agar media, the environment where pathogen resistance was measured?
__
We previously compared assays measuring food intake on solid agar media versus the liquid culture approach used in the current study to determine which method is the most robust (Wu et al. 2019, Cell Metabolism). While both assays produced similar results, performing the food intake assay on solid agar plates was much more variable as it is challenging to scrape off all of the uneaten bacteria from solid plates in order to measure it. Since the approach of measuring food intake in liquid media produces more consistent and reliable results, we chose to use this assay for the current study. We will update our manuscript to include this justification.
(__3) Does the p38 pathway single mutant nsy-1 or sek-1 live shorter than wild type on dead E. coli OP50 (Fig. S9) than they do on live OP50 (Fig. 3)? If so, what might that mean? These mutants are also living shorter than wild type on PA14 (Fig. 2), but live as long as wild type on OP50 (Fig. 3). What is in the live OP50 that allows these mutants to live like wild type?
__In a previous publication, we found that sek-1 mutants live shorter than wild-type worms, and nsy-1 live slightly shorter than wild-type worms in a lifespan assay performed in liquid medium with dead OP50 bacteria (Wu et al. 2019, Cell Metabolism). In the current study, we performed lifespan assays on solid NGM plates with live OP50 bacteria and observed a wild-type lifespan in sek-1 and nsy-1 worms. Since there are multiple experimental variables that are different between the previous and current study, most notably liquid versus solid media, the lifespan results cannot be directly compared. In the case of measuring survival of these strains on PA14, the simplest explanation is that they are dying sooner because their innate immune signaling pathway is disrupted, and so they are less able to mount an immune response against the pathogenic bacteria. We will update our manuscript to include these points.
__At the same time, wouldn't it be simpler to call the multiple antibiotic-treated OP50 as "dead bacteria", instead of "non-proliferating bacteria"? Some of the antibiotics used to treat OP50 are bactericidal and not bacteriostatic.
__
We previously monitored the OD600 of the antibiotic-treated, cold-treated OP50 that we used in our experiment, and found that there is only a very small decrease in OD600 after 10 days (Moroz et al. 2014, Aging Cell). Since dead bacteria are rapidly broken down leading to a decrease in OD600, this result is consistent with the bacteria being alive but not proliferating. We will include this point in our manuscript.
__(4) Since nuo-6 and isp-1 do not always behave exactly the same in their dependence on certain genes (e.g., Fig. 2C vs Fig 2D), what happens in isp-1; atfs-1 double mutants? Do these mutants behave in the same manner as nuo-6; atfs-1?
__
This is an interesting question. Unfortunately, isp-1;atfs-1 mutants arrest during development (Wu et al. 2018, BMC Biology), which is why we only examined the effect of atfs-1 deletion in nuo-6 mutants. We will update the manuscript to note this point.
__Regarding nuo-6; atfs-1, why does the double mutant live shorter on PA14 than either single mutant (Fig. 6A)? Is this because atfs-1 is needed to activate the p38 MAPK-dependent and -independent pathways? __
It is possible that the nuo-6 mutation makes worms more sensitive to bacterial pathogens, perhaps due to decreased energy production, and that activation of ATFS-1 is required not only to enhance their resistance to pathogens but also to increase their resistance back to wild-type levels. In a previous study, we showed that loss of ATFS-1 slows down the rate of nuclear localization of DAF-16. Thus, loss of atfs-1 may also be decreasing resistance to bacterial pathogens by diminishing the general stress resistance imparted by the DAF-16-mediated stress response pathway. We will update the manuscript to include these points.
__In Fig. 7B, the atfs-1(gof) appears to have slightly more phosphorylated p38 compared to wild type, although it is not statistically significant?
__
While there is a trend towards a very modest increase in phosphorylated p38 in the constitutively-active atfs-1 mutant compared to wild-type, quantification of four biological replicates indicated that the difference is not significant. This result is consistent with the fact that the levels of phosphorylated p38 are not significantly increased in nuo-6 or isp-1 mutants, both of which show activation of ATSF-1. We have provided raw images of all of these Western blots in our supplementals. In addition, we will repeat these Western blots to determine if this difference becomes significant with additional replicates.
__In Fig. 6B, the atfs-1 loss-of-function single mutant also increases the expression of Y9C9A.8, but suppresses it in a nuo-6 mutant background? What might that mean?
__
It is possible that in wild-type animals disruption of atfs-1 causes a compensatory upregulation of specific stress response genes. We have previously shown that deletion of atfs-1 results in upregulation of chaperone genes involved in the cytoplasmic unfolded protein response (hsp-16.11, hsp-16.2; Wu et al. 2018; BMC Biology). Perhaps Y9C9A.8 is acting in a similar way. In nuo-6, the upregulation of Y9C9A.8 is driven by activation of ATFS-1, and thus is prevented by atfs-1 deletion. We will add these points to the manuscript.
__Reviewer #3:
1) Some studies propose that OP50 offers some toxicity to worms which is not observed in other bacterial strains like HT115. The authors should test the role of the p38-innate immune signaling pathway in nuo-6 and isp-1 lifespan using other non-pathogenic E. coli strains.
__
To determine if the effect of disrupting the p38-mediated innate immune signaling pathway on the lifespan of isp-1 and nuo-6 mutants was simply the result of losing protection against OP50 bacteria, we examined the effect of nsy-1, sek-1 and atf-7(gof) mutations on isp-1 and nuo-6 lifespan using non-proliferating bacteria. We found that even when no proliferating bacteria are present, disruption of the p38-mediated innate immune signaling pathway markedly decreases isp-1 and nuo-6 lifespan. This suggests that the p38-mediated innate immune signaling pathway is required for their long lifespan independently of its ability to protect against bacterial infection. Similarly, we have previously shown that lifespan extension resulting from dietary restriction is dependent on the p38-mediated innate immune signaling pathway even when non-proliferating bacteria are used (Wu et al. 2019, Cell Metabolism). We will clarify this important point in the manuscript.
__ 2) The authors should measure food intake in worms exposed to pathogenic bacteria, given that reduced bacterial intake may be related to reduced mortality.
__
Unfortunately, it is not feasible to perform the food intake assay using the pathogenic bacteria because the bacteria cause death thereby complicating the calculation of food consumed per worm (which requires at least 3 days to assess). As an alternative to measuring food intake, we will attempt to measure intestinal accumulation of P. aeruginosa, which is a balance between food intake and other factors. To do this we will use a P. aeruginosa strain that expresses GFP and quantify the amount of intestinal fluorescence in wild-type, isp-1 and nuo-6 worms that have been grown on the GFP-labelled P. aeruginosa.
__3) The authors should check if ROS is required for the activation of the p38-mediated innate immune signaling pathway and reduction in food intake.
__
To determine if the elevated ROS that is present in isp-1 and nuo-6 worms affects activation of the p38-mediated innate immune signaling pathway, we will treat wild-type, isp-1 and nuo-6 worms with Vitamin C and measure the ratio of phosphorylated p38 to total p38 by Western blotting. Similarly, to examine the effect of ROS on food intake, we will treat wild-type, isp-1 and nuo-6 worms with Vitamin C and then quantify its effect on food intake. For these experiments, we will use 10 mM Vitamin C as we have previously shown that this concentration is effective at reducing ROS in isp-1 worms to decrease isp-1 lifespan (Van Raamsdonk and Hekimi 2012, PNAS).
__4) Since ATFS-1 and the p38 pathway control food intake, how related to dietary restriction the phenotypes the authors are studying are?
__
While the lifespan extension that results from mild impairment of mitochondrial function and the lifespan extension resulting from dietary restriction are both dependent on the p38-mediated innate immune signaling pathway, these interventions modulate innate immunity gene expression in opposite directions. We previously reported that dietary restriction primarily downregulates innate immunity genes (Wu et al. 2019 Cell Metabolism). Here, we show that mutations in isp-1 or nuo-6 primarily result in upregulation of innate immunity genes. To more globally examine gene expression changes between dietary restriction and mild impairment of mitochondrial function, we compared differentially expressed genes. We found that there was very little overlap of either upregulated or downregulated genes between dietary restriction and isp-1/nuo-6 mutants. We will add a supplementary figure to demonstrate this, and add these points to our manuscript.
__ 5) Somewhat related to the previous points, I am not so sure whether the changes in food intake are cause or consequence of the alterations in the innate immunity-related genes. Reduced food intake is depicted in Fig. 8 as the cause of the activation of the p38 pathway, but there is not enough evidence to unequivocally prove that. In fact, food intake might be controlled by the p38 or ATFS-1 pathway or by a common regulator such as ROS.
__
We apologize that we didn’t make this clearer. In our previous work, we showed that dietary restriction results in decreased activation of the p38 pathway (Wu et al. 2019, Cell Metabolism). Here, we show that activation of ATFS-1 results in decreased food intake. Based on our previous study, this decrease in food intake should similarly decrease p38 pathway activation. In Figure 8, we have depicted ATFS-1 inhibiting food intake, and food intake activating the p38-mediated innate immune signaling pathway. Combined, our model suggests that activation of ATFS-1 should act to decrease p38-mediated innate immune signaling. We will clarify this in the figure legend.
__6) I am not so convinced of the role of DAF-16. In fact, in Fig. 5A daf-16 mutation reduces pathogen resistance and that could represent a toxic effect of the mutation. Furthermore, the results in Fig. 4D do not exclude the possibility that daf-16 and isp-1 act in parallel.
__
We agree that the role of DAF-16 could be non-specific. While we show that disruption of daf-16 leads to decreased bacterial pathogen survival in isp-1 worms, it also decreases bacterial pathogen survival in wild-type worms. Since DAF-16 is known to be required for general resistance to stress, the decreased survival when daf-16 is disrupted could be due to a general toxic effect of reducing general stress resistance. This conclusion is consistent with our observation that DAF-16 is not involved in the upregulation of innate immunity genes in isp-1 worms. We will emphasize these points in our manuscript.
__ 7) Loss of innate immunity related genes may result in toxicity and sensitize worms to pathogenic bacteria. This is further supported by an even lower resistance to pathogens in the double mutants mainly in Fig. 2D.
__
We agree. Our data confirms that disruption of the p38-mediated innate immune signaling pathway makes worms more susceptible to bacterial pathogens. We will emphasize this point.
__ 8) The blots are saturated, particularly in Fig. 4A, and this can be masking the differences in p38 phosphorylation. In fact, the fact that p38 phosphorylation is not changed is contradictory to the other results. How is p38 regulated by mitochondrial mutations then? I am concerned that p38 is actually not altered and the changes in gene expression are exclusively due to ATFS-1. The interaction with the p38 pathway demonstrated genetically could be due to the toxicity elicited by the loss of function mutations in this pathway.__
To address this concern, we will repeat the Western blotting experiment to compare the ratio of phosphorylated p38 to total p38 between wild-type, isp-1 and nuo-6 worms. We will take multiple exposures to ensure that the blots are not over-saturated. Having already completed four replicates, we believe that there is not a major change in p38 activation. Our data suggests that the p38-mediated innate immunity pathway is playing a permissive role such that it is required for baseline expression of innate immunity genes, but that activation of ATFS-1 is driving the enhanced expression of innate immunity genes that we observe in the long-lived mitochondrial mutants and constitutively active atfs-1 mutants. We will update our manuscript to clarify this.
__ **Minor concerns**
1) Lines 167 and 174: What are these p values referred to?
__
The p-values indicate the significance of the overlap between the two gene sets. Given the size of the two gene sets, this is the probability that the observed number of overlapping genes would result by picking genes at random. We will clarify this in the manuscript.
__2) Line 258: I partially agree with the conclusions, since the functions may not necessarily be associated with innate immune signaling but rather other functions of p38.
__
Since isp-1 and nuo-6 worms have extended longevity even when grown on non-proliferating bacteria this indicates that their long life is not dependent on their enhanced resistance to bacterial pathogens. Similarly, since disruption of genes in the p38-mediated innate immune signaling pathway decrease isp-1 and nuo-6 lifespan even when the worms are grown on non-proliferating bacteria, this suggests that this pathway enhances longevity independently of its ability to increase innate immunity.
__ 3) Why in figures 4D and E different mutants were used?
__
We only used isp-1 mutants to examine the effect of daf-16 because we were unable to generate nuo-6;daf-16 mutants due to close proximity of the two genes on the same chromosome. We only used nuo-6 mutants to examine the effect of atfs-1 because isp-1;atfs-1 worms arrest during development. We will include this explanation in our manuscript.
__ 4) Line 498: revise writing.
__
We will rewrite this sentence to improve clarity.
__ 5) Show blots in Fig. 7B.
__
We will provide an image of a representative Western blot in Figure 7, and will provide the raw images for all of Western blots in our supplementals.
__ 6) It would be interesting to know where the activation of the immune-related genes by the mitochondrial mutations is happening, whether this is a cell autonomous or cell non-autonomous mechanism.
__While it would be interesting to explore whether specific tissues are important in sensing mitochondrial impairment in order to upregulate genes involved in innate immunity, it is beyond the scope of this manuscript. Previous work has shown that knocking down the expression of the cytochrome c oxidase gene cco-1 in neurons can activate the ATFS-1 target gene hsp-6 in the intestine (Durieux et al., 2011). Based on this, one could hypothesize that a similar cell non-autonomous mechanism might be involved. We will note this possible future direction in our discussion.
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Reply to the reviewers
__Reviewer #1: __ __ **Major concerns:**
1) This manuscript has some overlap with another manuscript from the same group recently submitted to EMBO Reports. Although I believe both manuscripts have sufficient elements to justify publication of two papers, I strongly recommend that these publications are made back-to-back and they should be discussed in context with one-another.
__
We agree that this manuscript is distinct from but highly complementary to our manuscript on innate immunity in the long-lived mitochondrial mutants, which has been invited for revision at EMBO Reports. According to this suggestion, we have arranged for these papers to be considered for publication at the same time in EMBO Reports and Life Science Alliance. We have updated the discussions of both manuscripts to incorporate the findings of the other manuscript.
__ 2) How is ATFS-1 function regulated in long-lived worms or under multiple stress conditions? Is there a common regulator such as oxidative stress or mitochondrial dysfunction? Both manuscripts would benefit from a clear understanding on how ATFS-1 is controlled under conditions where mitochondrial function is altered. Is mitoUPR required for this activation? If so, is mitoUPR upregulated in all interventions where ATFS-1 has been shown to play a role in stress response. __
We have previously used a reporter strain to determine which external stressors activate ATFS-1. The reporter strain has a transgene that links the promoter of the ATFS-1 target gene hsp-6 to GFP (Phsp-6::GFP) such that these worms exhibit increased fluorescence whenever ATFS-1 is activated. After exposing these worms to heat, cold, osmotic stress, anoxia, oxidative stress, starvation, ER stress and bacterial pathogens, we only observed increased fluorescence after exposure to oxidative stress (Dues et al. 2016, Aging). Here, we show that constitutive activation of ATFS-1 results in increased resistance not only to oxidative stress but also ER stress, osmotic stress, anoxia and bacterial pathogens (fast kill assay). Thus, ATFS-1 activation does not just protect against stresses that lead to its activation. Notably, the constitutively active atfs-1 mutants (et15 and et17) exhibit activation of the mitoUPR under unstressed conditions (e.g. upregulation of hsp-6 in Fig. 1A; increased fluorescence of hsp-6 and hsp-60 reporter strains in Rauthan et al. 2013, PNAS; upregulation of many other stress pathway target genes Fig. 2). It is likely that the activation of the mitoUPR and downstream stress response pathways under unstressed conditions results in the increased resistance to stress that we observe. We have included these points in the revised manuscript.
__Is there any intervention that controls longevity and does not trigger ATFS-1 response?
__
When we compared RNA-seq data on a panel of long-lived mutants representing multiple pathways of lifespan extension to ATFS-1 target genes (defined as genes that are upregulated by spg-7 RNAi in an ATFS-1 dependent manner from Nargund et al. 2012, Science), we found that seven of the nine long-lived mutants that we examined showed enrichment of ATFS-1 target genes (clk-1, isp-1, nuo-6, daf-2, glp-1, ife-2) while two did not (eat-2, osm-5) (Fig. 5). Interestingly, in six of these seven strains (all except ife-2), there is an increase in reactive oxygen species (ROS) that contributes to their longevity (treatment with antioxidants decreases their lifespan; Yang and Hekimi 2010, PLoS Biology; Zarse et al. 2012, Cell Metabolism; Wei and Kenyon 2016, PNAS). This observation is consistent with the idea that ROS/oxidative stress is sufficient to activate ATFS-1/mitoUPR. We have previously shown that exposure to a mild heat stress (35°C, 2 hours) or osmotic stress (300 mM, 24 hours) can extend lifespan but does not increase expression of the ATFS-1 target gene hsp-6 (Dues et al. 2016, Aging). Thus, there are multiple examples in which a genetic mutation or intervention increases longevity but does not trigger upregulation of ATFS-1 target genes. We have updated the manuscript to include these points.
__3) In Fig. 3, some of these genes appear to be unspecifically associated with different stressors. Therefore, it is difficult to rule out the participation of ATFS-1 in specific stress responses without looking at specific stress-responsive genes or a wider range of genes. For example, the conclusion that ATFS-1 does not control osmotic stress gene expression response comes from looking at 3 genes: sod-3, gst-4 and Y9C9A.8. gst-4 does not appear to be directly controlled by ATFS-1 regardless of the stressor. sod-3 is also upregulated by oxidative stress and Y9C9A.8 by anoxia. On the other hand, somewhat contradicting the authors' conclusions that ATFS-1 does not participate in osmotic stress response based on these 3 genes, ATFS-1 appears to be required for osmotic stress resistance.
__
In this experiment, we treated wild-type and atfs-1 deletion mutants with six different stressors (oxidative stress, bacterial pathogens, heat stress, osmotic stress, anoxia, and ER stress), isolated mRNA and then examined the expression of 14 different stress response genes. To select these genes, we chose a combination of the most established target genes of the stress response pathways that we examined in Figures 1/2, and genes that we had previously shown to be upregulated by specific stresses using fluorescent reporter strains (Dues et al. 2016, Aging). These genes included hsp-6, hsp-4, hsp-16.2, sod-3, gst-4, nhr-57, Y9C9A.8, trx-2, ckb-2, gcs-1, sod-5, T24B8.5, clec-67 and dod-22. To determine if ATFS-1 is required for gene upregulation in response to any of the six different stressors, we first identified which of these stress genes is significantly upregulated in response to each stressor and then looked to see if this upregulation is reduced or prevented by atfs-1 mutation. We found that there were multiple examples of this for both oxidative stress and bacterial pathogen stress, but not for other stresses. We selected three representative genes to display in Figure 3. Nonetheless, it is possible that there are genes that we didn’t examine that are upregulated by the other four stressors in an ATFS-1-dependent manner. To definitively address this question, one would have to do RNA sequencing on wild-type and atfs-1(gk3094) worms comparing untreated and stressed, but this is beyond the scope of the current manuscript. We have updated the manuscript to include these points, and noted the possibility that there are genes, which we didn’t measure, that are upregulated by the other four stressors in an ATFS-1-dependent manner. We have also included the qPCR data for all 14 genes for each of the six external stressors in Supplemental Figures S3-S8.
__ **Minor concerns:**
1) The paragraph starting in line 107 is confusing. They write that "Constitutive activation of ATFS-1 in atfs-1(et 15) and atfs-1(et17) mutants resulted in upregulation of most of the same genes that are upregulated in nuo-6 mutants, except for gst-4" and later they state that "Activating the mitoUPR through the nuo-6 mutation, or through the constitutively-active ATFS-1 mutants did not significantly increase the expression of target genes from the ER-UPR (hsp-4; Fig. 1B) or the cyto-UPR (hsp-16.2; Fig. 1C)." I understand the upregulation of ER-UPR and cyto-UPR is not statistically significant (isn't it for hsp-16.2?), but the first sentence is not accurate if statistics is considered.
__
To clarify this, we have modified the first sentence to describe which genes are significantly upregulated in atfs-1(et15) mutants, and separately describe the findings for atfs-1(et17) mutants in the second sentence. The results for hsp-16.2 are not significant because this gene shows highly variable expression between replicates and can be induced 60-fold. We have noted this in the text as well.
__ 2) The authors should discuss why they think atfs-1(et15) gain-of-function mutant exhibited decreased resistance to chronic oxidative stress, while it is protected from acute oxidative stress. In fact, the et15 allele differs in many aspects in relation to the et17 and in some cases it behaves similarly to the gk3094 loss-of-function allele.
__
While atfs-1(et15) and atfs-1(et17) mutants generally show similar results, they also exhibit differences. We previously used RNA sequencing to examine gene expression in these two strains. We found that atfs-1(et15) mutants have far more extensive changes in gene expression than atfs-1(et17) mutants (6227 differentially expressed genes versus 958 differentially expressed genes). It is possible that the et15 mutation is more disruptive to the mitochondrial targeting sequence than et17, thereby resulting in increased nuclear localization and more gene expression changes. The additional gene expression changes in the atfs-1(et15) mutant may contribute to their decreased resistance to chronic oxidative stress. We have included these points in the revised manuscript.
__ 3) Fig 4I is very similar to Fig. 6A of the other manuscript which strengthen the notion that ATFS-1 is not required (it is rather detrimental) for bacterial pathogen response when no underlying stress (most likely oxidative) occurs.
__
Yes, our results indicate that ATFS-1 is not required for wild-type survival of bacterial pathogen exposure. This is consistent with our findings in the other manuscript that baseline expression of innate immunity genes does not depend on ATFS-1 (innate immunity gene expression is similar between wild-type and atfs-1(gk3094) mutants). We have updated the manuscript to emphasize these points.
__ 4) In the paragraph starting in line 213, the authors conclude that "ATFS-1 is sufficient to protect against oxidative stress, osmotic stress, anoxia, and bacterial pathogens but not heat stress". The results do not unequivocally support a participation of ATFS-1 in oxidative stress or bacterial pathogen response, given the responses vary depending on the allele or condition.
__
We have modified this sentence by replacing “activation of ATFS-1 is sufficient to protect” with “activation of ATFS-1 can protect” to indicate that we didn’t observe protection in all cases.
__ 5) "Combined, this indicates that ATFS-1 does not play a major role in lifespan determination in a wild-type background despite having an important role in stress resistance." It actually does, since ATFS-1 gain-of-function decreases lifespan.
__
We have rewritten this sentence to say that constitutive activation of ATFS-1 does not extend lifespan, despite increasing resistance to multiple stresses.
__
__
__ __
__6) Paragraph starting in line 359 needs to be discussed in light of the results of the other manuscript submitted by the authors to EMBO.
__
Combined these two manuscripts indicate that baseline levels of innate immunity are dependent on the p38-mediated innate immune signaling pathway, and not dependent on ATFS-1. This idea is supported by the fact that deletion of atfs-1 does not decrease resistance to bacterial pathogens and does not reduce the expression of innate immunity genes. In contrast, disrupting genes involved in the p38-mediated innate immune signaling pathway does decrease resistance to bacterial pathogens and does decrease the expression of innate immunity genes. We have updated this paragraph to include these points and reference the findings from our manuscript on innate immunity in the long-lived mitochondrial mutants.
__ 7) In Fig. 1C, it appears that atfs-1 loss of function increases hsp-16.2. Is that significant?
__
While there is a strong trend towards increased hsp-16.2 expression in atfs-1(gk3094) mutants, this difference did not reach significance because this gene shows highly variable expression and can be induced 60-fold.
__ 8) In Fig. 2, 5 and S1, it would be interesting to build one single Venn Diagram with all the lists of genes to see if there are common genes associated with multiple pathways and if there are many ATFS-1 target genes not associated with these classical stress or longevity pathways.
__
While we would be very interested in performing this type of visualization, weighted Venn diagrams with more than 3 or 4 groups are challenging to generate and more challenging to interpret. Instead, we have generated an UpSetR plot to demonstrate the number of overlapping genes between each of the stress response pathways, as well as how many ATFS-1 target genes are not involved in stress response. We have included this plot in Figure 2, Panel I. We have also generated simpler figure to show the overlap between pairs of stress response pathways (Figure S1). In addition, we have also added Table S4 with these gene lists.
__ 9) In Fig. 2, 5 and S1: What are the p values referred to?
__
The p-values indicate the significance of the difference between the observed number of overlapping genes between the two gene sets, and the expected number of overlapping genes if the genes were picked at random. We have clarified this in the manuscript.
__ 10) In paragraph starting in line 85, the authors should include references that evidence the genes are bona fide markers of the stress response pathways.
__
We have added references for each of the genes that we examined to link it to the associated stress response pathway.
__ 11) Tables S2 and S3 are missing. __
Tables S2 and S3 were uploaded as Excel spreadsheets, not included with the supplemental figures as the other supplementary Tables were. We apologize that these were difficult to locate. In the revision, Table S1 is in the manuscript file, while Table S2 to S6 will be uploaded as separate files.
__ __
__Reviewer #2:
**Major comments:**
The only major conclusion that I would qualify is "ATFS-1 serves a vital role in organismal survival of acute stresses through its ability to activate multiple stress response pathways"-the data, as presented, does not make clear whether ATFS-1 directly activates these pathways (ie, by binding response elements in genes in those pathways), or indirectly influences them by altering the physiology of the worm).
__
We agree that our data does not determine precisely how ATFS-1 acts to modulate the expression of the different stress response pathways. To determine the extent to which ATFS-1 might be able to bind directly to the target genes of other stress response pathways, we have compared the ChIP-seq results for ATFS-1 to ChIP-seq studies for other stress responsive transcription factors (DAF-16, SKN-1, HSF-1, HIF-1, ATF-7). We found that in each case there are sets of genes that can be bound by both transcription factors. This suggests that ATFS-1 may be direct regulating at least some of the target genes from other stress response pathways. We have updated our manuscript to include these points and included the ChIP-seq data comparisons in Figure S2.
__ **Minor comments:**
In abstract, consider broadening/re-wording "Gene expression changes resulting from the activation of the mitoUPR are mediated by the transcription factor ATFS-1/ATF-5." Because a naïve reader may understand this to suggest that ATFS-1 is activated only by mitochondrial protein misfolding.
__
In this sentence we are describing the role of ATFS-1 in mediating the gene expression changes resulting from the activation of the mitoUPR. We would be happy to modify the sentence if this is unclear.
__Please indicate whether strains were outcrossed, and how often.
__
We have added these details to our materials and methods.
__ How was "young adult" defined? Were worms synchronized, and if so, how?
__
Young adult worms are picked on day 1 of adulthood before egg laying begins. The worms were not synchronized, but picked visually as close to the L4-adult transition as possible. We have added these details to our method section.
__ For the gene expression experiments, do I understand correctly that FUDR was used only for oxidative stress and adult day 2 experiments? Please clarify.__
Yes, that is correct. FUdR was used for these samples because (1) with the 2-day duration of this stress, worms can produce progeny which would complicate the collection of the experimental worms; and (2) 4 mM paraquat often results in internal hatching of progeny when FUdR is absent, which might have affected the results. The control worms for the 48-hour 4 mm paraquat stress were also treated with FUdR. We have clarified this in the manuscript and noted that the presence of FUdR has the potential to alter gene expression.
__ Important: Please make clear how many replicates were performed for each experiment, and where relevant, how many worms were measured per replicate (e.g., stress survival and lifespan). __
We have added a spreadsheet (Table S6) to include the number of replicates and number of worms per replicate for all experiments.__
For 2-way ANOVA analyses, please specify p values of both main factors as well as interaction terms and posthoc analyses where relevant.
__
We have included these additional details from our statistical analyses in Table S6.
__ In the second paragraph of the introduction, I suggest broadening slightly the description of why normal mitochondrial function is required for ATFS-1 important and degradation, because this helps the reader understand that any one of many perturbations to mitochondrial function (decreased bioenergetics, membrane potential, protein degradation, protein import; increased ROS; etc.) could prevent or reduce ATFS-1 import and degradation.
__
We have added these additional factors that might prevent ATFS-1 import and degradation in paragraph one of our introduction and broadened the description in paragraph two.
__ For Figure 1: The authors present their choice of genes to analyze as if, and interpret their results assuming, that each of these gene is ONLY regulated by the indicated stress response pathways. I think this is very unlikely. For example: is it certain that sod-3 and trx-2 are not also skn-1 regulated? How is "antioxidant" distinguished from the skn-1 pathway? Further clouding the water is the likelihood that nuo-6 and atfs-1 manipulations alter physiology in such a way that there are secondary/indirect stress pathways activated (for example: the authors show that ATFS-1 overexpression shortens lifespan. Perhaps this is why it appears that ATFS-1 overexpression also appears to cause a strong, although variable, upregulation of the cytosolic UPR?). The likelihood (in my opinion) that these genes are in fact regulated by more than one type of response element, and that the manipulations used to study these relationships have pleiotropic effects, do not invalidate the general conclusion that these pathways interact-but they do mean that the results should be discussed with more caveats regarding HOW they interact.
__
These are excellent points. The genes that we selected for Figure 1 are the genetic targets that in our reading of the literature have been most often used to represent a particular stress response pathway. We have added references to justify the association of each gene with the indicated stress response pathway. We have also noted that in at least some cases the stress response genes that have been typically used to represent a specific pathway can be activated by multiple pathways. We agree that the selection of genes for Figure 1 is not a comprehensive approach, and that it is possible that if we chose a different gene from each of these pathways, the results might be different. We have updated our manuscript to specifically note these limitations. To avoid these limitations, we examined the overlap between all of the genes significantly upregulated by ATFS-1 activation and all of the genes significantly upregulated by the different stress response pathways in Figure 2. In addition, to gain a better understanding of the overlap between these different stress response pathways globally, we have compared gene expression between each of the stress response pathways studied in Figure S1.
__Figure 1 also illustrates why a more detailed description of sample size and statistical analysis should be provided. What was the "n"? What were the main effects and interaction terms of each 2-way ANOVA? The design is not full factorial and therefore does not permit a simple 2-way ANOVA (i.e., not all condition combinations are performed)-which responses precisely were compared to which? Were 2 2-way ANOVAs performed per mRNA?
__
For Figure 1 we used a one-way ANOVA to compare all of the groups to wild-type with a Bonferroni’s Multiple Comparison post-hoc test. We have updated the manuscript to include the sample size and statistical details in Table S6.
__ The work shown in Figure 2 is a very nice way to leverage previous data to further explore this idea of cross-talk. I would suggest including a bit more meta-data in the supplemental data files related to each dataset. For example, what lifestages were used (were they all young adult?), was FUDR used, etc.
__
We have added these details to Table S3, which includes the lists of target genes from each stress response pathway.
__ However, again, I don't understand how the authors can reach this conclusion: "Combined, this indicates that activation of ATFS-1 is sufficient to upregulate genes in multiple stress response pathways." (lines 152-153 but similar phrasing occurs multiple times) Could it not simply be that one form of cellular stress often eventually triggers broader cellular dysfunction, thus activating other cell stress pathways? Ie-how do we know whether these genes are directly regulated by atfs-1 binding regulatory elements, as implied by this phrasing?
__
This conclusion is derived from our data showing that constitutively active ATFS-1 mutants have significant upregulation of target genes from multiple stress response pathways (Figure 2). As the worms in those experiments were not exposed to stress, we don’t have reason to believe that they are experiencing cellular stress or dysfunction. We think it is more plausible that activation of ATFS-1, which normally occurs in response to stress, leads to the activation of other stress response pathways, either directly or indirectly, and that these pathways are recruited to help regain mitochondrial homeostasis. We don’t mean to imply that activated ATFS-1 binds directly to the target genes of other stress response pathways. We have clarified this in the revised manuscript.
__ The stress response experiments are very nicely done and very interesting. I appreciate that the authors did not shy away from describing counterintuitive results (eg et15 mutants showing increased sensitivity to chronic oxidative stress), and think that these results should also be briefly considered in the Discussion.
__
We have updated our manuscript to discuss the observation that atfs-1(et15) mutants have increased sensitivity to chronic oxidative stress.
__
__
__ __
__Figure 3: please report ANOVA interaction terms-these are what tell whether the inductions are in fact dependent on atfs-1 (not the post-hoc analyses). Again, it also appears that in some cases, there is an upregulation of certain genes with atfs-1 knockdown-please report all p-values (because there will be many, I recommend a supplemental table with all main and interaction and posthoc analyses). Again, the "n" also needs to be specified.
__
We have added Table S6 to include all of these statistical details.
__ Figure 4 A-C appear to be lacking error bars? Please add. Perhaps relatedly-the effect size for 4A looks much larger than for 4B, but this does not come across in the text.
__
We have added error bars to Figure 4A-C. We think the difference in effect size might result from the fact that 4A is an acute assay and 4B is a chronic assay. We speculate that the negative effect of the et15 and et17 mutations on lifespan might be a stronger factor in the chronic assay. We have updated the text to comment on the relative effect sizes.
__ For Figures 4 and 6, please indicate sample size-number of independent experimental replicates, and number of worms per replicate (or range per replicate).
__
We have added the number of replicates and sample size in Table S6.
__ Lines 224-225 re. sod-2 mutants: these may also act by decreasing ROS signaling (less conversion of superoxide anon to hydrogen peroxide); also, why would this strain not be considered another long-lived mitochondrial mutant (like clk-1, isp-1 and nuo-6, to which it is contrasted)?
__
We think the sod-2 mutation extends lifespan by increasing ROS signaling, as treatment with antioxidants decreases their lifespan. The increased superoxide from the loss of sod-2 may be converted to H2O2 by sod-3 or sod-1, which are also present in the mitochondria. We don’t include sod-2 with the mitochondrial mutants because the mutation does not directly impact the mitochondrial electron transport chain, but may do so secondarily due to elevated ROS.
__ The confirmation that atfs-1 overexpressing strains are short-lived is very interesting. However, I think this statement "Combined, this indicates that ATFS-1 does not play a major role in lifespan determination in a wild-type background despite having an important role in stress resistance." (lines 265-267 and similar in several places throughout the Discussion, eg line 279) should be altered to indicate that this was observed under controlled laboratory conditions. Eg, "...this indicates that ATFS-1 does not play a major role in lifespan determination in a wild-type background under optimized laboratory conditions..."
__
This is an interesting point. It is possible that constitutive activation of ATFS-1 may be beneficial for lifespan in an environment where worms are exposed to external stressors. We have noted that our lifespan results were obtained under lab conditions, which are believed to be relatively unstressful.
__
__
__ __
__Discussion: consider adding in a consideration of dose-response, both of knockdown of mitochondrial genes (eg, k/d of many mitochondrial genes promotes lifespan at low levels, but decreases lifespan with greater knockdown) and of stressors (chemicals, heat, etc; for chemicals, at the least, dose-response is very important, with low levels not infrequently triggering apparently beneficial stress responses, and higher levels causing toxicity).
__
It is possible that the magnitude of ATFS-1 activation will impact its effect on stress resistance and lifespan. Perhaps, a milder activation of ATFS-1 will be more beneficial with respect to lifespan. The degree of ATFS-1 activation may also account for differences that we observe between atfs-1(et15) and atfs-1(et17) mutants. atfs-1(et15) has more differentially expressed genes than atfs-1(et17) suggesting the possibility that it has more ATFS-1 activation. We have updated our manuscript to include these points.
__ Section beginning on line 384 "ATFS-1 upregulates target genes of multiple stress response pathways"-again, please revise to make clear that this work does not demonstrate direct regulation.
__
We have clarified that our results don’t demonstrate direct regulation. In addition, we have examined published ChIP-seq datasets to determine if there is evidence of direct regulation.
__ It seems to me that our reviews are in pretty good agreement. I agree with Reviewers 1 and 3 where they commented on things that I did not. While I did not consider the manuscripts as overlapping in the sense of being redundant, I very much like Reviewer 1's suggestion that they be published back to back and that the Discussion of each incorporate consideration of the Results of the other.
__According to this suggestion, we have arranged for these papers to be considered for publication at the same time in EMBO Reports and Life Science Alliance. We have updated the discussions of both manuscripts to incorporate the findings of the other manuscript.
__ Reviewer #3:
**Major comments**
1.The authors mention that activation of the UPRmt by nuo-6 mutants or atfs-1(gf) do not activate the ER UPR or cyto-UPR gene expression targets (lines 111-113). However, they also find that atfs-1(gf) animals have 25% overlap with the ER UPR pathway (line 146-147). Is 25% overlap not substantial?
__
The genes that we are referring to in lines 111-113 are the genetic targets that in our reading of the literature have been most often used to represent the ER-UPR or Cyto-UPR. This is not a comprehensive approach, and it is possible that if we chose a different gene from each of these pathways, the result might be different. We have updated our manuscript to include this limitation. To avoid this limitation, we examined the overlap between all of the genes significantly upregulated by ATFS-1 activation and all of the genes significantly upregulated by the ER-UPR or Cyto-UPR in Figure 2. In both cases, we find the overlap is significant, indicating that activation of ATFS-1 leads to activation of ER-UPR and Cyto-UPR target genes.
__
__
__ __
__To determine whether ATFS-1 mediates any protective effect during ER stress, authors should test atfs-1(gf) and atfs-1(lf) animals' resistance to ER stress.
__
To examine the effect of ATFS-1 on resistance to ER stress, we exposed wild-type, atfs-1(gk3094), atfs-1(et15) and atfs-1(et17) worms to 50 µM tunicamycin beginning at young adulthood and monitor survival daily. We found that both constitutively active atfs-1 mutants, et15 and et17, have increased resistance to ER stress compared to wild-type worms, while atfs-1 deletion mutants have a similar survival to wild-type. We have added this new data to Figure 4.
__ Authors should comment on the difference in outcomes with atfs-1(et17) and atfs-1(et15) animals to chronic oxidative stress (line 184-187).
__
We have updated our manuscript to discuss the observation that atfs-1(et15) mutants have increased sensitivity to chronic oxidative stress.
__ Lines 258-260. The authors should make clear in this section that a previous study had already measured lifespans of atfs-1(gf) animals and found that it was reduced (PMID 24662282). Also, an elaboration on why this experiment was repeated would be warranted.
__
We have referenced the lifespan results from this previous study in our introduction (line 53-54, Bennett et al), in our results section (lines 342-343; “which is consistent with a previous study finding shortened lifespan in atfs-1(et17) and atfs-1(et18) worms”) and in our discussion (lines 429-431; “as well as previous results using constitutively active atfs-1 mutants (et17 and et18) show that constitutive activation of ATFS-1 in wild-type worms results in decreased lifespan”). The reasons that we repeated this result are (1) because the lifespan of the atfs-1(et15) mutant had not been measured and this was the allele that we used in our paper; and (2) because the shortened lifespan is a surprising result given the beneficial effect of ATFS-1 on stress resistance, we thought it was important to repeat this experiment under the same conditions that we measured stress resistance.
__ The authors find that atfs-1(gk3094) animals lived longer during infection with PA14 (line 208-211). Another study found that atfs-1(gk3094) animals died faster on PA14 (PMID 28283579), which should be mentioned and commented on.
__
We have added this finding to our discussion. We have also compared the protocols used by Jeong et al. (who observed decreased survival in atfs-1(gk3094) deletion mutants), Pellegrino et al. (who observed wild-type survival in atfs-1(tm4919) deletion mutants and our manuscript (in which we observed slightly increased survival in atfs-1(gk3094) deletion mutants), to see which parameters might account for the observed differences.
__**Minor comments**
Line 38: "Inside the mitochondria, ATFS-1 is degraded by the Lon protease CLPP-1/CLP1". The phrasing suggests that CLPP-1/CLP1 is a Lon protease, when in fact they are independent proteases.
__
We have removed the word “Lon” to clarify this.
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Referee #3
Evidence, reproducibility and clarity
The manuscript by Soo et al. investigates the role of the mitochondrial unfolded protein response (UPRmt) during different organismal stresses. Using both loss-of-function and gain-of -function alleles of atfs-1, the gene encoding the transcription factor and main regulator of the UPRmt, the authors discover that ATFS-1 is both required and sufficient for the expression of genes associated with different types of cellular stresses including hypoxia, innate immunity, and antioxidant defense. Consistent with these gene regulations, gain-of-function atfs-1 animals were more resistant to specific cellular stresses, while loss of ATFS-1 animals were generally more sensitive.
Major comments
1.The authors mention that activation of the UPRmt by nuo-6 mutants or atfs-1(gf) do not activate the ER UPR or cyto-UPR gene expression targets (lines 111-113). However, they also find that atfs-1(gf) animals have 25% overlap with the ER UPR pathway (line 146-147). Is 25% overlap not substantial?
To determine whether ATFS-1 mediates any protective effect during ER stress, authors should test atfs-1(gf) and atfs-1(lf) animals' resistance to ER stress.
- Authors should comment on the difference in outcomes with atfs-1(et17) and atfs-1(et15) animals to chronic oxidative stress (line 184-187).
- Lines 258-260. The authors should make clear in this section that a previous study had already measured lifespans of atfs-1(gf) animals and found that it was reduced (PMID 24662282). Also, an elaboration on why this experiment was repeated would be warranted.
- The authors find that atfs-1(gk3094) animals lived longer during infection with PA14 (line 208-211). Another study found that atfs-1(gk3094) animals died faster on PA14 (PMID 28283579), which should be mentioned and commented on.
- At the request of the Editor, I was asked to comment on potential overlap between this manuscript and a recently submitted article submitted by the current authors (RC-2021-00651). There is only minor overlap in my opinion, with the finding in the current manuscript that the UPRmt is associated with stimulation of a pathogen defense program (innate immunity). Manuscript RC-2021-00651 goes into more detail regarding the mechanism of the UPRmt/innate immunity association and regulation.
Minor comments
Line 38: "Inside the mitochondria, ATFS-1 is degraded by the Lon protease CLPP-1/CLP1". The phrasing suggests that CLPP-1/CLP1 is a Lon protease, when in fact they are independent proteases.
Significance
The finding that the UPRmt regulates other cellular stress response pathways which provides resistance to a variety of stressors is of interest. However, associations of the UPRmt with increased resistance to exogenous stresses such as hypoxia and pathogen infection have been reported before (PMID 26234215, 25274306, 28283579), which might reduce the impact of the current manuscript to some degree.
This work would be interest to those in the fields of mitochondria, stress responses, and longevity.
My expertise is in stress responses, longevity, and host-pathogen interactions.
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Referee #2
Evidence, reproducibility and clarity
Summary:
The authors carried out experiments, and mine published datasets, to further characterize the role of the ATFS1 transcription factor in mediating survival and lifespan in laboratory or stressed conditions. The role of ATFS-1 was assessed by using a loss-of-function deletion and two constitutive gain-of function mutants in which the mitochondrial leader sequence is not functional, resulting in continual nuclear translocation. The effect of ATFS1 loss or constitutive activation was assessed in both wild-type and mutant (mitochondrial function and long-lived mutants) strains, and either under standard laboratory conditions or in the context of a variety of physical, chemical, and pathogen stressors. Constitutive ATFS-1 activation upregulated genes from a number of stress-response pathways, and the loss of atfs-1 blocked upregulation of some stress-response genes by a variety of exogenous stressors, with little or no effect on baseline expression of those genes. Loss of atfs-1 also increased sensitivity to many exogenous stressors (not all mitochondria-targeting), and overexpression was generally protective. However, overexpression also decreased lifespan in the absence of exogenous stressor.
Major comments:
- Are the key conclusions convincing? Mostly, assuming sample size was adequate (see below). The only major conclusion that I would qualify is "ATFS-1 serves a vital role in organismal survival of acute stresses through its ability to activate multiple stress response pathways"-the data, as presented, does not make clear whether ATFS-1 directly activates these pathways (ie, by binding response elements in genes in those pathways), or indirectly influences them by altering the physiology of the worm).
- Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? No.
- Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation. No.
- Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments. N/A
- Are the data and the methods presented in such a way that they can be reproduced? Mostly; see below.
- Are the experiments adequately replicated and statistical analysis adequate? Unclear; see below.
Minor comments:
- Specific experimental issues that are easily addressable:
In abstract, consider broadening/re-wording "Gene expression changes resulting from the activation of the mitoUPR are mediated by the transcription factor ATFS-1/ATF-5." Because a naïve reader may understand this to suggest that ATFS-1 is activated only by mitochondrial protein misfolding. Please indicate whether strains were outcrossed, and how often.
How was "young adult" defined? Were worms synchronized, and if so, how?
For the gene expression experiments, do I understand correctly that FUDR was used only for oxidative stress and adult day 2 experiments? Please clarify. Important: Please make clear how many replicates were performed for each experiment, and where relevant, how many worms were measured per replicate (e.g., stress survival and lifespan).
For 2-way ANOVA analyses, please specify p values of both main factors as well as interaction terms and posthoc analyses where relevant. - Are prior studies referenced appropriately? Yes. - Are the text and figures clear and accurate? Yes. - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?
Yes:
In the second paragraph of the introduction, I suggest broadening slightly the description of why normal mitochondrial function is required for ATFS-1 important and degradation, because this helps the reader understand that any one of many perturbations to mitochondrial function (decreased bioenergetics, membrane potential, protein degradation, protein import; increased ROS; etc.) could prevent or reduce ATFS-1 import and degradation.
For Figure 1: The authors present their choice of genes to analyze as if, and interpret their results assuming, that each of these gene is ONLY regulated by the indicated stress response pathways. I think this is very unlikely. For example: is it certain that sod-3 and trx-2 are not also skn-1 regulated? How is "antioxidant" distinguished from the skn-1 pathway? Further clouding the water is the likelihood that nuo-6 and atfs-1 manipulations alter physiology in such a way that there are secondary/indirect stress pathways activated (for example: the authors show that ATFS-1 overexpression shortens lifespan. Perhaps this is why it appears that ATFS-1 overexpression also appears to cause a strong, although variable, upregulation of the cytosolic UPR?). The likelihood (in my opinion) that these genes are in fact regulated by more than one type of response element, and that the manipulations used to study these relationships have pleiotropic effects, do not invalidate the general conclusion that these pathways interact-but they do mean that the results should be discussed with more caveats regarding HOW they interact.
Figure 1 also illustrates why a more detailed description of sample size and statistical analysis should be provided. What was the "n"? What were the main effects and interaction terms of each 2-way ANOVA? The design is not full factorial and therefore does not permit a simple 2-way ANOVA (i.e., not all condition combinations are performed)-which responses precisely were compared to which? Were 2 2-way ANOVAs performed per mRNA?
The work shown in Figure 2 is a very nice way to leverage previous data to further explore this idea of cross-talk. I would suggest including a bit more meta-data in the supplemental data files related to each dataset. For example, what lifestages were used (were they all young adult?), was FUDR used, etc.
However, again, I don't understand how the authors can reach this conclusion: "Combined, this indicates that activation of ATFS-1 is sufficient to upregulate genes in multiple stress response pathways." (lines 152-153 but similar phrasing occurs multiple times) Could it not simply be that one form of cellular stress often eventually triggers broader cellular dysfunction, thus activating other cell stress pathways? Ie-how do we know whether these genes are directly regulated by atfs-1 binding regulatory elements, as implied by this phrasing?
The stress response experiments are very nicely done and very interesting. I appreciate that the authors did not shy away from describing counterintuitive results (eg et15 mutants showing increased sensitivity to chronic oxidative stress), and think that these results should also be briefly considered in the Discussion.
Figure 3: please report ANOVA interaction terms-these are what tell whether the inductions are in fact dependent on atfs-1 (not the post-hoc analyses). Again, it also appears that in some cases, there is an upregulation of certain genes with atfs-1 knockdown-please report all p-values (because there will be many, I recommend a supplemental table with all main and interaction and posthoc analyses). Again, the "n" also needs to be specified.
Figure 4 A-C appear to be lacking error bars? Please add. Perhaps relatedly-the effect size for 4A looks much larger than for 4B, but this does not come across in the text.
For Figures 4 and 6, please indicate sample size-number of independent experimental replicates, and number of worms per replicate (or range per replicate).
Lines 224-225 re. sod-2 mutants: these may also act by decreasing ROS signaling (less conversion of superoxide anon to hydrogen peroxide); also, why would this strain not be considered another long-lived mitochondrial mutant (like clk-1, isp-1 and nuo-6, to which it is contrasted)?
The confirmation that atfs-1 overexpressing strains are short-lived is very interesting. However, I think this statement "Combined, this indicates that ATFS-1 does not play a major role in lifespan determination in a wild-type background despite having an important role in stress resistance." (lines 265-267 and similar in several places throughout the Discussion, eg line 279) should be altered to indicate that this was observed under controlled laboratory conditions. Eg, "...this indicates that ATFS-1 does not play a major role in lifespan determination in a wild-type background under optimized laboratory conditions..."
Discussion: consider adding in a consideration of dose-response, both of knockdown of mitochondrial genes (eg, k/d of many mitochondrial genes promotes lifespan at low levels, but decreases lifespan with greater knockdown) and of stressors (chemicals, heat, etc; for chemicals, at the least, dose-response is very important, with low levels not infrequently triggering apparently beneficial stress responses, and higher levels causing toxicity).
Section beginning on line 384 "ATFS-1 upregulates target genes of multiple stress response pathways"-again, please revise to make clear that this work does not demonstrate direct regulation.
Significance
- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. The mitoUPR has generally been viewed and tested as an isolated mitochondrial stress-specific response; the authors have built upon previous work to convincingly show that it is integrated with a variety of other stress response pathways. This is an important contribution to the field.
- Place the work in the context of the existing literature (provide references, where appropriate). The authors have done a nice job of this in their discussion.
- State what audience might be interested in and influenced by the reported findings. Researchers interested in stress response in general, and mitochondrial homeostasis and stress response in particular, as well as the relation of these to lifespan.
- Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. Mitochondrial response to exogenous stressors, particularly pollutants.
Referees cross-commenting
It seems to me that our reviews are in pretty good agreement. I agree with Reviewers 1 and 3 where they commented on things that I did not. While I did not consider the manuscripts as overlapping in the sense of being redundant, I very much like Reviewer 1's suggestion that they be published back to back and that the Discussion of each incorporate consideration of the Results of the other.
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Referee #1
Evidence, reproducibility and clarity
Summary:
In this manuscript, Soo et al explore the role of the mitoUPR-associated transcription factor ATFS-1 as a hub in multiple stress response pathways controlling gene expression changes and resistance to a variety of exogenous and endogenous stressors. They found that ATFS-1 gain-of-function is sufficient to upregulate a number of genes involved in oxidative stress response, DAF-16-related response, hypoxia and innate immunity. Moreover, they found that many ATFS-1-responsive genes are upregulated under multiple stress conditions and by interventions that prolong lifespan. They also showed that ATFS-1 is required for stress response and resistance under different stress conditions. Finally, they demonstrate that ATFS-1 is not necessary for normal lifespan, but ATFS-1 gain-of-function decreases lifespan.
Major concerns:
1) This manuscript has some overlap with another manuscript from the same group recently submitted to EMBO J. Although I believe both manuscripts have sufficient elements to justify publication of two papers, I strongly recommend that these publications are made back-to-back and they should be discussed in context with one-another. While the main focus of the other manuscript is how mitochondrial mutations lead to improved bacterial pathogen response, it concludes ATFS-1 is key to explain how genes involved in this particular stress response are upregulated upon mitochondrial dysfunction. Their model is that mitochondrial mutations lead to upregulation of innate immunity genes via ATFS-1-mediated transcriptional activation. Here they show that ATFS-1 controls many other stress response pathways in addition to the innate immunity response. Somewhat contradicting their model in the other manuscript, here they show that ATFS-1 is not necessarily required for bacterial pathogen response. In contrast, they even found protection against PA in atfs-1 loss-of-function mutants. This could be explained in light of the fact that ATFS-1 appears to have a protective role under oxidative stress conditions (e.g., mitomutants or paraquat) whereas in worms that have no underlying stress, high ATFS-1 levels may be detrimental. This is consistent with the results in Figure 6. These aspects considered, I believe both manuscripts need to be revised back-to-back so that the data can be reconciled and discuss in context.
2) How is ATFS-1 function regulated in long-lived worms or under multiple stress conditions? Is there a common regulator such as oxidative stress or mitochondrial dysfunction? Both manuscripts would benefit from a clear understanding on how ATFS-1 is controlled under conditions where mitochondrial function is altered. Is mitoUPR required for this activation? If so, is mitoUPR upregulated in all interventions where ATFS-1 has been shown to play a role in stress response. Is there any intervention that controls longevity and does not trigger ATFS-1 response?
3) In Fig. 3, some of these genes appear to be unspecifically associated with different stressors. Therefore, it is difficult to rule out the participation of ATFS-1 in specific stress responses without looking at specific stress-responsive genes or a wider range of genes. For example, the conclusion that ATFS-1 does not control osmotic stress gene expression response comes from looking at 3 genes: sod-3, gst-4 and Y9C9A.8. gst-4 does not appear to be directly controlled by ATFS-1 regardless of the stressor. sod-3 is also upregulated by oxidative stress and Y9C9A.8 by anoxia. On the other hand, somewhat contradicting the authors' conclusions that ATFS-1 does not participate in osmotic stress response based on these 3 genes, ATFS-1 appears to be required for osmotic stress resistance.
Minor concerns:
1) The paragraph starting in line 107 is confusing. They write that "Constitutive activation of ATFS-1 in atfs-1(et 15) and atfs-1(et17) mutants resulted in upregulation of most of the same genes that are upregulated in nuo-6 mutants, except for gst-4" and later they state that "Activating the mitoUPR through the nuo-6 mutation, or through the constitutively-active ATFS-1 mutants did not significantly increase the expression of target genes from the ER-UPR (hsp-4; Fig. 1B) or the cyto-UPR (hsp-16.2; Fig. 1C)." I understand the upregulation of ER-UPR and cyto-UPR is not statistically significant (isn't it for hsp-16.2?), but the first sentence is not accurate if statistics is considered.
2) The authors should discuss why they think atfs-1(et15) gain-of-function mutant exhibited decreased resistance to chronic oxidative stress, while it is protected from acute oxidative stress. In fact, the et15 allele differs in many aspects in relation to the et17 and in some cases it behaves similarly to the gk3094 loss-of-function allele.
3) Fig 4I is very similar to Fig. 6A of the other manuscript which strengthen the notion that ATFS-1 is not required (it is rather detrimental) for bacterial pathogen response when no underlying stress (most likely oxidative) occurs.
4) In the paragraph starting in line 213, the authors conclude that "ATFS-1 is sufficient to protect against oxidative stress, osmotic stress, anoxia, and bacterial pathogens but not heat stress". The results do not unequivocally support a participation of ATFS-1 in oxidative stress or bacterial pathogen response, given the responses vary depending on the allele or condition.
5) "Combined, this indicates that ATFS-1 does not play a major role in lifespan determination in a wild-type background despite having an important role in stress resistance." It actually does, since ATFS-1 gain-of-function decreases lifespan.
6) Paragraph starting in line 359 needs to be discussed in light of the results of the other manuscript submitted by the authors to EMBO.
7) In Fig. 1C, it appears that atfs-1 loss of function increases hsp-16.2. Is that significant?
8) In Fig. 2, 4 and S1, it would be interesting to build one single Venn Diagram with all the lists of genes to see if there are common genes associated with multiple pathways and if there are many ATFS-1 target genes not associated with these classical stress or longevity pathways.
9) In Fig. 2, 4 and S1: What are the p values referred to?
10) In paragraph starting in line 85, the authors should include references that evidence the genes are bona fide markers of the stress response pathways.
11) Tables S2 and S3 are missing.
Significance
Nature and significance of the advance:
The study advances our knowledge about the role of ATFS-1 - a transcription factor involved in mitoUPR - in multiple stress response pathways.
Compare to existing published knowledge:
The role of ATFS-1 has been previously studied in the context of mitoUPR, although the present manuscript expands it to a variety of other stress response pathways. It is yet to be defined whether mitoUPR itself is promiscuously activated in response to different kinds of stressors or ATFS-1 may be activated independently of mitoUPR. As mentioned before, the present manuscript has considerable overlap with a manuscript from the same group under review in EMBO J. These manuscripts need to be discussed in light of one-another.
Audience:
The audience interested in this study is expected to be aging biologists, mitochondrial biologists, as well as researchers using C. elegans as a model organism.
Expertise:
I am interested in mechanisms of aging and their association with metabolism.
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Reply to the reviewers
Reviewer comments:
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
In this paper, the authors examine the relationship between the transcription factor Ribbon, its ribosomal protein gene (RPG) targets, and cell growth during the process of salivary gland tubulogenesis in the Drosophila embryo. This study builds upon previous work they published in 2016 (Loganathan et al., 2016). While the previous study identified RPGs as potential targets of Ribbon from ChIP-Seq analysis, they did not delve into the role of these targets in salivary gland morphogenesis. Here, the authors demonstrate that mutation of ribbon results in decreased cell volumes via immunostaining and image analysis. They identify and confirm RPGs as ribbon transcriptional targets using ChIP-SEQ, Microarray data, in situ hybridization, and qRT-PCR. They analyze these targets in an effort to identify a Rib consensus binding sites by MEME and find that Rib binding is not specific using EMSA. They suggest specificity arises from association with transcriptional cofactors. Binding with cofactors was confirmed by CO-IP and in vivo RNAi experiments demonstrated the requirement of these cofactors in mediating changes in cell volume during salivary gland tubulogenesis. They demonstrate that Ribbon regulation of cell growth via transcription of RPGs is not a universal mechanism for Ribbon function, as Ribbon regulates transcription of other genes in the context of tracheal development.
**Major comments:**
Results of all experiments are conclusive, and significant numbers of samples were noted for most figure panels. For a few panels the sample number/number of replicates was not noted, and it is recommended that the authors add this information (Figure 1F; 5B,C; 7B).
Additional experiments are not needed to support the conclusions presented in this work. The data and methods are presented clearly and the statistical analyses performed were appropriate.
In regard to microarray data, Figure 4E shows fold change as log2 values, but it is unclear if this is the case for Table S2. This should be clarified. The authors note in the text on page 7 that few targets show a greater than 1.5-fold change. Based on Figure 4E, this is a log2 value, and should be specified as such.
As the Rib antibody was generated in this study, it would be helpful to include data illustrating a confirmation of antibody specificity. This could include Rib antibody staining on rib mutant embryos, or showing a lack of band for ribbon in ribbon mutants on a Western blot. If the specificity has been published elsewhere, please add a reference.
**Minor Comments:**
As the microarray data was previously published in Loganathan et al 2016, as mentioned in the results section, this citation should also be included in the Methods section describing the Microarray data.
In the discussion section on page 15, a list of factors in the gene network are listed. What is viz.?
Reviewer #1 (Significance (Required)):
•As described in the introduction, the role of cell growth during embryonic tissue morphogenesis is a relatively unexplored topic. The authors point out that most previous studies describing regulation of tissue growth have focused on the role of mitosis and increased polyploidy, as in the gut (https://doi.org/10.1016/S0925-4773(00)00512-8 ), as primary mechanisms. In the case of the salivary gland, only a single endocycle occurs during embryogenesis and cells are post-mitotic, suggesting another mechanism is at play. This study identifies Ribbon as a mediator of cell growth and demonstrates that Ribbon mediates this function through transcriptional regulation of RPGs. In addition, they identify Ribbon cofactors that are important for salivary gland cell growth and tissue morphogenesis. Interestingly, they find that this mechanism for cell growth may be tissue specific, as Ribbon appears to regulate different genes in the trachea.
•This work has implications for the regulation of cell growth in other tissues and organisms and would be of broad interest to those studying organ development.
•In order to contextualize my review, I am a developmental biologist that works with Drosophila.
**Referees cross-commenting**
In regard to the comments by reviewer #2: I agree that point # 2 should be addressed to more thoroughly describe the method, but as the authors have looked at DNA Amplification at a time point following the normal endocycle, which occurs at stage 12, and DNA content is not significantly different, I don't think analysis of earlier stages would influence their conclusions.
Given that the authors do include some RNAi data for RPGs and Trf2, it would enhance the paper further to include M1BP and Dref RNAi data if quality reagents are available as described in point 5. Point 6 can be easily addressed. In regard to point 8, the effects of rib overexpression alone would be interesting to see given the ability of this construct to rescue the phenotype.
While I think points 3 and 7 are excellent ideas for a follow up study, I think they are outside of the scope of this paper. I do not view point 4 as essential to this study, as the study focuses on the regulation of transcription of the RPGs by Rib.
In regard to the comments by reviewer #3, I agree that points 1 and 2 should be addressed. It would be extremely difficult to address point #3 by dissecting out the tissue, but it could be addressed via further explanation in the text, as could point #4. I don't think minor points 4-6 need to be addressed, but the minor points 1-3 should addressed to improve the paper. For minor point #3, I would suggest the number of genes be included in Supplementary Table 1.
As reviewer #1, I think my comments should be addressed to improve the quality and clarity of the paper.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
This paper reported a role for the BTB/POZ-domain transcription factor rib in mediating early cell growth of embryonic salivary gland (SG) cells. the authors show that during tubulogenesis of the salivary glands, rib binds the transcription start site of almost all SG-expressed ribosomal protein gene (RPG) and promotes their transcription, thus providing a material foundation for cell growth. Interestingly, in embryo trachea cells, rib targets do not include RPGs, which indicates that rib may use different mechanisms to regulate cell growth of different organs. In general, this is a well-written, well designed research article with many conclusions well-supported by experimental evidence. Listed below are a few issues (mostly minor/unessential) for the authors to consider.
**Major comments:**
1.Although in Figure 1G, the nucleus size is indistinct in rib mutant and wt cells at stage 15 and 16, Figure 1C appeared to look like that the rib mutant nuclei at stage 11, 13 and 14 are significantly smaller than those in wild type cells. The authors need to make sure that the rib phenotype has nothing to do with DNA amplification.
2.Please describe the details on calculating DNA volume by DAPI staining in the method session.
3.The authors have demonstrated weak DNA binding ability of Rib, and physical interactions between Rib with the known regulators of RPG transcription (Trf2, M1BP, and Dref), but what is the functional relationships between Rib and the known RPG regulators? e.g., does Rib function to promote DNA binding and transcriptional activity of Trf2, M1BP, and Dref, or vice versa?
4.To confirm the rib function on RPG translation, it is recommended to examine ribosomal proteins by western, and comparing the total protein content would also be helpful.
5.As Trf2, M1BP and Dref are physically interacted with Rib, it would be helpful to determine Whether M1BP and Dref knockdown can phenocopy the cell growth deficit observed in rib mutant SGs.
6.Page12, paragraph 3, "Thus, despite the shared requirement for Rib in embryonic cell growth of both tubular organs, Rib-dependent growth in the trachea is likely through regulation of alternative growth-promoting factors." Please list the potential growth-promoting factors targeted by Rib according to the Chip-seq data, if possible.
7.It would be interesting to determine whether rib mutation differently affect the secretory function of salivary gland at embryo, larva, pupa or adult stage.
8.Does Rib overexpression have any effects to SG development? Considering the authors adopted GAL4-UAS system to rescue Rib under Rib-KO, it would be interesting to see if Rib overexpression could cause an opposite overgrowth phenotype.
Reviewer #2 (Significance (Required)):
This paper discovered a new mechanism underlying organ-specific cell growth regulation during a specific time-window of animal development, which should be of interest to the field of cell and developmental biology.
Drosophila genetics; Developmental biology
**Referees cross-commenting**
I agree with all the other referees that the comments raised by reviewer #1 should be addressed entirely.
In regard to the comments by reviewer #3, all of the 4 major points are excellent and should be addressed, but it is okay to address points #3 and 4 by simple explanation or re-wording. I find the minor point #6 is nice to have but not essential, the rest should be addressed.
In case of my comments (reviewer 2), points #1,2,5,8 should be addressed, others are nice to have.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
In the manuscript "The Ribb-osome: Ribbon boosts ribosomal protein gene expression to coordinate organ form and function" the authors show evidence that Ribbon mediates early cell growth in Drosophila embryonic salivary gland through direct interaction with ribosomal protein genes. The manuscript is well written while presenting novel and solid data. The data could be strengthened by some further analysis and clarification, but none of the issues raised represent major flaws.
**Key points:**
1.Cell segmentations: The way the cell segmentations / volume quantifications are presented it is impossible to judge their quality. The authors should provide the extracted geometries as Supplementary Data. The methods could be clearer on how the segmentations for cell and DNA volume were done; were the surfaces done manually, were there any image preprocessing steps etc.? In Figure 7C, it is not clear from the images whether cells or nuclei were segmented. Also, it would strengthen the work if the authors analysed the cell shapes (in particular cell height, and apical cell shape bias), considering that they mention it to be different in the Rib mutant. In addition, it would add to the manuscript if the authors could quantify the volume of the luminal space, of the epithelial layer in wt and mutant, and the bias in tube outgrowth.
2.The authors show nicely that the rib mutants have a smaller overall cell size, can this be the reason why the secretory tube in figure is smaller? In addition, if the overall size of the mutant and the WT is the same as suggested in figure 1H then why does the mutant larvae in figure 1f appear so much smaller than the WT in the same panel?
3.In figure 4f the authors see 4 out of 7 RPGs been significantly down-regulated, do they have an explanation for that? Why are not all 7 tested RPGs significantly down-regulated? Can it be that the results will be significantly improved by dissecting the tissue of interest instead of using whole embryos? Finally with what criteria were these 7 genes selected?
4.The authors state in their manuscript the limitations of the chip-seq and the fact that the 11 unbound RPGs are essentially a technical artifact. I suggest that the authors either perform ChIP on some of these RPGs to prove their point or that they ton down their statements about chip-seq limitations and Rib binding all SG-expressed RPGs
**Minor points**
The authors need to clarify in the text what is early and late stage of tubulogenisis.
In figure 1c the Mipp1 staining is of low quality and although the white lines help the reader on where to focus, noise vs signal is almost indistinguishable. Furthermore, the authors claim that they only take under consideration SG cells that show uniform membrane staining but Figure 1c does not show such uniform staining.
Figure 1d needs the addition of statistical analysis WT vs rib mutant st12 look very similar.
In their ChIP-seq data the authors identify 436 peaks that correspond to 413 genes. It is worth to add a pie chart depicting how many of those 413 are RPGs and how may are non-ribosomal.
Throughout the manuscript the authors exhibit nicely the effects of rib mutants. What happens to the tested genes in panel 4f when rib is overexpressed?
RPls are known to be involved in size regulation. If the authors use another driver than fkh to express Rib, Rpl19 etc will they still see similar phenotypes or not?
Figure 7b is hard to follow, the IP panels should be in agreement with the order that they appear in the text e.g., first experiment then controls
Reviewer #3 (Significance (Required)):
In the manuscript "The Ribb-osome: Ribbon boosts ribosomal protein gene expression to coordinate organ form and function" the authors show evidence that Ribbon mediates early cell growth in Drosophila embryonic salivary gland through direct interaction with ribosomal protein genes. As I am only vaguely familiar with the field, I would leave it to someone who is closer to judge the advance and relevance. But with the additional quantifications, the paper should be of interest more generally to developmental biologists who are interested in tubulogenesis, and if the authors make the 3D cell geometries available, the work should also be of interest to computational modellers with an interest in epithelial organization as segmented 3D cell geometries are still rare.
**Referees cross commenting**
Looking at all 3 referee reports, I find all points made by referee 1 either essential and/or easy to fix. As such, I would insist on all points made.
With regard to referee 2, I see points 1,5,8 as essential, and point 2 is too easy to do to not request it. The others I would consider nice-to-have, but not essential.
In case of my own report, I would insist on points 1 & 2. Among the minor points, points 4 & 6 are NOT essential. The others are either important or easy enough to fix.
I look forward to the views of my colleagues.
Our response to reviewer comments
We thank the reviewers for their very positive comments regarding the importance of this paper and for the constructive feedback they have provided. Indeed, we would be delighted to address every suggestion raised, but since we would also like to have this work published in a timely manner, it is quite helpful to have consensus among the three reviewers regarding which changes and experiments are the most important to include. Since all three reviewers felt it important to address all of the comments from Reviewer #1, we will do so. For the comments raised by reviewers #2 and #3, we will follow the consensus opinion and address those comments by changes in the text or by including more experiments. In this revision plan, we also address the comments that were considered to be beyond the scope of the current study.
Points raised by Reviewer #1
Include N values for all the figure panels: We will provide sample number information for those panels currently missing that information: Figures 1F; 5B, C; and 7B.
Microarray fold-change clarification: We will clarify that we are reporting the fold-change values in Table S2. As is standard with Volcano plots for reporting microarray data, Figure 4E is plotted as Log2 data.
Antibody validation: We will provide a supplemental figure with information about the Rib antiserum and its specificity.
Add citation regarding the microarray data: We will add the citation referring to the microarray data to the Methods section.
Uncommon word usage pg 15: We will remove “viz.”—contraction of a Latin phrase “videre licet” to mean “namely” or “specifically”—from the discussion of factors in the gene network, since it was clearly distracting.
Points raised by Reviewer #2
Appearance of Nuclei and Calculation of DNA volume: The rib mutant nuclei shown in Fig. 1C depict CrebA staining and were used only for identification of SG secretory cells – we did not measure nuclear volume in these samples. To eliminate any potential confusion, we have re-labelled the last column “3D cell volume”. All of the calculations of nuclear size (as a measure of DNA amplification) were carried out with DAPI-staining as shown In Fig 1G, which revealed no difference between WT and rib mutant SG secretory cells. Measurement of entire nuclear volume is critical, since, in any single focal plane, how much of the nucleus is captured varies. We will provide information detailing how DNA volume was obtained in the methods section.
SG cell size phenotypes of M1BP and Dref RNAi Knockdowns: We agree with the reviewers that determining if M1BP and Dref SG-specific RNAi also phenocopy the cell growth deficit observed in the rib mutant SGs is a meaningful experiment and could strengthen our conclusions. We will, therefore, perform this experiment. It should be noted, however, that whereas rib and Trf2 do not have significant levels of maternal mRNA or protein, both M1BP and Dref have high levels of both [based on ModEncode data; Flybase]. Thus, it may be challenging to deplete these genes with only SG driven expression of the RNAi constructs.
List of potential Rib-dependent growth promoting factors in the trachea: In the revised version, we provide the list of candidate growth genes bound by Rib from the tracheal Chip-Seq data as requested by reviewer #2 (and agreed upon by reviewer #1 as important) in the supplement.
Effects of Rib overexpression on SG cell growth: All of the reviewers agree that testing for a SG secretory cell over-growth phenotype with Rib overexpression is worthwhile and we will do this experiment. Nonetheless, we recognize that we may not see overgrowth phenotypes based on a few observations. Our ChIP-Seq data indicate that Rib binds neither the promoters of ribosomal RNAs [rRNAs; the other essential component of ribosomes] nor the promoters of known rRNA transcription factors. Based on a study from another group, it seems likely that Myc upregulates rRNA expression (Grewal et al., 2005). Correspondingly, myc is transcriptionally upregulated in the embryonic SG (supplemental panel 7C) and myc expression in the SG is independent of rib (i.e. Rib does not bind the myc gene based on the SG ChIP-Seq and myc levels in the embryonic SG do not change in rib null embryos based on microarray and whole mount in situs). Also based on ChIP-Seq, Rib binds its own promoter and, based on qRT-PCR experiments, represses its own expression (Loganathan et al., 2016). Thus, over-expression of Rib with GAL4:UAS driven expression may reduce rib transcription from the endogenous locus. Nonetheless, this experiment is still worth doing.
Points raised by Reviewer #3
Information on cell segmentations: In the revised manuscript, we will provide sample 3D views of cell volume quantifications as movie files. In the methods section, we will also make it clear that the surfaces were manually segmented and that no image preprocessing steps were performed. We will also provide the excel spread sheets on size calculations in a supplement. We will provide information in the legend for figure 7 that whole secretory cells were segmented for the calculations done for panel C. The information on cell shapes, apical membrane dynamics, and luminal volumes (including the assessment of developmental dynamics of tube elongation based on live-imaging construction of computational elastic and analytical viscoelastic models) has been presented in previous publications from our lab (Cheshire et al., 2008; Loganathan et al., 2016) and from work in other labs (Blake et al., 1998). We will include this information in the revised discussion and will include the appropriate citations.
Panel 1F and comment on the apparent smaller size of the rib mutant shown: rib mutant embryos show characteristic head invagination defects along with amioserosa and dorsal closure defects [Bradley and Andrew, 2001]. The partial embryo image in Panel 1F captures the head invagination defect making the embryo appear smaller. We will include images of whole embryos in the revised version to clarify that whole embryo volumes of rib mutants are comparable to WT for the representations shown in Fig. 1F.
Clarify early vs. late Tubulogenesis: Early SGs are stage 11, 12 – when the SG cells are internalizing. Late SGs are stages 13 – 16, when the glands are fully internalized. We will clarify this in the figure legend.
Statistics on Panel 1D: We will perform statistical analysis of growth profiles shown in Fig 1D as suggested by the reviewer and include the results in the figure or figure legend.
Pie-chart for RPG fraction: Given how crowded the figures currently are, instead of providing pie charts, we simply provide the fraction of the bound genes that are RP genes in the text. Using our set cut-off of 4.0: 12.9% of genes bound by Rib (with both drivers) were RP genes. Using the IDR platform for peak calling, 12.8% of bound genes were RP genes. In Fig 4A, we also include genes above the cut-off with one GAL4 driver, but not the other, as described in the legend.
Effects of Rib Overexpression: As discussed earlier, we will perform this experiment (please also see our response to the last comment by reviewer #2)
Order of presentation of co-IP results in Panel 7B: As requested, we will reorder the IP results in Fig. 7B as suggested by the reviewer to present first the results from the experiments and then the results from controls in accord with how we discuss the data in the results section.
Testing the functional relationships between Rib and known RPG regulators: We will not determine if Rib promotes DNA binding and transcriptional activity of Trf2, M1BP, and Dref, as this experiment was considered to not be critical for this paper by any of the three reviewers.
Panel 4F and tissue-specific RT-qPCR: We agree that it would be ideal to have tissue-specific qRT-PCR, but it is not technically feasible to dissect out enough embryonic SGs for analysis (as acknowledged by Reviewer 1). In future studies, we do plan to get that kind of information from single cell RNA sequencing (scRNA-Seq) of WT and rib mutant embryos, but there are a few hurdles to overcome before those experiments. In selecting the RP genes for qRT-PCR, we chose sample RpL and RpS genes, making sure to include at least one gene (RpS9) that was “not bound” by Rib based on ChIP-Seq criteria.
Determine Rib function on RPG translation: We will not examine levels of RP proteins by Western since this experiment was deemed be unnecessary for the current study by the three reviewers.
Effects of rib on the secretory function of the SG at the embryo, larva, pupa, or adult stage: We agree with the reviewer that these data would be interesting to have; as pointed out by reviewer #1, however, it’s a question for a future follow-up study.
Chip-Seq technical artifact / limitations: We don’t think we are incorrect in suggesting that the failure to detect Rib binding to all RP genes could be a technical artifact because of the following: (1) a direct examination of the binding tracts associated with every RP gene reveals a peak at/near the TSS. The values associated with those peaks do not always reach the cut-off, but when the peak values are lower than the cut-off, the signals in the flanking DNA are often also much lower than average (for details, see Supplemental Figure 1). (2) Among the RP genes whose expression went down significantly by qRT-PCR is RpS9 – an RP gene “not bound” by Rib, based on the cut-offs we followed.
Using another SG driver: We agree with reviewer #1 that the results obtained using the fkh-GAL4 driver for RNAi of RP regulators and RP genes are robust and sufficient to support the conclusion that Rib binds RPGs to regulate SG secretory cell size. Thus, we will not redo these experiments using another SG driver.
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Referee #3
Evidence, reproducibility and clarity
In the manuscript "The Ribb-osome: Ribbon boosts ribosomal protein gene expression to coordinate organ form and function" the authors show evidence that Ribbon mediates early cell growth in Drosophila embryonic salivary gland through direct interaction with ribosomal protein genes. The manuscript is well written while presenting novel and solid data. The data could be strengthened by some further analysis and clarification, but none of the issues raised represent major flaws.
Key points:
1.Cell segmentations: The way the cell segmentations / volume quantifications are presented it is impossible to judge their quality. The authors should provide the extracted geometries as Supplementary Data. The methods could be clearer on how the segmentations for cell and DNA volume were done; were the surfaces done manually, were there any image preprocessing steps etc.? In Figure 7C, it is not clear from the images whether cells or nuclei were segmented. Also, it would strengthen the work if the authors analysed the cell shapes (in particular cell height, and apical cell shape bias), considering that they mention it to be different in the Rib mutant. In addition, it would add to the manuscript if the authors could quantify the volume of the luminal space, of the epithelial layer in wt and mutant, and the bias in tube outgrowth.
2.The authors show nicely that the rib mutants have a smaller overall cell size, can this be the reason why the secretory tube in figure is smaller? In addition, if the overall size of the mutant and the WT is the same as suggested in figure 1H then why does the mutant larvae in figure 1f appear so much smaller than the WT in the same panel?
3.In figure 4f the authors see 4 out of 7 RPGs been significantly down-regulated, do they have an explanation for that? Why are not all 7 tested RPGs significantly down-regulated? Can it be that the results will be significantly improved by dissecting the tissue of interest instead of using whole embryos? Finally with what criteria were these 7 genes selected?
4.The authors state in their manuscript the limitations of the chip-seq and the fact that the 11 unbound RPGs are essentially a technical artifact. I suggest that the authors either perform ChIP on some of these RPGs to prove their point or that they ton down their statements about chip-seq limitations and Rib binding all SG-expressed RPGs
Minor points
The authors need to clarify in the text what is early and late stage of tubulogenisis.
In figure 1c the Mipp1 staining is of low quality and although the white lines help the reader on where to focus, noise vs signal is almost indistinguishable. Furthermore, the authors claim that they only take under consideration SG cells that show uniform membrane staining but Figure 1c does not show such uniform staining.
Figure 1d needs the addition of statistical analysis WT vs rib mutant st12 look very similar.
In their ChIP-seq data the authors identify 436 peaks that correspond to 413 genes. It is worth to add a pie chart depicting how many of those 413 are RPGs and how may are non-ribosomal.
Throughout the manuscript the authors exhibit nicely the effects of rib mutants. What happens to the tested genes in panel 4f when rib is overexpressed?
RPls are known to be involved in size regulation. If the authors use another driver than fkh to express Rib, Rpl19 etc will they still see similar phenotypes or not?
Figure 7b is hard to follow, the IP panels should be in agreement with the order that they appear in the text e.g., first experiment then controls
Significance
In the manuscript "The Ribb-osome: Ribbon boosts ribosomal protein gene expression to coordinate organ form and function" the authors show evidence that Ribbon mediates early cell growth in Drosophila embryonic salivary gland through direct interaction with ribosomal protein genes. As I am only vaguely familiar with the field, I would leave it to someone who is closer to judge the advance and relevance. But with the additional quantifications, the paper should be of interest more generally to developmental biologists who are interested in tubulogenesis, and if the authors make the 3D cell geometries available, the work should also be of interest to computational modellers with an interest in epithelial organization as segmented 3D cell geometries are still rare.
Referees cross commenting
Looking at all 3 referee reports, I find all points made by referee 1 either essential and/or easy to fix. As such, I would insist on all points made.
With regard to referee 2, I see points 1,5,8 as essential, and point 2 is too easy to do to not request it. The others I would consider nice-to-have, but not essential.
In case of my own report, I would insist on points 1 & 2. Among the minor points, points 4 & 6 are NOT essential. The others are either important or easy enough to fix.
I look forward to the views of my colleagues.
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Referee #2
Evidence, reproducibility and clarity
This paper reported a role for the BTB/POZ-domain transcription factor rib in mediating early cell growth of embryonic salivary gland (SG) cells. the authors show that during tubulogenesis of the salivary glands, rib binds the transcription start site of almost all SG-expressed ribosomal protein gene (RPG) and promotes their transcription, thus providing a material foundation for cell growth. Interestingly, in embryo trachea cells, rib targets do not include RPGs, which indicates that rib may use different mechanisms to regulate cell growth of different organs. In general, this is a well-written, well designed research article with many conclusions well-supported by experimental evidence. Listed below are a few issues (mostly minor/unessential) for the authors to consider.
Major comments:
1.Although in Figure 1G, the nucleus size is indistinct in rib mutant and wt cells at stage 15 and 16, Figure 1C appeared to look like that the rib mutant nuclei at stage 11, 13 and 14 are significantly smaller than those in wild type cells. The authors need to make sure that the rib phenotype has nothing to do with DNA amplification.
2.Please describe the details on calculating DNA volume by DAPI staining in the method session.
3.The authors have demonstrated weak DNA binding ability of Rib, and physical interactions between Rib with the known regulators of RPG transcription (Trf2, M1BP, and Dref), but what is the functional relationships between Rib and the known RPG regulators? e.g., does Rib function to promote DNA binding and transcriptional activity of Trf2, M1BP, and Dref, or vice versa?
4.To confirm the rib function on RPG translation, it is recommended to examine ribosomal proteins by western, and comparing the total protein content would also be helpful.
5.As Trf2, M1BP and Dref are physically interacted with Rib, it would be helpful to determine Whether M1BP and Dref knockdown can phenocopy the cell growth deficit observed in rib mutant SGs.
6.Page12, paragraph 3, "Thus, despite the shared requirement for Rib in embryonic cell growth of both tubular organs, Rib-dependent growth in the trachea is likely through regulation of alternative growth-promoting factors." Please list the potential growth-promoting factors targeted by Rib according to the Chip-seq data, if possible.
7.It would be interesting to determine whether rib mutation differently affect the secretory function of salivary gland at embryo, larva, pupa or adult stage.
8.Does Rib overexpression have any effects to SG development? Considering the authors adopted GAL4-UAS system to rescue Rib under Rib-KO, it would be interesting to see if Rib overexpression could cause an opposite overgrowth phenotype.
Significance
This paper discovered a new mechanism underlying organ-specific cell growth regulation during a specific time-window of animal development, which should be of interest to the field of cell and developmental biology.
Drosophila genetics; Developmental biology
Referees cross-commenting
I agree with all the other referees that the comments raised by reviewer #1 should be addressed entirely.
In regard to the comments by reviewer #3, all of the 4 major points are excellent and should be addressed, but it is okay to address points #3 and 4 by simple explanation or re-wording. I find the minor point #6 is nice to have but not essential, the rest should be addressed.
In case of my comments (reviewer 2), points #1,2,5,8 should be addressed, others are nice to have.
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Referee #1
Evidence, reproducibility and clarity
In this paper, the authors examine the relationship between the transcription factor Ribbon, its ribosomal protein gene (RPG) targets, and cell growth during the process of salivary gland tubulogenesis in the Drosophila embryo. This study builds upon previous work they published in 2016 (Loganathan et al., 2016). While the previous study identified RPGs as potential targets of Ribbon from ChIP-Seq analysis, they did not delve into the role of these targets in salivary gland morphogenesis. Here, the authors demonstrate that mutation of ribbon results in decreased cell volumes via immunostaining and image analysis. They identify and confirm RPGs as ribbon transcriptional targets using ChIP-SEQ, Microarray data, in situ hybridization, and qRT-PCR. They analyze these targets in an effort to identify a Rib consensus binding sites by MEME and find that Rib binding is not specific using EMSA. They suggest specificity arises from association with transcriptional cofactors. Binding with cofactors was confirmed by CO-IP and in vivo RNAi experiments demonstrated the requirement of these cofactors in mediating changes in cell volume during salivary gland tubulogenesis. They demonstrate that Ribbon regulation of cell growth via transcription of RPGs is not a universal mechanism for Ribbon function, as Ribbon regulates transcription of other genes in the context of tracheal development.
Major comments:
Results of all experiments are conclusive, and significant numbers of samples were noted for most figure panels. For a few panels the sample number/number of replicates was not noted, and it is recommended that the authors add this information (Figure 1F; 5B,C; 7B).
Additional experiments are not needed to support the conclusions presented in this work. The data and methods are presented clearly and the statistical analyses performed were appropriate.
In regard to microarray data, Figure 4E shows fold change as log2 values, but it is unclear if this is the case for Table S2. This should be clarified. The authors note in the text on page 7 that few targets show a greater than 1.5-fold change. Based on Figure 4E, this is a log2 value, and should be specified as such.
As the Rib antibody was generated in this study, it would be helpful to include data illustrating a confirmation of antibody specificity. This could include Rib antibody staining on rib mutant embryos, or showing a lack of band for ribbon in ribbon mutants on a Western blot. If the specificity has been published elsewhere, please add a reference.
Minor Comments:
As the microarray data was previously published in Loganathan et al 2016, as mentioned in the results section, this citation should also be included in the Methods section describing the Microarray data.
In the discussion section on page 15, a list of factors in the gene network are listed. What is viz.?
Significance
•As described in the introduction, the role of cell growth during embryonic tissue morphogenesis is a relatively unexplored topic. The authors point out that most previous studies describing regulation of tissue growth have focused on the role of mitosis and increased polyploidy, as in the gut (https://doi.org/10.1016/S0925-4773(00)00512-8 ), as primary mechanisms. In the case of the salivary gland, only a single endocycle occurs during embryogenesis and cells are post-mitotic, suggesting another mechanism is at play. This study identifies Ribbon as a mediator of cell growth and demonstrates that Ribbon mediates this function through transcriptional regulation of RPGs. In addition, they identify Ribbon cofactors that are important for salivary gland cell growth and tissue morphogenesis. Interestingly, they find that this mechanism for cell growth may be tissue specific, as Ribbon appears to regulate different genes in the trachea.
•This work has implications for the regulation of cell growth in other tissues and organisms and would be of broad interest to those studying organ development.
•In order to contextualize my review, I am a developmental biologist that works with Drosophila.
Referees cross-commenting
In regard to the comments by reviewer #2: I agree that point # 2 should be addressed to more thoroughly describe the method, but as the authors have looked at DNA Amplification at a time point following the normal endocycle, which occurs at stage 12, and DNA content is not significantly different, I don't think analysis of earlier stages would influence their conclusions.
Given that the authors do include some RNAi data for RPGs and Trf2, it would enhance the paper further to include M1BP and Dref RNAi data if quality reagents are available as described in point 5. Point 6 can be easily addressed. In regard to point 8, the effects of rib overexpression alone would be interesting to see given the ability of this construct to rescue the phenotype.
While I think points 3 and 7 are excellent ideas for a follow up study, I think they are outside of the scope of this paper. I do not view point 4 as essential to this study, as the study focuses on the regulation of transcription of the RPGs by Rib.
In regard to the comments by reviewer #3, I agree that points 1 and 2 should be addressed. It would be extremely difficult to address point #3 by dissecting out the tissue, but it could be addressed via further explanation in the text, as could point #4. I don't think minor points 4-6 need to be addressed, but the minor points 1-3 should addressed to improve the paper. For minor point #3, I would suggest the number of genes be included in Supplementary Table 1.
As reviewer #1, I think my comments should be addressed to improve the quality and clarity of the paper.
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Reply to the reviewers
1. General Statements [optional]
On behalf of the authors, I would like to thank the three reviewers for providing a valuable feedback on our manuscript. We appreciate that they all considered that the first part of our study conveys novelty to the field of neuroblastoma research, while being coherent with recent studies aimed at identifying the NB cells-of-origin through transcriptomics and single-cell RNA-sequencing approaches. We indeed identified a transcriptional signature that distinguishes LR-NBs from HR-NBs, revealing that these two NB subgroups are better discriminated by the core transcriptional signature shared by the distinct SA cell types, rather than by the transcriptional specificities of any of these cell types, as recently debated. Of note, our findings unveil that the sympatho-adrenal transcriptional program facilitates NB formation but concomitantly restricts its malignant potential. We also wish to thank the reviewers for acknowledging that, in contrast to previous studies, we pursued further by testing the functional relevance of this signature through a combination of in vitro and in vivo experiments. We thereby identified NXPH1 and its receptor α-NRXN1 as ones of the very first factors showing an anti-metastatic activity in the context of NB. Uncovering NXPH1/α-NRXN signaling as a possible target to treat metastatic HR-NBs gives our study considerable clinical relevance.
We consider that the critics and recommendations provided by the three reviewers are positive and pertinent. We are thus willing to address nearly all the reviewers’ concerns and suggestions within the scope of a revision, including performing additional in vivo experiments, as explained in details in the following section. We hope that the planned revisions will be sufficient to make our manuscript suitable for publication.
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2. Description of the planned revisions
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
**Summary:**
In the reviewed manuscript, the authors aimed to characterize the sympathoadrenal (SA) transcriptional landscape that defines low- and high-risk neuroblastomas (LR-NBs and HR-NBs respectively). In particular, they analyze previously published Affymetrix U219 expression profiles of 18 low- (n=8) and high-risk (n=10) neuroblastomas, and 2 fetal human adrenal glands. The authors define transcriptional signatures of LR- and HR-NBs, and further unbiasedly classified them in 4 clusters defining groups of patients with different prognosis (as tested using the 498 SEQC cohort). Within these transcriptional signatures, the authors delineated a SA signature using a human fetal adrenal gland transcriptional profile recently published (Kildisiute et al. 2021), that can discriminate low-risk neuroblastomas. From these genes, the authors further select NXPH1 and NRNX1-2 as promising targets for extensive experimental in vitro and in vivo validation, including validation in cell cultures, and xenografts, to determine that NXPH1/alpha-NRXN1-2 signaling is sufficient for NB tumor growth, and that the expression of either stimulates the metastatic potential of NB cells.
**Major comments:**
1- The cohorts and data used by the authors to conduct the main analysis of the paper are already published, and thus the contribution of the analysis is incremental. In particular, the authors analyzed arrays from a limited cohort size, in comparison with others available sequenced with RNA-seq (e.g. 176 HR- and 322 non-HR NB in 498 SEQC; 224 HR- and 342 non-HR NBs included in the Westermann-genecode19 cohort; and 80 HR- and 20 non-HR in the Jagannathan cohort). Furthermore, the 12,000 most-expressed genes (out of ~20,000 available) were analyzed by the authors, as opposed to more than 40,000 (coding and non-coding) included in a normal RNA-seq study. Only the 498 SEQC dataset provides 12,000 genes significantly up-regulated in either high-risk (n~5,500) or non-high-risk (n~7,000). The differences between datasets could influence the results of the study. For example, in the reviewed manuscript, genes with a high expression in LR-NB (compared to fAG) included DNMT3B, SEMA5A, SOX5, and TET1, all of which have a significantly higher expression in HR-NBs of the 498 SEQC cohort. The quality of the manuscript will be enhanced with consistent results obtained by conducting the same reported analysis in larger cohorts.
Reply: To define the transcriptional signatures associated to the aetiology of LR- and HR-NBs and to the malignant behavior of HR-NBs, we first needed to compare the transcriptomic signatures of LR-NB and HR-NB samples to the one of a non-tumoral, healthy tissue such as the fetal adrenal gland. The online databases cited by the reviewer #1 do not contain any healthy samples, thus precluding the possibility to use it for the first step of our analysis. This is why we decided to use as a starting point the cohort of samples from the Hospital Sant Joan de Déu (HSJD cohort), which further provided the advantage of comparing samples from patients all diagnosed and treated at the same facility. We then used the SEQC database in different steps of our analytic strategy (Fig.S1F, 2F-H, 3A-B, S3) to test the relevance and coherence of the results obtained with the HSJD cohort in the context of a larger cohort. As mentioned by the reviewer #1, we indeed focused our analysis on the 12,000 most-expressed genes. We did so to follow the recommendations of the software Phantasus for transriptomic analyses of mammalian datasets.
2- In the reviewed manuscript, PHOX2A and PHOX2B are significantly more expressed in both LR-NB and HR-NB compared to fAG. This is also the case for other adrenergic markers including TH and DBH. Oppositely, the expression of cortex markers (i.e. STAR and CYP11A1) is significantly higher in fAG. Nevertheless PNMT is not significantly up-regulated in fAG in comparison to LR-NB nor HR-NB. Is it possible that the fetal adrenal glands analyzed include a large proportion of cortex that confounds the transcriptional signals? The quality of the manuscript will be enhance if the authors could establish what proportion of the fAG transcriptional signal belongs to cortex, and if they account for its influence in the analysis.
Reply: The samples of human fetal adrenal gland from which RNA was extracted were obtained from donations (samples staged at 22 weeks post-conception or 2 days after birth) and evaluated by a pathologist who confirmed their correct preservation before sample processing. As fetal adrenal glands are very small tissues, successfully separating the cortical and adrenal regions requires micro-dissection, which was not applied. These samples were instead processed entirely, presenting a ratio of medulla/cortex tissue according to their developmental stage.
3- A recent published paper (Bedoya-Reina et al. 2021) study the differences of HR-NB and LR-NB from a single-cell perspective. In the published manuscript, the authors conclude that LR-NBs are enriched in cells that resemble chromaffin and sympathoblast cells, while the high-risk neuroblastomas are enriched in undifferentiated cells that resemble cells with progenitor characteristics in post-natal adrenal gland. This is broadly consistent with the conclusion reached by the authors in the manuscript under review. It will enhance the content of the reviewed manuscript if the authors compare their transcriptional signatures with the recently published transcriptional signatures in this paper to answer the following questions:
-1) to what extent the transcriptional signatures for HR-NBs (4 hierarchical clusters incl.) in the manuscript under review resembles that from the published undifferentiated cluster (nC3) enriched in HR-NBs, and the progenitor cluster (hC1) in post-natal adrenal gland;
-2) to what extent the transcriptional signatures for LR-NBs (4 hierarchical clusters incl.) in the manuscript under review resembles that from the published NOR (nC7, nC8, and nC9) enriched in LR-NBs, and that from the chromaffin cells (hC4) enriched in post-natal adrenal gland;
-3) how is the expression of NXPH1 and alpha-NRXN1-2 in the reported LR- and HR-NBs, and adrenal gland.
Reply: As stated by the reviewer #1, our findings are indeed in agreement with the main conclusions recently reported by Bedoya-Reina et al (Bedoya-Reina et al, 2021). We agree with the reviewer #1’s suggestion that a detailed comparison of our transcriptomic signatures with those of Bedoya-Reina et al would be interesting. We will thus perform this comparison and provide these complementary results in the revised version of the manuscript.
4- In the discussion, the authors indicate that they do not aim to identify the transcriptional signature associated to NB origin but rather use the component of the SA lineage that distinguish LR- and HR-NBs. This statement implies that neuroblastoma can originate from any cell in the developing SA lineage (i.e. SCP, bridge, chromaffin and sympathoblast), a controversial assumption that requires further proof. In particular, when discussing about the core sympathoadrenal signatures enriched in LR-NBs and HR-NBs, the authors obtained a SA signature of genes shared by at least 3 of the 4 SA cell signatures. Further justification needs to be provided as for why (in particular) one of these SA cell signatures exclude the sympathoblast/neuroblast contribution.
Reply: We decided to use as a core SA signature the genes shared by at least 3 of the 4 SA cell types to avoid being too restrictive, the SCP signature being particularly distant from the 3 others. As such, the list of genes shared by all 4 cell types consists of 663 genes, of which only 51 are retrieved in our list of 503 LR vs HR DEGs. Conversely, the list of genes shared by Bridge cells, Chromaffin cells and Sympathoblasts (but not shared by SCPs) consists 3,530 genes, of which 199 are retrieved in the list of 503 LRvsHR DEGs. These complementary results, which can be discussed and provided if required, therefore suggest that the core SA signature discriminating LR-NBs from HR-NBs represents mostly the Bridge-Chromaffin-Sympathoblast lineage and excludes the SCP identity. They are therefore in agreement with the recent notion that NB cells-of-origin derive from the sympathoblast-chromaffin lineage.
5- Some of the most interesting results in the paper are limited to proportions in a subset of top-ranked genes. It will be valuable to set the analysis in an hypothesis driven context, add probabilities, test names, and corrected p-values to the results.
Reply: Our study is based on the initial hypothesis that LR- and HR-NBs might differ in the way they exploit the transcriptional program underlying their developmental origin. To get a deeper insight into this notion we performed a sequential differential expression analysis of primary samples of LR-NBs, HR-NBs and human fetal adrenal gland using the web-based Phantasus software. This software identifies differentially expressed genes between groups using the Limma R package, as detailed in the Methods section. As such, basic statistics for significance analysis were performed using a modetared T-test (as specified in the Limma R package) and FDR-adjusted P-values were set to PAdditionally, top-ranked genes of SA clusters were selected as part of a heuristic approach aimed at highlighting the clinical implication of the transcriptional clusters retrieved in our analysis. The relationship between their expression levels and patient survival was further analyzed using the SEQC database (Fig. S1E). Next, and in contrast to previous studies, we tested experimentally the validity of our analytical findings correlating the expression of SA-c1 genes with a better patient prognosis. To this aim, we selected the candidate gene NXPH1, one of the top-ranked genes from the SA-c1 subset, on the basis of several complementary arguments (listed in response to the reviewer #2’ comment #4). We thereby analyzed how modulating the expression of NXPH1 or that of its receptor α-NRXN1 affect the growth and metastatic potential of human NB cells. The results obtained argue for the validity of our model, by proposing that the neural crest-derived sympatho-adrenal developmental program, in particular the SA-c1 signature, plays a complex role in NB tumorigenesis: it facilitates tumor growth but blocks metastasis formation, hence opposing NB malignancy.
**Minor comments.**
6- In comparison with other cohorts that include low- and intermediate-risk NBs as non-HR NBs, the reviewed data specifically includes low- and high- risk NBs. It is important that the authors include a characterization of intermediate-risk neuroblastomas in their analysis.
Reply: The samples forming the HSDJ cohort were all obtained from NB patients diagnosed and treated at the Hospital Sant Joan de Déu. Several clinical and biological parameters were used for classification, among which the age of the patient. If we apply the cut-off point of 1 year (used at the time the samples were obtained), our cohort does not include any intermediate-risk NB. If we apply the cut-off of 18 months which is now more usual, our cohort would contain only one case (#HSJD-NB14 - aged 16 months at the time of diagnosis) that could be classified as intermediate-risk.
7- Further details and figures on what precise criteria was used to remove the sample #LR-08 is required. How including this sample changes the reported results?
Reply: As explained in the Methods section, before comparing the transcriptomic landscapes of LR-NBs and HR-NBs, we first assessed the sample dispersion by performing a principal component analysis. This analysis identified one outlier (#LR-08) that was clearly distant from all the other NB samples (both LR- and HR-NBs). We thus removed this sample to limit the dispersion and variability that would have impacted the subsequent analyses, as recommended by the Phantasus guidelines. We will provide an illustration of the PCA including this outlier. We believe that performing de novo the whole bioinformatical analysis including this outlier would not bring any novel significant conclusion.
8- GO-term distribution was assessed using the 50 most-enriched GO-terms. How would the results change if all the significant GO terms were analyzed?
Reply: We will re-analyse the GO-term distribution by including all the significant terms.
9- Was the SEQC 498 (GSE62564) dataset obtained with microarrays (as indicated in the methods) or with RNA-seq (i.e. Illumina HiSeq 2000)?
Reply: Similar results were obtained using either the SEQC database obtained with microarrays or the one obtained with RNAseq. The data presented in our manuscript correspond to the ones obtained with the RNA-seq SEQC database.
10- In methods, the first quartile (Q1) in SA-c1 has a higher limit in 487 samples and the fourth quartile the lowest limit in 4, how many samples (out of 498 NBs) were excluded and why?
Reply: As explained in the Methods section and in Fig. 2F, the complete SEQC cohort was included in this survival analysis. To subdivide the 498 samples of the SEQC cohort into 4 expression quartiles, we evaluated whether the expression level of each of the 242 SA-c1 genes (corresponding to 573 ref-seq IDs) in a given patient sample was above or below the mean expression of that gene in the complete cohort. Samples were then distributed into quartiles based on the number of genes presenting an expression level above the mean. As detailed in the Methods section, the resulting sample distribution was as follows: 487≤Q1≤360 (124 patients); 359≤Q2≤250 (125 patients); 249≤Q3≤135 (126 patients) and 134≤Q4≤4 (123 patients).
11- In the 503 DEGs between LR-HR NBs, NTRK2 and MYCN are not included, even if the HR samples included MYCN amplified tumors. Can the authors comment on this?
Reply: MYCN did not pass the cut-off when comparing its expression levels in LR-NB and HR-NB samples (showing an adjusted P=0.10226 for a cut-off of adjusted PNTRK2 was present in our initial 12,000 gene dataset, but it was not differentially expressed in any of the comparisons made (LR vs fAG: adj-P=0.4916; HR vs fAG: adj-P=0.63757; HRvsLR: adj-P=0.90431)
12- The authors mention that the top 30 genes found in cluster c1 (and also in c2) are correlated with favorable patient prognosis. Is it the case that *all* the genes in c1 (and also c2, c3 and c4) are significantly associated with a favorable or else unfavorable prognosis?
Reply: As presented in the datasheet “KM analyses” of Table S3:
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32 out of the 33 genes (97%) of the cluster c2 correlate to an unfavorable prognosis, the 33th gene showing no particular correlation.
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29 out of the 32 genes (91%) of the cluster c3 correlate to a favorable prognosis, 1 gene correlates to an unfavorable prognosis and the last 2 do not show any particular correlation.
For the clusters c1 and c4, we focused on the top 30 genes because these clusters contain numerous genes (338 and 100, respectively). The results obtained showed:
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All the top 30 genes (100% of the genes tested) of the cluster c1 correlate to a favorable prognosis
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23 out of the top 30 genes (77% of the genes tested) of the cluster c4 correlate to an unfavorable prognosis, whereas the other 7 genes do not show any particular correlation
We believe that these results are convincing enough. However, if considered mandatory we will assess the prognosis of all the genes found in clusters c1 and c4.
13- The high expression of a (significant?) number of genes in cluster c4 is observed in patients with worst outcome (i.e. lower event-free survival), including ATR, HIF1A, ING2, POLR2L, SRPRB (498 SEQC, analyzed with R2).
Reply: Indeed, 23 out of the top 30 genes (77% of the genes tested) of the cluster c4 correlate to an unfavorable prognosis, which appears to us as a significant number of genes. We will test whether the expression of the remaining genes forming the cluster c4 also correlate to an unfavorable prognosis.
14- Regarding the 242 genes in the core SA signature, although its a smaller number, the expression of several genes in the core SA signature with a higher expression in HR compared to LR belonging to clusters 2, 3, and 4 is observed in worst outcome patients in the 498 SEQC cohort (CHD7, DNMT1, HMGA1, HSD17B12, LBR, LSM7, MCM4, NKAP, POLA1, and others). Is this small fraction significant?
Reply: The genes presenting a higher expression in HR-NBs than in LR-NBs are found either in cluster c2 or cluster c4. The core SA signature retrieved 262 genes of the 503 LR vs HR DEGs, of which 14 belong to cluster c2 (including CHD7, DNMT1, HMGA1, LBR, LSM7, MCM4, and POLA1) and 3 to cluster c4 (UQCRFS1, NKAP and HSD17B12). As shown in the “KM analyses” datasheet of Table S6, these 17 genes all correlated with an unfavorable prognosis.
15- In Kildisiute et al. 2021, NRXN1 is expressed in SCPs, while NXPH1 is expressed in bridge, chromaffin and sympathoblastic cells. How are the microenviroment of these cells regulating the expression of these genes in a developmental context (particularly as sympathoblastic cells are know to have larger proliferative capabilities than SCPs)? how is this cell heterogeneity replicated by a NB cell line? are mesenchymal and adrenergic cells expressing differentially NRXN1 and NXPH1?
Reply: Unfortunately, the literature about NXPH1 remains very limited (less than 40 articles referenced in Pubmed) and nothing is known about the regulation of its expression during development. The data from Kildisiute et al (Kildisiute et al, 2021) indeed identified NXPH1 in the signatures of bridge cells, chromaffin cells and sympathoblasts, while its receptors NRXN1 and NRXN2 were found in the transcriptomic signatures of all 4 SA cell types. Interestingly, the data provided by Kildisiute et al further established that the expression of NXPH1, NRXN1 and NRXN2 is specifically enriched during the pseudo-time transition from bridge cells to sympathoblasts. This suggests that NXPH1/α-NRXN signaling might be particularly important at that stage and could participate in regulating this transition. But this remains purely speculative and it will need further investigation.
We initially used a panel of 10 human NB cell lines harboring distinct characteristics in terms of genetic profile and morphological properties. We did not find any specific correlation between NXPH1 or α-NRXN1/2 expression and the different types of NB cell lines. We will provide an illustration of this observation in the revised version of the manuscript.
NB cells can convert or be reprogrammed from an adrenergic state, which is less chemoresistant in vitro, to a mesenchymal state (van Groningen et al, 2019). As asked by the reviewer #1, we investigated the expression of NXPH1 and α-NRXN1 in relation with the mesenchymal vs adrenergic status of NB cells (using the dataset GSE90803 from (van Groningen et al, 2019). We found that both genes are expressed at higher levels in cells of the adrenergic phenotype, suggesting that NXPH1/α-NRXN signaling might be particularly relevant for the maintenance of this phenotype. If needed, we will provide an illustration of this observation in the revised version of the manuscript.
16- Figure 1B and C, 2B,D: might the information provided be enhanced? otherwise these inserts might be excluded.
Reply: We thought that the panels presented as Fig.1B, C and 2B, D would be helpful to the readers. We could remove them if the editors and reviewers consider it mandatory.
17- Figure 3D: Kildisiute et al. 2021 data and GTEX available at human protein atlas indicate expression of NRXN1 and NXPH1 in developing and adult adrenal gland. Might the results illustrated suggest a confounding effect in the sampled fetal adrenal glands, perhaps from cortex?
Reply: The samples of human fetal adrenal gland from which RNA was extracted were obtained from donations (samples staged at 22 weeks post-conception or 2 days after birth) and evaluated by a pathologist who confirmed their correct preservation before sample processing. As fetal adrenal glands are very small tissues, successfully separating the cortical and adrenal regions requires micro-dissection, which was not applied. These samples were instead processed entirely, presenting a ratio of medulla/cortex tissue according to their developmental stage.
18- The authors conduct extensive experiments in NRXN1, and make conclusions about its role in for instance metastasis, nevertheless the LR-NB/HR-NB SA signal only includes NRXN2. Can the authors comment on the differences between NRXN1s and NRXN2?
Reply: NRXN1 and NRXN2 were both found to be differentially expressed between LR vs fAG and HR vs fAG, and were thus retrieved among the list of 3.096 common DEGs (Table S3). NRXN2 was further found in the list of 503 LR vs HR DEGs (adjusted P=0.037), showing higher expression levels in LR-NBs than in HR-NBs (see Fig.3D). NRNX1 presented an expression profile comparable to that of NRXN2 (Fig.3D) but did not pass the cut-off (adjusted P=0.38) due to an increased variability in HR-NB samples and was thus absent from the list of LR vs HR DEGs. In vitro, the expression levels of NRXN1 and NRXN2 showed comparable patterns. When we initiated the functional experiments there was no fluorescence-conjugated antibody available to detect and sort α-NRXN2, but there was for α-NRXN1. This is the practical reason that led us to focus on α-NRXN1 in the second part of our study.
Reviewer #1 (Significance (Required)):
The significance of the study relies in investigating the role of selected targets in neuroblastomas within a risk group. In particular, HR-NBs have poor outcomes and are generally metastatic at the time of diagnosis.
The results of the manuscript are somehow consistent with a recently published manuscript analyzing LR- and HR-NBs from a single-cell perspective. The manuscript will be enhanced by conducting the suggested comparison between the reviewed and the reported results. The authors further need to comment why HR-NBs markers, particularly MYCN is not recovered in the LR-NB/HR-NB and the LR-NB/HR-NB SA signals. Also they need to comment on possible confounding effects in the fetal adrenal gland.
The paper is directed to a broader audience of cancer and developmental biologists, and computational biologist. Yet further statistical support needs to be provided.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
**Summary:**
Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate). Please place your comments about significance in section 2.
In this paper, Fanlo-Escudero et al., determines gene signatures differentiating low-risk (LR) neuroblastoma (NB) from high-risk neuroblastoma (HR-NB), as well as LR- and HR-NB as an entity from human fetal adrenal gland. They identify a transcriptional signature corresponding to a core sympathoadrenal lineage that can discriminate between LR-NB and HR-NB. This signature is composed of genes associated with favorable patient outcome. The authors further choose one gene, NXPH1, for functional analysis and investigates the effects this gene has on NB progression using in vitro assays, chick CAM assay and mouse in vivo models. The authors conclude that this transcriptional signature can distinguish LR-NB from HR-NB and that NXPH1 is involved in NB cell growth.
**Major comments:**
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Are the key conclusions convincing? The key conclusions are 1) a core SA lineage signature can discriminate between LR-NB and HR-NB, and 2) NXPH1 represses NB malignancy (in terms of metastatic capacity) and is a therapeutic target. The first conclusion is indeed convincing, and not contradictive to common beliefs. The second conclusion is poorly supported by data. The authors perform a range of experiments using in vitro and in vivo settings, but lack some fundamental experiments and overstate their findings.
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Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? Several claims should be softened, re-phrased and/or clearly marked as preliminary or speculative. No data nor claims need to be removed.
For example, the following statements need to be changed:
1- Page 10, second paragraph: The authors state "Remarkably, NXPH1 and α-NRXN1/2 levels increased in all the NB cell lines harbouring a sphere-forming capacity (Fig. 3E), thereby revealing a strong positive correlation between the expression of NXPH1 and α-NRXN1/2 and the acquisition of a NCC stem cell identity". The authors only show that NXPH1 is expressed in 8 out of 10 NB cell lines. Sphere-forming capacity is displayed in a relative and not absolute scale which makes it difficult to assess which cell lines that do form spheres and to what extent. The capacity to form spheres (from low to high) does not correlate to the levels of NXPH1 in the different cell lines.
Reply: In its current form, Fig.3E presents via a heatmap representation how the expression of selected genes changes after growing cell lines in sphere-forming conditions as compared to basal (normal) ones. We understand from various reviewers’ comments that this representation has been misleading. We will change it, showing more explicitly the expression levels both in basal and sphere-forming conditions and will bring further details on how the sphere-forming ability of each cell line was assessed and characterized.
2- Page 13, paragraph 1. The authors write "...these data revealed that NXPH1/α-NRXN1 signaling is necessary and sufficient for NB tumor growth in vivo". This is an overstatement. Tumors still form, meaning that NXPH1 signaling is not sufficient.
Reply: Indeed, tumors still form after xenografting sh-NXPH1 or sh-NRNX1 cells but they form with a decreased frequency. Specifically, sh-NXPH1 cells formed tumors in 4 out of 6 xenografted mice, and the 4 tumors all showed a markedly reduced volume. Tumors formed from sh-NRNX1 cells were observed in only 2 out of 5 xenografted mice, with 1 of the 2 tumors showing a markedly reduced volume. We consider that these results support the conclusion that inhibiting NXPH1/α-NRXN1 signaling impairs tumor growth, affecting both tumor initiation and tumor growth. We however understand the reviewer #2’s comment and will thus rephrase this part accordingly. In addition, we will provide complementary data showing the mean volume of the tumors generated in the distinct experimental conditions.
3- Throughout the text, the authors convert their statements. One example is page 15, first paragraph. They write "...growth of NB cells but markedly restrict their metastatic potential", but they do not show this. Instead, they only address the opposite situation - Knockdown enhances metastasis. This is not equal to their statement. See other experiments in other sections. The authors need to go through the manuscript and make sure that they explain their conclusions to actually fit their experiments.
Reply: We will follow the reviewer #2’s recommendation and will rephrase the conclusions whenever needed to better fit to the experimental results.
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Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary to evaluate the paper as it is, and do not ask authors to open new lines of experimentation.
**Issues to respond to:**
4- The authors should more clearly explain why they choose to further study NXPH1 (second highest on the list), involved in synaptogenesis and neurotransmission, instead of SOX6, highest on the list, that is highly relevant in neural crest development.
Reply: The combination of several reasons led us to choose NXPH1 for functional studies:
- NXPH1 is a secreted factor, whose activity might be much more easy to target and modulate in future pharmacological/clinical assays than that of a transcription factor like SOX6
- NXPH1 and its receptor NRNX2 both came out in the list of SA-c1 genes, and NRXN1 presented a comparable expression profile (higher levels in LR-NBs than in HR-NBs, although it did not pass the cut-off required to appear in the SA-c1 list), emphasizing the putative importance of this signaling pathway in NB tumor biology
- The functional involvement of NXPH1 or that of its receptors has never been addressed in cancer formation or progression to date
5- When the authors investigate the expression of NXPH1 and other genes and compare that with sphere-forming capacity (Fig. 3E and page 10) they analyze expression in cells cultured in basal medium while sphere-forming capacity is measured after 5 weeks in restricted medium. How does the expression of the analyzed genes change under these conditions?
Reply: In its current form, our Fig.3E is actually showing how the expression of the analyzed genes changes after growing cell lines in sphere-forming conditions as compared to basal (normal) ones. As explained above (reviewer #1, point #1), we understand that this representation has been misleading and we will modify it.
6- Is the subpopulation of NRXN1+ cells low (1.5%) because these samples are from aggressive ("High-risk") cell lines?
Reply: All the NB cell lines used in this study derive from HR-NB tumors. As mentioned by the reviewer #3, there is no cell line modeling low-risk neuroblastoma to date. We indeed observed that the proportion α-NRXN1+ cells, as detected by FACS, was very low in all three cell lines tested. A similar observation was made using cells dissociated from three different patient-derived xenografts. We believe that a similar observation made in 6 samples of distinct origins highlights the consistency of finding a low proportion of α-NRXN1+ cells in NB samples.
7- The authors state "...the number of cells quantified per tumor section was decreased by ~50% for the α-NRXN1+-deprived cells relative to their control (Fig. 3J, K), thus revealing that α-NRXN1+ cells are required to support NB tumor growth in vivo". This is not a correct conclusion. This experiment shows that a-NRXN1- cells do not grow and expand to the same extent as control cells. They cannot say that a-NRXN1+ cells support NB growth without comparing growth between a pure a-NRXN1+ and control cells.
Reply: Unfortunately we could not assess the growth of purified α-NRXN1+ cells, given the low number of α-NRXN1+ cells that could be sorted as compared to the numbers of cells required to perform a CAM assay. We thus opted for comparing the growth of total SK-N-SH cells with that of SK-N-SH cells in which the α-NRXN1+ subpopulation had been experimentally removed. We believe that the results obtained convincingly argue for the importance of the α-NRXN1+ subpopulation in promoting NB proliferation and growth. Nevertheless, we understand the reviewer #2’s comment and will rephrase the conclusion.
8- The authors use shRNAs to knock down NXPH1. They enrich their cells by two means - puromycin or doxocycline. This results in equal cell populations. The authors however state that they use doxocycline to circumvent the growth arrest they observe with puromycin selection. They need to elaborate on this and show why this would be the case and what difference the two methods do and show.
Reply: We generated two types of knock-downs: a constitutive one and an inducible one. Puromycin was used to select constitutive knock-downs, whereas doxycycline was used to trigger sh-RNA production in an inducible manner, in stable clones previously established through neomycin selection. We apologize if this was not stated clearly enough in the manuscript and we will correct it.
9- The major flaw of this paper is that the authors use one cell line in total, and even more that they use the same cell line for both knockdown and activation. Since they do show that different NB cell lines have different expression levels (ranging from high to absent), they should choose one cell line for KD and one for overexpression. The authors could also do a rescue experiment with knockout and gain-of-function (e.g., construct that will not be targeted by the shRNA) in the same cells.
Reply: Since NXPH1 is a secreted protein, we needed a cell line that expresses both NXPH1 and its receptors to expect noticing effects on NB cell behavior when their expression is reduced. We reasoned that performing a gain-of-function of NXPH1 in a cell line that does not express its receptors would have no interest, and vice versa. We also believe that it is more conclusive to perform gain- and loss-of-function experiments in the same cell line, because of the likely differences in cell behavior and aggressiveness of distinct cell lines. We however agree that our conclusions would be strengthened if similar conclusions were reached using different cell lines. We will thus perform growth and metastasis assays both in vivo and in the CAM using NXPH1 and α-NRXN1 shRNAs in an additional cell line. We will moreover consider performing rescue experiments and will think about the best methodology to do so.
10- They only use one shRNA after trying several (Fig. S4). The efficiency is substantially variable and not convincing. As stated also elsewhere in this review, they need to check protein level. And to ensure that their results are not off-target they should perform at least some crucial experiments with two shRNAs.
Reply: We agree that the decreased mRNA levels caused by NXPH1 and α-NRXN1 shRNAs showed variability. Yet, they were sufficient to significantly reduce cell viability, which was impaired by two distinct shRNA constructs, both for NXPH1 and α-NRXN1. To complete these experiments as recommended by the reviewer #2, we will assess how NXPH1 and NRXN1 expression is altered at the protein level by western-blotting. We will moreover address possible off-target effects by RT-qPCR.
11- Why don't the authors add BrdU post-implantation? This is easily done in the egg considering the accessibility and would better reflect the proliferation in vivo.
Reply: Adding BrdU pre-implantation allowed us to get a read-out of the global proliferative behavior of NB cells over the whole post-implantation duration. Adding it at the end of the post-implantation would have only allowed us to assess the proliferative behavior of cells at the end of the experiment. We believe that this would have been less informative.
12- Why do the authors switch between CAM and mouse xenografts? I understand that the mouse model must be employed for "metastasis", but can it be explained why and when they perform the different "tumor growth" experiments?
Reply: The CAM assay was used for 2 reasons: 1) when cell numbers were limiting (i.e. testing the importance of the α-NRXN1+ subpopulation for tumor growth), and 2) to perform a gain-of-function strategy using a recombinant rNXPH1 protein and testing its effects on tumor growth over a duration of 1 week. Such experiment would not have been possible using mouse xenografts, due to the extended experimental duration (7-8 weeks) of this assay and to the need for repeated rNXPH1 injections. The rNXPH1 gain of function experiment in the CAM and the NXPH1/α-NRXN1 loss-of-function experiment using mouse xenografts were performed concomitantly.
13- Why do the authors do left ventricle injections for metastatic studies and not AG implantations?
Reply: We reasoned that cell injections into the left ventricle were ideal to test the organotropism of metastatic NBs, as this methodology facilitates cell dissemination and colonization of organs targeted by NB metastasis such as the liver and bone marrow. Cell implantation into the adrenal gland is especially useful to study cell growth in one of the primary sites of NB growth, which was not our experimental purpose at that stage of the study.
14- The measurement of bioluminescence is very difficult to interpret. The authors discuss metastatic spread, but the images show only large blobs covering the heart and areas surrounding it, especially for the sh-αNRXN1. To be able to say that the cells have colonized specific organs the authors need to dissect these organs and perform staining. I see it as tumors recur rather than particularly metastasize when they re-appear after 6 weeks.
Reply: The parameters for bioluminescence detection were set equally for all experimental conditions. The differences in bioluminescence intensities observed in mice injected with control cells compared to those injected with shNXPH1 and shNRXN1 cells explain why it is so intense in the case of shNXPH1 and shNRXN1 cells.
As suggested by the reviewer #2, we had further dissected the mice and recovered the organs of interest. We will provide additional data confirming that an intense bioluminescent signal was effectively detected in the liver and hind legs (containing bone marrow) of mice injected with shNXPH1 and shNRXN1 cells, but not in those injected with control cells.
15- How do the authors explain results presented in Fig. S5: There are more HuNu+ cells but tumor size is unchanged?
Reply: In our experimental CAM setup, 5·105 SK-N-SH cells were embedded in 10μl of Matrigel, which served as a matrix facilitating tridimensional NB cell proliferation. The addition of rNXPH1 increased the number of HuNu+ (NB) cells per section and per mm3 (Fig.4G, H and Fig.S5C), without significantly increasing the tumor area (Fig.S5D) nor the tumor/matrigel volume (data not shown in the current version, but it will be included in the revised version). As illustrated in Fig. 4G, the matrigel was not totally filled with NB cells, even at the time of recovery. We thus deduced from these observations that more NB cells progressively filled the matrigel, without reaching the point where they significantly altered the tumor/matrigel volume. Nevertheless, we can provide additional data revealing that the addition of rNXPH1 caused a slight, yet reproducible increase in the tumor/matrigel weight, in agreement with the increased cell density already shown in Fig.S5C.
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Are the suggested experiments realistic for the authors? It would help if you could add an estimated cost and time investment for substantial experiments.
Some suggested experiments take time (orthotopic implantations, rescue experiments, adding cell lines and # of shRNAs).
However, as long as the authors discuss and address their methods for in vivo growth (in ovo vs in vivo vs their choice of metastasis model), an orthotopic AG model is not necessary. The authors should however consider it for future studies.
Experiments required to support their conclusions: A rescue experiment and use of different cell lines for KD and overexpression is somewhat time-consuming, as well as ensuring KD at protein level and include an additional shRNA in crucial experiment. I expect that this would take 2-4 months.
- Are the data and the methods presented in such a way that they can be reproduced? 16- The authors should elaborate on their methods, but in general they are reproducible.
Reply: As requested, we will bring further details on the experimental setups.
Are the experiments adequately replicated and statistical analysis adequate?
17- In several places the number of replicates is questionable. Especially at the end of page 26, the authors state that they have performed n=1-4 replicates. N=1 replicate is never ok. In several instances they do n=2 replicates. This can be acceptable but the authors could address this.
Reply: When assessing the sphere-forming capacity of the different NB cell lines, some cell lines only produced spheroids in 1 of the 4 replicates tested. This is a result in itself, which helped us classifying cell lines based on their sphere-forming capacity. We understand that this Methods paragraph was elusive. We apologize for it and will clarify this aspect.
**Minor comments:**
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Specific experimental issues that are easily addressable. Mainly text changes can be seen as minor. For experiments and other issues, see other sections.
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Are prior studies referenced appropriately? Yes. 18- The reference list is not coherently styled.
Reply: We understand that using numbered references can be annoying. We will adapt the reference format to stick to the guidelines of the specific journal to which the manuscript will be addressed.
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Are the text and figures clear and accurate?
19- Overall, the paper is written in a complex way and difficult to easily comprehend. For example, the authors need to clarify several issues on the material they use and experiments they perform. I suggest substantial re-writing to better convey their messages. Sentences should be short and clear, data explained in the context of it was derived.
Reply: We will take into consideration the reviewer #2’s suggestion and modify the manuscript to facilitate its comprehension.
The following text edits and clarifications are required:
20- Page 3. The authors write "cell-of-origin" in several places, this should be changed to "cells-of-origin" (i.e., plural). The view that all NBs originate from only one cell is too simplistic, and the authors should definitely edit this considering that they are investigating different subgroups of NB.
Reply: We will correct this mistake.
21- Page 5. The authors MUST define where the material from the 18 NB patients as well as fetal AG derive from. There is no reference, and taken from the material&methods section, the transcriptome data from these data has not been generated by the authors themselves?
Reply: The transcriptomic data used herein have indeed been previously generated by two co-authors of the study, and the corresponding reference is cited (ref #20; (Gomez et al, 2015). All the NB samples included in our transcriptomic analysis were obtained at the time of diagnosis from patients attended at Hospital Sant Joan de Déu (HSJD, Barcelona, Spain). Tumors were evaluated by a pathologist and only the snap-frozen pre-treatment samples showing at least 70% of viable tumor content were included for analysis. Neuroblastoma risk assessment was defined by the International Neuroblastoma Staging System (INSS). Samples of normal fetal adrenal gland (n=2) were used as a non-tumoral reference tissue. Total RNA from frozen samples was extracted by TRIzol® Reagent. High quality RNA (RINe>7.00) was hybridized to Human Genome U219 microarray plates at the Functional Genomic Unit, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS, Barcelona, Spain) according to Affymetrix standard protocols. Microarray data are deposited in NCBI (#GSE54720). We will include this information in the Methods section of the revised version of the manuscript.
22- Why do the authors choose to work with the data set from Jansky et al in particular?
Reply: Actually, we chose to work with the data from Kildisiute et al. (ref #16 (Kildisiute et al, 2021). We selected this study because: 1) they identified the transcriptional signatures of the 4 SA subtypes relevant to our study and additional related signatures of interest (adrenal cortex, mesenchyme…), 2) these data were obtained from human tissues, and 3) these signatures were easily accessible from their supplementary data.
23- Page 7, paragraph 1. The authors write "Remarkably, 67% of the 503 genes found in this signature, which formed the cluster c1, are both associated with a neural identity and a better patient outcome". I do not agree that this is remarkable and the authors should remove this word.
Reply: We will remove the term “Remarkably”.
24- Page 10, second paragraph. The authors should clarify that the expression levels of the displayed genes are derived from qPCR analysis (if I have understood that correctly, I had to find and guess this from the M&M section), as well as explain how they have set the scale for sphere-forming capacity and what this corresponds to. What do the colors actually represent?
Reply: We apologize for this information lacking in the Results section. We will precise that the expression levels were assessed by RT-qPCR, we will add a representation of their expression levels in basal culture conditions and will improve the explanation of the assessment of the sphere-forming capacity of the NB cell lines.
25- Page 11 and onwards. The authors write "deprived of their a-NRXN1+ subpopulation". This is highly confusing and difficult to read. The authors should write "a-NRXN1- subpopulation"
Reply: We will follow the reviewer #2’s recommendation and change the text for "α-NRXN1- subpopulation".
26- Page 11, end of paragraph 2. The authors write "...arguing that NXPH1/α-NRXN signaling could control NB growth and/or aggressiveness". Number of cells do not directly correlate to aggressiveness, and this needs to be re-phrased to only state what the experiment actually shows - proliferation of a-NRXN1- cells.
Reply: We will rephrase this sentence according to the reviewer #2’s recommendation.
27- I am in favor of using the CAM assay as a complementary system. The authors however use this to define "...required to support tumor growth in vivo". The CAM assay using NB (i.e., transformed cells) shows the growth of these cells in response to presence of blood vessels and not a full tumor micro-environment. This should be clarified.
Reply: We will clarify this aspect.
28- As stated in the previous comment, the authors write "...required to support tumor growth in vivo". The next paragraph has the following headline "NXPH1/α-NRXN signaling stimulates NB growth". This is to me the same thing. The authors should elaborate on how these differ, or if they use them to show the same thing, clearly state that.
Reply: We will better explain how these distinct pieces of evidence are complementary and reinforce the conclusion that NXPH1/α-NRXN signaling stimulates NB growth.
29- The authors conclude that NXPH signaling can be used as a therapeutic target. This would however be extremely difficult considering the opposing effects shown on growth vs metastasis. I agree that it is important to find means to inhibit metastasis, but that does not mean we can allow for enhanced growth of the primary tumor. A better reflection would be to use this as a biomarker, but this can only be predictive/speculative since the authors do not perform for example a tissue microarray to show this at IHC protein level, something that is currently the practice in the clinic.
Reply: While we agree with reviewer #2 that we cannot allow for enhanced growth of the primary tumor, we believe that having identified a secreted factor whose activity inhibits NB metastatic potential is a novel and important finding. We did not wish to suggest that our experimental setup could be directly applied to inhibit the metastatic dissemination of HR-NB tumors. However, we believe that our findings can set the basis of a therapeutic design in which NXPH1/α-NRXN signaling would be enhanced locally to prevent/block metastases from HR-NB tumors.
As mentioned in the Discussion section (page 17), one study reported that NXPH1 can be used as a DNA methylation biomarker associated with a good prognosis for NB patients (reference #54, (Decock et al, 2016).
**Material and Methods:**
30- The authors have misspelled the cell line SK-N-BE(2)c.
Reply: Indeed. We will correct this mistake.
31- Why are some cell lines grown in 20% FBS? This is not standard and could impact the results.
Reply: The 3 cell lines of our panel which were grown in 20% FBS correspond to the 3 cell lines of the mixed subtype, including SK-N-SH, SK-N-Be(2)c and IMR-32 cells. These cell lines have been established and originally grown in presence of a high FBS content (Tumilowicz et al, 1970; Biedler et al, 1973; Ciccarone et al, 1989). In our hands and as recommended by the colleagues that provided us with these cell lines, growing the cell lines in presence of 20% FBS was indeed crucial to sustain their morphological heterogeneity. While we agree with the reviewer #2 that culture conditions could affect cell behavior in vitro, we wish to emphasize that our main conclusions are derived from in vivo experiments. We are thus convinced that our main findings were not impacted by in vitro culture conditions.
32- The authors should state what the tumor volume limit in their ethical permit is (page 30).
Reply: The tumor volume limit was set at 1,500 mm3 as specified by our ethical committee.
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Do you have suggestions that would help the authors improve the presentation of their data and conclusions?
33- Fig. 3E. As discussed elsewhere, the authors should clarify the scale/meaning for sphere-forming capacity
Reply: As explained above, we will modify the representation of the results shown in Fig.3E to improve their clarity and facilitate their comprehension.
34- Fig. 4D. What do the numbers (i - vi) refer to? I cannot find this in the figure, figure legend, text or material&methods.
Reply: These numbers were used to call different tumors and show their GFP content, as appearing in the lower part of this panel. We will precise this information in the corresponding figure legend.
35- The authors do not present the tumor volume in Fig. 4. The authors discuss tumor growth in the text and this data should be included.
Reply: We agree with the reviewer #2’ suggestion and will present tumor volumes in the revised version of the manuscript.
36- Fig. S4. Knockdown efficiency is variable, and efficiency does not correlate to growth capacity. Especially because of this, the authors need to investigate this at protein level.
Reply: As requested by the reviewer #2, we will investigate the knockdown efficiency at the protein level.
Reviewer #2 (Significance (Required)):
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Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. This paper is partly within the scope of the ongoing debate of NB origin (see references below). This paper is however not as extensive, provides no new patient material (applies data from Jansky et al), and do not address the actual cell-of-origin (which the authors themselves also clearly states). This paper provides new gene signatures that can be used to define low-risk vs high-risk patients which is highly important to the field, but these signatures are not unexpected and does not add a significant advance to the field. The authors also do not address how these signatures could be applied clinically. The authors do not use any new methodology. With this said, with revision of the paper, it will still add to the current focus on NB biology research.
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Place the work in the context of the existing literature (provide references, where appropriate). 37- There is a recently initiated, important and extensive debate about the cells-of-origin for NB. The authors indeed bring this up in the paper and also state that they do not intend to add to this debate. They use data from one paper from referenced debate above, and I would argue that because of this fact, and that they extract gene signatures from it, they do, at least partly, touch on the NB cells-of-origin debate, and the authors should put their results into context, from a big picture perspective. As of now, they dodge this complex issue.
References: Dong et al., Cancer Cell 2020; Hanemaaijer et al., PNAS 2021; Jansky et al., Nat Genet 2021; Kameneva et al., Nat Genet 2021; Kildisiute et al., Sci Adv 2021; Furlan et al., Science 2017).
Reply: We understand the reviewer #2’s argument. We will thus try to put our results in perspective regarding the NB cells-of-origin.
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State what audience might be interested in and influenced by the reported findings.
This paper will be interesting for scientists within the neuroblastoma field, in particular those working on defining NB subgroups in correlation to developmental stages.
- Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. Reviewer's expertise: Neuroblastoma, neural crest, trunk neural crest, chick embryos, mouse models, in vitro models
Parts of paper outside expertise of the reviewer: Analysis of the bioinformatics data (i.e., extracting signatures).
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
**Summary: short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).**
Dr. Le Dréau and colleagues provide a transcriptional analysis comparing low versus high-risk neuroblastomas using a cohort of 18 patients. By comparing them to normal transcriptional signatures obtained from human fetal adrenal glands, they conclude that low risk tumors share a specific core sympatho-adrenal developmental program, which is associated with favorable patient prognosis. Among this signature, they specifically assess the role of NXPH1/a-NRXN in vitro and in vivo using different models (cell lines, PDX-derived cell lines, mouse and chick xenografts). They propose that NXPH1/a-NRXN axis promotes neuroblastoma tumor growth via cell proliferation, and inhibits metastases.
**Major comments:**
- Are the key conclusions convincing?
There are major points that need to be addressed before being convinced by the conclusions.
1- The choice for using the SKNSH cell line, among others, to study the role of NXPH1/a-NRXN axis need to be explained. Indeed, whereas there is no cell line modeling low-risk neuroblastoma, authors should properly illustrate the basal expression of NXPH1/a-NRXN in the cell lines and not the ratio provided in Fig3E. If I understood well, according to Fig3F, only 1.5% of SKNSH cells express NRXN1. Could the authors provide FACS plots? And explain how they can sort high and low expressing cells among 1.5%? And then how do they justify using shRNA approach in a cell line in which only 1.5% are concerned cells? Also, the authors should prove the efficiency of the shRNA used on each specific target for both in vitro and in vivo studies by WB.
Reply: We chose the SK-N-SH cell line for experimental assays based on the following facts: 1) the expression levels of NXPH1 and α-NRXN1/2, 2) this cell line showed the highest sphere-forming capacity, 3) this cell line harbored the highest percentage of α-NRXN1+ cells detected by FACS among the different cell lines tested, and 4) this cell line is of a mixed type, which is supposed to encompass more cell heterogeneity than other (N, I and S) types, thus reproducing more faithfully the complex heterogeneity of primary NB tumors.
In a revised version of the manuscript we will provide data on NXPH1 and a-NRNX1/2 expression levels in basal culture conditions, and will improve the representation of Fig.3E to facilitate its comprehension. The method used to sort α-NRXN1+high, α-NRXN1+low and α-NRXN1- cells is explained in the Methods section (page 25). As requested by the reviewer #3, we will provide FACs plots to illustrate the sorting method.
As requested by the reviewers #2 and #3, we will assess the shRNA efficiency by testing the knockdown at the protein level.
2- The conclusion of inhibition of metastatic process is not supported by enough data. To achieve such a conclusion, authors should provide more than one in vivo experiment (which need to be completed already with the proof of protein deregulation). Some in vitro characterization of metastatic properties such as invasion and migration assays and/or transcriptional analyses could be done.
Reply: We agree with the reviewer #3 that that our conclusion on the anti-metastatic ability of NXPH1/α-NRXN signaling would be reinforced by complementary experiments. To this aim, we propose to test how NXPH1/α-NRXN knockdown affects the metastatic potential of the SK-N-SH cell line and of another cell line in the CAM assay. This assay can indeed be used to assess not only NB tumor growth (as we already did), but also NB cell invasion in target organs (such as the liver and the bone marrow), thus mimicking a metastatic dissemination.
3- Why don't the authors use the transcriptome dataset of 498 patients to realize a more powerful comparative study of low versus high risk tumors (Zhang et al, Genome biology, 2015)? The authors should show the expression plot of NXPH1/a-NRXN in low versus high risk patients, in their cohort of 18 patients but also in the cohort of 498 patients. How do the authors reconciliate the idea that NXPH1/a-NRXN could be associated to stem cell identity but low risk tumors?
Reply: As explained in response to the reviewer #1’ (comment #1), our strategy entailed comparing the transcriptomic signatures of LR-NB and HR-NB samples to the one of a non-tumoral, healthy tissue such as the fetal adrenal gland. The online SEQC database cited by reviewers #1 and #3 does not contain healthy samples, and therefore could not be used for this initial step of the analysis. This is why we decided to use as a starting point the cohort of samples from the Hospital Sant Joan de Déu, which further provided the advantage of comparing samples from patients all diagnosed and treated at the same facility. We then used the SEQC database at different steps of our analytic strategy (Fig.S1F, 2F-H, 3A-B, S3) to test the relevance and coherence of the results obtained with the HSJD cohort in the context of a larger cohort.
As requested by the reviewer #3, we will provide a dot-plot representation of the expression levels of NXPH1 and its receptors in both the HSDJ and SEQC cohorts.
At this point we can only speculate on the association between NXPH1/α-NRXN expression and stem cell identity. This correlation might simply reflect the fact that cells from human NB cell lines return to a transcriptional program closer to their neural crest-derived identity when grown in sphere-forming conditions (as suggested by the increased expression of p75/NTR). Alternatively, this correlation might reflect the ability of NXPH1/α-NRXN signaling to retain cells in an immature state. Such ability could explain how NXPH1/α-NRXN signaling participates in promoting primary tumor growth, and the fact that their expression is increased in LR-NBs as compared to normal fetal adrenal gland. On the other hand, NXPH1/α-NRXN expression is higher in LR-NBs than in HR-NBs, and our findings suggest that this is linked to the anti-metastatic ability of NXPH1/α-NRXN. We could further imagine that by favoring stem cell identity NXPH1/α-NRXN signaling might also provide LR-NB cells with an enhanced ability to “re-enter” a normal developmental path or to be eliminated. In this regard, it is worth reminding that LR-NBs are detected earlier during development than HR-NBs, and sometimes show the puzzling ability to regress spontaneously.
- Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?
Details of experiments and supplementary experiments have to be provided (see previous question).
- Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.
4- Additional details of the experiments have to be provided (cf above): levels of expression of NXPH1/a-NRXN in cell lines, FACS analyses to prove enrichment and/or depletion, WB for validation of shRNA knock-down etc...Moreover, the conclusion that NXPH1/a-NRXN axis inhibits metastatic potential of neuroblastoma is supported by only one in vivo experiment using SKNSH cell line with shRNAs anti-NXPH1/a-NRXN. It should be completed with invasion/migration assays in vitro for example, and/or transcriptional signature of tumors obtained +/- shRNAs anti-NXPH1/a-NRXN. Ideally, these results could be validated in an additional model.
Reply: As mentioned above, we will perform additional experiments following the reviewer #3’s recommendations, including assessing NXPH1 and α-NRXN1 knockdown efficiency at the protein level, assessing how knocking down NXPH1 and α-NRXN1 expression alters the in vivo growth and metastatic potential of an additional cell line, and studying how knocking down NXPH1 and α-NRXN1 alters the metastatic potential of NB cells using a second and complementary metastasis assay (CAM assay).
- Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.
Yes
- Are the data and the methods presented in such a way that they can be reproduced?
Yes, Material and methods section is well described. But, several points are missing:
5- The paragraphs describing how RNAs from 18 patients and fetal adrenal glands were obtained, how good was the quality, and how transcriptomes have been realized and sequenced are missing.
Reply: The transcriptomic data used herein have indeed been previously generated by two co-authors of the study, and the corresponding reference is cited (ref #20; (Gomez et al, 2015). All the NB samples included in our transcriptomic analysis were obtained at the time of diagnosis from patients attended at Hospital Sant Joan de Déu (HSJD, Barcelona, Spain). Tumors were evaluated by a pathologist and only the snap-frozen pre-treatment samples showing at least 70% of viable tumor content were included for analysis. Neuroblastoma risk assessment was defined by the International Neuroblastoma Staging System (INSS). Samples of normal fetal adrenal gland (n=2) were used as a non-tumoral reference tissue. Total RNA from frozen samples was extracted by TRIzol® Reagent. High quality RNA (RINe>7.00) was hybridized to Human Genome U219 microarray plates at the Functional Genomic Unit, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS, Barcelona, Spain) according to Affymetrix standard protocols. Microarray data are deposited in NCBI (#GSE54720). We will include this information in the Methods section of the revised version of the manuscript.
6- The sequences of shRNAs have to be provided.
Reply: We will provide the oligo sequences used to generate shRNAs against NXPH1 and aNRNX1.
-Are the experiments adequately replicated and statistical analysis adequate?
Yes
**Minor comments:**
- Specific experimental issues that are easily addressable.
7- The authors should show the basal expression of their genes of interest in the patients, cell lines and PDX-derived cell lines, to justify the choice to work then with SKNSH cell line. In addition to the ratio they show in Fig3.E.
Reply: As explained above, we will provide more details on the expression levels of NXPH1 and α-NRXN1/2 in the HSJD and SEQC cohorts and in the NB cell lines used in our study.
8- Authors should also illustrate the FACS analyses they realized in this study, in order to appreciate the quantity of positive cells that are either enriched or depleted.
Reply: The methodology used to sort α-NRXN1+high, α-NRXN1+low and α-NRXN1- cells is explained in the Methods section (page 25). As requested by the reviewer #3, we will provide FACs plots to illustrate this methodology.
9- Authors could precise in their schemes that DOX is maintained in vivo.
Reply: We will follow the reviewer #3’s suggestion.
10- The number of mice need to be integrated in each experiment.
Reply: The numbers of mice used to assess growth and metastasis in vivo were included in the corresponding figure legends (Figs. 4D, 4E and 5C) and appear discreetly on the panel 4D (to the right). We will follow the reviewer #3’s recommendation and add these details on the corresponding figure panels.
- Are prior studies referenced appropriately?
11- No some elements are not right in the introduction:
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Mutations of PHOX2B are not associated to poor prognosis.
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Original publications could be provided instead of reviews.
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Genetic alterations in NB are not only 16%, the authors forgot to mention TERT and ATRX.
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Maybe the NXPH1 methylation in ref 54 could be more explicit, if DNA methylation is detected on NXPH1, it would be of poor prognosis because driving low expression ..?
Reply: We will follow the reviewer #3’s critics and correct the mistakes and information lacking in the introduction and discussion sections.
- Are the text and figures clear and accurate?
Text is very clear and well written, as are the figures.
- Do you have suggestions that would help the authors improve the presentation of their data and conclusions?
Already mentioned before: FACS and WB are needed.
Reviewer #3 (Significance (Required)):
- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. This work aimed first at better understanding the fundamental transcriptional differences between two risk groups in Neuroblastoma. The authors defend the conceptual idea that these two groups represent two distinct diseases, which is not new in the area but requires attention, and indeed supported here by distinct transcriptional signatures.
Using published signatures of fetal sympatho-adrenal system, they define a core transcriptional program that is more strongly expressed in low-risk tumors, but we already know that tumors of this category are often more differentiated, and by definition expressed strong markers of SA differentiation, whereas high-risk tumors have a more undifferentiated phenotype.
Not surprisingly, several genes as well as a specific gene signature could be associated to better prognosis, a well-known characteristic of low-risk tumors. However, among them, a novel axis (NXPH1/a-NRXN) is proposed to explain the proliferation but absence of metastasis that define the low-risk group.
- Place the work in the context of the existing literature (provide references, where appropriate).
Neuroblastoma is a rare and very heterogeneous disease, in terms of biology and clinical presentation. To try to decipher such a heterogeneity, recent works have allied single cell transcriptome analyses on tumors and on human fetal cells during development. These studies are well cited in the discussion, and I agree that they yielded discrepant conclusions concerning the cell(s) of origin of Neuroblastoma. By comparing the normal developing human adrenal gland cells to cells from series of neuroblastomas, most of the studies converge towards that the tumors resembled differentiating adrenal neuroblasts. In one study, MYCN-amplified neuroblastoma cells (high risk group) were most similar to normal neuroblasts from seven- or eight-week post-conception, while lower-risk neuroblastomas included more cells resembling late neuroblasts (Janksy et al, 2021).
During the submission of this work, another paper using single cell technologies was published and supported the idea of two distinct tumor entities (Bedoya-reina et al, 2021), with also a stronger signature of sympatho-adrenal cells in low risk tumors.
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State what audience might be interested in and influenced by the reported findings. As the current clinical classification based on various criteria already allows clinicians to identify well low-risk tumors, I think this work would mainly attract fundamental researchers on the molecular differences between low-risk and high-risk tumors.
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Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. I'm specialized on pediatric cancers, and especially Neuroblastoma for the past 3 years. I'm interested in tumor cell identity and cell plasticity, particularly in response to treatment. I think that I have sufficient expertise to evaluate all parts of the manuscript.
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3. Description of the revisions that have already been incorporated in the transferred manuscript
Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.
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4. Description of analyses that authors prefer not to carry out
1: The reviewers #1 and #3 suggested using a large dataset (the SEQC database containing 498 samples) to realize a more powerful comparative transcriptomic study. However, to define the transcriptional signatures associated to the aetiology of LR- and HR-NBs and to the malignant behavior of HR-NBs, we first needed to compare the transcriptomic signatures of LR-NB and HR-NB samples to the one of a non-tumoral, healthy tissue such as the fetal adrenal gland. The online SEQC does not contain any healthy samples, thus precluding the possibility to use it for the first step of our analysis. This is why we decided to use as a starting point the cohort of samples from HSJD cohort, which further provided the advantage of comparing samples from patients all diagnosed and treated at the same facility. In different steps of our analytic strategy (Fig.S1F, 2F-H, 3A-B, S3), we already used the SEQC database to test the relevance of the results in the context of a larger cohort. In doing so we obtained coherent results. As recommended by the reviewer #1 (comment #3), we will further compare our data with the data from another study (Bedoya-Reina et al, 2021). We thus believe that the request of the reviewers #1 and #3 does not need to be addressed within the scope of a revision.
2: The reviewer #2 suggested that some crucial functional experiments should be repeated with another shRNA construct to ensure that our results are not off-target and because the knock-down efficiency of the shRNAs used was variable and not convincing. We have tested two distinct shRNA constructs for both NXPH1 and α-NRXN1, which all comparably reduced cell viability. Following this reviewer’s suggestion we will assess possible off-target effects of the sh-NXPH1 and sh-aNRXN1 constructs by RT-qPCR. Moreover, we will also test the effects of these sh-NXPH1 and sh-aNRXN1 constructs in another cell line and using a novel and complementary metastasis assay (CAM assay).
3: The reviewer #2 suggested to study the metastatic potential of NB cells by performing orthotopic implantations of NB cells into the mouse adrenal gland instead of performing cell injections into the mouse left cardiac ventricle. We reasoned that cell injections into the left cardiac ventricle were ideal to test the organo-tropism of metastatic NBs, as this methodology facilitates cell dissemination and colonization of organs targeted by NB metastasis such as the liver and bone marrow, as shown in Fig.5B. Cell implantation into the adrenal gland is especially useful to study cell growth in one of the primary sites of NB formation. We thus believe that our current experimental approach is more relevant to the question we wish to address within the scope of a revision.
References:
Bedoya-Reina OC, Li W, Arceo M, Plescher M, Bullova P, Pui H, Kaucka M, Kharchenko P, Martinsson T, Holmberg J, et al (2021) Single-nuclei transcriptomes from human adrenal gland reveal distinct cellular identities of low and high-risk neuroblastoma tumors. Nat Commun 12: 1–15
Biedler JL, Helson L & Spengler BA (1973) Morphology and Growth, Tumorigenicity, and Cytogenetics of Human Neuroblastoma Cells in Continuous Culture. Cancer Res 33: 2643–2652
Ciccarone V, Spengler BA, Meyers MB, Biedler JL & Ross RA (1989) Phenotypic Diversification in Human Neuroblastoma Cells: Expression of Distinct Neural Crest Lineages. Cancer Res 49: 219–225
Decock A, Ongenaert M, Cannoodt R, Verniers K, Wilde B De, Laureys G, Van Roy N, Berbegall AP, Bienertova-Vasku J, Bown N, et al (2016) Methyl-CpG-binding domain sequencing reveals a prognostic methylation signature in neuroblastoma. Oncotarget 7: 1960–72
Gomez S, Castellano G, Mayol G, Sunol M, Queiros A, Bibikova M, Nazor KL, Loring JF, Lemos I, Rodriguez E, et al (2015) DNA methylation fingerprint of neuroblastoma reveals new biological and clinical insights. Epigenomics 7: 1137–1153
van Groningen T, Akogul N, Westerhout EM, Chan A, Hasselt NE, Zwijnenburg DA, Broekmans M, Stroeken P, Haneveld F, Hooijer GKJ, et al (2019) A NOTCH feed-forward loop drives reprogramming from adrenergic to mesenchymal state in neuroblastoma. Nat Commun 10: 1–11
Kildisiute G, Kholosy WM, Young MD, Roberts K, Elmentaite R, van Hooff SR, Pacyna CN, Khabirova E, Piapi A, Thevanesan C, et al (2021) Tumor to normal single-cell mRNA comparisons reveal a pan-neuroblastoma cancer cell. Sci Adv 7: eabd3311
Tumilowicz JJ, Nichols WW, Cholon JJ & Greene AE (1970) Definition of a continuous human cell line derived from neuroblastoma. Cancer Res 30: 2110–2118
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Referee #3
Evidence, reproducibility and clarity
Summary: short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).
Dr. Le Dréau and colleagues provide a transcriptional analysis comparing low versus high-risk neuroblastomas using a cohort of 18 patients. By comparing them to normal transcriptional signatures obtained from human fetal adrenal glands, they conclude that low risk tumors share a specific core sympatho-adrenal developmental program, which is associated with favorable patient prognosis. Among this signature, they specifically assess the role of NXPH1/a-NRXN in vitro and in vivo using different models (cell lines, PDX-derived cell lines, mouse and chick xenografts). They propose that NXPH1/a-NRXN axis promotes neuroblastoma tumor growth via cell proliferation, and inhibits metastases.
Major comments:
- Are the key conclusions convincing? There are major points that need to be addressed before being convinced by the conclusions. • The choice for using the SKNSH cell line, among others, to study the role of NXPH1/a-NRXN axis need to be explained. Indeed, whereas there is no cell line modeling low-risk neuroblastoma, authors should properly illustrate the basal expression of NXPH1/a-NRXN in the cell lines and not the ratio provided in Fig3E. If I understood well, according to Fig3F, only 1.5% of SKNSH cells express NRXN1. Could the authors provide FACS plots? And explain how they can sort high and low expressing cells among 1.5%? And then how do they justify using shRNA approach in a cell line in which only 1.5% are concerned cells? Also, the authors should prove the efficiency of the shRNA used on each specific target for both in vitro and in vivo studies by WB. • The conclusion of inhibition of metastatic process is not supported by enough data. To achieve such a conclusion, authors should provide more than one in vivo experiment (which need to be completed already with the proof of protein deregulation). Some in vitro characterization of metastatic properties such as invasion and migration assays and/or transcriptional analyses could be done. • Why don't the authors use the transcriptome dataset of 498 patients to realize a more powerful comparative study of low versus high risk tumors (Zhang et al, Genome biology, 2015)? The authors should show the expression plot of NXPH1/a-NRXN in low versus high risk patients, in their cohort of 18 patients but also in the cohort of 498 patients. How do the authors reconciliate the idea that NXPH1/a-NRXN could be associated to stem cell identity but low risk tumors?
- Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? Details of experiments and supplementary experiments have to be provided (see previous question).
- Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation. Additional details of the experiments have to be provided (cf above): levels of expression of NXPH1/a-NRXN in cell lines, FACS analyses to prove enrichment and/or depletion, WB for validation of shRNA knock-down etc...
Moreover, the conclusion that NXPH1/a-NRXN axis inhibits metastatic potential of neuroblastoma is supported by only one in vivo experiment using SKNSH cell line with shRNAs anti-NXPH1/a-NRXN. It should be completed with invasion/migration assays in vitro for example, and/or transcriptional signature of tumors obtained +/- shRNAs anti-NXPH1/a-NRXN.
Ideally, these results could be validated in an additional model.
- Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments. Yes
- Are the data and the methods presented in such a way that they can be reproduced? Yes, Material and methods section is well described. But, several points are missing:
- The paragraphs describing how RNAs from 18 patients and fetal adrenal glands were obtained, how good was the quality, and how transcriptomes have been realized and sequenced are missing.
- The sequences of shRNAs have to be provided.
- Are the experiments adequately replicated and statistical analysis adequate? Yes
Minor comments:
- Specific experimental issues that are easily addressable. The authors should show the basal expression of their genes of interest in the patients, cell lines and PDX-derived cell lines, to justify the choice to work then with SKNSH cell line. In addition to the ratio they show in Fig3.E.
Authors should also illustrate the FACS analyses they realized in this study, in order to appreciate the quantity of positive cells that are either enriched or depleted.
Authors could precise in their schemes that DOX is maintained in vivo. The number of mice need to be integrated in each experiment. - Are prior studies referenced appropriately? No some elements are not right in the introduction: - Mutations of PHOX2B are not associated to poor prognosis. - Original publications could be provided instead of reviews. - Genetic alterations in NB are not only 16%, the authors forgot to mention TERT and ATRX. - Maybe the NXPH1 methylation in ref 54 could be more explicit, if DNA methylation is detected on NXPH1, it would be of poor prognosis because driving low expression ..? - Are the text and figures clear and accurate? Text is very clear and well written, as are the figures. - Do you have suggestions that would help the authors improve the presentation of their data and conclusions? Already mentioned before: FACS and WB are needed.
Significance
- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.. This work aimed first at better understanding the fundamental transcriptional differences between two risk groups in Neuroblastoma. The authors defend the conceptual idea that these two groups represent two distinct diseases, which is not new in the area but requires attention, and indeed supported here by distinct transcriptional signatures.
Using published signatures of fetal sympatho-adrenal system, they define a core transcriptional program that is more strongly expressed in low-risk tumors, but we already know that tumors of this category are often more differentiated, and by definition expressed strong markers of SA differentiation, whereas high-risk tumors have a more undifferentiated phenotype.
Not surprisingly, several genes as well as a specific gene signature could be associated to better prognosis, a well-known characteristic of low-risk tumors. However, among them, a novel axis (NXPH1/a-NRXN) is proposed to explain the proliferation but absence of metastasis that define the low-risk group.
- Place the work in the context of the existing literature (provide references, where appropriate). Neuroblastoma is a rare and very heterogeneous disease, in terms of biology and clinical presentation. To try to decipher such a heterogeneity, recent works have allied single cell transcriptome analyses on tumors and on human fetal cells during development. These studies are well cited in the discussion, and I agree that they yielded discrepant conclusions concerning the cell(s) of origin of Neuroblastoma. By comparing the normal developing human adrenal gland cells to cells from series of neuroblastomas, most of the studies converge towards that the tumors resembled differentiating adrenal neuroblasts. In one study, MYCN-amplified neuroblastoma cells (high risk group) were most similar to normal neuroblasts from seven- or eight-week post-conception, while lower-risk neuroblastomas included more cells resembling late neuroblasts (Janksy et al, 2021).
During the submission of this work, another paper using single cell technologies was published and supported the idea of two distinct tumor entities (Bedoya-reina et al, 2021), with also a stronger signature of sympatho-adrenal cells in low risk tumors.
- State what audience might be interested in and influenced by the reported findings. As the current clinical classification based on various criteria already allows clinicians to identify well low-risk tumors, I think this work would mainly attract fundamental researchers on the molecular differences between low-risk and high-risk tumors.
- Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. I'm specialized on pediatric cancers, and especially Neuroblastoma for the past 3 years. I'm interested in tumor cell identity and cell plasticity, particularly in response to treatment. I think that I have sufficient expertise to evaluate all parts of the manuscript.
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Referee #2
Evidence, reproducibility and clarity
Summary:
Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate). Please place your comments about significance in section 2.
In this paper, Fanlo-Escudero et al., determines gene signatures differentiating low-risk (LR) neuroblastoma (NB) from high-risk neuroblastoma (HR-NB), as well as LR- and HR-NB as an entity from human fetal adrenal gland. They identify a transcriptional signature corresponding to a core sympathoadrenal lineage that can discriminate between LR-NB and HR-NB. This signature is composed of genes associated with favorable patient outcome. The authors further choose one gene, NXPH1, for functional analysis and investigates the effects this gene has on NB progression using in vitro assays, chick CAM assay and mouse in vivo models. The authors conclude that this transcriptional signature can distinguish LR-NB from HR-NB and that NXPH1 is involved in NB cell growth.
Major comments:
• Are the key conclusions convincing?
The key conclusions are 1) a core SA lineage signature can discriminate between LR-NB and HR-NB, and 2) NXPH1 represses NB malignancy (in terms of metastatic capacity) and is a therapeutic target. The first conclusion is indeed convincing, and not contradictive to common beliefs. The second conclusion is poorly supported by data. The authors perform a range of experiments using in vitro and in vivo settings, but lack some fundamental experiments and overstate their findings.
• Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?
Several claims should be softened, re-phrased and/or clearly marked as preliminary or speculative. No data nor claims need to be removed.
For example, the following statements need to be changed:
- Page 10, second paragraph: The authors state "Remarkably, NXPH1 and α-NRXN1/2 levels increased in all the NB cell lines harbouring a sphere-forming capacity (Fig. 3E), thereby revealing a strong positive correlation between the expression of NXPH1 and α-NRXN1/2 and the acquisition of a NCC stem cell identity". The authors only show that NXPH1 is expressed in 8 out of 10 NB cell lines. Sphere-forming capacity is displayed in a relative and not absolute scale which makes it difficult to assess which cell lines that do form spheres and to what extent. The capacity to form spheres (from low to high) does not correlate to the levels of NXPH1 in the different cell lines.
- Page 13, paragraph 1. The authors write "...these data revealed that NXPH1/α-NRXN1 signaling is necessary and sufficient for NB tumor growth in vivo". This is an overstatement. Tumors still form, meaning that NXPH1 signaling is not sufficient.
- Throughout the text, the authors convert their statements. One example is page 15, first paragraph. They write "...growth of NB cells but markedly restrict their metastatic potential", but they do not show this. Instead, they only address the opposite situation - Knockdown enhances metastasis. This is not equal to their statement. See other experiments in other sections. The authors need to go through the manuscript and make sure that they explain their conclusions to actually fit their experiments. • Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary to evaluate the paper as it is, and do not ask authors to open new lines of experimentation.
Issues to respond to:
- The authors should more clearly explain why they choose to further study NXPH1 (second highest on the list), involved in synaptogenesis and neurotransmission, instead of SOX6, highest on the list, that is highly relevant in neural crest development.
- When the authors investigate the expression of NXPH1 and other genes and compare that with sphere-forming capacity (Fig. 3E and page 10) they analyze expression in cells cultured in basal medium while sphere-forming capacity is measured after 5 weeks in restricted medium. How does the expression of the analyzed genes change under these conditions?
- Is the subpopulation of NRXN1+ cells low (1.5%) because these samples are from aggressive ("High-risk") cell lines?
- The authors state "...the number of cells quantified per tumor section was decreased by ~50% for the α-NRXN1+-deprived cells relative to their control (Fig. 3J, K), thus revealing that α-NRXN1+ cells are required to support NB tumor growth in vivo". This is not a correct conclusion. This experiment shows that a-NRXN1- cells do not grow and expand to the same extent as control cells. They cannot say that a-NRXN1+ cells support NB growth without comparing growth between a pure a-NRXN1+ and control cells.
- The authors use shRNAs to knock down NXPH1. They enrich their cells by two means - puromycin or doxocycline. This results in equal cell populations. The authors however state that they use doxocycline to circumvent the growth arrest they observe with puromycin selection. They need to elaborate on this and show why this would be the case and what difference the two methods do and show.
- The major flaw of this paper is that the authors use one cell line in total, and even more that they use the same cell line for both knockdown and activation. Since they do show that different NB cell lines have different expression levels (ranging from high to absent), they should choose one cell line for KD and one for overexpression. The authors could also do a rescue experiment with knockout and gain-of-function (e.g., construct that will not be targeted by the shRNA) in the same cells.
- They only use one shRNA after trying several (Fig. S4). The efficiency is substantially variable and not convincing. As stated also elsewhere in this review, they need to check protein level. And to ensure that their results are not off-target they should perform at least some crucial experiments with two shRNAs.
- Why don't the authors add BrdU post-implantation? This is easily done in the egg considering the accessibility and would better reflect the proliferation in vivo.
- Why do the authors switch between CAM and mouse xenografts? I understand that the mouse model must be employed for "metastasis", but can it be explained why and when they perform the different "tumor growth" experiments?
- Why do the authors do left ventricle injections for metastatic studies and not AG implantations?
- The measurement of bioluminescence is very difficult to interpret. The authors discuss metastatic spread, but the images show only large blobs covering the heart and areas surrounding it, especially for the sh-aNRXN1. To be able to say that the cells have colonized specific organs the authors need to dissect these organs and perform staining. I see it as tumors recur rather than particularly metastasize when they re-appear after 6 weeks.
- How do the authors explain results presented in Fig. S5: There are more HuNu+ cells but tumor size is unchanged? • Are the suggested experiments realistic for the authors? It would help if you could add an estimated cost and time investment for substantial experiments.
Some suggested experiments take time (orthotopic implantations, rescue experiments, adding cell lines and # of shRNAs).
However, as long as the authors discuss and address their methods for in vivo growth (in ovo vs in vivo vs their choice of metastasis model), an orthotopic AG model is not necessary. The authors should however consider it for future studies.
Experiments required to support their conclusions: A rescue experiment and use of different cell lines for KD and overexpression is somewhat time-consuming, as well as ensuring KD at protein level and include an additional shRNA in crucial experiment. I expect that this would take 2-4 months.
• Are the data and the methods presented in such a way that they can be reproduced?
The authors should elaborate on their methods, but in general they are reproducible. • Are the experiments adequately replicated and statistical analysis adequate?
In several places the number of replicates is questionable. Especially at the end of page 26, the authors state that they have performed n=1-4 replicates. N=1 replicate is never ok. In several instances they do n=2 replicates. This can be acceptable but the authors could address this.
Minor comments:
• Specific experimental issues that are easily addressable.
Mainly text changes can be seen as minor. For experiments and other issues, see other sections. • Are prior studies referenced appropriately? Yes. The reference list is not coherently styled. • Are the text and figures clear and accurate?
Overall, the paper is written in a complex way and difficult to easily comprehend. For example, the authors need to clarify several issues on the material they use and experiments they perform. I suggest substantial re-writing to better convey their messages. Sentences should be short and clear, data explained in the context of it was derived.
The following text edits and clarifications are required: * Page 3. The authors write "cell-of-origin" in several places, this should be changed to "cells-of-origin" (i.e., plural). The view that all NBs originate from only one cell is too simplistic, and the authors should definitely edit this considering that they are investigating different subgroups of NB. * Page 5. The authors MUST define where the material from the 18 NB patients as well as fetal AG derive from. There is no reference, and taken from the material&methods section, the transcriptome data from these data has not been generated by the authors themselves? * Why do the authors choose to work with the data set from Jansky et al in particular? * Page 7, paragraph 1. The authors write "Remarkably, 67% of the 503 genes found in this signature, which formed the cluster c1, are both associated with a neural identity and a better patient outcome". I do not agree that this is remarkable and the authors should remove this word. * Page 10, second paragraph. The authors should clarify that the expression levels of the displayed genes are derived from qPCR analysis (if I have understood that correctly, I had to find and guess this from the M&M section), as well as explain how they have set the scale for sphere-forming capacity and what this corresponds to. What do the colors actually represent? * Page 11 and onwards. The authors write "deprived of their a-NRXN1+ subpopulation". This is highly confusing and difficult to read. The authors should write "a-NRXN1- subpopulation" * Page 11, end of paragraph 2. The authors write "...arguing that NXPH1/α-NRXN signaling could control NB growth and/or aggressiveness". Number of cells do not directly correlate to aggressiveness, and this needs to be re-phrased to only state what the experiment actually shows - proliferation of a-NRXN1- cells. * I am in favor of using the CAM assay as a complementary system. The authors however use this to define "...required to support tumor growth in vivo". The CAM assay using NB (i.e., transformed cells) shows the growth of these cells in response to presence of blood vessels and not a full tumor micro-environment. This should be clarified. * As stated in the previous comment, the authors write "...required to support tumor growth in vivo". The next paragraph has the following headline "NXPH1/α-NRXN signaling stimulates NB growth". This is to me the same thing. The authors should elaborate on how these differ, or if they use them to show the same thing, clearly state that. * The authors conclude that NXPH signaling can be used as a therapeutic target. This would however be extremely difficult considering the opposing effects shown on growth vs metastasis. I agree that it is important to find means to inhibit metastasis, but that does not mean we can allow for enhanced growth of the primary tumor. A better reflection would be to use this as a biomarker, but this can only be predictive/speculative since the authors do not perform for example a tissue microarray to show this at IHC protein level, something that is currently the practice in the clinic.
Material and Methods:
The authors have misspelled the cell line SK-N-BE(2)c. Why are some cell lines grown in 20% FBS? This is not standard and could impact the results. The authors should state what the tumor volume limit in their ethical permit is (page 30).
• Do you have suggestions that would help the authors improve the presentation of their data and conclusions? * Fig. 3E. As discussed elsewhere, the authors should clarify the scale/meaning for sphere-forming capacity * Fig. 4D. What do the numbers (i - vi) refer to? I cannot find this in the figure, figure legend, text or material&methods. * The authors do not present the tumor volume in Fig. 4. The authors discuss tumor growth in the text and this data should be included. * Fig. S4. Knockdown efficiency is variable, and efficiency does not correlate to growth capacity. Especially because of this, the authors need to investigate this at protein level.
Significance
• Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.
This paper is partly within the scope of the ongoing debate of NB origin (see references below). This paper is however not as extensive, provides no new patient material (applies data from Jansky et al), and do not address the actual cell-of-origin (which the authors themselves also clearly states). This paper provides new gene signatures that can be used to define low-risk vs high-risk patients which is highly important to the field, but these signatures are not unexpected and does not add a significant advance to the field. The authors also do not address how these signatures could be applied clinically. The authors do not use any new methodology. With this said, with revision of the paper, it will still add to the current focus on NB biology research.
• Place the work in the context of the existing literature (provide references, where appropriate).
There is a recently initiated, important and extensive debate about the cells-of-origin for NB. The authors indeed bring this up in the paper and also state that they do not intend to add to this debate. They use data from one paper from referenced debate above, and I would argue that because of this fact, and that they extract gene signatures from it, they do, at least partly, touch on the NB cells-of-origin debate, and the authors should put their results into context, from a big picture perspective. As of now, they dodge this complex issue. References: Dong et al., Cancer Cell 2020; Hanemaaijer et al., PNAS 2021; Jansky et al., Nat Genet 2021; Kameneva et al., Nat Genet 2021; Kildisiute et al., Sci Adv 2021; Furlan et al., Science 2017)
• State what audience might be interested in and influenced by the reported findings.
This paper will be interesting for scientists within the neuroblastoma field, in particular those working on defining NB subgroups in correlation to developmental stages.
• Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.
Reviewer's expertise: Neuroblastoma, neural crest, trunk neural crest, chick embryos, mouse models, in vitro models
Parts of paper outside expertise of the reviewer: Analysis of the bioinformatics data (i.e., extracting signatures).
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Referee #1
Evidence, reproducibility and clarity
Summary:
In the reviewed manuscript, the authors aimed to characterize the sympathoadrenal (SA) transcriptional landscape that defines low- and high-risk neuroblastomas (LR-NBs and HR-NBs respectively). In particular, they analyze previously published Affymetrix U219 expression profiles of 18 low- (n=8) and high-risk (n=10) neuroblastomas, and 2 fetal human adrenal glands. The authors define transcriptional signatures of LR- and HR-NBs, and further unbiasedly classified them in 4 clusters defining groups of patients with different prognosis (as tested using the 498 SEQC cohort). Within these transcriptional signatures, the authors delineated a SA signature using a human fetal adrenal gland transcriptional profile recently published (Kildisiute et al. 2021), that can discriminate low-risk neuroblastomas. From these genes, the authors further select NXPH1 and NRNX1-2 as promising targets for extensive experimental in vitro and in vivo validation, including validation in cell cultures, and xenografts, to determine that NXPH1/alpha-NRXN1-2 signaling is sufficient for NB tumor growth, and that the expression of either stimulates the metastatic potential of NB cells.
Major comments:
The cohorts and data used by the authors to conduct the main analysis of the paper are already published, and thus the contribution of the analysis is incremental. In particular, the authors analyzed arrays from a limited cohort size, in comparison with others available sequenced with RNA-seq (e.g. 176 HR- and 322 non-HR NB in 498 SEQC; 224 HR- and 342 non-HR NBs included in the Westermann-genecode19 cohort; and 80 HR- and 20 non-HR in the Jagannathan cohort). Furthermore, the 12,000 most-expressed genes (out of ~20,000 available) were analyzed by the authors, as opposed to more than 40,000 (coding and non-coding) included in a normal RNA-seq study. Only the 498 SEQC dataset provides 12,000 genes significantly up-regulated in either high-risk (n~5,500) or non-high-risk (n~7,000). The differences between datasets could influence the results of the study. For example, in the reviewed manuscript, genes with a high expression in LR-NB (compared to fAG) included DNMT3B, SEMA5A, SOX5, and TET1, all of which have a significantly higher expression in HR-NBs of the 498 SEQC cohort. The quality of the manuscript will be enhanced with consistent results obtained by conducting the same reported analysis in larger cohorts.
In the reviewed manuscript, PHOX2A and PHOX2B are significantly more expressed in both LR-NB and HR-NB compared to fAG. This is also the case for other adrenergic markers including TH and DBH. Oppositely, the expression of cortex markers (i.e. STAR and CYP11A1) is significantly higher in fAG. Nevertheless PNMT is not significantly up-regulated in fAG in comparison to LR-NB nor HR-NB. Is it possible that the fetal adrenal glands analyzed include a large proportion of cortex that confounds the transcriptional signals? The quality of the manuscript will be enhance if the authors could establish what proportion of the fAG transcriptional signal belongs to cortex, and if they account for its influence in the analysis.
A recent published paper (Bedoya-Reina et al. 2021) study the differences of HR-NB and LR-NB from a single-cell perspective. In the published manuscript, the authors conclude that LR-NBs are enriched in cells that resemble chromaffin and sympathoblast cells, while the high-risk neuroblastomas are enriched in undifferentiated cells that resemble cells with progenitor characteristics in post-natal adrenal gland. This is broadly consistent with the conclusion reached by the authors in the manuscript under review. It will enhance the content of the reviewed manuscript if the authors compare their transcriptional signatures with the recently published transcriptional signatures in this paper to answer the following questions: 1) to what extent the transcriptional signatures for HR-NBs (4 hierarchical clusters incl.) in the manuscript under review resembles that from the published undifferentiated cluster (nC3) enriched in HR-NBs, and the progenitor cluster (hC1) in post-natal adrenal gland; 2) to what extent the transcriptional signatures for LR-NBs (4 hierarchical clusters incl.) in the manuscript under review resembles that from the published NOR (nC7, nC8, and nC9) enriched in LR-NBs, and that from the chromaffin cells (hC4) enriched in post-natal adrenal gland; and 3) how is the expression of NXPH1 and alpha-NRXN1-2 in the reported LR- and HR-NBs, and adrenal gland.
In the discussion, the authors indicate that they do not aim to identify the transcriptional signature associated to NB origin but rather use the component of the SA lineage that distinguish LR- and HR-NBs. This statement implies that neuroblastoma can originate from any cell in the developing SA lineage (i.e. SCP, bridge, chromaffin and sympathoblast), a controversial assumption that requires further proof. In particular, when discussing about the core sympathoadrenal signatures enriched in LR-NBs and HR-NBs, the authors obtained a SA signature of genes shared by at least 3 of the 4 SA cell signatures. Further justification needs to be provided as for why (in particular) one of these SA cell signatures exclude the sympathoblast/neuroblast contribution.
Some of the most interesting results in the paper are limited to proportions in a subset of top-ranked genes. It will be valuable to set the analysis in an hypothesis driven context, add probabilities, test names, and corrected p-values to the results.
Minor comments.
1) In comparison with other cohorts that include low- and intermediate-risk NBs as non-HR NBs, the reviewed data specifically includes low- and high- risk NBs. It is important that the authors include a characterization of intermediate-risk neuroblastomas in their analysis.
3) Further details and figures on what precise criteria was used to remove the sample #LR-08 is required. How including this sample changes the reported results?
4) GO-term distribution was assessed using the 50 most-enriched GO-terms. How would the results change if all the significant GO terms were analyzed?
5) Was the SEQC 498 (GSE62564) dataset obtained with microarrays (as indicated in the methods) or with RNA-seq (i.e. Illumina HiSeq 2000)?
6) In methods, the first quartile (Q1) in SA-c1 has a higher limit in 487 samples and the fourth quartile the lowest limit in 4, how many samples (out of 498 NBs) were excluded and why?
7) In the 503 DEGs between LR-HR NBs, NTRK2 and MYCN are not included, even if the HR samples included MYCN amplified tumors. Can the authors comment on this?
8) The authors mention that the top 30 genes found in cluster c1 (and also in c2) are correlated with favorable patient prognosis. Is it the case that all the genes in c1 (and also c2, c3 and c4) are significantly associated with a favorable or else unfavorable prognosis?
9) The high expression of a (significant?) number of genes in cluster c4 is observed in patients with worst outcome (i.e. lower event-free survival), including ATR, HIF1A, ING2, POLR2L, SRPRB (498 SEQC, analyzed with R2).
10) Regarding the 242 genes in the core SA signature, although its a smaller number, the expression of several genes in the core SA signature with a higher expression in HR compared to LR belonging to clusters 2, 3, and 4 is observed in worst outcome patients in the 498 SEQC cohort (CHD7, DNMT1, HMGA1, HSD17B12, LBR, LSM7, MCM4, NKAP, POLA1, and others). Is this small fraction significant?
11) In Kildisiute et al. 2021, NRXN1 is expressed in SCPs, while NXPH1 is expressed in bridge, chromaffin and sympathoblastic cells. How are the microenviroment of these cells regulating the expression of these genes in a developmental context (particularly as sympathoblastic cells are know to have larger proliferative capabilities than SCPs)? how is this cell heterogeneity replicated by a NB cell line? are mesenchymal and adrenergic cells expressing differentially NRXN1 and NXPH1?
12) Figure 1B and C, 2B,D: might the information provided be enhanced? otherwise these inserts might be excluded.
13) Figure 3D: Kildisiute et al. 2021 data and GTEX available at human protein atlas indicate expression of NRXN1 and NXPH1 in developing and adult adrenal gland. Might the results illustrated suggest a confounding effect in the sampled fetal adrenal glands, perhaps from cortex?
14) The authors conduct extensive experiments in NRXN1, and make conclusions about its role in for instance metastasis, nevertheless the LR-NB/HR-NB SA signal only includes NRXN2. Can the authors comment on the differences between NRXN1s and NRXN2?
Significance
The significance of the study relies in investigating the role of selected targets in neuroblastomas within a risk group. In particular, HR-NBs have poor outcomes and are generally metastatic at the time of diagnosis.
The results of the manuscript are somehow consistent with a recently published manuscript analyzing LR- and HR-NBs from a single-cell perspective. The manuscript will be enhanced by conducting the suggested comparison between the reviewed and the reported results. The authors further need to comment why HR-NBs markers, particularly MYCN is not recovered in the LR-NB/HR-NB and the LR-NB/HR-NB SA signals. Also they need to comment on possible confounding effects in the fetal adrenal gland.
The paper is directed to a broader audience of cancer and developmental biologists, and computational biologist. Yet further statistical support needs to be provided.
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Referee #2
Evidence, reproducibility and clarity
Summary:
In this manuscript, Dennis et al. identify different secretory routes and cell exit sites involved in basement membrane secretion and diversification in epithelial cells. Using the follicular epithelium of the Drosophila ovary as their model system coupled with genetics, imaging, and image analysis approaches, they show that two previously identified RabGTPases, Rab8 and Rab10, work in parallel routes for basement membrane secretion. These two small GTPases work in a partially redundant manner, where Rab8 promotes basal secretion leading to a homogenous basement membrane, while Rab10 promotes lateral and planer-polarized secretion, leading to the formation of fibrils. The authors also show that Rab10 and the dystrophin-associated protein act together to regulate lateral secretion, and dystrophin (Dys) is necessary for dystroglycan (Dg) to recruit Rab10. Furthermore, DAPC is shown to be essential for fibril formation and is sufficient to reorient Collagen IV to the Rab10-dependent secretory route. Dys was also shown to interact directly with exocyst subunit Exo70. Using overexpression and loss of function approaches the authors claim that Exo70 limits the planer polarization of Dys, and as a result, Rab10, hence limiting basement membrane fibril formation. Finally, the authors state that the Exocyst (Exo70) is also required for the Rab8-dependent basement membrane route. Overall, the data described in this manuscript are convincing and the authors' claims are supported by the presented data. We have mainly minor comments and only a few major comments that need to be addressed.
Major Comments:
• In the text for Figure 1G-H (page 4), the authors stated that the basal secretion was not restored in Rab8, 10, and 11 triple KD, in our opinion, it is unclear how the authors came to this strong conclusion from the presented data. It would be good if the authors explicitly explain how they come to this conclusion. Is it only based on the weak Coll-IV-GFP signal in the Rab8, 10, and 11 triple KD data compare to the control? If so, the authors should statistically quantify the difference with the control. In Figure 1H, no statistical analysis is provided between the control and triple KD conditions.
• From the data presented in Figure S1B, the authors state that the basement membrane mislocalization observed in Rab8/10KD has no major impact on polarity maintenance. They based this statement only on the localization of the apical marker aPKC. Although the aPKC data are convincing, it would be more compelling if the authors observe the distribution of other polarity proteins such as Dlg, E-Cadherin, and armadillo to better assess if the overall epithelial polarity is maintained in this condition.
Minor Comments:
General comments:
• In the text describing their data, we recommend that the authors clearly indicate which panel(s) they are referring to.
• The authors should also be consistent with the diction throughout the manuscript when referring to the cortical domain or region of the cell (back/rear/trailing edge/leading edge).
• Several references are missing in the manuscript.
The following specific comments are in order of appearance in the manuscript.
Introduction Section:
The following statements in the introduction should be supported by specific references:
• "BM is critical for tissue development, homeostasis and regeneration, as exemplified in humans by its implication in many congenital and chronic disorders."
• "BM is assembled from core components conserved throughout evolution: type IV collagen (Col IV), the heparan sulfate proteoglycan perlecan, and the glycoproteins laminin and nidogen."
• "During development, the dynamic interplay between cells and BM participates in sculpting organs and maintaining their shape."
• "BM protein secretion shows some specificities, mainly because of the large size of the protein complexes (e.g., procollagen) that must transit from the endoplasmic reticulum to the cell surface". This statement could be supported with references including specific Drosophila references. Additionally, the authors need to clarify what they mean by "some specifies".
Results section:
• In the text describing Fig. 2 (page 5), the authors describe two different basement membrane types: fibrils and homogenous. Moreover, the manuscript focuses on the role of Rab8 and Rab10 in the formation of these two structures. Thus, the authors must better describe the two different types of basement membrane structures and their known roles. This will be helpful for the readers to analyze the presented data, especially for those that are not familiar with the system. In Figure 2A, the authors describe stage 3 basement membrane as uniform BM, do they mean homogenous?
• In the text describing the data for Fig. 3 (page 6), the authors should clearly explain the reason to use anti-GFP antibodies in a non-permeabilized condition (i.e., to detect specifically the extracellular secretion of BM proteins). This will help the readers to interpret the data presented.
• On page 9, the authors stated that the precise localization of Dg in follicle cells is unknown. This statement is incorrect. It has been shown, using a Dg antibody, that Dg localizes at a high level at the basal side of the follicle cells and at a lower level at the apical side (Deng et al, 2003 and Denef et al. 2008).
Discussion Section:
• The following statement is not clear: "Thus, three different Rab proteins are targeted towards the three distinct domains of epithelial cells defined by apical basal polarity, and at least of them is also planar polarized". The authors should rephrase and describe specifically which Rabs they are talking about.
• This statement is vague: "These three Rab GTPases have been jointly involved in different processes (Knödler et al, 2010; Sato et al, 2014; Vogel et al, 2015; Eguchi et al, 2018; Häsler et al, 2020)". The authors could also mention the processes in which Rab8, 10, and 11 are involved.
• The following statements need to be supported by references. "Therefore, more investigations are required to define exactly how the DAPC allows the formation of BM fibrils. Nonetheless, given the importance of the DAPC and BM proteins in muscular dystrophies, our results will pave the way to determine whether a similar function is present also in muscle cells. Interestingly, the extracellular matrix is different between the myotendinous junction and the interjunctional sarcolemmal basement membrane and may provide another developmental context where several routes targeted to different subcellular domains may be implicated".
Experimental Procedure Section:
• In the dissection and immunostaining section (p14), there is a typo: it should be for "20 min" instead of "2for 0 min"
• For the GST pulldown experiments, the authors mention that they use a standard protocol to produce S35 Exo 70 and the GST pulldown experiments. The authors should provide references.
Figure and Figure Legend: • General comment: The orientation of the images showing the rotation and leading and trailing edges need to be consistent in the different figures (e.g., In Figures 3 and 7, the leading edge is oriented to the top while in Figures 4, S4, 5, 6, the leading edge is oriented to the bottom). This will help the readers to analyze the data.
• In Figure 1 C-G the scale bars are missing and should be added as Fig. 1B.
• Figure S1A: The data presented in Figure S1A is convincing. However, a control panel should be added showing the absence of apical Coll IV for comparison. This information will help with the interpretation of the data.
• In Figure 3 legend: it should be "immunostained" for GFP instead of stain for f-actin and GFP.
• In Figure 4, some scale bars are missing.
• In Figure 4 legend: it should be "(A, E)" after (i.e 0.8 µm above the basal surface) instead of "(C, G)"
• In Figure 5A-E, the authors show quantification of the fibril fraction for Dys-, Rab10 OE, and Rab10OE+Dys, Rab8KD, and Rab8KD+Dys-, and images of the collagen fibril for all the conditions except Dys-, it will be informative that the authors present a representative image of the Coll IV fibril in Dys- condition for comparison. The above comment also applies to Figure 5F-J, and it will be also informative to have a representative image of Dys- condition.
• In Figure 5 legend (p23), it should be "plane" and not "plan".
• Overall, the legend for Fig. S5 is not clear and we recommend the authors to clearly described the different panels. (e.g., it should be "(D)" instead of "(H-J)")
• In Figure 6, some scale bars are missing.
Significance
Despite the important roles of the basement membrane for mechanical support, tissue and organ development, and function, the mechanisms that control the polarized deposition of basement membrane proteins are largely unknown. The contribution of Rab 8 and Rab 10 in the polarized deposition of the basement membrane was previously shown. However, by identifying two competitive secretory routes for the basal secretion of the basement membrane proteins that required these two different RabGTPases, controlled by the DAPC and the exocyst complexes, the authors make a novel contribution to our understanding of the mechanism that leads to the polarized secretion of basement membrane proteins (in that case Collagen IV). Since the basement membrane has critical roles in tissue and organ morphogenesis and functions, and its misregulation has been associated with developmental defects and pathological conditions, this research sheds light on the mechanisms important in these morphogenetic processes and will give insights into their deregulations in pathological conditions.
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Reply to the reviewers
A detailed point wise response has been uploaded along with revised manuscript files
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Reply to the reviewers
Manuscript number: RC-2023-02157
Corresponding author(s): Satish, Mishra
1. General Statements [optional]
We thank the editor and reviewers for their helpful comments. We have successfully addressed most of the comments. We are performing some additional experiments as suggested by the reviewers and will be included if considered further. We attempted to pulldown the S14 interacting partner using anti-mCherry antibody from S14-3XHA-mCherry transgenic sporozoites and then further tried to identify interactome using mass spectrometry but failed. So, accordingly, we have toned down the conclusion.
The point-by-point response to the reviewer’s comments is given as follows.
2. Description of the planned revisions
Reviewer #1:
Figure 1F You have not formally shown that this signal corresponds to palmitoylated S14. Could be heavy chain. Response: The possibility of a heavy chain is negligible because we have used sporozoite samples and probed it with an anti-rabbit antibody conjugated to HRP. Also, the size of the S14 bands does not correspond to heavy chain. However, we have toned down the conclusion. Currently, we are performing the depalmitoylation experiment, and data will be included in the next round of revision.
Reviewer #2
Line 149: To definitively state S14 is a membrane protein, biochemical assays proving such should be performed. (or perhaps genetic mutation of the predicted palmitoylation site?) Otherwise, this should be rephrased. Response: We are performing the depalmitoylation assay, and the data will be included during the second round of revision. However, we have rephrased the sentence in the current version of the manuscript.
Lines 257-258: for yeast 2-hybrid, the controls of expressing S14, GAP45 and MTIP together with control proteins where no interaction would be predicted are absent. Response: We are performing experiments with additional controls, and data will be included in the next round of revision.
Reviewer #3
Conclusions that S14 knockout does not impact the expression and organization of two surface proteins, CSP and TRAP, and two IMC rely on a qualitative analysis only. However, quantitative analysis to support their observations is missing. Response: We are quantifying the IFA images and data will be included in the next round of revision.
3. Description of the revisions that have already been incorporated in the transferred manuscript
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Summary: The authors have identified a sporozoite gliding motility protein through bioinformatic analysis. From the main text I do not know how, or what bioinformatic analysis was performed, in order to focus on this protein which is called S14. The authors then go on to tag the protein, produce a KO and show its involvement in gliding motility. The KO shows that parasites lacking S14 fail to invade the mosquito salivary glands. This is due to a motility defect. Y2H and docking studies are used to define an interaction with MTIP and GAP45, two known components of the glideosome. Response: We identified this gene from the Kaiser et al., 2004 paper (DOI: 10.1046/j.1365-2958.2003.03909.x). The S14 was found to be highly upregulated in salivary gland sporozoites but lacked signal sequence and transmembrane domain. Next, we looked into other sporozoite proteins lacking signal sequence and transmembrane domain and found several gliding-associated proteins with similar properties. By using the guilt-by-association principle (DOI: 10.1186/gb-2009-10-4-104), we studied the following properties of existing glideosome components along with S14: (1) Classical pathway secretion using the signal peptide (SignalP, https://services.healthtech.dtu.dk/services/SignalP-5.0) (http://dx.doi.org/10.1016/j.jmb.2004.05.028). (2) Nonclassical pathway secretion (SecretomeP , https://services.healthtech.dtu.dk/services/SecretomeP-1.0/) (10.1093/protein/gzh037). (3) Presence of transmembrane domains (TMHMM , https://services.healthtech.dtu.dk/services/TMHMM-2.0/) (10.1006/jmbi.2000.4315). (4) Presence of a potential palmitoylation site (CSS-Palm, http://bioinformatics.lcd-ustc.org/css_palm) (Ren et al, 2008). This is a similar association prediction method as employed by the STRING database. However, mentioning that we identified a gliding motility protein by bioinformatic analysis was wrong, and we modified the sentence.
Major comments: The paper is sometimes hard to follow and lacks clarity. The reason: important information is omitted, or explained at the end of a section rather than at first mention; experimental details that are of essence need to be mentioned or explained in the main text; there is ample use of the word 'bioinformatic' without explaining what kind of analysis was performed in the main text. I cite from the abstract: 'In silico analysis of a novel protein, S14, which is uniquely upregulated in salivary gland sporozoites, suggested its association with glideosome-associated proteins.' I cite from the introduction: 'A study comparing transcriptome differences between sporozoites and merozoites using suppressive subtraction hybridization found several genes highly upregulated in sporozoites and named them 'S' genes (Kaiser et al, 2004). We narrowed it down to a candidate named S14, which lacked signal peptide and transmembrane domains.' From reading the main text, I do not know why Plasmodium berghei S14 was chosen in this manuscript. S14 is one of 25 transcripts identified by Kappe et al in Plasmodium yoelii (https://doi.org/10.1046/j.1365-2958.2003.03909.x) to be upregulated in sporozoites. The material and methods section does not explain either why S14 was chosen. Perhaps the authors could update Figure 2 from Kappe et al with the most recent annotations from plasmodb. Response: We have edited the manuscript for clarity and mentioned the name of the bioinformatic analysis performed. We chose S14 from Kaiser et al., 2004 (https://doi.org/10.1046/j.1365-2958.2003.03909.x) that identified transcripts in P. yoelii. We work on the rodent malaria parasite P .berghei and validated S14 transcripts by qPCR which showed its upregulation in sporozoites.
Rodent malaria parasites P. berghei and P. yoelii have been used extensively as models of human malaria. Both species have been widely used in studies on the biology of Plasmodium sporozoites and liver stages due to the availability of efficient reverse genetics technologies, and the ability to analyze these parasites throughout the life cycle stages have made these two species the preferred models for the analysis of Plasmodium gene function. A genetic screen and phenotype analysis were performed in P. berghei (DOI: 10.1016/j.cell.2017.06.030 and DOI: 10.1016/j.cell.2019.10.030) that makes in-depth characterization easier due to the availability of reagents and preliminary gene-phenotype like its dispensability in the blood. As suggested by this reviewer, we have updated the most recent annotations from PlasmoDB.
Reproducibility: None of the main Figures or Figure legends define ' N = '. For example I cite: 'The S14 KO clonal lines were first analyzed for asexual blood-stage propagation, and for this, 200 µl of iRBCs with 0.2% parasitemia was intravenously injected into a group of mice.' There are 2 mentions of 'N=' in the supplementary figures. I have not found any others.
I'm not sure what the convention is. Should unpublished data for this gene (PBANKA_0605900) found in pberghei.eu (a database for mutant berghei parasites) be cited? After all it confirms their findings.
The authors need to use more recent references for some of their statements; see some comments below. __Response: __We have mentioned N in the figures legends of the revised manuscript and also mentioned the unpublished data of RMGM. We have also added recent references in the revised manuscript.
Minor comments:
line 1-2 Add the Plasmodium species of this study.
Response: Added.
abstract Which species do you work with?
Response: We have mentioned P. berghei in the abstract of the revised manuscript.
29 mosquito salivary glands and human host hepatocytes
Response: Corrected.
30 to the glideosome, a protein complex containing [...]
Response: Corrected.
32-33 What kind of in silico analysis suggested S14 is part of the glideosome? S14 is not uniquely upregulated; there are other S-type genes identified by Kappe and Matuschewski. 25 I believe.
Response: Mentioning that in silico analysis suggested S14 is part of the glideosome was a wrong statement, and we have modified the sentence for clarity in the revised manuscript.
32 Please point out he species were S genes were identified. SGS of which species?
Response: The S genes were identified in the transcriptomic study of Plasmodium yoelii.
34 expression: change to transcription
Response: Changed.
39 What kind of in silico analysis was used here? and therefore malaria transmission
__Response: __In silico, protein-protein docking interaction analysis was used.
55 A single zygote transforms into a single ookinete, which establishes a single oocyst, which in turn can produce thousands of midgut sporozoites. Please correct the life cycle passage.
Response: Corrected. located or anchored in the IMC? And located between the IMC and plasma membrane?
Response: Glideosome is located between the plasma membrane and IMC, and the same has been corrected in the revised manuscript.
61-63 Refer to Table S1 and its contents here 64 Name the known GAPs. Response: Done.
65-67 Which transmembrane domain proteins? Please add more recent references than King 1988.
Response: We have added TRAP as a transmembrane domain protein and updated the reference.
71-72 TRAP was the first protein found to be ...
Response: Corrected.
74-76 Add additional, more recent references: for example search Frischknecht and TRAP
Response: Added.
76 S6 (TREP) is also [...]
Response: Done.
88 Some of these proteins are also expressed in ookinetes.
Response: Corrected.
89-91 The sentence needs a verb.
Response: Added.
88-96 Please add some more recent glideosome papers. After 2013.
Response: Added.
91 Why do you call it a peripheral protein?
Response: Because the GAP45 was detected at the periphery of the merozoite in P. falciparum. As there are no such reports in sporozoites hence we have removed peripheral in the revised manuscript.
91-93 There are more recent citations for GAP45 and GAP50. Response: We have added recent citations.
96 Insert a reference here.
Response: Added.
99 Please define the gliding-associated proteins. What are they? Aren't there papers on GAP40, 45 and 50? DOI: 10.1016/j.chom.2010.09.002
Response: Done.
99 .... What prompted you to identify a novel GAP? And why is S14 classified as a GAP?
Response: This was a wrong statement, which we removed in the revised manuscript.
99-102 What kind of bioinformatic study? Why was S14 chosen? Please outline how you ended up with S14. Any other proteins that came out of the bioinformatic screen from the list of S genes?
Response: We identified S14 from the Kaiser et al., 2004 paper and analyzed its properties using the “guilt-by-association” principle. The analysis showed that S14 had properties similar to GAP45 and MTIP. The S14 upregulation in sporozoites and its properties similar to known GAPs, we were prompted to study this gene's function.
How many proteins were identified in the screen for sporozoite upregulated proteins by Kappe and Matuschewski?
Response: 25 genes were identified in that paper, including the two characterized genes CSP and TRAP during that study.
102-103 Define the nonclassical secretion pathway. Please reference GAP45 and GAP50 data for the nonclassical pathway.
Response: When proteins are secreted out of the cytosol without predictable or known signal sequences or secretory motifs are classified as non-classically secreted proteins, and the pathway is called a non-classical protein secretory pathway. References: https://doi.org/10.1371/journal.pone.0125191; https://doi.org/10.1016/S0171-9335(99)80097-1; doi: 10.3389/fmicb.2016.00194
105 Please add P. berghei to the title, the abstract, the introduction.
Response: Added.
111 The results section does not outline what bioinformatic analysis was used
Response: The guilt-by-association principle was used, and it is outlined in the revised manuscript.
112-114 Please specify the exact number of upregulated in sporozoites genes. I think it was 25. And add the species the study was performed in. Why did you choose the Kappe study but not the uis genes from berghei?
Response: It was 25, and the species was P. yoellli. The domains of all 25 proteins are shown in Figure 2 of Kappe study. It intrigued us after having a glance at it. Later, we confirmed the upregulation of S14 transcripts in P. berghei sporozoites and chose to study the function of this gene.
114-115 How did you narrow it down to S14? The Kappe paper lists 25 S-type genes from P. yoelii.
Response: The domains of all 25 proteins are shown in the Kappe study. Two proteins, S14 and S15, lack signal sequence and transmembrane domain, which intrigued us after glancing at them. We chose S14 because its microarray induction is higher compared to S15.
118 Plasmodia is not the plural for a group of different Plasmodium species. Use: [...] conserved among Plasmodium spp.
Response: Corrected.
118-119 Which proteins did you analyze? And how did you analyze them? Where is the data for this analysis? Outline the amino acids that predict palmitoylation? The nonclassical pathway?
Response: The proteins we analyzed are given in Table S1. We analyzed them by the guilt-by-association principle. The data for this analysis is shown in Table S1. The amino acids predicted to be palmitoylated are C59 and C228 (S14), C5 (GAP45), C8 and C5 (MTIP). Non-classical pathway secretion was predicted by SecretomeP ( 10.1093/protein/gzh037).
119-122 Here: do you mean S14 has similar properties as GAP 45 and GAP50? Define the nonclassical pathway? How do you know S14 is in the IMC?
Response: The similar properties of S14 and GAP45 are Signal Peptide Prediction, Prediction of Non-classical pathway secretion, number of predicted transmembrane domains and prediction of Palmitoylation signal. GAP50 was wrongly mentioned here and has been removed from the revised manuscript.
When proteins are secreted out of the cytosol without predictable or known signal sequences or secretory motifs are classified as non-classically secreted proteins. The pathway is called a non-classical protein secretory pathway.
Our colocalization data of S14 with GAP45 and MTIP indicated that S14 is in the IMC.
122-123 Please reference the bioinformatic analysis plus URL that allows targeting to the IMC to be analyzed.
Response: All the URLs with references are given in the method section, lines 348-358 in the revised manuscript.
123-124 Please reference the URLs for TM, palmitoylation, and interactions analyses.
Response: All URLs with references are given in the method section, lines 348-358 in the revised manuscript.
125-127 How did you predict that S14 is secreted via the nonclassical pathway?
Response: We predicted non-classical pathway secretion of S14 using - SecretomeP (https://services.healthtech.dtu.dk/services/SecretomeP-1.0/) (10.1093/protein/gzh037).
128-130 Define the nonclassical pathway when it first appears in your manuscript.
The citation Moskes 2004 is not in the reference list
Response: The nonclassical pathway is defined in lines 105-107. The citation Moskes 2004 has been included in the revised manuscript.
132 Which membrane?
Response: Live S14-mCherry localization on the membrane does not differentiate between the outer membrane or IMC. Hence, only membrane is mentioned. Next, in Figure 4A, we confirmed S14 localization on IMC by treating sporozoites with Triton X-100 and colocalizing with IMC proteins GAP45 and MTIP.
134-135 In which species?
Response: We have mentioned P. berghei in the text in the revised manuscript.
141-142 Please include images of blood stage and liver stage parasites.
Response: Blood and liver stage images are included in the revised manuscript as Figure S2.
142-143 Which membrane?
Response: Live S14-mCherry localization on the membrane does not differentiate between the outer membrane or IMC. Hence, only membrane is mentioned. Next, in Figure 4A, we confirmed S14 localization on IMC by treating sporozoites with Triton X-100 and colocalizing with IMC proteins GAP45 and MTIP.
148-149 I cannot find the specific figure you refer to; I checked the online version of the Frenal 2010 paper.
Response: Electromobility shifts of GAP45 due to the palmitoylation have been reported in (Rees-Channer et al, 2006; DOI: 10.1016/j.molbiopara.2006.04.008). Frenal 2010 paper has stated about two bands but experimentally, it was shown in Rees-Channer et al, 2006 in Figures 1 and 2B.
175 gland, we counted [...]
Response: Corrected.
177 Compared to the
Response: Corrected.
177-179 Failed to invade (absolutely)? Or invaded in highly reduced numbers?
Response: Corrected.
182-186 Please be precise: I think you mean you let all types of mosquitoes take a blood meal; s14 knockout-infected mosquitoes did not infect mice.
Response: Corrected.
181-202 Perhaps use paragraphs to indicate the different types of experiments performed here.
Response: Done.
204 Please introduce paragraphs to identify the different experiments in this section
Response: Done.
208 Outer or inner membrane of what? IMC, the plasma membrane?
Response: We treated sporozoites with Triton X-100 to analyze whether S14 is present on the outer membrane (plasma membrane) or IMC.
228 onwards Structural models were obtained from whom? Which species did you use for the docking study? Could you use in one approach 3 berghei proteins, and confirm your docking studies with the falciparum proteins? That would strengthen your model. Should you include a negative control protein in the approach? Response: The structural models were obtained using the trROSETTA server. We used P. berghei for the docking study. In the old annotation and RMGM, the ortholog of P. berghei (PBANKA_0605900) in P.falciparum (PF3D7_1207400) was indicated. However, the updated PlasmodDB does not show PBANKA_0605900 ortholog in P. falciparum. We did try to generate structure models of P. falciparum MTIP, GAP45 and S14 using the trROSETTA server. We successfully reproduced the structure of MTIP, and GAP45 but the quality of S14 structure was unsuitable for the interaction studies. The negative control cannot be included in this kind of study because it gives a false interface, and none of the previous studies have used negative control.
250-251 Was all of the gene cloned? Please define amino acid range. discussion
Response: Full-length gene of S14, MTIP and GAP45 was cloned and the same has been mentioned in materials and methods in the revised manuscript.
Please discuss data from https://elifesciences.org/articles/77447 in relation to your protein Response: Discussed.
298-300 More recent glideosome papers exist. For example https://doi.org/10.1038/s42003-020-01283-8
Response: Included.
340 List the proteins you analysed. Add URL (websites) to the analyses tools.
Response: They are listed in Table S1. The method section gives all the URLs with references, lines 348-358 in the revised manuscript.
343 Known association from the literature: how was this done?
Response: The interactions demonstrated by different groups have been summarized in the review by Boucher & Bosch, 2015 (doi: 10.1016/j.jsb.2015.02.008).
346-349 A few glideosome components? On what basis were they selected and which are they? Response: The analysis showed that S14 had properties similar to GAP45 and MTIP. Additionally, S14 localized with GAP45 and MTIP, hence selected for interaction studies.
471 Can AlphaFold Structure Predictions be used in the docking studies?
Response: Even the Alphafold AI is trained on existing sequence/structure information despite being advertised as a de novo prediction system. That's why it can't produce good quality structures of evolutionarily unique proteins such as S14. We initially started our protein model generation by alphafold2, but the quality of the structure was very low; then we further used the trRosetta server (https://yanglab.nankai.edu.cn/trRosetta/), which shows the quality of all three protein structures above 95 after validation by using UCLA-DOE LAB-SAVES V6.0 (https://saves.mbi.ucla.edu/).
tr-Rosetta includes inter-residue distance, orientation distribution by a deep-neural network, and homologous template to improve the accuracy of models (DOI: 10.1038/s41596-021-00628-9).
We have given the model structure generated using alphafold2 for your reference.
Model generated by using AlphaFold2.ipynb (https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb#scrollTo=kOblAo-xetgx).
Structure quality assessment by __http://saves.mbi.ucla.edu/.__
GAP 45
__S14 __
MTIP
487 What parts of theses genes was cloned? Define the amino acid range.
Response: The full-length protein-encoding gene was cloned.
714 Please split the table into A Mosquito bite and B haemolymph Sporozoites Response: Done.
Figure 1 For clarity, maybe write S14::mCherry
Response: Done.
Figure 1 It would be useful to show blood stage parasite images.
Response: Blood stage parasite image is included in the revised manuscript as Figure S2.
Figure 2G Haemolymph sporozoites ?
Response: Done.
Figure 8 You argued that S14 is a membrane-bound protein through palmitoylation. Here the protein is shown to be cytoplasmic. Please update our model with more recent ones. Response: We have shown that S14 colocalizes with GAP45 and MTIP, suggesting its IMC localization. We have updated our model in Figure 8.
Figure S2B It would be good to include a positive control for these PCRs.
Response: We have replaced the figure's new gel with a positive control.
Figure S3 It would be good to include a positive control for these PCRs. Response: We already have positive controls in Figure S3C and S3F for all the primer pairs used.
Tabel S1 Table S1 is only mentioned twice in the text: lines 124 and 128. There is no mention that the table contains all (??) known gliding motility proteins.
Response: The table does not contain all the gliding proteins; however, most of the proteins mentioned in the Boucher & Bosch, 2015 paper (doi: 10.1016/j.jsb.2015.02.008) were included.
Table S1 The algorithms / websites used for bioinformatic prediction need to be listed here.
Response: Included.
Table S2 Add the plasmodb gene identifiers here. The table does not show all Plasmodium spp. but a selection. Response: All the orthologs mentioned in Figure S1 and Table S2 are not shown in the updated PlasmoDB. Accordingly, we have removed the Figure S1 and Table S2 in the revised manuscript__.__
Reviewer #1 (Significance (Required)):
General assessment: The authors provide an in-depth analyses of the Plasmodium berghei protein S14 and its involvement in gliding motility. Response: Thank you.
Advance: This paper is the first analysis of the S14 protein. The authors suggest a bridging function for the protein between MTIP and GAP45. Response: Thank you.
Audience: Gliding motility is of interest to the apicomplexan field. I think this particular proteins is specific to Plasmodium spp. Response: Thank you.
Reviewer #2
Summary:
The authors tag the sporozoite protein S14 in P. berghei and show localization near the sporozoite plasma membrane. They also convincingly show, through the generation of S14 knockout lines, that S14 is required for sporozoite motility and thereby also salivary gland and hepatocyte invasion. Their bioinformatic results support possible interactions between S14 and the inner membrane complex proteins MTIP and GAP45. These analyses were performed with these specific candidate proteins rather than being unbiased searches for potential interaction partners. The yeast 2-hybrid data to support these possible protein interactions need further controls.
Lines 143-144: Unless the sporozoites were not permeablized prior to staining, it is not clear if the protein is "on" the plasma membrane or just under the plasma membrane. Furthermore, this statement anyway seems contradictory to the authors' interpretation of Figure 4A. Response: Live S14-mCherry localization on the membrane does not differentiate between the outer membrane or IMC. Next, in Figure 4A, we confirmed S14 localization on IMC by treating sporozoites with Triton X-100 and colocalizing with IMC proteins GAP45 and MTIP. Further, we ensured that mCherrey signals were bleached post-fixation and performed IFA with and without permeabilization. We revealed the mCherry and CSP signals using Alexa 488 and Alexa 594, respectively. We observed the mCherrey signal with permeablized sporozoites, not without permeabilization.
Line 218: "This result indicates that S14 is present within the inner membrane of sporozoites." While this data shows that S14 is not in the plasma membrane of the parasite, how can the authors be sure it is at the IMC? Response: S14 colocalization with MTIP and GAP45 suggested its localization on IMC.
Line 225-226: This sentence overreaches in its conclusion. There is no indication that this protein provides the power or force behind the sporozoites forward movement. Several proteins are known to be required for gliding motility, but they are not all force-providing factors. Response: We have modified the sentence, and now it states, ‘These data demonstrate that S14 is an IMC protein, essential for the sporozoite's gliding motility.
Minor comments:
Line 99: "the role of gliding-associated proteins is unexplored" There are several publications on GAP40, GAP45 and GAP50 (some of which are referenced in the previous paragraph). Response: We have included the reference for studied proteins and modified the sentence for clarity.
Line 114: "We narrowed it down to a candidate" Narrowed down how? Or rephrase. Response: We identified the S14 gene from the Kaiser et al., 2004 paper (DOI: 10.1046/j.1365-2958.2003.03909.x) and rephrased the sentence in the revised manuscript.
Lines 120-123 are strangely written, and I don't follow the logic. What "similar properties" do GAP45 and GAP50 have with S14 and are they really indicative of function? Also if palmitoylation and myristylation and nonclassical secretion are present in most eukaryotes, why would they necessarily be evidence of IMC targeting? Response: It was wrongly written, we have modified the sentence for clarity.
Line 148-149. I did not see examples of this electromobility shift of GAP45 in this publication (although I may have overlooked it).
Response: Electromobility shifts of GAP45 due to the palmitoylation have been reported in (Rees-Channer et al, 2006; DOI: 10.1016/j.molbiopara.2006.04.008). Frenal 2010 paper has stated about two bands, but experimentally it was shown in Rees-Channer et al, 2006 in Figure 1 and 2B.
Table 1 legend should preferably specify that hemolymph sporozoites were used for IV infections. Response: Done.
Line 228: Should be rephrased for accuracy. "revealed the" should be replaced with "suggests" Response: Replaced.
Lines 305-307: I don't entirely understand the logic laid out here.
Response: This was written about GAP45 and MTIP coordination; however, it has been removed in the revised manuscript.
Lines 320-322: "We hypothesize that S14 possibly plays a structural role and maintains the stability of IMC required for the activity of motors during gliding and invasion." The data about the IMC structure shown is fluorescence microscopy - and there no change is observed in the IMC in the knockout line. I suggest removing or rephrasing this point if no extra data is provided to show this. Response: We have removed this sentence in the revised manuscript.
Reviewer #2 (Significance (Required)):
The work gives insights into an unstudied, conserved Plasmodium protein, S14, which the authors show is critical for Plasmodium transmission from mosquitoes. The parasite genetics and phenotyping demonstrating this are strong. The conclusions about interactions with glideosome/inner membrane complex components need further experimental support. The work is of interest to the Plasmodium field and may be also of interest to people interested in other protozoan parasites or in cellular motility.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
The manuscript by Gosh and colleagues demonstrates that S14 is a glideosome-associated protein in sporozoites. S14 knockout sporozoites fail to infect mosquito salivary glands and liver cells in the mammalian host. These sporozoites are also defective in gliding motility as S14 localizes to the inner membrane. S14 was shown to interact with the glideosome-associated proteins GAP45 and MTIP using the yeast two-hybrid system. The authors also provide an in-silico prediction on the S14, GAP45 and MTIP interaction.
Major issues:
Overall, there is information lacking in the manuscript, including on the figure legends, regarding experiments replication and n analyzed.
For complementation, the authors engineered an independent S14 knockout line. For this line is clear that parasites failed to infect salivary glands contrarily to the knockout line. Despite not showing it, did the authors confirm that this knockout line has no defects in infecting mosquito midguts and producing sporozoites? Response: We analyzed the midgut for sporozoite formation, which was comparable to the original KO line, and included the data (Figure 2D) in the revised manuscript.
Did the authors conduct IV injections in mice with a higher number of sporozoites? Hemolymph sporozoites are less infectious than sporozoites collected from the salivary glands and I was wondering whether patent infections with S14 ko sporozoites can be obtained by injecting a higher inoculum. The same applies to the infectivity experiments with HepG2cells. Response: We increased the sporozoites dose and infected mice with 10,000 hemolymph sporozoites, but no infection was observed (Table 1). No EEFs were observed in HepG2 cells infected with 10,000 S14 KO hemolymph sporozoites.
Please provide information on the number of sporozoites that were analyzed in the trails experiment. Response: We analyzed 210, 225, and 212 sporozoites for WT GFP, S14 KO c1, and S14 KO c2, respectively.
Minor issues:
In Figure 1. F) WB on S14-3xHA-mCherry tagged sporozoites showing two bands on the WB. The Palm-band is only inferred thus I suggest correcting the figure to S14-3xHA-mcherry. On 1D all the mcherry signal is detected on the membrane but then on WB, a smaller fraction is palm? What is the explanation for the ratio between the two bands? Why so distinct CSP intensity bands between wt and tagged line? Were very distinct amounts of protein loaded?
Response: We have corrected the Palm-S14-3xHA-mcherry to S14-3xHA-mcherry.
This reviewer raises a valid point regarding the discrepancy between IFA and Western blot. The non-palmitoylated S14-mCherrey signal was possibly corrected after deconvolution in image 1D and mainly the membrane signal was prominent. In Figure 1C, many sporozoites show some cytosolic signal, perhaps representing non-palmitoylated S14. Western blot concentrates the protein of interest as a single band, allowing more accurate visualization of protein.
The distinct CSP intensity bands between wt and the tagged line are due to the loading of a higher amount of parasite lysate in WT lane. To ensure that the western blot signal is specific to S14, we loaded a higher amount of protein in WT.
Figure 1. A) Statistical analysis is missing. Not clear if the bars represent mean values +/- standard deviation. No information on the material and methods of how the relative expression was calculated. Response: No error bars are shown in Figure 1 because it was performed once.
In the introduction lines 54 and 58 I suggest replacing humans with mammalian host. Response: Replaced.
Line 58. Not clear why the ref Ripp et al., 2021 is used for a general sentence to introduce the Plasmodium life cycle. Response: Removed.
Line 72: I suggest replacing "TRAP mutant" with "TRAP knockouts" (Sultan et al., 1997). More recently there are TRAP mutants with impaired motility and normal invasion of mosquito salivary glands (Klug et al., 2020) Response: Replaced.
Lines 78 to 86: In this paragraph, authors refer to several proteins involved in sporozoite gliding motility and host cell invasion, however for most of the studies this conclusion comes from the characterization of knockouts defective phenotype and actually a direct role for some of these molecules in the process awaits clear demonstration. Response: We have replaced involved with implicated.
Line 78: Authors do not consider that maebl knockout sporozoites display reduced adhesion, including to cultured hepatocytes, which could contribute to the defects in multiple biological processes, such as in gliding motility, hepatocyte wounding, and invasion. Response: We have corrected maebl role in the revised manuscript.
Line 80: I suggest authors reconcile the contradictory reports in the literature on the role of TRSP in sporozoites invasion. Response: We have removed this reference in the revised manuscript.
Line 82-83: Please revise it. Response: Revised.
Table 1. Correct table as when sporozoites were transmitted by mosquito bite the term "number of sporozoites injected" does not apply. Please give more details on the bite experiments. Is this the number of mosquitoes for all four animals? For how long the mosquitoes were allowed to bite? Response: For clarity, we have split the table into A Mosquito bite and B haemolymph Sporozoites. We used ten mosquitoes/mice in the bite experiment. Mosquitos were allowed to probe for blood meal for 20 minutes, and the feeding was ensured by observing mosquitoes post-blood meal; approximately 70% of mosquitoes received the blood meal in all the cages.
Line 288 and 289. There are several publications showing that maebl knockout sporozoites are impaired at invading the mosquito salivary glands and at infecting the vertebrate host contradicting Kariu et al., 2002 findings in the vertebrate host. Response: We have removed maebl from this line.
Line 290. I suggest "was most likely due to" instead of " due to" as sporozoite adhesion to cells was not evaluated. Response: Corrected.
Line 291: "Cellular transmigration and host cell invasion are prerequisites for gliding motility" please revise. Response: Revised.
Line 437: indicate which clone was used.
Response: Indicated (3D11).
Line: 463: indicate the % of the gel in the SDS-PAGE Response: We have used 10% SDS-PAGE gel and it is indicated in the revised manuscript.
Line 499: indicate the version of the GraphPad Prism software. Response: GraphPad Prism version 9.
Figure S3 legend needs to be corrected. Panels in the figure are from A to F while in legend G and H are included. Response: Corrected.
4. Description of analyses that authors prefer not to carry out
Reviewer #2
Line 39-41: "Using in silico and the yeast two-hybrid system, we showed the interaction of S14 with the glideosome-associated proteins GAP45 and MTIP. Together, our data show that S14 is a glideosome-associated protein" Although these interactions can be speculated based on the results shown, these interactions were not confirmed in this study. Response: We attempted to pulldown the S14 interacting partner using anti-mCherry antibody from S14-3XHA-mCherry transgenic sporozoites and then further tried to identify interactome using mass spectrometry but failed. Hence, we selected two known IMC localized gliding proteins MTIP and GAP45. Performing pull-down from sporozoites is challenging, so we checked this interaction using yeast 2-hybrid assay and bioinformatic analysis for protein-protein interaction.
In order to claim interaction between S14 and IMC proteins, interaction needs to be shown experimentally. Well-controlled yeast 2-hybrid would be a start - then interaction would be more than just speculative. But immunoprecipitation from sporozoites or other biochemical interactions would give more support to this idea. Response: We attempted to pulldown the S14 interacting partner using an anti-mCherry antibody from S14-3XHA-mCherry transgenic sporozoites and then further tried to identify interactome using mass spectrometry but failed. Hence, we selected two known IMC localized gliding proteins MTIP and GAP45. Performing pull-down from sporozoites is challenging, so we checked this interaction using yeast 2-hybrid assay and bioinformatic analysis for protein-protein interaction.
Reviewer #3
The authors provide convincing data on the S14 localization in the inner membrane of sporozoites and interaction with GAP45 and MTIP using the yeast model. Did the authors consider conducting co-IP followed by MS analysis to pull down S14 in the complex with GAP45 and MTIP? Response: We attempted to pulldown the S14 interacting partner using an anti-mCherry antibody from S14-3XHA-mCherry transgenic sporozoites and then further tried to identify the interactome using mass spectrometry but failed. Hence, we selected two known IMC localized gliding proteins, MTIP and GAP45. Performing pull-down from sporozoites is challenging, so we checked this interaction using yeast 2-hybrid assay and bioinformatic analysis for protein-protein interaction.
__Reviewer #3 (Significance (Required)):____ __ Sporozoite gliding motility is a critical feature of parasite infectivity. Impairment of this important feature has been described for several mutant/knockout parasite lines. This study goes beyond the phenotypic analysis of mutant parasites to infer the role of S14 by providing more mechanistic evidence to show S14 interaction with other glideosome-associated proteins. However, this interaction was investigated using the two-hybrid system in yeast. Still, in sporozoites, no experiments were conducted to evaluate the interaction between these proteins.
Response: We attempted to pulldown the S14 interacting partner using an anti-mCherry antibody from S14-3XHA-mCherry transgenic sporozoites and then further tried to identify interactome using mass spectrometry but failed. Hence, we selected two known IMC localized gliding proteins, MTIP and GAP45. Performing pull-down from sporozoites is challenging, so we checked this interaction using yeast 2-hybrid assay and bioinformatic analysis for protein-protein interaction.
Please consider I'm not an expert on the in-silico interaction studies.
-
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Referee #3
Evidence, reproducibility and clarity
The manuscript by Gosh and colleagues demonstrates that S14 is a glideosome-associated protein in sporozoites. S14 knockout sporozoites fail to infect mosquito salivary glands and liver cells in the mammalian host. These sporozoites are also defective in gliding motility as S14 localizes to the inner membrane. S14 was shown to interact with the glideosome-associated proteins GAP45 and MTIP using the yeast two-hybrid system. The authors also provide an in-silico prediction on the S14, GAP45 and MTIP interaction.
Major issues:
Overall, there is information lacking in the manuscript, including on the figure legends, regarding experiments replication and n analyzed.
For complementation, the authors engineered an independent S14 knockout line. For this line is clear that parasites failed to infect salivary glands contrarily to the knockout line. Despite not showing it, did the authors confirm that this knockout line has no defects in infecting mosquito midguts and producing sporozoites?
Did the authors conduct IV injections in mice with a higher number of sporozoites? Hemolymph sporozoites are less infectious than sporozoites collected from the salivary glands and I was wondering whether patent infections with S14 ko sporozoites can be obtained by injecting a higher inoculum. The same applies to the infectivity experiments with HepG2cells.
The authors provide convincing data on the S14 localization in the inner membrane of sporozoites and interaction with GAP45 and MTIP using the yeast model. Did the authors consider conducting co-IP followed by MS analysis to pull down S14 in the complex with GAP45 and MTIP?
To characterize the gliding defective phenotype authors based their assessment on trails quantifications. This analysis is very limited as the percentage of attached, waving, floating sporozoites cannot be determined. Due to their conclusion on the role of S14 for sporozoite gliding motility, I recommend a more detailed investigation using live microscopy imaging. Please provide information on the number of sporozoites that were analyzed in the trails experiment.
Conclusions that S14 knockout does not impact the expression and organization of two surface proteins, CSP and TRAP, and two IMC rely on a qualitative analysis only. However, quantitative analysis to support their observations is missing.
Minor issues:
In Figure 1. F) WB on S14-3xHA-mCherry tagged sporozoites showing two bands on the WB. The Palm-band is only inferred thus I suggest correcting the figure to S14-3xHA-mcherry. On 1D all the mcherry signal is detected on the membrane but then on WB, a smaller fraction is palm? What is the explanation for the ratio between the two bands? Why so distinct CSP intensity bands between wt and tagged line? Were very distinct amounts of protein loaded?
Figure 1. A) Statistical analysis is missing. Not clear if the bars represent mean values +/- standard deviation. No information on the material and methods of how the relative expression was calculated.
In the introduction lines 54 and 58 I suggest replacing humans with mammalian host.
Line 58. Not clear why the ref Ripp et al., 2021 is used for a general sentence to introduce the Plasmodium life cycle.
Line 72: I suggest replacing "TRAP mutant" with "TRAP knockouts" (Sultan et al., 1997). More recently there are TRAP mutants with impaired motility and normal invasion of mosquito salivary glands (Klug et al., 2020)
Lines 78 to 86: In this paragraph, authors refer to several proteins involved in sporozoite gliding motility and host cell invasion, however for most of the studies this conclusion comes from the characterization of knockouts defective phenotype and actually a direct role for some of these molecules in the process awaits clear demonstration.
Line 78: Authors do not consider that maebl knockout sporozoites display reduced adhesion, including to cultured hepatocytes, which could contribute to the defects in multiple biological processes, such as in gliding motility, hepatocyte wounding, and invasion.
Line 80: I suggest authors reconcile the contradictory reports in the literature on the role of TRSP in sporozoites invasion.
Line 82-83: Please revise it.
Table 1. Correct table as when sporozoites were transmitted by mosquito bite the term "number of sporozoites injected" does not apply. Please give more details on the bite experiments. Is this the number of mosquitoes for all four animals? For how long the mosquitoes were allowed to bite?
Line 288 and 289. There are several publications showing that maebl knockout sporozoites are impaired at invading the mosquito salivary glands and at infecting the vertebrate host contradicting Kariu et al., 2002 findings in the vertebrate host.
Line 290. I suggest "was most likely due to" instead of " due to" as sporozoite adhesion to cells was not evaluated.
Line 291: "Cellular transmigration and host cell invasion are prerequisites for gliding motility" please revise.
Line 437: indicate which clone was used.
Line: 463: indicate the % of the gel in the SDS-PAGE
Line 499: indicate the version of the GraphPad Prism software.
Figure S3 legend needs to be corrected. Panels in the figure are from A to F while in legend G and H are included.
Significance
Sporozoite gliding motility is a critical feature of parasite infectivity. Impairment of this important feature has been described for several mutant/knockout parasite lines. This study goes beyond the phenotypic analysis of mutant parasites to infer the role of S14 by providing more mechanistic evidence to show S14 interaction with other glideosome-associated proteins. However, this interaction was investigated using the two-hybrid system in yeast. Still, in sporozoites, no experiments were conducted to evaluate the interaction between these proteins.
Please consider I'm not an expert on the in-silico interaction studies.
-
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
Learn more at Review Commons
Referee #2
Evidence, reproducibility and clarity
Summary:
The authors tag the sporozoite protein S14 in P. berghei and show localization near the sporozoite plasma membrane. They also convincingly show, through the generation of S14 knockout lines, that S14 is required for sporozoite motility and thereby also salivary gland and hepatocyte invasion. Their bioinformatic results support possible interactions between S14 and the inner membrane complex proteins MTIP and GAP45. These analyses were performed with these specific candidate proteins rather than being unbiased searches for potential interaction partners. The yeast 2-hybrid data to support these possible protein interactions need further controls.
Major comments:
Line 39-41: "Using in silico and the yeast two-hybrid system, we showed the interaction of S14 with the glideosome-associated proteins GAP45 and MTIP. Together, our data show that S14 is a glideosome-associated protein" Although these interactions can be speculated based on the results shown, these interactions were not confirmed in this study.
Lines 143-144: Unless the sporozoites were not permeablized prior to staining, it is not clear if the protein is "on" the plasma membrane or just under the plasma membrane. Furthermore, this statement anyway seems contradictory to the authors' interpretation of Figure 4A.
Line 218: "This result indicates that S14 is present within the inner membrane of sporozoites." While this data shows that S14 is not in the plasma membrane of the parasite, how can the authors be sure it is at the IMC?
Line 149: To definitively state S14 is a membrane protein, biochemical assays proving such should be performed. (or perhaps genetic mutation of the predicted palmitoylation site?) Otherwise, this should be rephrased.
Line 225-226: This sentence overreaches in its conclusion. There is no indication that this protein provides the power or force behind the sporozoites forward movement. Several proteins are known to be required for gliding motility, but they are not all force-providing factors.
Lines 257-258: for yeast 2-hybrid, the controls of expressing S14, GAP45 and MTIP together with control proteins where no interaction would be predicted are absent.
In order to claim interaction between S14 and IMC proteins, interaction needs to be shown experimentally. Well-controlled yeast 2-hybrid would be a start - then interaction would be more than just speculative. But immunoprecipitation from sporozoites or other biochemical interactions would give more support to this idea.
Minor comments:
Line 99: "the role of gliding-associated proteins is unexplored" There are several publications on GAP40, GAP45 and GAP50 (some of which are referenced in the previous paragraph).
Line 114: "We narrowed it down to a candidate" Narrowed down how? Or rephrase.
Lines 120-123 are strangely written, and I don't follow the logic. What "similar properties" do GAP45 and GAP50 have with S14 and are they really indicative of function? Also if palmitoylation and myristylation and nonclassical secretion are present in most eukaryotes, why would they necessarily be evidence of IMC targeting?
Line 148-149. I did not see examples of this electromobility shift of GAP45 in this publication (although I may have overlooked it).
Table 1 legend should preferably specify that hemolymph sporozoites were used for IV infections.
Line 228: Should be rephrased for accuracy. "revealed the" should be replaced with "suggests"
Lines 305-307: I don't entirely understand the logic laid out here.
Lines 320-322: "We hypothesize that S14 possibly plays a structural role and maintains the stability of IMC required for the activity of motors during gliding and invasion." The data about the IMC structure shown is fluorescence microscopy - and there no change is observed in the IMC in the knockout line. I suggest removing or rephrasing this point if no extra data is provided to show this.
Significance
The work gives insights into an unstudied, conserved Plasmodium protein, S14, which the authors show is critical for Plasmodium transmission from mosquitoes. The parasite genetics and phenotyping demonstrating this are strong. The conclusions about interactions with glideosome/inner membrane complex components need further experimental support. The work is of interest to the Plasmodium field and may be also of interest to people interested in other protozoan parasites or in cellular motility.
-
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Referee #1
Evidence, reproducibility and clarity
Summary: The authors have identified a sporozoite gliding motility protein through bioinformatic analysis. From the main text I do not know how, or what bioinformatic analysis was performed, in order to focus on this protein which is called S14. The authors then go on to tag the protein, produce a KO and show its involvement in gliding motility. The KO shows that parasites lacking S14 fail to invade the mosquito salivary glands. This is due to a motility defect. Y2H and docking studies are used to define an interaction with MTIP and GAP45, two known components of the glideosome.
Major comments: The paper is sometimes hard to follow and lacks clarity. The reason: important information is omitted, or explained at the end of a section rather than at first mention; experimental details that are of essence need to be mentioned or explained in the main text; there is ample use of the word 'bioinformatic' without explaining what kind of analysis was performed in the main text. I cite from the abstract: 'In silico analysis of a novel protein, S14, which is uniquely upregulated in salivary gland sporozoites, suggested its association with glideosome-associated proteins.' I cite from the introduction: 'A study comparing transcriptome differences between sporozoites and merozoites using suppressive subtraction hybridization found several genes highly upregulated in sporozoites and named them 'S' genes (Kaiser et al, 2004). We narrowed it down to a candidate named S14, which lacked signal peptide and transmembrane domains.' From reading the main text, I do not know why Plasmodium berghei S14 was chosen in this manuscript. S14 is one of 25 transcripts identified by Kappe et al in Plasmodium yoelii (https://doi.org/10.1046/j.1365-2958.2003.03909.x) to be upregulated in sporozoites. The material and methods section does not explain either why S14 was chosen. Perhaps the authors could update Figure 2 from Kappe et al with the most recent annotations from plasmodb.
Reproducibility: None of the main Figures or Figure legends define ' N = '. For example I cite: 'The S14 KO clonal lines were first analyzed for asexual blood-stage propagation, and for this, 200 µl of iRBCs with 0.2% parasitemia was intravenously injected into a group of mice.' There are 2 mentions of 'N=' in the supplementary figures. I have not found any others.
I'm not sure what the convention is. Should unpublished data for this gene (PBANKA_0605900) found in pberghei.eu (a database for mutant berghei parasites) be cited? After all it confirms their findings.
The authors need to use more recent references for some of their statements; see some comments below.
Minor comments:
line
1-2 Add the Plasmodium species of this study. abstract Which species do you work with? 29 mosquito salivary glands and human host hepatocytes 30 to the glideosome, a protein complex containing [...] 32-33 What kind of in silico analysis suggested S14 is part of the glideosome? S14 is not uniquely upregulated; there are other S-type genes identified by Kappe and Matuschewski. 25 I believe. 32 Please point out he species were S genes were identified. SGS of which species? 34 expression: change to transcription 39 What kind of in silico analysis was used here? and therefore malaria transmission 55 A single zygote transforms into a single ookinete, which establishes a single oocyst, which in turn can produce thousands of midgut sporozoites. Please correct the life cycle passage. located or anchored in the IMC? And located between the IMC and plasma membrane? 61-63 Refer to Table S1 and its contents here 64 Name the known GAPs.
65-67 Which transmembrane domain proteins? Please add more recent references than King 1988. 71-72 TRAP was the first protein found to be ... 74-76 Add additional, more recent references: for example search Frischknecht and TRAP 76 S6 (TREP) is also [...] 88 Some of these proteins are also expressed in ookinetes. 89-91 The sentence needs a verb. 88-96 Please add some more recent glideosome papers. After 2013. 91 Why do you call it a peripheral protein? 91-93 There are more recent citations for GAP45 andGAP50. 96 Insert a reference here. 99 Please define the gliding-associated proteins. What are they? Aren't there papers on GAP40, 45 and 50? DOI: 10.1016/j.chom.2010.09.002 99 .... What prompted you to identify a novel GAP? And why is S14 classified as a GAP? 99-102 What kind of bioinformatic study? Why was S14 chosen? Please outline how you ended up with S14. Any other proteins that came out of the bioinformatic screen from the list of S genes? How many proteins were identified in the screen for sporozoite upregulated proteins by Kappe and Matuschewski? 102-103 Define the nonclassical secretion pathway. Please reference GAP45 and GAP50 data for the nonclassical pathway. 105 Please add P. berghei to the title, the abstract, the introduction. 111 The results section does not outline what bioinformatic analysis was used 112-114 Please specify the exact number of upregulated in sporozoites genes. I think it was 25. And add the species the study was performed in. Why did you choose the Kappe study but not the uis genes from berghei? 114-115 How did you narrow it down to S14? The Kappe paper lists 25 S-type genes from P. yoelii. 118 Plasmodia is not the plural for a group of different Plasmodium species. Use: [...] conserved among Plasmodium spp. 118-119 Which proteins did you analyze? And how did you analyze them? Where is the data for this analysis? Outline the amino acids that predict palmitoylation? The nonclassical pathway? 119-122 Here: do you mean S14 has similar properties as GAP 45 and GAP50? Define the nonclassical pathway? How do you know S14 is in the IMC? 122-123 Please reference the bioinformatic analysis plus URL that allows targeting to the IMC to be analyzed. 123-124 Please reference the URLs for TM, palmitoylation, and interactions analyses. 125-127 How did you predict that S14 is secreted via the nonclassical pathway? 128-130 Define the nonclassical pathway when it first appears in your manuscript. The citation Moskes 2004 is not in the reference list 132 Which membrane? 134-135 In which species? 141-142 Please include images of blood stage and liver stage parasites. 142-143 Which membrane? 148-149 I cannot find the specific figure you refer to; I checked the online version of the Frenal 2010 paper. 175 gland, we counted [...] 177 Compared to the 177-179 Failed to invade (absolutely)? Or invaded in highly reduced numbers? 182-186 Please be precise: I think you mean you let all types of mosquitoes take a blood meal; s14 knockout-infected mosquitoes did not infect mice. 181-202 Perhaps use paragraphs to indicate the different types of experiments performed here. 204 Please introduce paragraphs to identify the different experiments in this section 208 Outer or inner membrane of what? IMC, the plasma membrane? 228 onwards Structural models were obtained from whom? Which species did you use for the docking study? Could you use in one approach 3 berghei proteins, and confirm your docking studies with the falciparum proteins? That would strengthen your model. Should you include a negative control protein in the approach? 250-251 Was all of the gene cloned? Please define amino acid range. discussion Please discuss data from https://elifesciences.org/articles/77447 in relation to your protein
298-300 More recent glideosome papers exist. For example https://doi.org/10.1038/s42003-020-01283-8 340 List the proteins you analysed. Add URL (websites) to the analyses tools. 343 Known association from the literature: how was this done? 346-349 A few glideosome components? On what basis were they selected and which are they?
471 Can AlphaFold Structure Predictions be used in the docking studies? 487 What parts of theses genes was cloned? Define the amino acid range. 714 Please split the table into A Mosquito bite and B haemolymph Sporozoites Figure 1 For clarity, maybe write S14::mCherry Figure 1 It would be useful to show blood stage parasite images. Figure 1F You have not formally shown that this signal corresponds to palmitoylated S14. Could be heavy chain. Figure 2G Haemolymph sporozoites ? Figure 8 You argued that S14 is a membrane-bound protein through palmitoylation. Here the protein is shown to be cytoplasmic. Please update our model with more recent ones.
Figure S2B It would be good to include a positive control for these PCRs. Figure S3 It would be good to include a positive control for these PCRs.
Tabel S1 Table S1 is only mentioned twice in the text: lines 124 and 128. There is no mention that the table contains all (??) known gliding motility proteins. Table S1 The algorithms / websites used for bioinformatic prediction need to be listed here. Table S2 Add the plasmodb gene identifiers here. The table does not show all Plasmodium spp. but a selection.
Significance
General assessment: The authors provide an in-depth analyses of the Plasmodium berghei protein S14 and its involvement in gliding motility.
Advance: This paper is the first analysis of the S14 protein. The authors suggest a bridging function for the protein between MTIP and GAP45.
Audience: Gliding motility is of interest to the apicomplexan field. I think this particular proteins is specific to Plasmodium spp.
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Reply to the reviewers
1. General Statements
This manuscript aimed at:
- a) producing the evidence that supports the need for performing RNA hydrolysis and applying the appropriate nucleoside MACs for the determination of nucleoside-modified mRNA concentrations using UV spectroscopy.
- b) Providing the m1Y MAC value and a new resource to the mRNA field community to perform the above-mentioned procedure. This piece is therefore a “resource” manuscript, rather than a biotechnological innovation or basic research manuscript.
2. Point-by-point description of the revisions
Considering that the reviewers coincided in some of their comments, we compiled them in topic and provided our response to the reviewers.
Topics:
- On the novelty in this manuscript
- On the impact of nucleoside modifications on the DFBHI-Broccoli complex.
- On the role of modified nucleosides on mRNA folding and the independent verification on a distinct mRNA.
- On the cap used for IVT in the webserver.
- On the average of Epsilon (e or MAC) values.
- Other minor comments. On the novelty in this manuscript:
Comments:
Reviewer #1
“Major Comments:
- In the introduction, the authors should discuss the novelty more by describing which techniques are currently available for quantification of modified RNA and how this study is novel.” __Reviewer #2 __
“(Significance (Required)):
The study develops an accurate method to measure RNA concentrations which can improve dosing accuracy. The methods developed here will be beneficial for a broad range of fields employing mRNA-therapies.”
__Reviewer #3 __
“(Significance (Required)):
Since 50 years, scientists that works in the field of modified nucleic acids have determined the concentration of the nucleic acids in the same way, which means by determining the epsilon values of the modified nucleosides, using the epsilon values of the natural nucleosides at same wavelength, and then calculating the concentration after measuring absorption at (for example) 260 nm (wavelength could change dependent on modified nucleoside that is incorporated). This manuscript is not really innovative.”
Response:
We thank the reviewers for bringing up this topic. We want to reassure to the reviewers, editors and readers that, throughout the manuscript, we have carefully selected the wording to avoid claiming any novelty on the principle of RNA hydrolysis or the use of nucleotide Molar absorption coefficients (MAC) and UV spectroscopy for the determination of RNA concentrations. We have “revised”, “assessed” and “examined” these experimental procedures, we “determined” the M1Y and we “developed” the mRNAcalc webserver.
This “resource” manuscript therefore mainly aims at introducing the mRNAcalc webserver to the community and providing the underlying biochemical principles of the methods suggested in the webserver. These articles are often published in webserver issues or as “resource” articles in certain journals, including some of the journals in Review Commons.
The authors understand that the data in our manuscript are not often provided for this type of “resource” manuscripts, and it might have led to a misunderstanding. For instance, the OligoCalc webserver was published in Nucleic Acid Research, it has become a valuable tool for the oligonucleotide research community (1621 citations in 15 years), and no experimental evidence supporting its underlying calculations is provided in the manuscript.
For our manuscript, we have cited the corresponding source of the principle of the experimental methods, and we additionally performed some experiments to reproduce the findings using nucleoside-modified mRNAs with the intention of highlighting the importance of performing RNA hydrolysis (Fig.2b) and implementing the MAC of modified nucleotides (Fig 1e and 1f) for the determination of modified-nucleoside mRNA concentration using the Beer-Lambert law. We have felt compelled to do so, despite the fact that they represent well-established science and methods, as correctly pointed out by one of the reviewers.
We have taken into account that a few dozen of non-RNA biochemistry focused laboratories around the world are currently embracing for the first time the nucleoside-modified mRNA technologies and, to our knowledge, not a single article in the nucleoside-modified mRNA field has mentioned the need of implementing a different MAC for the determination of nucleoside-modified mRNA concentration using UV spectroscopy in either its main text or Materials & Methods section. We want to reassure the reviewers that the authors, before starting the experimental investigation, performed an extensive literature search and failed to find the m1Y MAC at 260 nm. Our search included a few hundreds of research articles, several doctoral thesis (including Sister Miriam Michael Stimson’s work), classic books such as Hall, Ross “The modified nucleosides in nucleic acids” and nucleotide manufacturers’ datasheets. However, the authors cannot rule out that other investigators in the mRNA field have previously determined the m1Y MAC at 260 nm in aqueous buffered solution and this knowledge has remained hidden under the frequently used statement of “The mRNA concentrations were determined spectroscopically” or any alike statement.
Following the suggestion of Reviewer #1, we have also included a brief comment in the introduction on the fluorescence-based techniques for the determination of nucleic acid concentration (lines 87-91), as follows:
“Other non-UV-spectroscopic methods relying on the unspecific RNA binding of certain fluorophores (such as RiboGreen, Thermo Fisher Scientific) for the determination of RNA concentration may help to overcome any change in the MAC of modified nucleoside mRNA. However, the impact of RNA modifications on the binding affinity of these fluorophores also remains unknown.”
On the impact of nucleoside modifications on the DFBHI-Broccoli complex:
Comments:
Reviewer #1
“3) The broccoli aptamer has U in it which when mutated to pseudouridine (Ψ) or N1-methylpseudouridine may change the structure minutely affecting the cis-trans transition in aptamer- DFHBI-1 complex and hence in fluorophore properties. A control which shows the effect (or lack thereof) of aptamer modifications on fluorophore properties should be carried out. The ratio of A260/F507 can get affected by the denominator although it may/may not be insignificant.”
Reviewer #2
“Specific comments:
…
In Fig. 1D, the authors normalize the absorbance on mRNA to fluorescence of DFHBI-1T when bound to dBroccoli aptamer. The aptamer will contain uridines and therefore modified uridines. Will modified uridines affect binding affinity of the substrate to the aptamer? Could the differences in fluorescence be because of stronger/weaker binding of the substrate with modified uridines?”
Response:
We thank reviewers for enquiring about the effect of U-to-Y and U-to-M1Y substitutions on the DFHBI-1T-dBroccoli interaction, RNA folding or fluorophore properties. We have indeed investigated thoroughly and observed that there was no significant difference in the binding affinity, melting point, or relative brightness across the three DFHBI-1T-Broccoli complexes. These results go in line with the previously published photophysical and biochemical properties of the Broccoli−DFHBI-1T (reference 15 in manuscript). These data are provided as supplementary Table 1 in the revised manuscript.
Supplementary Table 1: photophysical and biochemical properties of mutated Broccoli−DFHBI-1T complexes.
Complex
Max em (nm)
Relative brightness*
KD (nM)+
Tm (°C)+
U-Broc−DFHBI-1T
(ref. 15)
507
360
48
U-Broc−DFHBI-1T
507
1.000 ± 0.002
379.6 ± 13.89
49.13 ± 0.13
Y-Broc-DFHBI-1T
507
1.005 ± 0.004
378.7 ± 8.11
49.46 ± 0.09
m1Y-Broc-DFHBI-1T
507
1.004 ± 0.003
375.6 ± 8.17
49.23 ± 0.07
*Relative to the U-Broc-DFHBI-1T complex. Data are shown as mean ± SD.
- Data are shown as KD ± Error of the fit or Tm ± Error of the fit.
On the role of modified nucleosides on mRNA folding and the independent verification on a distinct mRNA:
Comments:
Reviewer #1
“2) Fig 1d- In this experiment, the RNA is not hydrolyzed prior to concentration measurement. The authors should discuss how nucleoside modifications in the RNA may affect structure of the RNA, how significant that effect is on the ____e ____(MAC) and how justified it is to attribute the reduction in ____e ____(MAC) entirely to the mutations.
…
4) The reduction in A260 in modified nucleosides should be accurately measured and independent of the RNA. Hence, the values determined here should be shown to be independent of at least another RNA sequence.”
Response:
We want to express our gratitude to Reviewer #1 for enquiring about the potential impact of the modified nucleosides on the mRNA folding. We have further discussed this aspect on our interpretation of the data in Fig 1d. No doubt, this reviewer’s comment has substantially enriched the discussion in our manuscript.
For the revised version of the manuscript, we have also performed the same measurements using a different mRNA. We have used an mRNA with a higher m1Y composition. We have observed a stronger reduction in mRNA UV absorption (A260) in the m1Y-modified mRNA, confirming that the MAC of the nucleobase composition is the main determinant of mRNA UV absorption. We have appended these data to the manuscript as supplementary Figure 2 and the associated text can be found in lines 141-155 of the manuscript and in the following lines:
“By normalizing the UV absorbance (A260) of each mRNA by its corresponding fluorescence (F507), it was observed that in practice the relative UV absorbance of the nucleoside-modified mRNA was significantly reduced as compared to the standard mRNA (DA260 = -10.6%, Fig. 1d and 1e). The hypochromicity was more pronounced in a second m1Y-mRNA with higher m1Y composition (DA260 = -11.8%, Supplementary Figure 1). In principle, the modified nucleosides can also promote mRNA folding and reduce its UV absorption. This is particularly relevant for the pseudouridine modification. Its N1-hydrogen can engage in additional hydrogen bonds, promoting and stabilizing RNA folding. For instance, the U-to-Y substitution in tRNA stabilizes the folded structure that is essential for translation (reviewed in ref. 16). However, the m1Y nucleobase lacks this additional hydrogen bonding capability, and it is expected to have little or no effect on the RNA folding of low CG-content (1Y-mRNAs followed the anticipated hypochromicity associated to the nucleobase hypochromicity at 260 nm wavelength and not their expected contribution to RNA folding, these data suggest that the observed reduction in nucleoside-modified mRNA UV absorption is mainly determined by the nucleobase composition and the intrinsic MAC of the nucleosides in these mRNA.”
On the cap used for IVT in the webserver:
Comment:
__Reviewer #2 __
“(Evidence, reproducibility and clarity (Required)):
Specific comments:
- The authors should expand discussion on the effect of different caps used for IVT as the choice of the cap appears to be an important selection on the server.” Response:
We thank Reviewer #2 for spotting that we had forgotten to comment on this feature of mRNAcalc web server in the manuscript. We have included a short paragraph on the 5’ mRNA cap nucleotides and their implementation in the mRNACalc webserver (line 160-164), as follow:
*“In the mRNACalc webserver, the MACs of distinct modified nucleosides that form the capping nucleotide in the 5’ mRNA cap were also implemented for the sake of completeness. The capping nucleotide only represents one nucleotide out of thousands of nucleotides in a mRNA molecule and its contribution to the mRNA molar absorption is rather negligible.” *
On the averaged Epsilon (____e ____or____ MAC) values:
Comment:
__Reviewer #3 __
(Evidence, reproducibility and clarity (Required)):
“I find the way the authors use the epsilon value of pseudoU (and analogues), as a mean value of literature data to be incorrect. The epsilon value is absolute and can not vary from one measurement to another. In fact it is a good parameter to define concentration. When different values are obtained, it means that compound is not pure , or measured at different pH or solvent, or the compound is not weighted exactly. When publishing a methodology to determine concentration of nucleic acids, it might be good to determine the exact epsilon value you want to use, yourself.”
Response:
We thank Reviewer #3 for bringing up this topic. We had experimentally determined the MAC values when we observed that it was completely absent in the literature and it was essential to provide a valuable tool to the nucleoside-modified mRNA community, as it is the case of the m1Y nucleoside. For the Y and m5C nucleosides, we have now determined their MAC for this revised version of the manuscript and recalculated the average, this time including our own determination and the previously determined values (in aqueous buffered solution) from multiple sources in the literature or manufacturer’s product datasheets and we implemented it in the webserver. We agree that the different values may relate to distinct amount and nature of the impurities in the manufacturer’s preparation. We believe that the average of these values may reflect a better approximation to their absolute epsilon value. Detailed information on these calculations and methods are now provided in the supplementary notes (Lines 109 to 142).
Other minor comments:
Reviewer #1
“5) Fig 2c- The figure should be remade using larger symbols as it is difficult to see how different the concentration measurements are depending on the method of hydrolysis.”
Response:
Figure 2c is intended to show a schematic representation of the experimental workflow and use of the mRNAcalc webserver. We assume that Reviewer #1 referred to Fig 2b. We have enlarged the symbols to ease the visibility of the data.
“6) Lines 27, 45 and 33 have an incorrect symbol for ____e____(MAC) and for the word 'coefficient”
Response:
We thank Reviewer #1 for the interest of improving our manuscript in detail. The typos have been corrected in the revised manuscript.
We thank Reviewers for all the values comments to our manuscript; they have enriched and substantially improved its quality and readability.
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Referee #3
Evidence, reproducibility and clarity
I find the way the authors use the epsilon value of pseudoU (and analogues), as a mean value of literature data to be incorrect. The epsilon value is absolute and can not vary from one measurement to another. In fact it is a good parameter to define concentration. When different values are obtained, it means that compound is not pure , or measured at different pH or solvent, or the compound is not weighted exactly. When publishing a methodology to determine concentration of nucleic acids, it might be good to determine the exact epsilon value you want to use, yourself.
Significance
Since 50 years, scientists that works in the field of modified nucleic acids have determined the concentration of the nucleic acids in the same way, which means by determining the epsilon values of the modified nucleosides, using the epsilon values of the natural nucleosides at same wavelength, and then calculating the concentration after measuring absorption at (for example) 260 nm (wavelength could change dependent on modified nucleoside that is incorporated). This manuscript is not really innovative.
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Referee #2
Evidence, reproducibility and clarity
General Comments:
Finol and colleagues argue that uridine modifications absorb less UV light than uridine, leading to underestimation of RNA concentration for modified RNAs. Based on this observation, they created a web server for accurate calculation of RNA concentrations. This is an interesting manuscript and would benefit the field. Specific comments are listed below:
Specific comments:
- The authors should expand discussion on the effect of different caps used for IVT as the choice of the cap appears to be an important selection on the server.
- In Fig. 1D, the authors normalize the absorbance on mRNA to fluorescence of DFHBI-1T when bound to dBroccoli aptamer. The aptamer will contain uridines and therefore modified uridines. Will modified uridines affect binding affinity of the substrate to the aptamer? Could the differences in fluorescence be because of stronger/weaker binding of the substrate with modified uridines?
Significance
The study develops an accurate method to measure RNA concentrations which can improve dosing accuracy. The methods developed here will be beneficial for a broad range of fields employing mRNA-therapies. Reviewer expertise: Lipid nanoparticles, mRNA, self-amplifying RNA, immunoengineering
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Referee #1
Evidence, reproducibility and clarity
Summary: The study aims at developing a method of accurate quantification of concentration of self- amplifying RNA and modified RNA by determining molar absorption coefficient of modified nucleosides with higher accuracy.
Major Comments:
- In the introduction, the authors should discuss the novelty more by describing which techniques are currently available for quantification of modified RNA and how this study is novel.
- Fig 1d- In this experiment, the RNA is not hydrolyzed prior to concentration measurement. The authors should discuss how nucleoside modifications in the RNA may affect structure of the RNA, how significant that effect is on the (MAC) and how justified it is to attribute the reduction in (MAC) entirely to the mutations.
- The broccoli aptamer has U in it which when mutated to pseudouridine (Ψ) or N1-methylpseudouridine may change the structure minutely affecting the cis-trans transition in aptamer- DFHBI-1 complex and hence in fluorophore properties. A control which shows the effect (or lack thereof) of aptamer modifications on fluorophore properties should be carried out. The ratio of A260/F507 can get affected by the denominator although it may/may not be insignificant.
- The reduction in A260 in modified nucleosides should be accurately measured and independent of the RNA. Hence, the values determined here should be shown to be independent of at least another RNA sequence.
Minor comments:
- Fig 2c- The figure should be remade using larger symbols as it is difficult to see how different the concentration measurements are depending on the method of hydrolysis.
- Lines 27, 45 and 33 have an incorrect symbol for (MAC) and for the word 'coefficient'
Significance
Accurate measurement of RNA concentrations can be key where precise quantification of RNA is required and any error gets amplified such as RNA-based therapeutics dependent on RNA amplification or RNA modification. This study is also important for research on the effect of RNA modifications on RNA structure in vitro or in vivo.
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Reply to the reviewers
Manuscript number: RC-2023-02123
Corresponding author(s): Holger Sültmann
[The “revision plan” should delineate the revisions that authors intend to carry out in response to the points raised by the referees. It also provides the authors with the opportunity to explain their view of the paper and of the referee reports.
The document is important for the editors of affiliate journals when they make a first decision on the transferred manuscript. It will also be useful to readers of the preprint and help them to obtain a balanced view of the paper.
If you wish to submit a full revision, please use our "Full Revision" template. It is important to use the appropriate template to clearly inform the editors of your intentions.]
1. General Statements [optional]
This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.
We would like to thank the editorial team of Review Commons for sending our manuscript for peer review and all Reviewers for carefully reading our manuscript. The reviewer’s detailed and constructive feedback and comments were instrumental to improve the quality and rigor of our manuscript. We highly appreciate the thoroughness of the review and have carefully considered all suggestions and concerns. Below, we have made point-by-point responses to the reviewer’s comments, and outlined revisions we plan to make, or have made. Textual changes in the revised manuscript are marked in Red.
2. Description of the planned revisions
Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Daum AK et al. indicated that CAFs promote TKI resistance via lipid biosynthesis in ALK-driven lung adenocarcinoma cells. This work provides novel CAF-induced drug resistant mechanisms in ALK-driven lung cancer cells, however, there are several issues to be resolved by the following.
Major critiques:
- The authors claimed that CAF-produced HGF and NRG1 boosts AKT signaling and de novo lipogenesis to promote ALK-TKI resistance in lung cancer cells. They showed that CAF-secreted HGF and NRG1 inhibition attenuates tumor cell viability in the presence of FB2-CM, however, whether stromal HGF and NRG1 inhibition suppresses de novo lipogenesis in carcinoma cells has not yet been investigated. The authors should inhibit HGF and NRG1 expression in CAFs by shRNA prior to addition of CAF-CM onto cancer cells to evaluate AKT signaling and de novo lipogenesis.
Author response:
The necessity of demonstrating the direct impact of stromal HGF and NRG1 inhibition on de novo lipogenesis in cancer cells is a crucial aspect of our study, and appreciate the reviewer’s valuable suggestion to inhibit HGF and NRG1 expression in CAFs before assessing the effect on AKT signaling and de novo lipogenesis. To address this concern, we will conduct additional experiments to evaluate the impact of HGF and NRG1 expression knock-downs in CAFs by shRNA.
This work lacks human correlation. Are HGF and NRG1 expressions in CAFs related with de novo lipogenesis, drug resistance and poor outcomes in ALK-driven lung cancer patients?
__Author response: __
We agree with the reviewer’s point that human correlation would underline the significance of the given findings. However, conducting such analyses presents certain challenges.
- The availability of ALK-mutated samples among TCGA samples is limited and the sample size is quite small (n = 5), making survival analyses less statistically meaningful due to low statistical power.
- Using bulk RNA-seq data for this analysis necessitates deconvolution methods to differentiate between tumor and stromal cell compartments. While deconvolution methods are valuable, they have limitations, including potential inaccuracies in estimating cell-specific gene expression due to the inherent heterogeneity of cell populations (1). This may lead to imprecise conclusions about the specific contributions of stromal factors, such as CAF-secreted HGF and NRG1, in the tumor microenvironment. Nonetheless, we are considering leveraging the available dataset of Maynard et al.(2), to address the raised concerns by the reviewer. Here, the authors performed single-cell RNA-seq on clinical biopsies, including a number of ALK+ samples from both treatment-naive and progressive-disease patients. The analysis of this dataset could allow us to investigate whether the effects observed in our study hold true in the in vivo human tissue environment, providing a more direct and clinically relevant assessment.
The most experiments lack the appropriate control for CAFs. They would use primary isolated counterpart fibroblasts as the patient-specific control for lung cancer CAFs by extracting from non-cancerous regions of the same individual in their experiments.
__Author response: __
This point is well taken. Therefore, we will take this feedback into account and incorporate the suggested controls into our experimental design to enhance the robustness and validity of our results.
Minor issues:
In Fig 1B, coculture with TGF-b-treated MRC-5 attenuated cancer cell death with the lorlatinib treatment. However, HGF and NRG1 production is comparable between MRC-5 cells treated with or without TGF-b in Fig 5A. These data indicate that any fibroblasts but not CAFs could suppress cancer cell death with the lorlatinib treatment.
__Author response: __
We recognize the need for further clarification. To address this, we plan to include additional data to demonstrate the differences in tumor therapy response when cultured with CM derived from native fibroblasts versus TGF-β1-activated fibroblasts (CAFs). This will help elucidate the specific role of CAFs in suppressing cancer cell death with lorlatinib treatment and provide a more comprehensive understanding of the observed effects.
The pAKT induction of H3122 treated with lorlatinib in the presence of CAM-CM or HGF or NRG in Fig 7A, B is barely observed and lacks the significance.
Author response:
We acknowledge the need for more robust data to demonstrate the significance of the observed pAKT induction in H3122 cells treated with lorlatinib in the presence of CAF-CM, HGF or NRG. We will work to provide additional data that strengthens the significance of this effect.
Reviewer #1 (Significance (Required)):
The concept of this work is interesting, however, molecular mechanisms underlying CAF-medicated ALK-TKI resistance remain poorly elucidated. Characterization of human primary fibroblasts (FB1, FB2) is not clearly described, and the most experiments lack proper control. Immunoblot in Fig 6 and 7 looks snap-shot and the reviewer has concerns about the reproducibility.
Author response:
In response to the comments, we will revise our manuscript to provide a more detailed characterization of the human primary fibroblasts to ensure transparency. To address the concern regarding controls, we will implement further controls in our experimental procedures. Regarding the concern about the reproducibility of the immunoblots, we appreciate the feedback, and we will provide additional data in the manuscript to ensure the reproducibility of our results.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
In this review, the authors describe a possible mechanism of resistance in lung tumor cells that is induced by CAFs in response to ALK-specific inhibitors. The manuscript is well written and conclusions are in general well supported by experimental data, however additional experiments are needed to fully support the conclusions stated by the authors.
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Brigatinib and lorlatinib are used to target ALK-driven lung tumor cells. In many of the experiments described heterotypic 3D co-cultures are used. Although both inhibitors act preferentially over the cancer cells, and no effect is expected in the CAFs, it would be desirable to confirm it.
Author response:
We thank the reviewer for this endorsement of our study, and we are pleased to learn that our conclusions are generally supported by the experimental data. Regarding the use of brigatinib and lorlatinib to target ALK-driven lung tumor cells in our experiments, we acknowledge the importance of confirming the specific action of these inhibitors. While these inhibitors act preferentially on the cancer cells, we agree that it is desirable to confirm their limited effect on CAFs. Therefore, we will address the reviewer’s suggestion by performing further cell viability analyses of TKI-treated fibroblast spheriods to specifically assess the impact of brigatinib and lorlatinib on CAFs.
The authors demonstrated initial proliferation advantage and apoptosis protection of H2228 and H3122 cells in response to conditioned media (CM) of three independent CAF clones. However, once they identify lipid enzymes altered and specific ligand-receptor interaction, then they focus only in FB2. This imply that the mechanism described by authors is relevant for that particular clone, but it does not validate a general resistance mechanism induced by CAFs. In order to claim that this is a more general mechanism, other CAF clones should be tested.
Author response:
We appreciate the reviewer’s comment regarding the focus on a specific CAF clone (FB2) in our study. This point is well taken, and we understand the importance of demonstrating the generalizability of the resistance mechanism induced by CAFs.
Since our results indicated consistent therapy response across all CAF clones tested (Figure 1), with the most pronounced effect observed with FB2-CAFs, we chose to focus our efforts on conducting sc-RNAseq experiments with FB2-CAFs and subsequently performed downstream validation experiments to corroborate our findings. This approach allowed us to prioritize the CAF clone with the most robust response while acknowledging the broader therapy response observed in all tested clones. Nevertheless, as requested by the reviewer, we will perform additional experiments using other independent CAF clones to assess whether the identified mechanism is broadly applicable.
Authors show that the identified ligands secreted by CAFs (HGF, NRG1β1, etc) are found in conditioned media from CAFs. It would be good to determine if the amount of these ligands somehow is dependent on the presence of tumor cells and/or ALK-TKi. Additionally, both HGF, NRG1β1, are able to partially restore the expression of the lipogenic enzymes identified, or AKT activation pathway, but they are not able to completely restore it. Since CAF-derived CM would have both factors, maybe combination of both ligands may induce stronger rescue of the expression of these proteins.
Author response:
To provide a comprehensive understanding, we will investigate whether the levels of identified CAF-derived ligands are influenced by tumor cells and/or ALK-TKI treatment by performing additional assays on CAF-supernatants. Furthermore, we will explore the potential synergy between HGF and NRG1β1 in rescuing the expression of lipogenic enzymes and the AKT signal transduction pathway on protein level.
Does PI3K/mTOR inhibitors revert the proliferation advantage of 3D heterotypic cultures? And expression of lipid biosynthesis genes?
Author response:
To address the impact of PI3K/mTOR inhibitors on the proliferation advantage of CAFs on ALK+ lung tumor spheroids and the expression of lipid metabolic genes, we will conduct treatment experiments using agents such as alpelisib (PI3Kα inhibitor), ipatasertib (panAKT inhibitor), or everolimus (mTORC1/2 inhibitor). Cell viability will be assessed using the 3D CellTiterGlo assay, and we will investigate changes in the expression of lipid biosynthesis genes to comprehensively evaluate the effects of these inhibitors on the resistance mechanism induced by CAFs.
Reviewer #2 (Significance (Required)):
This study underscores the multifaceted nature of resistance mechanisms in ALK-rearranged lung adenocarcinomas, highlighting the pivotal role of CAFs and lipid metabolic reprogramming. Lung adenocarcinoma remains a challenge in oncology, and while targeted therapy with tyrosine kinase inhibitors (TKIs) has shown promise in treating ALK-rearranged lung adenocarcinomas, the development of resistance to these therapies is nearly inevitable. This study delves into a critical aspect of this resistance by describing an important aspect of the intricate interplay between cancer-associated fibroblasts (CAFs) and tumor cells within the tumor microenvironment.
One of the primary findings of this research is the impact of CAFs on the therapeutic response of ALK-driven lung adenocarcinoma cells. While intrinsic mechanisms within cancer cells are well-studied drivers of resistance, this study underscores the emerging importance of stromal components, particularly CAFs, in shaping therapeutic vulnerabilities. The observation that CAFs promote therapy resistance by hampering apoptotic cell death and fueling cell proliferation highlights the complexity of tumor-stroma interactions.
The study utilizes three-dimensional (3D) spheroid co-culture models, providing a more physiologically relevant platform to investigate these interactions. This approach bridges the gap between conventional monolayer cultures and in vivo models, allowing for a deeper understanding of the role of the tumor microenvironment.
Perhaps one of the most notable findings is the identification of lipogenesis-related genes as major players in TKI-treated lung tumor spheroids. This finding not only sheds light on a previously underexplored facet of cancer biology but also suggests that lipid metabolism may be a central determinant of therapeutic susceptibility in this context. Although data provided here suggests that it might not be the only mechanisms taking place in the development of resistance to ALK inhibitors, it clearly shows that it plays an important role in it.
The study proposes a potential solution to overcome CAF-driven resistance by targeting vulnerabilities downstream of oncogenic signaling. The simultaneous targeting of ALK and SREBP-1, a key regulator of lipogenesis, emerges as a promising strategy to thwart the established lipid metabolic-supportive niche within TKI-resistant lung tumor spheroids.
Author response:
We thank the reviewer for this endorsement of our study and are gratified that the reviewer recognizes the critical implications of our research in the context of ALK-rearranged lung adenocarcinomas and their treatment resistance.
One of the stronger limitations of this study is that it rely on a limited number of cell lines or patient-derived models, which may not fully capture the heterogeneity of ALK-rearranged lung adenocarcinomas. Furthermore, for most of the validatory assays performed, only a CAF cell line is used, higly limiting the significance of their conclusions to a more general resistance mechanism. Furthermore, the study provides valuable insights into the involvement of lipogenesis-related genes and AKT signaling but do not delve deeply into the precise molecular mechanisms underlying these processes. Further mechanistic studies are needed to understand the exact interactions and signaling pathways involved. In conclusion, while this study provides valuable insights into the role of CAFs and lipid metabolism in ALK-TKI resistance, its limitations underscore the need for further research, including more comprehensive in vivo models and clinical studies, to confirm and expand upon these findings.
Author response:
We appreciate the reviewer’s thoughtful assessment of our study and acknowledge the limitations highlighted in your comment. The limited number of cell lines and patient-derived models used in our study is indeed a limitation, and we agree that this may not fully capture the heterogeneity of ALK-rearranged lung adenocarcinomas. However, the number of ALK+ lung adenocarcinoma cell lines is limited (3), as is the availability of patient-derived tissue material. To address this, we are actively working on expanding our research to include a more comprehensive range of models (e.g. primary ALK+ lung cancer cells, patient-derived organoids (PDOs)) for future studies.
We also acknowledge the importance of the limitations related to the use of a single CAF cell line in many of our validation assays. We are committed to broadening our experimental scope to involve multiple CAF cell lines to strengthen the significance of our conclusions.
Regarding the need for deeper mechanistic studies to understand the precise molecular interactions and signaling pathways, we agree that this is a crucial point. To this end, we are planning additional mechanistic studies to uncover the exact molecular mechanisms underlying the described resistance processes in future studies.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
Summary: In this manuscript the authors use a spheroid co-culture model of human EML4-ALK non-small cell lung cancer (NSCLC) cell lines and fibroblasts to investigate the mechanism by which tumor-CAF crosstalk mediates non-genetic resistance to ALK inhibition. Spheroid culture of tumor cell lines with CAFs or CAF conditioned media was sufficient to reduce apoptosis and boost proliferation in the context of ALK inhibition. Using single-cell RNA-seq (scRNA-seq) of co-cultures treated with lorlatinib, the authors show that lipogenesis-associated genes are enriched in drug-treated tumor cells co-cultured with CAFs. Specifically, the authors propose that CAF-derived HGF and NRG1 derepress lorlatinib-induced changes in oncogenic Akt and mTOR signaling to boost expression of SREBP and FASN and restore lipid composition in NSCLC cancer cells.
Major comments:
The authors' conclusion that the effect of CAF CM on lorlatinib sensitivity is mediated by HGF and NRG is somewhat weak. Nrg1B1 was sufficient to rescue cell viability in the context of lorlatinib treatment (Figure 5B) only at a concentration significantly higher than that which was produced by fibroblast lines in culture (Figure 5A). Although the authors note that the real level of NRG1 could be higher than detected, this is speculative. Neither HGF nor NRG1 blocking antibodies appear to have rescued the elevated cell viability driven by CAF CM in the context of lorlatinib treatment (Figure 5C). These results, though statistically significant, do not appear biologically relevant. To strengthen their conclusions, the authors should consider ablating HGF or NRG1 in CAFs via shRNA or CRISPRi and then testing if CAF CM is no longer sufficient to rescue the viability of lorlatinib treated cancer spheroids.
Author response:
We refer the reviewer to our response to comment #1 of reviewer 1.
In addition to the western blots used to demonstrate and effect of CAF CM or HGF/NRG1 on Akt and mTOR signaling, the authors could strengthen their conclusions by testing the effect of Akt and mTOR inhibitors on the rescue effect of CAF CM.
Author response:
We refer the reviewer to our response to comment #6 of reviewer 2.
Minor comments:
-
In addition to the cell death and proliferation assays shown in Figure 1B-E, it would be helpful to show and quantify images of spheroid co-cultures treated +/- lorlatinib (as in Figure 1A, Supplemental Figure S9). Although the effects on percent cell death and proliferation are significant, images of spheroid size and morphology would make these results more convincing.
__Author response: __
We can certainly incorporate representative images in the revised manuscript. However, we'd like to clarify that comparing spheroid mono- and direct co-cultures can be challenging due to differences in initial cell seeding numbers, variations in the growth rates of fibroblasts and tumor cells, and subsequently, differences in spheroid sizes at the initiation of treatment. These factors can confound direct comparisons between the two culture conditions upon treatment.
In Figure 5B, the effect of CAF secreted factors on cell viability should be tested in comparison to CAF CM as a biological control. This would allow the reader to understand how the effect of each factor alone compares to the effect of CM.
__Author response: __
We will incorporate a comparison to CAF-CM as a biological control to provide a clearer understanding of the individual effects of CAF-secreted factors.
**Referees cross-commenting**
I am in agreement with all of the points made by Reviewer #1. I also suggested that the authors should use shRNA to inhibit HGF and NRG1 expression in CAFs, and am similarly concerned about both human and in vivo relevance of the authors' findings. The experiments suggested by Reviewer #1 to further characterize the fibroblast subtypes and to use non-CAF control cells are also reasonable.
Author response:
The reviewers alignment on the points raised is duly noted, and we understand the importance of addressing the concerns regarding the relevance of our findings. The use of shRNA to inhibit HGF and NRG1 expression in CAFs is a valuable suggestion, and we are actively considering this approach to enhance the specificity of our findings. Furthermore, we acknowledge the need for a deeper characterization of fibroblast subtypes and the inclusion of non-CAF control cells to strengthen the robustness of our research.
I am also in agreement with the critiques presented by Reviewer #2 and find them reasonable; they would strengthen the manuscripts and better support the authors' findings. The work is indeed limited by the models used here, and mechanistic findings would be better supported by further metabolic analysis such as Seahorse or assessment of lipid synthesis.
Author response:
We greatly appreciate your alignment with Reviewer #2's critiques and your recognition of their reasonableness. Expanding our research to include additional models and conducting further metabolic analyses are valuable suggestions that we are actively considering to bolster the mechanistic underpinnings of our work.
Reviewer #3 (Significance (Required)):
As a reviewer, I have expertise in fibroblast biology and the contributions of the tumor microenvironment to pancreatic tumor development. Although my research has not focused on lung cancer specifically, I also have experience in lipid metabolism, therapy resistance, and tumor heterogeneity. In this manuscript the authors use a co-culture system to show that soluble CAF factors drive tyrosine kinase inhibitor (TKI) resistance in vitro in Alk-fusion driven NSCLC in line with prior work (Reviewed by Wong et al. 2021, Domen et al. 2021, Li et al. 2022). Mechanistically, the authors propose that CAF-secreted HGF and NRG1 restore Akt and mTor signaling pathways suppressed by lorlatinib, thus rescuing SREBP expression and the phospholipidome in TKI-treated NSCLC cells. Prior work has specifically demonstrated the ability of CAFs to rescue the effect of lorlatinib on NSCLC cell lines with ALK fusions via HGF/Met signaling (Hu et al. 2021), and the general effect of CAF-secreted HGF on therapy resistance through Akt/mTor signaling has been well characterized (CITE). The regulation of SREBP and lipid metabolism by Akt/mTOR signaling in cancer and TKI resistance have been similarly described (CITE). Thus, the authors largely connect these well-known pathways, demonstrating that CAF co-culture restores lipid-associated transcriptional programs and lipidomic profile in lorlatinib-treated cells via HGF/NRG1 activation of Akt, mTOR, and SREBP. A few points presented in the manuscript that could represent potential scientific advances include scRNA-seq analysis of CAF/NSCLC co-cultures and the implication of CAFs in TKI-resistance through the modulation of lipid metabolism. However, the scientific and clinical significance of these findings are limited by the biological systems used and by their incremental contribution in context of the current literature.
The scRNA-seq analysis of NSCLC cells co-cultured with CAFs generated here could represent a potential advance and resource for future study; however, the application of this analysis to 2D cell lines in vitro may have limited utility as the heterogeneity of these long-culture lines is likely quite narrow (CITE OTHER scRNAseq in vivo or PDOs). The authors themselves did not leverage the scRNA-seq data for a deep analysis of cancer cell heterogeneity but rather uncovered lipid-associated transcriptional programs using bulk analysis across all tumor cells in their dataset. The significance of the authors' finding that CAF conditioned media (CM) mediates lorlatinib-sensitivity through the regulation of lipid metabolism is also somewhat limited by prior work directly implicating SREBP and phospholipid remodeling in TKI resistance (Xu et al. 2021, OTHERS). Although focusing on EGFR-mutant NSCLC, Xu et al. 2021 showed that SREBP upregulation and increased lipogenesis and decreased oxidative stress was associated with resistance to gefitinib and could be reversed by treatment with the SREBP inhibitor fatostatin in vivo. TKI-resistance is often driven by the activation of convergent signaling pathways (Akt, mTOR) in both in EGFR-mutant ALK-fusion NSCLC, so it is perhaps not particularly surprising that the authors find that lipid programs are similarly important in lorlatinib-resistance. The novelty in this manuscript is limited to the connection between CAFs and these critical lipid metabolism pathways, and the implication that SREBP inhibition similarly blocks non-genetic CAF-mediated TKI resistance. The significance of this finding might be greater if the authors explored whether fatostatin could improve therapy response to lorlatinib in vivo.
Author response:
We highly appreciate the reviewer’s detailed review and expertise in the fields of fibroblast biology, tumor microenvironment, lipid metabolism, and therapy resistance.
The reviewer’s perspective on the utility of scRNA-seq analysis in our study is justified. We acknowledge that applying this analysis to 2D cell lines in vitro may have limitations due to the narrow heterogeneity of long-culture lines. We therefore attempted to enhance the relevance of our findings by applying 3D cell culture models, which are known to resemble the in vivo situation more closely than conventional monolayer cultures (4, 5). Nevertheless, we agree that incorporation of additional models (e.g. patient-derived organoids) would better capture the heterogeneity of the tumor and its surrounding microenvironment. We concur that a deeper analysis would enhance our understanding of the interactions between CAFs and tumor cell (sub)populations.
The insights into the significance of our findings in the context of prior research on SREBP-dependent phospholipid remodeling in TKI resistance are well taken. We agree that the novelty of our study lies in the connection between CAFs and lipid metabolism pathways as a non-genetic CAF-mediated TKI resistance mechanism. However, it is also important to note that no prior studies have investigated stroma-driven lipid metabolic reprogramming in EML4-ALK-positive NSCLC. This unique aspect of our research adds to its originality and potential significance in advancing the understanding of ALK-positive NSCLC and therapy resistance.
We agree with the reviewer’s point that an in vivo study would be important in exploring whether fatostatin could improve therapy response to lorlatinib. However, due to technical and timing limitations, the establishment of corresponding mouse models is beyond the scope of our present study.
3. Description of the revisions that have already been incorporated in the transferred manuscript
Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.
Reviewer #1
Minor issues:
"----stronger in in case of-------" needs to be improved to "----stronger in case of-------", line 128, page 5.
__Author response: __
This was changed accordingly.
Reviewer #2
- Based on sc-RNA-seq data authors then explore molecular processes facilitating survival of ALK-driven lung cancer cells under the influence of CAFs and mention that "among the enriched biological processes and pathways related to metabolic activities, the most striking terms were linked to lipid metabolism" (lines 182-184). Attending to the graph depicted in Fig. 3A, that is not true, finding other processes more significant. In fact, linked to metabolism, glycolysis is more significant than lipid metabolism. This sentence should be changed accordingly and a different rationale should be done to focus on lipid metabolism.
__Author response: __
The phrasing was changed accordingly.
Reviewer #3
Major comments:
-
Although the authors note that the scRNA-seq data generated here may be an important resource in the field, it could also be explored in greater depth to further support the conclusions of this manuscript. As is, the scRNA-seq data is primarily analyzed at a bulk level to identify lipid-associated genes and gene sets as down-regulated by lorlatinib. In this case, it would be more useful and perhaps a better resource to conduct bulk RNA-seq in triplicate to generate a stronger dataset and generate a set of genes significantly regulated by lorlatinib and CAF co-culture or CAF CM. The scRNA-seq data could be leveraged to support the conclusions of the manuscript by plotting lipid-associated genes identified in Figure XB-C by U-map. This analysis would identify which clusters are enriched for lipid-associated genes and demonstrate whether these particular clusters are depleted by lorlatinib or rescued by CAF co-culture.
__Author response: __
We opted for scRNA-seq as it allowed us to simultaneously sequence co-cultivated tumor cells and fibroblasts, without the need for sorting experiments typically required for bulk RNA-sequencing experiments. With this we intended to avoid potential biases introduced by sorting procedures, which can be challenging, particularly in the case of identifying appropriate markers for fibroblasts.
In response to the reviewer’s suggestions we have now refined our analysis to depict lipid-associated genes in a cluster-dependent manner (Supplementary Figure S9). This analysis, however, did not showcase a cluster-specific enrichment of lipid-associated genes and a demonstrated a TKI-induced depletion of these genes across all tumor cell cluster.
The authors' conclusion that CAF co-culture restores the lipid profile of lorlatinib-treated tumor cells is somewhat weak due to the representation of lipidomic data. Although the Figure legends note that lipidomic analyses were conducted at n=3 replicates, the data as represented in Figure 8A-B do not allow the reader to assess variability across samples or the significance of the fold change differences in lipid species. Although it can be useful to view the data this way, the authors should also show variability across samples in some way via PCA plot or by including a heatmap of lipid abundance across all treatment groups and replicates. Especially as some differences appear subtle, it is also difficult to understand to what extent CAF CM rescues lorlatinib-induced effects on lipid species as values are shown as fold change relative to control for the independent groups. In this way, the reader cannot assess, for example, how lipid species abundance compares in lorlatinib-treated tumor cells +/- CAF CM. Again, a heatmap across treatment groups might be helpful in addition to an analysis for statistically significant differences in lipid abundance across treatment groups. The issues outlined here make it difficult to assess whether "addition of CAF-CM to H3122 lung tumor spheroids was able to partly abrogate this shift towards higher levels of poly-unsaturated lipid". As is, the statements describing the results in Figure 8A-B are vague and don't appear to totally align with the data. To my eyes, there is no apparent general trend in SFA or MUFA reduction in lorlatinib-treated cells as implied by the authors, though particular species may be down-regulated. The authors should also calculate saturation indices across lipid species to support their conclusion that lipid saturation is modulated by lorlatinib and rescued by CAF CM.
__Author response: __
Given the reviewer’s suggestions, we have made significant improvements to the presentation of our lipidomic data in the revised manuscript. We now provide a more comprehensive view of the data to allow for a better assessment of variability across samples and the significance of saturation index differences across lipid species. Specifically, we have included a PCA plot (Figure 8A) and a heatmap of lipid abundances across all treatment groups and replicates to address the issue of variability (Supplementary Figure S11). Furthermore, we have performed additional analyses to calculate saturation indices across lipid species (Supplementary Figure S12A), which support our conclusion that lipid saturation, i.e. de novo lipogenesis, is modulated by lorlatinib and rescued by CAF-CM. These additions provide a clearer visualization of the data and enhance the robustness of our findings.
Minor comments:
The ablation of specific cell clusters upon lorlatinib treatment in Figure 2 is compelling and visually striking. To make it easier for the reader to interpret this data, it might be useful to denote the general functional annotations of each cluster in the legend (for example, "cluster 3: proliferative"). This would allow the reader to visualize which populations are preferentially depleted by the inhibitor and rescued by CAF co-culture. Further, some quantification showing the number of cells in each cluster by treatment would group (or fold-reduction per cluster upon inhibitor treatment) would more clearly show how each cluster is impacted by the inhibitor and CAF co-culture.
__Author response: __
To facilitate a clearer understanding of which populations are preferentially affected by the ALK-inhibitor and rescued by CAF co-culture, we provided the cluster-specific annotations in the legend of Figure 2.
Furthermore, we included quantifications showing the number of cells in each cluster by culture condition and treatment group (Supplementary Table S3), to provide a more comprehensive view of how each cluster is impacted by the inhibitor and CAF co-culture.
In Supplemental Figure S3A please specify which gate is being used to quantify the percentage of dead cells shown in subsequent plots. It would also be useful to show the gating strategy used to separate labelled tumor cells and CAFs in heterotypic co-cultures by FACS so it is clear that CAF cells are not included in the cell death/proliferation analysis.
__Author response: __
Gates for quantification of dead cells are now specified, while the gating strategy used for analyzing cell death rates of separated tumor cells is given as requested in Supplementary Figure S3A. This gating strategy was likewise used to separate labelled tumor cells from CAFs to analyze cell cycle distributions.
In Supplemental Figure S1B and C, please briefly clarify in the legend how fibroblast lines were cultured for collection of RNA and protein. It would be useful to know if the cells were assessed in spheroid culture and thus representative of their cell state when used for the following heterotypic co-culture experiments.
__Author response: __
Culture conditions were added in the figure legend as requested. Prior to generation of heterotypic tumor spheroids, fibroblasts were cultured as monolayers. Nevertheless, we also verified that the activation status is maintained following TGF-β1 removal and subsequent cultivation as homotypic fibroblast spheroid via WB analysis. We added the results as shown in Supplementary Figure S1D.
In Figure 8C, the authors should plot all individual values in the bar graph as done in all other panels.
__Author response: __
This was changed accordingly
4. Description of analyses that authors prefer not to carry out
*Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. *
Reviewer #1
Major critiques:
The concept of this work is largely based on findings in vitro culture. The authors should perform animal experiments to convince their findings in vivo. CAFs and tumor cells would be implanted into recipient mice to determine whether AKT signaling, de novo lipogenesis and ALK-TKI resistance are increased in tumor cells by the presence of CAFs.
__Author response: __
We agree with the reviewer’s point that an in vivo study would be important in interrogating the full impact of CAFs on therapy response, AKT signaling, and de novo lipogenesis of ALK-driven lung adenocarcinoma cells. However, due to technical and timing limitations, establishing and performing co-injection and treatment experiments of corresponding mouse models is beyond the scope of our present study.
The author described that human primary fibroblasts (FB1, FB2) were derived from NSCLC adenocarcinoma patients---. Have FB1 and FB2 been isolated from tumor tissues or no-tumor tissues? If these fibroblasts were isolated from tumor tissues as CAFs, why the authors added TGF-b onto the cells? The TGF-b treatment generates myofibroblastic CAFs, which is one of CAF subtypes, but fails to have inflammatory CAFs, which is another CAF subtypes.
__Author response: __
The fibroblasts FB1 and FB2 were indeed isolated from tumor tissue obtained from lung adenocarcinoma patients. In our study, the addition of TGF-β1 was employed as a strategy to maintain the CAF phenotype. It's important to note that CAFs exhibit considerable plasticity and can potentially lose their distinctive CAF characteristics during in vitro cultivation (6). The introduction of TGF-β1 was aimed at mimicking the tumor microenvironment and assisting in the preservation of the CAF phenotype, which was partially reflected in the increased expression of CAF markers such as αSMA and FAP (Supplementary Figure S1B and C).
We acknowledge the existence of various CAF subtypes, including myofibroblastic and inflammatory CAFs, which can be induced by different stimuli. While TGF-β1 treatment tends to push fibroblasts more toward a myofibroblastic phenotype, other factors like IL-1 can induce an inflammatory phenotype (7). In our study, we chose to focus on the myofibroblastic CAF subtype. This decision was based on the prevalence of myofibroblastic CAFs in lung tumors and their established roles in tumor progression, poor prognosis across different cancer types, and resistance to immunotherapy in non-small cell lung cancer (NSCLC) (8, 9).
Reviewer #2
H2228 and H3122 cells are used indistinctively through the paper as ALK-driven lung tumor cells and, although in the discussion some reference is made regarding the worst outcomes observed for v3-driven ALK+ H2228 cells, results are considered similar for both cell lines, including sc-RNA-seq data. During analysis of sc-RNA-seq data numbers of specific genes identified at the different analysis are different, similar to the clusters identified (0-6). In order to determine the degree of overlap in the identified genes on the analysis and within clusters, it would be convenient to show tables with identified genes for each of the cell lines, together with the cluster classification of those genes.
Author response:
Regarding the comparison of v1- vs. v3-driven ALK+ tumors, we would like to clarify that the primary focus of our study is on the interactions CAFs and ALK-driven lung tumor cells, particularly in the context of therapy resistance. While the different ALK fusion variants are certainly of interest, our intention is not to delve into the comparative analysis of these variants in this paper. Instead, we aim to emphasize the broader impact of CAFs on ALK-driven lung tumors.
The comparison of v1- vs. v3-driven tumors, as well as a detailed analysis of the differences between H2228 and H3122 cells, goes beyond the main focus of this paper. Incorporating a comparative analysis of specific genes for each cell line and their cluster classification would require a significantly expanded scope and could lead to a more complex and detailed study.
In order to fully validate the effect of CAF/CAF CM in lipid biosynthesis in tumor cells, seahorse analysis would be highly beneficial, providing simultaneous measurement of multiple metabolic parameters, including glycolysis, oxidative phosphorylation, and fatty acid oxidation in homo (with and without CAF's CM or secreted ligands) and heterotypic conditions. Furthermore, it should be combined with specific substrates and inhibitors (i.e. glucose to measure acetyl-CoA production, labelled fatty acids, etc), to dissect various aspects of lipid biosynthesis and lipid metabolism and assess de novo lipogenesis, fatty acid uptake, or triglyceride.
Author response:
This point is well taken and we acknowledge the potential value of such comprehensive metabolic assessments. However, we would like to clarify that the Seahorse XF Analyzer can primarily measure oxygen consumption rate (OCR) and extracellular acidification rate (ECAR), in response to different substrates to interrogate key metabolic functions such as mitochondrial respiration and glycolysis. At least to our knowledge, the Seahorse analyzer does not specifically measure de novo lipogenesis and fatty acid uptake. Therefore, incorporating these assays in the revised manuscript may not directly address the central question of CAF-driven enhanced lipid biosynthesis.
Nonetheless, we do agree with the reviewer that a more in-depth investigation of various metabolic alterations could be of interest in future studies. Given the GSEA data derived from our scRNA-seq analysis, which hints at alterations in glycolysis (Figure 3A), exploring these aspects of metabolic alterations in the context of CAF-mediated resistance effects could indeed provide valuable insights in the broader mechanisms underlying ALK-TKI resistance.
This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.
References
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- Maynard A, McCoach CE, Rotow JK, Harris L, Haderk F, Kerr DL, et al. Therapy-Induced Evolution of Human Lung Cancer Revealed by Single-Cell RNA Sequencing. Cell. 2020;182(5):1232-51 e22.
- Bairoch A. The Cellosaurus, a Cell-Line Knowledge Resource. J Biomol Tech. 2018;29(2):25-38.
- Friedrich J, Seidel C, Ebner R, Kunz-Schughart LA. Spheroid-based drug screen: considerations and practical approach. Nat Protoc. 2009;4(3):309-24.
- Pampaloni F, Reynaud EG, Stelzer EH. The third dimension bridges the gap between cell culture and live tissue. Nat Rev Mol Cell Biol. 2007;8(10):839-45.
- Sahai E, Astsaturov I, Cukierman E, DeNardo DG, Egeblad M, Evans RM, et al. A framework for advancing our understanding of cancer-associated fibroblasts. Nat Rev Cancer. 2020;20(3):174-86.
- Biffi G, Oni TE, Spielman B, Hao Y, Elyada E, Park Y, et al. IL1-Induced JAK/STAT Signaling Is Antagonized by TGFbeta to Shape CAF Heterogeneity in Pancreatic Ductal Adenocarcinoma. Cancer Discov. 2019;9(2):282-301.
- Mhaidly R, Mechta-Grigoriou F. Fibroblast heterogeneity in tumor micro-environment: Role in immunosuppression and new therapies. Semin Immunol. 2020;48:101417.
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Referee #3
Evidence, reproducibility and clarity
Summary:
In this manuscript the authors use a spheroid co-culture model of human EML4-ALK non-small cell lung cancer (NSCLC) cell lines and fibroblasts to investigate the mechanism by which tumor-CAF crosstalk mediates non-genetic resistance to ALK inhibition. Spheroid culture of tumor cell lines with CAFs or CAF conditioned media was sufficient to reduce apoptosis and boost proliferation in the context of ALK inhibition. Using single-cell RNA-seq (scRNA-seq) of co-cultures treated with lorlatinib, the authors show that lipogenesis-associated genes are enriched in drug-treated tumor cells co-cultured with CAFs. Specifically, the authors propose that CAF-derived HGF and NRG1 derepress lorlatinib-induced changes in oncogenic Akt and mTOR signaling to boost expression of SREBP and FASN and restore lipid composition in NSCLC cancer cells.
Major comments:
- Although the authors note that the scRNA-seq data generated here may be an important resource in the field, it could also be explored in greater depth to further support the conclusions of this manuscript. As is, the scRNA-seq data is primarily analyzed at a bulk level to identify lipid-associated genes and gene sets as down-regulated by lorlatinib. In this case, it would be more useful and perhaps a better resource to conduct bulk RNA-seq in triplicate to generate a stronger dataset and generate a set of genes significantly regulated by lorlatinib and CAF co-culture or CAF CM. The scRNA-seq data could be leveraged to support the conclusions of the manuscript by plotting lipid-associated genes identified in Figure XB-C by U-map. This analysis would identify which clusters are enriched for lipid-associated genes and demonstrate whether these particular clusters are depleted by lorlatinib or rescued by CAF co-culture.
- The authors' conclusion that the effect of CAF CM on lorlatinib sensitivity is mediated by HGF and NRG is somewhat weak. Nrg1B1 was sufficient to rescue cell viability in the context of lorlatinib treatment (Figure 5B) only at a concentration significantly higher than that which was produced by fibroblast lines in culture (Figure 5A). Although the authors note that the real level of NRG1 could be higher than detected, this is speculative. Neither HGF nor NRG1 blocking antibodies appear to have rescued the elevated cell viability driven by CAF CM in the context of lorlatinib treatment (Figure 5C). These results, though statistically significant, do not appear biologically relevant. To strengthen their conclusions, the authors should consider ablating HGF or NRG1 in CAFs via shRNA or CRISPRi and then testing if CAF CM is no longer sufficient to rescue the viability of lorlatinib treated cancer spheroids.
- The authors' conclusion that CAF co-culture restores the lipid profile of lorlatinib-treated tumor cells is somewhat weak due to the representation of lipidomic data. Although the Figure legends note that lipidomic analyses were conducted at n=3 replicates, the data as represented in Figure 8A-B do not allow the reader to assess variability across samples or the significance of the fold change differences in lipid species. Although it can be useful to view the data this way, the authors should also show variability across samples in some way via PCA plot or by including a heatmap of lipid abundance across all treatment groups and replicates. Especially as some differences appear subtle, it is also difficult to understand to what extent CAF CM rescues lorlatinib-induced effects on lipid species as values are shown as fold change relative to control for the independent groups. In this way, the reader cannot assess, for example, how lipid species abundance compares in lorlatinib-treated tumor cells +/- CAF CM. Again, a heatmap across treatment groups might be helpful in addition to an analysis for statistically significant differences in lipid abundance across treatment groups. The issues outlined here make it difficult to assess whether "addition of CAF-CM to H3122 lung tumor spheroids was able to partly abrogate this shift towards higher levels of poly-unsaturated lipid". As is, the statements describing the results in Figure 8A-B are vague and don't appear to totally align with the data. To my eyes, there is no apparent general trend in SFA or MUFA reduction in lorlatinib-treated cells as implied by the authors, though particular species may be down-regulated. The authors should also calculate saturation indices across lipid species to support their conclusion that lipid saturation is modulated by lorlatinib and rescued by CAF CM.
- In addition to the western blots used to demonstrate and effect of CAF CM or HGF/NRG1 on Akt and mTOR signaling, the authors could strengthen their conclusions by testing the effect of Akt and mTOR inhibitors on the rescue effect of CAF CM.
Minor comments:
- In addition to the cell death and proliferation assays shown in Figure 1B-E, it would be helpful to show and quantify images of spheroid co-cultures treated +/- lorlatinib (as in Figure 1A, Supplemental Figure S9). Although the effects on percent cell death and proliferation are significant, images of spheroid size and morphology would make these results more convincing.
- The ablation of specific cell clusters upon lorlatinib treatment in Figure 2 is compelling and visually striking. To make it easier for the reader to interpret this data, it might be useful to denote the general functional annotations of each cluster in the legend (for example, "cluster 3: proliferative"). This would allow the reader to visualize which populations are preferentially depleted by the inhibitor and rescued by CAF co-culture. Further, some quantification showing the number of cells in each cluster by treatment would group (or fold-reduction per cluster upon inhibitor treatment) would more clearly show how each cluster is impacted by the inhibitor and CAF co-culture.
- In Supplemental Figure S3A please specify which gate is being used to quantify the percentage of dead cells shown in subsequent plots. It would also be useful to show the gating strategy used to separate labelled tumor cells and CAFs in heterotypic co-cultures by FACS so it is clear that CAF cells are not included in the cell death/proliferation analysis.
- In Supplemental Figure S1B and C, please briefly clarify in the legend how fibroblast lines were cultured for collection of RNA and protein. It would be useful to know if the cells were assessed in spheroid culture and thus representative of their cell state when used for the following heterotypic co-culture experiments.
- In Figure 5B, the effect of CAF secreted factors on cell viability should be tested in comparison to CAF CM as a biological control. This would allow the reader to understand how the effect of each factor alone compares to the effect of CM.
- In Figure 8C, the authors should plot all individual values in the bar graph as done in all other panels.
Referees cross-commenting
I am in agreement with all of the points made by Reviewer #1. I also suggested that the authors should use shRNA to inhibit HGF and NRG1 expression in CAFs, and am similarly concerned about both human and in vivo relevance of the authors' findings. The experiments suggested by Reviewer #1 to further characterize the fibroblast subtypes and to use non-CAF control cells are also reasonable.
I am also in agreement with the critiques presented by Reviewer #2 and find them reasonable; they would strengthen the manuscripts and better support the authors' findings. The work is indeed limited by the models used here, and mechanistic findings would be better supported by further metabolic analysis such as Seahorse or assessment of lipid synthesis.
Significance
As a reviewer, I have expertise in fibroblast biology and the contributions of the tumor microenvironment to pancreatic tumor development. Although my research has not focused on lung cancer specifically, I also have experience in lipid metabolism, therapy resistance, and tumor heterogeneity. In this manuscript the authors use a co-culture system to show that soluble CAF factors drive tyrosine kinase inhibitor (TKI) resistance in vitro in Alk-fusion driven NSCLC in line with prior work (Reviewed by Wong et al. 2021, Domen et al. 2021, Li et al. 2022). Mechanistically, the authors propose that CAF-secreted HGF and NRG1 restore Akt and mTor signaling pathways suppressed by lorlatinib, thus rescuing SREBP expression and the phospholipidome in TKI-treated NSCLC cells. Prior work has specifically demonstrated the ability of CAFs to rescue the effect of lorlatinib on NSCLC cell lines with ALK fusions via HGF/Met signaling (Hu et al. 2021), and the general effect of CAF-secreted HGF on therapy resistance through Akt/mTor signaling has been well characterized (CITE). The regulation of SREBP and lipid metabolism by Akt/mTOR signaling in cancer and TKI resistance have been similarly described (CITE). Thus, the authors largely connect these well-known pathways, demonstrating that CAF co-culture restores lipid-associated transcriptional programs and lipidomic profile in lorlatinib-treated cells via HGF/NRG1 activation of Akt, mTOR, and SREBP. A few points presented in the manuscript that could represent potential scientific advances include scRNA-seq analysis of CAF/NSCLC co-cultures and the implication of CAFs in TKI-resistance through the modulation of lipid metabolism. However, the scientific and clinical significance of these findings are limited by the biological systems used and by their incremental contribution in context of the current literature.
The scRNA-seq analysis of NSCLC cells co-cultured with CAFs generated here could represent a potential advance and resource for future study; however, the application of this analysis to 2D cell lines in vitro may have limited utility as the heterogeneity of these long-culture lines is likely quite narrow (CITE OTHER scRNAseq in vivo or PDOs). The authors themselves did not leverage the scRNA-seq data for a deep analysis of cancer cell heterogeneity but rather uncovered lipid-associated transcriptional programs using bulk analysis across all tumor cells in their dataset. The significance of the authors' finding that CAF conditioned media (CM) mediates lorlatinib-sensitivity through the regulation of lipid metabolism is also somewhat limited by prior work directly implicating SREBP and phospholipid remodeling in TKI resistance (Xu et al. 2021, OTHERS). Although focusing on EGFR-mutant NSCLC, Xu et al. 2021 showed that SREBP upregulation and increased lipogenesis and decreased oxidative stress was associated with resistance to gefitinib and could be reversed by treatment with the SREBP inhibitor fatostatin in vivo. TKI-resistance is often driven by the activation of convergent signaling pathways (Akt, mTOR) in both in EGFR-mutant ALK-fusion NSCLC, so it is perhaps not particularly surprising that the authors find that lipid programs are similarly important in lorlatinib-resistance. The novelty in this manuscript is limited to the connection between CAFs and these critical lipid metabolism pathways, and the implication that SREBP inhibition similarly blocks non-genetic CAF-mediated TKI resistance. The significance of this finding might be greater if the authors explored whether fatostatin could improve therapy response to lorlatinib in vivo.
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Referee #2
Evidence, reproducibility and clarity
In this review, the authors describe a possible mechanism of resistance in lung tumor cells that is induced by CAFs in response to ALK-specific inhibitors. The manuscript is well written and conclusions are in general well supported by experimental data, however additional experiments are needed to fully support the conclusions stated by the authors.
- Brigatinib and lorlatinib are used to target ALK-driven lung tumor cells. In many of the experiments described heterotypic 3D co-cultures are used. Although both inhibitors act preferentially over the cancer cells, and no effect is expected in the CAFs, it would be desirable to confirm it.
- H2228 and H3122 cells are used indistinctively through the paper as ALK-driven lung tumor cells and, although in the discussion some reference is made regarding the worst outcomes observed for v3-driven ALK+ H2228 cells, results are considered similar for both cell lines, including sc-RNA-seq data. During analysis of sc-RNA-seq data numbers of specific genes identified at the different analysis are different, similar to the clusters identified (0-6). In order to determine the degree of overlap in the identified genes on the analysis and within clusters, it would be convenient to show tables with identified genes for each of the cell lines, together with the cluster classification of those genes.
- Based on sc-RNA-seq data authors then explore molecular processes facilitating survival of ALK-driven lung cancer cells under the influence of CAFs and mention that "among the enriched biological processes and pathways related to metabolic activities, the most striking terms were linked to lipid metabolism" (lines 182-184). Attending to the graph depicted in Fig. 3A, that is not true, finding other processes more significant. In fact, linked to metabolism, glycolysis is more significant than lipid metabolism. This sentence should be changed accordingly and a different rationale should be done to focus on lipid metabolism.
- The authors demonstrated initial proliferation advantage and apoptosis protection of H2228 and H3122 cells in response to conditioned media (CM) of three independent CAF clones. However, once they identify lipid enzymes altered and specific ligand-receptor interaction, then they focus only in FB2. This imply that the mechanism described by authors is relevant for that particular clone, but it does not validate a general resistance mechanism induced by CAFs. In order to claim that this is a more general mechanism, other CAF clones should be tested.
- Authors show that the identified ligands secreted by CAFs (HGF, NRG1β1, etc) are found in conditioned media from CAFs. It would be good to determine if the amount of these ligands somehow is dependent on the presence of tumor cells and/or ALK-TKi. Additionally, both HGF, NRG1β1, are able to partially restore the expression of the lipogenic enzymes identified, or AKT activation pathway, but they are not able to completely restore it. Since CAF-derived CM would have both factors, maybe combination of both ligands may induce stronger rescue of the expression of these proteins.
- Does PI3K/mTOR inhibitors revert the proliferation advantage of 3D heterotypic cultures? And expression of lipid biosynthesis genes?
- In order to fully validate the effect of CAF/CAF CM in lipid biosynthesis in tumor cells, seahorse analysis would be highly beneficial, providing simultaneous measurement of multiple metabolic parameters, including glycolysis, oxidative phosphorylation, and fatty acid oxidation in homo (with and without CAF's CM or secreted ligands) and heterotypic conditions. Furthermore, it should be combined with specific substrates and inhibitors (i.e. glucose to measure acetyl-CoA production, labelled fatty acids, etc), to dissect various aspects of lipid biosynthesis and lipid metabolism and assess de novo lipogenesis, fatty acid uptake, or triglyceride.
Significance
This study underscores the multifaceted nature of resistance mechanisms in ALK-rearranged lung adenocarcinomas, highlighting the pivotal role of CAFs and lipid metabolic reprogramming. Lung adenocarcinoma remains a challenge in oncology, and while targeted therapy with tyrosine kinase inhibitors (TKIs) has shown promise in treating ALK-rearranged lung adenocarcinomas, the development of resistance to these therapies is nearly inevitable. This study delves into a critical aspect of this resistance by describing an important aspect of the intricate interplay between cancer-associated fibroblasts (CAFs) and tumor cells within the tumor microenvironment.
One of the primary findings of this research is the impact of CAFs on the therapeutic response of ALK-driven lung adenocarcinoma cells. While intrinsic mechanisms within cancer cells are well-studied drivers of resistance, this study underscores the emerging importance of stromal components, particularly CAFs, in shaping therapeutic vulnerabilities. The observation that CAFs promote therapy resistance by hampering apoptotic cell death and fueling cell proliferation highlights the complexity of tumor-stroma interactions. The study utilizes three-dimensional (3D) spheroid co-culture models, providing a more physiologically relevant platform to investigate these interactions. This approach bridges the gap between conventional monolayer cultures and in vivo models, allowing for a deeper understanding of the role of the tumor microenvironment.
Perhaps one of the most notable findings is the identification of lipogenesis-related genes as major players in TKI-treated lung tumor spheroids. This finding not only sheds light on a previously underexplored facet of cancer biology but also suggests that lipid metabolism may be a central determinant of therapeutic susceptibility in this context. Although data provided here suggests that it might not be the only mechanisms taking place in the development of resistance to ALK inhibitors, it clearly shows that it plays an important role in it.
The study proposes a potential solution to overcome CAF-driven resistance by targeting vulnerabilities downstream of oncogenic signaling. The simultaneous targeting of ALK and SREBP-1, a key regulator of lipogenesis, emerges as a promising strategy to thwart the established lipid metabolic-supportive niche within TKI-resistant lung tumor spheroids.
One of the stronger limitations of this study is that it rely on a limited number of cell lines or patient-derived models, which may not fully capture the heterogeneity of ALK-rearranged lung adenocarcinomas. Furthermore, for most of the validatory assays performed, only a CAF cell line is used, higly limiting the significance of their conclusions to a more general resistance mechanism. Furthermore, the study provides valuable insights into the involvement of lipogenesis-related genes and AKT signaling but do not delve deeply into the precise molecular mechanisms underlying these processes. Further mechanistic studies are needed to understand the exact interactions and signaling pathways involved.
In conclusion, while this study provides valuable insights into the role of CAFs and lipid metabolism in ALK-TKI resistance, its limitations underscore the need for further research, including more comprehensive in vivo models and clinical studies, to confirm and expand upon these findings.
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Referee #1
Evidence, reproducibility and clarity
Daum AK et al. indicated that CAFs promote TKI resistance via lipid biosynthesis in ALK-driven lung adenocarcinoma cells. This work provides novel CAF-induced drug resistant mechanisms in ALK-driven lung cancer cells, however, there are several issues to be resolved by the following.
Major critiques
- The authors claimed that CAF-produced HGF and NRG1 boosts AKT signaling and de novo lipogenesis to promote ALK-TKI resistance in lung cancer cells. They showed that CAF-secreted HGF and NRG1 inhibition attenuates tumor cell viability in the presence of FB2-CM, however, whether stromal HGF and NRG1 inhibition suppresses de novo lipogenesis in carcinoma cells has not yet been investigated. The authors should inhibit HGF and NRG1 expression in CAFs by shRNA prior to addition of CAF-CM onto cancer cells to evaluate AKT signaling and de novo lipogenesis.
- The concept of this work is largely based on findings in vitro culture. The authors should perform animal experiments to convince their findings in vivo. CAFs and tumor cells would be implanted into recipient mice to determine whether AKT signaling, de novo lipogenesis and ALK-TKI resistance are increased in tumor cells by the presence of CAFs.
- This work lacks human correlation. Are HGF and NRG1 expressions in CAFs related with de novo lipogenesis, drug resistance and poor outcomes in ALK-driven lung cancer patients?
- The author described that human primary fibroblasts (FB1, FB2) were derived from NSCLC adenocarcinoma patients---. Have FB1 and FB2 been isolated from tumor tissues or no-tumor tissues? If these fibroblasts were isolated from tumor tissues as CAFs, why the authors added TGF-b onto the cells? The TGF-b treatment generates myofibroblastic CAFs, which is one of CAF subtypes, but fails to have inflammatory CAFs, which is another CAF subtypes.
- The most experiments lack the appropriate control for CAFs. They would use primary isolated counterpart fibroblasts as the patient-specific control for lung cancer CAFs by extracting from non-cancerous regions of the same individual in their experiments.
Minor issues
- In Fig 1B, coculture with TGF-b-treated MRC-5 attenuated cancer cell death with the lorlatinib treatment. However, HGF and NRG1 production is comparable between MRC-5 cells treated with or without TGF-b in Fig 5A. These data indicate that any fibroblasts but not CAFs could suppress cancer cell death with the lorlatinib treatment.
- The pAKT induction of H3122 treated with lorlatinib in the presence of CAM-CM or HGF or NRG in Fig 7A, B is barely observed and lacks the significance.
- "----stronger in in case of-------" needs to be improved to "----stronger in case of-------", line 128, page 5.
Significance
The concept of this work is interesting, however, molecular mechanisms underlying CAF-medicated ALK-TKI resistance remain poorly elucidated. Characterization of human primary fibroblasts (FB1, FB2) is not clearly described, and the most experiments lack proper control. Immunoblot in Fig 6 and 7 looks snap-shot and the reviewer has concerns about the reproducibility.
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
1. EVIDENCE, REPRODUCIBILITY AND CLARITY
Summary:
Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate). Please place your comments about significance in section 2.
This work examines the active compensation of TDH3 by its paralogs TDH1 and TDH2 as a mechanism of robustness against genetic perturbations in yeast. The authors demonstrate that the paralogs compensate in a dose-dependent manner in response to TDH3's absence, mediated by shared transcriptional regulators Gcr1p and Rap1p. Furthermore, other glycolytic genes regulated by Gcr1p and Rap1p show similar changes in expression, indicating that active compensation of TDH3 is part of a greater homeostatic feedback mechanism. Additionally, the authors suggest that the ability of paralogs to actively compensate for each other and contribute to genetic robustness is actively selected for or is simply a side effect of their ancestrally shared regulators with sensitivity to feedback mechanisms.
Major comments:
- Are the claims and the conclusions supported by the data or do they require additional experiments or analyses to support them?
The authors present robust evidence in this paper to substantiate their claims and conclusions. The comprehensive data provided effectively establishes a clear and compelling case for the role of active compensation among the TDH paralogs. I think that the authors' conclusions are well-supported with the data. Further experiments are not warranted at this time.
- Please request additional experiments only if they are essential for the conclusions. Alternatively, ask the authors to qualify their claims as preliminary or speculative, or to remove them altogether.
No need for further experiments to support the manuscript's conclusions at this time.
- If you have constructive further reaching suggestions that could significantly improve the study but would open new lines of investigations, please label them as "OPTIONAL".
Dear authors, I have a couple of experiments to open further lines of investigation:
Considering the modest expression level increase resulting from gene duplication of TDH3 (~35%), it may be worthwhile to further explore this phenomenon and its potential relationship with the limited availability of GRC1 and RAP1 transcription factors. It is conceivable that an attenuation mechanism could be involved in regulating TDH3 expression, and an examination of this possibility would provide valuable insights. An experimental approach utilizing a titratable promoter and assessment of mRNA and protein levels would offer a compelling means to probe this inquiry. (OPTIONAL).
The strain expressing TDH3 at 135% of the wild-type expression level carries two copies of TDH3, but both copies have mutations in their promoter that reduce their individual expression relative to the wild-type alleles. We have clarified the text by adding “reducing expression levels from each promoter” on page 6, line 17.
The authors' discussion raises the question of whether the active compensation observed between the TDH paralogs is a result of selection or simply a consequence of their shared regulators. To address this question, one potential avenue for future research would be to test the ability of TDH1-2 gene products to compensate for the loss of TDH3 by expressing them under the TDH3 promoter, a stronger or an inducible promoter, and then, measuring the fitness of the resulting strains with a tdh3𝚫 background. This additional line of experimentation has the potential to improve our understanding of the regulatory networks involved and shed light on the selective pressures that contribute to the maintenance of these paralogs over evolutionary time. (OPTIONAL)
We agree that this question - how selection has acted on the catalytic activity of the three paralogous proteins in concert with their expression levels - is very interesting. In fact, experiments including those described by the reviewer are currently underway in the Wittkopp lab and will be the focus of a future manuscript.
- Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated time investment for substantial experiments.
Not applicable.
- Are the data and the methods presented in such a way that they can be reproduced?
Yes.
- Are the experiments adequately replicated and statistical analysis adequate?
Yes.
Minor comments:
- Specific experimental issues that are easily addressable.
In the introduction of the manuscript (pp. 4 para. 1), it would be useful to provide a more comprehensive overview of the gene expression patterns and protein abundances of the three TDH paralogs. Including such information would better enable readers to understand the functional roles of these paralogs.
We have added a new figure (Figure S1) showing differences in expression levels and patterns across the growth curve for the three paralogs. In addition, we have added some discussion of the differences in the effects of trans-regulatory mutations on protein abundance of each paralog that was recently published by another group and further indicates some level of regulatory divergence, particularly for TDH1 (pp.4, lines 12-19).
It would be helpful to report the phenotype of the tdh1𝚫/tdh2𝚫 double mutant to provide a clearer understanding of the functional overlap of these paralogs.
The revised manuscript includes additional information about divergence in expression patterns and differences in the effects of trans-regulatory mutations between TDH1 and the other two paralogs. Specifically, TDH1 is expressed under different conditions, and it is likely involved in different processes, than TDH2 and TDH3 (pp.4, lines 12-19, Figure S1). We have also added a sentence to the introduction stating that the double mutant deleting TDH1 and TDH3 has the same growth rate as TDH3 mutants alone, suggesting that TDH1 does not compensate for loss of TDH3 in the same way that TDH2 does. Because of these observations and because of the stronger overlap in expression profiles of TDH2 and TDH3, we have chosen to focus primarily on the compensation for TDH3 by TDH2 in the revised manuscript. We believe that these changes make the TDH1/TDH2 double mutant phenotype (which has not been studied as closely as the double mutants of TDH1 or TDH2 with TDH3) unnecessary for this study.
In the results section (pp. 5, para. 2), while it is understandable that the authors have focused on the transcriptional regulation of these paralogs, it would also be insightful to provide data on their respective protein abundances, as posttranslational regulation is often a crucial component of gene expression. This data may already be available in other high-throughput studies.
We have added new experimental data using fluorescent fusion proteins that shows that the protein abundance of TDH2 increases in response to deletion of TDH3 (Figure 1B). The results of our fluorescence measurements correspond well with transcriptional levels indicated by RNA-seq, indicating that the upregulation of TDH2 expression we saw in TDH3 mutants was controlled primarily at the transcriptional level.
It would be valuable to include more detailed information on the shared cis-regulatory elements between these genes, as this could provide further insight into their regulation and potential functional divergence.
According to experimental data compiled in http://www.yeastract.com/ , ChIP-exo data indicates that promoters for TDH1, TDH2, and TDH3 are all directly bound by Gcr1p (and the complex partner Gcr2p), although the evidence for Gcr1p binding is weaker at TDH1 than the other two paralogs, and this study does not identify Gcr1p TFBS motifs in the promoters of either TDH1 or TDH2 (Holland et al. 2019). However, we were able to locate Gcr1p TFBS motifs (CTTCC, Baker 1991) in the TDH2 promoter by manually searching regions annotated as bound by Gcr1’s complex partner Gcr2p in another publicly available ChIP-chip dataset (MacIsaac et al. 2006). We mutated these four motifs in a copy of the TDH2 promoter driving YFP expression to test for their role in upregulation using flow cytometry. We found that mutation or deletion of these putative TFBS reduced the overall activity of the promoter, indicating that these sequences are functional, and also observed that upon mutation or deletion of these putative TFBSs reduced the upregulation of TDH2 when TDH3 was deleted (Figure 3E). A schematic of the TDH2 promoter has been added to Figure 3 describing these experiments.
- Are prior studies referenced appropriately?
Yes.
- Are the text and figures clear and accurate?
The language used in this manuscript is clear and concise, making the material easily comprehensible to readers of various levels of expertise. The figures have a good quality for the most part and effectively complement the text to aid in the understanding of their findings.
- Do you have suggestions that would help the authors improve the presentation of their data and conclusions?
I have a few minor suggestions regarding your manuscript's figures:
In figures 1-3, it would be helpful to indicate the number of biological or technical replicates used for the statistical analyses displayed in the plots.
We have added the number of biological replicates for each genotype to our figure legends.
Please consider adding a sentence to the figure legends indicating that the raw data was generated in a previous study.
We have added a sentence indicating that the raw data was generated in a previous study to all relevant figure legends.
Figure 4E may benefit from alternative visualization methods, such as using lines or a different type of plot, to make it easier to distinguish each dataset.
In response to this and other reviewer comments, we have re-formatted Figure 4 to reduce the number of genes displayed in Figure 4E. We believe this greatly increases the readability of the figure and thank the reviewer for their suggestion.
Reviewer #1 (Significance (Required)):
SIGNIFICANCE
===============
- General assessment: provide a summary of the strengths and limitations of the study. What are the strongest and most important aspects? What aspects of the study should be improved or could be developed?
The study is noteworthy for its comprehensive analysis of previously reported data, offering a new understanding of the mechanisms behind the observed robustness of eukaryotic organisms, in particular the active compensation of TDH3 expression. The evidence presented in support of their conclusions is compelling. However, further research is required to investigate the role of active compensation at different regulation levels, in other paralogs, and under different environmental conditions.
- Advance: compare the study to the closest related results in the literature or highlight results reported for the first time to your knowledge; does the study extend the knowledge in the field and in which way? Describe the nature of the advance and the resulting insights (for example: conceptual, technical, clinical, mechanistic, functional,...).
This study provides new insights into the mechanisms of active compensation for the loss of gene expression in yeast. The authors demonstrate that the paralogs TDH1 and TDH2 upregulate in a dose-dependent manner in response to reductions in TDH3, mediated by shared transcriptional regulators Gcr1p and Rap1p. Furthermore, other glycolytic genes regulated by Rap1p and Gcr1p show similar changes in expression, indicating that active compensation of TDH3 by its paralogs is part of a larger homeostatic response. This study provides a mechanistic understanding of active compensation for the loss of gene expression in yeast and has potential implications for other organisms.
- Audience: describe the type of audience ("specialized", "broad", "basic research", "translational/clinical", etc...) that will be interested or influenced by this research; how will this research be used by others; will it be of interest beyond the specific field?
This study may attract a broad audience, as it provides insight into the mechanisms of active paralogous compensation. Their findings have potential implications beyond the yeast's specific field, as they may provide insight into the mechanisms of robustness in other genes and organisms. This research may be of interest in the fields of molecular biology and evolution in particular gene regulation.
- Please define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.
My field of expertise is molecular biology and evolution, specifically in the areas of gene duplication, gene expression and regulation, protein evolution, and interaction networks. I am familiar with some of the topics discussed in the paper, such as gene expression and regulation, and have a good understanding of the research related to these topics.
We thank the reviewer for their insightful comments and thorough reading of the manuscript. We believe that the revisions, as described in more detail below, improve the manuscript and we greatly appreciate the suggestions.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
The ms uses RNAseq data on S cerevisiae with TDH3 perturbations (cis and trans) from prior publication to look into RNA expression of TDH3 paralogues and genes within the same pathway. Analysis of both cis and trans TDH3 perturbation data suggests that the compensatory mechanisms (via either the paralogues or the upstream/downstream enzymes of the glycolytic pathway) are dependent on GCR1 and RAP1 transcription factors.
Major comment but OPTIONAL: The RNAseq data presented here convincingly convey the authors claims. Nevertheless, if any of the following data becomes available in the meantime, they will add a lot to the current ms: 1. Protein expression data can independently validate the findings and help support/clarify potential issues emerging from the data on the glycolytic pathway - see 2nd minor comment.
The revised manuscript includes new data showing increased expression of TDH2 upon deletion of TDH3 at the protein level using a TDH2:CFP fusion protein under the control of the native TDH2 promoter and at the native locus (Figure 1B). These protein-level data do indeed independently validate our RNA-seq findings for TDH2. We have also re-arranged Figure 4 and clarified the section of the manuscript describing changes in expression in the rest of the glycolytic pathway to better communicate that these changes in gene expression may or may not be part of an active compensation mechanism (see further discussion below).
- Any data that show expression of TDH3 as a result of TDH1/TDH2 expression changes occurring independently of Gcr1/Rap1 can support the claims on robustness as a consequence of multiple paralogues being around.
We have RNA-sequencing data for strains in which TDH1 or TDH2 was deleted individually (GSE175398, data from Vande Zande et al., 2022). We saw that in these strains TDH3 expression was not significantly increased. We believe that this finding is most likely due to the difference in basal expression levels between paralogs. TDH3 is expressed at approximately 6x the level of TDH2, and TDH1 is expressed in stationary phase rather than exponential growth as TDH2 and TDH3 are (See new supplementary Figure S1). Deletion or reduction of TDH3 expression represents a much larger change in total GAPDH levels in the cell, and therefore might elicit a much stronger compensation response than deletion of TDH2 or TDH1. We are interested in how the different expression levels, patterns, and enzymatic activity levels have diverged between paralogs and contribute to their relative function in the cell, and, as mentioned above, another member of the Wittkopp lab is currently working on a manuscript addressing these questions in greater detail. For these reasons, we have chosen not to include these data in the current manuscript.
Minor comments
1) Introduction and analysis framing: there seems to be two aspects for robustness and compensation that the manuscript focuses on. The one is through paralogues and the other via alteration in the expression of genes in the same pathway. The study shows both, yet there is particular weight on the paralogues.
The introduction should also mention both in a coherent and organized way. As an example, the second paragraph in the intro refers to 'upregulation of a paralog' in the 1st sentence, then it refers to an example that fits better to compensation through changes in expression of enzymes in the same pathway.
We have adjusted the language in the second paragraph of the introduction to clarify that the other enzymes that are actively compensating for CLV1 or SlCLV3 loss in arabidopsis and tomato are paralogs (pg.2 line 21- pg.3 line 8). In addition, we have adjusted our wording of the final introduction paragraph (pg. 5, lines 11-18), and the final results section (pg. 13, lines 17-23) to better communicate that the other genes are changing as part of a homeostatic response programmed into the regulatory network and may or may not contribute to fitness gains in a TDH3 mutant.
2)Figure 4 results/Discussion: Not unexpectedly, PFK1 and PFK2 (in panels 4D and 4B) have very similar expression profiles with respect to TDH3 expression. (Considering that they are part of the same complex, one would expect that their expression levels should correlate at the very least). Yet, PFK1 did not make the significance cutoff. That can be misleading, so it warrants a comment, either on the respective results section or discussion. For that reason and to make easier comparisons expression data should be shown on the same panel (consolidate 4B and 4D) with significance annotations. It would also be nice to see some commentary in terms of pathway output upon changes in TDH3 expression. It seems as if there is a 'diffused signal' through the whole pathway that compensates for TDH3 perturbations, meaning all enzymes may be compensating to different degrees.
We appreciate these suggestions and have used them to re-arrange Figure 4 to more clearly show the response of genes that function at each step in the glycolytic pathway. As noted in the reviewer’s comment, PFK1 and 2 are nearly identical in their expression profiles. The reason one is not significantly upregulated is that it has a higher variance among replicates than the other, which we now point out explicitly in the figure legend. By grouping these genes together in Figure 4, the similarity of their expression changes is much more obvious.
Reviewer #2 (Significance (Required)):
The study demonstrates an example of paralog-dependent changes in gene expression that contribute to phenotypic robustness. The active paralog compensation is transcription factor-dependent, and the same transcription factors are also responsible for compensatory changes in expression levels of genes in the same pathway. I believe that this is an interesting case showing how a negative feedback mechanism in place to maintain pathway output and contribute to phenotypic robustness, receives and integrates signaling from different components of a pathway, including paralogues. The study relies solely on RNAseq data. Although convincing, protein expression data not only could validate the RNAseq data, but also could give a more accurate view of the respective expression profiles. The study describes a molecular mechanism in pathway regulation with broad interest in basic research. It also has particular interest with respect to paralog evolution and brings up questions on the forces that drive paralog divergence.
We appreciate this reviewer’s comments and suggestions and have added several new figures that use fluorescent fusion proteins to provide a quantitative readout of protein expression levels. Specifically, we have added panels showing increased protein expression of TDH2 fused with CFP upon deletion of TDH3 (Figure 1B). We have also added expression of fluorescent reporter genes driven by the TDH1, TDH2, and TDH3 promoters showing the differences in their expression profiles across population growth stages (Supplementary Figure 1). Finally, we have analyzed fluorescent reporter genes with promoters containing mutated Gcr1p TFBS, which also suggest a dependence of the compensatory upregulation on GCR1p (Figures 2D, 3E).
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
In their preprint titled "Active compensation for changes in TDH3 expression mediated by direct regulators of TDH3 in Saccharomyces cerevisiae" Zande and Wittkopp attempt to delineate the molecular mechanism behind compensation. They have chosen 3 paralogs, TDH1, 2 and 3 in Saccharomyces cerevisiae as their system of choice, building on an earlier study where they have used RNA-seq transcriptomics to characterize how global gene expression is affected by strains harbouring regulatory mutations at the TDH3 locus or a TDH3 gene knockout. The major claims made by the authors in this study are as follows:
- The TDH1/2/3 system demonstrates compensation, such that changes in levels of expression of TDH3 result in altered expression levels of TDH1 and TDH2
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The mechanism of this compensation is "active", that is it involves modulating the transcription of paralogs in response to altered TDH3 in the cell. iii. The transcription factors Gcr1 and Rap1 are likely candidates mediating this compensation. The effects of these regulators on TDH1 and TDH2 differ and produces different profiles of compensatory expression for these two genes.
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Since Gcr1 and Rap1 regulate other genes coding for glycolytic enzymes, compensation is related to altered expression of a larger cohort of genes. Major comments:
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The authors' claims regarding the roles of Rap1 and Gcr1 as mechanisms of compensation are supported by correlative evidence from RNA seq data. To establish the causal relationships that the authors intend, more directed experiments like the ones listed below are required: i. Monitoring the activity of TDH1 and TDH2 promoters (as YFP-fused reporters) in the various strains
We have added new experimental data to the manuscript monitoring the activity of the TDH1 and TDH2 promoters driving YFP in different phases of the growth curve, demonstrating the divergence in their gene expression patterns (Supplementary Figure 1). Because of the divergence in expression patterns, we chose to focus additional efforts on TDH2 and have added new data showing an increase in expression of a CFP::TDH2 fusion protein upon deletion of TDH3. These new experiments provide strong evidence of the causal relationship between the deletion of TDH3 and an increase in TDH2 expression.
ii. Generating mutant promoter reporters for TDH1, 2 and 3 that are unable to bind to Gcr1 and Rap1 and testing their activity in the various mutant strains
We have added new experimental data to the manuscript demonstrating that increases in the activity of the TDH3 and TDH2 promoters upon deletion of TDH3 are dependent upon Gcr1p transcription factor binding sites, as originally hypothesized. Specifically, the new figure panel 3E consists of flow cytometry data showing that the TDH2 promoter driving YFP expression increases in fluorescence upon deletion of TDH3, but that a comparable increase does not occur when Gcr1p TFBSs in the TDH2 promoter are mutated. In addition, the new figure panel 2D shows that the TDH3 promoter driving YFP no longer increases in activity when a Gcr1 TFBSs is mutated. These new experiments provide strong evidence for the dependence of active compensation by upregulation upon shared transcription factor GCR1.
- The authors claim that Gcr1 and Rap1 have similar impact on other glycolytic enzymes. However, these conclusions are also based on RNA -seq data and hence remain correlative. Based on the presented results alone, and lack of a molecular mechanism for why the levels of Rap1 and Gcr1 change in TDH3 mutant strains, it may just as easily be argued that the change in expression of other glycolytic enzymes (and therefore glycolytic flux) may be the cause for altered Rap1/Gcr1 activity and not the consequence. To test which of these possibilities are true, I would recommend the following approaches:
i. Promoter reporters for glycolytic enzymes of interest, and mutant versions that don't respond to Rap1/Gcr1
ii. Change glycolytic flux by altering growth conditions (e.g. fermentable/non-fermentable carbon source) and check to see if compensation is altered
While it is possible that mutations in the TDH3 promoter that change TDH3 expression alter the expression of other glycolytic genes, and this in turn alters Rap1/Gcr1 activity, resulting in the upregulation of the TDH paralogs, we believe it is more likely that changes in the activity of Rap1/Gcr1 are a cause rather than a consequence of altered expression of glycolytic genes because it has previously been shown that these genes are under the control of Rap1 and Gcr1. We have adjusted the wording of the final results section, and throughout the paper, to clarify that we believe the similar expression patterns observed for other glycolytic genes suggest that the increase in paralog expression that results in active compensation is part of a larger regulon, which indeed may be responsive to changes in glycolytic flux. We cannot say, however, whether the upregulation of other glycolytic genes is part of the compensatory response per se. We believe this clarification, in addition to the new experiments showing the dependence of TDH3 and TDH2 upregulation on transcription factor binding sites for GCR1, addresses the issues raised above.
Reviewer #3 (Significance (Required)):
This study attempts to address the mechanistic basis for an important homeostatic mechanism, i.e. compensation. Compensation is an almost universal mechanism seen in pathways with genetic redundancy. As pointed out by the authors, compensation ensures that gene regulatory networks produce robust outcomes and are resistant to perturbation. Though compensation is often observed, the mechanistic basis is usually unclear. This study throws light on possible transcriptional mechanisms that orchestrate compensation by altering expression levels of paralogous enzymes. In this regard the study is novel, important, and fills a lacuna in the area. However, in its current form, the study lacks the necessary causal evidence needed to substantiate the claims made by the authors. Further, the mechanism linking transcriptional regulation and metabolic flux is still lacking. As a result, though interesting, the study doesn't provide a complete picture and fails to make an impact.
We thank the reviewer for their comments and believe that the additional experiments and data added to the revised manuscript, including using fluorescent reporter genes and mutant alleles to measure the activity of promoters and show their dependence on RAP1/GCR1 binding sites, provide the causal evidence necessary to make this an impactful study.
Since I am not a yeast geneticist, it is possible that several of the concerns raised by me are due to my lack of knowledge of the system and some of the links that I find missing may have been demonstrated by others. If this is the case, I would suggest that the authors provide adequate background to address these concerns in the manuscript itself. It is my opinion, that this study, once shored up, will be of interest to a wide-readership and could also provide important experimental data that could be used for mathematical modeling.
We appreciate the reviewer’s comments and believe that the changes we’ve made to the manuscript, including the addition of critical new data that complements and supports the RNA-seq data originally presented in the manuscript, does indeed make this a study that will be of interest to a wide readership.
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Referee #3
Evidence, reproducibility and clarity
In their preprint titled "Active compensation for changes in TDH3 expression mediated by direct regulators of TDH3 in Saccharomyces cerevisiae" Zande and Wittkopp attempt to delineate the molecular mechanism behind compensation. They have chosen 3 paralogs, TDH1, 2 and 3 in Saccharomyces cerevisiae as their system of choice, building on an earlier study where they have used RNA-seq transcriptomics to characterize how global gene expression is affected by strains harbouring regulatory mutations at the TDH3 locus or a TDH3 gene knockout. The major claims made by the authors in this study are as follows:
- i. The TDH1/2/3 system demonstrates compensation, such that changes in levels of expression of TDH3 result in altered expression levels of TDH1 and TDH2
- ii. The mechanism of this compensation in "active", that is it involves modulating the transcription of paralogs in response to altered TDH3 in the cell.
- iii. The transcription factors Gcr1 and Rap1 are likely candidates mediating this compensation. The effects of these regulators on TDH1 and TDH2 differ and produces different profiles of compensatory expression for these two genes.
- iv. Since Gcr1 and Rap1 regulate other genes coding for glycolytic enzymes, compensation is related to altered expression of a larger cohort of genes.
Major comments:
-
The authors' claims regarding the roles of Rap1 and Gcr1 as mechanisms of compensation are supported by correlative evidence from RNA seq data. To establish the causal relationships that the authors intend, more directed experiments like the ones listed below are required:
- i. Monitoring the activity of TDH1 and TDH2 promoters (as YFP-fused reporters) in the various strains
- ii. Generating mutant promoter reporters for TDH1, 2 and 3 that are unable to bind to Gcr1 and Rap1 and testing their activity in the various mutant strains
- The authors claim that Gcr1 and Rap1 have similar impact on other glycolytic enzymes. However, these conclusions are also based on RNA -seq data and hence remain correlative. Based on the presented results alone, and lack of a molecular mechanism for why the levels of Rap1 and Gcr1 change in TDH3 mutant strains, it may just as easily be argued that the change in expression of other glycolytic enzymes (and therefore glycolytic flux) may be the cause for altered Rap1/Gcr1 activity and not the consequence. To test which of these possibilities are true, I would recommend the following approaches:
- i. Promoter reporters for glycolytic enzymes of interest, and mutant versions that don't respond to Rap1/Gcr1
- ii. Change glycolytic flux by altering growth conditions (e.g. fermentable/non-fermentable carbon source) and check to see if compensation is altered
Significance
This study attempts to address the mechanistic basis for an important homeostatic mechanism, i.e. compensation. Compensation is an almost universal mechanism seen in pathways with genetic redundancy. As pointed out by the authors, compensation ensures that gene regulatory networks produce robust outcomes and are resistant to perturbation. Though compensation is often observed, the mechanistic basis is usually unclear. This study throws light on possible transcriptional mechanisms that orchestrate compensation by altering expression levels of paralogous enzymes. In this regard the study is novel, important, and fills a lacuna in the area. However, in its current form, the study lacks the necessary causal evidence needed to substantiate the claims made by the authors. Further, the mechanism linking transcriptional regulation and metabolic flux is still lacking. As a result, though interesting, the study doesn't provide a complete picture and fails to make an impact.
Since I am not a yeast geneticist, it is possible that several of the concerns raised by me are due to my lack of knowledge of the system and some of the links that I find missing may have been demonstrated by others. If this is the case, I would suggest that the authors provide adequate background to address these concerns in the manuscript itself. It is my opinion, that this study, once shored up, will be of interest to a wide-readership and could also provide important experimental data that could be used for mathematical modelling.
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Referee #2
Evidence, reproducibility and clarity
The ms uses RNAseq data on S cerevisiae with TDH3 perturbations (cis and trans) from prior publication to look into RNA expression of TDH3 paralogues and genes within the same pathway. Analysis of both cis and trans TDH3 perturbation data suggests that the compensatory mechanisms (via either the paralogues or the upstream/downstream enzymes of the glycolytic pathway) are dependent on GCR1 and RAP1 transcription factors.
Major comment but OPTIONAL: The RNAseq data presented here convincingly convey the authors claims. Nevertheless, if any of the following data becomes available in the meantime, they will add a lot to the current ms: 1. Protein expression data can independently validate the findings and help support/clarify potential issues emerging from the data on the glycolytic pathway - see 2nd minor comment. 2. Any data that show expression of TDH3 as a result of TDH1/TDH2 expression changes occurring independently of Gcr1/Rap1 can support the claims on robustness as a consequence of multiple paralogues being around.
Minor comments
- Introduction and analysis framing: there seems to be two aspects for robustness and compensation that the manuscript focuses on. The one is through paralogues and the other via alteration in the expression of genes in the same pathway. The study shows both, yet there is particular weight on the paralogues. The introduction should also mention both in a coherent and organized way. As an example, the second paragraph in the intro refers to 'upregulation of a paralog' in the 1st sentence, then it refers to an example that fits better to compensation through changes in expression of enzymes in the same pathway.
- Figure 4 results/Discussion: Not unexpectedly, PFK1 and PFK2 (in panels 4D and 4B) have very similar expression profiles with respect to TDH3 expression. (Considering that they are part of the same complex, one would expect that their expression levels should correlate at the very least). Yet, PFK1 did not make the significance cutoff. That can be misleading, so it warrants a comment, either on the respective results section or discussion. For that reason and to make easier comparisons expression data should be shown on the same panel (consolidate 4B and 4D) with significance annotations. It would also be nice to see some commentary in terms of pathway output upon changes in TDH3 expression. It seems as if there is a 'diffused signal' through the whole pathway that compensates for TDH3 perturbations, meaning all enzymes may be compensating to different degrees.
Significance
The study demonstrates an example of paralog-dependent changes in gene expression that contribute to phenotypic robustness. The active paralog compensation is transcription factor-dependent, and the same transcription factors are also responsible for compensatory changes in expression levels of genes in the same pathway. I believe that this is an interesting case showing how a negative feedback mechanism in place to maintain pathway output and contribute to phenotypic robustness, receives and integrates signaling from different components of a pathway, including paralogues. The study relies solely on RNAseq data. Although convincing, protein expression data not only could validate the RNAseq data, but also could give a more accurate view of the respective expression profiles. The study describes a molecular mechanism in pathway regulation with broad interest in basic research. It also has particular interest with respect to paralog evolution and brings up questions on the forces that drive paralog divergence.
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Referee #1
Evidence, reproducibility and clarity
Summary:
Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate). Please place your comments about significance in section 2.
This work examines the active compensation of TDH3 by its paralogs TDH1 and TDH2 as a mechanism of robustness against genetic perturbations in yeast. The authors demonstrate that the paralogs compensate in a dose-dependent manner in response to TDH3's absence, mediated by shared transcriptional regulators Gcr1p and Rap1p. Furthermore, other glycolytic genes regulated by Gcr1p and Rap1p show similar changes in expression, indicating that active compensation of TDH3 is part of a greater homeostatic feedback mechanism. Additionally, the authors suggest that the ability of paralogs to actively compensate for each other and contribute to genetic robustness is actively selected for or is simply a side effect of their ancestrally shared regulators with sensitivity to feedback mechanisms.
Major comments:
- Are the claims and the conclusions supported by the data or do they require additional experiments or analyses to support them?
The authors present robust evidence in this paper to substantiate their claims and conclusions. The comprehensive data provided effectively establishes a clear and compelling case for the role of active compensation among the TDH paralogs. I think that the authors' conclusions are well-supported with the data. Further experiments are not warranted at this time. - Please request additional experiments only if they are essential for the conclusions. Alternatively, ask the authors to qualify their claims as preliminary or speculative, or to remove them altogether.
No need for further experiments to support the manuscript's conclusions at this time. - If you have constructive further reaching suggestions that could significantly improve the study but would open new lines of investigations, please label them as "OPTIONAL".
Dear authors, I have a couple of experiments to open further lines of investigation: Considering the modest expression level increase resulting from gene duplication of TDH3 (~35%), it may be worthwhile to further explore this phenomenon and its potential relationship with the limited availability of GRC1 and RAP1 transcription factors. It is conceivable that an attenuation mechanism could be involved in regulating TDH3 expression, and an examination of this possibility would provide valuable insights. An experimental approach utilizing a titratable promoter and assessment of mRNA and protein levels would offer a compelling means to probe this inquiry. (OPTIONAL). The authors' discussion raises the question of whether the active compensation observed between the TDH paralogs is a result of selection or simply a consequence of their shared regulators. To address this question, one potential avenue for future research would be to test the ability of TDH1-2 gene products to compensate for the loss of TDH3 by expressing them under the TDH3 promoter, a stronger or an inducible promoter, and then, measuring the fitness of the resulting strains with a tdh3𝚫 background. This additional line of experimentation has the potential to improve our understanding of the regulatory networks involved and shed light on the selective pressures that contribute to the maintenance of these paralogs over evolutionary time. (OPTIONAL) - Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated time investment for substantial experiments.
Not applicable. - Are the data and the methods presented in such a way that they can be reproduced?
Yes. - Are the experiments adequately replicated and statistical analysis adequate?
Yes.
Minor comments:
- Specific experimental issues that are easily addressable.
In the introduction of the manuscript (pp. 4 para. 1), it would be useful to provide a more comprehensive overview of the gene expression patterns and protein abundances of the three TDH paralogs. Including such information would better enable readers to understand the functional roles of these paralogs. It would be helpful to report the phenotype of the tdh1𝚫/tdh2𝚫 double mutant to provide a clearer understanding of the functional overlap of these paralogs. In the results section (pp. 5, para. 2), while it is understandable that the authors have focused on the transcriptional regulation of these paralogs, it would also be insightful to provide data on their respective protein abundances, as posttranslational regulation is often a crucial component of gene expression. This data may already be available in other high-thrroughput studies. It would be valuable to include more detailed information on the shared cis-regulatory elements between these genes, as this could provide further insight into their regulation and potential functional divergence. - Are prior studies referenced appropriately?
Yes. - Are the text and figures clear and accurate?
The language used in this manuscript is clear and concise, making the material easily comprehensible to readers of various levels of expertise. The figures have a good quality for the most part and effectively complement the text to aid in the understanding of their findings.
- Do you have suggestions that would help the authors improve the presentation of their data and conclusions?
I have a few minor suggestions regarding your manuscript's figures: In figures 1-3, it would be helpful to indicate the number of biological or technical replicates used for the statistical analyses displayed in the plots. Please consider adding a sentence to the figure legends indicating that the raw data was generated in a previous study. Figure 4E may benefit from alternative visualization methods, such as using lines or a different type of plot, to make it easier to distinguish each dataset.
Significance
- General assessment: provide a summary of the strengths and limitations of the study. What are the strongest and most important aspects? What aspects of the study should be improved or could be developed?
The study is noteworthy for its comprehensive analysis of previously reported data, offering a new understanding of the mechanisms behind the observed robustness of eukaryotic organisms, in particular the active compensation of TDH3 expression. The evidence presented in support of their conclusions is compelling. However, further research is required to investigate the role of active compensation at different regulation levels, in other paralogs, and under different environmental conditions. - Advance: compare the study to the closest related results in the literature or highlight results reported for the first time to your knowledge; does the study extend the knowledge in the field and in which way? Describe the nature of the advance and the resulting insights (for example: conceptual, technical, clinical, mechanistic, functional,...).
This study provides new insights into the mechanisms of active compensation for the loss of gene expression in yeast. The authors demonstrate that the paralogs TDH1 and TDH2 upregulate in a dose-dependent manner in response to reductions in TDH3, mediated by shared transcriptional regulators Gcr1p and Rap1p. Furthermore, other glycolytic genes regulated by Rap1p and Gcr1p show similar changes in expression, indicating that active compensation of TDH3 by its paralogs is part of a larger homeostatic response. This study provides a mechanistic understanding of active compensation for the loss of gene expression in yeast and has potential implications for other organisms. - Audience: describe the type of audience ("specialized", "broad", "basic research", "translational/clinical", etc...) that will be interested or influenced by this research; how will this research be used by others; will it be of interest beyond the specific field?
This study may attract a broad audience, as it provides insight into the mechanisms of active paralogous compensation. Their findings have potential implications beyond the yeast's specific field, as they may provide insight into the mechanisms of robustness in other genes and organisms. This research may be of interest in the fields of molecular biology and evolution in particular gene regulation. - Please define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.
My field of expertise is molecular biology and evolution, specifically in the areas of gene duplication, gene expression and regulation, protein evolution, and interaction networks. I am familiar with some of the topics discussed in the paper, such as gene expression and regulation, and have a good understanding of the research related to these topics.
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Reply to the reviewers
*Reviewer #1 (Evidence, reproducibility and clarity (Required)): *
*In their study, Yamano et al. dissect the mechanism of TBK1 activation and downstream effects, especially in its relation to mitophagy adaptor OPTN. The authors find that OPTN's interaction with ubiquitin and the autophagy machinery, forming contact sites between mitochondria and autophagic membranes, results in TBK1 accumulation and subsequent autophosphorylation. Based on these findings, the authors propose a self-propagating feedback loop wherein OPTN phosphorylation by TBK1 promotes recruitment and accumulation of OPTN to damaged mitochondria and specifically the autophagosome formation site. This formation site is then involved in TBK1 autophosphorylation, and the activated TBK1 can then further phosphorylate other pairs of OPTN and TBK1. A OPTN monobody investigation strengthens their findings. *
*Critique: *
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It would be helpful if the authors could more clearly highlight the previous findings in OPTN-TBK1 relationship and which gaps in the understanding their study addresses.* We thank the reviewer for this comment. As suggested, we have highlighted previous findings and detailed in the Discussion how the study advances our understanding of TBK1 activation.
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It is not always clear whether experiments have been replicated sufficiently; this should be indicated in the figure descriptions.* In the original manuscript, most of the data shown was derived from duplicated experiments. For the revised version, we repeated experiments as needed to generate the replication necessary (i.e, N = 3) for determining statistical significance. Error bars and statistical significance have been added to the graphs and figure legends accordingly.
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During the discussion, references to the figures that indicate conclusions should be added where appropriate.* We thank the reviewer for the suggestion. References to figures have been added were appropriate to the Discussion.
*Figure 1 / Result "OPTN is required for TBK1 phosphorylation and subsequent autophagic Degradation": *
*o In a) the TBK1 and TOMM20 blots feature an image artefact that makes it appear like the blots are stitched together or there was a problem with the digital imager. The quantification in b) seems to be missing replications. *
We found that the artifact came from an automatic pixel interpolation process in Adobe Photoshop when the image was rotated by a small angle. We have provided the original immunoblotting data below as evidence that the data were not stitched from separate images. More accurate representations of the images without the artifact are now shown in Fig1 A of the revised manuscript.
For Fig 1b, the experiment was independently replicated three times with error bars added to each plot on the graph.
*o g) should feature the wt cell line on the same blot for better comparability as well as quantification and replication like done in f) *
As suggested, we have included the WT cell line in the immunoblot (See Fig 1g). In addition, Reviewer 2 asked that we provide data for Penta KO cells without exogenous expression of the autophagy adaptors and expressed concern regarding the lower expression of NDP52 relative to OPTN. To address these issues, we repeated the mitophagy experiments and detected phosphorylated TBK1 in six different cell lines: WT, Penta KO, Penta KO stably expressing OPTN at both low and high expression levels, and Penta KO stably expressing NDP52 at low and high expression levels. Immunoblots of phos-TBK1(pS172), TBK1, OPTN, NDP52, TOMM20, and actin were generated under four different conditions (DMSO, valinomycin for 1 hr, valinomycin for 3 hrs, and valinomycin in the presence of bafilomycin for 3 hrs). In addition, phos-TBK1 abundance in the six cell lines was determined in response to val and baf for 3 hrs and the expression levels of NDP52 and OPTN were similarly determined in response to DMSO. Error bars based on three independent experiments have been incorporated into the data, which are shown in Figure 1g and 1h of the revised manuscript.
*o h) is missing the blots for controls actin and TOMM20 *
Immunoblots for actin and TOMM20 have been added, please see Fig 1i in the revised manuscript.
*o In the text to e/f), the authors write that NDP52 KO effect on pS172 are comparable to controls, though the quantitation in f) indicates that pS172 signal is indeed significantly reduced compared to wt *
The reviewer is correct, the phos-TBK1 (pS172) signal in NDP52 KO cells is reduced compared to that in WT cells, but is only moderately lower in NDP52 KO cells relative to OPTN KO. We regret the error, which has been corrected in the revised manuscript.
*o In the text to h/i), the authors write "there was a significant increase in the TBK1 pS172 signal in cells overexpressing OPTN", though the quantification in i) does not indicate significance levels *
We performed statistical analyses on the phos-TBK1 (pS172) levels between cells with or without OPTN overexpression and have added the degree of significance to Fig 1j. As indicated in the original manuscript, there was a significant increase in phos-TBK1 (pS172) levels when OPTN was overexpressed.
*Figure 2 / Result "OPTN association with the autophagy machinery is required for TBK1 activation": ** o In b), pTBK1 at val 1 hr only features one dot/experiment per cell line *
Three independent replicates of the experiment (val 1 hr) were performed. The levels of phos-TBK1 (pS172), total TBK1, and actin were quantified, and the graph was remade with error bars and statistical significance incorporated. Please see Fig 2b in the revised manuscript.
*o In the text to c), the authors claim that the mutants reduce/abolish the recruitment of OPTN to the autophagosome site. A costain for LC3, as done for SupFig 1b, would be necessary to support that specific claim. *
To address the reviewer’s concern regarding the recruitment of OPTN mutants to the autophagosomal formation site, we performed two different experiments. First, when OPTN WT is recruited to the contact site between the autophagosomal formation site and damaged mitochondria, it should be heterogeneously distributed across mitochondria. In contrast, OPTN mutants that are unable to associate with the autophagosome formation sites should be largely localized to damaged mitochondria since the mutants are still capable of binding ubiquitin. When we examined the mitochondrial distribution of OPTN WT following valinomycin treatment for 1 hr, more than 80% of the Penta KO cells exhibited a heterogeneous distribution, whereas only 10% of the cells showed a similar distribution for OPTN 4LA or OPTN 4LA/F178A (please see Fig 2g in the revised manuscript). Although the OPTN F178A mutant exhibited 50% heterogeneous distribution (Fig 2g), this may be because OPTN F178A retains the ability to interact with ATG9A vesicles. In fact, our previous mitophagy analyses (Keima-based FACS analysis, Yamano et al 2020 JCB), which are strongly correlated with OPTN mitochondrial distribution, showed that the OPTN F178A mutant moderately (~ 60%) induced mitochondrial degradation. This degradation effect was slightly higher (80%) with OPTN WT but significantly lower (9%) with the 4LA/F178A mutant. In the second experiment, Penta KO cells expressing either OPTN WT or the OPTN mutants were immunostained for exogenous FLAG-tagged OPTN, endogenous WIPI2, and HAP60 (a mitochondrial marker) after valinomycin treatment for 1 hr (see Fig 2e and 2f in the revised manuscript). Because LC3B is assembled on the autophagosomal formation site as well as completed autophagosomes, we detected endogenous WIPI2 because WIPI2 is only recruited to autophagosomal formation sites (Dooley et al. 2014 Mol Cell). Confocal microscopy images and their associated quantification data indicate that WIPI2 foci formation during mitophagy was reduced in Penta KO cells expressing the OPTN mutants (4LA, F178A and 4LA/F178A) as compared to Penta KO cells expressing OPTN WT.
*o d) and g) as simple confirmations of KO/KD efficiency might be better suited for the supplemental part, or blots for FIP/ATG be included with the blots in e) and h) *
Based on the reviewer comments, we performed additional experiments related to Figure 2 and have incorporated the new data into the revised figure. The original Figure 2d, e, f, g, h, and I have been moved to supplemental Figure 5.
*o In the text to e), the authors claim that the levels of pS172 in the KO cell lines did not increase during mitophagy, though the blot and quantification in f) seem to indicate an increase. The results therefore don't seem to align completely with the claims that pS172 generation in response to mitophagy requires the autophagy machinery, or that FIP200 and ATG9A rather than ATG5 are critical for TBK1 phosphorylation. *
Although newly generated pS172 TBK1 was reduced in FIP200 KO and ATG9A KO cells relative to WT cells, the signals gradually increased. In the autophagy KO cell lines (FIP200 KO and ATG9A KO), phos-TBK1 accumulates prior to mitophagy stimulation. Although suggesting it is mitophagy-independent, phos-TBK1 accumulation prior to mitophagy stimulation in autophagy KO cell lines complicated interpretation of the results. To avoid this issue, we used siRNA to transiently knock down FIP200 and ATG9A. As shown in the original manuscript (Fig 2g, h, I in the original manuscript, supplementary Fig 5d, e, f in the revised manuscript), knockdown of FIP200 and ATG9A prior to mitophagy induction allowed us to observe mitophagy-dependent phosphorylation of TBK1. This result strongly suggests that the autophagy machinery does induce TBK1 phosphorylation in response to Parkin-mediated mitophagy. However, TBK1 phosphorylation still increases, albeit very slightly, in the FIP200 and ATG9A knock down cells. Thus, it may be reasonable to assume that OPTN-dependent phosphorylation of TBK1 can occur to a certain degree even in the absence of autophagy components. We have noted this in the Discussion.
While conducting experiments for the revised manuscript, we determined that TAX1BP1 is responsible for the accumulation of phos-TBK1 in the autophagy KO cell lines under basal conditions. When TAX1BP1 is knocked down in FIP200 KO or ATG9A KO cells, the basal accumulation of phos-TBK1 was eliminated and then we could observe mitophagy-specific TBK1 phosphorylation (please see Fig 2h, i, j, k in the revised manuscript). These results showed that mitophagy-dependent phos-TBK1 is largely attenuated in FIP200KO and was almost completely eliminated in ATG9A KO cells (Fig 2k in the revised manuscript).
*o f) is missing significance indications. Its description has a typo: "bad" instead of "baf" *
Newly synthesized pTBK1 (pS172) during mitophagy was quantified and statistical significance incorporated into the figure (please see supplementary Fig 5c). The identified typo has been corrected.
*Figure 3 / Result "TBK1 activation does not require OPTN under basal autophagy conditions": *
*o In the text to SupFig2, the authors claim that pS172 levels are significantly elevated, but no significance levels are indicated *
Statistical significance was determined for all proteins shown in original supplementary Fig 2 and the results have been incorporated into the relevant figure. The original supplementary Fig 2 is now supplementary Fig 6.
*o In the text to a), NBR1 is claimed to colocalize with Ub, but no costaining with Ub is shown. The claimed lacking colocalization of OPTN with Ub is not obvious from the images; a quantification might be appropriate. *
Since the anti-NBR1 antibody used in the original manuscript is derived from mouse, we were unable to use it in conjunction with the mouse ubiquitin antibody. Because ubiquitin-positive foci and NBR1-positive foci contain p62 (original Fig 3a) and NBR1 and p62 are known to tightly interact each other (Kirkin et al. 2009 Mol Cell and Sanchez-Martin et al. 2020 EMBO Rep), we stated that "NBR1 colocalizes with Ub". However, the reviewer is correct. To remedy this confusion, we obtained a rabbit anti-NBR1 antibody (a gift from the Masaaki Komatsu group) and used it to co-immunostain with anti-Ub antibodies (please see supplementary Fig 7a of the revised manuscript). NBR1 foci colocalize with both ubiquitin and p62 in FIP200 KO and ATG9A KO cells. Further, based on comments from Reviewer 2, we purchased several anti-TBK1 antibodies and identified one that was able to detect endogenous TBK1 by immunostaining (see Figure 1 for reviewers in our response to Reviewer 2 below). Using this anti-TBK1 antibody, we showed that a part of TBK1 also associates with ubiquitin and p62-positive aggregates.
*o In the text to b), the authors make reference to significant changes, but replication/ quantification/ significance testing is missing. *
We independently performed the same experiments three times. The levels of TBK1, phos-TBK1 (pS172), all five autophagy adaptors, and TOMM20 in both the supernatants and pellets have been quantified with error bars and statistical significance indicated. These results have been incorporated into Figure 3c in the revised manuscript.
*Figure 4b) is missing the pTBK1 data that is referenced in the text. In the text to figure 5 c/d), the authors claim that certain mutants have no significant effect on mitophagy, though d) is missing significance testing *
*Figure 6 c/d/i) appear to be missing replication. *
For Figure 4b, phos-TBK1 was immunoblotted (See Fig 4b of the revised manuscript). For Figure 5b and d, statistical significance was determined for the effect of TBK1 mutations on autophosphorylation and OPTN phosphorylation and the effect of the TBK1 mutants on Parkin-mediated mitophagy. For Figure 6 c/d/I, the experiment was repeated; error bars and statistical significance have been added to the associated graphs.
*Reviewer #1 (Significance (Required)): Removal of damaged mitochondria by the mitophagy pathway provides an important safeguarding mechanism for cells. The Pink1/Parkin mechanism linked to numerous modulators and adaptor proteins ensures an efficient targeting of damaged mitochondria to the phagophore. The Ser/Thr kinase TBK1, in addition of multiple roles in innate immunity, is a major mitophagy regulator as has been revealed by the Dikic and Youle groups in 2016 (Richter et al., PNAS). The mechanistic insights provided by this manuscript add to a growing body of studies of how the autophagy machinery interconnects with cellular signalling networks. Although parts of the results need to be further validated, the data shown is of high quality, revealing an important conceptual advance. The paper is interesting and of general relevance beyond the signalling and autophagy community. *
We would like to thank Reviewer 1 for the comments and suggestions, many of which improved our manuscript. We hope that the reviewer’s comments have been adequately addressed in the revised manuscript.
*Reviewer #2 (Evidence, reproducibility and clarity (Required)): Summary In this manuscript, Yamano and colleagues show that as for Sting-mediated TBK1 activation, Optn provides a platform for TBK1 activation by autophosphorylation and that TBK1 is activated after the interaction of Optn with the autophagy machinery and ubiquitin and not before. They show that TBK1 phosphorylation is blocked by bafilomycine A1, an inhibitor of vacuolar ATPases that blocks the late phase of autophagy. Furthermore, they demonstrate that Optn is require for TBK1 phosphorylation since variation of Optn expression regulates TBK1 phosphorylation in response to PINK/Parkin-mediated autophagy. Interestingly, using immunofluorescence microscopy, they show that Optn forms sphere like structures at the surface of damage mitochondria which are more dispersed in the absence of TBK1. In addition, TBK1 is also recruited at the surface of damage mitochondria and as Optn and NDP52 (but not p62) colocalize with LC3B in response to PINK/Parkin-mediated mitophagy. Next, it is demonstrated that the Leucin zipper and LIR domains of Optn (which modulate Optn interaction with autophagosome) play an important role for TBK1 activation. Additionally, the autophagy core is shown to be required for TBK1 activation. Under basal conditions, depletion of the autophagosome machinery leads to an increase in autophagy receptors (except Optn) and TBK1 phosphorylation which colocalize with ubiquitin in insoluble moieties. In contrast, Optn remains cytosolic and is dispensable for TBK1 activation in these conditions. Then, using the fluoppi technic, the authors demonstrate that the generation of Optn-Ubiquitin condensates recruits and activates TBK1. They express in HCT116 TBK1-deficient cells engineered or pathological ALS mutations of TBK1 that affect ubiquitin interaction, structure, dimerization and kinase activity of TBK1. The expression level of TBK1 was only affected by the dimerization-deficient mutations. None of the mutations impaired Optn and TBK1 ubiquitination. Interestingly, some ALS-associated mutations affect TBK1 activity and it is said in the text that the dimerization-deficient mutations of TBK1 affect its activity proportionally to their level of expression, which is not really correct (the expression level of the mutants is very heterogenous and not always correlate to their activity). Regarding their effect on mitophagy, the authors claim that the phosphorylation of TBK1 correlate with mitophagy which is not really the case. By using TBK1 inhibitor or TBK1-depleted cells, the authors conclude that TBK1 is the only kinase phosphorylating Optn. However, BX-795 is not completely specific to TBK1. Finally, the authors use monobodies against Optn effective in inhibiting mitophagy in NDP52 KO cells. Some of the monobodies have been shown to form a ternary complex with Optn and TBK1, while others compete for the interaction between Optn and TBK1 which involves the amino-terminal region of Optn and the C-terminal region of TBK1. Monobodies that compete for the interaction of Optn with TBK1 could alter the cellular distribution of Optn and inactivate TBK1, but they do not alter the ubiquitination of Optn. Finally, these monobodies inhibit 50% of mitophagy. *
*Major and minor points: Introduction The first paragraph of the Introduction section is confused and difficult to read. First and second paragraphs (page 3 and top of page 4) are dedicated to macroautophagy processes but ended with one sentence on Parkin-mediated autophagy without further introduction, while all processes regarding mitophagy are detailed in the next paragraph. Links between ideas developed are also somewhat missing. For example, in page 6, the three last sequences detailed the phosphorylation of autophagosome component, the fact that Optn and TBK1 genes are involved in neurodegenerative diseases and autophosphorylation of TBK1 as a pre-requirement for TBK1 activation without evident links between them, except "interestingly". *
In response to the reviewer’s suggestion, we have rewritten the Introduction. The first paragraph focused on introducing the molecular mechanism underlying macroautophagy and the second paragraph focused on Parkin-mediated mitophagy. As the reviewer indicated, the ALS mutations and TBK1 phosphorylation during Parkin-mediated mitophagy are not well related, so we moved the background material on the relationship between OPTN and TBK1 in neurodegenerative diseases to the beginning of the section describing Figure 5. We believe these changes have made the Introduction easier to read and understand.
*Results *
*Major points: *
*1- Results are often over-interpreted regarding data obtained leading to inadequate conclusions (see below for details); *
We regret the reviewer’s concerns regarding over-interpretation. To address this issue, we have carefully considered the data, performed additional experiments where necessary, and rewritten the results accordingly. Please see our point-by-point responses below.
*2- Quantification of protein levels detected by western blot are provided as "relative intensities" without referring to specific loading control or to total protein when -phosphorylated forms are quantified (Fig. 1b, 1d, 1f, 1i, 2b, 2f, 2i, 5b, 7b, supplemental figures 2b). *
For the immunoblots, we loaded the same amount of total cell lysate and the phosphorylated forms were quantified relative to the total protein input. This has been mentioned in the Materials and Methods.
*3- In western blotting experiments, authors described slower migrating bands as "ubiquitinated" forms of detected proteins, but never provided experimental evidences that it could be the case. Use of non-specific deubiquitinase incubation of extracts prior to western blot could help to correctly identified ubiquitination versus other post-translational modifications such as phosphorylation, glycosylation, acetylation etc... *
We appreciate the reviewer’s suggestion. The cell lysates after mitophagy induction were incubated in vitro with a recombinant USP2 core domain (non-specific DUB), and then immunoblotted. As shown in supplemental Fig 1 of the revised manuscript, the slower migrating OPTN bands disappeared in a USP2-dependent manner. The slower migrating NDP52 and TOMM20 bands likewise disappeared. These results confirm that the slower migrating OPTN, NDP52, and TOMM20 bands are ubiquitinated.
*4- Conclusions from data obtained by immunofluorescent imaging are often drawn from only one image presented without further statistical analysis. *
Statistical significance was determined for the immunofluorescent data (original figures 1j, 2c and 3a). Please see Fig 1l, 2f, 2g, and 3a in the revised manuscript.
*Page 7: - authors referred to TBK1 phosphorylation induced by mitophagy induction as "TBK1 phosphorylation induced by Parkin-mediated ubiquitination" while mitophagy can be induced independently of Parkin (ex: via mitochondrial receptors) and without any evidence (according to referee's knowledge) of a link between ubiquitination by Parkin and TBK1 phosphorylation. *
As the reviewer indicated, Parkin-independent and ubiquitination-independent mitophagy pathways are also known (i.e. receptor-mediated mitophagy driven by NIX, BNIP3, BCL2L13, FKBP8, FUNDC1, or Atg32). Therefore, references to "mitophagy" in our manuscript were reworded as "Parkin-mediated mitophagy". Since TBK1 phosphorylation is observed before mitochondria are degraded and is dependent on Parkin-mediated ubiquitin (for example, see Fig 1c), we use the phrase "TBK1 phosphorylation triggered by Parkin-mediated OMM ubiquitination".
*Fig 1g: Western blots performed in Penta KO cells without exogene expression of any autophagy receptors should be provided as control. Furthermore, lower expression of NDP52 relative to that of Optn (using flag antibodies) should be discussed as it can explained the differential levels in TBK1 phosphorylation observed. *
As suggested, we repeated the experiment using Penta KO cells in the absence of exogeneous autophagy adaptor expression. Furthermore, we expressed different amounts of NDP52 and OPTN (indicated as low and high in the figure) in Penta KO cells to rule out the possibility that higher TBK1 phosphorylation is induced by simple overexpression of autophagy adaptor (please see Fig 1g and h in the revised manuscript). At high NDP52 expression (2.5-3.0-fold higher than endogenous NDP52), phosphorylated TBK1 was reduced to ~30% the level of that observed in WT cells after 3 hrs with val and baf. In contrast, Penta KO cells with higher OPTN expression (3.0-fold higher than endogenous OPTN) had phosphorylated TBK1 signals that were 2-fold higher than those in WT cells. Based on these results, we concluded that OPTN is an important adaptor for TBK1 activation during Parkin-mediated mitophagy.
*Page 8: Supplemental Fig 1a: - The inability of authors to observe TBK1 endogenous signal in HeLa cells using commercially available antibodies is surprising as many publications reported successful staining (see Figure 1 of Suzuki et al. 2013 Cell type-specific subcellular localization of phospho-TBK1 in response to cytoplasmic viral DNA. PLoS One. 8:e83639 among others) as well as commercial promotion (see Anti-NAK/TBK1 antibody from Abcam reference: ab235253). *
For the original manuscript, anti-TBK1 antibodies purchased from abcam (ab235253), CST (#3013S), Proteintech (28397-1-AP), and GeneTex (GTX12116) for immunostaining were unable to yield TBK1-positive signals (please see Fig 1 for reviewers below). WT and TBK1-/- HCT116 cells stably expressing Parkin were treated with valinomycin for 1 hr and immunostained with the indicated antibodies. Anti-phos-TBK1 antibody (CST, #5483) was used as a positive control. Based on these results, we stated in the original manuscript that the "endogenous TBK1 signal could not be observed using commercially available antibodies". At the reviewer’s suggestion, we purchased anti-TBK1 antibodies from abcam (ab40676) and CST (#38066). As shown in the figure below, the immunofluorescent signals generated by these antibodies were detected in WT, but not in TBK1-/- cells. The CST (#38066) antibody yielded a stronger signal, most of which was on damaged mitochondria. Thanks to this suggestion, we repeated the experiment using the new anti-TBK1 antibody. Furthermore, based on a suggestion from Reviewer 3, we detected mitochondrial recruitment of TBK1 during mitophagy stimulation (valinomycin for 30 min or 2 hrs in the presence and absence of bafilomycin; supplemental Fig 2 in the revised manuscript). We also detected association of endogenous TBK1 with ubiquitin-positive condensates in WT, FIP200KO, and ATG9A KO cells (Fig 3a and supplementary Fig 7a in the revised manuscript).
*- Conclusions of the localization of signal on mitochondria (dispersed, in the periphery or at contact sites) are clearly over-interpreted in the absence of other membrane or autophagosome specific labeling and statistical colocalization analyses of multiple images. It is particularly difficult to assess any difference between Tax1BP1, p62 and NBR1 localization on mitochondria subdomains. *
We previously expressed each FLAG-tagged autophagy adaptor in Penta KO cells and observed their localization during Parkin-mediated mitophagy and found that exogenous FLAG-tagged OPTN and NDP52, but not p62, colocalized with LC3B (Yamano et al 2020 JCB). No one has assessed and compared the localization of all five endogenous autophagy adaptors. Although we still believe that the results (supplemental Fig1 in the original manuscript) are informative for researchers in the autophagy field, we decided to remove that data from the revised manuscript since they are not the main focus of the study. We will consider publishing those data elsewhere in the future after co-staining with autophagosome markers and assessing the statistical significance of colocalization as the reviewer suggested.
*Page 9: *
*- First part of results ended without any conclusions. *
As detailed in the previous response, we have removed results for mitophagic recruitment of autophagy adaptors (supplementary Figure 1 in the original manuscript).
*- The observation that "TBK1 phosphorylation was not apparent in the Optn mutant cell lines, even after 3 hrs of valinomycin, ..." is inconsistent with detection of bands with anti-pS172-TBK1 antibodies in Fig 2a detected at 1hr (with F178A) and 3 hrs (4LA, F178A, and 4LA/F178A mutants) of treatment. *
We apologize for the confusion. This statement was clearly our mistake. We had intended to state when "all autophagy adaptors are deleted" no phosphorylated TBK1 was observed. We have rewritten this part as "TBK1 phosphorylation was not apparent in the Penta KO cells even after 3 hrs with valinomycin".
*- Similarly, decreased levels of phosphorylated TBK1 stated for F178A mutant was only observed at 1 but not 3hrs or at 3hrs in the presence of bafilomycin. *
Based on the mitophagy assay previously reported (Yamano et al 2020 JCB), the F178A mutant only moderately inhibited mitophagy (60% mitophagy with the F178A mutant vs 80% mitophagy with OPTN WT). Conversely, the 4LA mutant and 4LA/F178A double mutant had stronger inhibitory effects on mitophagy (35% for 4LA and 9% mitophagy for 4LA/F178A). Therefore, the levels of phos-TBK1 after 1 hr with valinomycin or 3 hrs with valinomycin in the presence of bafilomycin are consistent with mitophagy progression. When mitophagy proceeds efficiently, the amount of phos-TBK1 in the 1 hr val samples is reduced relative to the 3 hr val samples due to autophagic degradation.
To more clearly observe and compare the levels of mitophagy-dependent phos-TBK1 among Penta KO cells expressing OPTN WT and the mutants, we treated cells with valinomycin in the presence of bafilomycin for 0, 0.5, 1, and 2 hrs and quantified phos-TBK1. The results are shown in Fig 2c and d in the revised manuscript. The phos-TBK1 signal increased over time with val and baf treatment in all OPTN expressing cells. Cells with OPTN WT generated the most phos-TBK1, whereas the signal generated by the F178A mutant was 75% that of the OPTN WT-expressing cells and the 4LA and 4LA/F178A mutants were about 40%. The experiments were independently replicated three times and error bars and statistical significance were incorporated into the associated graph. These results indicate that OPTN association with the autophagy machinery, in particular ATG9A vesicles, is important for TBK1 activation.
*Page 10: *
*The results and their repartition between figure 2 d, e, f, g, h, I and figure 3 is a bit confusing. In these experiments, it is shown Figure 2 that the absence or depletion of the autophagy machinery increase the phosphorylation of TBK1 and in Figure 3 it is shown that not only the phosphorylation of TBK1 accumulate but also the expression of NDP52, Tax1BP1 and p62. Is it because their degradation by autophagy is blocked (like for phosphoTBK1)? *
The reviewer is correct that autophagy adaptors other than OPTN (especially TAX1BP1, p62 and NBR1) are constantly degraded by macro/micro autophagy (Mejlvang et al. 2018 J Cell Biol and Yamano et al. 2021 BBA Gen Subj). Therefore, these adaptors accumulate in autophagy deficient cell lines (original Fig 3). In this study, we found that in the absence of mitophagy stimulation phos-TBK1 accumulates in autophagy deficient cell lines. This suggests that the accumulated autophagy adaptors induce TBK1 phosphorylation under basal conditions. In the original manuscript, we claimed that TBK1 phosphorylation under basal conditions does not require OPTN since in FIP200 KO and ATG9A KO cells it did not accumulate and did not primarily colocalize with ubiquitin- and TBK1-positive foci (original Fig 3). To gain more direct evidence for the revised manuscript, we performed additional experiments and discovered that TAX1BP1 is the adaptor responsible for TBK1 autophosphorylation under basal autophagy. We treated FIP200KO and ATG9A KO cells with siRNAs against OPTN, NDP52, TAX1BP, p62, and NBR1, and immunoblotted total cell lysates with an anti-phos-TBK antibody. As shown in Fig 3f in the revised manuscript, TAX1BP1 siRNA treatment decreased phos-TBK1 levels without affecting total TBK1. This result indicates that the accumulation of TAX1BP1 in the FIP200 KO and ATG9A KO cells induced TBK1 autophosphorylation under basal conditions. Considering this result, we treated WT, FIP200 KO, and ATG9A KO cells with TAX1BP1 siRNA, and then induced Parkin-mediated mitophagy with valinomycin in the presence of bafilomycin. This strategy eliminated the basal accumulation of phos-TBK1 and allowed us to focus on mitophagy-dependent TBK1 phosphorylation. Please see revised Fig 2h, I, j, and k. The results showed that mitophagy-dependent phos-TBK1 is predominantly attenuated in FIP200 KO and ATG9A KO cells. In Figs 2 and 3, we would like to emphasize that OPTN is required for TBK1 phosphorylation in response to Parkin-mediated mitophagy, whereas TAX1BP1 is required for TBK1 phosphorylation in basal autophagy. Since Reviewer 3 commented that interpretation of the data in original Figs 2d, e, and f was challenging, we elected to move those results to the supplemental figures. We have incorporated the newly acquired data (mitophagy using FIP200 KO or ATG9A KO with TAX1BP1 siRNA cells) into the main figure. We believe that this makes the text easier for readers to understand.
*- Fig 2c: conclusions on *
*the reduction of recruitment of Optn mutants on autophagosome formation seem over-interpreted as: *
*1- no labeling with LC3 has been used to identified autophagsome, *
*2- immunofluorescent signals observed with mutants are dispersed throughout the entire mitochondria network (see the merged images) rendering impossible to distinguish between autophagosome-associated mitochondria and others. *
*The following conclusive sentence stating that association of Optn to damaged mitochondria is not sufficient for TBK1 activation based solely on IF of figure 2c seems therefore unrelated to the obtained data. *
To address concerns about the recruitment of OPTN mutants to the autophagosome formation site, we performed additional experiments. Penta KO cells and those expressing OPTN WT and mutants were treated with valinomycin for 1 hr, and FLAG-tagged OPTN, endogenous WIPI2, and HAP60 (mitochondrial marker) were detected by immunostaining. We detected endogenous WIPI2 because WIPI2 is recruited only to autophagosome formation sites (Dooley et al. 2014 Mol Cell), whereas LC3B assembles on autophagosome formation sites and is also associated with completed autophagosomes. Confocal microscopy images showed that cup-shaped OPTN WT that had been recruited to damaged mitochondria colocalized with WIPI2. Quantification further showed that during mitophagy the number of WIPI2 foci seen in cells expressing OPTN WT decreased in Penta KO cells and cells expressing OPTN mutants (4LA, F178A and 4LA/F178A). These data are shown in Fig 2e and f in the revised manuscript. In addition, we quantified the number of cells that either exhibited heterogeneous or homogeneous recruitment of OPTN to damaged mitochondria after treatment with valinomycin for 1 hr. More than 80% of Penta KO cells with OPTN WT had heterogeneous OPTN recruitment, whereas this distribution was only present in 10% of cells expressing either OPTN 4LA or OPTN 4LA/F178A. Although cells expressing the OPTN F178A mutant exhibited 50% heterogeneous recruitment, this may be because the mutant can interact with ATG9A. As mentioned above, our previous mitophagy analyses (Keima-based FACS analysis, Yamano et al 2020 JCB) showed that the OPTN F178A mutant induced ~60% mitochondrial degradation (which is correlated strongly with OPTN distribution), whereas it was 80% with OPTN WT and 9% with 4LA/F178A.
*- Fig 2d: authors should explain why ATG KO cells displayed lipidated LC3B in the absence of efficient autophagy processes. *
We thank the reviewer for the suggestion. We added the following sentence to explain the function of ATG5 in LC3B lipidation. "Since LC3B lipidation is catalyzed by ATG5, but not FIP200 and ATG9A, the lipidated form disappears only in ATG5 KO cells (Hanada et al 2007 J Biol Chem). "
*- Fig 2e: despite authors statement that TBK1 phosphorylation did not increase during mitophagy in ATG KO cells, increased pS172-TBK1 is visible in FIP200 and ATG5 KO cells especially between 1 and 3 hrs of stimulation, leading to inaccurate conclusions that TBK1 phosphorylation requires the autophagy machinery. Therefore, overall assumption that both ubiquitination and autophagy subunits are required for TBK1 autophosphorylation appears erroneous. *
As the reviewer indicated, phos-TBK1 levels gradually increased in ATG KO cells. The main text was rewritten to more accurately reflect this increase. Based on experiments using the monobodies and those conducted during the revision process, it is apparent that although the autophagy machinery may not be completely essential for TBK1 phosphorylation, it clearly facilitates TBK1 phosphorylation in response to Parkin-mediated mitophagy.
*Page 12: *
*- Fig 3a: conclusion that Optn signal is more cytosolic and did not localize with Ub condensates seems speculative as based on: *
*1- only one immunofluorescence image without statistical analysis *
*2- Optn and Ub signals are lower in images with Optn is analyzed compared to other images in which NDP52, TAX1BP1 and NBR1 are detected. *
To address these concerns, we compared and quantified the signal intensities of all endogenous autophagy adaptors in FIP200 KO and ATG9A KO cells. The quantification data are shown in Fig 3a and the immunofluorescence images are shown in supplementary Fig 6a of the revised manuscript.
*- Fig 3b: interpretation of western blot data is uncertain due to lack of appropriate loading control, especially with pellets (P) extracts. In addition, it is not clear how to conclude from the experiments in Fig 3b that autophagy adaptors other than Optn mediate TBK1 phosphorylation. *
When autophagy is inhibited, p62 accumulates in the cytosol as aggregates (Komatsu et al. 2007 Cell). Therefore, p62 should be a positive control. Indeed, Fig 3b in the original manuscript (Fig 3b and c in the revised manuscript) showed that the amount of p62 in the pellet fraction was elevated in FIP200 KO and ATG9A KO cells. Furthermore, these aggregates were also observed in the imaging data (Fig 3a and supplementary Fig 7 in the revised manuscript). As the reviewer indicated, the original manuscript did not clarify whether autophagy adaptors other than OPTN mediated TBK1 phosphorylation; however, our revised results clearly demonstrate that TAX1BP1 is the adaptor responsible inducing TBK1 autophosphorylation when basal autophagy is impaired (please see Fig 3f in the revised manuscript).
*Minor point: reference is missing in the last sentence of the paragraph stating that K48-linked chains dominate when autophagy pathways are impaired. *
While several autophagy adaptors preferentially interact with K48-linked ubiquitin chains (Donaldson et al. 2003 PNAS etc), TRAF6 is recruited to ubiquitin-condensates via p62-mediated K63-linked ubiquitination (Linares et al. 2013 Mol Cell). Furthermore, K33-linked ubiquitin chains are also present in p62-positive condensates (Nibe et al. 2018 Autophagy). Because it’s not clear which ubiquitin-linkage is dominant in the condensates, we decided to delete the sentence. We regret the confusion.
*Page 13: *
*Conversely to Optn, they find that the other autophagic receptors localize in insoluble fractions (what does it mean?) independently of TBK1 expression (experiments with DKO cells) and also independently of Optn (where is this shown?). Altogether, these experiments are far from the message of the manuscript. The title of the paragraph "TBK1 activation does not require Optn under basal autophagy conditions" is not correct because even if the level of expression of autophagic receptors and TBK1 phosphorylation are increase in response to the depletion of the autophagy machinery, it does not increase autophagy. *
According to the suggestion, we changed the title of the paragraph to "TAX1BP1, but not OPTN, mediates TBK1 phosphorylation when basal autophagy is impaired." In addition, we rewrote this section.
*- Fig 3d: authors should mention the nature of the upper band observed in Optn western blot and show the same experiment in since solely TBK1 depleted cells since they stated that "electrophoretic migration of Optn was not affected by TBK1 deletion". In addition, suggesting from these sole experiments that "NP52, TAX1BP1, p62, NBR1 and AZI2 form Ub-positive condensates where TBK1 is activated" seems over-interpretated. *
Reviewer 3 suggested we characterize the upper band in the OPTN blot (Fig 3d in the original manuscript). To determine if the band is genuine OPTN, we used phostag-PAGE to analyze cell lysates from cells treated with control siRNA or OPTN siRNA and found that both the lower and upper bands were OPTN species (please see "Figure 2 for reviewers" in our response to Reviewer 3). The same pattern was reported by the Wade Harper group (Heo et al. 2015 Mol Cell). They showed that the OPTN double band pattern on phos-tag PAGE was not affected by TBK1 deletion. We have cited this literature where appropriate in the revised manuscript. In WT cells, it is difficult to detect phosphorylation of autophagy adaptors by TBK1 because basal autophagy constantly degrades them. That’s why we used autophagy KO cell lines.
*Page 14: *
*- Fig 4: TBK1 phosphorylation was analyzed in Fig4d and not in Fig4b as stated. In addition, it is rather difficult to conclude from artificial multimerization experiments, as the authors have done, that interaction between Optn and autophagy components contributes to Optn multimerization in genuine conditions. *
Detection of phos-TBK1 has been corrected to Fig 4b. Although artificial, the fluoppi assay provides insights into how OPTN activates TBK1 and how the autophagy machinery contributes to TBK1 activation via OPTN. To determine if artificial OPTN multimerization could bypass the autophagy machinery requirement, we used the fluoppi assay. This assay was important for us to conclude that the autophagy machinery and Parkin-mediated ubiquitination allow OPTN to be assembled in close proximity to where TBK1 is activated. The main text was rewritten to better convey the benefits of the fluoppi assay.
*Page 15: *
*This work could have therapeutic consequences but the pathological mutants of TBK1 used affect ALS (Figure 5) while in the discussion it is proposed that monobodies could have a therapeutic interest in familial forms of glaucoma due to the E50K mutation of Optn. It should be better to target only one pathology. *
Both TBK1 and OPTN are causative genes for ALS and many pathogenic mutations are known to impact their function. In this study, we focused on ALS mutations in TBK1 that affect self-dimerization and investigated their impact in response to Parkin-mediated mitophagy. We created the monobodies as a tool to physically inhibit OPTN assembly at the contact site. Although our monobodies inhibit Parkin-mediated mitophagy, they would not be a useful therapeutic strategy for ALS due to the loss of function with the TBK1 mutations. However, because TBK1 E50K is a glaucomatous mutation that causes OPTN-TBK1 to bind more tightly, our monobodies might be applicable to glaucomatous pathology since they could disrupt this interaction. We thus feel that it is appropriate to mention the potential of the monobodies and their future utility in the Discussion.
*- Fig 5c, d: Authors stated that degree of TBK1 autophosphorylation correlated with OPTN phosphorylation at S177 whereas phosphorylated TBK1 is unaffected by L693Q and V700Q mutants that display decreased phosphorylated Optn In addition, authors interpretation of Figure 5 data is clearly problematic as they stated that: *
*1- neither 693Q and V700Q mutants had "significant effect on mitophagy", while decreasing efficiency from 78% to 37-51% *
*2- but conclude that 49.7% mitophagy levels of R357Q mutant is significant mitochondrial degradation. *
*Overall conclusion that mitophagy efficiency is correlated with phosphorylated TBK1 levels is therefore inaccurate. *
We regret that this section did not sufficiently describe the data. Reviewer 3 also noted that the text referencing Fig 5 was difficult to interpret. One of the reasons for the complicated data interpretation is the number of TBK1 mutants used. The L693Q and V700Q mutations used by Li et al. (2016 Nat Commun) were expected to inhibit Parkin-mediated mitophagy since those authors reported that the mutations prevented interactions with OPTN. However, our in-cell assay showed that the two mutations only moderately affected Parkin-mediated mitophagy. Furthermore, both the L693Q and V700Q mutations were engineered based on the X-ray structure, rather than being authentic pathogenic ALS mutations. To avoid any potential confusion, we decided to remove the L693Q and V700A data. We have re-evaluated the other data and have rewritten this section accordingly. Please see the revised main text.
*Discussion *
*Minor points: *
*page 20: - reference is missing in the sentence "Optn cannot oligomerize on its own on ubiquitin-decorated mitochondria". *
We have provided the appropriate reference.
*Major points: *
*Authors stated that they showed that Optn recruitment to damaged mitochondria, itself, is insufficient for TBK1 autophosphorylation, but did not show experiment of Optn recruitment to mitochondria and its consequences on TBK1 phosphorylation in the absence of mitophagy induction signal. Authors could for example target HA-Ash-6Ub to mitochondria in order to artificially recruit hAG-Optn to "ubiquitinated" mitochondria in the absence of mitophagy signal. *
We showed that the efficiency of TBK1 autophosphorylation was reduced in cells expressing the OPTN 4LA/F178A mutant, which cannot interact with the autophagy machinery (Fig 2c and d in the revised manuscript). Cells with FIP200 or ATG9A knockdown also have reduced phos-TBK1 (pS172) as shown in supplementary Fig 5e and f. The rate of phos-TBK1 (pS172) generation in ATG9AKO cells during Parkin-mediated mitophagy is reduced relative to that in WT cells (Fig 2j and k). Since a small amount of phos-TBK1 was generated in both ATG9A knockdown and KO cells (supplementary Fig 5e, f, Fig 2j and k), we concur that it would be premature to conclude that phosphorylation of TBK1 does not occur at all when autophagy core components are absent. A small amount of phos-TBK1 may be generated by OPTN that is freely distributed on the outer mitochondrial membrane. In the revised manuscript, we mention the possibility that TBK1 might be phosphorylated by OPTN independent of the autophagy machinery and were careful to avoid over-interpretation.
As shown in Fig 4, fusing OPTN with an Azami-Green tag can induce artificial multimerization and trigger the generation of phos-TBK1 (pS172). Therefore, we expect that mitochondria-targeted HA-Ash-6Ub would induce TBK1 phosphorylation in a hAG-OPTN-dependent manner as was observed with cytosolic HA-Ash-6Ub (Fig 4). The accumulation of OPTN at the contact site in Parkin-mediated mitophagy is important for TBK1 phosphorylation. Even if OPTN is forced to anchor to the mitochondria, this would induce isolation membrane formation and subsequent autophosphorylation of TBK1. Therefore, the utility of forcing OPTN to anchor to mitochondria is questionable.
*Similarly, experimental approaches used by authors lack dynamics parameters to conclude on formation and elongation of isolation membranes and contacts sites that could be probably obtained through video microscopy. *
Based on the reviewer’s comment, we performed time-lapse microscopy to observe OPTN recruitment to the contact site and followed its movement along with the elongation of isolation membranes during Parkin-mediated mitophagy. HeLa cells stably expressing GFP-OPTN and pSu9-mCherry (a mitochondrial marker) were treated with valinomycin (please see Fig 2l in the revised manuscript). Initial recruitment of GFP-OPTN near mitochondria was evident as small dot-like structures that then elongated over time to become cup-shaped structures and culminated in the formation of spherical structures. Considering the colocalization of OPTN with WIPI1/WIPI2 (markers of autophagosome formation site) in Fig 2e and supplementary Fig 2a, the time-lapse images strongly suggest that OPTN assembles at contact sites followed by elongation in tandem with isolation membranes during Parkin-mediated mitophagy.
*Finally, the model proposed by the authors does not take into account data showing that Optn basally interacts with ubiquitinated mitochondria and LC3 family members (see Wild et al., Phosphorylation of the autophagy receptor optineurin restricts Salmonella growth. Science. 2011 333:228-33), although at lower levels compared to induced conditions, relativizing the impact of the proposed model. *
According to the Reviewer 2 comment, we again read the Science paper (Wild et al. 2011) but could not find data showing that OPTN basally interacts with ubiquitinated mitochondria. At least, we think that under steady state conditions without mitophagy induction, mitochondrial ubiquitination and mitochondrial localization of OPTN are undetectable as shown in supplementary Figure 2 in our revised manuscript.
*In conclusion, this manuscript represents a lot of work but the experiments often lack controls and are over-interpretated. *
***Referees cross-commenting** *
*In my opinion, what emerges from these 3 reviews is that the results lack controls or have not been repeated enough to support the message that the interaction of Optn with ubiquitin and the ubiquitination machinery is sufficient to activate TBK1. In particular, as reviewer 1 says, the phosphorylation kinetics shown in Figure 1a are not consistent with TBK1 phosphorylation following the interaction of Optn with the ubiquitination machinery and ubiquitin. In Figure 1e, there is a decrease in TBK1 phosphorylation in contrast to WTcells as mentioned by Reviewer 1. In agreement with Reviewer 1, we believe that the WT cells are missing in Figure 1g. *
*With regard to Figure 2c, we agree with reviewer 1 that an LC3 label is missing in order to be able to interpret the data. In Figure 2e and f, we agree with reviewer 1 that it is difficult to understand why TBK1 phosphorylation increases in the absence of the autophagy machinery (FIP200 KO and ATG5KO). In Figure 3, loading controls are missing for 3b and c. The TBK1 KO cells alone are missing in Fig 2d. In Figure 2b, pTBK1 is missing. In agreement with reviewer 3, we believe that the data with fluoppi contradict the message of the manuscript since they show that TBK1 can be phosphorylated by ubiquitin in the absence of the ubiquitination machinery. In agreement with reviewer 3, we believe that the experiments in Figure 5 are very difficult to interpret. The first reviewer is right to ask the question of the replicates for figures 6c and d. *
We appreciate the summary of the reviewers’ comments. To address their concerns, we have included the appropriate controls and included the results of three independent experiments in the graphs, which now include appropriate error bars and statistical significance. Thus, we believe we have answered the most critical comments concerning the lack of controls.
In Fig 1a, phos-TBK1 was maximal following 30 min of valinomycin treatment. We confirmed using microscopy-based observations that recruitment of endogenous TBK1 and OPTN and the generation of phos-TBK1 and phos-OPTN at contact sites (marked by WIPI1) near damaged mitochondria was also maximal after 30 min of valinomycin treatment (supplementary Fig 2 and 3). Therefore, the kinetics of phos-TBK1 and phos-OPTN generation are consistent with the recruitment of OPTN-TBK1 to the contact site.
The data presented in Fig 2 clearly indicate that the autophagy components are involved in phos-TBK1 generation during Parkin-mediated mitophagy. Therefore, the claim that ubiquitination machinery is sufficient for TBK1 activation is incorrect. Although we agree that the autophagy gene deletions cannot completely inhibit TBK1 autophosphorylation, mitophagy-dependent generation of phos-TBK1 is largely impaired by ATG9A KO (Fig 2j and k). Thus, there is no doubt that isolation membrane formation is important for TBK1 activation following Parkin-mediated mitophagy.
Fig 1e - The reviewer is correct that phos-TBK1 is reduced in the NDP52 knockout. We have rewritten the main text. It is also true that NDP52 has a smaller effect on TBK1 autophosphorylation as compared to OPTN.
Fig 1g - Immunoblots using total cell lysates prepared from six different cell lines (WT, Penta KO alone, Penta KO stably expressing low or high OPTN or NDP52) under four different conditions (DMSO, valinomycin 1 hr, valinomycin 3 hrs, valinomycin + bafilomycin 3 hrs) showed that OPTN is a rate-limiting factor for TBK1 phosphorylation. Please see Fig 1g and h in the revised manuscript
Fig 2c - The recruitment of OPTN WT and associated mutants to the contact site was re-examined by immunostaining with WIPI2 labeling. We found that OPTN WT was both efficiently recruited to and formed the contact site. In contrast, the OPTN 4LA/F178A mutant was unable to interact with FIP200/LC3/ATG9A and was uniformly (i.e. homogenously) distributed on damaged mitochondria with the rate of autophagosome site formation reduced. Please see Fig 2e, f, g in the revised manuscript.
Fig 2e and f - KO of the autophagy core components FIP200 and ATG9A increased phos-TBK1 under basal, non-mitophagy-associated conditions (see Fig 3). The levels of autophagy adaptors other than OPTN also increased in FIP200 KO and ATG9A KO cells. Furthermore, as shown in Fig 3a and supplementary Fig 7, both phos-TBK1 and the autophagy adaptors accumulated in Ub-positive condensates. Based on previous reports (Mejlvang 2018 J Cell Biol), TAX1BP1, p62, and NBR1 have short half-lives and are quicky degraded by macro/micro autophagy. The accumulation of phos-TBK1 in the absence of autophagy occurs because autophagy-dependent degradation of TAX1BP1 (and other adaptors) is inhibited. This allows for the formation of Ub-positive condensates, which brings TBK1 into sufficient proximity for activation. This has been noted in the revised manuscript.
Fig 3b and 3c - We wonder if the "loading controls are missing for Fig 3b and 3c" statement might be a misinterpretation by the reviewer as TOMM20 was used as the loading control in the original Fig 3b. It was recovered in the supernatant fractions of WT, FIP200 KO, and ATG9A KO cells, indicating that the accumulation of autophagy adaptors in the pellet fractions depends on autophagy gene deletion. Similarly, actin and TOMM20 were used as loading controls in the original manuscript Fig 3c.
Fig 2d (perhaps meant to be Fig 3d) – A previous study reported that phos-tag PAGE blot of OPTN in TBK1 KO cells alone revealed no differences between WT and TBK1 KO cells (Heo et al 2015 Mol Cell). We cited this reference in the revised manuscript.
Fig 2b (perhaps meant to be Fig 4b) - Immunoblots of phos-TBK1 have been incorporated into the results of Fig 4b in the revised manuscript.
Fig 4 - We show in Fig 2 that induction of Parkin-mediated mitophagy promotes OPTN accumulation at contact sites formed by isolation membranes and ubiquitinated mitochondria, and that autophagy core subunits are required for efficient generation of phos-TBK1. Fig 3 shows that phos-TBK1 accumulates in Ub-positive condensates with TAX1BP1, rather than OPTN, and that it is responsible for phos-TBK1 accumulation. Together, these results suggest a model in which TBK1 is activated when OPTN and TBK1 are positioned near each other. We hypothesized that if we could force OPTNs into close proximity the autophagy machinery requirement for TBK1 activation might be bypassed. To assess this model, we designed the fluoppi assay shown in Fig 4. This assay was critical in that it provided an important clue for the molecular mechanism that OPTN and the autophagy machinery use to cooperatively induce TBK1 trans-autophosphorylation. Because the original manuscript may not have sufficiently conveyed our reasoning for the fluoppi analysis, we have rewritten this section. The main point of the fluoppi assay is that engineered OPTN multimerization was able to bypass the autophagy requirement for TBK1 activation.
Fig 5 - For easier interpretation, the L693Q and V700Q data, which are not related to ALS pathology, have been removed.
Fig 5d – Statistical significance has been determined for the mitophagy results and the main text has been rewritten for better clarity.
Fig 6c, d, and I – The experiments were independently replicated more than three times with statistical support and error bars incorporated into the associated graphs.
*Reviewer #2 (Significance (Required)): *
*this manuscript represents a lot of work but the experiments often lack controls and are over-interpretated. The manuscript is for a broad audience. *
For the revised manuscript, additional experiments were carefully performed with appropriate controls and the manuscript was rewritten to address concerns regarding over-interpretation. We hope that we have adequately addressed the reviewer’s comments.
*Reviewer #3 (Evidence, reproducibility and clarity (Required)): *
*The authors investigated the mechanisms by which TBK1 is phosphorylated and thus activated in PINK1/Parkin-mediated mitophagy. They show data indicating that OPTN, by interacting both with ubiquitin-coated mitochondria and with the autophagy machinery, provides a platform where OPTN-bound TBK1 can be hetero-autophosphorylated by adjacent TBK1. *
*According to the prevailing model (prior to this manuscript), TBK1 activation via autophosphorylation leads to TBK1-mediated phosphorylation of OPTN S177 and subsequent pOPTN-mediated recruitment of autophagic isolation membranes to the mitochondria. However, based on the model provided in this manuscript, OPTN needs to interact first with both autophagic membranes and ubiquitin before TBK1 can become activated. *
*This is an important topic. Overall, the experimental data are of high scientific quality. For the most part, the manuscript is clearly written. The figures have been made with great care. The novel insights are relevant. However, a number of issues need to be addressed or clarified. *
*Major comments: *
- Fig. 1a-b shows that pTBK1 (pS172) formation already peaks after 30 min of valinomycin. Even when bafilomycin is added, pTBK1 level already reaches a near maximum after 30 min of valinomycin. If the model proposed by the authors is correct and pTBK1 (pS172) formation requires extensive interaction of OPTN with both ubiquitin and autophagic isolation membranes, they should be able to show (by immunostaining) that OPTN already extensively forms peri-mitochondrial cup/sphere-shaped structures that colocalize with isolation membrane markers after only 30 min of valinomycin. In the present manuscript, they only show formation of such structures after 1-3 h of valinomycin.* We thank the reviewer for the critical comments. Based on the suggestion, we performed immunostaining to observe the recruitment of TBK1 and OPTN to damaged mitochondria as well as the generation of phos-TBK1 (pS172) and phos-OPTN (pS177). HeLa cells stably expressing Parkin and 3HA-WIPI1 were treated with valinomycin for 30 min, and then TBK1, OPTN, phos-TBK1, and phos-OPTN were immunostained along with 3HA-WIPI1 (a marker of the autophagosome formation site) and TOMM20 (a mitochondria marker). Please see supplementary Fig 2a and 3a in the revised manuscript. The TBK1, OPTN, phos-TBK1, and phos-OPTN signals formed dot-like, cup-shaped, and/or spherical structures, most of which were peri-mitochondrial and colocalized with 3HA-WIPI1. In separate experiments, HeLa cells stably expressing Parkin were treated with valinomycin in the presence or absence of bafilomycin for 30 min or 2 hrs and then immunostained. Please see supplementary Fig 2b in the revised manuscript. After 30 min valinomycin in the absence of bafilomycin, many TBK1 and OPTN signals were observed on damaged mitochondria. These signals were quantified from more than 160 cells for each of the four conditions. Each microscopic image generated contained 18-36 cells and corresponds to one dot in supplementary Fig 2c. Based on these results, the abundance of TBK1 and OPTN on mitochondria after 30 min of valinomycin was much higher than that after 2 hrs with valinomycin (supplementary Fig 2c). Similar results were obtained for phos-TBK1 and phos-OPTN (supplementary Fig 3b and c). These results are consistent with the immunoblot data (Fig1a and b).
Furthermore, we show that Parkin expression levels affect the amount of phos-TBK1 generated during mitophagy. Please see supplementary Fig 4 in the revised manuscript. When PARKIN was integrated into HeLa cells under a CMV promoter via an AAVS1 (Adeno-associated virus integration site 1)-locus, the resultant cell line (referred to as high-Parkin) had higher Parkin levels than HeLa cells in which PARKIN was introduced by retrovirus infection (referred to as low-Parkin). In high-Parkin HeLa cells, phos-TBK1 levels reached a maximum after 30 min with valinomycin, while in low-Parkin HeLa cells, phos-TBK1 levels were comparable after 30 min and 1 hr. High-Parkin HeLa was used for Fig 1a, b, c, and d as well as supplementary Fig 1, 2, 3 and 4. For all other Figs, PARKIN genes were introduced by retrovirus infection. This is one of the reasons why val was used for 30 min in Fig1, but 1-3 hrs for the other Figs. Because 3 hrs valinomycin treatment may be unsuitable for evaluating OPTN recruitment to mitochondria/isolation membrane contact sites, we deleted the original Fig 2c and replaced it with the val 1 hr data (Please see Fig 2e in the revised manuscript).
- The authors propose that OPTN needs to interact both with ubiquitin on mitochondria and with isolation membrane proteins such as ATG9A to allow TBK1 phosphorylation. However, their fluoppi experiments in Fig. 4 seem to contradict this. In the fluoppi experiments, the authors generate multimeric OPTN-Ub foci and this is apparently sufficient to induced TBK1 phosphorylation at S172 (shown in 4d,f). In this experiment, there is no induction of autophagy or formation of isolation membranes, and TBK1 nevertheless gets activated.*
Figure 2 demonstrates that both ubiquitin on mitochondria and formation of the isolation membranes are needed to provide a platform for OPTN to assemble in close proximity to each other and subsequently induce TBK1 autophosphorylation. To determine if OPTN proximity is sufficient for TBK1 autophosphorylation (i.e., if engineered OPTN multimerization can bypass the autophagy machinery requirement for TBK1 autophosphorylation), we used the fluoppi assay. The results clearly showed that engineered OPTN multimerization induced TBK1 autophosphorylation without the need for the autophagy machinery. Although this is not a mitophagy experiment, the fluoppi assay provided crucial insights into the molecular mechanism underlying OPTN-mediated TBK1 autophosphorylation. The main text was rewritten to provide more clarity regarding the purpose of the fluoppi experiments.
- Can the authors be more concrete/specific in the discussion about the molecular mechanisms that explain why this 'platform' that is created by OPTN-autophagy machinery interactions is so crucial for TBK1 activation? If I understand the model in Fig. 7D correctly, the OPTN-autophagy machinery interactions are mainly important because they reduce the distance between OPTN-bound TBK1 molecules so that they can trans-phosphorylate each other. But if TBK1 autophosphorylation was just a matter of proximity between OPTN-bound TBK1 molecules, interaction of OPTN with densely ubiquitinated mitochondria should already be sufficient for TBK1 phosphorylation. When multiple OPTN molecule bind to one ubiquitin chain or to closely adjacent ubiquitin chains (similar to the fluoppi experiments), TBK1 molecules binding to OPTN would be expected to be already closely enough to one another for trans-autophosphorylation.*
The amount of phos-TBK1 during Parkin-mediated mitophagy was reduced in cells with the OPTN 4LA/F178A mutant, which cannot interact with the autophagy machinery (e.g. FIP200, ATG9A, and LC3) but can be targeted to mitochondria (see Fig 2c, d). ATG9AKO cells also had reduced amounts of phos-TBK1 relative to WT cells (See Fig 2j, k). Therefore, rather than OPTN-ubiquitin freely diffusing laterally on the outer membrane, we suggest that the contact site OPTN forms with ubiquitin and the autophagy machinery provides a more suitable platform for TBK1 autophosphorylation because it maintains TBK1 in a proximal position for a longer period of time.
The OPTN UBAN domain binds a ubiquitin-chain composed of two ubiquitin molecules (Oikawa et al. 2016 Nat Comm), and during Parkin-mediated mitophagy only shorter length poly-ubiquitin chains are generated on the mitochondrial surface (Swatek et al. 2019 Nature). Based on those findings, it is unlikely that multiple OPTN bind to one ubiquitin chain. Of course, we cannot rule out the possibility that TBK1 autophosphorylation does not occur on mitochondria in the absence of autophagy components. While full activation of TBK1 requires OPTN to associate with the isolation membrane, initial TBK autophosphorylation at the onset of mitophagy may occur based only on the OPTN-ubiquitin interaction. These explanations have been added to the Discussion in the revised manuscript.
Furthermore, based on comments from Reviewer 2, we performed time-lapse microscopy to observe OPTN dynamics during Parkin-mediated mitophagy (please see Fig 2l). HeLa cells stably expressing GFP-OPTN and pSu9-mCherry (a mitochondrial marker) were treated with valinomycin. GFP-OPTN was initially a peri- mitochondrial dot-like structure that elongated over time to a cup-shaped structure and which eventually ended up forming a spherical structure. The time-laps imaging showed that, at least in WT cells, OPTN is directly recruited to the contact sites and elongates along with the isolation membranes. We thus concluded that TBK1 is activated (autophosphorylated) at the contact site rather than on the outer membrane where OPTN-TBK can move freely.
- Fig. 5c,d and P. 16: the mitophagy experiments in TBK1-/- cells expressing the different mutant forms of TBK1 are hard to interpret because it is not clear which mitophagy differences are statistically significant. The main text about this part (p. 16) is also confusing.*
We regret the confusion. Reviewer 2 also noted that the main text for Fig 5 was difficult to interpret. One of the reasons that complicated interpretation of the data is the number of TBK1 mutants used. The L693Q and V700Q mutations used by Li et al. (2016 Nat Commun) were expected to inhibit mitophagy since those authors reported that the mutations prevented interactions with OPTN. However, our in-cell assay showed that the two mutants only moderately affected Parkin-mediated mitophagy. Furthermore, both L693Q and V700Q were engineered based on the X-ray structure and are not ALS pathogenic mutations. To simplify the data and to make data interpretation easier, we decided to delete the L693Q and V700A data. We also determined statistical significance and rewrote this section.
- Many graphs lack statistics: Fig. 2b (pTBK1), Fig. 2f, Fig. 5b, Fig. 5d, Fig. 6c.*
We apologize for the lack of statistical analyses. We repeated experiments (if the experiments had not been independently performed more than three times) with statistical significance and error bars incorporated into the relevant figures.
*Other comments: *
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Fig. 1a: how do they know that the upper OPTN band is ubiquitinated OPTN? Reviewer 2 raised the same question. To demonstrate that the upper OPTN band is ubiquitinated, cell lysates after mitophagy induction were incubated in vitro* with a recombinant USP2 core domain, and the samples immunoblotted. As shown in supplementary Fig 1 in the revised manuscript, the upper OPTN band disappeared in a USP2-dependent manner. The upper NDP52 and TOMM20 bands similarly disappeared. Therefore, the upper OPTN, NDP52 and TOMM20 bands observed after mitophagy induction are ubiquitinated.
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Fig. 1a,b: the bafilomycin stabilization of pTBK1, OPTN and pOPTN indicates that these proteins are substantially degraded by autophagy within 30-60 minutes. This seems extremely fast for mitophagy completion. Please discuss.*
According to Kulak et al. (2014 Nat Methods), autophagy adaptor abundance (OPTN: 2.32E+4 and NDP52: 3.34E+4 in HeLa cell line) is low compared to that of mitochondria (TOMM20: 1.45E+6 in HeLa cell line). This is one of the reasons why autophagic degradation of adaptors is easier to see. Degradation of phos-TBK1 was likewise easy to detect, whereas total TBK1 was not. This discrepancy is likely based on differences in the abundance of phos-TBK1 and total TBK1. In addition, because autophagy adaptors are localized outside of the mitochondrial membrane they may be easier targets for lysosomal degradation than matrix proteins, which are localized inside the outer and inner membranes.
- Fig. 1a and rest of the manuscript: is there a reason why the authors only looked at S177 phosphorylation of OPTN and not also at OPTN S473, which is also phosphorylated by TBK1?*
Both mass spectrometry and mutational analyses indicated that OPTN S473 is phosphorylated during Parkin-mediated mitophagy and that OPTN phosphorylated at S473 strongly binds ubiquitin chains (Richter et al. 2016 PNAS and Heo et al. 2015 Mol Cell). However, because a phos-S473 OPTN antibody is, to the best of our knowledge, currently not commercially available, we did not focus on S473 phosphorylation.
- Fig. 1e-f: the main text states that "NDP52 KO effects on the pS172 signal were comparable to controls", but the blot in 1e and the graph in 1f indicate a difference between NDP52KO and WT (significant difference shown in 1f). This is confusing.*
We regret the over-interpretation. As the reviewer indicated, the amount of phos-TBK generated in response to mitophagy was reduced in NDP52 KO cells relative to that in WT cells. This has been corrected. We would like to emphasize that, unlike OPTNdeletion, NDP52 deletion has relatively minor effects on TBK1 phosphorylation.
- P. 9: "TBK1 phosphorylation however was not apparent in the OPTN mutant lines, even after 3 hrs with valinomycin, indicating that autophagy adaptors are essential for TBK1 activation (Fig. 2a)". However, the pTBK1 blot in Fig. 1a does show pTBK1 formation in the OPTN mutant (4LA etc.) lines. This is confusing.*
We apologize for this error. We intended to state “TBK1 phosphorylation was not apparent in the Penta KO cells without OPTN expression even after 3 hrs with valinomycin, indicating that autophagy adaptors are essential for TBK1 activation”. This sentence has been corrected in the revised manuscript.
- P. 10: "we subtracted the basal phosphorylation signal from that generated post-valinomycin (1 hr) and bafilomycin (3 hr)". Do they mean "from that generated post-valinomycin (3 hr) and bafilomycin (3 hr)?*
The reviewer is correct, we have corrected the error.
- P. 10, same paragraph: "the phosphorylation signal was ~90 but was less than 30 in ATG9A KO cells." Unclear what they mean by 90 and 30. 90% and 30%? 90-fold and 30-fold?*
The newly generated pTBK1 levels following Parkin-mediated mitophagy were calculated as pTBK1 [val & baf 3 hrs] minus pTBK1 [DMSO]. Since pTBK1 [val & baf 3 hrs] in WT cells is set to 100%, the newly generated pTBK1 in WT cells was 100% - 5% = 95%. The calculated values for pTBK1 [DMSO] and pTBK1 [val & baf 3 hrs] in ATG9A KO cells were ~55% and ~85%, respectively. Consequently, newly generated pTBK1 in the ATG9A KO cells is ~85% - ~55% = 30%. For clarity, we modified the figure to make the meaning of the numbers more apparent.
- Fig. 3a: Do they have an idea what kind of ubiquitinated substrates are contained in the ubiquitin-positive condensates that accumulate in FIP200 KO and ATG9A KO cells (i.e. without valinomycin treatment)?*
According to Kishi-Itakura et al. (2014 J Cell Sci), ferritin accumulates in the p62 condensates in FIP200 KO and ATG9A KO cells. However, it is unknown if the ferritin in the condensates is ubiquitinated. In the original manuscript, we confirmed by immunostaining that the p62-NBR1 condensates contain ferritin (Fig 3a in the original manuscript and supplementary Fig 7b in the revised manuscript).
- P. 12 and Fig. 3a: please explain why they look at ferritin, to improve readability.*
We thank the reviewer for the suggestion. As mentioned, ferritin is a known substrate that accumulates in p62 condensates, we thus sought to confirm its presence. We have included this explanation in the revised manuscript.
- Fig. 3a: please also include Ub stain for NBR1.*
We thank the reviewer for the suggestion. We obtained a rabbit anti-NBR1 antibody that allowed us to co-immunostain with the mouse anti-ubiquitin antibody (please see supplementary Fig 7b in the revised manuscript).
- Fig. 3d: the OPTN blot shows 2 OPTN bands. What does the upper OPTN band represent here?*
To determine if the two bands are genuine OPTN, total cell lysates prepared from HeLa cells treated with control siRNA or OPTN siRNA were subjected to phos-tag PAGE followed by immunoblotting with an anti-OPTN antibody. As shown below (Figure 2 for reviewers), the two bands (indicated as blue arrowheads) were absent in the OPTN knock down cells, indicating that both are derived from OPTN. Since phosphorylated species migrate slower in phos-tag PAGE, the upper band might be a phosphorylated form. The specific Ser/Thr phosphorylated in OPTN, however, remains to be determined. Heo et al. (2015 Mol Cell) also reported the two OPTN bands on phos-tag PAGE and that both were unchanged in TBK1 KO cells, suggesting that at least the upper band is not affected by TBK1.
- P. 14 and Fig. 4b: "Here, we found that phosphorylation of ... TBK1 (S172) was induced by the OPTN-ub fluoppi (Fig. 4b)." However, Fig 4b does not show a pTBK1 blot.*
We immunoblotted phos-TBK1. Please see Fig 4b in the revised manuscript.
*Reviewer #3 (Significance (Required)): *
*The novel insights are relevant. *
*According to the prevailing model (prior to this manuscript), TBK1 activation via autophosphorylation leads to TBK1-mediated phosphorylation of OPTN S177 and subsequent pOPTN-mediated recruitment of autophagic isolation membranes to the mitochondria. However, based on the model provided in this manuscript, OPTN needs to interact first with both autophagic membranes and ubiquitin before TBK1 can become activated. *
Based on our time-lapse microscopy observations (Fig 2l), OPTN recruited to the vicinity of mitochondria was visible as a small dot-like structures that likely correspond to contact sites between mitochondria and the isolation membrane since OPTN colocalizes with WIPI1 (please see supplementary Fig 2). These results support our proposed model that OPTN interacts with both isolation membranes and ubiquitin at the onset of mitophagy. Without TBK1 activation, OPTN can interact with ATG9A vesicles, a seed for isolation membrane formation (Yamano et al 2020 JCB), and TBK1 can interact with the PI3K complex (Nguyen et al 2023 Mol Cell). Therefore, OPTN-TBK1 can be recruited to the contact site from the very beginning of mitophagy induction prior to TBK1 being fully activated. Furthermore, the proposed model also includes an OPTN-TBK1 positive feedback loop; however, the earliest reactions in the positive feedback loop are too difficult to observe. For example, it’s widely known that PINK1 and Parkin form a positive feedback loop to generate ubiquitin-chains on damaged mitochondria, but the initial reaction has yet to be observed. It remains unclear if PINK1 is the first to phosphorylate mitochondrial ubiquitin (if this is the case, it remains unknown how ubiquitin comes to mitochondria) or if cytosolic Parkin first adds ubiquitin to the outer membrane albeit with very weak activity. Similarly, in our proposed model, we cannot determine the earliest OPTN-TBK1 reaction. As described in the Discussion in the revised manuscript, it remains possible that in the absence of autophagy machinery OPTN distributed freely on the outer membrane can induce trans-autophosphorylation, albeit weakly, as the earliest reaction.
We would like to thank Reviewer 3 for the critical comments and suggestions. We have performed several of the suggested experiments, added new data, and rewritten the text. We hope that these changes have sufficiently addressed the reviewer’s concerns.
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Referee #3
Evidence, reproducibility and clarity
The authors investigated the mechanisms by which TBK1 is phosphorylated and thus activated in PINK1/Parkin-mediated mitophagy. They show data indicating that OPTN, by interacting both with ubiquitin-coated mitochondria and with the autophagy machinery, provides a platform where OPTN-bound TBK1 can be hetero-autophosphorylated by adjacent TBK1.
According to the prevailing model (prior to this manuscript), TBK1 activation via autophosphorylation leads to TBK1-mediated phosphorylation of OPTN S177 and subsequent pOPTN-mediated recruitment of autophagic isolation membranes to the mitochondria. However, based on the model provided in this manuscript, OPTN needs to interact first with both autophagic membranes and ubiquitin before TBK1 can become activated.
This is an important topic. Overall, the experimental data are of high scientific quality. For the most part, the manuscript is clearly written. The figures have been made with great care. The novel insights are relevant. However, a number of issues need to be addressed or clarified.
Major comments:
- Fig. 1a-b shows that pTBK1 (pS172) formation already peaks after 30 min of valinomycin. Even when bafilomycin is added, pTBK1 level already reaches a near maximum after 30 min of valinomycin. If the model proposed by the authors is correct and pTBK1 (pS172) formation requires extensive interaction of OPTN with both ubiquitin and autophagic isolation membranes, they should be able to show (by immunostaining) that OPTN already extensively forms peri-mitochondrial cup/sphere-shaped structures that colocalize with isolation membrane markers after only 30 min of valinomycin. In the present manuscript, they only show formation of such structures after 1-3 h of valinomycin.
- The authors propose that OPTN needs to interact both with ubiquitin on mitochondria and with isolation membrane proteins such as ATG9A to allow TBK1 phosphorylation. However, their fluoppi experiments in Fig. 4 seem to contradict this. In the fluoppi experiments, the authors generate multimeric OPTN-Ub foci and this is apparently sufficient to induced TBK1 phosphorylation at S172 (shown in 4d,f). In this experiment, there is no induction of autophagy or formation of isolation membranes, and TBK1 nevertheless gets activated.
- Can the authors be more concrete/specific in the discussion about the molecular mechanisms that explain why this 'platform' that is created by OPTN-autophagy machinery interactions is so crucial for TBK1 activation? If I understand the model in Fig. 7D correctly, the OPTN-autophagy machinery interactions are mainly important because they reduce the distance between OPTN-bound TBK1 molecules so that they can trans-phosphorylate each other. But if TBK1 autophosphorylation was just a matter of proximity between OPTN-bound TBK1 molecules, interaction of OPTN with densely ubiquitinated mitochondria should already be sufficient for TBK1 phosphorylation. When multiple OPTN molecule bind to one ubiquitin chain or to closely adjacent ubiquitin chains (similar to the fluoppi experiments), TBK1 molecules binding to OPTN would be expected to be already closely enough to one another for trans-autophosphorylation.
- Fig. 5c,d and P. 16: the mitophagy experiments in TBK1-/- cells expressing the different mutant forms of TBK1 are hard to interpret because it is not clear which mitophagy differences are statistically significant. The main text about this part (p. 16) is also confusing.
- Many graphs lack statistics: Fig. 2b (pTBK1), Fig. 2f, Fig. 5b, Fig. 5d, Fig. 6c.
Other comments:
- Fig. 1a: how do they know that the upper OPTN band is ubiquitinated OPTN?
- Fig. 1a,b: the bafilomycin stabilization of pTBK1, OPTN and pOPTN indicates that these proteins are substantially degraded by autophagy within 30-60 minutes. This seems extremely fast for mitophagy completion. Please discuss.
- Fig. 1a and rest of the manuscript: is there a reason why the authors only looked at S177 phosphorylation of OPTN and not also at OPTN S473, which is also phosphorylated by TBK1?
- Fig. 1e-f: the main text states that "NDP52 KO effects on the pS172 signal were comparable to controls", but the blot in 1e and the graph in 1f indicate a difference between NDP52KO and WT (significant difference shown in 1f). This is confusing.
- P. 9: "TBK1 phosphorylation however was not apparent in the OPTN mutant lines, even after 3 hrs with valinomycin, indicating that autophagy adaptors are essential for TBK1 activation (Fig. 2a)". However, the pTBK1 blot in Fig. 1a does show pTBK1 formation in the OPTN mutant (4LA etc.) lines. This is confusing.
- P. 10: "we subtracted the basal phosphorylation signal from that generated post-valinomycin (1 hr) and bafilomycin (3 hr)". Do they mean "from that generated post-valinomycin (3 hr) and bafilomycin (3 hr)?
- P. 10, same paragraph: "the phosphorylation signal was ~90 but was less than 30 in ATG9A KO cells." Unclear what they mean by 90 and 30. 90% and 30%? 90-fold and 30-fold?
- Fig. 3a: Do they have an idea what kind of ubiquitinated substrates are contained in the ubiquitin-positive condensates that accumulate in FIP200 KO and ATG9A KO cells (i.e. without valinomycin treatment)?
- P. 12 and Fig. 3a: please explain why they look at ferritin, to improve readability.
- Fig. 3a: please also include Ub stain for NBR1.
- Fig. 3d: the OPTN blot shows 2 OPTN bands. What does the upper OPTN band represent here?
- P. 14 and Fig. 4b: "Here, we found that phosphorylation of ... TBK1 (S172) was induced by the OPTN-ub fluoppi (Fig. 4b)." However, Fig 4b does not show a pTBK1 blot.
Significance
The novel insights are relevant.
According to the prevailing model (prior to this manuscript), TBK1 activation via autophosphorylation leads to TBK1-mediated phosphorylation of OPTN S177 and subsequent pOPTN-mediated recruitment of autophagic isolation membranes to the mitochondria. However, based on the model provided in this manuscript, OPTN needs to interact first with both autophagic membranes and ubiquitin before TBK1 can become activated.
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Referee #2
Evidence, reproducibility and clarity
Summary
In this manuscript, Yamano and colleagues show that as for Sting-mediated TBK1 activation, Optn provides a platform for TBK1 activation by autophosphorylation and that TBK1 is activated after the interaction of Optn with the autophagy machinery and ubiquitin and not before. They show that TBK1 phosphorylation is blocked by bafilomycine A1, an inhibitor of vacuolar ATPases that blocks the late phase of autophagy. Furthermore, they demonstrate that Optn is require for TBK1 phosphorylation since variation of Optn expression regulates TBK1 phosphorylation in response to PINK/Parkin-mediated autophagy. Interestingly, using immunofluorescence microscopy, they show that Optn forms sphere like structures at the surface of damage mitochondria which are more dispersed in the absence of TBK1. In addition, TBK1 is also recruited at the surface of damage mitochondria and as Optn and NDP52 (but not p62) colocalize with LC3B in response to PINK/Parkin-mediated mitophagy. Next, it is demonstrated that the Leucin zipper and LIR domains of Optn (which modulate Optn interaction with autophagosome) play an important role for TBK1 activation. Additionally, the autophagy core is shown to be required for TBK1 activation. Under basal conditions, depletion of the autophagosome machinery leads to an increase in autophagy receptors (except Optn) and TBK1 phosphorylation which colocalize with ubiquitin in insoluble moieties. In contrast, Optn remains cytosolic and is dispensable for TBK1 activation in these conditions. Then, using the fluoppi technic, the authors demonstrate that the generation of Optn-Ubiquitin condensates recruits and activates TBK1. They express in HCT116 TBK1-deficient cells engineered or pathological ALS mutations of TBK1 that affect ubiquitin interaction, structure, dimerization and kinase activity of TBK1. The expression level of TBK1 was only affected by the dimerization-deficient mutations. None of the mutations impaired Optn and TBK1 ubiquitination. Interestingly, some ALS-associated mutations affect TBK1 activity and it is said in the text that the dimerization-deficient mutations of TBK1 affect its activity proportionally to their level of expression, which is not really correct (the expression level of the mutants is very heterogenous and not always correlate to their activity). Regarding their effect on mitophagy, the authors claim that the phosphorylation of TBK1 correlate with mitophagy which is not really the case. By using TBK1 inhibitor or TBK1-depleted cells, the authors conclude that TBK1 is the only kinase phosphorylating Optn. However, BX-795 is not completely specific to TBK1. Finally, the authors use monobodies against Optn effective in inhibiting mitophagy in NDP52 KO cells. Some of the monobodies have been shown to form a ternary complex with Optn and TBK1, while others compete for the interaction between Optn and TBK1 which involves the amino-terminal region of Optn and the C-terminal region of TBK1. Monobodies that compete for the interaction of Optn with TBK1 could alter the cellular distribution of Optn and inactivate TBK1, but they do not alter the ubiquitination of Optn. Finally, these monobodies inhibit 50% of mitophagy.
Major and minor points:
Introduction
The first paragraph of the Introduction section is confused and difficult to read. First and second paragraphs (page 3 and top of page 4) are dedicated to macroautophagy processes but ended with one sentence on Parkin-mediated autophagy without further introduction, while all processes regarding mitophagy are detailed in the next paragraph.
Links between ideas developed are also somewhat missing. For example, in page 6, the three last sequences detailed the phosphorylation of autophagosome component, the fact that Optn and TBK1 genes are involved in neurodegenerative diseases and autophosphorylation of TBK1 as a pre-requirement for TBK1 activation without evident links between them, except "interestingly".
Results
Major points:
- Results are often over-interpreted regarding data obtained leading to inadequate conclusions (see below for details);
- Quantification of protein levels detected by western blot are provided as "relative intensities" without referring to specific loading control or to total protein when -phosphorylated forms are quantified (Fig. 1b, 1d, 1f, 1i, 2b, 2f, 2i, 5b, 7b, supplemental figures 2b).
- In western blotting experiments, authors described slower migrating bands as "ubiquitinated" forms of detected proteins, but never provided experimental evidences that it could be the case. Use of non-specific deubiquitinase incubation of extracts prior to western blot could help to correctly identified ubiquitination versus other post-translational modifications such as phosphorylation, glycosylation, acetylation etc...
- Conclusions from data obtained by immunofluorescent imaging are often drawn from only one image presented without further statistical analysis.
Page 7:
- authors referred to TBK1 phosphorylation induced by mitophagy induction as "TBK1 phosphorylation induced by Parkin-mediated ubiquitination" while mitophagy can be induced independently of Parkin (ex: via mitochondrial receptors) and without any evidence (according to referee's knowledge) of a link between ubiquitination by Parkin and TBK1 phosphorylation.
Fig 1g: Western blots performed in Penta KO cells without exogene expression of any autophagy receptors should be provided as control. Furthermore, lower expression of NDP52 relative to that of Optn (using flag antibodies) should be discussed as it can explained the differential levels in TBK1 phosphorylation observed.
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Supplemental Fig 1a:
- The inability of authors to observe TBK1 endogenous signal in HeLa cells using commercially available antibodies is surprising as many publications reported successful staining (see Figure 1 of Suzuki et al. 2013 Cell type-specific subcellular localization of phospho-TBK1 in response to cytoplasmic viral DNA. PLoS One. 8:e83639 among others) as well as commercial promotion (see Anti-NAK/TBK1 antibody from Abcam reference: ab235253).
- Conclusions of the localization of signal on mitochondria (dispersed, in the periphery or at contact sites) are clearly over-interpreted in the absence of other membrane or autophagosome specific labeling and statistical colocalization analyses of multiple images. It is particularly difficult to assess any difference between Tax1BP1, p62 and NBR1 localization on mitochondria subdomains.
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- First part of results ended without any conclusions.
- The observation that "TBK1 phosphorylation was not apparent in the Optn mutant cell lines, even after 3 hrs of valinomycin, ..." is inconsistent with detection of bands with anti-pS172-TBK1 antibodies in Fig 2a detected at 1hr (with F178A) and 3 hrs (4LA, F178A, and 4LA/F178A mutants) of treatment.
- Similarly, decreased levels of phosphorylated TBK1 stated for F178A mutant was only observed at 1 but not 3hrs or at 3hrs in the presence of bafilomycin.
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The results and their repartition between figure 2 d, e, f, g, h, I and figure 3 is a bit confusing. In these experiments, it is shown Figure 2 that the absence or depletion of the autophagy machinery increase the phosphorylation of TBK1 and in Figure 3 it is shown that not only the phosphorylation of TBK1 accumulate but also the expression of NDP52, Tax1BP1 and p62. Is it because their degradation by autophagy is blocked (like for phosphoTBK1)?
Fig 2c: conclusions on the reduction of recruitment of Optn mutants on autophagosome formation seem over-interpreted as:
1- no labeling with LC3 has been used to identified autophagsome,
2- immunofluorescent signals observed with mutants are dispersed throughout the entire mitochondria network (see the merged images) rendering impossible to distinguish between autophagosome-associated mitochondria and others.
The following conclusive sentence stating that association of Optn to damaged mitochondria is not sufficient for TBK1 activation based solely on IF of figure 2c seems therefore unrelated to the obtained data.
Fig 2d: authors should explain why ATG KO cells displayed lipidated LC3B in the absence of efficient autophagy processes.
Fig 2e: despite authors statement that TBK1 phosphorylation did not increase during mitophagy in ATG KO cells, increased pS172-TBK1 is visible in FIP200 and ATG5 KO cells especially between 1 and 3 hrs of stimulation, leading to inaccurate conclusions that TBK1 phosphorylation requires the autophagy machinery. Therefore, overall assumption that both ubiquitination and autophagy subunits are required for TBK1 autophosphorylation appears erroneous.
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- Fig 3a: conclusion that Optn signal is more cytosolic and did not localize with Ub condensates seems speculative as based on:
1- only one immunofluorescence image without statistical analysis
2- Optn and Ub signals are lower in images with Optn is analyzed compared to other images in which NDP52, TAX1BP1 and NBR1 are detected.
Fig 3b: interpretation of western blot data is uncertain due to lack of appropriate loading control, especially with pellets (P) extracts. In addition, it is not clear how to conclude from the experiments in Fig 3b that autophagy adaptors other than Optn mediate TBK1 phosphorylation. Minor point: reference is missing in the last sentence of the paragraph stating that K48-linked chains dominate when autophagy pathways are impaired.
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Conversaly to Optn, they find that the other autophagic receptors localize in insoluble fractions (what does it mean?) independently of TBK1 expression (experiments with DKO cells) and also independently of Optn (where is this shown?). Altogether, these experiments are far from the message of the manuscript. The title of the paragraph "TBK1 activation does not require Optn under basal autophagy conditions" is not correct because even if the level of expression of autophagic receptors and TBK1 phosphorylation are increase in response to the depletion of the autophagy machinery, it does not increase autophagy.
Fig 3d: authors should mention the nature of the upper band observed in Optn western blot and show the same experiment in since solely TBK1 depleted cells since they stated that "electrophoretic migration of Optn was not affected by TBK1 deletion". In addition, suggesting from these sole experiments that "NP52, TAX1BP1, p62, NBR1 and AZI2 form Ub-positive condensates where TBK1 is activated" seems over-interpretated.
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- Fig 4: TBK1 phosphorylation was analyzed in Fig4d and not in Fig4b as stated. In addition, it is rather difficult to conclude from artificial multimerization experiments, as the authors have done, that interaction between Optn and autophagy components contributes to Optn multimerization in genuine conditions.
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This work could have therapeutic consequences but the pathological mutants of TBK1 used affect ALS (Figure 5) while in the discussion it is proposed that monobodies could have a therapeutic interest in familial forms of glaucoma due to the E50K mutation of Optn. It should be better to target only one pathology.
Fig 5c, d: Authors stated that degree of TBK1 autophosphorylation correlated with OPTN phosphorylation at S177 whereas phosphorylated TBK1 is unaffected by L693Q and V700Q mutants that display decreased phosphorylated Optn In addition, authors interpretation of Figure 5 data is clearly problematic as they stated that:
1- neither 693Q and V700Q mutants had "significant effect on mitophagy", while decreasing efficiency from 78% to 37-51%
2- but conclude that 49.7% mitophagy levels of R357Q mutant is significant mitochondrial degradation. Overall conclusion that mitophagy efficiency is correlated with phosphorylated TBK1 levels is therefore inaccurate.
Discussion
Minor points:
page 20: - reference is missing in the sentence "Optn cannot oligomerize on its own on ubiquitin-decorated mitochondria".
Major points:
Authors stated that they showed that Optn recruitment to damaged mitochondria, itself, is insufficient for TBK1 autophosphorylation, but did not show experiment of Optn recruitment to mitochondria and its consequences on TBK1 phosphorylation in the absence of mitophagy induction signal. Authors could for example target HA-Ash-6Ub to mitochondria in order to artificially recruit hAG-Optn to "ubiquitinated" mitochondria in the absence of mitophagy signal.
Similarly, experimental approaches used by authors lack dynamics parameters to conclude on formation and elongation of isolation membranes and contacts sites that could be probably obtained through video microscopy.
Finally, the model proposed by the authors does not take into account data showing that Optn basally interacts with ubiquitinated mitochondria and LC3 family members (see Wild et al., Phosphorylation of the autophagy receptor optineurin restricts Salmonella growth. Science. 2011 333:228-33), although at lower levels compared to induced conditions, relativizing the impact of the proposed model.
In conclusion, this manuscript represents a lot of work but the experiments often lack controls and are over-interpretated.
Referees cross-commenting
In my opinion, what emerges from these 3 reviews is that the results lack controls or have not been repeated enough to support the message that the interaction of Optn with ubiquitin and the ubiquitination machinery is sufficient to activate TBK1. In particular, as reviewer 1 says, the phosphorylation kinetics shown in Figure 1a are not consistent with TBK1 phosphorylation following the interaction of Optn with the ubiquitination machinery and ubiquitin. In Figure 1e, there is a decrease in TBK1 phosphorylation in contrast to WTcells as mentioned by Reviewer 1. In agreement with Reviewer 1, we believe that the WT cells are missing in Figure 1g. With regard to Figure 2c, we agree with reviewer 1 that an LC3 label is missing in order to be able to interpret the data. In Figure 2e and f, we agree with reviewer 1 that it is difficult to understand why TBK1 phosphorylation increases in the absence of the autophagy machinery (FIP200 KO and ATG5KO). In Figure 3, loading controls are missing for 3b and c. The TBK1 KO cells alone are missing in Fig 2d. In Figure 2b, pTBK1 is missing. In agreement with reviewer 3, we believe that the data with fluoppi contradict the message of the manuscript since they show that TBK1 can be phosphorylated by ubiquitin in the absence of the ubiquitination machinery. In agreement with reviewer 3, we believe that the experiments in Figure 5 are very difficult to interpret. The first reviewer is right to ask the question of the replicates for figures 6c and d.
Significance
This manuscript represents a lot of work but the experiments often lack controls and are over-interpretated. The manuscript is for a broad audience.
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Referee #1
Evidence, reproducibility and clarity
In their study, Yamanato et al. dissect the mechanism of TBK1 activation and downstream effects, especially in its relation to mitophagy adaptor OPTN. The authors find that OPTN's interaction with ubiquitin and the autophagy machinery, forming contact sites between mitochondria and autophagic membranes, results in TBK1 accumulation and subsequent autophosphorylation. Based on these findings, the authors propose a self-propagating feedback loop wherein OPTN phosphorylation by TBK1 promotes recruitment and accumulation of OPTN to damaged mitochondria and specifically the autophagosome formation site. This formation site is then involved in TBK1 autophosphorylation, and the activated TBK1 can then further phosphorylate other pairs of OPTN and TBK1. A OPTN monobody investigation strengthens their findings.
Critique:
- It would be helpful if the authors could more clearly highlight the previous findings in OPTN-TBK1 relationship and which gaps in the understanding their study addresses.
- It is not always clear whether experiments have been replicated sufficiently; this should be indicated in the figure descriptions.
- During the discussion, references to the figures that indicate conclusions should be added where appropriate.
Figure 1 / Result "OPTN is required for TBK1 phosphorylation and subsequent autophagic Degradation":
- In a) the TBK1 and TOMM20 blots feature an image artefact that makes it appear like the blots are stitched together or there was a problem with the digital imager. The quantification in b) seems to be missing replications.
- g) should feature the wt cell line on the same blot for better comparability as well as quantification and replication like done in f)
- h) is missing the blots for controls actin and TOMM20
- In the text to e/f), the authors write that NDP52 KO effect on pS172 are comparable to controls, though the quantitation in f) indicates that pS172 signal is indeed significantly reduced compared to wt
- In the text to h/i), the authors write "there was a significant increase in the TBK1 pS172 signal in cells overexpressing OPTN", though the quantification in i) does not indicate significance levels
Figure 2 / Result "OPTN association with the autophagy machinery is required for TBK1 activation":
- In b), pTBK1 at val 1 hr only features one dot/experiment per cell line
- In the text to c), the authors claim that the mutants reduce/abolish the recruitment of OPTN to the autophagosome site. A costain for LC3, as done for SupFig 1b, would be necessary to support that specific claim.
- d) and g) as simple confirmations of KO/KD efficiency might be better suited for the supplemental part, or blots for FIP/ATG be included with the blots in e) and h)
- In the text to e), the authors claim that the levels of pS172 in the KO cell lines did not increase during mitophagy, though the blot and quantification in f) seem to indicate an increase. The results therefore don't seem to align completely with the claims that pS172 generation in response to mitophagy requires the autophagy machinery, or that FIP200 and ATG9A rather than ATG5 are critical for TBK1 phosphorylation.
- f) is missing significance indications. Its description has a typo: "bad" instead of "baf"
Figure 3 / Result "TBK1 activation does not require OPTN under basal autophagy conditions":
- In the text to SupFig2, the authors claim that pS172 levels are significantly elevated, but no significance levels are indicated
- In the text to a), NBR1 is claimed to colocalize with Ub, but no costaining with Ub is shown. The claimed lacking colocalization of OPTN with Ub is not obvious from the images; a quantification might be appropriate.
- In the text to b), the authors make reference to significant changes, but replication/quantification/significance testing is missing.
Figure 4b) is missing the pTBK1 data that is referenced in the text.
In the text to figure 5 c/d), the authors claim that certain mutants have no significant effect on mitophagy, though d) is missing significance testing
Figure 6 c/d/i) appear to be missing replication.
Significance
Removal of damaged mitochondria by the mitophagy pathway provides an important safeguarding mechanism for cells. The Pink1/Parkin mechanism linked to numerous modulators and adaptor proteins ensures an efficient targeting of damaged mitochondria to the phagophore. The Ser/Thr kinase TBK1, in addition of multiple roles in innate immunity, is a major mitophagy regulator as has been revealed by the Dikic and Youle groups in 2016 (Richter et al., PNAS). The mechanistic insights provided by this manuscript add to a growing body of studies of how the autophagy machinery interconnects with cellular signalling networks. Although parts of the results need to be further validated, the data shown is of high quality, revealing an important conceptual advance. The paper is interesting and of general relevance beyond the signalling and autophagy community.
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Reply to the reviewers
We thank the reviewers for their positive comments on our manuscript with the reviewer’s cross-commenting:
‘It is a scholarly work that makes a significant contribution detailing how network entropy and curvature relate in the context of biological networks.’
While reviewer #2 further comments:
‘This is an important paper…it will be of interest to a wide range of scientists that are involved in stem cell differentiation and biological networks.’
We also thank both reviewers for their constructive and insightful comments. In what follows we present a point-by-point response to the reviewer comments.
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Contrary to earlier studies, which proposed a positive correlation between network entropy and the total discrete curvature of biological networks, the paper proves that the network entropy and their defined total Forman-Ricci curvature are not guaranteed to be positively correlated. In the case of cell differentiation networks, a positive correlation is likely across highly promiscuous signalling regimes such as stem cells. While a negative correlation is likely for deterministic signalling (differentiated cells). Additionally, they found that cancer cells have a higher network entropy, but lower total Forman-Ricci curvature compared to healthy differentiated cells. This contrasts with stem cells, where both network entropy and total Forman-Ricci curvature are higher than healthy differentiated cells. They demonstrated this on the k-star network as a toy example, as well as different real-world biological datasets (embryonic stem cell differentiation, melanoma patients, and colorectal cancer patients) where the Forman-Ricci curvature and the network entropy follow two distinct regimes.
Finally, they apply their defined normalized discrete Ricci flow to predict the intermediate time-points of cellular differentiation by deriving the biological network rewiring trajectories based only on the first and last time points of time courses of cellular differentiation. Their approach accurately predicted intermediate time-points compared to the null Euclidean model, where a straight-line trajectory is considered. The proposed Ricci flow correctly orders the differentiation time courses during both the embryonic stem cell (ESC) differentiation and myoblast differentiation.
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The paper is well-written and easy to follow.
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The paper presents that the network entropy and the FRC do not always result in a positive correlation. This has been shown in a network toy example and several real-world cellular differentiation datasets.
We thank the reviewer for their positive and accurate summary of our manuscript’s results.
- [p.8-9, {section sign}2.1.2] "We note that these studies also employ slightly different constructions of Forman-Ricci curvature than our own." The Forman-Ricci curvature is formulated by defining the edge weights and node weights as in {section sign}2.1.2/{section sign}4.2.
- The node weights are defined as the reciprocal of the node degree. It is explained that this is "to normalize the sums in (4), preventing connected high-degree vertices from dominating the total FRC." Does this pay less importance to hub nodes? Is this consistent with the cellular differentiation biological application?
The reviewer raises the important point that computation of edge-wise Forman-Ricci Curvature (FRC) defined in eq. 4 requires stipulation of node and edge weights, which must be carefully defined to match the context of our problem of cellular differentiation.
We specify the edge weights to match the computation of network entropy (Banerji et al., Sci. Rep., 2013), which is based on a mass-action principle assumption. This assumes that the rate of reaction between two interacting proteins is proportional to the product of the concentration of the reactants. We assume that gene expression is a proxy for protein concentration and define interaction strength between nodes i and j by x_i*x_j, where x_i is the expression of gene i. In the context of edge-wise FRC, edge weights equate to a distance, and so nodes which have a strong interaction should have a low edge weight, implying close proximity. Hence we define edge weights for FRC as a_ij/x_i*x_j, where a_ij is the (i,j)^{th} element of the protein interaction network adjacency matrix.
For node weights we selected the reciprocal of the node degree as the reviewer recognises. The FRC computed on an edge (i,j) is essentially a pair of sums over edges connected to nodes i and j, with each sum normalised by the corresponding node weight W_i and W_j. The number of elements in each sum is deg(i) and deg(j) respectively. Thus choosing W_i as anything other than 1/deg(i), introduces a node degree bias to the edge-wise curvature measure.
Rather than paying less importance to hub nodes as the reviewer suggests, by selecting W_i=1/deg(i) we are ensuring that node degree does not alter the edge-wise Forman-Ricci curvature, and equal importance is paid to all nodes. This property is essential in assessing the correlation between network entropy and network average Forman-Ricci curvature, as it prevents trivial correlation.
To elaborate, to compute network average FRC we introduce a node wise FRC (eq. 24) as the mean edge-wise FRC over edges connected to a given vertex. Network average FRC (eq. 25) is then defined as a weighted sum over node averaged FRCs, where the weights employed are elements of the stationary distribution of P=(p_ij)_{ij \in V}. This construction mirrors the calculation of network entropy, where node-wise entropies are first computed (eq. 7) and an identical weighted sum over node-wise entropies (using the stationary distribution) defines network entropy.
If we consider eq. 7 for computing the node wise entropy of node i:
S_i = - \sum_{j \in V} p_{ij} log(p_{ij})
we see that S_i is bounded between [0,deg(i)]. Hence the node-wise of elements of the sum defining network entropy have a degree dependence. If we employ a different node weight in our definition of edge-wise FRC, we ensure that the node-wise elements of network average FRC also have a degree dependence. Such a dependency could lead to a trivial correlation between network entropy and network average Forman-Ricci curvature, confounding our results.
The reviewer makes the important observation that node degree often correlates with importance in biological networks and should be considered in a network average measure. We emphasise, that though we are removing degree dependency in the computation of edge-wise FRC, by using the stationary distribution for our network average FRC we re-introduce the importance of hub nodes (as high degree/strength nodes will have higher stationary distribution probabilities).
Thus, to answer the two questions raised by the reviewer: (1) The choice of node weights in eq. 4 ensure that edge-wise FRC is independent of node degree and does not pay more or less importance to hub nodes, this prevents trivial correlation between network average FRC and network entropy. (2) The fact that higher degree nodes are more biologically relevant is respected by the use of the stationary distribution in calculation of network average FRC, which gives higher weighting to hub nodes, without introducing the possibility of trivial correlation with network entropy.
We thank the reviewer for these important questions and will clarify this in an updated manuscript.
- How is the Forman-Ricci curvature/flow definition in earlier works applied to biological networks different? How does it impact the current conclusions if such FRC definitions were used?
Previous works investigating Forman-Ricci curvature in biological networks have defined edge-wise FRC using different node and edge weights, with various justification. All methods investigating gene expression data have used the same method to convert edge-wise FRC to network average FRC.
In our manuscript, for reasons explained above, we defined edge weights as \omega_{ij}=a_{ij}/(x_i*x_j) and node weights as W_i=1/deg(i). As with all prior studies we then compute node average FRCs and define network average FRC via a stationary distribution weighted sum of node-wise FRCs.
Murgas et al., Complex Networks & Their Applications X, 2021 defined edge weights via \omega_{ij}=x_i*x_j and node weights via W_i=x_i. Network average FRCs were computed via the same method we employ. The authors demonstrate a positive correlation between network average FRC and network entropy on stem cells. We note, however, that as the node weights do not depend on the node degree, the edge-wise FRCs have a degree dependence, leading to a possibly trivial correlation with network entropy as explained above. We further note that in this construction edge weights cannot be considered as “distances” between nodes, as they are large when interaction propensity is high and vice versa. This leads to a conceptual difficulty in interpreting the curvature. Finally, as the node weight depends on a temporal variable (gene expression), this construct is incompatible with a computable Ricci flow. To elaborate, at each time step of the Ricci flow equation we must compute all updated edge-weights and all updated edge-wise FRCs. However, the Ricci flow equation is defined on edges and thus produces a system of |E| equations at each time point, allowing us to solve for a maximum of |E| parameters. If the node weights also change over the time step, to compute the updated edge-wise FRCs, we will need to solve for |E|+|V| variables, which the system is insufficient to achieve. Thus the FRC defined by Murgas et al., 2021 is not suitable for our investigation as it may introduce trivial correlation with network entropy, is not well defined as a curvature in the biological context and is not compatible with a computable Ricci flow.
Murgas et al., Sci Rep. 2022 employed a different construction with edge weights \omega_{ij}=a_{ij}/(p_{ij}*deg(i) and node weights again W_i=x_i. Network average FRCs were computed via the same method we employ. The authors demonstrate a negative correlation between network average FRC and network entropy on stem cells. The fact that deg(i) is incorporated in the edge weight definition makes the degree dependence of edge wise FRC non-trivial, however the choice of node weights independent of degree still introduces the possibility of a correlation with node degree, which could confound any correlation with network entropy. The explanation given for a negative rather than positive correlation observed in this study, was the selection of edge weights, which the authors’ chose to better represent a ‘distance’ between nodes. While this permits a more well defined, intuitive curvature, we have shown here that both positive and negative correlations are possible with edge weights defined to represent distances. We also note that as the node weight is again a temporal variable, computation of a Ricci flow using these parameters is intractable.
Other studies investigating FRC in biological networks (e.g., Weber et al., Journal of Complex Networks, 2016) do so in a different context and do not consider single sample gene expression weighting, and so cannot be directly compared to our approach.
To our knowledge no other study has employed a Forman-Ricci flow on a biological network, but Forman-Ricci flow has been employed in the context of the Gnutella peer-to-peer file sharing network (Weber et al., Journal of Complex Networks, 2016). The major difference between the flow implemented by the authors and the one we present is that it was not normalised, but rather evolved the network towards a flat zero curvature. This would be suboptimal for our goal, which is to infer rewiring from one biological state to the another. We note that the concept of a normalised Ricci flow was introduced by Weber et al., 2016, but was not implemented in their example.
Other studies have employed Ollivier-Ricci curvature in the investigation of gene expression weighted biological networks, including using phenotype average curvatures (Sandhu et al Sci. Rep. 2015) and single sample curvatures (Elkin et al., NPJ Genom Med, 2021). We note that Ollivier-Ricci curvature is significantly more computationally expensive than Forman-Ricci curvature, and for our Ricci flow we must compute ~150,000 curvatures per time-step making Ollivier-Ricci curvature impractical. While Ollivier-Ricci curvature and Forman-Ricci curvature have been compared on biological networks, with some overlap in conclusions, the correlation between the two measures is not strong and our results cannot be generalised to cover this measure (Pouryahya et al., 2021, https://arxiv.org/abs/1712.02943).
We will update the discussion of our manuscript with details of these prior studies.
- Along these lines and especially considering the importance of curvature plays in network science and biology, the authors have to better discuss the existing work in developing various differential geometry approaches for molecular and system biology such as: "Ollivier-ricci curvature-based method to community detection in complex networks." Scientific reports 9, no. 1 (2019): 9800. "Inferring functional communities from partially observed biological networks exploiting geometric topology and side information." Scientific Reports 12, no. 1 (2022): 10883.
We thank the reviewer for this suggestion and will include a discussion of these studies in the updated manuscript.
- [{section sign}2.3, Fig. 3cd] For the cancer datasets shown in Fig. 3, I believe Pearson's r is shown for all the samples (malignant and healthy cells). It is mentioned that for cancerous cells, the network entropy is higher while the total FRC is lower. Is there a significant difference in correlation values if the healthy and cancerous cells are considered separately?
We thank the reviewer for this suggestion and indeed there is a difference in this correlation when healthy and cancerous samples are considered separately. Healthy samples have a stronger negative correlation between network entropy and total FRC than cancerous samples. Our theoretical results show that as intracellular signalling becomes less deterministic the correlation between our two measures becomes less negative. The findings of the analysis suggested by the reviewer, therefore supports the conclusion that intracellular signalling becomes less deterministic during oncogenesis.
For Figure 3C across control (healthy) samples there is a significant negative correlation between network entropy and total FRC (n=3256, Pearson’s r=-0.83, p-16), while there is no correlation between the two measures across melanoma (cancerous) samples (n=1257, Pearson’s r=-0.009, p=0.76). Using Fisher’s z-transformation to compare these correlations we find a highly significant difference (p-16).
For Figure 3D, the difference in more subtle. Across control (healthy) samples there is significant negative correlation between network entropy and total FRC (n=160, Pearson’s r=-0.90, p-16), while across colorectal cancer samples the negative correlation is slightly weaker (n=272, Pearson’s r=-0.83, p-16). Using Fisher’s z-transformation to compare these correlations we again find a significant difference (p-3).
We will include these results in an updated manuscript.
- [Methods, p.23] "For each gene expression time course we implemented one time step of the Ricci flow from the first time point $\mathbf{x^0}$ using each $\Delta t$ value and selected the optimal $\Delta t$ as the largest which does not admit negative values of $d_{0+\Delta t}$..." It seems that choosing the optimal $\Delta t$ is a heuristic; I wonder how sensitive is the proposed framework based on the choice of $\Delta t$ in accurately identifying the trajectory. i.e., assuming that the choice of $\Delta t$ does not admit to negative d values, are there choices of $\Delta t$ that are still too large that it does not correctly identify the intermediate time points?
The reviewer raises an important point on the selection of the parameter \Delta t in our Ricci flow equation. Selection of the optimal \Delta t is a trade off. Too small a \Delta t means many time-steps before convergence and the computational cost of implementing our flow (requiring the calculation of 150,000 Forman-Ricci curvatures per time step) becomes unmanageable. While too large a \Delta t may mean too coarse grain an approximation, inhibiting inference of the true underlying continuous network rewiring trajectory.
As the reviewer notes we are also subject to the constraint that edge weights (which in the context of Ricci flow correspond to distances) must be non-negative. If \Delta t is sufficiently small this guarantees non-negative edge weights are output from the Ricci flow. The precise \Delta t which prevents negative edge weights depends on the differences between the edge wise FRCs of the network at each time point and those of the normaliser. As these differences get smaller as we progress the flow and converge to the normaliser, we can determine the optimal \Delta t from the first time step as:
1/min_{(i,j)\in E}(Ric_{(i,j)}(x^0) - \bar{Ric_{(i,j)})
This is the largest possible value of \Delta t we can use in our framework without introducing negative values, and thus the \Delta t most likely to inhibit our approach from approximating the true intermediate time points.
In our paper, rather than using this formula we empirically searched for the largest \Delta t which admits only positive values. This was done by considering a range of possible values of \Delta t calculating one step of the Ricci flow equation for each value and identifying the largest \Delta t for which the smallest updated edge weight was non-negative. For both our time courses this identified \Delta t=0.06 as optimal, while the above formula gives maximal values for \Delta t at between 0.06 and 0.065.
The results we present are therefore calculated using very close to the maximum value of \Delta t possible in our framework and as the reviewer comments we
‘accurately predicted intermediate time-points compared to the null Euclidean model’
using this \Delta t.
Thus to answer the reviewer’s question, subject to the constraint of non-negative d values, there does not appear to be a choice of \Delta t which is too large to correctly identify the intermediate time points.
We will clarify this point in the updated manuscript.
Reviewer #1 (Significance (Required)):
Contrary to earlier studies, which proposed a positive correlation between network entropy and the total discrete curvature of biological networks, the paper proves that the network entropy and their defined total Forman-Ricci curvature are not guaranteed to be positively correlated.
We thank the reviewer for their kind and positive comments on our work.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
The paper brings in new insights, and corrects earlier ones, on how certain measures quantify structure and functionality of biological networks. It starts with the hypothesis that network entropy is a proxy for 'height' in Waddington's Landscape, with higher values on stem cells and malignant cells compared to healthy ones. In this context, it explores different notions of network curvature (one in particular, Forman-Ricci, in depth) in how it correlates with entropy for various types of networks. It draws a potentially transformative conclusion that promiscuity in signalling, that characterizes less differentiated and malignant cell, is also a key factor in how curvature and entropy relate to each other. Specifically, it observes for cases, and explains with an academic example that correlation between network curvature and entropy may change sign as e.g., stem cells differentiate.
It supports the conclusion that Forman-Ricci curvature has indeed a great biological relevance, and that curvature and entropy are complementary, and not interchangeable measures of cell potency, as suggested in earlier studies. It also hypothesizes that a version of Ricci flow (a dynamical model for how networks change in the strength of interactions and correlations; or, the metric in continuous spaces) correctly captures likely trajectories in cell differentiation. This provides an additional argument that highlights the importance of curvature as some sort of driving potential that effects, and is defined by, structural changes in biological networks.
The paper is well written, with a fairly accessible account of pertinent mathematics, in the body of the paper and the materials and methods. The component on the analysis of data is also well explained and supported. Minor suggestion, please point to the formula for S_R in the materials and methods when it is first referred to in the paper.
We thank the reviewer for their kind comments on our paper and will point to the formula for S_R as suggested in the updated manuscript.
**Referees cross-commenting**
It is a scholarly work that makes a significant contribution detailing how network entropy and curvature relate in the context of biological networks
We thank both referees for their kind and positive assessment of our work.
Reviewer #2 (Significance (Required)):
This is an important paper that explores in a systematic way certain mathematical concepts, as indicators of biological network functionality, to distinguish stages in cell differentiation that may be significant in understanding e.g., malignant versus healthy cells and the flow in stem cell differentiation. It supports its conclusions with an academic example as well as analysis of biological data sets. It draws a balanced view on how entropy and curvature relate and gives an accessible account to the relevant mathematics.
It will be of interest to a wide range of scientists that are involved in stem cell differentiation and biological networks.
We thank the referee for their kind words on our work and for emphasising the broad range of interest.
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Referee #2
Evidence, reproducibility and clarity
The paper brings in new insights, and corrects earlier ones, on how certain measures quantify structure and functionality of biological networks.
It starts with the hypothesis that network entropy is a proxy for 'height' in Waddington's Landscape, with higher values on stem cells and malignant cells compared to healthy ones. In this context, it explores different notions of network curvature (one in particular, Forman-Ricci, in depth) in how it correlates with entropy for various types of networks. It draws a potentially transformative conclusion that promiscuity in signaling, that characterizes less differentiated and malignant cell, is also a key factor in how curvature and entropy relate to each other. Specifically, it observes for cases, and explains with an academic example that correlation between network curvature and entropy may change sign as e.g., stem cells differentiate.
It supports the conclusion that Forman-Ricci curvature has indeed a great biological relevance, and that curvature and entropy are complementary, and not interchangeable measures of cell potency, as suggested in earlier studies. It also hypothesizes that a version of Ricci flow (a dynamical model for how networks change in the strength of interactions and correlations; or, the metric in continuous spaces) correctly captures likely trajectories in cell differentiation. This provides an additional argument that highlights the importance of curvature as some sort of driving potential that effects, and is defined by, structural changes in biological networks.
The paper is well written, with a fairly accessible account of pertinent mathematics, in the body of the paper and the materials and methods. The component on the analysis of data is also well explained and supported. Minor suggestion, please point to the formula for S_R in the materials and methods when it is first referred to in the paper.
Referees cross-commenting
It is a scholarly work that makes a significant contribution detailing how network entropy and curvature relate in the context of biological networks
Significance
This is an important paper that explores in a systematic way certain mathematical concepts, as indicators of biological network functionality, to distinguish stages in cell differentiation that may be significant in understanding e.g., malignant versus healthy cells and the flow in stem cell differentiation. It supports its conclusions with an academic example as well as analysis of biological data sets. It draws a balanced view on how entropy and curvature relate, and gives an accessible account to the relevant mathematics.
It will be of interest to a wide range of scientists that are involved in stem cell differentiation and biological networks.
-
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Referee #1
Evidence, reproducibility and clarity
Contrary to earlier studies, which proposed a positive correlation between network entropy and the total discrete curvature of biological networks, the paper proves that the network entropy and their defined total Forman-Ricci curvature are not guaranteed to be positively correlated. In the case of cell differentiation networks, a positive correlation is likely across highly promiscuous signaling regimes such as stem cells. While a negative correlation is likely for deterministic signaling (differentiated cells). Additionally, they found that cancer cells have a higher network entropy but lower total Forman-Ricci curvature compared to healthy differentiated cells. This is in contrast to stem cells, where both network entropy and total Forman-Ricci curvature are higher than healthy differentiated cells. They demonstrated this on the k-star network as a toy example, as well as different real-world biological datasets (embryonic stem cell differentiation, melanoma patients, and colorectal cancer patients) where the Forman-Ricci curvature and the network entropy follow two distinct regimes.
Finally, they apply their defined normalized discrete Ricci flow to predict the intermediate time-points of cellular differentiation by deriving the biological network rewiring trajectories based only on the first and last time points of time courses of cellular differentiation. Their approach accurately predicted intermediate time-points compared to the null Euclidean model, where a straight-line trajectory is considered. The proposed Ricci flow correctly orders the differentiation time courses during both the embryonic stem cell (ESC) differentiation and myoblast differentiation.
- The paper is well-written and easy to follow.
- The paper presents that the network entropy and the FRC do not always result in a positive correlation. This has been shown in a network toy example and several real-world cellular differentiation datasets.
- [p.8-9, {section sign}2.1.2] "We note that these studies also employ slightly different constructions of Forman-Ricci curvature than our own." The Forman-Ricci curvature is formulated by defining the edge weights and node weights as in {section sign}2.1.2/{section sign}4.2.
- The node weights are defined as the reciprocal of the node degree. It is explained that this is "to normalize the sums in (4), preventing connected high-degree vertices from dominating the total FRC." Does this pay less importance to hub nodes? Is this consistent with the cellular differentiation biological application?
- How is the Forman-Ricci curvature/flow definition in earlier works applied to biological networks different? How does it impact the current conclusions if such FRC definitions were used?
- Along these lines and especially considering the importance of curvature plays in network science and biology, the authors have to better discuss the existing work in developing various differential geometry approaches for molecular and system biology such as: "Ollivier-ricci curvature-based method to community detection in complex networks." Scientific reports 9, no. 1 (2019): 9800. "Inferring functional communities from partially observed biological networks exploiting geometric topology and side information." Scientific Reports 12, no. 1 (2022): 10883.
- [{section sign}2.3, Fig. 3cd] For the cancer datasets shown in Fig. 3, I believe Pearson's r is shown for all the samples (malignant and healthy cells). It is mentioned that for cancerous cells, the network entropy is higher while the total FRC is lower. Is there a significant difference in correlation values if the healthy and cancerous cells are considered separately?
- [Methods, p.23] "For each gene expression time course we implemented one time step of the Ricci flow from the first time point $\mathbf{x^0}$ using each $\Delta t$ value and selected the optimal $\Delta t$ as the largest which does not admit negative values of $d_{0+\Delta t}$..." It seems that choosing the optimal $\Delta t$ is a heuristic; I wonder how sensitive is the proposed framework based on the choice of $\Delta t$ in accurately identifying the trajectory. i.e., assuming that the choice of $\Delta t$ does not admit to negative d values, are there choices of $\Delta t$ that are still too large that it does not correctly identify the intermediate time points?
Significance
Contrary to earlier studies, which proposed a positive correlation between network entropy and the total discrete curvature of biological networks, the paper proves that the network entropy and their defined total Forman-Ricci curvature are not guaranteed to be positively correlated.
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
This work by Bruckman et al. showed how sperm can induce somatic cell syncytium upon binding. This finding has tremendous implications for the field. Although gamete fusion is one of the fundamental events in biology, the molecular details of this process in mammals is not well understood. As briefly mentioned in this work, spermatozoa contribute Izumo1 which forms a complex with SPACA6 and TMEM81; and probably with other proteins. On the other hand, the egg counterpart is JUNO, a GPI- anchored protein, and CD9. IZUMO1 and JUNO have been shown to interact and this interaction is essential for fusion. However, it is still not clear the extent that this binding is needed for attachment of for fusion between gametes. The authors have published before that somatic cells expressing IZUMO1 can fuse with other cells expressing JUNO; and that sperm can fuse to JUNO-expressing cells. This previous work strongly suggests that IZUMO and JUNO are sufficient for fusion; however, the efficiency of the fusion assay was low. In the present work, formation of syncytium is highly efficient, making possible to envision applications of this assay to research of the proteins involved in sperm-egg fusion as well as for molecular mechanisms. In addition, as mentioned in this work, the assay can be translated for the use for clinical diagnosis.
- We would like to thank the reviewer for the insightful comments that improved our manuscript. Furthermore, we appreciate the acknowledgment of the value of our findings for the field. Overall, the manuscript is straightforward, and the conclusions are sound. I have only a couple of comments:
1) As mentioned in this work, fusion of human sperm with hamster eggs was used in the past to evaluate human sperm fusogenic properties. Although this assay is not longer recommended, still highlight the fact that hamster egg's oolemma is promiscuous regarding species-specific fusion. Although I don't think the experiment is essential, if it were possible, trying hamster IZUMO1 in this assay will be a good addition to this manuscript.
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We have performed this experiment and it is now included in the revised manuscript (Figure 4). As discussed in the new version, hamster JUNO is as efficient as its murine counterpart in facilitating SPICER, confirming the well-known promiscuous nature of hamster eggs. 2) Is it possible to know how many sperm form part of the syncytium>?
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We have included in the revised manuscript a Figure showing the number of sperm fused in syncytia of different sizes (Figure S1). 3) Although I understand the relevance of this assay for diagnostic use, still the major contribution of this manuscript is to the sperm-egg fusion field of study. I encourage the authors to start the discussion with a more basic approach and add at least one paragraph summarizing how their discovery advanced the field. From my perspective the translational application should be at the end of the Discussion section, not at the start.
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We have modified the Introduction and Discussion sections according to this comment. In addition, we are now including new data further supporting that the SPICER assay can be used to predict sperm fertilizing capacity (Figure 3). **Referee Cross-Commenting**
The syncytia described in this work support the hypothesis that once Izumo and partners in sperm interact with Juno, the cells become able to undergo fusion events. It does not claim that this type of event happens in vivo. Although Juno is expressed in other cell types and not only in the egg, it is too soon to claim a physiological role for syncytia formation in vivo.
I agree with reviewer 2 comments. Both experiments proposed (paragraph 2 and 3) are relatively straightforward.
In general, I also agree with reviewer 3 comments, one of the experiments proposed is similar to the one proposed by reviewer 2. Having said this, I disagree that the human sperm experiment is needed for this paper to be significant. Although the diagnostic claim is important, the major contribution of this work is at the basic research level. Toning down the clinical relevance would be sufficient to make this paper a significant contribution to the field.
Mechanisms of sperm-egg fusion have been elusive for the last decades. In my view, syncytia formation and I don't expect a single paper to elucidate such a complex event.
Reviewer #1 (Significance (Required)):
General Assessment: This is an excellent manuscript contributing to our understanding of sperm-egg fusion. In addition, the tools described here warrant new approaches to study the molecules involved in this process using genetically modified mouse models.
Advance: Although the relevance of IZUMO1 and JUNO for sperm-egg fusion have been previously reported, as far as I know, this is the first study showing somatic cell fusion and syncitium formation using sperm and somatic cells. This finding will provide new tools to study the molecular basis of sperm-egg fusion as well as generate translational tools to evaluate human sperm ability to fertilize.
Audience: I believe that this manuscript will have broad interest. Although initially, the main audience will be related to reproductive biologists, this manuscript will be also highly relevant for other scientists studying fusion. In addition, this work has clear translational applications.
Keywords describing my expertise: sperm, eggs, in vitro fertilization, reproductive biology, embryos
We are pleased and grateful to read this reviewer’s comments. We have now implemented the suggestions in the revised version.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
This is a very nice manuscript that continues previous work of the lab. Authors in this manuscript goes further, showing that proteins from the plasma membrane of sperm end up in somatic cells plasma membrane after fusing, conferring them fusogenic capacity. The manuscript is very elegantly written, and of interest to the reproductive community. I have only some minor concerns to be addressed:
- We would like to thank the reviewer for the positive feedback as well as for the valuable suggestions.
- The authors indicate that this test "can help embryologists to better predict the success of the different ARTs for each individual.". This assumption should be taken as it is, an assumption. In order to state that in can be used, authors should first demonstrate, maybe with a correlation test hand by hand with a reproductive clinic, that this procedure indeed works for diagnosis. There is no doubt that it is a very nice proof of principle.
- We have changed the Introduction and Discussion to be more cautious about this point. Unfortunately, we currently do not have permission to work with human samples, however, we have added new data showing a positive correlation between the extent of cell-cell fusion and the fertilizing potential of mouse sperm (Figure 3).
Fig S1C. Although I agree with the ayuthors that the two models are possible, have the authors tried to wash sperm away from the fusing assay, and add new cells, maybe differentially tagged, to see whether they fuse to those already fused with sperm? This could shed light onto the mechanism.
- We have performed this experiment that was requested by Reviewer #1 and Reviewer #2 (Figure S3). The results show that when the sperm are added to the plate before the addition of the second population of cells, no fusion is observed. This argues against the model in which there is transfer of the fusion machinery to the somatic cells. We have now adjusted the manuscript accordingly.
Fig 3. Sperm incubated in the absence of BSA, failed tu fuse to cells. I wonder whether the lack of BSA precludes fusogenicity. Have authors tried removing HCO3 and keeping BSA? The presence of HCO3 still enables PKA activation even in the absence of BSA, also allowing hyperpolarization of the plasma membrane. Have authors evaluated acrosomal status after the selected treatments?
- As the co-incubation of the sperm and the cells is performed in the cell-culture incubator with CO2 supply, we were not able to evaluate a condition without HCO3. However, we have analyzed the levels of acrosome reaction after the incubation in media without calcium or BSA (Figure S4). For both conditions, the levels of acrosome reaction are significantly lower than the control consistent with previous reports (Visconti et al., 1995 PMID: 7743926). **Referee Cross-Commenting**
I thank reviewer 3 for his/her insight. I agree on being cautious about the relevance of this finding, as it is unclear if anything similar happens in vivo.
I agree with comments raised by rev 1. This reviewer clearly points out that the clinical relevance of the manuscript is not a strength of the manuscript. And I agree with reorganizing the discussion as suggested. In this line, I consider very important to tone down the clinical aspect of the manuscript. In this regard, I completely understand the experiments suggested by reviewer 3, but still, I think that the major contribution of this manuscript is to the sperm-egg fusion field of study. Thus, my suggestion is to tone down to a minimum level the clinical relevance of the study.
Reviewer #2 (Significance (Required)):
The work shows an interesting advance in the field. However, caution should be taken when referring to "capacitated sperm", since no controls are taken, and methods to inhibit capacitation are not solid.
- We thank the reviewer for this comment. We have improved the manuscript following their recommendations and now the pertinent controls are included (Fig. S4). Reviewer #3 (Evidence, reproducibility and clarity (Required)):
Summary:
The authors find that mouse sperm can fuse to hamster fibroblasts (Baby Hamster Kidney, BHK) and that consequently these cells form large multinucleated syncytia. They describe and quantify the formation of syncytia and suggest that the sperm ability to induce formation of syncytia is associated with sperm functionality.
Based on these findings, the authors propose a novel method for the diagnosis of male infertility.
Overall, the manuscript is clearly written, and the experimental procedures are described in detail.
- We sincerely appreciate the important comments and suggestions made by Reviewer #3. Major comments:
The main limitation of the study is that the data is obtained with mouse sperm, while the proposed application is for the evaluation of human sperm fertilizing ability. The authors need to repeat the assay of sperm fusion to somatic cells with human samples.
Furthermore, to correlate sperm functionality with cell fusion induction, it would be necessary to compare sperm from fertile and infertile men. The experiment reported in figure 3, where non-capacitated sperm, maintained in media without Calcium or without BSA, are used, does not mimic real cases of human male infertility where even fully capacitated sperm are unable to fertilize eggs.
The experiments with human sperm should not take longer than 6 months, depending on the local legislation regulating human sample collection and usage.
- We understand the point made by Reviewer #3 and agree to tone down the clinical significance of the finding. Unfortunately, we do not have permission to perform experiments with human samples.
- We also agree with Reviewers #1 and #2 in the sense that these experiments are out of the scope of this study. However, we have now included new data directly correlating mouse sperm fertilizing ability and syncytia formation (Figure 3) that further support our conclusions. Minor comments:
Figure 1. The % of multinucleation in panel C (~ 55%) and in panel D (25%) are substantially different. The authors should explain better how the percentages were calculated.
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As explained in the Results section, within the panels and in the legend of Figure 1, in panel C we quantify multinucleation separately for cells with and without sperm fused to them to distinguish the effect of sperm fusion. Instead, in panel D we quantify multinucleation in the whole population. This is better clarified in the revised version in the legend of Figure 1. Figure 2., panel C. It is unclear how the number of 'fluorescent cells IN CONTACT that do not fuse (NuC)' are evaluated given the lack of a marker for cell membranes or for actin. It is challenging to visualize all the filopodia at the edges of cells just with light microscopy, therefore adding a cell membrane dye could be helpful for visualization and counting.
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We agree with the reviewer that we cannot confirm that the cells are projecting filopodia between them. When we stated “in contact” we referred to the cell body, as we have done in several publications to date: Podbilewicz et al., 2006 (DOI 10.1016/j.devcel.2006.09.004); Avinoam et al., 2011 (DOI: 10.1126/science.1202333); Valansi et al., 2017 (DOI: 10.1083/jcb.201610093); Moi et al., 2022 (DOI: 10.1038/s41467-022-31564-1); and Brukman et al., 2023 (DOI: 10.1083/jcb.202207147). To be more specific we have now explained this in the revised version as “cells whose cell bodies are in contact”. Figure 3, panel A. The authors should specify if the 500 cells are single cells or nuclei, including those in syncytia.
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The figure refers to cells, not nuclei. It is better explained in the revised version of the Materials and Methods. Figure S1 panel C. To discern between the mechanism proposed in i) and ii), a possibility would be to add sperm to JUNO(GFPnes) cells, wash them out carefully after 4 hours in order to remove non-fused and unbound sperm, and then to add JUNO(H2B-RFP) cells. If sperm induce multinucleation by fusing with more than one cell, only GFP syncytia will be obtained. On the other hand, if fusion is due to the transfer of sperm fusing machinery, syncytia will be GFP positive and have red nuclei too.
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We performed this important experiment that was also requested by Reviewers #1 and #2 (Figure S3). We found that when the sperm were added to the plate before the addition of the second population of cells, no hybrid syncytia were formed, only multinucleated GFP cells. The manuscript has been modified to include this observation that points against the model in which there is transfer of the fusion machinery to the somatic cells. **Referee Cross-commenting**
As pointed out by reviewer 1 'this is the first study showing somatic cell fusion and syncitium formation using sperm and somatic cells' therefore if the clinical relevance of the manuscript is toned down, I believe that the authors need to add a more in-depth investigation of the cell fusion mechanism. Particularly in consideration of the data shown by other groups indicating that IZUMO1 and JUNO are not sufficient for cell fusion. In that regard, the experiments suggested by reviewers 1 and 2 would be helpful to start getting a better understanding of the mechanism. Anyhow, I would be extremely cautious about the relevance of this finding because it is unclear whether anything similar happens in vivo.
Reviewer #3 (Significance (Required)):
The mechanism of sperm-egg cell membrane fusion is still unclear, and the entire molecular machinery orchestrating fertilization is yet to be unveiled.
The sperm protein IZUMO1 and its egg receptor JUNO have been shown to be essential for sperm-egg binding in mouse, human and rats. Whether IZUMO1 also mediates membrane fusion is still debated because research groups who have adopted various assays came to different conclusions (PMID: 24739963; PMID: 35096839; PMID: 36394541; PMID: 26568141).
In a previous paper published earlier this year Brukman and colleagues showed that IZUMO1 is able to induce membrane fusion in a heterologous cell system (PMID: 36394541). In the current manuscript, they expand on this observation and report the formation of syncytia (large multinucleated cells) induced by the fusion of sperm with somatic cells. Whether this event that is observed in vitro has any relevance in vivo has to be investigated.
The large amount of work done on an unrelated cell membrane protein, the fibroblast growth factor receptor-like 1 (FGRL1), that also induces cell fusion in vitro, suggests a possible interpretation for the results described by Brukman et al. 'Cell fusion might just be the most extreme result of very tight cell adhesion [...] Under normal conditions, FgfrL1 might only bring together the cell membranes into intimate contact' https://link.springer.com/article/10.1007/s00018-012-1189-9 (PMID:23112089)
Therefore, it is reasonable to think that cell-cell fusion observed in vitro does not recapitulate the fusion of sperm and egg.
Regardless of whether sperm can induce fusion of somatic cells in vivo, the SPICER method proposed in this manuscript needs to be validated with human sperm in order to become a valuable tool for the assessment of male fertility.
We are truly grateful for the comments and suggestions made by Reviewer #3 that allowed us to improve our manuscript. We agree about the important questions that arise from our discovery and we believe that SPICER will be a powerful tool to address them in the future. We would only like to mention that the FGRL1-induced fusion was observed only when at least one of the fusing cells was a CHO cell (PMCID: PMC2988375 DOI: 10.1074/jbc.M110.140517). In our case, the fact that SPICER, as well as IZUMO1-induced fusion (Brukman et al., 2023, J Cell Biol.), was detected in two unrelated cell lines (i.e. hamster BHK and human HEK cells) argues against a cell-specific effect and suggests the involvement of a conserved mechanism.
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Referee #3
Evidence, reproducibility and clarity
Summary:
The authors find that mouse sperm can fuse to hamster fibroblasts (Baby Hamster Kidney, BHK) and that consequently these cells form large multinucleated syncytia. They describe and quantify the formation of syncytia and suggest that the sperm ability to induce formation of syncytia is associated with sperm functionality. Based on these findings, the authors propose a novel method for the diagnosis of male infertility. Overall, the manuscript is clearly written, and the experimental procedures are described in detail.
Major comments:
The main limitation of the study is that the data is obtained with mouse sperm, while the proposed application is for the evaluation of human sperm fertilizing ability. The authors need to repeat the assay of sperm fusion to somatic cells with human samples.
Furthermore, to correlate sperm functionality with cell fusion induction, it would be necessary to compare sperm from fertile and infertile men. The experiment reported in figure 3, where non-capacitated sperm, maintained in media without Calcium or without BSA, are used, does not mimic real cases of human male infertility where even fully capacitated sperm are unable to fertilize eggs. <br /> The experiments with human sperm should not take longer than 6 months, depending on the local legislation regulating human sample collection and usage.
Minor comments:
Figure 1. The % of multinucleation in panel C (~ 55%) and in panel D (25%) are substantially different. The authors should explain better how the percentages were calculated.
Figure 2., panel C. It is unclear how the number of 'fluorescent cells IN CONTACT that do not fuse (NuC)' are evaluated given the lack of a marker for cell membranes or for actin. It is challenging to visualize all the filopodia at the edges of cells just with light microscopy, therefore adding a cell membrane dye could be helpful for visualization and counting.
Figure 3, panel A. The authors should specify if the 500 cells are single cells or nuclei, including those in syncytia.
Figure S1 panel C. To discern between the mechanism proposed in i) and ii), a possibility would be to add sperm to JUNO(GFPnes) cells, wash them out carefully after 4 hours in order to remove non-fused and unbound sperm, and then to add JUNO(H2B-RFP) cells. If sperm induce multinucleation by fusing with more than one cell, only GFP syncytia will be obtained. On the other hand, if fusion is due to the transfer of sperm fusing machinery, syncytia will be GFP positive and have red nuclei too.
Referee Cross-commenting
As pointed out by reviewer 1 'this is the first study showing somatic cell fusion and syncitium formation using sperm and somatic cells' therefore if the clinical relevance of the manuscript is toned down, I believe that the authors need to add a more in-depth investigation of the cell fusion mechanism. Particularly in consideration of the data shown by other groups indicating that IZUMO1 and JUNO are not sufficient for cell fusion. In that regard, the experiments suggested by reviewers 1 and 2 would be helpful to start getting a better understanding of the mechanism. Anyhow, I would be extremely cautious about the relevance of this finding because it is unclear whether anything similar happens in vivo.
Significance
The mechanism of sperm-egg cell membrane fusion is still unclear, and the entire molecular machinery orchestrating fertilization is yet to be unveiled.
The sperm protein IZUMO1 and its egg receptor JUNO have been shown to be essential for sperm-egg binding in mouse, human and rats. Whether IZUMO1 also mediates membrane fusion is still debated because research groups who have adopted various assays came to different conclusions (PMID: 24739963; PMID: 35096839; PMID: 36394541; PMID: 26568141).
In a previous paper published earlier this year Brukman and colleagues showed that IZUMO1 is able to induce membrane fusion in a heterologous cell system (PMID: 36394541). In the current manuscript, they expand on this observation and report the formation of syncytia (large multinucleated cells) induced by the fusion of sperm with somatic cells. Whether this event that is observed in vitro has any relevance in vivo has to be investigated.
The large amount of work done on an unrelated cell membrane protein, the fibroblast growth factor receptor-like 1 (FGRL1), that also induces cell fusion in vitro, suggests a possible interpretation for the results described by Brukman et al. 'Cell fusion might just be the most extreme result of very tight cell adhesion [...] Under normal conditions, FgfrL1 might only bring together the cell membranes into intimate contact' https://link.springer.com/article/10.1007/s00018-012-1189-9 (PMID:23112089)
Therefore, it is reasonable to think that cell-cell fusion observed in vitro does not recapitulate the fusion of sperm and egg.
Regardless of whether sperm can induce fusion of somatic cells in vivo, the SPICER method proposed in this manuscript needs to be validated with human sperm in order to become a valuable tool for the assessment of male fertility.
-
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
Learn more at Review Commons
Referee #2
Evidence, reproducibility and clarity
This is a very nice manuscript that continues previous work of the lab. Authors in this manuscript goes further, showing that proteins from the plasma membrane of sperm end up in somatic cells plasma membrane after fusing, conferring them fusogenic capacity. The manuscript is very elegantly written, and of interest to the reproductive community. I have only some minor concerns to be addressed:
- The authors indicate that this test "can help embryologists to better predict the success of the different ARTs for each individual.". This assumption should be taken as it is, an assumption. In order to state that in can be used, authors should first demonstrate, maybe with a correlation test hand by hand with a reproductive clinic, that this procedure indeed works for diagnosis. There is no doubt that it is a very nice proof of principle.
- Fig S1C. Although I agree with the ayuthors that the two models are possible, have the authors tried to wash sperm away from the fusing assay, and add new cells, maybe differentially tagged, to see whether they fuse to those already fused with sperm? This could shed light onto the mechanism.
- Fig 3. Sperm incubated in the absence of BSA, failed tu fuse to cells. I wonder whether the lack of BSA precludes fusogenicity. Have authors tried removing HCO3 and keeping BSA? The presence of HCO3 still enables PKA activation even in the absence of BSA, also allowing hyperpolarization of the plasma membrane. Have authors evaluated acrosomal status after the selected treatments?
Referee Cross-Commenting
I thank reviewer 3 for his/her insight. I agree on being cautious about the relevance of this finding, as it is unclear if anything similar happens in vivo.
I agree with comments raised by rev 1. This reviewer clearly points out that the clinical relevance of the manuscript is not a strength of the manuscript. And I agree with reorganizing the discussion as suggested. In this line, I consider very important to tone down the clinical aspect of the manuscript. In this regard, I completely understand the experiments suggested by reviewer 3, but still, I think that the major contribution of this manuscript is to the sperm-egg fusion field of study. Thus, my suggestion is to tone down to a minimum level the clinical relevance of the study.
Significance
The work shows an interesting advance in the field. However, caution should be taken when referring to "capacitated sperm", since no controls are taken, and methods to inhibit capacitation are not solid.
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Referee #1
Evidence, reproducibility and clarity
This work by Bruckman et al. showed how sperm can induce somatic cell syncytium upon binding. This finding has tremendous implications for the field. Although gamete fusion is one of the fundamental events in biology, the molecular details of this process in mammals is not well understood. As briefly mentioned in this work, spermatozoa contribute Izumo1 which forms a complex with SPACA6 and TMEM81; and probably with other proteins. On the other hand, the egg counterpart is JUNO, a GPI- anchored protein, and CD9. IZUMO1 and JUNO have been shown to interact and this interaction is essential for fusion. However, it is still not clear the extent that this binding is needed for attachment of for fusion between gametes. The authors have published before that somatic cells expressing IZUMO1 can fuse with other cells expressing JUNO; and that sperm can fuse to JUNO-expressing cells. This previous work strongly suggests that IZUMO and JUNO are sufficient for fusion; however, the efficiency of the fusion assay was low. In the present work, formation of syncytium is highly efficient, making possible to envision applications of this assay to research of the proteins involved in sperm-egg fusion as well as for molecular mechanisms. In addition, as mentioned in this work, the assay can be translated for the use for clinical diagnosis.
Overall, the manuscript is straightforward, and the conclusions are sound. I have only a couple of comments:
- As mentioned in this work, fusion of human sperm with hamster eggs was used in the past to evaluate human sperm fusogenic properties. Although this assay is not longer recommended, still highlight the fact that hamster egg's oolemma is promiscuous regarding species-specific fusion. Although I don't think the experiment is essential, if it were possible, trying hamster IZUMO1 in this assay will be a good addition to this manuscript.
- Is it possible to know how many sperm form part of the syncytium>?
- Although I understand the relevance of this assay for diagnostic use, still the major contribution of this manuscript is to the sperm-egg fusion field of study. I encourage the authors to start the discussion with a more basic approach and add at least one paragraph summarizing how their discovery advanced the field. From my perspective the translational application should be at the end of the Discussion section, not at the start.
Referee Cross-Commenting
The syncytia described in this work support the hypothesis that once Izumo and partners in sperm interact with Juno, the cells become able to undergo fusion events. It does not claim that this type of event happens in vivo. Although Juno is expressed in other cell types and not only in the egg, it is too soon to claim a physiological role for syncytia formation in vivo.
I agree with reviewer 2 comments. Both experiments proposed (paragraph 2 and 3) are relatively straightforward.
In general, I also agree with reviewer 3 comments, one of the experiments proposed is similar to the one proposed by reviewer 2. Having said this, I disagree that the human sperm experiment is needed for this paper to be significant. Although the diagnostic claim is important, the major contribution of this work is at the basic research level. Toning down the clinical relevance would be sufficient to make this paper a significant contribution to the field.
Mechanisms of sperm-egg fusion have been elusive for the last decades. In my view, syncytia formation and I don't expect a single paper to elucidate such a complex event.
Significance
General Assessment: This is an excellent manuscript contributing to our understanding of sperm-egg fusion. In addition, the tools described here warrant new approaches to study the molecules involved in this process using genetically modified mouse models.
Advance: Although the relevance of IZUMO1 and JUNO for sperm-egg fusion have been previously reported, as far as I know, this is the first study showing somatic cell fusion and syncitium formation using sperm and somatic cells. This finding will provide new tools to study the molecular basis of sperm-egg fusion as well as generate translational tools to evaluate human sperm ability to fertilize.
Audience: I believe that this manuscript will have broad interest. Although initially, the main audience will be related to reproductive biologists, this manuscript will be also highly relevant for other scientists studying fusion. In addition, this work has clear translational applications.
Keywords describing my expertise: sperm, eggs, in vitro fertilization, reproductive biology, embryos
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- Oct 2023
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Reply to the reviewers
To,
The editors,
Review Commons.
31st Oct, 2023
We thank the editor and all the reviewers for the detailed critical feedback on our manuscript. We have substantially revised the manuscript to address all the queries, and have incorporated changes that address most of the suggestions made by the reviewers. The revised manuscript includes new experimental data, as well as text changes that address and clarify comments raised by the reviewers. The manuscript has been significantly strengthened by these revisions. A detailed response to reviewer comments is included below.
In the response letter below as well as the revised manuscript, we have addressed all the concerns raised by reviewers 1, 3, and 4, and most comments of reviewer 2. Some of the experiments suggested by reviewer #2 (related to an in-depth phosphoproteomics analysis) are unfortunately well beyond the scope of this manuscript, and infeasible at this stage (with the explanations provided below). We have divided our response document into three sections. In the first paragraph of the response, we briefly summarize the new data that we have included in the revision in response to reviewer queries. In the second section of this document, we have addressed the common comments raised by all the reviewers, which mostly address comments regarding the identification of Far11 phosphosites and mechanistic details about Far complex assembly. In the third part of this response, we include a detailed, point-by-point response to each of the reviewer's concerns, pointing to new data and specific changes made in the main manuscript. We also include a marked-up (in blue) version of the manuscript text, for easier follow up.
Our responses to queries are provided in blue text.
__Section I: A Brief summary of all new experimental data included in this manuscript __
- In this revised version of our study, we assessed the impact of the combined deletion of Ppg1 and components of Far complex. Our analysis revealed that double deletion of Ppg1 and Far complex does not result in any additive effect on metabolic regulation, indicating Ppg1 and Far function sequentially within the same pathway. Given these observations, the expectation will be that cells lacking Far complex will also have similar effects as the Ppg1 knock-out to adapt to glucose depletion. To assess this hypothesis, we conducted a competition experiment and indeed found that Far complex is required for proper adaptation to glucose depletion, very similar to Ppg1.
- To understand if Ppg1-Far regulate gene expression changes to modulate gluconeogenic outputs, we assessed transcript levels of gluconeogenic enzymes from ppg1D cells. Interestingly we observed that the transcript levels of these enzymes remain unchanged in ppg1D cells, suggesting mechanisms involving other modes of regulation.
- Additionally, our investigations revealed that the increased carbon allocation to cell wall precursors in ppg1D cells also makes them more susceptible towards the cell wall stress agent, Calcofluor white, similar to results obtained with Congo red.
- We have also carried out growth and competitive growth experiments with Far complex mutants, similar to those carried out with ppg1D cells, and find that these phenocopy ppg1D cells with respect to adapting to growth in glucose-depleted conditions. Section II: Regarding the identification of Far11 phosphosites dephosphorylated by Ppg1
Our response to this concern:
We agree with the reviewers that in order to obtain the full mechanism of Far complex assembly, it will be important to identify the Far11 phosphosites regulated by Ppg1. However, current evidence already suggests that it will be very challenging to identify specific phospho-site targets of Ppg1. Currently available data from extensive phosphoproteomics studies in Saccharomyces cerevisiae have identified a very large number of phosphorylation sites on Far11 (Bodenmiller et al, 2010; Swaney et al, 2013; Soulard et al, 2010). The identified Ser/Thr residues on Far11 that are known to be phosphorylated are shown in the figure below, and include at least 19 Ser/Thr residues. Considering that so many Ser/Thr residues of Far11 are phosphorylated, it is hard to pinpoint specific phosphosites that are recognized and uniquely dephosphorylated by Ppg1. Additionally, it is widely established that Ser/Thr phosphatases, especially the PP2A family, have little selectivity. At least in vitro, as well as often in vivo, multiple PP2A family members non-selectively target the same residue. Therefore, most of the function is only revealed by a combination of genetic and biochemical data that identify contextual phenotypes (as we have done so, and established in this manuscript). At this stage of this study, these phosphoproteomics experiments are well beyond the scope of the current manuscript.
Identified Ser/Thr residues known to be phosphorylated in only Far11
__However, we acknowledge this important point raised, and now include it in the discussion in Line 507. __
The revised text now reads:
“Additionally, large-scale studies suggest over 19 putatively phosphorylated Ser/Thr residues in Far11 alone, indicating multiple kinase-phosphatase interactions on this protein (Bodenmiller et al, 2010; Swaney et al, 2013; Soulard et al, 2010). Phosphoproteomics experiments with Ppg1 mutants are therefore a good starting point, but in themselves may be insufficient to specifically identify Ppg1 specific phosphosites on Far11.”
We also now have an extensive discussion section included, on the challenges of identifying specific phosphatase sites on proteins (and the contrast with kinase dependent phosphorylation sites). This section reads as (Line 500):
“Separately, phosphoproteomics-based studies could provide avenues for identifying as yet unidentified substrates of Ppg1. However, phosphoproteomics approaches have been far more suited for elucidating kinase-mediated regulation, due to high substrate specificity of kinases (Li et al, 2019). Identifying the specific substrates of phosphatases has posed significant challenges because of the nature of phosphatases like PP2A, which exhibit low substrate specificity and often have overlapping and compensatory outputs (Virshup & Shenolikar, 2009; Millward et al, 1999). Hence, determining the specific outputs or substrates of phosphatases through these methods presents a formidable challenge. Additionally, large-scale studies suggest over 19 putatively phosphorylated Ser/Thr residues in Far11 alone, indicating multiple kinase-phosphatase interactions on this protein (Bodenmiller et al, 2010; Swaney et al, 2013; Soulard et al, 2010). Phosphoproteomics experiments with Ppg1 mutants are therefore a good starting point, but in themselves may be insufficient to specifically identify Ppg1 specific phosphosites on Far11.”
Section III: Point-by-point response to all individual reviewer comments:
Reviewer #1:
The authors study metabolic adaptation to glucose depletion in budding yeast. A non-essential protein phosphatase mutant screen reveals adaptation to glucose depletion (growth in post-diauxic phase) requires Ppg1. The authors show i) that, in post-diauxic phase, cells lacking Ppg1 accumulate more trehalose, glycogen, UDP-glucose, UDP-GlcNAc (i.e., gluconeogenic outputs) than wild-type cells, ii) that, in post-diauxic phase, cells expressing a catalytically inactive version of Ppg1 accumulate more trehalose and iii) that Ppg1 is required for adaptation and growth post-glucose depletion. The authors find that Ppg1 interacts with Far11 (a member of the Far complex) in cells growing in the post-diauxic phase and that Ppg1 promotes Far complex stability. Finally, the authors conclude that the Ppg1 promotes Far complex stability to maintain gluconeogenic outputs after glucose depletion.
We thank the reviewer for a careful reading of this manuscript, and many constructive comments.
Major comments:
- Figures 1 to 4. The authors show that loss of Far components phenocopies loss of Ppg1 and conclude that Ppg1 is upstream of Far. However, the authors do not determine the combined effect of the two mutations. The authors should assess the phenotype (e.g., gluconeogenic output levels) of cells lacking both Ppg1 and Far (or in far9_deltaTA far10_deltaTA cells lacking Ppg1). The authors' conclusion would be strengthened if there was no additive effect between the mutations. Thank you for raising this point. We have now investigated the combined effect of deletion of Far11 and Ppg1 on post-diauxic carbon metabolism by measuring trehalose amounts. From these measurements, we did not observe any additional increase in trehalose amounts in the cells lacking both Ppg1 and Far11. We also assessed the growth of these cells in the presence of the cell wall stress agent, Congo red. There was no additional growth defect in the double deletion strain in the presence of Congo red. This data strongly indicates that the double deletion of Ppg1 and Far11 does not have an additive effect, indicating that both proteins function in the same pathway. This data is now included in Fig. S3 D and E. Text changes are made accordingly in the results section line 300:
“To determine if Ppg1 and Far complex independently regulate carbon metabolism or function within the same pathway, we generated double deletion mutants of Ppg1 and Far11 (ppg1Dfar11D). We assessed trehalose accumulation in ppg1Dfar11D cells and found no additional increase in trehalose levels (Figure 3H). Furthermore, we studied the growth of these ppg1Dfar11D cells in the presence of Congo red and observed no additional growth defects (Figure 3I). Overall, these findings strongly indicate that Ppg1 and Far complex function sequentially within the same pathway, with Ppg1 upstream of Far.”
Data showing trehalose levels from WT, far11D and ppg1Dfar11D cells after 24hrs of growth in YPD medium (now new Figure 3H):
Data showing the growth of WT, far11D and ppg1Dfar11D cells in the presence of Congo red (now new Figure 3I):
- Related to Figure 6A-C. One would expect that cells lacking Far components (or far9_delta TA far10_deltaTA cells) show a similar phenotype (fail to adapt to growth in changing glucose compared with wild-type cells) as cells lacking Ppg1. Is this the case? We agree with this expectation. The cells with loss of components of Far complex fully phenocopy ppg1D cells, and have an imbalanced carbon metabolism. Therefore, the expectation is that these cells will exhibit similar defects as ppg1D to properly adapt to glucose depletion. To address this question, we carried out a competition experiment with wild-type and Far9DTAFar10DTA cells (where the Far complex can now no longer be anchored, and therefore assemble properly, as shown in Fig. 4F, G), specifically assessing adaptation to environments as glucose depletes (and done identically to those with ppg1D). Note: the choice of this strain was to enable quantitative estimation, since we needed strains that had a ‘fluorescence’ mark (mNeonGreen or mCherry) to quantitatively assess changes in each genotype. Similar to ppg1D cells, the relative proportion of Far9DTAFar10DTA cells decreased during the competition experiment (Fig S6A). Independently, we estimated changes in post-diauxic growth of far11D cells, starting in glucose-rich conditions. In batch culture, the far11D cells showed reduced growth specifically in the post-diauxic phase (Fig S6B). Effectively, the loss of the Far complex nearly perfectly phenocopies the loss of Ppg1 in enabling effective adaptation to glucose-depleting environments. This data reiterates the importance of the Far complex in adaptation to glucose depletion, as the mechanistic target of Ppg1 function. This data is now included in the new Fig. S6 A and B. The text changes are made accordingly (Line 423):
“To concurrently address the role of Far complex in enabling cells to adapt to glucose depletion, we carried out a similar competition experiment (as with ppg1D) with WT and Far9DTA10DTA cells. Note: the Far9DTA10DTA cells will not allow the Far complex to anchor and assemble within cells, as shown earlier, and therefore phenocopies far9D, and was utilized in this experiment for easier quantitative estimations based on fluorescence. Expectedly, the relative proportion of Far9DTA10DTA cells decreased during the course of the competition experiment (Figure S6A). We next examined the effect of loss of Ppg1 on steady-state batch culture growth, starting from a glucose-replete medium. The loss of Ppg1 did not affect growth in the glucose-replete log phase, but after cells entered the post-diauxic (glucose-depleted) phase, ppg1D cells showed reduced growth and a reduction in biomass accumulation (Figure 6D). Independently, we assessed the growth of far11D cells starting in glucose-replete conditions, and observed reduced growth of these cells specifically in the post-diauxic phase (Figure S6B), similar to ppg1D cells. Effectively, the loss of Ppg1 or the Far complex phenocopied each other, and collectively, these data reveal that Ppg1-Far mediated regulation enables adaptation and competitive growth fitness after glucose depletion.”
Data showing growth competition between WT and Far9DTA10DTA cells in changing glucose conditions (now new Fig. S6A):
Data showing growth dynamics of WT and far11D cells in the YPD medium (now new Fig. S6B):
- The manuscript would be considerably strengthened if the authors provided more information on the mechanism by which Ppg1 controls Far complex stability, e.g., can the authors about the phosphosite(s) in Far11 regulated by Ppg1? As the authors mention, it has been already suggested that Ppg1 is required for Far complex assembly (PMID: 33317697). This comment has been commonly addressed in the section 2 of this document. Briefly, while we are equally excited about this direction, given the complexity of phosphorylation of the Far11 protein (and the challenges specifically in the context of PP2A family phosphatase action), this component is likely to take years to address and is beyond the scope of this manuscript. We do include a more speculative section in the discussion in this regard.
Minor comments:
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The authors may consider to include data from Figures 6A, 6B and 6C (failure to adapt to glucose-changing conditions) after Figure 2 to show a complete characterization of the phenotype of cells lacking Ppg1. Figure 6 could show only the "proposed model". We appreciate the reviewer’s suggestion and had indeed considered this possibility in an early version of the writing of the manuscript. However, we prefer the current flow of this manuscript, where we identify Ppg1 as a putative regulator of gluconeogenic flux, and end with the mechanistic confirmation of function that links Ppg1 and Far complex to the same adaptation function. We hope the reviewer appreciates this viewpoint.
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Related to Figure 1. The authors mention that Ppg1 is a "notable hit" and that the "increase in post-diauxic trehalose levels are considerable". However, there is no reference to use as a comparison. Is there any other mutant strain known to accumulate trehalose at the post-diauxic shift? If yes, it would be informative if the authors compared the effect of such mutant strain to a ppg1-delta mutant. For this experiment, we did not employ another mutant as a reference for increased trehalose accumulation. However, the point raised by the reviewer in itself was interesting in itself. Looking into the literature, we find that there are no reliable, quantitative estimates of how much trehalose increases in yeast in the post-diauxic phase (compared to the log phase), although numerous manuscripts (including some of our own earlier work) allude to this point. Therefore, we did this experiment, to obtain absolute quantitative information on the trehalose amounts in YPD in cells after 4 hrs of growth in 2% glucose, vs. in cells the post-diauxic phase (24 hrs after starting growth in 2% glucose). The amounts of trehalose increase >10-fold in the post-diauxic phase compared to the log phase. We now mention this in the text, and include absolute quantitation of trehalose amounts in Fig S1B. __Hence, a ~1.5-fold further increase in trehalose amounts in the post-diauxic ppg1D cells compared to post-diauxic wild-type cells is considerable. The revised text now reads (Line 121):__
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*
*“We initially estimated how much trehalose amounts increased in the post diauxic phase. Trehalose amounts increased over 10-fold in the post-diauxic phase after 24 hours of growth starting in 2% glucose, compared to cells after 4 hours of growth in the same condition (Figure S1B).” *
- *
*“Compared to the ~10 fold increase in trehalose (as shown in Figure S1B), the further increase of 1.5-fold in trehalose accumulation in the post-diauxic phase in cells lacking Ppg1 (Figure 1D) is substantial.” *
Data showing trehalose accumulation in log and post-diauxic phase wild-type cells (Figure S1B):
- Figures 4D and 4F. Regarding sensitivity to Congo Red and compared to wild-type cells, it seems that cells lacking Far9 are much more sensitive to Congo Red in Figure 4F than in Figure 4D. Is this just an image quality issue? The authors should address this apparent discrepancy. The small visual difference is likely because the images (which come from experiments done at different times) were taken at slightly different time points after spotting. To avoid any confusion, in the figure legends we have now mentioned the precise time at which each of the images were taken.
Reviewer #2:
Summary: In this manuscript, the authors screened the yeast phosphatase mutant that shows defective in metabolic adaptation and found that PP2A-like phosphatase Ppg1 is required for the appropriate gluconeogenic outputs after glucose depletion. Furthermore, they showed that Far complex which assembles with Ppg1 is also required to maintain gluconeogenic outputs. They also found that Ppg1 is required for assembly of Far complex and the assembly on the ER or mitochondrial membrane is important for their function. Ppg1 and Far complex dependent control of gluconeogenic outputs had important role on adaptive growth under glucose depletion.
Major comments:
In this study, the authors report new evidence that the Ppg1 and Far complexes are involved in the regulation of gluconeogenic outputs. However, the mechanism by which the Ppg1-Far complex is involved in gluconeogenic outputs has not been fully analyzed, and further analysis of the role of Ppg1 in Far complex assembly and the significance of Far11 phosphorylation is needed. The authors should consider the following points,
We thank the reviewer for valuable, constructive comments. Investigating the mechanism through which Ppg1, via the Far complex, regulates gluconeogenesis outputs and unravelling the added mechanism of Far complex assembly in this context are exciting areas of future research, and indeed where we hope to go. However, at this stage addressing some of these follow-up questions is beyond the scope of this manuscript. Our current findings unambiguously identify Ppg1 as a phosphatase that controls post-glucose depletion gluconeogenic flux, also identifies this mechanism to function through the proper assembly of the Far complex, and show that cells function through a Ppg1 - Far axis to adapt to glucose depletion. At this stage, we do not know what the Far complex might help assemble, and while this is an obvious follow-up, we anticipate years of effort to unravel this next question.
- In the mutant screen, both pph21Δ and pph22Δ cells showed increased levels of trehalose (figure 1C). Pph21 and Pph22 are catalytic subunits of protein phosphatase 2A (PP2A) and function redundantly. Thus, it may be possible that PP2A is more involved in gluconeogenic outputs regulation than Ppg1. In S. cerevisiae, the PP2A phosphatases regulate phosphorylation of transcription factors that control storage carbohydrate synthesis, and thereby regulate carbon metabolism (Bontron et al, 2013; Clotet et al, 1995; Dokládal et al, 2021). Notably, the deletion of Pph21 and Pph22 results in increased transcription of glucose repressed genes (Castermans et al, 2012), consequently resulting in increased gluconeogenesis and storage carbohydrate synthesis in these mutants. Additionally, our screen data also found an increase in trehalose accumulation in mutants of Pph21 and Pph22 (which were less than that of the Ppg1 mutants). Collectively, these observations emphasize the role of PP2A phosphatases in regulating gluconeogenic outputs, primarily through transcriptional control.
In notable contrast to the transcriptional changes observed with Pph21/22 mutants, the Ppg1-mediated regulation described in this manuscript does not involve any transcriptional changes of the enzymes of gluconeogenesis and related carbon metabolism (now included in Fig 2E ). This excitingly points towards regulation by some combination of post-translational modifications, allostery, or mass action. In light of these disparities, we believe that the function of Ppg1 elucidated in this study, operates independently of PP2A-mediated regulation of carbon metabolism. This point is now included in the discussion (Line 456). The revised text now reads:
“There are well studied examples of signaling systems regulating metabolic adaptation, which have typically focused on understanding the repression or activation of relevant transcriptional outputs. For example, upon glucose depletion, the Snf1 kinase activates transcription factors such as Cat8 and Rds2, resulting in an increase in transcripts of key gluconeogenic enzymes (Turcotte et al, 2010; Rashida et al, 2021; Vengayil et al, 2019). In this context, phosphatases belonging to PP2A family, particularly Pph21 and Pph22, regulate transcriptional outputs of glucose repressed genes (Bontron et al, 2013; Castermans et al, 2012). Interestingly, and in contrast to this, the Ppg1-mediated regulation we uncover in this study does not rely on changes in gene expression (Fig. 2E). Instead, this points towards regulation through other mechanisms that are driven by post-translational modifications, mass action, or enzyme concentration etc. This function of Ppg1, as uncovered in this study, differs from regulation mediated by related phosphatases.”
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Is it the Far complex or Ppg1 activity that is required for the regulation of gluconeogenic outputs? It seems that assembly of the Far complex requires Ppg1 and Ppg1 activity requires the Far complex. However, either one should be involved in the regulation of gluconeogenic outputs. For example, Innokentev et al, 2020 concluded that Ppg1 activity is critical for the regulation of mitophagy and that the Far complex serves only as a scaffold for Ppg1. by Ppg1 dephosphorylating an unidentified protein. The possibility that Ppg1 may be involved in the regulation of glycolytic output by dephosphorylating unidentified substrates needs to be fully tested. We agree with the reviewer's point. Our data very clearly now demonstrate that the Ppg1 activity is required for the assembly of Far complex (Fig. 3D). Subsequently, our data shows that the Far complex is required for regulation of gluconeogenic outputs. These observations together suggest the following two hypotheses:
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Ppg1 is required for assembly of the Far complex. The assembled Far complex could transiently interact with other proteins that regulate gluconeogenic outputs (as would be possible for a ‘scaffolding system’). Ppg1 is primarily required for the assembly of the Far complex, and may not directly regulate signaling proteins that control gluconeogenesis.
- Alternately, the Far complex only serves as a scaffold, and enables Ppg1 to interact and dephosphorylate as yet unidentified substrate(s). These two possibilities are also not mutually exclusive. Both these possibilities merit further investigation, but at this stage, all our direct biochemical experiments (including isolating the Far complex and Ppg1, in order to identify interacting proteins) have not yielded more than this connection between Ppg1 and Far itself. Given this (and what would be a reasonable expectation for a dynamic scaffold), it is likely that the subsequent targets of Ppg1 and Far are transient interactions. A future effort would involve creating proximity-based target identification systems in S. cerevisiae (that work effectively in glucose-depleted conditions) and identifying such mechanisms. Currently, no such system exists, and we are building these kinds of tools for future studies. Exploring such mechanisms of gluconeogenic outputs is therefore a very interesting area of future investigation, but well beyond the scope of this manuscript. We include an acknowledgement of the same in the discussion section____ (Line 490). The revised text now reads:
“Notably, the Ppg1 phosphatase regulates post-diauxic carbon metabolism by modulating the assembly of the Far complex (Fig. 3). Considering this requirement of Ppg1 to assemble this scaffolding complex and thereby constrain gluconeogenic flux, our study presents two intriguing possibilities: first, the Far complex scaffold could act as a facilitator, enabling interaction between Ppg1 and its other substrates (which regulate gluconeogenic outputs); and second, the primary function of Ppg1 is to facilitate Far complex assembly, which transiently brings to proximity other signaling proteins and enzymes that control gluconeogenesis. Both these possibilities (which are not mutually exclusive) merit detailed investigation. However, exploring these would require the development of new, proximity-based target identification systems for yeast that can identify transient protein-protein interactions. Separately, phosphoproteomics-based studies could provide avenues for identifying as yet unidentified substrates of Ppg1. However, phosphoproteomics approaches have been far more suited for elucidating kinase-mediated regulation, due to high substrate specificity of kinases (Li et al, 2019). Identifying the specific substrates of phosphatases has posed significant challenges because of the nature of phosphatases like PP2A, which exhibit low substrate specificity and often have overlapping and compensatory outputs (Virshup & Shenolikar, 2009; Millward et al, 1999). Hence, determining the specific outputs or substrates of phosphatases through these methods presents a formidable challenge.”
- Although there are no known substrates of Ppg1 other than Atg32, Atg32 is not involved in the regulation of gluconeogenic outputs. The identification of substrates of Ppg1 involved in the regulation of gluconeogenic outputs will help to elucidate the molecular mechanism of gluconeogenesis. We completely agree with the reviewer's point (and also see our response to the previous point). It’s important to note that the involvement of Ppg1 in regulating mitophagy is entirely independent of its role in regulating gluconeogenic outputs. This is something we firmly establish in this study, in Fig. S1C. This indicates that in addition to its recognized role in Atg32 dephosphorylation specific to extreme starvation conditions of mitophagy, Ppg1 activity functions to regulate gluconeogenesis, a critical homeostatic function. Our response to the previous comment indicates our future lines of inquiry, which are currently well beyond the scope of this manuscript. Included in the discussion (Line 500). The revised text now reads:
“Separately, phosphoproteomics-based studies could provide avenues for identifying as yet unidentified substrates of Ppg1. However, phosphoproteomics approaches have been far more suited for elucidating kinase-mediated regulation, due to high substrate specificity of kinases (Li et al, 2019). Identifying the specific substrates of phosphatases has posed significant challenges because of the nature of phosphatases like PP2A, which exhibit low substrate specificity and often have overlapping and compensatory outputs (Virshup & Shenolikar, 2009; Millward et al, 1999). Hence, determining the specific outputs or substrates of phosphatases through these methods presents a formidable challenge.”
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The authors conclude that Ppg1 dephosphorylates Far11 and that dephosphorylated Far11 assembles with the Far complex. However, there is a possibility that Ppg1 activity is required for Far complex assembly independently of dephosphorylation of Far11. To prove the authors' assertion, it is necessary to identify the phosphorylation site of Far11 and show that its phosphorylation affects the binding of Far11 to Far8. We address this point in the earlier section 2 of this document and hope that the reviewer will recognize the extreme challenges in the feasibility of these experiments at the current stage
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Several kinases have been reported to be involved in gluconeogenic outputs regulation. The initial aim of this study was to identify phosphatases involved in gluconeogenic outputs regulation by antagonizing these kinases. However, Ppg1 has not been shown to be involved in transcriptional regulation to control carbon metabolism by antagonizing any kinase. We thank the reviewer for raising this important point, which is one of the highlights of the findings of this manuscript. The regulation of gluconeogenesis enzymes by dephosphorylation/phosphorylation is not yet known at all, nor has there been a prior reason to look for such regulation. This paper will now provide strong reasons to look for such regulation. Separately, almost all past effort has been to look at transcriptional responses to changes in carbon availability. This is despite overwhelming evidence (Hackett et al, 2016) that over 50% of metabolic regulation in yeast can be direct - at the level of flux regulation by mass action, substrate availability and/or allostery- and precludes transcriptional changes. Since this interesting point was raised, we compared the expression of transcripts of the enzymes of gluconeogenesis, storage carbohydrate metabolism and cell wall synthesis in wild-type and ppg1D cells. Notably, we did not observe significant changes in transcript levels of these enzymes in ppg1D cells. This provides additional evidence that suggests the regulation of gluconeogenic flux (which we quantitatively demonstrate in Fig 2) must be via alternate mechanisms that involve increasing local substrate concentrations/PTMs/enzyme scaffolds or other mechanisms that need not invoke transcription. We therefore believe that it will be very interesting to study these possibilities in the future, and this can lead to a rich line of future inquiry. Our study also opens the possibility that Ppg1 might counteract kinase-mediated signaling (which, in the context of glucose, is better studied with PKA, TORC1 and other outputs). We have now included the transcript analysis of gluconeogenic enzymes in WT and ____ppg1____D cells, now included in the new Fig 2E.
We also include this revised text, to reiterate this point (Line 212):
“Finally, we asked if Ppg1 regulates the expression of transcripts of enzymes involved in gluconeogenesis, storage carbohydrate metabolism and cell wall synthesis proteins to modulate gluconeogenic outputs. For this, we measured the transcript levels of these enzymes from post-diauxic WT and ppg1D cells. Notably, the transcript levels of these enzymes remain unchanged in ppg1D cells (Figure 2E). This data suggests that Ppg1-mediated carbon flux regulation does not involve any transcript level changes, indicating that Ppg1 regulates gluconeogenic flux via mechanisms that involve allosteric, post-translational or mass action-based regulation.”
Data showing transcript levels of enzymes of gluconeogenesis, storage carbohydrate metabolism, and cell wall synthesis proteins in WT and ppg1D cells (now new Fig. 2E):
We also include a few lines in discussion, where we reiterate that while much of our understanding of the regulation of gluconeogenesis comes from changes in transcriptional programs, substantial regulation of metabolic flux involves direct regulation via allostery, post-translational modifications, mass action and concentration (Hackett et al, 2016). Ppg1, via the Far complex, appears to participate in one such example of regulation (Line 463).
“the Ppg1-mediated regulation we uncover in this study does not rely on changes in gene expression (Fig. 2E). Instead, this points towards regulation through other mechanisms that are driven by post-translational modifications, mass action, or enzyme concentration etc. This function of Ppg1, as uncovered in this study, differs from regulation mediated by related phosphatases. How might this occur? An underappreciated but important mediator of metabolic adaptation is the direct modulation of metabolic outputs or flux, through a combination of mass action and allosteric regulation (and without invoking transcriptional changes). Even in unicellular organisms like S. cerevisiae, over 50% of metabolic regulation occurs through such mechanisms (Hackett et al, 2016).”
- If the assembly of the Far complex is involved in gluconeogenic outputs regulation, what is the mechanism? The Far complex is a scaffold for enzymes. Therefore, the role of the Far complex in gluconeogenic outputs regulation will not be elucidated until the enzymes that function there are identified. We agree that the specific mechanism of how the Far complex functions, after being assembled by Ppg1, cannot be understood unless we find those targets. Indeed, the Far complex may potentially interact with signaling proteins or metabolic enzymes involved in post-diauxic carbon metabolism. However, many of these interactions are transient and identifying these interactions is an extremely challenging task. As pointed earlier, we will now have to establish effective methods in yeast for proximity-based substrate/target identification, establish effective mass spectrometry-based pipelines for the same, and then screen for new regulators. This is by no means a trivial task, and is well beyond the scope of this manuscript, and we hope the reviewer recognizes the same.
Recognizing this point, we had mentioned this in the discussion (Line 553):
“In order to understand how dynamically assembled scaffolds with varying localizations and modifications can regulate homeostatic outputs such as metabolic adaptations, we require new chemical biology approaches that stabilize low-affinity protein-protein interactions, or substrate-trapping mutants to identify transient substrates that are brought together by such signaling hubs (Qin et al, 2021). This remains a key challenge in the context of protein phosphatases, which naturally interact with substrates with low affinities (Bonham et al, 2023).”
Minor comments:
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Because TA of Far10 can tether Far complex on the membrane, Mito-Far and ER-Far experiments (Figure 4A-D) should be performed under Far10ΔTA conditions. We agree with the reviewers' comment. In the Mito-Far and ER-Far cells, the Far10 protein (with intact TA domain) can localize to the surface of both organelles. Our careful microscopy images show clear localization/targeting of components of Far complex to respective compartments in both Mito-Far and ER-Far cells. This data strongly indicates that regulating localization of Far9 by itself (at either surface location) is sufficient for Far complex to localise to these compartments.
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Figure 6B and 6C, total culturing time (hours) should be shown on X-axis in addition to number of transfers. We now include this in the figure legends.
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Figure 3E, additional explanation is needed as to why the molecular mobility of Far11-FLAG after CIP treatment differs between Ppg1-H111N and Ppg1. The difference in mobility of Far11 in wild-type and Ppg1-H111N cells can be because of post-translational modifications other than phosphorylation. However, these modifications are regulated in a Ppg1-dependent manner. It will be interesting to identify these modifications on Far11 and their role in stabilizing the Far complex assembly. We now include this in the text (Line 285):
“At this stage, while these experiments are consistent with a role of dephosphorylation in Far11 function and the assembly of the Far complex, these do not preclude other post-translational modifications in addition to phosphorylation.”
Reviewer #3:
The study performed by Niphadkar et al. seeks to uncover the role of the phosphatase Ppg1 in regulating gluconeogenesis during post-diauxic shift in S. cerevisiae. The authors show that loss or inactivation of Ppg1p affects production of gluconeogenic products incl. trehalose and glycogen. The authors show that assembly of the Far complex required the activity of Ppg1 and is required to maintain gluconeogenic
outputs after glucose depletion.
The manuscript is clearly written and methods well considered, no omics-methods have been included. Especially phosphoproteomics would be relevant to include. Specifically, the tracing experiments are an interesting and appropriate approach to confirm effects on gluconeogenesis etc. Yet, working with regulation of posttranslational modifications (phosphorylations) it is surprising that the authors only to a limited extent examine phosphorylation events, and not all examine or discuss specific phosphorylation events of e.g. Far11.
The study is interesting and provides new insights into regulation of glucose metabolism in yeast, however, there are serious concerns that need to be addressed before it can be reconsidered for publication.
Major points:
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The authors use electrophoretic mobility assays w/wo CIP to address the phosphorylation state of Far11. They show in figure 3E that the mobility of Far11 depends on Ppg1 activity and can be affected by CIP. Why is the mobility of Far11 not affected in e.g. figure 3D? For these experiments, protein gels with different acrylamide concentrations were used. For the shift experiment, proteins were resolved using 7% gel for the duration of 5 hours. In Fig. 3D, 4-12% gradient gels were used and proteins were resolved for the duration of 2 hours. We now mention this in the figure legends and methods section.
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There are several sites in Far11 previously reported to be phosphorylated, see e.g. Bodenmiller et al 2010 (Science Signal.) Are there sites that are specifically regulated (dephosphorylated) by Ppg1? or by other phosphatases? kinases? This is addressed in section 2 of this document. In that section, we summarize the very large number of putative Ser/Thr residues that are phosphorylated in Far11, and while there is no clear information on which kinases might act on these, it is extremely complex to identify specific phosphatase roles in dephosphorylating these.
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Here, it would be appropriate to apply phosphoproteomics to examine Far11 phosphorylation in Ppg1 knock out cells or in cells with inactivated Ppg1. We agree with the reviewer's comment. It will be very interesting to implement phosphoproteomics to identify phosphosites regulated by Ppg1. However, unlike kinases, the changes in the phosphoproteome with phosphatase knock-outs are very challenging to interpret, especially for a family of phosphatases from the PP2A family. Due to a range of overlapping substrate recognition sites, as well as a change in kinase outputs when a phosphatase is missing, interpreting phosphoproteomes with phosphatase knockouts, which function conditionally (during say post-diauxic conditions, like this study), will have substantial challenges. See for example this very recent, exhaustive, state-of-the-art study in yeast, quantifying kinase and phosphatase mutant phosphoproteomes (Li et al, 2019). While the analysis for kinase mutants were substantially more revealing, the data from phosphatase mutants were very convoluted, and could identify very little specific outputs of phosphatase function. This set of experiments is beyond the scope of this manuscript, but this manuscript provides compelling reasons to do so. We have included a couple of lines in the discussion, related to this specific component. Included in the discussion (Line 500).
“Separately, phosphoproteomics-based studies could provide avenues for identifying as yet unidentified substrates of Ppg1. However, phosphoproteomics approaches have been far more suited for elucidating kinase-mediated regulation, due to high substrate specificity of kinases (Li et al, 2019). Identifying the specific substrates of phosphatases has posed significant challenges because of the nature of phosphatases like PP2A, which exhibit low substrate specificity and often have overlapping and compensatory outputs (Virshup & Shenolikar, 2009; Millward et al, 1999). Hence, determining the specific outputs or substrates of phosphatases through these methods presents a formidable challenge. Additionally, large-scale studies suggest over 19 putatively phosphorylated Ser/Thr residues in Far11 alone, indicating multiple kinase-phosphatase interactions on this protein (Bodenmiller et al, 2010; Swaney et al, 2013; Soulard et al, 2010). Phosphoproteomics experiments with Ppg1 mutants are therefore a good starting point, but in themselves may be insufficient to specifically identify Ppg1 specific phosphosites on Far11.”
- The authors show that the levels of Ppg1 remain constant during growth in YPD medium, while the levels of Far11 increased after 24hrs of growth in YPD medium, and thus argue that the amount of Far complex itself increases in post-diauxic phase. The authors need to show that the level of complex indeed increases. In addition to Far11, we also compared the amounts of Far8 - another core component of Far complex. Similar to Far11, we observed an increase in the amounts of Far8 specifically in the post-diauxic phase. We also assessed the amounts of Far8 in response to glucose availability, and find that Far8 also decreases when glucose is added to the system. These data support our findings that the amounts of Far complex increase in the post-diauxic phase. These data are included in Fig. S5 B.
In the text, we reiterate (Line 396):
“Furthermore, the amounts of Far8 also were reduced after addition of glucose to post-diauxic cells (Figure S5B). Together, we infer that the activity and amounts of Ppg1 are constitutive, but the amounts of the Far proteins are glucose-responsive (Figure 5E).”
Data showing the effect of glucose availability on Far8 protein amounts are included in (Fig. S5B):
- The authors also apply fluorescence microscopy to address the localization of the Far11 complex etc. The quality of the shown images should be improved, also merged images should be shown. Only one single image containing one cell is shown, images should ideally show additional cells in the same image, alternatively, additional images should be shown. Good point. We have now included higher quality images which show more cells in each frame, as well as include the merge/overlap, in Fig. 4B. This should satisfy concerns.
For the reviewer’s reference, some additional images is are shown here:
Reviewer #4:
General comments
This paper reports a critical function of PP2A-like phosphatase encoded by PP1G in the post-diauxic shift of the yeast Saccharomyces cerevisiae. This function is mediated via the assembly of FAR complex that naturally sites at the ER-mitochondria outer membranes to ensure proper onset of growth at the diauxic shift by appropriate carbon allocation through gluconeogenesis. The identification of this PPase was based on a screen of yeast mutant defective in non-essential PPases for trehalose accumulation.
This is a bit surprising as it is known that trehalose accumulation sets in as soon as glucose is depleted and continues steadily during growth on other carbon source, which is merely ethanol, although it may depend whether the experiment was carried out in YPD or in mineral synthetic medium as YNB.
Although the work seems experimentally well conducted, in particular for the demonstration that bPP1G interacts with FAR complex, it raises several issues requiring a thorough revision and additional experiments to truly support the role of PP1F in regulating post-diauxic shift.
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all experiments were done using YPD medium and only a single value of trehalose at 24 h was recorded! It will be important to ensure that all mutant had exactly same growth rate, that at 24 h, glucose was totally gone. It should be relevant to have a more complete kinetic analysis of trehalose/ glycogen accumulation along growth, monitoring as well glucose consumption in WT and the pp1gD, to really convince that the metabolic difference is not associated with a difference in glucose consumption, such as that at 24 h, there is still some glucose remaining in the culture of the mutant! We thank the reviewer for raising these interesting points. We address this comment in following two points:
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In Fig.2 C, we have shown the kinetics of trehalose accumulation during the course of growth. Additionally, we measure amounts of other gluconeogenic outputs as cells grow and deplete glucose. Only after cells deplete glucose and enter the post-diauxic phase do we observe an increase in amounts of these metabolites in ppg1D cells.
We measured extracellular glucose concentration at 24hrs, and there was no detectable glucose present in the medium. This indicates that cells have completely consumed available glucose and have shifted to gluconeogenic metabolism. We now mention this in the text (Line 117):
“For the screen output, trehalose accumulation was assessed from these mutants after 24hrs. At this 24hrs time point, no glucose was detected in the medium, confirming that cells are in the post-diauxic phase. Trehalose synthesis increases in the post-diauxic phase and is a reliable readout of a gluconeogenic state.”
Note for further references: in several earlier studies, we have systematically established that trehalose production is a very reliable indicator of gluconeogenic flux (e.g. see PMID: 32876564, PMID: 31758251, PMID: 27090086, and PMID: 31241462). In addition, we have extensively described ways to look at trehalose production in this methods paper PMID: 32181267.
Finally, we will note that there are very few studies that actually have estimated flux through gluconeogenesis, and have made most inferences using only the expression of transcripts of gluconeogenic genes, and hence the interpretations have to be made accordingly. Our study, to our knowledge, is the first to provide quantitative carbon flux measurements through gluconeogenic intermediates, in the context of any phosphatase mutant studied in yeast. All other studies have not measured flux, but rely on changes in transcripts, or steady-state amounts of storage carbohydrates to draw conclusions.
- The experiment at least with pp1gD and WT should be redo in mineral synthetic medium with 2% glucose. There, only ethanol can be the sole carbon for growth resumption and thus this will ensure that the effects is linked to growth resumption at diauxic growth as YPD is a rich medium that contains excess of many amino acids and peptides that may interfere with your phenotype. We have examined the growth of ppg1D cells in a synthetic medium with glucose as the sole carbon source. These data indicate that ppg1D cells grow similar to wild-type cells in the log phase (glucose-replete). However, these cells show reduced growth post-glucose depletion, where the only available carbon source is secreted ethanol. Here, as expected in a synthetic minimal medium, the extent of difference in growth of ppg1D cells is not as pronounced as seen in YPD medium as the spent post-diauxic medium here may not provide enough carbon source to fuel further growth.
Data showing the growth dynamics of ppg1D cells in SD medium:
Additionally, to study the role of Ppg1 in regulating post-diauxic metabolism in cells growing in SD medium, we measured amounts of gluconeogenic outputs from the post-diauxic cells. Notably, after 18 hours of growth in the SD medium, the ppg1D cells showed increased amounts of gluconeogenic outputs (UDP-GlcNAc, F16BP). Collectively, this data suggests that Ppg1 regulates gluconeogenic outputs in cells growing in a synthetic medium with glucose as the only carbon source.
Data showing the relative levels of UDP-GlcNAc and F16BP in ppg1D cells after 18 hours of growth in SD medium:
__Given that all the experiments conducted in this manuscript were performed using YPD medium, and YPD is better reflective of a complex nutrient medium that natural yeasts would be exposed to (and more relevant to adaptation in changing nutrient sources), we feel that the manuscript remains more readable and relevant in YPD (complex medium). Incorporating these data from minimal medium in the manuscript would disrupt its coherence and the overall flow. In addition, the very elaborate estimates of carbon flux through gluconeogenesis, using 13C label tracking, have been done in this medium only. As noted earlier, this is the first study (to our knowledge) to do this in the context of any phosphatase regulating gluconeogenic flux. Repeating the entire study in minimal, defined medium is therefore impractical. However, we have included these data for the reference of the reviewer, and believe it addresses the primary concerns. __
- While loss of PP1G does not affect growth on glucose, cells entered post-diauxic shift show some latency, suggesting that they would resume more slowly on gluconeogenic substrates, which is mainly ethanol. Thus, it might be relevant to check whether this Ppase is not important growth on gluconeogenic substrate, such as ethanol and acetate (not glycerol at least if using mineral synthetic medium such as YNB as this is not a good substrate), and clearly do this minimal medium (YNB) to get rid of other carbon substrates. Our results (included in the manuscript) indicate that the ppg1D cells show reduced growth in the post-diauxic phase. We carried out a shift experiment to investigate if these cells resume growth slowly when shifted to gluconeogenic substrates. We cultured ppg1D cells in glucose-replete conditions and shifted them to a medium with ethanol as sole carbon source. As anticipated, we observed that the ppg1D cells resumed growth at a slower rate in ethanol containing medium.
Data showing the growth dynamics of ppg1D cells after shift to ethanol containing medium:
Given that all the experiments conducted in this manuscript were performed using YPD medium, and no shift experiments were included (as explained earlier), we believe that incorporating this data in the manuscript could disrupt its coherence, cause confusion to readers, and disrupt the overall flow. However, we include this for the reviewer, to address this point, but would prefer to not include it in the manuscript.
- Technical methods for quantifying intracellular metabolites are missing! There is a link to a paper from the same authors that is even not accessible! Measuring intracellular metabolites is very tricky as how quenching, sampling and extraction have been made are critical to get reliable data. We apologize for this. Having studied trehalose and flux towards this for so long (e.g. see PMID: 32876564, PMID: 31758251, PMID: 27090086, and PMID: 31241462), we inadvertently took for granted some of the details of these methodologies. We have extensively described many ways to quantitatively estimate trehalose production and flux in this methods paper PMID: 32181267 (also see some other references particularly PMID: 32876564, PMID: 31758251, PMID: 27090086, and PMID: 31241462. We have now modified the methods section and mentioned the detailed extraction protocols and methods to measure trehalose (Line 668).
“The metabolite extraction and analysis were carried out following protocols described in (Walvekar et al, 2019). For each experiment, 10 OD600 cells were used for metabolite extraction. First, the cells were quenched for 5 minutes in 60% methanol (maintained at -45oC). After centrifugation, the cell pellet was resuspended in the extraction buffer (75% ethanol) and kept at 80oC followed by incubation on ice and centrifugation. The supernatant was collected, dried, and then stored at -80oC till further use.”
In addition, we have now included all the mass spectrometry parameters, as well as all the mass spectrometry raw data values as supplementary tables, so that any reader can analyse and quantify these metabolites.
- Taking into account metabolites levels reported, the 3 to 4 fold levels of G6P and UDPGlc can account for higher capacity of trehalose accumulation because the trehalose synthase (TPS) displays Km that are in mM range for these metabolites and thus any increase of these metabolites will increase rate of TPS (old publication by {Vandercammen, 1989 #3278;Londesborough, 1993 #3899} Thanks for this note. Indeed, a major idea that is nucleated by our study is the possible roles of mass action based control of gluconeogenic flux. This is also related to some of our responses included earlier. We have now contextually discussed the importance of mass action-based regulation in the discussion section, and included key references. ____Included in the discussion (Line 466).
“This function of Ppg1, as uncovered in this study, differs from regulation mediated by related phosphatases. How might this occur? An underappreciated but important mediator of metabolic adaptation is the direct modulation of metabolic outputs or flux, through a combination of mass action and allosteric regulation (and without invoking transcriptional changes). Even in unicellular organisms like S. cerevisiae, over 50% of metabolic regulation occurs through such mechanisms (Hackett et al, 2016). In this study, the loss of Ppg1 increases the levels of gluconeogenic intermediates, precursors of cell wall and storage carbohydrates (Fig. 2A). Increasing flux towards G6P and UDP-glucose would be one way of supporting the increased synthesis of storage carbohydrates without requiring alterations in enzyme levels, driven primarily by mass action. Classic studies of the trehalose synthesis enzymes in yeast (Vandercammen et al, 1989; Londesborough & Vuorio, 1993) indicate this possibility.”
- 13C-labelling indicates a higher GNG flux in a pp1gD strain. Thus, one might expect faster growth resumption, which is the opposite that what is observed in a pp1g deletion strain? How to reconcile these data? In the post-diauxic phase, the ppg1D cells exhibit increased gluconeogenic flux, suggesting an imbalanced carbon allocation. However, this increased gluconeogenic flux need not necessarily support better adaptation on gluconeogenic substrates. What is really important to a cell is the balance of allocation of carbon resources (discussed more extensively in a recent study from our lab: (Rashida et al, 2021 PMID: 33853774), which we now contextually cite here). In ppg1D cells, this imbalance in carbon allocation results in increased consumption of amino acids towards gluconeogenic outputs and might limit their availability for other cellular processes resulting in reduced growth and biomass production. Hence, even though the gluconeogenic flux is higher in ppg1D cells, these cells have reduced growth in post-diauxic phase. Achieving a precise equilibrium of flux towards different outputs is crucial for optimum growth or appropriate adaptation. This is an interesting and non-intuitive point, hence we now more extensively discuss this in the manuscript. Included in the discussion (Line 477).
“This increase in gluconeogenic flux in ppg1D cells indicates an imbalance in carbon allocations, resulting in increased consumption of amino acids towards gluconeogenic outputs, and therefore might limit their availability for other cellular processes. Hence, even though the gluconeogenic flux is higher in ppg1D cells, these cells have reduced growth in the post-diauxic phase. This plausible mode of regulation via Ppg1 could be systematically investigated in future studies, as an example of regulation mediated via some combination of mass action, concentration, allostery and enzyme regulation. These additional mechanisms (through scaffolding systems working together with signaling systems) to mediate overall metabolic outputs might be more prevalent than currently appreciated. In this context, we recently identified a signaling axis with Snf1 (AMPK) and TORC1 (via Kog1) in enabling precise carbon allocations, ensuring optimum growth and adaptation during nutrient limitation (Rashida et al, 2021).”
- Sensibility of mutant cells to CR is borderline. Could you confirm with Calcofluor white which usually is more sensitive to minor cell wall modification, and notably when chitin is increased This is a good suggestion. We studied the growth of ppg1D cells in the presence of Calcofluor white and observed a similar growth defect as seen with CR, but the images are very clear. Some of this is a reflection of the nature of our light-box black-and white camera (and visibly and in color, the plates look much better!). We have now added this data in Fig S2D and mentioned this in the text (Line 206).
“Increased chitin accumulation is known to sensitize cells to cell wall stress (Ram & Klis, 2006; Vannini et al, 1983); hence, we studied the growth of ppg1D cells in the presence of two cell wall stress agents, Congo red and Calcofluor white. Expectedly, (and as observed earlier (Hirasaki et al, 2010)) the growth of ppg1D cells was reduced in the presence of either Congo red or Calcofluor white (Figure S2C, D).”
Data showing the growth of ppg1D cells in the presence of Calcofluor white (now new Fig. S2D):
- Is there any idea about the phosphorylation site on far11. I did not check on the phosphoproteomes data, but this might be worth to do and in that case, the loss of this phosphorylation shall be similar to loss of pp1G (no necessary to do that in this report) Addressed extensively in the section 2 of this document.
-
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Referee #4
Evidence, reproducibility and clarity
General comments
This paper reports a critical function of PP2A-like phosphatase encoded by PP1G in the post-diauxic shift of the yeast Saccharomyces cerevisiae. This function is mediated via the assembly of FAR complex that naturally sites at the ER-mitochondria outer membranes to ensure proper onset of growth at the diauxic shift by appropriate carbon allocation through gluconeogenesis. The identification of this PPase was based on a screen of yeast mutant defective in non-essential PPases for trehalose accumulation. This is a bit surprising as it is known that trehalose accumulation sets in as soon as glucose is depleted and continues steadily during growth on other carbon source, which is merely ethanol, although it may depend whether the experiment was carried out in YPD or in mineral synthetic medium as YNB.
Although the work seems experimentally well conduct, in particular for the demonstration that bPP1G interacts with FAR complex, it raises several issues requiring a thorough revision and additional experiments to truly support the role of PP1F in regulating post-diauxic shift
- all experiments were done using YPD medium and only a single value of trehalose at 24 h was recorded! It will be important to ensure that all mutant had exactly same growth rate, that at 24 h, glucose was totally gone. It should be relevant to have a more complete kinetic analysis of trehalose/ glycogen accumulation along growth, monitoring as well glucose consumption in WT and the pp1g, to really convince that the metabolic difference is not associated with a difference in glucose consumption, such as that at 24 h, there is still some glucose remaining in the culture of the mutant!
- The experiment at least with pp1g and WT should be redo in mineral synthetic medium with 2% glucose. There, only ethanol can be the sole carbon for growth resumption and thus this will ensure that the effects is linked to growth resumption at diauxic growth as YPD is a rich medium that contains excess of many amino acids and peptides that may interfere with your phenotype
- While loss of PP1G does not affect growth on glucose, cells entered post-diauxic shift show some latency, suggesting that they would resume more slowly on gluconeogenic substrates , which is mainly ethanol. Thus, it might be relevant to check whether this Ppase is not important growth on gluconeogenic substrate, such as ethanol and acetate (not glycerol at least if using mineral synthetic medium such as YNB as this is not a good substrate), and clearly do this minimal medium (YNB) to get rid of other carbon substrates
- Technical methods for quantifying intracellular metabolites are missing! There is a link to a paper from the same authors that is even not accessible! Measuring intracellular metabolites is very tricky as how quenching, sampling and extraction have been made are critical to get reliable data
- Taking into account metabolites levels reported, the 3 to 4 fold levels of G6P and UDPGlc can account for higher capacity of trehalose accumulation because the trehalose synthase (TPS) displays Km that are in mM range for these metabolites and thus any increase of these metabolites will increase rate of TPS (old publication by {Vandercammen, 1989 #3278;Londesborough, 1993 #3899}
- 13C-labelling indicates a higher GNG flux in a pp1g strain. Thus, one might expect faster growth resumption, which is the opposite that what is observed in a pp1g deletion strain? How to reconcile these data?
- Sensibility of mutant cells to CR is borderline. Could you confirm with Calcofluor white which usually is more sensitive to minor cell wall modification, and notably when chitin is increased
- Is there any idea about the phosphorylation site on far11. I did not check on the phosphoproteomes data, but this might be worth to do and in that case, the loss of this phosphorylatuion shall be similar to loss of pp1G (no necessary to do that in this report)
The references should be carefully revised because many of them are incomplete, lacking journal name or wrong name, number, issues, pages etc.
Significance
Although the work seems experimentally well conduct, in particular for the demonstration that bPP1G interacts with FAR complex, it raises several issues requiring a thorough revision and additional experiments to truly support the role of PP1F in regulating post-diauxic shift
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Referee #3
Evidence, reproducibility and clarity
The study performed by Niphadkar et al. seeks to uncover the role of the phosphatase Ppg1 in regulating gluconeogenesis during post-diauxic shift in S. cerevisiae. Thea authors show that loss or inactivation of Ppg1p affects production of gluconeogenic products incl. trehaloase and glycogen. The authors show that assembly of the Far complex required the activity of Ppg1 and is required to maintain gluconeogenic outputs after glucose depletion.
The manuscript is clearly written and methods well considered, no omics-methods have been included. Especially phosphoproteomics would be relevant to include. Specifically, the tracing experiments are an interesting and appropriate approach to confirm effects on gluconeogeneisis etc. Yet, working with regulation of posttranslational modifications (phosporylations) it is surprising that the authors only to a limited extent examine phosphorylation events, and not all examine or discuss specific phosphorylation events of e.g. Far11.
The study is interesting and provides new insights into regulation of glucose metabolism in yeast, however, there are serious concerns that need to be addressed before it can be reconsidered for publication.
Major points:
The authors use electrophoretic mobility assays w/wo CIP to address the phosphorylation state of Far11. They show in figure 3E that the mobility of Far11 depends on Ppg1 activity and can be affected by CIP. Why is the mobility of Far11 not affected in e.g. figure 3D?
There are several sites in Far11 previously reported to be phosphorylated, see e.g. Bodenmiller et al 2010 (Science Signal.) Are there sites that are specifically regulated (dephosphorylated) by Ppg1? or by other phosphatases? kinases?
Here, it would be appropriate to apply phosphoproteomics to examine Far11 phosphorylation in Ppg1 knock out cells or in cells with inactivated Ppg1.
The authors show that the levels of Ppg1 remain constant during growth in YPD medium, while the levels of Far11 increased after 24hrs of growth in YPD medium, and thus argue that the amount of Far complex itself increases in post-diauxic phase. The authors need to show that the level of complex indeed increases.
The authors also apply fluorescence microscopy to address the localization of the Far11 complex etc. The quality of the shown images should be improved, also merged images should be shown. Only one single image containing one cell is shown, images should ideally show additional cells in the same image, alternatively, additional images should be shown.
Minor points:
Does the FLAG tag affect activity of Ppg1?
Significance
The study is interesting and provides new insights into regulation of glucose metabolism in yeast, however, there are serious concerns that need to be addressed before it can be reconsidered for publication.
The manuscript is of broad interests for an audience primarily interested glucose metabolism and signalling in yeast.
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Referee #2
Evidence, reproducibility and clarity
Summary:
In this manuscript, the authors screened the yeast phosphatase mutant that shows defective in metabolic adaptation and found that PP2A-like phosphatase Ppg1 is required for the appropriate gluconeogenic outputs after glucose depletion. Furthermore, they showed that Far complex which assembles with Ppg1 is also required to maintain gluconeogenic outputs. They also found that Ppg1 is required for assembly of Far complex and the assembly on the ER or mitochondrial membrane is important for their function. Ppg1 and Far complex dependent control of gluconeogenic outputs had important role on adaptive growth under glucose depletion.
Major comments:
In this study, the authors report new evidence that the Ppg1 and Far complexes are involved in the regulation of gluconeogenic outputs. However, the mechanism by which the Ppg1-Far complex is involved in gluconeogenic outputs has not been fully analyzed, and further analysis of the role of Ppg1 in Far complex assembly and the significance of Far11 phosphorylation is needed. The authors should consider the following points,
1.In the mutant screen, both pph21Δ and pph22Δ cells showed increased level of trehalose (figure 1C). Pph21 and Pph22 are catalytic subunits of protein phosphatase 2A (PP2A) and function redundantly. Thus, it may be possible that PP2A is more involved in gluconeogenic outputs regulation than Ppg1. 2. Is it the Far complex or Ppg1 activity that is required for the regulation of gluconeogenic outputs? It seems that assembly of the Far complex requires Ppg1 and Ppg1 activity requires the Far complex. However, either one should be involved in the regulation of gluconeogenic outputs. For example, Innokentev et al, 2020 concluded that Ppg1 activity is critical for the regulation of mitophagy and that the Far complex serves only as a scaffold for Ppg1. by Ppg1 dephosphorylating an unidentified protein. The possibility that Ppg1 may be involved in the regulation of glycolytic output by dephosphorylating unidentified substrates needs to be fully tested. 3.Although there are no known substrates of Ppg1 other than Atg32, Atg32 is not involved in the regulation of gluconeogenic outputs. The identification of substrates of Ppg1 involved in the regulation of gluconeogenic outputs will help to elucidate the molecular mechanism of gluconeogenesis. 4. The authors conclude that Ppg1 dephosphorylates Far11 and that dephosphorylated Far11 assembles with the Far complex. However, there is a possibility that Ppg1 activity is required for Far complex assembly independently of dephosphorylation of Far11. To prove the authors' assertion, it is necessary to identify the phosphorylation site of Far11 and show that its phosphorylation affects the binding of Far11 to Far8. 5. Several kinases have been reported to be involved in gluconeogenic outputs regulation. The initial aim of this study was to identify phosphatases involved in gluconeogenic outputs regulation by antagonizing these kinases. However, Ppg1 has not been shown to be involved in transcriptional regulation to control carbon metabolism by antagonizing any kinase. 6. If the assembly of the Far complex is involved in gluconeogenic outputs regulation, what is the mechanism? The Far complex is a scaffold for enzymes. Therefore, the role of the Far complex in gluconeogenic outputs regulation will not be elucidated until the enzymes that function there are identified.
Minor comments:
- Because TA of Far10 can tether Far complex on the membrane, Mito-Far and ER-Far experiments (Figure 4A-D) should be performed under Far10ΔTA conditions.
- Figure 6B and 6C, total culturing time (hours) should be shown on X-axis in addition to number of transfers.
- Figure 3E, additional explanation is needed as to why the molecular mobility of Far11-FLAG after CIP treatment differs between Ppg1-H111N and Ppg1.
Significance
General assessment:
The discovery that Ppg1 and the Far complex are involved in the regulation of gluconeogenic outputs is novel. However, other studies on the assembly of the Far complex and the role of Ppg1 are very superficial and do not support the authors' claims.
Advance:
There are few reports of phosphatases involved in the regulation of gluconeogenesis. In this regard, the identification of Ppg1 and its involvement in the regulation of gluconeogenesis is a precedent.
Audience:
Cellular metabolism, yeast genetics
The expertise of this reviewer: yeast genetics, cell biology
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Referee #1
Evidence, reproducibility and clarity
The authors study metabolic adaptation to glucose depletion in budding yeast. A non-essential protein phosphatase mutant screen reveals adaptation to glucose depletion (growth in post-diauxic phase) requires Ppg1. The authors show i) that, in post-diauxic phase, cells lacking Ppg1 accumulate more trehalose, glycogen, UDP-glucose, UDP-GlcNAc (i.e., gluconeogenic outputs) than wild-type cells, ii) that, in post-diauxic phase, cells expressing a catalytically inactive version of Ppg1 accumulate more trehalose and iii) that Ppg1 is required for adaptation and growth post-glucose depletion. The authors find that Ppg1 interacts with Far11 (a member of the Far complex) in cells growing in post-diauxic phase and that Ppg1 promotes Far complex stability. Finally, the authors conclude that the Ppg1 promotes Far complex stability to maintain gluconeogenic outputs after glucose depletion.
Major comments:
- Figures 1 to 4. The authors show that loss of Far components phenocopies loss of Ppg1 and conclude that that Ppg1 is upstream of Far. However, the authors do not determine the combined effect of the two mutations. The authors should assess the phenotype (e.g., gluconeogenic outputs levels) of cells lacking both Ppg1 and Far (or in far9_deltaTA far10_deltaTA cells lacking Ppg1). The authors' conclusion would be strengthened if there was no additive effect between the mutations.
- Related to Figure 6A-C. One would expect that cells lacking Far components (or far9_delta TA far10_deltaTA cells) showing a similar phenotype (fail to adapt to growth in changing glucose compared with wild type cells) as cells lacking Ppg1. Is this the case?
- The manuscript would be considerably strengthened if the authors provided more information on the mechanism by which Ppg1 controls Far complex stability, e.g., can the authors about the phosphosite(s) in Far11 regulated by Ppg1? As the authors mention, it has been already suggested that Ppg1 is required for Far complex assembly (PMID: 33317697).
Minor comments:
- The authors may consider to include data from Figures 6A, 6B and 6C (failure to adapt to glucose changing conditions) after Figure 2 to show a complete characterization of the phenotype of cells lacking Ppg1. Figure 6 could show only the "proposed model".
- Related to Figure 1. The authors mention that Ppg1 is a "notable hit" and that the "increase in post-diauxic trehalose levels are considerable". However, there is no reference to use as a comparison. Is there any other mutant strain known to accumulate trehalose at the post-diauxic shift? If yes, it would be informative if the authors compared the effect of such mutant strain to a ppg1-delta mutant.
- Figures 4D and 4F. Regarding sensitivity to Congo Red and compared to wild type cells, it sems that cells lacking Far9 are much more sensitive to Congo Red in Figure 4F than in Figure 4D. Is this just an image quality issue? The authors should address this apparent discrepancy.
Significance
The manuscript is well written and easy to follow. Data and methods are presented in a clear way. It provides interesting and relatively novel insights on the function of Ppg1, a poorly characterized protein phosphatase. It will be interesting mainly for the yeast community working on metabolism.
This reviewer's area of expertise is budding yeast cation homeostasis, protein phosphatases, TOR signaling and nutrient sensing.
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Reply to the reviewers
Response to reviewer comments
R: We really appreciate the reviewer positive comments and consideration, and we believe that the review process has significantly strengthened our manuscript.
We have responded to all the reviewer comments, as follows:
Response (R)
From Reviewer #1
Major comments: The manuscript is mostly well written (it could use a few minor grammatical corrections), the significance of the problem is well described, and the results are clearly presented with adequate controls. The movies, provided as supplementary material, are of the highest quality and are essential additions to the stills provided in the figures. The data convincingly support the key conclusions of the manuscript.
R: We really appreciate the reviewer positive comments, and we have revised the manuscript accordingly
1) Does the MO knockdown both S and L homoeologs of X. laevis? Since the level of GAPDH in Figure 1H also looks reduced in Gai2 MO lane, it should be made clear that the apparent knockdown of Gai2 was normalized to GAPDH, rather than being the results of unequal loading of the gel. Yes, I recognize that Figure 1I says normalized, but this is not stated in the results or the methods. Also, was this experiment done with X. laevis or X. tropicalis? I could imagine that if done in X. laevis, the lack of complete knockdown might be due to only one homoeolog being affected.
R: We appreciate the reviewer comment, and we described in Material and Methods section the region targeted by the morpholino, in both Xenopus species. We added the next paragraph in the Material and Methods section, see page 23 paragraph 1 lines: 7-11.
“The Gαi2 morpholino (Gαi2MO) was designed as described in the results section to target Gαi2 in both Xenopus species (Xenopus tropicalis and Xenopus laevis). Specifically, it hybridizes with the 5’ UTR of X. tropicalis Gαi2 (NM_203919), 17 nucleotides upstream of the ATG start codon. For X. laevis Gαi2, the morpholino hybridizes with both isoforms described in Xenbase. It specifically targets the 5' UTR of the Gαi2.L isoform (XM_018258962), located 17 nucleotides upstream of the ATG start codon, and the 5' UTR of the Gαi2.S isoform (NM_001097056), situated 275 nucleotides upstream of the ATG.”
With respect to Figure 1H and 1I, we have specified in the Figure 1 legend that we normalized the data to GAPDH to quantifying the decrease in Gαi2 expression induced by the morpholino.
See page 37, Figure 1H-I, Legends section.
2) The knowledge of the efficacy of knockdown in each Xenopus species provided by the information requested in the previous point, would allow the reader to assess the level of knockdown in the remaining assays. To do this, the authors should tell us which assays were done in which species. I am not suggesting that each experiment needs to be done in each species, only that the information should be provided. If the MO is more effective in X. tropicalis - which assays used this species? If the knock down is partial, as shown in Figure 1H-I, which species this represents in the remaining assays would be useful knowledge.
R: We greatly appreciate the reviewer's valuable comments and suggestions, and as a response, we have incorporated a new supplementary figure (Figure S1). This figure includes a western blot and an in situ hybridization assay illustrating the efficiency of the knockdown in Xenopus laevis. The results presented in Figure S1 demonstrate that the knockdown efficiency is similar in both Xenopus species, allowing for a comparison between Figure 1A-I (X. tropicalis) and Figure 1S (X. laevis).
To complement this information, we have also improved the section of Material and Methods regarding the experiments in both Xenopus species (Xenopus tropicalis and Xenopus laevis). As detailed in the Materials and Methods section, we employed 20 ng of Gai2MO for Xenopus tropicalis embryos and 35 ng of Gai2MO for Xenopus laevis embryos to deplete cell migration. In both species, in vivo migration was analyzed, resulting in a substantial inhibition of cranial neural crest (NC) migration, ranging from 60% to 80%. Additionally, we conducted dispersion assays in both species. In X. laevis, in vitro migration was monitored for 10 hours, while in X. tropicalis, it was tracked for 4 hours, both yielding the same phenotype. We also studied cell morphology and microtubule dynamics in both Xenopus models. However, we used different tracer concentrations for each, with 200 pg for X. laevis and 100 pg for X. tropicalis, as specified in the Materials and Methods section. Our Rac1 and RhoA timelapse experiments were conducted in both species as well, employing pGBD-GFP and rGBD-mCherry probes, respectively, and different probe concentrations as outlined in the Materials and Methods section. These experiments revealed polarity impairment and consistent Rac1 behavior in both Xenopus species. The study of focal adhesion in vivo dynamics using the FAK-GFP tracer was carried out also in both species, resulting in the same phenotype. It is worth noting that the only experiment conducted exclusively in X. tropicalis was the focal adhesion disassembly assay with nocodazole.
Regarding the improvements of the Materials and Method section see page 22, paragraph 2.
We want to highlight that at the beginning of the Materials and Methods section, we incorporated a paragraph to clarify that “all experiments were conducted in both Xenopus species (X.t and X.l) using distinct concentrations of the morpholino (MO) and mRNA, as specified in each respective methodology description”. This approach consistently yielded similar results. It is important to note that for the figures, we selected the most representative images.
We have also specified in each figure legend which Xenopus species is depicted.
Minor comments: While prior studies are referenced appropriately, and the text and figures are mostly clear and accurately presented, the following are a few suggestions that would help the authors improve the presentation of their data and conclusions:
1) The cell biological experiments convincingly demonstrate that knockdown of Gai2 causes cells to move more slowly. It would be a nice addition to bring the explant experimental data back to the embryo by showing whether the slower moving NC cells in morphants eventually populate the BA. DO they cease to migrate or are they just slower getting to their destination? This could be done by performing snail2 ISH at a later stage (34-35?)
R: We appreciate the reviewer's insightful point and are currently conducting the in situ hybridization assay at stages 32-36 to address this question. Our plan includes incorporating a supplementary figure showing the results of this assay and integrating this information into both the results and discussion sections.
2) There are places in the manuscript where the authors use the terms "silencing" or "suppression" of Gai2, when they really mean reduced translation - their system is not a genetic knockout, as clearly demonstrated in Figure 1H-I. I suggest that more accurate wording be used.
R: We appreciate the reviewer's comment, and we agree that the Gαi2 morpholino impedes Gαi2 translation, leading to a reduction in Gαi2 protein expression. Consequently, we have revised the entire manuscript, replacing the terms “silencing” and “suppression” with “knockdown”.
3) In Figures 1-5 there are scale of bars on the cell images, but these are not defined in any of the figure legends.
R: We value the reviewer's comment, and we have revised all the figure legends by including the scale information. Each image has been scaled to 10 µm with varying magnifications.
4) The abstract is the weakest section of the manuscript, and would have greater impact if it were more clearly written.
R: We appreciate the reviewer's comment on the abstract, and we have revised and edited it to enhance its quality.
Abstract:
“Cell migration is a complex and essential process in various biological contexts, from embryonic development to tissue repair and cancer metastasis. Central to this process are the actin and tubulin cytoskeletons, which control cell morphology, polarity, focal adhesion dynamics, and overall motility in response to diverse chemical and mechanical cues. Despite the well- established involvement of heterotrimeric G proteins in cell migration, the precise underlying mechanism remains elusive, particularly in the context of development.
This study explores the involvement of Gαi2, a subunit of heterotrimeric G proteins, in cranial neural crest cell migration, a critical event in embryonic development. Our research uncovers the intricate mechanisms underlying Gαi2 influence, revealing its interaction with tubulin and microtubule-associated proteins such as EB1 and EB3, suggesting a regulatory function in microtubule dynamics modulation. Gαi2 knockdown leads to microtubule stabilization, alterations in cell morphology and polarity, increased Rac1-GTP concentration at the leading edge and cell-cell contacts, impaired cortical actin localization and focal adhesion disassembly. Interestingly, RhoA-GTP was found to be reduced at cell-cell contacts and concentrated at the leading edge, providing evidence of Gαi2 significant role in polarity. Remarkably, treatment with nocodazole, a microtubule-depolymerizing agent, effectively reduces Rac1 activity, restoring cranial NC cell morphology, actin distribution, and overall migration. Collectively, our findings shed light on the intricate molecular mechanisms underlying cranial neural crest cell migration and highlight the pivotal role of Gαi2 in orchestrating microtubule dynamics through EB1 and EB3 interaction, modulating Rac1 activity during this crucial developmental process.”
The molecular regulation of cell movement is a key feature of a number of developmental and homeostatic processes. While many of the proteins involved have been identified, how they interact to provide motility has not been elucidated in any great detail, particularly in embryo-derived cells (as opposed to cell lines). The results obtained from the presented experiments are novel, in-depth and provide a novel paradigm for how G proteins regulate microtubule dynamics which in turn regulate other components of the cytoskeleton required for cell movement. The results will be applicable to many migrating cell types, not just neural crest cells.
Because of the application of the data to many types of cells that migrate, the audience is expected to include a broad array of developmental biologists, basic cell biologists and those interested in clinically relevant aberrant cell migrations.
R: We really appreciate the reviewer positive comments and consideration
From Reviewer 2
Major comments
The authors aim to address two issues in this manuscript: a) the role of Gai2 in neural crest development; and b) the mechanism of Gai2 function. While they have done a good job demonstrating a role of Gai2 in NC migration both in vivo and in vitro as well as the effects of Gai2 knockdown on cytoskeleton dynamics, protein distribution of selected polarity and focal adhesion molecules, and Rac1 activation, the link between Gai2 and the downstream effectors is largely correlative. Because of this, the model suggesting the sequential events flowing from Gai2 to microtubule to Rac1 to focal adhesion/actin should be modified to allow room for direct and indirect regulation at potentially multiple entry points.
R: We appreciate the reviewer's valuable comments. We concur with the reviewer's observation that our experiments do not establish a causal link between Gαi2, EB1/EB3, and Rac1. We established a relationship between Gαi2 and microtubule dynamics (EB1 and EB3) to regulate Rac1 polarity through co-immunoprecipitation assays, which reveal protein interactions within an interactor complex. Therefore, while our findings support the involvement of Gαi2 in coordinating cranial NC cell migration alongside EB1, EB3, and Rac1, we cannot exclude the possibility that this regulation may occur through other intermediary proteins, such as GEFs, GAPs, GDIs, and others. As a result, we have revised our model and its description in accordance with the reviewer suggestion.
We have edited the discussion/conclusion, model and the legend at Figure 6. See page 16 (paragraph 2, last line), 17 (paragraph 1, last line), 22 (paragraph 1, last line 17-20), 42 (Legend Fig. 6).
Specific major comments are as the following: Strengths: -Determination of a role of Gai2 in neural crest migration is novel. -The effect of Gai2 knockdown on membrane protrusion morphology and microtubule stability and dynamics are demonstrated nicely. -Quantification of experimental perimeters has been performed throughout the manuscript in all the figures, and statistical analysis is included in the figures.
R: We appreciate the reviewer positive comments
Weaknesses: -The heavy focus of the study on microtubule is due to the previous publication on the function of Gai2 in regulation of microtubule during asymmetrical cell division. However, the activity of Gai2 is likely cell type-specific, as it has not been shown to control microtubule during cytokinesis in general. It is equally likely that Gai2 primarily regulates Rac1 or actin regulators to influence both microtubule and actin dynamics. The tone of the discussion should therefore be softened.
R: We greatly appreciate and agree with the comment from the reviewer, highlighting the possibility that Gαi2 primarily regulates Rac1 or actin regulators to influence both microtubule and actin dynamics. In this regard, we have revised our manuscript to include a discussion of this point. We added the next paragraph in the Discussion/Conclusion section, page 20-21.
“It is well established that the activity from the Rho family of small GTPases is controlling cytoskeletal organization during migration (Ridley et al., 2015). Contrariwise, it has been described in many cell types, that microtubules dynamic polymerization plays a crucial role in establishing the structural foundation for cell polarization, consequently influencing the direction of cell motility (Watanabe et al., 2005). Our results appear to align with this latter view. While it is reasonable to postulate the possibility that Gαi2 regulates Rac1 activity, subsequently influencing actin and microtubule dynamics, our findings in the context of cranial NC cells, lend support to an alternative sequence of events. Initially, Gαi2 knockdown leads to a decrease in microtubule dynamics, which in turn increase Rac-GTP towards the leading edge. This shift is accompanied by reduced levels of cortical actin and impaired focal adhesion disassembly, culminating in compromised cell migration. Notably, nocodazole, a microtubule-depolymerizing agent, not only diminishes Rac-GTP localization at the leading edge but also rescues cell morphology, restores normal cortical actin localization, and promotes focal adhesion disassembly, thereby facilitating cell migration. If Rac1 activity were indeed upstream of microtubules, it would be expected that nocodazole would not reduce Rac-GTP levels at the cell leading edge. These results suggest that the regulation of Rac1 activity may follow, rather than precede, alterations in microtubule dynamics, in the context of NC cells. Furthermore, in support of our model, our protein interaction analysis demonstrates Gαi2 interacting with microtubule components such as EB proteins and tubulin. As we already mention above, earlier studies have reported that microtubule dynamics promote Rac1 signaling at the leading edge and by releasing RhoGEFs promote RhoA signaling as well (Best et al., 1996; Garcin and Straube, 2019; Moore et al., 2013; Waterman-Storer et al., 1999). In addition, it is well-documented that RhoGEFs interact with microtubules, including bPix, a GEF for Rac1 and Cdc42, which, in turn, promotes tubulin acetylation (Kwon et al., 2020). Interestingly, in ovarian cancer cells, Gαi2 has been shown to activate Rac1 through an interaction with bPix, thereby jointly regulating migration in response to LPA (Ward et al., 2015). Taken together, these findings further support our proposed model (refer to Fig. 6).”
-The effect of rescue of NC migration with Rac1 inhibitor is marginal and the result is hard to interpret considering the inhibitor also blocks control NC migration. Either lower doses of Rac1 inhibitor can be used or the experiment can be removed from the manuscript, as Rac1 is required for membrane protrusions and the inhibitor doses can be hard to titrate.
__ R: We appreciate and agree with the reviewer's comments. To address this concern and enhance clarity, we have incorporated the following paragraph into the manuscript within the Discussion section. Additionally, we have included information on the range of NSC23766 concentrations used for this analysis in the Materials and Methods section. Page 24, __Explants and microdissection.
“It is worth noting that we conducted Rac inhibitor NSC23766 trials at concentrations ranging from 20 nM to 50 nM for X. laevis and between 10 nM to 30 nM for X. tropicalis. In both cases, higher concentrations of the Rac inhibitor proved to be lethal, underscoring the essential role of Rac1 in both cell migration and cell survival. Specifically, the described concentrations of 20 nM for X. laevis and 10 nM for X. tropicalis led to a partial rescue of the observed phenotype. This suggests that these concentrations are sufficient to demonstrate that the increase in Rac1-GTP resulting from Gαi2 morpholino knockdown impairs cell migration. The partial rescue can be attributed to the crucial role of microtubule dynamics in cell migration, which acts upstream of Rac activity. Additionally, Rac is pivotal for the modulation of cell polarity at the leading edge of migration. It is worth emphasizing that Rac1 levels are critical for cell migration, as demonstrated by other researchers. Lower concentrations of Rac1-GTP have been shown to hinder cell migration in cells deficient in Rac1, leading to a significant reduction in wound closure and random cell migration (Steffen et al., 2013).
Therefore, we believe that the lower concentration of NSC23766 used in our assay was adequate to reduce the abnormal Rac1-GTP activity in the morphant NC cells. However, it is important to note that for normal NC cell, this level of reduction in Rac1-GTP activity is critical and sufficient to impair normal migration”.
See page 12, paragraph 2, lines 8-11, 14-16, 23-25.
Steffen A, Ladwein M, Dimchev GA, Hein A, Schwenkmezger L, Arens S, Ladwein KI, Margit Holleboom J, Schur F, Victor Small J, Schwarz J, Gerhard R, Faix J, Stradal TE, Brakebusch C, Rottner K. Rac function is crucial for cell migration but is not required for spreading and focal adhesion formation. J Cell Sci. 2013 Oct 15;126(Pt 20):4572-88. doi: 10.1242/jcs.118232. Epub 2013 Jul 31. PMID: 23902686; PMCID: PMC3817791.
-Since the defects seem to result partially from the inability of the NC cells to retract and move away, it may help to either include some data on Rho activation patterns in knockdown cells or simply add some discussion about the issue.
R: We acknowledge and sincerely appreciate the reviewer's valuable comments on this pivotal aspect, which significantly enhances our capacity to elucidate the impact of Gαi2 knockdown on cell polarity. To address this crucial point, we have introduced an experiment that examines RhoA-GTP localization under Gαi2 knockdown conditions, and we have incorporated a supplementary figure S3 into our manuscript. This newly added figure clearly demonstrates that, under Gαi2 knockdown conditions, and in contrast to control cells, RhoA-GTP localization is substantially disrupted at cell-cell contacts and now detected at the leading edge of the cell, providing compelling evidence of cell polarity defects (refer to Figure S3). In response to these results, we have included a description of these findings in the Results section (please see page 11-12) and a dedicated paragraph in the Discussion section (please see page 18, paragraph 2, line 15-16, page 19, paragraph 1, lines 6-12).
Results section 1: “To achieve this, we explored whether Gαi2 regulates the subcellular distribution of active Rac1 and RhoA in cranial NC explants under Gαi2 loss-of-function conditions, considering their pivotal roles in cranial NC migration and contact inhibition of locomotion (CIL) (Carmona-Fontaine et al., 2011; Moore et al., 2013; Leal et al., 2018). Hence, we employed mRNA encoding the small GTPase-based probe, enabling specific visualization of the GTP-bound states of these proteins.”
Results section 2: “Consistent with earlier observations by Carmona-Fontaine et al. (2011), in control cranial NC cells, active Rac1 displayed prominent localization at the leading edge of migrating cells, whereas its presence was reduced at cell-cell contacts, coincident with an increase in RhoA-GTP levels (white arrows in Fig. 4A, supplementary material Figure S3A). On the contrary, in comparison to the control cells, Gαi2 morphants exhibit a pronounced accumulation of active Rac1 protein in the protrusions at cell-cell contacts, where active RhoA localization is conventionally expected (white arrow in Fig. 4B, supplementary material Figure S3A and movie S6). In contrast to control cells, a notable shift in the localization of active RhoA protein was observed, with its predominant accumulation now detected at the leading edge of the cell, instead of the typical localization towards the trailing edge or cell-cell contacts (__supplementary material Figure S3B). __These findings suggest a dysregulation of contractile forces that align with the observed distribution of active RhoA, cortical actin disruption, and diminished retraction in cell treated with Gαi2MO.”
Discussion section:
“Other studies have reported that microtubule assembly promotes Rac1 signaling at the leading edge, while microtubule depolymerization stimulates RhoA signaling through guanine nucleotide exchange factors associated with microtubule-binding proteins controlling cell contractility, via Rho-ROCK and focal adhesion formation (Krendel et al., 2002; Ren et al., 1999; Best et al., 1996; Garcin and Straube, 2019; Waterman-Storer et al., 1999; Bershadsky et al., 1996; Moore et al., 2013). This mechanism would contribute to establishing the antero-posterior polarity of cells, crucial for maintaining migration directionality, underscoring the significance of regulating microtubule dynamics in directed cell migration. These findings closely align with the results obtained in this investigation, demonstrating that Gαi2 loss of function reduces microtubule catastrophes and promotes tubulin stabilization, resulting in increased localization of active Rac1 at the leading edge and cell-cell contacts, while decreasing active RhoA at the cell-cell contact but increasing it at the leading edge. This possibly reinforces focal adhesion, which is consistent with the presence of large and highly stable focal adhesions under Gαi2 knockdown conditions. This finding also suggests a dysregulation of contractile forces in comparison to control cells, a result that aligns with the observed distribution of active RhoA, cortical actin distribution and diminished retraction in cells treated with Gαi2MO. This strikingly contrasts with the normal cranial NC migration phenotype, where Rac1 is suppressed while active RhoA is increased at cell-cell contacts during CIL, leading to a shift in polarity towards the cell-free edge to sustain directed migration (Theveneau et al., 2010; Shoval and Kalcheim, 2012; Leal et al., 2018).”
-To consider focal adhesion dynamics, live imaging should be used in the analysis. The fixed samples are different from each other, and natural variations of focal adhesion may exist among the samples. This can obscure data collection and quantification.
R: We agree with the reviewer that focal adhesion (FA) dynamics need to be analysed using live imaging. Indeed, Fig 5E-H shows an extensive analysis of FA using live imaging of neural crest expressing FAK-GFP. As complement to this live imaging analysis, and in order to analyse the effect on the endogenous levels of FA proteins, we performed immunostaining against FA. Both experiments using live imaging or fixed cells produce similar results, and they are consistent with our model on the role of Gαi2 on FA dynamics.
Minor comments -Fig. 2, the centrosomes in control cells are not always obvious. The microtubules simply seem to be more networked and more fluid in control cells. This should be clarified with either marking the centrosomes in the figure or modifying the wording in the manuscript.
R: We appreciate and concur with the reviewer's comment on this matter. As pointed out by the reviewer, the precise localization of the centrosome is not consistently clear in all cells. In response to this observation, we have revised the manuscript to emphasize this aspect solely as “microtubule morphology”. Please refer to the Results section description Figure 2.
-In Fig. 3, a better negative control for co-IP should be using anti-V5 antibody to IP against tubulin/EB1/EB3 in the absence of Gai2-V5.
R: We appreciate the reviewer's comment, and we agree about the controls that the reviewer suggest. We can inform that we have done by triplicate all the Co-IPP. Although, if is necessary we will do the controls suggested. We present this assay as a plan.
-The data for cell polarity proteins Par3 and PKC-zeta seem to be out of place. It is unclear whether mis-localization of these proteins has anything to do with NC migration defects induced by Gai2 knockdown. The conclusion does not seem to be affected if the data are taken out of the manuscript.
R: We appreciate the reviewer's concern, and we would like to highlight two points in this regard. Firstly, we have included these results as additional data to support the impact of Gai2 knockdown on cell polarity, given that these two proteins are commonly used polarity markers. Secondly, we have discussed this aspect extensively in the Discussion section of the manuscript. (See page 19, paragraph 1, lines 18-28)
In that section, we delve into the relationship between aPKC, Par3, and Gαi2 in controlling cell polarity during asymmetric cell division, as described in Hao et al., 2010. Par3 is known to play a role in regulating microtubule dynamics and Rac1 activation through its interaction with Rac-GEF Tiam1 (Chen et al., 2005). Additionally, it has been shown to promote microtubule catastrophes and inhibit Rac1/Trio signaling, regulating Contact Inhibition of Locomotion (CIL) as demonstrated in Moore et al., 2013. Thus, we believe that the data we present support the relationship between Par3 and aPKC localization changes and the neural crest migration defects induced by Gαi2 knockdown, probably by controlling microtubule dynamics. However, we have moved these results as part of the supplementary Figure S3.
-In Suppl. Fig. 1, protrusion versus retraction should be defined more clearly. The retraction shown in this figure seems to be just membrane between protrusions instead of actively retracting membrane.
R: We appreciate the reviewer's comments, and here we aim to provide a clearer description of our approach to this analysis. For the measurement of protrusion extension/retraction, we conducted two distinct experiments. The first, as described in Figure 1, involved measuring membrane extension and retraction in live cell using membrane-GFP by utilizing the image subtraction tool in ImageJ, which highlights changes in the membrane in red. Secondly, we employed ADAPT software to quantify cell perimeter based on fluorescence intensity in live cell using lifeactin-GFP, distinguishing membrane extension in green and retraction in red (as has been shown similarly in Barry et al., 2015). In both approaches, we observed a substantial increase in membrane protrusion (both in area and extension) and protrusion stability in Gαi2 morphants. Hence, we have revised the Materials and Methods section of the manuscript and included this clarification.
See Materials and Methods section, Cell dispersion and morphology, page 25-26.
Barry DJ, Durkin CH, Abella JV, Way M. Open source software for quantification of cell migration, protrusions and fluorescence intensities. J Cell Biol. 2015. Doi: 10.1083/jcb.201501081
-Discussion can be improved by better incorporating all the components to make a cohesive story on how Gai2 works to regulate migration in the context of the neural crest cells.
R: We appreciate the reviewer's comment and agree. To enhance the manuscript, we have included a new paragraph at the end of the Discussion/Conclusion section specifically addressing this point. For more details, please refer to page 21-22.
“In the context of collective cranial NC cells migration, our findings reveal the pivotal role played by Gαi2 in orchestrating the intricate interplay of microtubule dynamics and cellular polarity. When Gαi2 levels are diminished, we observe significant impediments in the ability of cells to efficiently navigate through their environment, resulting in a range of distinct effects. First and foremost, Gαi2 deficiency leads to the diminished ability of cells to adjust and reorient new protrusions effectively. Primary protrusions exhibit higher stability and heightened levels of active Rac1/RhoA when compared to control conditions in the leading edge. In addition, we observe a notable increase in protrusion area, a decrease in retraction velocity, and an enhanced level of cell-matrix adhesion in Gαi2 knockdown cells. These findings underscore the pivotal role that Gαi2 plays in the modulation of various cellular dynamics essential for collective cranial NC cells migration. Notably, the application of nocodazole, a microtubule-depolymerizing agent, and NSC73266, a Rac1 inhibitor, to Gαi2 knockdown cells leads to the rescue of the observed effects, thus facilitating migration. This observed response closely mirrors the outcomes associated with Par3, a known regulator of microtubule catastrophe during contact inhibition of locomotion (CIL) in NC cells. This parallel implies that there exists a delicate equilibrium between microtubule dynamics and Rac1-GTP levels, crucial for the establishment of proper cell polarity during collective migration. Our findings collectively position Gαi2 as a central master regulator within the intricate framework of collective cranial NC migration. This master regulator's role is pivotal in orchestrating the dynamics of polarity, morphology, and cell-matrix adhesion by modulating microtubule dynamics through interactions with EB1 and EB3 proteins, possible in a protein complex involving other intermediary proteins such as GDIs, GAPs and GEFs, thus fostering crosstalk between the actin and tubulin cytoskeletons. This orchestration ultimately ensures the effective collective migration of cranial NC cells (Fig. 6).”
From Reviewer #3
Major comments: 1. The authors focus exclusively on the analysis of the subcellular levels of Rac1, which is probably related to the fact that they observe large extended protrusions with high Rac1 activity. However, as the authors note, a global fine-tuning of Rho GTPase activity is required for neural crest migration. One of the observed phenotypes of Gαi2-morphant neural crest cells is a decrease in cell dispersion, which may be caused by defects in contact inhibition of locomotion (CIL). This process involves a local activation of RhoA at cell-cell contact sites (Carmona-Fontaine et al., 2008). Furthermore, in fibroblast, RhoA/ROCK activity is required for the front-rear polarity switch during CIL (Kadir et al., 2011). Interestingly, similar to the Gαi2 loss of function phenotype, ROCK inhibition leads to microtubule stabilization, which can be rescued by nocodazole treatment, restoring microtubule dynamics and CIL. Therefore, it would also be interesting to know how RhoA activity is affected in Gαi2-morphant NC cells. At a minimum, this point should be be included in the discussion.
R: We acknowledge and sincerely appreciate the reviewer's valuable comments on this pivotal aspect, which significantly enhances our capacity to elucidate the impact of Gαi2 knockdown on cell polarity. To address this crucial point, we have introduced an experiment that examines RhoA-GTP localization under Gαi2 knockdown conditions, and we have incorporated a supplementary figure S3 into our manuscript. This newly added figure clearly demonstrates that, under Gαi2 knockdown conditions and in contrast to control cells, RhoA-GTP localization is substantially disrupted at cell-cell contacts and now detected at the leading edge of the cell, providing compelling evidence of cell polarity defects (refer to Figure S2). In response to these results, we have included a description of these findings in the Results section (please see page 11-12) and a dedicated paragraph in the Discussion section (please see page 18, paragraph 2, line 15-16, page 19, paragraph 1, lines 6-12).
Results section 1: “To achieve this, we explored whether Gαi2 regulates the subcellular distribution of active Rac1 and RhoA in cranial NC explants under Gαi2 loss-of-function conditions, considering their pivotal roles in cranial NC migration and contact inhibition of locomotion (CIL) (Carmona-Fontaine et al., 2011; Moore et al., 2013; Leal et al., 2018). Hence, we employed mRNA encoding the small GTPase-based probe, enabling specific visualization of the GTP-bound states of these proteins.”
Results section 2: “Consistent with earlier observations by Carmona-Fontaine et al. (2011), in control cranial NC cells, active Rac1 displayed prominent localization at the leading edge of migrating cells, whereas its presence was reduced at cell-cell contacts, coincident with a increase in RhoA-GTP levels (white arrows in Fig. 4A, supplementary material Figure S2A). On the contrary, in comparison to the control cells, Gαi2 morphants exhibit a pronounced accumulation of active Rac1 protein in the protrusions at cell-cell contacts, where active RhoA localization is conventionally expected (white arrow in Fig. 4B, supplementary material Figure S3B and movie S6). In contrast to control cells, a notable shift in the localization of active RhoA protein was observed, with its predominant accumulation now detected at the leading edge of the cell, instead of the typical localization towards the trailing edge or cell-cell contacts (__supplementary material Figure S2). __These findings suggest a dysregulation of contractile forces that align with the observed distribution of active RhoA, cortical actin disruption, and diminished retraction in cell treated with Gαi2MO.”
Discussion section:
“Other studies have reported that microtubule assembly promotes Rac1 signaling at the leading edge, while microtubule depolymerization stimulates RhoA signaling through guanine nucleotide exchange factors associated with microtubule-binding proteins controlling cell contractility, via Rho-ROCK (cita) and focal adhesion formation (Krendel et al., 2002; Ren et al., 1999; Best et al., 1996; Garcin and Straube, 2019; Waterman-Storer et al., 1999; Bershadsky et al., 1996; Moore et al., 2013). This mechanism would contribute to establishing the antero-posterior polarity of cells, crucial for maintaining migration directionality, underscoring the significance of regulating microtubule dynamics in directed cell migration. These findings closely align with the results obtained in this investigation, demonstrating that Gαi2 loss of function reduces microtubule catastrophes and promotes tubulin stabilization, resulting in increased localization of active Rac1 at the leading edge and cell-cell contacts and decreasing active RhoA at the cell-cell contact but increasing at the leading edge, possibly reinforcing focal adhesion, which align with our result here that show large and highly stable focal adhesions under Gαi2 knockdown conditions. This finding also suggests a dysregulation of contractile forces in comparison to control cells, a result that aligns with the observed distribution of Active RhoA, cortical actin distribution and diminished retraction in cells treated with Gαi2MO. This strikingly contrasts with the normal cranial NC migration phenotype, where Rac1 is suppressed while active RhoA is increased at cell-cell contacts during CIL, leading to a shift in polarity towards the cell-free edge to sustain directed migration (Theveneau et al., 2010; Shoval and Kalcheim, 2012; Leal et al., 2018).”
The co-Immunoprecipitation data lack marker bands (larger images/sections of the blots would be preferable) and the labelling is not clear. What do the white arrows in Fig. 3H,I mean? What does "elu" and "non eluted" mean? Did the reverse IP work as well?
R: We appreciate the reviewer's comments, and here we intend to provide a more detailed explanation of our approach to this analysis. Since we do not possess a secondary antibody specific to the heavy chain, our method involves eluting the co-immunoprecipitated proteins to visualize those with weights close to that of the light chain (such as EB1). We have outlined this elution step in the “Cell lysates and co-immunoprecipitation” protocol in the Materials and Methods section. To ensure proper control, we load both fractions - the eluted (or supernatant) and non-eluted (or resin) fractions - to monitor the amount of protein extracted from the resin using a 1% SDS solution. It's important to note that the elution step, as indicated by the V5 signal, is not entirely efficient, and a significant portion of the protein remains bound to the resin. This issue may also apply to the EB1 protein; however, it is still possible to visualize both bands (Gαi2V5 and EB1).
We have revised the legend for Figure 3 to include an explanation of the terms 'elu' (eluted fraction) and 'non-eluted' (non-eluted fraction). We have also included the explanation of the white arrows’ significance in the legends for Figure 3H and 3I. These arrows indicate the bands corresponding to the immunoprecipitated proteins.
We also agree with the reviewer’s suggestion to conduct the reverse IP. We can inform that we have done by triplicate all the Co-IPP. Although, if is necessary we will do the controls suggested. We present this assay as a plan.
The presentation of the Delaunay triangulations varies in quality. In Fig. 1 J/K the cells are clearly visible in the images, while this is not the case in Fig. 3 J-M and Fig. 4K-N. Conversely, the Delaunay triangulations in Fig. 1L are mainly black, while they are clear in Fig. 3 and 4. Perhaps the authors could find a more consistent way to present the data. Were the explants all approximately the same size at the beginning of the experiment? The Gαi2-morphant explant in Fig. 3K appears to be unusually small.
R: We appreciate the reviewer’s concerns and have taken steps to address them. To improve the quality of our data, we have made enhancements to the presentation of Figures 3 (panels J-M) and Figure 4 (panels K-N). Specifically, we have standardized the Delaunay triangulation representations.
Regarding the size of the explants at the beginning of the experiments, they were indeed approximately similar in size. We confirmed this by including a reference point (point 0) for each condition in the figures 3. However, in the panels presented, we show the results after 10 hours (Figure 3, X. laevis) and 4 hours (Figure 4, X. tropicalis) to assess cell dispersion, as indicated in the respective figure legends. This uniformity in size was further ensured by the calculation used to quantify dispersion. For the dispersion assay, we normalized each initial size of the explant upon the control, and we have added another representative explant of Gαi2 morpholino with its Delaunay triangulation to facilitate the experiment interpretation. Every Delaunay triangulation calculates the area generated between three adjacent cells and it grows depending on how much disperse are the cells between each other in the final point. (See Material and Methods section, Cell dispersion and morphology). As we can see in the manuscript, in every dispersion experiment that we have performed with Gαi2 morpholino, the cells cannot disperse at all. Furthermore, to analyze the dispersion rate in this experiment we use Control n= 21 explants, Gαi2MO n= 24 explants, Gαi2MO + 65 nM Nocodazole n= 36 explants, Control + 65 nM Nocodazole n= 30 explants (as we mentioned in the manuscript legend).
Why was the tubulin distribution in Fig. 2F measured from the nucleus to the cell cortex? Would it not make more sense to include cell protrusions? This does not seem to be the case in the example shown in Fig. 2F.
R: We appreciate the reviewer's concern. We would like to clarify that for the tubulin distribution measurements, we indeed measured from the nucleus to the cell protrusion. As a result, we have made an edit to Figure 2 (panel 2F) to provide further clarity on this matter.
The immunostaining for acetylated tubulin (Fig. 3A,B) looks potentially unspecific and seems to co-localize with actin (for comparison see Bance et al., 2019). For imaging and quantification, it may be better to use tubulin co-staining to calculate the percentage of acetylated tubulin. Please also add marker bands to the Western blot in Fig. 3C. If this issue cannot be resolved it may be better to only include the Western blot data.
R: We appreciate the reviewer's concern about the potential unspecific nature of acetylated-tubulin and its co-localization with actin, particularly in Figure 3. Regarding the co-localization with actin, it is predominantly observed in panel B, and we attribute this phenomenon to the Gαi2 morphant phenotype, where cortical actin is notably reduced, creating the appearance of co-localization. However, we will assess the experiment as suggested by the reviewer. Therefore, our plan is to conduct an immunostaining for acetylated tubulin and tubulin in both control and Gαi2 knockdown conditions. This will allow us to calculate the percentage of acetylated tubulin and complement the western blot analysis.
We have included marker weight indications on the western blot panel in Figure 3C.
The model in Fig.6 indicates that Gαi2 inhibits EB1/3. What is the experimental evidence and the proposed mechanism for this? In the discussion, the authors cite evidence that Gαi activates the intrinsic GTPase activity of tubulin, which would destabilize microtubules by removing the GTP cap. However, this mechanism would not directly affect EB1 and EB3 stability as the Fig. 6A seems to suggest. The authors also mention that EB3 appears to be permanently associated with microtubules in Gαi2-morphant cells. How would this work, given that end-binding proteins bind to the cap region? Are the authors suggesting that there is an extended cap region in Gαi2 morphants?
R: We appreciate the reviewer's valuable comments. We agree with the reviewer's observation that our experiments do not establish a causal link between Gαi2, EB1/EB3, and Rac1. We established a relationship between Gαi2 and microtubule dynamics (EB1 and EB3) to regulate Rac1 polarity through co-immunoprecipitation assays, which reveal protein interactions within an interactor complex. In addition, in Gαi2 Knockdown conditions we have found a strong reduction in microtubules dynamics following EB-GFP comets. Regarding the observation that EB3 seems to be persistently associated with microtubules in Gαi2-morphant cells, we wish to clarify that this is a speculation based on the microtubule phenotype observed during our dynamic analysis, where they appear more like lines rather than comets. It is important to note that none of the experiments conducted in this study conclusively demonstrate this, and thus, it remains a suggestion. Therefore, while our findings support the involvement of Gαi2 in coordinating cranial NC cell migration alongside EB1, EB3, and Rac1, we cannot exclude the possibility that this regulation may occur through other intermediary proteins, such as GEFs, GAPs, GDIs, and others. As a result, we have revised our model in accordance with the reviewer suggestion.
We have edited both the model and the legend at Figure 6. Gαi2 controls cranial NC migration by regulating microtubules dynamics.
Considering this, we have reviewed the manuscript to provide clarity on this point. See page 16 (paragraph 2, last line), 17 (paragraph 1, last line), 22 (paragraph 1, last line 17-20), 42 (Legend Fig. 6).
Minor comments 1. The citation of Wang et al. 2018 in the introduction does not seem to fit.
R: We appreciate the correction provided by the reviewer. We have carefully reviewed the Introduction and Reference sections and have corrected this error.
2.Does the graph in Fig. 4S show average values for the three conditions? If so, what is the standard deviation?
R: We appreciate the reviewer’s concern and we have added the standard deviation to Figure 4S.
3.From the images in Fig. 2G and H, it is difficult to understand what the difference is between the four groups shown.
R: We appreciate the reviewer's comment, and to clarify this point, we would like to explain that the comparison has been made between each type of comet. The PlusTipTracker software separates comets based on their speed and lifetime, classifying them as fast long-lived, fast short-lived, slow long-lived, or slow short-lived. In both conditions (control and morphant cells), we compared the percentage of each type of comet, as previously described in Moore et al., 2013. The results demonstrate that morphant cells exhibit an increase in slow comets compared to control cells. The same explanation is described in the Material and Methods section on page 26, Microtubule dynamics analysis.
Reviewer #3 (Significance (Required)): Overall, the study is well executed and significantly advances our understanding of the control and role of microtubule dynamics in cell migration, which is much less understood compared to the function of the actin cytoskeleton in this process. The strength of the study is the use of state-of-the-art (live imaging) techniques to characterize the role of Gαi in neural crest migration at the cellular/subcellular level. This article will be of interest to a broad readership, including researchers interested in basic embryonic morphogenesis, cell migration, and cytoskeletal dynamics, as well as translational/clinical researchers interested in cancer progression or wound healing.
R: We really appreciate the reviewer positive comments and consideration. We believe that the review process has significantly strengthened our manuscript.
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Referee #3
Evidence, reproducibility and clarity
Summary: The manuscript by Villaseca et al. analyzes the role of Gαi2 in cranial neural crest migration and reveals a novel mechanistic link to microtubule dynamics. The authors nicely demonstrate that Gαi2 is required for Xenopus neural crest migration and affects cell dispersion, cell polarity, focal adhesion turnover, and microtubule dynamics. They find that Gαi2-morphant neural crest cells are elongated, have larger, more stable protrusions, higher active Rac1 levels, and a concentration of microtubules at the leading edge. Using co-immunoprecipitation, the authors show that Gαi2 forms a complex with α-tubulin and the microtubule plus-end binding proteins EB1 and EB3, which are known regulators of microtubule dynamics. Time-lapse imaging shows that Gαi2 loss of function increases microtubule stability, which is further supported by an increase in acetylated tubulin levels. Consistently, treatment with nocodazole, which inhibits microtubule polymerization, as well as treatment with a Rac1 inhibitor, is able to rescue cell dispersion and morphology of Gαi2-morphant neural crest cells. The authors propose a model, whereby Gαi2 interacts with components of the plus-tip microtubule-binding complex to control microtubule dynamics and Rac1 activity to establish cell polarity, disassemble focal adhesion, and thereby facilitate neural crest migration.
Major comments:
- The authors focus exclusively on the analysis of the subcellular levels of Rac1, which is probably related to the fact that they observe large extended protrusions with high Rac1 activity. However, as the authors note, a global fine-tuning of Rho GTPase activity is required for neural crest migration. One of the observed phenotypes of Gαi2-morphant neural crest cells is a decrease in cell dispersion, which may be caused by defects in contact inhibition of locomotion (CIL). This process involves a local activation of RhoA at cell-cell contact sites (Carmona-Fontaine et al., 2008). Furthermore, in fibroblast, RhoA/ROCK activity is required for the front-rear polarity switch during CIL (Kadir et al., 2011). Interestingly, similar to the Gαi2 loss of function phenotype, ROCK inhibition leads to microtubule stabilization, which can be rescued by nocodazole treatment, restoring microtubule dynamics and CIL. Therefore, it would also be interesting to know how RhoA activity is affected in Gαi2-morphant NC cells. At a minimum, this point should be be included in the discussion.
- The co-Immunoprecipitation data lack marker bands (larger images/sections of the blots would be preferable) and the labelling is not clear. What do the white arrows in Fig. 3H,I mean? What does "elu" and "non eluted" mean? Did the reverse IP work as well?
- The presentation of the Delaunay triangulations varies in quality. In Fig. 1 J/K the cells are clearly visible in the images, while this is not the case in Fig. 3 J-M and Fig. 4K-N. Conversely, the Delaunay triangulations in Fig. 1L are mainly black, while they are clear in Fig. 3 and 4. Perhaps the authors could find a more consistent way to present the data. Were the explants all approximately the same size at the beginning of the experiment? The Gαi2-morphant explant in Fig. 3K appears to be unusually small.
- Why was the tubulin distribution in Fig. 2F measured from the nucleus to the cell cortex? Would it not make more sense to include cell protrusions? This does not seem to be the case in the example shown in Fig. 2F.
- The immunostaining for acetylated tubulin (Fig. 3A,B) looks potentially unspecific and seems to co-localize with actin (for comparison see Bance et al., 2019). For imaging and quantification, it may be better to use tubulin co-staining to calculate the percentage of acetylated tubulin. Please also add marker bands to the Western blot in Fig. 3C. If this issue cannot be resolved it may be better to only include the Western blot data.
- The model in Fig.6 indicates that Gαi2 inhibits EB1/3. What is the experimental evidence and the proposed mechanism for this? In the discussion, the authors cite evidence that Gαi activates the intrinsic GTPase activity of tubulin, which would destabilize microtubules by removing the GTP cap. However, this mechanism would not directly affect EB1 and EB3 stability as the Fig. 6A seems to suggest. The authors also mention that EB3 appears to be permanently associated with microtubules in Gαi2-morphant cells. How would this work, given that end-binding proteins bind to the cap region? Are the authors suggesting that there is an extended cap region in Gαi2 morphants?
Minor comments
- The citation of Wang et al. 2018 in the introduction does not seem to fit.
- Does the graph in Fig. 4S show average values for the three conditions? If so, what is the standard deviation?
- From the images in Fig. 2G and H, it is difficult to understand what the difference is between the four groups shown.
Referees cross-commenting The concerns raised by my colleagues are entirely valid.
Significance
Overall, the study is well executed and significantly advances our understanding of the control and role of microtubule dynamics in cell migration, which is much less understood compared to the function of the actin cytoskeleton in this process. The strength of the study is the use of state-of-the-art (live imaging) techniques to characterize the role of Gαi in neural crest migration at the cellular/subcellular level. This article will be of interest to a broad readership, including researchers interested in basic embryonic morphogenesis, cell migration, and cytoskeletal dynamics, as well as translational/clinical researchers interested in cancer progression or wound healing.
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Referee #2
Evidence, reproducibility and clarity
Summary
The manuscript by Villaseca et al. describes functional analysis of Gai2 in cranial neural crest (CNC) migration using the frog Xenopus as their model system. The authors performed the loss-of-function assay to knock down expression of endogenous of Gai2 and discovered that CNC migration was impaired in the absence of changes of CNC fate specification. Based on the literature on Gai2 activities in other cellular contexts, the authors speculated that Gai2 might regulate microtubule dynamics and Rac1 function. Their studies using immunofluorescence (IF) and live-cell imaging indeed showed that microtubules were stabilized in membrane protrusions with concurrent activation of Rac1 in Gai2 knockdown cells. In addition, focal adhesion turnover was reduced. They further demonstrated that the CNC migration defects could be partially rescued by destabilization of microtubules with chemical treatment. The authors conclude from the studies that Gai2 orchestrates microtubule dynamics and modulates Rac1 activation during neural crest migration.
Major comments
The authors aim to address two issues in this manuscript: a) the role of Gai2 in neural crest development; and b) the mechanism of Gai2 function. While they have done a good job demonstrating a role of Gai2 in NC migration both in vivo and in vitro as well as the effects of Gai2 knockdown on cytoskeleton dynamics, protein distribution of selected polarity and focal adhesion molecules, and Rac1 activation, the link between Gai2 and the downstream effectors is largely correlative. Because of this, the model suggesting the sequential events flowing from Gai2 to microtubule to Rac1 to focal adhesion/actin should be modified to allow room for direct and indirect regulation at potentially multiple entry points.
Specific major comments are as the following:
Strengths:
-Determination of a role of Gai2 in neural crest migration is novel. -The effect of Gai2 knockdown on membrane protrusion morphology and microtubule stability and dynamics are demonstrated nicely. -Quantification of experimental perimeters has been performed throughout the manuscript in all the figures, and statistical analysis is included in the figures.
Weaknesses:
- The heavy focus of the study on microtubule is due to the previous publication on the function of Gai2 in regulation of microtubule during asymmetrical cell division. However, the activity of Gai2 is likely cell type-specific, as it has not been shown to control microtubule during cytokinesis in general. It is equally likely that Gai2 primarily regulates Rac1 or actin regulators to influence both microtubule and actin dynamics. The tone of the discussion should therefore be softened.
- The effect of rescue of NC migration with Rac1 inhibitor is marginal and the result is hard to interpret considering the inhibitor also blocks control NC migration. Either lower doses of Rac1 inhibitor can be used or the experiment can be removed from the manuscript, as Rac1 is required for membrane protrusions and the inhibitor doses can be hard to titrate.
- Since the defects seem to result partially from the inability of the NC cells to retract and move away, it may help to either include some data on Rho activation patterns in knockdown cells or simply add some discussion about the issue.
- To consider focal adhesion dynamics, live imaging should be used in the analysis. The fixed samples are different from each other, and natural variations of focal adhesion may exist among the samples. This can obscure data collection and quantification.
Minor comments
- Fig. 2, the centrosomes in control cells are not always obvious. The microtubules simply seem to be more networked and more fluid in control cells. This should be clarified with either marking the centrosomes in the figure or modifying the wording in the manuscript.
- In Fig. 3, a better negative control for co-IP should be using anti-V5 antibody to IP against tubulin/EB1/EB3 in the absence of Gai2-V5.
- The data for cell polarity proteins Par3 and PKC-zeta seem to be out of place. It is unclear whether mis-localization of these proteins has anything to do with NC migration defects induced by Gai2 knockdown. The conclusion does not seem to be affected if the data are taken out of the manuscript.
- In Suppl. Fig. 1, protrusion versus retraction should be defined more clearly. The retraction shown in this figure seems to be just membrane between protrusions instead of actively retracting membrane.
- Discussion can be improved by better incorporating all the components to make a cohesive story on how Gai2 works to regulate migration in the context of the neural crest cells.
Referees cross-commenting I agree with other reviewers' comments.
Significance
The authors demonstrate a role of Gai2 in regulation of neural crest migration in Xenopus by modulating microtubule dynamics. In addition, they show an effect of Gai2 knockdown on Rac1 spatial activation and focal adhesion stability. These are novel discoveries of the study. Some limitations exist in linking Gai2 with downstream effectors that directly or indirectly impact on cytoskeleton and Rac1 small GTPase.
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Referee #1
Evidence, reproducibility and clarity
Summary:
This manuscript examines the role of a G protein, Gai2, in regulating the migration of cranial neural crest cells. Although previous literature has established that heterotrimeric G proteins are involved in cell migration, a central process during embryogenesis and adult homeostasis, the underlying cell biological mechanisms of their activities have not been elucidated. This manuscript rigorously examines the various aspects of Gai2 protein interactions to generate an exciting new paradigm in which Gai2 maintains normal microtubule dynamics by binding to tubulin and EB proteins. This normally dynamic microtubular intracellular environment then promotes cortical actin formation in the leading edge of the migrating cell as well as rapid focal adhesion disassembly by controlling Rac1 activity. Under conditions in which the levels of Gai2 are reduced by MO-mediated knockdown, cells display reduced microtubule dynamics and a decreased catastrophe rate, resulting in slower and more stable microtubules to which EB3 is more persistently associated. A stable microtubule environment leads to enhanced Rac1 activation at the leading edge and stable and larger focal adhesions, resulting in reduced migration. The authors utilize cutting edge approaches to examine the interactions between Gai2 and these other cellular components, taking advantage of the well characterized cell migration model - the cranial neural crest - both in embryos and in cultured explants of these cells.
Major comments:
The manuscript is mostly well written (it could use a few minor grammatical corrections), the significance of the problem is well described, and the results are clearly presented with adequate controls. The movies, provided as supplementary material, are of the highest quality and are essential additions to the stills provided in the figures. The data convincingly support the key conclusions of the manuscript.
Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? No
Would additional experiments be essential to support the claims of the paper? No additional experiments are essential.
Are the experiments adequately replicated and statistical analysis adequate? The number of embryos/ explants per assay and the number of explant replicates for each assay and the statistical assessments are rigorous.
Are the data and the methods presented in such a way that they can be reproduced? Mostly, however, the description of the MO used for Gai2 knockdown needs more detail:
- Does the MO knockdown both S and L homoeologs of X. laevis? Since the level of GAPDH in Figure 1H also looks reduced in Gai2 MO lane, it should be made clear that the apparent knockdown of Gai2 was normalized to GAPDH, rather than being the results of unequal loading of the gel. Yes, I recognize that Figure 1I says normalized, but this is not stated in the results or the methods. Also, was this experiment done with X. laevis or X. tropicalis? I could imagine that if done in X. laevis, the lack of complete knockdown might be due to only one homoeolog being affected.
- The knowledge of the efficacy of knockdown in each Xenopus species provided by the information requested in the previous point, would allow the reader to assess the level of knockdown in the remaining assays. To do this, the authors should tell us which assays were done in which species. I am not suggesting that each experiment needs to be done in each species, only that the information should be provided. If the MO is more effective in X. tropicalis - which assays used this species? If the knock down is partial, as shown in Figure 1H-I, which species this represents in the remaining assays would be useful knowledge.
Minor comments:
While prior studies are referenced appropriately, and the text and figures are mostly clear and accurately presented, the following are a few suggestions that would help the authors improve the presentation of their data and conclusions:
- The cell biological experiments convincingly demonstrate that knockdown of Gai2 causes cells to move more slowly. It would be a nice addition to bring the explant experimental data back to the embryo by showing whether the slower moving NC cells in morphants eventually populate the BA. DO they cease to migrate or are they just slower getting to their destination? This could be done by performing snail2 ISH at a later stage (34-35?)
- There are places in the manuscript where the authors use the terms "silencing" or "suppression" of Gai2, when they really mean reduced translation - their system is not a genetic knockout, as clearly demonstrated in Figure 1H-I. I suggest that more accurate wording be used.
- In Figures 1-5 there are scale of bars on the cell images, but these are not defined in any of the figure legends.
- The abstract is the weakest section of the manuscript, and would have greater impact if it were more clearly written.
Referees cross-commenting
The concerns are fair assessments. However, most can be addressed in the text and by clearer presentation of existing data rather than more experimentation.
Significance
The molecular regulation of cell movement is a key feature of a number of developmental and homeostatic processes. While many of the proteins involved have been identified, how they interact to provide motility has not been elucidated in any great detail, particularly in embryo-derived cells (as opposed to cell lines). The results obtained from the presented experiments are novel, in-depth and provide a novel paradigm for how G proteins regulate microtubule dynamics which in turn regulate other components of the cytoskeleton required for cell movement. The results will be applicable to many migrating cell types, not just neural crest cells.
Because of the application of the data to many types of cells that migrate, the audience is expected to include a broad array of developmental biologists, basic cell biologists and those interested in clinically relevant aberrant cell migrations.
Reviewer keywords: Xenopus embryology; neural crest gene expression; use of MO-mediated knockdown of gene expression. Not an expert in microtubule dynamics.
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Reply to the reviewers
'The authors do not wish to provide a response at this time.'
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Referee #3
Evidence, reproducibility and clarity
This manuscript presents the cis-regulatory analysis of the enhancers controlling prox1a gene in zebrafish. Authors used both evolutionary conservation and existing single-cell ATAC data to highlight the major role of two elements. I feel that the transgenesis work is quite solid and the main conclusions interesting. However, I feel the authors need to provide some extra validations for some of the analysis.
- the authors did not discuss the fact that euteleosts underwent an extra whole genome duplication and that prox1a might have a paralogue. They also perform genome alignment using non-duplicated outgroups (gar, xenopus) without discussing. I am a bit skeptical about the use of mVISTA on relatively short expert of sequence aroudn a gene, as it is not able to capture the global molecular evolution parameters. I think the authors should also examine some of the precomputed phastCons / phylocons data performed and available on UCSC to confirm their findings. probably they should also examien a few more fish genome. I don't find this evolutioanry analysis extremely convinced and careful - which doesn't mean that the conclusions are wrong.
- I find the presentation, fairly obscure, the writing is quite convoluted, and the figures are very dense and not super explanatory, I would urge to improve (this is not helped by the fact that figure are their leged and presented at distinct places of this manuscript). For instance, I think having. a figure summarising signal from evolutionary conservation, scATAC and chromatin marks altogether would be quite essential.
- I also find the reanalysis of the single-cell ATAC described too scarcely: which are the genes used to identify the different cell populations?
- I feel the one additional experiment that the authors could have done would have been to use their construct to isolate the different cells population of interest and perform some regulatory profiling scuh as ATAC-seq or cut-and-tag on this population, to have a direct, in situ evidence of the activity of these regulatory elements.?
I also feel that the evolutionary aspect could be discussed a bit more, what are the differences between the diffeerent vertebrate lineage, etc...
(p7) active enhancer in a tissue: while ATAC gives a good indicated of accessibility it is not an indicate of activity as for instance H3K27Ac would be.
Significance
I think this is an interesting piece of work, which elaborates on previous studies on prox1a involvment in the lymphatic system but it doesn not bring essentially new perspective on the question.
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Referee #2
Evidence, reproducibility and clarity
Summary:
Panara et al. identified the enhancer regions necessary for the tissue- and organ-specific expression of Prox1a, which is essential for lymphatic vessel development in zebrafish. The authors compared the sequences of eight species of osteichthyes, identified Conserved Non-Coding Elements (CNEs), and further predicted active enhancers by combining public databases. This sequence was analyzed using the ZED system. As a result, they confirmed that two out of ten sequences caused GFP expression in a subset of lymphatic endothelial cells. +15.2prox1a was required for the prox1a expression in the facial collecting lymphatics, while -2.1prox1a was required in the facial lymphatic valves. They also predicted transcription factor binding to these enhancer regions and identified enhancer regions of -14, -71, -87Prox1a based on chromatin state. Additionally, they identified a core element within -2.1Prox1a, performed its loss-of-function experiments, and analyzed its phenotype, revealing the functional importance of these enhancer regions. The paper is logically well-structured and informative concerning the expression control of Prox1a.
Major comments:
- Are the key conclusions convincing?
I think so. - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?
No need to qualify. - Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.
Figure 4H is probably incorrect. Looking at the morphology of the valve, it's inferred that the lymph flow goes from right to left. In this case, if the valve function is abnormal, the lymph flow that entered from the right should reflux back to the left. Is the reviewer misunderstanding something here? Clarity on this point could be provided with video images, similar to echocardiograms in mice. - Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.
Yes, they are realistic. I would like further clarification to ensure the data is correct.
Minor comments:
- Specific experimental issues that are easily addressable.
It's important where Prox1 is expressed in the lymphatic system, but if the identified enhancers also regulate Prox1 expression in other tissues like the myocardium, it would be a significant finding. Do these enhancers have a role in controlling Prox1 expression in lymphatics and non-lymphatics? - Are prior studies referenced appropriately?
Yes. - Are the text and figures clear and accurate?
Yes. - Do you have suggestions that would help the authors improve the presentation of their data and conclusions? 1. Regarding Figure 4G, the actual image is unknown due to the red shading. Can you discuss or clarify how knocking out the Prox1 enhancer affects valve formation? If Prox1 expression is reduced, does the normal extracellular matrix change? Why do these morphological changes occur? 2. In mice, Prox1 is expressed in the heart valve endothelium. Have they checked the enhancer activity in other valves (heart valves or venous valves, if they exist)? 3. -2.1prox1a is said to be important for Prox1 expression in the facial lymphatic valves. Are valves only formed in this part of the lymphatics in zebrafish at this stage? Why is it important to identify the enhancer for this particular valve? Are there no other lymphatic valves? Please explain in the text. 4. They seem to emphasize +15.2's role in facial lymphatic expression. Compared to the trunk, why is it significant that there is a unique enhancer acting in this area? Is there something functionally or anatomically unique about facial lymphatics? Please discuss why the enhancer region's conservation is high in facial lymphatics.
Significance
- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.
In this paper, the authors have revealed previously unknown regulatory sequences of zebrafish Prox1a. They identified enhancer sequences that control Prox1a expression in the facial collecting lymphatics and lymphatic valves. The loss of the -2.1prox1a enhancer led to malformation in some lymphatic valves, suggesting the functional importance of this enhancer. - State what audience might be interested in and influenced by the reported findings.
Readers interested in vascular development and those interested in transcriptional control during development. - Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.
Lymphatic development in mammals, cardiac development, cardiology, cardiovascular pathology.
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Referee #1
Evidence, reproducibility and clarity
Through the study of cis-regulatory element/enhancer activity in zebrafish, this study from Panara and colleagues provides insight into the transcriptional control of Prox1, a master regulator of lymphatic endothelial cell (LEC) fate. The authors used conservation of non-coding DNA, and chromatin accessibility data to identify enhancers that drive expression of a fluorescent reporter in anatomically distinct subsets of LECs. Analysis of transcription factor binding motifs in these enhancers suggests that differences in enhancer activity throughout the lymphatic vasculature may be due to binding of distinct transcription factors to these elements. Importantly, the authors identify a conserved 200 bp element within the -2.1kb enhancer that could drive expression in the lymphatic valve. Analysis of mutants carrying a 102 bp deletion in this region (including an Nfatc1 binding site), revealed reduced Prox1 expression and valve defects.
Major comments
- In the text it is suggested that sequence conservation was assessed across 8 species: "We aligned the region of the PROX1/prox1a locus in eight Osteichthyes species using mVISTA (Fig. 1A, Table S1)." Fig. 1A contains 7 species, and I am not able to find Table S1.
- It would be important to discuss reasons that the +15.2kb enhancer is not clearly identifiable in the scATAC-seq analyses but drives expression. Is this due to the relatively limited activity in facial lymphatics? Furthermore, given the degree of conservation, it would be useful to mutate specific transcription factor binding motifs (e.g. Mafb, Sox18, etc) in the -15.2kb enhancer and assess activity in the FCLV.
- OPTIONAL : Mutate Nr2f2 and Gata2 binding sites in -87kb enhancer to test for impact on activity. This would allow the authors to imply functional rather than sequence conservation. On a similar note, it would be interesting to understand if these enhancers are active in the context of mammalian lymphatic development.
Minor comments:
- It would be good to clarify in the following sentence that these enhancer marks are present at the whole embryo level and were not specifically identified in LECs : "Using zebrafish public databases for H3K4me1 and H3K27ac, we identified that ten of the selected prox1a CNEs were primed or active enhancers (Aday et al., 2011; Bogdanovic et al., 2012) (Fig. 1A, S1C)."
- "....(Gupta et al., 2007) to determine the motifs and putative transcription factor binding sides." - should read "....(Gupta et al., 2007) to determine the motifs and putative transcription factor binding sites".
- It might be more accurate to use zebrafish protein nomenclature for the transcription factor motifs in Fig1D, G and Fig2E (i.e. Gata2 not GATA2)
Significance
- While the roles of Prox1 in lymphatic vascular development and homeostasis are well established, relatively little is known about the cis-regulatory mechanisms governing it's expression; recent work has described an enhancer in the mouse Prox1 locus (Kazenwadel et al., 2023). This study from Panara and colleagues characterises numerous cis-regulatory elements in the zebrafish prox1a locus and demonstrates heterogeneous activity in anatomically distinct parts of the lymphatic vasculature. The imaging and characterisation of enhancer elements is of a high standard. Experiments that addressed the regulation of enhancers by upstream transcriptional or signalling cues would strengthen this study. Furthermore, to understand if there is functional conservation across evolution, analysis of activity in mouse embryos would be of interest.
- This should be of broad interest to the vascular biology and developmental biology fields.
- My expertise are in vascular biology, lymphatic development and developmental genetics.
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Reply to the reviewers
We thank all the reviewers for positively evaluating our work and for their valuable comments and constructive suggestions. We will revise the manuscript in accordance with the raised points and ensure that all comments are addressed.
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Referee #3
Evidence, reproducibility and clarity
Summary:
The study of the formation and maintenance of the blood-brain barrier (BBB) is a growing field of study, partly due to its strong link with neurological disorders. The BBB depends on the role of multiple cell types and mechanisms. Mutations in the conserved phospholipase NTE/SWS can lead to neurodegeneration, and previous work from the authors shows that SWS loss leads to abnormal glial morphology. In this work, authors use Drosophila to further study this phenotype, showing that SWS is mostly expressed in the BBB-related glia and that its loss leads to abnormal BBB permeability, increased inflammatory response and neural cell death. Interestingly, authors observed a dependence for the BBB-defective phenotype on aging, with important implications for SWS/NTE and neurodegeneration. Overall, the work represents a clear advance in the poorly explored role of NTE/SWS in neurodegeneration, with a broad impact on the understanding of BBB maintenance. This work shows a combination of multiple and appropriate experimental approaches, including confocal microscopy, EM, RT-qPCR, or gas chromatography-mass spectrometry among others.
Major comments:
The use of sws1 and sws1/sws4 transheterozygous animals, together with the use of sws RNAi is a solid approach to validate that the reported phenotypes are due to SWS loss. Using these models, the authors performed a convincing structural analysis of the subperineurial glia phenotype, and showed that it is accompanied by a defective BBB, inflammation and neuronal cell death. The key conclusions are properly supported by the data. However, there are some claims in the text that are not supported by any data in the Figures, but only qualifications. This needs to be fixed:
- Page 6, third paragraph: "...we specifically downregulated sws in the nervous system using the double driver line that allows downregulation of sws in glia and neurons (repo, nSyb-Gal4, Suppl. Fig. 2C-Cʹ). Since these animals had the same disorganized structure of brain surface as the loss-of-function mutant..." Supp. Fig. 2C-C' only shows expression of CD8:GFP and nlacZ reporters by repo and nSyb-Gal4, but there is no data showing sws RNAi expression by these drivers.
"...Moreover, downregulation of sws in all glial cells (repo>swsRNAi) resulted in the same phenotype. At the same time, upon sws downregulation in neurons,... (Suppl. Fig. 4)..." Suppl. Fig. 4 only shows nsyb>swsRNAi data but not repo>swsRNAi - Page 6, fourth paragraph: "Importantly, expression of Drosophila or human NTE in these glia cells rescued this phenotype (Fig. 2H)" In addition to the indicated quantifications, it is essential to show some representative data showing the phenotype when Drosophila or human NTE are expressed in glial cells of sws mutant animals. - Page 9, last paragraph: "We found that in moody mutants, the surface glia phenotype analyzed using CoraC as a marker could also be suppressed by NSAID and rapamycin (Fig. 5A)." In addition to the indicated quantifications, it is essential to show some representative data showing the phenotype with and without treatments.
A more detailed analysis of two aspects of the data would clearly improve the manuscript, whose findings are a bit superficial in the current state:
- The exact mechanism by which BBB permeability leads to brain inflammation remains unknown. Authors show that accumulation of polyunsaturated fatty acids (known to regulate inflammation) occurs in sws-depleted animals. However, they only observed a correlation between this phenotype and the inflammatory response, while is not clear whether the accumulation of polyunsaturated fatty acids causes inflammation in this model or is a consequence of it. An attempt to rescue the accumulation of polyunsaturated fatty acids (i.e., knocking down a required enzyme for their production) in sws mutants might help to understand this. Also, the fact that the defective BBB phenotype observed in either sws KO and glia-specific KD can only be partially rescued by the use of inflammation inhibitors, suggests that other pathways are involved.
- While the differences between the phenotypes caused by sws or moody loss are well characterized, it would be key for this work to further study the mechanisms by which sws controls septate junctions. The authors propose the organization of lipid rafts, but some experiments in that direction to check this hypothesis. For example, can authors reproduce the septate junction phenotype of sws mutant (Fig. 6C) by using a different approach to induce defective lysosomes in subperineurial glia?
The attempt of the proposed approaches above should require about 3-6 months of investment, with limited economic effort, given the availability and diversity of lines found in the existing stock centres such as Bloomington or Vienna.
The data is presented very clearly, and the methods are adequately detailed, and the experiments and statistical analysis are adequate.
Minor comments:
Prior studies are referenced appropriately, but there is a case that should be addressed. On Page 3, first paragraph, regarding the sentence: "However, the molecular mechanisms underlying inflammaging remain unclear". I recommend specifying what is known and what is unknown in the field. Ideally describing (briefly) the knowledge about lipids, inflammaging and neurodegeneration, which are the specific topics of the research. Otherwise, the current sentence is too vague, while there is a lot of work published about it.
The text and figures are clear and accurate. The logic of the experiments and the results are exposed very clearly (for example, the Suppl. Tables are very helpful). There are a few minor issues, however, that should be addressed:
- Page 4, first paragraph: regarding the sentence: "For various obvious reasons, humans are not ideal subjects for age-related research.", I recommend specifying the main reasons (i.e. life cycle, ethical issues, etc.?).
- I would recommend moving the text "For various obvious reasons...disrupted upon ageing." From its current position to just before "Drosophila melanogaster is an excellent...". This would keep a better logic in the text by explaining NTE first and later introducing the models to study its function. Presenting then Drosophila.
- To support the sentence "Together, Drosophila satisfies...neurodegeneration during aging", instead of citing so many papers, I recommend citing just one current review about it, since the amount of literature supporting the claim is huge and should not be limited to a few "random" articles. An alternative might be indicating that the lab has used Drosophila for this aim before, and then citing the examples from the literature.
- Page 4, second paragraph: if NTE/SWS is going to be used as a synonym for NTE/SWS loss of function (or other type) model, it needs to be specified. Otherwise, refers to the proteins and sentences like "NTE/SWS has been shown to result in lipid droplet accumulation..." are misleading.
- Page 4, last paragraph: the first time that "BBB" is used, its meaning should be specified. And three lines below use "BBB" instead of blood-brain barrier.
Referees cross-commenting
I agree with the comments provided by the other reviewers. They are well reasoned and cover some aspects of the work that I did not see. Regarding the main issue, the three revisions point at the same direction, that is the limited analysis about the mechanism underlying the phenotypes.
Significance
- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.
This work represents a substantial advance in the understanding of NTE/SWS function in the context of neurodegeneration, and opens potential approaches to treat related disorders (they successfully use anti-inflammatory compounds to ameliorate some of the key phenotypes). However, the findings are a bit superficial in terms of mechanisms, and further analysis (see major comments) would notably improve the significance of the manuscript. This should be realistic and suitable, given the advantages of the Drosophila model and the availability of tools. - Place the work in the context of the existing literature.
The role of SWS in regulating lysosomal function is potentially supported by NTE-deficient mice data (Akassoglou et al., 2004; Read et al., 2009), where different types of neurons show similar dense bodies containing concentrically laminated and multilayered membranes than those observed in this work in Drosophila sws mutant. Potentially, the rest of the work has a translation to mammals, which is supported by the fact that ectopic expression of NTE rescues some of the key phenotypes described in the manuscript. - State what audience might be interested in and influenced by the reported findings.
Neuroscience in general, since the study of BBB and neurodegeneration has a clear general interest in the whole field. - Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.
Drosophila; Neurodegeneration; Hereditary Spastic Paraplegia; Alzheimer's disease; Motor neurons; Microglia; Endoplasmic reticulum; Mitochondria.
Lipid metabolism is the part of the manuscript where I have less expertise to evaluate, only having general knowledge about it.
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Referee #2
Evidence, reproducibility and clarity
The manuscript by Tsap et al describes a role of NTE/SWS in forming the BBB in Drosophila. Disruption of the BBB in SWS mutants and knockdown flies results in morphological changes of the glia forming the BBB, increased brain permeability, altered lysosomes, and an upregulation of innate immune genes. The experiments to show a function of SWS in surface glia and the resulting changes in permeability are well supported by the experiments and the statistics appears appropriate. The authors also show changes in innate immune genes and some fatty acids and that similar changes are found in another mutant affecting the BBB. They discuss that these changes are a consequence of the disruptions of the BBB but also that these changes induce changes in the BBB. To address this and confirm that the changes in immune genes and fatty acids is a consequence of the altered BBB, they should include experiment expressing SWS in the surface glia and measure if that normalizes these changes. Another major aspect that should be addressed is the effect of aging. As the authors point out, loss of SWS causes age-dependent phenotypes (shown by the author and others) and with the exception of figure 3F, the age isn't even mentioned in any of the other figures. Furthermore, at least some of the experiments should be done at different ages to determine whether the phenotype is progressive; this includes the permeability assays and the measurements of immune genes (the latter could also support whether changes in the immune genes affect the BBB or vice versa the BBB changes cause the upregulation of immune genes). Lastly, the authors claim that septate junctions are defective in sws mutants. However, this should be confirmed by EM studies (which the authors have already done) besides immunohistochemistry which doesn't provide enough resolution.
Significance
A role of SWS in maintaining the BBB and what consequences this has provides another insight how this protein (and its homolog NTE) affects brain health. Although a function of SWS in glia (as well as in neurons) has previously been described, changes in the surface glia and the BBB is a novel aspect. However, the causative role of SWS on some of the described consequences (see above) should be confirmed. Although the manuscript can add to a better understanding of the connection between disruptions of the BBB and neurodegenerative diseases, which is of interest for a broader field of researchers, the discussion of the results is quite speculative.
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Referee #1
Evidence, reproducibility and clarity
Summary
Elucidating the cellular and molecular mechanisms underlying age-related neurodegeneration remains a key challenge for neurobiologists. In this manuscript, Mariana Tsap and colleagues in the team of Halyna Shcherbata focus on the function of the neuropathy target esterase NTE/Swiss Cheese (Sws) in the Drosophila brain. The authors use an elegant combination of genetics, light and electron microscopy, RT-qPCR and GS-MS mass spectrometry to determine the complex role of Sws in cellular blood brain barrier (BBB) integrity, the brain inflammatory response and fatty acid metabolism. The study provides a detailed characterisation as to how the loss of sws affects glial cell morphology in the BBB revealing abnormal membrane accumulations and tight junctions, and in consequence causing permeability issues. Importantly, they observed the upregulation of antimicrobial peptides in the brain, indicative of neuroinflammation, as well as of fatty acids, equally connected with the inflammatory response.
Major comments
The study provides a detailed and comprehensive characterization of the sws mutant phenotype, and in particular the role of this gene in blood-brain barrier forming glia. - The study connects neurodegeneration and inflammation, but also makes a particular point about "inflammaging". However, the age contribution has not been studied in detail. Indeed, the flies analyzed are 15 days old (according to the Material and Methods section, with the exception of Figure 1 where flies are 30 days old), and hence have not been compared with younger or older flies to make a point of age as evoked in the abstract, introduction or discussion. The authors should either add experiments comparing differently aged flies or de-emphasize this point to a brief consideration in the discussion. Instead, it would be very helpful to provide concise information about the current knowledge concerning the inflammatory response in the Drosophila brain. - Related to this point, the authors convincingly show that sws is required in surface glia using rescue experiments. Nevertheless, all experiments rely on drivers and mutants that could cause the emergence of phenotypes during development. Thus, to strengthen the causative link between the breakdown of the BBB and the neuroinflammatory response, it would be helpful to consider an acute knock-down in adults after BBB formation has been completed. - To test the brain permeability barrier, the study uses a 10 KDa dextran permeability assay. Almost 25% of brain in controls show a leaky barrier. It would be helpful to describe the causes for this relatively high occurrence. - An important point in the study concerns the increase of free fatty acids as cause of the inflammatory response. The measurements were based on measurements of whole heads, which could include the hemolymph and fat body within the head in addition to brain. However, the causative relationship remains unclear and the question why a leaky blood brain barrier would increase the free fatty acid levels in the body or brain remains mainly an observation at the descriptive level. Here, it would be helpful to design an experiment, which could test the causative links or to modify the interpretation in scheme 6D and adjust the wording in the text. - Related to this, how do the levels of AMP caused by a leaky BBB would compare to an elicited neuroinflammation by the presence of bacteria? The neuroinflammatory response can be accompanied by macrophage entry into the brain following AMP induction. Could the authors detect this response (which could be envisioned as manipulations include pupal development, provided macrophages would persist into adulthood)? This would make a strong point regardless of the outcome. - Expression of sws is determined using sws-Gal4 driving membrane-tethered GFP. As sws is expressed very widely and classical Gal4 lines tend to be active in the BBB, it is important to provide the exact information about the nature of this driver. - The Material and Methods section should contain a proper Quantification and Statistical analysis section. In the Figures, it would be helpful to refer to the Table reporting sample numbers. - In Figure 5, it would be important to indicate sample numbers, the nature of the error bar, and show data points together with columns.
Minor comments
- On page 8, cell death is visualized using "the apoptotic marker Cas3". It should be Caspase-3. Moreover, it is not clear whether this antibody (directed against vertebrate Caspase-3) recognizes indeed Caspase-3 in Drosophila? This should be formulated more carefully.
- On Page 9 (3rd paragraph), the authors report that they "want to understand what signaling pathway is activated." However, the described experiments do not lead to a signaling pathway, but conclude that an antiflammatory response is evoked. This should thus be reworded.
- Figure 1 reports the expression pattern and phenotype of sws; thus, the title of the figure should be extended.
- Concerning the description of phenotypes, the authors use the term "clumps", but it is not clear what this entails (e.g., Page 6, or Figure 6). For the reader, it is also necessary to refer to original studies of moody to understand the septate junction phenotype represented in the figure.
Referees cross-commenting
I fully agree with the comments of the other two reviewers, as they were complementary and overlapping with mine (e.g. the contribution of age).
Significance
This study provides a detailed cellular and functional characterization of the swiss cheese phenotype in the blood-brain barrier so far not reported in previous studies, including the team's own earlier publications (e.g., Kretzschmar et al., 1997; Melentev et al., 2021 and Ryabova et al., 2021). Furthermore, it uses cutting-edge technology to provide links to neuroinflammation and neurodegeneration, Previous studies explored neuroinflammation in the brain of Drosophila by challenging the organism with bacteria to mount an inflammatory response (Winkler et al., 2021). Intriguingly, this current study provides evidence, that a leaky blood brain barrier alone could lead to an inflammatory response, and that in turn, treatment with anti-inflammatory agents could reduce the cellular defects in glia and in consequence neurodegeneration. This represents an important conceptual advance that will be of wide interest to neurobiologists interested in glial biology, neuroinflammation and neurodegeneration in Drosophila and in vertebrates. One possible limitation of the study may be that while complex cellular processes have been pinpointed, some of the causative links of the BBB with neuroinflammation remain unexplored, in particular the aspect of elevated free fatty acids/antimicrobial peptides.
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Reply to the reviewers
The authors do not wish to provide a response at this time.
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Referee #3
Evidence, reproducibility and clarity
In the manuscript entitled "The long non-coding RNA LINC00941 modulates MTA2/NuRD occupancy to suppress premature human epidermal differentiation", Morgenstern and colleagues describe a novel mechanism by which Nur complex interacting with a ncRNA LINC00947 represses expression of a late differentiation transcription factor EGR3 in the skin. These findings are novel and will be of interest in the field. Of note, the authors use a plethora of biochemical and cell biology techniques such as chromatin occupancy, transcriptomics and organotypic skin culture. Both the Introduction and Discussion outline a necessary background in the field of ncRNAs and skin biology as well as clearly state and discuss the scientific problem. The Methods are comprehensive and accurate. Importantly, any possible ethical issues are discussed. The Results are clear and support the data shown. Also the Figures are well organised and easy to follow. Overall, the manuscript is well prepared and will be of interest for broad readership. Before publication, however, the authors should address some points to further enhance their work:
Major points:
- It is peculiar that there is no evident K10 and FLG staining in the organotypic skin from siControl cells in Fig 2E but it is present on the Fig 5C. The authors should explain these inconsistences. Ideally the authors should provide a western blot analysis of these proteins which would help to give a more quantitative picture of the phenomenon.
- There iare no evidences at protein level of an efficiency of MTA2 and EGR3 knock-down. The authors use an MTA2 for western blot, so it should not be difficult to confirm. For EGR3, it is essential, as EFR3 is expected to increase only during late differentiation, while experiments from Fig 5B are performed only at 3 days of differentiation. Is it enough to induce EGR3 expression? Is the knock-down efficient at protein level at that time point? This is important as the work by Kim et al 2019 shows a detectable expression of EGR3 only after 7 days of differentiation.
- The authors should demonstrate an increase of EGR3 at protein level after LINC00947 knock-down (by western blot in vitro and/or in epidermal organotypic tissue).
- A key experiment to confirm the proposed model should be a double knock-down of both LINC00941 and EGF3. Will it rescue the observed increase of pro-differentiation genes?
Minor points:
- 4E - EGR3 or LINC00941 knock-down?
- 4E - The RNA seq label states "RNA seq (siLINC00941 d3)" but apparently shows both scr and siLINC00941
- Please add the recipe of the lysis buffer used for western blot analysis of keratinocytes lysates.
- Page 10 - Fig 4D description - is it "chromatin conformation state" or just "chromatin state"?
Significance
These findings are novel and will be of interest in the field.
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Referee #2
Evidence, reproducibility and clarity
In this manuscript, the authors investigate the role of LINC00941 epidermal differentiation. Specifically the authors show interaction with MTA2 and other NuRD subunits. Next, the authors show that LINC00941 and MTA2 restricts premature keratinocyte differentiation, where KD of either results in increased differentiation marker expression. To understand molecular impacts, the authors perform ChIPseq of MTA2 in control and LINC00941 depletion. Curiously, MTA2 binds in a trend differing from other cell types with predominant binding over active promoters. Upon LINC00941 KD, MTA2 binding is changed at 33 locations, where the majority show reduced binding. Overlapping binding changes with gene expression changes, the authors identify EGR3 as the only direct candidate upregulated upon LINC00941 KD and upregulated during differentiation. KD of EGR3 results in opposite trends of LINC00941 KD, suggesting the proposed mechanism of LINC00941 repressing EGR3 until appropriate time in differentiation. I have the following suggestions for this work:
- While data support MTA2 acting in NuRD, beyond Fig 1, the authors exclusively use MTA2 as a proxy for NuRD. Of course there are some subunits that are within other complexes and should not be used, others are options. While I do not expect the authors to perform all experiments on an additional subunit of NuRD, I do think there are a few things the authors should consider:
- a. Be more precise with language to point out only MTA2 rather than say NuRD complex throughout many aspects of the paper, and only assume the complex in limited settings and when it is clear it is speculative
- b. Perform a subset of experiments on another subunit. For example, the Mass Spec in Fig 1A/B shows an interaction with other subunits, but the verification was only done for MTA2 (Fig 1C/D). This could easily be blotted (or another Western performed) and/or primers for other subunits for the qPCR for a couple additional subunits. Similarly straightforward, looking at MTA2 RNA expression changes during differentiation (Fig 2A): if additional primers were used to other subunits, these additional subunits could be used to verify.
- Related to the above comment, does MTA2 KD (or LINC00941 KD for that matter) result in loss of NuRD complex formation? If so, this would be sufficient to address point 1.
- Finally, in relation to NuRD complex here, it is important to note that mutually exclusive NuRD complexes (MBD2/NuRD and MBD3/NuRD) have been documented. Because the Mass Spec did not show interaction to MBD2 or MBD3, it is not clear if this is limited to one of these complexes. Related to this, the authors show by Mass spec that LINC00941 interacts with CHD4, but not CHD3. Is this because Chd3 is not expressed in these cells, or because there is some mutual exclusivity to CHD4 and LINC00941 is acting through this subcomplex?
- Immunofluorescence images showing increased Keratin 10 and Filaggrin in LINC00941 or MTA2 KD (Fig 2E) and decreased Keratin 10 and Filaggrin in EGR3 KD (Fig 5C) are curious as the control look very different. In 2E, the control shows barely detectable levels, whereas in 5C the levels look similar to what is seen in Fig 2E KDs. Is this variability? If so, more representative images as well as quantification to the changes are necessary to make these two points.
- In Figure 4, the authors present ChIPseq data for MTA2 in LINC00941 KD. One interesting trend is that the KD alters binding of MTA2 at mostly bivalent/repressed locations, rather than at active locations which is the majority of MTA2 binding (from Fig 3). It would be nice to show then these data rather than only stating it. The authors include a browser track for 2 genes (Fig 4D and S4C), but for the other 31 locations, a heatmap or something to show the level of K27me3 vs K27ac/K4me3 would be helpful to support this claim. Notably saying "Most of the differential MTA2/NuRD occupied sites were marked by repressive histone modification H3K27me3..." is the point that doesn't seem to be shown, and also a precise number should be included. a. Related to this, I believe the authors performed K27me3 ChIPseq in the KD, and if so, it would be nice to see more genome wide effects here.
- This is perhaps beyond the scope of the paper, but the obvious question to me is if EGR3 is relocalized in LINC00941 KD. Specifically, we would anticipate that EGR3 localization in the KD would mimic that of a more differentiated cell (binding to differentiated genes). A quick ChIPqPCR experiment for a few locations would be sufficient to support this model.
Minor points:
- Importantly, CHD5 can also be incorporated in NuRD, in place of CHD3 or CHD4.
- The authors use heatmaps and metaplots in Fig S3 to show reproducibility of the ChIPseq datasets. Importantly, the PCA does show some variation. XY scatterplots for replicates vs one another would be a more robust QC.
- In figure 4D, the authors present nice data showing changes in histone mods during differentiation, but it is very hard to see the color changes and the tracks as presented. (same point for Fig S4C)
- it is unclear from the methods or the figure legend if RTqPCR data are biological or technical replicates.
Significance
In this manuscript, the authors present a molecular function for LINC00941 in epidermal differentiation, where it interacts directly with NuRD subunit MTA2. LINC00941 has been previously described but this activity was not described. LINC00941 seems to specifically help target or maintain MTA2 localization to EGR3 to promote repression of this gene. Then, the model suggests that during differentiation, LINC00941 and MTA2 levels decrease, permitting activation of EGR3 during epidermal differentiation and subsequent activation of appropriate genes. These findings will be of interest to individuals interested in NuRD function, lncRNA activity and/or epidermal cell fate.
My expertise is in chromatin biology, chromatin remodelers, epigenomics, and cell identity.
- While data support MTA2 acting in NuRD, beyond Fig 1, the authors exclusively use MTA2 as a proxy for NuRD. Of course there are some subunits that are within other complexes and should not be used, others are options. While I do not expect the authors to perform all experiments on an additional subunit of NuRD, I do think there are a few things the authors should consider:
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Referee #1
Evidence, reproducibility and clarity
In this manuscript, Morgenstern et al investigated the molecular mechanism by which LINC00941 regulates keratinocyte differentiation. They found the LINC00941 interacts with the NuRD chromatin remodeling complex in human primary keratinocytes. Furthermore, LINC00941 silencing by RNAi results in changes in the genomic occupancy of MTA2, a core NuRD subunit, especially near a number of bivalent genes. In particular, they showed that LINC00941 depletion resulted in reduced MTA2 occupancy at the EGR3 gene, increased EGR3 expression, and increased expression of EGR3-regulated epidermal differentiation genes. Together, they propose that LINC00941 prevents premature differentiation of human epidermal tissue by repressing EGR3 expression in non-differentiated keratinocytes via NuRD. The interaction between LINC00941 and NuRD is a novel finding and will likely provide new insights for the function of LINC00941, which has been implicated in keratinocytes, tissue homeostasis and cancer. It will also shed light on the role of lncRNAs in epigenetic gene regulation and cell fate transition in general. The conclusion of this study can be much strengthened if the authors can identify LINC00941-occupied genomic regions by ChIRP (PMID: 21963238) or RAP (PMID: 23828888). In addition, the authors are also encouraged to address the following questions and comments to further improve the manuscript.
Fig 1C: Since multiple NuRD subunits were identified in the LINC00941 pull-down (Fig 1B), can the authors validate at least one other subunit? CHD4 is also a NuRD-specific subunit and appears to be a strong hit based on supplemental Fig 1.
Fig 1D: Can the authors also try RNA-IP on MTA2 and endogenous LINC00941?
Fig 2B: It seems that MTA2 protein level still remains reasonably high at day-4.
Fig 3C: How many bivalent promoters are there in keratinocytes? How many of those are bound by MTA2?
Fig S3A: Can the authors examine MTA2 occupancy at TSS and bivalent TSS in control vs. siLINC00941 cells (by meta-gene analysis)? This will show whether LINC00941 KD affects MTA2 occupancy at bivalent TSS in general.
Fig 4B: Does LINC00941 KD only affect 33 out of the 3613 MTA2 peaks? If yes, can the authors comment on why only such a small fraction of MTA2 occupied regions are affected?
Fig 4C: The authors only examined a small number of MTA2-associated genes. To provide a more complete view of the potential involvement of LINC00941-regulated genes in keratinocytes differentiation, can the authors provide the total number of differentially expressed genes (DEGs) in LINC00941 KD, the total number of DEGs during keratinocytes differentiation, and the overlap between the two (maybe using a venn diagram)? In addition, among all the overlapping DEGs from above, how many of them have MTA2 peaks nearby? Finally, in the overlapping DEGs occupied by MTA2, can the authors compare MTA2 occupancy at up- vs. down-regulated DEGs caused by LINC00941 KD, to see whether reduced MTA2 occupancy associates with increased expression after LINC00941 KD?
Fig 4D: Can the authors add the H3K4me3 track to the figure? Can the authors provide ChIP-qPCR result to validate the changes in MTA2 occupancy near EGR3 after LINC00941 KD?
Fig 5A: Some of the EGR3 target genes (eg., GJB4, SERTAD1) appear to be expressed before EGR3 up-regulation in siCtrl, and some of them (eg. HMOX1, ESYT3, SMPD3) appear to show stronger up-regulation than EGR3 in siLINC00941. This is not entirely consistent with the idea that they are regulated by LINC00941 via EGR3.
Significance
The interaction between LINC00941 and NuRD is a novel finding and will likely provide new insights for the function of LINC00941, which has been implicated in keratinocytes, tissue homeostasis and cancer. It will also shed light on the role of lncRNAs in epigenetic gene regulation and cell fate transition in general. The conclusion of this study can be much strengthened if the authors can identify LINC00941-occupied genomic regions by ChIRP (PMID: 21963238) or RAP (PMID: 23828888). In addition, the authors are also encouraged to address the above-mentioned questions and comments to further improve the manuscript.
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Reply to the reviewers
[The “revision plan” should delineate the revisions that authors intend to carry out in response to the points raised by the referees. It also provides the authors with the opportunity to explain their view of the paper and of the referee reports.
1. General Statements [optional]
In this paper we describe the new finding that the epicardial deposits the extracellular matrix component laminin onto the apical ventricular surface during cardiac development. We identify a novel role for the apicobasal polarity protein Llgl1in timely emergence of the epicardium and deposition of this apical laminin, alongside a requirement for Llgl1 in maintaining integrity of the ventricular wall at the onset of trabeculation.
We thank the reviewers for their very positive appraisal of our manuscript, and for their helpful suggestions for useful revisions. In particular we would like to highlight the broad interest they feel this manuscript holds, not only contributing conceptual advances to our understanding of multiple aspects of cardiac development, but also to cell and developmental biologists working in epithelial polarity and extracellular matrix function. We also note their positive appraisal of the rigor of the study and quality of the manuscript.
2. Description of the planned revisions
Reviewer 1
1a) It is mentioned that llgl1 CRISPR/Cas9 mutants are viable as adults on pg. 3 of the Results section. Have the authors examined heart morphology in these mutants in juvenile or adult fish?
We have some historical data on adult llgl1 mutant survival that we plan to include in the study.
Reviewer 2
2a) The authors note an interesting observation with apical and basal laminin deposition dynamics surrounding cardiomyocytes, and that Llg1 has a role in apical Laminin deposition (however, highly variable at 80 hpf as Figure 3M shows). They carry out a very nice study in which they overexpress Llgl1 tagged with mCherry in the myocardium and show that there is no rescue of the extruding cardiomyocyte defect or Laminin deposition. However, there is still a possibility that the tagged Llgl1 in the transgene Tg(myl7:Llg1-mCherry)sh679 might not be functional due to improper protein folding or interference by the mCherry tag. The authors should supplement their approach with a transplantation experiment to generate mosaic llgl1 mutant animals and assess whether llgl1 mutant cardiomyocytes extrude at a higher rate than the control. This would provide definitive evidence that Llg1l acts in a cell non-autonomous manner.
We agree with the reviewer, and propose to perform transplant experiments, transplanting cells from llgl1 mutants into wild type siblings, and quantify cell extrusion to determine whether llgl1 mutant cells are extruded more frequently than wild type.
2b) The data in this manuscript appears to point that Llgl1 regulates Laminin deposition mainly in epicardial cells to regulate their dissemination/migration across the ventricular myocardial surface. It would be important to test this cell-autonomous function with the transplant experiment (above point) and examine whether llgl1 mutant epicardial cells fail to migrate and deposit Laminin. It might be possible to perform a rescue experiment through overexpression of Llgl1 in epicardial cells (if possible, there is a tcf21:Gal4 line available).
Similar to above, we propose to perform transplant experiments, transplanting cells from llgl1 mutants or wild type siblings into wild type siblings or llgl1 mutants, respectively, and in this instance quantify contribution of transplanted cells to epicardial coverage.
2c) In the Discussion, the authors propose that Llgl1 acts in two ways: Laminin deposition in epicardial cells that suppress cell extrusion and polarity regulation in cardiomyocytes to promote trabeculation. It would be important to test the second hypothesis on trabeculation and polarity regulation by using the myocardial-specific overexpression/rescue of Llgl1 in llgl1 mutants, and then quantifying the trabeculating cardiomyocytes and analyze Crb2a localization. This experiment can distinguish whether this trabeculation phenotype is rescued independently of the apical Laminin deposition that has been included in Figure S5.
To help address the second part of our hypothesis laid out in the discussion, we propose to quantify trabecular organisation and Crb2a localisation in llgl1 mutants either carrying the myl7:llgl1-mCherry construct, or mCherry-negative controls.
2d) The potential mis-localization of Crb2a in the llgl1 mutants is interesting, but this effect appears to be quite mild, and as the authors note, resolve by 80 hpf. Considering the role of Lgl in Drosophila in shifting Crb complex localization during early epithelial morphogenesis, it would be worth performing the analysis at earlier timepoints (between 55 and 72 hpf) to determine whether Llgl1 is indeed important for the progressive apical relocalization of Crb2a.
We will expand our description of this in the mutants by performing analysis of Crb2a at earlier timepoints in the llgl1 mutant (55hpf and 60hpf).
2e) OPTIONAL: It might be worth testing other antibodies that could mark the apical (particularly aPKC which is known to phosphorylate and regulate the Crb complex) and basolateral domains (Par1, Dlg) of the cardiomyocytes to definitively conclude that the epithelial integrity of the cells is affected. Although there are no reports of working antibodies marking the basal domain in zebrafish, there is at least a Tg(myl7:MARCK3A-RFP) line published (Jimenez-Amilburu et al. (2016)) - which the authors can inject to examine the localization in mosaic hearts.
We plan to assess localisation of aPKC (see section 4 for response to other suggested polarity protein analyses).
2f) Have the authors quantified the numbers of total cardiomyocytes in llgl1 mutants to correlate how many cells are lost as a consequence of extrusion? What is the physiological impact of this extrusion (ejection fraction, total cardiac volumes, sarcomere organization)?
We have some of this data already which we will include in the manuscript (cell number, myocardial volume). We agree that the analysis of cardiac function could be more extensive, and we will perform more detailed analysis of cardiac function, including e.g. ejection fraction. Sarcomere organisation has been previously described in llgl1 mutants by Flinn et al, 2020, so we do not plan to replicate this data.
2g) The lamb1a and lamc1 mutant phenotypes were nicely analyzed. However, there is basement membrane deposition on both the apical and basal sides of the cardiomyocytes. Therefore, it is unclear whether the cardiomyocyte extrusion is completely caused by loss of apical basement membrane, or whether the loss of basal basement membrane could compromise the myocardial tissue integrity. The authors should clarify this conclusion in the text.
We will address this further in the text, but will also include 55hpf Laminin staining data for llgl1 mutants to reinforce our message.
2h) The authors note that Llgl1-mCherry in the Tg(myl7:Llg1-mCherry)sh679 line localizes to the basolateral domain of the cardiomyocytes, which is valuable confirmation that Llgl1 protein is spatially restricted. However, only 1 timepoint (55 hpf) is noted. It would be important to perform Llgl1 localization across different developmental timepoints (at least until 80 hpf) to examine the dynamics of this protein during trabeculation and apical extrusion, and potentially correlate it with Crb2a localization for a better understanding of the apicobasal machinery in cardiomyocytes.
We already have some of this data and will include extra timepoints in a revised version of the manuscript
2i) The phenotypes of llgl1 mutants described here differ compared to the previous study by Flinn et al. (2020). In particular, whereas the mutants generated in this study have only mild pericardial edema and are adult viable, approximately one third of llgl1mw3 (Flinn et al. (2020)) died at 6 dpf. Is this caused by the different natures of the mutations in the llgl1 gene? Is there a possibility that the llgl1sh598 is a hypomorphic allele since the targeted deletion is in a more downstream sequence (in exon 2) compared to the llgl1mw3 (deletion in exon 1) allele?
We thank the reviewer for noticing these subtle differences between the two llgl1 mutants. Indeed, while we occasionally see llgl1sh598 mutants with the severe phenotype described by Flinn et al, this is a small minority which we did not quantify. Our mutation is indeed slightly further downstream than that described by Flinn et al, however we believe that this will have a neglible effect on Llgl1 function. Our llgl1sh589 mutation results in truncation shortly into the WD40 domain, and importantly completely lacks the Lgl-like domain, which is responsible for the specific function of Llgl1 likely through its ability to interact with SNAREs to regulate cargo delivery to membranes (Gangar et al, Current Biology 2005).
Interestingly, Flinn et al report no increased phenotypic severity in their maternal-zygotic llgl1 mutants when compared to zygotic mutants. Conversely, we often observed very severe phenotypes in MZ llgl1sh589 mutants, including failure of embryos during blastula stages, apparently through poor blastula integrity. We did not include this information in the manuscript due to space constraints. However, we argue that together these differences between the two alleles may not be due to hypomorphism of our llgl1sh589 allele, but rather differences in genetic background that may amplify specific phenotypes. We plan to include a short sentence summarising the above in combination with planned experiments described below to address the reviewer’s next comment.
2j) Suggested experiment: qPCR of regions downstream of the deletion to make sure that the transcript is absent/reduced in the llgl1sh598 mutants. Alternatively, immunostaining or Western blot would be an even better option to ensure there is no Llgl1 protein production - there is an anti-Llgl1 antibody available that works for Western blots in zebrafish (Clark et al. (2012)).
We plan to analyse llgl1 expression in llgl1 mutants using qPCR.
Reviewer 3
3a) Major - the authors describe that llgl1 mutants exhibit transient cardiac edema at 3 dpf, which is resolved by 5 dpf, and claim that the mutants are viable. This statement needs to be better supported - What is the proportion of mutants that survive to adulthood? The embryonic phenotypes are pretty variable - are the mutants that survive the ones with a less severe phenotype? Is there a gross defect in the adult heart of these animals?
In line with comments from Reviewers 1 and 2 above, we will include a description of the data we have from adult animals (historical data, not generation of new animals).
3b) Major - Many of the phenotypes described here -most importantly, the defects on epicardial development- could result from hemodynamic defects in llgl1 mutants. The authors claim that function is unaffected in these animals, but this has only been addressed by measuring heartbeat. The observation that the cardiac function in these animals is normal would conflict with a previous description (PMID: 32843528) that demonstrates that llgl1 mutant animals show significant hemodynamic defects, which would cause epicardial defects. Thus, this aspect of the work needs to be better addressed.
In line with our comments to point 2f) from Reviewer 2, we will perform a more in-depth functional analysis on llgl1 mutant larvae.
3c) The phenotypes related to forming multiple layers in the heart (Fig. 1) could be more convincing. In some figures, the authors use a reporter that labels the myocardial cell membrane, but in Figure 1 this is not used. Showing a myocardial membrane marker (for example, the antibody Alcama, Zn-8) would significantly strengthen this observation.
We will describe trabecular phenotypes in more detail using the suggested antibody to highlight membranes.
3d) The analysis of Crumbs redistribution (Fig. 2) is quite interesting. Still, given that the authors have a transgenic model to rescue llgl1 expression in cardiomyocytes, they could move from correlative evidence to experimental demonstration of the role of llgl1 in Crumbs localization.
Similar to our response to comment 2c) from Reviewer 2, we plan to address this
3. Description of the revisions that have already been incorporated in the transferred manuscript
Reviewer 1:
Although information is provided in the introduction and discussion on the role of the Llgl1 homolog in Drosophila and speculation on LLGL1 contributing to heart defects in SMS patients in the discussion, have Llgl1 homologs been examined in other vertebrate animal models during heart development or regeneration?
With the exception of the Flinn et al paper, we find no published studies assessing the role of Llgl1 in heart development or regeneration in other vertebrates, and have updated the introduction to highlight this fact:
‘Zebrafish have two Lgl homologues, llgl1 and llgl2, and llgl1 has previously been shown to be required for early stages of heart morphogenesis (Flinn et al. 2020). However, although Llgl1 expression has also been reported in the developing mouse heart and both adult mouse and human hearts (Uhlén et al. 2015; Klezovitch et al. 2004), whether llgl1 plays a role in ventricular wall development has not been examined.’
In Fig. 4J-M', there is no Cav1 signals after wt1a MO but still laminin signals. Where these laminins come from?
The residual laminin staining observed in wt1a morphants is located at the basal surface of cardiomyocytes (while the apical laminin signal is lost, in line with the epicardial deposition of laminin at the apical ventricle surface). This basal laminin is likely deposited earlier during heart tube development by either the myocardium, endocardium or both, and thus unaffected by later formation of the epicardium. We reason this since a) it is present at the basal cardiomyocyte surface at 55hpf (see Fig 2); b) we have previously identified both myocardial and endocardial expression of laminin subunits at 26hpf and 55hpf (Derrick et al, Development, 2021); c) sc-RNA-seq analysis of hearts at 48hpf demonstrates that laminin subunits, e.g. lamc1 are expressed in myocardial and endocardial cells (Nahia et al, bioRxiv, 2023), also in line with our previous ISH analysis. We have included a sentence to reflect this in the results section:
Conversely, *wt1a* morphants retain deposition of laminin at the basal CM surface, likely from earlier expression and deposition of laminin by either myocardial or endocardial cells (Derrick et al. 2021; Nahia et al. 2023), which is unaffected by later epicardial development.
On page 3 of the manuscript, Fig. 1A should be included with Fig. 1B in the first sentence of paragraph 2 of the Results subsection "Llgl1 regulates ventricular wall integrity and trabeculation".
Amended
It would be beneficial to readers to briefly describe what cell type the transgenic reporters label when mentioned in the Results section to help readers unfamiliar with zebrafish.
We have updated the text to read:
We further analysed heart morphology using live lightsheet microscopy of *Tg(myl7:LifeActGFP);Tg(fli1a:AC-TagRFP)* double transgenic wild-type and *llgl1* mutant embryos, allowing visualisation of myocardium (green) and endocardium (magenta) respectively. Comparative analysis of overall heart morphology between 55hpf and 120hpf when looping morphogenesis is complete, revealing that *llgl1* mutants continue to exhibit defects in heart morphogenesis (Fig S1S-X).
Reviewer 3
(Optional) There is laminin in the luminal side of the heart before there is any epicardial invasion. What is the source of this laminin? The techniques the authors have used (i.e., chromogenic ISH) are fine, but a more detailed analysis using fluorescent ISH (i.e., RNAScope) would be much more definitive.
This is related to our response to Reviewer 1 (above) – where we have included the following text included in manuscript:
Conversely, *wt1a* morphants retain deposition of laminin at the basal CM surface, likely from earlier expression and deposition of laminin by either myocardial or endocardial cells (Derrick et al. 2021; Nahia et al. 2023), which is unaffected by later epicardial development.
We hope this clarifies our proposed origins for the earlier laminin deposition.4. Description of analyses that authors prefer not to carry out
Reviewer 1:
As pan-epicardial transgenes like tcf21 reporters have been widely used, the authors should use such reporters to verify the expression of laminin gene expression in epicardial cells, and the efficacy and efficiency of depleting epicardial cells after wt1 MO injection.
Several studies have demonstrated that the epicardium is not a heterogeneous population – for example, tcf21 is not expressed in all epicardial cells and thus not a pan-epicardial reporter (Plavicki et al, BMC Dev Biol, 2014, Weinberger et al, Dev Cell, 2020) – the suggested analysis would not necessarily be conclusive, and more detailed study would require acquisition of three new transgenic lines. Furthermore, we believe the evidence we present in the paper supports our claim: 1) We show expression of two laminin subunits in a thin mesothelial layer directly adjacent to the myocardium, specifically in the location of the epicardium; 2) sc-RNA seq analyses have also identified laminin expression in epicardial cells at 72hpf (where lamc1a is identified as a marker of the epicardium); 3) We demonstrate 100% efficacy of our wt1a knockdown as assayed by Cav1 expression, an established epicardial marker (Grivas et al, 2020, Marques et al, 2022) which in sc-RNA seq data is expressed at high levels broadly in the epicardial cell population (Nahia et al, 2023), representing a good assay for presence of epicardium. However, we propose to perform ISH analysis of laminin subunit expression in wt1a MO to investigate whether the mesothelial laminin-expressing layer we observe adjacent to the myocardium is absent upon loss of wt1a.
Reviewer 2:
The data in this manuscript appears to point that Llgl1 regulates Laminin deposition mainly in epicardial cells to regulate their dissemination/migration across the ventricular myocardial surface. It would be important to test this cell-autonomous function with the transplant experiment (above point) and examine whether llgl1 mutant epicardial cells fail to migrate and deposit Laminin. It might be possible to perform a rescue experiment through overexpression of Llgl1 in epicardial cells (if possible, there is a tcf21:Gal4 line available).
We do not propose to perform this experiment using a tcf21:Gal4 line, as this would likely require at least 6 months to either import and quarantine, or generate the necessary stable lines. Furthermore, as mentioned above, tcf21 is not a pan-epicardial marker, and the extent and timing of the Gal4:UAS system may make this challenging to determine whether llgl1 has been expressed early or broadly enough. We will instead attempt transplantation experiments.
OPTIONAL: It might be worth testing other antibodies that could mark the apical (particularly aPKC which is known to phosphorylate and regulate the Crb complex) and basolateral domains (Par1, Dlg) of the cardiomyocytes to definitively conclude that the epithelial integrity of the cells is affected. Although there are no reports of working antibodies marking the basal domain in zebrafish, there is at least a Tg(myl7:MARCK3A-RFP) line published (Jimenez-Amilburu et al. (2016)) - which the authors can inject to examine the localization in mosaic hearts.
We will assess localisation of aPKC, but we do not plan to analyse the other components. Analysis of basolateral domains (Par1, Dlg, Mark3a-RGP), will not necessarily assess epithelial integrity, as suggested, but rather apicobasal polarity – which we already assess using Crb2a, and additionally plan to assess aPKC to accompany the Crb2a analysis. Since the reviewer suggests this as an optional experiment we prioritise their other suggested experiments that we think more directly address the main messages of the manuscript.
OPTIONAL: Gentile et al. (2021) found that reducing heartbeat led to decreased cardiomyocyte extrusion in snai1b mutants. The authors could look into the contribution of mechanical pressure through contraction in the apical cardiomyocyte extrusion, and test whether reducing contraction (tnnt2 morpholino, chemical treatments) partly rescues the llgl1 mutant phenotypes.
The relationship between cardiac function and myocardial wall integrity appears to be complex. The paper referred to by the reviewer indeed finds that reduction in heartbeat leads to decreased CM extrusion upon loss of the EMT-factor Snai1b. Previous studies have also found that endothelial flow-responsive genes klf2a/b are required to maintain myocardial ventricular wall integrity at later stages in a contractility-dependent manner (Rasouli et al, 2018). However, contractility is also required early for pro-epicardial emergence, but plays a lesser role in expansion of the epicardial layer on the myocardial surface (Peralta, 2013). Unpicking the relationship between the forces induced by mechanical contraction of the ventricular wall, contractility-based induction of e.g klf2 expression, and the impact of contractile forces on proepicardial development or epicardial expansion will be complex. We therefore think the proposed experiment will be difficult to interpret whatever the outcome, and argue that dissecting this relationship is beyond the scope of revisions for this paper.
Reviewer 3
How llgl1 relates to epicardial biology is left entirely unexplored in this work. Do proepicardial cells show any defect in cell polarization related to llgl1 absence?
We agree with the reviewer that we do not delve into the mechanisms underlying regulation of epicardial development by llgl1, and that this is an interesting question. Our scope for this manuscript was to understand the mechanisms by which llgl1 regulates integrity of the ventricular wall, and feel that uncovering the molecular mechanisms by which llgl1 regulates timely epicardial emergence is a larger question that would require substantial investigation (for example, if and when llgl1 PE cells do exhibit apicobasal defects, how this impacts timing of cluster release etc). We think these are important questions that would be better answered in detail in a separate manuscript.
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Referee #3
Evidence, reproducibility and clarity
This manuscript from Pollitt and colleagues analyzes the role of lethal(2) giant larvae homolog 1 (llgl1) in cardiac development in zebrafish. Llgl1 has been previously involved in regulating epithelial polarity, which raises the possibility that this gene might play a role during the trabeculation of the zebrafish heart. To examine the role of llgl1 in this phenomenon, the authors generated a new loss of function mutant using CRISPR/Cas9. Animals lacking llgl1 initially exhibited abnormal cardiac development, manifested by defects in cardiac looping and pericardial edema. This phenotype, however, was transient. A detailed analysis of the developing heart showed, albeit with significant variability, defects in trabeculation and an interesting cardiomyocyte extrusion phenotype, described before in mutants that lack epicardium. During their analysis, the authors discovered a switch in the localization of the extracellular matrix protein laminin from the luminal to the apical side of the cardiomyocytes that temporally correlates with the process of trabeculation. The accumulation of laminin in the epicardial side was affected in llgl1 mutants, which also showed a defect in epicardial development. Coincidentally, the epicardial cells appear to be the primary source of laminin. This work suggests that llgl1 acts in epicardial cells to maintain ventricular wall integrity during heart development.
Major Comments:
- Major - the authors describe that llgl1 mutants exhibit transient cardiac edema at 3 dpf, which is resolved by 5 dpf, and claim that the mutants are viable. This statement needs to be better supported - What is the proportion of mutants that survive to adulthood? The embryonic phenotypes are pretty variable - are the mutants that survive the ones with a less severe phenotype? Is there a gross defect in the adult heart of these animals?
- Major - Many of the phenotypes described here -most importantly, the defects on epicardial development- could result from hemodynamic defects in llgl1 mutants. The authors claim that function is unaffected in these animals, but this has only been addressed by measuring heartbeat. The observation that the cardiac function in these animals is normal would conflict with a previous description (PMID: 32843528) that demonstrates that llgl1 mutant animals show significant hemodynamic defects, which would cause epicardial defects. Thus, this aspect of the work needs to be better addressed.
- The phenotypes related to forming multiple layers in the heart (Fig. 1) could be more convincing. In some figures, the authors use a reporter that labels the myocardial cell membrane, but in Figure 1 this is not used. Showing a myocardial membrane marker (for example, the antibody Alcama, Zn-8) would significantly strengthen this observation.
- The analysis of Crumbs redistribution (Fig. 2) is quite interesting. Still, given that the authors have a transgenic model to rescue llgl1 expression in cardiomyocytes, they could move from correlative evidence to experimental demonstration of the role of llgl1 in Crumbs localization.
- (Optional) There is laminin in the luminal side of the heart before there is any epicardial invasion. What is the source of this laminin? The techniques the authors have used (i.e., chromogenic ISH) are fine, but a more detailed analysis using fluorescent ISH (i.e., RNAScope) would be much more definitive.
- How llgl1 relates to epicardial biology is left entirely unexplored in this work. Do proepicardial cells show any defect in cell polarization related to llgl1 absence?
Significance
General Assessment. Overall, this is an interesting manuscript put together with rigor. The strongest aspect of this work is the discovery of a switch in the localization of laminin in the developing heart and the potential implications of this process in regulating correct trabeculation versus cardiomyocyte extrusion. Although the text itself is very well written, with clear statements of the hypotheses and the findings that led the authors to each experiment, I found myself wondering what the unifying theme and central message of the manuscript is and whether this has been appropriately supported with experimental data. Specifically, although the authors included a very detailed analysis of the myocardium, their results (including the last supplementary figure) suggest that these phenotypes might be secondary to a defect in epicardial development. Still, it is entirely unclear how the loss of llgl1 would affect epicardial development.
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Referee #2
Evidence, reproducibility and clarity
Summary: The manuscript by Pollitt et al. explores the functions of llgl1, which encodes a critical component of the basolateral domain complex, during cardiac development in zebrafish. The authors observed that llgl1 mutants exhibited compromised myocardial tissue integrity with significantly higher numbers of apically extruding cardiomyocytes. Llgl1 appears to primarily function during epicardial cell spreading on the myocardial tissue, as myocardial-specific overexpression of llgl1 did not rescue llgl1 mutant phenotypes. llgl1 mutants exhibited impaired epicardial coverage and subsequently Laminin deposition on the apical side of the cardiomyocytes. Functional linkage between Laminin/the basement membrane was identified, as extruding cardiomyocytes were also observed in mutants of two core laminin genes, lamb1a and lamc1. The epicardial defects were transmitted to myocardial tissue defects, marked by mis-localization of the apical polarity protein Crumbs2a during early heart development. Overall, the authors provide a nice study that strengthens the role of apicobasal factors in myocardial tissue morphogenesis and that sheds light on the role of epicardial-derived basement membrane in maintaining myocardial tissue integrity.
Major comments
- The authors note an interesting observation with apical and basal laminin deposition dynamics surrounding cardiomyocytes, and that Llg1 has a role in apical Laminin deposition (however, highly variable at 80 hpf as Figure 3M shows). They carry out a very nice study in which they overexpress Llgl1 tagged with mCherry in the myocardium and show that there is no rescue of the extruding cardiomyocyte defect or Laminin deposition. However, there is still a possibility that the tagged Llgl1 in the transgene Tg(myl7:Llg1-mCherry)sh679 might not be functional due to improper protein folding or interference by the mCherry tag. The authors should supplement their approach with a transplantation experiment to generate mosaic llgl1 mutant animals and assess whether llgl1 mutant cardiomyocytes extrude at a higher rate than the control. This would provide definitive evidence that Llg1l acts in a cell non-autonomous manner.
- The data in this manuscript appears to point that Llgl1 regulates Laminin deposition mainly in epicardial cells to regulate their dissemination/migration across the ventricular myocardial surface. It would be important to test this cell-autonomous function with the transplant experiment (above point) and examine whether llgl1 mutant epicardial cells fail to migrate and deposit Laminin. It might be possible to perform a rescue experiment through overexpression of Llgl1 in epicardial cells (if possible, there is a tcf21:Gal4 line available).
- In the Discussion, the authors propose that Llgl1 acts in two ways: Laminin deposition in epicardial cells that suppress cell extrusion and polarity regulation in cardiomyocytes to promote trabeculation. It would be important to test the second hypothesis on trabeculation and polarity regulation by using the myocardial-specific overexpression/rescue of Llgl1 in llgl1 mutants, and then quantifying the trabeculating cardiomyocytes and analyze Crb2a localization. This experiment can distinguish whether this trabeculation phenotype is rescued independently of the apical Laminin deposition that has been included in Figure S5.
- The potential mis-localization of Crb2a in the llgl1 mutants is interesting, but this effect appears to be quite mild, and as the authors note, resolve by 80 hpf. Considering the role of Lgl in Drosophila in shifting Crb complex localization during early epithelial morphogenesis, it would be worth performing the analysis at earlier timepoints (between 55 and 72 hpf) to determine whether Llgl1 is indeed important for the progressive apical relocalization of Crb2a. OPTIONAL: It might be worth testing other antibodies that could mark the apical (particularly aPKC which is known to phosphorylate and regulate the Crb complex) and basolateral domains (Par1, Dlg) of the cardiomyocytes to definitively conclude that the epithelial integrity of the cells is affected. Although there are no reports of working antibodies marking the basal domain in zebrafish, there is at least a Tg(myl7:MARCK3A-RFP) line published (Jimenez-Amilburu et al. (2016)) - which the authors can inject to examine the localization in mosaic hearts.
- Have the authors quantified the numbers of total cardiomyocytes in llgl1 mutants to correlate how many cells are lost as a consequence of extrusion? What is the physiological impact of this extrusion (ejection fraction, total cardiac volumes, sarcomere organization)?
- The lamb1a and lamc1 mutant phenotypes were nicely analyzed. However, there is basement membrane deposition on both the apical and basal sides of the cardiomyocytes. Therefore, it is unclear whether the cardiomyocyte extrusion is completely caused by loss of apical basement membrane, or whether the loss of basal basement membrane could compromise the myocardial tissue integrity. The authors should clarify this conclusion in the text.
Minor comments
- The authors note that Llgl1-mCherry in the Tg(myl7:Llg1-mCherry)sh679 line localizes to the basolateral domain of the cardiomyocytes, which is valuable confirmation that Llgl1 protein is spatially restricted. However, only 1 timepoint (55 hpf) is noted. It would be important to perform Llgl1 localization across different developmental timepoints (at least until 80 hpf) to examine the dynamics of this protein during trabeculation and apical extrusion, and potentially correlate it with Crb2a localization for a better understanding of the apicobasal machinery in cardiomyocytes.
- The phenotypes of llgl1 mutants described here differ compared to the previous study by Flinn et al. (2020). In particular, whereas the mutants generated in this study have only mild pericardial edema and are adult viable, approximately one third of llgl1mw3 (Flinn et al. (2020)) died at 6 dpf. Is this caused by the different natures of the mutations in the llgl1 gene? Is there a possibility that the llgl1sh598 is a hypomorphic allele since the targeted deletion is in a more downstream sequence (in exon 2) compared to the llgl1mw3 (deletion in exon 1) allele? Suggested experiment: qPCR of regions downstream of the deletion to make sure that the transcript is absent/reduced in the llgl1sh598 mutants. Alternatively, immunostaining or Western blot would be an even better option to ensure there is no Llgl1 protein production - there is an anti-Llgl1 antibody available that works for Western blots in zebrafish (Clark et al. (2012)).
- Closeups needed for Figure 3I-L' - difficult to assess mis-localization or differences in Laminin staining. Contrary to the quantification or conclusion, the Laminin staining appears stronger in llgl1 mutants compared to wild types in Figure 3I' and J'.
- OPTIONAL: Gentile et al. (2021) found that reducing heartbeat led to decreased cardiomyocyte extrusion in snai1b mutants. The authors could look into the contribution of mechanical pressure through contraction in the apical cardiomyocyte extrusion, and test whether reducing contraction (tnnt2 morpholino, chemical treatments) partly rescues the llgl1 mutant phenotypes.
Significance
As someone with expertise in cardiac development and cellular behaviours, I find this study provides strong and convincing quantitative data on the role of Llgl1 in suppressing cardiomyocyte extrusion and promoting epicardial dissemination on the ventricular surface. The genetic experiments, including mutant analysis and myocardial-specific rescue, were carefully performed in a region-specific manner, which provides much insight into the non-uniformity of myocardial tissue integrity. The generation of Tg(myl7:llgl1-mCherry) line is also a valuable tool for researchers in the field interested in understanding apicobasal polarity and cardiomyocyte development and regeneration.
A limitation of the study is the unclear link between epithelial polarity and basement membrane deposition, and how they synchronize to regulate cardiomyocyte integrity. The llgl1 mutant phenotype in increasing cardiomyocyte apical extrusion and Crb2 localization is interesting; however, the authors note that this appears to be a phenotype induced by epicardial defects. Epicardial cells are not known to exhibit apicobasal polarity and are fibroblastic by nature. Thus, the cellular mechanisms by which Llg1 regulates epicardial cell morphology or behaviours, and how it functions to regulate polarity in cardiomyocytes are not clearly defined in this work. In addition, clarification of the cell autonomous functions of Llgl1 in epicardial cells and/or cardiomyocytes would strengthen the findings.
Overall, the findings of this study would be of interest to cell and developmental biologists in the fields of epithelial polarity, cardiac morphogenesis, and extracellular matrix function. It provides nice conceptual advance in further elucidating the mechanisms that underlie myocardial tissue integrity and epicardial-myocardial interactions.
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Referee #1
Evidence, reproducibility and clarity
Pollitt et al. investigated the role of Llgl1 in maturation of the ventricular wall in zebrafish. They examined zebrafish heart morphology with microscopy analysis of fluorescent reporter lines crossed with their CRISPR/Cas9 llgl1 line and observed apically extruding CMs in llgl1 mutant embryos, implicating a role for llgl1 in ventricular wall integrity. Further, they examined apicobasal polarity in the ventricular wall through quantification of Crb2a distribution at the apical membrane surface, with enhanced Crb2a retention at CM junctions in llgl1 mutant embryos in comparison to WT at 72 hpf but similar levels at 80 hpf. Further analyses indicated a requirement for llgl1 for the temporal establishment of the apical laminin sheath, llgl1 is required for the timely dissemination of epicardial cells which deposit laminin to maintain the integrity of the ventricular wall. This work is written well and provides new information on heart development in zebrafish, and provides additional information on the role of the epicardium in supporting the integrity of the ventricular wall during trabeculation.
Major:
- Although information is provided in the introduction and discussion on the role of the Llgl1 homolog in Drosophila and speculation on LLGL1 contributing to heart defects in SMS patients in the discussion, have Llgl1 homologs been examined in other vertebrate animal models during heart development or regeneration?
- In Fig. 3I-O: The authors described the spatial dynamics of laminin in llgl1 mutants at 72 and 80m hpf. However, it is hard to say the schematic depicting of laminin for llg1 mutant in Fig. 3O reflect the real laminin staining signals in Fig. 3J' and 3L'.
- It is mentioned that llgl1 CRISPR/Cas9 mutants are viable as adults on pg. 3 of the Results section. Have the authors examined heart morphology in these mutants in juvenile or adult fish?
- In Fig. 4J-M', there is no Cav1 signals after wt1a MO but still laminin signals. Where these laminins come from?
- As pan-epicardial transgenes like tcf21 reporters have been widely used, the authors should use such reporters to verify the expression of laminin gene expression in epicardial cells, and the efficacy and efficiency of depleting epicardial cells after wt1 MO injection.
Minor:
- On page 3 of the manuscript, Fig. 1A should be included with Fig. 1B in the first sentence of paragraph 2 of the Results subsection "Llgl1 regulates ventricular wall integrity and trabeculation".
- Fig. 2E: Is Fig. 2E from WT or llgl1 embryos? This information isn't indicated in the image panels or the figure legend. It might also be beneficial to include a similar representative image for the WT or llgl1 mutant embryo as this was used for quantification.
- Fig. 3G: As the entirety of Fig. 3 used violet coloring to depict Laminin, it would be more consistent to change the blue coloring used to depict Laminin in panel G to the same violet coloring used for Laminin in the other panels of Fig. 3.
- It would be beneficial to readers to briefly describe what cell type the transgenic reporters label when mentioned in the Results section to help readers unfamiliar with zebrafish.
Significance
This work is written well and provides new information on heart development in zebrafish, and provides additional information on the role of the epicardium in supporting the integrity of the ventricular wall during trabeculation.
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Referee #3
Evidence, reproducibility and clarity
Summary:
In this paper, Hama et al look to address ongoing questions regarding the recruitment of core autophagy factors to protein aggregates during aggrephagy. Using cells that lack all known aggrephagy receptors (PentaKO HeLa cells) provides the authors with a 'blank canvas' with which to cleanly dissect a process otherwise fraught with mechanistic redundancy. By this approach, the authors isolate a previously unidentified mechanism by which TAX1BP1 recruits ATG9A vesicles to ubiquitin-positive aggregates. Using mass spectrometry, the authors identify SCAMP3 as a component of ATG9 vesicles that is responsible for recruiting the vesicles to cargo. Moreover, they provide additional mechanistic insight through the subsequent identification and mutagenesis of a putative interaction interface between TAX1BP1 and SCAMP3.
Major comments:
- Statistics:
- a) The description of the statistical methods is sparse. In the methods section, the authors' state that statistical methods are described in each figure legend. However, for most figures they were excluded.
- b) Where statistics are discussed, Mann-Whitney was used. However, this appears to be the incorrect test in many cases. Per GraphPad (the author's preferred statistical package) "Use Mann-Whitney test only to compare two groups. To compare three or more groups, use the Kruskal-Wallis test followed by post tests. It is not appropriate to perform several Mann-Whitney (or t) tests, comparing two groups at a time."
- Rigor and reproducibility - The authors report n=~30 cells in most experiments. However, it's unclear if these 30 cells represent more than a single experimental replicate. While the trends in the data are quite convincing, this a significant limitation of this study.
- Puncta identification - it's unclear how the authors called puncta. The quantification of these images (i.e. the box and whiskers plots) makes compelling points that support the authors' interpretation. However, in many cases the authors are calling puncta amidst a fairly speckled image. How were puncta distinguished from speckles? When did something rise to the level of a puntum? If it was computationally called, please provide the methods and pipeline. If data were manually scored, then the lack of replicates rises to a more significant concern - would other investigators have scored the data similarly? Is it possible that the data were scored with implicit bias based on expected outcomes of the investigator? Where data scored in a blinded fashion?
- Do the findings presented here have functional implications? While the data on recruitment of ATG9A to TAX1BP1 is clear, it's unclear whether the SCAMP3/TAX1BP1 interaction is functionally important for lysosomal delivery of cargo. In part, this is because the authors must work in autophagy-deficient cells (ATG9AKO or FIP200KO) to observed many of their effects. One way to address function might be to ask whether lysosomal delivery of TAX1BP1 is affected upon SCAMP3KO (e.g. in PentaKO cells to remove the effects of other receptors).
Minor comments:
- The authors IP TAX1BP1CC1 and SCAMP3, but an IP between full length TAX1BP1 (WT and K248E) and SCAMP3 would more fully demonstrate the sufficiency of this binding site.
- Optional: Is the TAX1BP1 and SCAMP3 interaction direct?. The data are suggestive, but this question is not fully resolved due to the in vivo nature of the assays. Formally, there could be an adapter that facilitates TAX1BP1/SCAMP3 interaction. This could be formalized by testing binding between purified soluble domains of SCAMP3 and TAX1BP1CC1.
- Figure 4F - the y axis has a typographical error
Significance
This is a crisply written manuscript with a generally clean experimental approach. While there are many directions the authors could take this work in the future, the data largely stand on their own as a short, concise advance. The work adds to our current understanding of the regulation of ATG9A recruitment during selective autophagy, which is under-explored in comparison to starvation-induced autophagy. Mechanistic insights are provided in the form of a new interacting protein SCAMP3, which is present in ATG9A vesicles and is required for the recruitment of ATG9A vesicles to TAX1BP1. The data are predominantly convincing, albeit with significant caveats. Limited sample sizes and lack of functional effects (see 'major concerns') limit the impact of this work. However, the data have merit on their own. This is a specialized study that will be appreciated by those interested in selective autophagy and the mechanism of autophagosome formation.
Reviewer expertise: selective autophagy and autophagosome formation
- Statistics:
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Referee #2
Evidence, reproducibility and clarity
Ubiquitin-binding adaptors are a group of autophagy adaptor proteins that facilitates the formation of isolating membrane around a specific substrate during selective autophagy. At least two ubiquitin-binding adaptors, NBR1 and OPTN, have previously been demonstrated to recruit ATG9-positive vesicles to ubiquitin-positive biomolecular condensates. It is postulated that this recruitment mediates the formation of the isolating membrane around the ubiquitinated biomolecular condensates. In this manuscript, the authors utilized the HeLa cells deficient in the expression of five ubiquitin-binding adaptors, p62, NBR1, OPTN, NDP52 and TAX1BP1, to show that expressing TAX1BP1 colocalizes with ATG9A. The authors interpreted this observation to suggest that TAX1BP1 can recruit ATG9A vesicles independent of other adaptors. Interestingly, the TAX1BP1 positive structures differ from that of OPTN and NBR1 because they do not contain ubiquitin, suggesting a difference in substrate specificity. They further show that TAX1BP1 differs from OPTN and NBR1 because ATG9 recruitment required SCAMP3 to recruit ATG9A. Based on these observations, the authors propose that TAX1BP1 recruits ATG9A via SCAMP3.
Major comments
- The work presented in this manuscript is of high quality, however, the reliance on a single experimental assay (colocalization microscopy studies) limits the relevance of its findings. The conclusions are based on colocalization studies in wild-type and CRISPR/cas9 knockout HeLa cell lines. Therefore, it is not certain that their findings are restricted to these PentaKO cell lines. Given that these adaptors are required for selective autophagy, the pentaKO cells may have adapted. Unfortunately, the current study lacks additional systems physiologically relevant models or systems. Such experiments are needed to validate their findings in the HeLa pentaKO cell. (Optional) Alternatively, identify the substrate(s) specific to the TAX1BP1-SCAMP3-ATG9A mediated autophagosome vs. that of NBR1 and/or OPTN in these cell lines.
- There are several issues with the immunofluorescence data throughout the manuscript. For example, in Figure 1C, the authors quantify 30 cells per condition and perform a Mann-Whitney test, presumably using each cell as a data point, as this is not specified in the legend. There are two problems with this approach, the first being there is only one indicated independent trial performed, and the second that treating each cell as a data point for statistical analysis overinflates the power. These experiments should instead be performed across at least 3 independent trials (especially given the wide range of values seen in some conditions such as Fig. 2b) with 30 cells counted per trial, and statistical analysis should be performed using the means from each trial. See Lord, S.J. et al. (JCB 2020, PMID: 32346721) for a detailed explanation. Further, how each statistical test is performed (using means vs. all points) and the number of independent trials conducted should be communicated in the figure captions. Finally, beyond Fig. 1, the statistical test used is not reported. If a Mann-Whitney test was used for all statistical analyses, this should be revisited as with two or more variables (cell type, treatment, etc.), this is not the appropriate test.
Minor Points
- The experimental design rationale is not clear throughout the manuscript. For example, why FIP200 KO cells are used in Fig. 3c and Fig. 4 is not apparent, and only in Fig. 6 (Lines 211-213) is it clearly stated. The authors should revisit the results section and ensure the experimental rationale is clearly explained.
- It is curious that condensates formed in muGFP-NDP52 and muGFP-TAX1BP1 cells lack ubiquitin (Fig. 3c). Is this image representative of most cells? if so it should be discussed.
- The authors are missing some relevant citations:
- Lines 57-58, the authors may consider citing more recent literature (Olivas, T.J. et al., JCB 2023; Broadbent, D.G. et al., JCB 2023; Nguyen, A. et al., Mol Cell 2023) investigating ATG9 vesicles in autophagosome biogenesis.
- In lines 58-66, the authors do not mention that NBR1 was also shown to bind FIP200 (Turco, E. et al., Nat Communications 2021, PMID: 34471133).
- Some minor changes are recommended for clarity:
- In lines 106-110, the authors may consider explaining that "growing" conditions are cells grown in regular culture conditions, as it is a bit unclear whether these cells are treated with a drug, etc., to induce p62-condensate formation. Something as simple as "...p62-double-positive structures under normal culture conditions, hereafter referred to as growing conditions," would help the reader.
- The general axis labelling of "Ubiquitin-positive rate of FIP200 puncta (%)" (Fig 1D) is confusing wording. The authors may consider changing to "% of ubiquitin-positive FIP200 puncta" for readability.
- The axis title in Fig. 4F has a typo.
- Several figure legends include data interpretation, which should generally be excluded from figure legends. For example, the first line of Fig. 4B should be removed.
- The authors may consider how ATG9A is recruited to ubiquitin-independent selective autophagy cargoes that are degraded independently of NBR1, p62, OPTN, NDP52, and TAX1BP1 in their discussion.
- Supplementary figure S2-right side blot. For best practice in publications, I encourage the authors to provide a single blot of FIP200, ATG13, and ATG9A immunoblot for Penta KO and its KO derivatives. Presently, the blot appears to have been spliced together
Significance
Although different ubiquitin-binding adaptor proteins have been shown to have some substrate specificity, how they mediate the selective degradation of a substrate remains unclear. This manuscript provides evidence that TAX1BP1, like NBR1 and OPTN, can also recruit ATG9A positive structures independent of the autophagosome initiation complex ULK1-complex. However, they do show that the mechanism of ATG9A vesicles differs from the other two adaptors in that it requires SCAMP3, a membrane protein found in endosomes. The data provided by the authors are of high quality. However, the study relies on only one experimental system/assay, which limits the relevance of its findings and could benefit from testing these findings using additional systems and/or physiologically relevant models, as well as increasing the rigor of the quantitative analysis.
Besides these two major shortcomings, the finding is novel and adds to our current understanding of autophagy adaptor proteins. The difference in the mechanism of ATG9 vesicles compared to NBR1 and OPTN may contribute to the selective nature of these autophagy adaptors.
This manuscript is most appropriate for specialized basic researchers in the field of selective autophagy.
Our expertise is in selective autophagy regulation and substrate selectivity of selective autophagy.
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Referee #1
Evidence, reproducibility and clarity
Macroautophagy is a catabolic pathway for various intracellular components mediated by the formation of autophagosomes followed by lysosomal degradation. ATG9 vesicles provide the initial autophagosomal membrane source and thereby recruitment of ATG9 vesicles to the autophagosome formation sites serves as a critical step for autophagy induction. However, the precise molecular mechanism of the ATG9A recruitment is not fully understood. In this study, Hama et al. report two distinct pathways; ULK complex-dependent ATG9 vesicle recruitment during starvation-induced autophagy, and selective autophagy receptor TAX1BP1-dependent ATG9 vesicle trafficking through the binding to SCAMP3, which was identified as an ATG9 vesicle component by the authors. Unfortunately, the authors were unable to demonstrate the impact of the TAX1BP1-SCAMP3-ATG9 vesicle axis on cellular physiology, presumably due to the existence of compensatory mechanisms mediated by other selective autophagy receptors and ULK complex, which limits the impact of the findings presented. Having said that, the study is technically well executed and provides a new insight into the regulation of cargo/receptor-mediated ATG9 vesicle recruitment. This reviewer has a few comments that should be addressed to strengthen the authors' conclusions.
- In the Co-IP experiments (Fig 5A-C), binding of TAX1BP1 to SCAMP3 is assessed by using the CC1 domain fraction of TAX1BP1, which may yield an artificial binding to SCAMP3. Could the authors confirm binding of full length TAX1BP1 wild-type and K248E mutant to SCAMP3?
- In Fig 5D, the authors showed that SCAMP3 localises to immuno-isolated ATG9A-positive vesicles. Is it a direct interaction between the two proteins? Could the authors provide the evidence that the interaction is retained in the presence of a detergent by immunoprecipitation? If the interaction is indirect, can the authors discuss candidate proteins that mediate binding of SCAMP3 to ATG9 vesicles?
- Related to the comment #2, it is interesting that the knockout of ATG9A does not affect SCAMP3-positive "ATG9 vesicle" formation. What is the nature of "ATG9 vesicles" lacking ATG9A?
- Could the authors confirm that K284E mutation in TAX1BP1 abrogates the localisation of SCAMP3 to the TAX1BP1 condensates as in Fig 6E? This will reinforce the claim that TAX1BP1 binding to SCAMP3 facilitates ATG9 vesicle recruitment.
- Could the authors discuss the potential reasons differentiating TAX1BP1 from other CC-domain containing autophagy receptor proteins (NDP52, OPTN and NBR1), which enables it to bind to SCAMP3. For instance, does TAX1BP1 have charged residues facing outwards in its CC domain that could be responsible for this specificity?
- In Fig 3C and 6E, no colocalisation of TAX1BP1 and ubiquitin was observed in TAX1BP1 condensates. In the context of "cargo-driven recruitment" of ATG9 vesicles, what cellular component(s) could trigger TAX1BP1-mediated SCAMP3/ATG9 vesicle recruitment? In the Discussion, authors mentioned that ferritin-NCOA4 was not the target of the TAX1BP1-SCAMP3 axis. Could the authors test if any of the other known TAX1BP1 cargo proteins localise to TAX1BP1 condensates in Penta KO/FIP200 KO/muGFP-TAX1BP1 cells?
Minor:
- Fig 4F: Typo in y axis.
Significance
General assessment: provide a summary of the strengths and limitations of the study. What are the strongest and most important aspects? What aspects of the study should be improved or could be developed?
The main finding of the study is a new pathway of ATG9 vesicle recruitment through the interaction of TAX1BP1 with SCAMP3, which provides a novel insight into molecular mechanisms of autophagosome biogenesis. However, the axis is implied to be redundant for functional autophagy in wild-type cells, and lack of data providing a biological function of the axis in cellular physiology will limit impact attracting broader readers outside of molecular mechanism of autophagy.
Advance: compare the study to the closest related results in the literature or highlight results reported for the first time to your knowledge; does the study extend the knowledge in the field and in which way? Describe the nature of the advance and the resulting insights (for example: conceptual, technical, clinical, mechanistic, functional,...).
Interaction between ATG9A and selective autophagy receptors OPTN and NBR1 has been reported (doi: 10.1083/jcb.201912144; 10.15252/embr.201948902). This study provides an additional mechanistic insight into the regulation of ATG9 vesicle recruitment through another autophagy receptor TAX1BP1 interacting with SCAMP3 which was newly identified as an ATG9 vesicle component in this study. Given the predominant functions of ATG9A in TNF cytotoxicity and plasma membrane integrity as well as TAX1BP1 in neuronal proteostasis and iron homeostasis (doi: 10.1126/science.add6967; 10.1038/s41556-021-00706-w; 10.1016/j.molcel.2020.10.041; 10.15252/embr.202154278), the interaction between TAX1BP1 and ATG9A would potentially have uncovered but important role in mammals. Autophagy-independent lysosomal degradation regulated via ULK component, ATG9 and TAX1BP1 might be related in this context (10.1016/j.celrep.2017.08.034).
Audience: describe the type of audience ("specialized", "broad", "basic research", "translational/clinical", etc...) that will be interested or influenced by this research; how will this research be used by others; will it be of interest beyond the specific field?
This study will be of interest to the basic researchers working on molecular and structural mechanisms of bulk and selective macroautophagy. Unfortunately, the lack of data demonstrating the relevance of the findings for cellular physiology will limit the impact on researchers in broader fields such as pathology and drug discovery.
Please define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.
Molecular cell biology of autophagy; neurodegenerative and lysosomal storage disorders.
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Referee #3
Evidence, reproducibility and clarity
Summary:
The article describes a study on two effector nucleases, V2c and V2a, encoded by the T6SS cluster in Agrobacterium tumefaciens 1D1609. The study shows that V2c is a DNase belonging to the Tox-SHH clade of His-Me finger superfamily, exhibiting DNase activity in vivo and in vitro. The SHH and HNH motif of V2c were found to be involved in DNase activity. The study also demonstrates that V2c induces DNA degradation and cell elongation, which can be neutralized by its cognate immunity protein V3c. Furthermore, V2a, also exhibits DNase activity-dependent cell elongation phenotype. Both V2a and V2c nucleases function synergistically for antibacterial activity against Dickeya dadantii, resulting in elongated and lysed cells. The study suggests that 1D1609 uses V2a and V2c DNase effectors with synergistic antibacterial activity against Dickeya dadantii.
Major issues:
- The cell elongation phenotype was an important focus of the paper and the authors did a solid job quantifying this phenotype. However, cell elongation does not seem to be associated with the mechanism of toxicity. Just a small part of the cells are elongated while you have more than one log of killing (Figure 5C and 5D).<br /> Many stresses can result in cell elongation, such as cell-wall targeting antibiotics. The signal narrows down to the very well-known mechanism of SulA activated by SOS response. There is no evidence "cell elongation independent of nuclease activity may represent a new mechanism of stress response #339". I strongly suggest the authors to use an SOS like pPrecA-gfp from ref 17 if they want to invest in the cell elongation phenotype. As it is, cell elongation is a distraction from the most exciting things in the manuscript. One potential hypothesis is that the catalytic mutants may bind DNA without cleaving, and while bound to the DNA, the mutants may interfere with DNA metabolism, replications, transcription, etc... This interference could create breaks in the DNA and cause cell wall elongation.
- The manuscript does not have biological replicates in many experiments. At first glance, the tiny error bars in many graphs raised a red flag. It is very difficult, if not impossible, to have the date so tight when working with bacterial cultures. Many of these graphs have "independent experiments", but in Fig 5 the authors mention " 6 repeats from three independent experiments". My understanding is that the independent experiments are from the same cultures, which makes them technical and not biological replicates. The authors need to have replicates from independent cultures. I ask the authors to explain better what they mean by replicates and their rationale.
Minor issues:
- Many figures have all the data with conditions with and without arabinose. This makes the figure very polluted and difficult to follow. I suggest the authors keep only the data from induced cultures and move the figures with all the data to supplement. This is a big issue with Fig 3
- The entire "V2c V2a nucleases function synergistically for antibacterial activity against the soft rot phytopathogen, Dickeya dadantii" #212 section is difficult to follow because the name of the toxins are not in the name of the strains. The reader should be able to associate the toxin with the strain.
- The in vitro essay #427 should have information about the amount of enzyme used.
- The competition essay #474 method does not have enough information to allow reproducibility and must be expanded.
- Figure 2B Y-axis needs one log increase between marks, and not two.
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256 is difficult to understand and not very scientific. DNses are potent because they target essential molecules, the same for lipases and muramidases. This is not related to the origin of life.
Significance
Strengths:
Data is clear about the toxicity and DNA degradation phenotypes The synergy of toxins is a very exciting topic; it helps to explain why some strains have redundant toxins
Weakness:
Cell elongation claims are not supported Replicates are inadequate Methods needs to be more specific
The manuscript provides incremental data about bacteria-to-bacteria toxins. The major finding of predicted nuclease acting as nuclease toxins is not particularly innovative. This work will benefit a smaller audience in the field of toxins.
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Referee #2
Evidence, reproducibility and clarity
Santos et al demonstrate the activity and confirm predictions of the mechanism of action of SHH nuclease toxin encoded in auxiliary T6SS cluster of Agrobacterium tumefaciens. Study includes demonstration of nuclease activity in vitro and its manifestation in vivo, mutational analysis of the predicted catalytic site, and assessment of its role in inter-bacterial competition using strains deleted for the T6SS effector(s).
Major comments: major issues affecting the conclusions
Lines 159 and further - I am not sure that large deletion as an entire region between residues 380-409 is a very healthy approach. Without showing that such protein can be produced and fold, it can be very risky to draw any conclusions. While expression of other mutants are shown in Figure 2D, this construct is not included. Also, it is not clear (starting from line 160) what is the utility of double catalytic mutant if single mutants are already inactive. Was there residual activity of single mutants after all?
Line 187 and Figure 3 - cell elongation of CR mutant and HAHA mutant seems to be independent of expression of the construct. It is therefore incorrect to write that "E. coli expressing ...", since theoretically it should not express anything in the absence of arabinose. Also, how do would authors interpret these findings? Again, at Line 211 - in the same paragraph authors say "V2c shows DNase activity-independent cell elongation" and then conclude "cell elongation phenotype may be specific to toxicity of nuclease effectors". These two phrases seem to contradict each other.
Line 272 on - authors speculate about dependency on the metals, which is a hallmark of the his-me finger nucleases. A simple test could have been adding the EDTA control to chelate the metal in the in vitro experiment such as one presented in figure 2D. (OPTIONAL)
Minor comments
Figure 1 - Auxiliary cluster with accession numbers, in addition to the domain composition of the toxin could be demonstrated. This would help the readers to identify which effector is studied and link it to other studies.
Line 127 and Figure 1 C and line 303 - the last option, named "FIX RhsA AHH HNH-like" seems to be a mix of multiple things, First, FIX domain is already included as a first option; AHH HNH-like corresponds to toxic domain (although it often ends up in annotation of the entire protein). All His-Me finger nucleases at some point were annotated as HNH-like and thus HNH, AHH, SHH, ... all belong to the same clade of nuclease domains; RhsA is probably a correct domain/protein detected here and to be represented here as a separate option. I would call it "Rhs", not RhsA, since RhsA,B,C,... is part of an historical systematics from E.coli, but is probably true for one strain only, since Rhs ends (C-terminal toxic domains) are highly variable between species and even strains. To my knowledge, Rhs do encode AHH-like domains and quite often. In conclusion, this is just a mess of protein naming that was picked up from databases, I would correct this name for "Rhs".
Line 181-182 - text is speaking about v2c H383A v2c H384A, but in figure 3, it is v2c HAHA. It might be just naming differences, but it should be consistent.
Line 246 - expression "detoxify D. dadantii" is unclear and a little confusing here, did authors mean kill or eliminate?
Line 263 - "that consisting of" should be "that comprise" or "that possess"
Referees cross-commenting
I agree with the reviewer #1 that the text could be improved by better explaining the nomenclature, and reasoning behind certain experiments such as choice of prey cells. Regarding the novelty, to my opinion it is a choice of authors - either limit their study as it is now and it does not stand out by neither approach nor subject, or as suggested by the reviewers explore the mode of action, specificity and the reason behind the cell filamentation.
I agree with the reviewer #3 that the cell elongation seems to be central interest but it is not investigated properly. I do think, that the SOS reporter would strengthen the study and would help to support (or not) some of the statements. I appreciate the scrutiny of reviewer #3 and I agree that the replicates seem to have extremely low variation and authors should provide precise explication on the reproducibility.
Overall, I agree with the two other reviewers that the weakness of this manuscript is lack of innovation and to some extent lack of support for certain claims. The study could be improved by making it more profound, but a lot of additional work will be needed to bring it to another level.
Significance
Overall, this study is rather detailed, but not very novel - SHH and other HNH nucleases have been already assessed in literature using very similar methods, even by the same authors (Santos et al., Front Microbiol 2020). On the other hand, the study presents in depth investigation of auxiliary toxin, but shows that it is fully functional and has a role in killing, which is important and interesting for the field of Agrobacterium.
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Referee #1
Evidence, reproducibility and clarity
This study characterised one of the four previously identified T6SS effectors of Agrobacterium tumefaciens strain 1D1609. The effector, named V2c, is a His-Me finger nuclease likewise the previously characterised V2a, although V2c has a distinct SHH motif. V2c was found to induce growth inhibition, plasmid degradation, and cell elongation in E. coli. The cognate immunity protein V3c can neutralise the DNase activity of V2c. V2a and V2c are found to function synergistically to exhibit stronger antibacterial activity against a phytopathogen, Dickeya dadantii.
Although the manuscript provides a decent characterisation of the T6SS effector V2c of strain 1D1609 of A. tumefaciens, there are several areas where the authors could make significant improvements to their work.
- The introduction section of the manuscript would benefit from a more detailed background of the research to situate the reader in the appropriate context for a better understanding of the results of this study. Specifically, the authors could provide more information regarding the four effectors of A. tumefaciens 1D1609, their domains, their genetic context, and their immunity proteins. In fact, three of the effectors are, to a different extent, named in the manuscript, whereas the fourth one is not mentioned at all. The authors should also aim to provide more context and explanation of the nomenclature of the effectors. This will make the paper more understandable for readers unfamiliar with the terminology of Agrobacterium effectors. In addition, the authors should consider giving more attention to the phenotype of previously described nucleases, such as cell elongation, so that the reader would better understand the result section when encountering this phenotype. The authors only explain this in the discussion, and it would be helpful to have more clarification in the Introduction as well. The authors should also explain the reasoning behind using that prey cell (Dickeya) and not E. coli or another organism for the antibacterial activity experiments.
- The novelty of the study could be improved by providing a better explanation of the specific mechanism or mode of action through which V2c works. For example, the authors could study more deeply the puzzling fact that the elongation phenotype is independent of the nuclease activity for this effector but not for V2a. Another interesting approach would be to study the putative specificity of these nuclease effectors, as not all of them are effective against bacterial targets. This is an unexplored area of great interest for a more innovative study because nucleases do not seem to be specific, they all degrade nucleic acids, and somehow they have different capacities to kill different prey cells.
- In Figure 1B, it would be convenient to include in the alignment the two nucleases of the same family as V2c that have been already described in the literature and are named in the main text (Tke4 and Txe4) to better illustrate their similarities and differences. In Figure 1C, it would be convenient to add the PAAR-RHS domain to the list of N-terminal domains found with Tox-SHH nucleases.
- When it comes to the microscopy experiments (Figure 5), the authors should work to improve the relevance and quantification of the data they present. This could involve using more rigorous analysis techniques, such as statistical analysis, to support their findings more convincingly. In fact, the authors could enhance the rigour of their analysis (Dickeya cell lysis) by gathering more supporting evidence before drawing their conclusions. The authors should explain why they are using two identical strains (attackers) in the competition assays (Fig 5C) d3EIbcd 1 and 2.
- Regarding the inter-bacterial competition settings between Agrobacterium and Dickeya, the authors should explain why they used TssB-GFP prey cells when this is not necessary for the assays they performed. Furthermore, the authors should clarify the statement in the discussion (lane 353-355) regarding the absence of antibacterial activity of V2c against E. coli, while the results of this work show inhibition of E. coli cells (Fig. 2). The authors should rethink whether the video is necessary for this section, as it does not provide additional relevant information.
- To ensure that the paper is easily comprehensible and effectively conveys its message, the authors should meticulously examine all aspects of their writing, including language usage, grammatical accuracy, and syntax structure. The authors should also ensure consistency in their writing style and language usage throughout the paper. As one example of writing style inconsistency, the authors used both Gram-negative and gram-negative in the manuscript.
Significance
The study expands the knowledge in the specific field of SHH nucleases and T6SS effectors. This could be of interest to T6SS researchers and more broadly to researchers in the field of bacterial toxins.
The novelty of the study is very limited since the authors functionally characterised a T6SS effector with a well-described function, a DNAse (DNA degradation) and a recognised structure (SHH domain). As expected for a nuclease, it degrades DNA and provokes cell elongation, which has also been described before.
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The authors do not wish to provide a response at this time.
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Referee #2
Evidence, reproducibility and clarity
Summary:
The authors provide insight into the gaps within BRAFi research in an effort to further understand how elements such as mechanisms of resistance and clinically observed adverse events in melanoma patients occur. This manuscript more specifically highlights the effects of BRAFi treatment on endothelial cells in the context of vasculature. The authors begin to explore how traditional BRAFi therapies may lend to such adverse events due to the role they play alongside that of targeting melanoma cells such as off-target effects, paradoxical endothelial signaling, and inducing a pro-tumorigenic microenvironment. The conducted studies demonstrate simple and effective methodology, focusing on proteomic and phosphoproteomic analysis, to elucidate the endothelial consequences of BRAFi treatment. The authors provide sound conclusions from the presented data and validate their in vitro findings with clinical observations using patient tissue. The analysis within this manuscript is just scratching the surface and leaves the authors with much to explore in future manuscripts.
Major comments:
The authors provide a solid story outlining the pitfalls in BRAFi therapy research and the consequences on endothelial vasculature in the treatment of BRAF mutant melanoma. The manuscript details clinical relevance of the research, functional impact to the field, and a thorough discussion on the scope of this work and where it may be lacking, which allows for the opportunity for future directions.
Minor comments:
The authors may consider revising minor errors within the Discussion as indicated below. Discussion - Paradoxical MAPK activation Missing comma between cells and the; "For endothelial cells, the concentration of BRAFi measured in the patient circulation is critical." Discussion - Off targets in endothelial cells Missing comma between range and it; "At concentrations in the low µM range, it inhibits numerous other kinases." Missing commas around apart from MAPK; "This suggests that, apart from MAPK, other signaling pathways would also be affected by BRAFi treatment"
Significance
This manuscript poses a key discussion in the importance of expanding research of molecular targeted therapies on more than just the target cells as the consequences to surrounding cell types can give vital insight into potential adverse effects in the clinic. The authors note that while this is not a novel concept, there are still gaps that prove vital in understanding clinical impact, which they hope to fill with this manuscript. They provide support to their conclusions using primarily proteomic approaches with the addition of some comparative analysis of a publicly available dataset, and patient tissue samples in order to validate their findings. Whether in the context of treating melanoma or any other disease. this manuscript serves as a helpful reminder to pre-clinical and clinical researchers alike in how important it is to factor in the patient as a whole, not just the disease when identifying effective treatment options.
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Referee #1
Evidence, reproducibility and clarity
Summary:
The author Bromberger and colleagues have submitted a MS # RC-2023-02152 entitled "Off-targets of BRAF inhibitors disrupt endothelial signaling and differentially affect vascular barrier function" for review via Review Commons. In the MS they have investigated four BRAF inhibitors with different pharmacodynamics; Vemurafenib, Dabrafenib, Encorafenib and PLX8394 and their specific effect on vascular endothelial cells but also on melanoma cells. The study is composed of in vitro studies using in-house isolated human dermal endothelial cells. Also, melanoma cells and skin biopsies from 5 melanoma patients were analysed. The authors conclude that the BRAF inhibitor Vemurafenib caused strong effect on the endothelial cells' barrier function in comparison to the other three BRAF inhibitors.
Major comments: major issues affecting the conclusions:
In general; a major issue that is affecting the whole story is the rather high concentration of Vemurafenib (100 uM) used in the study. The authors do not provide any data describing the viability and function of the endothelial cells after exposure to 100 uM of Vemurafenib. Instead they have chosen two concentrations with a large (10X) difference. Where the cells viable at 100 uM of Vemurafenib? If the endothelial cells were suffering from 100 uM Vemurafenib, they will immediately loose the cell-cell contacts/junctions and thereby any performed permeability assay would be pointless. Furthermore, isolation of skin endothelial cells is at risk to be accompanied with lymphatic endothelial cell contamination. The authors should provide data ensuring that the cells are of >90% endothelial cell purity by checking for PROX1-positive cells together with endothelia cells markers (CD31, VE-cadherin, uptake of AcLDL etc).
The work is of importance in understanding consequences for endothelial cells exposed to BRAF inhibitors used in the clinic using clinically relevant concentrations of the drugs investigated in vitro. If the authors provide with a major revision, the work could be acceptable for publication.
- The Western blots in this manuscript are in general overexposed (saturated) and therefore differences between treatment conditions are not possible to be clearly defined. Therefore, quantifications of the experiments should be done and combined with representative Western blots.
- Figure 1A-C n=?, notice no standard deviations in 1C. Does this mean that in 1C n=1?
- Figure 1D, no significant differences? If there is no significant difference, then there is no difference between the treatments.
- Regarding concentrations of Vemurafenib; it is needed that the authors define endothelial cell viability (proliferation, Caspase-9 staining or LIVE/DEAD fixative stains) at this high concentration. Then 10 uM is probably a too low concentration (see data in figure 2 where 10 uM gives no data of relevance). Cell toxic effects could be the reason of increased passage of N-Fluorescein upon 100 uM Vemurafenib treatment or the cause of cell-cell gaps (Figure 5). If 100 uM truly shows that the cells are viable without any signs of toxicity, the paper would be more clear if main figures contain only 100 uM Vemurafenib. It is recommended that cell cytotoxicity is tested for all compounds in this short- and long-term treatments
- Figure 5; the authors should demonstrate the effect of BRAF inhibitors using a different approach. Trans-endothelial migration (trans-well), or similar methods would enforce the main message. Furthermore, migration defects could be evaluated by scratch-wound assay. Comment: the imaging in figure 5A is not clear enough to truly show the cell morphology and to define the cell status (see point 4 related to cell viability). We also advice that figure 5A also contains stainings for all other treatment conditions (or included in a supplement figure). What´s the mechanism behind junctional rearrangement? Internalization, degradation or actin cytoskeleton-dependent mechanisms? Figure 5A, stainings should be quantified. Figure 5C; with three asterisks in the figure, what is the actual significance and is it compared to DMSO? With the large SD the significance can hardly fit with three asterisks (<0.001).
- Valuable skin biopsies of patients before and after treatment have been used for figure 6. The authors should pay more careful attention to what vasculature they are investigating in the biopsy material. The authors mainly focus on large arteries (large vascular lumens with a thick layer of ASMA-positive cells). We recommend that they investigate capillaries (5-10 um in diameter) which are more plastic and susceptible towards treatment. Claudin-5 is a vascular marker but the antibody chosen clearly provides with high autofluorescence stains detecting blood cells in the vascular lumen and not only the endothelial cells. We therefore recommend to use another claudin-5 antibody that will stain dermal vasculature better. Which patient is imaged in figure 6? Please prepare a supplement figure with patient 1-4 to show representative images of the main differences. Do the authors expect that Vemurafenib 100µM will also decrease VE-cadherin and claudin-5 total protein levels?
- Table 2: The quantification is not clear. The authors should describe the data in a more descriptive way. For example, what does it means to have more than 100% (181.41% of claudin-5 for patient 5) of the vascular markers? Also, it is not realistic to describe percentage data with 2 decimals. The authors should also classify their quantification based on vessel type (large caliber vessels vs capillaries), cancer and pseudo-normal tissue. As a way to validate their in vitro findings (permeability and junctional disruption in these patient tissue biopsies), the authors should check for leakage by staining for serum proteins like IgG, fibrinogen or serum albumin.
Minor comments: important issues that can confidently be addressed:
- The authors want to fill a gap in knowledge related to BRAF inhibitors effect on endothelial cells, which a limited number of publications are available.
- Why are the authors using CellTracker for visualize cell morphology. It would be better if cells were stained for VE-cadherin and beta-actin including nuclear stain with DAPI. This would far better define the cell morphology after treatments.
- Please in Material & Methods describe KinSwing activity predictions index to help the reader to follow the results better.
- Table 1 could be reformatted to be more easily to read.
- Figure 1, is ERK= ERK1/2?
- The discussion text should be shortened and more focused towards their findings and with conclusions of performed experiments. How is the paradoxical effect of Vemurafenib (figure 1) related to their later findings (Figure 2 and 3)? In other words, what is the relation between figure 1 and figure 2 and 3?
- For the discussion; is the result in figure 5C supported by data that patients on Vemurafenib treatment would be exposed to a higher risk of metastasis?
- Figure 3B, resolution of text needs to be improved and the full compound names could be written in figure 3A.
- Figure 4A are any of the results statistically significant? If not, then there is no difference.
- The authors should elaborate a hypothesis based on their phosphoproteomics data. Which of the off-targeted molecule(s) could impact endothelial barrier?
Referees cross-commenting
With our deep knowledge in endothelial cell biology, we would like to emphasize the need of Bromberger et al to reply to our comments. Additional experiments and verifications will improve the impact of the performed research. With reviewer 2 demanding far less additional work to be done there is a discrepancy between the two reviewers of the estimated time needed for performing a revision (1-3 months for reviewer 1 versus 1 month for reviewer 2). I (reviewer 1) believe that at least three (3) moths will be needed to collect additional data to reply to the questions.
Significance
This manuscript addresses an important question; how is the vasculature affected by cancer treatments? It is not unusual that the vascular status is neglected in clinical treatment studies. The manuscript provides valuable phosphoproteomics data of great interest related to this topic. The major weakness of the work is the lack of data verifying the chosen concentrations for the BRAF inhibitors used in the study. There is a great risk that several results based on the 100 uM Vemurafenib treatment (of high impact for the story) are based on cell toxicity due to a high concentration treatment in vitro. Also, the link between the strategy of performed in vitro experiments isn't clear and there is a lack of connecting the in vitro data to the validation performed on melanoma patient tissue biopsies. It is a great strategy to investigate skin biopsies before and after treatment. The precious biopsy material should be more carefully investigated and evaluated.
Audience: after improvement of the manuscript by better presentation of existing data and by additional experiments the work presented would be of interest to a pre-clinical and clinical audience investigating cancer treatments.
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Please see below for the detailed description of the changes made in response to the reviewers’ comments.
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
The manuscript investigated the composition of the plastid proteomes of seven distantly-related kareniacean dinoflagellates, including newly-sequenced members of three genera (Karenia, Karlodinium, and Takayama). Using a custom plastid-targeting predictor, automatic single-gene tree building and phylogenetic sorting of plastid-targeted proteins for plastid proteome construction, the authors suggest that the haptophyte order Chrysochromulinales is the closest living relative of the fucoxanthin plastid donor. Interestingly, the N-terminal targeting sequences of kareniacean plastid signal peptides, reveal a high sequence conservation. Moreover, ecological and mechanistic factors are suggested that may have driven the endosymbiotic acquisition of the fucoxanthin plastid. Overall, this is a comprehensive and interesting analysis.
Other comments.
- For analyses of N-terminal targeting sequences, why did the authors not consider to employ Predalgo as an additional tool? Author response: We thank the reviewer for their suggestion. To our understanding, PredAlgo is a targeting predictor trained on primary green algae, which have two-membrane bound plastids and purely hydrophilic N-terminal plastid targeting sequences. It thus would be expected to perform poorly for the prediction of N-terminal targeting sequences in complex plastids such as those of the Kareniaceae bound by three or more membranes, who are located within endomembrane-derived compartments and which utilise plastid-targeting sequences based on an N-terminal hydrophobic signal peptide for ER import.
We considered the application of PredAlgo for the identification of downstream hydrophilic transit peptide regions in Kareniacean presequences, but note that the specific residue positioned after the signal peptidase cleavage site is typically a much better predictor than transit peptide hydrophobicity for identifying plastid-targeting sequences (Gruber et al., Plant J 2015, and citing references). We found that other targeting prediction tools based primarily on hydrophobicity (e.g., HECTAR) performed poorly in identifying probable plastid-targeting sequences in our control Kareniacean dataset, and therefore chose to prioritise a modified version of ASAFind that takes into account the residue context of Kareniacean signal peptidase cleavage site for our targeting predictor, which works with high sensitivity and specificity on our control dataset. We summarise these observations in Fig. S15.
Given the fact that peridinin or fucoxanthin pigment binding is in the focus of the paper, a more detailed introduction of the peridinin and fucoxanthin light-harvesting systems should be given.
Author response: A brief introduction to the pigment-binding proteins in dinoflagellates was added, “These include a unique carotenoid pigment… massively paralogized and synthesized as polyproteins” (lines 86-89).
The authors state "It is also possible that there has been a direct niche competition between the peridinin and fucoxanthin plastid that may have coexisted in the same host for a period of time with possibly different selective pressure on retention of their respective proteins based on their interaction with plastid-encoded components, e.g., extrinsic photosystem subunits not assembling correctly with their intrinsic haptophyte-like counterparts." It is tempting to ask, whether peridinin light-harvesting systems have left traces in the fucoxanthin plastid, possibly due to mistargeting of peridinin light-harvesting systems into the fucoxanthin plastid? Are some photosynthetic subunits "in-between" peridinin and fucoxanthin plastids?
Author response: We did not identify any other peridinin-like photosystem subunits than the ones visualized in the map schematic (i.e., ferredoxin/PetF in both Karenia and Karlodinium and PsaD of Karlodinium micrum) and discussed in the supplementary text. PetF is the only consistently retained peridinin-like photosystem protein, likely due to the fact that it is not strictly linked to photosynthesis: it is expressed in plant leucoplasts, and plastid-encoded in some non-photosynthetic chrysophytes. We have added a sentence in Supporting Text 6.4 that “we detect no possible homologues of peridinin-chlorophyll binding proteins (PCP) in any kareniacean transcriptome” (line 91).
Figure 3 is difficult to understand, e.g. for PSI and PSII which subunits are shown, why has PSI "more" contribution from dinoflagellates as compared to PSII?
Author response: The photosystem subunits are ordered numerically in the schematic, and detailed information on each protein and the corresponding sequences with their origin are included in the supplementary table S3. A single subunit of photosystem I (PsaD) was determined to be of plastid-early (peridinin-like) origin in Karlodinium (while the same protein is plastid-encoded in Karenia and undetermined in Takayama). We believe this may be simply due to an evolutionarily neutral differential loss / non-adaptive retention of photosynthesis-related proteins in a secondarily non-photosynthetic host before the acquisition of a replacement plastid. We note that there are only two (incomplete) kareniacean plastid genomes available so we cannot rule out the possibility of this subunit being plastid-encoded in Karlodinium as well (which would mean that both plastid-late and plastid-early homologs co-occur in this genus).
Fig. 3 is necessarily complex due to the size and multiplicity of the dataset considered. To facilitate reader navigation, we have added the following text to the figure legend (lines 1128-1140) text “Plastid proteins are arranged by major metabolic pathway or biological process, with each protein shown as rosettes … Proteins of plastid-late (haptophyte) origin, such as are concentrated in photosystem and ribosomal processes, are coloured red; and proteins of plastid-early (dinoflagellate) origin, such as are concentrated in carbon and amino acid metabolism are coloured blue. … In certain cases (shown as rosettes with multiple colours), homologues from different species have different evolutionary origins, e.g. Karenia possessing plastid-late and Karlodinium/ Takayama plastid-early”.
Data shown in figure 4, is there experimental evidence for signal peptide cleavage site(s). Could these data been used to predict mature plastid targeted protein sequence?
Author response: We were able to determine the conserved motives in signal peptide, including its cleavage site (GRR) which we exploited in the design of kareniaceae-specific matrix for ASAFind. We show these residues in Fig. 4. We note that these motifs were identified based on homology to known signal processing peptidase recognition sites, as opposed to experimentally determined protein N-termini.
Consistent with previous studies (e.g. Yokoyama et al., J Phycol 2011) we see limited evidence for consensus plastid transit peptide cleavage motifs in kareniacean presequences, and do not discuss this further as a result.
The authors state "Partial Least Square (PLS) analysis shows a set of environmental variables (salinity, silicate, iron) positively correlated with abundances of both Karenia and Takayma and also haptophytes as a whole, but at the same time negatively correlated to Karlodinium (Figure S8), further illustrating that the latter genus is quite distant from the rest in its biogeographical pattern." How could this be interpreted in the light of the plastid proteomes
Author response: We believe that this may be due to the more cosmopolitan distribution of Karlodinium, and possibly also a result of bias stemming from our strategy of grouping the organisms at the genus level (as not enough data was available at species level) which may obscure the potential outlier status of only some species/ subpopulations. This is particularly true for the haptophytes, where in the absence of specific ancestry for individual kareniacean plastids we are only able to consider distributions at the levels of entire orders. We now acknowledge this in the Discussion: “specific ecological interactions between the progenitors … via ancestral niche reconstruction for each lineage” (lines 473-475).
Please note, that the results might have changed slightly from the previous version due to the re-calculation following additional normalization of the data (see below).
Reviewer #1 (Significance (Required)):
The current manuscript gives insights into the endosymbiotic acquisition of the fucoxanthin plastids.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
This is a well done, detailed bioinformatic analysis of genomic and transcriptomic data from an important lineage of dinoflagellates that have undergone serial substitution of their plastid. On the whole I am enthusiastic about the paper; it presents valuable new insights, and is rigorously performed. However, I have to object to the way the term "proteome" is used in the paper; the manuscript is talking about the predicted proteome, not a measured proteome. This is something of a technical distinction, but it is an important one because the transcriptome and the proteome don't necessarily track each other, and there is little or no actual proteomic data available from dinoflagellates. We assume that transcript abundance has something to do with proteome abundance, but this is often violated. What this paper is really addressing is the potential proteome, because if a given gene is completely absent from the genome and the transcriptome we can be confident it will not be present in the proteome. The converse is not true. For this reason I feel it is important to be clear on the distinction. I would be satisfied in this regard by minor modifications, using the term "predicted proteome" in the title, and being more direct in the introduction about the distinction.
Author response: We agree that the usage of the word proteome for in silico predictions is not entirely correct, and have used the term “predicted proteome” where possible in the text to clarify this.
We have also, as described in our response to Reviewer 1 above, included a statement in the Discussion that our largely bioinformatic results will be transformed by an experimentally realised kareniacean plastid proteome, which we nonetheless feel goes beyond the scope of our manuscript.
Overall the analyses are impressive. I do have to squirm a little when I see automated analyses generating alignments where the threshold is less than 75% gaps and at least 100 nucleotides aligned. I looked at the supplementary data and the figshare files and could not find the alignments themselves, so I don't know what fraction of the sequences are in that territory. Because phylogenetic analysis (as performed here) treats the alignments as an observation, and because the alignments include sequences with more than 50% gaps, it is entirely possible that some taxa, or even whole segments of the tree, are based on non-overlapping data.
Author response: We thank the reviewer for their comment and have added in three new supplementary figures (S16-S18) providing statistics on alignment size, length, and average gap percentage distribution. We report that most of the alignments contained relatively little gaps: 90% of the alignments contained between 1.1 and 24.5% of gaps with median value of 6.6%.
Mind you, we have done similar analyses, and I don't think this invalidates the results, but it does open up the possibility of some dramatic artifacts. Consequently, I would recommend a) making the alignments available (or more obvious where to find them), and b) providing more detail on the alignments, including, if possible, to add a figure (probably in the supplementary data) that visualizes them. It is not given in the text itself, but according to the figure 2 caption there are 22 sequences thought to be "plastid late", and 241 in the pan-eukaryotic dataset. This is a scale that is feasible to put in a figure showing, for example, each aligned residue as a color and indels as grey. Such a figure is readable even when the individual residues are only a few pixels in size (less than a millimeter when printed). I also recommend describing the final alignments more fully in the text. Most of the summary statistics are presented in normalized form, and that can obscure patterns that come from poorly sampled taxa. Better clarify on the characteristics of the alignments will make it easier to interpret the findings overall. Although this is critical to interpreting the results, gappy alignments are not uncommon in analyses of this sort, and setting that aside the analyses presented are comprehensive and thorough. The discussion does a good job of addressing the significance of the work, and potential causes of error are addressed adequately (aside from the matter of the alignments).
Author response: We thank the reviewer for their comment and have provided alignments for all single-gene trees, in a dedicated online supporting repository (https://figshare.com/articles/dataset/all-automatically-generated-alignments_rar/24347032). The datasets and alignments used for PhyloFisher and plastid-encoded gene trees are included directly in the supplementary files (phylofisher_files.tar, plastid_genome_phylogeny_files.tar and plastid_protein_phylogeny_files.tar).
We have additionally included three new supporting figures (S16-S18) showing the distributions of lengths, gaps and homologues in each single-gene tree. These data project largely completion of individual alignments, with only 5% containing > 20% gapped positions (see Fig. S18), for example. We have additionally clarified in the Methods that “The trimmed alignments were then filtered by a custom python script that discarded sequences comprising of more than 75% gaps and then rejected alignments shorter than 100 positions or containing fewer than 10 taxa.” (lines 571-573).
For the two concatenated trees presented, we have clarified in the Methods the alignment lengths (PhyloFisher: 72, 162 positions; plastid genes: 2,404 positions), and that we removed sequences containing >66% of gaps from the final alignment. Reflecting on the congruency assumptions required to concatenated alignments, we have chosen to replace the plastid-late concatenated tree (which may group proteins with multiple phylogenetic signals) with a new main text figure 2 providing an overview of the plastid signals we observe across the entire dataset (see comments below to Reviewer 3).
Reviewer #2 (Significance (Required)):
I find the paper to be exciting and important. These organisms are economically important, particularly as potential nuisance organisms, but also because of their role in primary productivity. They also have extremely complex evolutionary histories and similarly complex genomes. performing any bioinformatic analysis of these organisms is a substantial challenge because almost every gene exists in high copy number and with complex and often obscure patterns of homology. The manuscript brings forward these challenges, and makes a substantial step forward in elucidating the evolution of a group that is fascinating and important, but remarkably difficult to work with. I feel that it is an important analysis, and should be of interest to a broad audience.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
Summary
This manuscript entitled "Divergent and diversified proteome content across a serially acquired plastid lineage" by Novak Vanclova et al. proposes the origin and evolution of plastids in kareniacean dinoflagellates. The authors generated new transcriptome data from Karenia mikimotoi, Karenia papilionacea, Karlodinium micrum, Karlodinium armiger, and Takayama helix. Combining them to the previously published transcriptome data from kareniacean dinoflagellates, they constructed the pan-kareniacean transcriptome library. They surveyed plastid-targeted protein-coding transcripts in the dataset, and consequently they estimated ~14.5% of the transcriptome data were of plastid-targeted ones. Of them, 65-80% were derived from a peridinin-containing dinoflagellate ancestor while ~15% were derived from EGTs from a haptophyte endosymbiont of the current plastid origin. By using the plastid-targeted transcript dataset, they investigated 1) origins of the plastid-targeted protein-coding transcripts by single gene-trees, 2) the plastid origin and evolution by the multigene dataset of 22 conserved plastid-targeted protein-coding transcripts and of 3) plastid genome-derived transcripts, 4) plastid functions, 5) diversity of plastid-targeted signals in kareniacean dinoflagellates, and 6) the distributions of kareniacean species by using the Tara Oceans database. On the basis of their results, they proposed many hypotheses regarding kareniacean dinoflagellate evolution, such as i) the chrysochromulinales-origin of the plastids, ii) more recent acquisition of the plastid than previously thought, iii) a plastid replacement within kareniaceae evolution, iv) the strict selection of signal peptides but non-conserved transit peptides in the kareniacean plastid-targeted proteins, and v) correlated or non-correlated distribution patterns of kareniaceaen dinoflagellates to specific haptophyte lineages.
Although their proposals are interesting, I have many concerns to be addressed. Especially, their analyses on which the above proposals are based seem to be still preliminary and inconclusive. To support their proposals more confidently, I also suggest some additional analyses.
Major comments
- seemingly inconsistency between the authors' claims The most striking is inconsistency of the authors' claims proposed in this manuscript. Their proposals include a) the common ancestor of kareniaceans has not possessed a fucoxanthin plastid but the plastid has been acquired more recently, b) an ancestor of Takayama and Karlodinium has gained a fucoxanthin plastid from a (chrysochlomulinales) haptophyte, c) an ancestor of Karenia has gained a fucoxanthin plastid from Karlodinium. However, they also demonstrate a higher proportion of plastid-late proteins in Karenia than Karlodinium and Takayama. If I understand correctly, "a higher proportion of plastid-late proteins in Karenia than Karlodinium and Takayama" would seemingly be inconsistent to and challenge two of the authors' claims: no haptophyte-derived plastid in the common ancestor of kareniacean dinoflagellates and a Karlodinium-to-Karenia plastid transfer (Fig. 7). If the Karenia plastid is derived from Karlodinium, I have no idea why haptophyte-derived plastid proteome of Karenia is larger than that of Karlodinium. After the plastid acquisition in Karenia, Karenia might have gained more genes for plastid-targeted proteins from haptophytes by LGTs. If this is true, many single gene trees would suggest different origins of plastid-targeted proteins between Karenia and Karlodinium/Takayama. Can we see it in the single gene analyses? I would like authors to rationalize the inconsistency in the main text.
Author response: We agree with the reviewer that the evolutionary origins and dynamics of the kareniacean plastid proteome are complex, and thank them for their suggestion.
First, to take into account the different evolutionary scenarios that could explain the present-day distribution of the kareniacean plastids, including the new plastid genome sequences identified in response to the reviewer’s suggestions, we have made a revised version of Fig. 8 evaluating three different hypotheses (see below). Nonetheless, we feel that the Karlodinium-to-Karenia model we propose is plausible, based on the following observations:
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We identify 1,418 plastid protein gene trees in which at least two of the three studied genera (Karenia, Karlodinium, Takayama), and 748 in which all three resolve as monophyletic, and with a haptophyte sister-group (i.e., a common plastid-late origin; Fig. S2). This points to a common haptophyte ancestry in all three groups, as opposed to independent endosymbiotic consumptions of free-living haptophytes in Karenia and Karlodinium micrum.
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We see no such shared signal with the RSD, which shares only 42 proteins with at least two other kareniacean genera (Fig. S4). Thus, and consistent with previous studies (Hehenberger et al., PNAS 2019) we cannot invoke an ancestral presence of a fucoxanthin plastid shared with the RSD in the last common kareniacean ancestor. This discrepancy thus likely points to a serial transfer of the kareniacean plastid from either Karlodinium into Karenia or vice versa (Fig. 8).
- Concerning the direction of this transfer, among 1,059 gene trees of plastid-late origin found in both Takayama, Karenia and Karlodinium, 873 place Takayama as basal to a monophyletic clade of Karenia and Karlodinium, i.e. support a specific plastid transfer between the latter two genera. The most parsimonious explanation for this is the origin of the fucoxanthin plastid in the common Takayama/ Karlodinium ancestor, which was subsequently transferred into Karenia. It is true that Karenia contains both a greater absolute proportion of predicted plastid-targeted proteins (Fig. 1) and greater number of unique KO number annotations (Table S4) of plastid-late origin than either Karlodinium or Takayama. That said, this signal may be influenced by multiple other factors beyond how old the given endosymbiosis is (i.e., longer coexistence implies more EGT). For example, the number of plastid-late gene in a host genome may depend on the frequency of duplication of plastid-late genes and the receptiveness of the host nuclear genome to incoming horizontally derived genes. It may further be influenced by the presence and relative selective advantage or disadvantage of competing genes of host nuclear origin (i.e. plastid-early genes) that may be differentially selected over plastid-late genes, which might vary between Karenia and Karlodinium due to differential retention of the ancestral peridinin-type plastid in each lineage.
We have elaborated on this point in the Discussion, noting that there may have been “a direct niche competition between the peridinin and fucoxanthin plastid … with possibly different selective pressure on retention of individual imported proteins” (lines 370-372), “relatively recent origin and spread throughout the kareniacean genome, e.g., via gene duplications” (line 459), and finally that precedent for divergent evolutionary trajectories in different Kareniaceae exists from the Karenia and Karlodinium plastid genomes that “contain partially non-overlapping sets of genes that suggest independent post-endosymbiotic plastid genome reduction” (lines 403-404). Nonetheless, we acknowledge that the evolutionary model we propose is not definitive, and that alternative explanations may find more favour with increased genome data.
Signal peptide prediction I think the modified ASAFind would be greatly helpful for future studies on automatic prediction of plastid proteomes in kareniacean dinoflagellates. However, I found no data on selection criteria for the signal peptide prediction program SignalP5.0 used. I believe such data would be very important to interpret the previously published paper by Gruber et al. in which prediction methods for plastid-targeting sequences are compared to each other to see how sensitively and specifically they can capture the plastid proteomes.
Gruber et al. 2020. Comparison of different versions of SignalP and TargetP for diatom plastid protein predictions with ASAFind.
According to Gruber et al. (2020), signalP5.0 is not suitable for prediction of signal peptides for diatoms, in consistent with the authors' claim for kareniacean dinoflagellates. This inconsistency would be difference of the nature in signal peptides between diatoms and kareniacean dinoflagellates. Even if so, it would be useful to see quantitatively how much different their signal peptides are in terms of their suitable prediction programs.
Author response: In our preliminary benchmarking using only the previously published transcriptomes (see additional sheet in Supplementary tables), SignalP 5.0 performed substantially better in terms of specificity than SignalP 3.0 (i.e., 22 versus 34/ 728 retrieved positive hits of proteins with uniquely non-plastidial functions), with comparable sensitivity in the correct prediction of positive control proteins. Given the size of our dataset, and the substantial risk of false positive detection in the highly expanded and redundant dinoflagellate transcriptomes we have used, we feel that the greater specificity of SignalP 5.0 is important to integrate in our model selection. We have clarified this position in the Methods, stating “First, the relative effectiveness of two SignalP versions … SignalP 5.0 was used for all subsequent analysis.” (lines 525-529).
I also have a concern about use of the combination of PrediSI and ChloroP, combination which is suitable for the plastid proteome prediction in Euglena gracilis. The authors should rationalize why the method for Euglena plastids can be applicable without any modification to the plastid proteome prediction in kareniacean dinoflagellates. Although Euglena plastids are enclosed by three membranes, kareniacean plastids are by four. Therefore, from the side of molecular mechanisms in protein import, the method suitable for Euglena plastids is not necessarily suitable for kareniacean dinoflagellate plastids.
By using PrediSI and ChloroP, they detected additional "candidate plastid proteomes" including several proteins not detectable by SignalP5.0 and the modified ASAFind. That seems great. However, they did not seem to consider false positives since there is no mention on it. Although the additional candidates predicted by PrediSI and ChloroP included true plastid proteins of kareniacean dinoflagellates, many might not be. Nevertheless, the authors suggest 7.5 to 14.5% in K. micrum and K. brevis, respectively, are of plastid-targeted ones. I am so afraid if the proportions would be highly overestimated due to false positives by PrediSI and ChloroP. To rationalize the use of PrediSI and ChloroP, the authors should show sensitivity and specificity by quantitative analyses with a benchmark dataset.
Author response: We thank the author for this comment. The reasoning behind using the parallel PrediSI+ChloroP strategy was the previously reported similarity of the plastid signal structure between euglenids and peridinin dinoflagellates (c.f., Lukes et al., PNAS, 2009) and the previous observation that some kareniaceae posses plastid-targeting sequences resembling those of peridinin dinoflagellates (c.f., Hehenberger et al., PNAS, 2019). Per the reviewers’ suggestion, we present a modified sensitivity/ specificity testing PrediSI+ChloroP, alongside other alternative targeting predictors in Figure S15. While the PrediSI+ChloroP sensitivity is very low, its specificity is comparable with the modified ASAFind, and in this regard outperforms other targeting predictor tools, thus rationalising the use of both targeting prediction tools together.
Origin and evolution of kareniacean plastids The authors suggest the chrysochromulinales origin of the kareniacean dinoflagellate plastids and the Karlodinium-to-Karenia plastid transfer, on the basis of phylogenetic analyses using the concatenated datasets with the 22 conserved plastid-targeted proteins and with plastid-genome derived transcripts. It is very interesting that those plastid-targeted proteins in kareniacean dinoflagellates might be phylogenetically closely related to chrysochromulinales haptophyte I have suggestions on the analyses and interpretation
As the 22 analyzed genes are nuclear-encoded plastid targeted genes, they are a quite small portion of entire plastid proteins. I am not convinced by that evolution of the small number of genes reflects evolution of fucoxanthin plastids of which proteomes are comprised of >1000 proteins. How many genes for haptophyte-derived plastid-targeted proteins suggest the monophyly of kareniaceaen dinoflagellates and chrysochromulinales haptophytes should be investigated by, for example, a coalescence-based analysis such as Astral for all the detected haptophyte-derived plastid-targeted proteins including the 22 genes. This is because the monophyly could be reconstructed only by one or few, limited number of proteins even if the concatenated dataset is analyzed.
Relevant to this, plastid-targeted proteins derived from a peridinin-containing ancestor might still have phylogenetic signals of host evolution. I am interested in whether such analyses with peridinin plastid-derived plastid-targeted proteins reconstruct Takayama and Karlodinium as monophyletic but separate Karenia from them, as suggested in the phylogenomics with non-plastid proteins.
Author response: We agree with the reviewer concerning the problematic nature of concatenations with small numbers of genes, particularly if the underlying gene trees are not phylogenetically congruent to one another, and have chosen to replace the concatenation with a more global evaluation of the different plastid protein origins across our entire dataset. Using automated sorting approaches, we have evaluated the support for our evolutionary model across hundreds of gene trees. We feel that this approach supercedes coalescence-based techniques, as it enables us to treat each gene topology as an independent event, and to consider multiplicity in the origin of the kareniacean plastid proteome. We present these data in a new Fig. 2 and S2.
As stated above, these data strongly support monophyly of all three Kareniacean genera. Concerning the potential Chrysochromulinalean plastid signal in our dataset, we have reanalysed our data and quantify a substantial number of trees (220/ 1,418 of plastid-late origin) that specifically place multiple kareniacean genera within the Chrysochromulinales. This figure is more than twice the number (91) that place the kareniaceae with the next most occurrent haptophyte group in our dataset, Isochrysidales. We nonetheless have chosen to no longer present this as a cryptic plastid endosymbiosis, in the absence of clear examples of extant kareniaceae still possessing this plastid, saying purely in the Discussion that “a common ancestor of the studied organisms either possessed a stable plastid or had a long-term symbiotic relationship (e.g., kleptoplastidic) with a haptophyte lineage related to the extant Chrysochromulinaceae” (lines 363-365).
Concerning the phylogenetic placement of each karenicean genus, the majority of our plastid-late trees specifically recover the monophyly of Karenia and Karlodinium. Remarkably, we find that Takayama and Karlodinium only resolve together in 69/ 1,039 plastid-late gene trees in which all three genera are represented, strongly refuting a vertical origin of the haptophyte-derived components of their plastid proteome. This is not due to the Phaeocystales origin of the current Takayama plastid genome, which is found in only 21 of our plastid protein trees. Nonetheless, as the reviewer suggests, the opposite trend (1,505/ 2,804 gene trees grouping Takayama and Karlodinium as monophyletic) was observed amongst plastid-early gene trees, which might reflect a cryptic peridinin plastid shared between these groups. We expand on these results in the Discussion, stating “Many of the plastid-early gene trees copy the organismal topology …this awaits structural confirmation via microscopy” (lines 383-386).
Finally, to enable reviewer comprehension of the relationships shown, we have presented some exemplar topologies of some of the trees previously displayed in the concatenation, provided in a new Fig. S5.2.
For the phylogenetic analysis of plastid genome-derived transcripts, I might be wrong, but I could not find any information on dataset sizes (i.e., the numbers of sites) and evolutionary models for the analyses in the main text nor supplementary document. Although one may see the dataset sizes when looking at the original datasets in the supplementary files, such information is substantial and thus is to be described in the materials and methods section. I am afraid if this analysis was performed with a small dataset size. I would like to know total lengths of the concatenated sequences and especially that for Takayama. The phylogenetic position of Takayama, distantly related to the other kareniaceans, in this tree might be caused by a larger portion of gaps in the Takayama sequences than in the other kareniaceans.
Author response: As noted in our response to Reviewer 2, we have included three new supplementary figures (S16-S18) with statistics on alignment size, length, and average gap percentage distribution. The average and median values of these three measurements do not differ significantly when calculated separately for different organisms. We have clarified in the Methods that the concatenated alignments retained (PhyloFisher, and plastid-encoded genes) were “constructed by IQ-TREE with the LG+C60+F model for the plastid matrices and posterior mean site frequency (PMSF) model (LG+C60+F+G with a guide tree constructed with C20) for PhyloFisher matrix” (lines 630-632).
Moreover, due to lack of the plastid genome sequence of Takayama, no one could confidently identify plastid genome-derived transcripts: some of those could be derived from second, nuclear copies that might be pseudogenes. Otherwise, even if they are plastid-derived, no one can evaluate whether they are transcripts after or prior to RNA editing. I am afraid if the dataset used is comprised of a mixture of edited and non-edited sequences in kareniacean sequences. Either of sequences after or prior to RNA editing, latter of which are identical with DNA sequences, should be consistently used for the phylogenetic analysis. In any case, the plastid genomes are necessary for this analysis, and the authors can easily obtain them by DNAseq as they have the cultures.
Author response: We thank the Reviewer for their insightful response. We agree that understanding the evolution of kareniacean plastid genomes are crucial to understanding their evolutionary history.
We have accordingly, as described above, integrated a new main text Fig. 5 building a concatenated tree of plastid marker genes (psbA, psych, psbD, psaA, rbcL, and 16S rDNA) historically and commonly used to assess the evolutionary origins of fucoxanthin plastids (e.g., Takishita et al., Phycol Res 1999; Dorrell and Howe, PNAS 2012). These sequences were amplified cryopreserved stocks of total RNA and specific primers, amplified by RT-PCR. We have chosen here to use RNA sequences, to account for the presence of plastid RNA editing, which has been shown to play an important role in maintaining sequence identity between kareniaceaen plastids and haptophyte relatives despite a high DNA mutation rate in the former (Jackson et al., MBE 2013; Klinger et al., GBE 2018), rather than DNA sequences for this analysis.
Additionally, we would like to note that while plastid genomes are generally relatively simple to sequence and assemble, this is not the case in Kareniaceae. The existing plastid genome assemblies are partially incomplete and suggest more complex and possibly unstable structures (e.g., involving at least some minicircles in Karlodinium micrum, Espelund et al., PLoS One 2012; Richardson et al., MBE 2014). From personal communication with our colleagues, we are aware of some efforts to sequence additional kareniacean plastid genomes that unfortunately have not yielded satisfactory results and publications to this day. This strongly invites a separate project focused on kareniacean plastid genomes but is vastly out of scope of this study.
As described above, we have obtained striking new results which we are happy to report in the revised manuscript and which suggest even more, so far unnoticed, plastid replacements in the kareniacean lineage. In light of these finding, parts of the Results and Discussion sections have been extensively rewritten, and the schematic models presented in Fig. 8 has been updated to account for the distinct evolutionary origins of the Karlodinium armiger and Takayama helix plastids.
In addition, although I might be wrong, the phylogenomic analysis for plastid-encoded transcripts might be performed with their nucleotide sequences according to the figure title and legend of Figure S4 mentioning "nucleotide phylogenetic matrix" and the file name "plastid_coded_nt_concatenation_files.tar". If so, translated amino acid sequences should be subjected to phylogenetic analysis, to avoid a well-known artifact that is caused by saturation of substitutions at the 3rd codon.
Author response: With the exception of our 16S rDNA trees (in supporting data), all of our trees were generated with conceptual amino acid translations using a standard codon translation table, in accordance with previous studies (e.g., Klinger et al. GBE 2018). We have revised the file and figure names accordingly.
Duplication of an ATP synthase subunit Duplication and relocation of ATP synthase subunit delta seems interesting. In figure S6.4.1, could you clarify why the possible extensions containing signal peptides lack the initiation methionine at N-termini? I wonder they are 5′ UTRs but artifactually detected as signal peptides, if they all indeed lack Met. To evaluate this point, I recommend 5′ RACE followed by transformation into a model organism as performed in previous studies by some of the authors.
Author response: We reinvestigated these sequences more thoroughly using raw nucleotide data and conclude that the evidence for their retargeting to plastids is very weak and the reported extensions more likely represent untranslated regions some of which were falsely predicted as signal peptides. This section was removed from the new version of the manuscript, although we have noted in Supplementary Text 6.4 that: “A targeted HMMER search for possible distant homologs revealed that the distantly related functional analog of this protein in mitochondrial F-type ATP synthase (ATP5D, K02134) is duplicated in all species except Takayama. The additional copies, however, do not possess a detectable plastid-targeting signal and the specific functions of this duplicated subunit remain to be determined” (lines 107-111).
Comparison of transit peptides Amino acid compositions in transit peptides would vary when targeted compartments are different. In complex plastids, there are functionally distinct compartments: lumen, stroma, periplastidal compartment (PPC). Comparison should therefore be conducted separately for lumen-targeted, stroma-targeted and PPC-targeted proteins in order to claim their transit peptides are not conserved.
Author response: We acknowledge that this question was not explored in our analysis. We therefore re-analyzed our datasets taking the inferred sub-plastidial (thylakoid vs other, based on function) localization of the proteins into account. Our results showed no notable differences between these subsets and are reported in supplementary figure S10.
RDS never possessed a stable fucoxanthin plastid Although the authors cite Hehenberger et al. 2019 for that RDS never possessed a stable fucoxanthin plastid, as far as I know, that paper seems not to mention it. Could you let me know where that is mentioned in the paper? Hehenberger et al. instead proposed the retention of non-photosynthetic peridinin plastid.
Author response: We have modified the Results text, noting that we only identify 42 plastid-late proteins shared between RSD and other Kareniaceae, and in the Discussion that these data provide only limited support for a shared fucoxanthin plastid. We further clarify in the Introduction that “In some cases, the co-existence of a new organelle or endosymbiont with a remnant of the ancestral plastid has been proposed” (lines 106-108) and “It has previously been suggested that the RSD retains a non-photosynthetic form of peridinin plastid” (lines 378-379) with regard to the Hehenberger paper.
Regardless of whether Hehenberger et al. mentioned or not, Novák Vanclová et al. propose that RDS never possessed a stable fucoxanthin plastid because, if I understand correctly, they detected no or few haptophyte-derived RDS genes for plastid-targeted proteins of which origins are shared with those of Karlodinium, Karenia, and Takayama. What about the possibility that the last common ancestor of kareniacean dinoflagellates possessed a fucoxanthin plastid in addition to peridinin plastid followed by almost complete losses of those haptophyte-derived genes after loss of a fucoxanthin plastid in evolution leading to RSD? Free living eukaryotes were appeared to have lost a plastid in recent studies and they have only a few or no genes showing evidence of a plastid previously retained. We cannot rule out that an ancestor of kareniacean dinoflagellates possessed both of peridinin and fucoxanthin plastids, as the authors mention in the main text, and either plastid was inherited to each lineage by differential losses. Accordingly, I would say Fig. 7 is a too much strong proposal as alternative hypotheses are still present. They should be introduced equally.
Author response: We thank the reviewer for this comment. As discussed above, we evaluate the possibility of a cryptic peridinin plastid shared in different kareniaceae, which is suggested at a genetic level by our data but awaits structural confirmation.
We agree that alternative hypotheses may be invoked for the origins of the current kareniacean plastids, and have modified our Fig. 8 to present three alternative possibilities: serial transfer, independent acquisition, and coexistence of an ancestral peridinin and fucoxanthin plastid, as the reviewer suggests. The presence of an ancestral fucoxanthin plastid that was subsequently replaced in Takayama and Karlodinium armiger is strongly suggested by the monophyly of the plastid-late signal across all kareniacean species studied, except RSD. We nonetheless feel that the frequent monophyletic placement of the Karenia and Karlodinium micrum plastids to the exclusion of Takayama in our plastid-late gene trees strongly argues against a vertical inheritance of this plastid from the common kareniacean ancestor, and more likely reflects a serial transfer between the Karenia and Karlodinium / Takayama branches. We have evaluated the evidence for and against each hypothesis in the Discussion and in the Fig. 8 legend.
rRNA copy numbers in dinoflagellates It is known that the rRNA gene copy number varies among populations or strains in dinoflagellates; some possess several dozens of times as many rRNA gene copies as others (Galluzzi et al. 2010). Is it informative to see the ocean wide rRNA gene amplicon data for the kareniacean dinoflagellates? The numbers of rRNA gene-derived reads would not necessarily reflect the cell abundance of dinoflagellates.
Galluzzi et al. 2010. Analysis of rRNA gene content in the Mediterranean dinoflagellate Alexandrium catenella and Alexandrium taylori: implications for the quantitative real-time PCR-based monitoring methods. J Appl Phycol 22:1-9
Author response: We thank the reviewer for raising this point. The exploration of Kareniaceae distribution was intended primarily to investigate their respective ecological relevance in terms of niche diversity, in particular compared with the well-known cosmopolitan patterns of haptophytes, rather than comparing their abundance patterns. We feel that our approach, treating each Kareniacean genus independently, is sufficient for this, but have now clarified in the Results that the different abundances observed “may be biased by the different ribosomal DNA copy numbers in different genera” (lines 330-331) and have cited the reference the reviewer has kindly supplied.
We further note in the Discussion that “It will therefore be worthwhile in the future to assess the distributions of other more recently developed marker genes (Penot et al., 2022; Pierella Karlusich et al., 2023)” (lines 371-372).
Minor points
- the dataset size for the 241 protein-based host phylogeny should also be described in the main text. Author response: The information (72,162 positions241 genes, removal of sequences with >66% gaps) has been included in the Materials and Methods.
The authors mention in Discussion "Thus, our results illuminate the mechanistics of a fundamental process that may under pin vast tracts of chloroplast evolution". If I understand correctly, I think this is based on "shopping bag model" when considering plastid replacements in dinoflagellates. It is helpful to add more details to clarify why the authors would like to claim so. "Chloroplast" should be replaced with "plastid".
Author response: We agree that the term plastid is more appropriate in this context, and have used it globally throughout the manuscript. We have mentioned once in the Introduction “primary plastids, i.e. chloroplasts” to orient the non-specialist reader.
We have elaborated on our definition of the Shopping Bag model, and the specific importance of the Kareniaceae, in the Discussion: “The idea that individual genes encoding plastid-targeted proteins may exhibit evolutionary affiliation with other groups than the plastid donor, typifying the “shopping bag” model (Larkum et al., 2007), is well-established in many plastid lineages” (lines 350-352).
Nonetheless, we feel that our data are in many ways different to those previously observed in other plastid lineages. This may reflect that the kareniacean plastid has undergone one, and potentially multiple, recent replacement events. Nonetheless, the predominant contribution of the host to the plastid proteome is striking, which we elaborate in the Discussion: “Our data show that the dinoflagellate host was the principal contributor of nucleus-encoded proteins supporting the kareniacean plastid proteome” (lines 352-353).
Supplementary document S6.6 I found the term nitrogen fixation, but should this be replaced with "nitrogen assimilation"?
Author response: We have corrected the text as requested.
Figure S5 For those LGTs, all the trees should be shown in supplementary text as they are only 11 or 12 trees. Especially, please add the chlorophyllide b reductase and chlorophyllase in the figure.
Author response: Trees for all laterally transferred genes mentioned in the text have been provided among supplementary figures (S7.1-10).
References I am not picky about a format of the reference list, but I think it should be consistent throughout the list. I recommend adding journals, volumes, and pages precisely for cited papers. I found lack of them at least in Novak Vanclova et al. and Pierella Karlusich et al.
Author response: We corrected the incomplete citations and will perform a complete reformatting of the references to comply with the requirements of a concrete affiliate journal.
Figures In figure 3, I strongly recommend adding RDS data, while distinguishing them by another color if they are derived from different origins from those of Karenia, Karlodinium, and Takayama. This would make the authors claim clearer that there are few haptophyte-derived genes for plastid targeted proteins of which origins are shared with those of the other kareniacean dinoflagellates.
Author response: We believe the comparison to RSD is not among the main stories of our study and adding this dimension to the already complex discussion and metabolic map schematic would compromise the overall clarity. This point is already noted by Reviewer 1 (above). However, this question may indeed be asked by some readers, therefore we decided to include the results for RSD as an additional column in the supplementary table S3 and as an additional graphical element in the supplementary version of the map schematic (figure S8). Per the reviewer’s comments above, we have further stated the number of plastid-late trees shared (42) between the RSD and other kareniaceae in the Results text.
In figures S5.1-2 showing LGTs, I found two paralogs of kareniacean dinoflagellates. What does "CP" mean? If "CP" means ChloroPlast-targeted, both paralogs of K. brevis in HARS and those of K. micrum are of plastid-targeted in TARS and they do not have cytosolic ones. I am afraid if these cases are caused by false positives of detection for plastid-targeted proteins by PredSI and ChloroP. Similarly, in figure S5.4, I found two distant paralogs of heam oxygenase in the tree and the taxon names for both types in kareniaceans include "CP." Are both targeted to the plastids or of false positives?
Author response: The annotation with “CP” and darker colour denotes proteins that were predicted as plastid-targeted by our pipeline. We have clarified in supporting text 6.8 that we investigated our aminoacyl-tRNA synthetases for possible dual targeting to both plastid and mitochondria but found no evidence for it.
We have searched the K. brevis SP3 HARS sequence (CAMPEP-0189291366) by CD-search and note that the conserved domain (underlined) starts at residue 24 after the first predicted methionine (bold), which is inconsistent with the probable length a plastid-targeting sequence, and we have noted in the figure legend that this is likely to represent a false positive.
CAMPEP_0189291366_Karenia-brevis-SP3-20130916
SWLVLLAFALTTPGPVVAVSATILRGLLVGLQRPCAAALRLSCCAATRALPLPGASELGSRFAAAAASSAR__M__GKEGKKKEDGKKKKDETKTEKLIGLEPPSGTRDFFPAEMRQQRYIFNKFRETANLYGFQEYDAPVLEHQELYIRKQGEEITDQMYSFDDKEGAKVTLRPEMTPTLARMVLNLMRVETGEMAAQLPLKWFSIPQCWRFETTQRGRKREHYQWNMDIVGVTSIYAEAELLSAICNFFESVGITSKDVGLRVNSRKVLNAVTKLAGVPDDRFAETCVIIDKLDKIGAEAVKTEMREKIGLPEEVGERIVKATGAKSLEEFADLAGVGQNNPEVLELKHLFELAEDYGYGDWLIFDASVVRGLGYYTGVVFEGFDRAGVLRAICGGGRYDRLLTKFGSPKEIPCVGFGFGDCVIAELLKEKGVTPSLPEHIDFVVAAFNSEMMGKAMNAARRLRLGGKSVDIFTEPGKKVGKAFNYADRVGADMVAFIAPDEWAKGLVRIKALRMGQDVPDDQKQKDVPLEDLANVDSYFGLAPAAAPVMSAAPAASTVKSTAPALAVPAAAKASAPKAAAPSGTGADVEAFLVDHPYVGGFRPCARDRTLFDELRLTSGRPSTPALGRWYDHIDSFPAVVRASWC
The green HARS sequences (including that of Karenia brevis SP1) in contrast typically have conserved domains starting after residues 50-60, and are likely to be genuinely plastid-targeted. Reflecting that the automated prediction approach used within our dataset may contain other such false positive results (c.f., Fig. S18), we have chosen for tree-sorting and pathway reconstruction analyses to only consider genes in which we can identify plastid-targeted homologues of the same inferred phylogenetic origin in at least two distinct Kareniacean genera (Figs. 2, 3).
For the Karlodinium micrum TARS sequence we have identified a second TARS sequence (CAMPEP_0200847158) that is of apparent dinoflagellate origin and lacks a credible targeting sequence, and have updated the tree accordingly.
In the case of heme oxygenases, we are convinced that (at least) two paralogs of distinct origins are indeed plastid targeted. The presence of multiple copies of this enzyme has been noticed in other organisms including some plants (e.g., Dammeyer and Frankenberg-Dinkel, Photochemical & Photobiological Sciences, 2008) and may be reflective of functional specialization or regulation / expression under different conditions. We have discussed this in the supporting text 6.1: “Two evolutionarily distinct versions of the biliverdin-producing haem oxygenase seem to be present …the specific metabolic functions of the green- and haptophyte-like haem oxygenases in the fucoxanthin plastid await experimental characterisation.” (lines 52-58).
Reviewer #3 (Significance (Required)):
Significance
General assessment: provide a summary of the strengths and limitations of the study. What are the strongest and most important aspects? What aspects of the study should be improved or could be developed?
This study by Novak Vanclova et al. provide new transcriptome datasets from multiple species in kareniacean dinoflagellates including harmful and toxic species. Their transcriptome datasets would help understand their biology, evolution, and ecology. The authors also provide a program that predicts plastid proteomes in those dinoflagellates, which would be useful for future studies to focus on kareniacean dinoflagellate plastids, after further refinement. The most important aspect of this study is that many plastid-targeted proteins might be derived from a particular haptophyte lineage, although it is still not sure whether they are derived from LGTs or EGTs. Phylogenetic analyses performed in this study should be improved by adding some plastid genomes, in order to gain more conclusive results. In addition to methods, interpretation of the current results and proposals on plastid evolution should be toned-down.
Advance: compare the study to the closest related results in the literature or highlight results reported for the first time to your knowledge; does the study extend the knowledge in the field and in which way? Describe the nature of the advance and the resulting insights (for example: conceptual, technical, clinical, mechanistic, functional,...).
Although there are technical issues, this study improves our conceptual understanding the plastid proteome evolution in Kareniacean dinoflagellates. The plastid proteomes are comprised of proteins with more various origins in those dinoflagellates, suggesting more complex plastid proteome evolution than previously thought.
Audience: describe the type of audience ("specialized", "broad", "basic research", "translational/clinical", etc...) that will be interested or influenced by this research; how will this research be used by others; will it be of interest beyond the specific field?
This study seems to be "basic research".
Please define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.
algal evolution, eukaryotic evolution, mitochondrial metabolisms, plastid metabolisms, phylogenomics
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Referee #3
Evidence, reproducibility and clarity
Summary
This manuscript entitled "Divergent and diversified proteome content across a serially acquired plastid lineage" by Novak Vanclova et al. proposes the origin and evolution of plastids in kareniacean dinoflagellates. The authors generated new transcriptome data from Karenia mikimotoi, Karenia papilionacea, Karlodinium micrum, Karlodinium armiger, and Takayama helix. Combining them to the previously published transcriptome data from kareniacean dinoflagellates, they constructed the pan-kareniacean transcriptome library. They surveyed plastid-targeted protein-coding transcripts in the dataset, and consequently they estimated ~14.5% of the transcriptome data were of plastid-targeted ones. Of them, 65-80% were derived from a peridinin-containing dinoflagellate ancestor while ~15% were derived from EGTs from a haptophyte endosymbiont of the current plastid origin. By using the plastid-targeted transcript dataset, they investigated 1) origins of the plastid-targeted protein-coding transcripts by single gene-trees, 2) the plastid origin and evolution by the multigene dataset of 22 conserved plastid-targeted protein-coding transcripts and of 3) plastid genome-derived transcripts, 4) plastid functions, 5) diversity of plastid-targeted signals in kareniacean dinoflagellates, and 6) the distributions of kareniacean species by using the Tara Oceans database. On the basis of their results, they proposed many hypotheses regarding kareniacean dinoflagellate evolution, such as i) the chrysochromulinales-origin of the plastids, ii) more recent acquisition of the plastid than previously thought, iii) a plastid replacement within kareniaceae evolution, iv) the strict selection of signal peptides but non-conserved transit peptides in the kareniacean plastid-targeted proteins, and v) correlated or non-correlated distribution patterns of kareniaceaen dinoflagellates to specific haptophyte lineages.
Although their proposals are interesting, I have many concerns to be addressed. Especially, their analyses on which the above proposals are based seem to be still preliminary and inconclusive. To support their proposals more confidently, I also suggest some additional analyses.
Major comments
- seemingly inconsistency between the authors' claims The most striking is inconsistency of the authors' claims proposed in this manuscript. Their proposals include a) the common ancestor of kareniaceans has not possessed a fucoxanthin plastid but the plastid has been acquired more recently, b) an ancestor of Takayama and Karlodinium has gained a fucoxanthin plastid from a (chrysochlomulinales) haptophyte, c) an ancestor of Karenia has gained a fucoxanthin plastid from Karlodinium.
However, they also demonstrate a higher proportion of plastid-late proteins in Karenia than Karlodinium and Takayama. If I understand correctly, "a higher proportion of plastid-late proteins in Karenia than Karlodinium and Takayama" would seemingly be inconsistent to and challenge two of the authors' claims: no haptophyte-derived plastid in the common ancestor of kareniacean dinoflagellates and a Karlodinium-to-Karenia plastid transfer (Fig. 7). If the Karenia plastid is derived from Karlodinium, I have no idea why haptophyte-derived plastid proteome of Karenia is larger than that of Karlodinium. After the plastid acquisition in Karenia, Karenia might have gained more genes for plastid-targeted proteins from haptophytes by LGTs. If this is true, many single gene trees would suggest different origins of plastid-targeted proteins between Karenia and Karlodinium/Takayama. Can we see it in the single gene analyses? I would like authors to rationalize the inconsistency in the main text. 2. Signal peptide prediction I think the modified ASAFind would be greatly helpful for future studies on automatic prediction of plastid proteomes in kareniacean dinoflagellates. However, I found no data on selection criteria for the signal peptide prediction program SignalP5.0 used. I believe such data would be very important to interpret the previously published paper by Gruber et al. in which prediction methods for plastid-targeting sequences are compared to each other to see how sensitively and specifically they can capture the plastid proteomes.
Gruber et al. 2020. Comparison of different versions of SignalP and TargetP for diatom plastid protein predictions with ASAFind.
According to Gruber et al. (2020), signalP5.0 is not suitable for prediction of signal peptides for diatoms, in consistent with the authors' claim for kareniacean dinoflagellates. This inconsistency would be difference of the nature in signal peptides between diatoms and kareniacean dinoflagellates. Even if so, it would be useful to see quantitatively how much different their signal peptides are in terms of their suitable prediction programs.
I also have a concern about use of the combination of PrediSI and ChloroP, combination which is suitable for the plastid proteome prediction in Euglena gracilis. The authors should rationalize why the method for Euglena plastids can be applicable without any modification to the plastid proteome prediction in kareniacean dinoflagellates. Although Euglena plastids are enclosed by three membranes, kareniacean plastids are by four. Therefore, from the side of molecular mechanisms in protein import, the method suitable for Euglena plastids is not necessarily suitable for kareniacean dinoflagellate plastids. By using PrediSI and ChloroP, they detected additional "candidate plastid proteomes" including several proteins not detectable by SignalP5.0 and the modified ASAFind. That seems great. However, they did not seem to consider false positives since there is no mention on it. Although the additional candidates predicted by PrediSI and ChloroP included true plastid proteins of kareniacean dinoflagellates, many might not be. Nevertheless, the authors suggest 7.5 to 14.5% in K. micrum and K. brevis, respectively, are of plastid-targeted ones. I am so afraid if the proportions would be highly overestimated due to false positives by PrediSI and ChloroP. To rationalize the use of PrediSI and ChloroP, the authors should show sensitivity and specificity by quantitative analyses with a benchmark dataset. 3. Origin and evolution of kareniacean plastids The authors suggest the chrysochromulinales origin of the kareniacean dinoflagellate plastids and the Karlodinium-to-Karenia plastid transfer, on the basis of phylogenetic analyses using the concatenated datasets with the 22 conserved plastid-targeted proteins and with plastid-genome derived transcripts. It is very interesting that those plastid-targeted proteins in kareniacean dinoflagellates might be phylogenetically closely related to chrysochromulinales haptophyte I have suggestions on the analyses and interpretation
As the 22 analyzed genes are nuclear-encoded plastid targeted genes, they are a quite small portion of entire plastid proteins. I am not convinced by that evolution of the small number of genes reflects evolution of fucoxanthin plastids of which proteomes are comprised of >1000 proteins. How many genes for haptophyte-derived plastid-targeted proteins suggest the monophyly of kareniaceaen dinoflagellates and chrysochromulinales haptophytes should be investigated by, for example, a coalescence-based analysis such as Astral for all the detected haptophyte-derived plastid-targeted proteins including the 22 genes. This is because the monophyly could be reconstructed only by one or few, limited number of proteins even if the concatenated dataset is analyzed.
Relevant to this, plastid-targeted proteins derived from a peridinin-containing ancestor might still have phylogenetic signals of host evolution. I am interested in whether such analyses with peridinin plastid-derived plastid-targeted proteins reconstruct Takayama and Karlodinium as monophyletic but separate Karenia from them, as suggested in the phylogenomics with non-plastid proteins.
For the phylogenetic analysis of plastid genome-derived transcripts, I might be wrong, but I could not find any information on dataset sizes (i.e., the numbers of sites) and evolutionary models for the analyses in the main text nor supplementary document. Although one may see the dataset sizes when looking at the original datasets in the supplementary files, such information is substantial and thus is to be described in the materials and methods section. I am afraid if this analysis was performed with a small dataset size. I would like to know total lengths of the concatenated sequences and especially that for Takayama. The phylogenetic position of Takayama, distantly related to the other kareniaceans, in this tree might be caused by a larger portion of gaps in the Takayama sequences than in the other kareniaceans. Moreover, due to lack of the plastid genome sequence of Takayama, no one could confidently identify plastid genome-derived transcripts: some of those could be derived from second, nuclear copies that might be pseudogenes. Otherwise, even if they are plastid-derived, no one can evaluate whether they are transcripts after or prior to RNA editing. I am afraid if the dataset used is comprised of a mixture of edited and non-edited sequences in kareniacean sequences. Either of sequences after or prior to RNA editing, latter of which are identical with DNA sequences, should be consistently used for the phylogenetic analysis. In any case, the plastid genomes are necessary for this analysis, and the authors can easily obtain them by DNAseq as they have the cultures.
In addition, although I might be wrong, the phylogenomic analysis for plastid-encoded transcripts might be performed with their nucleotide sequences according to the figure title and legend of Figure S4 mentioning "nucleotide phylogenetic matrix" and the file name "plastid_coded_nt_concatenation_files.tar". If so, translated amino acid sequences should be subjected to phylogenetic analysis, to avoid a well-known artifact that is caused by saturation of substitutions at the 3rd codon. 4. Duplication of an ATP synthase subunit Duplication and relocation of ATP synthase subunit delta seems interesting. In figure S6.4.1, could you clarify why the possible extensions containing signal peptides lack the initiation methionine at N-termini? I wonder they are 5′ UTRs but artifactually detected as signal peptides, if they all indeed lack Met. To evaluate this point, I recommend 5′ RACE followed by transformation into a model organism as performed in previous studies by some of the authors. 5. Comparison of transit peptides Amino acid compositions in transit peptides would vary when targeted compartments are different. In complex plastids, there are functionally distinct compartments: lumen, stroma, periplastidal compartment (PPC). Comparison should therefore be conducted separately for lumen-targeted, stroma-targeted and PPC-targeted proteins in order to claim their transit peptides are not conserved. 6. RDS never possessed a stable fucoxanthin plastid Although the authors cite Hehenberger et al. 2019 for that RDS never possessed a stable fucoxanthin plastid, as far as I know, that paper seems not to mention it. Could you let me know where that is mentioned in the paper? Hehenberger et al. instead proposed the retention of non-photosynthetic peridinin plastid. Regardless of whether Hehenberger et al. mentioned or not, Novák Vanclová et al. propose that RDS never possessed a stable fucoxanthin plastid because, if I understand correctly, they detected no or few haptophyte-derived RDS genes for plastid-targeted proteins of which origins are shared with those of Karlodinium, Karenia, and Takayama. What about the possibility that the last common ancestor of kareniacean dinoflagellates possessed a fucoxanthin plastid in addition to peridinin plastid followed by almost complete losses of those haptophyte-derived genes after loss of a fucoxanthin plastid in evolution leading to RSD? Free living eukaryotes were appeared to have lost a plastid in recent studies and they have only a few or no genes showing evidence of a plastid previously retained. We cannot rule out that an ancestor of kareniacean dinoflagellates possessed both of peridinin and fucoxanthin plastids, as the authors mention in the main text, and either plastid was inherited to each lineage by differential losses. Accordingly, I would say Fig. 7 is a too much strong proposal as alternative hypotheses are still present. They should be introduced equally. 7. rRNA copy numbers in dinoflagellates It is known that the rRNA gene copy number varies among populations or strains in dinoflagellates; some possess several dozens of times as many rRNA gene copies as others (Galluzzi et al. 2010). Is it informative to see the ocean wide rRNA gene amplicon data for the kareniacean dinoflagellates? The numbers of rRNA gene-derived reads would not necessarily reflect the cell abundance of dinoflagellates.
Galluzzi et al. 2010. Analysis of rRNA gene content in the Mediterranean dinoflagellate Alexandrium catenella and Alexandrium taylori: implications for the quantitative real-time PCR-based monitoring methods. J Appl Phycol 22:1-9
Minor points
- the dataset size for the 241 protein-based host phylogeny should also be described in the main text.
- The authors mention in Discussion "Thus, our results illuminate the mechanistics of a fundamental process that may under pin vast tracts of chloroplast evolution". If I understand correctly, I think this is based on "shopping bag model" when considering plastid replacements in dinoflagellates. It is helpful to add more details to clarify why the authors would like to claim so. "Chloroplast" should be replaced with "plastid".
- Supplementary document S6.6 I found the term nitrogen fixation, but should this be replaced with "nitrogen assimilation"?
- Figure S5 For those LGTs, all the trees should be shown in supplementary text as they are only 11 or 12 trees. Especially, please add the chlorophyllide b reductase and chlorophyllase in the figure.
- References I am not picky about a format of the reference list, but I think it should be consistent throughout the list. I recommend adding journals, volumes, and pages precisely for cited papers. I found lack of them at least in Novak Vanclova et al. and Pierella Karlusich et al.
- Figures In figure 3, I strongly recommend adding RDS data, while distinguishing them by another color if they are derived from different origins from those of Karenia, Karlodinium, and Takayama. This would make the authors claim clearer that there are few haptophyte-derived genes for plastid targeted proteins of which origins are shared with those of the other kareniacean dinoflagellates. In figures S5.1-2 showing LGTs, I found two paralogs of kareniacean dinoflagellates. What does "CP" mean? If "CP" means ChloroPlast-targeted, both paralogs of K. brevis in HARS and those of K. micrum are of plastid-targeted in TARS and they do not have cytosolic ones. I am afraid if these cases are caused by false positives of detection for plastid-targeted proteins by PredSI and ChloroP. Similarly, in figure S5.4, I found two distant paralogs of heam oxygenase in the tree and the taxon names for both types in kareniaceans include "CP." Are both targeted to the plastids or of false positives?
Significance
General assessment: provide a summary of the strengths and limitations of the study. What are the strongest and most important aspects? What aspects of the study should be improved or could be developed?
This study by Novak Vanclova et al. provide new transcriptome datasets from multiple species in kareniacean dinoflagellates including harmful and toxic species. Their transcriptome datasets would help understand their biology, evolution, and ecology. The authors also provide a program that predicts plastid proteomes in those dinoflagellates, which would be useful for future studies to focus on kareniacean dinoflagellate plastids, after further refinement. The most important aspect of this study is that many plastid-targeted proteins might be derived from a particular haptophyte lineage, although it is still not sure whether they are derived from LGTs or EGTs. Phylogenetic analyses performed in this study should be improved by adding some plastid genomes, in order to gain more conclusive results. In addition to methods, interpretation of the current results and proposals on plastid evolution should be toned-down.
Advance: compare the study to the closest related results in the literature or highlight results reported for the first time to your knowledge; does the study extend the knowledge in the field and in which way? Describe the nature of the advance and the resulting insights (for example: conceptual, technical, clinical, mechanistic, functional,...).
Although there are technical issues, this study improves our conceptual understanding the plastid proteome evolution in Kareniacean dinoflagellates. The plastid proteomes are comprised of proteins with more various origins in those dinoflagellates, suggesting more complex plastid proteome evolution than previously thought.
Audience: describe the type of audience ("specialized", "broad", "basic research", "translational/clinical", etc...) that will be interested or influenced by this research; how will this research be used by others; will it be of interest beyond the specific field?
This study seems to be "basic research".
Please define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.
algal evolution, eukaryotic evolution, mitochondrial metabolisms, plastid metabolisms, phylogenomics
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Referee #2
Evidence, reproducibility and clarity
This is a well done, detailed bioinformatic analysis of genomic and transcriptomic data from an important lineage of dinoflagellates that have undergone serial substitution of their plastid. On the whole I am enthusiastic about the paper; it presents valuable new insights, and is rigorously performed. However, I have to object to the way the term "proteome" is used in the paper; the manuscript is talking about the predicted proteome, not a measured proteome. This is something of a technical distinction, but it is an important one because the transcriptome and the proteome don't necessarily track each other, and there is little or no actual proteomic data available from dinoflagellates. We assume that transcript abundance has something to do with proteome abundance, but this is often violated. What this paper is really addressing is the potential proteome, because if a given gene is completely absent from the genome and the transcriptome we can be confident it will not be present in the proteome. The converse is not true. For this reason I feel it is important to be clear on the distinction. I would be satisfied in this regard by minor modifications, using the term "predicted proteome" in the title, and being more direct in the introduction about the distinction.
Overall the analyses are impressive. I do have to squirm a little when I see automated analyses generating alignments where the threshold is less than 75% gaps and at least 100 nucleotides aligned. I looked at the supplementary data and the figshare files and could not find the alignments themselves, so I don't know what fraction of the sequences are in that territory. Because phylogenetic analysis (as performed here) treats the alignments as an observation, and because the alignments include sequences with more than 50% gaps, it is entirely possible that some taxa, or even whole segments of the tree, are based on non-overlapping data.
Mind you, we have done similar analyses, and I don't think this invalidates the results, but it does open up the possibility of some dramatic artifacts. Consequently, I would recommend a) making the alignments available (or more obvious where to find them), and b) providing more detail on the alignments, including, if possible, to add a figure (probably in the supplementary data) that visualizes them. It is not given in the text itself, but according to the figure 2 caption there are 22 sequences thought to be "plastid late", and 241 in the pan-eukaryotic dataset. This is a scale that is feasible to put in a figure showing, for example, each aligned residue as a color and indels as grey. Such a figure is readable even when the individual residues are only a few pixels in size (less than a millimeter when printed). I also recommend describing the final alignments more fully in the text. Most of the summary statistics are presented in normalized form, and that can obscure patterns that come from poorly sampled taxa.
Better clarify on the characteristics of the alignments will make it easier to interpret the findings overall. Although this is critical to interpreting the results, gappy alignments are not uncommon in analyses of this sort, and setting that aside the analyses presented are comprehensive and thorough. The discussion does a good job of addressing the significance of the work, and potential causes of error are addressed adequately (aside from the matter of the alignments).
Significance
I find the paper to be exciting and important. These organisms are economically important, particularly as potential nuisance organisms, but also because of their role in primary productivity. They also have extremely complex evolutionary histories and similarly complex genomes. performing any bioinformatic analysis of these organisms is a substantial challenge because almost every gene exists in high copy number and with complex and often obscure patterns of homology. The manuscript brings forward these challenges, and makes a substantial step forward in elucidating the evolution of a group that is fascinating and important, but remarkably difficult to work with. I feel that it is an important analysis, and should be of interest to a broad audience.
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Referee #1
Evidence, reproducibility and clarity
The manuscript investigated the composition of the plastid proteomes of seven distantly-related kareniacean dinoflagellates, including newly-sequenced members of three genera (Karenia, Karlodinium, and Takayama). Using a custom plastid-targeting predictor, automatic single-gene tree building and phylogenetic sorting of plastid-targeted proteins for plastid proteome construction, the authors suggest that the haptophyte order Chrysochromulinales is the closest living relative of the fucoxanthin plastid donor. Interestingly, the N-terminal targeting sequences of kareniacean plastid signal peptides, reveal a high sequence conservation. Moreover, ecological and mechanistic factors are suggested that may have driven the endosymbiotic acquisition of the fucoxanthin plastid. Overall, this is a comprehensive and interesting analysis.
Other comments.
- For analyses of N-terminal targeting sequences, why did the authors not consider to employ Predalgo as an additional tool?
- Given the fact that peridinin or fucoxanthin pigment binding is in the focus of the paper, a more detailed introduction of the peridinin and fucoxanthin light-harvesting systems should be given.
- The authors state "It is also possible that there has been a direct niche competition between the peridinin and fucoxanthin plastid that may have coexisted in the same host for a period of time with possibly different selective pressure on retention of their respective proteins based on their interaction with plastid-encoded components, e.g., extrinsic photosystem subunits not assembling correctly with their intrinsic haptophyte-like counterparts." It is tempting to ask, whether peridinin light-harvesting systems have left traces in the fucoxanthin plastid, possibly due to mistargeting of peridinin light-harvesting systems into the fucoxanthin plastid? Are some photosynthetic subunits "in-between" peridinin and fucoxanthin plastids?
- Figure 3 is difficult to understand, e.g. for PSI and PSII which subunits are shown, why has PSI "more" contribution from dinoflagellates as compared to PSII?
- Data shown in figure 4, is there experimental evidence for signal peptide cleavage site(s). Could these data been used to predict mature plastid targeted protein sequence?
- The authors state "Partial Least Square (PLS) analysis shows a set of environmental variables (salinity, silicate, iron) positively correlated with abundances of both Karenia and Takayma and also haptophytes as a whole, but at the same time negatively correlated to Karlodinium (Figure S8), further illustrating that the latter genus is quite distant from the rest in its biogeographical pattern." How could this be interpreted in the light of the plastid proteomes?
Significance
The current manuscript gives insights into the endosymbiotic acquisition of the fucoxanthin plastids.
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General Statements
Ezrin, Radixin, and Moesin (ERMs) serve as crucial cytoskeletal linker proteins, connecting the actin cytoskeleton to the plasma membrane upon activation. ERMs are essential regulators of cell morphogenesis across every cell types reported so far, and have been implicated in vital cellular functions such as migration, and invasion. In our study, we discovered that ERMs are dispensable for the cortical organization of macrophages. In accordance with this surprising finding, we found that the migration of macrophages was not affected upon knock-out of the three ERMs. Our findings challenge the prevailing belief that ERMs universally regulate cortical organization. Instead, they indicate that the actin cortex of macrophages has evolved to possess a high degree of adaptability and plasticity, enabling these immune cells to function independently of ERM proteins.
We thank the editors of Review Commons that handled our manuscript and all three reviewers for their positive assessment of our manuscript and for their constructive suggestions.
Description of the planned revisions
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
In the manuscript, the authors systematically test the role of ERM proteins in macrophages using RNA silencing as well as the genetic knockout approaches. Previous studies have highlighted the fundamental importance of ERM proteins as structural and regulatory components of the cell cortex governing several essential functions such as the generation of surface features such as filopodia, maintenance of cortex-plasma membrane attachment, bleb retraction, cortical mechanics, and cell migration. The authors performed a series of experiments to comprehensively test each of these functions (including cell migration in 2D surface and 3D matrix in vitro, ex vivo on tumor implants, as well as in vivo) and found that none of these are significantly affected when ERM proteins are downregulated in macrophages. Overall, the paper is solid, the experiments are well-designed and conclusive, and the manuscript is written well.
We thank the reviewer for these encouraging comments.
I have no significant concerns with the study. My only experimental suggestion is related to a previously shown function of ERM protein in macrophages- the ERM proteins play an important role in phagosome maturation in macrophages (Defacque et al., EMBO, 2000; Lars-Peter et al., PNAS, 2006; Mylvaganam et al., Current Biology, 2021). It would be nice if authors could explore this phenotype in their perturbation system.
We thank the reviewer for this valuable suggestion. ERM proteins have indeed been proposed as important for macrophage phagocytosis. Importantly, their necessity for the early steps of this process is still debated, as conclusions differ depending on the cellular model used and the type of particle to be internalised (Erwig et al., PNAS USA 2006; Di Pietro et al., Sci. Rep. 2017; Gomez and Descoteaux, Biochem. Biophys. Res. Commun. 2018; Mu et al., Nat. Commun. 2018; Okazaki et al., J. Physiol. Sci. 2020). While the implication of ERMs in the early steps of phagocytosis remains controversial, there seems to be a consensus to implicate ezrin and moesin in phagosome maturation (Defacque et al. EMBO 2000 ; Erwig et al., PNAS USA 2006; Marion et al., Traffic 2011; Gomez and Descoteaux, Biochem. Biophys. Res. Commun. 2018).
We have already started addressing the ability of ERM-depleted macrophages to perform phagocytosis. In particular, we quantified the dynamics of phagocytosis of ovalbumin-coated or IgG-opsonized polystyrene beads, which did not reveal any difference between WT and ERM-depleted macrophages.
Proposed revision: We propose to include in the manuscript our quantification of IgG-coated and non-coated phagocytosis, and evaluate whether phago-lysosome fusion is delayed in ERM-depleted macrophages.
A minor concern with the study is, as the authors have already pointed out, that ERM proteins may still be required for some functions in macrophages under specific (environmental?) conditions. It is of course impossible to experimentally test all possible conditions that may involve ERMs, however, the authors should include a note on the hypothetical conditions that may require ERMs in macrophages. They should also discuss possible hypothetical reasons why macrophages may have evolved a cortex that does not rely on ERM proteins for specific functions. Overall, a more extended discussion on the role of ERM proteins (or the lack of them) in macrophages is required.
As suggested, in the revised version of the manuscript we will add a more extensive discussion of the role of ERM proteins in macrophages, and in particular the hypothetical conditions that might require their presence, as well as the reasons why macrophages have developed a particular cortex.
Reviewer #1 (Significance (Required)):
The manuscript is important on many accounts: The ERM proteins are considered crucial membrane-cytoskeletal linkers in many cellular systems. The study presents a surprising finding that cortical phenomena requiring membrane-cytoskeletal attachment do not essentially need ERM proteins providing a fundamental conceptual advance. The results from this study will also inform both experimental as well as theoretical studies of cortical organization and dynamics in the future. Furthermore, overexpressed mutant forms of ERMs are used as sensors as well as perturbing agents of cortical actin dynamics in many cellular systems. These utilities can now be further substantiated and if required, revised in light of the results from this study.
I am an immune cell biologist specializing in early lymphocyte activation and cytoskeleton dynamics.
We would like to thank the reviewer for pointing out the importance of our work for our understanding of the function of the cellular cortex, and for highlighting the fact that it may lead to a reinterpretation of the results obtained using ERM mutants.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
Summary ERM proteins are known to play a central role in linking the cortical actin cytoskeleton with the plasma membrane, which is involved in regulating a diverse range of actin-rich membrane structures. The authors question the role that ERM proteins play in regulating cell shape changes and migration, specifically in macrophages. To test this, they designed an approach to systematically delete each ERM in macrophages - followed by the production of a triple-ERM ko line (tKO) using HoxB8 myeloid progenitor cells. The tKO line was subjected to a series of in vitro and in vivo experiments - all of which involve a series of imaging techniques to monitor membrane dynamics, protein subcellular organisation and cellular behaviours (e.g. rolling fraction, sticking fraction and chemotaxis). Their overall conclusion is that ERM are dispensable for macrophage membrane structures and migration.
General comments.
The experiments are very well executed. The manuscript in short demonstrates that the ERM proteins are dispensable for macrophage migration (both in 2D and 3D contexts), but there is very little beyond this work that points to what they might be doing instead. In this regard, given that the focus is exclusively on macrophage migration, the work comes across as quite specialised.
We thank the reviewer for appreciating the quality of our work.
We respectfully disagree to their assessment of the limited scope of our findings. Given the crucial importance of the migration of macrophages for so many of our body's functions, our findings will have a wide-ranging impact. Furthermore, and as acknowledged by Reviewers 1 and 3, we believe that the discovery that ERMs do not play a universal role in cortical mechanics and in cell migration, as hitherto believed, reaches a much wider scientific audience than that of the macrophage field. By proposing a unique research model (a triple KO for ERMs), our work allows to question many studies carried out with less direct molecular tools, such as the use of drugs or mutants of ERMs.
We acknowledge the fact that although our data convincingly demonstrate that the ERM proteins are dispensable for macrophage migration, they do not reveal alternative functions for these proteins. We agree that it could be interesting to search for alternative functions for ERM proteins in macrophages in future studies. However, we believe that such studies are out of the scope of the present manuscript.
The biggest concern I have is with the in vivo part. It should be noted that the work outlined in the manuscript does not actually address diapedesis, which is monitoring transmigration from the blood into tissue. Rolling and sticking do not define diapedesis. The experiments that the authors have conducted may have captured diapedesis events, but that very much depends on the length of time that the IVM was conducted. The authors would need to qualify their claims in this regard. Removing this work altogether would not lessen the impact, given that diapedesis is not shown. The work would therefore be very much in vitro/ex vivo.
We agree with the reviewer that, due to technical limitations, we only measured the rolling and sticking capacity of the +/-ERM cells and did not measure diapedesis directly. Following Reviewer's comments, we have thus modified the text of the manuscript and no longer use the term ‘diapedesis’ to describe our in vivo intravital imaging studies.
We also clarified the fact that we did not inject differentiated macrophages into the circulation, but macrophage precursors obtained by the treatment of progenitors with a 1 day treatment only (and not a 7 day treatment) with 20 ng/mL M-CSF.
Here, highlighted in yellow, are the changes to the text (in the Introduction, Results, Methods and Legends sections):
Introduction, p3:
“Surprisingly, we found that ERMs are dispensable for macrophages to migrate in diverse contexts, including in vitro 2D migration and 3D invasion of extracellular matrix, ex vivo tissue infiltration through healthy dermis and tumor tissue, and for the in vivo adhesion of macrophage precursors to an activated endothelium.”
Results, p6:
“ERM tKo cells without ezrin, radixin, and moesin exhibit no impairment in their ability to adhere to vascular endothelium in vivo and infiltrate the ear derma or fibrosarcoma.
To further investigate the migratory properties of ERM-deficient cells in vivo, we first assessed their ability to adhere to activated vascular endothelium into mice bearing a fibrosarcoma (Gui et al., 2018).”
Results, p8:
“Our study uncovered a surprising finding: ezrin, radixin and moesin are dispensable for key aspects of macrophage behavior, including the formation of lamellipodia and filopodia, the dynamics of membrane ruffles and podosomes, migration in vitro (in 2D or 3D matrices) and ex vivo (into dermis or tumor tissues) as well as for the in vivo adhesion of macrophage precursors to activated vascular endothelium).”
Methods, p14:
“In vivo analysis of adhesion to vascular endothelium with wide-field intravital microscopy”
And
“HoxB8-progenitors were directed towards monocyte/macrophage differentiation using a 1 day treatment with 20 ng/mL mouse M-CSF.”
Figure 4 legend, p33:
“Fig. 4: ERM tKO cells have no defect in adhesion to vascular endothelium in vivo and infiltrate tissues explants ex vivo
- In vivo adhesion to vascular endothelium
Fibrosarcoma cells were injected into the flank of a mice. After a week, tumor was exposed for intravital microscopy, and the femoral artery of recipient mice was catheterized for injection of exogenous cells. Differentially labeled WT and TKO-ERM macrophage precursors were injected in the blood and their behaviour in tumor blood vessels was assessed by real-time imaging. Rolling fractions were quantified as the percentage of rolling cells in the total flux of cells in each blood vessel, and sticking fractions were quantified as the percentage of rolling cells that firmly adhered for a minimum of 30 seconds.”
Proposed revision: We propose to keep the results of the in vivo experiments in the manuscript, including the modifications proposed by the reviewer and listed above.
Specific questions
How sure are the authors that they are capturing these events in cremasteric venules?
As described in the Results and Methods section, these measurements were not captured in cremasteric venules but in fibrosarcoma tumour blood vessels, where we have previously demonstrated strong recruitment of circulating monocytes to infiltrate tumor tissue (Gui et al., Cancer Immunol. Res. 2018).
Is there any sign of cells being trapped in the microcirculation?
The diameter of the tumor blood vessels analysed is consistent with tumor post-capillary venules, and we have not seen cells trapped in these tumor blood vessels.
The reason for injecting macrophages intravenously is not explained.
We injected cells intravenously in order to compare their capacity to adhere to activated tumor blood vessels by intravital microscopy. This is now clarified in the corresponding result section (p6):
“For that purpose, one day differentiated wild-type or ERM-deficient cells were fluorescently labelled with two different cell trackers, mixed in a 1:1 ratio, and co-injected intra-arterially into recipient mice in order to analyse their behaviour in tumor blood vessels by intravital microscopy.”
Are these experiments modelling intravascular (patrolling) macrophages? Monocytes will typically differentiate into macrophages in tissue.
We again apologize for the lack of clarity. In these experiments, we did not inject fully differentiated (seven days) macrophages but progenitors directed towards monocyte/macrophage differentiation using a 1 day treatment with 20 ng/mL mouse M-CSF. We believe that these experiments model the adhesion/recruitment of monocytes by activated vascular endothelium in the tumor microenvironment.
The fact that the cells are able to "roll" and "stick" suggests that they have the complimentary cell adhesion molecules, although this is not addressed in the study.
We agree with the reviewer. Our intravital microscopy analyses indicate that the injected cells have the complementary cell adhesion molecules for firm adhesion to activated tumor blood vessels. Importantly, our data clearly demonstrate that the capacity of ERM-tKO cells to bind vascular endothelium in the tumor microenvironment is similar to that of WT cells (Fig. 4A).
Reviewer #2 (Significance (Required)):
The strength of the manuscript is based on the robust in vitro experiments, however such experiments are difficult to address in vivo - mainly because of the issue that macrophages (unless patrolling macrophages) are not a useful model to investigate for ivm experiments.
We thank the reviewer for recognizing the robustness of our in vitro experiments. We fully agree with the reviewer that the in vivo experiments are more challenging and that the behaviour of monocytes/(patrolling) macrophages is difficult to mimic in vivo. However, we believe that our intravital microscopy analyses are important because they demonstrate that ERM-tKO cells retain the capacity to bind firmly (sticking) to activated tumor blood vessels in vivo.
This would be of great interest to the macrophage field, which is quite limited in scope. An advancement in the field would be to learn what is taking over the role of ERM in macrophages. As such, this becomes a report with a series of experiments to confirm that ERM are not involved.
Again, we respectfully disagree with the reviewer, as this work goes against the dogma that ERMs are generally the most important mechanical links between the plasma membrane and the cytoskeleton. By clearly establishing that this is not the case in macrophages, cells whose importance for our immunity justifies the importance of their investigation, this study could make it possible to reconsider the functioning of the cellular cortex and the role of ERMs in other cellular systems.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
Verdys and colleagues report an elegant study in which the authors describe that ERM proteins are dispensable for migrating monocyte-derived macrophages. The methods are adequate and the results support the conclusions.
We thank the reviewer for these very supportive comments.
Major points:
- Although the authors demonstrate, by multiple methods, the dispensability of ERM proteins in the migration of macrophages derived from monocytes, the role of these proteins must also be evaluated in the phagocytosis process (another relevant functional aspect of macrophages).
This is an excellent suggestion, which should make it possible to clarify the role of ERMs in this important function of macrophages.
ERM proteins have indeed been proposed as important for macrophage phagocytosis. Importantly, their necessity for the early steps of this process is still debated, as conclusions differ depending on the cellular model used and the type of particle to be internalised (Erwig et al., PNAS USA 2006; Di Pietro et al., Sci. Rep. 2017; Gomez and Descoteaux, Biochem. Biophys. Res. Commun. 2018; Mu et al., Nat. Commun. 2018; Okazaki et al., J. Physiol. Sci. 2020). While the implication of ERMs in the early steps of phagocytosis remains controversial, there seems to be a consensus to implicate ezrin and moesin in phagosome maturation (Defacque et al. EMBO 2000 ; Erwig et al., PNAS USA 2006; Marion et al., Traffic 2011; Gomez and Descoteaux, Biochem. Biophys. Res. Commun. 2018).
We have already started addressing the ability of ERM-depleted macrophages to perform phagocytosis. In particular, we quantified the dynamics of phagocytosis of ovalbumin-coated or IgG-opsonized polystyrene beads, which did not reveal any difference between WT and ERM-depleted macrophages.
Proposed revision: We propose to include in the manuscript our quantification of IgG-coated and non-coated phagocytosis, and evaluate whether phago-lysosome fusion is delayed in ERM-depleted macrophages.
- How is the activation of key downstream targets of ERM proteins involved in macrophage migration in KO models?_
This is a very pertinent question. However, while ERMs have been described as being downstream of several signalling pathways, their own downstream targets are unfortunately poorly documented and, to our knowledge, none are known in macrophages.
In different cellular contexts, it has been proposed that ERMs regulate PI3K (Gautreau et al. PNAS USA 1999), Ras (Sperka et al. Plos One 2011) or that they are involved in the initiation of protein translation (Briggs et al. Neoplasia 2012), but these results have not yet been confirmed and we believe they are outside the scope of this study.
During macrophage migration, we consider that their obvious main target is cortical actin, and demonstrate in this manuscript that the functional coupling between actin and the plasma membrane is not affected by full ERM knockout.
Reviewer #3 (Significance (Required)):
Advance: The present study fills a gap in the participation of ERM proteins in cell migration. The results obtained on the dispensability of these proteins in macrophage migration can pave avenues for identifying new processes and proteins associated with migration in this context.
Audience: The audience for this study is very broad.
We again thank the reviewer for recognising the importance of this work for the understanding of cell migration.
My expertise: I have expertise in cellular and molecular biology with a focus on processes associated with cancer. Among the numerous research fronts of the group led by me, we recently identified the EZR gene (which encodes the ezrin protein) as a prognostic marker and molecular target in acute leukemias.
Description of the revisions that have already been incorporated in the transferred manuscript
In the revised version of the article, we have taken into account all relevant changes proposed by the reviewers. We modified the text of the manuscript and no longer use the term ‘diapedesis’ to describe our in vivo intravital imaging studies, and clarified the fact that we did not inject differentiated macrophages into the circulation, but macrophage precursors obtained by the treatment of progenitors with a 1 day treatment only (and not a 7 day treatment) with 20 ng/mL M-CSF.
Here, highlighted in yellow, are the changes to the text (in the Introduction, Results, Methods and Legends sections):
Introduction, p3:
“Surprisingly, we found that ERMs are dispensable for macrophages to migrate in diverse contexts, including in vitro 2D migration and 3D invasion of extracellular matrix, ex vivo tissue infiltration through healthy dermis and tumor tissue, and for the in vivo adhesion of macrophage precursors to an activated endothelium.”
Results, p6:
“ERM tKo cells without ezrin, radixin, and moesin exhibit no impairment in their ability to adhere to vascular endothelium in vivo and infiltrate the ear derma or fibrosarcoma.
To further investigate the migratory properties of ERM-deficient cells in vivo, we first assessed their ability to adhere to activated vascular endothelium into mice bearing a fibrosarcoma (Gui et al., 2018). For that purpose, one day differentiated wild-type or ERM-deficient cells were fluorescently labelled with two different cell trackers, mixed in a 1:1 ratio, and co-injected intra-arterially into recipient mice in order to analyse their behaviour in tumor blood vessels by intravital microscopy.”
Results, p8:
“Our study uncovered a surprising finding: ezrin, radixin and moesin are dispensable for key aspects of macrophage behavior, including the formation of lamellipodia and filopodia, the dynamics of membrane ruffles and podosomes, migration in vitro (in 2D or 3D matrices) and ex vivo (into dermis or tumor tissues) as well as for the in vivo adhesion of macrophage precursors to activated vascular endothelium).”
Methods, p14:
“In vivo analysis of adhesion to vascular endothelium with wide-field intravital microscopy”
And
“HoxB8-progenitors were directed towards monocyte/macrophage differentiation using a 1 day treatment with 20 ng/mL mouse M-CSF.”
Figure 4 legend, p33:
“Fig. 4: ERM tKO cells have no defect in adhesion to vascular endothelium in vivo and infiltrate tissues explants ex vivo
- In vivo adhesion to vascular endothelium
Fibrosarcoma cells were injected into the flank of a mice. After a week, tumor was exposed for intravital microscopy, and the femoral artery of recipient mice was catheterized for injection of exogenous cells. Differentially labeled WT and TKO-ERM macrophage precursors were injected in the blood and their behaviour in tumor blood vessels was assessed by real-time imaging. Rolling fractions were quantified as the percentage of rolling cells in the total flux of cells in each blood vessel, and sticking fractions were quantified as the percentage of rolling cells that firmly adhered for a minimum of 30 seconds.”
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Referee #3
Evidence, reproducibility and clarity
Verdys and colleagues report an elegant study in which the authors describe that ERM proteins are dispensable for migrating monocyte-derived macrophages. The methods are adequate and the results support the conclusions.
Major points:
- Although the authors demonstrate, by multiple methods, the dispensability of ERM proteins in the migration of macrophages derived from monocytes, the role of these proteins must also be evaluated in the phagocytosis process (another relevant functional aspect of macrophages).
- How is the activation of key downstream targets of ERM proteins involved in macrophage migration in KO models?
Significance
Advance: The present study fills a gap in the participation of ERM proteins in cell migration. The results obtained on the dispensability of these proteins in macrophage migration can pave avenues for identifying new processes and proteins associated with migration in this context.
Audience: The audience for this study is very broad.
My expertise: I have expertise in cellular and molecular biology with a focus on processes associated with cancer. Among the numerous research fronts of the group led by me, we recently identified the EZR gene (which encodes the ezrin protein) as a prognostic marker and molecular target in acute leukemias.
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Referee #2
Evidence, reproducibility and clarity
Summary
ERM proteins are known to play a central role in linking the cortical actin cytoskeleton with the plasma membrane, which is involved in regulating a diverse range of actin-rich membrane structures. The authors question the role that ERM proteins play in regulating cell shape changes and migration, specifically in macrophages. To test this, they designed an approach to systematically delete each ERM in macrophages - followed by the production of a triple-ERM ko line (tKO) using HoxB8 myeloid progenitor cells. The tKO line was subjected to a series of in vitro and in vivo experiments - all of which involve a series of imaging techniques to monitor membrane dynamics, protein subcellular organisation and cellular behaviours (e.g. rolling fraction, sticking fraction and chemotaxis). Their overall conclusion is that ERM are dispensable for macrophage membrane structures and migration.
General comments.
The experiments are very well executed. The manuscript in short demonstrates that the ERM proteins are dispensable for macrophage migration (both in 2D and 3D contexts), but there is very little beyond this work that points to what they might be doing instead. In this regard, given that the focus is exclusively on macrophage migration, the work comes across as quite specialised.
The biggest concern I have is with the in vivo part. It should be noted that the work outlined in the manuscript does not actually address diapedesis, which is monitoring transmigration from the blood into tissue. Rolling and sticking do not define diapedesis. The experiments that the authors have conducted may have captured diapedesis events, but that very much depends on the length of time that the IVM was conducted. The authors would need to qualify their claims in this regard. Removing this work altogether would not lessen the impact, given that diapedesis is not shown. The work would therefore be very much in vitro/ex vivo.
Specific questions
How sure are the authors that they are capturing these events in cremasteric venules? Is there any sign of cells being trapped in the microcirculation? The reason for injecting macrophages intravenously is not explained. Are these experiments modelling intravascular (patrolling) macrophages? Monocytes will typically differentiate into macrophages in tissue. The fact that the cells are able to "roll" and "stick" suggests that they have the complimentary cell adhesion molecules, although this is not addressed in the study.
Significance
The strength of the manuscript is based on the robust in vitro experiments, however such experiments are difficult to address in vivo - mainly because of the issue that macrophages (unless patrolling macrophages) are not a useful model to investigate for ivm experiments.
This would be of great interest to the macrophage field, which is quite limited in scope.
An advancement in the field would be to learn what is taking over the role of ERM in macrophages. As such, this becomes a report with a series of experiments to confirm that ERM are not involved.
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Referee #1
Evidence, reproducibility and clarity
In the manuscript, the authors systematically test the role of ERM proteins in macrophages using RNA silencing as well as the genetic knockout approaches. Previous studies have highlighted the fundamental importance of ERM proteins as structural and regulatory components of the cell cortex governing several essential functions such as the generation of surface features such as filopodia, maintenance of cortex-plasmamembrane attachment, bleb retraction, cortical mechanics, and cell migration. The authors performed a series of experiments to comprehensively test each of these functions (including cell migration in 2D surface and 3D matrix in vitro, ex vivo on tumor implants, as well as in vivo) and found that none of these are significantly affected when ERM proteins are downregulated in macrophages. Overall, the paper is solid, the experiments are well-designed and conclusive, and the manuscript is written well.
I have no significant concerns with the study. My only experimental suggestion is related to a previously shown function of ERM protein in macrophages- the ERM proteins play an important role in phagosome maturation in macrophages (Defacque et al., EMBO, 2000; Lars-Peter et al., PNAS, 2006; Mylvaganam et al., Current Biology, 2021). It would be nice if authors could explore this phenotype in their perturbation system.
A minor concern with the study is, as the authors have already pointed out, that ERM proteins may still be required for some functions in macrophages under specific (environmental?) conditions. It is of course impossible to experimentally test all possible conditions that may involve ERMs, however, the authors should include a note on the hypothetical conditions that may require ERMs in macrophages. They should also discuss possible hypothetical reasons why macrophages may have evolved a cortex that does not rely on ERM proteins for specific functions. Overall, a more extended discussion on the role of ERM proteins (or the lack of them) in macrophages is required.
Significance
The manuscript is important on many accounts: The ERM proteins are considered crucial membrane-cytoskeletal linkers in many cellular systems. The study presents a surprising finding that cortical phenomena requiring membrane-cytoskeletal attachment do not essentially need ERM proteins providing a fundamental conceptual advance. The results from this study will also inform both experimental as well as theoretical studies of cortical organization and dynamics in the future. Furthermore, overexpressed mutant forms of ERMs are used as sensors as well as perturbing agents of cortical actin dynamics in many cellular systems. These utilities can now be further substantiated and if required, revised in light of the results from this study.
I am an immune cell biologist specializing in early lymphocyte activation and cytoskeleton dynamics.
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Reply to the reviewers
We would like to thank all reviewers for their careful evaluation of our manuscript and their thoughtful feedback, which we could use to improve its quality significantly.
Reviewer #1 (Evidence, reproducibility and clarity):
Summary: This study addresses the problem of what is the optimal ribosome composition in terms of relative RNA and protein content, to ensure optimal growth rate and minimal energy waste. The RNA-world hypothesis suggests that primitive ribosomes were RNA-only objects, and in fact this would appear to be very advantageous from an energetic point of view, since RNA synthesis requires a much lower energy expenditure than protein synthesis. Yet a large fraction of present-day ribosome mass is protein, ranging from 30% to nearly 70% depending on the organism. The authors hypothesize that one of the main functions of ribosomal proteins is to stabilize the RNA and to protect it against degradation. According to their idea, the fast degradation of a protein-free rRNA would offset the energetic advantage given by its cheaper synthesis. To test the hypothesis, they developed a mathematical model whereby to evaluate the optimal ribosome composition under a number of different conditions.
Major comments: The paper is well-written and very readable. I am not an expert of mathematical modelling, so I cannot go into the details of the model presented. As a biologist, I can say that the conclusion arrived at are reasonable and well-justified.
We thank the reviewer for the positive evaluation.
Perhaps the point of view is rather narrow, since ribosomal proteins are known to be important not only for RNA protection and ribosome stability, but also to ensure the accuracy of decoding and, in certain contexts, to allow the ribosomes to interact with other cellular ligands. The authors make only very slight reference to these questions, so it would be worthwhile to further comment on them.
Thank you for your suggestion. To address it, we expanded the discussion as follows:<br /> "Finally, we need to consider that ribosomal proteins may play other roles in the cells, especially in eukaryotic organisms. Ribosomal proteins participate in translation processes, for example, binding of translation factors, release of tRNA, and translocation. They may also affect the fidelity of translation (Nikolay et al., 2015). Furthermore, they play roles in various cellular processes such as cell proliferation, apoptosis, DNA repair, cell migration and others (Kisly and Tamm, 2023). These additional functions might have conferred evolutionary fitness advantages. Nevertheless, the primary role of ribosomal proteins seems to be stabilization and folding of rRNA (Nikolay et al., 2015; Kisly and Tamm, 2023)."
Furthermore, their explanation of why ribosome composition should be so different in different organisms (e.g. protein-poor bacterial ribosomes versus protein-rich archaeal ones) is not entirely convincing. For instance, they suggest that archaea may have protein-richer ribosomes than bacteria because they live in extreme environments, thus needing a further aid to stabilize the organelle. While this may be a factor, one must point out that non-extremophilic archaea (e.g. methanogens) have protein-rich ribosomes, making it obvious that other factors must be at play.<br />
We appreciate the reviewer's feedback. Ribosome composition is indeed complex and influenced by various factors. While extreme environments (may) contribute to protein-rich ribosomes in archaea, it's important to note that not all archaea share this characteristic. Some, like Halobacteriales, Methanomicrobiales, and Methanobacteriales, have ribosomes with protein content similar to bacteria.
Furthermore, there are species in both archaea and bacteria with low protein content in their ribosomes despite extreme habitats. This suggests that alternative strategies, possibly involving specific sequence variants in the rRNA (Nissley et al., 2023), play a role in stabilizing ribosomes. In our model, these findings would correspond to a decreased kdegmax. However, these sequence variants are not universal.
Amils et al. (1993) suggest that protein-rich ribosomes in archaea are (more) ancient and proteins may have been lost in some species, possibly to favor higher growth rates (and in agreement with our theoretical analysis). An intriguing avenue for further research would be a phylogenetic analysis of archaeal evolution to investigate the emergence of different ribosome compositions.
To address your concerns, we added the following paragraph to the discussion:<br /> "Additionally, some extremophilic organisms, such as the bacteria Chloroflexus aurantiacus or Fervidobacterium islandicum, exhibit ribosomes with lower protein content (approximately 40%) compared to extremophilic archaea (50%). It has been suggested that protein-rich ribosomes can be traced back to the oldest phylogenetic lineages, with some ribosomal proteins being lost over time (Amils et al., 1993; Acca et al., 1993). Organisms with lower protein content in their ribosomes may have evolved alternative strategies to thrive in extreme conditions. Examples of such strategies include the presence of specific rRNA sequence variants or base modifications, as recently discussed by Nissley et al. (2023).
Moreover, certain archaeal species, such as those from Methanobacteriales or Halobacteriales, have transitioned to milder environmental conditions and subsequently shed unnecessary ribosomal proteins (Acca et al., 1993; Amils et al., 1993).
To gain a comprehensive understanding of ribosome evolution in response to changing conditions, a thorough phylogenetic analysis is warranted. This analysis should be complemented by measurements of growth rate, translation rate, RNA degradation rate, among other parameters, to delineate the order of protein loss or gain, and the emergence of sequence variations and base modifications."
Minor comments: none in particular. Referencing is adequate, text is clear and the figures are clear and well-organized.
Thank you.
Reviewer #1 (Significance):
As I stated above, the main weakness of this study may be that it concentrates overwhelmingly on a single problem, i.e. the energetic cost of adding proteins to an RNA-only ancestral ribosome. On the other hand, this is a question seldom addressed when talking about ribosome composition, which indeed makes this paper valuable and interesting. The authors expand and advance a previous study of the same kind (to which they make ample reference).
Although rather specialized, I think this paper, in its general conclusions, may be of interest to most of those working in the field of protein synthesis and ribosome evolution.
Referee's keywords: archaea, ribosome evolution, translation, translation initiation
Reviewer #2 (Evidence, reproducibility and clarity):
The authors explore a mathematical model to rationalize the variable RNA content in ribosomes across species. The mathematical model particularly considers the idea that the protein-to-RNA ratio in ribosomes emerges as a consequence of faster rRNA than r-protein synthesis coupled with a faster degradation of rRNA. This is an interesting analysis. The idea is well explained and the math of the model is overall well explained. Overall, I thus support publication of this analysis.
We thank the reviewer for the positive evaluation.
However, while reading the manuscript I was continuously wondering about two major aspects which, I suggest, should be considered more prominently in the text:
- How clear is it that rRNA is more unstable than r-protein?
- Why should the translation rate (the speed with which ribosomes assemble new proteins) not be highly dependent on the ribosome-to-protein ratio (with some intermediate ratio ensuring efficient synthesis and efficient translation?
Currently these points are considered briefly in the discussion part. I suggest that these points should at least be discussed more prominently in the introduction. I further appreciate any more detailed thoughts the authors have on these questions.
Finally, I think the discussion section would benefit strongly from a more detailed consideration of possible future experiments. Which data is needed to probe the idea? What types of experiments could be performed to probe the model.
We added a paragraph to the discussion with suggestions for experiments:<br /> "There are still many open questions about ribosome biogenesis and evolution. Our model could guide future experiments. There are a few studies that assessed the effect of individual rP deletions in E. coli, for example mutation in S10 increased RNA degradation (Kuwano et al., 1977), and mutation in L6 lead to disrupted ribosomal assembly (Shigeno et al., 2016). A systematic knock-out screen of all ribosomal proteins could be done (as in Shoji et al. (2011)), complemented with quantification of RNA degradation and misfolding.
In case of extremophilic organisms with protein-rich ribosomes, temperature sensitivity could also be assessed. We would expect that deletion of the extra proteins would cause growth defects only at high temperatures.
Furthermore, after removal of proteins from archaeal protein-rich ribosomes, laboratory evolution could be performed to see whether growth rate increases beyond wild-type.
Comprehensive datasets, akin to the work of Bremer and Dennis in 2008 for E. coli, should be generated for non-standard organisms by measuring various parameters such as transcription and translation rates, ribosome and RNAP activities, and other relevant factors.
Finally, as mentioned earlier, phylogenetic analysis or ribosome evolution across different species and environments could be done."
More detailed comments:
Regarding i: rRNA is pretty stable compared to other RNA types in the cell. The authors argue it is unstable. The specific question then seems to become how stable rRNA is compared to r-protein? Generally, proteins are also stable, but what data is available to support that r-proteins are more stable than rRNA?
While rRNA that is already integrated into a ribosome is stable, nascent RNA may be susceptible to degradation (Jain, 2018). It has been observed that even during exponential growth, some rRNA is degraded (Gausing, 1997; Jain 2018) and the degradation rate increases if ribosome assembly is delayed (Jain, 2018). This suggests that rRNA that is synthesized in excess cannot be stored and used later. Furthermore, when rRNA is overexpressed in excess of rPs, it is rapidly degraded (half life 15-70 min) (Siehnel and Morgan, 1985).
On the other hand, the turnover of proteins is negligible (Bremer and Dennis 2008), and most ribosomal proteins can exist in a free form without RNA. For example, under starvation/in stationary phase, rRNA is degraded, but most proteins are stable and can be reused later (Reier et al., 2022; Deutscher, 2003).
The precise mechanisms of the rRNA instability are not clear. The simplest explanation is that rRNA that is not protected by rPs is attacked by RNases. Another option is that rRNA without proteins is difficult to fold and can get trapped in misfolded states. These are then degraded as a part of quality control. The model developed in this paper allows for both of these mechanisms.
We added these references to the discussion:<br /> "In order to explain a mixed (RNA+protein) ribosome, we consider rRNA degradation in our extended model, thereby increasing the costs for RNA synthesis. While rRNA that is already integrated into a ribosome is stable, nascent RNA may be susceptible to degradation (Jain, 2018). Indeed, it has been experimentally observed that even at maximum growth rate, 10% of newly synthesized rRNA is degraded (Gausing, 1977), and the degradation rate increases if ribosome assembly is delayed (Jain, 2018). Furthermore, when rRNA is overexpressed in excess of rPs, it is rapidly degraded (Siehnel and Morgan, 1985). Due to the extremely high rates at which rRNA is synthesized, errors become inevitable, necessitating the action of quality control enzymes such as polynucleotide phosphorylase (PNPase) and RNase R to ensure ribosome integrity (Dos Santos et al., 2018). The absence of the RNases results in the accumulation of rRNA fragments, ultimately leading to cell death (Cheng and Deutscher, 2003; Jain, 2018).
In contrast, protein turnover is negligible (Bremer & Dennis, 2008), and most ribosomal proteins can exist without rRNA and can be reused (Reier et al., 2022; Deutscher, 2003). Therefore, we do not consider protein degradation in our model."
Regarding ii: Building on their model results, the authors rationalize the highly varying RNA-to-protein ratio in ribosomes across species. The model considers a non-varying rate with which ribosomes synthesize new proteins. This is briefly discussed in the discussion section. However, this appears to be a major assumption that, I think, should be stated clearly stated earlier in the text, including the abstract and introduction. Second, I wonder how the authors then rationalize variations in translation rate across species. Translation rates and the speeds with which ribosomes are varying strongly across species (indicated for example well by the change in the slope between ribosome content/rRNA and growth rate - slope in Fig. 2A). Why could the rRNA-to-protein ratio not be important in playing a role here?
We decided not to consider the effect of rRNA/protein ratio in ribosomes on translation rate mainly because it is not clear in what way it affects it. Proteins are better catalysts than rRNA. Yet, eukaryotic ribosomes which have higher protein content, have lower translation rates. For archaea and mitochondria, we were not able to find data but it is unlikely that the translation rates are faster because the growth rates are not faster.
We added a paragraph to the introduction that explains our assumption:<br /> "We focus on the primary role of ribosomal proteins, which is stabilizing rRNA (by preventing its degradation or misfolding).
Ribosome protein content might also affect other parameters, such as translation rate. Proteins are generally better catalysts than RNA (Jeffares et al., 1998), but the ribosome's catalytic core is formed by rRNA (Tirumalai et al., 2021) and operates at a relatively slow catalytic rate compared to typical enzymes. This suggests that there is little evolutionary pressure to increase the catalytic rate. Furthermore, ribosomes with the lowest protein content, like the E. coli ribosome, exhibit the highest translation rates (Bonven and Gulløv, 1979; Hartl and Hayer-Hartl, 2009; Bremer and Dennis, 2008). Therefore, we do not consider the impact on translation rate in this study."
And a sentence to the abstract:<br /> "In this study, we develop a (coarse-grained) mechanistic model of a self-fabricating cell and validate it under various growth conditions. Using resource balance analysis (RBA), we examine how the maximum growth rate varies with ribosome composition, assuming that all kinetic parameters remain independent of ribosome composition."
More minor point, but I was also not sure about the justification that ribosome mass is constant (line 111). The mass of an amino acid and a nucleotide is quite different. Why should overall mass matter, and not for example the number of amino acids and proteins. I think it also would be good here to motivate the assumption better early on instead of commenting on it in the discussion section.
Thank you for your suggestion. We agree with the reviewer that we should make our assumption of keeping the ribosome mass constant, which we used for simplicity, clearer from the beginning. Therefore, we have added the following statement to the introduction:<br /> "For simplicity, we assume a constant ribosome mass."
Reviewer #2 (Significance):
Protein synthesis by ribosomes is a major determinant of the rate with which microbes and other fast growing cells accumulate biomass. To better understand cell growth it is thus essential to better understand the makeup of ribosomes. Széliová et al present a mathematical model to entertain the idea that the varying RNA content in ribosomes across species is a consequence of RNA degradation. The model makes clear predictions which can guide future experiments.
Reviewer #4 (Evidence, reproducibility and clarity):
Summary
In this manuscript, Széliová et al. used a simple self-replicating cell model to study why the ribosome consists of both RNA and protein from an economic point of view. Their base model predicts an RNA-only ribosome, which is not surprising since the smaller RNAP has a higher turnover number compared to the larger ribosome. When rRNA instability is included, the model predicts an "RNA+Protein" ribosome. In particular, the predicted ribosome composition is comparable to the measured ribosome composition when strong cooperative binding of ribosomal proteins to rRNA is considered. The authors conclude that the maximal growth rate is achieved by the real ribosome composition when rRNA instability is taken into account.
Major comments:
- The authors modeled the rRNA degradation rate as a function of the concentration of fully assembled ribosomes (equation 5). However, only partially assembled ribosomes are susceptible to RNase, and they make up only a small fraction of total ribosomes. The majority of ribosomes are fully assembled. In addition, the turnover number obtained from Fazal et al. (2015) and used here is the degradation rate of double-stranded RNA, not the fully assembled ribosomes, which have a stable tertiary structure. In my opinion, the rRNA degradation rate should be modeled as a function of the concentration of partially assembled ribosomes (i.e., pre-R in Figure 7) rather than the concentration of fully assembled ribosomes.
We agree with the reviewer that the way we model the process is not entirely biologically accurate. The problem is that even if we add the assembly intermediates, their concentration would be zero as they do not catalyze any reaction (similarly to the metabolites). Therefore, the degradation rate would also always be zero. Given the current modeling setup, the obvious proxy for the intracellular rRNA concentration is the rRNA concentration in the (assembled) ribosome, c_R*(1-x_rP).
- Compared to the work by Kostinski and Reuveni (2020), the authors have made an improvement by avoiding the use of constant ribosome allocation to ribosomal protein (Φ_rP^R) and RNAP (Φ_RNAP^R), allowing these parameters to vary with predicted growth rates (by changing 𝑥_rP). This is indeed important, as bacteria are very likely to adjust these parameters in response to different growth conditions. However, certain other growth rate-dependent parameters are still treated as constants (or treated as nutrient-specific parameters) across predicted growth rates under given conditions. For example, experiments have shown that the fraction of active RNAP (f_RNAP^act) and the ribosome elongation rate (k_R^el) are growth rate-dependent (Bremer and Dennis, 1996). In contrast, when the authors predict the maximum growth rate by changing 𝑥_rP, f_RNAP^act and k_R^el are held constant regardless of the predicted growth rates.
The fraction of active RNAP (f_RNAP^act) was growth-rate dependent in all our simulations (see Table 2), only the fraction of active ribosomes (f_R^act) was kept constant according to Bremer and Dennis, 1996 & 2008.
We decided to keep the elongation rate (k_R^el) constant similar to Scott et al. 2010 (their explanation is in the supplementary material “Correlation [1] and the control of ribosome synthesis”).
We reran the simulations with variable k_R^el. It has no impact on the predictions of optimal ribosome composition. However, the linear dependence of RNA/protein ratio is less steep and predicts an offset at zero growth rate.
We added the results to the supplementary material and the following text to the results section (for the base model):<br /> "…the base model correctly recovers the well-known linear dependence of the RNA to protein ratio and growth rate (Scott et al. 2010), see Figure 2a, but not the offset at zero growth rate, since our model does not contain any non-growth associated processes and we assume constant translation elongation rate kelR as in Scott et al. (2010). At low growth rate, kelR decreases, most likely because of the lower availability of the required substrates (Bremer and Dennis, 2008; Dai et al., 2016). Interestingly, when we use variable kelR, we observe a nonzero offset (Appendix 1, Figure 2)."
and in a later section:<br /> "Using variable or constant kelR has no impact on the predicted optimal ribosome composition. As in the base model, variable kelR leads to predicted non-zero offset of RNA/protein ratio at zero growth rate (Appendix 1, Figure 6)."
- _If amino acids or nucleotides are provided in the media, the cell does not have to synthesize all of them de novo. However, the model assumes that the cell always synthesizes all amino acids or nucleotides de novo for growth on growth on amino acid-supplemented media or on LB. This problem could in principle be solved by assuming very fast kinetics of the metabolic reactions in these media, but that should be discussed in the manuscript. Furthermore, why does the turnover number for EAA depend on the growth rate while that of ENT is constant?<br /> > _
We focused on the “enzyme” EAA because it forms a significant fraction of the proteome. However, for consistency, we now also made ENT turnover number depend on growth rate. It made no significant impact on the simulation results.
We agree with the reviewer that the model is currently very simplified and the enzymes ENT and EAA are used even in the media supplemented with AAs/NTs. However, these enzymes represent lumped pathways that aim to take into account not only AA/NT synthesis but also the different ‘nutrient efficiencies’ of the carbon sources (as in Scott et al. 2010). Therefore, to approximate these effects we increase the kcat of EAA (and now also ENT) with growth rate.
We added a paragraph to the results section to explain these simplifications:<br /> "We used parameters from E. coli grown in six different media. Three of them are rich media (Gly+AA, Glc+AA, LB) where amino acids (and nucleotides) are provided so cells only have to express the corresponding transporters instead of the synthesis pathways. In our model, the enzymes ENT and EAA represent lumped pathways for glycolysis and nucleotide / amino acid synthesis, and we only consider one type of transporter. Therefore, to model the changing `nutrient quality' of the different media (inspired by Scott et al. 2010), we assume that turnover numbers of EAA and ENT increase with growth rate."
- All parameters related to transcription (RNAP) and translation (ribosome) used in this manuscript are adopted from Kostinski and Reuveni (2020), which are slightly modified based on Bremer and Dennis' research (1996, 2008). However, the authors changed some of the original parameters or data points, but did not provide explanations for these changes:
(a) The original data depicted a growth rate-dependent translation elongation rate, but Table 2 presents it as a constant value.
Please see the reply to point 2 above.
(b) Figure 2b displays five experimental data points, as opposed to the six data points in the original dataset and other figures in this manuscript.
The values for the transcription rate were taken from Bremer and Dennis’s paper from 1996 which only contains five growth rates. We updated the Figure 2b – it now displays data from Bremer and Dennis 2008 for six growth rates.
(c) The model does not consider the fraction of RNAP transcribing rRNA (Φ_rRNA^RNAP), except in Appendix Figure 4. In the original data (Bremer and Dennis 1996), the fraction of RNAP transcribing rRNA increases dramatically with growth rate; however, in this study, it remains constant at 1.
Our goal was to keep the model as simple as possible and keep the number of required parameters to a minimum. We only included the figure in the supplementary material because it does not change the conclusions, even though it makes the predictions quantitatively better. In the future we would like to achieve this improvement by expanding the model (with mRNA, tRNA, non-specific RNAP binding to DNA etc.). We added a sentence to the discussion to point out again how the results are affected if Φ_rRNA^RNAP is included, and how this parameter could be mechanistically included in the model in the future.
"Furthermore, incorporating other types of RNA (mRNA, tRNA) and energy metabolism, or even constructing a genome-scale RBA model (Hu et al., 2020), will likely lead to more quantitative predictions of fluxes and growth rate. A strong indication of this is that including a variable RNAP allocation into the model leads to quantitatively better predictions (see Appendix 1, Figure 5). Therefore, in the future, we aim to model RNAP allocation mechanistically. This will involve for example adding other RNA species (mRNA, tRNA), and considering non-specifically bound RNAP which is a significant fraction of RNAP (Klumpp and Hwa, 2008)."
Furthermore, Φ_rRNA^RNAP was first introduced in line 205 but was not explained until line 337.
We added an explanation to the sentence in line 205:<br /> "If we consider RNAP allocation to rRNA (k_RNAP^el^bar = k_RNAP^el f_act^RNAP Φ_rRNA^RNAP, where Φ_rRNA^RNAP is the fraction of RNAP allocated to the synthesis of rRNA), the results get closer to the experimental data (Appendix 1, Figure 5)."
The value(s) of Φ_rRNA^RNAP for Appendix Figure 4 are also missing from this manuscript.
We added the missing values to the figure caption.
- How, exactly, is the unit of flux converted to mmol g-1 h-1?
We are not exactly sure what the reviewer means by this question. As an example of unit conversion, we provide an explanation for the conversion of literature RNAP fluxes. The RNAP fluxes predicted by the model are in mmol g^-1 h^-1. The RNAP fluxes in Bremer and Dennis (2008) were in nt min^-1 cell^-1. To convert them to mmol g^-1 h^-1, we used the values of dry mass/cell from Bremer and Dennis (2008) and the number of nucleotides in rRNA (the stoichiometric coefficient n_rRNA). The code for the conversion is available on GitHub (https://github.com/diana-sz/RiboComp) in the script fluxes_vs_growth_rate.py.
- What is the (dry) mass constraint and how is it defined? In the manuscript, both the second equation in line 101 and the bottom row of Table 1 are dry mass constraint(s). Why are they different? Furthermore, why is the right-hand side of the second equation in line 101 a dimensionless 1, and how does the last row of Table 1 result in the unit of growth rate, time^(-1)?
These are two forms of the same constraint. We added a paragraph to the methods section that explains how to convert the equations (capacity constraints, dry mass constraint) into the form in Table 1.
In the first form of the equation, ⍵Tc = 1, the units of ⍵ are g/mmol, and the units of c are mmol/g, so they cancel out.
The rows in Table 1 are multiplied by the vector of fluxes, so we get ⍵C [g/mmol] * vIC [mmol/gh] = μ [1/h].
- The concentrations of all components that serve as "substrates" will be zero when growth rate is maximized, as these molecules do not catalyze any reactions, nor do they influence reaction kinetics in the model. These "0" concentration components are C, AA, NT, rP, and rRNA. Why are these concentrations even included in the model?
The reviewer is correct in pointing out that these species have zero concentrations at maximum growth, and it would be possible to simplify the model accordingly. However, we have chosen not to merge these reactions to maintain clarity in distinguishing between metabolic and macromolecular synthesis processes. Additionally, while we currently use the model to predict optimal behavior, it is not inherently limited to this purpose, as it can equally describe sub-optimal states (as in Figure 2b). Finally, if needed, we can easily introduce minimum concentration constraints (e.g. obtained from measurements) for any of these species without affecting our overall arguments.
Minor comments:
- Questions regarding Figure 2:
(a) The explanation of Figure 2a is unclear. Intuitively, I assumed that it was a comparison between model predictions and experimental data, with the points representing experimental data and the line representing predictions; and the authors wrote in the figure legend "The points represent maximum growth rates in six experimental conditions". However, the growth rates shown in the figure do not match the original experimental data. Are all the data in the figure predictions?
Yes, the points are predictions and the line is a linear fit. We changed the figure caption as follows:<br /> "The model predicts a linear relationship between RNA to protein ratio and growth rate. The points represent the predicted maximum growth rates in six experimental conditions (Table 2). The line is a linear fit."
(b) Figure 2b is difficult to understand. This figure shows the non-optimal solutions of the model. It is unclear how these solutions are achieved and why there are three lines in the figure.
We expanded the figure caption to make it clearer:<br /> "Alternative RNAP fluxes at different non-optimal growth rates in glucose minimal medium. These are obtained by varying the growth rate step by step from zero to maximum and enumerating all solutions (elementary growth vectors as defined in Müller et al. (2022)) for each growth rate. The grey and blue lines are the alternative solutions. The blue line corresponds to solutions, where rRNA and ribosomes do not accumulate (constraints
rRNA' and
cap R' in Table 1 are limiting)."- Table 1 is also difficult to understand. While the stoichiometric constraints can be easily derived, the capacity constraints and the dry mass constraint cannot be easily derived from related equations from the text.
We added a paragraph into the methods section that explains how to convert the equations (capacity constraints, dry mass constraint) into matrix form.
- As the authors ask a question in the title, they should provide an explicit answer in the abstract.
We added a sentence to the abstract:<br /> "Our model highlights the importance of RNA instability. If we neglect it, RNA synthesis is always ``cheaper' than protein synthesis, leading to an RNA-only ribosome at maximum growth rate. However, when we account for RNA turnover, we find that a mixed ribosome composed of RNA and proteins maximizes growth rate."
- The authors should cite a seminal modeling paper, which was the first to examine resource allocation in simplified self-replicating cell systems (Molenaar et al. 2009, Molecular Systems Biology 5:323).
The citation was added.
- The meaning of v is not consistently defined throughout the manuscript. It refers to the fluxes of enzymatic reactions in some instances, but in other contexts, it refers to the fluxes of the entire network of enzymatic reactions and protein synthesis reactions (Figure 1, Equation 1, and Line 386).
We have made the notation more consistent. When we refer to the fluxes of the entire network we now use v_tot instead of v.
- Line 85, it might be difficult to interpret "RNAP fluxes" as the flux of rRNA synthesis without reading the subsequent text.
_We added the explanation in brackets.<br /> "_We validate the model by predicting RNAP fluxes (rRNA synthesis fluxes)."
- Typo in line 102-103. "...protein fluxes 𝒘" → "...protein synthesis fluxes 𝒘".
Thank you for spotting that, we added the missing word.
- Line 104, f_RNAP^act and f_R^act are not explained in the text; and their biological significance cannot be understood from their names in Table 2 ("RNAP activity" and "Ribosome activity").
We added a sentence that explains these parameters:<br /> "f_RNAP^act is the fraction of actively transcribing RNAPs, and f_R^act is the fraction of actively translating ribosomes."
- Notion "**" in Table 2. The coupling between transcription and translation means the coupling of "mRNA" transcription and translation, not rRNA. At least in E. coli, the transcription rate of rRNA is faster than that of mRNA.
The transcription rate of the archaeal RNAP was determined in vitro. To our knowledge, data for transcription rates of rRNA vs. mRNA in vivo are not available. Therefore, the translation rate is only a very rough estimate.
- Is the citation correct in line 136? I didn't find related information in Bremer and Dennis' paper after a quick scan.
We corrected the citation. Additionally, we added references that indicate that if rRNA is transcribed in excess of available r-proteins, it gets rapidly degraded:<br /> "In fact, the accumulation of free rRNA in a cell is biologically not realistic as it is bound by rPs already during transcription (Rodgers and Woodson, 2021). Furthermore, if rRNA is expressed in excess of rPs, it is rapidly degraded (Siehnel and Morgan, 1985)."
- Lines 136-138. The statement is not accurate, as the fraction of inactive ribosomes increases with decreasing growth rate in E. coli (Dai et al. 2016, Nat Microbiol 2, 16231). If the studied growth rates are relatively high, it is acceptable to use a constant active ribosome fraction as an approximation, but this approximation should be made explicit.
We used the fractions of active ribosomes as reported in Bremer and Dennis, 2008 which are constant between growth rates of 0.4-2.1 1/h. In Dai et al. 2016, it was similarly observed that above the growth rate of ~0.5 1/h, the active fraction is quite constant. We rephrase the text to make it more accurate:<br /> "For the growth rates studied here (0.4-2.1 1/h), the fraction of inactive ribosomes stays roughly constant at 15-20% (Bremer and Dennis, 1996, 2008; Dai et al., 2016). In our model, we have already incorporated this fraction using the effective translation elongation rate (k_R^el^bar = k_R^el*f_R^act). However, below the growth rate of ~0.5 1/h, the fraction of active ribosomes rapidly decreases (Dai et al. 2016)."
- The citation in line 142 is not accurate. It should be (Bremer and Dennis, 1996).
We corrected the citation.
- Lines 192-193: "six" different growth media, not five.
Thank you for pointing that out, we corrected it.
- Line 287: The statement "... translation rate does not increase in ribosomes with a higher protein content" could be misinterpreted as discussing translation elongation rate changes with different protein content in ribosomal protein mutant strains in a given species. It should be rephrased to remove ambiguity.
We rephrased the sentence as follows:<br /> "…translation rate does not increase in ribosomes from different species which have higher protein content."
- Parameters for the three panels in Figure 8 are missing.
The parameters used for mitochondria are the same as for E. coli in glucose minimal media. The only difference is that a fraction of rPs can be imported. We added a sentence to the figure caption to clarify this:<br /> "The model can be adjusted to predict mitochondrial protein-rich ribosome composition. All parameters used for the simulation of mitochondria are the same as for E. coli in glucose minimal media, except a fraction of rPs can be imported for free from the cytoplasm and does not have to be synthesized. For simplicity, we assumed that 1/3 of rPs are imported. (In reality, almost all rPs are imported, but mitochondria make additional proteins to provide energy for the whole cell.)"
Reviewer #4 (Significance):
Strengths: Why the ribosome is composed of RNA and protein parts is a fundamental biological question. This manuscript proposes a very interesting hypothesis, arguing that the mixed ribosome composition results from rRNA instability. To test their hypothesis, the authors parameterize a simplified self-replicating cell model with realistic parameters. The model is first developed/parameterized for E. coli, and it could be easily adapted to other organisms with higher ribosomal protein content.
Limitations: The main limitations of this manuscript lie in the development of the model, especially the modeling of rRNA degradation and the use of constant values for growth rate-dependent parameters.
Advances: (1) This manuscript proposes a new hypothesis that rRNA instability is a universal factor that influences the ribosome composition across living organisms. (2) Compared to Kostinski and Reuveni's work, the authors have made certain improvements by including adjustable ribosome allocation to RNA and ribosomal protein when maximizing growth rate, which may lead to more realistic conclusions.
Audience: This work will be of interest to people in the field of theoretical biology, computational biology, and evolution, as well as to researchers studying ribosome structure and function.
Areas of expertise: Microbial systems biology, computational biology, and prokaryotic genomics.
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Referee #4
Evidence, reproducibility and clarity
Summary
In this manuscript, Széliová et al. used a simple self-replicating cell model to study why the ribosome consists of both RNA and protein from an economic point of view. Their base model predicts an RNA-only ribosome, which is not surprising since the smaller RNAP has a higher turnover number compared to the larger ribosome. When rRNA instability is included, the model predicts an "RNA+Protein" ribosome. In particular, the predicted ribosome composition is comparable to the measured ribosome composition when strong cooperative binding of ribosomal proteins to rRNA is considered. The authors conclude that the maximal growth rate is achieved by the real ribosome composition when rRNA instability is taken into account.
Major comments:
- The authors modeled the rRNA degradation rate as a function of the concentration of fully assembled ribosomes (equation 5). However, only partially assembled ribosomes are susceptible to RNase, and they make up only a small fraction of total ribosomes. The majority of ribosomes are fully assembled. In addition, the turnover number obtained from Fazal et al. (2015) and used here is the degradation rate of double-stranded RNA, not the fully assembled ribosomes, which have a stable tertiary structure. In my opinion, the rRNA degradation rate should be modeled as a function of the concentration of partially assembled ribosomes (i.e., pre-R in Figure 7) rather than the concentration of fully assembled ribosomes.
- Compared to the work by Kostinski and Reuveni (2020), the authors have made an improvement by avoiding the use of constant ribosome allocation to ribosomal protein (Φ_rP^R) and RNAP (Φ_RNAP^R), allowing these parameters to vary with predicted growth rates (by changing 𝑥_rP). This is indeed important, as bacteria are very likely to adjust these parameters in response to different growth conditions. However, certain other growth rate-dependent parameters are still treated as constants (or treated as nutrient-specific parameters) across predicted growth rates under given conditions. For example, experiments have shown that the fraction of active RNAP (f_RNAP^act) and the ribosome elongation rate (k_R^el) are growth rate-dependent (Bremer and Dennis, 1996). In contrast, when the authors predict the maximum growth rate by changing 𝑥_rP, f_RNAP^act and k_R^el are held constant regardless of the predicted growth rates.
- If amino acids or nucleotides are provided in the media, the cell does not have to synthesize all of them de novo. However, the model assumes that the cell always synthesizes all amino acids or nucleotides de novo for growth on growth on amino acid-supplemented media or on LB. This problem could in principle be solved by assuming very fast kinetics of the metabolic reactions in these media, but that should be discussed in the manuscript. Furthermore, why does the turnover number for EAA depend on the growth rate while that of ENT is constant?
- All parameters related to transcription (RNAP) and translation (ribosome) used in this manuscript are adopted from Kostinski and Reuveni (2020), which are slightly modified based on Bremer and Dennis' research (1996, 2008). However, the authors changed some of the original parameters or data points, but did not provide explanations for these changes:
(a) The original data depicted a growth rate-dependent translation elongation rate, but Table 2 presents it as a constant value.
(b) Figure 2b displays five experimental data points, as opposed to the six data points in the original dataset and other figures in this manuscript.
(c) The model does not consider the fraction of RNAP transcribing rRNA (Φ_rRNA^RNAP), except in Appendix Figure 4. In the original data (Bremer and Dennis 1996), the fraction of RNAP transcribing rRNA increases dramatically with growth rate; however, in this study, it remains constant at 1. Furthermore, Φ_rRNA^RNAP was first introduced in line 205 but was not explained until line 337. The value(s) of Φ_rRNA^RNAP for Appendix Figure 4 are also missing from this manuscript.<br /> 5. How, exactly, is the unit of flux converted to mmol g-1 h-1?<br /> 6. What is the (dry) mass constraint and how is it defined? In the manuscript, both the second equation in line 101 and the bottom row of Table 1 are dry mass constraint(s). Why are they different? Furthermore, why is the right-hand side of the second equation in line 101 a dimensionless 1, and how does the last row of Table 1 result in the unit of growth rate, time^(-1)?<br /> 7. The concentrations of all components that serve as "substrates" will be zero when growth rate is maximized, as these molecules do not catalyze any reactions, nor do they influence reaction kinetics in the model. These "0" concentration components are C, AA, NT, rP, and rRNA. Why are these concentrations even included in the model?
Minor comments:
- Questions regarding Figure 2:
(a) The explanation of Figure 2a is unclear. Intuitively, I assumed that it was a comparison between model predictions and experimental data, with the points representing experimental data and the line representing predictions; and the authors wrote in the figure legend "The points represent maximum growth rates in six experimental conditions". However, the growth rates shown in the figure do not match the original experimental data. Are all the data in the figure predictions?
(b) Figure 2b is difficult to understand. This figure shows the non-optimal solutions of the model. It is unclear how these solutions are achieved and why there are three lines in the figure.<br /> 2. Table 1 is also difficult to understand. While the stoichiometric constraints can be easily derived, the capacity constraints and the dry mass constraint cannot be easily derived from related equations from the text.<br /> 3. As the authors ask a question in the title, they should provide an explicit answer in the abstract.<br /> 4. The authors should cite a seminal modeling paper, which was the first to examine resource allocation in simplified self-replicating cell systems (Molenaar et al. 2009, Molecular Systems Biology 5:323).<br /> 5. The meaning of v is not consistently defined throughout the manuscript. It refers to the fluxes of enzymatic reactions in some instances, but in other contexts, it refers to the fluxes of the entire network of enzymatic reactions and protein synthesis reactions (Figure 1, Equation 1, and Line 386).<br /> 6. Line 85, it might be difficult to interpret "RNAP fluxes" as the flux of rRNA synthesis without reading the subsequent text.<br /> 7. Typo in line 102-103. "...protein fluxes 𝒘" → "...protein synthesis fluxes 𝒘".<br /> 8. Line 104, f_RNAP^act and f_R^act are not explained in the text; and their biological significance cannot be understood from their names in Table 2 ("RNAP activity" and "Ribosome activity").<br /> 9. Notion "**" in Table 2. The coupling between transcription and translation means the coupling of "mRNA" transcription and translation, not rRNA. At least in E. coli, the transcription rate of rRNA is faster than that of mRNA.<br /> 10. Is the citation correct in line 136? I didn't find related information in Bremer and Dennis' paper after a quick scan.<br /> 11. Lines 136-138. The statement is not accurate, as the fraction of inactive ribosomes increases with decreasing growth rate in E. coli (Dai et al. 2016, Nat Microbiol 2, 16231). If the studied growth rates are relatively high, it is acceptable to use a constant active ribosome fraction as an approximation, but this approximation should be made explicit.<br /> 12. The citation in line 142 is not accurate. It should be (Bremer and Dennis, 1996).<br /> 13. Lines 192-193: "six" different growth media, not five.<br /> 14. Line 287: The statement "... translation rate does not increase in ribosomes with a higher protein content" could be misinterpreted as discussing translation elongation rate changes with different protein content in ribosomal protein mutant strains in a given species. It should be rephrased to remove ambiguity.<br /> 15. Parameters for the three panels in Figure 8 are missing.
Significance
Strengths: Why the ribosome is composed of RNA and protein parts is a fundamental biological question. This manuscript proposes a very interesting hypothesis, arguing that the mixed ribosome composition results from rRNA instability. To test their hypothesis, the authors parameterize a simplified self-replicating cell model with realistic parameters. The model is first developed/parameterized for E. coli, and it could be easily adapted to other organisms with higher ribosomal protein content.
Limitations: The main limitations of this manuscript lie in the development of the model, especially the modeling of rRNA degradation and the use of constant values for growth rate-dependent parameters.
Advances: (1) This manuscript proposes a new hypothesis that rRNA instability is a universal factor that influences the ribosome composition across living organisms. (2) Compared to Kostinski and Reuveni's work, the authors have made certain improvements by including adjustable ribosome allocation to RNA and ribosomal protein when maximizing growth rate, which may lead to more realistic conclusions.
Audience: This work will be of interest to people in the field of theoretical biology, computational biology, and evolution, as well as to researchers studying ribosome structure and function.
Areas of expertise: Microbial systems biology, computational biology, and prokaryotic genomics.
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Referee #2
Evidence, reproducibility and clarity
The authors explore a mathematical model to rationalize the variable RNA content in ribosomes across species. The mathematical model particularly considers the idea that the protein-to-RNA ratio in ribosomes emerges as a consequence of faster rRNA than r-protein synthesis coupled with a faster degradation of rRNA. This is an interesting analysis. The idea is well explained and the math of the model is overall well explained. Overall, I thus support publication of this analysis. However, while reading the manuscript I was continuously wondering about two major aspects which, I suggest, should be considered more prominently in the text:
i. How clear is it that rRNA is more unstable than r-protein?
ii. Why should the translation rate (the speed with which ribosomes assemble new proteins) not be highly dependent on the ribosome-to-protein ratio (with some intermediate ratio ensuring efficient synthesis and efficient translation?
Currently these points are considered briefly in the discussion part. I suggest that these points should at least be discussed more prominently in the introduction. I further appreciate any more detailed thoughts the authors have on these questions. Finally, I think the discussion section would benefit strongly from a more detailed consideration of possible future experiments. Which data is needed to probe the idea? What types of experiments could be performed to probe the model.
More detailed comments:
Regarding i: rRNA is pretty stable compared to other RNA types in the cell. The authors argue it is unstable. The specific question then seems to become how stable rRNA is compared to r-protein? Generally, proteins are also stable, but what data is available to support that r-proteins are more stable than rRNA?
Regarding ii: Building on their model results, the authors rationalize the highly varying RNA-to-protein ratio in ribosomes across species. The model considers a non-varying rate with which ribosomes synthesize new proteins. This is briefly discussed in the discussion section. However, this appears to be a major assumption that, I think, should be stated clearly stated earlier in the text, including the abstract and introduction. Second, I wonder how the authors then rationalize variations in translation rate across species. Translation rates and the speeds with which ribosomes are varying strongly across species (indicated for example well by the change in the slope between ribosome content/rRNA and growth rate - slope in Fig. 2A). Why could the rRNA-to-protein ratio not be important in playing a role here?
More minor point, but I was also not sure about the justification that ribosome mass is constant (line 111). The mass of an amino acid and a nucleotide is quite different. Why should overall mass matter, and not for example the number of amino acids and proteins. I think it also would be good here to motivate the assumption better early on instead of commenting on it in the discussion section.
Significance
Protein synthesis by ribosomes is a major determinant of the rate with which microbes and other fast growing cells accumulate biomass. To better understand cell growth it is thus essential to better understand the makeup of ribosomes. Széliová et al present a mathematical model to entertain the idea that the varying RNA content in ribosomes across species is a consequence of RNA degradation. The model makes clear predictions which can guide future experiments.
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Referee #1
Evidence, reproducibility and clarity
Summary: This study addresses the problem of what is the optimal ribosome composition in terms of relative RNA and protein content, to ensure optimal growth rate and minimal energy waste. The RNA-world hypothesis suggests that primitive ribosomes were RNA-only objects, and in fact this would appear to be very advantageous from an energetic point of view, since RNA synthesis requires a much lower energy expenditure than protein synthesis. Yet a large fraction of present-day ribosome mass is protein, ranging from 30% to nearly 70% depending on the organism. The authors hypothesize that one of the main functions of ribosomal proteins is to stabilize the RNA and to protect it against degradation. According to their idea, the fast degradation of a protein-free rRNA would offset the energetic advantage given by its cheaper synthesis. To test the hypothesis, they developed a mathematical model whereby to evaluate the optimal ribosome composition under a number of different conditions.
Major comments: The paper is well-written and very readable. I am not an expert of mathematical modelling, so I cannot go into the details of the model presented. As a biologist, I can say that the conclusion arrived at are reasonable and well-justified. Perhaps the point of view is rather narrow, since ribosomal proteins are known to be important not only for RNA protection and ribosome stability, but also to ensure the accuracy of decoding and, in certain contexts, to allow the ribosomes to interact with other cellular ligands. The authors make only very slight reference to these questions, so it would be worthwhile to further comment on them.<br /> Furthermore, their explanation of why ribosome composition should be so different in different organisms (e.g. protein-poor bacterial ribosomes versus protein-rich archaeal ones) is not entirely convincing. For instance, they suggest that archaea may have protein-richer ribosomes than bacteria because they live in extreme environments, thus needing a further aid to stabilize the organelle. While this may be a factor, one must point out that non-extremophilic archaea (e.g. methanogens) have protein-rich ribosomes, making it obvious that other factors must be at play.
Minor comments: none in particular. Referencing is adequate, text is clear and the figures are clear and well-organized.
Significance
As I stated above, the main weakness of this study may be that it concentrates overwhelmingly on a single problem, i.e. the energetic cost of adding proteins to an RNA-only ancestral ribosome. On the other hand, this is a question seldom addressed when talking about ribosome composition, which indeed makes this paper valuable and interesting. The authors expand and advance a previous study of the same kind (to which they make ample reference).
Although rather specialized, I think this paper, in its general conclusions, may be of interest to most of those working in the field of protein synthesis and ribosome evolution.
Referee's keywords: archaea, ribosome evolution, translation, translation initiation
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1. General Statements
We thank the reviewers for their comments, and their appreciation of the value and thoroughness of our work in identifying a novel and clinically relevant consequence of centrosome amplification in favoring cell death. The reviewers accurately identify weaknesses of our work which we had also pointed out in the discussion of our manuscript. In particular, we agree as pointed out by Reviewer 1 that direct links between our cellular and clinical observations are difficult to establish given the low level of centrosome amplification observed in the tumor samples. Although multiple hypothesis might explain why preferentially eliminating a small population of cells is beneficial for the patients, we consider that this is out of the scope of this manuscript. However, given that our cellular and clinical observations point in the same direction, we remain confident in the value of presenting them together in this manuscript. We have made it clearer both in the results section and discussion that further work is required to better understand the influence of low levels of centrosome amplification on chemotherapy responses in patients.
We also thank the reviewers for their suggestions to improve the in vitro work we have performed. Our point by point response below lists the experiments we plan to perform, and corrections we have already included in this submitted version. Although Reviewer 3 points out that the molecular mechanism underlying apoptotic priming in cells with centrosome amplification remains a mystery, we argue that the identification of this priming already provides a mechanistic explanation for enhanced chemotherapy responses. Our careful and thorough analysis of these responses, using a diversity of advanced technical approaches was key to achieve this. We were also able to clearly rule out that priming is caused by a previously characterized centrosome amplification consequence, demonstrating its novelty. Reviewer 3’s characterization of our work as “archival” is diminishing to say the least, and we believe multiple aspects of this work will be built upon, even beyond the identification of a molecular mechanism which will of course also be important. Indeed, centrosome amplification is observed in a diversity of healthy cell types (megakaryocytes, B cells, hepatocytes…) and could contribute to the homeostasis of these tissues via apoptotic priming. We predict the translational perspectives of this work also to be important, from the point of view of centrosome amplification in disease, but also in understanding apoptotic priming and responses to BH3-mimetics.
2. Description of the planned revisions
Reviewer 1, Major comments:
- The conclusion that centrosome amplification primes to apoptosis irrespective of mitotic defects is largely based on low resolution timelapse analysis (20x magnification, 10 minute imaging intervals, no tubulin). Imaging at this resolution is likely to miss mitotic defects, reducing the confidence with which this conclusion can be drawn.
We were unsure of the exact point brought up by Reviewer 1 here and we have consulted Reviewer 1 (through Reviews commons) to confirm the revision plan. In Figure 5A, we show that the level of heterogeneity stemming from chromosome instability is lower for PLK4OE than for MPS1 inhibition, and Figure 5D then shows that apoptotic priming only occurs in PLK4OE, and not in response to MPS1 inhibition. Combined, these experiments allow us to conclude that apoptotic priming occurs independently of mitotic defects. Nevertheless, we propose to reproduce the live-imaging of mitosis, increasing the resolution and including tubulin, to better visualize mitotic defects in response to the different mitotic perturbations induced.
- Data from timelapse analysis of DNA content in Fig. 2 are used to conclude that Plk4OE cells are more sensitive to carboplatin due to mitotic defects that occurred without multipolar spindles. However, it is premature to conclude that multipolar spindles were not involved in DNA mis-segregation without visualizing the spindles themselves. While DNA positioning can be used as a proxy for spindle morphology, as performed here, it only reliably detects multipolar spindles when all poles are relatively equal in size and the multipolar spindle is maintained throughout mitosis. However, the poles in multipolar spindles often differ in size and ability to recruit DNA. Additionally, they often cluster over time, which can preclude their identification when only visualizing DNA, especially at 20x magnification. Compelling evidence that high mis-segregation is occurring without multipolar spindles would require visualizing the spindles and also demonstrating the cause of the increased chromosome mis-segregation. (Are acentric fragments being mis-segregated as lagging chromosomes?)
We agree with Reviewer 1 that the “High mis-segregation” mitotic phenotype we describe is poorly characterized in our original manuscript, and that we cannot formally exclude that multipolar spindles are involved, although we observe this phenotype in similar proportions in PLK4Ctl and PLK4OE. We also agree that identifying the origin of increased chromosome mis-segregation is relevant here. We therefore propose to characterize spindles and mis-segregating chromosomes by imaging fixed mitotic figures upon Carboplatin treatment, staining for Tubulin, centrosomes, and centromeres. This will allow us to better determine if Carboplatin induces the same mitotic phenotypes in PLK4OE and PLK4Ctl cells.
- The images in Fig. 3D and 4D are not of sufficient resolution to support the central conclusion that centrosome amplification primes cells for MOMP. This conclusion is further weakened by the facts that 1) Plk4OE was the only source of centrosome amplification tested and 2) Plk4OE was reported to prime for MOMP in only 2 of 3 cell lines. Potential explanations for the lack of priming in SKOV3 cells should be discussed. Additionally, the sensitization in Fig. S4H-J appears quite modest. (These data are also difficult to see, perhaps because the Plk4Ctl -/+ chemo conditions are overlapping.)
We have made images in Fig. 3D and 4D larger. We hope that this makes our observations of Cytochrome C release (quantified in Fig. 3E and 4E) easier to visualize for readers. We would like to point out that our conclusion that centrosome amplification primes for MOMP in OVCAR8 does not only arise from the assays presented in these figures. In Figures 4A and 4C we show by MTT assays and detection of apoptotic cells by cytometry respectively, that PLK4OE OVCAR8 are sensitized to BH3-mimetic WEHI-539 compared to PLK4Ctl cells. We also show this priming using BH3-mimetic Navitoclax in Fig S4F. Regarding the source of centrosome amplification, we use OVCAR8 cells devoid of the inducible PLK4OE transgene and show that “natural” centrosome amplification also makes OVCAR8 cells more sensitive to WEHI-539 (Fig. S4C). This strongly suggests that priming does indeed stem from centrosome amplification and not from other consequences of PLK4 over-expression. Nevertheless we are currently generating cells to induce centrosome amplification via SAS-6 over-expression, to test an alternative source of centrosome amplification.
We do not claim that this apoptotic priming is a universal consequence of centrosome amplification, as indeed we show that it is not observed for the cell line SKOV3 (Fig. S4F). For now, we do not have a clear hypothesis of the reason for the cell line differences. Initially we hypothesized that p53 status could be in cause because OVCAR8 and COV504 both express mutant p53 whereas p53 protein is completely absent in SKOV3. However we observe that p53 does not seem to affect cell death signaling in OVCAR8 (Fig. S3C-D), making this hypothesis less likely. The 3 cell lines are indeed very different in terms of origin and genomic alterations, making it difficult at this stage to propose an evidence-based explanation of differences in terms of apoptotic priming.
Finally, regarding the sensitization to chemotherapy associated priming presented in Fig. S4H-J, we have made the figures bigger and non-overlapping hoping that this makes them easier to read. Additionally, we agree that the effect of centrosome amplification appears modest. The trypan-blue assays we used have the advantage of being relatively high-throughput, but they only detect a fraction of the killed cells: only cells that are late in the apoptotic process but that haven’t yet been degraded. This makes the assay less sensitive. The general tendency we observe nevertheless proposes an association between apoptotic priming induced by centrosome amplification, and an enhanced sensitivity to a diversity of chemotherapy agents. We propose to confirm this for OVCAR8 cells treated with Olaparib, by performing cytometry experiments staining for AnnexinV and Propidium Iodide in order to increase sensitivity by detecting earlier signs of apoptosis.
Reviewer 2, Major comments:
- The authors state that (Line 133) "the increased multipolarity we observe in presence of the combined chemotherapy is caused by the effect of Paclitaxel on the capacity of cells to cluster centrosomes." Could the authors to back up this claim by reanalyzing the imaging data to look for clustering as a survival mechanism versus inhibition of clustering in Paclitaxel-treated cells? Or indeed test any of the range of available clustering inhibitors directly on PLK4OE and thus prove the contribution of clustering to survival?
We agree that the conclusion that Paclitaxel suppresses centrosome clustering is not sufficiently backed by experimental data. We cannot directly view clustering in the live-imaging experiments we performed, because we are visualizing neither tubulin nor centrosomes. To clarify this point, we will:
- Image fixed mitotic figures of Plk4Ctl and PLK4OE cells untreated or treated with Paclitaxel, staining for tubulin and centrosomes to identify if indeed Paclitaxel increases the proportion of anaphases with multiple poles characterized by the presence of centrosomes. We will use this type of assay as live imaging approaches to film both microtubules and centrosomes will not be feasible within the timing of this revision, but also because paclitaxel responses maybe modified if tubulin dyes are used such as Sir-tubulin.
- We will use HSET inhibitor CW069, to test if this also prevents centrosome clustering.
- If this is the case, we will then test if CW069 also preferentially kills PLK4OE compared to PLK4Ctl by Trypan-blue viability assays.
3. Description of the revisions that have already been incorporated in the transferred manuscript
Reviewer 1, Major comments:
- In its current form, the title suggests that the major role of centrosome amplification in sensitizing to chemotherapy is independent of multipolar divisions. Based on Figure 1, this is misleading. Figure 1D shows that in centrosome amplified cells treated with combination chemotherapy, the most common cause of death is high mis-segregation on multipolar spindles. Modifying the title to "Centrosome amplification favors the response to chemotherapy in ovarian cancer by priming for apoptosis in addition to promoting multipolar division" would more accurately reflect the data.
We agree with Reviewer 1 that our title should include the promotion of multipolar divisions, and have modified the title accordingly.
- Line 191 points out that more Plk4OE cells that were in G1 at the beginning of carboplatin died than Plk4Ctl cells. However, in Fig. 2H-I, it looks like longer G1 durations in the presence of carboplatin led to increased cell death and that the Plk4OE cells happened to spend more time in G1 at the beginning of carboplatin treatment than Plk4Ctl cells did. Is this the case? Quantification of the average time spent in G1 for each group would be helpful.
Upon Carboplatin exposure, G1 length is indeed longer for PLK4OE cells compared to PLK4Ctl cells, as shown in Fig. S2D for complete G1 phases observed after the first mitosis (although the induced lengthening is mild compared to the observed extension of G2 induced upon carboplatin exposure shown in Fig. S2D). The same tendency, although not significant, is observable for cells in G1 at the beginning of carboplatin treatment, although it is harder to conclude because these are not complete G1 phases.
To assess links between G1 phase length and cell fate, we have plotted the length of G1 depending on whether cells live or die, focusing on cells of the second generation for which G1 length is complete. We observed no link between G1 length and cell fate, and have added this figure as Fig. S1E.
Reviewer 1, minor comments:
- The authors cite Fig. 1B when drawing the conclusion that "combined chemotherapy induced a stronger reduction of viable cells produced per lineage in PLK4OE compared to PLK4Ctl". But Fig. 1B shows that combination chemotherapy produced a similar decrease in viable cells per lineage +/- Plk4OE. If anything, the Plk4OE+ cells showed slightly less sensitivity because they proliferated more poorly in the absence of chemotherapy. This is also true for carboplatin sensitivity in Fig. 2D (line 156).
Here our focus is actually more on the proportion of cell death that on the number of viable cells. We agree that the way we wrote this makes it confusing so we have re-written this paragraph to make it clearer.
- Line 202 concludes that Fig. S2H-I shows that Plk4OE doesn't affect recruitment of DNA damage repair factors. The dotted outlines around the nuclei in Fig. S2H-1 make it very difficult to see, but it appears that gH2AX, FancD2, and 53BP1 signals are lower in Plk4OE cells.
We have made images in Fig. S2H bigger and the dotted outlines around nuclear less strong, and we hope this makes the signal easier to see. Strong cell to cell variations in signal make it hard to draw conclusions from images, although we have aimed to present this heterogeneity. The quantifications shown in Fig. S2I however show that there are no differences in Rad51 or FANCD2 recruitment in PLK4OE cells. For 53BP1 however, we observed less recruitment in PLK4OE for one biological replicate (squares in quantification shown in Fig. S2I) but not in the two other replicates. Although there may be some interesting observation here, this difference does not appear sufficiently robust to consider it as relevant in the context of this study.
- The images for "dies in interphase" and "dies in mitosis" in Fig. 1B are suboptimal. Alternative images would be beneficial.
We have modified the images and added timepoints to make the phenotypes clearer. We have also added supplementary movies to better visualize the events (See Movie S1A for cell death events).
- It would be helpful to discuss the clinical relevance of WEHI-539 and Navitoclax.
We have further developed the section of our discussion about the clinical relevance of BH3-mimetics and these drugs.
- The discussion states that "mitotic drugs that limit centrosome clustering have had limited success in the clinic". I am not aware of any drugs that limit centrosome clustering that are suitable for in vivo use and the citation provided does not mention centrosome clustering.
We thank Reviewer 1 for this comment. Indeed, we oversimplified things a bit here, and have rewritten this paragraph. We have however kept the citation because although this reference does not directly mention centrosome clustering, some of the discussed drugs have been shown to kill cancer cells via centrosome unclustering in vitro in other studies.
- The dark purple and black are very difficult to discriminate (Figure 1,2 and S1), as are the light green and light turquoise (Fig. 4A,S4A-B, S4F, S4H).
We changed these colors in the indicated graphs, and also in other figures where they were used. We hope these changes make the figures easier to read.
- Line 246 claims that Fig. S3B shows that p21 and PUMA mildly increase upon carboplatin exposure, but it isn't clear that these increase in a biologically or statistically significant manner.
We have modified this paragraph because indeed it seems unlikely that the differences are statistically or biologically significant.
- The green used to indicate S/G2 in Fig. S2A-B is different in Plk4 Ctrl vs Plk4OE cells.
We thank Reviewer 1 for spotting this and have changed the colors.
- I do not believe that carboplatin + paclitaxel is standard of care treatment for breast or lung cancer, as stated on line 48-49.
Based on the guidelines of the NIH, we believe that these two drugs are indeed used in combination for the treatment of Stage IV non small cell lung cancer, as well as triple-negative breast cancer. (https://www.cancer.gov/types/lung/hp/non-small-cell-lung-treatment-pdq#_48414_toc, https://www.cancer.gov/types/breast/hp/breast-treatment-pdq#_1049).
However, given the complexity and diversity of treatment protocols, we have modified the text so as not to convey the idea that these are the only drugs used.
- This study advances, but does not complete our understanding of centrosome amplification in breast cancer, as stated on line 75.
Agreed and changed.
- Line 297 describes Navitoclax as an "inhibitor of BCL2, BCL-XL and BCL2". (ie BCL2 is listed twice).
Thank you for noticing this, we have corrected this.
- It's not clear why line 120, which refers to effects of combined chemotherapy, cites Fig. S1G-I, which apparently show data from untreated (even without dox?) Plk4Ctrl and Plk4OE cells.
We indeed meant to refer to Fig. 1E-F and have therefore made this change.
- In Fig. S6A, how can the mitotic index be 200%?
We thank Reviewer 1 for noticing the poor labelling of this figure. It is not a percentage of cells we are presenting, but the number of mitotic figures identified in 10 analyzed fields. We have corrected the figure.
Reviewer 2 major comments:
- Fig 3: Results line 229-236 refer to quantification of fragmented nuclei which the authors interpret as poised for apoptosis. Micronuclei are also quantified- do the authors interpret this phenotype as advanced apoptosis? There is no mention of apoptotic bodies in the analysis. I would ask the authors to provide a bank of representative images with explanations to illustrate their interpretation of the range of morphologies - differences between nuclear fragmentation, versus micronuclei versus DNA contained in apoptotic bodies.
The cells we defined as “poised for apoptosis” are cells that release cytochrome C in presence of a pan-caspase inhibitor. These cells are therefore activating mitochondrial outer membrane permeabilization without executing apoptosis. It is then within these cells that we observe different nuclear morphologies, reflecting different behaviors in mitosis rather than apoptosis advancement. Apoptotic bodies are not observed in these cells, because they are actually not executing apoptosis owing to the presence of the pan-caspase inhibitor. We visualized apoptotic bodies only in absence of pan-caspase inhibitor. These are indicated by white arrow-heads, in Fig. 3D which was made bigger for more clarity. We have also added images of nuclei to present the different morphologies we describe in Fig. 3F.
- Although this patient cohort is described in a previous publication, authors should include a cohort description in a table within supplemental for this manuscript: age range of patients, number of patients in each stage, size of tumours, and most relevant to this study, treatment regimens- adjuvant versus neoadjuvant, surgery vs no surgery? How is the cohort selected- sequentially selected? inclusion/exclusion criteria? Statement in abstract "we show that high centrosome numbers associate with improved chemo responses" is too specific as we have no information on the treatment regimens received by the patients (neo or adjuvant chemo versus surgical/radiological interventions?). Maybe treatment response would be more appropriate? Were there any cases of Pathological complete (or even near complete) response in this cohort and if so, what was the CNR in those cases?
We have included the cohort description in Supplementary Table 1. This is a retrospective cohort, so no specific inclusion criteria were applied. Treatment mainly consisted of surgery (100% patients) followed by adjuvant chemotherapy consisting of platinium salts and/or taxanes (84% of patients, 67% treated with both). Despite the wide common ground of treatment (surgery followed by taxanes and/or platinium salts for 84% of patients), we have nevertheless modified the abstract as suggested by Reviewer 2. Regarding complete or near-complete response, there were no such cases in this cohort.
Reviewer 2, minor comments:
Just some minor points on language:<br /> Line 54: Suggest rephrasing of the statement "and this can be favored by centrosome amplification (29) "<br /> Perhaps a word like potentiated instead of favored?<br /> Line 67: Again consider using an alternative to favored "We show that centrosome amplification favors the response to combined Carboplatin and Paclitaxel via multiple mechanisms."<br /> Favored is in fact used throughout the manuscript text- in my opinion this is not a scientific enough term and would consider replacing with alternative.
We have replaced the term favor with more appropriate terms, depending on the context.
4. Description of analyses that authors prefer not to carry out
Reviewer 1, major comments:
- In a previous technical tour de force (Morretton et al, EMBO Mol Med 2022), the Basto lab quantified centriole numbers in the ovarian patient cancer samples analyzed here, and found that the percentage of cells with centrosome amplification in a given ovarian tumor is quite small, only reaching a maximum of 3.2%. It is critical background information to cite that quantification here. This information also begs the question of whether introducing this low rate of centrosome amplification is sufficient to cause a more global apoptotic priming in the sample, as suggested.
We have now included this important background information in our results section. We agree with Reviewer 1 that the low levels of centrosome amplification in tumors may not cause a more global apoptotic priming in the whole tumor. However, based on our findings, this low proportion of cells will most likely be more sensitive to chemotherapy. We cannot affirm for now what will be the consequences of the preferential elimination of these cells. However, given centrosome amplification’s potential to promote malignant behaviors such as genetic instability or invasiveness, we hypothesize that the elimination of these cells may have effects on tumor survival that are not proportional to their numbers. Testing this hypothesis would require many more experiments using in vivo models, which we cannot carry out within the scope of this study.
Reviewer 2, major comments:
- Fig 6: While the authors have already acknowledged this as a weakness of the study, can the patient data really be compared to cell line data on CA because inclusion of CNRs between 1.4 and 2 as "high CNR" is questionable given that this ratio represents a completely normal centrosome complement? Are the authors confident enough in the imaging technique that all centrosomes are being detected? Can the authors justify the inclusion of the 1.4-2 CNR tumours by breaking down individual patient data on response to various treatments? Have the authors tried to analyse the cohort for OS and RFS using only those 9% of tumours exhibiting CA? What does the analyses of Fig 6 and S6 look like with a CNR cut-off of 2 instead of 1.45? Does the re-analysis show a better correlation between CNR and FIGO stage?
At the single-cell level, centrosome amplification is indeed defined as the presence of more than 2 centrosomes per cell. Tumor samples are characterized by heterogeneous centrosome numbers, with some regions showing extensive centrosome loss, and some others showing nuclei associated with either one, two or various levels of centrosome amplification. In such a heterogeneous population of cells, it is therefore not straight-forward to use the cut-off CNR=2 to define tumors with centrosome amplification. We have nevertheless analyzed the clinical parameters using the cut-off for CNR at 2 as proposed. Using this cut-off, High CNR patients still show improved overall survival, but a non-statistically significant extension of time to relapse. There are very few patients with CNR>2 (6 for overall survival and 5 for time to relapse), and therefore we remain unconvinced by the statistical value of such an analysis.
The definition of a cut-off at 1.45 was not arbitrary. We dichotomized the population into two groups using the Classification And Regression Trees (CART) method. Taking into consideration the binary outcome “relapse within 6 months or no relapse within 6 months” this method resulted in the categorization of the cohort into low CNR (£ 1.45) and high CNR (> 1.45). Independently, we also used predictiveness curves to estimate an optimal cut-off parameter for a continuous biomarker such as the CNR. The threshold obtained by this robust methodology was in agreement with CART approach with a cut-off of 1.40.
Dichotomizing the population does not guarantee the identification of significant clinical differences between the generated groups. We therefore analyzed overall survival and time to relapse ex post, and observed that high CNR and low CNR populations differed significantly for both these parameters.
Regarding FIGO stage, given frequent late detection of ovarian cancer, 59% of the cohort is considered at stage III (See Fig. S6C). All patients with CNR>2 are in the group of Stage III, except one which is Stage II. However, no patient with CNR>2 is in Stage IV, arguing that even with a higher CNR cut-off, there is no association between CNR and Figo stage.
- The experimental PLK4 overexpression system is an accepted and clean method to induce CA in vitro. Could the authors comment in the discussion on how they envision CA being induced as a sensitizing agent in the clinical setting to support the translational aspects of their work?
In the clinical setting, we do not suggest to induce centrosome amplification as a sensitization agent. Indeed, centrosome amplification induces multiple phenotypes associated with malignancy (genomic instability, invasiveness). The translational aspects of our work relate more to the detection of centrosome amplification as a potential biomarker of chemotherapy responses, from conventional chemotherapies to BH3-mimetics for which biomarkers are absent. This aspect we have commented on in our discussion.
Reviewer 2, minor comments:
Line 263: "Centrosome amplification primes for MOMP and sensitizes cells to a diversity of chemotherapies." CA primes to one very specific BCL-XL inhibitor in this section so consider modifying the title of the section.
We agree that centrosome amplification makes cells sensitive to a specific BCL-XL inhibitor. However, we nevertheless claim that this very specific priming, can potentiate these cells responses to a diverse range of chemotherapies with different targets (paclitaxel, carboplatin, and olaparib).
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Referee #3
Evidence, reproducibility and clarity
This is a valuable archival paper that catalogues the effects of combined treatment with paclitaxel and carboplatin predominantly on one ovarian cancer cell line, OVCAR8, in which extra centrosomes can be induced by induced overexpression of Plk4. It systematically examines cellular responses to this drug treatment regimen in control and Plk4 overexpressing cells. Together the experiments show that Plk4-mediated formation of extra-centrosomes sensitizes cells to cell death independently of any effect upon spindle multipolarity and chromosome segregation, irregular spindle formation and mitotic errors, and of the DNA damage response. The authors then go on to show that Plk4 over expression results in premature cleavage of Caspase 3 and so favors the apoptotic response. \This appears to be mediate through increased mitochondrial outer membrane permeabilization. The PIDDosome is believed to contribute to apoptosis in the presence of extra centrosomes through a p%£ mediated pathway. However, in this case, apoptosis appears to be independent of p53 and also of the PIDDosome, as show by deleting a key PIDDosome component. The authors are therefore left with a bit of a mystery in terms of providing a mechanistic explanation of their findings.<br /> I do recommend publication of this paper in its present form as the study has been carried out very carefully and it is very important for workers in the field to know what has been tried in attempt to explain the phenomenon of increased cell death following Plk4 overexpression. It does not lead to a new mechanistic discovery but highlights an important phenomenon that we still have to explain.
Significance
valuable archival information
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Referee #2
Evidence, reproducibility and clarity
Summary
The authors' findings suggest that induction of centrosome amplification synergises with and potentiates the cytotoxic effects of standard chemo in epithelial ovarian cancer cell lines via a mechanism involving mitochondrial membrane priming and Cyt C release. CA has differential apoptotic priming effects depending on the cell line context. The authors use single cell analysis to characterise the range of mitotic defects through to cell fate following PLK4 OE and the combination treatments. The studies are extended to an ovarian cancer patient cohort where elevated centrosome numbers are associated with better OS and RFS. These findings have the potential to improve future patient stratification and treatment in EOC in addition to prognosis of treatment response.
Major comments
- The authors state that (Line 133) "the increased multipolarity we observe in presence of the combined chemotherapy is caused by the effect of Paclitaxel on the capacity of cells to cluster centrosomes." Could the authors to back up this claim by reanalysing the imaging data to look for clustering as a survival mechanism versus inhibition of clustering in Paclitaxel-treated cells? Or indeed test any of the range of available clustering inhibitors directly on PLK4OE and thus prove the contribution of clustering to survival?
- Fig 3: Results line 229-236 refer to quantification of fragmented nuclei which the authors interpret as poised for apoptosis. Micronuclei are also quantified- do the authors interpret this phenotype as advanced apoptosis? There is no mention of apoptotic bodies in the analysis. I would ask the authors to provide a bank of representative images with explanations to illustrate their interpretation of the range of morphologies - differences between nuclear fragmentation, versus micronuclei versus DNA contained in apoptotic bodies.
- Fig 6: While the authors have already acknowledged this as a weakness of the study, can the patient data really be compared to cell line data on CA because inclusion of CNRs between 1.4 and 2 as "high CNR" is questionable given that this ratio represents a completely normal centrosome complement? Are the authors confident enough in the imaging technique that all centrosomes are being detected? Can the authors justify the inclusion of the 1.4-2 CNR tumours by breaking down individual patient data on response to various treatments? Have the authors tried to analyse the cohort for OS and RFS using only those 9% of tumours exhibiting CA? What does the analyses of Fig 6 and S6 look like with a CNR cut-off of 2 instead of 1.45? Does the re-analysis show a better correlation between CNR and FIGO stage?
- Although this patient cohort is described in a previous publication, authors should include a cohort description in a table within supplemental for this manuscript: age range of patients, number of patients in each stage, size of tumours, and most relevant to this study, treatment regimens- adjuvant versus neoadjuvant, surgery vs no surgery? How is the cohort selected- sequentially selected? inclusion/exclusion criteria?<br /> Statement in abstract "we show that high centrosome numbers associate with improved chemo responses" is too specific as we have no information on the treatment regimens received by the patients (neo or adjuvant chemo versus surgical/radiological interventions?). Maybe treatment response would be more appropriate? Were there any cases of Pathological complete (or even near complete) response in this cohort and if so, what was the CNR in those cases?
- The experimental PLK4 overexpression system is an accepted and clean method to induce CA in vitro. Could the authors comment in the discussion on how they envision CA being induced as a sensitizing agent in the clinical setting to support the translational aspects of their work?
Minor comments
The manuscript is well written and all data clearly and thoroughly presented.
Just some minor points on language:
Line 54: Suggest rephrasing of the statement "and this can be favored by centrosome amplification (29)"<br /> Perhaps a word like potentiated instead of favored?<br /> Line 67: Again consider using an alternative to favored "We show that centrosome amplification favors the response to combined Carboplatin and Paclitaxel via multiple mechanisms."<br /> Favored is in fact used throughout the manuscript text- in my opinion this is not a scientific enough term and would consider replacing with alternative.<br /> Line 263: "Centrosome amplification primes for MOMP and sensitizes cells to a diversity of chemotherapies." CA primes to one very specific BCL-XL inhibitor in this section so consider modifying the title of the section.
Significance
Overall, this well-written work extends and provides mechanistic detail to understand the role of CA in priming cells for cytotoxicity in response to commonly used chemo agents in the EOC context. It is a thorough study with sound conclusions drawn from the data provided. It also employs a broad range of assays and techniques to explore the hypotheses from every angle. In view of this, this manuscript is a valuable contribution to the literature on the role of CA in ovarian cancer and its treatment.
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Referee #1
Evidence, reproducibility and clarity
In this manuscript, Edwards et al analyze OVCAR8 cells with dox inducible expression of Plk4. Doxycycline treatment induces centrosome amplification in ~80% of cells. 72 hour timelapse analysis of cells with fluorescent chromosomes revealed that cell death after carboplatin+paclitaxel was more common in Plk4OE than Plk4Ctl cells. Cell death was most common after high chromosome mis-segregation/multipolar division, which resulted in death of ~80% of the daughter cells. However, death was also elevated in cells with no or slight mis-segregation when comparing Plk4OE to PlkrCtl (40% vs 12%), suggesting an additional sensitization effect. Plk4OE also increased cell death in carboplatin alone, most notably after high mis-segregation but also to a lesser extent in cells with no or slight mis-segregation. 17% of Plk4OE cells exposed to carboplatin in G1 died in S/G2 vs 6% of Plk4Ctl. This difference did not appear to be due to DNA damage response or PIDDosome activity. Carboplatin caused caspase 3 cleavage and cytochrome C release to a greater extent in Plk4OE than Pl4Ctl cells, suggesting MOMP priming. Plk4OE sensitizes OVCAR8 cells to the BCL-XL inhibitor WEHI-539, and Plk4OE sensitized COV504 but not SKOV3 cells to the less specific inhibitor Navitoclax. In 88 patients with high grade serous ovarian carcinoma, a high (>1.45) centrosome-to-nucleus ratio was associated with increased relapse-free and overall survival. The authors conclude that centrosome amplification primes ovarian cancer cells to chemotherapy independent of mitotic defects.
Major comments
- In its current form, the title suggests that the major role of centrosome amplification in sensitizing to chemotherapy is independent of multipolar divisions. Based on Figure 1, this is misleading. Figure 1D shows that in centrosome amplified cells treated with combination chemotherapy, the most common cause of death is high mis-segregation on multipolar spindles. Modifying the title to "Centrosome amplification favors the response to chemotherapy in ovarian cancer by priming for apoptosis in addition to promoting multipolar division" would more accurately reflect the data.
- In a previous technical tour de force (Morretton et al, EMBO Mol Med 2022), the Basto lab quantified centriole numbers in the ovarian patient cancer samples analyzed here, and found that the percentage of cells with centrosome amplification in a given ovarian tumor is quite small, only reaching a maximum of 3.2%. It is critical background information to cite that quantification here. This information also begs the question of whether introducing this low rate of centrosome amplification is sufficient to cause a more global apoptotic priming in the sample, as suggested.
- The conclusion that centrosome amplification primes to apoptosis irrespective of mitotic defects is largely based on low resolution timelapse analysis (20x magnification, 10 minute imaging intervals, no tubulin). Imaging at this resolution is likely to miss mitotic defects, reducing the confidence with which this conclusion can be drawn.
- Data from timelapse analysis of DNA content in Fig. 2 are used to conclude that Plk4OE cells are more sensitive to carboplatin due to mitotic defects that occurred without multipolar spindles. However, it is premature to conclude that multipolar spindles were not involved in DNA mis-segregation without visualizing the spindles themselves. While DNA positioning can be used as a proxy for spindle morphology, as performed here, it only reliably detects multipolar spindles when all poles are relatively equal in size and the multipolar spindle is maintained throughout mitosis. However, the poles in multipolar spindles often differ in size and ability to recruit DNA. Additionally, they often cluster over time, which can preclude their identification when only visualizing DNA, especially at 20x magnification. Compelling evidence that high mis-segregation is occurring without multipolar spindles would require visualizing the spindles and also demonstrating the cause of the increased chromosome mis-segregation. (Are acentric fragments being mis-segregated as lagging chromosomes?)
- The images in Fig. 3D and 4D are not of sufficient resolution to support the central conclusion that centrosome amplification primes cells for MOMP. This conclusion is further weakened by the facts that 1) Plk4OE was the only source of centrosome amplification tested and 2) Plk4OE was reported to prime for MOMP in only 2 of 3 cell lines. Potential explanations for the lack of priming in SKOV3 cells should be discussed. Additionally, the sensitization in Fig. S4H-J appears quite modest. (These data are also difficult to see, perhaps because the Plk4Ctl -/+ chemo conditions are overlapping.)
- Line 191 points out that more Plk4OE cells that were in G1 at the beginning of carboplatin died than Plk4Ctl cells. However, in Fig. 2H-I, it looks like longer G1 durations in the presence of carboplatin led to increased cell death and that the Plk4OE cells happened to spend more time in G1 at the beginning of carboplatin treatment than Plk4Ctl cells did. Is this the case? Quantification of the average time spent in G1 for each group would be helpful.
Minor comments
- The authors cite Fig. 1B when drawing the conclusion that "combined chemotherapy induced a stronger reduction of viable cells produced per lineage in PLK4OE compared to PLK4Ctl". But Fig. 1B shows that combination chemotherapy produced a similar decrease in viable cells per lineage +/- Plk4OE. If anything, the Plk4OE+ cells showed slightly less sensitivity because they proliferated more poorly in the absence of chemotherapy. This is also true for carboplatin sensitivity in Fig. 2D (line 156).
- Line 202 concludes that Fig. S2H-I shows that Plk4OE doesn't affect recruitment of DNA damage repair factors. The dotted outlines around the nuclei in Fig. S2H-1 make it very difficult to see, but it appears that gH2AX, FancD2, and 53BP1 signals are lower in Plk4OE cells.
- The images for "dies in interphase" and "dies in mitosis" in Fig. 1B are suboptimal. Alternative images would be beneficial.
- It would be helpful to discuss the clinical relevance of WEHI-539 and Navitoclax.
- The discussion states that "mitotic drugs that limit centrosome clustering have had limited success in the clinic". I am not aware of any drugs that limit centrosome clustering that are suitable for in vivo use and the citation provided does not mention centrosome clustering.
- The dark purple and black are very difficult to discriminate (Figure 1,2 and S1), as are the light green and light turquoise (Fig. 4A,S4A-B, S4F, S4H).
- Line 246 claims that Fig. S3B shows that p21 and PUMA mildy increase upon carboplatin exposure, but it isn't clear that these increase in a biologically or statistically significant manner.
- The green used to indicate S/G2 in Fig. S2A-B is different in Plk4 Ctrl vs Plk4OE cells.
- I do not believe that carboplatin + paclitaxel is standard of care treatment for breast or lung cancer, as stated on line 48-49.
- This study advances, but does not complete our understanding of centrosome amplification in breast cancer, as stated on line 75.
- Line 297 describes Navitoclax as an "inhibitor of BCL2, BCL-XL and BCL2". (ie BCL2 is listed twice).
- It's not clear why line 120, which refers to effects of combined chemotherapy, cites Fig. S1G-I, which apparently show data from untreated (even without dox?) Plk4Ctrl and Plk4OE cells.
- In Fig. S6A, how can the mitotic index be 200%?
Significance
The importance of centrosome amplification in cancer has long been debated. The possible effects of extra centrosomes on multipolar divisions are well known. An independent apoptosis-priming effect of additional centrosomes is novel and of interest. However, in their previous manuscript (Morretton et al, EMBO Mol Med 2022), the Basto lab showed that centrosome amplification only occurs in a maximum of 3.2% of cells in a given ovarian cancer. Given the large discrepancy between the rate of centrosome amplification in the models here and in ovarian cancers ({greater than or equal to}80% vs {less than or equal to}3%), it is unclear whether the mechanism of apoptosis priming reported here is at play in a clinical setting. It is unclear whether the low rate of centrosome amplification observed in cancers can predispose response to a particular inhibitor, as suggested, particularly when centrosome amplification in {greater than or equal to}80% of cells 1) only induced apoptosis priming in 2 of 3 cell lines (Fig. S4F) and 2) induced relatively modest drug sensitivity (Fig. S4J). If it were shown in an additional experiment that induction of centrosome amplification in a small minority of cells, as occurs in patient tumors, increases MOMP priming and drug response, this would substantially increase the significance of the study.
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1. General Statements [optional]
The findings presented in this manuscript are original and have not been previously published, nor is the manuscript under consideration for publication by another journal. The authors of this manuscript declare to have no conflicts of interest.
- Description of the planned revisions
We believe that incorporating the suggested corrections and conducting the additional experiments recommended by the reviewers will significantly enhance the quality of this study. These revisions will not only bolster the current conclusions but also broaden the relevance and applicability of our work to a wider scientific audience, extending beyond the field of virology.
As outlined in the following sections, we are fully committed to implementing the experiments proposed by the reviewers and making the necessary modifications to the manuscript in line with their suggestions. Our responses to each specific comment are provided below.
Reviewer #1
Evidence, reproducibility and clarity
Summary: Several target cell entry pathways have been described for different viruses, including endocytic/ fusion pathways, some which are dynamin-dependent.
Here the authors exploited cell lines with multiple dynamin gene disruptions and other cell biological tools, as well as a phenotypic range of previously characterized viruses, to evaluate the relative importance of dynamin and actin for entry of viruses, including SARS-CoV-2.
In cells that lack the serine protease TMPRSS2, dynamin depletion blocked uptake and infection by SARS-CoV-2. Increasing the input virus partially rescued SARS-CoV-2 infection in the absence of dynamin, and both dynamin-dependent and dynamin-independent entry pathways were inhibited by drugs that disrupt actin polymerization.
Examination by electron microscopy indicated that the dynamin-independent endocytic process was clathrin-independent, in that, in the absence of dynamin, the majority of Semliki Forrest Virions were detected in bulb-shaped, non-coated pits. When TMPRSS2 was expressed, SARS-CoV-2 infection was rendered dynamin-independent.
Significance
Overall, the experiments are expertly performed, the results and conclusions are convincing, the text is clearly written and accurately describes the data, and the manuscript makes an important contribution to a complex and important topic in the cell biology of virus infection. It would be reasonable for the authors to publish the manuscript with the current data.
That being said, we have two main questions/comments:
- The authors point out that SFV differs from SARS-CoV-2 in that it required actin only for the dynamin-independent entry. The EM experiments were done with SFV, not with SARS-CoV-2. This raises the question of the relevance for SARS-CoV-2 of the interesting finding that, in the absence of dynamin, SFV associated with non-coated pits.
If the authors had the tools to do similar EM experiments with SARS-CoV-2, it would be nice to include those results. Otherwise, it is fine to discuss/speculate about SARS-CoV-2 regarding this issue.
RESPONSE:As requested by the reviewer, we are currently perform the suggested EM analysis of SARS-CoV-2 entry in the presence and absence of dynamins.
- The authors show that TMPRSS2 allows the original Wuhan strain and Delta Variant of SARS-CoV-2 to bypass the need for dynamin. This is presumably because TMPRSS2 allows SARS-CoV-2 to fuse at the plasma membrane, precluding need for endocytosis altogether. The authors also mention literature claiming that Omicron is more dependent upon endocytosis than the Wuhan and Delta variants. If the authors had data with Omicron it would be really nice to include it.
RESPONSE: We have already conducted this experiment and have incorporated the quantitative results into the updated version of the manuscript, now presented as Figure 8.
There were some minor typos/grammar/other quoted here:
- Ultrastructural analysis by electron microscopy showed that this dynamin-independent endocytic processes - cell injests particles and nutrients by encoulfing them - some viruses have been show
RESPONSE: Thank you for noticing the error. We have modified the text as: “Ultrastructural analysis by electron microscopy showed that this dynamin-independent endocytic processes appeared as 150-200 nm non-coated invaginations that have been shown to be efficiently used by numerous mammalian viruses, including alphaviruses, influenza, vesicular stomatitis, bunya, adeno, vaccinia, and rhinovirus.”.
- The final step of an endocytic vesicle formation culminates with the pinching of vesicle off from the PM into the cytoplasm
RESPONSE: We have modified the sentence as: “The concluding stage of endocytic vesicle formation is marked by the vesicle being pinched off from the plasma membrane and released into the cytoplasm.”
- For other viruses, such as respiratory viruses (This word is a little strange here since influenza was mentioned in the last sentence.)
RESPONSE: Thank you for noticing the error, we have removed the mention to respiratory viruses: “ For other viruses (including coronaviruses), the fusion is triggered by proteolytic cleavage of the spike proteins that, once cleaved, undergo conformational changes leading first to the insertion of the viral spike into the host membrane and, upon retraction, the fusion of viral and cellular membranes9,10.”.
- Viruses that use a receptor that is internalized by dynamin-dependent endocytosis (e.g. CPV and the TfR) (just reminding that TfR is not a virus)
RESPONSE: We have amended the sentence to avoid misunderstandings: “Viruses (e.g. CPV) that use a receptor (e.g. TfR) that are internalized by dynamin-dependent endocytosis cannot efficiently infect cells in the absence of dynamins.”.
- that appeared surrounded by an electron dense coated
RESPONSE: We have corrected the typo: “In MEFDNM1,2 DKO cells treated with vehicle control, TEM analysis revealed numerous viruses at the outer surface of the cells (Figure 4 A), as well as inside endocytic invaginations that were surrounded by an electron dense coat, consistent with the appearance of clathrin coated pits47,48 (CCP) (Figure 4 B).”
- The main virial receptor could be internalized using two endocytic
RESPONSE: We have corrected the typo: “The main viral receptor could be internalized using two endocytic mechanisms, one mainly available in unperturbed cells (e.g. dynamin-dependent), the other activated upon dynamin depletion (i.e. dynamin independent).”
- Virus infection was determined by FACS analysis of virial induced EGFP
RESPONSE: We have corrected the typo: ‘Virus infection was determined by FACS analysis of EGFP (VAVC and VSV), mCherry (SINV) or after immunofluorescence of viral antigens using virus-specific antibodies (IAV X31 and UUKV).”.
Reviewer #2
Evidence, reproducibility and clarity
Summary: Ohja et al. present an interesting study investigating dynamin independent endocytic entry mechanism of viral infection. Using a genetic KO of 2 dynamin isoforms they show impacts on the infection of a range of large and small DNA and RNA viruses.
They go onto show that SARS-CoV-2 may utilise a dynamin independent mechanism of infection that requires an intact actin cytoskeleton.
Significance
This work is of interest to the field of virology and has the potential to answer previously understudied entry mechanisms important for a wide range of viruses. It is a well presented piece of work overall.
Major Comments:
- The abstract does not in my opinion reflect the content of the paper and is too 'SARS-CoV-2' centric. The work involves the use of a range of viruses to first define a mechanism that is applicable to SARS-CoV-2 and I think the abstract and title should reflect this.
RESPONSE: As per the reviewer's request, we will make revisions to the Title and Abstract. As a ‘non SARS-CoV-2-centric’ title we have amended the title to: Multiple animal viruses, including SARS-CoV-2, can infect cells using alternative entry mechanisms.
- In figure 1H the authors postulate that the reduced impact of dyn1,2 KO on SFV infection may be due to the interaction with heparin sulphate proteoglycans. Have the authors considered performing experiments using Heparin to block infection in their KO cells -/+ tamoxifen treatment?
RESPONSE: As per the reviewer's request, we will perform the proposed heparin experiments for SFV.
- In Figure 2 the authors assess infection of a range of viruses in dyn1,2 KO cells showing differential effects in some viruses but not all.
Have the authors confirmed whether tamoxifen treatment and the subsequent KD of dyn1,2 effect surface expression of the entry receptors for the viruses tested?
RESPONSE: Although in general blocking receptor endocytosis results in an increase in its cell surface levels, we agree with the Reviewer that the effect of dynamin depletion on receptors levels should be monitored at least for some of the viruses. To address the question raised by the reviewer, we will monitor the surface expression of SFV receptors VLDLR and ApoER2, and of the CPV receptor TfR in the presence and absence of dynamins.
We have already confirmed that there are no changes in the surface expression of SARS-CoV-2 receptor ACE2 in the absence of dynamin and this new data will be added to Figure 7.
- Additionally in this setting, dyn1,2 KD may impact on post entry steps in the virus life cycle such as the initial establishment of viral replication.
Can the authors either provide evidence as to how they have delineated measurement entry over replication or support their findings with psuedotyped virus-like-particles?
RESPONSE: This is an important point. As suggested by the reviewer, we will perform infection experiments in the presence or absence of Dynamins using VLPs pseudotyped with SFV and VSV spikes.
In addition, several of our experiments already indicate that upon dynamin depletion, the main block in virus infection is at the step of cell entry: 1) Upon DNM-depletion, the decrease in SARS-CoV-2 infection strongly correlates with a proportional block in spike (Figure 5) and virions (Figure 7) endocytosis; 2) exogenous expression of even low levels of the cell surface protease TMPRSS2 rescued SARS-CoV-2 infection in cells devoid of dynamins, indicating that merely by-passing endocytosis restores virus infection; 3) as shown in Figure 1 H for SFV, and in Figure 2 for multiple viruses, increasing the multiplicity of infection increases the number of infected cells, indicating that when virions access the dynamin-independent entry route, cells can be efficiently infected; 4) the infection of both negative strand (i.e. Uukuniemi virus, UUKV, Figure 2 ) and positive strand (i.e. human Rhino virus, HRVA1, Figure S3 D-E) RNA viruses, as well as DNA viruses (i.e. Vaccinia, Figure 2, and Adenovirus-5, Figure S3 B-C) are not affected by dynamin depletion, arguing against a general negative impact of dynamin depletion on cellular protein synthesis or other basic cell functions required for virus replication.
- Figure 3, given the unexpected results with the dynamin inhibitors, could this experiment be repeated with the dyn1-3 triple KD presented in figures 5-8?
RESPONSE: As requested by the reviewer, we will repeat the main inhibitor experiments presented in Figure 3 for SFV also in DNM TKO cells.
- Statistical analysis of imaging data in figure 4 would help with the conclusions.
RESPONSE: We have already performed the requested statistical analysis and modified Figure 4 accordingly.
- Additionally, the authors comment that in the KD cells the viruses were trapped in 'stalled CCPs'. What morphological changes determine this classification?
RESPONSE: As previously reported by Ferguson et al. (Developmental Cell, 2009), who developed the conditional MEF DNM knock out cell models, all CCPs are stalled at 6 days post induction of dynamin depletion. When observed by electron microscopy, stalled CCPs are readily identified by the presence of elongated, membranous narrow neck structures that connects the vesicle to the plasma membrane. We have clarified this description in the manuscript text and indicated the morphological features typical for a ‘stalled’ clathrin coated pit in Figure 4 F (black asterisk and white arrowheads).
- Concerning the SARS-CoV-2 work presented in figures 6-8, the use of exogenous expression of the viral entry receptors ACE2 and TMPRSS2 is a concern.
RESPONSE: While the reviewer appreciates that this is a necessary step to allow entry into their MEF-dyn1-3 KD cells, exogenous receptor expression can result in artificial entry of the virus.
- To support their findings, can the authors perform experiments in either cell lines endogenously expressing ACE-2/TMPRSS2 such as Calu3 or Caco2 and KD dyn1-3 using transient siRNA?
RESPONSE: This experiment poses a challenge due to the inherent difficulty of transfecting Caco2 and Calu3 cells and the potential difficulty of achieving a robust (>80%) simultaneous knockdown of all three dynamin isoforms. This is one of the reasons why we chose the conditional knock out approach. Nevertheless, we are committed to attempting this experiment.
- This approach would also provide more evidence for the role of TMPRSS2 presented in SF5 as the limited expression of this protease limits the robustness of the conclusions one can draw from the data presented.
RESPONSE: We appreciate the reviewer's observation, and to address this concern, we plan to not only perform siRNA knockdown of dynamins in cells with endogenous ACE2 and TMPRSS2 but also endeavor to elevate the expression levels of TMPRSS2 in our MEF DNM1,2,3 TKO ACE2 cells. It's worth noting, however, that this task presents a unique challenge since expression of TMPRSS2, a trypsin-like cell surface protease, leads to cell detachment even when expressed at moderate levels.
Minor comments & typo:
- Introduction paragraph 1 engulfing
RESPONSE: The sentence has been amended: “To gain access into the host cell's cytoplasm where viral protein synthesis and genome replication take place, most animal viruses hijack cell’s endocytic pathways1 by which the cell engulfs particles and nutrients into vesicular compartments. “.
- Pg 13 - typo in 'Figurre 6B'
RESPONSE: The typo has been corrected.
2. Description of the revisions that have already been incorporated in the transferred manuscript
- Regarding the Reviewer 1 request on the use of Omicron variants, we have already conducted the requested experiments and have incorporated the quantitative results into the updated version of the manuscript, now presented as Figure 8.
- Regarding the Reviewer 2 request on the EM data, we have already performed the requested statistical analysis and modified Figure 4 accordingly. We have also clarified the EM descriptions in the manuscript text and indicated the morphological features typical for a ‘stalled’ clathrin coated pit in Figure 4 F (black asterisk and white arrowheads).
3. Description of analyses that authors prefer not to carry out
none
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Referee #2
Evidence, reproducibility and clarity
Summary
Ohja et al present an interesting study investigating dynamin independent endocytic entry mechanism of viral infection. Using a genetic KO of 2 dynamin isoforms they show impacts on the infection of a range of large and small DNA and RNA viruses. They go onto show that SARS-CoV-2 may utilise a dynamin independent mechanism of infection that requires a intact actin cytoskeleton.
Major Comments
The abstract does not in my opinion reflect the content of the paper and is too 'SARS-CoV-2' centric. The work involves the use of a range of viruses to first define a mechanism that is applicable to SARS-CoV-2 and I think the abstract and title should reflect this.
In figure 1H the authors postulate that the reduced impact of dyn1,2 KO on SFV infection may be due to the interaction with heparin sulphate proteoglycans, have the authors considered performing experiments using Heparin to block infection in their KO cells -/+ tamoxifen treatment
In Figure 2 the authors assess infection of a range of viruses in dyn1,2 KO cells showing differential effects in some viruses but not all. Have the authors confirmed whether tamoxifen treatment and the subsequent KD of dyn1,2 effect surface expression of the entry receptors for the viruses tested? Additionally in this setting, dyn1,2 KD may impact on post entry steps in the virus life cycle such as the initial establishment of viral replication. Can the authors either provide evidence as to how they have delineated measurement entry over replication or support their findings with psuedotyped virus-like-particles?
Figure 3, given the unexpected results with the dynamin inhibitors could this experiment be repeated with the dyn1-3 triple KD presented in figures 5-8.
Statistical analysis of imaging data in figure 4 would help with the conclusions. Additionally, the authors comment that in the KD cells the viruses were trapped in 'stalled CCPs'. What morphological changes determine this classification?
Concerning the SARS-CoV-2 work presented in figures 6-8, the use of exogenous expression of the viral entry receptors ACE2 and TMPRSS2 is a concern. While the reviewer appreciates that this is a necessary step to allow entry into their MEF-dyn1-3 KD cells exogenous receptor expression can result in artificial entry of the virus.
To support their findings, can the authors perform experiments in either cell lines endogenously expressing ACE-2/TMPRSS2 such as Calu3 or Caco2 and KD dyn1-3 using transient siRNA. This approach would also provide more evidence for the role of TMPRSS2 presented in SF5 as the limited expression of this protease limits the robustness of the conclusions one can draw from the data presented.
Minor comments
typo: Introduction paragraph 1 engulfing
Pg 13 - typo in 'Figurre 6B'
Significance
This work is of interest to the field of virology and has the potential to answer previously understudied entry mechanisms important for a wide range of viruses. It is a well presented piece of work overall.
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Referee #1
Evidence, reproducibility and clarity
Several target cell entry pathways have been described for different viruses, including endocytic fusion pathways, some which are dynamin-dependent. Here the authors exploited cell lines with multiple dynamin gene disruptions and other cell biological tools, as well as a phenotypic range of previously characterized viruses, to evaluate the relative importance of dynamin and actin for entry of viruses, including SARS-CoV-2. In cells that lack the serine protease TMPRSS2, dynamin depletion blocked uptake and infection by SARS-CoV-2. Increasing the input virus partially rescued SARS-CoV-2 infection in the absence of dynamin, and both dynamin-dependent and dynamin-independent entry pathways were inhibited by drugs that disrupt actin polymerization. Examination by electron microscopy indicated that the dynamin-independent endocytic process was clathrin-independent, in that, in the absence of dynamin, the majority of Semliki Forrest Virions were detected in bulb-shaped, non-coated pits. When TMPRSS2 was expressed, SARS-CoV-2 infection was rendered dynamin-independent.
Significance
Overall, the experiments are expertly performed, the results and conclusions are convincing, the text is clearly written and accurately describes the data, and the manuscript makes an important contribution to a complex and important topic in the cell biology of virus infection.
It would be reasonable for the authors to publish the manuscript with the current data. That being said, we have two questions/comments:
- The authors point out that SFV differs from SARS-CoV-2 in that it required actin only for the dynamin-independent entry. The EM experiments were done with SFV, not with SARS-CoV-2. This raises the question of the relevance for SARS-CoV-2 of the interesting finding that, in the absence of dynamin, SFV associated with non-coated pits. If the authors had the tools to do similar EM experiments with SARS-CoV-2, it would be nice to include those results. Otherwise, it is fine to discuss/speculate about SARS-CoV-2 regarding this issue.
- The authors show that TMPRSS2 allows the original Wuhan strain and Delta Variant of SARS-CoV-2 to bypass the need for dynamin. This is presumably because TMPRSS2 allows SARS-CoV-2 to fuse at the plasma membrane, precluding need for endocytosis altogether. The authors also mention literature claiming that Omicron is more dependent upon endocytosis than the Wuhan and Delta variants. If the authors had data with Omicron it would be really nice to include it.
- There were some minor typos/grammar/other quoted here:
- Ultrastructural analysis by electron microscopy showed that this dynamin-independent endocytic processes
- cell injests particles and nutrients by encoulfing them
- some viruses have been show
- The final step of an endocytic vesicle formation culminates with the pinching of vesicle off from the PM into the cytoplasm
- For other viruses, such as respiratory viruses (This word is a little strange here since influenza was mentioned in the last sentence.)
- Viruses that use a receptor that is internalized by dynamin-dependent endocytosis (e.g. CPV and the TfR) (just reminding that TfR is not a virus)
- that appeared surrounded by an electron dense coated
- The main virial receptor could be internalized using two endocytic
- Virus infection was determined by FACS analysis of virial induced EGFP
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Reply to the reviewers
FULL REVISION
The preprint of this article is uploaded in bioRxiv (doi:10.1101/2023.06.03.543550) (June 2023) and has been previously submitted and revised by peers in Review Commons. We attach the full Review, with a detailed answer to the Reviewers and a new revised version of the manuscript following the Reviewer’s comments and suggestions, adding new data, a new Supplementary figure, as well as a revision of the text and discussion. We are thankful to the reviewers for their helpful comments, which have further clarified some of aspects of our work and overall improved the quality of our manuscript.
All the line’s numbering mentioned in the point by point answer to the reviewers refer to the converted pdf of the revised manuscript, including the main figures.
Answers to Reviewer #1
Aísa-Marín et al. present a detailed scRNAseq description of two Nr2e3 mouse models the authors had published in Neurobiology of Disease in 2020. These two mouse models do not mirror known pathogenic variants in human patients, but are useful animal models to understand disease mechanisms in NR2E3-linked retinal degenerations. The present follow-up study has been carefully done, the bioinformatic analysis is sound and selected pathways have been validated at protein level by immunohistochemistry and Western blot. The impact of this data on photoreceptor development and maintenance is somewhat decreased because previous data by the groups of Joseph Corbo, Connie Cepko, Jeremy Nathans, Anand Swaroop and others have already shown several years ago upregulation of cone-specific genes in a spontaneous Nr2e3 mutant mouse, the rd7 mouse. In these papers, hybrid cones both expressing cone- and rod-specific genes have been described and coined 'cods'. The authors are encouraged to use already defined terms in their paper. For instance, the concept of 'half differentiated' photoreceptors is unclear and must be rephrased. In general, existing literature is not always integrated adequately into the submitted work. The detailed remarks are listed below.
We thank the reviewer for this suggestion to incorporate previous terminology in the field for the sake of clarity. Although it is true that previous authors have described hybrid photoreceptors, they have not quantified them, or compared their contribution to the different subpopulations of photoreceptors in different models of disease. For instance, there may be different types of hybrid photoreceptors, as they can be identified both within the cone and the rod populations, they show different characteristics and might perform different roles or else, be indicative of a pathological phenotype.
Taking into account the reviewer’s suggestion, we have carefully revised our text and figures and the terms “intermediate” or “half differentiated photoreceptors” have been substituted by the already existing term “hybrid photoreceptors” of “incompletely differentiated”. We have also included some context in previous work regarding ‘cods’.
See now lines 60, 146, 215, 271, 638-645 in the Discussion, Figure 7 legend and the Graphical Abstract for changes in terminology, and also sentences in lines 518-522, for reference to previous works.
Reviewer #1
One important question the authors must also address in their scRNAseq analysis is why the well described 'mixed' S- and M-opsin expressing cones do not seem detectable, are actually not even mentioned?
We sincerely thank the reviewer for this comment, as it is a relevant piece of information that we missed in our previous analysis. Cones are usually classifed according to the type of opsin they express. In mouse, previous work has described cones expressing solely S- or M-opsins, but also cones that co-express both types of opsins. Indeed, we find these three types of cones, overlapping partially with our cone subpopulations, which are defined by a larger number of signature genes. As previously described, merging the results of the wild-type with the two mutants demonstrate that most cones co-express both opsins (roughly 46%), S- and M-opsins are expressed exclusively in around 13% cone cells each, and somewhat surprisingly around 28% of cones do not express any opsin (Fig. EVF6). However, the dissection of cone results by genotype is more informative, as the percentages vary according to the mutant genotype. In the mutants, the percentage of cones expressing no opsins is higher than in the wild-type at the expense of the cones that should express solely S-opsin (data in EVF5). Again reflecting the impact of Nr2e3 mutations on the differentiation of cones and reinforcing our main message.
We have introduced several sentences in the text to reflect this results and refer to this new figure EVF6 (see lines 292-307 in the Results as well as 575-580 in the Discussion sections.
Reviewer 1. Detailed comments:
- Graphical abstract: replace 'half differentiated' by incompletely differentiated or similar
As detailed above, we have followed the suggestion of the reviewer and throroughly revised all the text and all Figures. “Half differentiated” and “intermediate photoreceptors” have been substituted by “incompletely differentiated” and “hybrid photoreceptors”, respectively.
- l.86: to my best knowledge, the shorter isoform has only been described at transcript level in humans, no evidence at protein level, please clarifiy. Please also state that the isoform lacking exon 8 is due to retention of intron 7.
Following the suggestion of the reviewer, we have clarified that the short NR2E3 transcript isoform is due to retention of intron 7, both in human and mouse. We have also clarified that in human, the protein has been computationally predicted, whereas there are experimental evidences in mouse. (lines 94-104).
- l.89: lacks repressor and dimerization domains. Exon 8 is not coding all repressor and dimerization domains. The authors do not mention neither the D-box in the DBD that also contributes to dimerization, in addition to the LBD (von Alpen et al., Hum Mut, 2015). Furthermore, repressor domain should be presented in the context of the auto-repressed structure of NR2E3 (Zhu et al., Genes Dev, 2015).
We agree with the reviewer that the original text was simplified. For the sake of clarity and following his/her suggestion we now include more context about the structure of NR2E3 and included the suggested references (lines 87-93).
See also below the answer to the points 6, 9 and 13, which are also related.
- l.90: typo NR2E3
The corresponding line is now on line 105 and the typo has been corrected.
- l.93-106: incorrect, please rewrite whole paragraph. There is only one single pathogenic variant leading to NR2E3-Gly56Arg-linked autosomal dominant retinitis pigmentosa, all other pathogenic variants are recessive and cause ESCS!
Now these paragraph starts in line 105. We agree with the reviewer that so far only mutation Gly56Arg in NR2E3 is associated to autosomal dominant retinitis pigmentosa. however there are also some (even if few) NR2E3 mutations associated to autosomal recessive forms of RP, in addition to the most well known autosomal recessive mutations that cause ESCS. Here we provide a selection of reports supporting NR2E3 as causative of arRP (that are some more included in HMGD).
In order to avoid further confusion to some readers, we also include these references in the new version (see lines 109-112).
- Gerber, S. et al. The photoreceptor cell-specific nuclear receptor gene (PNR) accounts for retinitis pigmentosa in the Crypto-Jews from Portugal (Marranos), survivors from the Spanish Inquisition. Hum Genet 107, 276–284 (2000). https://doi.org/10.1007/s004390000350
- Kannabiran, et al. Mutations in TULP1, NR2E3, and MFRP genes in Indian families with autosomal recessive retinitis pigmentosa. Mol. Vis. 2012; 18:1165-74..
- Bocquet, B. et al. Homozygosity mapping in autosomal recessive retinitis pigmentosa families detects novel mutations. Mol Vis 19:2487-500.
- l.110: see comment above about dimerization.
We have modified the sentence in the text accordingly, see now line 125.
- l.162: ok, but the main reason for restricting the analysis to photoreceptors should be the photoreceptor-specific expression of Nr2e3 though...
We have specified that Nr2e3 is solely expressed in photoreceptor cells (see line 176).
- l.165: please specify what is the percentage of rods with respect to all retinal cells
The percentage of rods is around 77.7% of all cells (now on line 180). We value the suggestion, as it adds information to the reader. Therefore, the percentage of each main cell type is now included in Figure 1B.
- l.210: idem l.89
We have modified the sentence in the text accordingly (now on lines 226-227).
- l.310: replace 'halfway'
As specified in the answer to the first main point, we have throroughly revised the text and amended the references towards hybrid and incompletely differentiated photoreceptors.
- l.337: as expected? please detail
We agree that the sentece was not clear. We have now clarified that our results agree with previous studies (see lines 370-371).
- l.388: discuss also crystallins in other RD models, v.g. Rpe65 ko mice
We thank the reviewer for this suggestion and have included a brief description of the expression of crystallins in response to retinal stress in other RD models, and include the appropriate references (now on lines 422-428).
- l.469: idem l.89
We have modified the sentence in the text accordingly (see lines 506-509).
- l.526: Please discuss increase in non-apoptotic cell death markers with respect to published data in the rd7 mouse (Venturini et al., Sci Rep, 2021)
We have included published data in the rd7 mouse and discussed that multiple non-apoptotic cell death markers might be activated in response to NR2E3 disfunction (see lines 587-593).
- l.580: the proposed dominant negative effect is overtly speculative and not supported by any presented data, please remove.
We have rewritten the sentence removing the dominant negative effect and referring exclusively to our results.
Answers to Reviewer #2
Aísa-Marín et al. present a detailed scRNAseq description of two Nr2e3 mouse models the authors had published in Neurobiology of Disease in 2020. These two mouse models do not mirror known pathogenic variants in human patients, but are useful animal models to understand disease mechanisms in NR2E3-linked retinal degenerations. The present follow-up study has been carefully done, the bioinformatic analysis is sound and selected pathways have been validated at protein level by immunohistochemistry and Western blot.
Major comments:
- Overall, the conclusions of the study are well supported by the results. The findings provide valuable insights into retinal development and the pathogenesis of NR2E3-associated retinal dystrophies in an animal model, which need to be validated in humans. This limitation should be noted in the manuscript.
Indeed, we agree that our work has used animal models, but further work in human-derived models (e.g. retinal organoids) should be performed to confirm these results. We have clarified this point in the Discussion section (see lines 649-650).
Include an explanation in the text about how the specificity of the different signals detected by immunohistochemistry was assessed.
The specificity of each antibody was assessed by introducing a negative control into the immunostaining procedure, wherein only the secondary antibody was used, as well as by comparison to the staining of the protein of interest in wild type or basal conditions reported in previous work from ours or other groups. This has been now specified in the corresponding Methods section (see lines 912-916).
Explain the observed high variability in the percentage of cones, particularly between the two deltaE8 mutants (Figure 3C).
We believe that the main characteristic of the Nr2e3-mutant retinas is that photoreceptors are not adequately differentiated and thus, affected photoreceptor subpopulations, in this case cones, degnerate and die. The pathogenic phenotype might be somewhat variable between animals from the same genotype (as it also happens in siblings carrying the same mutation). The deltaE8 mutants show a very different cone subpopulation pattern compared to wild-types and they clearly cluster with the other mutant retinas. For instance, cones of subpopulation cone4 might have all died.
We take note of the question posed by the reviewer, and thus have included a graph of the absolute number of cones in each subcluster per retina and genotype, which may help the reader (see new Fig 3C, panel on the right).
Explain why PARP-1 signals in Figure 6E are so thick and intense. Why this thick pattern is also present in the wild type retina?
The IHC of mouse tissue sections using antibodies of mouse origin can result in background by secondary antibody binding to the endogenous Igs (as reported in Eng et al, 2016, https://doi.org/10.1093/protein/gzv054). The PARP-1 antibody is a mouse monoclonal antibody (Abcam, ab14459). The strong signal that we observe in the INL, IPL, and GL of the retina is typically found when using primary mouse antibodies in mouse tissues and corresponds to the reaction of the secondary anti-mouse antibodies binding to the endogenous IgGs in the blood vessels of the retina (we obtain the same result using the secondary antibody as a negative control, see answer to point 2).
For the sake of clarity, we have clarified this background staining in the corresponding figure legend (lines 831-834).
Reviewer #2
Minor comments:
- Describe the abbreviations used in the text, such as PARP-1, MLKL, IRD, and VADC.
A list of abbreviations has been included at the end of the main text (see lines 663-672).
- Improve the visibility of number 4 in Figure 3A and describe the meaning of the insert.
Figure 3A has been amended accordingly
- Label the X-axis (cone subclusters) in Figure 3E.
The X-axis is now correctly labelled in Figure 3E.
- Describe the meaning of the insert in Figure 4A.
Figure 4A legend now contains the meaning of the insert.
- Indicate the relationship among inserts in Figure 4F.
Figure 4F and legend have been modified to clarify the meaning of the insert.
- Use an arrow to indicate the higher expression of CSTB in the cone-rich invaginations in the mutant retinas (Figure 6A).
The Figure 6A has been modified to include white arrowheads indicating the high expression of CSTB in the invaginations of the mutant retinas. This is also indicated in the corresponding Figure 6 Legend (lines 819-820.
- Revise the Y-axis values in Figure 6B, as they do not correspond to a percentage. Please, provide an explanation for the number of symbols displayed in this panel.
The Y-axis was previously expressed on a per-unit basis. For the sake of clarity and following the reviewer’s suggestion, it has now been appropriately modified to percentage of CSTB colocalizing with opsins.
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Referee #2
Evidence, reproducibility and clarity
Aísa-Marin et al. present a study on alterations in photoreceptor cell fate in NR2E3-associated diseases. The results convincingly reveal the presence of heterogeneous rod and cone populations in the mouse retina, as well as the existence of a novel cone pathway that results in hybrid cones characterized by the expression of genes associated with both cones and rods. Furthermore, the authors show that functional alteration of NR2E3 affects the expression of rod and cone signature genes, providing an interesting animal model to unveil the molecular mechanism underlying these retinal disorders.
Major comments:
- Overall, the conclusions of the study are well supported by the results. The findings provide valuable insights into retinal development and the pathogenesis of NR2E3-associated retinal dystrophies in an animal model, which need to be validated in humans. This limitation should be noted in the manuscript.
- Include an explanation in the text about how the specificity of the different signals detected by immunohistochemistry was assessed.
- Explain the observed high variability in the percentage of cones, particularly between the two deltaE8 mutants (Figure 3C).
- Explain why PARP-1 signals in Figure 6E are so thick and intense. Why this thick pattern is also present in the wild type retina?
Minor comments:
- Describe the abbreviations used in the text, such as PARP-1, MLKL, IRD, and VADC.
- Improve the visibility of number 4 in Figure 3A and describe the meaning of the insert.
- Label the X-axis (cone subclusters) in Figure 3E.
- Describe the meaning of the insert in Figure 4A.
- Indicate the relationship among inserts in Figure 4F.
- Use an arrow to indicate the higher expression of CSTB in the cone-rich invaginations in the mutant retinas (Figure 6A).
- Revise the Y-axis values in Figure 6B, as they do not correspond to a percentage. Please, provide an explanation for the number of symbols displayed in this panel.
Significance
While it is known that NR2E3, an orphan nuclear receptor, plays a role in photoreceptor fate and differentiation during retinal development, its precise biological function remains poorly characterized. Additionally, the mechanisms by which inherited functional alterations of NR2E3 lead to RP or ESCS are not well understood. To address these unresolved questions, the authors employ a comprehensive experimental approach, including scRNAseq, RT-PCR, and immunohistochemistry, using retinas from two NR2E3 mutant mice. The technical procedures are well executed, and the complex scRNAseq data are rigorously analyzed and presented in a clear manner. Overall, this is a careful and meticulous study, that provides the fundamental basis for further verification in humans.
In my view, this research is highly relevant for the scientific community interested in the study of retinal development and inherited retinal dystrophies, specifically retinitis pigmentosa (RP) and enhanced S-cone syndrome (ESCS).
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Referee #1
Evidence, reproducibility and clarity
Aísa-Marín et al. present a detailed scRNAseq description of two Nr2e3 mouse models the authors had published in Neurobiology of Disease in 2020. These two mouse models do not mirror known pathogenic variants in human patients, but are useful animal models to understand disease mechanisms in NR2E3-linked retinal degenerations. The present follow-up study has been carefully done, the bioinformatic analysis is sound and selected pathways have been validated at protein level by immunohistochemistry and Western blot. The impact of this data on photoreceptor development and maintenance is somewhat decreased because previous data by the groups of Joseph Corbo, Connie Cepko, Jeremy Nathans, Anand Swaroop and others have already shown several years ago upregulation of cone-specific genes in a spontaneous Nr2e3 mutant mouse, the rd7 mouse. In these papers, hybrid cones both expressing cone- and rod-specific genes have been described and coined 'cods'. The authors are encouraged to use already defined terms in their paper. For instance, the concept of 'half differentiated' photoreceptors is unclear and must be rephrased. In general, existing literature is not always integrated adequately into the submitted work. The detailed remarks are listed below.
One important question the authors must also address in their scRNAseq analysis is why the well described 'mixed' S- and M-opsin expressing cones do not seem detectable, are actually not even mentioned?
Detailed comments:
Graphical abstract: replace 'half differentiated' by incompletely differentiated or similar l.86: to my best knowledge, the shorter isoform has only been described at transcript level in humans, no evidence at protein level, please clarifiy. Please also state that the isoform lacking exon 8 is due to retention of intron 7.
l.89: lacks repressor and dimerization domains. Exon 8 is not coding all repressor and dimerization domains. The authors do not mention neither the D-box in the DBD that also contributes to dimerization, in addition to the LBD (von Alpen et al., Hum Mut, 2015). Furthermore, repressor domain should be presented in the context of the auto-repressed structure of NR2E3 (Zhu et al., Genes Dev, 2015).
l.90: typo NR2E3
l.93-106: incorrect, please rewrite whole paragraph. There is only one single pathogenic variant leading to NR2E3-Gly56Arg-linked autosomal dominant retinitis pigmentosa, all other pathogenic variants are recessive and cause ESCS!
l.110: see comment above about dimerization
l.162: ok, but the main reason for restricting the analysis to photoreceptors should be the photoreceptor-specific expression of Nr2e3 though...
l.165: please specify what is the percentage of rods with respect to all retinal cells
l.210: idem l.89
l.310: replace 'halfway'
l.337: as expected ? please detail
l.388: discuss also crystallins in other RD models, v.g. Rpe65 ko mice
l.469: idem l.89
l.526: Please discuss increase in non‑apoptotic cell death markers with respect to published data in the rd7 mouse (Venturini et al., Sci Rep, 2021)
l.580: the proposed dominant negative effect is overtly speculative and not supported by any presented data, please remove.
Significance
Strength: first scRNAseq analysis in Nr2e3 mouse models, validation at protein level
Limitations: descriptive of gene expression, no mechanims identified, previous literature not adequately incorporated or missing
Audience: specialized, basic research
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www.biorxiv.org www.biorxiv.org
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Reply to the reviewers
Reviewer #1
The paper provides models of essential complexes formed in bacteria. These models have been predicted by AlphaFold2 and in some of the models, information from existing experimental structures is utilized. The predicted models have been calculated based on standard workflow procedures which are explained in detail and can be reproduced by others. The figures are informative and clear.
We are grateful for the reviewer's insightful comments, which have significantly contributed to improve our manuscript.
Suggestions for improvement:
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The PDB accession codes of the experimental structures should be providedb. A comparison of the predicted models with the experimental structures should be provided (e.g. same orientation, superposition). In Fig. 6 for example, a figure with superposition or use of the same orientation would be more informative.
As suggested by the reviewer, we have included a new table (Table 1) listing all experimental structures discussed in the main text, with the corresponding PDB codes. All predictions are listed in Supplementary File 1. For instances with available PDB codes, we compared the predicted structures to the experimental ones (new Supplementary Figure 3). In Fig. 6, the structures were difficult to superimpose because the subunits in the complexes have different relative orientations. To help comparing both models, we have added a schematic representation (new Fig. 6c).
The paper will certainly generate many hypotheses based on the predicted models. In this respect, it would be useful for a wide audience in the bioscience field. However, the discussed models will need experimental verification by various techniques, such as X-ray crystallography, cryo-EM, SAXS, and structural proteomics. A more thorough analysis of the literature may help to improve the paper in this respect.
We acknowledge the reviewer's emphasis on the importance of experimental verification of the predicted models. We have conducted a thorough analysis of the literature to identify instances where experimental verification could complement our predictions. We identified several mutations in BirA, documented in the literature, that affect its interaction with AccB. __In BirA mutations M310L and P143T were found to induce a superrepressor phenotype (BirA lacks the capacity to biotinylate AccB). These mutations do not significantly affect the BirA active site, but can destabilize the BirA-AccB interface. __We have added this information in the main text. Also, we investigated whether our complexes have known crosslinks in the xlinkdb database(https://xlinkdb.gs.washington.edu/xlinkdb/). We found information for five of our predicted complexes. In all cases, the distance restraints identified by crosslinking (crosslinked lysines are ~15Å apart) are compatible with our models. We have incorporated these references into a new table in Supplementary File 1. Unfortunately, we could not find more information in the xlinkdb that can be used to further validate our complexes.
Supplementary table. Selected binary complexes modeled by AF2 whose structure is experimentally verified by cross-linking mass spectrometry.
Protein 1
Protein 2
Peptide 1
Pepitde 2
Species
acca
accd
VNMLQYSTYSVISPEGCASILWKSADK
IKSNITPTR
E. coli
dnak
grpe
DDDVVDAEFEEVKDKK
VKAEMENLR
E. coli
rpob
rpoc
GKTHSSGK
KGLADTALK
E. coli
bama
bamd
TVDIKPAR
DVSYLKVAYQNFVDLIR
A. baumannii
secd
secf
ILGKTANLEFR
MPSEDPELGKK
P. aeruginosa
Reviewer #2
This study attempts to identify the 'essential interactome' through combining information in presence/absence genomics across bacteria, information in the STRING database, and predictions from alpha-fold. Overall, the strategy is clear, and I do not have concerns about reproducibility and clarity.
We value the reviewer's constructive evaluation of our manuscript and we would like to thank the reviewer's feedback as it has significantly helped us in improving our manuscript.
Strengths: Clever approach to get at the essential interactome.
Weaknesses: Putative impact. It is clear why understanding which interactions are present are important. But even as the authors suggest, interactions are dynamic and there are plenty of other tools that people could use to find interactions (including AA Coev that the authors themselves cite). The counter argument the authors bring up is the high false positive rate of interactions that is solved by this method. While true, the stringency criteria for what constitutes an interaction in this paper is remarkably high: each protein within the interaction needs to be essential, and needs to have a high confidence score in STRING, and then there is a hyperparameter that dictates the level at which AlphaFold 2 is providing confident answers. In this sense, this is less about an 'essential' interactome, and more about an interactome that is present with the highest true positive rate (trading off with the ability to discover new interactions at a reasonable breadth).
We appreciate the reviewer's insights concerning the stringency criteria for defining interactions. Here, we provide a detailed justification for our selection criteria and show how it aligns with our goal of identifying high-confidence interactions.
- Protein essentiality: In our model, interactions are considered essential if, and only if, both proteins involved are essential, providing a conservative estimate for the essential interactome. In our revised manuscript, we explored the possibility the potential for two non-essential proteins to form an essential interaction by investigating synthetically lethal interactions. Out of all synthetic lethal interactions in * coli*, only 28 interactions were identified, and only two could be modeled with an ipTM score > 0.6. Likely, these non-essential proteins operate in parallel or compensatory pathways instead of interacting directly. These findings lend support to our hypothesis and suggest that our interactome encompasses most essential interactions.
- Conditional essentiality: While we concur with the reviewer that our study does not address conditional essentiality, we would like to note that exploring conditional essential interactions across all the bacterial species discussed in our manuscript is currently unviable. Just as a matter of example, we checked the overlap in essential genes between Acinetobacter baumannii and Pseudomonas aeruginosa in the lung environment (Wang et al., 2014; Potvin et al., 2003). In that case, there is a minimal overlap between the two species, suggesting that conditional interactions might also be species-dependent. In our manuscript, we aimed to describe the core essential interactions for Gram-negative and Gram-positive bacteria under standard laboratory growth conditions. We agree that further research is needed to incorporate specific, context-dependent interactions to provide a complete, comprehensive view of the interactome. Nonetheless, we define here the first bacteria essential interactome that, in our opinion, marks a significant step towards understanding bacterial cell metabolism and holds relevance in applications such as developing broad-spectrum antibiotics.
- Confidence of the interaction: All existing methods to predict protein-protein interactions, including those based on coevolution, suffer from poor performance metrics. Most of them generate many false positive interactions while missing important ones. Without the aim of being exhaustive, here we reproduce a table of some of the latest computational methods to predict PPIs. Table 1. Performance of state-of-the-art PPI prediction methods (Huang et al., 2023).
Methods
AUPRCa
*SGPPI *
0.422
Profppikernelb
0.359
PIPRc
0.342
PIPE2b
0.220
SigProdb
0.264
a AUPRC denotes the average AUPRC value of 10-fold cross-validation.
It is clear from the data that such methods are not mature enough to be used as confident predictors. Hence, we decided to resort to validated interactions in the String database, which is one of the most comprehensive PPI databases__. In this revised version, we have expanded our data set to include all experimentally labeled interactions in the String database, even those with a low probability (experimental score > 0.15). The addition of these new interactions __increased the total number of interactions tested from 1089 to 1402 and generated 38 new models for Gram-negative species (13 with high accuracy) and 275 new models for Gram-positive bacteria (18 with high accuracy). All interactions are now included in the Supplementary File 1 and high accuracy models will be deposited on Model Archive after acceptance.
Alphafold (AF2) criterion for complex prediction. Although AF2 has its limitations, its accuracy in predicting bacterial complexes is consistently high. In various benchmarking studies, AF2 Multimer accurately predicted between 70-75% of tested complexes, with almost 90% of them being of medium-to-high quality (Evans et al., Yin et al., 2022). While there might be some minor deviations, AF2 can largely capture the bacterial essential interactome accurately. In the revised version, we compare pDockQ and pDockQ2 metrics with our ipTM criterion to define confident models. We observed that both pDockQ and pDockQ2 metrics were capable of identifying highly reliable complexes, but also disregarded actual complexes (Supplementary Figure 1). Thus, we decided to retain our initial criterion, based on ipTM scores, which is consistent with other authors who used similar ipTM thresholds to model bacterial interactions (e.g., O’Reilly et al., 2023).
In summary, although our methodology has inherent limitations, we believe that our approach is sound and can give a comprehensive and realistic view of the bacterial essential interactome. We hope that these new insights further substantiate our approach.
I don't know of too many studies that use AlphaFold 2 in this way. This was clever. However, there are plenty of studies that use phylogenomic information to infer interactions. In this sense, the core idea of the paper is not intrinsically novel.
We thank the reviewer for valuing our approach. Although other methods have been used to predict interactomes, our study, to the best of our knowledge, provides the first high-quality essential interactome for bacteria. We used experimental data (analysis of single deletion mutants) to define the essential interactions in bacteria. Other methods, either using phylogenomic information and/or deep learning tools to infer interactions, have a poor performance, as illustrated in the preceding table. Often, these methods yield a high number of interactions and, in many cases, show a bias towards overrepresented entries in the positive databases used to train the predictors (Macho Rendón et al., 2022). Also, while other methods lack detailed structural insights into the interactions, we offer structural models for every interaction tested.
Overall, I do feel this would be worth publishing as an expose of AF2 is capable of. I'm not sure of the impact it will have on researchers, however.
We appreciate the reviewer's positive feedback on our manuscript. Using AF2, we identified key interactions using only gene deletion mutant data. __This manuscript reveals new insights into the assembly of essential bacterial complexes, providing specific structural details to understand their stability and function. Additionally, __our work seeks to establish a methodology applicable to all bacterial species, guiding future research in this field. The approach taken in this study may expand drug targeting opportunities and accelerate the development of more effective antibiotics aimed to disrupt these essential interactions. In conclusion, the impact of the paper lies in its novel use of Alphafold2 to understand essential bacterial protein interactions, providing key insights into assembly mechanisms, and identifying new potential drug targets.
Reviewer #3
The selection of "essential" interactions is a bit arbitrary, given that their main criterion for selection is that both proteins are essential. Unfortunately, it's not always clear where the essential protein data is coming from. Authors cite Mateus et al. (ref 15) as source for E. coli, but I don't see an explicit list of essential genes in this paper (nor its supplement). For Pseudomonas the citation doesn't contain author information and for Acinetobacter essentiality only seems to refer to "essentiality" in the lung.
As a minimum, the author should provide a table with summary statistics for the essential proteins they are using, as this is the basis for the whole paper. Such a table should include the names of the species, the number of genes that are considered as essential, a very brief characterization of how essentiality was determined and the source for this information. For instance, are the genes listed in the Supplementary File congruent with the genes in the Database of Essential Genes (DEG) for these organisms? Finally, authors should indicate in that table which (essential) protein pairs are conserved across species, as this is another one of their selection criteria. Conservation is not necessary for an essential interaction, but it certainly makes it more likely.
We understand the reviewer's concerns regarding the selection of essential interactions and the need for a more thorough description of the sources of essential protein data. To address these concerns in the revised manuscript:
- __We included a clear explanation of the sources for essential protein data, including proper citations for each organism in Supplementary File 1. __The selected studies were primarily sourced from the DEG database. If data was unavailable, we revised the literature for relevant studies. The DEG database's most recent update was on September 1, 2020. __A graphical summary of the datasets has been included in Supplementary Figure 12, __that shows the overlapping between the different studies.
- We included comprehensive information for the essential proteins used in our study in Supplementary File 1. The file provides two tables detailing genes for both Gram-positive and Gram-negative datasets. Each table lists the gene names and their corresponding Uniprot IDs for every species in our study, as well as their orthologues in other organisms. Also, the reviewer was right in pointing out that for Acinetobacter baumannii, the study was conducted in the lung, which may bias the results as all other studies were performed in the test tube. To solve this, we replaced this study for Bai et al., 2021, that was performed in rich medium.
Author should also state whether they have verified that none of the random pairs are in the positive set.
We thank the reviewer for this comment. We certainly checked that none of the random pairs was present in the positive dataset. This clarification has now been added to the methods section.
This is also relevant because authors "retrieved all high-confidence PPIs between these proteins from the STRING database" which provides compound scores for interactions but that has often little to do with physical interactions (given that the scores factor in co-expression and several other criteria). In fact, I find STRING scores difficult to interpret for that very reason.
We appreciate the reviewer's comment to the use of combined interaction scores from the STRING database. We agree with the reviewer that STRING combined scores are somehow difficult to interpret because they combine different evidence of interaction. We decided to use the STRING combined scores to include interactions that may not have direct experimental evidence but are probable to interact according to other information (e.g., co-expression). However, to further examine the interactome we have also included in the revised version all interactions with experimental evidence in String to complete our interactome. As mentioned in the response to Reviewer 1, __we expanded the tested interactions from 1089 to 1402. This resulted in 38 new models for Gram-negative species, with 13 being highly accurate, and 275 for Gram-positive bacteria, of which 18 were highly accurate. All interactions are now included in the Supplementary File 1 __and high accuracy models will be deposited on the Model Archive after acceptance.
The authors "reasoned that a given interaction would only be essential if and only if both proteins forming the complex are essential" - this sounds reasonable but doesn't capture synthetically lethal (genetic) interactions, that is, interactions between two proteins that are both non-essential but are essential in combination. Admittedly, I don't have a number of how many such cases exist, but there are such cases in the literature (e.g. Hannum et al. 2009, PLoS Genet 5[12]: e1000782, for yeast).
We thank the reviewer for bringing this point into discussion. We acknowledge that our reasoning does not capture synthetic lethality, which occurs when the loss of one of two individual genes has no effect on cell survival, but the simultaneous loss of both leads to cell death. In this case, the two genes or proteins are non-essential individually but become essential in combination. To cover synthetic lethality, we retrieved all synthetically lethal interactions found in Escherichia coli, strain K12-BW25113 from the Mlsar database and included them in our pipeline. We identified 28 synthetically lethal PPIs (involving 45 proteins) and we modeled them with AF2. Only two interactions displayed an ipTM score > 0.6 (nadA-pncB and nuoG-purA). Hence, the number of interactions due to synthetic lethality seems to contribute low to the overall interactome. We believe that synthetic lethal partners often function in parallel or compensatory pathways, rather than directly interacting with each other. For example, in yeast, the genes RAD9 and RAD24 are synthetic lethal. RAD9 is involved in cell cycle checkpoints, while RAD24 is involved in DNA damage response. They function in related pathways but do not encode proteins that directly interact with each other. Hence, finding specific examples of proteins that are both synthetic lethal and directly interact might be challenging as the synthetic lethal relationship often reveals functional rather than physical interactions.
Apart from that, one could question the selection method more generally, given that for a biological process always essential and non-essential proteins work together, so I wonder why the authors didn't include additional proteins known to be involved in specific processes as this could make their predictions much more biologically meaningful.
We agree with the reviewer that accessory proteins are important to understand the biological context of interactions. In fact, in several sections of our manuscript, we included accessory proteins to fully describe the essential complexes. For example, in the cell division complex, we incorporated proteins like MreCD-RodZ from the elongasome to enhance the structural context of the interactions. However, a comprehensive explanation of all identified interactions and accessory proteins would extend beyond the scope of this manuscript and further lengthen an already extensive document. In our study, we sought to describe the fundamental interactions for both Gram-negative and Gram-positive bacteria. We anticipate that our findings will prompt additional research to confirm our hypotheses and enhance knowledge of these protein complexes within the proper cellular context.
In any case, to understand their choice better, authors should provide a table (in the main text) summarizing the proteins they actually analyze and discuss in more detail in their models. This would allow a reader to see which proteins are considered essential and which ones are missing. I would organize this by function / pathway / process, so these proteins are listed in a functional context.
We added Table 1 in the main text, listing all interactions described in the text. Table 1 includes the proteins involved in each complex, the ipTM score of the interaction, whether a PDB code is available for comparison and the functional classification of the interaction.
With regard to docking, please also discuss why you focus on iPTM, as there are other derived metrics from AF2 scores, such as pdockq based on if_plddt (e. g. Bryant et al, 2022), as well as external metrics to AF2 (physics-based methods such as Rosetta). Another option may be a modified versions of AF2 multimer, such as AFSample, which produces a greater diversity of models, allowing for more "shots on goal" and ultimately a higher success rate, assuming one has a reliable QC filter (I wonder how those compares to iPTM).
We did not use AFsample because is a very expensive computational approach that would require too many resources for the batch prediction of more than 1.400 complexes. AFsample generates 240x models, and including the extra recycles, the overall timing is around 1,000x more costly than the baseline. However, we acknowledge that using other metrics can be useful to further evaluate our models. Hence, we investigated how pDockQ and pDockQ2 metrics compare with ipTM score. We observed that pDockQ hardly correlates with ipTM (R = 0.328) whereas the improved metric pDockQ2 correlates much better (R = 0.649). All complexes described in the manuscript, which have an ipTM score higher than our threshold (0.6), have also a pDockQ2 score higher than 0.23, except for six interactions that have a lower pDockQ2 score. However, these scores improve when the interactions are modeled with accessory proteins in the complex. This somehow suggests that the ipTM metric better captures binary interactions when these are excluded from their context. __It is possible however, that pDockQ scores are better in discriminating false positive interactions than ipTM scores. Based on the strong correlation between the two metrics and the observation that ipTM may better capture binary interactions, we decided to keep our method in the manuscript. Other authors have employed analogous ipTM thresholds to model bacterial interactions (e.g., O’Reilly et al., 2023). Notwithstanding, __we also included pDockQ and pDockQ2 metrics in Supplementary File 1, so readers can evaluate complexes based on these metrics.
Minor comments:
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1, 3rd last line: "the essential interactome is a potentially powerful strategy to [...] identify new targets for discovering new antibiotics"
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Figures and figure legends need to be explicit which species is represented (ideally with a Uniprot ID) and which structure was predicted by alphafold and which one has an experimental structure. Known structures should be indicated in a table, as suggested above.
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Figure 5: LptF is too dark when printed, so a lighter color may be better.
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Figure 6: The cryoEM and alphafold structures look quite different, so please discuss discrepancies between them (in terms of prediction or cryEM modeling). A schematic may be helpful to illustrate the differences in more clarity.
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Figure 7: LolC is also too dark when printed. Make lighter.
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Maybe in some cases it may be worthwhile looking at Consurf structures to see if the predicted inferfaces are indeed more conserved than the non-conserved parts.
We thank the reviewer for his/her insightful feedback on our manuscript. We have addressed all these comments as follows:
- The statement on page 1 was revised as suggested.
- We revised all figure legends to include the Uniprot IDs, and distinguish between predicted and experimental structures. We also included Table 1 and Supplementary File 1 for known structures.
- We adjusted the colors in Figures 5 and 7 to enhance print visibility.
- We provided a schematic to illustrate discrepancies between cryoEM and AlphaFold structures in Figure 6c.
- We used Vespa to highlight conserved interfaces in the complexes described in the manuscript, as suggested. The figures displaying the conservation of interfaces in the complexes are now depicted in Supplementary Figure 2. A comparison between interface and surface conservation can be found in Figure 1f.
The main significance of this study is its potential use for a better understanding of the protein complexes described in more detail (and the fact that alphafold can be applied in a similar fashion to many other complexes). This is why the individual sections need to be evaluated to process-specific experts (disclaimer: I have only worked on some of the complexes, but I am not an expert on any of them). I wonder if it would make more sense to break out some of the sections on individual complexes into separate papers, and then discuss them in more detail and with more context from previous studies. Complexes such as the divisome have a huge body of literature and it may be worth reviewing which structures are known and which ones are not. However, the dynamic and labile nature of these complexes have made it difficult for both crystallography as well as modeling to get a good structural understanding, but some of the models proposed here may be useful for overcoming some of these hurdles.
We appreciate the reviewer's suggestion. While we acknowledge the complexity of some of the individual complexes, such as the divisome, and the wealth of existing literature, we believe that the current manuscript provides a valuable comprehensive view on how AF2 can be used to predict essential protein complexes in bacteria. In our opinion, dividing the manuscript in separate pieces might dilute its scope. Nonetheless, we are exploring in our laboratory the interactions detailed in the manuscript, aiming to further expand the knowledge on these important complexes and their potential as targets for new antimicrobials.
References:
Bai J, Dai Y, Farinha A, et al. Essential Gene Analysis in Acinetobacter baumannii by High-Density Transposon Mutagenesis and CRISPR Interference. J Bacteriol. 2021; 203(12):e0056520.
Evans R, O’Neill M, Pritzel A, et al. Protein complex prediction with AlphaFold-Multimer.
bioRxiv. 2021; 2021.10.04.463034.
Huang Y, Wuchty S, Zhou Y, Zhang Z. SGPPI: structure-aware prediction of protein-protein interactions in rigorous conditions with graph convolutional network. Brief Bioinform. 2023; 24(2):bbad020
Macho Rendón J, Rebollido-Ríos R, Torrent Burgas M. HPIPred: Host-pathogen interactome prediction with phenotypic scoring. Comput Struct Biotechnol J. 2022; 20:6534-6542.
O'Reilly FJ, Graziadei A, Forbrig C, et al. Protein complexes in cells by AI-assisted structural proteomics. Mol Syst Biol. 2023; 19(4):e11544.
Potvin, E., Lehoux, D.E., Kukavica-Ibrulj, I., et al. In vivo functional genomics of Pseudomonas aeruginosa for high-throughput screening of new virulence factors and antibacterial targets. Environmental Microbiology. 2003; 5: 1294-1308.
Wang N, Ozer EA, Mandel MJ, Hauser AR. Genome-wide identification of Acinetobacter baumannii genes necessary for persistence in the lung. mBio. 2014; 5(3):e01163-14.
Yin, R, Feng, BY, Varshney, A, Pierce, BG. Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants. Protein Science. 2022; 31(8):e4379.
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Referee #3
Evidence, reproducibility and clarity
Summary:
Gómez-Borrego & Torrent-Burgas selected and modelled 1089 interactions between "essential" proteins in bacteria and generated 115 what they call "high-accuracy" models (using alphafold2). Some of the models potentially provide new insight into structure-function relationships of various biological processes and thus may serve as basis for further exploration.
Major comments
Methods
The selection of "essential" interactions is a bit arbitrary, given that their main criterion for selection is that both proteins are essential. Unfortunately, it's not always clear where the essential protein data is coming from. Authors cite Mateus et al. (ref 15) as source for E. coli, but I don't see an explicit list of essential genes in this paper (nor its supplement). For Pseudomonas the citation doesn't contain author information and for Acinetobacter essentiality only seems to refer to "essentiality" in the lung.
As a minimum, the author should provide a table with summary statistics for the essential proteins they are using, as this is the basis for the whole paper. Such a table should include the names of the species, the number of genes that are considered as essential, a very brief characterization of how essentiality was determined and the source for this information. For instance, are the genes listed in the Supplementary File congruent with the genes in the Database of Essential Genes (DEG) for these organisms? Finally, authors should indicate in that table which (essential) protein pairs are conserved across species, as this is another one of their selection criteria. Conservation is not necessary for an essential interaction, but it certainly makes it more likely.
Author should also state whether they have verified that none of the random pairs are in the positive set.
This is also relevant because authors "retrieved all high-confidence PPIs between these proteins from the STRING database" which provides compound scores for interactions but that has often little to do with physical interactions (given that the scores factor in co-expression and several other criteria). In fact, I find STRING scores difficult to interpret for that very reason.
The authors "reasoned that a given interaction would only be essential if and only if both proteins forming the complex are essential" - this sounds reasonable but doesn't capture synthetically lethal (genetic) interactions, that is, interactions between two proteins that are both non-essential but are essential in combination. Admittedly, I don't have a number of how many such cases exist, but there are such cases in the literature (e.g. Hannum et al. 2009, PLoS Genet 5[12]: e1000782, for yeast, or Babu et al. 2014 PLoS Genet 10[2]: e1004120, for E. coli).
Apart from that, one could question the selection method more generally, given that for a biological process always essential and non-essential proteins work together, so I wonder why the authors didn't include additional proteins known to be involved in specific processes as this could make their predictions much more biologically meaningful.
In any case, to understand their choice better, authors should provide a table (in the main text) summarizing the proteins they actually analyze and discuss in more detail in their models. This would allow a reader to see which proteins are considered essential and which ones are missing. I would organize this by function / pathway / process, so these proteins are listed in a functional context.
With regard to docking, please also discuss why you focus on iPTM, as there are other derived metrics from AF2 scores, such as pdockq based on if_plddt (e. g. Bryant et al, 2022), as well as external metrics to AF2 (physics-based methods such as Rosetta).
Another option may be a modified versions of AF2 multimer, such as AFSample, which produces a greater diversity of models, allowing for more "shots on goal" and ultimately a higher success rate, assuming one has a reliable QC filter (I wonder how those compares to iPTM).
These details are required to make the study truly transparent and reproducible.
Results
Given the methodological caveats given above, some of the results are certainly convincing and interesting to a broader readership.
However, since their models are predictions, it would be important to provide some guidance on which interactions are the highest-scoring and thus the most promising for further validation. I would thus include a list of interactions for each functional group and their scores. This would be more useful than the rather difficult to interpret Figure 2 (even though it looks nice - or just add a table and leave Figure 2). Such a table could (and should) also include other data, such as references that support those top-ranking (but still unknown) interactions, or which structure are already known.
Minor comments
P. 1, 3rd last line: "the essential interactome is a potentially powerful strategy to [...] identify new targets for discovering new antibiotics"
Figures and figure legends need to be explicit which species is represented (ideally with a Uniprot ID) and which structure was predicted by alphafold and which one has an experimental structure. Known structures should be indicated in a table, as suggested above.
Figure 5: LptF is too dark when printed, so a lighter color may be better.
Figure 6: The cryoEM and alphafold structures look quite different, so please discuss discrepancies between them (in terms of prediction or cryEM modeling). A schematic may be helpful to illustrate the differences in more clarity.
Figure 7: LolC is also too dark when printed. Make lighter.
Maybe in some cases it may be worthwhile looking at Consurf structures to see if the predicted inferfaces are indeed more conserved than the non-conserved parts.
Significance
The main significance of this study is its potential use for a better understanding of the protein complextes described in more detail (and the fact that alphafold can be applied in a similar fashion to many other complexes).
This is why the individual sections need to be evaluated to process-specific experts (disclaimer: I have only worked on some of the complexes but I am not an expert on any of them).
I wonder if it would make more sense to break out some of the sections on individual complexes into separate papers, and then discuss them in more detail and with more context from previous studies. Complexes such as the divisome have a huge body of literature and it may be worth reviewing which structures are known and which ones are not. However, the dynamic and labile nature of these complexes have made it difficult for both crystallography as well as modeling to get a good structural understanding, but some of the models proposed here may be useful for overcoming some of these hurdles.
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Referee #2
Evidence, reproducibility and clarity
This study attempts to identify the 'essential interactome' through combining information in presence/absence genomics across bacteria, information in the STRING database, and predictions from alpha-fold. Overall, the strategy is clear, and I do not have concerns about reproducibility and clarity.
Significance
General Assessment:
Strengths: Clever approach to get at the essential interactome.
Weaknesses: Putative impact. It is clear why understanding which interactions are present are important. But even as the authors suggest, interactions are dynamic and there are plenty of other tools that people could use to find interactions (including AA Coev that the authors themselves cite). The counter argument the authors bring up is the high false positive rate of interactions that is solved by this method. While true, the stringency criteria for what constitutes an interaction in this paper is remarkably high: each protein within the interaction needs to be essential, and needs to have a high confidence score in STRING, and then there is a hyperparameter that dictates the level at which AlphaFold 2 is providing confident answers. In this sense, this is less about an 'essential' interactome, and more about an interactome that is present with the highest true positive rate (trading off with the ability to discover new interactions at a reasonable breadth).
Advance: I don't know of too many studies that use AlphaFold 2 in this way. This was clever. However, there are plenty of studies that use phylogenomic information to infer interactions. In this sense, the core idea of the paper is not intrinsically novel.
Audience: specialized. Overall, I do feel this would be worth publishing as an expose of AF2 is capable of. I'm not sure of the impact it will have on researchers however.
Field of expertise: Statistical genomics.
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Referee #1
Evidence, reproducibility and clarity
The paper provides models of essential complexes formed in bacteria. These models have been predicted by AlphaFold2 and in some of the models, information from existing experimental structures is utilized. The predicted models have been calculated based on standard workflow procedures which are explained in detail and can be reproduced by others. The figures are informative and clear.
Suggestions for improvement:
- a. The PDB accession codes of the experimental structures should be provided
- b. A comparison of the predicted models with the experimental structures should be provided (e.g. same orientation, superposition). In Fig. 6 for example, a figure with superposition or use of the same orientation would be more informative.
Significance
The paper will certainly generate many hypotheses based on the predicted models. In this respect, it would be useful for a wide audience in the bioscience field. However, the discussed models will need experimental verification by various techniques, such as X-ray crystallography, cryo-EM, SAXS, and structural proteomics. A more thorough analysis of the literature may help to improve the paper in this respect.
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Reply to the reviewers
The authors do not wish to provide a response at this time.
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Referee #3
Evidence, reproducibility and clarity
In the present paper, authors study the effect of the L250P mutation on Fbxo7, leading to a severe infantile onset motor impairment.
They show by co-IP that the mutation selectively ablates the interaction of Fbxo7 with the protesomal adaptor PI31. It induced a reduction in endogenous Fbxo7, which had a reduced half-life, and concomitantly of PI31 levels, without affecting its half-life, and a reduction in the expression of some subunits of the proteasome, and altogether, a reduction in its activity. To identify substrates of SCF Fbxo7 reliant on PI31 they used some databases and proteins arrays and identified MiD49 and MiD51, involved in mitochondrial fission machinery. Authors show PI31 acts as an adaptor and allows SCFfbxo7 ligase to ubiquitinate MiD49, therefore L250P mutation alters its substrate repertoire. It is shown Fbxo7 stabilizes Mid49 and Mid51. Importantly, reduced levels of Fbxo7 (KD) mimic the effect of the mutation.
Despite the affectation of MiD49, mitochondrial network appeared unaffected. However they observed that Fbxo7 L250P mutation led to a general alteration of the mitochondrial function: reduction of the mitochondrial mass, leading to reduced oxygen consumption, lower mitophagy and biogenesis and increased ROS production. Data are well presented, findings are convincing and complete and discussion seems appropriate.
I just have some minor comments:
According to the data showing that Fbxo7 KD mimics the effects of the L250P mutation, it appears that the altered stabilization of Fbxo7 is a key event in the process and results observed. How do the authors explain the reduction of Fbxo7 half-life induced by the mutation?
I would find more appropriate that to estimate the mitochondrial mass, the relative area or volume of Mitotracker green fluorescence is quantified, rather than its average intensity.
It is not accurate to say TMRE is only able to enter mitochondria and fluorescence when there is an intact membrane potential. Rather, its accumulation is dependent on the mitochondrial membrane potential.
Significance
Authors provide a comprehensive study of the effects of the novel mutation L250P in Fbxo7 gene, leading to infantile onset PD. Findings are new and describe the mechanism this mutation changes the substrate repertoire of SCFFbxo7 ligase and affect mitochondrial function. The paper could be of interest to both clinical and basic researchers focused on PD and mitochondrial function.
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Referee #2
Evidence, reproducibility and clarity
Summary
In this article, Sara Al Rawi and colleagues hypothesize that the homozygous L250P FBXO7 mutation identified in an infant with reduced facial and limb movements and axial hypotonia affects the Fbxo7-PI31 interaction domain. The authors used patient fibroblasts carrying the pathogenic mutation to show reduced expression of Fbxo7 and PI31 on those cells, and reduced proteasome levels and activity. Moreover, their data suggest that the L250P mutation affects the mitochondrial function and promotes increased levels of reactive oxygen species (ROS).
Major comments:
- The only thing that be argued is that Supplementary Table 1 containing the list of whole exome sequencing (WES) hits is missing. Please add it to the manuscript to check that the FBXO7 variant is the most plausible one considering the clinical phenotype of the affected individual.
Minor comments:
There are a few minor details that I would modify to improve the general readability of the manuscript: - The authors state that parents of the affected individual are "related", but do not specify the degree of consanguinity that they have. It was only in my second read of the manuscript that I realized that parents were consanguineous, and that the presence of a homozygous mutation made sense. It would help to state the degree of consanguinity between parents so that the reader can understand that a homozygous mutation is plausible. - In the clinical description of the patient, I would add the interpretation of a "developmental quotient (DQ) = 40", such as "a score <75 indicates a developmental delay", since some basic scientists may not be used to interpret those scores and cannot identify right away the degree of clinical impairment. - Many researchers are used to interpret the potential pathogenicity of a "candidate variant" using the CADD score. You can add this score to the paragraph where you mention the predicted pathogenicity of other in silico tests. - In the section "Fbxo7 stabilizes MiD49/51 protein levels", the beginning of the second paragraph: "To test whether knock-down of Fbxo7 levels phenocopies the effect of the L250P point mutation, [...]" is hard to understand. I would rewrite this first sentence in a different way to make it easier to read.
Significance
The manuscript is well written, and the results are coherent and supported by impressively detailed methods. The results of this manuscript are important for the advance of the movement disorders field. It shows how the novel L250P FBXO7 mutation in homozygous status can cause a very early onset (neonatal) movement disorder. Additionally, the functional analyses performed in patients' fibroblasts characterize very well the effect of this mutation on the cellular machinery showing proteasomal dysfunction, mitochondrial dysregulation, and ubiquitination alteration.
The content of the manuscript is interesting for a broad spectrum of individuals: from pediatric movement disorders specialists to basic researchers interested in proteasomal and mitochondrial dysfunction.
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Referee #1
Evidence, reproducibility and clarity
This study reports a human pediatric patient carrying a mutation in the Fbxo7/PARK15 gene. Several previous reports demonstrated that loss of Fbxo7/PARK15 gene function causes early-onset parkinsonian-pyramidal syndrome, but the precise underlying molecular mechanism remains to be elucidated. Based on studies with patient fibroblasts the authors suggest that Fbxo7 and its conserved binding partner PI31 regulate proteasomes and mitochondria, and that PI31 is an adaptor for the SCF Fbxo7 E3 ubiquitin ligase. The observations presented here are potentially interesting, but at this point they lack sufficient experimental evidence to support the main conclusions.
A general weakness of this study is that experiments focus on patient fibroblasts, and not neurons. Fbxo7/PARK15 patients as well as mouse mutants have neurological phenotypes, but no overt defects in skin or connective tissues. Therefore, it is not clear how the reported observations relate to the clinical symptoms. This is of particular concern since a conflicting report (Kraus et al., 2023) has found no change in basal mitophagy in Fbxo7 KO iNeurons.
The authors show that the proteasome regulatory protein PI31 is cleaved in Fbxo7 mutant cells. The cleavage and inactivation of PI31 upon inactivation of Fbxo7 was originally reported in Bader et al., 2011 (Cell 145, 371-82), but this paper is not cited. On the other hand the authors are citing a yet unpublished biorxiv preprint by Sanchez-Martinez et al. (https://www.biorxiv.org/content/10.1101/2022.10.10.511602v3) on the Drosophila Fbxo7 ortholog but another highly relevant preprint showing that transgenic expression of PI31 can extensively compensate for the inactivation of Fbxo7 in mice is not included (https://www.biorxiv.org/content/10.1101/2020.05.05.078832v1). This lack of consistency in citing prior works is concerning and should imperatively be rectified to provide a transparent and more accurate account of the novelty of the presented findings. Therefore, the bibliography of the manuscript is incomplete and relevant citations are missing.
The idea that PI31 is an adaptor for SCF-FBXO7 ubiquitination has not sufficient experimental support. The authors use the correlation of the L250P mutation not interacting with PI31 and not ubiquitinating MiD49 to propose that PI31 is an adaptor needed for MiD49 (and TOMM22 and Rpl23) ubiquitination by FBXO7 - this is an over-interpretation; this claim should be toned down or supported by further investigations.
FBXO7 can ubiquitinate MiD49 in vitro, but in vivo it appears to protect MiD49 from ubiquitination. Moreover, reduced FBXO7 levels (either by L250P mutation or knock down) result in decreased MiD49 and MiD51. There is no mechanistic explanation for these seemingly contradictory findings.
Mitochondrial homeostasis of patient fibroblasts appears aberrant. In particular, there seems a reduction of mitochondrial mass, reduced basal mitophagy and reduced respiration, which contrasts the report by Krauss et al. (2023). A potential explanation for these disparate observation is suggested by the decreased transcription of two mitochondrial transcription factors, PGC1α and PPARγ. How FBXO7 inactivation leads to this decrease in PGC1α and PPARγ or a decrease in basal autophagy is not clear.
In sum, there are some potentially interesting preliminary observations, but the study is not convincing because the results are over-interpreted and the analysis is not sufficiently rigorous.
Specific comments:
Figure 1 panel C has no loading control for total lysates.
Figure 2 panel B. The authors state that this experiment suggests direct interaction of PI31 with MiD49 (in the discussion they drop "suggests"). GST-pulldown with bacterially expressed GST-PI31 with MiD49/51 in vitro transcribed in rabbit reticulocyte lysate, which is less complex than HEK293 lysate, but not a purified system (for example, they contain proteasomes and other UPS components). This does not prove direct interaction. They show a Coomassie gel of the purified GST and GST-PI31, but not the reticulocyte lysate.
Figure 2 panels I and J. In panel I they show a decrease of Mi49 following FBXO7 KD, a main point of the paper that MiD49 is a FBXO7/PI31 substrate. However, in panel J for time zero of their hydrogen peroxide treatment time course it appears that Mi49 from the FBXO7 KD is as abundant if not greater than the Mi49 for the control time zero. Why? Even in the graph in panel K they start at the same level, though that probably is due to the way they normalize the data, which is not stated clearly.
The legend for figure 3 panel C states that the figure shows cells from control and the patient imaged under basal conditions or following treatment with 2-deoxyglucose, yet in the figure there are only two panels!?
For the proteasome activity assay, in the text they state that they use Suc-LLVY-AMC, which releases a fluorescent signal when proteolytically cleaved, but in the Materials and Methods they say that they use the Proteasome-Glo Chymotrypsin-like assay (Promega G8621), which is a two-step luminescent assay. These are not the same assays.
Finally, this ms is very similar to a bioxrchive paper from the Laman-lab, but there are some notable omissions:
- In their bioxrchive paper they show a very clear decrease in LMP7(beta5i) protein in figure 1 panel D. This would go along with the decrease in other proteasome subunits, but this result is not mentioned in this manuscript. Why?
- The bioXrchive figure 1 panel E was replaced here with a considerably lower quality Western blot (Figure 1 panel F, using tubulin instead of GAPDH as the loading control). Again, the reason is not clear.
Significance
A better understanding of how mutations in Fbxo7/PARK15 cause juvenile onset neuronal degeneration would be very important and significant. Unfortunately, the current study is not sufficiently rigorous while the results are over-interpreted which will confuse readers.
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Reply to the reviewers
Please see the attached pdf "Response to Reviewer" with all reviewer comments, our responses, and descriptions of the edits in the text supplementary information, and figures.
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Referee #3
Evidence, reproducibility and clarity
This paper by Sharma et al describes ultrastructural changes in the polar tube (PT) of the microsporidian species Varimorpha necatrix upon PT firing. The relationship between cargo transport and the diameter of the PT, as well as the thickness of the PTP coat, are investigated. Moreover, low-resolution sub-tomogram averaging (STA) reveals that ribosome dimers occasionally arrange into spiral-like arrays on the inner surface of the bilayer lining the PTP coat. The data are well presented and, in most parts, appropriately interpreted. I have the following comments that I suggest should be addressed.
Major:
- The authors suggest that the ribosomes in the arrays are dimers. Yet, figure 2c only shows a STA map of a 70S particle. A map of the dimer should be included to support this significant message of the paper.
- Do the authors see any 70S particles and if so, how common are they? 3D classification would clarify this.
- The authors put a lot of emphasis on the finding of array-like ribosomes within PTs. However, these appear to be present in only a minority of the cases. Moreover, similar ribosome arrays have previously not been seen in other microsporidian PTs. This raises the question of how significant these arrays are. Do they only occur in some microsporidia, or only at certain time points? This should be more clearly discussed, for example in lines 232 - 233. Also, do the authors suggest that this is a specific organisation or just a matter of close packing?
- As the arrays are only occasionally observed, the statement that ribosomes are transported through PTs in a spiral-like fashion should also be toned down in the abstract and throughout the manuscript. The fact that the arrays are only seen sometimes, makes the finding even more interesting, as it may infer a dynamic reorganisation process.
- The ribosome arrays appear to co-localise with the membrane. If this is the case, does the membrane show up in their STA? If so, it would be essential to show this.
- How do the ribosomes in the arrays differ from the free-floating ones? Are the latter not associated with the membrane, while the former are not? Can differences be visualised through 3D classification?
- The difference in the PTP coat in empty vs. filled PTs are very interesting. Can the authors clarify how this was measured and mention the number of measurements, mean, and standard deviation in the main text? Line plots would help substantiate the measurements.
- Do the authors observe any differences in the regularity of the array? This could be assessed by investigating power spectra of tomograms of STAs.
- How do the authors suggest such large changes in thickness come about? Is the PT coat "bunched up", as the PT compresses and stretched out, as the PT extends?
- How often are each of the described PT stages seen as a percentage of all data? Are some observed more often than others or is the distribution equal?
- Line 125. How do the authors know that they observe nuclei? Can they identify nuclear envelopes? Are nuclear pores evident?
- Line 168 - 169: How many measurements were taken from each state? What was the mean and SD for membranes and coats? This will be interesting, especially, as the thickness of the PT coat can vary along the length of one PT.
Minor:
- Line 102 "optimal conditions" sounds obscure, please briefly mention what these are.
- Line 119. Are membrane-less PTs ever seen?
- Line 156, the word "remodelling" may be too specific, considering that only differences in thickness were measured.
- Line157: "Visualising PT sections ...." Sections sounds like physical cryosections were investigated. Perhaps better: "Inspecting tomograms of PT segments in different states..."
- Line 161: "subtomogram averaging particles picked on the tube wall from both states" better: "subtomogram averaging of the tube wall from both states"
- Line 162: delete "it be"
- Line 201: Is organ the right word here?
- Fig 2: Increase transparency to reveal the atomic model in C more clearly.
- Fig 2b. I suspect the beige ribosomes are ones that do not follow the array? If so, can you please clearly state it? Also, are these dimers too? And can you tell if they are different?
- Fig. 3e: The diagonal lines in the schematic infer that the data provide some level of insight into the PT lattice structure. As this is not the case, it would be better to remove these lines.
- A flow chart highlighting the sub-tomogram averaging workflows employed should be included.
Significance
This paper advances our knowledge of the microsporidian polar tube with regard to its structure, dynamics, and transported content. Ribosome arrays have not been described before in extended PTs, so this is an interesting discovery, which adds to the complexity of ribosome regulation in microsporidia.
Strengths are the novelty of the findings, in particular the ribosome arrays, PT dynamics, and PT composition.
As a weakness, I feel that the tomography data could have been analysed in more depth. For example, at least a low-resolution map of the ribosome dimer would be important to show that the ribosomes in the arrays are indeed dimers. In addition, 3D classification would be useful to understand, if all ribosomes occur as dimers or only a fraction.
The paper is clearly written and well presented and thus suitable for a wider audience, including researchers studying microsporidia, infection biology, host-pathogen interactions, and ribosome biology.
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Referee #2
Evidence, reproducibility and clarity
In this paper Sharma et al. use cryo-electron tomography to study structural properties of the polar tube invasion apparatus from the microsporidian parasite Vairimorpha necatrix. The main conclusions of the paper are related to the unique organization of ribosomes in the polar tube, and the organization of the surface layer of the tube. The cryo-ET data presented in this paper are of high quality, and add new insights into the structure of the polar tube, which have not been reported previously. The authors also purify an endogenous polar tube protein, PTP3, via a native cluster of histidines, and identify co-purifying proteins, which provides new insights into the proteins present in the polar tube that may interact directly or indirectly with PTP3. The endogenous purification was innovative and well carried out, a very nice result.
Major comments:
- Our biggest comment on this manuscript is that we feel the cryo-ET data are often over-interpreted. We would like to request the authors to ensure that their conclusions are justified by the data. We realize that this is a general weakness of cryo-ET at the moment, and that often features that are observed may not be able to be unambiguously defined. The interpretation of the data does need to reflect this. Below are several of the main examples we found, but we urge the authors to keep this in mind as they revise the whole manuscript.
- a) Assignment of densities: Ribosomes and lipid bilayer are reasonable to assign, because STA in Fig. S3 supports this. Fig. 1 and through the text, eg. lines 120, 126 - proteasomes, PTPs, assigning the outer layer to PTPs, is not justified based on the data. For these, it would be reasonable to speculate in the discussion, it is a reasonable hypothesis, but currently there are no data that directly support this assignment/interpretation. Statements such as in Line 184 "large-scale remodeling of the PTP layer" - are misleading, and do need to be worded with the appropriate level of certainty, currently it is only a hypothesis that this layer is, in fact, composed of PTPs.
- b) Definition and classification of Cargo: The authors observe polar tubes with different cargos in them. From the cryo-ET data itself, it is not clear what the cargo is. Based on the timescale of the event, it is unlikely that one would catch a substantial number of tubes in the process of transporting cargo. The data are still valuable, but the authors should take care in how the cargo are interpreted, and what the relationship may be to transport of sporoplasm through the tube.
- c) Time component in interpretation: The authors discuss data as a function of time, for example one section is entitled, "Remodeling of the polar tube protein layer during cargo transport". These data are simply 45 random snapshots of polar tubes, so currently there is no time component in these data. Such a section could be valuable to add to a discussion section, but since it is quite speculative, it would be misleading to a reader for these to be presented as results. Along these lines, Line 118 correlates a "germination phase" with tube thickness. As these experiments have no time component, there is no basis for this correlation. These data can of course be used to generate a hypothesis, which would be appropriate for the discussion section, or clearly indicated that it is speculative (not a direct conclusion from the data presented)
- d) Line 156-158 - is an overinterpretation of the data, because in our understanding it is currently not known what is in the tubes, and what state they are in. Please re-word.
- One of the main conclusions of the paper is the arrangement of ribosomes in the PT. Yet, these are only observed in 5/45 tomograms. What is the authors' interpretation of this observation? Are they just stuck in the tube in some cases?
- We request the authors to please provide sufficient information in their methods for reproducibility of their experiments, specifically in these sections:
- a) In the germination section please provide information on reproducibility of the spore preparation, and information on germination rates. Line 104: "with spores consistently displaying high germination efficiencies" - please clarify what "high" means.
- b) Light microscopy: please specify rates of incomplete and complete germination, how this was evaluated, how many events were analyzed, and any differences between complete (sporoplasm visible) or incomplete (sporoplasm not visible) germination.
- c) Line 325: please provide detailed information on CNN-based picking and segmentation, for example, parameters used for optimization
- d) Line 330: please specify number of tomograms
- e) Line 331: please specify how manual alignment and particle centering was achieved
- f) Line 332: please provide information on template-matching options / thresholds used
- Supporting Fig 2c: we found it confusing to understand how Pempty is defined, it does not look empty in some cases, and the 3 shown look very different. On what basis is the tube labeled "empty"? The definition provided in line 131 does not seem to match the figures.
Minor comments:
Fig. 1: The lipid bi-layer in parts a to e seems different, and we found this confusing. Is the pink label in A not pointing to the correct layer? The corresponding segmentation is also confusing - does the lipid bilayer not go all the way around the tube? This would be important to clarify, since a lipid bilayer is one of the major components of the tube.
The following publications have shown cryo-ET of the polar tube, and should be referenced appropriately in the introduction, as well as during interpretation: 1) BioRxiv, https://doi.org/10.1101/2023.05.01.538940 Figure 1 and 2) PMID: 31332877. The second is referenced but should be mentioned around line 70 in the introduction
In the introduction, it would be helpful if the authors mention something about their microsporidia species of interest, and reason for choosing to study this species.
Fig. S1C - please show individual data points
Line 100 - the data presented do not show deformation of the cargo, so please reword to reflect the data being discussed
Line 140: could the authors please outline how they confirmed that the handedness of the reconstructed tomogram is correct?
Line 250: re-word to ensure that appropriate credit is given to previous work in the field; the large-scale rearrangement of the polar tube has been observed for many decades
Line 352: out of curiosity, why could resolution not be determined for PTcargo?
Fig S2a: diagram of the polar tube within the spore shows the polar tube with opposite handedness to what has been previously determined
Supporting table 1 - is missing frames per movie and which mode data were collected in
Fig 2b: We did not follow the rationale for the 3 colors of ribosomes
Sup Fig 3a: please specify in legend and/or workflow software packages used in panel (a)
Fig 2e: it is unclear whether averages presented are from 1 tomogram, or all tomograms where that pattern is visible - Is the measurement coming from all 5 tomograms?
Fig 3 c-e: it is unclear how many tomograms were used for these averages. Was 1 STA per tomogram performed, or 1 STA per type of PT?
Significance
Overall, this paper provides interesting new insights into knowledge of the microsporidian polar tube. We thank the authors for making this paper available to the community on BioRixiv, and we summarize a few main comments below, which we hope will be helpful in preparing a revised version of the manuscript. There is a substantial advance in applying cryo-ET to studying the polar tube of microsporidian parasites. The audience this will be interesting to are those studying microsporidian parasites.
- Our biggest comment on this manuscript is that we feel the cryo-ET data are often over-interpreted. We would like to request the authors to ensure that their conclusions are justified by the data. We realize that this is a general weakness of cryo-ET at the moment, and that often features that are observed may not be able to be unambiguously defined. The interpretation of the data does need to reflect this. Below are several of the main examples we found, but we urge the authors to keep this in mind as they revise the whole manuscript.
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Referee #1
Evidence, reproducibility and clarity
Summary: Sharma, et al. report the characterization of the polar tube (PT) from the microsporidian species, Vairimorpha necatrix, using a combination of optical microscopy, cryo-ET, and proteomics. The polar tube is a fascinating invasion apparatus which mediates the translocation of the parasite into the inside of a host cell to initiate infection. Similar to results obtained previously in other species, the authors show that PT firing in Vairimorpha necatrix is extremely fast, occurring on the order of 1 sec, and that the extruded PT is over 100 microns long in this species. Using cryo-ET to image the PT at a high resolution, they find that it exists in two major states: both an empty state and a state filled with cargo, and that the thickness of the tube wall changes when cargo is present. Strikingly, the authors observed that one of the cargo components, the ribosomes, are organized ordered array that may have helical symmetry. Finally, the authors took advantage of a naturally occurring "His tag" on PTP3 to affinity purify PTP3-containing protein complexes and analyze the composition using proteomics.
Major comments
ln 139-140: The absolute handedness of something can be very tricky to determine by cryo-ET (but certainly is possible). Variable hardware configurations between microscopes and differing conventions between software packages (e.g., for what direction is a positive tilt angle) can lead to inversion of the apparent handedness in the final tomogram. How certain are the authors that the absolute handedness is indeed right handed, as this seems to vary between the various display items in the manuscript? For example, in Fig 1c, my impression is that ribosome helices are left handed, as they are also in the supplemental movie. If this isn't known with certainty, perhaps it would be sufficient to describe the apparent helical symmetry but state that the handedness is ambiguous.
Minor comments
ln 39-40: Perhaps also cite the E. cuniculi genome paper?
ln 97-98: It is interesting that the PT shortens in V. necatrix as well, and while I can pick this out in some of the individual traces in Sup Fig. 1b, it seems to get washed out in the trend line and isn't super obvious. If it isn't to laborious, it could be nice to add a panel showing the quantification of this (e.g., plotting the final length of each PT as a percentage of the maximum length achieved).
ln 98-100: Strictly speaking, I don't think the referenced figure shows the sporoplasm being transformed into an extended conformation, only that it is spherical upon exit. Simply reword this to make clear that the deformations are inferred to occur but not directly observed.
Because PT firing is so fast, the probability of trapping a PT in the process of transporting cargo would be pretty low. So then why does the PT still contain cellular cargo like ribosomes inside in the tomograms? Should these not have emerged in the sporoplasm which would enter the host cell? Are these "defective" spores that have failed to complete sporoplasm transport? Perhaps this is worth discussing.
ln 118: The authors note an apparent correlation between the phase of germination and the thickness of the tube wall but don't specify what this correlation is. Is it thicker in the early phase and thinner in later phase, or vice versa? One could imagine "empty" tubes existing before or after sporoplasm transport, for example, so I'm not sure I follow how the phase is being inferred from the tomograms.
ln 119-120: What is the evidence that the outer layer is made of PTPs, or that it is even protein (for example, as opposed to cell wall-like carbohydrate polymers)? I think this seems like a very reasonable hypothesis, but I would suggest explaining the logic and ensuring the degree of uncertainty is conveyed clearly. In light of this, I would also suggest changing figure labels, etc, that refer to the PTP layer (e.g., Fig. 3, PTPc and PTPe labels).
ln 121, 123: "sheathed by a thin layer" and "enveloped by a thick outer layer": is this an additional layer being described? Or is this referring to the putative PTP layer, and that its thickness is variable?
ln 125-126: While I understand how some features, like ribosomes, proteasomes, and generic membrane compartments could be identified, it is unclear to me how one would recognize the nucleus when inside the PT, nor are any examples shown. If the data is clear, perhaps the authors could show it in a figure? Otherwise, I suggest removing the claim regarding the nucleus.
The arrangement of the ribosomes in a subset of tubes is really fascinating! While the number of observations is relatively small (n=5), it seems like it should be possible to comment preliminarily on whether there is much variability in their helical arrangement. Do the helical parameters vary much between observations? Does the til, pitch, etc vary much, are the 5 occurrences very similar? Is there any sign that they are associated with a membrane? Also, since the ribosomes form a lattice-like arrangement, it seems like it would be possible to trace ribosome helices in both the left and right handed directions. How did the authors decide between the two possibilities? This doesn't seem to be discussed.
Fig. 2e: Are the two different colors/orientations meant to represent the two protamers of the ribosome dimer? When refined subvolumes are mapped back onto the original tomogram do the authors observe a similar crystalline arrangement of particles as in their segmentation? Are the orientations of the ribosomes correlated, and do the provide any evidence for the dimeric arrangement mentioned? The PlaceObjects plugin for Chimera can be very helpful for visualizing this: https://www.biochem.mpg.de/7939908/Place-Object
Supp figure 4(b-d): Perhaps these models could be colored by pLDDT scores (with a key indicating the color scheme), so the reader can assess the quality of the predictions?
How were the measurements of the membrane thickness and putative PTP layer carried out? On the tomogram projections? STAs? How were the boundaries of the layers established (e.g., map threshholding if STA?)? This information appears to be missing from the methods.
Some tubes that are labeled as 'PTempty' actually contain cargo and look dense (example supp. Fig 2c, left and middle panels). Is it fair to classify these as empty tubes?
Fig. 3d: I am not entirely clear on what is being shown here. Are independent reconstructions of PTcargo and PTempty superposed (aligned on membrane)? The description in the figure legend doesn't clearly say what is being displayed. I think it might be more clear to show these side-by-side instead of superposed (i.e., 4 panels instead of 2).
Sup Fig 1: Define S and SP in legend or just spell out on figure? Missing x-axis label on panel b.
Fig. 4b and Sup Fig 2a: The depictions of the PT in the spore here are left-handed. In a few species, the coil of the PT was found to form a right-handed helix (Jaroenlak, et al.), and it seems plausible that this may be a general feature that would be conserved across microsporidia. I appreciate that it might not be actually known to be right-handed in V. necatrix, but if there is no strong data either way, perhaps it would make sense for these depictions of the PT to be right-handed.
I think all three of us are more or less in consensus about this manuscript, and I largely agree with the other reviewers comments. I think after addressing reviewer suggestions, this will be a pretty nice story.
Significance
Overall, this manuscript from Sharma, et al. presents interesting new findings about the structure and cargo transport function of the microsporidian PT. Microsporidia infect a wide range of hosts, including humans, and how the PT mediates parasite entry into cells is poorly understood. The approaches used in this study are appropriate for tackling the questions at hand, and appear to be generally well executed and interpreted. The observation that ribosomes assemble into an array within the PT is very unexpected and quite fascinating, and may be of broader interest to researchers working on ribosome structure and function, in addition to researchers studying microsporidia. The approach to investigating proteins interacting with PTP3 was quite elegant, and yielded a list of potential interactors that appears to be of very high quality and is highly plausible based on the literature field. We think this work is a substantial advance in the field and provides important new insights into the organization of the PT. - Please define your field of expertise with a few keywords to help the authors contextualize your point of view:
Structural biology, microsporidia - Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.
We are not experts in proteomics/mass spectrometry
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www.biorxiv.org www.biorxiv.org
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
The authors describe a broad-scale phylogenetic survey of chemokine-related ligand and receptors from representative vertebrates, invertebrates, and viruses. They collect ligand and receptor sequences from available genome sequences, and use phylogenetic and CLANS analysis to group these into similar gene types. They then overlay these onto a validated species phylogeny in order to evaluate relationships of orthology and paralogy to pinpoint gene duplication and loss events. They carry out these analyses for canonical chemokine ligands receptors and for other closely related protein families. They conclude that the canonical chemokine system is restricted to vertebrates but that closely related ligands and receptors can be found in invertebrate chordates. More divergent but related gene systems are found in more distant invertebrates. They define more limited expansions of some ligand-receptor systems in certain jawed vertebrate groups and specifically in mammals.
Overall, the paper addresses a complex and important system of signaling proteins with a rigorous and comprehensive set of analyses. The finding will be of interest to a diverse group of scientists. My comments listed below mainly consist of suggestions to help clarify the presentation.
- Pg 2, Lns 21-24: The canonical and non-canonical chemokine subclasses are introduced in the abstract without definition. A very brief explanation would be useful.
We've included a brief description of "non-canonical" components in the abstract (lines 21-24). These non-canonical components fall into at least one of three categories: 1) molecules with sequence similarities to canonical components, 2) those that bind to a canonical component (either ligand or receptor), 3) those involved in chemokine-like functions, such as chemoattraction. More comprehensive explanations and examples of these non-canonical components are provided in the Introduction section.
- Some general contexts of chemokine functions are listed, including inflammation and homeostasis. A little more detail of how these signals are used and the molecular consequences of signaling may be useful in the introduction to set the biological context of the analysis (e.g., how do the signals regulate homeostasis?).
We have added at the beginning of the introduction (lines 39 – 46) some details of how chemokine signalling typically occurs at a mechanistic level. We also provided few examples of homeostatic functions regulated by chemokine signalling and clarified different expression strategies for inflammatory versus homeostatic chemokines.
It may help to summarize the known chemokine and chemokine-related gene systems in some type of table at the beginning of the results. This could serve as a convenient reference to guide the reader through the more detailed results. The manuscript addresses a complex set of ligands and receptors with names that may be confusing to the non-expert.
We agree with the reviewer on this and moved Table S1 to the main text (now Table 1). This table contains all the information on ligands, receptors, and relative citations (lines 741-744).
Pg 5, Ln 98: Fig 1C is introduced before Fig 1B. Can the panels be switched or the descriptions be rearranged?
We have switched the panels in Figure 1. Now, Figure 1A and 1B refer to CLANS analyses and Figure 1C and 1D refer to phylogenetic trees of ligand groups. We have corrected all the references in the main text and in Figure 1 caption. Now the panels are mentioned in the correct alphabetical order within the text.
Cytokine and chemokine ligands are small proteins that diverge quickly in different species and are difficult to identify in divergent genomes even within vertebrates. Conclusions about the absence of these types of factors are notorious for being disproven in subsequent analyses. Some discussion of what may have been missed in the survey for homologs (or reasons to think that ligands were not missed) would be useful in the Discussion.
We concur with the reviewer's observation, and we used three distinct strategies to address the issue:
- E-value Threshold Adjustment: Initially, we utilized a relatively low e-value threshold of These three strategies collectively contribute to a more robust and comprehensive approach to address the challenges associated with the bioinformatic identification of canonical and non-canonical chemokines. We briefly mentioned the technical difficulty of working with short sequences in our Introduction (lines 75-76).
Reviewer #1 (Significance (Required)):
This paper presents a thorough analysis of chemokines and related gene systems across a wide phylogenetic landscape. The authors have expertise in these gene families and in the techniques that they use to identify and relate family members. The chemokines are an important set of signals that are used across several biological systems. These findings will be of wide interest to immunologists, neurobiologists, developmental and evolutionary biologists.
We thank reviewer 1 for their comments – they have been very valuable to improve our manuscript.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
This paper applies phylogenetic clustering methods to a large taxonomical sampling to interrogate the relationship between canonical and non-canonical chemokine ligands and receptors. The results suggest that 1) unrelated proteins evolved "chemokine-like" ligand function multiple times independently; and 2) all the canonical and non-canonical chemokine receptors (except ACKR1) originated from a single duplication in the vertebrate stem group, which also gave rise to many GPCRs. In addition, the authors characterized the complement of canonical and non-canonical components in the common ancestor of vertebrates and identified several other ligands and receptors with potential chemokine related properties.
Comments: 1. There are many places in the paper, too many to list, where the authors refer to chemokine receptors but call them 'chemokines'.
We have corrected this oversight throughout the manuscript.
In Figure 1, CX3CL is referred to as 'X3CL'
We have corrected this. Now CX3CL is referred to correctly in Figure 1. We also found that it was incorrectly spelt in Figure 2 as well and corrected it there too.
- CXCL17 was originally reported to be chemokine-like based on sequence threading methods. The authors refer to a 2015 paper indicating that it has chemokine-like activity at GPR35, which had been renamed provisionally CXCR8. To my knowledge that result was not based on direct binding data but inferred from a functional response. Moreover, to my knowledge it has not been independently confirmed. Instead there is a recent paper in JI from the Pease lab showing extensive experimental results that fail to demonstrate CXCL17 activity at GPR35. This uncertainty regarding a potential mistake in the literature should be addressed and integrated in the points made about CXCL17 being an outlier.
We thank the reviewer for pointing this out. To account for this suggestion, we have modified the text as follows:
Lines 105-108: “The distinction between CXCL17 and all other canonical chemokines is consistent with our receptor results showing that the potential receptor for CXCL17, GPR35 (41), is also not within the canonical chemokine receptor group (see below). Although it is important to note that recent studies fail to demonstrate CXCL17 activity at GPR35 (42, 43).”
Lines 240-241: “Another orphan GPCR, GPR35, had been proposed as a potential chemokine receptor (41); however, this was later questioned (42, 43) and GPR35 is still generally considered orphan (55–57).”
Lines 312-315: “CXCL17 is mammal-specific and likely unrelated to canonical chemokines (similar to its controversial putative receptor, GPR35 (41-43), that is not a canonical chemokine receptor).”
References: [41] J. L. Maravillas-Montero, et al., Cutting Edge: GPR35/CXCR8 Is the Receptor of the Mucosal Chemokine CXCL17. The Journal of Immunology 194, 29–33 (2015).
[42] S.-J. Park, S.-J. Lee, S.-Y. Nam, D.-S. Im, GPR35 mediates lodoxamide-induced migration inhibitory response but not CXCL17-induced migration stimulatory response in THP-1 cells; is GPR35 a receptor for CXCL17? British Journal of Pharmacology 175, 154–161 (2018).
[43] N. A. S. B. M. Amir, et al., Evidence for the Existence of a CXCL17 Receptor Distinct from GPR35. The Journal of Immunology 201, 714–724 (2018).
[55] S. Xiao, W. Xie, L. Zhou, Mucosal chemokine CXCL17: What is known and not known. Scandinavian Journal of Immunology 93, e12965 (2021).
[56] S. P. Giblin, J. E. Pease, What defines a chemokine? – The curious case of CXCL17. Cytokine 168, 156224 (2023).
[57] J. Duan, et al., Insights into divalent cation regulation and G13-coupling of orphan receptor GPR35. Cell Discov 8, 1–12 (2022).
Can the authors use alpha fold to address whether any of these non-canonical molecules actually is predicted to fold like a chemokine? More generally, based on the paper's analysis, how do the authors propose to define a chemokine? It is well-accepted that chemokines are defined by structure, not function (e.g. limited truncation of any chemokine abrogates activity, but it is still a chemokine structurally, not semantically, folds like a chemokine, aligns with other chemokines).
In response to the recommendation from reviewer 2 to incorporate AlphaFold data, we leveraged AFDB Clusters (foldseek.com), a recently developed tool that clustered over 200 million Uniprot proteins based on their predicted AlphaFold structures (as described in this Nature paper: https://www.nature.com/articles/s41586-023-06510-w). We utilised this pre-computed dataset of clustered proteins to query with representative human proteins, both canonical and non-canonical chemokine ligands, and the results are summarised in the table below. Notably, we observed that canonical chemokines were distributed across different AlphaFold clusters, each corresponding to different ligand types (e.g., CC and CXC). Interestingly, despite this, all these clusters exhibited similar descriptions (e.g. CC or CXC), indicating that the method effectively recovers well-characterized chemokines. Conversely, when analysing non-canonical chemokine ligands, none of them were classified within the canonical chemokine clusters. This observation strongly suggests that canonical and non-canonical ligands do not share the same protein fold. Additionally, we identified intriguing correlations between these structure-based clusters and the results from our phylogenetic analyses. For instance, CXCL14 was clustered within a CC-type group, consistent with our reconciled tree positioning it within the broader CC-type clade (as shown in Figure 2A). Similarly, CXCL16 formed its own unique cluster, which aligns with our CLANS analysis, where it is the last group to connect with canonical chemokines (illustrated in Figure 1A and Figure S1). Furthermore, TAFA5 was found in a distinct cluster, mirroring our phylogenetic analyses that place it as the most basal TAFA clade (as depicted in Figure 2A and Figure S19). While these findings are intriguing, we acknowledge that additional in-depth analyses, beyond the scope of this paper, will be necessary to confirm these results.
In response to the reviewer's inquiry regarding how to define a chemokine, it is essential to recognise that many proteins can exhibit similar 3D structures without being considered homologous. A notable example is the opsins, which are present in both bacteria and animals. Despite sharing a common 3D structure that is characterised by seven transmembrane domains (TMDs) and serves similar functions, they are not regarded as homologous, as highlighted in this study (https://doi.org/10.1186/gb-2005-6-3-213). Considering these findings, we propose that, like various other gene families, the primary criterion for assessing protein homology should be rooted in shared evolutionary ancestry and common origin, and this should take precedence over structural similarities.
Human gene
Uniprot Accession
AFDB Cluster
Accession
Description
Canonical CKs
CXCL14
O95715
A0A3Q3M453
C-C motif chemokine
CCL24
O00175
A0A4X1T574
C-C motif chemokine
CX3CL1
P78423
A0A7J8CF84
C-X3-C motif chemokine ligand 1
CXCL1
P09341
A0A1S2ZIJ4
C-X-C motif chemokine
CXCL13
O43927
A0A1S2ZIJ4
C-X-C motif chemokine
CXCL8
P10145
A0A1S2ZIJ4
C-X-C motif chemokine
CCL20
P78556
A0A6P7X7F3
C-X-C motif chemokine
XCL1
P47992
A0A6P7X7F3
C-X-C motif chemokine
CXCL16
Q9H2A7
A0A6P8SIS6
C-X-C motif chemokine 16
CCL27
Q9Y4X3
A0A1L8GBB9
SCY domain-containing protein
CCL1
P22362
A0A3B4A358
SCY domain-containing protein
CCL5
P13501
A0A3B4A358
SCY domain-containing protein
CCL28
Q9NRJ3
A0A3Q0SB19
SCY domain-containing protein
CXCL12
P48061
A0A401SMI2
SCY domain-containing protein
CXCL17
CXCL17
Q6UXB2
No cluster found
No cluster found
TAFA
TAFA1
Q7Z5A9
Q96LR4
Chemokine-like protein TAFA-4
TAFA2
Q8N3H0
Q96LR4
Chemokine-like protein TAFA-4
TAFA3
Q7Z5A8
Q96LR4
Chemokine-like protein TAFA-4
TAFA4
Q96LR4
Q96LR4
Chemokine-like protein TAFA-4
TAFA5
Q7Z5A7
A0A7M4EYY1
TAFA chemokine like family member 5
CYTL
CYTL1
Q9NRR1
A0A673GVE4
Cytokine-like protein 1
CKLFSF
CMTM5
Q96DZ9
A0A4W2H069
CKLF like MARVEL transmembrane domain containing 5
CMTM8
Q8IZV2
U3IR50
CKLF like MARVEL transmembrane domain containing 7
CMTM7
Q96FZ5
A0A6G1PQK5
CKLF-like MARVEL transmembrane domain-containing protein 7
CMTM6
Q9NX76
A0A814ULI9
Hypothetical protein
CKLF
Q9UBR5
A0A3M0K8M7
MARVEL domain-containing protein
CMTM1
Q8IZ96
A0A3M0K8M7
MARVEL domain-containing protein
MAL
P21145
A0A402F5Z5
MARVEL domain-containing protein
CMTM2
Q8TAZ6
A0A6G1S7Y0
MARVEL domain-containing protein
PLP2
Q04941
A0A667IJ27
Proteolipid protein 2
CMTM3
Q96MX0
A0A3B1ILJ1
Zgc:136605
CMTM4
Q8IZR5
A0A3B1ILJ1
Zgc:136605
PLLP
Q9Y342
A0A3B1ILJ1
Zgc:136605
Chemokine genes are found on many human chromosomes with large clusters on chromosome 2 and 17. Can the authors address the syntenic relationships phylogenetically?
There are cases where synteny data have been used to infer the relationship between species (e.g. https://doi.org/10.1038/s41586-023-05936-6); however, to our knowledge, they cannot be used to infer the pattern of gene duplications and losses, as we have done here with gene tree to species tree reconciliations. However, the two approaches are extremely powerful combined and compared as they provide independent evidence. For example, with our phylogenetic analysis of chemokine ligands, we found that CXCL1-10 plus CXCL13 form a monophyletic clade (Figure 2A); this is consistent with their location on the human chromosome 4 (Zlotnik and Yoshie 2012). Similarly, most of the CC-type chemokines, that we find monophyletic in our trees, are located in a locus in human chromosome 17. Likewise, chemokine receptor phylogenetic relationships are largely consistent with macro and micro syntenic patterns. Most of the chemokine receptors are on human chromosome 3 (Zlotnik and Yoshie 2012) and they all belong to a large monophyletic clade in our tree (Figure 4A). Smaller clusters also maintain correspondence, such as the mini cluster of CXCR1 and CXCR2 on human chromosome 2 corresponding to a monophyletic clade in our phylogenetic analysis (Figure 4A).
We have incorporated the above considerations in our manuscript at the lines:
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Lines 140-148 (ligands)
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Lines 256-272 (receptors)
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Lines 375 – 483 (discussion)
The authors indicate that 'CXCL8 is present in all jawed vertebrates except in the cartilaginous fishes lineage'. However, they should point out that CXCL8 is not represented in mice. The notion that the repertoire of chemokine and chemokine receptor genes can be different in even closely related species as well as in individuals of the same species is well-documented but not mentioned here.
We thank the reviewer for these suggestions, and we have modified the text in lines 137-138.
The analysis suggests that chemokine gene repertoires start small and grow non-linearly to 45 in mammals. However DeVries et al (JI 2005) published that zebrafish have the most chemokines, 63, and chemokine receptors, 24. Do the authors disagree? This should be addressed.
The significant increase in the number of ligands and receptors in zebrafish, compared to their last common mammalian ancestor, can be attributed to an additional round of whole-genome duplication (WGD) (https://doi.org/10.1016/S0955-0674(99)00039-3).
Concerning ligands, the count in zebrafish varies from 63 in DeVries et al. 2005 to 111 in Nomiyama et al. 2008, and to 35 in our study. This variation can be attributed to several factors:
- Genome Versions: The disparities may arise from the use of different versions of the zebrafish genome. We utilised an improved version known for its higher contiguity and reduced fragmentation (https://www.nature.com/articles/nature12111). It is possible that the additional ligands identified by DeVries, Nomiyama, and others were partial sequences.
- Methodology: Methodological differences are at play. DeVries et al. employed tblastN, while we opted for BLASTP. Nomiyama et al. do not specify the type of BLAST performed.
- Stringency: We collected our sequences based on a BLASTP search using as query sequences only manually curated sequences from UniProt. This additional precaution allowed us to identify sequences with high-confidence chemokine ligand characteristics.
- Sequence Characteristics: Ligands typically have shorter sequences and exhibit less sequence conservation compared to receptors. Zebrafish represents a case in which working with short sequences may lead to missed homologs.
- Species-Specific Nature: Our approach successfully recovered the complete set of ligands in other species, such as humans and mice. Zebrafish appears to be an exception rather than the norm. When it comes to receptors, which typically have longer sequences, making it easy to identify distant homologs, our results closely mirror those of DeVries in 2005. In our study, we identified 28 canonical receptors, compared to their count of 24. However, it is worth highlighting that within our dataset, four of these receptors appear as species-specific duplications, potentially indicating that they are actually isoforms or related variants.
Nonetheless, it is essential to emphasise that our work does not aim to precisely reconstruct the entire complement of ligands and receptors in zebrafish or other species. Achieving this would require further validation, including the expression analysis of potential transcripts.
Did the authors find any species in which a chemokine/chemokine receptor pair are not found together? That is, if the system is irreducibly complex, requiring both a ligand and receptor, the probability of both genes arising simultaneously is essentially zero. So how do the authors theorize that such a system actually arose, and is there any evidence in their data set for convergence of separately evolved ligand and receptor?
Our data strongly support the hypothesis that the canonical chemokine system originated within the stem group of vertebrates, likely as a consequence of two rounds of genome duplication. This likely accounts for the simultaneous emergence of both ligands and receptors. While the receptors (both canonical and non) can be traced back to a single-gene duplication event (with the exception of ACKR1), the evolution of ligand families capable of interacting with chemokine receptors occurred independently, although further experiments are required to validate this in vivo in a broader set of organisms. In our study, we successfully identified the complete set of receptors and ligands in well-established model systems like humans and mice. However, when it comes to interactions between ligands and receptors outside these model organisms, the picture becomes less clear. Similarly, the exact pairings of non-canonical components are also not fully clarified (see lines 404-406). As a result, speculating about evolutionary conservation in these contexts requires caution and further investigation. It's worth noting that chemokines and their corresponding chemokine receptors do not necessarily evolve in tandem. Since they are encoded by different genes, they evolved from separate duplication events occurring at different points in evolutionary history. In certain instances, due to the system's flexibility, chemokines binding orthologous receptors may not be orthologous themselves but may have independently acquired the ability to activate the same receptor in various species.
Line 180, 181 and elsewhere: GPCR1 and GPCR33 should be GPR1 and GPR33
We have corrected this throughout the manuscript.
Line 185: ACKR1 exceptionalism is noted, but there is no discussion of the remarkable structure-function paradox that the most distantly related chemokine receptor is also the most highly promiscuous receptor, binding many but not all CC and CXC chemokines with high affinity.
We added in the discussion section this consideration regarding the wide binding of ACKR1 (Lines 341-343) and its ability to bind both CC and CXC chemokines (DOI: 10.1126/science.7689250 and 10.3389/fimmu.2015.00279), highlighting the intriguing contrast with the fact that it is the most distantly related receptor.
Line 196: the viral receptors cluster with the vertebrate receptors, suggesting that the viruses captured the receptor gene from the host. Authors might mention this obvious point regarding origins, and discuss how it relates to the monophyly and paraphyly that emerges from the phylogenetic analysis.
We added a comment to the discussion section (Lines 348-352) regarding the potential origins of the viral chemokine receptors.
Any discussion of chemokine-like convergent evolution presupposes that the activity is real and actually occurs in vivo. The authors should make clear to what extent the existing literature supports this. As mentioned above, CXCL17 interaction with GPR35 has been challenged in vitro and has never been demonstrated to occur in vivo. To what extent is the same limitation a problem in considering co-evolution of the other non-canonical chemokines? I agree that classification based solely on function is inappropriate, but so is phylogenetic analysis without direct knowledge of in vivo function. It is no feasible to address this in a phylogenetic analysis, but there ought to be at least one species in which the non-canonicals have been rigorously shown to act at specific receptors in vivo before grouping them with the canonicals in a co-evolutionary sense.
We agree with the referee that evidence of real chemokine-like activity is important to consider the activity in vivo.
In our work, the molecules examined were chosen based on previous evidence of chemokine-like sequence similarity, ability to bind canonical components and/or chemokine-like function. For example, CKLF (also called CKLF1) has been shown, through calcium mobilisation and chemotaxis assays using the human cell line HEK293, to bind CCR4 and to induce cell migration via CCR4 respectively (https://doi.org/10.1016/j.lfs.2005.05.070). Numerous papers are studying the in vitro and in vivo effects of CKLF in murein and human models (https://doi.org/10.1016/j.cyto.2017.12.002), therefore, we found it compelling to investigate its evolutionary relationship with canonical chemokines. Similarly, CYTL1, that had been predicted to possess an IL8-like fold (https://doi.org/10.1002/prot.22963), has been found to bind CCR2 (https://doi.org/10.4049/jimmunol.1501908) and in vitro and in vivo studies showed chemotactic activity for neutrophils (https://doi.org/10.1007/s10753-019-01116-9). Ongoing research into this molecule are focusing on a wide array of immune functions (https://doi.org/10.1007/s00018-019-03137-x).
We mentioned these considerations in our introduction to explain why we were interested in investigating these molecules (lines 50-57). We have also added a line in the Discussion (lines 323-324) where we reinforce the idea that in vitro and in vivo experiments for all chemokine-like molecules are required to validate computation predictions.
The discussion of homeostatic vs inflammatory chemokine/receptors in the last section of the Discussion would be enhanced by pointing out that the chemokine specificities are numerically totally different for these two groupings, homeostatics tending to have monogamous ligand-receptor relationships and inflammatories being highly promiscuous.
To account for the reviewer’s comment, we have added this consideration in a paragraph of the discussion (see Line 389-394).
Reviewer #2 (Significance (Required)):
Much of the paper's results are confirmatory of previous work based on less extensive sequence analysis. One could say more generally that unrelated chemical forms, not just unrelated proteins, have chemokine-like ligand function. For example leukotriene B4 is a powerful leukocyte chemoattractant for neutrophils working through a GPCR. That proteins might also independently evolve common functions does not add insight beyond what is already appreciated. The notion that chemokine receptors have a common ancestor is also generally accepted and that ACKR1 is an outlier is already appreciated. The present work adds phylogenetic and statistical precision to these points.
Our discoveries clarify various aspects of the chemokine system's evolution, and we are confident that the "phylogenetic and statistical precision" of our findings will provide a solid cornerstone for future research aimed at unravelling the function and evolution of the system. Specifically, our work clarified:
- The presence only in Vertebrates: We have confirmed, through a comprehensive taxonomic sampling (we use many more species than previous works), that the chemokine system is exclusive to vertebrates. However, intriguingly, we identified a TAFA chemokine-like family in urochordates.
- Relationships between Ligands: We conducted a thorough examination of the relationships between canonical and non-canonical ligands and suggested that several unrelated molecules might have evolved independently their ability to interact with the chemokine receptors. We appreciate the comment of the reviewer regarding the fact that unrelated chemical forms such as leukotriene B4 may have chemokine-like functions. However, in our work all the non-canonical components examined are proteins and as such could have an evolutionary relationship with chemokines. Furthermore, we chose to consider only proteins that showed multiple lines of evidence implicating them in the chemokine system and that are currently the topic of interest in the field (see replies to reviewer 1’s comment #5 and to reviewer 2’s comment #12). Seeing the general interest in the topic, and especially seeing as this had never been clarified before, in this work, we set ourselves the goal to investigate the evolutionary relationship amongst these non-canonical ligands and canonical chemokines.
- Duplication Events: We pinpoint the specific gene duplication events responsible for the emergence of chemokine receptors.
- Atypical Receptor Paraphyly: Our work highlights the paraphyletic nature of atypical receptors, in contrast to previous research (see https://doi.org/10.1155/2018/9065181).
- Viral Receptor Phylogenetics: To our knowledge, this is the first work to investigate the phylogenetic affinities of viral receptors.
- GPCR182 and Atypical Receptor Affinities: We clarify the affinity of GPCR182 with atypical receptor 3, offering different insights compared to prior studies (see figure S3C in https://doi.org/10.1038/s41467-020-16664-0).
- Additionally, our study represents the first analysis of the chemokine system in the basal vertebrate hagfish and provides insights into the ancestral form of the chemokine system.
- Ultimately, our research identifies numerous molecules and receptors with potential chemokine functions. In conclusion, we contribute to resolving uncertainties surrounding the system's origin, including the complex duplication events that have shaped receptor evolution. As evident from the extensive comments provided by the reviewer, our work addresses various controversies in the field (e.g. the inclusion of CXCL17 as a chemokine). Nonetheless, like any new set of findings, our work amalgamates confirmatory results (as highlighted in point 1) with innovative discoveries (as outlined in points 2-8). However, the latter category significantly outweighs the former, underscoring the richness of novel insights.
Finally, we would like to thank reviewer 2 for their comments, as these have contributed to greatly improve our manuscript.
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Referee #2
Evidence, reproducibility and clarity
This paper applies phylogenetic clustering methods to a large taxonomical sampling to interrogate the relationship between canonical and non-canonical chemokine ligands and receptors. The results suggest that 1) unrelated proteins evolved "chemokine-like" ligand function multiple times independently; and 2) all the canonical and non-canonical chemokine receptors (except ACKR1) originated from a single duplication in the vertebrate stem group, which also gave rise to many GPCRs. In addition, the authors characterized the complement of canonical and non-canonical components in the common ancestor of vertebrates and identified several other ligands and receptors with potential chemokine related properties.
Comments:
- There are many places in the paper, too many to list, where the authors refer to chemokine receptors but call them 'chemokines'.
- In Figure 1, CX3CL is referred to as 'X3CL'
- CXCL17 was originally reported to be chemokine-like based on sequence threading methods. The authors refer to a 2015 paper indicating that it has chemokine-like activity at GPR35, which had been renamed provisionally CXCR8. To my knowledge that result was not based on direct binding data but inferred from a functional response. Moreover, to my knowledge it has not been independently confirmed. Instead there is a recent paper in JI from the Pease lab showing extensive experimental results that fail to demonstrate CXCL17 activity at GPR35. This uncertainty regarding a potential mistake in the literature should be addressed and integrated in the points made about CXCL17 being an outlier.
- Can the authors use alpha fold to address whether any of these non-canonical molecules actually is predicted to fold like a chemokine? More generally, based on the paper's analysis, how do the authors propose to define a chemokine? It is well-accepted that chemokines are defined by structure, not function (e.g. limited truncation of any chemokine abrogates activity, but it is still a chemokine structurally, not semantically, folds like a chemokine, aligns with other chemokines).
- Chemokine genes are found on many human chromosomes with large clusters on chromosome 2 and 17. Can the authors address the syntenic relationships phylogenetically?
- The authors indicate that 'CXCL8 is present in all jawed vertebrates except in the cartilaginous fishes lineage'. However, they should point out that CXCL8 is not represented in mice. The notion that the repertoire of chemokine and chemokine receptor genes can be different in even closely related species as well as in individuals of the same species is well-documented but not mentioned here.
- The analysis suggests that chemokine gene repertoires start small and grow non-linearly to 45 in mammals. However DeVries et al (JI 2005) published that zebrafish have the most chemokines, 63, and chemokine receptors, 24. Do the authors disagree? This should be addressed.
- Did the authors find any species in which a chemokine/chemokine receptor pair are not found together? That is, if the system is irreducibly complex, requiring both a ligand and receptor, the probability of both genes arising simultaneously is essentially zero. So how do the authors theorize that such a system actually arose, and is there any evidence in their data set for convergence of separately evolved ligand and receptor?
- Line 180, 181 and elsewhere: GPCR1 and GPCR33 should be GPR1 and GPR33
- Line 185: ACKR1 exceptionalism is noted, but there is no discussion of the remarkable structure-function paradox that the most distantly related chemokine receptor is also the most highly promiscuous receptor, binding many but not all CC and CXC chemokines with high affinity.
- Line 196: the viral receptors cluster with the vertebrate receptors, suggesting that the viruses captured the receptor gene from the host. Authors might mention this obvious point regarding origins, and discuss how it relates to the monophyly and paraphyly that emerges from the phylogenetic analysis.
- Any discussion of chemokine-like convergent evolution presupposes that the activity is real and actually occurs in vivo. The authors should make clear to what extent the existing literature supports this. As mentioned above, CXCL17 interaction with GPR35 has been challenged in vitro and has never been demonstrated to occur in vivo. To what extent is the same limitation a problem in considering co-evolution of the other non-canonical chemokines? I agree that classification based solely on function is inappropriate, but so is phylogenetic analysis without direct knowledge of in vivo function. It is no feasible to address this in a phylogenetic analysis, but there ought to be at least one species in which the non-canonicals have been rigorously shown to act at specific receptors in vivo before grouping them with the canonicals in a co-evolutionary sense.
- The discussion of homeostatic vs inflammatory chemokine/receptors in the last section of the Discussion would be enhanced by pointing out that the chemokine specificities are numerically totally different for these two groupings, homeostatics tending to have monogamous ligand-receptor relationships and inflammatories being highly promiscuous.
Significance
Much of the paper's results are confirmatory of previous work based on less extensive sequence analysis. One could say more generally that unrelated chemical forms, not just unrelated proteins, have chemokine-like ligand function. For example leukotriene B4 is a powerful leukocyte chemoattractant for neutrophils working through a GPCR. That proteins might also independently evolve common functions does not add insight beyond what is already appreciated. The notion that chemokine receptors have a common ancestor is also generally accepted and that ACKR1 is an outlier is already appreciated. The present work adds phylogenetic and statistical precision to these points.
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Referee #1
Evidence, reproducibility and clarity
The authors describe a broad-scale phylogenetic survey of chemokine-related ligand and receptors from representative vertebrates, invertebrates, and viruses. They collect ligand and receptor sequences from available genome sequences, and use phylogenetic and CLANS analysis to group these into similar gene types. They then overlay these onto a validated species phylogeny in order to evaluate relationships of orthology and paralogy to pinpoint gene duplication and loss events. They carry out these analyses for canonical chemokine ligands receptors and for other closely related protein families. They conclude that the canonical chemokine system is restricted to vertebrates but that closely related ligands and receptors can be found in invertebrate chordates. More divergent but related gene systems are found in more distant invertebrates. They define more limited expansions of some ligand-receptor systems in certain jawed vertebrate groups and specifically in mammals.
Overall, the paper addresses a complex and important system of signaling proteins with a rigorous and comprehensive set of analyses. The finding will be of interest to a diverse group of scientists. My comments listed below mainly consist of suggestions to help clarify the presentation.
- Pg 2, Lns 21-24: The canonical and non-canonical chemokine subclasses are introduced in the abstract without definition. A very brief explanation would be useful.
- Some general contexts of chemokine functions are listed, including inflammation and homeostasis. A little more detail of how these signals are used and the molecular consequences of signaling may be useful in the introduction to set the biological context of the analysis (e.g., how do the signals regulate homeostasis?).
- It may help to summarize the known chemokine and chemokine-related gene systems in some type of table at the beginning of the results. This could serve as a convenient reference to guide the reader through the more detailed results. The manuscript addresses a complex set of ligands and receptors with names that may be confusing to the non-expert.
- Pg 5, Ln 98: Fig 1C is introduced before Fig 1B. Can the panels be switched or the descriptions be rearranged?
- Cytokine and chemokine ligands are small proteins that diverge quickly in different species and are difficult to identify in divergent genomes even within vertebrates. Conclusions about the absence of these types of factors are notorious for being disproven in subsequent analyses. Some discussion of what may have been missed in the survey for homologs (or reasons to think that ligands were not missed) would be useful in the Discussion.
Significance
This paper presents a thorough analysis of chemokines and related gene systems across a wide phylogenetic landscape. The authors have expertise in these gene families and in the techniques that they use to identify and relate family members. The chemokines are an important set of signals that are used across several biological systems. These findings will be of wide interest to immunologists, neurobiologists, developmental and evolutionary biologists.
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Reply to the reviewers
The authors do not wish to provide a response at this time
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Referee #3
Evidence, reproducibility and clarity
There is mounting evidence pointing towards an association of HSV with ApoE and Alzheimer's disease. Although it has been shown that ApoE impacts HSV-1 spread in animal models in an isoform specific fashion, the molecular relationship between the virus and ApoE is unclear. The present study probes the role of ApoE on the viral life cycle, clearly an important aspect if one is to better understand how the virus may influence the disease. Using assays monitoring various steps of viral replication, the authors report that ApoE perturbs the interaction of the virus with the cell surface, both during the initial binding and following viral release. Furthermore, they show that ApoE 3 and 4 exert a proviral effect, with a smaller impact with ApoE 2.
General Comments
The comprehensive study addresses an important point that may help clarify the interaction between HSV-1 and Alzheimer's disease. It major strength is that it is systematic and elegantly performed. The paper convincingly shows that ApoE impact cell adhesion at the cell surface. In general, the data have properly been analyzed statistically. Some aspects are however enigmatic. First of all, how can ApoE be proviral if it prevents the initial binding of the virus to the cells? Second, less viral binding should mean less entry (as shown in figure 2) and subsequently fewer genome copies, but that is not what is reported in figure 3. This is not clearly stated in the discussion (lines 457-459 and again in lines 490-491). Finally, if ApoE is unstable as indicated on lines 495-497, how can it be active later on at 24 hpi and prevent viral release? How do the authors reconcile these observations? Of interest, ApoE 3 has a slightly greater impact on viral growth than other isoforms (fig 1), which is not quite fitting the model that ApoE4 is the main culprit for Alzheimer's disease. Could the authors comment? Where are the ApoE proteins normally expressed in cells? At the cell surface or intracellularly? This may provide a hint as to where the virus picks it up when incorporating it. Immunofluorescence would be a great addition (e.g., Huh-7 cell line). Similarly, does the virus impact the expression level of ApoE? One could resolve the dilemma that ApoE blocks the initial binding of the virus but stimulates viral release at later time points if the virus induces ApoE at those late times. Furthermore, if the Vero or SH-SY5Y cells don't normally express ApoE, then it should not be important for the virus in that context. How about keratinocytes, the normal host of the virus? These considerations should be addressed in the manuscript by Western blotting at different time points.
Significance
This study adds an interesting twist and advances the infectious etiology model of Alzheimer's disease and should appeal to a broad authorship ranging from the neurobiology to virology.
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Referee #2
Evidence, reproducibility and clarity
In the manuscript "Recruitment of apolipoprotein E facilitates Herpes simplex virus 1 release", by Liu et al, the authors investigate the effect of ApoE protein on HSV-1 replication. Treatment of infected cultures with ApoE proteins appeared to increase the production of infectious titer. The authors perform several experiments to determine which steps of the virus replication cycle are affected. ApoE proteins reduce virus attachment, but subsequent reductions in entry are explained by the reduction in attachment, suggesting that the efficiency of entry is not affected. Viral DNA replication and cell-surface virus amounts appear to be unaffected, but the release of virus titer to the supernatant is increased. These results suggest that ApoE effects virus replication in two ways: reduces attachment if inoculum, but subsequently increases release of progeny. To determine whether this is the case, the authors then measure the release of attached virus particles from native membranes in the presence or absence of ApoE4, and derived from cells inducibly expressing ApoE4.
The manuscript is generally well written and the experiments generally appear to be performed well. However, the importance and impact of this manuscript are limited by two major weaknesses:
- It seems that effects are only seen with high concentrations of ApoE. How does this concentration compare to what would be found in blood plasma/tissues/secreted by Huh-7 cells? Thus, these results may not be biologically relevant. It is difficult to determine what concentrations of ApoE are used in some cases, e.g. Fig 6. Please provide this information in the figure or figure caption.
- While there are some interesting results here, this manuscript does not get to the point of establishing mechanism. In the discussion, it is speculated that ApoE functions via GAGs/HSPGs, which are known to affect HSV-1 attachment/release. It would make the manuscript much stronger to include experiments adding soluble heparin or treating cells with heparinase, or producing gC-null virus particles, to see if this abolishes the attachment/release effects of ApoE.
Minor points:
Fig. 3B: It is difficult to compare virus genomes by qPCR to virus titer of supernatants. If ApoE is promoting release of cell surface virus, why does an increase in titer in the supernatants not show a corresponding decrease in cell surface virus?
Fig. 6B: "Spdi I" term is not used in the results section or figure legend. I do not know what "Spdi I" means.
Fig. 6E, Fig 7: Normalized values can be misleading. Please provide raw values. Please show dissociation curves, as in Fig. 6D, for Fig.7.
It would be nice to perform the same analysis of supernatant vs. cell surface vs. intracellular virus as in Fig. 3B, and the release on ice measures as in Fig 4, using the inducible expression HEK cell line.
Discussion: Degradation of ApoE (line 495-500). The degradation of ApoE in these infection experiments could be measured by e.g. western blot of cell supernatants. This suggestion is a bit troubling: If the ApoE is degraded during the first replication cycle, how is it able to have an ongoing effect? How can ApoE simultaneously be present to promote release of progeny, while being degraded so as not to prevent attachment to subsequent cells.
I do not see that "GMK AH-1" cells are available from ATCC, as stated in the methods. Is this a synonym for Vero cells?
Although I understand what is meant, "dissolvent" is not a common term. "diluent" or "vehicle" is more common.
Significance
General assessment: The manuscript is generally well written and the experiments generally appear to be performed well. However, the significance of this manuscript are limited by two major weaknesses:
- It seems that effects are only seen with high concentrations of ApoE. How does this concentration compare to what would be found in blood plasma/tissues/secreted by Huh-7 cells? Thus, these results may not be biologically relevant. It is difficult to determine what concentrations of ApoE are used in some cases, e.g. Fig 6. Please provide this information in the figure or figure caption.
- While there are some interesting results here, this manuscript does not get to the point of establishing mechanism. In the discussion, it is speculated that ApoE functions via GAGs/HSPGs, which are known to affect HSV-1 attachment/release. It would make the manuscript much stronger to include experiments adding soluble heparin or treating cells with heparinase, or producing gC-null virus particles, to see if this abolishes the attachment/release effects of ApoE.
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Referee #1
Evidence, reproducibility and clarity
This manuscript attempts to identify the molecular basis for the reported interactions between apolipoprotein E (ApoE) and herpes simplex virus 1 (HSV1), known to be a significant marker for Alzheimer's disease. The authors employ a combination of cellular and in vitro assays designed to assess the effect of ApoE on different stages of the HSV1 life cycle. These experiments reveal an effect of ApoE on virus binding to and detachment from cell membranes, but not in other aspects of the viral life cycle. Further, the isoform ApoE4 was found to be the most effective in exerting these changes, possibly due to competitive binding with cell surface receptors that can associate with both ApoE and HSV1. Prior studies were referenced appropriately. For the most part, sufficient details about the data acquisition and analysis workflow were included in the study, although a couple of exceptions have been noted. More information about number of data sets for each panel and statistical analysis need to be included in the manuscript (figure legends and a separate section in Materials & Methods). Some of the key conclusions from the data require additional context and information to justify their interpretation in the present form. The additional experiments suggested are reasonable in terms of time and resources and are critical for strengthening the key conclusions in the manuscript. These have been noted below. Comments on evidence, reproducibility and clarity
Major Comments:
- The authors mention in the Discussion section that they have ruled out interaction of ApoE with HSV1 glycoproteins B, C, D, and E based on immunoprecipitation data that is not included in the manuscript. In view of this, how do they justify using HSV1 gC as a marker for checking for association of HSV1 with ApoE (Fig 5)? Further, the authors should consider including said immunoprecipitation data in the manuscript, since those would be of immense value in further studies looking for other interaction partners of ApoE (as the authors have stated in the Discussion section).
- How strong are the interactions between ApoE and HSV-1? In other words, what fraction of the available ApoE could be expected to associate stably with HSV1? Can ApoE or ApoE-associated complexes act as a trap for the virus and therefore represent latent virus pools in cells? How were possible contributions to HSV1 detachment from other cellular factors associated with ApoE ruled out?
- The Discussion section of the manuscript clearly places the detected interaction of ApoE with HSV1 in the context of previous literature on related facets of the crosstalk between ApoE and viral infections. The authors may also consider including a paragraph on the more physicochemical attributes of ApoE interaction with HSV1, which would make the Discussion section more well-rounded and provide some background for understanding the biophysical experiments reported here (Fig. 6, 7). For example, how do the dissociation rates they report in Fig. 6 compare to those reported earlier for viruses on SLBs or cellular membranes? Could these dissociation rates be readily converted to (at least semi-quantitative) estimates of the thermodynamics of ApoE binding to HSV1 or would other factors need to be explicitly considered for such analytical exercises? Would it be difficult to measure binding affinities using HSV1 and purified ApoE by complementary approaches such as calorimetry or surface plasmon resonance? On a related note, what kind of ApoE concentration could HSV1 encounter in a cellular milieu and would it be in the range (5 μM) at which they report significant effects of ApoE on HSV1? What could be expected to happen at even higher concentrations of ApoE (reported for other cellular pathologies)?<br /> Why is the isoform dependence of ApoE effects observed predominantly in case of HSV1? Could this be related to the more complex fusion machinery available to this virus?
- It is strongly recommended that details about ApoE purification and characterization are included in the manuscript, along with appropriate references. It is also not clear how a 4h period was deemed to be sufficient for incubation with ApoE (Fig. 2).
- The data in Fig 4 is not entirely sufficient to support claims of ApoE enrichment in virus particles released into the supernatant. The authors may consider including additional experiments to check for (and quantify, if possible) ApoE levels in these virus fractions (since these conditions are drastically different than that used for reporting co-sedimentation of ApoE and HSV-gC in Fig. 6B).
- Fig 5: It is not clear why the authors tested only for gC (especially when they note that co-immunoprecipitation experiments have ruled out gC as a possible interaction partner for ApoE; also see comment 1). Is the shift in the ApoE band to higher kD values from fraction 1 to 6 significant? What do the error bars in Fig 5B represent, if data was generated from two independent replicates? How do you reconcile the very high viral titer of fraction 3 (Fig 5C) with the moderate level of gC_HSV-1 in the same fraction (Fig 5B)? Does this indicate heterogeneity in gC content across seemingly equivalent viral titers (fractions 1-3, based on Fig 5C) ?
- Several inconsistencies were noted in figures and figure legends that could affect a clear understanding of the data by readers. For all figures, please indicate clearly if no notation for statistical tests denote an absence of significance (ns) or that significance was not tested (such as for Fig. 2B, C). Please include sufficient information regarding number of independent replicates for each panel (i.e., what do error bars represent). For t-tests, please indicate clearly the reference data sets used for testing statistical significance and define symbols used for different p-values only for that specific figure. For example, a p-value corresponding to * is defined in the legend to Fig. 2, although that p-value is not indicated in the figure.
Minor Comments:
- P. 4: abbreviation HSPG is not defined
- Inconsistent figure formatting noted in terms of non-uniform axis labels, color coding, inclusion of error bars, clear label of all lanes in blots. These should be reviewed and modified as appropriate.
- Fig 1A: The authors may consider reverting the order of ApoE concentration (ascending, instead of descending) in X-axis label to make it more intuitive.
- Line 167: please specify that data in Fig S1b refers only to SH-SY5Y cells.
- In Fig 2A, value for dissolvent should be set to 100%, since rest of the data are normalized with respect to that.
- Line 279 refers to Fig. 3C (not present in the manuscript).
- Ladders not clearly visible in some blots, such as that for 50 kD in Fig S1 and Fig 5A (HSV1 infected panel).
- Please indicate clearly in the Materials & Methods if ApoE induction (Fig. 7) is performed in HEK cells or HEK-293T cells.
Significance
This study sheds light on the molecular basis of the interaction between HSV1 and ApoE and represents a conceptual advance in the field. Since such interactions have been reported to be a marker for patients at high risk of developing Alzheimer's disease, these findings would be important in designing future clinical studies on prognostic and diagnostic advances in neurodegenerative diseases. As such, this manuscript would be of interest to a broad spectrum of scientists and clinicians including virologists, biochemists, and biophysicists.
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Hermanns et al. report a robust biochemical and structural characterization of the TSSM virulence effector of Burkholderia pseudomallei, the causative agent of melioidosis, with TSSM being the only USP-type deubiquitinase of bacterial origin. The authors demonstrate ubiquitin specificity, and the capacity of the DUB to cleave most Ubiquitin chain types in vitro. By solving crystal structures of TSSM in isolation as well as in covalent complex with Ubiquitin, the authors rationalize how the truncated fold (compared to all other eukaryotic USPs) is capable of binding ubiquitin. The most surprising finding is the conformation of the so-called little finger loop, unique to TSSM, which engages the distal Ubiquitin in a manner not seen in any other DUB. These findings are convincingly validated by point mutations in biochemical assays and bioinformatic analyses. The structures also yielded information on the fold an N-terminal Ig-like domain with unknown function.
The authors are encouraged to address the following main points before publication can be supported:
• On page 6, the authors state "the little finger loop contacts hydrophobic residues around Ile-44 of ubiquitin, an important interaction interface that is not observed in USP7 and is not part of the canonical USP: ubiquitin interface". The latter statement I believe is simply wrong as Ile44 is at the centre of the large canonical USP:ubiquitin interaction interface observed in all structurally studied USP proteins so far (the recognition is through varying residues, that are not always strictly hydrophobic, but it is always contacted). This is occurring at the hinge between thumb and fingers. In USP7, this is for example occurring through the hydrophobic part of Arg301. The authors are referred to O'Dea et al. Nat Chem Biol 2023, where same conformation of the equivalent residue in USP36 is discussed in the context of Ile44-Ubiquitin vs. Fubi recognition.
We thank reviewer #1 for this detailed explanation. Initially we made this statement based on the fact that in other USPs, the interaction is not always hydrophobic. However, we now agree with reviewer #1 and have removed this statement from the manuscript.
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This should also be phrased very precisely, as Ile44 itself is not actually contacted by the little finger loop (see Fig. 3c), but other hydrophobic residues commonly included in the Ile44 patch are. However, also those are always in the Ubiquitin:USP interface.
These section was removed in line with the previous point. Additionally, we added the information that Ile44 itself is not contacted to the main text.
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It appears as if the little finger loop takes up the exact position of what is commonly referred to as blocking loop 1 (terminology introduced by Hu et al EMBO J 2005, which the authors could also use when referring to canonical USPs). The connectivity in the domain is not equivalent to BL1 though (this could be shown with topology diagrams of a canonical USP and of TSM), but the relative position is. Importantly, BL1 in USPs typically engage the Ile36 patch with a large aromatic residue (e.g., a Trp in USP30 or a Tyr in USP7), however, in TSSM Ubiquitin is rotated such that the Ile44 patch is recognised at the exact spot where normally Ile36 is engaged. Can the authors comment on this interpretation? This reviewer would also find a comparison to the canonical USP recognition mode in the main figures beneficial (however one that is less crowded than in the SI) to make this very important finding come across well. Even if the loop is more like a BL1 (which is highly diverse in the USP family, see reference above), it can still be called "little finger", but the analogies should be carefully phrased throughout the text (e.g., page 9: "functionally but not structurally analogous" would not be correct if it does not replace the fingers which contact Phe4 as BL1 is not part of the fingers).
The reviewer is correct. The positioning of BL1 from canonical USPs and the TssM Littlefinger loop is superficially similar. Since the Littlefinger is derived from a different part of the sequence (Fig 1a) and contacts a different patch of ubiquitin, it is still distinct from the BL1. We have incorporated this information into the main text and generated a new superposition to show the structural similarities (Supplementary Fig 3c). Additionally, we highlighted BL1 and Littlefinger in Fig1a to show the different position within the amino acid sequence. Since BL1 and Littlefinger are distinct from each other we still classify the Littlefinger as functionally (but not structurally) analogous to the fingers domains. Both serve as the main contacts to ubiquitin and (partially) interact with the Ile44 patch, while BL1 contacts the Ile36 patch.
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The assessment of the molecular weight of apo TSSM in solution by SEC is flawed in its current form (page 7, Fig. 4b). There are only two, seemingly randomly chosen, comparison proteins, and the GST-Ub is unsuitable for 36 kDa because it is an obligate dimer (due to the GST) and as a fusion protein has a much higher hydrodynamic radius than a globular protein. There are commercial reference proteins that can be used which have defined oligomerisation states and are (reasonably globular) so that the retention times can be quantitatively analysed. Such a reference, which also includes proteins of smaller sizes, must be used. Alternatively, the authors should use light-scattering (SEC-MALS or SEC-RALS) to unequivocally demonstrate the molecular weight / oligomeric state of TSSM.
The experiment has been re-done using commercially proteins routinely used for the calibration of SEC columns. Additionally, a 75pg column was used to achieve a better resolution of the proteins. The new data confirm the original conclusion that TssM behaves like a monomer in solution.
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The abstract ends with speculation on the presence of the strand-swapping in live cells and of the role of the Ig-fold domain, but not a proper conclusion. The same applies to the ending of the results section (page 8), where it is not clear where the structural analysis of the Ig-like domains is leading. The authors then speculate about sugar binding (page 10) in the discussion. The latter could be substantiated by an analysis of the residues in the possible sugar binding site (if present in their TSSM), and the text be more rounded off at these regions.
We admit that our manuscript does not reveal the function of the Ig-like domain and the strand swap observed in the apo structure. To really address these questions, an infection model would be required, which is outside the scope of our manuscript focussing on the TssM mechanism. As have showed that deletion of the Ig-domain does not alter the DUB properties, the remaining questions cannot be addressed in vitro. To address the concerns of the reviewer, we have changed the ending of the result section (page 8) to summarize our in vitro data on the Ig-like domain. We have also clarified the paragraph discussing the possibility of sugar binding on pages 10/11. In brief, this speculation was not based on the identification of a possible sugar binding site, but rather on the presumed positioning of the Ig-like domain relative to the bacterial surface and its evolutionary relationship to other sugar-binding domains.
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The authors show in Fig. 1b TSSM reactivity with both Ubiquitin and Nedd8 probes but then qualify the Nedd8-reactivity with the assays shown in Figs. 1cd, which currently looks like it is an issue with the comparison of probe to substrate. However, their probe assay is very long with 18 h. It should be repeated at shorted times (for Ub and Nedd8-PA only) to test if the strong Ub preference seen in the kinetic assay can also be visualized with the probes.
We measured the requested time curve and added the data to the newly generated Supplementary Figure 1. Using very short time points of 3 – 20min, a preferential reactivity with the ubiquitin probe became visible, which supports the AMC results.
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Moreover, the authors prepare Ubl-PA probes by aminolysis of C-terminal thioesters but at very different stoichiometries as reported for Ub-PA (e.g., in Gersch et al. NSMB 2017). The precise amounts should be cross-checked. This reviewer would not be surprised if the conditions of only 2-fold excess of propargylamine over Ubl thioester and the high amount of NaOH led also to hydrolysis of the C-terminus, which by size exclusion chromatography would not be separated. For all probes, intact mass spectra of the used aliquots must be shown to demonstrate the identity of the used reagents. The presence of other species of course does not per se disqualify the assay in Fig. 1b.
Our protocol for generating the Ub-PA probe follows exactly the one described in Gersch et al 2017. While this publication describes the amounts and concentrations of the individual reagents, our description of the protocol documents their final concentration in the reaction mixture. We double-checked the data and found them to be identical. Thus, there is no reason to assume elevated amounts of hydrolysis products. Moreover, the purity of our probes is regularly controlled by intact mass spectrometry (shown below for Ub-PA). The observed ~5% of hydrolysis product should not pose a problem for the assays, which routinely involve a 10x excess of probe over DUB. As documented in the Materials section, the Nedd-PA probe was obtained from Monique Mulder in Leiden.
In addition, the following minor points should be addressed:
• On page 2, the authors state that the 1-2 DUBs typically present in bacteria either target K63-chains or lack specificity (without reference), but on page 10 they state that bacteria typically only have 1 DUB with little specificity. This is contradicting and should be fixed.
We have rephrased the introductory sentence on page 2 and added a reference. The issue on page 10 has been corrected
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On page 3, the authors make the case for TSSM from B. pseudomallei by calling it representative. This reviewer would appreciate if the authors could expand on this towards the end of the manuscript and comment on whether they expect the identified Ubiquitin recognition mode to be present also in all (!) other DUB-TSSM proteins, at least all analysed bioinformatically. Importantly, they should include the sequence of TSSM of B. mallei (the only in vitro studied TSSM so far) into their analyses in the SI.
We have expanded the paragraph (page 9) where we discuss the conservation of the Littlefinger-based ubiquitin recognition mode. Among the TssM-like DUBs that we identified, only those from other Burkholderia species contain the Littlefinger conservation and are predicted (by alphafold) to use this region for S1-ubiquitin recognition. The two non-Burkholderia TssM-like DUBs (one from Chromobacterium sinusclupearum, the other from an unidentified bacterial metagenome) lack the Littlefinger region and the associated ubiquitin recognition mode. This is documented in Supplementary Fig 4. We also added B. mallei to the alignment in Supplementary Fig 4a, although it is almost identical to the B. pseudomallei sequence.
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On page 4, the authors start with TSSM (193-490) but do not comment on the role of the first 192 residues. What was the rational for these boundaries?
The N-terminal 192 amino acids are predicted to be unstructured (by alphafold, IUPRED, etc). We therefore decided to use the entire folded part of TssM for the first experiments. The information has been added to the manuscript and a domain scheme was added to the supplementary data (Supplementary Figure 1a).
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On page 4, the authors introduce the use of the fluorogenic AMC substrates with "more quantitative analysis", however, the experiments are rather minimalistic. Only one substrate concentration, and two enzyme concentrations (at least for one substrate) are used, and the data are not really analysed in a quantitative manner. Through curve fitting of the existing data (with fixed restraints) the authors may be able to determine an estimate of the Ub/Nedd8-specificity factor. Moreover, negative controls should be shown in Figure 1c to demonstrate that the Nedd8-curve is above a possible baseline drift.
Following this reviewer’s suggestion, we estimated a 116x better cleavage of ubiquitin-AMC by determination of the initial velocities using the linear range of the data presented in Figure 1c.
For ensuring better clarity, we have omitted the negative control data from Fig 1 panel c (comparing Ub-AMC and Nedd8-AMX) but are showing them in panel 1d (comparing Nedd8-AMC to negative control). This figure clearly shows that Nedd8-AMC is really cleaved, whereas no baseline drift is visible.
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On page 4, the authors mention a possible relationship with the Josephin family (which they later disprove through structural comparisons, page 6). It may be helpful to briefly explain the rational (i.e., why one would even consider a relationship with the Josephin family DUB given the higher homology to the USP fold).
The rationale for considering a relationship to the Josephin family is explained at the end of the introduction section (page 3). We never considered TssM to be closer related to Josephins than to USPs. We rather speculated that the entire USP and Josephin families are distantly related to each other, and that TssM-like USPs might form a kind of missing link. This latter aspect has been disproved (page 6), since the TssM structure is no more Josephin-like than that of any other USP.
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On page 5, the "shortened C-terminus" compared to USP7 is mentioned. This is misleading, as USP7 has an elongated C-terminus compared to most other USPs, and so the TSSM C-terminus is the canonical ending of the USP fold.
The section has been rephrased and now points out that TssM shares the canonical USP C-terminus.
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On page 6, "ubiquitin has multiple specific contacts" - why are the contacts named "specific"?
We have removed the word ‘specific’
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On page 10, the TSSM from C. sinusclupearum appears without any context. Chromobacterium should be spelled out, and it should be discussed if this is the odd one out. It would also be appreciated if the authors could state whether their bioinformatic analysis is comprehensive (i.e., do only the known or all Burkholderia strains have TSSM with a DUB profile). And why is there a A0A1J5... sequence included in Supp Fig. 3a without any context?
We agree that this part of the text was confusing. We have now bundled all information on other TssM-like sequences on page 9. There, we explain that TssM-like DUBs are only found in selected Burkholderia lineages, and spell out some examples. We also introduce Chromobacterium sinusclupearum with its full name and explain that the remaining non-Burkholderia TssM homolog is from an unidentified metagenomic sequence. This is also the reason why we refer to this sequence by its Uniprot accession number.
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Some minor polishing in the methods would increase consistency (cloning is only mentioned for pOPIN-K, but TSSM is also expressed from pOPIN-S; mutants are only mentioned for the 292-490 construct, but Fig. 2d shows one in the 193-490 construct; Hampton Additive Screens I-III are likely an internal name and not used by Hampton itself; "different TSSM concentrations (as indicated in the figure legends" are mentioned in the methods, but e.g. in the caption to Fig. 1 no concentration is given).
We have added the pOPIN-S cloning method and corrected the name of the additive screen. We now provide all mutant data in the 292-490 background (Figure 2d was updated accordingly). We made sure that all figure captions mention the enzyme concentrations.
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Likewise, Table 1 needs polishing as commas and points are used interchangeably.
Table 1 has been corrected
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In Fig. 2c, it looks like the catalytic His is built such that there is no hydrogen bonding between Cys and Asp - is there a particular reason for this? If not, the side chain should be flipped so that the nitrogens are positioned for ideal hydrogen bonding.
Fig 2c had been accidentally exported from an early version of the structure. It has now been replaced by a panel that uses the final and deposited version of the structure. Here, the His position is correct.
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In Fig. 3, dotted lines are introduced as "hydrophobic interactions" as per the captions, but some are clearly hydrogen bonds (e.g., from the amide backbone), and for some others one does not see as they emerge from a cartoon.
In Fig 3, dotted lines are not generally introduced as "hydrophobic interactions". Instead, the individual panels have their own definition for the dotted lines. c) hydrophobic d) hydrogen bonds e) hydrophobic.
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In Fig. 4, context should be given to "3VYP" and why it is used here.
We have added an explanation of 3VYP to the figure legend (The reason for showing it is also explained in the main text)
**Referees cross-commenting**
Reviewer comments appear to be fair, balanced and complementary.
Re Reviewer 2's comment on the pre-print: It includes a structure of TSSM bound to ubiquitin (but not of the apo protein). I am not sure if it is appropriate to follow up on the esterase activity. Firstly, there are no tools for it commercially available or readily made in vitro, and secondly it would appear a bit "copycat"-like. Especially since the molecular determinants of what makes a DUB a good esterase are still elusive. A narrower focus of this manuscript, but done very well (also according to what reviewer 3 suggested), might be a more fitting option.
Reviewer #1 (Significance (Required)):
The findings are novel and of high relevance to the broad ubiquitin and bacterial pathogen communities as the study addresses an enigmatic USP-type deubiquitinase which, as the authors reveal, recognizes Ubiquitin in an entirely different way than its eukaryotic counterparts. This is basic research of pronounced conceptual and mechanistic advance, as it demonstrates that the USP domain can be much more diversified than previously assumed. The structural analysis is very thorough, the data are scholarly presented, and interactions/mechanisms carefully validated by mutations.
The strongest point is clearly the structural analysis of Ubiquitin recognition by this extremely truncated USP fold, and the introduction of the little finger motif. The manuscript does not provide cellular validation of the findings, which is fine to this reviewer for the DUB catalysis part. For the part of the N-terminal domain, the manuscript would benefit from a cellular validation or some localisation studies, however, this is beyond what is established in the authors' lab, and therefore has not been asked. This in turn limits the study to a very thorough in-vitro analysis of this DUB for TSSMs with DUB activity, using conventional substrates like polyUb chains and fluorogenic substrates, and providing convincing conclusions.
My expertise lies in DUBs, biochemistry, and structural biology, and I this believe to have sufficient expertise to evaluate the majority of the findings except the bioinformatic algorithms used which are however not an emphasis in this manuscript.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
In this manuscript, the authors solved crystal structures of apo-TssM and its complex with Ubiquitin. Together with the biochemical assays, the authors highlighted the differences of TssM from other USP family. TssM do not contain the classical finger domain while it has little finger domain that authors defined. The Ile44 patch on the ubiquitin is mainly used for interacting with TssM.
For the clarity of there findings several points should be edited.
1) In Fig 2d, the authors used different constructs for testing catalytic residues. It will be better to be consistent. Though the authors showed that the deletion of Ig-like domain does not affect the catalytic activity of TssM by showing the AMC assay, it does not guarantee that the effect of mutation on catalytic sites is same for both construct.
The experiment has been repeated with TssM292-490 C308A and the panel in Fig 2d has been replaced
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2) In fig 2d, authors need to put the label to indicate which linkage of di-Ub is used. Authors did it for figure 2f.
Chain type was already stated in the legend, but was now also added to the respective panel.
3) By showing AMC assay (fig 2e) and K63 Ub2 cleavage assay (fig 2f), authors concluded that the deletion of Ig-like domain does not affect the activity of TssM. However, as authors found that the Ig-like domain forms dimer at least in their crystallization condition, one cannot exclude the possibility of the role of Ig-like domain in recognising different ubiquitin chains. I would clarify the words by saying "The Ig-like domain does not affect the cleavage K63-Ub2), or authors can expand the cleavage assay with all the linkages.
We agree with reviewer #2 and rephrased the corresponding sentence.
4) In the apo structure, authors found a domain swapped dimer. One can expect that this dimer is crystallographic artifact and not found in the nature. Indeed, authors could not observe this dimer in the solution when they performed SEC analysis and there was no effect on the catalytic activity when the Ig-like domain is deleted. Because there is no clear evidence of functional importance of this dimer in the manuscript, authors need to clarify about this dimer.
We agree with reviewer #2 that there is no evidence for functional importance of the dimer. We had already addressed this issue in our discussion section, where we hypothesized a potential in vivo function. We have now rephrased this section of the discussion in order to make it more precise.
**Referees cross-commenting**
Agree with Reviewer #1's comment on my points.
Reviewer #2 (Significance (Required)):
The structure of TssM is recently reported in a preprint ( https://doi.org/10.21203/rs.3.rs-2986327/v1) from Pruneda (OHSU). In this preprint, they suggested the role of TssM as Ubiquitin esterase which is not explored in the this manuscript. As it is already published and freely available, authors can explore the role of TssM in that direction as well. Because the preprint do not contain the complex structure of TssM with Ubiquitin, authors can also examine the roles of Ile44 patch-interacting residues on the catalytic activities.
Said preprint contains the TssM~Ub complex structure, but not the structure of the apo form. The role of the Ile44-patch contacting residues is also already analysed in regards of esterase activity. As explained in the ‘general points’ at the top of the rebuttal letter, the two manuscripts were meant to be submitted simultaneously. Due to a delay in our PDB deposition/validation, we submitted our version two weeks later. Thus, we share the opinion of reviewer #1 that it would be inappropriate to use our competitors’ data and analyse esterase activity in our manuscript. Instead, we added a reference to the competing preprint to our discussion section and briefly compare the key findings. The authors of the preprint have agreed to reciprocate and reference our preprint in their revised manuscript.
Also, authors can compare their little finger structure with the preprint.
See point above.
In general, this manuscript is providing several interesting points to the readers working on the ubiquitin, structures of proteins, host-pathogen interactions and especially those who studying deubiquitinases.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
In this study, Hermanns et al. have examined the Burkholderia TssM deubiquitinase (DUB) for its ubiquitin chain cleavage specificity using in vitro analyses and for a structural rationalization of its specificity by solving crystal structures with and without covalently bound ubiquitin. TssM was previously shown to be a DUB of the USP family capable of cleaving several ubiquitin chain types. From bioinformatic comparisons, the authors inferred that TssM lacks the 'fingers' domain of classic USP enzymes, and this was shown in the co-crystal structure to be replaced by a 'Littlefinger' loop. The work here is well described and appears to be overall technically solid.
My enthusiasm is reduced for several reasons. First, there is no analysis in an (infected) cell model to evaluate the significance of the TssM mutations, for example, the mutations in ubiquitin-interacting surfaces that are not seen in classic USPs. Second, there is very little quantitation of cleavage rates; while I would not demand derivation of kinetic values for all mutants, the qualitative treatment was not always convincing. For example, Y443A was said to cause "strongly reduced cleavage" (Fig. 4h) whereas E378A was said to "only mildly affect cleavage" (Fig. 4i): to my eye, these cleavage rates are only slightly different (2-3 fold?).
In order to support the findings of our gel based assay and to get more quantitative data, we tested all mutants against Ub-AMC. The results are depicted in supplementary figures 3d/f/g and correspond to results of the chain cleavage assays.
Finally, there is a preprint available on Research Square (doi: 10.21203/rs.3.rs-2986327/v1) that shows a potent esterase activity of TssM against ubiquitinated LPS, which is probably its key role in avoiding surveillance and elimination by the host. This paper, although still a preprint, is far more quantitative, includes similar crystallographic data as in the current paper, and describes cellular assays of function. Even without the RS preprint, the Hermanns et al. study provides a fairly modest advance in our knowledge of TssM function; with the preprint, its novelty is, unfortunately, severely reduced.
As explained in the ‘general points’ at the top of the rebuttal letter, the two manuscripts were meant to be submitted simultaneously. Due to a delay in our PDB deposition/validation, we submitted our version two weeks later. The competing work is currently under review at a top-tier journal and far from being accepted. We therefore ask to consider the significance of our own manuscript based on the peer-reviewed state-of-the-art.
In our revised manuscript, we have added a brief discussion of the pre-published results. The authors of the preprint have agreed to reciprocate and address our data in their revised manuscript.
Minor comments:
Full genus names should be spelled out when they first appear in the text (such as Burkholderia and Chromobacterium).
Genus names are now spelled out.
Fig. 3a is described out of sequence.
A reference to figure 3a was missing in the beginning of the chapter. The reference was added and the figure is now described in sequence.
In Fig 4a, there still seem to be extensive contacts between monomers but the viewing angle could be misleading.
There are no extensive contacts between the two DUB monomers in the right panel (complex structure). We slightly changed the viewing angle in Fig 4a to make this clearer.
**Referees cross-commenting**
I also agree with Reviewer #1's comments
Reviewer #3 (Significance (Required)):
Even without the Research Square preprint (doi: 10.21203/rs.3.rs-2986327/v1), the Hermanns et al. study provides a fairly modest advance in our knowledge of TssM function; but with the preprint, its novelty is, unfortunately, severely reduced.
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Referee #3
Evidence, reproducibility and clarity
In this study, Hermanns et al. have examined the Burkholderia TssM deubiquitinase (DUB) for its ubiquitin chain cleavage specificity using in vitro analyses and for a structural rationalization of its specificity by solving crystal structures with and without covalently bound ubiquitin. TssM was previously shown to be a DUB of the USP family capable of cleaving several ubiquitin chain types. From bioinformatic comparisons, the authors inferred that TssM lacks the 'fingers' domain of classic USP enzymes, and this was shown in the co-crystal structure to be replaced by a 'Littlefinger' loop. The work here is well described and appears to be overall technically solid.
My enthusiasm is reduced for several reasons. First, there is no analysis in an (infected) cell model to evaluate the significance of the TssM mutations, for example, the mutations in ubiquitin-interacting surfaces that are not seen in classic USPs. Second, there is very little quantitation of cleavage rates; while I would not demand derivation of kinetic values for all mutants, the qualitative treatment was not always convincing. For example, Y443A was said to cause "strongly reduced cleavage" (Fig. 4h) whereas E378A was said to "only mildly affect cleavage" (Fig. 4i): to my eye, these cleavage rates are only slightly different (2-3 fold?). Finally, there is a preprint available on Research Square (doi: 10.21203/rs.3.rs-2986327/v1) that shows a potent esterase activity of TssM against ubiquitinated LPS, which is probably its key role in avoiding surveillance and elimination by the host. This paper, although still a preprint, is far more quantitative, includes similar crystallographic data as in the current paper, and describes cellular assays of function. Even without the RS preprint, the Hermanns et al. study provides a fairly modest advance in our knowledge of TssM function; with the preprint, its novelty is, unfortunately, severely reduced.
Minor comments:
Full genus names should be spelled out when they first appear in the text (such as Burkholderia and Chromobacterium).
Fig. 3a is described out of sequence.
In Fig 4a, there still seem to be extensive contacts between monomers but the viewing angle could be misleading.
Referees cross-commenting
I also agree with Reviewer #1's comments
Significance
Even without the Research Square preprint (doi: 10.21203/rs.3.rs-2986327/v1), the Hermanns et al. study provides a fairly modest advance in our knowledge of TssM function; but with the preprint, its novelty is, unfortunately, severely reduced.
-
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
Learn more at Review Commons
Referee #2
Evidence, reproducibility and clarity
In this manuscript, the authors solved crystal structures of apo-TssM and its complex with Ubiquitin. Together with the biochemical assays, the authors highlighted the differences of TssM from other USP family. TssM do not contain the classical finger domain while it has little finger domain that authors defined. The Ile44 patch on the ubiquitin is mainly used for interacting with TssM.
For the clarity of there findings several points should be edited.
- In Fig 2d, the authors used different constructs for testing catalytic residues. It will be better to be consistent. Though the authors showed that the deletion of Ig-like domain does not affect the catalytic activity of TssM by showing the AMC assay, it does not guarantee that the effect of mutation on catalytic sites is same for both construct.
- In fig 2d, authors need to put the label to indicate which linkage of di-Ub is used. Authors did it for figure 2f.
- By showing AMC assay (fig 2e) and K63 Ub2 cleavage assay (fig 2f), authors concluded that the deletion of Ig-like domain does not affect the activity of TssM. However, as authors found that the Ig-like domain forms dimer at least in their crystallization condition, one cannot exclude the possibility of the role of Ig-like domain in recognising different ubiquitin chains. I would clarify the words by saying "The Ig-like domain does not affect the cleavage K63-Ub2), or authors can expand the cleavage assay with all the linkages.
- In the apo structure, authors found a domain swapped dimer. One can expect that this dimer is crystallographic artifact and not found in the nature. Indeed, authors could not observe this dimer in the solution when they performed SEC analysis and there was no effect on the catalytic activity when the Ig-like domain is deleted. Because there is no clear evidence of functional importance of this dimer in the manuscript, authors need to clarify about this dimer.
Referees cross-commenting
Agree with Reviewer #1's comment on my points.
Significance
The structure of TssM is recently reported in a preprint ( https://doi.org/10.21203/rs.3.rs-2986327/v1) from Pruneda (OHSU). In this preprint, they suggested the role of TssM as Ubiquitin esterase which is not explored in the this manuscript. As it is already published and freely available, authors can explore the role of TssM in that direction as well.
Because the preprint do not contain the complex structure of TssM with Ubiquitin, authors can also examine the roles of Ile44 patch-interacting residues on the catalytic activities.
Also, authors can compare their little finger structure with the preprint.
In general, this manuscript is providing several interesting points to the readers working on the ubiquitin, structures of proteins, host-pathogen interactions and especially those who studying deubiquitinases.
-
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
Learn more at Review Commons
Referee #1
Evidence, reproducibility and clarity
Hermanns et al. report a robust biochemical and structural characterization of the TSSM virulence effector of Burkholderia pseudomallei, the causative agent of melioidosis, with TSSM being the only USP-type deubiquitinase of bacterial origin. The authors demonstrate ubiquitin specificity, and the capacity of the DUB to cleave most Ubiquitin chain types in vitro. By solving crystal structures of TSSM in isolation as well as in covalent complex with Ubiquitin, the authors rationalize how the truncated fold (compared to all other eukaryotic USPs) is capable of binding ubiquitin. The most surprising finding is the conformation of the so-called little finger loop, unique to TSSM, which engages the distal Ubiquitin in a manner not seen in any other DUB. These findings are convincingly validated by point mutations in biochemical assays and bioinformatic analyses. The structures also yielded information on the fold an N-terminal Ig-like domain with unknown function.
The authors are encouraged to address the following main points before publication can be supported:
- On page 6, the authors state "the little finger loop contacts hydrophobic residues around Ile-44 of ubiquitin, an important interaction interface that is not observed in USP7 and is not part of the canonical USP: ubiquitin interface". The latter statement I believe is simply wrong as Ile44 is at the centre of the large canonical USP:ubiquitin interaction interface observed in all structurally studied USP proteins so far (the recognition is through varying residues, that are not always strictly hydrophobic, but it is always contacted). This is occurring at the hinge between thumb and fingers. In USP7, this is for example occurring through the hydrophobic part of Arg301. The authors are referred to O'Dea et al. Nat Chem Biol 2023, where same conformation of the equivalent residue in USP36 is discussed in the context of Ile44-Ubiquitin vs. Fubi recognition.
- This should also be phrased very precisely, as Ile44 itself is not actually contacted by the little finger loop (see Fig. 3c), but other hydrophobic residues commonly included in the Ile44 patch are. However, also those are always in the Ubiquitin:USP interface.
- It appears as if the little finger loop takes up the exact position of what is commonly referred to as blocking loop 1 (terminology introduced by Hu et al EMBO J 2005, which the authors could also use when referring to canonical USPs). The connectivity in the domain is not equivalent to BL1 though (this could be shown with topology diagrams of a canonical USP and of TSM), but the relative position is. Importantly, BL1 in USPs typically engage the Ile36 patch with a large aromatic residue (e.g., a Trp in USP30 or a Tyr in USP7), however, in TSSM Ubiquitin is rotated such that the Ile44 patch is recognised at the exact spot where normally Ile36 is engaged. Can the authors comment on this interpretation? This reviewer would also find a comparison to the canonical USP recognition mode in the main figures beneficial (however one that is less crowded than in the SI) to make this very important finding come across well. Even if the loop is more like a BL1 (which is highly diverse in the USP family, see reference above), it can still be called "little finger", but the analogies should be carefully phrased throughout the text (e.g., page 9: "functionally but not structurally analogous" would not be correct if it does not replace the fingers which contact Phe4 as BL1 is not part of the fingers).
- The assessment of the molecular weight of apo TSSM in solution by SEC is flawed in its current form (page 7, Fig. 4b). There are only two, seemingly randomly chosen, comparison proteins, and the GST-Ub is unsuitable for 36 kDa because it is an obligate dimer (due to the GST) and as a fusion protein has a much higher hydrodynamic radius than a globular protein. There are commercial reference proteins that can be used which have defined oligomerisation states and are (reasonably globular) so that the retention times can be quantitatively analysed. Such a reference, which also includes proteins of smaller sizes, must be used. Alternatively, the authors should use light-scattering (SEC-MALS or SEC-RALS) to unequivocally demonstrate the molecular weight / oligomeric state of TSSM.
- The abstract ends with speculation on the presence of the strand-swapping in live cells and of the role of the Ig-fold domain, but not a proper conclusion. The same applies to the ending of the results section (page 8), where it is not clear where the structural analysis of the Ig-like domains is leading. The authors then speculate about sugar binding (page 10) in the discussion. The latter could be substantiated by an analysis of the residues in the possible sugar binding site (if present in their TSSM), and the text be more rounded off at these regions.
- The authors show in Fig. 1b TSSM reactivity with both Ubiquitin and Nedd8 probes but then qualify the Nedd8-reactivity with the assays shown in Figs. 1cd, which currently looks like it is an issue with the comparison of probe to substrate. However, their probe assay is very long with 18 h. It should be repeated at shorted times (for Ub and Nedd8-PA only) to test if the strong Ub preference seen in the kinetic assay can also be visualized with the probes.
- Moreover, the authors prepare Ubl-PA probes by aminolysis of C-terminal thioesters but at very different stoichiometries as reported for Ub-PA (e.g., in Gersch et al. NSMB 2017). The precise amounts should be cross-checked. This reviewer would not be surprised if the conditions of only 2-fold excess of propargylamine over Ubl thioester and the high amount of NaOH led also to hydrolysis of the C-terminus, which by size exclusion chromatography would not be separated. For all probes, intact mass spectra of the used aliquots must be shown to demonstrate the identity of the used reagents. The presence of other species of course does not per se disqualify the assay in Fig. 1b.
In addition, the following minor points should be addressed:
- On page 2, the authors state that the 1-2 DUBs typically present in bacteria either target K63-chains or lack specificity (without reference), but on page 10 they state that bacteria typically only have 1 DUB with little specificity. This is contradicting and should be fixed.
- On page 3, the authors make the case for TSSM from B. pseudomallei by calling it representative. This reviewer would appreciate if the authors could expand on this towards the end of the manuscript and comment on whether they expect the identified Ubiquitin recognition mode to be present also in all (!) other DUB-TSSM proteins, at least all analysed bioinformatically. Importantly, they should include the sequence of TSSM of B. mallei (the only in vitro studied TSSM so far) into their analyses in the SI.
- On page 4, the authors start with TSSM (193-490) but do not comment on the role of the first 192 residues. What was the rational for these boundaries?
- On page 4, the authors introduce the use of the fluorogenic AMC substrates with "more quantitative analysis", however, the experiments are rather minimalistic. Only one substrate concentration, and two enzyme concentrations (at least for one substrate) are used, and the data are not really analysed in a quantitative manner. Through curve fitting of the existing data (with fixed restraints) the authors may be able to determine an estimate of the Ub/Nedd8-specificity factor. Moreover, negative controls should be shown in Figure 1c to demonstrate that the Nedd8-curve is above a possible baseline drift.
- On page 4, the authors mention a possible relationship with the Josephin family (which they later disprove through structural comparisons, page 6). It may be helpful to briefly explain the rational (i.e., why one would even consider a relationship with the Josephin family DUB given the higher homology to the USP fold).
- On page 5, the "shortened C-terminus" compared to USP7 is mentioned. This is misleading, as USP7 has an elongated C-terminus compared to most other USPs, and so the TSSM C-terminus is the canonical ending of the USP fold.
- On page 6, "ubiquitin has multiple specific contacts" - why are the contacts named "specific"?
- On page 10, the TSSM from C. sinusclupearum appears without any context. Chromobacterium should be spelled out, and it should be discussed if this is the odd one out. It would also be appreciated if the authors could state whether their bioinformatic analysis is comprehensive (i.e., do only the known or all Burkholderia strains have TSSM with a DUB profile). And why is there a A0A1J5... sequence included in Supp Fig. 3a without any context?
- Some minor polishing in the methods would increase consistency (cloning is only mentioned for pOPIN-K, but TSSM is also expressed from pOPIN-S; mutants are only mentioned for the 292-490 construct, but Fig. 2d shows one in the 193-490 construct; Hampton Additive Screens I-III are likely an internal name and not used by Hampton itself; "different TSSM concentrations (as indicated in the figure legends" are mentioned in the methods, but e.g. in the caption to Fig. 1 no concentration is given).
- Likewise, Table 1 needs polishing as commas and points are used interchangeably.
- In Fig. 2c, it looks like the catalytic His is built such that there is no hydrogen bonding between Cys and Asp - is there a particular reason for this? If not, the side chain should be flipped so that the nitrogens are positioned for ideal hydrogen bonding.
- In Fig. 3, dotted lines are introduced as "hydrophobic interactions" as per the captions, but some are clearly hydrogen bonds (e.g., from the amide backbone), and for some others one does not see as they emerge from a cartoon.
- In Fig. 4, context should be given to "3VYP" and why it is used here.
Referees cross-commenting
Reviewer comments appear to be fair, balanced and complementary.
Re Reviewer 2's comment on the pre-print: It includes a structure of TSSM bound to ubiquitin (but not of the apo protein). I am not sure if it is appropriate to follow up on the esterase activity. Firstly, there are no tools for it commercially available or readily made in vitro, and secondly it would appear a bit "copycat"-like. Especially since the molecular determinants of what makes a DUB a good esterase are still elusive. A narrower focus of this manuscript, but done very well (also according to what reviewer 3 suggested), might be a more fitting option.
Significance
The findings are novel and of high relevance to the broad ubiquitin and bacterial pathogen communities as the study addresses an enigmatic USP-type deubiquitinase which, as the authors reveal, recognizes Ubiquitin in an entirely different way than its eukaryotic counterparts. This is basic research of pronounced conceptual and mechanistic advance, as it demonstrates that the USP domain can be much more diversified than previously assumed. The structural analysis is very thorough, the data are scholarly presented, and interactions/mechanisms carefully validated by mutations.
The strongest point is clearly the structural analysis of Ubiquitin recognition by this extremely truncated USP fold, and the introduction of the little finger motif. The manuscript does not provide cellular validation of the findings, which is fine to this reviewer for the DUB catalysis part. For the part of the N-terminal domain, the manuscript would benefit from a cellular validation or some localisation studies, however, this is beyond what is established in the authors' lab, and therefore has not been asked. This in turn limits the study to a very thorough in-vitro analysis of this DUB for TSSMs with DUB activity, using conventional substrates like polyUb chains and fluorogenic substrates, and providing convincing conclusions.
My expertise lies in DUBs, biochemistry, and structural biology, and I this believe to have sufficient expertise to evaluate the majority of the findings except the bioinformatic algorithms used which are however not an emphasis in this manuscript.
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The authors do not wish to provide a response at this time.
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Referee #3
Evidence, reproducibility and clarity
Summary
In this manuscript Salvador-Garcia et al. examine how several mutations in the Dynein heavy chain (DHC) influence Dynein function in an in vitro reconstituted system and in Drosophila embryos. Most importantly, they identify a novel substitution mutation (S3372C, generated as a by-product of a targeted CRISPR mutagenesis screen) that leads to a novel phenotype specifically in early Drosophila embryos (metaphase arrest) and that only impairs DHC function under load in vitro. Most surprisingly, the metaphase arrest in embryos does not appear to be due to a failure to inactive the spindle-assembly-checkpoint (SAC), a known DHC function. This suggests that DHC has a hitherto unappreciated function in allowing spindles in the Drosophila embryo to progress from metaphase-to-anaphase.
The manuscript is generally well written and conveys the major conclusions clearly and concisely. The data is generally of high quality, and largely supports the main conclusions, although there is one set of relatively straight-forward experiments that I think would be an important addition (see major comment #1, below).
Major Comments
- The observation that the S3372C mutation causes a mitotic arrest that is not SAC dependent (i.e. it still largely occurs even in a Mad2 mutant background) is very surprising, and is the basis of the authors claim of a new, DHC-dependent, mechanism that allows embryo spindles to progress into anaphase. I think it would be important to assess whether the SAC components are still localising to the kinetochores in these S3372C, Mad2 double mutants (e.g. is Rod still recruited to high-levels in the double mutant?). If the SAC components are still being recruited to the spindle (suggesting that they are still detecting that the spindle is not ready to go into anaphase), is it worth considering that Mad2 may not be essential for SAC function in these embryos? I say this because I find it hard to imagine how any, presumably mechanical, failure at the kinetochore that leads to the improper metaphase/anaphase transition in the S3372C mutants, would signal to the rest of the spindle to not transition to anaphase if the SAC is truly inactivated. Do the authors think these embryos have a completely unrelated surveillance system that detects the S3372C-dependent error (whatever that is) and arrests the spindles specifically in embryos? Or is the error itself sufficient to cause a spindle-wide arrest, which seems improbable?
- I was surprised the authors made no attempt to quantify the level of over-accumulation of Dlic (Figure 6) or Rod (Figure 7) (and the lack of over-accumulation in other regions of the spindle). The images are convincing, so I don't doubt that this is the case, but I think some sort of quantification would be useful and I don't think it would be hard to come up with a way to do this (even just drawing a ROI around the approximate areas of interest). It would also be interesting to know whether other proteins like Spc25 (Figure 6) and Cdc20 (Figure S6) are recruited to normal levels at kinetochores.
Minor Comments
- In the Discussion the authors state: "Our discovery of a missense mutation that strongly affects nuclear divisions in the embryo without disrupting other dynein functions offers a unique tool to study the mitotic roles of the motor". This should be reworded, as it suggests that the mutation effects all mitotic functions of DHC, which is clearly not the case (and also applies only to the embryo).
- I think it worth more explicitly stating that there is no evidence that the defects the S3372C mutation lead to in the in vitro reconstituted system are the cause of the in vivo defects observed in the embryo. The authors are careful not to directly claim this, and I agree with their assertion that this is the most "parsimonious explanation" for their data, but I'm sure they would agree that this is far from proven, and it might be worth emphasising this point a little more.
Significance
This is a well conducted study that significantly extends the author's previous work on how mutations in DHC (initially indentified in human patients) effect DHC function (Hoang et al., PNAS, 2017). The paper reports the striking central finding that the S3372C mutation produces a very unusual mitotic arrest phenotype specifically in Drosophila embryos, and the authors link this to the also striking finding that this mutation only disrupts DHC function in vitro when DHC is working under load. As mentioned above, this link is not proven here, but this is a solid working hypothesis that is potentially of significant interest to those working on molecular motors and their role in fundamental cell biology and human disease.
I am a cell biologist with expertise in the cytoskeleton, particularly during early Drosophila embryogenesis.
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Referee #2
Evidence, reproducibility and clarity
In the manuscript by Salvador-Garcia et al., the authors assess the physiological consequences of dynein mutations in flies and in vitro. In addition to characterizing the manner by which disease causing mutations affect fly development and some aspects of their cell physiology, the authors focus on sporadic mutations that arose during the course of generating their mutant fly lines. Of particular interest was a mutation in the dynein MTBD: S3722C. This mutation caused very interesting phenotypes in flies (e.g., infertility in females likely due to mitotic arrest) as well as in reconstituted motility assays (e.g., reduced stall force). The authors posit that the mitotic arrest phenotype is a consequence of a dynein's role in initiating anaphase onset, and that this role is distinct from its well established role in silencing the spindle assembly checkpoint.
The paper is very well written, and the data are of high quality. Most of the claims - with one major exception (described below) - are well supported by the data. I have a few comments that might help to strengthen the conclusions.
Major comment:
- In brief, I'm not convinced the mitotic arrest phenotype is not a consequence of impaired SAC silencing by the mutant dynein. The main tool the authors use to support their claim is a Mad2 mutant. They use this to determine if preventing SAC function (with the mutant Mad2) can override the ability of S3722C cells to progress into anaphase. The Mad2 mutant does in fact increase the proportion of cells exiting mitosis (from 0.8 to 14% of cells); however, the low number (14%) suggests that an inability to silence the SAC is not the reason the cells are not entering anaphase (i.e., it is SAC silencing-independent). My question is how penetrant the Mad2 mutant is? For example, how many cells with this Mad2 mutant would exit mitosis if the authors perturbed mitosis some other way (e.g., treatment with high concentrations of nocodazole)? If the number is still low (~14%), then this might be why the mutant can't rescue the mitotic exit phenotype for S3722C cells, and would challenge the following statements: "...it suggests that this can make, at best, a minor contribution to the mitotic arrest phenotype"; and "Remarkably, the MTBD mutation does not appear to block anaphase progression in embryos by preventing the well-characterized role of kinetochore-associated dynein in silencing the SAC, as the defect persists when the checkpoint is inactivated by mutation of Mad2. Collectively, these observations indicate that kinetochore dynein has a novel role in licensing the transition from metaphase to anaphase." Although previous studies might have assessed the penetrance of this mutant in other cell types, given the cell specificity of the mitotic arrest phenotype for S3722C (in embryonic cells, but not L3 neuroblasts), it will be important to provide such additional evidence in embryonic cells (e.g., nocodazole treatment of embryonic cells) to support these statements, especially in light of the bold conclusions and hypotheses they are making (e.g., "For example, the apparent variability in tension between sister kinetochores in S3372C embryos, which could reflect abnormal force generation by the mutant motor complex, might prevent APC/C activation through the complex series of signaling events that respond to chromosome biorientation."). Although it would be fascinating if the authors are correct that dynein provides another role in licensing anaphase onset, the well-established role for dynein in checkpoint silencing currently seems like the most parsimonious explanation.
Minor comments:
- The S3722C mutant appears to accumulate to higher-than-normal levels at KTs and to some extent along the spindle MTs. In addition to the representative images, it would be helpful to see a quantitation of this phenomenon for WT, S3722C, and S3722C/+ along with statistics.
- Although the mean inter-KT distance was unchanged between WT and S3722C cells, the authors note that the deviation from the mean was higher. Could this simply be a consequence of more highly dynamic oscillations of KT pairs (similar to that seen with Hec1-S69D in DeLuca et al., JCB 2018)? More dynamic oscillations could potentially lead to more variable distances between KT pairs.
- It is interesting that the S3722C/Mad2-mutant cells are enriched in telophase (Fig. 7G). Does this not suggest another arrest point for these cells?
- The authors state: "Immunostaining revealed that whereas α1-tubulin was present throughout the spindle apparatus, α4-tubulin was enriched at the spindle poles (Figure S7A)." Although I agree the a4-tubulin appears somewhat enriched at the poles with respect to a1-tubulin, a quantitation (with statistics) would be needed to support this claim. That being said, I agree the isotype is unlikely to account for the S3722C phenotype.
- Trapping data show reduced stall force, yet increased stall time at low resistive forces for the mutant. This finding could potentially account for the reduced velocity of GFP-Rod noted in cells; however, I wonder if the authors noted altered velocity for dynein-driven bead movement under load in their trapping assays? This information would be useful to include in their manuscript.
- Is there a defocused spindle pole phenotype in the mutant cells? The cells in Video 1 and Fig. S6c appear to show as much, although other cells do not.
- The authors state: "This may reflect the relatively short length of Drosophila neurons making them less sensitive to partially impaired cargo transport." Could the extent of the phenotypes also be related to the lifespan of the flies? Do any of the diseases caused by these mutations have late-onset in patients? I wonder if a subtle defect in dynein behavior might not manifest for numerous years due to only minor changes in motility?
Referees cross-commenting
I wanted to reiterate my skepticism regarding the possibility that their data strongly support a SAC-independent role for dynein in the metaphase-to-anaphase transition (it seems Reviewer #3 might agree with me). I don't think it's impossible, but I'm not convinced they've made a very strong case for this model, which is noted in the title. The fact that proteins are accumulating at aligned kinetochores in the mutant cells (e.g., Rod and DLIC) in fact are consistent with a SAC silencing defect. Along these lines, I think reviewer #1's point regarding RZZ is a good one (that the mutant dynein is incapable of evicting RZZ specifically from aligned KTs), and should be tested prior to publication.
My primary concern is that their conclusion is based entirely on the fact that the Mad2 mutant does not fully restore mitotic exit to the dynein mutant cells. Given the cell-specificity of their dynein phenotype (in embryonic cells, but not L3 neuroblasts), I think testing the penetrance of their Mad2 mutant in the embryonic cells would need to be assessed. In my review I suggested nocadazole, when I realized I meant to say reversine (oops!).
Significance
The manuscript by Salvador-Garcia et al. is a very interesting study dissecting the physiological consequences of dynein mutations in flies and in vitro. This study will be of high interest to those in the dynein/molecular motor field, as well as those that study mitosis and kinetochore function. One of the most interesting findings in the study is the identification of a mutation in dynein that specifically impacts its motility in conditions of high load. This mutant provides a novel tool to dissect load-dependent transport for dynein in other systems. Moreover, the study suggests a novel role for dynein in promoting anaphase onset; if the authors can provide additional support for this claim, the impact of this study would be greater.
Field of expertise: molecular motors, kinetochore function
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Referee #1
Evidence, reproducibility and clarity
The authors start out by examining the cellular and organismal effects of 6 human disease-linked mutations, as well as the mouse legs-at-odd-angles (Loa) mutation, after introducing the mutations into the D. melanogaster dynein heavy chain (Dhc) by genome editing. This reveals an overall correlation between the severity of the effect on dynein motor activity in vitro (determined in a previous study) and the penetrance of the corresponding mutant phenotype in the fly, with a couple of interesting exceptions that illustrate the value of performing structure-function analysis of dynein in animal models. The authors then focus on an additional missense mutation in the Dhc microtubule binding domain, fortuitously generated by imprecise editing, that results in a striking phenotype. The S3372C mutation supports normal development, including normal axonal transport of mitochondria and asymmetric mitosis of larval neuroblasts, but female flies are infertile. Through elegant genetics, ectopic disulfide bond formation with a nearby residue is ruled out as the cause of the maternal effect. S3372C results in metaphase arrest of early embryonic divisions, characterized by over-accumulation of dynein light intermediate chain (Dlic) and the dynein recruitment factor Rod at kinetochores, as well as by reduced poleward streaming of Rod along spindle microtubules. Surprisingly, the S3372C-induced metaphase arrest cannot by bypassed by inhibiting the spindle assembly checkpoint, implying that dynein promotes the metaphase-to-anaphase transition not solely through its known function in spindle assembly checkpoint silencing. In vitro motility and optical trapping experiments show that the mutant motor performs normally in a load-free regime but exhibits reduced peak force production and excessive pausing under load. Furthermore, molecular dynamics simulations reveal how the mutation affects dynein's interaction with microtubules, including a change in the positioning of the stalk. The authors conclude that the S3372C mutation specifically perturbs high-load functions of dynein, explaining the selective phenotype observed in vivo.
The experiments are technically on a very high level, the results are presented in a clear manner, and the conclusions are fully supported by the data.
Minor suggestions (optional):
- In the first part of the paper, where Dhc mutations associated with neurological disease are examined, the H3808P and F579/Loa mutations are shown to cause mis-accumulation of synaptic vesicles in axons. The authors may want to perform this assay for the K129I, R1557Q, and K3226T mutations, as this would strengthen the comparative analysis of in vitro versus in vivo effects, summarized in Figure 1C. For example, K129I has a more severe effect in vitro than the Loa mutation, but the Loa mutation has a more pronounced phenotype on the organismal level. Would this also be the case in a cell-based assay?
- The observation that the metaphase arrest of S3372C mutant embryos cannot be alleviated by the checkpoint-defective Mad2 mutant is very intriguing, as is the observation that Dlic and the RZZ subunit Rod over-accumulate at/near kinetochores. As discussed by the authors, one possibility is that the arrest is a consequence of dynein's failure to disassemble the corona by stripping, but, surprisingly, in a manner unrelated to dynein's role in SAC silencing. In this regard, it is interesting to note that fly RZZ mutants do not undergo metaphase arrest in the early embryo (Williams and Goldberg, 1994; Défachelles et al., 2015), whereas knockdown of Spindly, which functions in dynein recruitment downstream of RZZ, does lead to arrest (see Figure 2 in Clemente et al., 2018; PMID 29615558). Taken together, this raises the possibility that it is the failure to remove RZZ (and other associated corona components) from kinetochores that inhibits anaphase onset in S3372C embryos. It would therefore be interesting to test whether the metaphase arrest in S3372C embryos is alleviated in RZZ mutants.
Referees cross-commenting
The Mad2 mutant the authors use is a P-element insertion that was described by Buffin et al 2007 as a null mutant with regards to SAC signaling (it also does not produce any detectable protein by Western blot; Figure 1b). Nevertheless, since the analysis in Buffin et al was restricted to larval brains, I agree with reviewer #2 that it remains to be formally demonstrated that this Mad2 mutant fully abolishes the SAC in the early embryo. Unfortunately, as far as I am aware, reversine does not work well in Drosophila. An alternative would be to combine Dhc(S3372C) with the other Mad2 mutant used by Buffin et al, which (besides not producing detectable protein) lacks the Mad1 binding domain and can therefore be expected to be a definitive checkpoint null in all tissues.
Significance
The cytoplasmic dynein 1 motor complex participates in a multitude of cellular processes that require microtubule minus-end-directed motility. Whereas in vitro reconstitution efforts have led to a detailed understanding of the motor's motile properties, the essential requirement of dynein for development has made it challenging to dissect how the motor contributes to specific aspects of intracellular organization and cell division in vivo. The need for a mechanistic understanding of how dynein motility is used for diverse functions in vivo is underscored by missense mutations in the motor subunit that cause human neurological disease. In this interesting and insightful study, Salvador-Garcia and colleagues characterize several missense mutations in dynein heavy chain (Dhc) using biochemical assays and genetic approaches is the fly, which reveals the distinct effects of disease-causing mutations in vivo and uncovers an unanticipated novel function of dynein in regulating mitotic progession.
This beautifully executed study has important implications for dynein's role in mitosis, in particular its role at the kinetochore, and is of broad interest to cell biologists studying the cytoskeleton, as it demonstrates that examining motor mutants with altered mechanical properties in vivo can reveal specific motor functions.
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Reply to the reviewers
Response to Reviewers
We are grateful to the Reviewers for their insightful thoughts and suggestions for improving the manuscript for publication. We have addressed all Reviewers’ comments, and detailed responses have been provided below (in blue font). We have uploaded a revised manuscript version, and have made a few small improvements to the text to improve readability. Line and figures numbers refer to the revised version of the manuscript.
‘Reviewer #1 (Evidence, reproducibility and clarity (Required)):
In this study, Wenner et al. used various in vitro methods, including transposon mutagenesis, screening of known regulatory proteins and isolation of spontaneous mutants to discover 11 mutations in genes that promote bacterial growth under succinate-mediated inhibition. Through additional experiments, the manuscript provides evidence for factors that underlie several layers of succinate regulation. These layers include sRNAs, OxyRS, succinate transport antibiotics and rRNA. The study then characterized the molecular mechanisms regulating succinate utilization by these mutations, revealing a RpoS-independent mechanism for succinate uptake via the dctA transporter and mechanisms for RpoS regulation.
Overall, the manuscript is very unfocused and uneven in the level of details of each of these factors and could be much more compelling if more focus was given to several factors and providing more mechanistic insight of these factors.
We thank Reviewer #1 for the constructive criticism and suggestions. We do recognize the limitations of our study, clearly more work is required to unravel the complex phenomenon of the inhibition of succinate utilisation by Salmonella. We welcome Reviewer #1’s suggestions to shorten the manuscript, which has allowed us to focus the paper on our key findings.
Major comments
- The authors discuss virulence-mediated succinate but disregard some important features of succinate utilization, only referring to dctA and disregarding the overlap with other C4-dicaroxy transporters (Spiga, wolf, PMID). Furthermore, the study found that a mutation in the IscR binding site on the DctA promoter region reversed the effects of succinate-dependent growth inhibition generated under aerobic conditions but other succinate transporters are expressed under different physiological conditions (Janausch et al. 2002, Spiga et al. 2017). Does the IscR binding site motif can be found in promoters of other succinate transporters? Analysis of IscR in aerobic/ anaerobic conditions can be useful. Do mutations in IcsR lead to increased expression of other succinate transporters in aerobic or anaerobic conditions?
The Reviewer’s question of the regulatory role of IscR on anaerobic C4-dicarboxylate transporters is particularly relevant in the context of the role of succinate catabolism in pathogenesis and could be studied in a follow-up investigation. However, further analysis of the influence of mutations that modulate the expression or activity of IscR are beyond the scope of our study. Here, we have focused on succinate utilisation under in vitro, aerobic conditions: under these conditions, growth upon succinate is robustly repressed, allowing the selection of Succ+ mutants. To emphasise that our study was done under aerobic conditions, we have rephrased the Introduction (line 93).
Transposon screen - There is no comprehensive description of the results and it is not clear why mutations found in the evolution experiments or regulatory proteins that were shown to allow bacteria growth under succinate treatment were not detected in the transposon screening?
Different selection protocols were used to isolate the Succ+ mutants and the experimental approaches are detailed in the Methods and in the strain list for each mutant (Supplementary_Resource_Table S1). Selection was performed in liquid M9+Succ for Tn5 mutagenesis (in the rpoS2X background), or in solid and liquid M9+Succ media for the spontaneous mutants (the mutations are all listed and detailed in Table 1).
Therefore, the different selection conditions and the presence of an extra rpoS copy may have favored certain mutants, especially when the pools of Tn5 mutants were grown with succinate together (mutants in competition).
We recognize that our experimental approach had limitations, and that a Tn-seq methodology would have been more comprehensive. However, the robustness of the phenotypes of the mutants (all re-constructed and complemented, when possible) demonstrated that the genes of interest had direct impact upon the control of succinate metabolism with novel implications for the field.
Figure 4: The authors claim: "that the fast growth of the Δhfq and Δpnp strains reflected both the dysregulation of the sRNA-mediated repression of sdh and the activation of rpoS translation". However, they provide no evidence for SDH regulation. The experiment is correlative, the activity of pnp regulating rpoS was done with overexpression without the proper controls. The authors should look at rpoS expression in Δpnp. It does not seem reasonable that transcription of SDH mRNA can explain lack of succinate utilization. What about the SDH protein? is it at all changed? The authors claim "none of the sRNA mutants tested displayed the same fast-growing pattern of the Δhfq mutant" but they action can involve completely different mechanisms, that the authors do not study. This part does not seem to contribute any novel information on Δhfq and Δpnp on Succ+ with the sRNAs seem not to provide any clear mechanism. The authors should consider removing this part or moving to supplementary.
We appreciate this comment, and agree that this section does not provide critical novel insight. However, our findings provide valuable data concerning the role that Hfq, PNPase and sRNAs play in succinate utilisation. Therefore, we have briefly mentioned the role of Hfq, PNPase and sRNAs in the main text (Lines 333-338) and moved the original Figure 4 to the Supplementary (Figure S5), with a supplementary text section (Supplementary Text T1).
If OxyS, an Hfq-binding sRNA, is related to Succ+ in Δhfq, then why all the other sRNAs are relevant? This is not clear. The authors could have focused here on the oxyS instead of other sRNAs. "The same plasmid did not stimulate the growth of the ΔoxyR strain indicating that a functional OxyR is required for growth in M9+Succ (Fig 5D)" - is it because of other targets of OxyR?
The reviewer’s interpretation is correct. To clarify this point, we have rephased the sentence (Lines 274-276) to “The same plasmid did not stimulate the growth of the ∆oxyR strain indicating that other OxyR-dependent genes are required to grow under this condition”
It seems that an RNA-seq analysis in the conditions of succinate growth with OxyRmut vs. WT could hint towards this.
Indeed, it would be very interesting to compare the transcriptomic landscape of the WT and of the oxyRmut mutant and other Succ+ mutants in succinate minimal medium. However, the lack of growth of S. Typhimurium WT in M9+Succ, would make these experiments unlikely to succeed.
"We previously showed that Hfq inactivation boosted succinate utilization (Fig 4A), but in the oxyRmut genetic background the same Hfq inactivation dramatically reduced growth and extended the duration of lag time in M9+Succ (Fig 5 E)"
The reviewer is correct, we had hypothesised that Hfq is necessary to stimulate succinate utilisation by OxyS. Therefore, we have rephrased to: “We previously showed that Hfq inactivation boosted succinate utilisation, but in the oxyRmut genetic background the same Hfq inactivation dramatically reduced growth and extended the duration of lag time in M9+Succ (Fig 4E). Collectively, our findings show that the OxyS sRNA orchestrates the de-inhibition of succinate utilisation in concert with Hfq” (Lines 278-281)
- this seems like an interesting finding, but the authors don't offer any follow-up? Is it related to oxyS activity?
The role of Hfq on succinate utilisation appeared to be dual, we have added a sentence to this effect (Lines 335-338).
Figure 6: "OxyS acts as an indirect repressor of RpoS expression, probably via the titration of Hfq". the yobF::sfgfp activity was significantly lower in the oxyRmut strain (~2-fold repression), confirming that OxyS represses the expression of the yobF cspC operon in Salmonella - can the authors show this directly with oxyS in succinate?
Because Salmonella WT and ∆oxyS strains do not grow in succinate media (M9+Succ), we had to investigate the regulation of yobF-cspC operon with a translational gene fusion in non-selective LB media.
Why use OxyRmut here? This is indirect.
In Figure 5C we first used the oxyRmut Succ+ strain to demonstrate that this mutation leads to the repression of yobF-cspC. In Figure 6F, we used the oxyRmut allele to allow a constitutive expression of oxyS WT or oxySGG : allele oxySGG was introduced into the chromosome and relies on an active OxyR to be transcribed. The direct role of OxyS is demonstrated in Figure 5 E &F.
The authors already show that OxyRmut does not act solely via Oxys...can the authors directly show RpoS and SDH levels by qRT-PCR in ΔcspC? Again - the appropriate control for RpoS overexpression in the WT was not done (Fig. 6G). Furthermore, expression analysis of the sdhCDAB operon over the background of the oxyR mutant will confirm the author suggestion for the mechanism by which the OxyS-driven inhibition of CspC expression impacts upon the catabolism of succinate.
The reviewer’s comments are valid, more work is required to understand how OxyS stimulates succinate utilisation via the repression of cspC. The fact that Salmonella WT does not grow with succinate as a sole carbon source makes such comparisons technically challenging. Yes, the repressive role of CspC remains enigmatic. However, RNA-seq data following growth in LB media have already been provided by others, suggesting that CspEC may repress TCA cycle genes in Salmonella (PMID: 28611217), consistent with the repression of succinate catabolism by CspC.
The fact that the plasmid-borne overexpression of rpoS completely represses growth upon succinate in the ∆rpoS background (Figure S3 B) validated the usage of the prpoS plasmid in other genetic backgrounds, in order to reveal whether the other Succ+ mutations were stimulating succinate utilisation via rpoS repression or not. Because WT Salmonella does not grow in M9+Succ, presenting the growth curve of the WT strain carrying the prpoS plasmid would not be informative here, and would make the figure overly complex.
Figure 7: the authors check growth in M9+succ in the absence of DctA - but the experiment duration should be carried out for longer, as previous experiments with WT (intact dctA in Fig. 2A) and check if in the absence of dctA there are mutations that allow succinate growth.
We agree with the reviewer’s comment and we have performed a new growth curve (over 65 hours) of the ∆dctA strain to clarify that DctA is the only succinate transporter involved in Salmonella growth under our experimental conditions (Figure S8).
It seems that the results here contradict some of the previous - if succinate uptake through dctA is intact then there is no repression of SDH? rpoS? In figure 7E - is this difference only through dctA activity?
The reviewer is raising an important point and it is possible that the de-repression of succinate uptake via DctA could impact upon the expression of the succinate catabolic genes and more work is required to understand this phenomenon. We have discussed this possibility in the main text (Lines 424-432) and in Figure 8C.
It seems that icsR is not repressing dctA expression to WT levels - are there other factors? Can the authors show that dctA repression by IscR is direct?
We agree with the reviewer, we have not shown that IscR represses dctA directly. Electrophoretic mobility shift assays could be performed to prove that IscR interacts with the dctA promoter region, but this would be beyond the scope of the paper. We have clearly stated in the discussion that indirect effects of iscR on dctA expression cannot be ruled out (Lines 419-422).
Figure 9 is very descriptive and does not provide any evidence to support the authors hypothesis. The authors should either provide more substantial evidence connecting ribosomal RNA levels and succinate utilization and similarly Cm concentrations or either remove this part or move it to the supplementary.
We agree that the data do not conclusively support the hypothesis, but we believe that the impact of anti-SD mutation and chloramphenicol on Salmonella carbon metabolism are valuable observations for the community. Therefore, we have moved the data to supplementary Figures S11 and S12 in the revised version, with a supplementary text section (Supplementary Text T2). We also removed this aspect from the model Figure (Figure 8) and only mentioned the phenomenon briefly in the main text, Lines 482-485.
Can any of the mutations characterized in this work be found in the genome of Newport or LT2 strains that can grow with succinate as a sole carbon source? (Fig 1)
Very good questions. Yes, S. Typhimurium strain LT2 has an altered rpoS allele that attenuates virulence of the strain in the murine infection model (PMID: 8975913) and promotes growth with succinate (PMID: 33593945). We have added a sentence and cited the reference at Lines 129-131.
To address the S. Newport question, we performed an analysis of the genome of the S. Newport strain LSS-48, and did not identify any mutations in regulatory or catabolic genes that could explain the faster growth on M9+succinate. However, in comparison with fast-growing enteric bacteria (i.e. E. coli MG1655) or Succ+ S. Typhimurium mutants, S. Newport LSS-48 grows much slower on succinate and has an intermediate growth phenotype. It remains unclear why S. Newport does grow better than other serovars.
Although the author suggested that regulation of succinate uptake is critical for Salmonella colonization and virulence in various metabolic conditions, the study lacks sufficient evidence to support these claims and further research is necessary to establish these statements.
We agree that our findings are not directly linked to Salmonella host colonisation or virulence. However, we do believe that our study will contribute to a better understanding of Salmonella metabolic control, in the context of pathogenesis. To address Reviewer #3’s comment, we have moderated our claims about the likely impact of our findings on the understanding of Salmonella pathogenesis in the Perspective section.
Minor comments
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Table summarizing the growth curves lag phase of the different mutants might help in the data interpretation.
We appreciate the Reviewer’s suggestion and have prepared a supplementary figure (Figure S4) indicating the average lag time of the Succ+ mutants and of the complemented mutants.
In lines 245-248 the author describes the eleven novel Succ+ mutations however in this gene list only ten gene names are mentioned. DctA is missing from this list.
We appreciate the Reviewer’s comment and we have modified the sentences in the revised manuscript (Line 244).
** Referees cross-commenting**
I agree with both reviewer that there is a large amount of data in the paper, and willing to accept their point that asking for further experiments would exceed the scope of the paper. In that case, the authors should address the mechanistic options in the discussion
Reviewer #1 (Significance (Required)):
In this work, Wenner et al. characterized the molecular mechanisms regulating Salmonella growth inhibition when succinate is the sole carbon source in the culture. This work revealed new layer of regulations for rpoS activation, the sigma factor previously characterized to control this growth inhibition mechanism. In addition, this work revealed novel RpoS-independent mechanisms for succinate utilization and highlighted the crucial role of succinate processing in Salmonella physiology.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
In the manuscript titled "Salmonella succinate utilisation is inhibited by multiple regulatory systems", Wenner et al., explored how Salmonella regulates the utilization of succinate, an important carbon source for Salmonella gut colonization as well as a molecule that regulates intracellular adaptation in the SCV. As Salmonella exhibits a slow growth rate when succinate is provided as the sole carbon source, the authors explored the underlying genetic regulation by isolating fast-growing mutants (Succ+) using an experimental evolution approach. By combining the screen for mutants lacking key regulatory proteins, an elegantly designed Tn5 transposon mutagenesis, and selection of spontaneous Succ+ mutants, the authored identified a library of mutations that led to the Succ+ phenotype. Using classical bacterial genetics, Wenner et al characterized how Hfq, PNPase and cognate sRNA inhibit succinate utilization. They went on to show, clearly and convincingly, that IscR inhibits growth upon succinate by repressing DctA expression, and succinate utilization can also be repressed by RbsR and FliST via RpoS. Lastly, they provided evidence supporting that anti-Shine-Dalgarno mutations and low concentrations of chloramphenicol can boost succinate utilization. Overall, this paper is well written, and the experiments were rigorously designed and executed. This is a beautiful example of deciphering complex regulatory nodes in the succinate utilization using elegant genetics approaches. Very nicely done!
We thank the Reviewer #2 for the very positive evaluation of our work and the constructive comments.
Minor issues:
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While rpoS2X strain is an clever way to avoid the selection of Succ+ rpoS mutants, it is unclear why "identified an iraP::Tn5 mutant was an effective validation of the use of the rpoS2X genetic background". IraP stabilizes Rpos, and this mutant could have been selected in the wild-type background (rpoS1X).
The reviewer’s comment is helpful, we have removed this sentence from the revised manuscript.
The description between line 356-357 is confusing as it reads like the author constructed a "oxyRmut oxySGG pPL-OxySGG" strain, while the experiments that followed actually used a " ∆oxyS, yobF::sfgf, pPL-OxySGG" strain.
We have modified these sentences in the revised manuscript (Lines 303-308).
An alternative explanation for the Succi+ phenotype in aSD mutant and bacteria treated with low Cm is the reduced translation fidelity, which leads to selectively degradation of inhibitors of succinate utilization.
We thank Reviewer #2 for the suggestion. This phenomenon is really enigmatic and as previously discussed in Reviewer #1’s section, we have now moved Figure 9 to supplementary data. Further discussion of how the aSD mutations and chloramphenicol can affect Salmonella succinate metabolism would require a lot more experimental data.
** Referees cross-commenting**
Most of the comments from Reviewer 1 are valid but excessive. Most of the experiments presented in this paper were rigorously controlled and executed. While some parts of the paper could be more mechanistic but they could also leave room for future studies. Also, some of the points raised, the 1st major concern, for example, may have exceeded the scope of the paper.
We agree. We have performed a new experiment (Figure S8) to address Reviewer #1’s comments.
Reviewer #2 (Significance (Required)):
Overall, this paper is well written, and the experiments were rigorously designed and executed. This is a beautiful example of deciphering complex regulatory nodes in the succinate utilization using elegant genetics approaches.
We appreciate Reviewer #2’s feedback that the quality of the text and our experiments was viewed so highly.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
In this work, Wenner and colleagues use experimental evolution to define a range of spontaneous mutations in Salmonella that allow it to overcome its aversion to using succinate as a carbon source in vitro.
This work cites the literature extensively and the scholarship is very very good. I appreciate the effort they put into the manuscript, which made it easy to read. Quite a relief to get a paper in this good of shape compared to most.
We appreciate Reviewer #3’s positive comments on our work and the constructive suggestions.
Shortcomings - although I don't think they are necessary for *this* paper to be published include:
- not defining what could be 'bad' about eating succinate in the wrong place. The fact that succinate import is a problem (dctA is what is being regulated ant its a transporter) suggests one of the following: (1) excess succinate would block the utilization of fumarate by fumarate reductase, (2) succinate is a powerful buffer and, if protonated, would acidify the cytoplasm of Salmonella if it were brought in - note that there is a lot of work on RpoS controlling cytoplasmic acidification, (3) a drop in succinate (because Salmonella eats it) would allow more flux by macrophages or the microbiota in a bad way...maybe the Salmonella 'wants' macrophages to have lots of succinate *because* its pro-inflammatory (and therefore more tetrathionate for its friends...etc), (4) it could be the transporter that also bring in antimicrobial itaconate?...so the succinate phenotype is a red herring and really this is about preventing taconite from getting into the cell?
We thank the reviewer for all these suggestions and for highlighting the reasons why the avoidance of Salmonella utilising succinate is a key point. We have emphasized this key question to conclude our manuscript (Lines 500-501). Whilst all the hypotheses are valid, we believe that further speculation should not be added to the “Perspective” section.
- no proof that any of this is relevant in infection except citing old papers. Again - this work is already VERY expansive and we could propose experiments until the end of time. Next paper should take the dctA and other mutations and put them into mice to see if they fail in either germ free mice (no microbial produced succinate around) or in systemic infections.
The reviewer’s comment is welcomed. As discussed in our response to Reviewer #1, we have scaled back our discussion of the impact of our findings for the understanding of Salmonella pathogenesis*. *
Most of the mutations they find are 'regulatory' and the only proximal effector of succinate utilization seems to be dctA...suggesting that dctA expression is the 'rate limiting' or 'blocked' step that decides whether succinate is being used or not.
We agree that dctA regulation is a central element of the story. As discussed in Reviewer #1 comments, it is not clear how de-repression of dctA leads to the increased catabolism of succinate in the presence of RpoS (particularly because RpoS represses several succinate catabolic genes, PMID: 24810289 and PMID: 25578965). We also discovered other Succ+ mutants that did not affect DctA expression but stimulated growth on succinate as a sole carbon source. Consequently, it is uncertain whether the uptake of succinate is really the limiting factor. We have added sentences about this paradox, Lines 424-432.
The data is extensive and generally well controlled. Where appropriate they either complement mutations or reconstruct them denovo. The findings of the various genes range in novelty but many are new.
** Referees cross-commenting**
I agree that the work was valid and well controlled. The 'story' was a bit disjointed at times primarily because the range of mutations identified were diverse and pleiotropic. Given the large amount of data already in the paper and the nature of the mutations identified I worry about embarking on an endless cycle of new experiments. I think it's at a publishable stopping point.
In response to Reviewer #3 & #1’s comments, we have now improved the flow of the manuscript.
Reviewer #3 (Significance (Required)):
This seemingly mundane phenotype (Salmonella 'choosing' to not use succinate even though it's perfectly capable of doing so) has been known for years but only recently has its potential relevance become more clear in the context of infection and microbiota metabolism.
The authors propose that succinate utilization is to be used at the right time and right place.
I sympathize with the authors that they keep hitting very pleiotropic regulators (RpoS has ten million upstream inputs and outputs. The ribosome? How is that going to be figured out in one or two simple experiments?). My money is on figuring out exactly how dctA is regulated and whether there's differences in the dctA regulation between E. coli and Klebsiella/Salmonella.
So I think the work is extensive and generally well done. I think the paper will be well cited...and I think it's importance will grow over time and it will continue to be relevant years from now. I can't say that about most work in the field.
We agree with Reviewer #3’s assessment that other scientists in the Salmonella field are likely to cite our paper, and to perform experiments that will build on our findings in the future.
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Referee #3
Evidence, reproducibility and clarity
In this work, Wenner and colleagues use experimental evolution to define a range of spontaneous mutations in Salmonella that allow it to overcome its aversion to using succinate as a carbon source in vitro.
This work cites the literature extensively and the scholarship is very very good. I appreciate the effort they put into the manuscript, which made it easy to read. Quite a relief to get a paper in this good of shape compared to most.
Shortcomings - although I don't think they are necessary for this paper to be published include:
- not defining what could be 'bad' about eating succinate in the wrong place. The fact that succinate import is a problem (dctA is what is being regulated ant its a transporter) suggests one of the following: (1) excess succinate would block the utilization of fumarate by fumarate reductase, (2) succinate is a powerful buffer and, if protonated, would acidify the cytoplasm of Salmonella if it were brought in - note that there is a lot of work on RpoS controlling cytoplasmic acidification, (3) a drop in succinate (because Salmonella eats it) would allow more flux by macrophages or the microbiota in a bad way...maybe the Salmonella 'wants' macrophages to have lots of succinate because its pro-inflammatory (and therefore more tetrathionate for its friends...etc), (4) it could be the transporter that also bring in antimicrobial itaconate?...so the succinate phenotype is a red herring and really this is about preventing taconite from getting into the cell?
- no proof that any of this is relevant in infection except citing old papers. Again - this work is already VERY expansive and we could propose experiments until the end of time. Next paper should take the dctA and other mutations and put them into mice to see if they fail in either germ free mice (no microbial produced succinate around) or in systemic infections.
Most of the mutations they find are 'regulatory' and the only proximal effector of succinate utilization seems to be dctA...suggesting that dctA expression is the 'rate limiting' or 'blocked' step that decides whether succinate is being used or not.
The data is extensive and generally well controlled. Where appropriate they either complement mutations or reconstruct them denovo. The findings of the various genes range in novelty but many are new.
** Referees cross-commenting**
I agree that the work was valid and well controlled. The 'story' was a bit disjointed at times primarily because the range of mutations identified were diverse and pleiotropic. Given the large amount of data already in the paper and the nature of the mutations identified I worry about embarking on an endless cycle of new experiments. I think it's at a publishable stopping point.
Significance
This seemingly mundane phenotype (Salmonella 'choosing' to not use succinate even though it's perfectly capable of doing so) has been known for years but only recently has its potential relevance become more clear in the context of infection and microbiota metabolism.
The authors propose that succinate utilization is to be used at the right time and right place.
I sympathize with the authors that they keep hitting very pleiotropic regulators (RpoS has ten million upstream inputs and outputs. The ribosome? How is that going to be figured out in one or two simple experiments?). My money is on figuring out exactly how dctA is regulated and whether there's differences in the dctA regulation between E. coli and Klebsiella/Salmonell.
So I think the work is extensive and generally well done. I think the paper will be well cited...and I think it's importance will grow over time and it will continue to be relevant years from now. I can't say that about most work in the field.
-
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Referee #2
Evidence, reproducibility and clarity
In the manuscript titled "Salmonella succinate utilisation is inhibited by multiple regulatory systems", Wenner et al., explored how Salmonella regulates the utilization of succinate, an important carbon source for Salmonella gut colonization as well as a molecule that regulates intracellular adaptation in the SCV. As Salmonella exhibits a slow growth rate when succinate is provided as the sole carbon source, the authors explored the underlying genetic regulation by isolating fast-growing mutants (Succ+) using an experimental evolution approach. By combining the screen for mutants lacking key regulatory proteins, an elegantly designed Tn5 transposon mutagenesis, and selection of spontaneous Succ+ mutants, the authored identified a library of mutations that led to the Succ+ phenotype. Using classical bacterial genetics, Wenner et al characterized how Hfq, PNPase and cognate sRNA inhibit succinate utilization. They went on to show, clearly and convincingly, that IscR inhibits growth upon succinate by repressing DctA expression, and succinate utilization can also be repressed by RbsR and FliST via RpoS. Lastly, they provided evidence supporting that anti-Shine-Dalgarno mutations and low concentrations of chloramphenicol can boost succinate utilization. Overall, this paper is well written, and the experiments were rigorously designed and executed. This is a beautiful example of deciphering complex regulatory nodes in the succinate utilization using elegant genetics approaches. Very nicely done!
Minor issues:
- While rpoS2X strain is an clever way to avoid the selection of Succ+ rpoS mutants, it is unclear why "identified an iraP::Tn5 mutant was an effective validation of the use of the rpoS2X genetic background". IraP stabilizes Rpos, and this mutant could have been selected in the wild-type background (rpoS1X).
- The description between line 356-357 is confusing as it reads like the author constructed a "oxyRmut oxySGG pPL-OxySGG" strain, while the experiments that followed actually used a " ∆oxyS, yobF::sfgf, pPL-OxySGG" strain.
- An alternative explanation for the Succi+ phenotype in aSD mutant and bacteria treated with low Cm is the reduced translation fidelity, which leads to selectively degradation of inhibitors of succinate utilization.
** Referees cross-commenting**
Most of the comments from Reviewer 1 are valid but excessive. Most of the experiments presented in this paper were rigorously controlled and executed. While some parts of the paper could be more mechanistic but they could also leave room for future studies. Also, some of the points raised, the 1st major concern, for example, may have exceeded the scope of the paper.
Significance
Overall, this paper is well written, and the experiments were rigorously designed and executed. This is a beautiful example of deciphering complex regulatory nodes in the succinate utilization using elegant genetics approaches.
-
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Referee #1
Evidence, reproducibility and clarity
In this study, Wenner et al. used various in vitro methods, including transposon mutagenesis, screening of known regulatory proteins and isolation of spontaneous mutants to discover 11 mutations in genes that promote bacterial growth under succinate-mediated inhibition. Through additional experiments, the manuscript provides evidence for factors that underlie several layers of succinate regulation. These layers include sRNAs, OxyRS, succinate transport antibiotics and rRNA. The study then characterized the molecular mechanisms regulating succinate utilization by these mutations, revealing a RpoS-independent mechanism for succinate uptake via the dctA transporter and mechanisms for RpoS regulation.
Overall, the manuscript is very unfocused and uneven in the level of details of each of these factors and could be much more compelling if more focus was given to several factors and providing more mechanistic insight of these factors.
Major comments
- The authors discuss virulence-mediated succinate but disregard some important features of succinate utilization, only referring to dctA and disregarding the overlap with other C4-dicaroxy transporters (Spiga, wolf, PMID). Furthermore, the study found that a mutation in the IscR binding site on the DctA promoter region reversed the effects of succinate-dependent growth inhibition generated under aerobic conditions but other succinate transporters are expressed under different physiological conditions (Janausch et al. 2002, Spiga et al. 2017). Does the IscR binding site motif can be found in promoters of other succinate transporters? Analysis of IscR in aerobic/ anaerobic conditions can be useful. Do mutations in IcsR lead to increased expression of other succinate transporters in aerobic or anaerobic conditions?
- Transposon screen - There is no comprehensive description of the results and it is not clear why mutations found in the evolution experiments or regulatory proteins that were shown to allow bacteria growth under succinate treatment were not detected in the transposon screening?
- Figure 4: The authors claim: "that the fast growth of the Δhfq and Δpnp strains reflected both the dysregulation of the sRNA-mediated repression of sdh and the activation of rpoS translation". However, they provide no evidence for SDH regulation. The experiment is correlative, the activity of pnp regulating rpoS was done with overexpression without the proper controls. The authors should look at rpoS expression in pnp. It does not seem reasonable that transcription of SDH mRNA can explain lack of succinate utilization. What about the SDH protein? is it at all changed? The authors claim "none of the sRNA mutants tested displayed the same fast-growing pattern of the Δhfq mutant" but they action can involve completely different mechanisms, that the authors do not study. This part does not seem to contribute any novel information on Δhfq and Δpnp on Succ+ with the sRNAs seem not to provide any clear mechanism. The authors should consider removing this part or moving to supplementary.
- If OxyS, an Hfq-binding sRNA, is related to Succ+ in Δhfq, then why all the other sRNAs are relevant? This is not clear. The authors could have focused here on the oxyS instead of other sRNAs. "The same plasmid did not stimulate the growth of the ΔoxyR strain indicating that a functional OxyR is required for growth in M9+Succ (Fig 5D)" - is it because of other targets of OxyR? It seems that an RNA-seq analysis in the conditions of succinate growth with OxyRmut vs. WT could hint towards this. "We previously showed that Hfq inactivation boosted succinate utilization (Fig 4A), but in the oxyRmut genetic background the same Hfq inactivation dramatically reduced growth and extended the duration of lag time in M9+Succ (Fig 5 E)" - this seems like an interesting finding, but the authors don't offer any follow-up? Is it related to oxyS activity?
- Figure 6: "OxyS acts as an indirect repressor of RpoS expression, probably via the titration of Hfq". the yobF::sfgfp activity was significantly lower in the oxyRmut strain (~2-fold repression), confirming that OxyS represses the expression of the yobF cspC operon in Salmonella - can the authors show this directly with oxyS in succinate? Why use OxyRmut here? This is indirect. The authors already show that OxyRmut does not act solely via Oxys...can the authors directly show RpoS and SDH levels by qRT-PCR in ΔcspC? Again - the appropriate control for RpoS overexpression in the WT was not done (Fig. 6G). Furthermore, expression analysis of the sdhCDAB operon over the background of the oxyR mutant will confirm the author suggestion for the mechanism by which the OxyS-driven inhibition of CspC expression impacts upon the catabolism of succinate
- Figure 7: the authors check growth in M9+succ in the absence of DctA - but the experiment duration should be carried out for longer, as previous experiments with WT (intact dctA in Fig. 2A) and check if in the absence of dctA there are mutations that allow succinate growth. It seems that the results here contradict some of the previous - if succinate uptake through dctA is intact then there is no repression of SDH? rpoS? In figure 7E - is this difference only through dctA activity? It seems that icsR is not repressing dctA expression to WT levels - are there other factors? Can the authors show that dctA repression by IscR is direct?
- Figure 9 is very descriptive and does not provide any evidence to support the authors hypothesis. The authors should either provide more substantial evidence connecting ribosomal RNA levels and succinate utilization and similarly Cm concentrations or either remove this part or move it to the supplementary.
- Can any of the mutations characterized in this work be found in the genome of Newport or LT2 strains that can grow with succinate as a sole carbon source? (Fig 1)
- Although the author suggested that regulation of succinate uptake is critical for Salmonella colonization and virulence in various metabolic conditions, the study lacks sufficient evidence to support these claims and further research is necessary to establish these statements.
Minor comments
- Table summarizing the growth curves lag phase of the different mutants might help in the data interpretation.
- In lines 245-248 the author describes the eleven novel Succ+ mutations however in this gene list only ten gene names are mentioned. DctA is missing from this list.
** Referees cross-commenting**
I agree with both reviewer that there is a large amount of data in the paper, and willing to accept their point that asking for further experiments would exceed the scope of the paper. In that case, the authors should address the mechanistic options in the discussion
Significance
In this work, Wenner et al. characterized the molecular mechanisms regulating Salmonella growth inhibition when succinate is the sole carbon source in the culture. This work revealed new layer of regulations for rpoS activation, the sigma factor previously characterized to control this growth inhibition mechanism. In addition, this work revealed novel RpoS-independent mechanisms for succinate utilization and highlighted the crucial role of succinate processing in Salmonella physiology.
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Reply to the reviewers
__Reviewer #1 (Evidence, reproducibility and clarity (Required)): __
The manuscript investigates the role of PAT1 gene family in Arabidopsis thaliana. Though the PAT1 protein has been previously investigated and displayed immune-related and developmental phenotypes, the other two members of the family, PATH1 and PATH2, have not been well studied. The authors set out to understand the role of these proteins in relation to the role of PAT1. They thus generated single, double, and triple mutants of the possible combinations of PAT1 genes and examined their phenotypes. As the study focused on the developmental effects of PAT1, the mutants were generated on the background of the summ2 mutant to avoid phenotypes related to immune response. The authors notice a developmental difference between the pat1 mutant combinations, suggesting that PAT1 acts differently than PATH1 and PATH2 and that the PATH proteins serve a redundant function. They also performed RNA-seq analysis to identify differentially-regulated genes in the mutant combinations. The study is interesting and well-executed, yet I believe some questions should still be addressed:
__Our response: __We thank the reviewer for acknowledging the significance of our findings. Please see our detailed answers to the reviewer’s suggestions in the following.
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The research mainly focuses on the developmental phenotype of pat mutants but also tests the interaction of PATH proteins with RNA decapping enzymes to check their function and localization during different treatments. I found it a bit confusing since Figure 1 also shows the developmental phenotype of the mutants. I think editing the order of the figures would make the overall story more coherent.
__Our response: __We agree with the reviewer thus we moved old Fig 1C to new Fig 3A, we believe the new figure orders make the overall story more coherent.
My main concern is the correlation between the developmental phenotype of the mutants and the gene expression. Leaf samples for RNA extraction were taken when the plants were 6 weeks old, and the developmental phenotype is very evident. It is thus not possible to tell whether the differences in gene expression are a cause or effect of the developmental phenotype. I think performing qPCR of selected candidates at earlier developmental times might help solve this issue, as well as the characterization of younger plants for the developmental phenotypes (such as leaf number).
__Our response: __We followed the reviewer’s suggestions and performed qRT-PCR on IAA19, IAA29, SAUR23 and PIL2 in pats mutants under different developmental stages (Line 162, 169; Fig S4), we also characterized leaf number of pats mutants from younger stages (Line 109; new Fig 3C).
Overall, the manuscript is missing data regarding replicate numbers in the IP and confocal microscopy experiments.
__Our response: __We thank the reviewer for pointing this it out, the replicate numbers are provided now in our new figure legends.
Minor comments:
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Figure 1C - the authors should add a picture of Col0 plants as well as the mutants.
Our response: To be reader friendly, the picture of Col-0 plant is added in Fig S1A. For the reviewer’s information, plant pictures in FigS1A and old Fig1C (new Fig 3A) were taken at the same time. 2.
Figure 3 - Calculating the leaf-to-petiole ratio in the different mutants would be good.
Our response: We now calculate PBR (petiole blade ratio) of all pats mutants in Fig3F (Line 121).
Figure 4 - the details in the figure are very unclear, especially in the PCA. It would be good to display the data in 2D for PC1 and PC3 and change the colors a bit.
Our response: We agree with the reviewer; thus, we remade the PCA plot from RNA-seq reads data in a 2D style and also changed the colors for each mutant (Fig 4A). We need to point out that the PCs number also changed because the old PCA plot were made by mistake from expression data.
Reviewer #1 (Significance (Required)): Both PATH proteins have been less investigated than PAT1, and in that sense, the work is novel. However, it seems that most of the phenotype is attributed to PAT1 rather than the other family members, limiting the interest to the broad plant science community.
Our response: We appreciate the reviewer think our work is novel. We agree that PAT1 plays the main role during plant development (old Line 171), however the pat triple mutant exhibit the most severe dwarfism as well as the most mis-regulated genes compared to any single or double mutants, indicating all 3 PATs are essential for development.
__Reviewer #2 (Evidence, reproducibility and clarity (Required)): __
Zuo et al., characterize the role of three cytoplasmic mRNA-decay activator proteins PAT1, PATH1 and PATH2 in the context of plant development and leaf morphology in Arabidopsis thaliana and Nicotiana benthamiana. The authors show that the triple pat mutant displays the most severe dwarfism of all combinatorial mutants. Through treatment with different stimulants the authors found that only IAA treatment induces the three homologues to form condensates (possibly PBs), while PAT1 forms condensates upon every tested stimulus. An extensive RNA seq experiment revealed miss-regulation of several hundred genes in the higher order mutants, several of which were involved in auxin responsive and leaf morphology determinant genes.
__Our response: __We thank the reviewer for the peer review. Please see our detailed answers to the reviewer’s suggestions in the following.
Major points: 1.Title is not meaningful as is and, in my opinion, does not reflect the main findings in the manuscript.
Our response: We now changed our title into “PAT mRNA decapping factors are required for proper development in Arabidopsis”.
The results section could benefit from improved flow between the paragraphs and more reasoning for the next steps taken to help readers understand the aims of the authors.
Our response: We followed the reviewer’s suggestion and modified the wording in our result part(Line 79,81,94,146-151).
L46: "So far little is known about the functions of these three PATs in plant development.", The authors themselves have studied these proteins in the context of seed germination and ABA control, as well as apical hook formation and auxin responses. Should at least be mentioned and the results discussed in this context.
Our response: We thank the reviewer for noticing our other work and we now included this information in the new introduction and discussion part (Line56&237).
What are the expression levels and patterns of PATH1 and PATH2 compared to PAT1? Is anything known about spatial or temporal regulation of these proteins?
Our response: All three PATs are expressed in roots, stems, leaves, flowers, siliques, and seeds during the whole developmental stages, PAT1 has higher expression level in leaves but lower expression levels in petals. (Klepikova et al., 2016;
https://www.arabidopsis.org/servlets/TairObject?id=138009&type=locus for PAT1; https://www.arabidopsis.org/servlets/TairObject?id=38646&type=locus for PATH1 and https://www.arabidopsis.org/servlets/TairObject?id=128694&type=locus for PATH2).
Figure 1:
o I do not agree that the authors have shown that "PATH1 and PATH2 are also mRNA decapping factors", rather that these proteins can co-localize (and possibly interact) with LSM1. Decapping assays for example with the known PAT1 de-capping targets from their previous work and their extensive mutant collection could be used to test this.
Our response: We thank the reviewer for pointing it out and reminding us about the characterized mRNA decapping target from our previous work, we now include the decapping assays in new Fig5 (Line 197).
From the BiFC experiment (Figure 1B) it looks like PATs are mostly soluble in the cytoplasm (like LSM1) and might be stress-induced components of PBs (like LSM1). Do PATs co-localize with other canonical PB markers that are more prone to condensation, like DCPs or VCS? BiFC could be performed after IAA treatment to confirm that the cytoplasmic foci are indeed LSM1-positive PBs.
Our response: We agree with the reviewer that PATs behave more like LSM1. Given time limit of the project, we unfortunately are not able to check the colocalization of PATs with DCPs or VCS. However, we performed BIFC after IAA treatment, and the cytoplasmic foci are indeed LSM1-positive foci (new Fig1B).
A: please provide uncropped images of all Western blots in supplemental data.
Our response: To be reader friendly, we decide to show the original western blots here (see in the file named "RC-Full-revision"), instead of in supplemental data. However, we will leave the final decision to the editor.
I applaud the authors for establishing this great higher order mutant collection that will be very useful for researchers in the field. However, I am confused about the description of these mutants. If I understood it correctly, these mutants were already used in a previous study by the authors, namely “Zuo, Z., et al., Molecular Plant-Microbe Interactions, 35(2), 125-130.” & Zuo, Z., et al., (2021). FEBS letters, 595(2), 253-263.” In this study the authors refer to a BioRxiv “Zuo, Z., et al., (2019).” As the reference for these Arabidopsis lines. Is this current manuscript a continuation of the BioRxiv? Please elaborate whether these lines have been used and described In previous studies.
Our response: We truly appreciate the reviewer for acknowledging the significance of our work. These pats mutants have been used in the FEBS letters paper (2021), MPMI paper (2022), and the new published paper in Life Science Alliance (2023, but preprinted in BioRxiv 2019 and 2022). However, they have not been fully described or characterized in any of the mentioned published stories. Characterization of these pats mutants were originally only included in preprint 2019 which was cited in FEBS letters paper (2021) and MPMI paper (2022).
L72: Is the strong developmental phenotype of the higher order mutants persistent under long day conditions? Considering the strong developmental phenotypes of the mutants, the flowering transition and morphology could be an interesting trait to study. Why did you choose short day conditions for this study?
Our response: The pat triple mutant also has strong developmental phenotype under long day condition and exhibits early flowering phenotype. We are currently preparing a manuscript regarding mRNA decay and flowering. We did not “choose” short day condition, we just started with short day condition and observed phenotypical differences hence we kept this condition.
L78: This statement is hard to see in Figure 1C and best described for Figure 3A.
Our response: We now change this statement for Fig 3.
L82: Please include a reasoning for testing PATs localization after hormone treatment. Do you have any indication that other PB proteins behave similar to either PAT1 or the PATHs after hormone treatment to substantiate that these foci observed are indeed PBs? What is known about PBs after hormone treatment in planta?
Our response: We were interested in investigating if all three PAT proteins may also form PBs in Arabidopsis thus we tested PATs localization with/without hormone treatment (old Line 84, new line 81). For the reviewer’s interest we also observe LSM1 localization after hormone treatment (Fig 2). PBs have been published to respond to light, cold treatment, PAMPs, ABA, ACC and auxin (Line 39-42).
Figure 2:
o How does the localization of LSM1 change under the same treatments? Does ist behave like PAT1 or the homologues?
Our response: Please see our new Fig 2 for LSM1 localization, and it behaves more like PAT1.
Which part of the root was imaged for this experiment? Is it possible that the observed foci are ARF-condensates as reported by Jing et al., 2022? Do you observe a gradual change in numbers or morphology along the root?
Our response: We use root elongation zone for this experiment. We don’t know if the foci are ARF-condensates, but it’s possible to study in the future. If the reviewer is interested, we are happy to share our materials. We do observe more foci in the cell division zone and less in the mature zone.
How did the authors decide on the concentrations for the stimulant treatments? Have you tried different doses, and could the responses be dose-dependent?
Our response: We did not try different doses; we searched for and applied the commonly used concentrations for different hormones.
A representative image is not sufficient for quantitative responses, like RNA granule condensation. Please provide a quantification of stimulant-induced foci after the different treatments.
Our response: Please see the quantification in our new Fig 2.
L91: Does that mean that most co-precipitated signal comes from the soluble fraction and not PB-localized? Would an RNAse treatment step eliminate the co-precipitation (optional)?
Our response: We believe it means LSM1 and PATs are in the same complex regardless of PB localization.
L92/93: Or alternatively that PAT1 localizes to PBs independent of the stress, while PATHs are signal-specific PB components?
Our response: We think PAT1 aggregates upon broad stimuli/stress, while PATHs respond to specific/limited stimuli, for example, auxin.
Figure 3:
o I wonder if these results fit better in conjunction with Figure 1, either as a combined figure or move before Figure 2.
Our response: We agree with the reviewer thus we moved old Fig 1C into Fig 3.
It is interesting that path2/pat1, while being dwarfed, is less serrated compared to pat1 or path1/pat1. Can you find any indications in your RNAseq set which genes might be involved?
Our response: ANAC016 might be involved, but more research needs to be done to confirm it and this is not the focus of the current project.
Indicate statistical test used to determine p-value
Our response: We now indicate the statistic test in Materials and Methods part (Line 369).
L116/L117: Doesn't the result in Figure 3E indicate that PATH1 and PATH2 are not fully redundant, but that PATs have specific and narrow roles in leaf development? L116 goes against your statement in L150 & L160. What is known about the expression patterns of PAT1, PATH1 and PHATH2?
Our response: We agree and thus modified our statement (Line 137). All three PATs are expressed in roots, stems, leaves, flowers, siliques, and seeds during the whole developmental stages. Please also see our answer to major comment #4.
L123: PC3 only explains 0.55% of the variance, so differences along this axis will be overinflated. In my interpretation the pat1/path2 mutant is clustering apart from the other higher order mutants, which is also reflected in the leaf phenotypes. A 2D PCA would be sufficient to describe most of the variation.
Our response: We agree and thus we changed the PCA plot into a 2D style, please also see our response to reviewer 1 minor comment #3.
Figure 4: o A: The 3D-PCA inflates the differences between higher order mutants along PC3, even though this axis explains only 0.55% of the variance, maybe a 2D-PCA would more intuitively cluster the samples together?
Our response: Please see our new PCA plot in Fig4A.
B: Please explain the scale in the figure legend and which genes were included? Only DEGs between triple mutant and summ2-8 or DEGs that were different in at least one higher order mutant?
Our response: We now explained more details in the figure legends. The genes which were included in Fig4B were DEGs that were differently expressed in at least one of the pat mutants.
C: several comparisons are missing from the upset-plot. Please show the complete plot, also is there a white box laid over the second bar in the upper graph? It would help the reader, if the results section would explain the plots and the comparisons took. Which differences are the authors interested in?
Our response: We covered all the comparisons we wanted to show, but we thank the reviewer for suggesting a more detailed explanation and we therefore explain Fig4C more in detail in Line 146. There is no white box over the second bar, it’s only 1 gene mis-regulated specifically by PATH1 (mis-regulated in plants with path1 mutation).
From Figure 4B, the triple mutant has an almost inverted expression of mis-regulated genes. High expression genes are now lowly expressed and vice-versa. Has this been reported for other RNA decay mutants before?
Our response: Our RNA-seq data indicate the pat tripe mutant has more than 1000 mis-regulated genes and based on microarray data on 2-week-old lsm1alsm1b plants (Perea-Resa et al, 2012), more than 600 genes are misregulated in lsm1alsm1b mutant.
How do you explain that mutants in RNA decay have a large group of repressed transcripts and a large group of enriched transcripts? Wouldn't you suspect a general higher expression in RNA decay mutants or which kind of feedback loop would you propose is happening here? Also, since both kinds of expression changes are recorded in your RNA seq can you speculate on the specificity? Why are some genes up- and others downregulated? Would you suspect that transcription factors are under PATs control?
Our response: We assume that the mRNA decapping machinery target genes should accumulate in mRNA decapping mutants, pat mutants in our case. On the other hand, the down-regulated genes could be target genes of other mRNA degradation pathways such as exosome pathway (Line 257); We agree with the reviewer that the down regulated genes in pat triple could also be negatively regulated by the mRNA decapping targets which could be transcription factor genes. For example, our previous research indicates the transcription factor gene ASL9/LBD3 is mRNA decapping targets under PATs control.
Where is the sequencing data deposited? This dataset can be of great value for researchers in the field, but the raw data needs to be made commonly available.
Our response: We thank the reviewer for acknowledging the significance of our work. The raw data has been submitted to NCBI, accession number is PRJNA1006171(Line 307)
Minor points:
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Check order and nomenclature for protein / gene names in Abstract and Introduction
Our response: We now carefully double check the order and nomenclature for protein / gene names in abstract and introduction (Line 8,11,14,18,19,24)
L26 / L83 "aggregate" implies non-functionality, I would use "concentrate", "condensate" or "accumulate".
Our response: We thank the reviewer for pointing it out, we now use “concentrate” (Line 29&80)
L35, L45 & L54 all state the same. Maybe remove at least one mention to reduce redundancy?
Our response: We modified these statements hopefully in a satisfactory way. (Line 56)
L211: Did you use the same imaging settings for all lines?
Our response: We used the same settings for all the lines and treatment (Line 284)
L217: RNA quality "control" word missing?
Our response: The word “control” is added in Line 296
L477: Authors should cite the newest version of their BioRxiv: Zuo, Z., Roux, M. E., Chevalier, J. R., Dagdas, Y. F., Yamashino, T., H�jgaard, S. D., ... & Petersen, M. (2022). The mRNA decapping machinery targets LBD3/ASL9 to mediate apical hook and lateral root development in Arabidopsis. bioRxiv, 2022-07.
Our response: The latest version is cited in our new manuscript (Line 42)
Figure 3B-F, Figure 4C: check spelling on the axis titles.
Our response: We carefully checked the spelling on the axis titles in our new manuscript.
Reviewer #2 (Significance (Required)):
This manuscript represents a continuation of the author's characterization of the 3 PAT1s in Arabidopsis development after Zuo et al., 2021; Zuo et al., 2022a; Zuo et al., 2022b. The mutants and the corresponding RNA sequencing experiments are of value to the community working on RNA regulation and degradation or plant development. While the initial findings are interesting, the authors do not explore the stimulus-induced condensation differences between the homologues or try to link the extreme differences in expression profiles mechanistically or functionally. I think the manuscript could greatly benefit from contextualizing their work within the frame of their previous studies and what is known about PBs in terms of plant development. While the RNA seq is a comprehensive data set, a closer examination and a better representation of the results would help readers to access the findings.
__Our response: __We thank the reviewer for the constructive criticism. We hope the reviewer is satisfied by our modified manuscript.
Reviewer expertise: RNA granule biology, Arabidopsis, molecular biology
__Reviewer #3 (Evidence, reproducibility and clarity (Required)): __
Summary:
In the study "PAT mRNA decapping factors function specifically and redundantly during development in Arabidopsis" authors investigate potential specific functions of Arabidopsis PAT1 orthologs in plant development. Authors observe differences in rosette phenotypes (leaf size, serration and number) of single and multiple mutants of PAT1 gene family, show variation in translocation of the corresponding PAT1 proteins to processing bodies under a set of stress conditions and perform transcriptomics on the established mutants to elucidate the impact of individual PATs on posttranscriptional regulation of plant gene expression. Authors conclude that PAT1 orthologs have both overlapping and specific roles in plant development.
__Our response: __We thank the reviewer for the peer review. Please see our detailed answers to the reviewer’s suggestions in the following.
Major comments:
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The study contains intersting transcriptomics data that will be of use for the scientific community. However, analysis of the transcriptomics results could be discussed a bit more in depth. Authors could express their opinion about what gene expression changes might be caused by direct degradation via PAT1-dependent decapping mechanism and what changes are more likely to have occurred indirectly via other factors.
__Our response: __We followed the reviewer’s suggestion and thus we analysed and discussed more in depth about the transcriptomic data (Line145, 220 &232)
The intersting phenotypic observations are currently poorly linked to the transcriptomics/qPCR data provided, resulting in a somewhat fragmented story flow.
__Our response: __We appreciate the reviewer thought the pat mutants’ phenotype are interesting, however we disagre with the reviewer on the statement of “poorly linked to the transcriptomics/ qPCR data”. For instance, downregulation of developmental and auxin responsive genes could explain the stunt growth phenotype in the pat triple mutant. Furthermore, the published petiole elongation regulator genes XTR7/XTH15 and PIL2/PIF6 exhibit decreased expression level only in mutants with shorter petioles. Nevertheless, we hope our new data and analysis will satisfy the reviewer.
The transcriptomics was performed on the 6-weeks old plants. It would be helpful to learn more about authors reasoning for choosing this developmental stage for sampling. Why did authors decide against sampling at the earlier stages, before the observed leaves phenotypes were established?
__Our response: __The pat mutants growth phenotypes showed bigger difference among each other at the late stage, therefore we performed RNA-seq on these samples. But we agree with the reviewer (also reviewer 1, major comment #2), transcriptomic shift at earlier stage could also be responsible for the observed phenotype, thus we performed qRT-PCR on the pat mutants at earlier stages for certain genes to examine this (Line 162 &169)
Authors obtained intriguing results on specific translocation of PAT1, PATH1 and PATH2 to processing bodies in the root cells upon various stresses. Perhaps root transcriptomics of single PAT1, PATH1 and PATH2 knockouts under control conditions, treatment that translocate all three proteins to PBs(IAA) and selectively translocate only PAT1 (e.g. cytokinin) could shed more light on the redundancy an specificity of these proteins as the mRNA decapping factors.
__Our response: __We appreciate the reviewer found our findings interesting. The specific translocation of PAT1, PATH1 and PATH2 to PBs in the root cells upon various stimuli indicates functional specificity and redundancy in cellular level which correlates with mutants’ growth phenotype. However, we agree with the reviewer that root transcriptomic data on pat mutants are very interesting, we are more than willing to share these mutants with peers who want to persue this in more detail.
Do authors consider PAT1, PATH1 and PATH2 to be localized to different PBs sub-populations? It could be intersting to check co-localization of PAT1, PATH1 and PATH2 under various stress conditions. Could authors elaborate on their view of PBs composition and fate to which different PAT1s are recruited?
__Our response: __We agree with the reviewer that it’s interesting to check co-localization of PAT1, PATH1 and PATH2. We observed partial localization of CFP-PATH2(in blue) and Venus-PAT1(in yellow) when transiently expressed in Benthmiana. But for permanent lines, we failed at observing separate CFP-PATH2(Blue) signal due to too much signal leakage from Venus-PAT1(Green). Given the fact that PATs function redundantly, we would assume they are partially co-localized in cellular level.
Could authors speculate what features in the PAT1 protein might cause it being recruited to PBs more efficiently (or better to say, under a broader range of stresses) in comparison to PATH1 and 2?
__Our response: __The release of ribosome-free mRNPs induces PB formation (Brengues et al., 2005). We suspect PAT1 could bind broader mRNAs compared to PATH1 and PATH2, therefor PAT1-mRNPs could form PBs more efficiently. Moreover, Sachdev et al found yeast PAT1 enhances the condensation of Dhh1 and RNA and PAT1-DHH1 interaction is essential for PB assembly (Sachdev et al., 2019), we assume PAT1 might have better interaction with DHH1 compared to PATH1 and PATH2 thus promote PB formation more efficiently. Please see our discussion part (Line 252)
Are all three Arabidopsis PAT paralogs co-expressed in the same tissues /developmental stages?
__Our response: __Please see our response to reviewer 2 major comment #4.
Could authors elaborate a bit more why the triple pat1 knockout has a much more severe phenotype in comparison to a single pat1 loss-of-function mutant or any of the double pat1 mutants. Do authors observe complementary changes in the PAT1 genes expression in the mutant lines, e.g. is PATH1 expressed at a higher level in the absence of PAT1 and PATH2?
__Our response: __We now elaborate more about the reason why triple pat1 knockout has the most severe phenotype in the multiple pat mutants (Line 210). We do see higher transcriptional level of PAT1 in path1-4path2-1summ2-8 and also higher transcriptional level of PATH1 in pat1-1path2-1summ2-8 but the same PATH2 transcriptional level in pat1-1path1-4summ2-8 compared to summ2-8 (Fig S1C, Line 104)
Please provide the name of the used statistical test in all figure legends.
__Our response: __We now provide the statistical test in “Material and Methods” part (Line 367).
Minor comments:
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Authors might want to reconsider the title as it is somewhat too vague in its current form.
__Our response: __We now changed our title into “ PAT mRNA decapping factors are required for proper developmental in Arabidopsis”
Line 9: explanation of PAT1 and PATH1 and 2 abbreviations is best placed at the first mentioning of the name.
__Our response: __We carefully followed the reviewer’s suggestion (Line 10)
Line 10: mRNA degradation is rather a posttranscriptional regulation of gene expression.
__Our response: __We agree and changed our statement in the new ms (Line 9).
Lines 11 and 12: path1 and path2 abbreviation are not explained. Please note that on the Figure 1A the same proteins are labelled as PAT1H1 and PAT1H2
__Our response: __We thank the reviewer for pointing it out, we now have PATH1 and PATH2 abbreviations explained in Line 10 and also correct the labels in Fig 1A.
Lines 22-25: Would you be so kind to rephrase or elaborate on what yoPBu mean. LSM1-7/PAT1 complex are known to bind oligoadenylated transcripts indeed and even stabilize their 3' ends, it is not clear what "engage transcripts containing deadenylated tails" means in this context.
__Our response: __We hope we now rephrase the statement in a clear way (Line 25)
Line 29: for the sake of clarity, it might be beneficial to list the known activators of the decapping DCP2 enzyme, including the VCS. Generally the introduction could benefit from a bit more in depth review of the decapping mechanism.
__Our response: __We hope the more detailed introduction will satisfy the reviewer (Line 27).
Line 51:"other 2 PATs" => "other two PATs". Generally the text is quite well written, but might need a bit of polishing.
__Our response: __The text is corrected now (Line 64).
Authors are absolutely correct in their attempt to provide full information about mutant backgrounds. However, for the sake of comprehension, it would be great to grant the double and triple mutants in the summ2 background shorter and more legible names. For example, the pat1-1path1-4path2-1summ2-8 mutant could be named as pat1/h1/h2/s.
__Our response: __We originally used pat1/h1/h2/s for the triple but a colleague pointed out “h1” or “h2” are not proper gene names and suggested us to rename them. But we agree that the double and triple pat names are comprehensive, to compromise we change the triple pat mutants into pat triple.
Figure 1B:
- it would be intersting to have authors opinion on why PBs are formed in this case under non-stress(?) conditions.
__Our response: __Forming PBs is a dynamic process, and we assume that even under normal conditions, there is still ongoing mRNA decay and translational repression which should be seen as some background level of PBs (Line 85).
Please note that expressing only the N-terminal part of CFP is a weak negative control for BiFC. No restoration of CFP can occur in such case and thus it is a given that no fluorescence can be observed in these samples. For example, co-expression of nCFP-PAT1 with cCFP-GUS, would be a more rigorous negative control, better aligned with the coIP experiments.
__Our response: __We had nCFP-Gus with cCFP-LSM1 as real negative control in old Fig 1B (bottom lane). We also agree with the reviewer that only the N-terminal part of CFP is a weak negative control for BiFC, therefore we removed the weak control and only left the rigorous negative control (new Fig 1B).
Please note that some arrows point at a structure that seems to be not discernible a signal.
__Our response: __It’s due to the poor quality of the picture from the PDF file, arrows in the original high-resolution figure do point at discernible foci.
Figure 1C: It might be helpful to also include a Col-0 WT plant
__Our response: __Col-WT plant is now included in Fig S1A.
It is not clear how qPCR data and complementation lines help to characterize the established PATH1 and PATH2 loss-of-function mutants. There is no immunodetection of the corresponding proteins in the knockouts, qPCR shows no dramatic decrease in the transcript level of PATH1 and H2 and the phenotypes of complemented lines presented in the Fig S1E at a glance look quite similar to the phenotypes of the corresponding knockout mutants. Complementation lines are not used for any other experiments in this study and it is not clear why authors decided to include this material into the article.
__Our response: __To characterize the path1 and path2 mutants, we first did qRT-PCR to check the transcriptional level expression, but like the reviewer mentioned, there was no dramatic decrease indicating the mutations of path1-4 and path2-1 did not change PATH1 and PATH2 transcriptional level expression. We also tried to raise antibodies against PATH1 and PATH2, however the antibodies failed to recognize any PAT proteins. Therefore, we used the complementation lines to characterize the mutations in PATH1 and PATH2. Since path1 and path2 single mutants don’t have obvious growth phenotype and the dwarf pat triple is barely possible to transform, we had to complement the pat1path1 and pat1path2 double mutants. If the reviewer takes a closer look, the growth phenotype of the complementation lines Venus-PATH1/ pat1-1path1-4summ2-8 and Venus-PATH2/ pat1-1path2-1summ2-8 are similar to pat1-1summ2-8 but not the background pat double mutants. The complementation lines were also used to study PATH1 and PATH2 cellular localization.
Figure S1C misses labels indicating what detection of what gene is shown on what chart.
__Our response: __We thank the reviewer for pointing it out, the gene names are indicated now in new FigS1C.
Experiments to visualize PBs under various stress stimuli were conducted on roots for the Figure 2 while coIP was performed on the green tissue. Could authors elaborate on whether PB formation could be expected to be the same in different plant organs? Somewhat related to the same topic, Figure 2 contains micrographs obtained on meristematic, transition and elongation root zones, in which epidermal cells are present at various developmental stages. Since PAT proteins are suggested to impact plant development, it might be prudent to obtain observations for all samples at the same developmental stage. Could authors provide their opinion about how representative the provided micrographs are for all root zones? Furthermore, Venus-PATH2 under ACC treatment shows punctate localization only in a single cell out of the three-ish cells visible on the micrograph, potentially indicating differences in PAT2 recruitment to PBs in trichoblasts and atrichoblasts. This in itself could be an intersting observation helpful for elucidating the specific roles of PAT1 orthologs.
__Our response: __CoIP results from Benthamiana leaves indicate Arabidopsis PATs and LSM1 are in the same complex, and PB visualization in root area suggests PATs respond to different hormone treatments. flg22 treatment has been published to induce PB formation in Arabidopsis root but dissemble PBs in Arabidopsis protoplasts, indicating a tissue specific manner of PB formation. We randomly chose 1 picture/treatment from 9 (3 plants * bio-triplicates) which showed the same. However, we thank the reviewer for pointing out the confocal pictures we chose were not all from elongation zone, we now carefully checked all our confocal pictures and made sure they are from the same developmental stages. We also discuss more of PATH2 localization in response to ACC (Line 251).
Figure 4C would greatly benefit from a more detailed description in the main text and figure legend of what authors show/conclude.
__Our response: __We thank the reviewer for the suggestion hence we describe Fig 4C in more detail in our new manuscript (Line 146).
Figure 5, please avoid using the same color for the bars for the triple pat knockout and the control summ2-8 line
__Our response: __We changed the colour scheme for all the mutants (new Fig 4E).
Figure 5B legend should include the name of the statistical test.
__Our response: __We now include the name of the statistical test in “Material and Methods” (Line 367).
Figure S2: The coIP experiment is a bit difficult to interpret due to the extremely low protein quantities in some of the input samples. Perhaps a repetition with more balanced input quantities would be beneficial. The figure legend does not contain information on how normalized intensity values were obtained.
__Our response: __We used the same amount of total protein for each sample (3mg) for each IP, PATH1 and PATH2 don’t express as high as PAT1. The numbers indicate the comparative ratio between PAT-HA protein signal and LSM1-GFP signal, and PAT1-HA/LSM1-GFP under non-treatment condition is normalized as 1.
Line 130: Fig S2 is referenced but Fig S3 is meant
__Our response: __We thank the reviewer for pointing out our mistake, the correct figure is now referenced.
Reviewer #3 (Significance (Required)):
Strength:
Regulation of gene expression by mRNA decay is an extremely intersting topic and is highly relevant in plant stress and developmental biology. This study provides a more in depth view on the potential specific roles of the three PAT1 orthologs in Arabidopsis plants. Authors established loss-of-function mutants of the corresponding genes and performed transcriptomics analysis that will be a valuable source for future studies. Furthermore, microscopy analysis of PATH1 and PATH2 translocation to PBs indicates their potential specific roles in plant stress response.
Weakness: The current version of this study suffers from vague presentation of the results. Starting from the title and ending with discussion authors provide a "general" view on their results and do not go into detailed interpretations. Thus, no mechanistic insight has been derived or at least suggested from the wealth of the transcriptomics, phenotypic and microscopy data.
The introduction should provide more detailed information on what is known on the PAT1 role in the mRNA decapping pathway and its relevance for plant stress response and development.
Please note, that the above mentioned suggestion of different sampling for transcriptomics analysis is not meant as a request for this particular study, but rather as an illustration of an expectation a reader would built while following the current version of the text. A thorough description of the strategy for transcriptomics and a more in depth analysis might significantly strengthen the study's coherence and impact.
Advance:
At this stage, the study looks more like an incremental advance of the work from the same laboratory performed for the single PAT1 protein. However, as mentioned in the comments above, the study might be made significantly stronger by elaborating the results analysis and highlighting potential discoveries.
Audience:
The topic of this study is of a significant interest to a broad audience performing research in plant stress biology and also developmental plant biology.
__Our response: __We thank the reviewer for acknowledging the significance of our work and the structural criticism. We hope our detailed answers to the reviewer’s suggestions and the additional data we included in the manuscript will satisfy the reviewer.
Reviewer's and co-reviewer's fields of expertise:
Molecular Biology, Plant cell biology, Plants Stress response, Autophagy, Stress granules
__Reviewer #4 (Evidence, reproducibility and clarity (Required)): __
PAT1 (Protein Associated with Topoisomerase II) are RNA-binding proteins involved in the control of mRNA decay in the cytoplasm. Plants possess multiple PAT1 family members, three in Arabidopsis, PAT1, PATH1 and PATH2. According to the literature, the pat1 mutant shows dwarfism and de-repressed immunity. In this paper, Zou et al. describe the function of PATH1 and PATH2. Two pieces of evidence are consistent with their role in the control of mRNA decay. First, Co-IP and bimolecular Fluorescence Complementation assays in tobacco indicate physical interaction and co-localization of PAT1, PATH1 or PATH2 with LSM1 (Fig. 1), which is a protein present in decapping complexes that form the cytoplasmic foci involved in mRNA decay. Second, PAT1, PATH1 and PATH2 are present in these cytoplasmic Processing Bodies (Fig. 2). Zou et al. generated path1 and path2 mutants, double mutants with pat1 and the triple mutant using independent alleles and the summ2 background to avoid autoimmunity interference. The mutants show leaf growth (Fig. 3) and gene expression (Fig. 4) phenotypes that are not exactly similar among the different family members, but there is significant redundancy revealed by these phenotypes.
__Our response: __We thank the reviewer for the peer review. Please see our detailed answers to the reviewer’s suggestions in the following.
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The conclusions are straight forward and, apparently, well supported by the data. However, the authors should confirm that when they provide the number of replicates (n) in the legends to the figures, this actually refers to the number of biological replicates. The statements should be based on true biological replicates (not technical replicates). The statistical tests should also be explicitly indicated (including that used to identify DEG in the RNAseq experiment).
__Our response: __We carefully went through our figures and made sure the number of replicates (n) were correctly stated in figure legends and the statistical tests were indicated (Line 367)
Reviewer #4 (Significance (Required)):
The results are useful but mainly descriptive. Personally, I am interested in the mechanisms involved in the control of growth and the manuscript does not mechanistically link the action of PAT1, PATH1 and PATH2 to the transcriptome and the latter to the growth patterns.
__Our response: __We thank the reviewer for acknowledging the significance of our work of characterizing PATs and we hope our new data could satisfy the reviewer in regarding to “mechanistical link”.
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Referee #4
Evidence, reproducibility and clarity
PAT1 (Protein Associated with Topoisomerase II) are RNA-binding proteins involved in the control of mRNA decay in the cytoplasm. Plants possess multiple PAT1 family members, three in Arabidopsis, PAT1, PATH1 and PATH2. According to the literature, the pat1 mutant shows dwarfism and de-repressed immunity. In this paper, Zou et al. describe the function of PATH1 and PATH2. Two pieces of evidence are consistent with their role in the control of mRNA decay. First, Co-IP and bimolecular Fluorescence Complementation assays in tobacco indicate physical interaction and co-localization of PAT1, PATH1 or PATH2 with LSM1 (Fig. 1), which is a protein present in decapping complexes that form the cytoplasmic foci involved in mRNA decay. Second, PAT1, PATH1 and PATH2 are present in these cytoplasmic Processing Bodies (Fig. 2). Zou et al. generated path1 and path2 mutants, double mutants with pat1 and the triple mutant using independent alleles and the summ2 background to avoid autoimmunity interference. The mutants show leaf growth (Fig. 3) and gene expression (Fig. 4) phenotypes that are not exactly similar among the different family members, but there is significant redundancy revealed by these phenotypes.
The conclusions are straight forward and, apparently, well supported by the data. However, the authors should confirm that when they provide the number of replicates (n) in the legends to the figures, this actually refers to the number of biological replicates. The statements should be based on true biological replicates (not technical replicates). The statistical tests should also be explicitly indicated (including that used to identify DEG in the RNAseq experiment).
Significance
The results are useful but mainly descriptive. Personally, I am interested in the mechanisms involved in the control of growth and the manuscript does not mechanistically link the action of PAT1, PATH1 and PATH2 to the transcriptome and the latter to the growth patterns.
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Referee #3
Evidence, reproducibility and clarity
Summary:
In the study "PAT mRNA decapping factors function specifically and redundantly during development in Arabidopsis" authors investigate potential specific functions of Arabidopsis PAT1 orthologs in plant development. Authors observe differences in rosette phenotypes (leaf size, serration and number) of single and multiple mutants of PAT1 gene family, show variation in translocation of the corresponding PAT1 proteins to processing bodies under a set of stress conditions and perform transcriptomics on the established mutants to elucidate the impact of individual PATs on posttranscriptional regulation of plant gene expression. Authors conclude that PAT1 orthologs have both overlapping and specific roles in plant development.
Major comments:
- The study contains intersting transcriptomics data that will be of use for the scientific community. However, analysis of the transcriptomics results could be discussed a bit more in depth. Authors could express their opinion about what gene expression changes might be caused by direct degradation via PAT1-dependent decapping mechanism and what changes are more likely to have occurred indirectly via other factors.
- The intersting phenotypic observations are currently poorly linked to the transcriptomics/qPCR data provided, resulting in a somewhat fragmented story flow.
- The transcriptomics was performed on the 6-weeks old plants. It would be helpful to learn more about authors reasoning for choosing this developmental stage for sampling. Why did authors decide against sampling at the earlier stages, before the observed leaves phenotypes were established?
- Authors obtained intriguing results on specific translocation of PAT1, PATH1 and PATH2 to processing bodies in the root cells upon various stresses. Perhaps root transcriptomics of single PAT1, PATH1 and PATH2 knockouts under control conditions, treatment that translocate all three proteins to PBs(IAA) and selectively translocate only PAT1 (e.g. cytokinin) could shed more light on the redundancy an specificity of these proteins as the mRNA decapping factors.
- Do authors consider PAT1, PATH1 and PATH2 to be localized to different PBs sub-populations? It could be intersting to check co-localization of PAT1, PATH1 and PATH2 under various stress conditions. Could authors elaborate on their view of PBs composition and fate to which different PAT1s are recruited?
- Could authors speculate what features in the PAT1 protein might cause it being recruited to PBs more efficiently (or better to say, under a broader range of stresses) in comparison to PATH1 and 2?
- Are all three Arabidopsis PAT paralogs co-expressed in the same tissues /developmental stages?
- Could authors elaborate a bit more why the triple pat1 knockout has a much more severe phenotype in comparison to a single pat1 loss-of-function mutant or any of the double pat1 mutants. Do authors observe complementary changes in the PAT1 genes expression in the mutant lines, e.g. is PATH1 expressed at a higher level in the absence of PAT1 and PATH2?
- Please provide the name of the used statistical test in all figure legends.
Minor comments:
- Authors might want to reconsider the title as it is somewhat too vague in its current form.
- Line 9: explanation of PAT1 and PATH1 and 2 abbreviations is best placed at the first mentioning of the name.
- Line 10: mRNA degradation is rather a posttranscriptional regulation of gene expression.
- Lines 11 and 12: path1 and path2 abbreviation are not explained. Please note that on the Figure 1A the same proteins are labelled as PAT1H1 and PAT1H2
- Lines 22-25: Would you be so kind to rephrase or elaborate on what you mean. LSM1-7/PAT1 complex are known to bind oligoadenylated transcripts indeed and even stabilize their 3' ends, it is not clear what "engage transcripts containing deadenylated tails" means in this context.
- Line 29: for the sake of clarity, it might be beneficial to list the known activators of the decapping DCP2 enzyme, including the VCS. Generally the introduction could benefit from a bit more in depth review of the decapping mechanism.
- Line 51:"other 2 PATs" => "other two PATs". Generally the text is quite well written, but might need a bit of polishing.
- Authors are absolutely correct in their attempt to provide full information about mutant backgrounds. However, for the sake of comprehension, it would be great to grant the double and triple mutants in the summ2 background shorter and more legible names. For example, the pat1-1path1-4path2-1summ2-8 mutant could be named as pat1/h1/h2/s.
- Figure 1B:
- it would be intersting to have authors opinion on why PBs are formed in this case under non-stress(?) conditions.
- Please note that expressing only the N-terminal part of CFP is a weak negative control for BiFC. No restoration of CFP can occur in such case and thus it is a given that no fluorescence can be observed in these samples. For example, co-expression of nCFP-PAT1 with cCFP-GUS, would be a more rigorous negative control, better aligned with the coIP experiments.
- Please note that some arrows point at a structure that seems to be not discernible a signal.
- Figure 1C: It might be helpful to also include a Col-0 WT plant
- It is not clear how qPCR data and complementation lines help to characterize the established PATH1 and PATH2 loss-of-function mutants. There is no immunodetection of the corresponding proteins in the knockouts, qPCR shows no dramatic decrease in the transcript level of PATH1 and H2 and the phenotypes of complemented lines presented in the Fig S1E at a glance look quite similar to the phenotypes of the corresponding knockout mutants. Complementation lines are not used for any other experiments in this study and it is not clear why authors decided to include this material into the article.
- Figure S1C misses labels indicating what detection of what gene is shown on what chart.
- Experiments to visualize PBs under various stress stimuli were conducted on roots for the Figure 2 while coIP was performed on the green tissue. Could authors elaborate on whether PB formation could be expected to be the same in different plant organs? Somewhat related to the same topic, Figure 2 contains micrographs obtained on meristematic, transition and elongation root zones, in which epidermal cells are present at various developmental stages. Since PAT proteins are suggested to impact plant development, it might be prudent to obtain observations for all samples at the same developmental stage. Could authors provide their opinion about how representative the provided micrographs are for all root zones? Furthermore, Venus-PATH2 under ACC treatment shows punctate localization only in a single cell out of the three-ish cells visible on the micrograph, potentially indicating differences in PAT2 recruitment to PBs in trichoblasts and atrichoblasts. This in itself could be an intersting observation helpful for elucidating the specific roles of PAT1 orthologs.
- Figure 4C would greatly benefit from a more detailed description in themain text and figure legend of what authors show/conclude.
- Figure 5, please avoid using the same color for the bars for the triple pat knockout and the control summ2-8 line
- Figure 5B legend should include the name of the statistical test.
- Figure S2: The coIP experiment is a bit difficult to interpret due to the extremely low protein quantities in some of the input samples. Perhaps a repetition with more balanced input quantities would be beneficial. The figure legend does not contain information on how normalized intensity values were obtained.
- Line 130: Fig S2 is referenced but Fig S3 is meant
Significance
Strength: Regulation of gene expression by mRNA decay is an extremely intersting topic and is highly relevant in plant stress and developmental biology. This study provides a more in depth view on the potential specific roles of the three PAT1 orthologs in Arabidopsis plants. Authors established loss-of-function mutants of the corresponding genes and performed transcriptomics analysis that will be a valuable source for future studies. Furthermore, microscopy analysis of PATH1 and PATH2 translocation to PBs indicates their potential specific roles in plant stress response.
Weakness: The current version of this study suffers from vague presentation of the results. Starting from the title and ending with discussion authors provide a "general" view on their results and do not go into detailed interpretations. Thus, no mechanistic insight has been derived or at least suggested from the wealth of the transcriptomics, phenotypic and microscopy data.<br /> The introduction should provide more detailed information on what is known on the PAT1 role in the mRNA decapping pathway and its relevance for plant stress response and development. Please note, that the above mentioned suggestion of different sampling for transcriptomics analysis is not meant as a request for this particular study, but rather as an illustration of an expectation a reader would built while following the current version of the text. A thorough description of the strategy for transcriptomics and a more in depth analysis might significantly strengthen the study's coherence and impact.
Advance: At this stage, the study looks more like an incremental advance of the work from the same laboratory performed for the single PAT1 protein. However, as mentioned in the comments above, the study might be made significantly stronger by elaborating the results analysis and highlighting potential discoveries.
Audience: The topic of this study is of a significant interest to a broad audience performing research in plant stress biology and also developmental plant biology.
Reviewer's and co-reviewer's fields of expertise:
Molecular Biology, Plant cell biology, Plants Stress response, Autophagy, Stress granules
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Referee #2
Evidence, reproducibility and clarity
Zuo et al., characterize the role of three cytoplasmic mRNA-decay activator proteins PAT1, PATH1 and PATH2 in the context of plant development and leaf morphology in Arabidopsis thaliana and Nicotiana benthamiana. The authors show that the triple pat mutant displays the most severe dwarfism of all combinatorial mutants. Through treatment with different stimulants the authors found that only IAA treatment induces the three homologues to form condensates (possibly PBs), while PAT1 forms condensates upon every tested stimulus. An extensive RNA seq experiment revealed miss-regulation of several hundred genes in the higher order mutants, several of which were involved in auxin responsive and leaf morphology determinant genes.
Major points:
- Title is not meaningful as is and, in my opinion, does not reflect the main findings in the manuscript.
- The results section could benefit from improved flow between the paragraphs and more reasoning for the next steps taken to help readers understand the aims of the authors.
- L46: "So far little is known about the functions of these three PATs in plant development.", The authors themselves have studied these proteins in the context of seed germination and ABA control, as well as apical hook formation and auxin responses. Should at least be mentioned and the results discussed in this context.
- What are the expression levels and patterns of PATH1 and PATH2 compared to PAT1? Is anything known about spatial or temporal regulation of these proteins?
- Figure 1:
- I do not agree that the authors have shown that "PATH1 and PATH2 are also mRNA decapping factors", rather that these proteins can co-localize (and possibly interact) with LSM1. Decapping assays for example with the known PAT1 de-capping targets from their previous work and their extensive mutant collection could be used to test this.
- From the BiFC experiment (Figure 1B) it looks like PATs are mostly soluble in the cytoplasm (like LSM1) and might be stress-induced components of PBs (like LSM1). Do PATs co-localize with other canonical PB markers that are more prone to condensation, like DCPs or VCS? BiFC could be performed after IAA treatment to confirm that the cytoplasmic foci are indeed LSM1-positive PBs.
- A: please provide uncropped images of all Western blots in supplemental data.
- I applaud the authors for establishing this great higher order mutant collection that will be very useful for researchers in the field. However, I am confused about the description of these mutants. If I understood it correctly, these mutants were already used in a previous study by the authors, namely "Zuo, Z., et al., Molecular Plant-Microbe Interactions, 35(2), 125-130." & Zuo, Z., et al., (2021). FEBS letters, 595(2), 253-263." In this study the authors refer to a BioRxiv "Zuo, Z., et al., (2019)." as the reference for these Arabidopsis lines. Is this current manuscript a continuation of the BioRxiv? Please elaborate whether these lines have been used and described in previous studies.
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L72: Is the strong developmental phenotype of the higher order mutants persistent under long day conditions? Considering the strong developmental phenotypes of the mutants, the flowering transition and morphology could be an interesting trait to study. Why did you choose short day conditions for this study?
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L78: This statement is hard to see in Figure 1C and best described for Figure 3A.
- L82: Please include a reasoning for testing PATs localization after hormone treatment. Do you have any indication that other PB proteins behave similar to either PAT1 or the PATHs after hormone treatment to substantiate that these foci observed are indeed PBs? What is known about PBs after hormone treatment in planta?
- Figure 2:
- How does the localization of LSM1 change under the same treatments? Does ist behave like PAT1 or the homologues?
- Which part of the root was imaged for this experiment? Is it possible that the observed foci are ARF-condensates as reported by Jing et al., 2022? Do you observe a gradual change in numbers or morphology along the root?
- How did the authors decide on the concentrations for the stimulant treatments? Have you tried different doses, and could the responses be dose-dependent?
- A representative image is not sufficient for quantitative responses, like RNA granule condensation. Please provide a quantification of stimulant-induced foci after the different treatments.
- L91: Does that mean that most co-precipitated signal comes from the soluble fraction and not PB-localized? Would an RNAse treatment step eliminate the co-precipitation (optional)?
- L92/93: Or alternatively that PAT1 localizes to PBs independent of the stress, while PATHs are signal-specific PB components?
- Figure 3:
- I wonder if these results fit better in conjunction with Figure 1, either as a combined figure or move before Figure 2.
- It is interesting that path2/pat1, while being dwarfed, is less serrated compared to pat1 or path1/pat1. Can you find any indications in your RNAseq set which genes might be involved?
- Indicate statistical test used to determine p-value
- L116/L117: Doesn't the result in Figure 3E indicate that PATH1 and PATH2 are not fully redundant, but that PATs have specific and narrow roles in leaf development? L116 goes against your statement in L150 & L160. What is known about the expression patterns of PAT1, PATH1 and PHATH2?
- L123: PC3 only explains 0.55% of the variance, so differences along this axis will be overinflated. In my interpretation the pat1/path2 mutant is clustering apart from the other higher order mutants, which is also reflected in the leaf phenotypes. A 2D PCA would be sufficient to describe most of the variation.
- Figure 4:
- A: The 3D-PCA inflates the differences between higher order mutants along PC3, even though this axis explains only 0.55% of the variance, maybe a 2D-PCA would more intuitively cluster the samples together?
- B: Please explain the scale in the figure legend and which genes were included? Only DEGs between triple mutant and summ2-8 or DEGs that were different in at least one higher order mutant?
- C: several comparisons are missing from the upset-plot. Please show the complete plot, also is there a white box laid over the second bar in the upper graph? It would help the reader, if the results section would explain the plots and the comparisons took. Which differences are the authors interested in?
- From Figure 4B, the triple mutant has an almost inverted expression of mis-regulated genes. High expression genes are now lowly expressed and vice-versa. Has this been reported for other RNA decay mutants before?
- How do you explain that mutants in RNA decay have a large group of repressed transcripts and a large group of enriched transcripts? Wouldn't you suspect a general higher expression in RNA decay mutants or which kind of feedback loop would you propose is happening here? Also, since both kinds of expression changes are recorded in your RNA seq can you speculate on the specificity? Why are some genes up- and others downregulated? Would you suspect that transcription factors are under PATs control?
- Where is the sequencing data deposited? This dataset can be of great value for researchers in the field, but the raw data needs to be made commonly available.
Minor points:
- Check order and nomenclature for protein / gene names in Abstract and Introduction
- L26 / L83 "aggregate" implies non-functionality, I would use "concentrate", "condensate" or "accumulate".
- L35, L45 & L54 all state the same. Maybe remove at least one mention to reduce redundancy?
- L211: Did you use the same imaging settings for all lines?
- L17: RNA quality "control" word missing?
- L477: Authors should cite the newest version of their BioRxiv: Zuo, Z., Roux, M. E., Chevalier, J. R., Dagdas, Y. F., Yamashino, T., Højgaard, S. D., ... & Petersen, M. (2022). The mRNA decapping machinery targets LBD3/ASL9 to mediate apical hook and lateral root development in Arabidopsis. bioRxiv, 2022-07.
- Figure 3B-F, Figure 4C: check spelling on the axis titles.
Significance
This manuscript represents a continuation of the author's characterization of the 3 PAT1s in Arabidopsis development after Zuo et al., 2021; Zuo et al., 2022a; Zuo et al., 2022b. The mutants and the corresponding RNA sequencing experiments are of value to the community working on RNA regulation and degradation or plant development. While the initial findings are interesting, the authors do not explore the stimulus-induced condensation differences between the homologues or try to link the extreme differences in expression profiles mechanistically or functionally. I think the manuscript could greatly benefit from contextualizing their work within the frame of their previous studies and what is known about PBs in terms of plant development. While the RNA seq is a comprehensive data set, a closer examination and a better representation of the results would help readers to access the findings.
Reviewer expertise: RNA granule biology, Arabidopsis, molecular biology
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Referee #1
Evidence, reproducibility and clarity
The manuscript investigates the role of PAT1 gene family in Arabidopsis thaliana. Though the PAT1 protein has been previously investigated and displayed immune-related and developmental phenotypes, the other two members of the family, PATH1 and PATH2, have not been well studied. The authors set out to understand the role of these proteins in relation to the role of PAT1. They thus generated single, double, and triple mutants of the possible combinations of PAT1 genes and examined their phenotypes. As the study focused on the developmental effects of PAT1, the mutants were generated on the background of the summ2 mutant to avoid phenotypes related to immune response. The authors notice a developmental difference between the pat1 mutant combinations, suggesting that PAT1 acts differently than PATH1 and PATH2 and that the PATH proteins serve a redundant function. They also performed RNA-seq analysis to identify differentially-regulated genes in the mutant combinations. The study is interesting and well-executed, yet I believe some questions should still be addressed:
- The research mainly focuses on the developmental phenotype of pat mutants but also tests the interaction of PATH proteins with RNA decapping enzymes to check their function and localization during different treatments. I found it a bit confusing since Figure 1 also shows the developmental phenotype of the mutants. I think editing the order of the figures would make the overall story more coherent.
- My main concern is the correlation between the developmental phenotype of the mutants and the gene expression. Leaf samples for RNA extraction were taken when the plants were 6 weeks old, and the developmental phenotype is very evident. It is thus not possible to tell whether the differences in gene expression are a cause or effect of the developmental phenotype. I think performing qPCR of selected candidates at earlier developmental times might help solve this issue, as well as the characterization of younger plants for the developmental phenotypes (such as leaf number).
- Overall, the manuscript is missing data regarding replicate numbers in the IP and confocal microscopy experiments.
Minor comments:
- Figure 1C - the authors should add a picture of Col0 plants as well as the mutants.
- Figure 3 - Calculating the leaf-to-petiole ratio in the different mutants would be good.
- Figure 4 - the details in the figure are very unclear, especially in the PCA. It would be good to display the data in 2D for PC1 and PC3 and change the colors a bit.
Significance
Both PATH proteins have been less investigated than PAT1, and in that sense, the work is novel. However, it seems that most of the phenotype is attributed to PAT1 rather than the other family members, limiting the interest to the broad plant science community.
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
The study largely focuses on the use of a 293 cell line that lacks a functional Dicer gene originally identified by the Cullen group. Baldaccini use this cell line, referred to as NoDice cells, to reconstitute various Dicer isoforms that have thus far been described in a variety of settings (e.g., stem cells and oocytes). Collectively, these data demonstrate the capacity of certain N-terminal truncations of Dicer to inhibit Sindbis virus and reduce the presence of viral dsRNA, supporting some of the observations made thus far concerning an antiviral role for mammalian Dicer. For other viruses, this impact was significantly more modest (SFV reduction is less than a log) or was not observed at all (VSV and SARS-CoV-2). The authors then go on to characterize the nature of the observed antiviral activity and ultimately implicate PKR and the induction of NF-kB in priming the cell's antiviral defenses. Importantly, the group also found that this antiviral activity neither required the nuclease activity of Dicer nor the kinase activity of PKR - providing evidence against antiviral RNAi in mammals. In all, the data would seem to suggest that Dicer can act as a dsRNA sensor and can mediate the activation of an NF-kB response - akin to what is observed in response to NOD or some TLR engagement. In all, it is the opinion of this reviewer that this work brings additional clarity to a concept that remains controversial in the field and therefore embodies something meaningful for the community.
With that said, there are a few issues that require additional attention. The first of these is textual. The introduction of the paper accurately describes the evidence in support of mammalian RNAi but does not invest the same time in discussing the data to the contrary. For example, Seo et al demonstrated that virus infection results in poly-adp-ribosylation of RISC preventing RNAi activity (PMID: 24075860), Uhl et al showed that IFN-induced ADAR1 resolves dsRNA in the cell and prevents RNAi (PMID: 37017521), and Tsai et al showed that virus-derived small RNAs are not loaded into the RISC in a manner that would enable antiviral activity (PMID 29903832). None of this work is referenced in this manuscript and it generates an unbalanced introduction as it relates to the controversy surrounding the idea of RNAi in mammals.
Reply: We thank the reviewer for their positive comments and suggestions. In the revised version of this manuscript, we will rewrite the introduction to take into account the published data that are not in favor of an antiviral role of RNAi in mammals and we will add the suggested references
The second issue that would further strengthen this paper relates to the fact that the authors spend a considerable amount of time discussing the data of Figure 6 and 7 as conditions that are defined by a Dicer that can not be processive in its nuclease activity (WT) vs. one that can (N1). However, there seems to be little consideration about the fact that the introduction of WT Dicer into these cells also restores miRNA biology whereas N1 appears to remain only partially functional (based on the data of Fig 3E). Given this, it seems the authors should verify that the high baseline of NFkB signaling that is being observed when comparing WT to N1 is not a product of restored miRNA function in WT cells, in contrast to the hypotheses outlined in the manuscript. This could be addressed by silencing Drosha or DGCR8 in the Dicer knockout cells prior to their reconstitution of Dicer. In the opinion of this reviewer, this experimental control would significantly strengthen the conclusions the authors are making here.
Reply: This would indeed be an ideal experiment to rule out the contribution of miRNAs in the observed phenotype. We believe however that this particular experiment would prove difficult to realize given that we reconstitute Dicer expression by lentiviral transduction and keep the cells under selection for a couple of weeks before using them for further experiments. This time frame is therefore not compatible with the use of siRNA to knock-down Drosha or DGCR8. Alternatively, we could knock them out by CRISPR-Cas9, but this would take too long and is not feasible in the frame of this work.
We can however address the concern regarding the role played by miRNAs in the observed phenotype of the Dicer N1 cells. Indeed, we can determine the miRNA profile from our small RNA sequencing data and compare them between the Dicer WT and Dicer N1 cells. We have done this comparison and could not find striking differences in miRNA expression between the two cell lines. We will add this additional piece of evidence in our revised manuscript.
Reviewer #1 (Significance (Required)):
In the manuscript entitled, "Canonical and non-canonical contributions of human Dicer helicase domain in antiviral defense" Baldaccini et al. describe their findings concerning the ability of certain N-terminal deletion variants of Dicer in contributing to mammalian antiviral activity. The concept of a functional antiviral RNAi system in mammals is a contentious one with many publications including data both in support of its existence and opposing this idea. In this manuscript, Baldaccini et al. perform a wide range of well-controlled experiments to specifically address aspects of those reports to both provide clarity in what has been documented thus far and to expand on those concepts further.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
Summary
Whether RNAi is used as an antiviral mechanism in mammals has been a hotly debated issue. The research team previously published several papers on the roles of Dicer in siRNA/miRNA biogenesis and in antiviral responses. They have recently reported that the helicase domain of human Dicer specifically interacts with several proteins that are involved in the IFN response, including PKR. In this study, Baldaccini et al. investigated the involvement of Dicer in antiviral response using various mutants of human Dicer. They showed that deletion mutants of helicase domain exhibit antiviral activity that requires the presence of PKR. They further demonstrated that one of the mutants, N1-Dicer showed antiviral activity in an RNAi-independent manner but depending on the presence of either native PKR or kinase deficient mutants. Transcriptomic analysis revealed that numerous genes involved the IFN and inflammatory response were upregulated in the cells that express N1-Dicer, which is likely due to an increased activation of the NFκB pathway. Based on these findings, the authors propose that Dicer may act as antiviral molecule using its helicase domain, which representing a novel non-canonical function of Dicer.
Major comments:
1.The results from experiments with SARS-CoV2 are intriguing (Fig.2). The authors speculated that NFkb activation is in favor of the replication of this virus. It would be interesting to see the infection and replication of SARS-CoV2 in PKR deficient cells and cells expressing PKR mutants (as described in Fig.5). The results may prove/disapprove the authors' speculation and yield additional findings.
Reply: We thank the reviewer for this suggestion. We have cells that are double knock-out for Dicer and PKR (NoDice/∆PKR) that were transduced to stably express Dicer WT or Dicer N1 and further transduced to express ACE2. We will infect those cell lines with SARS-CoV-2, which will allow us to see whether the difference in viral accumulation can still be observed in the absence of PKR. However, it might prove more difficult to reconstitute PKR expression (WT or mutants) in these cells since they are already transduced twice with two different constructs (Dicer and ACE2).
Western blot analysis. In the method section, it is stated that proteins were quantified with Bradford method and equal loading was verified by Ponceau S staining. The members of also probed with gamma-tubulin (It was stated that antibodies against alpha-tubulin was used in the method section) as a loading control, however, the bend intensity of tubulin shows great variations among different lanes in several figures while Ponceau S staining is similar (Fig.s, 4, 5, and 8). The differences compromise the accuracy of the results.
Reply: We apologize for the difference in Tubulin signal in some of our western blots. There are several possibilities to explain those inconsistencies between Ponceau staining and Tubulin blotting, including an effect of viral infection on Tubulin expression. To remove ambiguities around this issue, we could quantify the signal across several blot replicates and provide the quantification after normalization. In addition, we would like to stress that regarding quantification of the infection, we think that the plaque assay experiments are more reliable than quantification of western blot signals.
3.RNA-seq analysis revealed that Dicer N1 cells have significantly increased expression levels of signaling molecules in type I IFN response even in uninfected cells. While this provides a potential explanation for the antiviral phenotype of N1-Dicer cells. I wonder why the expression levels of type I IFNs (probably the most potent antiviral molecules) were not analyzed in WT and Dicer N1 cells. Measurement of the levels of IFNα and IFNβ by ELISA in the cells before and after infection could provide the important and direct data to support their conclusion.
Reply: This an interesting suggestion but unfortunately, we do not believe that it would possible to quantify IFNα and IFNβ by ELISA in the cell line that we used in our experiments. Indeed, the level of expression might just be too low to be able to measure something meaningful. We could measure the induction of IFNβ expression at the mRNA level by RT-qPCR though. However, we do not believe that the observed increased expression of genes that belong to the type I IFN response is solely the effect of an increased production of IFN. These genes are also under the control of other transcription factors, including NF-kB for some of them, and it might prove difficult to make a direct link with IFNα or IFNβ production.
4.While the data presented in Fig. 5 provides convincing evidences that the antiviral activity of mediated by PKR against SINV is independent of its kinase activity in N1-Dicer cells. An interesting question is that whether antiviral activity associated with PKR is N1-Dicer dependent, which could be addressed by comparing the viral infection of NoDice∆PKR and NoDicer expressing PKR mutants.
Reply: Yes indeed, we have generated NoDice/∆PKR cells expressing PKR WT or mutant and we will infect them with SINV to confirm whether the presence of Dicer N1 is needed for the observed phenotype.
5.In the concluding paragraph of the discussion, the authors presented an oversimplified discerption of a complex model that involves a crosstalk between IFN-I and RNAi and Dicer-PKR interaction, which is difficult for the reader to compose a clear picture of mechanisms involved. It could be helpful to use a schematic illustration to summarize the action model of PKR incorporated with the canonical and non-canonical Dicer functions.
Reply: We will add a schematic model in the revised version of our manuscript to summarize our main findings.
Minor comments:
1.It stated that NoDice FHA-Dicer WT #4 and NoDice FHA:Dicer N1 110 #6 are referred to as Dicer WT and Dicer N1 cells (p.6). For simplicity, Dicer WT and Dicer N1 cells should be used throughout manuscript, including in all figures. The labels in the figures are difficult to read and are confusing in some cases.
Reply: This will be changed in the revised version to increase the clarity of the figures.
2.It is to note that p-PKR was only detected at in N1-Dicer cells at 24 hpi (Fig.8A). This is an interesting observation that was not discussed. It appears that this could be due to a delayed viral replication since these cells are already in an elevated antiviral state. This possibility could be tested by examining viral replication and dsRNA accumulation at more time points in the experiments described in Fig.1.
Reply: We have performed a kinetic of infection at more time points and we will incorporate these experiments in the revision.
3.The authors may point out the limitations of the studies. For examples, all cells used in the study are engineered HEK cell lines and were tested with limited number of viruses. As such, the observations may reflect Dicer-PKR interaction under artificially overexpressed conditions, but how the model established from the current study applies to primary cells require further investigation.
Reply: This is indeed important, we will add a sentence about this in the discussion.
Reviewer #2 (Significance (Required)):
The findings reported in this study shed some new light on a long-debated issue regarding the potential roles of RNAi as physiologically relevant antiviral mechanism in mammals. Identification of a new antiviral function of Dicer helicase domain via interaction with PKR is a new advancement of the field, and it also adds a new dimension to a complex subject that overlaps of innate immunity , RNA biology, and developmental biology associated with Dicer.
Field of expertise: Innate immunity, cell signaling, cytokine biology
Areas that that I do not have sufficient expertise to evaluate: Small RNA cloning, sequencing and, analysis.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
This work by Baldaccini et al. explores the interplay between Dicer and the antiviral protein PKR in the context of viral infection. It builds on a previous publication of the team which demonstrates that the Dicer helicase interacts with multiple RNA binding proteins, including PKR (see Montavon et al.). In this work from 2021, they demonstrate that an artificially-truncated form of Dicer (Dicer-N1) lacking part of the helicase is antiviral against RNA viruses in a PKR-dependent fashion. This was an interesting finding because the field largely assumed that Dicer-N1 performs its antiviral function via canonical dicing of dsRNA, as part of an antiviral RNAi pathway. The present manuscript follows up on this initial discovery and deciphers the specifics of Dicer-N1 antiviral phenotype, as well as delineates the interplay between Dicer's helicase and PKR. The authors main claims are as follow:
- i) Dicer-N1 antiviral effect does not require its catalytic activity, therefore is completely RNAi-independent.
- ii) Neither does it require canonical PKR activation, but relies instead on NF-kB-driven inflammation. The origin of this inflammation is not studied.
- ii) Truncated Dicers other than Dicer-N1 are antiviral through RNAi, but are also PKR-dependent. The authors claims are mostly supported by the data, although I suggest below some improvements regarding experimental approaches and data presentation. This work details in an interesting manner the interplay between the machinery of RNAi and the classical pathway of innate immunity (PKR). As explained by the authors, there is solid data in the literature demonstrating the mutual exclusivity of IFN and antiviral RNAi in differentiated cells. This mostly goes through the receptors LGP2, which inhibits dsRNA dicing by Dicer. The authors data suggest that, conversely, Dicer may play a role in preventing the unwanting activation of PKR (a non-canonical activation leading to inflammation). Given that PKR activation does not depend on virus, the authors discuss potential mechanisms of PKR triggering. This is an interesting topic that deserves further investigation (not necessarily within the frame of this work - it can be a follow-up). Another interesting piece of information is that different truncated Dicers behave differently with respect to implementing antiviral RNAi. Whilst Dicer-N1 isn't proficient in doing so, the other forms are. It shows that lab-generated truncations do not fully recapitulate what is observed with existing truncated Dicers (DicerO and aviD).
Experimental design and data interpretation
- The authors should compare infection between different cell lines across a range of time points (ie, a virus growth curve). In Fig 4E for example, I worry that cells expressing or not PKR will reach the plateau of viral particle accumulation at different time points. One could imagine that cells lacking PKR do not show any differences in particle production at 24h, but do at earlier time points.
Reply: This is an interesting suggestion, we can perform a kinetic experiment by looking at more time points to address this point. This will allow us to determine the time needed for every cell line to reach the plateau of infection.
Western blots should be accompanied with proper quantifications plotted as bar graph with biological replicates (p-PKR, p-eIF2a and capsid).
Reply: We have biological replicates for our western blot experiments, and we will quantify those to better determine the observed changes. However, in the case of p-eIF2a, we do not think it is pertinent to measure it since there are other kinases than PKR that are known to induce eIF2a phosphorylation upon SINV infection. It might therefore not prove very informative to precisely quantify this particular signal.
Microscopy images should be properly quantified across biological replicates (Fig 1&2 for the J2 staining, for example).
Reply: We could do a proper quantification of the J2 signal across replicates, but we do not think it would bring much to our message. Here, we mostly used J2 staining as a qualitative indication that the infection was impacted or not. We have a proper quantification of the effect with our plaque assay experiments, which are way more robust to determine the levels of infection between conditions.
Confounding factors hinder the interpretation of siRNA accumulation (Suppl Fig 2): i) the efficiency of dsRNA dicing from different Dicers will generate different amounts of siRNAs from a given amount of dsRNA and ii) the higher antiviral response translates into decreased infection, so decreased dsRNA substrate. I suggest that the authors normalise the amount of viral siRNAs over the total amount of viral genomes. This should allow to assess if Dicer-N1 is better at dicing dsRNA than WT in these conditions.
Reply: This is a valid concern and we agree that it is important to be able to normalize small RNA reads between conditions before reaching a conclusion. The problem is that there is no easy way to do this since we do not get a direct measurement of viral genomes accumulation from our small RNA sequencing data. To better compare the two conditions, we could normalize the individual viral siRNA to the total number of viral reads. Another problem that we face is that we are looking here at the AGO-loaded small RNAs, which makes it more difficult to assess dicing efficiency since not every generated siRNA might be loaded into an Argonaute protein. In fact, this has been proposed by the Cullen laboratory in a paper published in 2018 (Tsai et al. doi: 10.1261/rna.066332.118). They showed that although viral siRNAs were generated during IAV infection, those were inefficiently loaded and thus did not significantly impacted the infection.
In Fig 8, the authors should verify that phospho-p65 increase depends on PKR by repeating the experiment in PKR KO cells.
Reply: Yes, good point. We will check what happens to phosphorylation of p65 in PKR KO cells. In addition, we can also measure the effect on a known NF-kB target by RT-qPCR (e.g. PTGS2).
Data representation
- Levels of phospho-PKR and eIF2a need to be normalised on the total amount of PKR and eIF2a, respectively. The authors should quantify the blots and present bar graphs with biological replicates and statistics.
Reply: As mentioned above in our reply to point 2, we can add the quantification for phospho PKR, but we do not think it is pertinent to do it for eIF2a.
Could the authors add the names of representative genes on the volcano plots of Fig 7?
Reply: Yes, this will be done.
Points of discussion
- In Fig 4C, catalytically-dead mutants of truncated Dicers (other than N1) do not display an antiviral effect. Presumably, such proteins implement canonical antiviral RNAi. Is there a reason why the authors interpret this data as Dicers being "partially" antiviral through RNAi (l. 92). This data instead suggest that is it totally dependent on RNAi.
Reply: Indeed, and we do not say the contrary. It seems that some of this helicase-truncated Dicer proteins can act through RNAi. However, they also depend on PKR, so in the end it might be a combination of the two that allows their antiviral effect.
Gurung et al. demonstrate that PKR is activated in Dicer KO mouse ES cells, which results in phosphorylation of eIF2a at steady-state. This is different from the authors' data, in which PKR activation does not affect eiF2a phosphorylation. Could the authors discuss this discrepancy?
Reply: The problem that we face here is that SINV is known to also activate GCN2 and therefore eIF2a phosphorylation does not strictly rely on PKR in our experimental conditions. In addition, we did not check eIF2a phosphorylation in Dicer KO cells, but we always compare Dicer WT and Dicer N1 expressing cells.
Do the authors expect that truncated Dicers other than N1 trigger an inflammatory response such as the one described for N1? Would it be possible to have this antiviral inflammatory response in conjunction with antiviral RNAi?
Reply: This goes back to Point 1 mentioned previously. We think indeed that there might be a dual action of Dicer and that it will be important to check whether in other cellular systems or animal model such a phenomenon can be observed as well. This is a point that we did address in the discussion of our manuscript (line 522-525).
Reviewer #3 (Significance (Required)):
This is a study that conceptually advances the field of antiviral RNAi in mammals, including its interplay with the machinery of innate immunity. It is of interest for virologists and immunologists. My expertise is centered on the mechanisms of innate immunity in mammalian cells, including antiviral RNAi.
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Referee #3
Evidence, reproducibility and clarity
This work by Baldaccini et al. explores the interplay between Dicer and the antiviral protein PKR in the context of viral infection. It builds on a previous publication of the team which demonstrates that the Dicer helicase interacts with multiple RNA binding proteins, including PKR (see Montavon et al.). In this work from 2021, they demonstrate that an artificially-truncated form of Dicer (Dicer-N1) lacking part of the helicase is antiviral against RNA viruses in a PKR-dependent fashion. This was an interesting finding because the field largely assumed that Dicer-N1 performs its antiviral function via canonical dicing of dsRNA, as part of an antiviral RNAi pathway. The present manuscript follows up on this initial discovery and deciphers the specifics of Dicer-N1 antiviral phenotype, as well as delineates the interplay between Dicer's helicase and PKR. The authors main claims are as follow:
i) Dicer-N1 antiviral effect does not require its catalytic activity, therefore is completely RNAi-independent.
ii) Neither does it require canonical PKR activation, but relies instead on NF-kB-driven inflammation. The origin of this inflammation is not studied.
ii) Truncated Dicers other than Dicer-N1 are antiviral through RNAi, but are also PKR-dependent.
The authors claims are mostly supported by the data, although I suggest below some improvements regarding experimental approaches and data presentation. This work details in an interesting manner the interplay between the machinery of RNAi and the classical pathway of innate immunity (PKR). As explained by the authors, there is solid data in the literature demonstrating the mutual exclusivity of IFN and antiviral RNAi in differentiated cells. This mostly goes through the receptors LGP2, which inhibits dsRNA dicing by Dicer. The authors data suggest that, conversely, Dicer may play a role in preventing the unwanting activation of PKR (a non-canonical activation leading to inflammation). Given that PKR activation does not depend on virus, the authors discuss potential mechanisms of PKR triggering. This is an interesting topic that deserves further investigation (not necessarily within the frame of this work - it can be a follow-up). Another interesting piece of information is that different truncated Dicers behave differently with respect to implementing antiviral RNAi. Whilst Dicer-N1 isn't proficient in doing so, the other forms are. It shows that lab-generated truncations do not fully recapitulate what is observed with existing truncated Dicers (DicerO and aviD).
Experimental design and data interpretation
- The authors should compare infection between different cell lines across a range of time points (ie, a virus growth curve). In Fig 4E for example, I worry that cells expressing or not PKR will reach the plateau of viral particle accumulation at different time points. One could imagine that cells lacking PKR do not show any differences in particle production at 24h, but do at earlier time points.
- Western blots should be accompanied with proper quantifications plotted as bar graph with biological replicates (p-PKR, p-eIF2a and capsid).
- Microscopy images should be properly quantified across biological replicates (Fig 1&2 for the J2 staining, for example).
- Confounding factors hinder the interpretation of siRNA accumulation (Suppl Fig 2): i) the efficiency of dsRNA dicing from different Dicers will generate different amounts of siRNAs from a given amount of dsRNA and ii) the higher antiviral response translates into decreased infection, so decreased dsRNA substrate. I suggest that the authors normalise the amount of viral siRNAs over the total amount of viral genomes. This should allow to assess if Dicer-N1 is better at dicing dsRNA than WT in these conditions.
- In Fig 8, the authors should verify that phospho-p65 increase depends on PKR by repeating the experiment in PKR KO cells.
Data representation
- Levels of phospho-PKR and eIF2a need to be normalised on the total amount of PKR and eIF2a, respectively. The authors should quantify the blots and present bar graphs with biological replicates and statistics.
- Could the authors add the names of representative genes on the volcano plots of Fig 7?
Points of discussion
- In Fig 4C, catalytically-dead mutants of truncated Dicers (other than N1) do not display an antiviral effect. Presumably, such proteins implement canonical antiviral RNAi. Is there a reason why the authors interpret this data as Dicers being "partially" antiviral through RNAi (l. 92). This data instead suggest that is it totally dependent on RNAi.
- Gurung et al. demonstrate that PKR is activated in Dicer KO mouse ES cells, which results in phosphorylation of eIF2a at steady-state. This is different from the authors' data, in which PKR activation does not affect eiF2a phosphorylation. Could the authors discuss this discrepancy?
- Do the authors expect that truncated Dicers other than N1 trigger an inflammatory response such as the one described for N1? Would it be possible to have this antiviral inflammatory response in conjunction with antiviral RNAi?
Significance
This is a study that conceptually advances the field of antiviral RNAi in mammals, including its interplay with the machinery of innate immunity. It is of interest for virologists and immunologists. My expertise is centered on the mechanisms of innate immunity in mammalian cells, including antiviral RNAi.
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Referee #2
Evidence, reproducibility and clarity
Summary
Whether RNAi is used as an antiviral mechanism in mammals has been a hotly debated issue. The research team previously published several papers on the roles of Dicer in siRNA/miRNA biogenesis and in antiviral responses. They have recently reported that the helicase domain of human Dicer specifically interacts with several proteins that are involved in the IFN response, including PKR. In this study, Baldaccini et al. investigated the involvement of Dicer in antiviral response using various mutants of human Dicer. They showed that deletion mutants of helicase domain exhibit antiviral activity that requires the presence of PKR. They further demonstrated that one of the mutants, N1-Dicer showed antiviral activity in an RNAi-independent manner but depending on the presence of either native PKR or kinase deficient mutants. Transcriptomic analysis revealed that numerous genes involved the IFN and inflammatory response were upregulated in the cells that express N1-Dicer, which is likely due to an increased activation of the NFκB pathway. Based on these findings, the authors propose that Dicer may act as antiviral molecule using its helicase domain, which representing a novel non-canonical function of Dicer.
Major comments:
1.The results from experiments with SARS-CoV2 are intriguing (Fig.2). The authors speculated that NFkb activation is in favor of the replication of this virus. It would be interesting to see the infection and replication of SARS-CoV2 in PKR deficient cells and cells expressing PKR mutants (as described in Fig.5). The results may prove/disapprove the authors' speculation and yield additional findings. 2. Western blot analysis. In the method section, it is stated that proteins were quantified with Bradford method and equal loading was verified by Ponceau S staining. The members of also probed with gamma-tubulin (It was stated that antibodies against alpha-tubulin was used in the method section) as a loading control, however, the bend intensity of tubulin shows great variations among different lanes in several figures while Ponceau S staining is similar (Fig.s, 4, 5, and 8). The differences compromise the accuracy of the results. 3.RNA-seq analysis revealed that Dicer N1 cells have significantly increased expression levels of signaling molecules in type I IFN response even in uninfected cells. While this provides a potential explanation for the antiviral phenotype of N1-Dicer cells. I wonder why the expression levels of type I IFNs (probably the most potent antiviral molecules) were not analyzed in WT and Dicer N1 cells. Measurement of the levels of IFNα and IFNβ by ELISA in the cells before and after infection could provide the important and direct data to support their conclusion. 4.While the data presented in Fig. 5 provides convincing evidences that the antiviral activity of mediated by PKR against SINV is independent of its kinase activity in N1-Dicer cells. An interesting question is that whether antiviral activity associated with PKR is N1-Dicer dependent,, which could be addressed by comparing the viral infection of NoDice∆PKR and NoDicer expressing PKR mutants. 5.In the concluding paragraph of the discussion, the authors presented an oversimplified discerption of a complex model that involves a crosstalk between IFN-I and RNAi and Dicer-PKR interaction, which is difficult for the reader to compose a clear picture of mechanisms involved. It could be helpful to use a schematic illustration to summarize the action model of PKR incorporated with the canonical and non-canonical Dicer functions.
Minor comments:
1.It stated that NoDice FHA-Dicer WT #4 and NoDice FHA:Dicer N1 110 #6 are referred to as Dicer WT and Dicer N1 cells (p.6). For simplicity, Dicer WT and Dicer N1 cells should be used throughout manuscript, including in all figures. The labels in the figures are difficult to read and are confusing in some cases. 2.It is to note that p-PKR was only detected at in N1-Dicer cells at 24 hpi (Fig.8A). This is an interesting observation that was not discussed. It appears that this could be due to a delayed viral replication since these cells are already in an elevated antiviral state. This possibility could be tested by examining viral replication and dsRNA accumulation at more time points in the experiments described in Fig.1. 3.The authors may point out the limitations of the studies. For examples, all cells used in the study are engineered HEK cell lines and were tested with limited number of viruses. As such, the observations may reflect Dicer-PKR interaction under artificially overexpressed conditions, but how the model established from the current study applies to primary cells require further investigation.
Significance
The findings reported in this study shed some new light on a long-debated issue regarding the potential roles of RNAi as physiologically relevant antiviral mechanism in mammals. Identification of a new antiviral function of Dicer helicase domain via interaction with PKR is a new advancement of the field, and it also adds a new dimension to a complex subject that overlaps of innate immunity , RNA biology, and developmental biology associated with Dicer.
Field of expertise: Innate immunity, cell signaling, cytokine biology
Areas that that I do not have sufficient expertise to evaluate: Small RNA cloning, sequencing and, analysis.
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Referee #1
Evidence, reproducibility and clarity
The study largely focuses on the use of a 293 cell line that lacks a functional Dicer gene originally identified by the Cullen group. Baldaccini use this cell line, referred to as NoDice cells, to reconstitute various Dicer isoforms that have thus far been described in a variety of settings (e.g., stem cells and oocytes). Collectively, these data demonstrate the capacity of certain N-terminal truncations of Dicer to inhibit Sindbis virus and reduce the presence of viral dsRNA, supporting some of the observations made thus far concerning an antiviral role for mammalian Dicer. For other viruses, this impact was significantly more modest (SFV reduction is less than a log) or was not observed at all (VSV and SARS-CoV-2). The authors then go on to characterize the nature of the observed antiviral activity and ultimately implicate PKR and the induction of NF-kB in priming the cell's antiviral defenses. Importantly, the group also found that this antiviral activity neither required the nuclease activity of Dicer nor the kinase activity of PKR - providing evidence against antiviral RNAi in mammals. In all, the data would seem to suggest that Dicer can act as a dsRNA sensor and can mediate the activation of an NF-kB response - akin to what is observed in response to NOD or some TLR engagement. In all, it is the opinion of this reviewer that this work brings additional clarity to a concept that remains controversial in the field and therefore embodies something meaningful for the community.
With that said, there are a few issues that require additional attention. The first of these is textual. The introduction of the paper accurately describes the evidence in support of mammalian RNAi but does not invest the same time in discussing the data to the contrary. For example, Seo et al demonstrated that virus infection results in poly-adp-ribosylation of RISC preventing RNAi activity (PMID: 24075860), Uhl et al showed that IFN-induced ADAR1 resolves dsRNA in the cell and prevents RNAi (PMID: 37017521), and Tsai et al showed that virus-derived small RNAs are not loaded into the RISC in a manner that would enable antiviral activity (PMID 29903832). None of this work is referenced in this manuscript and it generates an unbalanced introduction as it relates to the controversy surrounding the idea of RNAi in mammals.
The second issue that would further strengthen this paper relates to the fact that the authors spend a considerable amount of time discussing the data of Figure 6 and 7 as conditions that are defined by a Dicer that can not be processive in its nuclease activity (WT) vs. one that can (N1). However, there seems to be little consideration about the fact that the introduction of WT Dicer into these cells also restores miRNA biology whereas N1 appears to remain only partially functional (based on the data of Fig 3E). Given this, it seems the authors should verify that the high baseline of NFkB signaling that is being observed when comparing WT to N1 is not a product of restored miRNA function in WT cells, in contrast to the hypotheses outlined in the manuscript. This could be addressed by silencing Drosha or DGCR8 in the Dicer knockout cells prior to their reconstitution of Dicer. In the opinion of this reviewer, this experimental control would significantly strengthen the conclusions the authors are making here.
Significance
In the manuscript entitled, "Canonical and non-canonical contributions of human Dicer helicase domain in antiviral defense" Baldaccini et al. describe their findings concerning the ability of certain N-terminal deletion variants of Dicer in contributing to mammalian antiviral activity. The concept of a functional antiviral RNAi system in mammals is a contentious one with many publications including data both in support of its existence and opposing this idea. In this manuscript, Baldaccini et al. perform a wide range of well-controlled experiments to specifically address aspects of those reports to both provide clarity in what has been documented thus far and to expand on those concepts further.
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Authors used organoid technology to study the effects of the serum from lupus patients on intestinal epithelium. By culturing organoids derived from human colon crypts, they specifically determined the response of epithelial cells to inflammatory mediators present in lupus serum. Using bulk and scRNA-seq, authors found that secretory cells function and differentiation were impaired as well as the mitochondrial metabolism. These effects were shown to be mediated by type 1 interferon in combination with other pro-inflammatory cytokines present in lupus serum.
The reduction of mucus secretion after SLE-serum treatment and the downregulation of tight junctions' genes seem to indicate an increased permeability of the epithelial barrier, thus it would be interesting to determine the expression and distribution of tight junction proteins and to test in the organoids whether the paracellular permeability is increased upon SLE-serum treatment. These analyses will give a functional result of this in vitro model.
If the organoids take a few days to culture and the material is available, the measurement of paracellular permeability may take no more than 2 weeks. It is true that they will need a microneedle to inject the FITC-Dextran 4K into the organoids and record the images for 24h.
- We thank the reviewer for suggesting this experiment. Reviewer #2 had the same suggestion. The results obtained elevate our overall results and the quality of our research.
- Tight junction protein expression is cell-type dependent as shown in literature1,2 and in our scRNA-seq analysis (Suppl. Table 7). Additionally, we observed that the expression changes of ZO-1(TJP1; Fig. 7A) are accumulated in colonocytes. That might be the reason we could not detect significant changes in organoid sections with staining for tight junction proteins ZO-1 and Occludin and analyzing all cell types. The data of the permeability assay however was able to show that the function of the tight junctions is altered. If this change is caused by changes in protein abundance as suggested by reported transcriptomic changes remains to be elucidated in future research using single-cell proteomic approaches.
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For the permeability assay we stimulated organoid monolayers for 72h with SLE or control serum. The measured translocation of FITC showed an increased barrier leakiness in SLE compared to control condition. These functional results not only support our findings presented in our study that the epithelial barrier is altered upon SLE serum stimulation, but they also provide the definitive proof of concept highlighting the crucial connection between SLE and intestinal barrier integrity. Furthermore, it shows that changes on transcriptional level and alterations in cell type composition translate into a barrier dysfunction which could have potentially detrimental effects in vivo. The data has been added to Figure 1H, line 722-731; 746-751.
I would like to know which of the donor's cells were used from figure 2 on and why.
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Organoid line I was used for the 24h stimulation and for the 72h stimulation whereas organoid line II was used for the 72h stimulation only. Due to limitations in serum availability we had to limit the experiments that exceeded the initial transcriptomic analysis to one organoid line. After confirming that the serum was affecting both organoid lines, we continued using organoid line II.
The bioinformatics analyses using gene expression data and scRNASeq were well done. No comments.
Reviewer #1 (Significance (Required)):
For the field of autoimmunity, to study the crosstalk between the systemic response and the gut epithelium response results quite important as the increased permeability of the gut epithelial barrier has been suggested to fuel the systemic inflammation in lupus. However, as the author mention, there is not enough information about the interaction of epithelial cells and the systemic inflammatory mediators in lupus. This system can be useful to determine a personalized treatment for patients by testing the effect of individual serum on organoids. Moreover, the use of organoids can be extended to study the gut epithelium response in other autoimmune diseases mediated by type 1 interferon.
Increased permeability of the gut epithelial barrier has been related with lupus development. In humans, it is not known whether it is a cause or consequence, but in lupus mouse models it has been demonstrated that there is a reduction of the systemic autoimmune response concomitant with a reduction of gut permeability. The authors have validated an in vitro model that can be used to study how gut epithelium is affected by systemic inflammatory mediators and that will help to develop novel therapeutic approaches or personalize treatments.
Thank you for your insightful comments. We are encouraged to see that the reviewer has accurately grasped the core purpose and implications of our research. The intricate relationship between gut epithelial barrier permeability and lupus development is indeed vital for expanding our scientific knowledge and for future therapeutic breakthroughs. We are happy that the overall message we aimed to convey with our research was well captured and appreciated by the reviewer. We believe our in vitro model will serve as a foundation for further detailed studies and for the development of therapeutic strategies in this field. Your acknowledgment of our work inspires us to persist in our research efforts.
Interest stakeholders: Clinical and basic researchers in autoimmunity, gastroenterology, and rheumatology.
My field of expertise is systemic and organ-specific autoimmunity at cellular and molecular level. My work covers autoimmunity and gut microbiota. I study how B cells regulate the microbiota composition and how that microbiota impacts gut permeability and inflammation in mouse lupus models. On the other hand, the bioinformatics analyses are well-done for both bulk RNASeq and scRNASeq.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
The group of Dr. Resnik-Docampo provides a very elegant study on two patient lines for SLE. The study is definitely very interesting and opens many scientific avenues that are worthy of being explored further. Major comments: -Barrier integrity or its alterations can be tested in organoids with specific dyes, I feel this would give definitive proof of concept.
- We thank the reviewer for the suggestion. Reviewer #1 had the same suggestion. The results obtained elevate our overall results and the quality of our research.
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For the permeability assay we stimulated organoid monolayers for 72h with SLE or control serum. The measured translocation of FITC showed an increased barrier leakiness in SLE compared to control condition. These functional results not only support our findings presented in our study that the epithelial barrier is altered upon SLE serum stimulation, but they also provide the definitive proof of concept highlighting the crucial connection between SLE and intestinal barrier integrity. Furthermore, it shows that changes on transcriptional level and alterations in cell type composition translate into a barrier dysfunction which could have potentially detrimental effects in vivo. The data has been added to Figure 1H, line 722-731; 746-751.
-In supplementary figure 1 a caspase 3 staining is presented, please show a positive control for caspase 3 staining on organoids or alternstively use a different method to prove no differential cell death.
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Due to the processing of the organoids it is difficult to have a positive control. However, we can confirm that the used cleaved Caspase 3 antibody is able to detect cell death by the following staining. There are positive cells in the center of the organoid where dead cells accumulate. Additionally in Figure 1C we show with a cytotoxicity assay measuring LDH release that there is no significant difference between both groups. Furthermore, brightfield imaging showed no obvious differences and the DEGs show also no evidence of increased cell death.
-Serum from SLE patients reduces drastically Edu positivity, it would be interesting to see a clonogenicity assay to see whether this reflects on reduced stem cell clonogenic potential
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We agree. However, this analysis goes beyond the time and material limitations we have in our project.
-Goblet cells in the colon are very heterogeneous, which subpopulations of goblet cell are reduced? how does this affect mucus composition?
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We see that subpopulation GC4 is significantly reduced upon SLE serum stimulation (Fig. 7C and Suppl. Fig. 7C). Overall, we see a trend of a general reduction of all GC subpopulation and an indication that there might be also a shift in subtype abundance. We would need to increase the study population to be able to draw any conclusions on how exactly the GC subpopulations change. So far not much is known about how the different GC subpopulations affect mucus composition. This field is completely understudied especially in the human intestine. Future projects should focus on mucus composition and how it is changed with changes of the subpopulations (even under physiological conditions).
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We thank the reviewer for the question about the mucus composition. We included the analysis of FCGBP protein abundance (Fig. 4C and Suppl. Fig. 4D; line 245-247) in our study. The increased FCGBP protein abundance upon SLE serum stimulation which is in line with our transcriptomic changes complements our data and supports our hypothesis that the mucus composition is altered. This new data improves the quality of our research.
Minor comments: - Please provide an hypothesis on how mitochondrial alterations are linked to altered lineage progeny of stem cells. This should be discussed more in depth.
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It is known that cells in the crypt compartment that undergo rapid division depend on glycolysis for ATP production once the cell differentiates it switches to oxidative phosphorylation.3 In vitro it has been shown that differentiation coincides with the switch to oxidative phosphorylation.4 There is a complex interplay between Notch signaling and FoxO transcription factors which is driving differentiation and cell fate decision.5 Especially the differentiation towards the secretory lineage highly depends on the metabolic switch from glycolysis to oxidative phosphorylation. Furthermore, even mucus secretion itself is dependent on oxidative phosphorylation.6 This is in line with our differentiation experiment where less oxidative phosphorylation coincided with the absence of goblet cells. We hypothesize that the changed cellular composition upon SLE serum stimulation is at least partly reflected in the altered mitochondrial function. If the decrease in the secretory lineage alone can explain the seen mitochondrial changes needs to be further elucidated. While a detailed examination at single-cell level analysis of mitochondrial function might be able to answer if they drive the altered cell differentiation, such an in-depth analysis is beyond the scope of this article and would be best addressed in a dedicated study on the topic. Within the scope of our analysis and considering the still limited knowledge of mitochondrial function in specific cell types of the human colon we included some more discussion (line 696-698 and 702-703).
-Many antimicrobial peptides are changed, this could reflect on microbiome composition as well as mucus composition and properties, which I am sure will be the topic of future studies. This should be discussed more in depth.
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We hypothesize that the alterations in antimicrobial peptide expression along with the seen changes in major mucus components would be translated into changes in mucus composition. Since the mucus serves as the niche for gut microbiota this could lead to changes in microbiome composition. It is of high interest to analyze the mucus composition of the stimulated organoids. Furthermore, assays analyzing the killing capacity of the potentially secreted antimicrobial peptides could help us to understand the relevance of the observed changes. We addressed this in line 714-715 and line 750-752.
Reviewer #2 (Significance (Required)):
The study is useful for both broad and specialized audiences. The findings are interesting and of relevance to the field of SLE, gut epithelial biology. The strength of the manuscript is that it opens many scientific avenues, its weakness is that they are not mechanistically dissected to the fullest rendering the study a bit descriptive. Nonetheless, I consider positively the manuscript after a minor revision given the major message of the paper can be proven.
Thank you for your constructive feedback. We are genuinely encouraged by your recognition of the utility and relevance of our study for both broad and specialized audiences in the fields of SLE and gut epithelial biology. Your acknowledgment resonates with our larger objectives: beyond merely exploring the specific connection between SLE and intestinal leakiness, our aim has been to create a methodological approach that illuminates novel avenues to study complex diseases. It is deeply heartening to realize that this overarching message and intent were clearly understood and agreed upon by the reviewer. We recognize and appreciate your insights into the strengths and areas for improvement of our manuscript, and we are committed to addressing the highlighted points to further enhance our contribution to the field.
__ Reviewer #3 (Evidence, reproducibility and clarity (Required)):__
Inga Viktoria Hensel et al. used colon organoid to study the impact of lupus patients' serum on gut epithelial barrier. The exposure of SLE serum on colon organoids increased gene expression related to cell cycle, chromosome organization, mitochondrial function as well as interferon signaling, but downregulated that related to secretion, cytoskeleton, and anchoring junctions of the cells. Higher type I IFN in the SLE serum and unregulated interferon signature genes post stimulation suggest a potential role of type I interferon in this process. The addition of a type 1 interferon receptor (IFNAR1) antagonist, Anifrolumab, blocked the stimulation function of SLE serum but the combination of IFN-2α and control serum failed to recapitulate the results from SLE serum, suggesting that more than one cytokine was involved. SLE serum exposure altered metabolic profiles of organoids with a significant increase of basal respiration and ATP production. Stimulating organoid with SLE serum confirmed an alteration in cell differentiation with a loss of secretory lineage. scRNA-seq analysis revealed that colon organoid had all major cell types from colon in vivo. SLE serum stimulation shifted cell differentiation with decreased number of goblet cells and downregulated mucin, AMP and other components that were required for gut barrier integrity.
Finally, the authors performed a gene expression analysis of colon biopsies derived from SLE patients and healthy controls. While the authors should be commended to attempt a validation of the results obtained with organoids, the small sample size and patient heterogeneity prevented a statistical analysis. Some genes involved in absorption and ion transport as well as secretory lineage showed a similar trend with organoid assay, suggesting that colon organoids may be a good tool for future studies. However, it is noticeable that the biopsies from SLE patients did not show the IFN signature and the decreased in Muc2 expression, which dominated the gene signature of organoids exposed to SLE serum. There is no information about the disease activity of the SLE patients, as well as their IFN activity, which makes difficult to interpret these results.
- We thank the reviewer for the questions and suggestions. They helped us to improve our manuscript and make it more concise for the reader.
Specific concerns: 1. Line 107, Why did The authors use 72 hours post treatment. Are other timepoints available and have similar results?
- We used two different time points, 24h and 72h. The nature of the organoid culture limits the total length of the experiment. Organoids can be cultured for a maximum of 5-7 days. Differentiation from the stem cell to the fully differentiated cell takes 5-7 days. Preliminary results showed that serum stimulation prior to day 2 led to a decrease in organoid survival, most likely because serum stimulation in general induces differentiation. We therefore chose 72h as the stimulation that mimics a chronic exposure as a differentiating cell would face in vivo. With this time span we were able to see manifestations in cell differentiation changes but avoided beginning cell death to prolonged culture. However, we also wanted to understand a more acute exposure to the serum. Therefore, we chose 24h as a second stimulation duration. With this time point we were able to detect initial changes in cell fate decision which was important in the interpretation of the accumulated effects seen after 72h.
Figure 1D, how do the authors explain the heterogeneity among SLE samples (2, 3, 4, 5) on organoid line II? These samples do not seem to correlate with cytokine levels shown in Fig. 2. This issue may be worth exploring further, such as correlation between cytokine levels and gene expression.
- The heterogeneity seen among the SLE samples most likely reflects the complex composition of the serum itself. A similar heterogeneity (PC1 axis) is also seen for the controls. The epithelial cells eventually will react to all contained factors showing an integrated response that makes it difficult to correlate to a single cytokine.
Line 144, the 2 outlier SLE serum samples are not same between organoid lines with NO. 1&5 in Organoid line II and with NO. 1&4 in Organoid line I. The statement is misleading.
- We corrected the statement according to the suggestions (Line 146-148).
Line 169, IFN-a2 and IL-6 are not significantly different.
- The statement was rephrased (Line 166-174).
Line 179-180, Reduced fitness of organoids exposed to SLE serum is an overstatement. It was not directly tested, and there is no difference in apoptosis.
- The term ‘reduced fitness’ referred to the results seen in the mitochondrial stress test. We rephrased the parts to make the statement more concise (Line 182).
Line 242-243: SLE serum stimulation induced MUC2 high expression in Organoid II but lower level in organoid I (Figure 4B & Figure S4C). This is a major discrepancy that needs to be addressed.
- This discrepancy comes from the different differentiation dynamics observed in both organoid lines. Therefore, for the downstream analysis we considered the results from both lines to have a robust analysis. With the results from the 24h timepoint and the scRNA-seq which were performed with either of the lineages respectively, we can be certain that the overall seen effect on the secretory lineage is a valid finding (we addressed this in line 229-232).
Line 251-252: How do authors make sure that "we were facing an effect on the differentiation process rather than cell type loss"?
- We can exclude an increase of cell death since we did not see changes in LDH release (Fig. 1C) and cleaved Caspase 3 abundance (Suppl. Fig. 1A) when we compared both conditions. Additionally, the data from the 24h stimulation time point showed the reduction of transcription factors (Fig. 4J) important for differentiation which manifested in a reduction of secretory cell markers upon longer stimulation (72h) (edited statement in line 256-258).
Line 259, How about apoptosis gene levels here?
- Apoptosis markers BAX and BCL2 are not amongst the differentially expressed genes. (see Suppl. Table 4).
Line 290 : It has been shown that the response of colon explants to IFN-α was variable among donors (https://www.sciencedirect.com/science/article/pii/S2352345X16301084#undfig1). This study should be cited. Was the response to IFNa tested on both organoids?
- The reference was included (Line 669-671). The response to IFNα was only tested in organoid line II given the limited availability of the serum that was used for co-stimulation. Overall, however, we saw an effect in both donor lines and less response was rather connected to the serum, not the organoid donor. In the publication reported interindividual heterogeneity depends on the therewith connected release of IL-18. Future studies could include analysis of cytokine release from the organoids after serum stimulation and a higher number of organoid lines to validate our findings.
Line 305-307: Is single cell sequencing from single organoid line or from combined? Do two organoid lines show different distributions?
- scRNA-seq was performed using organoid line II. Due to limited resources, we unfortunately could not include another organoid line.
Reviewer #3 (Significance (Required)):
While there is mounting evidence of an altered intestinal barrier integrity in SLE patients, there is little insights in the mechanisms. Using colon organoids is a novel approach with great potentials to investigate this issue. The strongest signature found by the authors was type IFN, which is indicates that the colon epithelial cells respond in a similar manner to other cell types. The secretory and absorption genes are of potential greater interest to unraveling mechanisms to gut alteration in SLE. This study is of interest for audiences interested in lupus basic and clinic research, as well as investigators working of gut barrier integrity.
Thank you for your constructive feedback. We are encouraged by your acknowledgment of our novel approach using colon organoids to explore the altered intestinal barrier integrity in SLE patients. Your emphasis on the significance of the type IFN signature and the importance of secretory and absorption genes aligns with our perspective.
Incorporating the intestinal barrier functional experiment emphasizes the translational nature of our study. We agree with your view that our findings are relevant for those involved in both basic and clinical lupus research, as well as specialists in intestinal biology. Your encouraging feedback strengthens our conviction in the wider significance of our work. We are motivated to keep bridging the gap between these two essential areas of study.
References
- Pearce, S. C. et al. Marked differences in tight junction composition and macromolecular permeability among different intestinal cell types. BMC Biol. 16, 1–16 (2018).
- Kishida, K., Pearce, S. C., Yu, S., Gao, N. & Ferraris, R. P. Nutrient sensing by absorptive and secretory progenies of small intestinal stem cells. Am. J. Physiol. Liver Physiol. 312, G592–G605 (2017).
- Rath, E., Moschetta, A. & Haller, D. Mitochondrial function — gatekeeper of intestinal epithelial cell homeostasis. Nat. Rev. Gastroenterol. Hepatol. 15, 497–516 (2018).
- Rodríguez-Colman, M. J. et al. Interplay between metabolic identities in the intestinal crypt supports stem cell function. Nature 543, 424–427 (2017).
- Ludikhuize, M. C. et al. Mitochondria Define Intestinal Stem Cell Differentiation Downstream of a FOXO/Notch Axis. Cell Metab. 32, 889-900.e7 (2020).
- Sünderhauf, A. et al. Loss of Mucosal p32/gC1qR/HABP1 Triggers Energy Deficiency and Impairs Goblet Cell Differentiation in Ulcerative Colitis. Cmgh 12, 229–250 (2021).
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Referee #3
Evidence, reproducibility and clarity
Inga Viktoria Hensel et al. used colon organoid to study the impact of lupus patients' serum on gut epithelial barrier. The exposure of SLE serum on colon organoids increased gene expression related to cell cycle, chromosome organization, mitochondrial function as well as interferon signaling, but downregulated that related to secretion, cytoskeleton, and anchoring junctions of the cells. Higher type I IFN in the SLE serum and unregulated interferon signature genes post stimulation suggest a potential role of type I interferon in this process. The addition of a type 1 interferon receptor (IFNAR1) antagonist, Anifrolumab, blocked the stimulation function of SLE serum but the combination of IFN-2α and control serum failed to recapitulate the results from SLE serum, suggesting that more than one cytokine was involved. SLE serum exposure altered metabolic profiles of organoids with a significant increase of basal respiration and ATP production. Stimulating organoid with SLE serum confirmed an alteration in cell differentiation with a loss of secretory lineage. scRNA-seq analysis revealed that colon organoid had all major cell types from colon in vivo. SLE serum stimulation shifted cell differentiation with decreased number of goblet cells and downregulated mucin, AMP and other components that were required for gut barrier integrity.
Finally, the authors performed a gene expression analysis of colon biopsies derived from SLE patients and healthy controls. While the authors should be commended to attempt a validation of the results obtained with organoids, the small sample size and patient heterogeneity prevented a statistical analysis. Some genes involved in absorption and ion transport as well as secretory lineage showed a similar trend with organoid assay, suggesting that colon organoids may be a good tool for future studies. However, it is noticeable that the biopsies from SLE patients did not show the IFN signature and the decreased in Muc2 expression, which dominated the gene signature of organoids exposed to SLE serum. There is no information about the disease activity of the SLE patients, as well as their IFN activity, which makes difficult to interpret these results.
Specific concerns:
- Line 107, Why did The authors use 72 hours post treatment. Are other timepoints available and have similar results?
- Figure 1D, how do the authors explain the heterogeneity among SLE samples (2, 3, 4, 5) on organoid line II? These samples do not seem to correlate with cytokine levels shown in Fig. 2. This issue may be worth exploring further, such as correlation between cytokine levels and gene expression.
- Line 144, the 2 outlier SLE serum samples are not same between organoid lines with NO. 1&5 in Organoid line II and with NO. 1&4 in Organoid line I. The statement is misleading.
- Line 169, IFN-a2 and IL-6 are not significantly different.
- Line 179-180, Reduced fitness of organoids exposed to SLE serum is an overstatement. It was not directly tested, and there is no difference in apoptosis.
- Line 242-243: SLE serum stimulation induced MUC2 high expression in Organoid II but lower level in organoid I (Figure 4B & Figure S4C). This is a major discrepancy that needs to be addressed.
- Line 251-252: How do authors make sure that "we were facing an effect on the differentiation process rather than cell type loss"?
- Line 259, How about apoptosis gene levels here?
- Line 290 : It has been shown that the response of colon explants to IFN-α was variable among donors (https://www.sciencedirect.com/science/article/pii/S2352345X16301084#undfig1). This study should be cited. Was the response to IFNa tested on both organoids?
- Line 305-307: Is single cell sequencing from single organoid line or from combined? Do two organoid lines show different distributions?
Significance
While there is mounting evidence of an altered intestinal barrier integrity in SLE patients, there is little insights in the mechanisms. Using colon organoids is a novel approach with great potentials to investigate this issue. The strongest signature found by the authors was type IFN, which is indicates that the colon epithelial cells respond in a similar manner to other cell types. The secretory and absorption genes are of potential greater interest to unraveling mechanisms to gut alteration in SLE. This study is of interest for audiences interested in lupus basic and clinic research, as well as investigators working of gut barrier integrity.
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Referee #2
Evidence, reproducibility and clarity
The group of Dr. Resnik-Docampo provides a very elegant study on two patient lines for SLE. The study is definitely very interesting and opens many scientific avenues that are worthy of being explored further.
Major comments:
- Barrier integrity or its alterations can be tested in organoids with specific dyes, I feel this would give definitive proof of concept.
- In supplementary figure 1 a caspase 3 staining is presented, please show a positive control for caspase 3 staining on organoids or alternstively use a different method to prove no differential cell death.
- Serum from SLE patients reduces drastically Edu positivity, it would be interesting to see a clonogenicity assay to see whether this reflects on reduced stem cell clonogenic potential
- Goblet cells in the colon are very heterogeneous, which subpopulations of goblet cell are reduced? how does this affect mucus composition?
Minor comments:
- Please provide an hypothesis on how mitochondrial alterations are linked to altered lineage progeny of stem cells. This should be discussed more in depth.
- Many antimicrobial peptides are changed, this could reflect on microbiome composition as well as mucus composition and properties, which I am sure will be the topic of future studies. This should be discussed more in depth.
Significance
The study is useful for both broad and specialised audiences. The findings are interesting and of relevance to the field of SLE, gut epithelial biology. The strength of the manuscript is that it opens many scientific avenues, its weakness is that they are not mechanistically dissected to the fullest rendering the study a bit descriptive. Nonetheless, I consider positively the manuscript after a minor revision given the major message of the paper can be proven.
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Referee #1
Evidence, reproducibility and clarity
Authors used organoid technology to study the effects of the serum from lupus patients on intestinal epithelium. By culturing organoids derived from human colon crypts, they specifically determined the response of epithelial cells to inflammatory mediators present in lupus serum. Using bulk and scRNA-seq, authors found that secretory cells function and differentiation were impaired as well as the mitochondrial metabolism. These effects were shown to be mediated by type 1 interferon in combination with other pro-inflammatory cytokines present in lupus serum.
The reduction of mucus secretion after SLE-serum treatment and the downregulation of tight junctions' genes seem to indicate an increased permeability of the epithelial barrier, thus it would be interesting to determine the expression and distribution of tight junction proteins and to test in the organoids whether the paracellular permeability is increased upon SLE-serum treatment. These analyses will give a functional result of this in vitro model.
If the organoids take a few days to culture and the material is available, the measurement of paracellular permeability may take no more than 2 weeks. It is true that they will need a microneedle to inject the FITC-Dextran 4K into the organoids and record the images for 24h.
I would like to know which of the donor's cells were used from figure 2 on and why.
The bioinformatics analyses using gene expression data and scRNASeq were well done. No comments.
Significance
For the field of autoimmunity, to study the crosstalk between the systemic response and the gut epithelium response results quite important as the increased permeability of the gut epithelial barrier has been suggested to fuel the systemic inflammation in lupus. However, as the author mention, there is not enough information about the interaction of epithelial cells and the systemic inflammatory mediators in lupus. This system can be useful to determine a personalized treatment for patients by testing the effect of individual serum on organoids. Moreover, the use of organoids can be extended to study the gut epithelium response in other autoimmune diseases mediated by type 1 interferon.
Increased permeability of the gut epithelial barrier has been related with lupus development. In humans, it is not known whether it is a cause or consequence, but in lupus mouse models it has been demonstrated that there is a reduction of the systemic autoimmune response concomitant with a reduction of gut permeability. The authors have validated an in vitro model that can be used to study how gut epithelium is affected by systemic inflammatory mediators and that will help to develop novel therapeutic approaches or personalize treatments.
Interest stakeholders: Clinical and basic researchers in autoimmunity, gastroenterology, and rheumatology.
My field of expertise is systemic and organ-specific autoimmunity at cellular and molecular level. My work covers autoimmunity and gut microbiota. I study how B cells regulate the microbiota composition and how that microbiota impacts gut permeability and inflammation in mouse lupus models. On the other hand, the bioinformatics analyses are well-done for both bulk RNASeq and scRNASeq.
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Reply to the reviewers
We thank the reviewers to appreciating the depth and quality of the results presented in our manuscript. We are happy that he/she has appreciated our contemporary approach to addressing an important problem but also a thorny and debated topic in the field that has lasted for over 30 years since it was first proposed by Mike Berridge. Our work not only addresses mechanisms in cultured human cells but also for the very first time addresses the key problem of Li action in the human brain using human iPSC derived forebrain cortical neurons.
We are delighted with the reviewer’s comment that “This effort highlighted the author's commendable goal to develop a thoughtful story and not just another publication.” We thank the reviewer for appreciating our detailed multi-disciplinary approach to addressing a long standing (> 4 decades) problem of key significance in human psychiatry and neuroscience. We thank him/her for noting that the work deserves to be presented in a journal with a cross-disciplinary focus. A few technical points that have been noted by the reviewer have been addressed below. Most of these can be effectively addressed by modest rewriting of text to make certain points clearer. The one experiment that has been suggested can be done and will be completed within 1 month.
- Description of the planned revisions
Reviewer #1 (Evidence, reproducibility and clarity):
The authors functionally define in the inositol monophosphate phosphatase IMPA1, as the true target of lithium regulating phosphatidylinositol turnover and calcium signalling. While the observed IMPA1 inhibition by lithium led to the historical 'inositol depletion hypothesis' over the past 30+ years were published evidence both in support and against this concept. These contradictory sets of results have led to decreased interest in phosphoinositides as the signalling pathway affected by the therapeutic action of lithium in bipolar disorder (BD) patients. The remarkable results shown here will revert this trend since the data clearly demonstrate a key role of IMPA1 in setting the rate of phosphatidylinositol turnover, and consequentially the extent of calcium signalling. While the data are consistent with the 'inositol depletion hypothesis' the authors do not prove or disprove the validity of this hypothesis since the actual levels of inositol were not measured in their experiments. However, this is not a criticism, since quantifying cellular inositol is complex, it is just a suggestion for future work. After clarifying the points listed below this work will be suitable for publication.
• The experiments using inositol rich DMEM (reported on page 17 and in Fig 2I,J) require a better explanation and an adequate material and method section. It is not clear if the 'normal/control' condition uses inositol-free DMEM. The standard concentration of inositol in DMEM is 40uM. Thus, are the ~155uM (28 mg/litre) added by the authors at the high end of the 40uM? Given that FBS contains inositol have the authors used dialyzed serum? While adding 155uM of inositol on either inositol-free medium or to medium containing 40uM inositol does not alter the author's message, this technical information are important for the reproducibility of the data presented and to understand how HEK293T manages inositol homeostasis.
The standard concentration of inositol in DMEM high Glucose media (Dulbecco’s Modified Eagle Medium; Life Technologies) is 40 µM (7 mg/ litre) and our HEK293T cells were maintained under standard conditions at (37oC, with 5% CO2) in this media, supplemented with 10% Foetal bovine serum (FBS). This FBS was not dialyzed, so it might contain trace amounts of inositol.
For our inositol rich DMEM, 117 μl of 100 μM of inositol (18 mg/ml) was added to 100 ml of DMEM high Glucose media, supplemented with 10% FBS- this led to the final effective concentration of inositol in the media ~155 µM (28 mg/ litre). This media was referred to as the inositol rich media and used for the inositol supplementation in Fig. 2I-J.
Therefore, the inositol supplementation we refer to is effectively raising the extracellular inositol concentration from ca. 40mm to 155mM which is 3X elevation. This information has been added to the results section.
- It would be helpful to know if store operated calcium entry is altered in impa1-/--M1 cells. This information would nicely complement Fig.3 C-E data.
We have studied store operated calcium entry in IMPA1-/- cells and it is decreased. The quantification of this reduction can be added to the paper.
- In the Introduction at the end of page 4, the evidence not supporting the inositol depletion hypothesis is correctly discussed. This section lacks the discussion of another work questioning this theory (PMID: 30171184). The conclusion of this work is also in agreement with the authors finding that lithium affects the rare/turnover (lines 490/506) of PIP2 synthesis.
Saiardi A, Mudge AW. Lithium and fluoxetine regulate the rate of phosphoinositide synthesis in neurons: a new view of their mechanisms of action in bipolar disorder. Transl Psychiatry. 2018 Aug 31;8(1):175. doi: 10.1038/s41398-018-0235-2. PMID: 30171184.
This paper suggests that lithium mediated inhibition of IMPase leads to an accumulation of IP1 and this elevated IP1 leads to a competitive inhibition of the rate of synthesis of PI, and hence turnover of PIP2. Combined with lithium’s inhibition of inositol uptake, this inhibition of PI synthesis can lead to the mood stabilizing effect of lithium, rather than the inositol depletion. This point will be added to our manuscript and the reference cited.
- In the material and methods, Liquid Chromatography Mass spectrometry is abbreviated to LCMS while in the main text (line 493) LC-MS is used. The dashed version should be used throughout the manuscript.
Liquid Chromatography Mass spectrometry will be abbreviated to LC-MS throughout the text in the revised version.
- I suggest to define (line 500) phosphatidylinositol 4-phosphate as (PI(4)P simplified as PIP). This will be consistent with the phosphatidylinositol 4,5-bisphosphate abbreviation as (PIP2) as reported in the introduction (line 97)
PIP refers to all the functional isoforms of Phosphatidylinositol phosphate- PI 3P, PI 4P and PI 5P. By the LC-MS/MS analysis, we had measured the total PIP masses but we cannot distinguish between the individual functional isomers of PIP. However, pre-existing literature suggests that PI 4P is the most abundant isoform of PIP present in cell- its level is approximately 50 folds higher than that of PI 5P (Rameh et al., 1997). Hence we can suggest that the change in the total mass of PIP (as seen by the LC-MS/MS) is mainly reflective of the PI 4P.
- Line 646: Instead of using [this study] the authors should refer to the Figure panels supporting the discussed argument.
The identity of the channels mediating Ca2+ transients in this system was shown by us in Sharma et al., 2020. In the revision, we will cite this paper.
Referees cross-commenting
Reviewer #2 main message is identical to my. The work is a "contemporary re-evaluation of the inositol depletion hypothesis" but it does settle the debate. Say that reviewer #2 also recognises the importance of the work in defining IMPA1 as the only lithium target affecting the PI cycle removing GSK3 from the picture. Additionally, we agree that the thorough transcriptional analysis of the effect of lithium on human cortical neurons will be very informative for any researcher interested in psychiatric disorders.
Reviewer #2 requests are rational and not demanding. Most queries require extra information or the reformatting of the data presented.
Reviewer #1 (Significance):
The submitted manuscript addresses an important topic. The authors developed HEK293 stable expressing muscarinic receptor to study the effect of lithium (without or after receptor activation) on PI(4,5)P2 turnover using two approaches, by microscopy and biochemically by LC-MS. These analyses were followed by a thorough characterization of the effect of lithium on calcium signalling. The generation of HEK293 impa1-/- line has allowed the authors to demonstrate that the observed effect of lithium on PI(4,5)P2 turnover and calcium signalling were IMPA1 dependent. The authors pushed the work to a higher level by studying the effect of lithium on iPSC-derived human cortical neurons demonstrating that lithium reduces neurons excitability and calcium signalling. Although previously published attempts failed to generate IMPA1 deficient human cortical neurons the authors managed to produce iPSC impa1-/- but, as reported and consistent with previous literature, this cell line failed to differentiate into neurons. This effort highlighted the author's commendable goal to develop a thoughtful story and not just another publication. The work is complemented by a very informative transcriptional analysis characterising the effect of lithium on human cortical neurons. Noteworthy is also the author's efforts to functionally and transcriptionally define the effect of another lithium target, GSK-3. These experiments emphasize that GSK-3 does not phenocopy the effect of lithium. This is another utterly important message of the paper.
In conclusion, the authors presented an easily readable, comprehensive, and experimentally convincing story. Furthermore, the developed experimental tools (HEK293-m1AchR) and the extensive data set (transcriptomic analysis) will be instrumental to further studies aimed at elucidating mechanistically how phosphoinositide signalling affects BD pathophysiology.
Reviewer #2 (Evidence, reproducibility and clarity):
This manuscript seeks to test if inhibition of the phosphoinositide (PI) cycle is the relevant pathway targeted by lithium in bipolar affective disorder (BPAD). Firstly, a cultured model system (HEK293T) is used to test the effects of lithium on the PI cycle. Using PI(4,5)P2 probes along with mass spectrometry, Li is shown to inhibit PI(4,5)P2 re-synthesis after PLC activation, though not to perturb pre-stimulus levels. Release of calcium from intracellular stores along with refilling from extracellular calcium is also inhibited - though there are no effects on stored calcium capacity. Crucially, with the exception of the calcium refilling step, these effects if Li can be abolished by genetic ablation of IMPA1, the proposed molecular target of Li. Having established the affected pathway, the manuscript then studies the effects of Li treatment on iPSC-differentiated cultured cortical neurons. Spontaneous and muscarinic evoked calcium transients are shown to be abolished by Li. None of these effects in HEK293T or neurons can be recapitulated by an inhibitor of GSK3beta, another proposed target for Li. Finally, a transcriptomic analysis of Li treated neurons is presented, showing down regulation of relevant genes, especially genes involved in neuronal calcium signaling and glutamatergic signaling.
The inositol depletion hypothesis has been debated for nearly four decades. As it stands, this manuscript does not settle this debate once and for all, but it does add some novel and important insights: that 1) IMPA1 is certainly the target of lithium, at least in terms of the PI cycle and 2) Lithium treatment can lead to longer-term transcriptional changes in neuronal calcium and glutamatergic signaling that can dampen excitability. The paper is on the whole clearly written, and the data are easy to follow. That said, there are a number of areas where the manuscript is lacking key details, or where the results do not fully support the conclusions. Specific suggestions for amendment are as follows:
(1) The PH-PLCdetla1 PH domain has been used to follow PI(4,5)P2 turnover in HEK293T cells. Although long established, the manuscript does not discuss the fact that this domain also binds to IP3, which given high enough concentrations, can compete the PH domain off the membrane. As such, what is being measured is the convolution of PI(4,5)P2 decreases and IP3 increases (see for example doi: 10.1083/jcb.200301070 ). Ideally, a non-IP3 binding probe would have been used, such as the Tubby c-terminal domain (doi: 10.1186/1471-2121-10-67; doi: 10.1113/jphysiol.2008.153791). As it stands, the failure of the PH domain to return to the membrane after Li treatment reported in figures 1G, 3F and 3L could either be due to a failure of PI(4,5)P2 re-synthesis, or a failure to breakdown IP3 - either of which are plausible explanations given inhibition of IMPA1. This concern is somewhat mitigated by the inclusion of mass determinations of the lipids in figure 1H-J, which support the PI(4,5)P2 re-synthesis defect. However, the potential problems with interpretation of the data with the PH domain should be discussed.
PH-PLCδ-GFP probe is used in the field as a biosensor for PI 4,5-P2 (Chakrabarti et al., 2015; Várnai and Balla, 1998) and has been used to monitor the PIP2 turn-over rate. However, as the reviewer has pointed out, this probe also has an affinity towards IP3 (Xu et al., 2003) and therefore the failure of the probe to return to the plasma membrane could also, in principle, reflect the accumulation of IP3.
We are well aware of this discussion in the field and to make sure that our measurements using the PH-PLCδ-GFP probe are indeed a true reflection of PIP2 re-synthesis , we have also used a biochemical method to establish the levels of PIP2. Our measurements of the total mass of PIP2 by LC-MS/MS corroborate our findings using the probe, on the delay in PIP2 resynthesis. Nonetheless, we will explicitly mention this point in the discussion.
Drawback of the Tubby c-terminal domain-
Most of the biosensors for PI 4,5-P2 have distinctive advantage and disadvantages- PH-PLCδ-GFP probe is a more sensitive reporter but its IP3 binding may compromise its accuracy to measure PI 4,5-P2 changes. However, the Tubby c-terminal domain has exhibited lower sensitivity to report on changes of PI 4,5-P2 during PLC activation, although being more specific in its affinity towards PI 4,5-P2 (Szentpetery et al., 2009). Furthermore, recent studies have revealed that Tubby c-terminal domain can also bind to PI 3,4-P2 as well as PI 3,4,5-P3 (Hammond and Balla, 2015). Lastly a very recent study has noted that in contrast to PH-PLCδ-GFP probe, the tubby domain binds selectively to certain domains of the plasma membrane at membrane contact sites making it not a detector of PIP2 levels across the plasma membrane (Thallmir et,al., 2023, PMCID: PMC10445746 DOI: 10.1242/jcs.260848 ).
(2) The strongest evidence for the effects of IMPA1 inhibition coming from inositol depletion are given by the experiment reported in figure 2I and J, where inositol supplementation rescues calcium mobilization. This should also be performed for the PIP2 re-synthesis experiments.
We thank the reviewer for this suggestion.
We will perform this experiment and add the results to the revised manuscript. Duration estimated for this experiment- 30 days.
(3) It is implicit in the manuscript that DMEM does not contain inositol. This is not true; Life technologies' formulation for DMEM contains 40 micromol/l myo-inositol, which is sufficient to support activity of both proton/myo-inositol and sodium/myo-inositol symporters (HMIT and SMIT). On the face of it, therefore, inositol depletion seems unlikely. The reviewer wonders what concentration of added inositol mediated the rescue? This key fact is missing from the manuscript. At the very least, the details should be included and the reason for rescue of already inositol replete cells discussed. Ideally, the key experiments would be repeated with inositol-free medium and supplementation.
Repeated cycles of GPCR linked PLC signalling depend on a stable and on a continuous supply of PIP2 at the cell membrane, which in turn depends on the cytoplasmic pool of inositol in the cell. Inositol pool can be maintained by three avenues- via the recycling of the inositol by the stepwise dephosphorylation of IP3, de novo synthesis of inositol from glucose 6-phosphate and the transport of inositol from the extracellular environment across the plasma membrane. Li inhibits IMPase, an enzyme that dephosphorylates inositol monophosphate to generate free inositol.
Due to Li’s inhibition of IMPase, the inositol pool cannot be regenerated by the first two avenues since both of them need IMPase. However, restriction of the inositol pool by Li’s inhibition of IMPase can be bypassed by the transport of inositol from the extracellular media via SMIT (Sodium-dependent myo-inositol co-transporter) and/or HMIT (Proton-dependent myo-inositol co-transporter). At steady state, the low amount of inositol in the DMEM media (the inositol concentration in normal DMEM media being approximately 7 mg/litre or 40 µM) might be sufficient for maintaining the inositol pool and thereby PIP2 levels at the steady state. But this amount of inositol in the DMEM media appeared to be limiting to sustain the inositol pool and thereby PIP2 levels under conditions of hyperactivated PIP2 cycle. This is likely the reason why Li mediated inositol depletion (by inhibition of IMPA1) leads to a decreased rate of PIP2 synthesis at the plasma membrane as well as decreased PLC mediated Ca2+ release, in the background of activated PLC.
However, when the cells were grown in an inositol rich DMEM media (inositol concentration is ca. 28 mg/litre or approximately 155 µM; which is similar to the inositol concentration in the cerebrospinal fluid (Swahn, 1985; Shetty et al., 1996)); transport of extracellular inositol by SMIT/HMIT could sustain a continuous level of PIP2 levels even in a PLC activated background. This explains the rescue of the decreased PLC mediated Ca2+ release phenotype in the control-M1 cells grown in inositol supplemented DMEM despite the Li treatment.
This section can be added to the Results section (page 17) in the revised version.
(4) The introduction refers to lithium as a "non-competitive" inhibitor of IMPA1. This is erroneous, as lithium is in fact an uncompetitive inhibitor. This is a key distinction: since the uncompetitive inhibitor blocks the enzyme:substrate complex, it is most effective where substrate accumulates the most - in this context, sites of intense PLC activity. This was central to Berridge's inositol depletion hypothesis. Also, the Allison et al citation is incorrect here. The correct citation is PMID: 2833231.
Li inhibits IMPA1 in an uncompetitive manner.
This will be corrected in the revised version of the manuscript, with the appropriate references.
Ref cited by reviewer- Gee NS, Ragan CI, Watling KJ, Aspley S, Jackson RG, Reid GG, Gani D, Shute JK. The purification and properties of myo-inositol monophosphatase from bovine brain. Biochem J. 1988 Feb 1;249(3):883-9. doi: 10.1042/bj2490883. PMID: 2833231; PMCID: PMC1148789.
Ref- Hallcher LM, Sherman WR. The effects of lithium ion and other agents on the activity of myo-inositol-1-phosphatase from bovine brain. J Biol Chem. 1980 Nov 25;255(22):10896-901. PMID: 6253491.
(5) other key experimental details are missing from the figures/figure legends/results and or methods. Namely, what concentration of carbachol was used? What was the optimum concentration of thapsigargin? For figure 2 B-C, was carbachol used to evoke calcium mobilization?
For the agonist mediated Ca2+ release, 20 µM of carbachol was used. This is mentioned in the Materials and Methods section (line 238); however, we will mention this in the figure legends and results for clarity, in the revised version.
For the store depletion in the SOCE experiments, 10 µM of thapsigargin was used. This is mentioned in the Materials and Methods section (line 249); however, we will mention this in the figure legends and results for clarity.
In Fig. 2B, C, Carbachol was used to evoke calcium mobilization in the cells. This is mentioned in the Results section (line 510)- we will mention this in the figure legends for clarity.
(6) The effects of IMPA1 knockout and rescue in figure 3F are rather unconvincing. All treatment groups' means fall within 1 SD; are the changes statistically significant? Plotting 95% C.I. or standard error may be more informative for these experiments.
This can be addressed in the revised version.
Minor comments:
() There are some inconsistencies in the figure panels. Arrows labelled "CCh" are used to denote CCh addition in e.g. Fig. 3D, whereas simple arrows are used elsewhere e.g. Fig. 3F or no arrow at all e.g. Fig. 3B.
All the points where CCh has been added to stimulate PLC will be denoted with an arrow labelled Cch for clarity and consistency. This will be addressed in the revised version.
() Details of what the error denotes is missing in Figs. 2B-C, 3B-C, F - as is N for 3A.
Whiskers in box plots show the minimum and maximum values with a line at the median. This will be addressed in the revised version.
() Fig. 2D: It should be made explicit that arrows indicate TTX addition in the figure. More importantly, it should be clarified whether this is also the case for Fig. 5D? Transients do not appear to be depleted by this addition.
This has been mentioned in the figure legends for the figure (line 1077)- we will address this in the revised version.
Few of the neuronal transients are not abolished by TTX- this variability can be addressed by other representative traces.
() In figure 6C, it is stated that "there was no down regulation in transcripts for SCN1A (Nav1.1) or SCN9A (Nav7.1)" but this is not strictly true from the data; the Li-treated cells definitely trend lower. The effect is clearly not statistically significant, so although it is not possible to state that they reduced, this is not the same thing as being able to assert that they are not reduced. Perhaps it could be more helpful to plot the size of the reduction between these transcripts?
This will be addressed as- “there was no significant downregulation in transcripts for SCN1A (Nav1.1) or SCN9A (Nav7.1)".
The difference in the transcript level for SCN1A (Nav1.1) or SCN9A (Nav7.1) can be plotted for further clarification.
Reviewer #2 (Significance):
The manuscript is ultimately a contemporary re-evaluation of the inositol depletion hypothesis for Li treatment of BPAD, first proposed by Berridge in 1989. The manuscript certainly does not end the debate - other pathways and mechanisms for lithium's actions on a complex human behavioral phenotype will surely persist. However, it does add some important new insights: firstly, that IMPA1 is certainly the target mediating the effects of Li on the PI cycle; and secondly, that long-term, Li may exert effects at the transcriptional level to down regulate calcium and glutamatergic signaling in the brain. However, there is no mechanism presented to link these two findings. The work is therefore of interest to researchers with an interest on studying psychiatric disorders, basic mechanisms of neuronal excitability as well as molecular mechanisms of cell signaling. It therefore deserves to be published in a journal with a cross disciplinary focus.
2. Description of the revisions that have already been incorporated in the transferred manuscript
Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.
3. Description of analyses that authors prefer not to carry out
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Referee #2
Evidence, reproducibility and clarity
This manuscript seeks to test if inhibition of the phosphoinositide (PI) cycle is the relevant pathway targeted by lithium in bipolar affective disorder (BPAD). Firstly, a cultured model system (HEK293T) is used to test the effects of lithium on the PI cycle. Using PI(4,5)P2 probes along with mass spectrometry, Li is shown to inhibit PI(4,5)P2 re-synthesis after PLC activation, though not to perturb pre-stimulus levels. Release of calcium from intracellular stores along with refilling from extracellular calcium is also inhibited - though there are no effects on stored calcium capacity. Crucially, with the exception of the calcium refilling step, these effects if Li can be abolished by genetic ablation of IMPA1, the proposed molecular target of Li. Having established the affected pathway, the manuscript then studies the effects of Li treatment on iPSC-differentiated cultured cortical neurons. Spontaneous and muscarinic evoked calcium transients are shown to be abolished by Li. None of these effects in HEK293T or neurons can be recapitulated by an inhibitor of GSK3beta, another proposed target for Li. Finally, a transcriptomic analysis of Li treated neurons is presented, showing down regulation of relevant genes, especially genes involved in neuronal calcium signaling and glutamatergic signaling.
The inositol depletion hypothesis has been debated for nearly four decades. As it stands, this manuscript does not settle this debate once and for all, but it does add some novel and important insights: that 1) IMPA1 is certainly the target of lithium, at least in terms of the PI cycle and 2) Lithium treatment can lead to longer-term transcriptional changes in neuronal calcium and glutamatergic signaling that can dampen excitability. The paper is on the whole clearly written, and the data are easy to follow. That said, there are a number of areas where the manuscript is lacking key details, or where the results do not fully support the conclusions. Specific suggestions for amendment are as follows:
- The PH-PLCdetla1 PH domain has been used to follow PI(4,5)P2 turnover in HEK293T cells. Although long established, the manuscript does not discuss the fact that this domain also binds to IP3, which given high enough concentrations, can compete the PH domain off the membrane. As such, what is being measured is the convolution of PI(4,5)P2 decreases and IP3 increases (see for example doi: 10.1083/jcb.200301070 ). Ideally, a non-IP3 binding probe would have been used, such as the Tubby c-terminal domain (doi: 10.1186/1471-2121-10-67; doi: 10.1113/jphysiol.2008.153791). As it stands, the failure of the PH domain to return to the membrane after Li treatment reported in figures 1G, 3F and 3L could either be due to a failure of PI(4,5)P2 re-synthesis, or a failure to breakdown IP3 - either of which are plausible explanations given inhibition of IMPA1. This concern is somewhat mitigated by the inclusion of mass determinations of the lipids in figure 1H-J, which support the PI(4,5)P2 re-synthesis defect. However, the potential problems with interpretation of the data with the PH domain should be discussed.
- The strongest evidence for the effects of IMPA1 inhibition coming from inositol depletion are given by the experiment reported in figure 2I and J, where inositol supplementation rescues calcium mobilization. This should also be performed for the PIP2 re-synthesis experiments.
- It is implicit in the manuscript that DMEM does not contain inositol. This is not true; Life technologies' formulation for DMEM contains 40 micromol/l myo-inositol, which is sufficient to support activity of both proton/myo-inositol and sodium/myo-inositol symporters (HMIT and SMIT). On the face of it, therefore, inositol depletion seems unlikely. The reviewer wonders what concentration of added inositol mediated the rescue? This key fact is missing from the manuscript. At the very least, the details should be included and the reason for rescue of already inositol replete cells discussed. Ideally, the key experiments would be repeated with inositol-free medium and supplementation.
- The introduction refers to lithium as a "non-competitive" inhibitor of IMPA1. This is erroneous, as lithium is in fact an uncompetitive inhibitor. This is a key distinction: since the uncompetitive inhibitor blocks the enzyme:substrate complex, it is most effective where substrate accumulates the most - in this context, sites of intense PLC activity. This was central to Berridge's inositol depletion hypothesis. Also, the Allison et al citation is incorrect here. The correct citation is PMID: 2833231.
- other key experimental details are missing from the figures/figure legends/results and or methods. Namely, what concentration of carbachol was used? What was the optimum concentration of thapsigargin? For figure 2 B-C, was carbachol used to evoke calcium mobilization?
- The effects of IMPA1 knockout and rescue in figure 3F are rather unconvincing. All treatment groups' means fall within 1 SD; are the changes statistically significant? Plotting 95% C.I. or standard error may be more informative for these experiments.
Minor comments:
- There are some inconsistencies in the figure panels. Arrows labelled "CCh" are used to denote CCh addition in e.g. Fig. 3D, whereas simple arrows are used elsewhere e.g. Fig. 3F or no arrow at all e.g. Fig. 3B.
- Details of what the error denotes is missing in Figs. 2B-C, 3B-C, F - as is N for 3A.
- Fig. 2D: It should be made explicit that arrows indicate TTX addition in the figure. More importantly, it should be clarified whether this is also the case for Fig. 5D? Transients do not appear to be depleted by this addition.
- In figure 6C, it is stated that "there was no down regulation in transcripts for SCN1A (Nav1.1) or SCN9A (Nav7.1)" but this is not strictly true from the data; the Li-treated cells definitely trend lower. The effect is clearly not statistically significant, so although it is not possible to state that they reduced, this is not the same thing as being able to assert that they are not reduced. Perhaps it could be more helpful to plot the size of the reduction between these transcripts?
Significance
The manuscript is ultimately a contemporary re-evaluation of the inositol depletion hypothesis for Li treatment of BPAD, first proposed by Berridge in 1989. The manuscript certainly does not end the debate - other pathways and mechanisms for lithium's actions on a complex human behavioral phenotype will surely persist. However, it does add some important new insights: firstly, that IMPA1 is certainly the target mediating the effects of Li on the PI cycle; and secondly, that long-term, Li may exert effects at the transcriptional level to down regulate calcium and glutamatergic signaling in the brain. However, there is no mechanism presented to link these two findings. The work is therefore of interest to researchers with an interest on studying psychiatric disorders, basic mechanisms of neuronal excitability as well as molecular mechanisms of cell signaling. It therefore deserves to be published in a journal with a cross disciplinary focus.
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Referee #1
Evidence, reproducibility and clarity
The authors functionally define in the inositol monophosphate phosphatase IMPA1, as the true target of lithium regulating phosphatidylinositol turnover and calcium signalling. While the observed IMPA1 inhibition by lithium led to the historical 'inositol depletion hypothesis' over the past 30+ years were published evidence both in support and against this concept. These contradictory sets of results have led to decreased interest in phosphoinositides as the signalling pathway affected by the therapeutic action of lithium in bipolar disorder (BD) patients. The remarkable results shown here will revert this trend since the data clearly demonstrate a key role of IMPA1 in setting the rate of phosphatidylinositol turnover, and consequentially the extent of calcium signalling. While the data are consistent with the 'inositol depletion hypothesis' the authors do not prove or disprove the validity of this hypothesis since the actual levels of inositol were not measured in their experiments. However, this is not a criticism, since quantifying cellular inositol is complex, it is just a suggestion for future work. After clarifying the points listed below this work will be suitable for publication.
The experiments using inositol rich DMEM (reported on page 17 and in Fig 2I,J) require a better explanation and an adequate material and method section. It is not clear if the 'normal/control' condition uses inositol-free DMEM. The standard concentration of inositol in DMEM is 40uM. Thus, are the ~155uM (28 mg/litre) added by the authors at the high end of the 40uM? Given that FBS contains inositol have the authors used dialyzed serum? While adding 155uM of inositol on either inositol-free medium or to medium containing 40uM inositol does not alter the author's message, this technical information are important for the reproducibility of the data presented and to understand how HEK293T manages inositol homeostasis.<br /> It would be helpful to know if store operated calcium entry is altered in impa1-/--M1 cells. This information would nicely complement Fig.3 C-E data.<br /> In the Introduction at the end of page 4, the evidence not supporting the inositol depletion hypothesis is correctly discussed. This section lacks the discussion of another work questioning this theory (PMID: 30171184). The conclusion of this work is also in agreement with the authors finding that lithium affects the rare/turnover (lines 490/506) of PIP2 synthesis.<br /> In the material and methods, Liquid Chromatography Mass spectrometry is abbreviated to LCMS while in the main text (line 493) LC-MS is used. The dashed version should be used throughout the manuscript.<br /> I suggest to define (line 500) phosphatidylinositol 4-phosphate as (PI(4)P simplified as PIP). This will be consistent with the phosphatidylinositol 4,5-bisphosphate abbreviation as (PIP2) as reported in the introduction (line 97)<br /> Line 646: Instead of using [this study] the authors should refer to the Figure panels supporting the discussed argument.
Referees cross-commenting
Reviewer #2 main message is identical to my. The work is a "contemporary re-evaluation of the inositol depletion hypothesis" but it does settle the debate. Say that reviewer #2 also recognises the importance of the work in defining IMPA1 as the only lithium target affecting the PI cycle removing GSK3 from the picture. Additionally, we agree that the thorough transcriptional analysis of the effect of lithium on human cortical neurons will be very informative for any researcher interested in psychiatric disorders.
Reviewer #2 requests are rational and not demanding. Most queries require extra information or the reformatting of the data presented.
Significance
The submitted manuscript addresses an important topic. The authors developed HEK293 stable expressing muscarinic receptor to study the effect of lithium (without or after receptor activation) on PI(4,5)P2 turnover using two approaches, by microscopy and biochemically by LC-MS. These analyses were followed by a thorough characterization of the effect of lithium on calcium signalling. The generation of HEK293 impa1-/- line has allowed the authors to demonstrate that the observed effect of lithium on PI(4,5)P2 turnover and calcium signalling were IMPA1 dependent. The authors pushed the work to a higher level by studying the effect of lithium on iPSC-derived human cortical neurons demonstrating that lithium reduces neurons excitability and calcium signalling. Although previously published attempts failed to generate IMPA1 deficient human cortical neurons the authors managed to produce iPSC impa1-/- but, as reported and consistent with previous literature, this cell line failed to differentiate into neurons. This effort highlighted the author's commendable goal to develop a thoughtful story and not just another publication. The work is complemented by a very informative transcriptional analysis characterising the effect of lithium on human cortical neurons. Noteworthy is also the author's efforts to functionally and transcriptionally define the effect of another lithium target, GSK-3. These experiments emphasize that GSK-3 does not phenocopy the effect of lithium. This is another utterly important message of the paper.
In conclusion, the authors presented an easily readable, comprehensive, and experimentally convincing story. Furthermore, the developed experimental tools (HEK293-m1AchR) and the extensive data set (transcriptomic analysis) will be instrumental to further studies aimed at elucidating mechanistically how phosphoinositide signalling affects BD pathophysiology.
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The authors do not wish to provide a response at this time.
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Referee #3
Evidence, reproducibility and clarity
In this work, the authors generate a multi-omic dataset (RNA, proteomic and metabolomic) from fibroblast cell-lines of human and bat origins, in study of the specific differences in bat that allows them to have a good cancer resistance and longevity. They specifically focus on metabolic differences between humans and bats. They perform differential analysis followed by GO enrichment analysis to highlight differences related to the electron transport chain both at the level of RNA and protein abundance. They then use FBA sampling and specific constraints to propose an hypothesis of reverse direction of the second complex of the ETC, as well as better resistance to ROS, which they support with several subsequent experiments.
Overall, the paper is very well written, the findings are presented clearly and efficiently. For the most part, the assumption and limits of the study are clearly stated by the author (notably with respect to the limits of using only cell lines). In my opinion, the goal of the paper, which is presented as a stepping stone into further characterisation of the metabolic differences between human and bat for potential oncological research benefits, is clearly stated and appropriate.
There are however several points that I think are important to address inorder to improve the quality of the scientific work and its interest for the rest of the scientific community.
Major
The authors state:<br /> "We then set the lower bound of the PaLung Complex I reaction flux to a value equal to 70% of its theoretical maximum. Similarly, we set the upper bound of the WI-38 Complex I reaction at a value equal to 30% of its theoretical maximum value. This ensured that the PaLung model would have higher flux through the Complex I reaction, in comparison to the WI-38 model."
How do the results hold with different thresholds ? Are these findings robust with e.g. in ranges between 10 to 50% (90-50%) (instead of only 30% and 70%). Furthermore, the histogram figures doesnt seem to reflect a 70% of maximum lower bound for complex I (threshold at a value of 30 seems like extremity of tail).
Number of differentially expressed genes is extremely high because such cutoffs are not really meaningful given the comparison between two organisms. No need to refer to the 6247 above cutoff as differentially regulated genes (see: https://elevanth.org/blog/2023/07/17/none-of-the-above/ and https://daniel-saunders-phil.github.io/imagination_machine/posts/if-none-of-the-above-then-what/ for pointers toward current best practice in biological statistics). Enough to simply note that 6247 are above the cutoffs, which suggest a drastic (and expected) difference in expression profiles between the two organisms.
Please highlight the RNA and proteomic analysis assumption and present results within those boundaries (e.g. how are the transcript matched between human and bat, the use of human gene ontologies, etc...). Are the human GO set definitions relevant in bat (it is a common practice with mice and rats, are bats close ?)?
Are oxphos and hypoxia responses the most extreme pathway scores in the GSEA ? Instead of barcode plots that are generally not a very useful use of figure space, use fig 1C to show the top e.g.20 (positive and negative) pathway scores so that we can see how much those two actually stand out. Same for the proteomic analysis. Also, need to show an unbiased side by side comparison of the pathway enrichments for RNA and proteomic, the reported results in main text and figures are too cherry picked to be of interest as they stand.
Finally, and very importantly, please upload ALL the code used for the analysis, with instructions to run it and all the required inputs and source files. The computational analysis is only as credible as it is easy to reproduce.
Minor
Introduce GeTMM, what are its key specificities ?
Fig 1C code bar plot useless, simply report ES and NES and pathway absolute rank in text.
Report Foldchange/p-value/rank of complex-I members and other genes of interest for the narrative of the paper.
Referees cross-commenting
I also think the comments from the other reviewers are appropriate.
Significance
In my opinion, the goal of the paper, which is presented as a stepping stone into further characterisation of the metabolic differences between human and bat for potential oncological research benefits, is clearly stated and appropriate.
Broadly interesting for oncological research.
My espertise is multi-omic data analysis and integration with prior knowledge in the context of complexe diseases.
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Referee #2
Evidence, reproducibility and clarity
Jagannathan et al. performed a multi-omics comparative analysis between fibroblast cells from bats of the species P. alecto and humans. Using a combination of transcriptomics, proteomics and metabolomics, the authors showed differences in central metabolism between the cells of the two species. Specifically, the authors noted higher expression of Complex I components of the electron transport chain, as well as low activity of the Complex II. The computational modeling suggested that the latter is indicative of a state resembling the state of ischemia. Furthermore, the expression of antioxidant components was interpreted as higher in the bat compared to human cells, which is in accordance with previous reports.
Overall, it is a comprehensive multi-omics approach performed with a very interesting biological object, such as a bat. However, some aspects, especially the part of bioinformatic analysis, needs to be enhanced. The existence of differences in mitochondrial metabolism is also not surprising, given the evolutionary distance and very different ecology and lifestyle between the two species, but a more mechanistic follow-up would be of greater interest. Nevertheless, it is the first study using integrated omics approach of that sort done on bats.
Major:
- The authors compared a fibroblast cell line derived from adult bats with a human embryonic cell line. Please discuss whether mitochondrial metabolism in embryonic cells might be different and how it could have affected the obtained results. Please describe in more detail how the cells were established, what population doubling they were used at (both bat and human cells). Were the cells cultured in atmospheric oxygen or low-oxygen conditions. The exposure of cells to atmospheric oxygen might affect the many mitochondrial parameters measured in this study and could influence the main finding about ischemic-like state. Additionally, please mention in the limitations of the study that only biological n=1 was compared (since cells only from 1 individual per species was used in experimental groups), despite n=3 technical replicates.
- Reference genomes for bats are not as well annotated as for human. Downregulation of a pathway may result from some genes being excluded from the analysis because of poor annotation of the P. Alecto genome compared to human. The authors state: "Genes with counts per million (CPM) < 1 in more than 3 out of 6 samples were discarded from downstream analysis". So, if the gene was not annotated, was it assigned a zero value and discarded? Was it discarded if it was zero in one species (e.g. bat) or set to 0? If such genes were excluded, while in reality not being mapped, they could have skewed the pathway analysis.
- All conclusions are based on high-throughput data, however it is accepted that some validation should be provided. Please provide qPCR or WB (if good antibodies are available) validation for several most significantly differentially expressed genes supporting the pathways identified in Figure 2 (preferably supporting the conclusions about Complexes I/II).
- The major findings of this paper were based on the omic data, followed by some experimental validations. However, the quality of these omic data or the results are not solid enough to motivate the authors to validate these findings. For example, both of the GO terms enriched by the DEGs in Fig.1 are not the top terms as claimed by the authors (not even significant after multiple test correction). Also, even though the 2 GO terms in Fig.2 are quite significant, the expression pattern seems not very consistent among the replicates, which make the enrichments not so solid. This highlights an inconsistency among different omic datasets, which may generate some conflicting results. For example, the low level of metabolites from TCA cycle (Fig.4c) seems not consistent with the high level of TCA-related protein, as described in Fig.2c & d. For the purpose of improving the manuscript quality, the authors may have to evaluate the consistency among the multiple omic datasets or to optimize their bioinformatic pipeline to enhance the results.
- The dominant up-regulation of complex I in ETC is interesting and is the main finding of this paper. However, no experimental evidence was provided to prove the greater activity of Complex I, for example, metabolites changes. In addition, the genes encoding proteins belong to ETC complex I, II, III and IV vary a lot, with much more genes encoding complex I. Therefore, the author should consider the background gene number when they compare the up-regulated gene number differences in each complex. For example, a fisher-exact test could be done to see if complex I has significantly more genes been up-regulated than a random expectation.
- If the main findings of this paper can be further confirmed by additional experiments or data, it will be a very nice paper. This could be a potential mechanism that bats used to switch metabolism modes between two metabolic extremes: flight and hibernation, which require high and low energy. However, the usage of only the lung fibroblasts of human and bat may limit the ability of generalizing this 'ischemic-like state' of ETC in most of the bats tissue/organs. While I agree what the authors mentioned in the discussion section, that to extend to primary cells of other species can help generalize this finding, studying the metabolism state of different cell type of bats (e.g., muscle cells responsible for flight; myocytes and neurons for hibernation) probably can provide more insights into the evolution of various interesting phenotypes of bats.
Minor:
- the author may have to add the p value or FDR for each GSEA plot, even though some of the FDR are not significant. Also, it will be better to show the normalized enrichment score (NES) instead of the ES.
- the gene set name in several supplementary tables contains many '%' characters and those needs to be removed.
- in Line 302, "...combined with the earlier findings of downregulated OxPhos expression and low OCR, we conclude...". If my understanding is right, the authors only mentioned the up-regulation of Oxphos expression, instead of down-regulation. This sentence may need to be clarified.
- How did mitochondrial DNA content per cell compared between the two species? Could the results be affected by the number and size of the mitochondria per cell in each species? An indirect measurement of mitochondrial DNA yield in the fractionation experiment would be the total DNA amount that was obtained in mitochondrial fractions per cell lysed.
Significance
The work is significant considering the limitations stated above. This may be considered a pilot study of brand significance.
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Referee #1
Evidence, reproducibility and clarity
Summary
In this study, the authors conducted a multi-omics analysis comparing cells from the long-lived bat, Pteropus alecto, and human cells. Their findings revealed that bat cells express higher levels of mitochondrial complex I components and exhibit a lower rate of oxygen consumption. Moreover, computational modeling suggested that the activity of complex II in bat cells might be low or even reversed, similar to the conditions observed during ischemia. The decrease in central metabolites and the increased ratio of succinate to fumarate in bat cells might indicate an ischemia-like metabolic state. Despite having high mitochondrial ROS levels, bat cells exhibit higher levels of total glutathione and a higher ratio of NADPH to NADP. Additionally, bat cells showed resistance to glucose deprivation and induction of ferroptosis.
Major comments
- Regarding Figure 1A, the authors mention 'n = 3' for a single cell line. Does this refer to three different passages or three independent experiments? Please provide a more detailed description to clarify.
- In relation to Figures 1C and 1D, the authors state in the figure legend that the 'GSEA analysis identifies Respiratory electron transport and Cellular response to hypoxia as the top metabolic pathways that are differentially regulated between PaLung and WI-38 cells.' (Lines 140-144). However, the criteria for selecting these terms as the top metabolic pathways is not clear. In the lists in Supplementary Tables 2 and 3, the authors' proposed term, 'Respiratory electron transport,' is ranked 126th, and 'Cellular response to hypoxia' is ranked 79th. Conversely, terms related to the TCA cycle are ranked 66th and 82nd, and another term that seems to be related to hypoxia, 'OXYGEN-DEPENDENT PROLINE HYDROXYLATION OF HYPOXIA-INDUCIBLE FACTOR ALPHA,' is ranked 62nd. Could the authors please provide a clarification for their choice of 'Respiratory electron transport' and 'Cellular response to hypoxia' as the top metabolic pathways?
- In the Materials and Methods section (lines 419-421), the authors mention, 'GSEA was run against the complete Gene ontology biological process (GO BP) gene set list (containing 18356 gene sets).' However, they narrow down the gene dataset for analysis (lines 136-138, 'we filtered our gene dataset to contain only genes listed under the Gene ontology category Cellular Metabolic Process (GO ID:0044237), resulting in a truncated list of 4794 genes.'). I'm concerned that this selective approach might introduce bias into the resultant pathways. Is this selective approach commonly employed in this type of analysis? And isn't there a need for adjustments to avoid potential bias?
- The authors noted that the number of differentially expressed genes (DEGs) is quite high (6,247 out of 14,986) as per lines 134-135, stating that "The number of differentially expressed genes (6,247) was extremely high, suggesting that multiple pathways are differentially regulated between the two species." However, this large number of DEGs could indicate either an improper correction procedure or a need for a more stringent threshold. The authors should address this issue to avoid potential misinterpretation of the results.
- In Figure 2B, the samples labeled as W1 and P1 appear to be outliers. This raises questions about the integrity of the sampling or analysis process. Please describe about this.
- Regarding the GSEA analysis of Fig. 2, they are using the full set of GSEA. However, this reviewer is wondering if this is appropriate when analyzing mitochondrial fractions, as I believe using the entire GSEA set could introduce a bias. Is this a common approach? Shouldn't the authors be focusing on mitochondrial-related sets within the GSEA, and then determining the upregulated and downregulated pathways from there?
- The authors describe in lines 195-197, "GSEA-flagged upregulation in OxPhos was driven mostly by the upregulation of Complex I subunits, for both the proteomic and transcriptomic data (Figure 2G, Supplementary Figure S1D)." However, within this analysis, the number of genes composing each subgroup of the mitochondrial Complexes are 44 for Complex I, 4 for Complex II, 10 for Complex III, and 19 for Complex IV (https://www.genenames.org/data/genegroup/#!/group/639). The authors mention that the genes of Complex I were dominant in the ETC, but, might this just be reflecting the original difference in the number of genes? As this reviewer believes this could have a significant impact on the authors' current claims, this reviewer suggest the authors to carefully reconsider this point, comparing the actual results with the proportion expected from the difference in gene numbers. (Even in Fig. S1D, it appears to correlate with the number of genes: C1 39.3%, C3 10.7%, C4 10.7%, C2 3.5%)
- As pointed out in Major Point 7, if the authors' claim of enrichment in Complex I is indeed due to the large number of genes included in the Complex I subgroup (https://www.genenames.org/data/genegroup/#!/group/639), can the assumption of High Complex I flux truly be considered valid? In that case, this constraints model would become inappropriate, and the validity of the inferred low or reverse activity of Complex II would be diminished. Therefore, a careful re-examination is desirable.
- (option, takes about 1-2 months). This reviewer believes that the authors' most important claim, concerning the high activity of Complex I and the low activity of Complex II, lacks strong evidence as no biochemical data of the activities of each mitochondrial complex are presented to substantiate this. Unless additional biochemical experimental data is provided, the assertions should be toned down. While the abstract mentions "complex II activity may be low or reversed," it is stated with certainty in line 108 of the introduction, "associated with the low or reverse activity of Complex II." Based on the present data, this reviewer believes that the claim remains speculative. Therefore, I suggest moderating the overall argument or adding the biochemical data. While the results from metabolomics are supportive, they do not serve as direct evidence.
- Regarding Figure 5, the title of the figure states "lower antioxidant response", but it doesn't seem that the data in the figure actually shows a lower antioxidant response.
- In lines 109-110 of the Introduction, the authors state, "we confirmed our prediction of ischemic-like basal metabolism in PaLung cells by characterizing the response of bat cells to cellular stresses such as oxidative stress, nutrient deprivation, and a type of cell death related to ischemia, viz. ferroptosis." However, can the assertion that the cells are in an ischemic-like state be confirmed simply because they are resistant to several types of cellular stress?
Minor points:
- The authors mention the use of cufflinks/Tophat for mapping/quantification. However, support for these software programs has ended and the creators of these programs themselves recommend using the successor programs. I recommend re-analysis using a more current pipeline (such as HISAT2/StringTie, STAR/RSEM, etc.). Furthermore, the transcriptomics section of the methods should also include the program used for cleaning and trimming.
- As for the Oxygen Consumption Rate (OCR) data presented in Figure 2F, it makes sense that it's low at the basal level. However, it's perplexing that it is also low even under uncoupled conditions, especially considering the high energy demand associated with flight in this species. Could the authors provide their interpretation on this apparent contradiction?
- In line 156, the authors mention that 'Profiling detected a total of 1,469 proteins.' Please provide more details in the explanation. Specifically, does this total of 1,469 proteins represent a combined count from both humans and bats, or is this the number of proteins for which orthologs could be identified in both species, just like the authors did with the transcript results.
- In Supplementary Table 4, only 127 mitochondrial proteins are listed out of the 405 proteins mentioned in "Of these 405 proteins, we identified 127 to be core mitochondrial proteins (lines 161-163)". As there is no explanation for this within Supplementary Table 4, it would be better to include one.
- In line 472, the phrase "GO BB gene set list" is used. Could this potentially be a typographical error, and should it instead be "GO BP gene set list"?
- In the volcano plot of Fig. S3B, it appears that the side with lower P/W values generally corresponds with lower p-values. I wonder if there might have been any oversight or mistake in the data analysis process that could explain this observation?
- In lines 249-252, it is stated, "The low or negative flux values for Complex II in our PaLung simulations indicate that the electrons obtained from Complex I may accumulate at Complex II or potentially even get consumed by Complex II operating in reverse (bypassing the rest of the ETC) in PaLung cells." However, isn't the basic process of electron transfer done through Complex I-III-IV, independent of Complex II?
- Regarding Figure 4F, the authors state, 'PaLung cells displayed higher viability than WI-38 cells after glucose deprivation (Figure 4F).' However, in addition to the cell images, it would be beneficial to perform experimental quantification of cell death to provide more rigorous data. Additionally, the cells appear to be over-confluent, which might influence the results. Also, scale bars should be included in all photos, including Fig. 6.
- Regarding Figure 5B, it is stated that 'the expression levels of differentially expressed antioxidant genes' are shown, but it includes those that are not significant. It would be helpful if the authors could clarify how this gene set was selected.
- Regarding Figure 6C, the values for total glutathione seem to significantly differ from those in Figure 5C. An explanation for this discrepancy would be appreciated to ensure the consistency and reliability of the data.
Referees cross-commenting
I think the comments from the other reviewers are appropriate.
Significance
Collectively, these intriguing results from the interspecies comparison provide novel insights into the differences in metabolism and cellular characteristics between bat and human cells. However, the study has some limitations, notably certain weaknesses in the data and potential overstating of certain interpretations. Addressing these issues would enhance the overall quality and robustness of the manuscript. Furthermore, if feasible, conducting a biochemical analysis of each mitochondrial complex activity would solidify the authors' main conclusions.
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Referee #3
Evidence, reproducibility and clarity
Summary:
This paper provides an elegant investigation into the dysmorphology of skeletal structures upon loss of the extracellular Fraser Complex through use of a zebrafish fras1 mutant. The adult phenotypes of this mutant had not been previously explored in detail. The authors use transgenics, histological staining and immunostaining to visualize the morphological defects, then examine Fraser Complex localization and effects on Shh signaling upon loss of Fras1. They analyse both steady state and regeneration contexts. Most importantly, they demonstrate the blistering that has been shown to occur in larval fins, also occurs during regeneration of the adult fins, and that there is a disorganization of the pre-osteoblasts due to basement membrane corruption. This work provides novel investigation into how Fraser Complex leads to skeletal defects.
Major comments:
Overall the data presented by the authors is logical and very well presented. They make reasonable claims that are supported by sound experimentation. Statistics are used appropriately and the authors combine different approaches to make their points. For example they draw on previous single cell RNA-seq Data sets to define the cell type expressing Fraser complex components but then also use immunostaining and ins situ hybridisation to localise expression domains. I thought the analysis using the ptch1:kaede line to be particularly elegant and informative.
My first major question relates to Figure 7. The authors cage their analysis under the impetus of understanding how the distal anomalies and skeletal defects in Fraser Syndrome arise in development. They present convincing images of distal blistering during regeneration. Were similar blisters seen at any stage during adult fin development prior to the full fin formation? The authors might have noted this during their imaging of the ptch1 reporter Figure 6 Supplement 1. Or they might need to look earlier. Figure 7 is informative analysis. It's a shame to limit it to regeneration and not look during adult fin formation which would have direct inferences for human development.
Secondly, was osteoblast morphology affected as well as their patterning in the fras1 mutants? Perhaps the authors could look at zns-5 antibodies or sp7:egfp line in transverse cryosections to assess if there was a change in osteoblast morphology. Figure 5B' certainly suggests they might be more cuboidal and have lost their flattened shape. This change in osteoblast morphology has been noted in other ECM mutants affecting the lepidotrichia.
Minor comments:
I have only a few minor comments.<br /> Line 55 Introduction. For clarity change this sentence to "variable expressivity and incomplete penetrance reminiscent of the variability seen in distal limb defects in humans with Fraser Syndrome."
Referring to Figure 2, is each fin ray thicker in fras1 mutants than in WT? It appears so but might be just an optical illusion. Would be easy to measure and state in the text.
Line 242, 243 and Figure 5. The text refers to Fig 5C' and Fig 5D', but I could only find Fig 5C' and Fig 6C'. Do you mean E, F??
Related you claim that in fras1 mutants, frem2 protein remains intracellular. How do you know that protein signal is intracellular yet in the WT it is extracellular? Please reword this or show more conclusively. This is also repeated on line 360 in the discussion.
Fig 6B, D are not referred to in the main text.
I couldn't find details of the Runx2 antibody in the materials and methods
Significance
This paper provides novel insights into how loss of Fraser Complex might alter morphology of post-embryonic structures, and gives novel, visual indication of the effect basement membrane disruption has on osteoblast patterning. This will give novel guidance to explaining the basis of skeletal presentations of Fraser Syndrome for basic researchers interested in the importance of osteoblast environment on patterning and clinicians examining similar rare skeletal congenital syndromes.
It elegantly demonstrates that cellular topology, organisation and morphology is just as important as signalling in defining organ growth and shape. The authors also suggest this model of an indirect disruption of osteoblast patterning might explain why there is such variability in distal skeletal phenotype severity in Fraser patients. This is a valid and reasonable hypothesis.
My background directly relates to mutant analysis of zebrafish fins
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Referee #2
Evidence, reproducibility and clarity
Summary:
In this manuscript, Robbins et al. develop the adult zebrafish caudal fin as a model for limb deformities in Fraser syndrome. They show that the zebrafish fras1 mutant can survive to adulthood and investigate how loss of fras1 influences the caudal fin during its regeneration. The fin ray branching defects and chondral bone defects examined in this study show a degree of variability akin to many defects found in Fraser syndrome, including limb defects. The paper is an easy read, has beautiful imaging, and shows compelling quantification. As such, the authors set up the zebrafish fras1 mutant caudal fin as a novel and interesting platform to investigate the onset and variability of limb defects in Fraser syndrome.
Major comments:
Figure 7 makes some critical claims based solely on H&E staining; better markers are needed. I also strongly recommend that the authors repeat this Figure 7 experiment with immunolabel or other techniques that mark the epithelial and mesenchymal layers distinctly. In particular, since the author's discussion focuses on osteoblast positioning, it seems important to mark the osteoblasts alongside the epithelium.
The authors posit that background mutations explain Fraser syndrome variation, but it's worth also considering non-genomic origins of variation, including potential micro-environmental differences and/or stochasticism. The caudal fin seems like an ideal system to test the idea - one could injure the fin and test how well initial defects predict defects after regeneration. It looks like the authors started to do so in panel 3R, but don't comment on the finding. Also, it looks like the initial severity doesn't completely predict regenerated severity - for instance, the least severe fin pre-injury becomes the most severe fin post-injury. I recommend explaining panel 3R in results text and returning to it in the discussion, to contextualize how this finding might influence understanding of fras1-dependent variability. OPTIONAL: Variability could also be compared between different fins in the same animal; does the severity of caudal fin defect correlate with severity in the pelvic or dorsal fin? If so or if not, what might this suggest about the origins of phenotypic variation in this model?
The study is focused on core Fraser complex, particularly fras1 and the Frem genes which are mutually-stabilizing components of anchoring cords that link nascent epithelia to underlying mesenchyme. The authors could broaden the paper's impact and scope by considering other proteins bound by the core Fraser complex, such as AMACO, Integrins, Npnt, fibrillin, etc. Likewise, examination of Collagen expression, such as collagen VII, may help explain why there is a dissipation over time for the requirement for fras1 to prevent blistering. It may be outside the scope of this study to delve deep into these 'nearby genes' but it seems reasonable to examine them bioinformatically in the scRNA-seq (perhaps adding a supplemental figure if the results are unsatisfying), or at least to discuss them and thereby contextualize the overall findings.
Minor comments:
Figure 8 feels like a visual abstract, summarizing findings and contextualizing existing models to the caudal fin, instead of putting forward a truly new model. It focuses on the concept that Fras1 is needed for epithelial-mesenchymal attachments and that Fraser-components stabilize one another; both of these ideas are over a decade old. Although the concepts are cited appropriately within the discussion narrative, it would be nice if Figure 8 did a bit more. The model could be revised in a way that offers new insights into how the specific defects seen in this study arise, by hinting at answers to some of the many questions raised by the study. What is the source of variation? Why do the fin rays fail to branch in the mutant? (could there be more to that decision than simple osteoblast mispositioning?) Why does defect severity change during regeneration vs. development? Why does an extra hypural cartilage form in the mutant? Is there any similarity between the failure to branch fin rays and the presence of fusions in chondral skeleton? These fusions could also be compared and contrasted with non-limb skeletal fusions? It's certainly optional to tackle all of these issues, but discussing any of them could increase the manuscript's impact and establish interesting fodder for future papers. These changes are important, but I place this remark in 'minor comments', because an improved final figure would not be critical to publication in some journals.
This sentence on line 273-274 is confusing and should be revised: "However, the Fraser Complex at least supports the Shh/Smo downstream cell behaviors that split pre-osteoblasts given frequent ray branching defects in fras1-/- mutants." What do the authors mean by "at least supports"? The phrasing may imply 'Provides physical support essential to,' but that reading does not explain why the fin rays branch in Shh expressing cells regardless of the presence of Fras1. (this comment is actually 'minor')
The Figure 7 title states that the Fraser complex is needed for normal "epithelial-mesenchymal tissue layering." I am not certain what the authors mean by this. The phrase written in the figure title implies that mesenchymal cells start appearing inside of epithelial regions, but that interpretation doesn't match the figure nor results text. An alternative read - one consistent with the final figure - is that the authors are simply trying to show that the blisters form at the interface of the fin and underlying mesenchyme. If this is the primary thrust of the argument, then it could be stated plainly - and shown more clearly in the results section. (this comment is also truly 'minor')
Significance
This manuscript establishes the zebrafish adult caudal fin as a new model to investigate epithelial-mesenchymal interaction defects and skeletal variation caused by loss of the central Fraser complex gene. It is an important contribution to existing models because of the similarity between patterning mechanisms in the caudal fin and tetrapod limbs, which are variably disrupted in Fraser's syndrome. This variability is itself of interest, because the profound variability of Fraser phenotype offers an experimental model for high-variance diseases, which remain poorly understood. The caudal fin is an exciting system to study variation, in particular because it can be cut and regrown rapidly with phenotypic severity compared between regenerates in a clonal system; that regenerative tractability is particularly potent when paired with the zebrafish transgenic toolbox, as the authors show in Figure 6. The present manuscript feels almost complete and yet it opens up many questions, suggesting that it can serve as a good foundation for future studies.
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Referee #1
Evidence, reproducibility and clarity
Robbins and colleagues present a new zebrafish model for studying the rare disorder Fraser Syndrome in a well written manuscript. The authors present an interesting phenotype caused by a mutation in fras1: mutants have small, misshapen fins with rays that frequently fail to form branches. The authors show that the Fras1 protein localizes to the distal portions of regenerating fin rays and that in its absence, another component of the Fraser Complex (Frem2) is decreased and mislocalized. The authors show that Fras1 is not required for Shh/Smo signal transduction but that branching is nonetheless disrupted.
Major comments:
Since the basement membrane ends up being such a big part of the story, it would add support to the model to specifically present a laminin stain in both WT and the mutant. This could potentially support to the idea that the FPC is integrating the BM with the osteoblasts and it would be helpful to see where the BM is relative to the 'blisters.'
The fras1-/- mutants show a near-absence of Frem2 protein; the authors conclude that Fras1 supports Frem2 secretion and assembly into Fraser Complex-containing BM. However have the authors considered the possibility that Fras1 directly or indirectly regulates Frem2 transcription? (The model in Fig 8 seems to show a relative increase in Frem2, which should be altered)<br /> Do the other components of the FPC (Frem1a/b, Frem3) show similarly disrupted levels and localization? As presented, since the Frem1a/b, Frem3 are not actually examined in a fras1-/- context, it is not justified to show their intracellular localization in the model in Fig 8b.
The relative activities of osteoblasts and osteoclasts may regulate the relative location of branches (Cardeira-da-Silva et al 2022). In the fras1 system mutant, it would be beneficial to examine osteoclasts to rule out the idea that Fras1 is simply required for osteoclast activity. This could additional lend support to the idea that disrupted integration between the BE and Obs underlies that failure of fras1-/- mutants to form branches. This could be done within a few months by crossing the mutant into an osteoclast reporter, or more rapidly by using an osteoclast specific antibody or ISH.
The authors look only at the meristic presence/absence of fin ray branches, and do not take fin length into account. Longer rays are more likely to form branches and the fras1-/- have shorter fins. Some of the branches may be absent in the mutant background simply because the rays are not long enough to have formed them. How far are the branches from the body in each background? Where do the branches form along the total length of the rays?<br /> In the regeneration experiments, do the mutants regenerate at the same relative rate as WTs? Could the decrease in the number of branched rays simply be due to the fact that the mutants may not have not regenerated to their original length by 28 dpa? Has the distance from the body to branch changed? These are all addressable with the data the authors have already collected.
Minor comments:
The protruding lower jaw phenotype mentioned in the results should either be shown, or if it is redundant with previous publications please add the citation here.
Please show Amputation planes in Fig 4E-G. It is notable that Fras1 seems to be present in the proximal region (proximal to the amputation plane?). This seems like it deserves mention in the manuscript.
In line 195, please clarify what you mean by ray proportions. The proportion of longest/shortest, presumably?
Please change the title of Fig 3: Fras1 mutants robustly restore skeletal patterning. Also, the ray length ratio in Fig 3Q has been tested with an unpaired t test. It should be a paired test as in R.
This is an unbelievably a minor point, but for consistency with the other panels I think that in Fig 5 E and F should be C' and D'.
The titles of Fig 6 and Fig 7 should indicate indicate Fras1 not the Fraser Complex. Eg. Fras1 is not essential for sonic hedgehog/smoothed signal transduction during fin regeneration
There are also a few grammar errors that should be fixed.
Significance
Robbins and colleagues present a new zebrafish model for studying the rare disorder Fraser Syndrome.
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Reply to the reviewers
First and foremost, we would like to extend our thanks to the two referees and managing editor of Review Commons for their constructive feedback and careful consideration of this manuscript. We have taken into consideration all of the suggestions from the reviewers and address all points below and in the following sections.
Minor comments from Reviewer #1:
“It is not clear why the authors used cell number as a measure of viability compared to the MTS assay used in figure 2.”
Response:
In Fig. 2, both MTS assay and CellTiter-Blue assay are used to assess cell viability at 24h and 72h, respectively, whereas counterstaining with DAPI is used to quantify cell number in viral infection assays. Quantification of viral infection using immunofluorescence requires fixation and permeabilization of the cells which is not compatible with assessment of metabolic activity through either the MTS assay or CellTiter-Blue assay post-imaging. One can perform these assays (e.g., MTS and CellTiter-Blue) prior to imaging, however there were concerns regarding viral assay image quantification after running these assays due to the metabolic demand of dye processing which may influence susceptibility to viral infection/propagation, and fluorescence of the metabolic sensor interfering with subsequent IF staining.
DAPI staining is not meant to show viability per se, however, one can gate for fragmented cell nuclei (indicative of lysed cell) to remove dead cells or nuclear debris from the measurement. However, pre-apoptotic or dying cells cannot be accounted for in this measurement.
In our case, due to the rapid doubling time of the immortalized cell lines used (e.g., Vero E6 ~24h, Calu-3 ~35-48h, L929 ~24h), we are able to see large differences in cell number as infected or lysed cells fail to replicate over the 48h experiment. Thus, DAPI staining can be used as a proxy to determine the overall health of the culture but is not directly a measure of cell viability.
Minor comments from Reviewer #1:
“Why did the authors adopt a different pre-treatment infection protocol for SARS-CoV-2 compared to MHV and OC43?”
Response:
The pre-treatment protocol was developed by collaborators in the BSL3 facility as standard practice for rapid treatment/screening of multiple compounds in a BSL3 lab where hands-on time was limited due to immense research focus on infectious disease (e.g., SARS-CoV-2) at the time. We screened many different nanoparticle formulations with this protocol before assessing nanoparticle effect on MHV and OC43 where we also used standardized protocols.
In reference to comments from Reviewer #2:
“The substance of the work is very interesting, but experience shows that the idea that by testing one isolate you can generalize about all other viruses is not accurate. Viruses mutate. SARS-CoV-2 has shown an exceptional capacity for mutation, and the mechanism by which viruses enter cells by endocytosis or membrane fusion plays a role in their exposure to products concentrated in the lysosome. Viruses that enter by membrane fusion (including certain isolates of SARS-CoV-2) should not a priori be as sensitive because they are not subject to phagolysosome fusion.”
“In this sense, it is important to evaluate efficacy on several viral strains and not on a single strain, as has long been the case for acute viral infections. As with HIV, viruses can present natural or acquired resistances linked to evolution under selection pressure or not.<br /> In practice, we cannot rely on testing two viruses, one that has disappeared (Wuhan) and the first virus of the Omicron generation (B1), to assess the therapeutic capacity of a new strategy.”
Response:
We would like to highlight that MFQ-NP efficacy was evaluated in several different coronavirus models (MHV, HCoV-OC43 and SARS-CoV-2) in addition to two distinct viral strains (SARS-CoV-2 WT-WA1 and Omicron BA.1) in this study. Not only this, but we have selected viruses from distinct Betacoronavirus lineages which infect different species (homo sapiens and mus musculus). At the time, we had chosen Omicron BA.1 as our model strain as it was the dominant strain in circulation and was temporally separated and genetically distinct from the original WT-WA1 strain.
We can appreciate that viruses mutate, and SARS-CoV-2 has exemplified this through its life cycle. Due to this rapid mutation rate, it is challenging to assess the current dominant variant (e.g., XBB.1.16 as of writing this) and complete manuscript preparation in addition to full peer-review prior to the emergence of a new, potentially more relevant, dominant variant. Additionally, it remains challenging to accurately predict the emergence and genotypic/phenotypic changes of new strains before they arise, necessitating investigation on ancestral strains or, at the least, the current dominant strain.
Lastly, we do not wish to speculate/generalize that MFQ-NPs or a similar approach works for “all other viruses”. As exemplified by efficacy studies in three Betacoronavirus lineages, and one of which using two distinct strains, we do argue that this approach is an effective means to inhibit Betacoronavirus infection. We speculate in the discussion that MFQ-NPs may be used as either a prophylactic or treatment for an array of other respiratory coronaviruses, however we neither show data or speculate efficacy against viruses outside of the coronavirus family.
In reference to comments from Reviewer #1:
“The claim that MFQ may impact cell entry is not supported by the data in the paper. At minimum, the impact of treatment on expression of viral entry receptors for all 3 viruses should be performed, viral attachment assays (see PMID 35176124) and viral pseudoparticle assays. Further the conclusion that MFQ inhibits replication as well as entry is not fully supported by the data presented. This could be improved using a single cycle infection experiment using a synchronised infection protocol. The gold standard to determine impacts on replication would be the use of a viral replicon however I appreciate the technical difficulties in performing these experiments.”
Response:
We agree that the mechanism by which MFQ-NPs inhibit coronavirus infection has not been fully interrogated through this work. We do have preliminary evidence addressed in Fig. 4 which suggests that mechanistically MFQ-NPs may work through targeting pH-dependent protease activity and lysosomal function downstream of viral uptake. However, we have not yet investigated the effect that MFQ-NPs may have on viral entry receptors or viral attachment.
To that end, we are proposing to perform RT-qPCR to measure changes in expression level of key membrane bound proteins responsible for viral uptake. For these assays, we plan to treat Calu-3 cells with MFQ-NPs, unloaded PGC-NPs, equivalent concentration of molecular MFQ, or DMSO as control to gauge expression level of ACE2 (Hs01085333_m1), TMPRSS2 (Hs01122322_m1), sialate O-acetyltransferase gene (CasD1, Hs01082700_m1), and sialic acid acetylesterase gene (SIAE, Hs00405149_m1) relative to expression of glyceraldehyde-3-phosphate dehydrogenase (GAPDH, Hs02786624_g1) using TaqMan probes (ThermoFisher, USA). Additionally, we will also measure the expression level of Cathepsin L (Hs00964650_m1). Cathepsin L is not a transmembrane protein, however strong evidence suggests that this lysosomal protease is essential for S protein processing and viral membrane – endolysosomal membrane fusion in the SARS-CoV-2 endocytic infection route.
These proteins are chosen as ACE2 and TMPRSS2 are known mediators of SARS-CoV-2 uptake and fusion, and HCoV-OC43 relies on uptake via sialoglycan-based receptors with 9-O-acetylated sialic acid (9-O-Ac-Sia) as a key component. Although CasD1 and SIAE themselves are not transmembrane receptors for HCoV-OC43, they regulate the addition or removal of O-acetyl ester groups from sialic acids, respectively.
Similarly, we plan to treat L929 cells with MFQ-NPs, unloaded PGC-NPs, equivalent concentration of molecular MFQ, or DMSO as control to gauge expression level of CEACAM1 (Mm04204476_m1) relative to expression of GAPDH (Mm99999915_g1). MHV spike protein binds to murine carcinoembryonic antigen-related cell adhesion molecule 1a (mCEACAM1a) facilitating infection. NP treatment duration will be consistent with viral infection assays (e.g., 48 h) prior to RNA isolation and qPCR.
Results from PCR measurements may warrant further investigation into protein level expression measured by Western Blotting. However, we plan to begin with PCR as blotting for multiple membrane bound proteins is considerably challenging and higher cost than qPCR.
In addition to expression of viral entry receptors, we will also perform a viral attachment assay and internalization assay to further interrogate MFQ’s mechanism of action. To determine whether mefloquine inhibits SARS-CoV-2 binding/attachment, we will perform viral binding assays in Calu-3/ Vero E6-TMPRSS2-T2A-ACE2 cells that express ACE2 at high levels, and HEK293T cells that express ACE2 at lower levels. Assays will be performed using SARS-CoV-2 Spike pseudo-typed lentivirus which expresses a fluorescent reporter upon mammalian cell infection. SARS-CoV-2 Spike pseudo-virus is advantageous as it mimics SARS-CoV-2 entry mechanisms; however, it can be handled at BSL2, and it can be used to accurately quantify viral uptake since this virus is not replication competent.
SARS-CoV-2 can be internalized primarily via receptor-mediated endocytosis in cells which do not express TMPRSS2 (e.g., Vero E6) or via direct plasma membrane fusion in cells which do express TMPRSS2 (e.g., Calu-3 and Vero E6-TMPRSS2-T2A-ACE2). To test whether mefloquine inhibits the endocytosis of SARS-CoV-2, Vero E6 cells will be pre-treated with effective concentrations of MFQ-NPs, equivalent concentration of molecular MFQ, or unloaded PGC-NPs/DMSO as controls. Next, cells will be inoculated with SARS-CoV-2 Spike pseudo-virus at MOI 0.5 at 37 oC for 1 h. Cells will then be washed with PBS to remove unbound virus, media containing treatments will be reintroduced, and the pseudoviral reporter fluorescence will be measured 18-24 h after inoculation.
To test whether mefloquine inhibits TMPRSS2-mediated fusion during SARS-CoV-2 infection we will use TMPRS2 expressing Vero E6-TMPRSS2-T2A-ACE2 and Calu-3 cells. Cells will be pre-treated with Leupeptin/Pepstatin (inhibitors of endolysosomal proteases), camostat mesylate (serine protease inhibitor), MFQ-NPs, molecular MFQ, unloaded PCG-NPs, or DMSO as control for 1 h, and then inoculated with SARS-CoV-2 Spike pseudo-virus (MOI = 0.5). Cells will then be washed with PBS to remove unbound virus, media containing treatments will be reintroduced, and the pseudoviral reporter fluorescence will be measured 18-24 h after inoculation.
Minor comments from Reviewer #1
_<br /> “Further discussion in the introduction as to the potential mechanism of action of MFQ should be included. I would also suggest the authors read the work by Elizabeth Campbell and Bruno Canard concerning the potential difficulties in designing direct acting antivirals for coronaviruses.”
“The figure legends lack detail concerning the number of replicates and statistical comparisons. For viral infections MOIs used are also absent.”_
Response:
We have included a brief summary describing what the field knows of the mechanism of action of MFQ. Namely that MFQ does not directly inhibit the virus/cell membrane attachment process, rather MFQ somehow inhibits viral entry after attachment. We speculate a few mechanisms by which this may occur, which include: (1) inhibiting viral membrane fusion with the cell membrane or endolysosomal membrane, (2) inhibiting proteases responsible for processing SARS-CoV-2 S protein and exposing the fusion peptide, (3) modulating expression levels of ACE2, TMPRSS2, and/or cathepsin, or (4) promoting exocytosis of SARS-CoV-2 particles after uptake.
While it is not the primary goal of the manuscript to determine this mechanism of action, we have evidence currently that MFQ inhibits endolysosomal proteolysis (e.g., mechanism 2 above). Through viral attachment assays and evaluation of receptor expression levels we will also probe mechanism 3 in the revision process.
We have updated the figure legends and methods section to include further details concerning replicates and statistical comparisons. For viral infections we had previously listed the MOIs used in the Materials & Methods section but have also included them in the figure legends.
In reference to comments from Reviewer #2:
“On the one hand, both the introduction and the abstract contain too many elements that give a biased view of the extremely controversial literature. For example, the activity of Hydroxychloroquine and its toxicity have been explored most extensively on the "C19Early" website, which reports on trials carried out in a multitude of countries, including more than 300 trials with Hydroxychloroquine, and it is unreasonable in a scientific paper, designed to last, to report on the major beliefs at a given moment in order to develop a work that has nothing to do with this debate. The same applies to the efficacy of the vaccine. It is difficult to say that the vaccine was poorly distributed, with 20 billion doses, making it the most widely distributed vaccine in the history of mankind in such a short space of time, with results that were not as spectacular as the studies predicted, since the epidemic continued at a comparable level. I suggest that the authors concentrate on their work rather than getting involved in the controversies that are developing around treatments and vaccination.”
Response:
When possible, we have tried to highlight the mixed/controversial results, both positive and negative, of pre-existing therapeutics targeting SARS-CoV-2 and COVID-19 and cited them accordingly. Conjectural phrases such as “selective pressure may lead to 3CLpro mutations conferring nirmatrelvir resistance to new viral mutants” or “there is a growing concern that Molnupiravir, especially when administered at sub therapeutic doses, may result in the creation of more virulent SARS-CoV-2 mutants” are supported by observations in the literature and have been proposed by other experts in the field. Otherwise, we have revised the text to remove any unsupported speculative phrases.
We believe it is worth noting the existing therapeutic strategies in the field to provide context and rationale for our differentiated approach. We have extensively explored the C19Early site as well, and although it does a fantastic job of compiling relevant literature, this site also appears to have a biased view. Throughout preparation of this manuscript, we have considered FDA recommendations and clinical practice in prophylactic protection/treatment of COVID-19 paramount in guiding our introduction and discussion.
We have removed phrasing regarding the limited distribution of vaccines.
In reference to comments from Reviewer #1:
_“The authors claim that MFQ loaded nanoparticles have reduced cytotoxicity compared to 'free MFQ' dissolved in DMSO, however with the NP data and free MFQ data not plotted with the same units this conclusion is hard to reach (Fig.2). While I appreciate the molar units are presented in the text - the reduction in cytotoxicity with MFQ-NP appears to be relatively minor in the both the Calu3 and Vero cell models (doubling in IC50 in both instances). This may indicate quite a narrow therapeutic window for antiviral efficacy without unwanted cytotoxicity. Can the authors replot the data on scales using either molar or ug/ml and use the same dose range for all treatments to enable statistical determination as to whether MFQ-NP significantly reduce cytotoxicity.
To test the antiviral efficacy of the MFQ-NP the authors adopt 2 infection systems, either treating cells pre or post infection. The authors either use fluorescently tagged reporter viruses (OC-43/MHV) or immunofluorescence to visualise and quantify viral infection in cell-line models. Given a key aim of this paper is to determine whether MFQ-NP rather than free MFQ is a superior treatment option, it is challenging to assess this with the data presented in figures 5-6. The units of treatment between NP, MFQ-NP and free MFQ again differ, and the molar dose range of MFQ-NP and free NP is not the same. This makes it very hard to conclude whether MFQ-NPs are more effective then free MFQ. Formal dose response curves with the same dose of empty-NPs, MFQ-NPs and free MFQ are needed here, preferably with match cell viability data using the same assay as figure 2.”_
Response:
We will include additional plots with axis scaling for MFQ-NPs as concentration of MFQ in µM rather than concentration of NPs in µg/mL for further comparison to the free MFQ group. We chose concentration of NPs to match with the equivalent unloaded NP controls. Unfortunately, it would not be possible to create a formal dose response curve with the same dose of empty-NPs and free MFQ as those entities only co-exist in the MFQ-NPs treatment group.
We agree with the observation that the therapeutic window is likely narrow in vitro. This is observed in the viral inhibition and cytotoxicity experiments, where the most efficacious dosing of NPs (e.g., 50 – 100 µg/mL) in inhibiting viral infection is similar to the IC50 value (e.g., 54 µg/mL in Calu-3) at 24 h. Similarly, we see a biphasic dose response in our protease activity assay, suggesting that low doses actually increase endolysosomal protease activity which may promote viral infection.
We speculate this dose limiting toxicity and narrow therapeutic window will likely be improved more drastically in vivo (ongoing continuation of this study), however in vitro we still see at least a minor improvement in MFQ tolerability.
In reference to comments from Reviewer #1:
“Finally while animal experiments are likely beyond the scope of this study, use of air-liquid interface cultures of lung epithelial cells would be a significant improvement to the work and provide further support to their conclusions in a physiologically relevant system.”
Response:
While we appreciate the suggestion of ALI models and animal models to further assess MFQ-NPs in a more physiologically relevant system, we agree that these studies would be beyond the scope of this study. This investigation is planned as a continuation of the current study.
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Referee #2
Evidence, reproducibility and clarity
This work is interesting but poses two problems.
On the one hand, both the introduction and the abstract contain too many elements that give a biased view of the extremely controversial literature. For example, the activity of Hydroxychloroquine and its toxicity have been explored most extensively on the "C19Early" website, which reports on trials carried out in a multitude of countries, including more than 300 trials with Hydroxychloroquine, and it is unreasonable in a scientific paper, designed to last, to report on the major beliefs at a given moment in order to develop a work that has nothing to do with this debate. The same applies to the efficacy of the vaccine. It is difficult to say that the vaccine was poorly distributed, with 20 billion doses, making it the most widely distributed vaccine in the history of mankind in such a short space of time, with results that were not as spectacular as the studies predicted, since the epidemic continued at a comparable level. I suggest that the authors concentrate on their work rather than getting involved in the controversies that are developing around treatments and vaccination.<br /> The substance of the work is very interesting, but experience shows that the idea that by testing one isolate you can generalize about all other viruses is not accurate. Viruses mutate. SARS-CoV-2 has shown an exceptional capacity for mutation, and the mechanism by which viruses enter cells by endocytosis or membrane fusion plays a role in their exposure to products concentrated in the lysosome. Viruses that enter by membrane fusion (including certain isolates of SARS-CoV-2) should not a priori be as sensitive because they are not subject to phagolysosome fusion.
In this sense, it is important to evaluate efficacy on several viral strains and not on a single strain, as has long been the case for acute viral infections. As with HIV, viruses can present natural or acquired resistances linked to evolution under selection pressure or not.<br /> In practice, we cannot rely on testing two viruses, one that has disappeared (Wuhan) and the first virus of the Omicron generation (B1), to assess the therapeutic capacity of a new strategy.
Significance
This work is interesting but poses two problems.
On the one hand, both the introduction and the abstract contain too many elements that give a biased view of the extremely controversial literature. For example, the activity of Hydroxychloroquine and its toxicity have been explored most extensively on the "C19Early" website, which reports on trials carried out in a multitude of countries, including more than 300 trials with Hydroxychloroquine, and it is unreasonable in a scientific paper, designed to last, to report on the major beliefs at a given moment in order to develop a work that has nothing to do with this debate. The same applies to the efficacy of the vaccine. It is difficult to say that the vaccine was poorly distributed, with 20 billion doses, making it the most widely distributed vaccine in the history of mankind in such a short space of time, with results that were not as spectacular as the studies predicted, since the epidemic continued at a comparable level. I suggest that the authors concentrate on their work rather than getting involved in the controversies that are developing around treatments and vaccination.<br /> The substance of the work is very interesting, but experience shows that the idea that by testing one isolate you can generalize about all other viruses is not accurate. Viruses mutate. SARS-CoV-2 has shown an exceptional capacity for mutation, and the mechanism by which viruses enter cells by endocytosis or membrane fusion plays a role in their exposure to products concentrated in the lysosome. Viruses that enter by membrane fusion (including certain isolates of SARS-CoV-2) should not a priori be as sensitive because they are not subject to phagolysosome fusion.
In this sense, it is important to evaluate efficacy on several viral strains and not on a single strain, as has long been the case for acute viral infections. As with HIV, viruses can present natural or acquired resistances linked to evolution under selection pressure or not.<br /> In practice, we cannot rely on testing two viruses, one that has disappeared (Wuhan) and the first virus of the Omicron generation (B1), to assess the therapeutic capacity of a new strategy.
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Referee #1
Evidence, reproducibility and clarity
Summary
Petcherski et al describe a nanoparticle delivery system to deliver mefloquine, a inhibitor of viral endocytosis, into cell line models of coronavirus replication. They describe the generation of these nanoparticles and test their antiviral efficacy against 3 different beta coronaviruses OC43, MHV and SARS-CoV-2, including the omicron variant.
Major Comments
The authors claim that MFQ loaded nanoparticles have reduced cytotoxicity compared to 'free MFQ' dissolved in DMSO, however with the NP data and free MFQ data not plotted with the same units this conclusion is hard to reach (Fig.2). While I appreciate the molar units are presented in the text - the reduction in cytotoxicity with MFQ-NP appears to be relatively minor in the both the Calu3 and Vero cell models (doubling in IC50 in both instances). This may indicate quite a narrow therapeutic window for antiviral efficacy without unwanted cytotoxicity. Can the authors replot the data on scales using either molar or ug/ml and use the same dose range for all treatments to enable statistical determination as to whether MFQ-NP significantly reduce cytotoxicity.
To test the antiviral efficacy of the MFQ-NP the authors adopt 2 infection systems, either treating cells pre or post infection. The authors either use fluorescently tagged reporter viruses (OC-43/MHV) or immunofluorescence to visualise and quantify viral infection in cell-line models. Given a key aim of this paper is to determine whether MFQ-NP rather than free MFQ is a superior treatment option, it is challenging to assess this with the data presented in figures 5-6. The units of treatment between NP, MFQ-NP and free MFQ again differ, and the molar dose range of MFQ-NP and free NP is not the same. This makes it very hard to conclude whether MFQ-NPs are more effective then free MFQ. Formal dose response curves with the same dose of empty-NPs, MFQ-NPs and free MFQ are needed here, preferably with match cell viability data using the same assay as figure 2.
The claim that MFQ may impact cell entry is not supported by the data in the paper. At minimum, the impact of treatment on expression of viral entry receptors for all 3 viruses should be performed, viral attachment assays (see PMID 35176124) and viral pseudoparticle assays. Further the conclusion that MFQ inhibits replication as well as entry is not fully supported by the data presented. This could be improved using a single cycle infection experiment using a synchronised infection protocol. The gold standard to determine impacts on replication would be the use of a viral replicon however I appreciate the technical difficulties in performing these experiments.
Finally while animal experiments are likely beyond the scope of this study, use of air-liquid interface cultures of lung epithelial cells would be a significant improvement to the work and provide further support to their conclusions in a physiologically relevant system.
Minor Comments
It is not clear why the authors used cell number as a measure of viability compared to the MTS assay used in figure 2.
Why did the authors adopt a different pre-treatment infection protocol for SARS-CoV-2 compared to MHV and OC43?
Further discussion in the introduction as to the potential mechanism of action of MFQ should be included. I would also suggest the authors read the work by Elizabeth Campbell an Bruno Canard concerning the potential difficulties in designing direct acting antivirals for coronaviruses.
The figure legends lack detail concerning the number of replicates and statistical comparisons. For viral infections MOIs used are also absent.
Significance
This work has good potential but is let down by the execution of the experimental design. The use of NP for antiviral drugs is a very interesting area and could greatly contribute to drug design for this viral family.
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity):
In this study, the authors made a two-component homing modification gene drive in Anopheles coluzii with a different strategy than usual. The final drive itself targets and disrupts the saglin gene that is nonessential for mosquitoes, but important for the malaria parasite. The drive uses several gRNAs, and some of these target the Lp gene where an anti-malaria antibody is added, fused to the native gene (this native gene is also essential, removing nonfunctional resistance alleles at this locus). In general, the system is promising, though imperfect. Some of the gRNAs self-eliminate due to recombination of repetitive elements, and the fusion of the antimalaria gene had a modest fitness cost. Additionally, the zpg promoter was unable to operate at high efficiency, requiring use of the vasa promoter, which suffers from maternal deposition and somatic expression (the latter of which increased fitness costs at the Lp target). The manuscript has already undergone some useful revisions since its earliest iteration, so additional recommended revisions are fairly modest.
Line 43-45: The target doesn't need to be female sterility. It can be almost any haplosufficient but essential target (female sterility works best, so it has gotten the most study, but others have been studied too).
--- We agree. However, this paragraph focused on previous achievements in malaria mosquitoes, for which suppression gene drives spreading lethality rather than female sterility have not been reported to our knowledge. Even the targeting of doublesex, which is a sex determination rather than female fertility gene, results in female sterility (Kyrou et al. 2018). However, we inserted the possibility of female killing by X-shredder GD (Simoni et al., 2020).
Line 69: A quick motivation for studying Anopheles coluzii should be added here (since gambiae is discussed immediately before this).
---Thank you for drawing our attention to this point. We modified the sentence to:
_Here, we present the engineering of the Lipophorin (Lp) essential gene in Anopheles coluzzii, a prominent member of the A. gambiae species complex and a major malaria vector in sub-Saharan Africa.
_
Introduction section: It might be helpful to break up the introduction into additional paragraphs, rather than just two.
--- We followed this suggestion and broke up the introduction into 5 paragraphs to make it more breathable.
Introduction last part: The last part of the introduction reads more like an abstract or conclusions section. Perhaps a little less detail would fit better here, so the focus can be on introducing the new drive components and targets
--- We have followed this suggestion and substantially shortened this last part of the introduction.
Line 207-213: This material could go in the methods section. There are some other examples in the results that could be similarly shortened and rearranged to give a more concise section.
--- We moved the long description from lines 207-213 to the Methods as suggested, and summarized it simply as:
Only mosquitoes displaying GFP parasites visible through the cuticle were used to infect mice.
We emphasize this point because in subsequent experiments using Saglin knockout mosquitoes, this enrichment for infected mosquitoes will probably attenuate the Plasmodium-blocking phenotype caused by Saglin KO, since mosquitoes lacking Saglin tend to be less infected (Klug et al., 2023). Elsewhere in the Results, we still provide detailed descriptions of procedures because we believe they aid understanding and assessing the quality of the experiments.
Line 283-287: I couldn't find the data for this.
--- Indeed we only summarized the data about the progeny of the [zpg-Cas9; GFP-RFP] line crossed to WT, as we didn’t judge these results worth detailing. Here is our record from one such cross:
GFP-RFP females x WT males 486 (50.7%) GFP+ and 472 (49.3%) GFP- larvae
GFP-RFP males x WT females 1836 (48.9%) GFP+ and 1925 (51.1%) GFP- larvae
This shows no significant gene drive. However in these progenies, a few GFP+ and non-RFP larvae, and a few RFP+ non-GFP larvae were noted by visual examination under the fluorescence microscope, without counting them precisely. Their existence testified to some weak homing activity mediated by zpg-Cas9 in the Lp locus.
We modified the sentence as follows to support our conclusion, and we propose to leave these detailed numbers here in our response, which will be published along with the paper.
In spite of the presence of the zpg-Cas9 and gRNA-encoding cassettes in the GFP-RFP allele, it was inherited in about 50% of male or female progenies, demonstrating little homing activity of the GFP-RFP locus after crosses to WT, except for the appearance of rare GFP-only or RFP-only progeny larvae, …
Line 291: Replace "lied" with "was".
----done.
Line 356: Homing in the zygote would be considered very unusual and is thus worthy of more attention. While possible (HDR has been shown for resistance alleles in the zygote/early embryo), this would be quite distinct from the mechanism of every other reliable gene drive that has been reported. Is the flow cytometry result definitely accurate? By this, I mean: could the result be explained by just outliers in the group heterozygous for EGFP, or perhaps some larvae that hatched a little earlier and grew faster? Perhaps larvae get stuck together here on occasion or some other artifact? Was this result confirmed by sequencing individual larvae?
---- We agree with your skepticism, especially given that the same is not seen in Suppl Fig 2A with a similar genotype setup, i.e., the vasa gene drive at the Lp locus, or in the G1 of populations 6 or 8 at the Saglin locus (Suppl. File 2). Unfortunately, it would take too much time at this point to re-create this line (which has been discarded) to re-examine this issue. Therefore, we acknowledge that another explanation than homing in the zygote may account for this result. Based on our empirical experience COPAS outputs are reliable: such outliers from the heterozygous population are usually not seen, and we always sort neonate larvae a few hours from hatching. Those 6% homozygous-looking larvae may come from a contamination with male pupae when female pupae were manually sorted for the cross to WT males, a human error that we cannot exclude. In this case, the true GFP inheritance would be closer to 79% than to 85%. For these reasons, we must back up from our initial statement as follows:
The progeny of these triple-transgenic females crossed to WT males showed markedly better homing rates (>79% GFP inheritance)
And edit the figure legend of Figure 4B to account for the alternative possibility of a contamination with males:
6% of individuals appeared to be homozygous, revealing either unexpected homing in early embryos due to maternal Cas9 deposition, or accidental contamination of the cross with a few transgenic males.
Results in general: Why is there no data for crosses with male drive heterozygotes? Even if some targets are X-linked, performance at others is important (or did I miss something and they are all X-linked). I see some description near line 400, but this sort of data is figure-worthy (or at least a table).
--- For the only example of functioning split gene drive at the Lipophorin locus on chromosome III, we do show homing results from heterozygous GD males in Suppl. Fig. 2A (91.2% homing in males inferred from ((40.7+53.1+1.8)-50)x2). We added this calculation of the homing rates in the figure legend. For full drive constructs in the Saglin locus on chromosome X (our final functional design), in addition to the data described in the text near line 400, male data showing “teleguided” homing at the Lipophorin locus on chromosome II is shown in Suppl. File 2 (see G2 of population 7, showing close to 100% homing at the GFP locus); the same data (less easy to assess) being converted into the G2 point of the graphs in Figure5.
Lines 362-367: What data (figure/table) does this paragraph refer to?
--- We apologize for the fact that this sentence was misleading. In this population, the genotype frequencies were not tracked at each generation but measured once after 7 generations. We rephrased (now lines 401-403) and now provide the measured values directly in the text:
We maintained one mosquito population of Lp::Sc2A10 combined with SagGDzpg (initial allele frequencies: 25% and 33%, respectively) and measured genotype frequencies after 7 generations. This showed an increase in the frequency of both alleles (G7: GFP allelic frequency = 59.2%, phenotypic expression of DsRed in >90% of larvae, n=4282 larvae),
Lines 405-406: There may be a typo or miscalculation for the DsRed inheritance and homing rate here. Should DsRed inheritance be 90.7%?
--- Thank you for spotting this. You are right, DsRed inheritance would be 90.7% if the homing rate were 81.4% as we mistakenly wrote. Actually DsRed inheritance was really 80.7% so our mistake was in calculating the homing rate: 61.4% is the correct value ((80.7-50)x2), now corrected in the manuscript.
Figure 5: The horizontal axis font size for population 8 is a little smaller than the others.
--- True. Corrected.
Line 454: In addition to drive conversion only occurring in females and the somatic fitness costs, embryo resistance from the vasa promoter would prevent the daughters of drive females from doing drive conversion. This means that drive conversion would mostly just happen with alleles that alternate between males and females.
--- We agree with this idea, although the impact of this phenomenon will depend on the extent of resistance allele formation in early embryos. We observed (Fig. 6) that failed homing mutagenesis in Saglin is not that intense, the sequenced non-drive alleles that were exposed 1-4 times to mutagenic activity in females either being mostly wild-type, or carrying mutations that often still left one or two gRNA target sites intact and vulnerable to another round of Cas9 activity. Therefore, alleles passed on from female to female may still undergo drive conversion to a large extent, that future experiments may be able to quantify.
Line 481: Deletions between gRNAs certainly happen, but I wouldn't necessarily expect this to be the "expectation". In our 2018 PNAS paper, it happened in 1/3 of cases. There were less I think in our Sciences Advances 2020 and G3 2022 paper. All of these were from embryo resistance from maternal Cas9 (likely also the case with your drive due to the vasa promoter). When looking at "germline" resistance alleles, we have recently noticed more large deletions.
--- We agree that the early embryo with maternally deposited Cas9 is probably the most prominent source of mutations at gRNA target sites. Perhaps naïvely we imagined that it would be easier for cells to repair two closely spaced DNA breaks by eliminating the intervening sequence, rather than stitching each break individually. Given that we sequenced many alleles carrying a single mutation, the lack of larger deletions may be explained by lower rates of Cas9 activity in Saglin, with mostly a single break at a time, due to limiting Cas9 amounts and their partial saturation with Lp gRNAs, and/or lesser accessibility of the Saglin locus compared to Lipophorin… We deleted the phrase “Contrarily to our expectation”.
Figure 6C: It may be nice to show the wild-type and functional resistance sequence side-by-side.
--- done
Lines 642-644: This isn't necessarily the case. At saglin, the nonfunctional resistance alleles may still be able to outcompete the drive allele in the long run. This wasn't tested, but it's likely that the drive allele has at least some small fitness costs.
--- We agree. We inserted this comment in a parenthesis in the text (now lines 644-645):
Unlike the first approach, this design may allow Cas9 and gRNA-coding genes to persist indefinitely within the invaded mosquito population (unless nonfunctional resistance alleles outcompete the drive allele in the long run).
A few comments on references to some of my studies:
Champer, Liu, et al. 2018a and 2018b citations are the same paper.
--- Duplicate in our reference library. Corrected.
For Champer, Kim, et al. 2021 in Molecular Ecology, there was a recent follow-up study in eLife that shows the problem is even worse in a mosquito-specific model (possibly of interest as an alternate or supporting citation): https://elifesciences.org/articles/79121
--- Citation added (line 68).
One of my other previous studies was not cited, but is quite relevant to the manuscript: https://www.science.org/doi/10.1126/sciadv.aaz0525<br /> This paper demonstrates multiplexed gRNAs and also models them, showing their advantages and disadvantages in terms of drive performance. Additionally, it models and discusses the strategy of targeting vector genes that are essential for disease spread but not the vectors themselves (the "gene disruption drive"), showing that this can be a favorable strategy if gene knockout has the desired effect (nonfunctional resistance alleles contribute to drive success).
--- your 2020 study will indeed now be useful to inform the design of multiplex gRNAs for various gene drives designs, in terms of number of gRNAs, distribution of their target sites, necessity to generate loss-of-function rather than functional resistance allele in the target gene (such as our Lp and Saglin pro-parasitic genes)… The notion of Cas9 saturation with increasing gRNA numbers is also important. When we initiated this project in 2018, we only had intuitive notions that multiplex gRNAs could improve the durability of GD and increase the chances of resistance alleles to be loss-of-function. We thus arbitrarily maximized the number of gRNAs for each of the two targets: 3 for each target in one design, 3 and 4 in another, which, according to your modelling, is luckily close to the optimal numbers for each locus. We now cite your paper as a GD design tool in the discussion about pathways to optimizing our system:
To further optimize GD design, modeling studies can now aid in determining the optimal number of gRNAs in a multiplex, depending on the specific GD design and purpose (Champer et al., 2020)__.
In addition to this and to the stabilization of multiplex gRNA arrays, other paths to improvement (…)
This one is less relevant, but is still a "standard" homing modification rescue type drive that could be mentioned (and owes its success to multiplexing): https://www.pnas.org/doi/abs/10.1073/pnas.2004373117<br /> The recoded rescue method was also used in mosquitoes (albeit without gRNA multiplexing) by others, so this may be a better one to mention: https://www.nature.com/articles/s41467-020-19426-0
--- We added the two references on what is now Line 663:
Lp::Sc2A10 depends on SagGD for its long-term persistence and spread in a population, and SagGD depends on Lp::Sc2A10 as a rescue allele of the essential Lp target for its survival. This design can be seen as a two-locus variation of rescue-type GDs (Adolfi et al., 2020; Champer et al., 2020)
Sincerely,<br /> Jackson Champer
Referees cross-commenting<br /> Other comments look good. One thing that I forgot to mention: for the 7-gRNA construct with tRNAs, the authors mentioned that it was harder to track, but it sounds like they obtained some data for it that showed similar performance. Even if this one is not featured, perhaps they can still report the data in the supplement?
--- This GD required examination of the mosquitoes at late developmental stages, such as the pupa, to score red fluorescence under control of the OpIE2 promoter, that is unfortunately late-active when expressed from the Lp locus. We precisely scored only the first 128 pupae arising from the progeny of the first obtained G1 [SagGD/+ ; Lp-2A10/+] females crossed to WT males. Among these:
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115 were GFP+, DsRed+ (89.8%)
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12 were GFP+, DsRed- (9.3%)
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1 was GFP-, DsRed- (<1%)
This allowed us to roughly estimate the homing rates at 98.2% at the Lipophorin locus and 79.7% at the Saglin locus, which is similar to the other construct without tRNA spacers.
These approximate rates were confirmed by visual examination of progenies in two subsequent generations of [SagGD/+; Lp-2A10/+] males and females backcrossed to WT.
Reviewer #1 (Significance):
Overall, this study represents a useful advance. Aside from being the first report for gene drive in A. coluzii, it also is the first that investigates the gene disruption strategy and is the first report of gRNA multiplexing in Anopheles. The study can thus be considered high impact. There are also other aspects of the study that are of high interest to gene drive researchers in particular (several drives were tested with some variations).
--- We are grateful for your positive, constructive and in-depth analysis of our study!
Reviewer #2 (Evidence, reproducibility and clarity):
The authors initially created a transgenic mosquito colony expressing the Sc2A10 antibody fused to the lipid transporter Lipophorin, and tested the transmission-blocking activity of this transgene. Building off of previous findings that the Sc2A10 antibody inhibits sporozoite infectivity when expressed in mosquito salivary glands, the authors showed that found it was also efficient at inhibiting sporozoite infectivity when secreted into the hemolymph expressed under the lipophorin endogenous promoter in An. coluzzii. They then designed and tested two different gene drives utilizing the Sc2A10-Lipophorin fusion protein. In the first, the authors used a recoded allele of Lp-Sc2A10 while simultaneously utilizing gRNAs that targeted endogenous Lp in an effort to select for mosquitoes that expressed transgenic Lp-Sc2A10 due to the essential nature of Lp. However, this drive was unsuccessful because recoded Lp is necessarily heterozygous while the GD is entering the population, and Lp proved to be largely haploinsufficient. Further, the zpg promoter expressing cas9 was not effective in promoting homing of the gRNAs. In the second gene drive that was tested, authors made use of the endogenous Saglin locus, which expresses a natural agonist for Plasmodium, and is thus desirable to target for disruption in a gene drive that aims to reduce vector competence for Plasmodium. This gene drive also uses recoded Lp-Sc2A10 to replace the wild-type Lp allele, thus selecting for Sc2A10 expression, however this drive is not dependent on fitness of individuals with only one functional copy of Lp.<br /> The authors discovered that the efficacy of the zpg promoter to drive homing of cas9 is locus-dependent, limiting the success of their gene drive designs. They do show, however, that the Saglin gene drive succeeds at reaching high frequencies in mosquito populations using instead the vasa promoter to express cas9, and that these transgenic mosquitoes are able to reduce infectivity of sporozoites in a bite-back mouse model. However, they observe gene drive refractory mutations in the Lp gene, despite its highly conserved nature, showcasing the difficulty of avoiding drive resistance even in small populations of mosquitoes, and also observed deletions of gRNAs targeting both Lp and Saglin, further highlighting possible shortcomings in gene drive approaches. Together, these findings are useful to the field in walking the readers through an interesting and promising approach for a novel gene drive, and illustrating the challenges in engineering an efficacious and long-lasting drive.
Major comments:
As the authors are able to observe Plasmodium within mosquitoes, it would be useful to have these data in the manuscript pertaining to the prevalence and intensity of infection in mosquitoes prior to bite-back assays. If there are data or images that the authors could include, it would be helpful to show if there is a possibility that infection intensity is a variable that contributes to whether or not mice develop an infection. It would also be interesting to note whether there is a different in infection (oocysts or sporozoites) between transgenic mosquitoes and wild type mosquitoes.
--- This is a valuable suggestion. Please note that, in order to evaluate the transmission-blocking properties of the Lp-2A10 allele (acting at the sporozoite level), we discarded non-infected mosquitoes prior to bite-back experiments, so that infection prevalence was 100% in the mosquitoes retained for the bite-back. We have not systematically compared parasite loads between transgenic and control mosquitoes. In some experiments comparing Lp-2A10 mosquitoes and their control, we dissected a subset of the mosquito midguts after bite-back to visually ascertain that they showed roughly equivalent oocyst numbers between transgenic and controls. However, we have not precisely recorded these data. It is possible that slightly decreased lipid availability in Lp::2A10 mosquitoes (their lipophorin allele producing slightly less Lp than the WT) negatively affects the parasite, as suggested by previous studies highlighting the role of host lipophorin-derived lipids for parasite development in the mosquito (Costa et al, Nat Commun 2018; Werling et al. Cell 2019; Kelsey et al. PLoS Path 2023).
In the case of Lp-2A10 mosquitoes additionally containing a GD in Saglin, it is expected that they should carry lower parasite numbers than their controls, an effect of the Saglin knockout mutation alone (Klug et al., PLoS Path 2023). Re-inforcing the transmission blocking effect of the 2A10 antibody by reducing parasite loads via the Saglin KO was indeed our intention. Hence, having selected the most infected mosquitoes for our bite-back experiments likely attenuated this desired effect, but we still observed a 90% transmission decrease when the two modifications were combined, compared to a 70% decrease with Lp-2A10 alone. We do not plan to perform additional infections experiments for the current manuscript on Plasmodium berghei expressing Pf-CSP, but we do intend to record parasite counts in a follow-up study with an optimized SagGD transgene and Plasmodium falciparum infections. This will be of high relevance for potential future applications in malaria control.
The authors also go into significant detail in the discussion exploring ideas of how to optimize or improve this specific gene drive design. The authors should also stress further the applicability of their discoveries in other gene drive designs, and emphasize the lessons they learned in the difficulties encountered in this study and how these findings could guide others in their decision making process when choosing targets or elements to include in a potential gene drive approach.
--- We feel that we already emphasized these lessons in the manuscript, in the discussion and when justifying the chosen strategies in the Results section. Lessons for future designs include:
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inserting an antimalarial factor into an essential endogenous gene, preserving its function, can provide many benefits (high expression level, secretion signal that can be hijacked, endogenous introns can be hijacked to host a marker, inactivation by mutagenesis or epigenetic silencing being more difficult…);
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a distant-locus gene drive (as here in Saglin) could potentially drive several antimalarial cargoes at the same time, inserted in different loci;
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non-essential mosquito genes agonistic to Plasmodium are attractive host loci for a GD, an already old idea illustrated here by the case of Saglin;
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multiplex gRNAs are a viable approach to reduce the formation of GD-resistant alleles in essential genes and/or to increase the frequency of loss-of-function alleles, which will either disappear if the gene is essential or decrease vector competence if the gene is pro-parasitic. Hence gRNAs targeting intron sequences should be avoided in order to preserve this benefit, as illustrated by one of our Lp gRNAs targeting the first intron and that contributed to generate the only Lp viable resistance allele identified in this study;
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To increase long-term stability of the GD construct, repeats should be minimized in gRNA multiplexes through the use of a single promoter and various spacers (tRNAs, ribozymes?) – it remains to be seen if the 76-nucleotide gRNA constant sequence itself, necessarily repeated, will stimulate unit losses in a gRNA multiplex;
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The best promoter to restrict Cas9 expression to the germ line may be zpg in some but not all loci; the vasa promoter causing maternal Cas9 deposition may still be envisaged if resistance allele formation can be prevented by other means (targeting hyper-conserved essential sequence, multiplexing the gRNAs against an essential gene…).
Minor comments:
Line 44 - female sterility but also female killing approaches to crash pop. like X shredder, if authors would like to expand
--- Female killing citation of Simoni et al, 2020 added (line 45).
Lines 48-60 - Authors should add some references from the literature surrounding ethics and ecology studies related to gene drive release
--- we added: (e.g., National Academies of Science, Engineering, and Medicine, 2016; Courtier-Orgogozo et al., 2017; de Graeff et al., 2021) on lines 49-51.
Line 114 - Given the only moderate impacts of Saglin's role in Plasmodium invasion, I am not sure this saglin deletion is a convincing benefit for GD as it is probably not impactful enough alone - can the authors soften this statement?
--- while it’s correct that Saglin KO mosquitoes show a significant decrease only in P. berghei oocyst counts and not in prevalence when mosquitoes are heavily infected, they do show a significant decrease in both counts and prevalence upon infection with P. berghei and, most importantly_, P. falciparum_ when parasite loads are lower —a situation that is more physiological (e.g. prevalence of 65% and 13% in WT and Sag(-)KI mosquitoes, respectively, upon infection with P. falciparum - Klug et al., PLoS Path 2023). Therefore, for human-relevant P. falciparum infections, an impactful decrease in vector competence can be legitimately expected.
Line 126 -Can the authors provide rationale for expressing Sc2A10 with Lp instead of expressing it from salivary glands?
--- There are three reasons for this. First, we knew from the cited Isaacs et al. papers that the 2A10 antibody was efficient against transmission when expressed in the fat body, and from unpublished work (Maria Pissarev, Elena Levashina and Eric Marois) that anti-CSP ScFvs expressed in the fat body of transgenic mosquitoes blocked sporozoite transmission as efficiently as when expressed from salivary glands. This is certainly favored by the easy sporozoite accessibility to the antibody when both are in mosquito hemolymph. Of note, the transmission blocking results suggest that the binding of ScFv to CSP withstands the crossing of the salivary gland epithelium by sporozoites. Second, we were looking for a host gene expressed as high as possible to produce high levels of Sc2A10 antibody. Third, the host gene must be essential so that resistance alleles would not be viable.
We agree that it would also be possible to use a salivary gene instead of Lp as a host for this antimalarial factor. In this case, a same-locus gene drive may have functioned, but the advantages of the host locus being an essential gene would be lost, at least partially, as genetic ablation of the salivary gland, albeit slowing blood uptake, does not prevent mosquito viability and reproduction (Yamamoto et al., PLoS Path 2016).
Line 140 - Can authors give any comment on why these regions of Lp were chosen to be recoded / targeted with gRNAs?
--- inserting Sc2A10 just after the cleaved Lp secretion signal, and N-terminally to the rest of the Lp protein, was the goal, so that 2A10 would be secreted together with Lp and separated from both signal peptide and Lp by naturally occurring proteolysis. This constrained the choice of the target site to be at the junction between signal peptide and the remainder of Lp protein. An alternative design could have been to insert it between the two subunits ApoLpI and ApoLpII, with duplication of the protease cleavage site, or on the C-terminal extremity of the protein, but there would have been no intron in the immediate vicinity to knock-in a selection marker at the same time.
Line 171 - "stoichiometric"
--- Corrected.
Line 186 - Can the authors comment or speculate on why the expression levels of the fusion protein are expected to be lower than endogenous Lp?
--- We did not expect this. It is hard to predict whether and explain how insertion of exogenous sequences in a gene can alter its expression. Possible explanations include: the existence of harder-to-translate mRNA sequences in the Sc2A10 moiety; the addition of seven exogenous amino acids on the N-terminal side of ApoLpII (mentioned in M&M) possibly modifying the stability of the Lp protein; the modification of the intron sequence perturbing efficient intron excision and/or pre-mRNA expression due to the disruption of regulatory elements or to the new presence of the GFP gene in the antisense orientation (albeit expressed in the nervous system and not in the fat body); the presence of the exogenous Tub56D transcription terminator used to arrest GFP transcription possibly possessing bidirectional termination activity and lowering the mRNA level of the Lp allele…
Line 211 - Why were 6 mosquitoes used for these assays, and 10 mosquitoes used in later assays (Line 223)?
--- Mice were always exposed to groups of 10 mosquitoes, but not all 10 mosquitoes were necessarily biting the mice. We retained mice bitten by at least 6 mosquitoes for further analysis (M&M, lines 871-873 of the revised file).
Line 212 - I would also suggest using letters (Suppl. Table 2A,B,C etc) to refer the specific experiments and sections in the Table.
--- Implemented.
Line 225- 228 - The authors should mention in the text that homozygotes and heterozygotes do not differ in infection assays.
--- Added: Therefore, heterozygous mosquitoes showed a transmission blocking activity comparable to that seen in homozygotes.
Line 249 - Can the author comment on the impacts of population influx / exchange on the idea that the GD cassette need only be transiently in the population?
--- If Lp::Sc2A10 is fixed in the population and the GD gone, indeed an influx of WT alleles through mosquito immigration will begin to replace the antimalarial factor and drive it to extinction due to its fitness cost. As mentioned in the final paragraph of the discussion, this could be seen as an advantage to restore the original natural state—hopefully after malaria eradication! However, we regard a situation where Lp::2A10 never reaches fixation as more likely, with its spread being re-ignitable by updated GDs (line 741 of the revised file).
Line 273 - Can the authors comment on why this may have occurred more frequently than the expected integration of the GD cassette?
--- When a chromosome break is repaired, each side of the cut must recombine with the repair template. A possible explanation for our observation is that one side of the break recombined with the injected repair plasmid, while the other recombined with the intact sister chromosome (physiologically probably the preferred option). Since this situation still leaves truncated chromosomes, another repair event can join the plasmid-bearing chromosome end to the sister chromosome. The observation that complex rearrangement occurred frequently suggests that such events can be very common, but will usually go undetected due to the absence of genetic markers. Here, GFP on the intact sister chromosome served as a genetic marker to betray its unexpected involvement in the repair process.
Line 314 - Not all fitness costs are apparent through standard laboratory rearing as was performed in Klug et al. Authors could consider "no known fitness cost" instead.
--- We agree. This is what we meant by “no fitness cost in laboratory mosquitoes”. We changed this to “no fitness cost at least in laboratory conditions (Klug et al., 2023)” to make clear that this was tested.
Line 407 - don't start new paragraph (same with 409)
--- we removed these two lines, as we realized they contained an error, and made a correction on line 420 of the revised manuscript.
Line 408 - I'm not sure it's clear why all these populations were kept for a different number of generations - can the authors clarify?
--- Populations 1 and 2 were the oldest founder populations, therefore maintained for the longest time. As described in the text, all other populations were derived from populations 1 and 2 later in time by outcrossing a subset of individuals to WT mosquitoes. For these derived populations, we reset the clock of generation counting to 0 as we monitored the homing phenomenon “from scratch” in transgenic males crossed to WT, and in transgenic females crossed to WT. Resetting the clock resulted in an apparent lower number of generations for these derived populations. In addition, some of them were discarded early, usually after reaching a stable state, as it was difficult to maintain so many populations in parallel over a long period of time.
Line 558 - "10/12 mice" not immediately clear - the authors could be more specific about how data was combined here
--- Thank you for pointing out this ambiguity. We replaced by: the absence of infection in a total of 10 out of 12 mice showed… (line 561)
Line 586 - Since there do appear to be some fitness costs associated with the Sc2A10 version of Lp, might it be expected that fitness costs imposed by the transgene itself could lead to selection pressures leading to its loss? Or do the authors think that these fitness costs are prevented from causing selection against Sc2A10 due to the design of the transgene such that its translation is a prerequisite for Lp's translation? Is the fact that its removal occurs more rapidly than Lp's any indication that selection against the persistence of Sc2A10 may occur?
--- Yes, we believe that Lp::Sc2A10 will progressively disappear, replaced by the WT allele, as shown in Figure 1C, in the absence of a GD stimulating its maintenance and spread. In the Lp::Sc2A10 transgene, translation of Sc2A10 is indeed a prerequisite for Lp translation, imposing a degree of genetic stability of this transgene in terms of sequence integrity, but this does not mean that the locus cannot be outcompeted by the WT under natural selection, so that long-term persistence of Lp::Sc2A10 depends on the presence of the GD, as outlined in lines 669-672. As the GD itself can disappear due to the accumulation of resistance alleles, we expect a progressive lift of its pressure to maintain Lp::Sc2A10 and both loci to be progressively lost, a form of reversibility that may be regarded as desirable (lines 773-776 in v2, 741-743 in v3). Alternatively, both transmission blocking alleles could be maintained by releasing an updated version of the dual GD.
Line 659 - add some further detail to this - how do you envision this to occur?
--- We have deleted this paragraph, as it hypothesized that SagGD could frequently be transmitted to the next generation in the absence of Lp::2A10, which is not the case (it would be lethal, and Lp::2A10 homing is anyway extremely efficient). After a putative field release of [SagGD / Y; Lp::2A10/ Lp::2A10] males, both transgenes should rapidly be introgressed in the field’s genetic background.
Line 635 - Long paragraph, should be broken up or removal of text. Some of these ideas could possibly be made more concise to improve readability. There are many different hypotheticals that are expanded upon in the discussion.
--- We admit that this paragraph in the discussion was long and dense. We have split it into 4 smaller paragraphs to better separate the concepts that we want to discuss, and have deleted the part mentioned in the above point.
Line 677 - This scenario seems potentially unrealistic considering the only subtle impacts of Saglin deletion on vector competence, and the potential for population exchange in mosquito populations to dilute out these alleles if the drive begins to fail. Can the author comment or potentially decrease emphasis on such scenarios?
--- while Saglin KO mosquitoes show a moderate decrease of infection prevalence in the context of high infections, the Saglin KO decreases parasite loads in all cases, and most importantly, also prevalence upon physiological infections with P. falciparum (Klug et al., PLoS Path 2023 and see our response to your comment to line 114 above). This yields a higher proportion of non-infected mosquitoes. Therefore, the impact of Saglin mutations should be stronger for the epidemiology of human infections with P. falciparum than in laboratory models of infections where parasite loads are very high.
We agree that mosquito migration in natural populations would progressively dilute out the beneficial alleles once the GD effect ceases. The epidemiological impact is difficult to predict and will strongly depend on the durability of the GD and on the intensity of genetic influx from adjacent mosquito populations.
Line 708 - Can the authors speculate on why zpg is sensitive to local chromatin and elaborate on possible solutions or consequences for other drive ideas? This seems broadly important.
--- We do not precisely know why the zpg promoter is more sensitive to local influences than the vasa promoter, but this phenomenon seems common for other promoters as well (e.g., the sds3 promoter as opposed to the shu promoter in Aedes aegypti (Anderson et al., Nat Comm 2023)). It is possible that the vasa promoter is better insulated from local repressive influences, perhaps by insulating elements akin to gypsy insulators in Drosophila. Knowledge of genetic insulators active for mosquito genes is lacking as far as we know. Characterization of efficient mosquito insulators, for example if one could be identified within vasa, and their combination with zpg or sds3 promoter elements, could potentially improve the locus-independent activity of such promoters. Alternatively, a natural and ideal promoter may still be found showing both an optimal window of expression of Cas9 in the germline, and little susceptibility to local repression.
Line 737 - The suggestion of releasing laboratory-selected resistance alleles in the absence of further context may be provocative and unnecessary here.
--- We didn’t intend to sound provocative, but are interested in the idea of simple resistance alleles with limited sequence alteration that could be selected in the lab, and released to block a gene drive that turned undesirable, so we wanted to share it with the reader. Mutations in the Lp and Saglin loci, preserving their functions, can be limited to one or few nucleotide changes in the gRNA target sites, as illustrated by the mutants we sequenced. Lab population of GD mosquitoes can, therefore, be a source of GD refractory mutants that could be leveraged in recall strategies.
Line 850 - unnecessary comma
--- Corrected.
Line 854 - change to "after infection, moquitoes were "
--- Changed.
Figure 1 - Not clear what is intended to be communicated by shapes portraying proteins / subunits - consider more detailed illustration of mosquito fat body cells synthesizing and secreting proteins rather than words in text box with arrow to clearly demonstrate the point of this figure.
--- We propose a new version of figure 1 to better illustrate the fat body origin of Lp and 2A10. We have also re-worked the graphic design to improve several figures.
Figure 3 - I recommend rearranging this figure so that B comes before C, visually. The proportions for the design of in B should also match those used for A.
--- We have followed these recommendations in the new Figure 3, and also used more logical color codes for the gRNAs and their target genes.
Figure 5 - It is unclear to me why some Populations were maintained for such different lengths of time.
--- Same point as above for line #408: Populations 1 and 2 are the oldest founder populations, therefore maintained for the longest time. As described in the text, all other populations were derived from populations 1 and 2 later in time by outcrossing to WT mosquitoes, resulting in a lower number of generations for these derived populations. In addition, some of them were discarded earlier, usually after reaching a stable state, as it was not possible to maintain so many populations in parallel for a long period of time.
Figure 7 - Ladder should be labeled on the gel. It may also be helpful for the author to indicate clearly exactly which mosquitoes were shown by sequencing to have these different deletions, as it is occasionally unclear based on band sizing.
--- we have added the ladder sizes as well as a numbering of individual mosquitoes on Figure 7. We sequenced 4 gel-purified small -type B- amplicons of Population 1 individually (#1, 2, 4, 6), and a pool of 4 type B amplicons from Population 7 (pooled #2, 4, 5, 6) as well as two samples of several pooled gel-purified large -type A- amplicons from Population 2 (pool of samples #2, 3, 4, 5, 6, 8, 9, 11, 12) and from Population 7 ( pool of #1, 3, 7, 11, 12). This information now also appears in the material and methods section (PCR genotyping of the SagGDvasa gRNA array).
Line 996 - given that there is a size band on the right line of this gel also, can authors crop the gel image to eliminate unnecessary lanes a and b from this figure without losing information needed to interpret this blot?
--- we agree that this would make the message easier to understand, but cropping lanes a and b would place WT control and Lp::Sc2A10 homozygotes on two separate images, even if a size marker is present on each. We prefer keeping the raw image to facilitate direct comparison of the band sizes, making clear that this was a single protein gel.
Line 1070 - 12 out of how many sequenced mosquitoes?
--- 12 mosquitoes from each of these four populations served as PCR templates to generate figure 7. A subset of amplicons were sequenced individually or pooled, as described above and now in Methods. All sequencing reactions of type A and type B amplicons showed consistent results.
Line 1078 - Can remove some detail like % of agarose, and replication of results with different polymerase as these are already in methods.
--- Done.
Line 1098 - "Unbless"
--- Corrected
Reviewer #2 (Significance):
This study illustrates a wide range of issues pertinent for gene drive implementation for malaria control, and as such is of value to the field of entomologists, genetic engineers, parasitologists and public health professionals. The gene drive designs explored for this study are interesting largely from a basic biology perspective pertinent mostly to specialists in the field of genetic engineering and vector biology, but highlight challenges associated with this technology that could also be of interest to a broader audience. A transmission blocking gene drive has not yet been achieved in malaria mosquitoes, and is thus a novel space for exploration. As a medical entomologist that works predominantly outside of the genetic engineering space, I have appreciated the detail the authors have provided with regard to their rationale and findings, even when these findings were inconsistent with the authors' primary objectives or expectations.
--- Thank you for your positive assessment and for this in-depth evaluation of our data.
Reviewer #3 (Evidence, reproducibility and clarity):
The study by Green et al. generated a gene drive targeting both Saglin and Lipophorin in the Anopheles mosquito, with a view to blocking Plasmodium parasite transmission. This is a highly complex but elegant study, which could significantly contribute to the design of novel strategies to spread antimalarial transgenes in mosquitoes.<br /> Overall, this is a complex study which, for a non-specialist reader gets quite technical and heavy in most parts. Despite this, there are key points showing that suppression gene drive may not be the way forward in this instance. However, I would advise explaining certain elements in more detail for the benefit of the general readers. I only have minor points for the authors to address:<br /> 1) Please point out for the general reader that Anopheles coluzzii belongs to the gambiae complex, since you explain that gambiae are the major malaria spreaders in sub-Saharan Africa.
--- done in the introduction (lines 71-73) also in response to Rev. 1
2) The authors pretty much give all results in the last part of the introduction, could the intro be shortened by removing these parts, or just highlighting in a single paragraph the main take home message?
--- We have condensed this part to highlight the take home messages in the last paragraph, also in response to Rev. 1.
3) Why is Vg mentioned? It is only mentioned once and doesn't have any other mention through the manuscript.
--- this introduces the two proteins that are by far the most abundant, and present at similar levels, in the hemolymph of blood-fed females, Vg being also prominent on the Coomassie stained gel of fig.1. We mention Vg also because it represents another excellent candidate locus to host anti-plasmodium factors, as discussed later on lines 600-610 of the Discussion section.
4) Please make it clearer for non-specialists why Cecropin wasn't used.
---On lines 630-636 we explain that we decided to leave out Cecropin to avoid potential additional fitness costs due to expression at all life stages in the fat body, as opposed to solely in the midgut after blood meal (Isaacs et al. PNAS 2012); and to avoid complexifying the anti-Plasmodium Lipophorin locus in a way that could further reduce the functionality of the Lp gene. We also had prior knowledge from unplublished work that Sc2A10 alone was sufficient to block sporozoite infectivity.
5) Why were homozygous and not heterozygous transgenics transfected if there is such as fitness cost to homozygous mosquitoes?
--- the fitness cost of homozygous mosquitoes is actually mild, unnoticeable if homozygotes are bred in the absence of competing heterozygotes and wild-types (lines 151-156). Microinjection experiments to obtain the different versions of SagGD were, therefore, performed on either the heterozygous or homozygous line. As for infection assays, the anticipated effect of gene drive is to promote homozygosity at the Lp::Sc2A10 locus. For this reason, it made sense to test the vector competence of homozygotes, in addition to the fact that the Plasmodium-blocking phenotype was expected to be stronger (and thus, easier to document) with two copies of the transgene. Only after obtaining a large dataset from infection assays with homozygotes did we test heterozygotes and found that they actually had a similar phenotype.
6) Line 211 - what was the average number of infected mosquitoes used per infection for each mosquito strain?
--- As described in the text (lines 204-206 of v2; 208-212 of the revision) and in the Methods (lines 868-873), non-infected mosquitoes were discarded prior to performing the experiment using 10 infected mosquitoes per mouse, and we discarded mice bitten by fewer than 6 mosquitoes. So at least 6 infected mosquitoes bit each mouse (often 8-9).
7) Line 219 - please be clearer regarding this being infection detected in the blood.
--- We replaced « infection » with « detectable parasitemia in the blood »
8) Line 320 - please clarify why the zpg promoter was used.
--- The advantages of zpg are mentioned in lines 257-258 and 320-322 (revised file).
9) Line 375 - what was the rationale for using so many gRNAs?
--- 3 or 4 gRNAs against Lipophorin and 3 gRNAs against Saglin, amounting to a total of 6 or 7 gRNAs against the two loci. The rationale is explained on lines 249-253 : the goal was to maximize the chance of causing loss-of-function mutations in the essential Lp gene and to favor elimination of GD resistant alleles by natural selection, in case of failed homing. For Saglin which is a non-essential gene, we wanted to ensure loss-of-function of failed homing alleles to achieve a reduction in vector competence, even if GD-resistant alleles accumulate. We sought to make this rationale clearer by adding a sentence on lines 328-332:
Multiplexing the gRNAs was intended to promote the formation of loss-of-function alleles in case of failed homing at the Lp and Saglin loci: non-functional alleles of the essential Lp gene would be eliminated by natural selection while non-functional Saglin alleles would reduce vector competence.
Line 555 - please state how long post bite back parasite appears in infected mice.
--- We changed this sentence to : …two of these six mice developed parasitemia six days after infection<br /> (line 556).
Reviewer #3 (Significance):
This is potentially a highly significant study that could provide a vital mechanism for generating efficient gene drives. Although highly technical and complex in most parts, with a little clarification in certain areas this manuscript could be of great value to a general readership.
--- Thank you for your appreciation and thoughtful evaluation of our manuscript.
Reviewer #4 (Evidence, reproducibility and clarity):
The authors hijacked the Anopheles coluzzii Lipophorin gene to express the antibody 2A10, which binds sporozoites of the malaria parasite Plasmodium falciparum. The resulting transgenic mosquitoes showed a reduced ability to transmit Plasmodium.
The authors also designed and tested several CRISPR-based gene drives. One targets Saglin gene and simultaneously cleaves the wild-type Lipophorin gene, aiming to replace the wildtype version with the Sc2A10 alele while bringing together the Saglin gene drive.
Drive-resistant alleles were present in population-caged experiments, the Saglin-based gene drive reached high levels in caged mosquito populations though, and simultaneously promoted the spread of the antimalarial Lp::Sc2A10 allele.
This work contributes to the design of novel strategies to spread antimalarial transgenes in mosquitoes. It also displays issues related to using multiplexing gene-drive designs due to DNA rearrangements that could prevent the efficient spread of the gene drive in the long term.
This is tremendous work considering how many transgenic lines and genetic crosses are performed using mosquitoes. The conclusions are supported by the data presented, and some modifications regarding the experimental design description through text/figure improvements would facilitate the reading and flow of the paper.
Here some questions/comments:
- Line 124-125: Reference?
--- added
- Line 133-134: Reference?
--- added
- Table 1: It seems the authors have some issues recovering a good amount Sc2A10 from hemolymph samples. Is this a problem of the antibody per se? Is it the Lp endogenous promoter weak? Could this be improved by placing the antibody in a different genomic region? Alternatives could be discussed.
--- The 2A10 antibody must be initially produced in the same, very high, amounts as the Lp endogenous protein with which it is co-translated. Therefore, its low relative abundance must result from faster turnover or stickiness to tissue, as hypothesised on lines 176-177. We believe that virtually any other endogenous promoter would be weaker than Lp and produce lower Sc2A10 levels.
- Fig.1B: It would be nice to have a representation of the genome after integration. You could add a B' panel or just another schematic under the current one.
--- In agreement with this suggestion and that of rev. 3, we added a new panel in 1B.
- Supplementary Fig.1b: Could the authors explain the origin of the (first) zpg promoter used? Is it from An. Coluzzii? It seems they use a different one in the gene drive designs later (see comments below too).
--- We initially cloned a PCR-amplified zpg promoter region of the same size as the version published by Kyrou et al., from genomic DNA from our colony of A. coluzzii. The resulting promoter fragment harbored several single nucleotide polymorphisms (SNPs) compared to published sequences, as typically observed when cloning genomic fragments due to high genetic diversity in Anopheles species. Such SNPs are not usually expected to affect promoter activity, but are difficult to distinguish from PCR mutations which, in turn, could decrease or abolish promoter activity if mutating an essential transcription factor binding site. For this reason, our next constructs were based on the validated zpg sequences from Kyrou et al. The first cloning strategy was described in the results section but was missing in the material and method section. This is now corrected (lines 773-779).
- Fig.3: Please, correct to A, B, C order. Current one is A, C, B.
--- Done.
Could the authors include a schematic of the final mosquito genome after integration? I can see they are targeting two different locations (Saglin and Lp). It is unclear though from the figure where the Sc2A10-GFP is coming from. I understand this represents the mosquito genome as you injected heterozygous animals already containing the Sc2A10-GFP. Maybe label the Sc2A10-GFP as mosquito genome or similar? A schematic showing mosquito embryos already carrying this and then the plasmid being injected could help.
--- Figure 3 does not represent the injection of new transgenic constructs. Instead, it shows the conversion process of chromosomes X and II in a germ cell carrying both transgenes in the heterozygous state, to illustrate how the dual gene drive can spread in a population after WT mosquitoes mated with transgenics carrying both the SagGD and Lp-2A10 alleles. We have re-worked the graphic design of this figure and modified its title to make this more clear.
- Line 330-331: Do you know the transgenesis efficiency? Did the authors make single or pools for crossing and posterior screening? It would be interesting to know about transgenesis rates to inform the community.
--- we no longer perform single crosses for transgenesis, as batch crosses ensure higher recovery of transgenics due to the collective reproductive behavior (swarming) in Anopheles. Therefore, we cannot precisely calculate the transgenesis efficiency. However, >60 positive G1s from a pool of 36 G0 males crossed to WT females is indicative of a rather high integration efficiency. We consistently observe high efficiency of transgene integration when using the CRISPR/Cas9 system, that we estimate to be about 5-fold more efficient than docking site transgenesis, and much more efficient than piggyBac mediated transgenesis.
- Line 357/Fig.4B: Could the authors explain in the text GFP+ vs. GFP++?
--- GFP++ was meant to indicate higher intensity of GFP fluorescence than GFP+, due to two copies of the transgene versus one, but see our response to reviewer 1’s comment to line 356 about the questionability of homing in the zygote.
- Line 357: Where is the vasa promoter that made the "rescue" coming from? Is it amplified from Coluzzii? Please, include this explanation for clarification. Why the authors think the zpg from Kyrou et al 2018 works for the cassette integration but not for homing? They discuss positional effects, any references showing that?
--- We amplified the vasa promoter from A. coluzzii using primers CggtctcaATCCcgatgtagaacgcgagcaaa and CggtctcaCATAttgtttcctttctttattcaccgg (annealing sequence underlined) to have a fragment equivalent to that (vas2) characterized in Papathanos et al, 2009. We have now added this information in the Methods under Plasmid construction. This is the only source of vasa promoter used in this work.
About zpg promoter activity : we have past experience suggesting that promoters, such as the hsp70 promoter from Drosophila, can be sufficient to express enzymatic activities in embryos injected with helper plasmids, even though the same promoters appear to become inactive once integrated in the genome. This may be due to injected “naked” plasmids being readily accessible to the transcription machinery, unlike organized chromatin. A recent reference showing genomic positional influences on promoter efficiency is Anderson et al., 2023, which we have added on line 710 of the Discussion.
- Line 362: No reference to figure nor table.
--- These data (numbers from a COPAS analysis) are provided directly in the text in this sentence (which has been clarified in response to Reviewer 1). See lines 364-369 of the revision.
- Line 417: The text brings the reader back to Fig.3C. Could the authors move this panel for easier flow of the paper?
--- We agree that positioning of this panel in Figure 3 is a bit awkward, but this western blot pertains to the characterization of the insertion shown in Fig. 3. Placing it after COPAS analyses would be equally awkward.
- Line 472-474: How many WT alleles were recovered? It is not stated unless I missed anything, which is possible.
--- We refrained from providing a quantification of this, and focussed on qualitative results, as we didn't trust the quantitative representativity of our high-throughput amplicon sequencing results in terms of allele frequency in the sampled mosquito population. A large fraction of sequenced reads corresponded to PCR artefacts such as primer dimers and unspecific short amplicons, potentially affecting the relative frequencies of gene-specific amplicons. However, among the sequenced gene-specific amplicons, WT alleles were the majority (lines 474-475).
- Fig.5. Could the authors discuss why the observed DsRed-gene drive drop in population 1 at ~18 generation? The population gets to the point where only 50% of the population carries the Cas9-DsRed cassette. Considering that the Saglin gene drive only converts through females (inserted into the X chr.), and some indels could be generated by generation 20, how do you explain the great recovery until fully spreading into the population?
--- We agree that this is somewhat puzzling. We don’t have a satisfactory explanation beyond stochastic effects, possibly promoted by population bottlenecks: although we strived to maintain these populations at a high number of individuals at each generation, we cannot exclude that at a given generation only a relatively small fraction of individuals contributed to the next generation, leading to fluctuations in allelic frequencies. This would be possible particularly for populations 1 and 2, which were not monitored frequently between generations 10 and 18, at which point additional populations 5-8 were established and it was decided that close monitoring of all populations was important.
It seems to me populations 3-8 are new cage experiments by randomly picking mosquitoes from populations 1 and 2 (at a specific generation) and mixing them with WT individuals. Could the authors explain the reasoning for these experiments? I believe populations 3-8 deserves a different figure (main or supplementary) describing how they were seeded. It is confusing having everything together as these experiments were performed differently way and for a different reason compared to populations 1 and 2. Some cage schematics and drawings would help in understanding the protocol strategy for populations 3-8.
--- This is correct for populations 3 and 4 that indeed originated from randomly picking mosquitoes from populations 1 and 2 at generation 10 and mixing them with WT individuals. Populations 5, 6, 7 and 8 are crosses between generation 16 transgenic partners of one sex to WT of the other sex, as indicated above the COPAS diagrams provided in Suppl. File 2. We apologize for having insufficiently described how each population was assembled and now provide more details (lines 422-429, in the figure 5 legend, and G0 crosses spelled out on top of each population diagram). In setting up these populations, we wanted to test the effects of various routes by which the transgenes may be introduced into a wild mosquito population: release of unsorted transgenic males + females, or release of one sex only (probably males in the field, but the crosses with transgenic females as with transgenic males also served to re-quantify homing in the second generation of each cross).
The modified text reads as follows:
Populations 3 and 4 were established by mixing randomly selected transgenic mosquitoes (both males and females of generation 10) from populations 1 and 2, respectively, with wild-types, to mimic what may occur in a mixed-sex field release. Populations 5-8 were established by crossing single-sex transgenic mosquitoes to WT of the opposite sex, both to mimic a single-sex field release and to re-assess homing efficiency after 16 generations.
Also, could you add homozygous and heterozygous labels in the figure legend to help understanding the different lines.
--- As indicated on the side of the figure and in the figure legend, lines don’t represent homozygous vs. heterozygous frequency, but allele frequency (continuous lines), and frequency of mosquitoes carrying the transgene (dotted lines). In the figure legend we now provided the calculation formulas for gene frequency: [ 2 x (number of homozygotes) + (number of heterozygotes)] / 2 x (total number of larvae) for the autosomal Lp::2A10 transgene, and [ 2 x (number of homozygotes) + (number of heterozygotes) ] / 1.5 x (total number of larvae) for the X-linked SagGD transgene.
- Fig.6: The authors sequenced non-DsRed individuals from generations 3-4. The authors also mentioned they sequenced mosquitoes from generation 32 (Fig.7). Interestingly, they observed that these mosquitoes were missing a piece of the cassette (they contained 2 gRNAs instead of 7). Since the amplicons only cover the gRNA portion, a PCR covering the Zpg-Cas9 portion would be ideal to confirm that only the gRNAs are missing. Sampling DsRed+ mosquitoes from generations 3, 18 and 31 (populations 1 and 2) and carrying out these experiments is recommended. Although unlikely, I would be worried about the Cas9 being deleted due to unexpected DNA rearrangements; in that case, the cassette would contain the DsRed marker alone.
--- Thank you for this suggestion. We no longer have DNA samples from the earlier generations. Thus, we genotyped 7 DsRed positive male mosquitoes from each of current populations 1, 2 and 7 (generation 41 since transgenesis) for the presence of Cas9. We detected a Cas9-specific amplicon of 1.6 kb in 21/21 sampled DsRed positive mosquitoes, in parallel to the same shortened gRNA arrays detected in earlier generations. This suggests that the Cas9 part of the transgene was not affected by the loss of gRNA units. We made a panel C in Figure 7 showing these results and mentioned them on lines 537-538. Of note, the Cas9 moiety of the gene drive construct shows no repetitive sequence and should therefore not be as unstable as the gRNA multiplex array. The observed excisions of gRNA expression units were strictly due to recombinations between repeated U6 promoter sequences (Fig. 7).
The authors employ 3 different gRNAs that are 43 and 310 nts apart. It has been shown that only 20 nt lack of homology produces an important reduction on gene drive performance (Lopez del Amo et al 2020, Nat Comms). Also, it has been shown that gRNA multiplexing approaches should be kept with a low number of gRNAs, 2 being maybe the best one depending on the design (Samuel Champer 2020, Sciences Advances). This could be discussed more.
--- Thank you for this suggestion. These results were not published when this study was initiated, so that our gene drive constructs could only be designed on empirical bases. For gRNA numbers, see the new discussion point and inclusion of a reference to the study by S. Champer et al., on line 700-702. The reduction of drive performance with longer non-homologous stretches is indeed also a very important point, that we now discuss on lines 713-717, citing your study:
Finally, tighter clustering of gRNA target sites at target homing loci, especially Saglin, should improve gene drive performance by reducing the length of DNA sequences flanking the cut site that bear no homology to the repair template on the sister chromosome and need to be resected by the repair machinery to allow homing (López Del Amo et al., 2020)__.
Reviewer #4 (Significance):
There are different novelty aspects from my point of view in this work. While most of the scientists focus on developing CRISPR-based gene drives in An. Stephensi and gambiae, this work employs An. Coluzzii. Some limitations regarding fitness cost associated with the Lp gene were also noted and discussed by the authors.
--- To be fair, earlier gene drive studies were performed on the G3 laboratory strain, traditionally named A. gambiae, although it is probably itself a hybrid strain from gambiae and coluzzii. Still, the Ngousso strain from Cameroon that was used in this study is thought to be a bona fide A. coluzzii. We have also added a reference to a recent paper (Carballar-Lejarazu et al., 2023) that also describes a population modification GD in A. coluzzii.
First, they show that An. Coluzzii mosquitoes infect less when containing the antimalarial effector cassette inserted in their genomes. Second, a gene drive is showing super-Mendelian inheritance in An. Coluzzii, which would be the second example of a gene drive in these mosquitoes so far to my knowledge.
I believe this is the first manuscript experimentally using multiplexing approaches (multiple gRNAs) in mosquitoes (all previous works I saw were performed in flies). While previous gene-drive works employ only one gRNA in mosquitoes, this works explores the use of different gRNAs targeting nearby locations to potentially improve HDR rates and gene drive spread. Although they observe gene drive activity, they also show DNA rearrangements due to the intrinsic nature of multiplexing gene drives that can generate multiple DNA double-strand breaks, impeding proper HDR and clean replacement of the wildtype alleles. This is important from a technical point of view as it shows this approach requires optimization. They included 3 gRNAs targeting the Saglin gene, and trying 2gRNAs instead could be interesting for future investigations.
--- We now discussed optimization with the help of modeling, in response to Reviewer 1, on lines 701-702.
This work will be very useful for the CRISPR-based gene drive field, which seeks to develop genome editing tools to control mosquito populations and reduce the impact of vector-borne diseases such as malaria.
This reviewer intended to understand the work and provide constructive feedback to the best of my abilities. I apologize in advance if I misunderstood anything.
--- Thank you for your appreciation, insight, and constructive evaluation of our manuscript.
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Referee #4
Evidence, reproducibility and clarity
The authors hijacked the Anopheles coluzzii Lipophorin gene to express the antibody 2A10, which binds sporozoites of the malaria parasite Plasmodium falciparum. The resulting transgenic mosquitoes showed a reduced ability to transmit Plasmodium.
The authors also designed and tested several CRISPR-based gene drives. One targets Saglin gene and simultaneously cleaves the wild-type Lipophorin gene, aiming to replace the wildtype version with the Sc2A10 alele while bringing together the Saglin gene drive.
Drive-resistant alleles were present in population-caged experiments, the Saglin-based gene drive reached high levels in caged mosquito populations though, and simultaneously promoted the spread of the antimalarial Lp::Sc2A10 allele.
This work contributes to the design of novel strategies to spread antimalarial transgenes in mosquitoes. It also displays issues related to using multiplexing gene-drive designs due to DNA rearrangements that could prevent the efficient spread of the gene drive in the long term.
This is tremendous work considering how many transgenic lines and genetic crosses are performed using mosquitoes. The conclusions are supported by the data presented, and some modifications regarding the experimental design description through text/figure improvements would facilitate the reading and flow of the paper.
Here some questions/comments:
- Line 124-125: Reference?
- Line 133-134: Reference?
- Table 1: It seems the authors have some issues recovering a good amount Sc2A10 from hemolymph samples. Is this a problem of the antibody per se? Is it the Lp endogenous promoter weak? Could this be improved by placing the antibody in a different genomic region? Alternatives could be discussed.
- Fig.1B: It would be nice to have a representation of the genome after integration. You could add a B' panel or just another schematic under the current one.
- Supplementary Fig.1b: Could the authors explain the origin of the (first) zpg promoter used? Is it from An. Coluzzii? It seems they use a different one in the gene drive designs later (see comments below too).
- Fig.3: Please, correct to A, B, C order. Current one is A, C, B.<br /> Could the authors include a schematic of the final mosquito genome after integration? I can see they are targeting two different locations (Saglin and Lp). It is unclear though from the figure where the Sc2A10-GFP is coming from. I understand this represents the mosquito genome as you injected heterozygous animals already containing the Sc2A10-GFP. Maybe label the Sc2A10-GFP as mosquito genome or similar? A schematic showing mosquito embryos already carrying this and then the plasmid being injected could help.
- Line 330-331: Do you know the transgenesis efficiency? Did the authors make single or pools for crossing and posterior screening? It would be interesting to know about transgenesis rates to inform the community.
- Line 357/Fig.4B: Could the authors explain in the text GFP+ vs. GFP++?
- Line 357: Where is the vasa promoter that made the "rescue" coming from? Is it amplified from Coluzzii? Please, include this explanation for clarification. Why the authors think the zpg from Kyrou et al 2018 works for the cassette integration but not for homing? They discuss positional effects, any references showing that?
- Line 362: No reference to figure nor table.
- Line 417: The text brings the reader back to Fig.3C. Could the authors move this panel for easier flow of the paper?
- Line 472-474: How many WT alleles were recovered? It is not stated unless I missed anything, which is possible.
- Fig.5. Could the authors discuss why the observed DsRed-gene drive drop in population 1 at ~18 generation? The population gets to the point where only 50% of the population carries the Cas9-DsRed cassette. Considering that the Saglin gene drive only converts through females (inserted into the X chr.), and some indels could be generated by generation 20, how do you explain the great recovery until fully spreading into the population?
It seems to me populations 3-8 are new cage experiments by randomly picking mosquitoes from populations 1 and 2 (at a specific generation) and mixing them with WT individuals. Could the authors explain the reasoning for these experiments? I believe populations 3-8 deserves a different figure (main or supplementary) describing how they were seeded. It is confusing having everything together as these experiments were performed differently way and for a different reason compared to populations 1 and 2. Some cage schematics and drawings would help in understanding the protocol strategy for populations 3-8.
Also, could you add homozygous and heterozygous labels in the figure legend to help understanding the different lines.
- Fig.6: The authors sequenced non-DsRed individuals from generations 3-4. The authors also mentioned they sequenced mosquitoes from generation 32 (Fig.7). Interestingly, they observed that these mosquitoes were missing a piece of the cassette (they contained 2 gRNAs instead of 7). Since the amplicons only cover the gRNA portion, a PCR covering the Zpg-Cas9 portion would be ideal to confirm that only the gRNAs are missing. Sampling DsRed+ mosquitoes from generations 3, 18 and 31 (populations 1 and 2) and carrying out these experiments is recommended. Although unlikely, I would be worried about the Cas9 being deleted due to unexpected DNA rearrangements; in that case, the cassette would contain the DsRed marker alone.
The authors employ 3 different gRNAs that are 43 and 310 nts apart. It has been shown that only 20 nt lack of homology produces an important reduction on gene drive performance (Lopez del Amo et al 2020, Nat Comms). Also, it has been shown that gRNA multiplexing approaches should be kept with a low number of gRNAs, 2 being maybe the best one depending on the design (Samuel Champer 2020, Sciences Advances). This could be discussed more.
Significance
There are different novelty aspects from my point of view in this work. While most of the scientists focus on developing CRISPR-based gene drives in An. Stephensi and gambiae, this work employs An. Coluzzii. Some limitations regarding fitness cost associated with the Lp gene were also noted and discussed by the authors.
First, they show that An. Coluzzii mosquitoes infect less when containing the antimalarial effector cassette inserted in their genomes. Second, a gene drive is showing super-Mendelian inheritance in An. Coluzzii, which would be the second example of a gene drive in these mosquitoes so far to my knowledge.
I believe this is the first manuscript experimentally using multiplexing approaches (multiple gRNAs) in mosquitoes (all previous works I saw were performed in flies). While previous gene-drive works employ only one gRNA in mosquitoes, this works explores the use of different gRNAs targeting nearby locations to potentially improve HDR rates and gene drive spread. Although they observe gene drive activity, they also show DNA rearrangements due to the intrinsic nature of multiplexing gene drives that can generate multiple DNA double-strand breaks, impeding proper HDR and clean replacement of the wildtype alleles. This is important from a technical point of view as it shows this approach requires optimization. They included 3 gRNAs targeting the Saglin gene, and trying 2gRNAs instead could be interesting for future investigations.
This work will be very useful for the CRISPR-based gene drive field, which seeks to develop genome editing tools to control mosquito populations and reduce the impact of vector-borne diseases such as malaria.
This reviewer intended to understand the work and provide constructive feedback to the best of my abilities. I apologize in advance if I misunderstood anything.
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Referee #3
Evidence, reproducibility and clarity
The study by Green et al. generated a gene drive targeting both Saglin and Lipophorin in the Anopheles mosquito, with a view to blocking Plasmodium parasite transmission. This is a highly complex but elegant study, which could significantly contribute to the design of novel strategies to spread antimalarial transgenes in mosquitoes.<br /> Overall, this is a complex study which, for a non-specialist reader gets quite technical and heavy in most parts. Despite this, there are key points showing that suppression gene drive may not be the way forward in this instance. However, I would advise explaining certain elements in more detail for the benefit of the general readers. I only have minor points for the authors to address:
- Please point out for the general reader that Anopheles coluzzii belongs to the gambiae complex, since you explain that gambiae are the major malaria spreaders in sub-Saharan Africa.
- The authors pretty much give all results in the last part of the introduction, could the intro be shortened by removing these parts, or just highlighting in a single paragraph the main take home message?
- Why is Vg mentioned? It is only mentioned once and doesn't have any other mention through the manuscript.
- Please make it clearer for non-specialists why Cecropin wasn't used.
- Why were homozygous and not heterozygous transgenics transfected if there is such as fitness cost to homozygous mosquitoes?
- Line 211 - what was the average number of infected mosquitoes used per infection for each mosquito strain?
- Line 219 - please be clearer regarding this being infection detected in the blood.
- Line 320 - please clarify why the zpg promoter was used.
- Line 375 - what was the rationale for using so many gRNAs?<br /> Line 555 - please state how long post bite back parasite appears in infected mice.
Significance
This is potentially a highly significant study that could provide a vital mechanism for generating efficient gene drives. Although highly technical and complex in most parts, with a little clarification in certain areas this manuscript could be of great value to a general readership.
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Referee #2
Evidence, reproducibility and clarity
The authors initially created a transgenic mosquito colony expressing the Sc2A10 antibody fused to the lipid transporter Lipophorin, and tested the transmission-blocking activity of this transgene. Building off of previous findings that the Sc2A10 antibody inhibits sporozoite infectivity when expressed in mosquito salivary glands, the authors showed that found it was also efficient at inhibiting sporozoite infectivity when secreted into the hemolymph expressed under the lipophorin endogenous promoter in An. coluzzii. They then designed and tested two different gene drives utilizing the Sc2A10-Lipophorin fusion protein. In the first, the authors used a recoded allele of Lp-Sc2A10 while simultaneously utilizing gRNAs that targeted endogenous Lp in an effort to select for mosquitoes that expressed transgenic Lp-Sc2A10 due to the essential nature of Lp. However, this drive was unsuccessful because recoded Lp is necessarily heterozygous while the GD is entering the population, and Lp proved to be largely haploinsufficient. Further, the zpg promoter expressing cas9 was not effective in promoting homing of the gRNAs. In the second gene drive that was tested, authors made use of the endogenous Saglin locus, which expresses a natural agonist for Plasmodium, and is thus desirable to target for disruption in a gene drive that aims to reduce vector competence for Plasmodium. This gene drive also uses recoded Lp-Sc2A10 to replace the wild-type Lp allele, thus selecting for Sc2A10 expression, however this drive is not dependent on fitness of individuals with only one functional copy of Lp.
The authors discovered that the efficacy of the zpg promoter to drive homing of cas9 is locus-dependent, limiting the success of their gene drive designs. They do show, however, that the Saglin gene drive succeeds at reaching high frequencies in mosquito populations using instead the vasa promoter to express cas9, and that these transgenic mosquitoes are able to reduce infectivity of sporozoites in a bite-back mouse model. However, they observe gene drive refractory mutations in the Lp gene, despite its highly conserved nature, showcasing the difficulty of avoiding drive resistance even in small populations of mosquitoes, and also observed deletions of gRNAs targeting both Lp and Saglin, further highlighting possible shortcomings in gene drive approaches. Together, these findings are useful to the field in walking the readers through an interesting and promising approach for a novel gene drive, and illustrating the challenges in engineering an efficacious and long-lasting drive.
Major comments:
As the authors are able to observe Plasmodium within mosquitoes, it would be useful to have these data in the manuscript pertaining to the prevalence and intensity of infection in mosquitoes prior to bite-back assays. If there are data or images that the authors could include, it would be helpful to show if there is a possibility that infection intensity is a variable that contributes to whether or not mice develop an infection. It would also be interesting to note whether there is a different in infection (oocysts or sporozoites) between transgenic mosquitoes and wild type mosquitoes.
The authors also go into significant detail in the discussion exploring ideas of how to optimize or improve this specific gene drive design. The authors should also stress further the applicability of their discoveries in other gene drive designs, and emphasize the lessons they learned in the difficulties encountered in this study and how these findings could guide others in their decision making process when choosing targets or elements to include in a potential gene drive approach.
Minor comments:
Line 44 - female sterility but also female killing approaches to crash pop. like X shredder, if authors would like to expand
Lines 48-60 - Authors should add some references from the literature surrounding ethics and ecology studies related to gene drive release
Line 114 - Given the only moderate impacts of Saglin's role in Plasmodium invasion, I am not sure this saglin deletion is a convincing benefit for GD as it is probably not impactful enough alone - can the authors soften this statement?
Line 126 -Can the authors provide rationale for expressing Sc2A10 with Lp instead of expressing it from salivary glands?
Line 140 - Can authors give any comment on why these regions of Lp were chosen to be recoded / targeted with gRNAs?
Line 171 - "stoichiometric"
Line 186 - Can the authors comment or speculate on why the expression levels of the fusion protein are expected to be lower than endogenous Lp?
Line 211 - Why were 6 mosquitoes used for these assays, and 10 mosquitoes used in later assays (Line 223)?
Line 212 - I would also suggest using letters (Suppl. Table 2A,B,C etc) to refer the specific experiments and sections in the Table.
Line 225- 228 - The authors should mention in the text that homozygotes and heterozygotes do not differ in infection assays.
Line 249 - Can the author comment on the impacts of population influx / exchange on the idea that the GD cassette need only be transiently in the population?
Line 273 - Can the authors comment on why this may have occurred more frequently than the expected integration of the GD cassette?
Line 314 - Not all fitness costs are apparent through standard laboratory rearing as was performed in Klug et al. Authors could consider "no known fitness cost" instead.
Line 407 - don't start new paragraph (same with 409)
Line 408 - I'm not sure it's clear why all these populations were kept for a different number of generations - can the authors clarify?
Line 558 - "10/12 mice" not immediately clear - the authors could be more specific about how data was combined here
Line 586 - Since there do appear to be some fitness costs associated with the Sc2A10 version of Lp, might it be expected that fitness costs imposed by the transgene itself could lead to selection pressures leading to its loss? Or do the authors think that these fitness costs are prevented from causing selection against Sc2A10 due to the design of the transgene such that its translation is a prerequisite for Lp's translation? Is the fact that its removal occurs more rapidly than Lp's any indication that selection against the persistence of Sc2A10 may occur?
Line 659 - add some further detail to this - how do you envision this to occur?
Line 635 - Long paragraph, should be broken up or removal of text. Some of these ideas could possibly be made more concise to improve readability. There are many different hypotheticals that are expanded upon in the discussion.
Line 677 - This scenario seems potentially unrealistic considering the only subtle impacts of Saglin deletion on vector competence, and the potential for population exchange in mosquito populations to dilute out these alleles if the drive begins to fail. Can the author comment or potentially decrease emphasis on such scenarios?
Line 708 - Can the authors speculate on why zpg is sensitive to local chromatin and elaborate on possible solutions or consequences for other drive ideas? This seems broadly important.
Line 737 - The suggestion of releasing laboratory-selected resistance alleles in the absence of further context may be provocative and unnecessary here.
Line 850 - unnecessary comma
Line 854 - change to "after infection, moquitoes were "
Figure 1 - Not clear what is intended to be communicated by shapes portraying proteins / subunits - consider more detailed illustration of mosquito fat body cells synthesizing and secreting proteins rather than words in text box with arrow to clearly demonstrate the point of this figure.
Figure 3 - I recommend rearranging this figure so that B comes before C, visually. The proportions for the design of in B should also match those used for A.
Figure 5 - It is unclear to me why some Populations were maintained for such different lengths of time.
Figure 7 - Ladder should be labeled on the gel. It may also be helpful for the author to indicate clearly exactly which mosquitoes were shown by sequencing to have these different deletions, as it is occasionally unclear based on band sizing.
Line 996 - given that there is a size band on the right line of this gel also, can authors crop the gel image to eliminate unnecessary lanes a and b from this figure without losing information needed to interpret this blot?
Line 1070 - 12 out of how many sequenced mosquitoes?
Line 1078 - Can remove some detail like % of agarose, and replication of results with different polymerase as these are already in methods.
Line 1098 - "Unbless"
Significance
This study illustrates a wide range of issues pertinent for gene drive implementation for malaria control, and as such is of value to the field of entomologists, genetic engineers, parasitologists and public health professionals. The gene drive designs explored for this study are interesting largely from a basic biology perspective pertinent mostly to specialists in the field of genetic engineering and vector biology, but highlight challenges associated with this technology that could also be of interest to a broader audience. A transmission blocking gene drive has not yet been achieved in malaria mosquitoes, and is thus a novel space for exploration. As a medical entomologist that works predominantly outside of the genetic engineering space, I have appreciated the detail the authors have provided with regard to their rationale and findings, even when these findings were inconsistent with the authors' primary objectives or expectations.
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Referee #1
Evidence, reproducibility and clarity
In this study, the authors made a two-component homing modification gene drive in Anopheles coluzii with a different strategy than usual. The final drive itself targets and disrupts the saglin gene that is nonessential for mosquitoes, but important for the malaria parasite. The drive uses several gRNAs, and some of these target the Lp gene where an anti-malaria antibody is added, fused to the native gene (this native gene is also essential, removing nonfunctional resistance alleles at this locus). In general, the system is promising, though imperfect. Some of the gRNAs self-eliminate due to recombination of repetitive elements, and the fusion of the antimalaria gene had a modest fitness cost. Additionally, the zpg promoter was unable to operate at high efficiency, requiring use of the vasa promoter, which suffers from maternal deposition and somatic expression (the latter of which increased fitness costs at the Lp target). The manuscript has already undergone some useful revisions since its earliest iteration, so additional recommended revisions are fairly modest.
Line 43-45: The target doesn't need to be female sterility. It can be almost any haplosufficient but essential target (female sterility works best, so it has gotten the most study, but others have been studied too).
Line 69: A quick motivation for studying Anopheles coluzii should be added here (since gambiae is discussed immediately before this).
Introduction section: It might be helpful to break up the introduction into additional paragraphs, rather than just two.
Introduction last part: The last part of the introduction reads more like an abstract or conclusions section. Perhaps a little less detail would fit better here, so the focus can be on introducing the new drive components and targets
Line 207-213: This material could go in the methods section. There are some other examples in the results that could be similarly shortened and rearranged to give a more concise section.
Line 283-287: I couldn't find the data for this.
Line 291: Replace "lied" with "was".
Line 356: Homing in the zygote would be considered very unusual and is thus worthy of more attention. While possible (HDR has been shown for resistance alleles in the zygote/early embryo), this would be quite distinct from the mechanism of every other reliable gene drive that has been reported. Is the flow cytometry result definitely accurate? By this, I mean: could the result be explained by just outliers in the group heterozygous for EGFP, or perhaps some larvae that hatched a little earlier and grew faster? Perhaps larvae get stuck together here on occasion or some other artifact? Was this result confirmed by sequencing individual larvae?
Results in general: Why is there no data for crosses with male drive heterozygotes? Even if some targets are X-linked, performance at others is important (or did I miss something and they are all X-linked). I see some description near line 400, but this sort of data is figure-worthy (or at least a table).
Lines 362-367: What data (figure/table) does this paragraph refer to?
Lines 405-406: There may be a typo or miscalculation for the DsRed inheritance and homing rate here. Should DsRed inheritance be 90.7%?
Figure 5: The horizontal axis font size for population 8 is a little smaller than the others.
Line 454: In addition to drive conversion only occurring in females and the somatic fitness costs, embryo resistance from the vasa promoter would prevent the daughters of drive females from doing drive conversion. This means that drive conversion would mostly just happen with alleles that alternate between males and females.
Line 481: Deletions between gRNAs certainly happen, but I wouldn't necessarily expect this to be the "expectation". In our 2018 PNAS paper, it happened in 1/3 of cases. There were less I think in our Sciences Advances 2020 and G3 2022 paper. All of these were from embryo resistance from maternal Cas9 (likely also the case with your drive due to the vasa promoter). When looking at "germline" resistance alleles, we have recently noticed more large deletions.
Figure 6C: It may be nice to show the wild-type and functional resistance sequence side-by-side.
Lines 642-644: This isn't necessarily the case. At saglin, the nonfunctional resistance alleles may still be able to outcompete the drive allele in the long run. This wasn't tested, but it's likely that the drive allele has at least some small fitness costs.
A few comments on references to some of my studies:
Champer, Liu, et al. 2018a and 2018b citations are the same paper.
For Champer, Kim, et al. 2021 in Molecular Ecology, there was a recent follow-up study in eLife that shows the problem is even worse in a mosquito-specific model (possibly of interest as an alternate or supporting citation): https://elifesciences.org/articles/79121
One of my other previous studies was not cited, but is quite relevant to the manuscript: https://www.science.org/doi/10.1126/sciadv.aaz0525<br /> This paper demonstrates multiplexed gRNAs and also models them, showing their advantages and disadvantages in terms of drive performance. Additionally, it models and discusses the strategy of targeting vector genes that are essential for disease spread but not the vectors themselves (the "gene disruption drive"), showing that this can be a favorable strategy if gene knockout has the desired effect (nonfunctional resistance alleles contribute to drive success).
This one is less relevant, but is still a "standard" homing modification rescue type drive that could be mentioned (and owes its success to multiplexing): https://www.pnas.org/doi/abs/10.1073/pnas.2004373117<br /> The recoded recuse method was also used in mosquitoes (albeit without gRNA multiplexing) by others, so this may be a better one to mention: https://www.nature.com/articles/s41467-020-19426-0
Sincerely,<br /> Jackson Champer
Referees cross-commenting<br /> Other comments look good. One thing that I forgot to mention: for the 7-gRNA construct with tRNAs, the authors mentioned that it was harder to track, but it sounds like they obtained some data for it that showed similar performance. Even if this one is not featured, perhaps they can still report the data in the supplement?
Significance
Overall, this study represents a useful advance. Aside from being the first report for gene drive in A. coluzii, it also is the first that investigates the gene disruption strategy and is the first report of gRNA multiplexing in Anopheles. The study can thus be considered high impact. There are also other aspects of the study that are of high interest to gene drive researchers in particular (several drives were tested with some variations).
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Reply to the reviewers
We are grateful to the Referees for their detailed evaluation of our data and insightful remarks. We have now addressed all comments and amended the manuscript accordingly. Below we detail how we have addressed each point.
Reviewer #1 (Evidence, reproducibility and clarity):
Summary:
In this manuscript Lafzi et al. present a novel computational framework (ISCHIA) for the analysis of spatial occurrence patterns, be it of cells or transcript species, found in spatial transcriptomics datasets. The authors also show its applications in finding differentially co-occurring ligand-receptor pairs, as well as inter-species analysis to find conserved cell signalling pathways. ISCHIA consists of a well-documented R package and utilizes empirical probabilistic estimations of non-random co-occurrence, as used in the field of ecology, which to my knowledge is novel in the field. The authors also validate their predictions using an orthogonal technology (in situ hybridization-based spatial transcriptomics), which is a nice addition to the computational work presented in the manuscript.
Major:
When determining the composition classes, the authors discard 4 out of 8 clusters of composition classes, partly due to being highly patient-specific. It's unclear how sensitive ISCHIA is for batch effects which might affect the measured cellular fractions. Given that the presence of batch effects is highly likely with ST methods, due to the sample processing procedures, it would help the reader/potential user to estimate the impact these could have on the resulting output. It would also be useful to plot a version of the UMAP with sample labels as to see if the remaining clusters are properly mixed (at least between replicates of the same condition).
We thank the reviewer for the comment. We have included in Supplemental Figure S1c a UMAP colored by sample (patients), and a barplot in d) that illustrates how CC8 and CC4 are quite specific to patients 2 and 3. These graphs illustrate how the composition classes 1, 3, 5 and 6 are represented in all patients, albeit in different abundances. We would like to point out that the choice to exclude 4 out of 8 composition classes was not dictated by batch effects, but rather by the specific morphology of the samples available to us. Indeed, we excluded composition classes that were mapping to submucosal and muscular areas because only 2 out of 4 colon resections contained these anatomical compartments, and because we wanted to focus on cell type co-occurrences in the epithelial and subepithelial layers.<br /> Additionally, in order to explain the sensitivity of ISCHIA for batch effects or sample-specific variation, we performed principal component (PC) analysis, and measured the standard deviation explained by each PC on the cell type deconvolution matrix (which is used to calculate composition clusters). Next, we tried to find an association of the covariate “batch” with the PCs that explain a high amount of variation in the data. In the deconvoluted matrix, while the first 4 PCs explain more than 80% of the variation in the data, we couldn’t find association of the covariate “batch” to any of the first 4 PCs. We now include this analysis in the Result section:
Principal component analysis of the deconvolution matrix reveals no association of a particular sample with the first 4 principal components, which cumulatively explain 80% of the variance in the data (Supplemental Figure 2c, d). K-means clustering of the deconvolution matrix revealed 8 CCs of co-localizing cell types present in all samples (Fig 1c, d).
While other methods assign a cell-type identity to spots based on the most abundant cell type detected by deconvolution algorithm, ISCHIA summarizes spot gene expression data in a presence-absence matrix. ISCHIA is therefore robust to variation in expression levels due to batch effects. This was also recently reported in the analytical tool Starfysh (He et al. 2022), which performs similar clustering of spots based on cell type composition. This is included in the Discussion:
While preserving the complexity of the cell type composition of the analyzed tissue, composition-based clustering of spots also confers robustness towards variations in expression levels due to batch effects. Indeed, other spatial analysis methods such as Starfysh66 have found that finding inter-sample commonalities using composition-based clusters is easier compared to finding common transcriptome-based clusters between samples. Still, batch analysis and, if needed, correction of the ST data is recommended prior to analysis with ISCHIA.
Additionally, it would help to illustrate that the biological findings reported in the manuscript are supported across more than 1 biological replicate.
We agree with the reviewer that the sample size of Visium and Resolve datasets is limited, however greater than 1 (3 and 4 patients, respectively). ISCHIA is meant as a tool for hypothesis generation. Its findings require independent functional validation in model systems and bigger patient cohorts.
In the LR analysis, the authors state that ISCHIA's predictions are agnostic to gene expression levels, as the authors model expression as a Boolean (gene count threshold > 0). Wouldn't low expression levels result in increased drop-out due to imperfect sensitivity? This would likely inflate false negative predictions at low expression levels.
We agree with the reviewer that dropouts due to low capture rate of Visium will lead to false negative predictions. Indeed, we chose to set the threshold for gene count > 0 to account for the sparsity of captured transcripts. In single cell RNA sequencing analysis, gene expression is analyzed in cell clusters rather than in single cells. Similarly, ISCHIA calculates LR co-occurrence in composition classes, that is clusters of spots of similar cell composition. Aggregating spots in composition classes thus mitigates the effects of low capture rate and consequent false negative predictions. We now include a sentence explaining this concept in the Results section:
The count threshold is a user defined parameter that can be increased to restrict the co-occurrence analysis to highly expressed ligands and receptors. To account for the sparsity of ST data, ISCHIA calculates LR co-occurrence within composition classes, that is clusters of spots with similar cell mixtures. Aggregating spots in composition classes thus mitigates the effects of low transcript capture rate and consequent false negative predictions.
The authors show the enrichment of particular pathways/genesets in differential gene expression comparing interacting vs noninteracting spots (through LR expression) within the same CC. It is however unclear if this enrichment stems from a random sampling of the CC (with possible confounding factors such as batch effect, QC metrics, which might also have a spatial component such as localized tissue degradation) or from the actual interaction. Adding a measure of uncertainty, such as by permuting over interaction-labels to generate a proper null distribution, would help the user to ascertain the robustness of the results. For clarity, it would also be good to add how this is exactly computed to the Methods section.
We thank the reviewer for this remark and now perform a gene expression noise estimation. As suggested by the reviewer, we employed a permutation-based approach to assess the significance of differentially expressed genes (DEGs) identified by comparing spots that are double positive for expression of a ligand-receptor pair vs spots that are not expressing any of the genes in a specific CC. To do so, we performed 1000 random sampling of spots into two groups, and calculated DEGs between these groups, consequently generating a null distribution of DEGs. We next calculated Monte Carlo p-values for the LR-associated DEGs, comparing the initially computed p-values DEG with the null distribution, and adjusting them for multiple testing using FDR. Significant adjusted p-values were indicative of genes whose differential expression was robust and unlikely to result from random spot sampling.
From the Method section:<br /> For the calculation of L-R-associated DEGs, ISCHIA computes differential gene expression between spots that are double positive or double negative for a given L-R pair. The significant DEGs are then used for pathway enrichment with any tool of choice, such as EnrichR (https://bio.tools/enrichr). We employed a permutation-based approach to assess the significance of the obtained DEGs. Specifically, we generated a null distribution of DEGs (noise estimation) by 1000-fold random sampling of spots into two groups, and calculating DEGs between these groups. Next FDR-adjusted Monte Carlo p-values were calculated for each LR-associated DEG, comparing the initially computed p-values DEG from with the null distribution, and subsequently adjusting for multiple testing. DEGs with Monte Carlo FDRs < 0.05 are likely specific to the presence of a given LR and unlikely to result from random spot sampling.
It's unclear if the p-values in the manuscript are adjusted for multiple comparisons or not. Given the number of hypotheses being tested here, this is a crucial issue.
We agree with the reviewer that this is a crucial issue. In the ecology papers we consulted and in the original co-occur R package (Griffith et al. 2016), multiple testing correction was not applied when computing co-occurrence of species. We believe however, that it is necessary to correct p values when computing co-occurrence of ligands and receptor pairs, and now implement FDR correction in the ISCHIA pipeline. We have amended all the relative figures and tables.
The authors don't really mention any of the existing state-of-the-art methods (e.g. Squidpy, Spacemake, Giotto, ...). This doesn't necessitate a full benchmark, but at least the authors should then state qualitatively what the difference is between the chosen approach and already available packages, with their respective added advantages/disadvantages.
We thank the reviewer for the remark and we have compared the analysis performed by ISCHIA with other state-of-the-art tools. The main difference between ISCHIA with respect to other methods such as Spacemake (Sztanka-Toth et al. 2022), Squidpy (Palla et al. 2022) and Giotto (Del Rossi et al. 2022), is that ISCHIA computes cell-type and LR co-occurrence within individual spots, not between neighboring spots. We include here an extensive comparison with other methods for this reviewer, and now discuss the differences between ISCHIA and other tools in the manuscript.
For neighborhood analysis, Spacemake and Squidpy use spatial coordinates of spots to identify neighbors among them (neighborhood sets are defined as a fixed number of adjacent spots in a square or hexagonal grid). Squipdy computes co-occurrence of clusters in spatial dimensions, however it uses the coordinates of spots and clusters to calculate co-occurrence of entire clusters of spots, not of cell-types within spots. This approach ignores the missing data between spots, as well as the multicellular nature of each spot. Similarly to Squidpy, Giotto assigns a score of a cell type to each spot upon deconvolution, to further identify the spatial patterns of the major cell taxonomies across all the spots on the tissue. For image-based spatial technologies with single cell resolution, Giotto creates a neighborhood graph of the single-cells to study gene expression patterns. This is similar to the approach we used to validate our predictions from Visium data in the Resolve dataset.
Tangram (Biancalani et al. 2021) is a deep learning approach to harmonize sc/snRNA-seq data with in situ, histological, and anatomical data, toward a high-resolution, integrated atlas. Tangram focuses on learning spatial gene-expression maps transcriptome-wide at single-cell resolution, and relating those to histological and anatomical information from the same specimens. However, it does not address cell-cell and ligand receptor interactions, nor co-occurrences from spatial data. Therefore, Tangram can be used to improve the deconvolution step of ISCHIA, to improve the definition of cellular composition in multicellular spatial spots.
Starfysh (He et al. 2022) is a computational toolbox for joint modeling of ST and histology data, dissection of refined cell states, and systematic integration of multiple ST datasets from complex tissues. It uses an auxiliary deep generative model that incorporates archetypal analysis and any known cell state markers to avoid the need for a single-cell-resolution reference. Starfysh also clusters spots based on cell type composition, and terms group of spots with similar composition “spatial hubs”. They use spatial hubs to integrate multiple samples, and to uncover regions with varying composition of cell states. As we propose in ISCHIA, the Starfysh authors also suggest that finding inter-sample commonalities using spatial hubs is easier compared to finding common clusters between samples. Starfysh addresses the co-localization of cell states by calculating the spatial correlation index (SCI) within a certain hub and penalizing the calculated correlation with a weight matrix τ in a way that : τ_(between two spots i,j)=1 if the coordinate distance of spot i and spot j was less than √3 else τ_(between two spots i,j)=0 . While this approach provides a measure of cell state co-localization across a spatial hub, it looks at the problem from an inter-spot perspective, similar to Squipdy and Giotto. Again, this is different from ISCHIA that calculates co-occurrence within spots.
In conclusion, none of the current approaches focuses on addressing the co-occurrence of cell types and molecules within individual Visium spots. As the analysis is fundamentally different, we did not perform a full quantitative benchmark. We agree, however, that these differences need to be addressed in the manuscripts. To illustrate the different results obtained, we ran ISCHIA on a Visium slide of a coronal section of the mouse brain, which was also analyzed using Squidpy (https://squidpy.readthedocs.io/en/stable/notebooks/tutorials/tutorial_visium_hne.html) and Giotto (https://rubd.github.io/Giotto_site/articles/mouse_visium_brain_201226.html#part-9-spatial-network). We clustered the spots based on cell composition and then ran celltype co-occurrence analysis within each composition class (Supplementary Figure 1).
From the Results section:
State-of-the-art analysis tools for Visium data often treat every spot as a single datapoint, and compute co-localization, network or cell-cell interactions analysis between neighboring spots (inter-spot analysis). We hypothesized that CNs would be best reconstructed within individual spots (intra-spot analysis), as their mixed transcriptome contains information about locally occurring cell types, expressed ligands and receptors, and activated signaling pathways. As inferring CNs in each individual spot separately would be noisy, sparse, computationally intensive, and would lack statistical power, ISCHIA first divides the tissue into clusters of spots with similar cellular composition - termed composition classes (CCs) (Fig 1a). CCs are thus groups of spots containing similar mixtures of cells, or cellular communities, e.g, all spots capturing colonic crypts. To achieve the division of the tissue into CCs, spot transcriptomes are deconvoluted, yielding a cell type composition matrix (spot × contribution of each cell type), which is then subjected to dimensionality reduction and k-means clustering. ISCHIA allows for both reference-based deconvolution, with tools such as SPOTlight7 or RCTD8, and reference-free deconvolution9. Upon deconvolution, ISCHIA summarizes spot gene expression data in a cell type presence-absence matrix, where each listed cell type is associated with a probability to be present in a given spot (p > 0.1). Each spot is thus represented as a mixture of cell types, and similar mixtures are then clustered together in CCs. We applied ISCHIA on a publicly available Visium slide of a coronal section of the mouse brain (10x Genomics), using as a reference for deconvolution a scRNA-seq dataset of ~14,000 adult mouse cortical cells from the Allen Institute10. Composition-based clustering of the spots yielded 5 CCs, which broadly reflect the annotated anatomical regions (Supplemental Figure 1a). ISCHIA then computes cell type co-occurrence for every CC separately, identifying spatial association of cells in close proximity (Supplemental Figure 1b). Intra-spot analysis reconstructs cellular networks with cell types as nodes, and is distinct from inter-spot networks analysis employed by other tools on this sample, in which spots are used as nodes11,12.
We also discuss differences between ISCHIA and other tools in the Discussion:
ISCHIA differs from other analysis tools for Visium data in that it predicts CNs within spots and not across spots. Indeed, spot data from sequencing-based ST methods such as Visium, simultaneously captures information about 1) cell types, 2) expressed LR genes, and 3) associated transcriptional responses at multiple spatially restricted locations. As proximity is a prerequisite for juxtacrine and paracrine cell-cell communication, which in turn constitutes the basis for the coordinated function of CNs, we hypothesized that CNs would best be reconstructed within individual spots, rather than across neighboring spots. To increase robustness, spots are grouped in clusters of similar cellular composition, termed composition classes. Composition-based clustering of the tissue represents a major advantage of this method, and distinguished it from other methods, such as Squidpy11 or Giotto12, that assign an identity to each spot based on marker gene expression or on the most abundant cell type. While preserving the complexity of the cell type composition of the analyzed tissue, composition-based clustering of spots also confers robustness towards variations in expression levels due to batch effects. Indeed, other spatial analysis methods such as Starfysh66 have found that finding inter-sample commonalities using composition-based clusters is easier compared to finding common transcriptome-based clusters between samples. Still, batch analysis and, if needed, correction of the ST data is recommended prior to analysis with ISCHIA. Composition-based clustering of spots allows to restrict downstream analysis to similar mixtures of cells, filtering out transcriptome heterogeneity arising from distinct cellular compositions, which might act as a confounder variable when performing differential gene expression or cell-cell interaction predictions.<br /> To reconstruct CNs, ISCHIA performs co-occurrence analysis of cell types within CCs. Other tools build a neighborhood graph using spatial coordinates of spots and a fixed number of adjacent spots11,12,66, and therefore ignoring the missing data between spots as well as the multicellular nature of each spot, ISCHIA leverages the inherent proximity of mixed transcriptomes within individual spots to infer cellular neighborhoods. Hence, the cell types within the spots, rather than the spots themselves, are the nodes of the CN. This approach allows for reconstruction of much smaller CNs, operating in close spatial proximity, a prerequisite for juxtacrine and paracrine signaling between cells. ISCHIA further predicts LR interaction as edges connecting cell types within spots, not across multiple spots. Finally, by integrating co-occurrence of cell types, co-occurrence of LR pairs, and associated gene signatures, ISCHIA infers CN function.
Minor:
When a priori testing for LR interactions without restricting these interactions to predicted interactions, it would be informative to have an estimate of how many of the positively co-occurring interactions coincide with their predictions. As the authors state, it's hard to judge novel interactions without orthogonal validation, but a large overlap between predictions and the results presented here might instill confidence in the novel findings.
We thank the reviewer for the comment and now label in green, in Fig 4a, the positively co-occurring interactions that are also predicted by Omnipath, NicheNet or CellTalkDB.
Fig 4D: It's hard to judge very small p-values on this plot, might be better to plot -log10(pval).
We have now changed this plot to display the differential co-occurrence score, calculated as FDRinflamed - FDRnon-inflamed.
The axes on some of the plots should be better defined in the figure legends (e.g. Fig 4D, 5C)
We have included better axis descriptions.
I'm not an expert in inflammation or IBD biology, so I will defer that to other reviewers more suited to comment on this.
Reviewer #1 (Significance):
The proposed method provides a reasonable framework for studying co-occurences of cell types and transcripts (particularly ligand-receptor pairs), which are currently questions of great interest to the community applying novel spatial transcriptomics technologies in many different domains of life sciences. The manuscript is very well written, and provides a clear and consistent logical flow. The manuscript can be easily read and understood both by specialized users as well as biologist/clinical end-users wanting to apply the proposed technique. The addition of experimental data using an orthogonal technology to validate computational predictions illustrates nicely the power of the proposed approach.
Although the presented approach is methodologically rather simple (which is not necessarily a disadvantage), it is novel in the field as far as I know and a good implementation is likely to see great adoption by the field, especially if it's well documented, maintained and integrated into existing data processing workflows. The authors should however compare their approach fairly with the rest of the available packages in order to convince the reader.
Although the presented data seems convincing to me, the authors should take greater care of defining good practice statistical reporting of their findings. Even though these tools are often hypothesis-generating and predictions should always be experimentally validated, some end-users might interpret p-values literally. As such, proper multiple-testing correction and analysis of critical confounding factors should be carried out as to set an example.
I'm a computational biologist with expertise in method development (machine learning and statistical modelling) for spatial multi-omics assays. I'm not an expert in inflammation or IBD biology, so I will defer that to other reviewers more suited to comment on this.
Reviewer #2 (Evidence, reproducibility and clarity):
The authors developed ISCHIA to study co-occurence of cell types and transcript species. This work was further extended to study cell-cell interactions based on ligand-receptor co-expression. The observation by ISCHIA was further validated using hybridization based spatial transcriptomics approaches. ISCHIA was applied to study healthy and inflamed human colons.
Referees cross-commenting
As the reviewer #1 pointed, there is no description about existing methods. The reviewer #1 only asked stating qualitative differences.
If the manuscript is mainly for IBD and ISCHIA is the bioinformatics steps they followed, I would agree with the reviewer #1. However, the authors wanted to say that it is a new software. I still think that full benchmarking is needed in this circumstance.
We thank the reviewer for the remark and we have compared the analysis performed by ISCHIA with other state-of-the-art tools. The main difference between ISCHIA with respect to other methods such as Spacemake (Sztanka-Toth et al. 2022), Squidpy (Palla et al. 2022) and Giotto (Del Rossi et al. 2022), is that ISCHIA computes cell-type and LR co-occurrence within individual spots, not between neighboring spots. We include here an extensive comparison with other methods for this reviewer, and now discuss the differences between ISCHIA and other tools in the manuscript.
For neighborhood analysis, Spacemake and Squidpy use spatial coordinates of spots to identify neighbors among them (neighborhood sets are defined as a fixed number of adjacent spots in a square or hexagonal grid). Squipdy computes co-occurrence of clusters in spatial dimensions, however it uses the coordinates of spots and clusters to calculate co-occurrence of entire clusters of spots, not of cell-types within spots. This approach ignores the missing data between spots, as well as the multicellular nature of each spot. Similarly to Squidpy, Giotto assigns a score of a cell type to each spot upon deconvolution, to further identify the spatial patterns of the major cell taxonomies across all the spots on the tissue. For image-based spatial technologies with single cell resolution, Giotto creates a neighborhood graph of the single-cells to study gene expression patterns. This is similar to the approach we used to validate our predictions from Visium data in the Resolve dataset.
Tangram (Biancalani et al. 2021) is a deep learning approach to harmonize sc/snRNA-seq data with in situ, histological, and anatomical data, toward a high-resolution, integrated atlas. Tangram focuses on learning spatial gene-expression maps transcriptome-wide at single-cell resolution, and relating those to histological and anatomical information from the same specimens. However, it does not address cell-cell and ligand receptor interactions, nor co-occurrences from spatial data. Therefore, Tangram can be used to improve the deconvolution step of ISCHIA, to improve the definition of cellular composition in multicellular spatial spots.
Starfysh (He et al. 2022) is a computational toolbox for joint modeling of ST and histology data, dissection of refined cell states, and systematic integration of multiple ST datasets from complex tissues. It uses an auxiliary deep generative model that incorporates archetypal analysis and any known cell state markers to avoid the need for a single-cell-resolution reference. Starfysh also clusters spots based on cell type composition, and terms group of spots with similar composition “spatial hubs”. They use spatial hubs to integrate multiple samples, and to uncover regions with varying composition of cell states. As we propose in ISCHIA, the Starfysh authors also suggest that finding inter-sample commonalities using spatial hubs is easier compared to finding common clusters between samples. Starfysh addresses the co-localization of cell states by calculating the spatial correlation index (SCI) within a certain hub and penalizing the calculated correlation with a weight matrix τ in a way that : τ_(between two spots i,j)=1 if the coordinate distance of spot i and spot j was less than √3 else τ_(between two spots i,j)=0 . While this approach provides a measure of cell state co-localization across a spatial hub, it looks at the problem from an inter-spot perspective, similar to Squipdy and Giotto. Again, this is different from ISCHIA that calculates co-occurrence within spots.
In conclusion, none of the current approaches focuses on addressing the co-occurrence of cell types and molecules within individual Visium spots. As the analysis is fundamentally different, we did not perform a full quantitative benchmark. We agree, however, that these differences need to be addressed in the manuscripts. To illustrate the different results obtained, we ran ISCHIA on a Visium slide of a coronal section of the mouse brain, which was also analyzed using Squidpy (https://squidpy.readthedocs.io/en/stable/notebooks/tutorials/tutorial_visium_hne.html) and Giotto (https://rubd.github.io/Giotto_site/articles/mouse_visium_brain_201226.html#part-9-spatial-network). We clustered the spots based on cell composition and then ran celltype co-occurrence analysis within each composition class (Supplementary Figure 1).
From the Results section:
State-of-the-art analysis tools for Visium data often treat every spot as a single datapoint, and compute co-localization, network or cell-cell interactions analysis between neighboring spots (inter-spot analysis). We hypothesized that CNs would be best reconstructed within individual spots (intra-spot analysis), as their mixed transcriptome contains information about locally occurring cell types, expressed ligands and receptors, and activated signaling pathways. As inferring CNs in each individual spot separately would be noisy, sparse, computationally intensive, and would lack statistical power, ISCHIA first divides the tissue into clusters of spots with similar cellular composition - termed composition classes (CCs) (Fig 1a). CCs are thus groups of spots containing similar mixtures of cells, or cellular communities, e.g, all spots capturing colonic crypts. To achieve the division of the tissue into CCs, spot transcriptomes are deconvoluted, yielding a cell type composition matrix (spot × contribution of each cell type), which is then subjected to dimensionality reduction and k-means clustering. ISCHIA allows for both reference-based deconvolution, with tools such as SPOTlight7 or RCTD8, and reference-free deconvolution9. Upon deconvolution, ISCHIA summarizes spot gene expression data in a cell type presence-absence matrix, where each listed cell type is associated with a probability to be present in a given spot (p > 0.1). Each spot is thus represented as a mixture of cell types, and similar mixtures are then clustered together in CCs. We applied ISCHIA on a publicly available Visium slide of a coronal section of the mouse brain (10x Genomics), using as a reference for deconvolution a scRNA-seq dataset of ~14,000 adult mouse cortical cells from the Allen Institute10. Composition-based clustering of the spots yielded 5 CCs, which broadly reflect the annotated anatomical regions (Supplemental Fig 1). ISCHIA then computes cell type co-occurrence for every CC separately, identifying spatial association of cells in close proximity (Supplemental Fig xx). Intra-spot analysis reconstructs cellular networks with cell types as nodes, and is distinct from inter-spot networks analysis employed by other tools on this sample, in which spots are used as nodes11,12.
We also discuss differences between ISCHIA and other tools in the Discussion:
ISCHIA differs from other analysis tools for Visium data in that it predicts CNs within spots and not across spots. Indeed, spot data from sequencing-based ST methods such as Visium, simultaneously captures information about 1) cell types, 2) expressed LR genes, and 3) associated transcriptional responses at multiple spatially restricted locations. As proximity is a prerequisite for juxtacrine and paracrine cell-cell communication, which in turn constitutes the basis for the coordinated function of CNs, we hypothesized that CNs would best be reconstructed within individual spots, rather than across neighboring spots. To increase robustness, spots are grouped in clusters of similar cellular composition, termed composition classes. Composition-based clustering of the tissue represents a major advantage of this method, and distinguished it from other methods, such as Squidpy11 or Giotto12, that assign an identity to each spot based on marker gene expression or on the most abundant cell type. While preserving the complexity of the cell type composition of the analyzed tissue, composition-based clustering of spots also confers robustness towards variations in expression levels due to batch effects. Indeed, other spatial analysis methods such as Starfysh66 have found that finding inter-sample commonalities using composition-based clusters is easier compared to finding common transcriptome-based clusters between samples. Still, batch analysis and, if needed, correction of the ST data is recommended prior to analysis with ISCHIA. Composition-based clustering of spots allows to restrict downstream analysis to similar mixtures of cells, filtering out transcriptome heterogeneity arising from distinct cellular compositions, which might act as a confounder variable when performing differential gene expression or cell-cell interaction predictions.
To reconstruct CNs, ISCHIA performs co-occurrence analysis of cell types within CCs. Other tools build a neighborhood graph using spatial coordinates of spots and a fixed number of adjacent spots11,12,66, and therefore ignoring the missing data between spots as well as the multicellular nature of each spot, ISCHIA leverages the inherent proximity of mixed transcriptomes within individual spots to infer cellular neighborhoods. Hence, the cell types within the spots, rather than the spots themselves, are the nodes of the CN. This approach allows for reconstruction of much smaller CNs, operating in close spatial proximity, a prerequisite for juxtacrine and paracrine signaling between cells. ISCHIA further predicts LR interaction as edges connecting cell types within spots, not across multiple spots. Finally, by integrating co-occurrence of cell types, co-occurrence of LR pairs, and associated gene signatures, ISCHIA infers CN function.
Reviewer #2 (Significance):
As the authors wanted to introduce ISCHIA as a new tool, discussion and comparison with the previous approaches are essential. The manuscript lacks discussion and the comparison with others. Co-localization has been discussed already in many articles including [PMID:325799]. It does not seem to require additional packages to study co-localization for cell type. There are many cell-cell interaction studies using ligand-receptor co-localization [ref; stLern, SpaGene, and many]. It is not well documented about the relationships with the previous works. Given the advances in algorithms for spatial transcriptomics, it is very uncertain that ISCHIA can provide additional knowledge or contribute to algorithmic development.
We agree with the reviews that there is an increasing body of work addressing co-localization of cell type and cell-cell interactions in spatial transcriptomic data. However, we assert that the introduction of our method holds substantial relevance and adds value to the field, notwithstanding its ostensible simplicity. Our method has been well-received within the scientific community, indicating its applicability and potential significance in deciphering complex cellular ecosystems. We believe our approach offers a distinct conceptual advantage by enabling the analysis of cellular communities within individual Visium spots, rather than solely between them, allowing for a more refined exploration of cellular interactions and co-localizations within specific spatial domains.
Previously, Visium data were generated by Elmentaite et al. (Nture 2021) against healty and IBD samples. what are the new findings of the manuscript?
Visium data generated by Elmentaite et al is from pediatric Crohn's disease, not adult Ulcerative colitis. Spatial analysis of IBD samples has been performed by Nanostring and by CODEX (Garrido-Trigo et al. 2022; Mayer et al. 2023). Publication of our datasets (both Visium and Resolve) will increase the body of patient data available to the community, and should be considered positively.
Here, we use our dataset to demonstrate the ability of ISCHIA to reconstruct cellular networks within Visium spots, and identify a M-cell-fibroblast network in inflamed regions of UC patients. We further identify differentially co-occurring LRs in the inflammatory CC5 centered around EDN1, SEMA3C, and CXCL5. We further reveal inflammation-induced, protective responses from the colonic crypt involving the complement cascade and the immuno-modulator SECTM1. Finally, we apply co-occurrence analysis to an independent mouse Visium dataset and uncover differentially co-occurring LR pairs shared between the inflamed human and murine colon. ISCHIA is an hypothesis generating tool, and its findings should be extensively characterized and validated in larger cohorts.
Reviewer #3 (Evidence, reproducibility and clarity):
Summary
The authors provide a framework to analyze spatial transcriptomics (ST) data in terms of spatial co-occurrence of cell types, and ligand-receptor pairs. The method was applied to an ulcerative colitis sequencing-based data set (10x Visium) and validated using a matched hybridization-based data set (Molecular Cartography).
Major Comments
The Visium data set consisted of a single slide with four samples. The authors should clarify if the current implementation of their method is limited to a single Visium slide.
We thank the reviewer for the remark and now state in the text that ISCHIA can be applied to any Visium, Resolve or other spatial transcriptomic dataset. Visium samples originating from different slides and datasets can be fed into the ISCHIA pipeline, as it is quite robust to batch effects. Indeed, while other methods assign a cell-type identity to spots based on the most abundant cell type detected by deconvolution algorithm, ISCHIA summarizes spot gene expression data in a presence-absence matrix. ISCHIA is therefore robust to variation in expression levels due to batch effects. This was also recently reported in the analytical tool Starfysh (He et al. 2022), which performs similar clustering of spots based on cell type composition. This is included in the Discussion:
While preserving the complexity of the cell type composition of the analyzed tissue, composition-based clustering of spots also confers robustness towards variations in expression levels due to batch effects. Indeed, other spatial analysis methods such as Starfysh66 have found that finding inter-sample commonalities using composition-based clusters is easier compared to finding common transcriptome-based clusters between samples. Still, batch analysis and, if needed, correction of the ST data is recommended prior to analysis with ISCHIA.
In Supplementary Table 1, I think it would be useful to include the minimum number of counts for the Ligand-Receptor genes. Given that the current threshold is 1, I think it warrants a discussion if the minimum number of counts has an effect on whether the ligand-receptor pair is significantly co-occurring (i.e. if ligand-receptor pairs with more counts are more likely to be significant).
We agree with the reviewer that increasing the threshold to >1 will reduce the number of significantly co-occurring LRs, but also increase false negative predictions. We chose to set the threshold for gene count > 0 to account for the sparsity of captured transcripts. Indeed, dropouts due to low capture rate of Visium will lead to false negative predictions. In single cell RNA sequencing analysis, gene expression is analyzed in cell clusters rather than in single cells. Similarly, ISCHIA calculates LR co-occurrence in composition classes, that is clusters of spots of similar cell composition. Aggregating spots in composition classes thus mitigates the effects of low capture rate and consequent false negative predictions. We now include a sentence explaining this concept in the Results section:
The count threshold is a user defined parameter that can be increased to restrict the co-occurrence analysis to highly expressed ligands and receptors. To account for the sparsity of ST data, ISCHIA calculates LR co-occurrence within composition classes, that is clusters of spots with similar cell mixtures. Aggregating spots in composition classes thus mitigates the effects of low transcript capture rate and consequent false negative predictions.
Given the effect of outliers in the Pearson correlation and the nature of the expression values for VIsium data, I think that the Spearman rank correlation is better suited to estimate the correlation between the expression values of the ligand-receptor pairs than the Pearson correlation (the default in R).
We thank the reviewer for the comment and now rank positively co-occurring LR based on Spearman correlation. See Fig 3a and Supplementary Table 1.
In the section titled "Differential co-occurrence identifies niche-specific response programs", it is unclear whether the spatial co-occurrence analysis was done within each CC.
We now specify that we computed co-occurrence analysis of ligands and receptor genes in all spots of our dataset across all CCs. We now include FDR corrected p-values in this analysis. Only after computing this broad co-occurrence analysis, we focus on differential co-occurrence comparing conditions or composition classes.
Minor Comments
I found a few typos in the manuscript
In the Abstract, "tecniquee" instead of "techniques"
On page 10, under "Integration and annotation of scRNASeq data.", "W" instead of "We"
On page 11, there is an equation rendering error: P(lt) = $p_lt
We thank the reviewer for these comments and have now corrected the typos.
Reviewer #3 (Significance):
The method proposed takes advantage of work done in ecology to leverage the spatial context of ST data. Furthermore, the methods proposed goes beyond describing spatial patterns present in the data, but allows for the comparison between two conditions of interest. The method proposed will be of interest to the growing number of researchers generating ST data.
My expertise is in statistical methods for single cell and spatial transcriptomics data. Furthermore, I have extensive experience analyzing single cell and spatial transcriptomics data in the context of liver diseases.
-
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Referee #3
Evidence, reproducibility and clarity
Summary
The authors provide a framework to analyze spatial transcriptomics (ST) data in terms of spatial co-occurrence of cell types, and ligand-receptor pairs. The method was applied to an ulcerative colitis sequencing-based data set (10x Visium) and validated using a matched hybridization-based data set (Molecular Cartography).
Major Comments
The Visium data set consisted of a single slide with four samples. The authors should clarify if the current implementation of their method is limited to a single Visium slide.
In Supplementary Table 1, I think it would be useful to include the minimum number of counts for the Ligand-Receptor genes. Given that the current threshold is 1, I think it warrants a discussion if the minimum number of counts has an effect on whether the ligand-receptor pair is significantly co-occurring (i.e. if ligand-receptor pairs with more counts are more likely to be significant).
Given the effect of outliers in the Pearson correlation and the nature of the expression values for VIsium data, I think that the Spearman rank correlation is better suited to estimate the correlation between the expression values of the ligand-receptor pairs than the Pearson correlation (the default in R).
In the section titled "Differential co-occurrence identifies niche-specific response programs", it is unclear whether the spatial co-occurrence analysis was done within each CC.
Minor Comments
I found a few typos in the manuscript
In the Abstract, "tecniquee" instead of "techniques"
On page 10, under "Integration and annotation of scRNASeq data.", "W" instead of "We"
On page 11, there is an equation rendering error: P(lt) = $p_lt
Significance
The method proposed takes advantage of work done in ecology to leverage the spatial context of ST data. Furthermore, the methods proposed goes beyond describing spatial patterns present in the data, but allows for the comparison between two conditions of interest. The method proposed will be of interest to the growing number of researchers generating ST data.
My expertise is in statistical methods for single cell and spatial transcriptomics data. Furthermore, I have extensive experience analyzing single cell and spatial transcriptomics data in the context of liver diseases.
-
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Referee #2
Evidence, reproducibility and clarity
The authors developed ISCHIA to study co-occurence of cell types and transcript species. This work was further extended to study cell-cell interactions based on ligand-receptor co-expression. The observation by ISCHIA was further validated using hybridization based spatial transcriptomics approaches. ISCHIA was applied to study healthy and inflamed human colons.
Referees cross-commenting<br /> As the reviewer #1 pointed, there is no description about existing methods. The reviewer #1 only asked stating qualitative differences.
If the manuscript is mainly for IBD and ISCHIA is the bioinformatics steps they followed, I would agree with the reviewer #1. However, the authors wanted to say that it is a new software. I still think that full benchmarking is needed in this circumstance.
Significance
As the authors wanted to introduce ISCHIA as a new tool, discussion and comparison with the previous approaches are essential. The manuscript lacks discussion and the comparison with others. Co-localization has been discussed already in many articles including [PMID:325799]. It does not seem to require additional packages to study co-localization for cell type. There are many cell-cell interaction studies using ligand-receptor co-localization [ref; stLern, SpaGene, and many]. It is not well documented about the relationships with the previous works. Given the advances in algorithms for spatial transcriptomics, it is very uncertain that ISCHIA can provide additional knowledge or contribute to algorithmic development.
Previously, Visium data were generated by Elmentaite et al. (Nture 2021) against healty and IBD samples. what are the new findings of the manuscript?
-
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Referee #1
Evidence, reproducibility and clarity
Summary:
In this manuscript Lafzi et al. present a novel computational framework (ISCHIA) for the analysis of spatial occurrence patterns, be it of cells or transcript species, found in spatial transcriptomics datasets. The authors also show its applications in finding differentially co-occurring ligand-receptor pairs, as well as inter-species analysis to find conserved cell signalling pathways. ISCHIA consists of a well-documented R package and utilizes empirical probabilistic estimations of non-random co-occurrence, as used in the field of ecology, which to my knowledge is novel in the field. The authors also validate their predictions using an orthogonal technology (in situ hybridization-based spatial transcriptomics), which is a nice addition to the computational work presented in the manuscript.
Major:
- When determining the composition classes, the authors discard 4 out of 8 clusters of composition classes, partly due to being highly patient-specific. It's unclear how sensitive ISCHIA is for batch effects which might affect the measured cellular fractions. Given that the presence of batch effects is highly likely with ST methods, due to the sample processing procedures, it would help the reader/potential user to estimate the impact these could have on the resulting output. It would also be useful to plot a version of the UMAP with sample labels as to see if the remaining clusters are properly mixed (at least between replicates of the same condition). Additionally, it would help to illustrate that the biological findings reported in the manuscript are supported across more than 1 biological replicate.
- In the LR analysis, the authors state that ISCHIA's predictions are agnostic to gene expression levels, as the authors model expression as a Boolean (gene count threshold > 0). Wouldn't low expression levels result in increased drop-out due to imperfect sensitivity? This would likely inflate false negative predictions at low expression levels.
- The authors show the enrichment of particular pathways/genesets in differential gene expression comparing interacting vs noninteracting spots (through LR expression) within the same CC. It is however unclear if this enrichment stems from a random sampling of the CC (with possible confounding factors such as batch effect, QC metrics, which might also have a spatial component such as localized tissue degradation) or from the actual interaction. Adding a measure of uncertainty, such as by permuting over interaction-labels to generate a proper null distribution, would help the user to ascertain the robustness of the results. For clarity, it would also be good to add how this is exactly computed to the Methods section.
- It's unclear if the p-values in the manuscript are adjusted for multiple comparisons or not. Given the number of hypotheses being tested here, this is a crucial issue.
- The authors don't really mention any of the existing state-of-the-art methods (e.g. Squidpy, Spacemake, Giotto, ...). This doesn't necessitate a full benchmark, but at least the authors should then state qualitatively what the difference is between the chosen approach and already available packages, with their respective added advantages/disadvantages.
Minor:
- When a priori testing for LR interactions without restricting these interactions to predicted interactions, it would be informative to have an estimate of how many of the positively co-occurring interactions coincide with their predictions. As the authors state, it's hard to judge novel interactions without orthogonal validation, but a large overlap between predictions and the results presented here might instill confidence in the novel findings.
- Fig 4D: It's hard to judge very small p-values on this plot, might be better to plot -log10(pval).
- The axes on some of the plots should be better defined in the figure legends (e.g. Fig 4D, 5C)
I'm not an expert in inflammation or IBD biology, so I will defer that to other reviewers more suited to comment on this.
Significance
The proposed method provides a reasonable framework for studying co-occurences of cell types and transcripts (particularly ligand-receptor pairs), which are currently questions of great interest to the community applying novel spatial transcriptomics technologies in many different domains of life sciences. The manuscript is very well written, and provides a clear and consistent logical flow. The manuscript can be easily read and understood both by specialized users as well as biologist/clinical end-users wanting to apply the proposed technique. The addition of experimental data using an orthogonal technology to validate computational predictions illustrates nicely the power of the proposed approach.
Although the presented approach is methodologically rather simple (which is not necessarily a disadvantage), it is novel in the field as far as I know and a good implementation is likely to see great adoption by the field, especially if it's well documented, maintained and integrated into existing data processing workflows. The authors should however compare their approach fairly with the rest of the available packages in order to convince the reader.
Although the presented data seems convincing to me, the authors should take greater care of defining good practice statistical reporting of their findings. Even though these tools are often hypothesis-generating and predictions should always be experimentally validated, some end-users might interpret p-values literally. As such, proper multiple-testing correction and analysis of critical confounding factors should be carried out as to set an example.
I'm a computational biologist with expertise in method development (machine learning and statistical modelling) for spatial multi-omics assays. I'm not an expert in inflammation or IBD biology, so I will defer that to other reviewers more suited to comment on this.
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity):
Major comments:
- 1/ The model system used in this work is referred to as "organoids", with the premise that organoids, as representatives of the original tissue, can be used to study tissue development. However, the organoids presented in the study are spherical structures, and the paper does not provide any information about how and to what extent these organoids represent the original tissue. Furthermore, it would be difficult to expect these organoids to accurately represent human breast tissue, as they are derived, to the best of the reviewer's understanding (it is not explicitly noted, but rather the text refers the reader to several previous works), from primary human breast epithelial cells that had been cultured for 6 passages in 2D culture. The CD24/CD44 flow cytometry profile of these 2D cultures, as well as the organoids derived from them, shows a unimodal distribution of CD24 and CD44 expression, and does not show distinct cell populations, as typical in primary breast epithelial cells that have not been cultured in 2D
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Response 1:
We understand the concern raised by the reviewer. Before initiating the study we have carefully characterized our model (See Revision Section part 2.2, for the revisions that have already been carried out).
In parallel, to further address this point, we plan to perform an extensive characterization of our mammary primary cells as well as our 3D-matrigel embedded culture. This will be achieved through flow cytometry using other multiple lineage-specific surface markers for luminal progenitor cells (such as CD49f−/low/EpCAM+), and basal/myoepithelial cells (such as CD49f+/EpCAM−/low).
Thus, overall our experiments will confirm that our growth conditions (which we intend to describe in greater details in the methods section) for multipotent mammary stem cells can generate multi-lineage organoids.
2/ There appears to be confusion between concepts: primarily confusing a basal phenotype with a stem cell phenotype, and progenitor activity with stem cell activity. Because the 3D organoid model does not display a replication of luminal differentiation, it cannot be used as a proxy for stem cell function. The results from the miRNA screen and the subsequent experiments support two conclusions: 1. miR-160b-3p leads to an enhanced mesenchymal phenotype, manifested by reduced CD24 and enhanced CD44 expression. 2. miR-160b-3p leads to increased organoid formation. The latter may be interpreted, at best, as higher basal progenitor activity, but is not a measure of stem cell activity, as that would require the cells to give rise to more than one lineage, which is not shown here.
- Response 2:
We agree with the reviewer on the confusions between several concepts that we did not discriminate enough. The planned characterization of our cultures will allow us to better define the nature of our primary cell population and avoid any confusion (See Response 1). Indeed, an organoid is a self-organized 3D tissue that typically originates from stem cells (pluripotent, fetal or adult). Therefore, we will replace the term “stem cell activity” through the article by “stem/progenitor activity”.
3/ Overall, the information from figures 1-4 indicate that miR-160b-3p is driving and is associated with mesenchymal differentiation, possibly with EMT.
Response 3:
We agree with the reviewer that our data suggest that miR-160b-3p is driving and is associated with mesenchymal differentiation. As suggested by reviewer, to further evaluate the possible involvement of EMT, we will analysed using RT-qPCR, as we described in Fessart et al, (May 30, 2016 https://doi.org/10.7554/eLife.13887), the expression of the main EMT genes in miR-106a-3p cells. These results will be also confirmed at the protein level.
4/ In contrast to the work in the breast 3D culture model, the experiments with hESCs are interesting and do support a role of this miR in stemness.
Response 4:
We would like to extend our gratitude to the reviewer for their valuable comments on this aspect of our work.
There are several relatively minor comments that cumulatively somewhat undermine the strength of the work:
5/ Abstract: the sentence "organoids can be directly generated from human epithelial cells by only one miRNA, miR-106a-sp" needs better clarification.
- We agree with the reviewer, this sentence will be modified accordingly.
6/ Page 5 line 103: HMECs is a term that generally refers to human mammary epithelial cells, not a specific derivation or subpopulation thereof.
- We will replace HMEC by human primary mammary epithelial cells.
7/ The graph in Fig. 1A is unnecessary, it shows only one bar.
- We will remove this panel in the revised version of the manuscript.
8/ It is unclear if all the HMECs were derived from the same donor, or several donors. There is no information about the donor and how the tissue and cells were derived. In general, it is not entirely clear how the cells were collected, processed, stored, and cultured from the time they were obtained from the donor until their use in the current study.
- We will include in the material section the details information on our primary cells and our culture conditions
9/ The key for Fig. 2D is unclear. The axes read "density" but the text refers to "intensity". Fluorescence intensity in flow cytometry is usually measured on a log scale. Differences on a linear scale are not usually considered meaningful. The authors should clarify why they chose a linear scale for this screen.
- The screening was conducted using a high throughput microscope that measures the relative signal intensity, thus the scale is based on linear signal intensity.
10/ In the miRNA screen, how long were the cells cultured after transfection, and was it enough time for them to shift phenotype?
- The timeline of the screening process is detailed in the Material section. The cells were cultured for 6 days following transfection for the CD44/CD24 staining and 8 days following transfection for the 3D culture, which provides sufficient time for shifting the phenotype.
11/ Page 6 line 154: The authors likely mean z-score, not z factor (two different things).
- We thank the reviewer for pointing this, this will be corrected.
12/ Page 7 line 161: "mir-106a-3p directly promotes the "transdifferentiation" of CD44low/CD24high cells phenotype into CD44high/CD24low cell phenotype" - is an unsupported statement, given that there could be several alternative explanations for the observed change in population ratios, including effects on survival or growth of cells of a certain population.
- Transdifferentiation (also known as lineage reprogramming, or -conversion), is a process in which one mature, specialized cell type changes into another without entering a pluripotent state. This process involves the ectopic expression of transcription factors and/or other stimuli. We agree with the reviewer's observation that we did not fully demonstrate the transdifferentiation process in terms of lineage. Therefore, we will use flow cytometry to analyse multiple lineage-specific surface markers for luminal progenitor cells (such as CD49f−/low/EpCAM+), and basal/myoepithelial cells (such as CD49f+/EpCAM−/low) following miR-106a-3p expression.
- Additionally, we acknowledge that there may be other alternative explanations; so we have already assessed the impact of mir-106a-3p on the population doubling time in culture to determine whether it affects the cells' survival or growth of the cells (See Section Part 2.2 for a description of experiments already carried out).
13/ There is need for quantification of the phenotypes described in Fig. 3C
- We thank the reviewer for this valuable suggestion and we will indeed characterize the 3D structure using flow cytometry.
14/ Figures 3 D-F it is not clear if the graphs display percentage or mean number (there is discrepancy between figure text and figure legend text), and when percentage, not clear for figure 3F out of what.
- We appreciate the reviewer's suggestion, and we have replaced the term 'axis' with 'Organoid Formation Capacity (OFC),' which is the commonly used nomenclature for this measure. OFC corresponds to the percentage of organoids per cell seeded, as explained in the legend.
15/ Fig. 5B, what is the statistical significance of the enrichment?
- In Figure 5B, the statistical significance of the enrichment is an FDR value of 0.024 and it is based on the multiple rotation gene-set testing (mroast) from the Limma R package (https://doi.org/10.1093/bioinformatics/btq401). This information will be included in the figure legend.
Reviewer #2 (Evidence, reproducibility and clarity):
In this work, Robert et al. utilize mammary organoid culture as an in vitro model of stem cell renewal and maintenance. Authors show that a putative mammary stem cell population, characterized as CD44high CD24low, is enriched in organoid culture relative to 2D monolayer culture. They conduct a microRNA (miR) screen and identify miR-106a-3p as a miRNA that enriches for stem cell (CD44high CD24low) and organoid formation capacity, confirming these findings using miR-106a-3p overexpressing cells. Authors also show that CBX7 overexpression achieves a similar enrichment in CD44high CD24low and organoid formation capacity in an miR-106a-3p dependent manner, though the rationale behind the connection between CBX7 and miR-106a-3p is not well defined. Finally, the manuscript conducts a transcriptomic analysis on miR-106a-3p-OE cells and aims to functionally validate its role in embryonic stem cells (ESCs) as a model that is versatile for renewal and differentiation studies. How this relates to mammary adult stem cells (ASCs) is not entirely clear. Overall, the manuscript contains significant technical and conceptual limitations, and the data presented do not support the major conclusions of the study. There are two overarching issues that question the validity of most of the findings in this study. Firstly, there is no clear demonstration that the structures reported as 3D organoids are indeed driven by multipotent stem cells (in all data and experimental approaches associated with this). Secondly, there is a lack of evidence to support the claim that CD44high CD24low cells are stem cells with an exclusive or enhanced capacity to generate multi-lineage organoids.
In addition to these general points, there are several specific major issues summarized below:
1/ Authors base their findings about mammary ASC renewal using a poorly defined organoid culture system that they claim is driven by ASC renewal and differentiation. These organoid growth conditions were originally developed to support growth of bronchial epithelial cells, and the authors have not presented data to objectively assess the validity of this extrapolation to expansion of mammary organoids. The authors did not present data to support the notion that these growth conditions generate multi-lineage organoids that expand from multipotent mammary stem cells.
- Response 1:
- This concern has been also raised by the 1st reviewer (See Response 12 from Reviewer 1).
- Our experiments will confirm that our growth conditions (which will be more comprehensively described in the methods section) can generate multi-lineage organoids from multipotent mammary stem cells. We will include these characterizations in the new version of the manuscript.
2/ Authors claim that miR-106a-3p downregulates stem cell differentiation and utilize 'organoid' culture to track the temporal expression of OCT4, SOX2 and NANOG during phases of organoid renewal and differentiation. However, mammary stem cell differentiation is associated with the emergence of luminal progenitor, mature luminal and myoepithelial lineages that are characterized by the expression of a well-defined set of markers. The authors did not investigate the expression of these markers in their 'differentiation' settings. Furthermore, modulators of major pathways reported to be critical for mammary ASC maintenance are lacking, including modulators of WNT, TGF/BMP, Notch and other pathways. As such, it is difficult to ascertain that organoids emerging under such culture conditions are the result of stem cell renewal and differentiation, as opposed to lineage-restricted proliferative non-ASCs, thus questioning the validity of many of the findings in this work.
- Response 2:
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As suggested by the reviewer, we will investigate the expression of emergence of luminal progenitor, mature luminal and myoepithelial lineages in our 'differentiation' settings using flow cytometry. This analysis will involve the examination of multiple lineage-specific surface markers, including CD49f−/low/EpCAM+ for luminal progenitor cells and CD49f+/EpCAM−/low for basal/myoepithelial cells in the 3D context.
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Secondly, we agree with the reviewer that major pathways such as Wnt, TGFb/BMP and Notch have been reported to be critical for mammary ASC maintenance. As suggested by the reviewer, to further evaluate the possible involvement of these pathways, we have analyzed our transcriptomic data, using PROGENy pathway, to investigate which pathways are regulated. The major pathways are depicted in a new figure and will be included in the manuscript (See Revision Section part 2.2, for the revisions that have already been carried out).
- There was significant enrichment indicating down-regulation of TGFβ, MAPK, WNT, PI3K genes along with gene sets representing other oncogenic pathways, are up-regulated such as the hypoxia response, JAK/STAT pathway, and p53 pathway activity (Figure A and B). Stem cells possess self-renewal activities and multipotency, characteristics that tend to be maintained under hypoxic microenvironments [1], thus this is not surprising to observe an up-regulation of Hypoxia pathway and activation of HIF1α transcription factor. In mammary stem cells, it has also been shown that p53 is critical to control the maintenance of a constant number of stem cells pool [2, 3]. Remarkably, it has been shown that cell sorting of the cells with a putative cancer stem cell phenotype (CD44+/CD24 low) express a constitutive activation of Jak-STAT pathway [4], which we observed as up-regulated along with STAT1 and STAT2 transcription factors. In parallel, we observe a down-regulation of Wnt signaling as well as PI3K pathways. Wnt is known to play important role in the maintenance of stem cells; its inhibition has been shown to lead to the inactivation of PI3 kinase signaling pathways to ensures a balance control of stem cell renewal [5]. Moreover, we observe a down-regulation of SMAD4 and SMAD3 transcription factors correlated with a down-regulation of TGFβ pathway. Signals mediated by TGF-β family members have been implicated in the maintenance and differentiation of various types of somatic stem cells [6].
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References
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Semenza GL. Dynamic regulation of stem cell specification and maintenance by hypoxia-inducible factors. Molecular aspects of medicine2016 Feb-Mar;47-48:15-23.
- Solozobova V, Blattner C. p53 in stem cells. World journal of biological chemistry2011 Sep 26;2(9):202-14.
- Cicalese A, Bonizzi G, Pasi CE, Faretta M, Ronzoni S, Giulini B et al. The tumor suppressor p53 regulates polarity of self-renewing divisions in mammary stem cells. Cell2009 Sep 18;138(6):1083-95.
- Hernandez-Vargas H, Ouzounova M, Le Calvez-Kelm F, Lambert MP, McKay-Chopin S, Tavtigian SV et al. Methylome analysis reveals Jak-STAT pathway deregulation in putative breast cancer stem cells. Epigenetics2011 Apr;6(4):428-39.
- He XC, Zhang J, Tong WG, Tawfik O, Ross J, Scoville DH et al. BMP signaling inhibits intestinal stem cell self-renewal through suppression of Wnt-beta-catenin signaling. Nature genetics2004 Oct;36(10):1117-21.
- Watabe T, Miyazono K. Roles of TGF-beta family signaling in stem cell renewal and differentiation. Cell research2009 Jan;19(1):103-15.
3/ The authors base their work on the claim that CD44high CD24low cells represent bona fide mammary ASCs. This claim is not support by functional work to show that organoid generation is exclusive to or enhanced in CD44high CD24low cells relative to other cells. The claim of differentiation of this stem cell population in vitro is not supported by data to show emergence of luminal progenitor, mature luminal and myoepithelial lineages that are characterized by the expression of a well-defined set of reported markers as abovementioned. Although miR-106a-3p is claimed to downregulate stem cell differentiation based on a gene set enrichment analysis, authors did not investigate the expression of mammary differentiation markers or the association of miR-106a-3p-transfected HMECs with gene set pathways involved in mammary gland differentiation.
- Response 3:
- This concern has been also raised by the 1st reviewer (See Response 1 from Reviewer 1). As explained in response 1, to address this point, we will perform an extensive characterization of our mammary primary cells as well as our 3D-matrigel embedded cultures using flow cytometry This analysis will involve multiple lineage-specific surface markers, including luminal alveolar progenitor (such as CD49f−/low/EpCAM+) and basal/myoepithelial cells (such as CD49f+/EpCAM−/low).
- Secondly, as suggested by the reviewer, we will investigate the expression of mammary differentiation markers or the association of miR-106a-3p-transfected human mammary epithelial cells with gene set pathways involved in mammary gland differentiation by bioinformatics using our transcriptomic data.
4/ Line 129: "Together, these results indicate that cells grown as organoids acquired a CD44high / CD24low expression pattern similar to that of stem/progenitor cells, which suggests that 3D organoids can be used to enrich breast stem cell markers for further screening". This conclusion is not supported by the data presented. It is not clear if the expression of these markers was acquired upon 3D culture. It could be that 3D culture better maintained and expanded already existing CD44high CD24low cells in 2D culture. It is also unclear if these cells are indeed organoid-forming. Authors have not isolated and tested the organoid-forming capacity of CD44high CD24low cells relative to other cells. Along the same line, the conclusion that " mir-106a-3p directly promotes the "transdifferentiation" of CD44low/CD24high cell phenotype into CD44high/CD24low cell phenotype" isn't justified. Experiments required to conclude that 'transdifferentiation' is involved are lacking. miR-106a-3p overexpression could be creating conditions that are permissive for expansion of already existing CD44high CD24low cells as opposed to 'transdifferentiation' of other cell types into this phenotype. The authors could FACS-isolate CD44low CD24low cells and treat these with control or miR-106a-3p to conclusively establish 'transdifferentiation'.
- Response 4:
- This concern has been also raised by the 1st reviewer (See Response 12 from Reviewer 1). Transdifferentiation (lineage reprogramming, or -conversion), is a process in which one mature, specialized cell type changes into another without entering a pluripotent state. This process involves the ectopic expression of transcription factors and/or other stimuli. We agree with the reviewer that we did not demonstrate the transdifferentiation process in terms of lineage. Therefore, we will use flow cytometry to analyze multiple lineage-specific surface markers for luminal progenitor (CD49f−/low/EpCAM+), and basal/myoepithelial cells (CD49f+/EpCAM−/low) following miR-106a-3p expression.
- Additionally, we cannot exclude the possibility that the 3D culture better maintained and expanded the pre-existing CD44high CD24low cells, as suggested by the reviewer. The reviewer recommends FACS-isolating CD44low CD24low cells. We apologise for any lack of clarity in our description. Technically, our primary cells have a low percentage of colony-forming efficiency (CFE) in 3D and not enough cells for FACS isolation of CD44low and CD24low cells. Therefore, we leveraged our knowledge that the expression of CBX7 potentiates the growth of 3D structures. We decided to FACS-isolate the different subpopulations of CD44 and CD24 cells to further elucidate the expression of miR-106a-3p in the different CD44/CD24 cell subpopulations. CBX7-transfected human mammary epithelial cells showed enrichment in CD44high/CD24low cells as compared to empty vector-transfected human mammary epithelial cells (Figure 4A-B). Subsequently, we separated the CD44high/CD24low (green) population from the CD44high/CD24high cell populations (blue) using flow cytometry to analyze the role of the endogenous expression of miR-106a-3p. The CD44high/CD24low population was the only one to exhibit endogenous expression of miR-106a-3p (Figure 4C). Blocking the endogenous expression of miR-106a-3p with LNA-anti-miR-106a-3p or LNA-control (Figure 4D) impacted organoid generation (Figure 4E-F). We will modify the text accordingly to include this point and this figure will be moved in the supplemental figure.
5/ Line 242: "These data demonstrate that miR-106a-3p is involved in the early cell differentiation process into the three germ layers ". The authors have not conducted functional or mechanistic work to show that miR-106a-3p is involved in morphological or transcriptomic differentiation changes in ESCs. This and other data would be necessary to substantiate this conclusion.
- Response 5:
- Indeed, we agree that we cannot conclude that the miR-106a-3p is involved in the early cell differentiation process into the three germ layers without demonstrating the differentiation changes in ESCs through transcriptomic analysis. To clarify the take-home message, we did not include the transcriptomic characterization of the differentiation changes in ESCs. Instead, we focused on assessing the impact on Oct4, Sox2, and Nanog expression. However, to further understand the impact of miR-106a-3p depletion on hESCs differentiation, we have also monitored the expression of specific genes upon induction of the three embryonic germ layers (See Section Part 2.2 for a description of the experiments already carried out).
Selected technical points:
6) Figure 1A: The Y-axis label is misleading and suggests multiple organoids seeded per cell? Organoid Formation Efficiency (OFE) or Organoid Formation Capacity (OFC) are the commonly used nomenclature for this.
- Response 6:
- Since reviewer 1 found that the graph in Fig. 1A to be unnecessary, we have decided to remove this panel in the revised version of the manuscript. Furthermore, as suggested, we will replace the axis “Number of organoids per cell seeded” with “Organoid Formation Capacity” (OFC), which is the commonly used nomenclature for this measure in the article.
7) Figure 2G: X-axis unclear. Is this meant to investigate the percentage of cells expressing CD44/CD24 as double high, double low, high/low and low/high?
- Response 7:
- We thank the reviewer for this careful examination of the manuscript. We apologize for this error, and will make the corresponding adjustment in Figure 2G.
8) Figure 4C: In HMEC-CBX7 cells, it is unclear whether the high miR-106a-3p levels in CD44high CD24low cells are due to CBX7 expression. An important control, HMEC-Control Vector, is missing.
- Response 8:
- This control has been done (see Section 3, for details on experiments that have been carried out).
Reviewer #3 (Evidence, reproducibility and clarity):
In this study the authors identify miR-106a-3p as a potent inducer of organoid formation from HMECs. Overexpression of miR-106a-3p induced formation of more organoids, increased the number of stem/progenitor cells and overall positively affected the stemness properties of the organoids, likely by affecting SOx2, Oct4 and Nanog expression.
Major comments:
1/ the flow of the paper is confusing, it appears that the authors are trying to combine several non-completed studies into one paper. It is not immediately evident how is the rationale or the conclusion supported by published data. For example, is there published evidence that Sox2/Oct4/Nanog are expressed in healthy mammary gland stem cells in vivo, or whether they have a role in establishment of stem cell population?
- Response 1:
- There is still a controversy about the existence of unipotent, bipotent or multipotent stem cells in mammary gland tissue [7-9]. Notably, OCT4, SOX2, and NANOG collectively form the core transcriptional network responsible for maintaining pluripotency in embryonic stem cells [10]. Evidence has accumulated over the past few years, accumulating evidence has supported the presence of stem cells in both mouse and human mammary [11]. Various strategies have been used to identify and isolate human breast stem/progenitor cells, including FACS sorting based on cell surface antigen expression. In addition, an in vitro cell culture system has been described allowing the propagation of human mammary epithelial cells in an undifferentiated state through their ability to proliferate in suspension as non-adherent mammospheres [12].. Simoe et al have demonstrated that stem cells isolated from both normal human breast and breast tumor cells display an increased expression of the embryonic stem cell genes NANOG, OCT4 and SOX2 [13]. Moreover, they have shown that the ectopic expression of any one of these factors, but in particular NANOG and SOX2, in breast cancer cells increases the pool of stem cells and enhances the cells' ability to form mammospheres. They observed higher expression of NANOG, OCT4, and SOX2 in the stem cell populations CD44+CD24−/low and EMA+CALLA+ compared to the rest of the sample population. Cells overexpressing these factors displayed an increase in the stem cell populations, thereby confirming the role of Nanog, Oct4, and Sox2 in the maintenance of human mammary stem cells. We will include this rationale in the manuscript.
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References
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Van Keymeulen A, Rocha AS, Ousset M, Beck B, Bouvencourt G, Rock J et al. Distinct stem cells contribute to mammary gland development and maintenance. Nature2011 Oct 9;479(7372):189-93.
- Deome KB, Faulkin LJ, Jr., Bern HA, Blair PB. Development of mammary tumors from hyperplastic alveolar nodules transplanted into gland-free mammary fat pads of female C3H mice. Cancer research1959 Jun;19(5):515-20.
- Shackleton M, Vaillant F, Simpson KJ, Stingl J, Smyth GK, Asselin-Labat ML et al. Generation of a functional mammary gland from a single stem cell. Nature2006 Jan 5;439(7072):84-8.
- Boyer LA, Lee TI, Cole MF, Johnstone SE, Levine SS, Zucker JP et al. Core transcriptional regulatory circuitry in human embryonic stem cells. Cell2005 Sep 23;122(6):947-56.
- LaMarca HL, Rosen JM. Minireview: hormones and mammary cell fate--what will I become when I grow up? Endocrinology2008 Sep;149(9):4317-21.
- Dontu G, Abdallah WM, Foley JM, Jackson KW, Clarke MF, Kawamura MJ et al. In vitro propagation and transcriptional profiling of human mammary stem/progenitor cells. Genes & development2003 May 15;17(10):1253-70.
- Simoes BM, Piva M, Iriondo O, Comaills V, Lopez-Ruiz JA, Zabalza I et al. Effects of estrogen on the proportion of stem cells in the breast. Breast cancer research and treatment2011 Aug;129(1):23-35.
2/ The organoids are all spherical, while mammary gland is characterized by branching. Therefore, the organoids are not recapitulating the gland morphology and the validation should include wider range of molecular markers.
- Response 2:
- This concern has been also raised by both reviewers 1 and 2. As explained in response 1 to reviewer 1, we will perform a comprehensive characterization of our mammary primary cells as well as our 3D-matrigel embedded culture culture using flow cytometry to examine multiple lineage-specific surface markers, including luminal alveolar progenitor (such as CD49f−/low/EpCAM+), and basal/myoepithelial cells (such as CD49f+/EpCAM−/low). As a result, our experiments will collectively confirm that our growth conditions (which will be more thoroughly described in the methods section) for multipotent mammary stem cells can indeed generate multi-lineage organoids.
3/ The choice of control is not clear. For example, why was miR-106a-5p chosen as a control? And why choose miR-106a-5p when a better candidate would be miR-106b-3p, that produces very high number of organoids as well? And how did the other miRNAs affected the CD44/24 profile?
- Response 3:
- We apologize for any lack of clarity in our description. It's important to note that miRNAs consist of two strands, -5p and -3p, and both strands can coexist and play distinct roles. For example, paired species of members in the let-7 and mir-126 families coexist and have different regulatory functions in reprogramming and differentiation of embryonic stem cells [1]. Several deep sequencing studies have demonstrated the coexistence of 5p/3p pairs in approximately half of the miRNA populations analyzed [2, 3]. Therefore, to determine whether the biological effect specifically resulted from the -3p strand, we also assessed the -5p strand.
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References
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Koh W, Sheng CT, Tan B, Lee QY, Kuznetsov V, Kiang LS et al. Analysis of deep sequencing microRNA expression profile from human embryonic stem cells derived mesenchymal stem cells reveals possible role of let-7 microRNA family in downstream targeting of hepatic nuclear factor 4 alpha. BMC genomics2010 Feb 10;11 Suppl 1(Suppl 1):S6.
- Jagadeeswaran G, Zheng Y, Sumathipala N, Jiang H, Arrese EL, Soulages JL et al. Deep sequencing of small RNA libraries reveals dynamic regulation of conserved and novel microRNAs and microRNA-stars during silkworm development. BMC genomics2010 Jan 20;11:52.
- Kuchenbauer F, Mah SM, Heuser M, McPherson A, Ruschmann J, Rouhi A et al. Comprehensive analysis of mammalian miRNA* species and their role in myeloid cells. Blood2011 Sep 22;118(12):3350-8.
4/ What is the CD44/CD24 profile in the organoid cultures from Figure 7?
- Response 4:
- The CD44/CD24 profile from the organoid cultures from Figure 7 will be assessed in the revised manuscript.
Part 2.2 Following the experiment that have been already carried out that will be included in the manuscript after revisions.
Reviewer #1 (Evidence, reproducibility and clarity):
Major comments:
The model system used in this work is referred to as "organoids", with the premise that organoids, as representatives of the original tissue, can be used to study tissue development. However, the organoids presented in the study are spherical structures, and the paper does not provide any information about how and to what extent these organoids represent the original tissue. Furthermore, it would be difficult to expect these organoids to accurately represent human breast tissue, as they are derived, to the best of the reviewer's understanding (it is not explicitly noted, but rather the text refers the reader to several previous works), from primary human breast epithelial cells that had been cultured for 6 passages in 2D culture. The CD24/CD44 flow cytometry profile of these 2D cultures, as well as the organoids derived from them, shows a unimodal distribution of CD24 and CD44 expression, and does not show distinct cell populations, as typical in primary breast epithelial cells that have not been cultured in 2D.
- Response:
- First, we assessed the aldehyde dehydrogenase activity (ALDH) [14] in our primary cells to demonstrate that these culture conditions preserve stem/progenitor properties (See Figure A, below). Additionally, we conducted immuno-staining of primary cells in 2D culture for lineage-specific luminal markers (CK18, MUC1) and basal markers (CK14, CK5), which revealed the heterogeneity of our population (See Figure B, below).
FIGURE FOR REVIEWERS
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Reference
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Ginestier C, Hur MH, Charafe-Jauffret E, Monville F, Dutcher J, Brown M et al. ALDH1 is a marker of normal and malignant human mammary stem cells and a predictor of poor clinical outcome. Cell stem cell2007 Nov;1(5):555-67.
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For the characterization of the 3D culture, we have presented early time points of 3D cell growth, which are mainly spherical (until Day 8). However, differentiation occurs in a stepwise manner, where single stem cells proliferate to form small spheroids before undergoing multilineage differentiation into organoids that initiate branching (Day 10). Subsequently, ducts undergo budding and lobule formation (Day 20). We have included a time-course representation of this 3D growth to illustrate the differentiation process (See Figure 1C).
- Under these culture conditions, the 3D structure retains its self-renewal activity and the ability to reseed and regenerate secondary tissues. We have also assessed the self-renewal capacity of our 3D structure (See Figure 1D).
FIGURE FOR REVIEWERS<br />
Page 7 line 161: "mir-106a-3p directly promotes the "transdifferentiation" of CD44low/CD24high cells phenotype into CD44high/CD24low cell phenotype" - is an unsupported statement, given that there could be several alternative explanations for the observed change in population ratios, including effects on survival or growth of cells of a certain population.
- Indeed, we cannot exclude the possibility of other alternative explanations. Therefore, we have already assessed the impact of miR-106a-3p on population doubling in culture to determine whether it has an effect on the survival or growth of the cells. We observed that miR-V cells stopped growing after approximately 15 population doublings, indicating their limited proliferative potential. In contrast, we found that miR-106a extended the lifespan of the cells, suggesting an effect on cell survival (Figure below). We will include this information in the manuscript.
FIGURE FOR REVIEWERS
- Why was GATA3 not included in the last analysis depicted in Fig. 7?
- We apologize for any lack of clarity in our description. It's important to note that GATA3 was not included in Figure 7. As explained in the text, GATA3 plays a role in regulating Nanog expression. However, it's worth noting that the cells did not form organoids when GATA3 was depleted, as illustrated in the results below. We will include these results in the revised version of the manuscript.
FIGURE FOR REVIEWERS
Reviewer #2:
2/ Authors claim that miR-106a-3p downregulates stem cell differentiation and utilize 'organoid' culture to track the temporal expression of OCT4, SOX2 and NANOG during phases of organoid renewal and differentiation. However, mammary stem cell differentiation is associated with the emergence of luminal progenitor, mature luminal and myoepithelial lineages that are characterized by the expression of a well-defined set of markers. The authors did not investigate the expression of these markers in their 'differentiation' settings. Furthermore, modulators of major pathways reported to be critical for mammary ASC maintenance are lacking, including modulators of WNT, TGF/BMP, Notch and other pathways. As such, it is difficult to ascertain that organoids emerging under such culture conditions are the result of stem cell renewal and differentiation, as opposed to lineage-restricted proliferative non-ASCs, thus questioning the validity of many of the findings in this work.
- We agree with the reviewer that major pathways such as Wnt, TGFβ/BMP and Notch have been reported to be critical for mammary ASC maintenance. As suggested by the reviewer, to further evaluate the possible involvement of these pathways, we have analyzed our transcriptomic data, using the PROGENy R package (version 1.16.0) (https://doi.org/10.1038/s41467-017-02391-6) and DoRothEA (version 1.6.0)-decoupleR (version 2.1.6) computational pipeline (https://doi.org/10.1101/gr.240663.118, https://doi.org/10.1093/bioadv/vbac016), to investigate the differential activation of major signaling pathways and transcriptional factors. The major pathways are depicted in the figure below and will be included in the manuscript.
FIGURE FOR REVIEWERS
- There was significant enrichment indicating down-regulation of TGFβ, MAPK, WNT, PI3K genes along with gene sets representing other oncogenic pathways, are up-regulated such as the hypoxia response, JAK/STAT pathway, and p53 pathway activity (Figure A and B). Stem cells possess self-renewal activities and multipotency, characteristics that tend to be maintained under hypoxic microenvironments [15], thus this is not surprising to observe an up-regulation of Hypoxia pathway and activation of HIF1α transcription factor. In mammary stem cells, it has also been shown that p53 is critical to control the maintenance of a constant number of stem cells pool [16,17]. Remarkably, it has been shown that cell sorting of the cells with a putative cancer stem cell phenotype (CD44+/CD24 low) express a constitutive activation of Jak-STAT pathway [18], which we observed as up-regulated along with STAT1 and STAT2 transcription factors. In parallel, we observe a down-regulation of Wnt signaling as well as PI3K pathways. Wnt is known to play important role in the maintenance of stem cells; its inhibition has been shown to lead to the inactivation of PI3 kinase signaling pathways to ensure a balance control of stem cell renewal [19]. Moreover, we observe a down-regulation of SMAD4 and SMAD3 transcription factors correlated with a down-regulation of TGFβ pathway. Signals mediated by TGF-β family members have been implicated in the maintenance and differentiation of various types of somatic stem cells [20].
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References
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Semenza GL. Dynamic regulation of stem cell specification and maintenance by hypoxia-inducible factors. Molecular aspects of medicine2016 Feb-Mar;47-48:15-23.
- Solozobova V, Blattner C. p53 in stem cells. World journal of biological chemistry2011 Sep 26;2(9):202-14.
- Cicalese A, Bonizzi G, Pasi CE, Faretta M, Ronzoni S, Giulini B et al. The tumor suppressor p53 regulates polarity of self-renewing divisions in mammary stem cells. Cell2009 Sep 18;138(6):1083-95.
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Hernandez-Vargas H, Ouzounova M, Le Calvez-Kelm F, Lambert MP, McKay-Chopin S, Tavtigian SV et al. Methylome analysis reveals Jak-STAT pathway deregulation in putative breast cancer stem cells. Epigenetics2011 Apr;6(4):428-39.
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He XC, Zhang J, Tong WG, Tawfik O, Ross J, Scoville DH et al. BMP signaling inhibits intestinal stem cell self-renewal through suppression of Wnt-beta-catenin signaling. Nature genetics2004 Oct;36(10):1117-21.
- Watabe T, Miyazono K. Roles of TGF-beta family signaling in stem cell renewal and differentiation. Cell research2009 Jan;19(1):103-15.
5) Line 242: "These data demonstrate that miR-106a-3p is involved in the early cell differentiation process into the three germ layers ". The authors have not conducted functional or mechanistic work to show that miR-106a-3p is involved in morphological or transcriptomic differentiation changes in ESCs. This and other data would be necessary to substantiate this conclusion.
- Response:
- Indeed, we cannot conclude that miR-106a-3p is directly involved in the early cell differentiation process into the three germ layers without demonstrating the differentiation changes in ESCs through transcriptomic analysis. To clarify the take-home message, it's important to note that we did not include the transcriptomic characterization of differentiation changes in ESCs. Instead, we focused on assessing the impact of miR-106a-3p depletion on the expression of Oct4, Sox2, and Nanog. However, to gain a deeper understanding of the effects of miR-106a-3p depletion on hESCs differentiation, we also monitored the expression of specific genes upon the induction of the three embryonic germ layers (see Figure A, B, and C below). We observed that the expression of endodermal genes was not or only weakly affected by the level of miR-106a-3p expression (Figure A), whereas the expression of mesoderm- and ectoderm-specific genes increased upon miR-106a-3p down-regulation (Figure B and C). We will include these findings in the manuscript.
FIGURE FOR REVIEWERS
Reviewer 2 (Minor points)
3) Figure 4C: In HMEC-CBX7 cells, it is unclear whether the high miR-106a-3p levels in CD44high CD24low cells are due to CBX7 expression. An important control, HMEC-Control Vector, is missing.
- Response
- The control HMEC-Vector is shown in panel Figure 4A for the FACS analysis. In Figure 4C, we did not include the control since there is no expression of miR-106a-3p, but it's important to note that this control was included in the experiment. As illustrated, there is no miR-106a-3p expression in the HMEC control cells. We intend to include this panel in Figure 4 of the manuscript.
FIGURE FOR REVIEWERS
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Referee #3
Evidence, reproducibility and clarity
In this study the authors identify miR-106a-3p as a potent inducer of organoid formation from HMECs. Overexpression of miR-106a-3p induced formation of more organoids, increased the number of stem/progenitor cells and overall positively affected the stemness properties of the organoids, likely by affecting SOx2, Oct4 and Nanog expression.
Major comments: the flow of the paper is confusing, it appears that the authors are trying to combine several non-completed studies into one paper. It is not immediately evident how is the rationale or the conclusion supported by published data. For example, is there published evidence that Sox2/Oct4/Nanog are expressed in healthy mammary gland stem cells in vivo, or whether they have a role in establishment of stem cell population?<br /> The organoids are all spherical, while mammary gland is characterized by branching. Therefore, the organoids are not recapitulating the gland morphology and the validation should include wider range of molecular markers.<br /> The choice of control is not clear. For example, why was miR-106a-5p chosen as a control? And why choose miR-106a-5p when a better candidate would be miR-106b-3p, that produces very high number of organoids as well? And how did the other miRNAs affected the CD44/24 profile?<br /> What is the CD44/CD24 profile in the organoid cultures from Figure 7?
Significance
The study provides a novel methodology to enrich for the mammary stem cells. However, many of the experiments need further clarification.
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Referee #2
Evidence, reproducibility and clarity
In this work, Robert et al. utilize mammary organoid culture as an in vitro model of stem cell renewal and maintenance. Authors show that a putative mammary stem cell population, characterized as CD44high CD24low, is enriched in organoid culture relative to 2D monolayer culture. They conduct a microRNA (miR) screen and identify miR-106a-3p as a miRNA that enriches for stem cell (CD44high CD24low) and organoid formation capacity, confirming these findings using miR-106a-3p overexpressing cells. Authors also show that CBX7 overexpression achieves a similar enrichment in CD44high CD24low and organoid formation capacity in an miR-106a-3p dependent manner, though the rationale behind the connection between CBX7 and miR-106a-3p is not well defined. Finally, the manuscript conducts a transcriptomic analysis on miR-106a-3p-OE cells and aims to functionally validate its role in embryonic stem cells (ESCs) as a model that is versatile for renewal and differentiation studies. How this relates to mammary adult stem cells (ASCs) is not entirely clear.
Overall, the manuscript contains significant technical and conceptual limitations, and the data presented do not support the major conclusions of the study. There are two overarching issues that question the validity of most of the findings in this study. Firstly, there is no clear demonstration that the structures reported as 3D organoids are indeed driven by multipotent stem cells (in all data and experimental approaches associated with this). Secondly, there is a lack of evidence to support the claim that CD44high CD24low cells are stem cells with an exclusive or enhanced capacity to generate multi-lineage organoids.
In addition to these general points, there are several specific major issues summarized below:
- Authors base their findings about mammary ASC renewal using a poorly defined organoid culture system that they claim is driven by ASC renewal and differentiation. These organoid growth conditions were originally developed to support growth of bronchial epithelial cells, and the authors have not presented data to objectively assess the validity of this extrapolation to expansion of mammary organoids. The authors did not present data to support the notion that these growth conditions generate multi-lineage organoids that expand from multipotent mammary stem cells.
- Authors claim that miR-106a-3p downregulates stem cell differentiation and utilize 'organoid' culture to track the temporal expression of OCT4, SOX2 and NANOG during phases of organoid renewal and differentiation. However, mammary stem cell differentiation is associated with the emergence of luminal progenitor, mature luminal and myoepithelial lineages that are characterized by the expression of a well-defined set of markers. The authors did not investigate the expression of these markers in their 'differentiation' settings. Furthermore, modulators of major pathways reported to be critical for mammary ASC maintenance are lacking, including modulators of WNT, TGF/BMP, Notch and other pathways. As such, it is difficult to ascertain that organoids emerging under such culture conditions are the result of stem cell renewal and differentiation, as opposed to lineage-restricted proliferative non-ASCs, thus questioning the validity of many of the findings in this work.
- The authors base their work on the claim that CD44high CD24low cells represent bona fide mammary ASCs. This claim is not support by functional work to show that organoid generation is exclusive to or enhanced in CD44high CD24low cells relative to other cells. The claim of differentiation of this stem cell population in vitro is not supported by data to show emergence of luminal progenitor, mature luminal and myoepithelial lineages that are characterized by the expression of a well-defined set of reported markers as abovementioned. Although miR-106a-3p is claimed to downregulate stem cell differentiation based on a gene set enrichment analysis, authors did not investigate the expression of mammary differentiation markers or the association of miR-106a-3p-transfected HMECs with gene set pathways involved in mammary gland differentiation.
- Line 129: "Together, these results indicate that cells grown as organoids acquired a CD44high / CD24low expression pattern similar to that of stem/progenitor cells, which suggests that 3D organoids can be used to enrich breast stem cell markers for further screening".
This conclusion is not supported by the data presented. It is not clear if the expression of these markers was acquired upon 3D culture. It could be that 3D culture better maintained and expanded already existing CD44high CD24low cells in 2D culture. It is also unclear if these cells are indeed organoid-forming. Authors have not isolated and tested the organoid-forming capacity of CD44high CD24low cells relative to other cells. Along the same line, the conclusion that " mir-106a-3p directly promotes the "transdifferentiation" of CD44low/CD24high cell phenotype into CD44high/CD24low cell phenotype" isn't justified. Experiments required to conclude that 'transdifferentiation' is involved are lacking. miR-106a-3p overexpression could be creating conditions that are permissive for expansion of already existing CD44high CD24low cells as opposed to 'transdifferentiation' of other cell types into this phenotype. The authors could FACS-isolate CD44low CD24low cells and treat these with control or miR-106a-3p to conclusively establish 'transdifferentiation'.<br /> 5. Line 242: "These data demonstrate that miR-106a-3p is involved in the early cell differentiation process into the three germ layers "
The authors have not conducted functional or mechanistic work to show that miR-106a-3p is involved in morphological or transcriptomic differentiation changes in ESCs. This and other data would be necessary to substantiate this conclusion.
Selected technical points:
- Figure 1A: The Y-axis label is misleading and suggests multiple organoids seeded per cell? Organoid Formation Efficiency (OFE) or Organoid Formation Capacity (OFC) are the commonly used nomenclature for this.
- Figure 2G: X-axis unclear. Is this meant to investigate the percentage of cells expressing CD44/CD24 as double high, double low, high/low and low/high?
- Figure 4C: In HMEC-CBX7 cells, it is unclear whether the high miR-106a-3p levels in CD44high CD24low cells are due to CBX7 expression. An important control, HMEC-Control Vector, is missing.
Significance
The findings in this report do not represent a sufficient conceptual advance in mammary stem cell biology. There are some interesting data on the role of miR-106a-3p in differentiation and regulation of pluripotency-inducing factors OCT4, SOX2 and NANOG. This is, however, more relevant to ESC biology. The data presented do not sufficiently explain the proposed transcriptional and molecular influences of miR-106a-3p on ESC maintenance and differentiation. Furthermore, there are multiple instances in the manuscript of overstating conclusions in a manner that is not supported by the data presented, as well as several instances of a conceptually premature extrapolation of various aspects of ESC biology to mammary ASC biology. It is not clear what literature or experimental findings serve as the basis for the author's connection between these two developmentally and temporally distinct aspects of tissue biology.
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Referee #1
Evidence, reproducibility and clarity
Major comments:
- The model system used in this work is referred to as "organoids", with the premise that organoids, as representatives of the original tissue, can be used to study tissue development. However, the organoids presented in the study are spherical structures, and the paper does not provide any information about how and to what extent these organoids represent the original tissue. Furthermore, it would be difficult to expect these organoids to accurately represent human breast tissue, as they are derived, to the best of the reviewer's understanding (it is not explicitly noted, but rather the text refers the reader to several previous works), from primary human breast epithelial cells that had been cultured for 6 passages in 2D culture. The CD24/CD44 flow cytometry profile of these 2D cultures, as well as the organoids derived from them, shows a unimodal distribution of CD24 and CD44 expression, and does not show distinct cell populations, as typical in primary breast epithelial cells that have not been cultured in 2D.
- There appears to be confusion between concepts: primarily confusing a basal phenotype with a stem cell phenotype, and progenitor activity with stem cell activity. Because the 3D organoid model does not display a replication of luminal differentiation, it cannot be used as a proxy for stem cell function. The results from the miRNA screen and the subsequent experiments support two conclusions: 1. miR-160b-3p leads to an enhanced mesenchymal phenotype, manifested by reduced CD24 and enhanced CD44 expression. 2. miR-160b-3p leads to increased organoid formation. The latter may be interpreted, at best, as higher basal progenitor activity, but is not a measure of stem cell activity, as that would require the cells to give rise to more than one lineage, which is not shown here.
- Overall, the information from figures 1-4 indicate that miR-160b-3p is driving and is associated with mesenchymal differentiation, possibly with EMT.
- In contrast to the work in the breast 3D culture model, the experiments with hESCs are interesting and do support a role of this miR in stemness.<br /> There are several relatively minor comments, that cumulatively somewhat undermine the strength of the work:
- Abstract: the sentence "organoids can be directly generated from human epithelial cells by only one miRNA, miR-106a-sp" needs better clarification.
- Page 5 line 103: HMECs is a term that generally refers to human mammary epithelial cells, not a specific derivation or subpopulation thereof.
- The graph in Fig. 1A is unnecessary, it shows only one bar.
- It is unclear if all the HMECs were derived from the same donor, or several donors. There is no information about the donor and how the tissue and cells were derived. In general, it is not entirely clear how the cells were collected, processed, stored, and cultured from the time they were obtained from the donor until their use in the current study.
- The key for Fig. 2D is unclear. The axes read "density" but the text refers to "intensity". Fluorescence intensity in flow cytometry is usually measured on a log scale. Differences on a linear scale are not usually considered meaningful. The authors should clarify why they chose a linear scale for this screen.
- In the miRNA screen, how long were the cells cultured after transfection, and was it enough time for them to shift phenotype?
- Page 6 line 154: The authors likely mean z-score, not z factor (two different things).
- Page 7 line 161: "mir-106a-3p directly promotes the "transdifferentiation" of CD44low/CD24high cells phenotype into CD44high/CD24low cell phenotype" - is an unsupported statement, given that there could be several alternative explanations for the observed change in population ratios, including effects on survival or growth of cells of a certain population.
- There is need for quantification of the phenotypes described in Fig. 3C
- Figures 3 D-F it is not clear if the graphs display percentage or mean number (there is discrepancy between figure text and figure legend text), and when percentage, not clear for figure 3F out of what.
- Fig. 5B, what is the statistical significance of the enrichment?
- Why was GATA3 not included in the last analysis depicted in Fig. 7?
Significance
This manuscript describes experiments that aim to explore the role of miR-160b-3p in stem cells. It uses primarily a model of breast epithelial cells in 3D Matrigel culture, termed organoids.
The first part of the paper describes a screen that identified miR-160b-3p as changing the expression profile of the two surface markers CD24 and CD44 in breast epithelial cells, which the authors refer to as a stem cell population, and enhancing organoid formation capability, which the authors interpret as stem cell capacity. In the second part of the paper, the experiments use another cell model - human embryonic stem cells (ESCs). In the second part, the authors link the expression of miR-160b-3p to the expression of Nanog, Sox2, and Oct4, which are key transcription factors that play essential roles in maintaining pluripotency and self-renewal of ESCs.<br /> The premise of the work is strong, namely that genetic screens that use an organoid model have the potential to uncover genes and pathways that direct tissue development and regulate stem cell fate and function. Several issues make it difficult to draw conclusions about stem cell function from the first part of the paper, namely the experiments with breast epithelial organoids. The second part of the paper is stronger and more convincing of the main claim, which is that miR-160b-3p has a role in stem cell maintenance.
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Reply to the reviewers
'The authors do not wish to provide a response at this time.' The response has been included in a PDF
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Referee #3
Evidence, reproducibility and clarity
Summary:
This manuscript attempts to answer how the maternal and paternal chromosomes are organized, and probe the determinants of this organization.
The authors use two divergent strains of worms - Bristol(N2) and Hawaiian - and hybrids progeny to study this as there are large regions of sequence variants between chromosome V in the two strains, making it an ideal candidate to design specific FISH probes. The authors build on their previously published work and optimize a protocol to trace chromosomes by using a multiple-probe FISH approach to investigate chromosome architecture. This approach is well illustrated in Figure 1.
Overall, this manuscript does a good job of describing a potentially useful technique with wide application. The claims about differences (and similarities) require statistical analysis to be appreciated, and much work is necessary to make the analysis approachable to readers outside the immediate field.
Major comments:
Nearly all claims regarding the organization, compactness and pair-wise distances of chromosomes lack any statistical measures of significance. This is particularly important for the clustering and scaling analysis. This makes interpretation of the claims made in the text impossible. For example, claims such as "the step size remained virtually unchanged" or "the paternal chromosomes adopt the maternal conformation in hybrids" cannot be currently analyzed.
Throughout the manuscript (including in the abstract), the authors use the term "sister chromosomes" to (presumably) refer to the maternal and paternal chromosomes. This is a confusing term, since "sister chromatids" usually refers to the identical products of DNA replication, and "homologous chromosomes" is usually used to describe the parental chromosomes. The term would ideally be changed, and at the very least, it should be clearly defined.
Presentation: The manuscript in its current state (excluding figure one) is essentially impossible to interpret by readers who are unfamiliar with this subfield. The authors could include a blurb on the methodology behind each data type to help the manuscript reach a larger audience. The pipeline, meaning, and potential caveats of the clustering analysis should also be explicated.
Other suggestions:
The use of Hi-C-like heatmaps is good, since they are commonly used and are clear and easy to understand. However, it would be best to explain how the FISH data were used to construct the maps. The implications of the map could also be better explained (e.g., that the red cluster means relatively looser regions, and the blue means more tightly compacted ones).
The last sentence in the Introduction does not make clear sense. How does the similarity between N2 & HI open up the possibility of interrogating inheritance effects?
Several of the additional analysis that could improve the paper are: 1. Measure the nucleus diameter in N2/HI hybrid and HI/N2 hybrid. 2. Normalize the spatial distance to the nucleus size, rather than directly using the distance. 3. Explore some of the patterns in the spatial distance plots (e.g., red/blue lines and boxes). Are the sequences that are in them any different between N2 and HI in a way that might be able to account for these patterns?
Significance
The manuscript introduces a technical advance in the study of chromosomal territories - an important area of study that benefitted from recent advances in microscopy and in development of FISH approaches. However, it lacks mechanistic analysis and remains almost purely descriptive. It is also not clear what motivated the work beyond the technical feasibility. These issues make it impossible to assign biological significance to the seemingly minor differences that are documented.
However, as a report of a technical advance, it could be useful to many chromosome biologists who might apply it to diverse organisms and biological questions.
I am a chromosome biologist working on worms. However, my research does not directly deal with parental effects or with the development of novel FISH methodologies, so I did not examine claims regarding these specific points.
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Referee #2
Evidence, reproducibility and clarity
Building on their previous work using sequential fluorescent in situ hybridization (FISH) to follow the path of entire chromosomes in C. elegans (Sawh et al. 2020; Sawh and Mango 2020), the authors developed and then applied a method that distinguishes maternal and paternal homologs. In particular, they designed probes specific for ChrV in two strains, N2 and HI, and then demonstrated their effectiveness in homozygotes and hybrids. They found that HI ChrV is more compact in HI homozygotes as compared to N2 homozygotes, but decompacts in the F1 of N2 hermaphrodites x HI males. A different outcome was observed with respect to decompaction in the reverse cross, where both N2p and HIm ChrV chromosomes (p=paternal, m=maternal) exhibit decompaction. Through unsupervised clustering, the authors further found both dominant and minor patterns of chromosome-wide organization, with maternal chromosomes similar in terms of their dominant clusters regardless of the direction of the cross, but paternal chromosomes less so. Finally, the authors measured the degree to which homologous chromosomes interact, concluding that, although homologs overlap quite frequently, they rarely align (pair).
In sum, the significance of the authors' work lies in its chromosome-level observations and the application of computationally designed probes that distinguish homologs by targeting strain-specific insertions. While homolog distinction by FISH has been previously demonstrated, this study is the first to demonstrate this approach in C. elegans as well as implement it via insertions in a chromosome-wide manner. As such, the manuscripts should be of interest to a broad range of researchers, especially those in the fields of genetics, genomics, and 3D genome organization. That said, the study falls short in several ways, and we recommend the authors i) present a more thorough summary of what is known about homolog positioning across species, ii) describe how homologs have been previously distinguished by FISH and, thus, more clearly elucidate the specific advances they have enabled, iii) ground their work in more rigorous quantitation, and iii) provide a better description of the technologies (strengths as well as limitations) carried over from their previous studies so that readers can better evaluate the current study. We detail our suggestions and questions, below:
Major Comments:
- Page 1: We suggest the authors broaden the reach of their introduction to include well-known examples outside of C. elegans of the impact of parent-of-origin on 3D genome organization. Such examples would include X-inactivation, selective silencing of paternal genomes, the physical elimination of paternal chromosomes, and the like.
- Page 1: We also suggest the authors consider including mention of observations from the following research publications and review:
Mayer W et al. Spatial separation of parental genomes in preimplantation mouse embryos. 2003 PMC2169371
Reichmann J et al. Dual-spindle formation in zygotes keeps parental genomes apart in early mammalian embryos. 2018 PMID: 30002254
Nagele R, Freeman T, McMorrow L, Lee HY. Precise spatial positioning of chromosomes during prometaphase: evidence for chromosomal order. 1995 PMID: 8525379
Hua LL et al. Mitotic antipairing of homologous and sex chromosomes via spatial restriction of two haploid sets. 2018 PMID: 30530674
Hua LL, Casas CJ, Mikawa T. Mitotic antipairing of homologous chromosomes. 2022 PMC9731508 3. Page 5: The authors designed their strain-specific probes to target 172 insertions that are over 1 kb in size and distributed across ChrV. We ask the authors to describe these insertions in greater detail, especially as, later in the manuscript, the authors touch on the possibility that sequence differences between the two strains may account for differences in chromatin architecture. How many insertions were on the N2 and HI chromosomes, respectively? What is the range and distribution of insertion sizes for all insertions as well as specifically for the N2 and HI ChrV chromosome? Do the insertions contain repetitive sequences, or are they predominantly composed of unique sequences? What is their distribution with respect to genes, active regions, TADs? Is there an explanation for why some are clustered? If they contain genes, are the genes enriched in certain GO categories? Do the insertions differ in their characteristics across the different chromosomes? This information could be included as graphs and/or tables. 4. Page 5: Given that N2 and HI ChrV chromosomes differ by the number of insertions and, ultimately, probes, how might these differences have skewed the authors' results, especially with respect to overlap between the homologs? Here, simulations of different ratios of insertions between N2 and HI could be clarifying. 5. What difficulties were encountered when tracing the paths of overlapping homologs, and how were these difficulties accounted for and/or solved? Did the different numbers and distributions of insertions between N2 and HI exacerbate the challenge? What confidence levels accompanied their findings? 6. Page 4: It would be helpful if the authors to put their insertion-based method into the context of other studies that have developed and used FISH to distinguish homologs. 7. Pag 6: It would clarifying if the authors provided details about the mis-annotation of the Thompson genome and how it is pertains to probe design. 8. Page 6: The authors state that "...N2 and HI...harbor sequence differences, some of which are predicted to affect chromatin architecture" and that HI lacks ppw-1. We ask the authors to provide a more thorough discussion. To what extent might such predictions rest on the insertional differences between the strains? 9. Page 7: How much smaller is the HI genome and what percent of this difference is due to insertions (deletions)? Related to this, how much smaller is the HI ChrV chromosome as compared to N2? 10. Page 7 and throughout: For those readers who have not read Sawh and Mango 2020 and Sawh et al. 2020, or who are unfamiliar with the broader category of imaging technologies that support the current study, we ask the authors to provide much more background and citations to key methods. Without this information, many readers will neither sufficiently understand the strategies used for imaging acquisition, processing, and analysis nor grasp the relevance of terms such as step size, polymer step size, power-law fitting, etc. and therefore be less able to assess the authors' data and conclusions. 11. General statement: When the authors infer similarity or differences between power-law fittings, scaling exponents, step sizes, etc. what is the statistical significance of those comparisons? 12. Experiments in general: We suggest the authors provide considerably more quantitation. For example, we urge them to provide the number of trials, sample sizes, numbers of embryos examined for all experiments. Equally important would be information regarding the stage of embryos examined and, where more than one embryonic stage was involved, the number of embryos for each stage. Are there stage-specific changes? We are also concerned about the impact of mixed populations of embryos on studies using unsupervised clustering. In other words, what was the contribution of developmental stage to the outcome of the clustering? Furthermore, if not all nuclei in an embryo were captured, we ask the authors to give the percent of nuclei captured from an embryo and reasons why only a subset of nuclei were included in the analysis. 13. With regard to cluster analyses both here and elsewhere, will the authors please include statements of statistical significance whenever they note differences and/or similarities? 14. Page 8: It would be helpful if the authors explained how they implemented the nearest-neighbor approach, including caveats and limitations (success rates, drop-out rates, etc.) and providing statistical assessment wherever possible. 15. Page 8: "In some instances (6-8% of traces), traces were ambiguous and excluded from further analysis". We ask the authors to provide more detail. For example, what does ambiguous mean and, with respect to the 6-8% value, what was the total number of traces? What was the distribution of all traces (prior to filtering) with respect to percent of targets detected? Also, how coincident were the two homologs of a nucleus to each other in terms of capturing all the targets? 16. Page 8: "...we classified traces into N2 or HI based on whether the trace was located closest to a strain marking volume for N2 or HI." Will the authors please quantify "closest" and explain what this means, whether there was a cut-off and, if there had been, how it was determined and implemented? What percent of cells were problematic, and were there traces that did not overlap the strain marking volumes at all? As stated in the Materials and Methods, only a subset of traced chromosomes were analyzed for overlap - why were only a subset analyzed for overlap, how was the subset selected, and how many/what percentage did these traces represent? It would also be helpful if the authors provided a quantitative summary of the traces. 17. Page 8: Did the authors account for chromatic aberration and, if so, what protocol did they use? 18. Page 8: "...counting how often more than two N2 or HI traces were detected in one nucleus." This is puzzling, and we suggest that the authors include explanations for how this might have happened. Did the nuclei not contain signal from the other strain marking probe at all? Was there a bias for this to happen with N2 or HI chromosomes? Could this have been a consequence of biology or of the algorithm for tracing? The authors' observations are reminiscent of the many implications raised by Jia et al. (2023; A spatial genome aligner for resolving chromatin architectures from multiplexed DNA FISH. PMID: 36593410), and we ask the authors to comment on the relevance of their observations to those in this recent publication. 19. Page 8: "We found only a minority of 2% of HI traces and 7% of N2 traces were mis-assigned and excluded these from downstream analysis." 2% and 7% of what total? 20. Page 8: How do pairwise distances remain almost identical between N2 and HI and yet generate different scaling exponents? 21. Page 9: Figure 1F shows images of embryos derived from N2 hermaphrodites x HI males. It would be helpful if the authors added analogous images from the reciprocal cross as a supplementary figure. 22. Page 2, 9, 9, and 12: The authors make several comments regarding the action of factors in trans: "... factors from the mother impact chromosome folding in trans (p. 2)"; "... the HIp decompacts when subjected to the N2m environment and implies that the paternal chromosome is influenced by the maternal environment in trans (p. 9)"; "...N2 chromosomes influence HI chromosomes in trans, while N2 chromosome structure seems to be resistant to influences by the HI chromosomes (p. 9)"; and "...implicating maternal factors that act in trans (p. 12). While provocative, these statements call for more concrete consideration. Are the authors using "in trans" in lieu of "indirectly", or are they alluding to factors, such as ppw-1, or direct physical contact? Without further substantiation or argument, mention of in trans activity might best be reserved for the Discussion. 23. Page 10: "While HIp subpopulations were characterized by folding of one or the other chromosome arm, N2p clusters were more open and a subpopulation with a highly folded right arm was not present" (Figure 5CD). Was there a significant correlation between left vs. right arm folding and overall genome organization and function? 24. Page 11: Will the authors please provide a more explicit definition of alignment as well as a more detailed description of how alignment is quantified in the main text? 25. Page 11: With respect to direct physical contact, the authors mention transvection, which they conclude in the abstract is unlikely because pairing between homologs was observed to be rare. As transvection and pairing can both be short-lived, and the data are not compelling, the statement may need to be toned down considerably and/or be moved to the discussion. 26. Page 11: The authors draw a distinction between % territory overlap and physical distances between homologs. In particular, the differences between % overlap across different stages is quite interesting and potentially suggests embryo stage-specific changes. Could the authors explore this further by breaking down mean pairwise distances into different embryo stages (Figure 6B and C)? 27. Pages 2, 11-13: When the authors use "sisters", do they mean "homologs"? If the latter, we recommend they use "homologs", only, as "sisters" refers to the sister chromatids after replication. If, however, the authors are using "sisters" to mean sister chromatids, will they please explain how their data can distinguish sisters? 28. Discussion: We encourage the authors to speculate further regarding the basis of decompaction. Is it a hybrid-specific phenomenon?
Minor comments:
- Page 1: Although the introduction focuses on C. elegans, the genome length that is mentioned (2 meters) is more aligned with that of mammalian species. The authors could cite a range of lengths or make more clear which species is being discussed.
- Page 5: As the figures label the strains as Hawaiian and Bristol, the authors might wish to include this nomenclature in the main text. Curiously, the authors use several different spellings for the Hawaiian/Hawai'ian/Hawaiin/Hawaii strain.
- Page 5: Will the authors please explain why the common whole-chromosome tracing probes have only one tail, while the strain-specific probes have two tails, as shown in Figure 1D?
- Figures in general: The axes of a number graphs and heat maps need to be labeled.
- Materials and Methods: The section on "Cluster analysis" is missing units for the resolution.
- Page 8: Will the authors please give a reference for and explain watershed segmentation?
Significance
In sum, the significance of the authors' work lies in its chromosome-level observations and the application of computationally designed probes that distinguish homologs by targeting strain-specific insertions. While homolog distinction by FISH has been previously demonstrated, this study is the first to demonstrate this approach in C. elegans as well as implement it via insertions in a chromosome-wide manner. As such, the manuscript should be of interest to a broad range of researchers, especially those in the fields of genetics, genomics, and 3D genome organization. That said, the study falls short in several ways, and we recommend the authors i) present a more thorough summary of what is known about homolog positioning across species, ii) describe how homologs have been previously distinguished by FISH and, thus, more clearly elucidate the specific advances they have enabled, iii) ground their work in more rigorous quantitation, and iii) provide a better description of the technologies (strengths as well as limitations) carried over from their previous studies so that readers can better evaluate the current study.
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Referee #1
Evidence, reproducibility and clarity
The authors designed an elegant series of FISH probes taking advantage of insertions that are divergent between HI and N2 strains of C. elegans to identify the maternal versus paternal chromosomes in hybrid embryos. Overall, the conclusions are well supported by the data. I only have minor comments, which are numbered below in relation to each figure.
In figure 1 they demonstrate that the probes can specifically recognize their corresponding chromosome in hybrid embryos
In figure 2 they demonstrate that overall chromosome 5 adopts a similar shape from both strains.
In figure 3, they demonstrate that in the hybrids, the chromosomes are generally the same as in the homozygous embryos. However, when the HI chromosome is brought in paternally in the hybrids, it is more decompacted. In this cross, the maternal N2 chromosome is normal. In figure 5, when the N2 chromosome is now brought in paternally, it is similarly decompacted. However, in this cross, the HI chromosome that is brought in maternally is also decompacted. This appears to be the biggest difference, but is somewhat mitigated by a decrease in the scaling component, which I believe means it takes a straighter path? This is in contrast to the reciprocal cross where the maternal N2 chromosome is normal.
- In figures 2-4, it is important to note what stages of embryos are being analyzed and whether any analysis was done to determine if the chromosomes varied with embryonic stage?
- The authors need to clearly define chromosomal step size, scaling coefficient and pair wise distance and then describe the difference between these measurements. For example, it would be nice in relation to figure 4F if it was described exactly what it means to have an increase in step size along with a decrease in scaling exponent. This will enable the reader to more easily interpret the results.
- Is there any way to determine if changes in the step size and scaling are significant? It would be good to know if the changes are actually significant.
In figure 5, the authors examine sub-clusters of where individual chromosomes locate. 4. In figure 5 (and maybe in earlier figures as well), it would also be helpful to mark the different clusters in the figures to show what is meant by a folder arm or a compacted central domain and refer to every panel in the text (only some descriptions in the text reference specific panels).
In figure 6, the authors determine whether the two chromosomes 5's overlap in nuclear territory and whether they the align along the length of the chromosome. From this analysis, they conclude that the chromosomes overlap a fair amount of time, but do not align. This makes it unlikely that transvection might occur. 5. In figure 6, the authors need to do a better job of describing how the data is being presented. The % overlap is being graphed by density, but density of what? Also, the text mentions the total number of nuclei that overlap, but how is that number derived from the presented data? Finally, the data are broken down by embryonic stage, but there is no mention of this in the text. It is not mentioned until the discussion. Overall, this makes it very difficult to determine what the data are showing. 6. In the discussion, the authors perhaps should spend more time interpreting their results in light of others work on the maternal and paternal inheritance of chromatin in C. elegans. For example, Arico et al 2011 Plos Genetics from the Kelly Lab. In addition to examining chromatin in the early embryo, in this paper the authors examine translocations, which might be interesting to look at using the technique presented here. Also, a number of recent papers from the Strome lab have examined chromatin inheritance from sperm. It would be interesting to interpret the finding that paternal chromosomes are influenced by the maternal environment, in light of this work.
Significance
These studies are the important extension of the elegant chromosome tracing that the Mango lab has pioneered. The authors have clearly demonstrated that the technique works well to identify individual chromosomes in a hybrid background. This provides the opportunity for the system to be used in numerous different ways, not just in C. elegans. This is the most significant advance of this paper and should be of interest to a fairly broad audience. However, using this technique, the authors also provide the initial characterization of sister chromosomes in C. elegans embryos and draw important initial conclusions, such as finding that the chromosomes do not pair, as they do in Drosophila. This makes the paper of interest to the C. elegans audience, as well as the general field of chromosomal organization. My expertise is in C. elegans biology and chromatin biology in general. I also am familiar with the field of chromosome biology. As a result, I believe I am capable of judging the significance of this paper in these areas.
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- Sep 2023
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Reply to the reviewers
Point-by-point response to reviewers, including our plans for the revision:
Review____er #1 (Evidence, reproducibility and clarity (Required)):
* Summary: In this manuscript by the Sanson group, Lye and colleagues try to definitively answer the question of whether pulling forces from the ventral mesoderm have significant effects on convergent extension in the Drosophila germband (germband extension). While germband extension does occur in mutant embryos lacking mesoderm invagination, it has long been an open question in the field as to whether ventral pulling forces from the mesoderm have significant effects (positive or negative) on cell intercalation during germband extension. To definitely address this question, Lye and colleagues generated high-quality, directly comparable datasets from wild-type and twist mutant embryos, and then systematically assessed nearly all aspects of cell intercalation, myosin recruitment, and tissue elongation over time. They demonstrate that pulling forces from the ventral mesoderm have negligible impacts on the course of germband extension. While there are indeed some interesting differences between wild-type and twist embryos with respect to cell intercalation and myosin recruitment, such differences are relatively minor. They conclude that the events of germband extension neither require nor are strongly affected by external forces from the mesoderm. While this is largely a negative results paper, I believe that it should be published and that it will be an impactful paper within the field. Namely, it will settle once and for all the question of whether mesoderm invagination is required for optimal germband extension in the early Drosophila embryo, and it suggests that tissues are largely autonomous developmental units that are buffered from outside mechanical inputs.*
- * *Major comments: *
* It seems to me that the one obvious omission from this paper is a general measure of convergent extension over time. I think it would be useful to the reader to include some measure of change in tissue aspect ratio over time between wild-type and twist embryos. This could be included in Figure 5 or 6. *
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We are happy to include a graph with what we call “tissue strain rate”, which measures the deformation of the germ-band in the direction of extension (along AP) over time, and propose to add it as a panel in Supplementary Figure 6. Note that in our measures, the “tissue” strain rate is decomposed into contributions from two cell behaviors, the “cell intercalation” strain rate and the “cell shape” strain rate (Blanchard et al., 2009). “Tissue” and “cell shape” strain rate are directly measured, and “cell intercalation” strain rate is what remains when “cell shape” strain rate is removed from “tissue” strain rate. The “cell intercalation” strain rate calculated in that way is a “continuous” measure of cell intercalation, measuring the progressive shearing of cells during convergent extension. We also use a “discrete” measure of cell intercalation, which measures the number of cell neighbor exchanges, also called T1 swaps. We found that both “continuous” and “discrete” measures of cell intercalation are unchanged in twist mutant compared to wild-type embryos (Fig. 6F and 6E, respectively). In contrast, we find that the “cell shape” strain rate is increased in twist mutants (Fig. 5B and Fig. 5S1A). Consistent with this finding, the “tissue” strain rate is also increased in twist mutants (see graph below).
Otherwise, I have no major comments on the experimental approach or the findings of this manuscript. It seems to me a straightforward and systematic approach for determining whether mesoderm invagination affects germband extension. I do have several minor comments that should be addressed prior to publication (below).
*Minor comments: *
*I understand why cells would initially stretch more along the DV axis in wild-type embryos compared with twist embryos, but why do cells become so much more stretched along the AP axis (and become smaller apically) after 10 minutes of GBE in wild type compared with twist (Figure 2C and E). *
*I think this is an interesting and non-intuitive result that would warrant a bit of explanation/conjecture. *
This is not what Fig. 2C and E show, and we realize now that our schematics on the graphs might have been confusing. We will work on those to improve their clarity (or remove them), and also review our text.
Figure 2C shows how cells deform along DV (cell shape strain rate projected onto the DV axis). So the graph does not show that the cells are elongating in AP, as only the DV component of the strain rate is shown in this figure. In the wild type, the DV strain rate is positive (the cells are elongating in DV) at developmental times when the mesoderm invaginate (from about -10 minutes to until 7.5 minutes). The DV strain shows an acceleration until about 5 mins, then decelerates, crossing the x-axis to become negative at 7.5 minutes. From this timepoint and until the end of GBE, the DV strain rate is negative (the cells are contracting along DV). Mirroring the positive section of the curve, the DV contraction of the cells accelerate until about 12 mins and then slows down. The strong rate of DV contraction between 7.5 and 20 mins could in part be due to the endoderm invagination pulling in the orthogonal direction (AP) and helping the cells regaining a more isotropic shape. We could add a mention about this in the discussion.
In Figure 2E, the rate of change in cell area follows a similar time course in the wild type, showing that the cells are increasing their areas until about 10 mins (positive values) and then reduce their areas again until the end of GBE (negative values). Note that the graph does not show raw (instantaneous) cell areas as suggested by the comment, but rather a rate of change.
So in wild type, the cells get stretched by the invaginating mesoderm, and once the mesoderm is not pulling anymore, the cells appear to relax back. As there is no stretching in twist mutants, there is no equivalent relaxation of the cells along DV. Note that in twist, there is a milder increase in cell area in the first 15 mins of GBE (Fig. 2E). This could again be caused by the pull from endoderm invagination stretching the cells along AP, which, as we have shown before, increases both cell shape strain rates along AP and cell areas (Butler et al., 2009). So the pull from endoderm invagination (along AP) will have an impact on cell area rates of change and possibly also, indirectly, on DV cell shape strain rates, in both twist and wild type embryos, during most of GBE. Therefore cell area and DV cell shape strain rates are affected by more than one process during GBE. In this paper, we are focusing on the impact of mesoderm invagination, which happens around the start of GBE, so have focused our analysis of the graphs in the results section to this period, and the differences between wildtype and *twist. *
*I don't understand how you are defining cell orientation in Figure 2G. How are you choosing the cell axis that you are then comparing with the body axis? Is it the long axis, or something more complicated than that? I think you should briefly provide this information in the results section. If it is included in the methods, I wasn't able to locate it. *
Yes, it is the orientation of the long axis of the cell relative to the antero-posterior embryonic axis. We will clarify this in the text, in particular in the Methods, and also try improve our schematics.
Figure 2: Since you have the space, it might help the reader if you simply wrote out "strain rate" for panels B, D, and F, rather that used the abbreviation "SR." Thank you for this suggestion, we will reduce use of abbreviations where space permits.
*Please ensure that all axis labels are fully visible in the final figures. In several figures, the Y-axis labels were cut off (e.g., Fig 2I, 4A, 4D, 6B, 6C). *
These were visible to us in our submitted version, but of course we will ensure everything is visible on the final version.
*Where space permits, I would suggest using fewer abbreviations in axis labels to increase readability of the figures (e.g., in Figures 3H or 4D). *
Thank you for this suggestion, will do.
* In Figure 7, I would move the wild-type panels to the left and the twist panels to the right. I think it is more conventional to describe the normal wild-type scenarios first, and then contrast the mutant state.*
Will do.
To be consistent with the literature, "wildtype" should be hyphenated (wild-type) when used as an adjective, or two separate words (wild type) when used as a noun. Thank you, we will change this.
Review*er #1 (Significance (Required)): *
* Advance: The advances in this manuscript are largely methodological, but the experiments and analyses are quite rigorous and allow the authors to make strong conclusions concerning their hypotheses. Their findings are based on a high-quality collection of movies from control and twist mutant embryos expressing a cell membrane marker and knock-in GFP-tagged myosin. Importantly, I think the researchers were correct in choosing to analyze twist single-mutant embryos (as opposed to snail or twist, snail double-mutant embryos), as the overall embryo geometry of these mutants is fairly similar to wild-type embryos, allowing the researchers to directly compare cell behaviors and myosin dynamics during germband extension. This approach also allows them to avoid indirect effects on the germband due to a completely non-internalized mesoderm. *
*
Audience: The primary audience for this article will be basic science researchers working in the early Drosophila embryo who are interested in the interplay between the germband and neighboring tissues. Secondary audiences will include developmental biologists more broadly who are interested in biomechanical coupling (or in this case decoupling) of neighboring tissues. *
*
Describe your expertise: I have been a Drosophila developmental geneticist for over twenty years, and I have been working directly on Drosophila germband extension for over a decade. I have published numerous papers and reviews in this field, and I am very familiar with the genetic backgrounds and types of experimental analyses used in this manuscript. Therefore, I believe I am highly qualified to serve as a reviewer for this manuscript.*
Review____er #2 (Evidence, reproducibility and clarity (Required)):
*
In the present manuscript, Lye et al. describe a highly detailed quantification of cell shape changes during germband extension in Drosophila melanogaster early embryo. During this process, ectodermal tissue contracts along the dorso-ventral axis, simultaneously expanding along the perpendicular antero-posterior direction, migrating from the ventral to the dorsal surface of the embryo as it extends. This important morphogenetic event is preceded by ventral furrow formation when mesodermal tissue (located in the ventral part of the embryo) contracts along the dorso-ventral axis and invaginates into the embryonic interior. The study compares cell shape dynamics in the wildtype Drosophila with that in the twist mutant, which largely lacks mesoderm and does not form ventral furrow. The major motivation of the study is to examine whether cellular behaviors and myosin recruitment in the ectoderm is cell autonomous, or if those cellular behaviors depend on mechanical interactions between mesoderm and ectoderm.*
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The authors first examine whether transcriptional patterning of key genes involved in germband extension is different between the wildtype and the twist mutant and find no significant difference. Next, the authors thoroughly quantify cellular behaviors and patterns of myosin recruitment in the two genetic backgrounds. A number of different measures are investigated, notably the rate of change in the degree of cellular asymmetry, rate of cell area change, rate of change of cell orientation, differences in myosin recruitment to cell edges of various orientation, as well as the rates of growth, shrinkage, and re-orientation of the various cellular interfaces. It is thoroughly documented how these quantities change as a function of developmental timing and spatial position within the embryo. These data serve basis for quantitative comparison between cellular dynamics in the two genetic backgrounds considered.*
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Overall, the study shows that cellular behaviors observed in the ectoderm are largely the same during the period of time following ventral furrow formation, as would be expected if those cellular behaviors were predominantly cell autonomous and not dependent on stresses generated in the mesoderm.*
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The data presented in the manuscript are of excellent quality and presentation is very clear.
Minor comments: none *
* Reviewer #2 (Significance (Required)): *
* I find that the study provides a thorough quantification of cell behaviors in a widely studied important model of morphogenesis. The work may be of particular interest for future model-to-data comparison, perhaps providing a basis for future modeling work. I therefore certainly think that this work warrants publication.*
- However, the results of the study largely parallel previous findings and do not appear novel or surprising. It is well established that in snail mutant that lack mesoderm entirely, germband extension proceeds largely normally. This well-established fact suggests that since tissue dynamics in complete absence of mesoderm are largely unaffected, behaviors of individual cells are likely to not be affected either*.
*The work is pretty much entirely observational, and for most part provides a more detailed documentation/quantification of previous findings. I do not think it is appropriate for high profile publication. *
We are not sure which evidence the reviewer is referring to here specifically. We agree that the single mutants twist or snail, or the double twist snail mutants do extend their germ-band. However, the question we are asking here, is how well do they extend their germband and to answer this question, quantitation is needed. The first quantitation of GBE were performed by (Irvine and Wieschaus, 1994). While they quantified GBE in various mutant contexts, they did not perform quantitation for snail, twist, or twist snail mutants. Instead, they refer to these mutants once in p839, with the following sentence: ”Additionally, twist and snail mutant embryos, which lack mesoderm, extend their germbands almost normally (Leptin and Grunewald, 1990; Simpson, 1983)*.” *
Following these earlier qualitative observations, various studies have quantified different aspects of GBE in mesoderm invagination mutants, with contradictory results. For example, some studies, including from our own lab, report a reduction in cell intercalation in the absence of mesoderm invagination (Butler et al., 2009; Wang et al., 2020), but there have also been reports that tissue extension and T1-transistions occur normally (Farrell et al., 2017)(see also introduction of our manuscript). These contradictory results have motivated our present study, and we have implemented rigorous comparison between wild type and mesoderm invagination mutants, being careful i) to check that the regions analyzed were comparable in terms of cell fate, and ii) to control for any confounding effects between experiments (see also response to reviewer 4, main question 2). We have also considered which mesoderm invagination mutants to use. We rejected snail or twist snail mutants because the absence of snail means that the mesodermal cells do not contract and thus stay at the surface of the embryo, which changes the spatial configuration of the embryo considerably and would make a fair quantitative comparison very difficult. Instead, we decided to use twist mutants, as in those, cell contractions still happen so the cells do not take as much space at the surface of the embryo, but the contractions are uncoordinated which means that there is no invagination (and we demonstrate here, no significant pulling on the ectoderm). We note that reviewer 1 highlights the merit of settling the question of the impact of mesoderm invagination on GBE and the pertinence of choosing twist mutants versus the alternatives (see also response to reviewer 4, suggestion 1).
__Review____er #3 (Evidence, reproducibility and clarity (Required)): __
During morphogenesis, the final shape of the tissue is not only dictated by mechanical forces generated within the tissue but can also be impacted by mechanical contributions from surrounding tissues. The way and extent to which tissue deformation is influenced by tissue-extrinsic forces are not well understood. In this work, Lye et al. investigated the potential influence of Drosophila mesoderm invagination on germband extension (GBE), an epithelial convergent extension process occurring during gastrulation. Drosophila GBE is genetically controlled by the AP patterning system, which determines planar polarized enrichment of non-muscle myosin II along the DV-oriented adherens junctions. Myosin contractions drive shrinking of DV-oriented junctions into 4-way vertices, followed by formation of new, AP-oriented junctions. This process results in cell intercalation, which causes tissue convergence along the DV-axis and extension along the AP-axis. In addition, GBE is facilitated by tissue-extrinsic pulling forces produced by invagination of the posterior endoderm. Interestingly, some recent studies suggest that the invagination of the mesoderm, which occurs immediately prior to GBE, also facilitates GBE. In the proposed mechanism, invaginating mesoderm pulls on the germband tissue along the DV-axis; the resulting strain of the germband cells generates a mechanotransduction effect that promotes myosin II recruitment to the DV-oriented junctions, thereby facilitating cell intercalation. Here, the authors revisited this proposed mechanotransduction effect using quantitative live imaging approaches. By comparing the wildtype embryos with twist mutants that fail to undergo mesoderm invagination, the authors show that although the DV-oriented strain of the germband cells was greatly reduced in the absence of mesoderm pulling, this defect had a negligible impact on junctional myosin density, myosin planar polarity, the rate of junction shrinkage or the rate of cell intercalation during GBE. A mild increase in the rate of new junction extension and a slight defect in cell orientation were observed in twist mutants, but these differences did not cause obvious defects in cell intercalation. The authors conclude that myosin II-mediated cell intercalation during GBE is robust to the extrinsic mechanical forces generated by mesoderm pulling.
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* *Overall, I found that the results described here are very interesting and of high quality. The data acquisition and analyses were elegantly performed, statistics were appropriately used, and the manuscript was clearly written. However, there are a few points where some further explanation or clarification is necessary, as detailed below: *
- The main conclusion of the manuscript relies on appropriate quantification of myosin intensity at cell junctions. It is therefore important that the methods of quantification are well justified. Below are a few questions regarding the methods used in the analyses:*
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-For myosin quantification, the authors state that "Background signal was subtracted by setting pixels of intensities up to 5 percentile set to zero for each timepoint" [Line826]. The rationale for selecting 5 percentile as the threshold for background should be explained. Also, how does this background value change over time? *
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For our normalization method, we stretched the intensity histogram of images to use the full dynamic range for quantification and enable meaningful comparison of intensities between different movies. The 5th percentile was chosen to set to zero intensity as this removed background signal without removing any structured Myosin signal (i.e., non-uniform, low level fluorescence - this was assessed by eye). We will provide some before and after normalization images at different timepoints to illustrate this (See reviewer 3, minor point 4 below). Since the cytoplasmic signal is uniform, it is difficult to discern from true ‘background’, therefore some cytoplasmic signal might be set to zero with this method, but all medial and junctional Myosin structures will still be visible and have none-zero intensity values. However, since cytoplasm takes up a large majority of pixels in the image, and we only set 5% of pixels to zero, the majority of the cytoplasm will have non-zero pixel values. ‘Background’ changes increases slightly as Myosin II levels increase in general over time, as expected from the embryo accumulating Myosin II as they develop.
-The authors mention that "Intensities varied slightly between experiments due to differences in laser intensity and therefore histograms of pixel intensities were stretched" [Line828]. The method of intensity justification should be justified. For example, does this normalization result in similar cytoplasmic myosin intensity between control and twist mutant embryos?
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As stated above, we stretched the intensity histogram of images to enable meaningful comparison of intensities between different movies, as stretching the histograms would bring Myosin II structures of similar intensities into the same pixel value range. We chose to stretch histograms using a reference timepoint (30 minutes, the latest timepoint analyzed), rather than on a per timepoint basis, because we saw a general increase in Myosin II over time, and we wanted to ensure that this increase was preserved in our analysis.
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Note that we quantify Myosin from 2 µm above to 2 µm below the level of the adherens junctions (see Methods), not throughout the entire cell, and therefore we have no true measure of cytoplasmic Myosin. However, we can plot non-membrane Myosin from this same apicobasal position in the cell. Non-membrane Myosin will include both the cytoplasmic signal and the Myosin II medial web (see above). When plotting these, we find that Myosin II intensities in this pool are similar in wildtype and twist (see graph below, dotted lines show standard deviations), confirming that that we are not inappropriately brightening one set of images compared to the other (e.g., twist versus wildtype).
Finally, our observations of rate of junction shrinkage and intercalation are consistent with our Myosin II quantification results (see Figures 4A, 4D and 6F). This further validates our methods.
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- A previous study demonstrates that the accumulation of junctional myosin is substantially reduced in twist mutant embryos compared to the wild type (Gustafson et al., 2022). In that work, junctional myosin was quantified as (I_junction - I_cytoplasm)/I_cytoplasm. In contrast, the cytoplasmic myosin intensity does not appear to be subtracted from the quantification in this study. How much of the difference in the conclusions of the two studies can be explained by this difference in myosin quantification?
As explained above, we choose to normalize our data by stretching histograms, rather than subtracting and dividing intensities between different pools of Myosin. The setting pixels of intensities up to 5 percentiles set to zero for each will have a similar effect to subtracting a small fraction of the cytoplasmic pool. We note that the intensity measurements in (Gustafson et al., 2022) are in the apical-top 5µm of the cell, and therefore their ‘cytoplasmic’ signal is likely to also include the apical medial web of Myosin. Also, after subtraction they use division by the cytoplasmic intensity in an attempt to bring pixel intensities between different movies into a comparable range, whereas we do this by stretching the histograms themselves (see above). We carefully designed our method to preserve the increase in Myosin levels that we see over time in our post-normalization data. This is something that their method of normalization would not be predicted to capture, if their ‘cytoplasmic’ signal increase over time as well as their junctional signal. Indeed, in FigS6D of their paper, Myosin II levels do not appear to increase over time in these (presumably normalized) images.
Additionally, we note that in (Gustafson et al., 2022), not all Myosin II is fluorescently tagged since they use a sqhGFP transgene located on the balancer chromosome. This means that the line they use will have a pool of exogeneous Myosin tagged with GFP (expressed from the CyO balancer) and a pool of endogenous Myosin (expressed from the sqh gene on the X chromosome. It is not known whether endogenous and exogeneous GFP-tagged Myosin II will be recruited equally to cell junctions when in competition with each other. Therefore, in their genetic background, the ratio of junctional/cytoplasmic sqhGFP might not reflect the true ratio. To avoid this potential caveat, in our study we have used a new knock-in of Myosin, which tags the sqh gene at the endogenous locus (Proag et al., 2019). The line is homozygous viable and thus all the molecules of Myosin II Regulatory Light Chain (encoded by sqh), and thus the Myosin II mini-filaments, are labelled with GFP.
Additionally, we note that when comparing their images of Myosin II in wildtype and twist (Figure 5D and D’), the overall Myosin signal appears reduced in twist mutants (including in the head and posterior midgut, which is outside the area that they are claiming Myosin II is recruited in response to mesoderm invagination). This suggests that Myosin II is generally reduced in their twist mutants (or images thereof), which is not expected and might indicate issues with their methods.
Therefore differences in the methods may explain the discrepancies between studies. Importantly, we have quantified junctional shrinkage rates and intercalation, and our analysis of these rates is consistent with our Myosin II quantification results (see above).
-The authors used the tissue flow data to register the myosin channel and the membrane channel, which were acquired at slightly different times. The accuracy of this channel registration should be demonstrated.
As stated in our methods: “the channel registration was corrected post-acquisition in order that information on the position of interfaces in the Gap43 channel could be used to locate them in the Myosin channel. Therefore the local flow of cell centroids between successive pairs of time frames in the Gap43 channel is used to give each interface/vertex pixel a predicted flow between frames. A fraction of this flow is applied, equal to the Myosin II to Gap43 channel time offset, divided by the frame interval. Because cells deform as well as flow, the focal cell’s cell shape strain rate is also applied, in the same fractional manner as above.”
The images in Figure 3C and C’ show the Myosin II, with quantified membrane Myosin superimposed on the image as a color-code. Images in Figure 3B and B’ show the (normalized) Myosin II. Comparison of these images demonstrates that the channel registration is accurate. We will add a reference to these images in the methods.
- The authors show that cell intercalation is not influenced in twist mutant embryos. However, a previous study demonstrates that the speed of GBE is substantially reduced in twist mutants (Gustafson et al., 2022). It would be interesting to see whether a similar reduction in the speed of GBE was observed in this study. *
We do not see a reduction in the speed of GBE as reported by (Gustafson et al., 2022), we will add “tissue strain rate” graphs to demonstrate this. On the contrary, we find a slight increase in the “tissue strain rate”, because there is a slight increase in the “cell shape strain rate” contributing to extension (while “cell intercalation strain rate” is unchanged). See also response to Reviewer 1 (major comment) .
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It has been previously shown that contractions of medioapical myosin in germband cells also contribute to cell intercalation. The authors should explain why medioapical myosin was not included in the comparison between wildtype and twist mutant embryos. *
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*
Indeed, it has been shown that there is a flow of medial Myosin towards the junctions (Rauzi et al., 2010). However, and as described in that paper, this flow ‘feeds’ the enrichment of Myosin II at shrinking junctions, and thus the junctional Myosin II can be taken as a readout of polarized Myosin II behavior. Additionally, medial flows are more technically challenging to quantify, especially when quantification is required in a large number of cells as is the case for our study.
Importantly, our junctional Myosin II and junctional shrinkage rate results are consistent with each other, therefore it is very unlikely that analyzing medial Myosin II would lead us to form a different conclusion. We will add a sentence to explain why we chose to quantify junctional, and not medial, Myosin II.
*Minor points: *
- * Fig. 1-S1 panel C: the number of cyan cells changes non-monotonically. It first decreases from -10 min to 10 min, then increases from 10 min to 20 min. This is confusing since in theory the number of tracked cells should not increase over time if the cells are tracked from the beginning of the movie. *
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The cyan cells highlight tracked mesodermal and mesectodermal cells, which are not included in the analysis. The low number of mesodermal cells highlighted at 10mins germband extension is because mesodermal and mesectodermal cells are not always tracked successfully at this time. Note that the legend includes a note that ‘”Unmarked cells are poorly tracked and excluded from the analysis”. Also see Methods: “Note on number of cells in movies, for notes on changes to the number of tracked ectodermal cells throughout the timecourse of the movies.”
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Fig. 1-S2: the vnd band in panel A appears to be much narrower than in panel B. *
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*
These are fixed embryos, therefore this could be (at least partially) due to slight differences in exact developmental age of the embryo. Note that we wanted to check that vnd and ind are expressed in the correct places in the ectoderm. We were motivated to check this because the width of mesoderm is reduced in twist, so we thought it was important to verify that there is not a population of ‘ectodermal’ cells with a strange fate (i.e., negative for both vnd and ind). Our experiments show that vnd abuts the mesoderm/mesectoderm in twist as in wildtype, and that the cells immediately lateral to the vnd cell population express ind as expected.
It is possible that there is a slight difference in the number of vnd cells in twist mutants compared to wildtype, but we see no differences in Myosin II bipolarity that would coincide with the vnd/ind boundary (Fig3-S1). Therefore, this would not change the interpretation of our results. Counting the number of rows of vnd cells prior to any cell intercalation (the number of rows will reduce as cells intercalate) would be technically challenging as the lateral border of vnd expression is hard to discern at this time due to lower levels of vnd expression laterally within the vnd expression domain.
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The schematic in Fig. 2J suggests that at the onset of mesoderm pulling the germband cells have a uniform angle of rotation (towards bottom right). Is this the case?*
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*
No, this schematic is purely supposed to show that as cells stretch, they also reorient. Note that we will review our schematics in Fig. 2 to increase clarity (see response to reviewer 1, first minor comment).
- The description of myosin intensity normalization in the Methods section is somewhat difficult to follow [Line 829 - 832]. It would be helpful if the authors can show one or two images before and after intensity normalization as examples. *
We will add some examples of before and after normalization images to this section. We will also review the Methods to improve the text’s clarity.
- Line 704: "Z-stacks for each channel were collected sequentially" - the step size in Z-axis should be reported. *
Thank you for this, the step size was 1µm. We will add this information.
- Fig. 4C: what are the thin, black lines in the image? *
This image is a 2D representation of the Gap43Cherry signal at the level of the adherens junctions extracted for tracking, not a simple confocal z-slice. When viewing these representations, you can see lines showing borders between where information from different z-stacks was used for the tracking layer. Unfortunately, our software does not allow us to remove these lines, but they do not affect tracking, quantification etc.
Reviewer #3 (Significance (Required)):
While most previous work on tissue mechanics and morphogenesis focuses on tissue-intrinsic mechanical input, recent studies have started to emphasize the contribution of tissue-extrinsic forces. An important challenge in understanding the function of tissue-extrinsic forces lies in the difficulties in properly comparing the wild type and the mutant samples that disrupt extrinsic forces, in particular when cell fate specification is altered in the mutants. In this work, the authors addressed this challenge by employing a number of approaches to warrant a parallel comparison between genotypes, including examining the AP- and DV-patterning of the tissue, selecting sample regions with comparable cell fate for analysis, and carefully aligning the stage of the movies. With these approaches, the authors provide compelling evidence to support their main conclusions. By teasing apart the role of the intrinsic genetic program and the extrinsic tissue forces, the work provides important clarifications on the function of mesoderm pulling in GBE and adds new insights into this well-studied tissue morphogenetic process. This work should be of interest to the broad audience of epithelial morphogenesis, tissue mechanics and myosin mechanobiology.
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Review____er #4 (Evidence, reproducibility and clarity (Required)):
*Lye and colleagues investigate the impact of tissue-tissue interactions on morphogenesis. Specifically, they ask how disrupting mesoderm internalization affects convergence and extension of the ectoderm (germband) in Drosophila embryos. Using twi mutants in which mesoderm invagination fails, the authors find that the invagination of the mesoderm deforms germband cells, but does not significantly contribute to patterning, cell alignment, myosin polarization and cell-cell contact disassembly (which drive germband convergence). The authors find modest effects of mesoderm invagination on new junction formation and orientation (which drive extension), but these changes do not have a significant effect on germband elongation. The authors conclude that germband extension is robust to external forces from the invagination of the mesoderm. *
*MAIN 1. The authors clearly show that myosin density is not different in wild-type and twi mutant embryos, and subsequently argue that the pulling force from the mesoderm does not elicit a mechanosensitive response in early germband extension. But if the cell density is constant, doesn't that mean that the longer, DV-oriented interfaces in the wild type accumulate more total myosin than their shorter counterparts in twi mutants? Assuming that the total number of myosin molecules per cell is not greater in the wild type, wouldn't increased total myosin at the membrane suggest a response to the increased deformation? Certainly the cells are able to maintain the same cell density despite the pulling force from the mesoderm, so can the authors rule out a mechanosensing mechanism? *
- *
We do not rule out a mechanosensing mechanism. We agree the total Myosin at stretched interfaces is higher than at unstretched interfaces and proposed a homeostatic mechanism to maintain Myosin II density on the cortex upon rapid stretching (summarized in Fig. 7). Indeed it is possible that this mechanism could itself be due to mechanosensitive recruitment of Myosin II (though there are also other possibilities). We have tried to address this in our discussion (under “Mechanisms regulating Myosin II density at the cortex and consequences for cell intercalation” and “Restoration of DV cell length after being stretched by mesoderm invagination”), but we will amend the wording the make the possibility of mechanosensitive recruitment of Myosin II to maintain cortical density more explicit.
*What happens to the Gap43mCherry signal? From Figure 2A, it seem to be diluted ventrally in the wild type as compared to twi mutants? Comparing myosin and Gap43 dynamics may shed light on whether myosin accumulates more or less than one would expect simply on the basis of having longer contacts. *
We quantify the density of Myosin, rather than the total amount. Therefore, the length of the contact should not matter. The suggestion of comparing Myosin density to Gap43Cherry density is in principle a good one, as it would allow us to compare a protein which is not diluted as cell contact length increases (Myosin) to one which appears to be (Gap43). However, it is not essential for the conclusions that we make. However, in practice quantifying the Gap43Cherry signal would not be straightforward on our existing movies due to the imaging parameters used. We capture the Gap43Cherry channel (but not the Myosin channel) with a ‘spot noise reducer’ tuned on in the camera software, due to very occasional bright spot noise, which confuses the tracking software. Therefore, our Gap43Cherry signal is manipulated during acquisition and to quantify from these images would not be appropriate. Therefore, we would have to acquire, track and quantify some new movies, which is not possible within the timeframe of a revision.
In summary, we think that we have sufficient evidence from our analysis that Myosin II is not diluted upon junctional stretching without comparing to quantification of Gap43Cherry, and the time investment required to quantify the Gap43Cherry would not be worthwhile as it would require more data to be acquired and processed.
- The authors previously argued that mesoderm invagination was required for the fast phase of cell intercalation [Butler et al., 2009]. However, here the authors interpret that loss of twi does not significantly slow down interface contraction, but accelerates the elongation of junctions and cells along the AP axis, which overall would mean that mesoderm invagination is (slightly) detrimental for axis elongation. The discrepancy between their previous and current results should be discussed. *
We are happy to add more information about these discrepancies in the discussion. In a nutshell, we think that these discrepancies arise from the challenges of comparing wildtype and twist mutant embryos relative to each other, and as a consequence we have made various improvements to our methods since (Butler et al., 2009). These improvements included using markers that would be expressed at the same levels in wildtype and twist embryos. Additionally, we did not use overexpressed cadherin-FPs (namely, the ubi-CadGFP transgene), which may have confounding effects, and we used a knock-in sqhGFP to ensure we could all Myosin II molecules were labelled by GFP. We also carefully controlled the temperature at which we acquired the movies, standardized the level at which to track cells and quantify Myosin between movies, as well as improving the accuracy of our image segmentation and cell type identification since our previous study (Butler et al., 2009). See also response to reviewer 2.
- Related to the previous point, it is surprising that the differences shown in Figure 4A-B are not significant. This is particularly troubling when in Figure 5B the authors claim a significant difference in cell elongation rate, which is higher in twi mutants (but only in very short time intervals and actually switches sign at the end of germband extension). These are just two examples, but I think the analysis of significance on a per-time point basis is problematic. *
*Have the authors considered analyzing their results as time series rather than comparing individual time points? Or perhaps integrating the different metrics over the duration of germband extension (e.g. using areas under the curve)? That way they would not have to arbitrarily decide if significant differences in a few time points should or not be interpreted as significant overall differences. *
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For graphs plotted against time of germband extension, we do not think it is appropriate to analyze as a time series rather than comparing individual time points, since different developmental events (such as mesoderm invagination) occur at different times. For graphs plotted against time to/from cell neighbor swap, these can also change over time (e.g., ctrd-ctrd orientation, Fig6D). Therefore we do not feel that it appropriate to run statistical analyses as a timeseries for these comparisons either. Statistically cut-offs are by their nature arbitrary. We have tried to highlight non-significant trends throughout the text (including for Fig4A&B), in addition to stating where we see significant differences to highlight where there may be minor (but not significant) differences.
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While the number of cells analyzed is impressive, the number of embryos is relatively low, particularly for the wild type (only four embryos analyzed). If I understood correctly (if not, please clarify) the authors ran their statistics using cells and not embryos as their measurement unit. But I could not find any evidence that cells from the same embryo can be considered as independent measurements. This could be easily done by demonstrating that the variance of any of the measurements (e.g. elongation, area change rate, etc.) for cells in an embryo is comparable to that calculated when mixing cells from different embryos. *
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*
We do not simply use the number of cells as an n for our experiments. We use a mixed effects model for our statistics as previously (Butler et al., 2009; Finegan et al., 2019; Lye et al., 2015; Sharrock et al., 2022; Tetley et al., 2016). This estimates the P value associated with a fixed effect of differences between genotypes, allowing for random effects contributed by differences between embryos within a given genotype. We will make sure that this is clear in the Methods.
MINOR 1. Figure 4D: the authors show no difference in the proportion of neighbor swaps per minute between wild-type and twi- mutant embryos. But how about the absolute number of neighbour swaps per minute? Does that change in twi mutants (and if so, why?).
The number of interfaces involved in a T1 swap are expressed as a proportion of the total number of DV-oriented interfaces for all tracked ectodermal germband cells, to take account of differences in the number of tracked cells between different timepoints and different movies. Presenting the absolute number of swaps per minute could lead to misleading interpretations.
- I was a bit confused about the reason why in Figure 4A the authors measure the rate of interface contraction in units of “proportion/min”, but in Figure 5A they measure interface elongation in units of “um/min”. Unless there is a good reason not to, these two metrics should be reported using the same units. Is there a difference in the rate of interface contraction when measured in absolute units (um/min)? *
Thank you, we will amend so that both measures are expressed in the same units.
- The discussion of previous work on cell deformation within the mesoderm (page 16, first paragraph) should probably include recent work from Adam Martin's lab (e.g. [Heer et al., 2017]; or [Denk-Lobnig et al., 2021]). *
Thank you, and apologies for this oversight, we will add these references__.__
SUGGESTIONS 1. While I appreciate the arguments that the authors provide to use twi mutants rather than sna mutants or twi sna double mutants, as the authors indicate, in twi mutants there is still contractility in the mesoderm (albeit not ratcheted). Therefore, it is possible that contractile pulses from the mesoderm in twi mutants could still facilitate cell alignment and polarization of myosin in the germband. Given the previous results from the Zallen lab using twi sna double mutants (see above) this is unlikely to be the case, but the findings in this manuscript would be significantly stronger if they included similar analysis in the double mutants.
We had concerns about using sna or twi sna double mutants due to the large amount of space the un-internalized mesoderm takes up on the exterior of the embryo. This concern is also shared by reviewer 1 “Importantly, I think the researchers were correct in choosing to analyze twist single-mutant embryos (as opposed to snail or twist, snail double-mutant embryos), as the overall embryo geometry of these mutants is fairly similar to wild-type embryos, allowing the researchers to directly compare cell behaviors and myosin dynamics during germband extension. This approach also allows them to avoid indirect effects on the germband due to a completely non-internalized mesoderm.” * In addition to this concern, imaging of snail or twist snail* embryos by confocal imaging to include the ventral midline (which is required to define embryonic axes) is problematic as the un-constricted mesodermal cells occupy virtually all the field of view, leaving very few ectodermal cells to analyze.
Whilst we acknowledge that there are some (un-ratcheted) contractions of mesodermal cells in twist mutants, we have clearly shown that there is no DV stretch and very little reorientation of cells. Therefore, any residual contractile activity in the mesodermal cells of twist mutants does not appear to have a mechanical impact on the ectoderm. We cannot exclude the possibility that there is some transmission of forces between contracting cells of the mesoderm and the ectoderm in twist mutants. However, our evidence suggests that the large tissue scale force that transmits to the ectoderm from the invaginating mesoderm is missing in twist mutants, and it was the effects of that force that we wished to investigate (See also response to reviewer 2).
Review*er #4 (Significance (Required)): *
*This is an interesting study, with careful quantitative analysis of cellular and subcellular dynamics. The results follow previous findings from Jennifer Zallen and the authors themselves. The Zallen lab showed that cell alignment, myosin polarization and germband extension are normal in sna twi mutants [Fernandez-Gonzalez et al., 2009], a result that the authors fail to cite. The results in the present manuscript are similar, but the analysis is much more in depth here, so the findings by Lye and colleagues certainly warrant publication. *
We did not specifically cite this result from (Fernandez-Gonzalez et al., 2009), because the subject of their study is the formation of multicellular rosettes, not whether a pull from mesoderm affects Myosin II polarity and cell intercalation. The formation of multicellular rosettes occurs later in germband extension, and therefore these results are not directly relevant to our study. Additionally, their measures of alignment are defined as linkage to other approximately DV oriented interfaces, rather than directly measuring orientation compared to the embryonic axes as we do here, as a different question is being addressed. Specifically, the quoted sna twi experiment is interpreted as extrinsic forces from the mesoderm not being required for linkage of Myosin enriched DV-oriented interfaces together. Myosin II quantification is more rudimentary with edges being assigned as Myosin positive or Myosin negative, as opposed to quantifying the density of Myosin on each interface and we cannot see any comparison of Myosin II quantification between wildtype and twist embryos.
So, although the results are consistent with each other, they are not directly comparable due to methods used and we are happy that the reviewer acknowledges that our analysis is more in depth, which was necessary to address the specific questions that we investigate in our study.
In general, there have been inconsistencies in results between previous studies, leading reviewer one to recognize that *“…it should be published and that it will be an impactful paper within the field. Namely, it will settle once and for all the question of whether mesoderm invagination is required for optimal germband extension in the early Drosophila embryo.” *The high amount of conflicting information in the literature led us to not exhaustively describe individual findings, but we will ensure the results from the Zallen lab are appropriately cited.
However, there are a number of experimental points that I think need to be addressed to solidify the manuscript, particularly in terms of statistical analysis.
Please see more details above (main points 3 and 4) regarding specific concerns about experimental points and statistics. Additionally, we note that reviewer 3 states “statistics were appropriately used”, and our statistical methods are the same as we have used in previous studies comparing live imaging data (Butler et al., 2009; Finegan et al., 2019; Lye et al., 2015; Sharrock et al., 2022; Tetley et al., 2016).
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__REFERENCES
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Blanchard, G. B., Kabla, A. J., Schultz, N. L., Butler, L. C., Sanson, B., Gorfinkiel, N., Mahadevan, L. and Adams, R. J. (2009). Tissue tectonics: morphogenetic strain rates, cell shape change and intercalation. Nat Methods 6, 458-464.
Butler, L. C., Blanchard, G. B., Kabla, A. J., Lawrence, N. J., Welchman, D. P., Mahadevan, L., Adams, R. J. and Sanson, B. (2009). Cell shape changes indicate a role for extrinsic tensile forces in Drosophila germ-band extension. Nat Cell Biol 11, 859-864.
Farrell, D. L., Weitz, O., Magnasco, M. O. and Zallen, J. A. (2017). SEGGA: a toolset for rapid automated analysis of epithelial cell polarity and dynamics. Development 144, 1725-1734.
Fernandez-Gonzalez, R., Simoes Sde, M., Roper, J. C., Eaton, S. and Zallen, J. A. (2009). Myosin II dynamics are regulated by tension in intercalating cells. Dev Cell 17, 736-743.
Finegan, T. M., Hervieux, N., Nestor-Bergmann, A., Fletcher, A. G., Blanchard, G. B. and Sanson, B. (2019). The tricellular vertex-specific adhesion molecule Sidekick facilitates polarised cell intercalation during Drosophila axis extension. PLoS Biol 17, e3000522.
Gustafson, H. J., Claussen, N., De Renzis, S. and Streichan, S. J. (2022). Patterned mechanical feedback establishes a global myosin gradient. Nat Commun 13, 7050.
Irvine, K. D. and Wieschaus, E. (1994). Cell intercalation during Drosophila germband extension and its regulation by pair-rule segmentation genes. Development 120, 827-841.
Leptin, M. and Grunewald, B. (1990). Cell shape changes during gastrulation in Drosophila. Development 110, 73-84.
Lye, C. M., Blanchard, G. B., Naylor, H. W., Muresan, L., Huisken, J., Adams, R. J. and Sanson, B. (2015). Mechanical Coupling between Endoderm Invagination and Axis Extension in Drosophila. PLoS Biol 13, e1002292.
Proag, A., Monier, B. and Suzanne, M. (2019). Physical and functional cell-matrix uncoupling in a developing tissue under tension. Development 146.
Rauzi, M., Lenne, P. F. and Lecuit, T. (2010). Planar polarized actomyosin contractile flows control epithelial junction remodelling. Nature 468, 1110-1114.
Sharrock, T. E., Evans, J., Blanchard, G. B. and Sanson, B. (2022). Different temporal requirements for tartan and wingless in the formation of contractile interfaces at compartmental boundaries. Development 149.
Simpson, P. (1983). Maternal-Zygotic Gene Interactions during Formation of the Dorsoventral Pattern in Drosophila Embryos. Genetics 105, 615-632.
Tetley, R. J., Blanchard, G. B., Fletcher, A. G., Adams, R. J. and Sanson, B. (2016). Unipolar distributions of junctional Myosin II identify cell stripe boundaries that drive cell intercalation throughout Drosophila axis extension. Elife 5.
Wang, X., Merkel, M., Sutter, L. B., Erdemci-Tandogan, G., Manning, M. L. and Kasza, K. E. (2020). Anisotropy links cell shapes to tissue flow during convergent extension. Proc Natl Acad Sci U S A 117, 13541-13551.
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Referee #4
Evidence, reproducibility and clarity
Lye and colleagues investigate the impact of tissue-tissue interactions on morphogenesis. Specifically, they ask how disrupting mesoderm internalization affects convergence and extension of the ectoderm (germband) in Drosophila embryos. Using twi mutants in which mesoderm invagination fails, the authors find that the invagination of the mesoderm deforms germband cells, but does not significantly contribute to patterning, cell alignment, myosin polarization and cell-cell contact disassembly (which drive germband convergence). The authors find modest effects of mesoderm invagination on new junction formation and orientation (which drive extension), but these changes do not have a significant effect on germband elongation. The authors conclude that germband extension is robust to external forces from the invagination of the mesoderm.
Main
- The authors clearly show that myosin density is not different in wild-type and twi mutant embryos, and subsequently argue that the pulling force from the mesoderm does not elicit a mechanosensitive response in early germband extension. But if the cell density is constant, doesn't that mean that the longer, DV-oriented interfaces in the wild type accumulate more total myosin than their shorter counterparts in twi mutants? Assuming that the total number of myosin molecules per cell is not greater in the wild type, wouldn't increased total myosin at the membrane suggest a response to the increased deformation? Certainly the cells are able to maintain the same cell density despite the pulling force from the mesoderm, so can the authors rule out a mechanosensing mechanism? What happens to the Gap43mCherry signal? From Figure 2A, it seem to be diluted ventrally in the wild type as compared to twi mutants? Comparing myosin and Gap43 dynamics may shed light on whether myosin accumulates more or less than one would expect simply on the basis of having longer contacts.
- The authors previously argued that mesoderm invagination was required for the fast phase of cell intercalation [Butler et al., 2009]. However, here the authors interpret that loss of twi does not significantly slow down interface contraction, but accelerates the elongation of junctions and cells along the AP axis, which overall would mean that mesoderm invagination is (slightly) detrimental for axis elongation. The discrepancy between their previous and current results should be discussed.
- Related to the previous point, it is surprising that the differences shown in Figure 4A-B are not significant. This is particularly troubling when in Figure 5B the authors claim a significant difference in cell elongation rate, which is higher in twi mutants (but only in very short time intervals and actually switches sign at the end of germband extension). These are just two examples, but I think the analysis of significance on a per-time point basis is problematic. Have the authors considered analyzing their results as time series rather than comparing individual time points? Or perhaps integrating the different metrics over the duration of germband extension (e.g. using areas under the curve)? That way they would not have to arbitrarily decide if significant differences in a few time points should or not be interpreted as significant overall differences.
- While the number of cells analyzed is impressive, the number of embryos is relatively low, particularly for the wild type (only four embryos analyzed). If I understood correctly (if not, please clarify) the authors ran their statistics using cells and not embryos as their measurement unit. But I could not find any evidence that cells from the same embryo can be considered as independent measurements. This could be easily done by demonstrating that the variance of any of the measurements (e.g. elongation, area change rate, etc.) for cells in an embryo is comparable to that calculated when mixing cells from different embryos.
Minor
- Figure 4D: the authors show no difference in the proportion of neighbor swaps per minute between wild-type and twi-mutant embryos. But how about the absolute number of neighbour swaps per minute? Does that change in twi mutants (and if so, why?).
- I was a bit confused about the reason why in Figure 4A the authors measure the rate of interface contraction in units of "proportion/min", but in Figure 5A they measure interface elongation in units of "um/min". Unless there is a good reason not to, these two metrics should be reported using the same units. Is there a difference in the rate of interface contraction when measured in absolute units (um/min)?
- The discussion of previous work on cell deformation within the mesoderm (page 16, first paragraph) should probably include recent work from Adam Martin's lab (e.g. [Heer et al., 2017]; or [Denk-Lobnig et al., 2021]).
Suggestions
- While I appreciate the arguments that the authors provide to use twi mutants rather than sna mutants or twi sna double mutants, as the authors indicate, in twi mutants there is still contractility in the mesoderm (albeit not ratcheted). Therefore, it is possible that contractile pulses from the mesoderm in twi mutants could still facilitate cell alignment and polarization of myosin in the germband. Given the previous results from the Zallen lab using twi sna double mutants (see above) this is unlikely to be the case, but the findings in this manuscript would be significantly stronger if they included similar analysis in the double mutants.
Significance
This is an interesting study, with careful quantitative analysis of cellular and subcellular dynamics. The results follow previous findings from Jennifer Zallen and the authors themselves. The Zallen lab showed that cell alignment, myosin polarization and germband extension are normal in sna twi mutants [Fernandez-Gonzalez et al., 2009], a result that the authors fail to cite. The results in the present manuscript are similar, but the analysis is much more in depth here, so the findings by Lye and colleagues certainly warrant publication. However, there are a number of experimental points that I think need to be addressed to solidify the manuscript, particularly in terms of statistical analysis.
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Referee #3
Evidence, reproducibility and clarity
During morphogenesis, the final shape of the tissue is not only dictated by mechanical forces generated within the tissue but can also be impacted by mechanical contributions from surrounding tissues. The way and extent to which tissue deformation is influenced by tissue-extrinsic forces are not well understood. In this work, Lye et al. investigated the potential influence of Drosophila mesoderm invagination on germband extension (GBE), an epithelial convergent extension process occurring during gastrulation. Drosophila GBE is genetically controlled by the AP patterning system, which determines planar polarized enrichment of non-muscle myosin II along the DV-oriented adherens junctions. Myosin contractions drive shrinking of DV-oriented junctions into 4-way vertices, followed by formation of new, AP-oriented junctions. This process results in cell intercalation, which causes tissue convergence along the DV-axis and extension along the AP-axis. In addition, GBE is facilitated by tissue-extrinsic pulling forces produced by invagination of the posterior endoderm. Interestingly, some recent studies suggest that the invagination of the mesoderm, which occurs immediately prior to GBE, also facilitates GBE. In the proposed mechanism, invaginating mesoderm pulls on the germband tissue along the DV-axis; the resulting strain of the germband cells generates a mechanotransduction effect that promotes myosin II recruitment to the DV-oriented junctions, thereby facilitating cell intercalation. Here, the authors revisited this proposed mechanotransduction effect using quantitative live imaging approaches. By comparing the wildtype embryos with twist mutants that fail to undergo mesoderm invagination, the authors show that although the DV-oriented strain of the germband cells was greatly reduced in the absence of mesoderm pulling, this defect had a negligible impact on junctional myosin density, myosin planar polarity, the rate of junction shrinkage or the rate of cell intercalation during GBE. A mild increase in the rate of new junction extension and a slight defect in cell orientation were observed in twist mutants, but these differences did not cause obvious defects in cell intercalation. The authors conclude that myosin II-mediated cell intercalation during GBE is robust to the extrinsic mechanical forces generated by mesoderm pulling.
Overall, I found that the results described here are very interesting and of high quality. The data acquisition and analyses were elegantly performed, statistics were appropriately used, and the manuscript was clearly written. However, there are a few points where some further explanation or clarification is necessary, as detailed below:
- The main conclusion of the manuscript relies on appropriate quantification of myosin intensity at cell junctions. It is therefore important that the methods of quantification are well justified. Below are a few questions regarding the methods used in the analyses:
- For myosin quantification, the authors state that "Background signal was subtracted by setting pixels of intensities up to 5 percentile set to zero for each timepoint" [Line826]. The rationale for selecting 5 percentile as the threshold for background should be explained. Also, how does this background value change over time?
- The authors mention that "Intensities varied slightly between experiments due to differences in laser intensity and therefore histograms of pixel intensities were stretched" [Line828]. The method of intensity justification should be justified. For example, does this normalization result in similar cytoplasmic myosin intensity between control and twist mutant embryos?
- A previous study demonstrates that the accumulation of junctional myosin is substantially reduced in twist mutant embryos compared to the wild type (Gustafson et al., 2022). In that work, junctional myosin was quantified as (I_junction - I_cytoplasm)/I_cytoplasm. In contrast, the cytoplasmic myosin intensity does not appear to be subtracted from the quantification in this study. How much of the difference in the conclusions of the two studies can be explained by this difference in myosin quantification?
- The authors used the tissue flow data to register the myosin channel and the membrane channel, which were acquired at slightly different times. The accuracy of this channel registration should be demonstrated.
- The authors show that cell intercalation is not influenced in twist mutant embryos. However, a previous study demonstrates that the speed of GBE is substantially reduced in twist mutants (Gustafson et al., 2022). It would be interesting to see whether a similar reduction in the speed of GBE was observed in this study.
- It has been previously shown that contractions of medioapical myosin in germband cells also contribute to cell intercalation. The authors should explain why medioapical myosin was not included in the comparison between wildtype and twist mutant embryos.
Minor points:
- Fig. 1-S1 panel C: the number of cyan cells changes non-monotonically. It first decreases from -10 min to 10 min, then increases from 10 min to 20 min. This is confusing since in theory the number of tracked cells should not increase over time if the cells are tracked from the beginning of the movie.
- Fig. 1-S2: the vnd band in panel A appears to be much narrower than in panel B.
- The schematic in Fig. 2J suggests that at the onset of mesoderm pulling the germband cells have a uniform angle of rotation (towards bottom right). Is this the case?
- The description of myosin intensity normalization in the Methods section is somewhat difficult to follow [Line 829 - 832]. It would be helpful if the authors can show one or two images before and after intensity normalization as examples.
- Line 704: "Z-stacks for each channel were collected sequentially" - the step size in Z-axis should be reported.
- Fig. 4C: what are the thin, black lines in the image?
Significance
While most previous work on tissue mechanics and morphogenesis focuses on tissue-intrinsic mechanical input, recent studies have started to emphasize the contribution of tissue-extrinsic forces. An important challenge in understanding the function of tissue-extrinsic forces lies in the difficulties in properly comparing the wild type and the mutant samples that disrupt extrinsic forces, in particular when cell fate specification is altered in the mutants. In this work, the authors addressed this challenge by employing a number of approaches to warrant a parallel comparison between genotypes, including examining the AP- and DV-patterning of the tissue, selecting sample regions with comparable cell fate for analysis, and carefully aligning the stage of the movies. With these approaches, the authors provide compelling evidence to support their main conclusions. By teasing apart the role of the intrinsic genetic program and the extrinsic tissue forces, the work provides important clarifications on the function of mesoderm pulling in GBE and adds new insights into this well-studied tissue morphogenetic process. This work should be of interest to the broad audience of epithelial morphogenesis, tissue mechanics and myosin mechanobiology.
- The main conclusion of the manuscript relies on appropriate quantification of myosin intensity at cell junctions. It is therefore important that the methods of quantification are well justified. Below are a few questions regarding the methods used in the analyses:
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Referee #2
Evidence, reproducibility and clarity
In the present manuscript, Lye et al. describe a highly detailed quantification of cell shape changes during germband extension in Drosophila melanogaster early embryo. During this process, ectodermal tissue contracts along the dorso-ventral axis, simultaneously expanding along the perpendicular antero-posterior direction, migrating from the ventral to the dorsal surface of the embryo as it extends. This important morphogenetic event is preceded by ventral furrow formation when mesodermal tissue (located in the ventral part of the embryo) contracts along the dorso-ventral axis and invaginates into the embryonic interior. The study compares cell shape dynamics in the wildtype Drosophila with that in the twist mutant, which largely lacks mesoderm and does not form ventral furrow. The major motivation of the study is to examine whether cellular behaviors and myosin recruitment in the ectoderm is cell autonomous, or if those cellular behaviors depend on mechanical interactions between mesoderm and ectoderm. The authors first examine whether transcriptional patterning of key genes involved in germband extension is different between the wildtype and the twist mutant and find no significant difference. Next, the authors thoroughly quantify cellular behaviors and patterns of myosin recruitment in the two genetic backgrounds. A number of different measures are investigated, notably the rate of change in the degree of cellular asymmetry, rate of cell area change, rate of change of cell orientation, differences in myosin recruitment to cell edges of various orientation, as well as the rates of growth, shrinkage, and re-orientation of the various cellular interfaces. It is thoroughly documented how these quantities change as a function of developmental timing and spatial position within the embryo. These data serve basis for quantitative comparison between cellular dynamics in the two genetic backgrounds considered. Overall, the study shows that cellular behaviors observed in the ectoderm are largely the same during the period of time following ventral furrow formation, as would be expected if those cellular behaviors were predominantly cell autonomous and not dependent on stresses generated in the mesoderm.
The data presented in the manuscript are of excellent quality and presentation is very clear.
Minor comments: none
Significance
I find that the study provides a thorough quantification of cell behaviors in a widely studied important model of morphogenesis. The work may be of particular interest for future model-to-data comparison, perhaps providing a basis for future modeling work. I therefore certainly think that this work warrants publication. However, the results of the study largely parallel previous findings and do not appear novel or surprising. It is well established that in snail mutant that lack mesoderm entirely, germband extension proceeds largely normally. This well-established fact suggests that since tissue dynamics in complete absence of mesoderm are largely unaffected, behaviors of individual cells are likely to not be affected either. The work is pretty much entirely observational, and for most part provides a more detailed documentation/quantification of previous findings. I do not think it is appropriate for high profile publication.
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Referee #1
Evidence, reproducibility and clarity
Summary: In this manuscript by the Sanson group, Lye and colleagues try to definitively answer the question of whether pulling forces from the ventral mesoderm have significant effects on convergent extension in the Drosophila germband (germband extension). While germband extension does occur in mutant embryos lacking mesoderm invagination, it has long been an open question in the field as to whether ventral pulling forces from the mesoderm have significant effects (positive or negative) on cell intercalation during germband extension. To definitely address this question, Lye and colleagues generated high-quality, directly comparable datasets from wild-type and twist mutant embryos, and then systematically assessed nearly all aspects of cell intercalation, myosin recruitment, and tissue elongation over time. They demonstrate that pulling forces from the ventral mesoderm have negligible impacts on the course of germband extension. While there are indeed some interesting differences between wild-type and twist embryos with respect to cell intercalation and myosin recruitment, such differences are relatively minor. They conclude that the events of germband extension neither require nor are strongly affected by external forces from the mesoderm. While this is largely a negative results paper, I believe that it should be published and that it will be an impactful paper within the field. Namely, it will settle once and for all the question of whether mesoderm invagination is required for optimal germband extension in the early Drosophila embryo, and it suggests that tissues are largely autonomous developmental units that are buffered from outside mechanical inputs.
Major comments:
It seems to me that the one obvious omission from this paper is a general measure of convergent extension over time. I think it would be useful to the reader to include some measure of change in tissue aspect ratio over time between wild-type and twist embryos. This could be included in Figure 5 or 6.
Otherwise, I have no major comments on the experimental approach or the findings of this manuscript. It seems to me a straightforward and systematic approach for determining whether mesoderm invagination affects germband extension. I do have several minor comments that should be addressed prior to publication (below).
Minor comments:
I understand why cells would initially stretch more along the DV axis in wild-type embryos compared with twist embryos, but why do cells become so much more stretched along the AP axis (and become smaller apically) after 10 minutes of GBE in wild type compared with twist (Figure 2C and E). I think this is an interesting and non-intuitive result that would warrant a bit of explanation/conjecture.
I don't understand how you are defining cell orientation in Figure 2G. How are you choosing the cell axis that you are then comparing with the body axis? Is it the long axis, or something more complicated than that? I think you should briefly provide this information in the results section. If it is included in the methods, I wasn't able to locate it.
Figure 2: Since you have the space, it might help the reader if you simply wrote out "strain rate" for panels B, D, and F, rather that used the abbreviation "SR."
Please ensure that all axis labels are fully visible in the final figures. In several figures, the Y-axis labels were cut off (e.g., Fig 2I, 4A, 4D, 6B, 6C).
Where space permits, I would suggest using fewer abbreviations in axis labels to increase readability of the figures (e.g., in Figures 3H or 4D).
In Figure 7, I would move the wild-type panels to the left and the twist panels to the right. I think it is more conventional to describe the normal wild-type scenarios first, and then contrast the mutant state.
To be consistent with the literature, "wildtype" should be hyphenated (wild-type) when used as an adjective, or two separate words (wild type) when used as a noun.
Significance
Advance: The advances in this manuscript are largely methodological, but the experiments and analyses are quite rigorous and allow the authors to make strong conclusions concerning their hypotheses. Their findings are based on a high-quality collection of movies from control and twist mutant embryos expressing a cell membrane marker and knock-in GFP-tagged myosin. Importantly, I think the researchers were correct in choosing to analyze twist single-mutant embryos (as opposed to snail or twist, snail double-mutant embryos), as the overall embryo geometry of these mutants is fairly similar to wild-type embryos, allowing the researchers to directly compare cell behaviors and myosin dynamics during germband extension. This approach also allows them to avoid indirect effects on the germband due to a completely non-internalized mesoderm.
Audience: The primary audience for this article will be basic science researchers working in the early Drosophila embryo who are interested in the interplay between the germband and neighboring tissues. Secondary audiences will include developmental biologists more broadly who are interested in biomechanical coupling (or in this case decoupling) of neighboring tissues.
Describe your expertise: I have been a Drosophila developmental geneticist for over twenty years, and I have been working directly on Drosophila germband extension for over a decade. I have published numerous papers and reviews in this field, and I am very familiar with the genetic backgrounds and types of experimental analyses used in this manuscript. Therefore, I believe I am highly qualified to serve as a reviewer for this manuscript.
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Reply to the reviewers
1. General Statements
We thank the editors for sending our manuscript for peer review and the reviewers for careful reading and their critical comments to improve the manuscript. Below, we describe the experiments that have been carried out in response to the reviewers and incorporated in the preliminary revision. We also describe our plan for the revisions that will address the remaining comments of the reviewers. Most of the comments are addressable with additional experiments (some of which are already ongoing) and these experiments will surely strengthen the study reported in this manuscript without affecting the fundamental findings. We would require up to 4-6 weeks to complete these experiments.
2. Description of the planned revisions
Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Summary: The authors used a conditional transgenic mouse model to demonstrate that deletion of serum response factor (SRF) from adult astrocytes provides neuroprotection in various insult/ diseases contexts without promoting any obvious phenotypic deficiencies. The work builds on the group’s previous study where SRF was embryonically deleted from astrocytes and their precursor cells. Given the role of SRF in promoting glial cell differentiation, the adult conditional KO used in the current study was designed to circumvent the limitations of the previous approach. The authors used a variety of complementary approaches (including immunohistochemistry, electrophysiology, transcriptomics, and behavior) to demonstrate the therapeutic potential of their approach. However, I have questions regarding the validity of the behavioral analyses as well as some of the imaging results that dampen my overall enthusiasm.
Major Comment #1
The synaptogenic factors probed in Figure 3C (e.g. glypicans, thrombospondins, etc.) are not likely to play major roles in the adult brain in a non-injury context, so I do not know that these analyses provide any significant insight into potential functional changes in the mutant mice. Along the same lines, the analysis of synapse count (Figure 3D-E) seems inconsequential given that SRF was knocked out well after the period of developmental synaptogenesis. It would have been much more interesting to have performed these analyses following insult (such as the kainate injury model used by the authors) or in one of the disease models presented later in the manuscript. As it stands, I don't think they add very much to the study.
Response: We are grateful to the reviewer for the careful reading of the manuscript. Astrocytes are known to regulate the formation, maintenance, and elimination of synapses. It has been previously shown that LPS-induced reactive astrocytes exhibit reduced expression of several synaptogenic factors, were unable to promote synapse formation and showed reduced phagocytic activity (PMID: 28099414). We wanted to determine whether the SRF-deficient reactive-like astrocytes were likely compromised in their ability to produce pro-synaptogenic factors and/or adversely affect synapse maintenance. We agree with the reviewer that analysis of synapses in the adult brain may not address the role of these mutant astrocytes in synaptogenesis. But our results indicate that the mutant astrocytes are likely not affecting synapse maintenance or exhibit altered phagocytotic activity that would result in increased or decreased synapse numbers. We will make this clearer in the revised manuscript.
Minor Comment #2:
The authors should note that the use of GluA1 as a postsynaptic marker will not identify silent synapses (i.e. structurally "normal" but functionally inert).
Response: We agree with the reviewer that GluA1 will not identify silent synapses. To study silent vs functional synapses, we will stain for Piccolo (presynaptic) and NMDA receptor NR1 subunit (post-synaptic) to label all synapses and compare this with Piccolo/GluA1 co-localized synapses to identify the functional synapses.
Reviewer #2 (Significance (Required):
The manuscript addresses the important area of the cellular mechanisms that underlie neuroprotection. The ms adds to our understanding of genetic control of neuroprotection and should be of significant interest to others in the field. The experimental approach systematic and the data presented are generally of high quality and believable. While the ms presents quite a bit of overall cellular data that underlies various areas of neuronal and brain function that may be affected by loss of SRF, it is still somewhat descriptive. It is unclear what aspect of astrocyte reactivity is determinative, how mechanistically in normal cells SRF suppresses reactivity, and how SRF -negative reactive astrocytes confer such broad neuroprotection. While the latter is well beyond the scope of this study, the authors do propose SRF may be involved in regulating oxidative stress and amyloid plaque clearance as a potential pathway to account for SRF's role, however a more systematic discussion based on the gene expression data and known pathways would be welcome. Overall, this is a high quality ms that should be of interest to the field that identifies a SRF as a novel player in neuroprotection.
Response: We thank the reviewer for the careful reading of the manuscript and for the positive comments. We will include a more detailed discussion on the genes and pathways based on our gene expression data that may provide insights into how SRF may regulate astrocyte reactivity and neuroprotection.
Additional considerations:
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Quantification of the extent of SRF loss in astrocytes in conditional tamoxifen knockout would strengthen the quality of the data.
Response: We will provide this data in the revised manuscript.
While the authos did use a Sholl analysis to show hypertophic changes in SRF negative astrocytes, given that SRF is an important regulator of actin and other cytoskeletal related proteins in other cell types, and that cytoskeletal components can play an important role in cell signaling, it is somewhat surprising that the gene array analysis did not include actin and other cytoskeletal proteins, nor did the authors consider a more careful analysis of intracellular cytoskeletal changes and the potential mechanistic implications of this for observed reactivity and neuroprotection.
Response: We agree with the reviewer that SRF is a well-established regulator of actin cytoskeleton. However, we did not any significant changes in gene expression for actin or actin-regulatory proteins. We would have expected a decrease in astrocyte morphology similar to the neurite/axon defects exhibited by SRF-deficient neurons. It is unclear whether the hypertrophic morphology is due to transcriptional regulation of actin/actin-binding proteins or due to astrocyte reactivity. This would be a very interesting question and we will investigate these aspects in future studies.
Reviewer #3 (Evidence, reproducibility and clarity (Required):
Summary: The study by Thumu et al., suggests that astrocytic specific deletion of SRF in mice results in morphological changes in these cells that does not affect neuronal survival, synapse number, plasticity or cognition. However, in in vivo mouse models of excitotoxic damage and neurodegenerative disease, deletion of SRF reduced neurotoxicity. The authors provide sufficient evidence to suggest that astrocytic SRF contributes to neurotoxicity in various models however some claims are made that are currently not supported by evidence.
Major comments:
2) The authors claim that SRF KO astrocytes undergo hypertrophy. However, the quantification of the number of intersections gives information about morphology rather than hypertrophy. Quantification of cell size (area of S100B staining) should be provided.
Response: We will provide the data suggested by the reviewer.
6) For the RNAseq of isolated astrocytes did the authors confirm that other cell types (e.g microglia) did not contaminate their samples?
Response: We will provide the information requested by the reviewer.
Reviewer #3:
Minor comments:
1) The authors say that in Figure 1B many astrocytes did not show any SRF expression. However, overall averages of SRF intensity are plotted in Figure 1C. It would support their claim to instead to calculate the percentage of SRF expressing cells above a certain threshold in each condition, rather than plotting the mean intensity. As a control for their method of quantifying SRF intensity in Figure 1B, demonstrating no change in SRF in neurons would provide confidence for the specificity of the knockout.
Response: We will provide the quantification of the extent of SRF loss in astrocytes (percent astrocytes that are deleted for SRF) as suggested by Reviewer 2. We will also provide SRF intensity from neurons as suggested by the reviewer.
2) The authors use the term "reactivation" throughout the manuscript. This could be misconstrued as re-activation and so I would suggest using the terms "reactivity" or "reactive transformation". Furthermore, only one region is quantified in Figure 1C while in later figures multiple regions are quantified. The authors should justify this decision or update the figures with data from missing regions.
Response: We will make this change in using the term “reactivity” as suggested by the reviewer.
3) In Figure S2 the authors should provide a positive control for their staining.
Response: We will provide the positive control data for this experiment.
4) Can the authors explain the large amount of variability in number of synapses in 15 mpi in Figure 3E?
Response: We will perform more immunostainings and update the data presented in this figure.
5) Images in Figure 2C are poorly visible and should be improved in terms of either quality or magnification.
Response: We will provide better quality image for Figure 2C.
8) The authors should provide a list of differentially expressed genes from RNAseq of SRF KO mice. No information is currently given in the text about the number of differentially expressed genes in the conditional knockout.
Response: We will include this information in the revised manuscript.
9) In figure 5A data would be better illustrated as a volcano plot (similar to Fig. S7C).
Response: We will provide this in the revised manuscript.
3. Description of the revisions that have already been incorporated in the transferred manuscript
Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.
Reviewer #1: Major Comment #2
There is considerable variability in the behavioral results, particularly the fear conditioning and Barnes maze tasks (Figures 4F-G). Given the extremely low sample size for mouse behavior (n=5 in on group, n=7 in the other), it is highly likely that the behavioral tests were done with a single cohort of animals (which would be far from ideal) and that these experiments are significantly underpowered. Furthermore, it does not appear that the fear conditioning task was properly optimized. For example, in the control mice in context A, there were two animals that were at or very close to 0 percent freezing; these were likely outliers, or even an indication that the foot shock conditioning protocol was not working as it should. The highest percent freezing of either group was ~70%, which would have been an ideal starting place as an average for the control group. In addition, sex of the animals was not reported for these experiments. If the authors combined sexes as they did in other analyses in this paper, it is possible that they missed reaching the appropriate reaction threshold for the foot shock for some of the animals, as sex differences have previously been demonstrated in mice (DOI: 10.1037/bne0000248). Given the age at which the animals are assessed with these tasks, these specific revisions would require greater than 6 months to complete. However, as currently presented, there simply are not enough data points to make any conclusions regarding behavior.
Response: We have performed the behavioural experiments with an additional cohort of animals for both control and mutant groups and reanalysed the data. We now have n=11 for control and n=9 for mutant group. Only males were used for the behaviour experiments, and we do not see any significant difference in behaviour between the two groups. These results are included in revised Figure 4E-G in the Preliminary Revision of the manuscript. However, we are waiting to perform the remote recall memory for the fear conditioning experiment and will include this date in the revised manuscript.
Minor Comment #1:
The representative GFAP images (Figure 1 E/G) do not appear to have been taken at the same magnification. This was particularly apparent in the comparison between the control and CKO hippocampus at 12mpi. It is difficult to say with certainty, due to the lack of fiducial markers in many of the images. Inclusion of a nuclear stain (DAPI) would be highly beneficial to allow the reader to make a more informed comparison.
Response: These images were taken at the same magnification. We have included the DAPI staining for these images in Suppl. Figure 2 in the Preliminary Revision of the manuscript.
**Referees cross-commenting**
After reading the comments of the other reviewer, I think we're in agreement that the cellular and molecular data, while descriptive, is of mostly excellent quality. Moreover, the significance of the study is high, and the potential readership broad. However, I stand by my initial assessment of the behavioral data and find the manuscript quite lacking in this regard. Proper revisions would take at least half a year or more, so the authors may be disinclined to go this route. That being said, if the behavioral data were to be excised, I would be happy to sign off on the rest of the manuscript provided that the other major criticisms are addressed.
Response: We thank the reviewer for the appreciation of our work. We have increased the number of animals in the behavioural experiments and do not see any significant difference between the two groups. These results are included in revised Figure 4E-G in the Preliminary Revision of the manuscript.
In response cross-comment of Rev 2:
Agreed that if properly conducted and presented, the behavioral data would indeed provide a nice functional correlate to the cellular work. In its current state, I'm afraid that it is instead a hindrance to the study and I would recommend that they just remove it if they choose not to address my concerns with the quality (particularly the extreme variability and the complete lack of freezing by several of the animals, especially in the controls).
Response: We hope that the revised behaviour data would provide a strong functional correlate to the other findings in the study.
Additional cross-comments:
I agree with the added criticisms raised by Reviewer #3, and I think that the manuscript would be greatly improved by revisions that address those and the original criticisms from myself and Reviewer #2. I still think that the behavioral data should be omitted, provided that the authors are not capable or willing to appropriately address those concerns within a reasonable time frame.
Response: We will address all the concerns raised by the reviewers with the required experiments to further strengthen the findings in this study.
Reviewer #3
Major Comment
3) In Figure S1 the authors provide evidence showing lack of B-gal in cell types other than astrocytes (neurons/OPCs). However, microglia are missing, which could be important as later they show that microglia undergo changes in the SRF knockout model. This staining should be provided.
Response: We have performed double immunostaining for b-gal and IbaI and do not see any overlap between IbaI and b-gal, suggesting that there is no Cre expression in microglia. We have included this data in revised Figure S1F in the Preliminary Revision of the manuscript.
5) The authors claim in the text that microglia have thicker processes and an amoeboid shape however no evidence of this is provided in Figure S5.
Response: We have provided data to show larger microglia area and morphology in revised Figure S5 in the Preliminary Revision of the manuscript.
7) In the text "Enrichment analysis of Gene Ontology terms for Biological Process (GO BP) revealed that Srf deficient astrocytes showed enrichment of pathways related to cellular response to beta amyloid and beta-amyloid clearance." This is not shown in fig 5. It would be more accurate to say that there is a downregulation of genes involved in B amyloid metabolic process.
Response: We apologize for the omission in showing that this data was presented in Suppl. Fig. S8E. We have now indicated this in the main text.
Minor Comments:
4) Figure 1E is missing body weight data noted in the figure legend.
Response: We apologize for this oversight. This data was actually included in Suppl. Figure S3E and not in Figure 1. We have made the appropriate correction to Figure legend 1.
6) In Figure 2B figure labels are missing.
Response: We thank the reviewer for pointing out this omission. We have added the missing labels.
7) Details of houskeeping gene normalisation are missing from qPCR data.
Response: We apologize for not providing this information. We have included this in the revised Methods section.
4. Description of analyses that authors prefer not to carry out
Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.
Reviewer #3, Major Comment 1:
1) The title of the manuscript is "SRF-deficient astrocytes provide neuroprotection in mouse models of excitotoxicity and neurodegeneration". It would be more accurate to say that SRF is involved in neurotoxicity in these models. To make a comment on the role of SRF in neuroprotection, experiments should be performed in spinal cord injury or ischaemia, where deficiency of SRF would be hypothesised to worsen recovery.
Response: We disagree with the reviewer with this assessment. There is no evidence to suggest that SRF is involved in neurotoxicity. What our data suggests is that SRF deficiency results in a reactive astrocyte state that is neuroprotective in these models. We hypothesize that in injury/infection/disease conditions that would result in generation of neuroprotective astrocytes, SRF expression or function may be negatively regulated. It would be interesting to see whether the SRF-deficient astrocytes alleviate or exacerbate pathology and recovery following spinal cord injury and ischaemia.
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Referee #3
Evidence, reproducibility and clarity
Summary: The study by Thumu et al., suggests that astrocytic specific deletion of SRF in mice results in morphological changes in these cells that does not affect neuronal survival, synapse number, plasticity or cognition. However, in in vivo mouse models of excitotoxic damage and neurodegenerative disease, deletion of SRF reduced neurotoxicity. The authors provide sufficient evidence to suggest that astrocytic SRF contributes to neurotoxicity in various models however some claims are made that are currently not supported by evidence.
Major comments: The title of the manuscript is "SRF-deficient astrocytes provide neuroprotection in mouse models of excitotoxicity and neurodegeneration". It would be more accurate to say that SRF is involved in neurotoxicity in these models. To make a comment on the role of SRF in neuroprotection, experiments should be performed in spinal cord injury or ischaemia, where deficiency of SRF would be hypothesised to worsen recovery.
The authors claim that SRF KO astrocytes undergo hypertrophy. However, the quantification of the number of intersections gives information about morphology rather than hypertrophy. Quantification of cell size (area of S100B staining) should be provided.
In Figure S1 the authors provide evidence showing lack of B-gal in cell types other than astrocytes (neurons/OPCs). However, microglia are missing, which could be important as later they show that microglia undergo changes in the SRF knockout model. This staining should be provided.
Can the authors explain the large amount of variability in number of synapses in 15 mpi in Figure 3E?
The authors claim in the text that microglia have thicker processes and an amoeboid shape however no evidence of this is provided in Figure S5.
For the RNAseq of isolated astrocytes did the authors confirm that other cell types (e.g microglia) did not contaminate their samples?
In the text "Enrichment analysis of Gene Ontology terms for Biological Process (GO BP) revealed that Srf deficient astrocytes showed enrichment of pathways related to cellular response to betaamyloid and beta-amyloid clearance." This is not shown in fig 5. It would be more accurate to say that there is a downregulation of genes involved in B-amyloid metabolic process.
OPTIONAL: Figure 6 would be greatly strengthened by functional in vivo experiments showing reversal of motor/ cognitive phenotypes.
OPTIONAL: The study would be improved by isolating astrocytes from the models used in figure 6 and performing RNAseq to provide information about how SRF knockout affects astrocyte reactivity in these models.
Minor comments: The authors say that in Figure 1B many astrocytes did not show any SRF expression. However, overall averages of SRF intensity are plotted in Figure 1C. It would support their claim to instead to calculate the percentage of SRF expressing cells above a certain threshold in each condition, rather than plotting the mean intensity. As a control for their method of quantifying SRF intensity in Figure 1B, demonstrating no change in SRF in neurons would provide confidence for the specificity of the knockout.
The authors use the term "reactivation" throughout the manuscript. This could be misconstrued as re-activation and so I would suggest using the terms "reactivity" or "reactive transformation".
Furthermore, only one region is quantified in Figure 1C while in later figures multiple regions are quantified. The authors should justify this decision or update the figures with data from missing regions.
In Figure S2 the authors should provide a positive control for their staining.
Figure 1E is missing body weight data noted in the figure legend.
Images in Figure 2C are poorly visible and should be improved in terms of either quality or magnification.
In Figure 2B figure labels are missing.
Details of houskeeping gene normalisation are missing from qPCR data.
The authors should provide a list of differentially expressed genes from RNAseq of SRF KO mice. No information is currently given in the text about the number of differentially expressed genes in the conditional knockout. In figure 5A data would be better illustrated as a volcano plot (similar to Fig. S7C).
Significance
The strength of the manuscript is that the authors demonstrate in more than one model that astrocyte specific knockout of SRF rescues neuronal death, implicating SRF in astrocyte mediated neurotoxicity. The limitations of the study are that the mechanism by which SRF deletion reduces excitotoxicity is not addressed and there is no supporting data beyond neuronal survival in the excitotoxicity/OHDA models or plaque density in the APP/PS1 model.
This study adds SRF to an expanding understanding of the neurotoxic capacity of astrocytes in certain reactive states. It will be of broad interest to the astrocyte reactivity field.
My field of expertise is in astrocyte and microglia interactions in neurodegenerative diseases.
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Referee #2
Evidence, reproducibility and clarity
The manuscript, "SRF-deficient astrocytes provide neuroprotection in mouse models of excitotoxicity and neurodegeneration" by Thumu et al., describes the observation astrocyte specfic SRF deficient mice exhibit neuroprotection against a broad range of brain pathologies. The current ms follows up on previous work done by the corresponding author Ramanan and colleagues in which they showed that astrocyte-specific deletion of SRF early during mouse development resulted in persistent reactive-like astrocytes throughout the postnatal mouse brain. In the current ms the authors present data that adult astrocyte specific conditional deletion of serum response factor results in reactive-like hypertrophic astrocytes that localize throughout the mouse brain. They further show that SRF deficient astrocytes do not affect neuron survival, synapse numbers, synaptic plasticity or learning and memory. Strikingly, they further show that brains of Srf knockout mice exhibit protection against neurodegenerative disease related pathologies including induced excitotoxic cell death and that SRF-deficient astrocytes abrogate dopaminergic neuron death and reduce beta-amyloid plaques in mouse models of Parkinson's and Alzheimer's disease. Based on their results, the authors proposes that SRF is a key molecular switch for the generation of reactive astrocytes with neuroprotective functions can attenuate neuronal injury in the setting of neurodegenerative diseases.
Referees cross-commenting
Reviewer #1 raises an important concern regarding the quality of the behavioral studies. I would also agree that the ms is still strong and the findings are significant without them, although they do extend the functional dimensions of the overall study.
Significance
The manuscript addresses the important area of the cellular mechanisms that underlie neuroprotection. The ms adds to our understanding of genetic control of neuroprotection and should be of significant interest to others in the field. The experimental approach systematic and the data presented are generally of high quality and believable. While the ms presents quite a bit of overall cellular data that underlies various areas of neuronal and brain function that may be affected by loss of SRF, it is still somewhat descriptive. It is unclear what aspect of astrocyte reactivity is determinative, how mechanistically in normal cells SRF suppresses reactivity, and how SRF -negative reactive astrocytes confer such broad neuroprotection. While the latter is well beyond the scope of this study, the authors do propose SRF may be involved in regulating oxidative stress and amyloid plaque clearance as a potential pathway to account for SRF's role, however a more systematic discussion based on the gene expression data and known pathways would be welcome. Overall, this is a high quality ms that should be of interest to the field that identifies a SRF as a novel player in neuroprotection.
Additional considerations:
- Quantification of the extent of SRF loss in astrocytes in conditional tamoxifen knockout would strengthen the quality of the data.
- While the authos did use a Sholl analysis to show hypertophic changes in SRF negative astrocytes, given that SRF is an important regulator of actin and other cytoskeletal related proteins in other cell types, and that cytoskeletal components can play an important role in cell signaling, it is somewhat surprising that the gene array analysis did not include actin and other cytoskeletal proteins, nor did the authors consider a more careful analysis of intracellular cytoskeletal changes and the potential mechanistic implications of this for observed reactivity and neuroprotection.
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Referee #1
Evidence, reproducibility and clarity
Summary: The authors used a conditional transgenic mouse model to demonstrate that deletion of serum response factor (SRF) from adult astrocytes provides neuroprotection in various insult/diseases contexts without promoting any obvious phenotypic deficiencies. The work builds on the group's previous study where SRF was embryonically deleted from astrocytes and their precursor cells. Given the role of SRF in promoting glial cell differentiation, the adult conditional KO used in the current study was designed to circumvent the limitations of the previous approach. The authors used a variety of complementary approaches (including immunohistochemistry, electrophysiology, transcriptomics, and behavior) to demonstrate the therapeutic potential of their approach. However, I have questions regarding the validity of the behavioral analyses as well as some of the imaging results that dampen my overall enthusiasm.
Major Comments:
- The synaptogenic factors probed in Figure 3C (e.g. glypicans, thrombospondins, etc.) are not likely to play major roles in the adult brain in a non-injury context, so I do not know that these analyses provide any significant insight into potential functional changes in the mutant mice. Along the same lines, the analysis of synapse count (Figure 3D-E) seems inconsequential given that SRF was knocked out well after the period of developmental synaptogenesis. It would have been much more interesting to have performed these analyses following insult (such as the kainate injury model used by the authors) or in one of the disease models presented later in the manuscript. As it stands, I don't think they add very much to the study.
- There is considerable variability in the behavioral results, particularly the fear conditioning and Barnes maze tasks (Figures 4F-G). Given the extremely low sample size for mouse behavior (n=5 in on group, n=7 in the other), it is highly likely that the behavioral tests were done with a single cohort of animals (which would be far from ideal) and that these experiments are significantly underpowered. Furthermore, it does not appear that the fear conditioning task was properly optimized. For example, in the control mice in context A, there were two animals that were at or very close to 0 percent freezing; these were likely outliers, or even an indication that the foot shock conditioning protocol was not working as it should. The highest percent freezing of either group was ~70%, which would have been an ideal starting place as an average for the control group. In addition, sex of the animals was not reported for these experiments. If the authors combined sexes as they did in other analyses in this paper, it is possible that they missed reaching the appropriate reaction threshold for the foot shock for some of the animals, as sex differences have previously been demonstrated in mice (DOI: 10.1037/bne0000248). Given the age at which the animals are assessed with these tasks, these specific revisions would require greater than 6 months to complete. However, as currently presented, there simply are not enough data points to make any conclusions regarding behavior.
Minor Comments:
- The representative GFAP images (Figure 1 E/G) do not appear to have been taken at the same magnification. This was particularly apparent in the comparison between the control and CKO hippocampus at 12mpi. It is difficult to say with certainty, due to the lack of fiducial markers in many of the images. Inclusion of a nuclear stain (DAPI) would be highly beneficial to allow the reader to make a more informed comparison.
- The authors should note that the use of GluA1 as a postsynaptic marker will not identify silent synapses (i.e. structurally "normal" but functionally inert).
Referees cross-commenting
After reading the comments of the other reviewer, I think we're in agreement that the cellular and molecular data, while descriptive, is of mostly excellent quality. Moreover, the significance of the study is high, and the potential readership broad. However, I stand by my initial assessment of the behavioral data and find the manuscript quite lacking in this regard. Proper revisions would take at least half a year or more, so the authors may be disinclined to go this route. That being said, if the behavioral data were to be excised, I would be happy to sign off on the rest of the manuscript provided that the other major criticisms are addressed.
In response cross-comment of Rev 2:
Agreed that if properly conducted and presented, the behavioral data would indeed provide a nice functional correlate to the cellular work. In its current state, I'm afraid that it is instead a hindrance to the study and I would recommend that they just remove it if they choose not to address my concerns with the quality (particularly the extreme variability and the complete lack of freezing by several of the animals, especially in the controls).
Additional cross-comments:
I agree with the added criticisms raised by Reviewer #3, and I think that the manuscript would be greatly improved by revisions that address those and the original criticisms from myself and Reviewer #2. I still think that the behavioral data should be omitted, provided that the authors are not capable or willing to appropriately address those concerns within a reasonable time frame.
Significance
General assessment: Overall strengths of this study are the implications of SRF as a broad spectrum anti-neurodegeneration agent and the variety of techniques used. Limitations of this study include a lack of meaningful synaptic comparisons and underpowered behavioral assays.
Advance: Provided the above limitations are addressed, this study would provide a meaningful advance in our understanding of controlled reactive astrogliosis as a potential therapeutic strategy for neuroprotection.
Audience: This study would be of interest to a wide audience, particularly neuro- and gliobiologists as well as clinicians who deal with brain disorders and injury.
Expertise: imaging; behavior; synaptic development
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Reply to the reviewers
We appreciate the valuable suggestions and the overall highly positive review of our manuscript. We have now included many suggestions provided by the reviewers, which have made our manuscript much stronger and more rigorous. One reviewer acknowledged, “This study uncovers sex-dependent mechanisms underlying cold sensitivity between male and female mice. The detailed IHC analysis of MHCII expression in DRG neurons is a clear strength of this study and supports flow cytometry results as well as existing literature. The specificity of MHCII expression on small diameter is well characterized and supported by conditional knockout mouse models of MHCII in TRVPV1-lineage neurons.”
R1: It is not, yet, possible to conclude that all experiments are adequately powered as N's for some studies are not provided.
All experiments include N’s both in the text and in the figure legend.
R1: It is unclear what is meant by "novel" expression, used throughout the manuscript.
MHCII is traditionally thought to be constitutively expressed on antigen-presenting cells (APCs) and induced by inflammation on some non-APCs, including endothelial, epithelial, and glial cells (van Velzen et al., 2009). RNA seq data sets (Nguyen et al., 2021, Tavares- Ferreira et al., 2022, Usoskin et al., 2015, Lopes et al., 2017) demonstrate that mouse and human DRG neurons express transcripts for MHCII and MHCII-associated genes. However, there are no reports to date that demonstrate MHCII protein expression in terminally differentiated neurons. To the best of our knowledge, we are the first group to show that MHCII protein is expressed in DRG neurons.
R1: The statement at the end of the abstract, "and that neuronal MHCII may also contribute to many other neurological disorders" seems premature, beyond the scope of the present study.
We agree with the reviewer’s comment and have changed the sentence to the following: “Collectively, our results demonstrate expression of MHCII on DRG neurons and a functional role during homeostasis and inflammation” (pg. 1).
R1: While cold allodynia (hypersensitivity) is a clinically important feature of CIPN, especially in CIPN associated with the platinum based chemotherapeutic agents, it is less so taxane CIPN. Do 60% of patients with PTX CIPN express cold allodynia or does that number refer to CIPN in general?
This statistic is based on a study that conducted a meta-analysis of CIPN incidence and prevalence with paclitaxel, bortezomib, cisplatin, oxaliplatin, vincristine or thalidomide. However, we now include another reference (PMID: 15082135) that demonstrates patients receiving PTX experience cold hypersensitivity (pg.3).
R1: Again, the future direction of expanding studies of the role of MHCII in other aspects of the CIPN phenotype might bear mention.
We have included future directions regarding other aspects of CIPN phenotype in the discussion. We state, “Reducing the expression of MHCII in TRPV1-lineage neurons exacerbated PTXinduced cold hypersensitivity in both male and female mice. Future studies will evaluate the role of MHCII in PTX-induced mechanical hypersensitivity, another prominent feature of CIPN” (pg. 29).
R1: Is there any evidence that IL-4 and/or IL-10 influence cold sensitivity?
IL-10 and IL-4 have been shown to suppress spontaneous activity from sensitized nociceptors (Krukowski et al., 2016; Laumet et al., 2020; Chen et al., 2020) and to reduce neuronal hyperexcitability (Li et al., 2018), respectively. In addition, IL-10 has been shown to reduce mechanical hypersensitivity (Krukowski et al., 2016); however, cold sensitivity has not been evaluated. IL-4 KO mice do not have an increase in tactile allodynia or cold sensitivity after CCI; however, there is an increase in anti-inflammatory cytokines, specifically IL-10, and opioid receptors, which may be a compensatory mechanism that protects against enhanced pain after injury (Nurcan Üçeyler et al. 2011).
R1: Are these experiments run blinded?
Yes, this is discussed in the materials and methods section (pg. 31).
R1: The term "directly contacts" is unclear. No synaptic structure is identified. It might be more accurate to estimate the actual proximity between the two cells, especially as direct contact would not be necessary for the type of intercellular communication they are studying. This is not an EM study.
We agree with the reviewer’s comment and have changed the wording to “in close proximity” (pgs. 1,5, 7, 27).
R1: Two abbreviations are used for immunohistochemistry, ICC and IHC.
IHC refers to immunohistochemistry, and ICC refers to immunocytochemistry. We accidently wrote ICC in the immunohistochemistry section in the materials and methods section. We have now corrected it to say IHC (pg. 32).
R1: In some figure, group sizes are not indicated (e.g., Fig. 4D).
All group sizes are indicated in the text and figure legends.
R1: "small non-nociceptive neurons" - seems to refer to TRPV1+ neurons. There are, however, TRPV1-nociceptors. "Therefore, the majority of MHCII+ neurons in the DRG of naïve female mice were not TRPV1- lineage neurons but non-nociceptive C-LTMRs." Could use some clarification here. Are the authors suggesting that being TRPV1- defines a neuron a non-nociceptive?
We never said small non-nociceptive neurons are TRPV1+ neurons. We crossed TRPV1 lineage mice with td-tomato to label TRPV1 lineage neurons, which include TRPV1 neurons, IB4, and a subset of Aẟ neurons. We found that TRPV1 lineage neurons comprise about 65% of small diameter neurons, so 35% of small diameter neurons are not TRPV1 lineage cells. These non- TRPV1 lineage small diameter neurons are non-nociceptive LTMRs, most likely TH and MrgB4 neurons.
R2: The most pressing concern regarding this study is a lack of a vehicle control group. It is not appropriate to be comparing paclitaxel treated mice to naïve mice. Please include a vehicle treatment (cremophor:ethanol 1:1 diluted 1:3 in PBS) group for all experiments involving paclitaxel.
We believe the most appropriate control to paclitaxel treatment is the naïve control because clinically, paclitaxel is always administered to the patient in a formulation of 50% Cremophor and 50% ethanol. In clinical studies, the controls are healthy no-pain individuals and patients receiving paclitaxel without pain. However, the percentage of patients receiving paclitaxel that do not develop CIPN is low, emphasizing the need for healthy individuals not taking paclitaxel.
R2: Figure 1A only includes representative images of a small number of T cells in presumable contact with DRG neurons in female Day 14 paclitaxel mice but does not include images from other groups. Similarly, B-D show a single CD4+ T cell in contact with DRG neurons in Day 14 paclitaxel and naïve female mice. Please include quantification of the frequency of CD4+ T cells interacting with DRG neurons in the different experimental groups utilized in this study.
We have now quantified the number of CD4+ T cells per mm2 of DRG tissue, which is found in the text (pg. 5) and figures (Fig. S1 and Fig. 1A). We plan to add the quantification of CD4+ T cells per mm2 of DRG tissue for naïve and day 14 PTX-treated male mice. This data will be included in the text (pg. 5) and in Fig. S1.
R2: Please include entire blot for Figure 2A (or at least more of the blot). There is plenty of space in the figure and as it currently appears is not free from apparent manipulation.
We included a larger area of the western blot in Fig 2A (pg. 9).
R2: The authors conclude that MHCII helps to suppress chemotherapy-induced peripheral neuropathy, resolving cold allodynia following paclitaxel treatment. To support this conclusion, I think it is necessary to include a time-course experiment highlighting whether cKO of MHCII in TRPV1 neurons indeed increases the duration for cold hypersensitivity to resolve following paclitaxel treatment.
We conclude that neuronal MHCII suppresses cold hypersensitivity in naïve male mice and reduces the severity of PTX-induced cold hypersensitivity at the peak of the response (day 6) (pg. 1-2). In addition, knocking out one copy of MHCII in male TRPV1-lineage mice reduced total neuronal MHCII in naïve and PTX-treated mice (day 7 and 14) (pgs. 21-22; Fig.7). Moreover, knocking out one copy of MHCII in female TRPV1-lineage mice reduced surface- MHCII in female 7 days post-PTX (pgs. 19-20; Fig.6). Future studies will investigate the distinct roles of surface and intracellular neuronal MHCII and the contribution of MHCII to the resolution of CIPN.
R2: The graphical abstract is misleading. The authors suggest paclitaxel is acting exclusively via TLR4 and that signaling is resolved at Day 14 which their data does not support. Please adjust to reflect findings from the experiments included in this study.
We have removed TLR4 from our graphical abstract as we do not investigate the role of TLR4 in this manuscript. However, we do not suggest paclitaxel is acting exclusively through TLR4. We modified our wording to indicate both pro-inflammatory cytokines and PTX act on neurons to induce hyperexcitability and neurotoxicity: “Pro-inflammatory cytokines and PTX act on DRG neurons inducing hyperexcitability (Li et al., 2018, Boehmerle et al., 2006, Li et al., 2017) and neurotoxicity (Goshima et al., 2010, Flatters and Bennett, 2006), which manifests as pain, tingling, and numbness in a stocking and glove distribution (Rowinsky et al., 1993)” (pg. 9).
R2: Figure 4 and 6 MHCII labelling is oversaturated in most of the images, creating a blurry hue in the representative images. This should be fixed.
The signal intensity of immune cell MHCII is >5 times greater than neuronal MHCII; therefore, in order to visualize neuronal MHCII, the immune cell MHCII is oversaturated. We reference this in the discussion (pg. 26).
R2: The effects of the PTX cHET group are very mild in both the male and female cohorts, and specific to 1 trial. R3: Furthermore, the behavioral effect is seemingly variable, with only one of the three trials being significantly different between groups. This variable response needs to be discussed further.
This behavioral assay was developed by the UNE COBRE Behavior Core, under the guidance of Dr. Tamara King, who has extensive experience in using learning and memory measures to determine changes in pain such as development of thermal hypersensitivity (1-3, King et al, Nat Neuro 2009). Methodologically, the process is as follows: In the temperature placed preference assay, mice are placed on the reference plate (25 °C) to begin each 3-minute trial. For the habituation trial, both the test and reference plates are set to 25 °C, and the mice are allowed to explore for 3 minutes. The following 3 trials are the acquisition trials where the reference plate is set to 25 °C and the test plate to 20 °C. If the animals have cold hypersensitivity, modeling cold allodynia, then they will demonstrate faster acquisition of a learned avoidance response compared to the WT controls. For the results, we will clarify our findings, which are outlined below: 1) We will change the axis labels to better distinguish BL/habituation trial from reference trials in the graphs. 2) We will add graphs comparing naïve versus PTX for male and female WT mice. 3) The changes in the graphs will now reflect 3 key findings: First, we note that PTX-treated mice learn to avoid the cold test plate faster than the naive controls in the WT mice reflecting PTX-induced cold hypersensitivity. Of interest, both males and females demonstrate learned avoidance by trial 2 and that the percent of time on the cold plate continued to decline only in the PTX-treated mice. We had not graphed this in the original figure and plan to add graphs for both male and female WT mice. These graphs are important to include as it validates that this TPP can capture the expected PTX-induced cold hypersensitivity in WT mice. Second, in terms of the naïve cHET mice, these data show that both female and male cHET mice demonstrate faster learning to avoid the cold (20 °C) plate compared to the WT mice (Fig. 8A, B. We note that the males demonstrate a more robust effect, (faster learned avoidance of the cold plate) with significant avoidance to the cold plate emerging in the cHET mice by trial 3 compared to trial 4 in the females (sig diff compared to BL trial). Third, we observed that cHET mice treated with PTX demonstrate even more accelerated learning to avoid the cold plate compared to WT mice treated with PTX. This observation suggests that PTX-treated cHET mice have heightened cold allodynia compared to the WT mice.
R2: The statistical analysis (for the behavior) should also have been a mixed-effects repeated measures between groups ANOVA.
We agree and re-analyzed our behavior data using repeated measures mixed-effects model (REML) with Dunnett’s multiple comparison test comparing trials 2-4 to trial 1 within same group, and Sidak’s multiple tests for significance between groups at the same trial (pgs. 23-25; Fig. 8)
R3: Presented in Figure 3, the authors present data to show surface expression of MHCII, along with the ability of MHCII to present OVA peptide, on naïve and PTX-treated DRG neurons. These data are probably the most relevant in terms of expression as they look at the surface expression of MHCII along with the potential of MHCII to function; therefore, it is unclear why the authors only conducted this analysis on female neurons, and not both male and female neurons. Given the claims of the paper in terms of sex differences for MHC expression, I strongly suggest this is done in order to put the other observations into context.
We completely agree and have added male mice data in Figs. 2 and 3. By western blot, we show that PTX increased the amount of MHCII protein 14 days post-PTX in DRG neurons from female mice, but there’s no change in MHCII protein after PTX in male mice (Fig. 2). In agreement with the western blot, surface-MHCII determined by flow cytometry did not increase after PTX on DRG neurons from male mice (Fig. 3B). Moreover, the frequency of DRG neurons from male mice with surface-MHCII (determined by ICC) and OVA peptide did not change after PTX treatment (Fig. 3D, E). However, the percent area with polarized MHCII on DRG neurons from male mice increased 14 days post-PTX, indicating a modest PTX-induced response in males (Fig. 3F). We have now included the frequency of surface-MHCII on DRG neurons from male and female mice after PTX treatment, and again there was no change in surface-MHCII in male mice (Fig. 6). Collectively, our data demonstrates that neuronal MHCII in male mice is not strongly regulated by PTX treatment.
R3: Given the data presented in Figure 3, it is not clear what the relevance of investigating the subcellular puncta expression of MHCII neurons is, particularly when considering the sex differences observed, and how this was not been performed for surface expression.
We now include surface and total MHCII quantification for male and female WT and cHET mice (Figs. 6,7). In the text, we describe the significance of surface versus endosomal MHCII. “While endosomal MHCII can promote TLR signaling events(Liu et al., 2011), expression of MHCII on the cell surface is required to activate CD4+ T cells.” (pg. 10). “Although the major role for surface MHCII is to activate CD4+ T cells, cAMP/PKC signaling occurs in the MHCII-expressing cell(Harton, 2019). In addition, it has recently been shown that endosomal MHCII plays an important role in promoting TLR responses(Liu et al., 2011), and since DRG neurons are known to express TLRs (Lopes et al., 2017, Wang et al., 2020, Cameron et al., 2007, Barajon et al., 2009, Xu et al., 2015, Zhang et al., 2018), this suggests the potential for T-independent responses in MHCII+ neurons. Knocking out one copy of MHCII in TRPV1- lineage neurons (cHET) from female mice did not change total MHCII 7 days post-PTX but reduced surface-MHCII. Accordingly, PTX-treated cHET female mice were more hypersensitive to cold than PTX-treated WT female mice, suggesting a role for neuronal MHCII in CD4+ T cell activation and/or neuronal cAMP/PKC signaling. In contrast, knocking out one copy of MHCII in TRPV1-lineage neurons (cHET) from male mice did not change surface-MHCII in naïve or PTX-treated mice but reduced total MHCII, indicating endosomal MHCII and potentially a role in TLR signaling. Future studies are required to delineate MHCII surface and endosomal signaling mechanisms in naïve and PTX-treated female and male mice.” (pg. 28).
R3: Furthermore, the authors should provide details of what the abundant non-neuronal structures are within the DRG images that appear positive for MHCII staining.
We now include an image of the high MHCII+ cells in mouse DRG co-stained with macrophage and dendritic cell markers (CD11b/c), indicating the presence of immune cells (Fig. S6).
R3: The behavioral data presented in Figure 7 is somewhat confusing. Can the authors confirm how many alleles of MHCII were knocked out from the Trpv1-lineage neurons for these experiments? In Figure 7, it states cKO Het, which suggests that only one allele was deleted within the Trpv1 population. If this is the case, this needs to be clearly outlined within the results section and not simply referred to as "knocking out MHCII in Trpv1-lineage neurons". In addition, an explanation as to why heterozygous cKO were used rather than homozygous cKO needs to be provided. This is particularly relevant when discussing potential sex differences.
The mouse behavior is performed in wild type and TRPV1lin MHCII+/- heterozygote mice (Fig 8). Instead of saying we knocked out MHCII, we changed the text to “knocking out one copy of MHCII in TRPV1-lineage neurons” (pgs. 23, 29). In the methods section, we state that “cHET×MHCIIfl/fl crosses only yielded 8% cKO mice (4% per sex) instead of the predicted 25% (12.5% per sex) based on normal Mendelian genetics. Thus, cKO mice were only used to validate MHCII protein in small nociceptive neurons” (pg. 30) (Fig 7).
R3: A significant gap in the current manuscript is the functional assessment of MHCII protein expressed on DRG neurons in terms of T cell activity. I would suggest the authors consider performing a co-culture DRG-T cell (i.e. Treg) assay where anti-inflammatory cytokine release can be measured in the presence and absence of MHCII on DRG neurons.
The functional implication of MHCII protein on DRG neurons in terms of T cell activity is out of the scope of this manuscript. We currently have another manuscript in progress investigating CD4+ T cell signaling and cytokine production when co-cultured with DRG neurons. R3: Within the first paragraph of the results section, the authors reference Goode et al, 2022, stating that they have previously shown that CD4+ T cells in the DRG secrete anti-inflammatory cytokines. I have read this paper and could not find any data that showed increased secretion of cytokines, only that there is an increase in T-cell populations that contain anti-inflammatory markers. Please consider rewording to reflect the observations made in the original paper. We have changed “secrete” to “produce” (pg. 5) because we detected anti-inflammatory cytokines (IL-10 and IL-4) within CD4+ T cells using intracellular staining and multi-color flow cytometry.
R3: Figure 1A states that it is "day 14 PTX", however, there is no reference to this in the corresponding text - please state what Figure 1A is showing in the main text and legend regarding PTX treatment.
We have now included text and Fig. 1. legend that states that the images in Fig1A are of DRG tissue collected from female mice 14 days after PTX treatment (pg. 5).
R3: Throughout the results section (Figure 3-Figure 6), the authors provide percentage changes in observed difference in expression, however, in addition to this, it would be valuable to have the actual number of neurons analysed for each group and sex.
We now report in the materials and methods section the number of neurons that were analyzed (pg. 33).
R3: For Figure 5, can the authors confirm whether this was performed on tissue sections or dissociated cell culture?
This analysis was performed in DRG tissue sections. The legend now states, “Gaussian distribution of the diameter of MHCII+ DRG neurons in DRG tissue from naïve (pink), day 7 (orange) and day 14 PTX-treated (blue) (A) female and (E) male mice (n=8/sex, pooled neurons).”
R3: Can the authors comment on why surface expression for MHCII was not performed on the these reporter neurons?
In the future, we plan to delineate which subsets of neurons express MHCII by co-staining for MHCII and specific neuronal markers. However, these studies are beyond the scope of the current manuscript.
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Referee #3
Evidence, reproducibility and clarity
This manuscript sets out to investigate the mechanism by which the previously reported infiltration CD4+ T cells into the DRG parenchyma can mediate analgesia in the paclitaxel (PTX) model of chemotherapy-induced neuropathic pain (CIPN). The authors provide good rationale for the purpose of the study and make a number of interesting observations, including the expression of MHCII on DRG neurons, the effect of PTX on MHCII expression on DRG neurons, and the effect of targeted deletion of MHCII on Trpv1-expressing putative nociceptive neurons in exacerbating the effect of PTX-induced cold hypersensitivity. These data culminate to a hypothesis that MHCII expression on DRG neurons may drive T-cell mediated anti-inflammatory effects (and analgesia) in models where their recruitment is notable. Overall I enjoyed reading the manuscript, however, I believe there are a number of points that need to be considered further.
Major comments.
- Presented in Figure 3, the authors present data to show surface expression of MHCII, along with the ability of MHCII to present OVA peptide, on naïve and PTX-treated DRG neurons. These data are probably the most relevant in terms of expression as they look at the surface expression of MHCII along with the potential of MHCII to function; therefore, it is unclear why the authors only conducted this analysis on female neurons, and not both male and female neurons. Given the claims of the paper in terms of sex differences for MHC expression, I strongly suggest this is done in order to put the other observations into context.
- Given the data presented in Figure 3, it is not clear what the relevance of investigating the subcellular puncta expression of MHCII neurons is, particularly when considering the sex differences observed, and how this was not been performed for surface expression. Furthermore, the authors should provide details of what the abundant non-neuronal structures are within the DRG images that appear positive for MHCII staining.
- The behavioural data presented in Figure 7 is somewhat confusing. Can the authors confirm how many alleles of MHCII were knocked out from the Trpv1-lineage neurons for these experiments? In Figure 7, it states cKO Het, which suggests that only one allele was deleted within the Trpv1 population. If this is the case, this needs to be clearly outlined within the results section and not simply referred to as "knocking out MHCII in Trpv1-lineage neurons". In addition, an explanation as to why heterozygous cKO were used rather than homozygous cKO needs to be provided. This is particularly relevant when discussing potential sex differences. Furthermore, the behavioural effect is seemingly variable, with only one of the three trials being significantly different between groups. This variable response needs to be discussed further.
- A significant gap in the current manuscript is the functional assessment of MHCII protein expressed on DRG neurons in terms of T cell activity. I would suggest the authors consider performing a co-culture DRG-T cell (i.e. Treg) assay where anti-inflammatory cytokine release can be measured in the presence and absence of MHCII on DRG neurons.
Minor comments.
- Within the first paragraph of the results section, the authors reference Goode et al, 2022, stating that they have previously shown that CD4+ T cells in the DRG secrete anti-inflammatory cytokines. I have read this paper and could not find any data that showed increased secretion of cytokines, only that there is an increase in T-cell populations that contain anti-inflammatory markers. Please consider rewording to reflect the observations made in the original paper.
- Figure 1A states that it is "day 14 PTX", however, there is no reference to this in the corresponding text - please state what Figure 1A is showing in the main text and legend regarding PTX treatment.
- Throughout the results section (Figure 3-Figure 6), the authors provide percentage changes in observed difference in expression, however, in addition to this, it would be valuable to have the actual number of neurons analysed for each group and sex.
- For Figure 5, can the authors confirm whether this was performed on tissue sections or dissociated cell culture? In addition, can the authors comment on whey surface expression for MHCII was not performed on the these reporter neurons?
Significance
This paper presents interesting data on the expression of MHCII on DRG neurons, which corroborates existing and published RNA expression data from the literature. In addition, this paper builds on our current understanding of how T-cells may be able to interact with DRG neurons in order to modulate their responses in instances of nerve injury. However, there are significant gaps in the data presented which prevent a more informative conclusion being drawn regarding the role of MHCII in modulating neuronal responses following PTX-induced CIPN.
Audience: I would suggest basic scientists working within the field of pain and neuroimmunology would be interested in this work.
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Referee #2
Evidence, reproducibility and clarity
In this manuscript, Whitaker EE and co-authors implicate MHCII expression in DRG neurons in the resolution of pain following paclitaxel treatment. The authors demonstrate that CD4 T cells closely interact with DRG neurons, which also express MHCII proteins. They further characterize neuronal MHCII expression in naïve and paclitaxel treated mice in small diameter TRPV1+ neurons. Utilizing genetic animal models with MHCII knockout in TRPV1-lineage neurons, the authors highlight that loss of MHCII in TRPV1 neurons exaggerates cold sensitivity in naïve male mice, and in both sexes following paclitaxel treatment.
Major concerns:
The most pressing concern regarding this study is a lack of a vehicle control group. It is not appropriate to be comparing paclitaxel treated mice to naïve mice. Please include a vehicle treatment (cremophor:ethanol 1:1 diluted 1:3 in PBS) group for all experiments involving paclitaxel. This would also improve statistics as unpaired T tests comparing naïve vs paclitaxel is not convincing.
Figure 1A only includes representative images of a small number of T cells in presumable contact with DRG neurons in female Day 14 paclitaxel mice, but does not include images from other groups. Similarly, B-D show a single CD4+ T cell in contact with DRG neurons in Day 14 paclitaxel and naïve female mice. Please include quantification of the frequency of CD4+ T cells interacting with DRG neurons in the different experimental groups utilized in this study.
Please include entire blot for Figure 2A (or at least more of the blot). There is plenty of space in the figure and as it currently appears is not free from apparent manipulation.
The authors conclude that MHCII helps to suppress chemotherapy-induced peripheral neuropathy, resolving cold allodynia following paclitaxel treatment. To support this conclusion, I think it is necessary to include a time-course experiment highlighting whether cKO of MHCII in TRPV1 neurons indeed increases the duration for cold hypersensitivity to resolve following paclitaxel treatment.
Minor concerns:
The graphical abstract is misleading. The authors suggest paclitaxel is acting exclusively via TLR4 and that signaling is resolved at Day 14 which their data does not support. Please adjust to reflect findings from the experiments included in this study.
Figure 4 and 6 MHCII labelling is oversaturated in most of the images, creating a blurry hue in the representative images. This should be fixed
The effects of the PTX cHET group are very mild in both the male and female cohorts, and specific to 1 trial. I believe these assessments were conducted at Day 6 post injection. Why was this timepoint chosen considering differences in MHCII expression in small neurons was only present at Day 14 relative to naïve? The statistical analysis should also have been a mixed-effects repeated measures between groups ANOVA.
Significance
This study uncovers sex-dependent mechanisms underlying cold sensitivity between male and female mice. The detailed IHC analysis of MHCII expression in DRG neurons is a clear strength of this study, and supports flow cytometry results as well as existing literature. The specificity of MHCII expression on small diameter is well characterized and supported by conditional knockout mouse models of MHCII in TRVPV1-lineage neurons. The clear limitations of this study is the lack of a vehicle control group and limited behavioral analysis. They undermine the conclusions made by the author, and in extension, the significance of this study.
This study adds to the understanding of neuro-immune signaling in peripheral neuropathic pain. As far as I am aware, this is the first study to investigate MHCII expression in DRGs in relation to development of chemotherapy-induced peripheral neuropathy. Thus this study provides an incremental advance in neuroimmune mechanisms contributing to the development of chemotherapy-induced peripheral neuropathy in mice.
This study would be of interest to basic researchers interested in neuropathic pain, with particularly researchers with a focus on neuroimmunology and chemotherapy-induced peripheral neuropathy models. The sex differences observed in naïve mice would also be of interest to basic researchers within the wider pain field. Given the preliminary nature of the findings, I do not think this would be of interest to broader neuroimmunology or clinical audiences.
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Referee #1
Evidence, reproducibility and clarity
Section A:
The key conclusions of this study are quite robust and compelling.
While no claims need qualification clarification of some conclusions could improve the impact of this study.
Additional experiments are not essential to support the claims of this study.
Sufficient details are provided to allow reproduction of the key findings of this study.
It is not, yet, possible to conclude that all experiments are adequately powered as N's for some studies are not provided.
Significance
Section B:
- State what audience might be interested in and influenced by the reported findings.
This study should be of broad interest not only in the field of the neurobiology of pain but in broader issues related to neuroimmunology.<br /> - Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.
I am a clinician-scientist with clinical responsibilities in immunologic disorders and a basic scientist with expertise in the area of pain, including chemotherapy-induced painful peripheral neuropathies and neuroimmune mechanisms.<br /> - Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.<br /> - Place the work in the context of the existing literature (provide references, where appropriate).
The authors provide compelling evidence that MHCII expression of MHCII in primary sensory neurons, is regulated in painful chemotherapy-induced peripheral neuropathy (CIPN) induced by the commonly used taxane class of chemotherapy drugs, paclitaxel (PTX).
The present studies build on recent literature demonstrating that PTX CD4+ T cells in DRG. This is key to their hypothesis as T cells and anti-inflammatory cytokines protect against CIPN. In the present study these investigators studied how CD4+ T cells are activated role of cytokines released from these cells on CIPN. To key findings of the present study: the expression of functional MHCII protein in DRG neurons and the proximity of the DRG neurons and CD4+ T cells. While the MHCII protein was expressed in small-diameter, nociceptive, DRG neurons, in male mice, in females it was induced by PTX. Compatible with the hypothesis that the anti-inflammatory CD4+ T cells attenuate CIPN. Finally, in support of the contribution of this mechanism to CIPN pain, they demonstrated that attenuation of MHCII protein from nociceptors produced the predicted increase in cold hypersensitivity. Taken together their findings support suppression of CIPN by MHCII
While the experiments are well designed and executed and the results clearly presented, I have some relatively minor concerns that, if addressed, might improve the ability of a general scientific audience to appreciate the impact of the findings presented (possibly a penultimate paragraph covering caveats and limitations of the present study).
It is unclear what is meant by "novel" expression, used throughout the manuscript.
The statement at the end of the abstract, "and that neuronal MHCII may also contribute to many other neurological disorders" seems premature, beyond the scope of the present study.
While cold allodynia (hypersensitivity) is a clinically important feature of CIPN, especially in CIPN associated with the platinum based chemotherapeutic agents, it is less so taxane CIPN. Do 60% of patients with PTX CIPN express cold allodynia or does that number refer to CIPN in general? Again, the future direction of expanding studies of the role of MHCII in other aspects of the CIPN phenotype might bear mention. Is there any evidence that IL-4 and/or IL-10 influence cold sensitivity? Are these experiments run blinded?
The term "directly contacts" is unclear. No synaptic structure is identified. It might be more accurate to estimate the actual proximity between the two cells, especially as direct contact would not be necessary for the type of intercellular communication they are studying. This is not an EM study.
Two abbreviations are used for immunohistochemistry, ICC and IHC.
In some figure, group sizes are not indicated (e.g., Fig. 4D).
"small non-nociceptive neurons" - seems to refer to TRPV1+ neurons. There are, however, TRPV1-nociceptors.
"Therefore, the majority of MHCII+ neurons in the DRG of naïve female mice were not TRPV1-lineage neurons but non-nociceptive C-LTMRs." Could use some clarification here. Are the authors suggesting that being TRPV1- defines a neuron a non-nociceptive?
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Reply to the reviewers
We would like to thank all reviewers for their highly valuable comments, reviewing the article, and suggesting changes to improve its overall structure, clarity, and comprehension. Please find below our point-by-point responses to each reviewer’s comments. Lines, figures, and tables we refer to in the responses correspond to the clean copy of the revised manuscript.
Reviewer #1:
1) In the introduction the authors list 4 databases of ultra-conserved non-coding elements, and choose to work with the UCNEbase that detects UCNE regions by comparing human and chicken, as they state that it is one of the most comprehensive resources. Can the authors comment on the extent of overlap of UCNE regions identified in this database compared to other databases. It would also be helpful to add an explanation in the Introduction on the advantage of comparing the human genome to the chicken genome, e.g., is chicken the most distant vertebrate with a high quality genome, or another reason? And can the authors comment on the extent of conservation of the UCNEs compared to other species - in the UCNEbase paper, orthologues for the UCNEs are identified in 18 vertebrate species including reptiles, amphibians and fish.
We have addressed these comments and incorporated them in the introduction (lines 46-47; 49-52). As stated, it is not straightforward to evaluate to which extent UCNE regions overlap with those collected in other databases due to the different scopes and methods of these resources. We clarified why we selected this database, which was precisely based on the comments mentioned by the reviewer, i.e., sufficient evolutionary distance to identify functional regions confidently and high quality genome assemblies. Regarding the extent to which UCNEs are conserved in other species, the UCNEdatabase indeed provides additional information with respect to UCNE orthologues in other vertebrate species, including reptiles, amphibians, and fish. As we consider that this comparison is beyond the scope of the present study, it was not included in the main manuscript. However, to address this interesting point raised by the reviewer, we evaluated the proportion of UCNEs found in different species with respect to those annotated for human-chicken. As show in the figure below, UCNEs are conserved to a larger extent up to Xenopus tropicalis, for which most of the UCNEs annotated for human-chicken have corresponding orthologues in this species. Although to a minor extent, UCNEs are also conserved across more distant species (e.g., fish), for which approximately half of the UCNEs annotated for human-chicken have orthologues.
2) The authors briefly describe how potential target genes were assigned to UCNEs in the Method section (section begins on page 21, line 407 and on lines 379-381). They note that they used the Genomic Regions Enrichment of Annotations Tool (GREAT). Can the authors provide additional details on what this method does and also provide a high level sentence in the Results section on page 8, lines 144-146, on how target genes were assigned to UCNEs, as well as in the Discussion on page 16, line 289. Based on the Methods description on lines 409-414, a UCNE was associated with any gene within 1Mb of the UNCE that was expressed in retina and whose curated regulatory domains overlapped the UNCE. Is this correct? How were the regulatory domains curated (line 411-412)? Can the authors please clarify this important point.
Additional details about the GREAT algorithm have been included in the Methods section (lines 410-424) as well as a high-level sentences in the Results (lines 140-141) and Discussion (lines 276-280). It is correct that a UCNE was associated with any gene within 1Mb of the UCNE that was expressed in retina and whose curated regulatory domains overlapped the UCNE. As stated now (lines 420-424), these regulatory domains are supported by experimental evidence demonstrating that a gene is directly regulated by an element located beyond of its putative regulatory domain. We specified these domains for the utilized version of GREAT as well.
3) There are other approaches for linking putative transcriptionally active genomic regions with target genes in specific tissues or cell types through correlation analysis of ATAC-seq peaks (chromatin accessibility) and gene expression in RNA-seq data. A commonly used peak-to-gene linkage method is implemented in ArchR (Granja et al, 2021, PMID: 33633365). The authors note that they used scATAC-seq gene scores and scRNA-seq gene expression to further characterize the proposed target genes of UCNEs. It is not clear what the authors mean by "scATAC-seq gene scores". Please define "scATAC-seq gene score" and add a reference. Can the authors compare the target gene mapping to UCNEs using GREAT and gene expression filtering to that using peak-to-gene linkage based on retina tissue or single cell ATAC-seq and RNA-seq data?
We have defined explicitly what scATAC-seq gene scores are in the Methods section (lines 436-437) along with its reference. We have also addressed this important point and compared the overlap between the set of target genes predicted by GREAT and those assigned by the peak-to-gene linkage method implemented by ArchR. Details of this analysis, its results, interpretation are included in the Methods (lines 441-446), Results (lines 158-163; Supplementary Table 4), and Discussion (lines 280-282) sections.
4) For gene expression filtering (line 419) the authors quantify transcript expression of retina from FASTQ samples using the Kallisto method, and then note that they did the filtering on gene expression levels (TPM<0.5). Please add details on how you went from transcript expression levels to gene expression levels for the filtering, or was the filtering performed at the transcript level?
We have added details on how transcript-level quantification estimates were summarized at gene level, for which the filtering was performed (lines 429-430).
5) The authors use the words "active UCNE", first mentioned in the Results on line 144. Can the authors define what they mean by "active UCNE". What information/evidence is used to ascertain that a UCNE is indeed active. Overlap of a UCNE with a chromatin accessibility region from ATAC-seq or DNAase-Seq would only suggest that the UCNE may be active. Intersection with enhancer activity measured with in vivo enhancer reporter assays in transgenic mice from the VISTA enhancer browser provides stronger evidence of transcriptional activity. The authors might want to distinguish between putatively actively and active based on the functional support.
We thank the reviewer for this relevant comment to address the nuances of defining active UCNEs. The reviewer’s assumptions are correct and hence these terms were clarified throughout the entire text. The term functional is now only used when referring to UCNEs for which there is functional support (e.g., PAX6-associated UCNE in line 193) .
6) The authors assessed the significance of depletion of common variation (MAF>1%) in the UCNE regions compared to a background of randomly selected genomic regions. In generating the random distribution of regions, did the authors match on the distribution of distances of the UNCEs to the TSS of genes in the randomly selected regions? This may be a confounder. Also, in the legend of Figure 3, lines 191-192, it is stated: "Variant population frequencies within putative retinal UCNEs normalized to a background composed of randomly selected sequences (see Methods).", but we did not find a description of this analysis in the Methods section.
Evaluating the potential confounding effect of the genomic background was indeed a very important point. We have now incorporated the details showing the suitability of such background well as a detail description of how such background was generated (lines 479-481; Figure S1). Additionally, to further support our analysis demonstrating the depletion of common variation within UCNEs, we have included an evaluation of the distribution of genome-wide residual variation intolerance score (gwRVIS) values (PMID: 33686085) compared to this background of randomly selected genomic regions in a human reference cohort (lines 173-178; 487-495; Fig. 3C).
7) In regards to intersecting UCNEs with epigenetic marks that detect active or repressed enhancers in retina, the authors used data from Aldiri et al 2017 that measured epigenetic changes during retinal development. Did this dataset contain epigenetic measurements in adult retina? The authors might want to consider using the epigenetic marks/ChIP-seq data from adult human retina in Cherry et al. PNAS 2020 (PMID: 32265282)
We have incorporated the adult-stage data suggested by the reviewer to provide a more comprehensive characterization. Details about the integration of this dataset as well as the results and their corresponding interpretation have been incorporated in the Methods (lines 372-374), Results (lines 115-117; Supplementary Table 2), and Discussion (lines 271-273) sections accordingly.
8) With respect to the examination of rare variants that may be associated with rare eye disease in retina active UCNEs, for the interpretation of the results, it would be helpful to get more information on the distribution of rare variants found in UCNEs associated in this study to known IRD genes in all affected individuals in families, if this information is available in the 100,000 Genomes Project.
Although it is indeed a relevant point, this information cannot be retrieved in the 100,000 Genomes Project. As it is a restricted research environment, we are only allowed to query sequencing data corresponding to participants enrolled within the framework of our specific sub-domain, namely “Hearing and Sight”, and therefore evaluating the distribution of rare variants in all affected individuals is not feasible.
9) In the Methods section on lines 450-451, the authors mention that they performed variant screening of retinal disease genes, referencing the Genomics England Retinal Disorder panel and Martin et al., 2019. Can the authors add to the Methods and Results sections how many retinal disease genes were initially tested. Also, to get a sense of the specificity of the overlap of rare variants in the 100k Genome Project cohort with UNCE regions, it would be informative to show a distribution of the number of rare variants <0.5% that passed the filtering in gnomAD per eye disease gene before the overlap with UNCEs.
We specified the number of retinal genes that were tested in the Method section (line 471). In addition, as suggested by the reviewer, we generated allele frequency distributions for all variants retrieved within a selection of 25 disease-gene associated loci and their corresponding UCNEs in order to assess the specificity of the overlap between rare variants and UCNE regions (lines 181-182, 496-501; Figure S2).
10) The authors found "an ultrarare SNV (chr11:31968001T>C) within a candidate cis-regulatory UCNE located ~150kb upstream of PAX6. This variant was found in four individuals of a family segregating autosomal dominant foveal abnormalities". They tested the functional effect of this element with a reporter assay in zebrafish and found that the UNCE affects expression in the eye, forebrain, and neural tube. It would add further value if the authors were to test the effect of this SNV in the UCNE on the reporter expression pattern, using CRISPR/cas9?
That is a very relevant point. We have tested the effect of this SNV in the UCNE on the reported expression pattern using the same experimental setup that we used for testing the wild-type construct, namely transgenic enhancer zebrafish assays. However, we could not obtain conclusive results, most likely due to the limitations posed by testing these regions outside their native genomic context. Therefore, additional experimental work (e.g., CRISPR-based) should be performed to address this question. This is, however, beyond the scope of the present study, for which the main focus was the identification and functional annotation of ultraconserved cCREs. We have incorporated the details, results, and interpretation of the assays performed mutant construct in the Methods (lines 525-527; Supplementary Table 12), Results (lines 235-238; Supplementary Table 10), and Discussion (lines 350-353) sections.
11) The authors found rare variants in UCNEs linked to 45 IRD genes. Can the authors provide additional information on the functional genomic annotations of these UCNEs and distance to the target genes. The UCNEs were characterized with respect to their genic features in the original paper (UCNEbase, Dimitrieva et al., NAR, 2013), e.g., intergenic, intronic and 3'/5' UTR. Also, it would be useful for clinical applications to provide the start and end positions of the UCNEs that contain the rare variants associated with their 45 IRD genes in Supplementary Table 6.
Additional functional genomic annotations, genic features following those of the original UCNE paper, and the distance to the TSS of these 45 target disease-associated genes have been incorporated in (new) Supplementary Table 5. The start and end positions of the UCNEs that contain the rare variants have also been indicated in new Supplementary Table 7.
12) A total of 724 target genes were assigned to 1,487 UCNEs that displayed candidate cis-regulatory activity. Given the interest in using UCNEs to help identify potential pathogenic mutations that lead to IRDs, can the authors note in the Results section how many of the 724 target genes are IRDs.
We thank the reviewer for this important point. From the total of 724, a total of 27 genes are annotated as IRD genes, of which (interestingly) 23 were kept as found to be expressed in the retina. This has been clarified in the Results section (line 166-168).
13) In the Discussion on page 15 line 259, can the authors clarify if variation found in UCNEs were only associated with rare disease or also with common diseases.
We have clarified that variation found in UCNEs has only been associated with rare diseases (line 247).
Minor edits:
1) In abstract, the authors might consider changing the words "rare eye diseases" on lines 20 and 22 to "rare retina degeneration diseases", and on lines 88-89.
We thank the reviewer for this comment. However, we consider that rare eye diseases is a more suitable term for our purpose as it includes diseases primarily characterized by stationary and non-progressive phenotypes such as North Carolina Macular Dystrophy and fovea hypoplasia.
2) In the Introduction on line 49, there seems to be a typo in the number of UCNE regions reported. 4,135 UCNE regions is supposed to be 4,351, based on the original paper (https://academic.oup.com/nar/article/41/D1/D101/1057253).
We have corrected this typo accordingly.
3) In the introduction on lines 75-76, these references: Lyu et al., Cell Reports 2021, PMID: 34788628, and Zhang et al., Trends in Genetics 2023, PMID: 37423870, could be added to the following sentence to provide additional: "This cellular complexity is the result of spatiotemporally controlled gene expression programs during retinal development”.
We have now included these relevant references.
4) On lines 77 and 84, I would write IRD as plural: IRDs.
This has been amended in the new version.
5) In introduction on lines 89-90, it can be further added that you provide experimental support for an ultra rare SNV in a cis regulatory UCNE affecting PAX6.
We have explicitly stated that we provided functional evidence for this UCNE.
6) On line 98, the authors refer to Figure 1A when noting that the integration of UCNEs with multi-omics data in human retina allows to evaluate the regulatory capacity of UCNEs across the major developmental stages of human retina. However panel A in figure 1 does not seem to show this point. It shows the comparison of elements across species. Please make appropriate changes to the main text and figure legend.
We have made the appropriate changes and located the reference to this figure in a more relevant part of the text (line 87).
7) Please explain what the names appended to the gene symbols in the first column "UCNE ID" in Supplementary Tables 1 and 2 refer to.
We have clarified what these refer to.
8) On line 145, can the authors clarify what they mean by "active gene expression in the retina". Is this just another way of referring to genes found to be expressed in retina? If so, it might be clearer just to write: "We annotated the identified active UCNEs to assign them potential target genes and thus assess their association with genes expressed in the retina"
We indeed meant genes found to be expressed in retina. As this phrasing might not be completely clear, we have now changed to the wording suggested by the reviewer.
9) One line 156, I would write "regulation of transcription" as listed in the gene ontology terms in Figure 2C, instead of "regulation of gene expression". The authors might want to add this to the Discussion. Can the authors include the full gene set enrichment results from Enrichr in a supplementary table at Padj<0.05 since only the top gene sets are shown in Fig. 2C (at P<1E-13)?
We changed the term to “regulation of transcription” to keep the nomenclature consistent to that of Figure 2C. We have also provided a full gene set enrichment from Enrichr as well (Supplementary Table 3).
10) On page 12, line 214, what does "EH38E1530321" Stand for? It seems to specify a distal enhancer-like signatures in bipolar neurons, but I couldn't find this ID in any database.
This refers to the identifier of ENCODE:
https://screen-v2.wenglab.org/search/?q=EH38E1530321&assembly=GRCh38
Additionally, when mentioning a specific UCNE, VISTA enhancer, or ENCODE cCRE (as in this case), we have explicitly included its corresponding identifier.
11) In the Methods section on lines 391-392, can the authors give some high-level explanation of the unconstrained integration method: "Single-nucleus RNA-seq of the same tissue and timepoints (GSE183684) were integrated using the unconstrained integration method". Also, can they comment on how retinal cell class identities were assigned (line 393). Was it based on known markers or on previous identification of cell classes and highly variable genes between clusters?
We have included a high-level explanation of the unconstrained method in the Methods section (lines 387-392). We also clarified that the assignment of cell class identities was based on known markers (line 394).
12) In the integration of UCNEs with bulk and single cell ATAC-seq and Dnase hypersentitivity regions, can the authors note in the Methods section (lines 400-404) what peak width was used to test for overlap with the UCNEs.
We have specified the peak widths that were used for the overlap with UCNEs (lines 397 and 403).
13) On line 436, the word 'and' is missing between "(SNVs, and indels < 50bp)" and "large structural variants".
This has been corrected.
14) On lines 443-444, please provide references to the computational tools listed. Please note if default settings/parameters were used.
We have specified that default parameters were used in the analysis (line 464).
15) In the following sentence in the Methods section on lines 447-449, it is not clear in the Results section how this was used in the flow of the analysis, and how many cases showed such a similarity in phenotype: "For each candidate variant, we compared the similarities between the participant phenotype (HPO terms) and the ones known for its target gene through literature search and clinical assessment by the recruiting clinician when possible." Can the authors add more detail to the Results section.
As the evaluation of the candidate variants was essentially performed on a case-by-case basis, we opted to include a rather general description of the workflow, which indeed included a comparison of the reported phenotypes with those associated with the putative target gene. An example of such comparison has been included in the Results (lines 186-187) section regarding the cases for which a NCMD-like phenotype was suspected.
16) It would be helpful to have a table that describes the different omics datasets used in the paper, with some basic annotations (tissue type, sample size, reference).
This has now been incorporated in Supplementary Table 11.
17) Can the authors add references to their sentence in the Discussion on page 17 lines 299-301: "As has been shown before, the phenotype caused by a coding mutation of a developmental gene can be different from the phenotype caused by a mutation in a CRE controlling spatiotemporal expression of this gene."
We clarified that this phrase referred to the case of PRDM13, for which bi-allelic coding pathogenic variants are linked to hypogonadotropic hypogonadism and perinatal brainstem dysfunction in combination with cerebellar hypoplasia (Whittaker et al., 2021), while non-coding variants within its regulatory regions are associated with NCMD.
Reviewer #2:
Minor discretionary suggestions for improving the presentation:
1) Wherever a specific UCNE, Vista enhancer or ENCODE cCRE is mentioned, the element should be identified by name or accession code: For instance (Iine 212): "this variant is located within a UCNE (PAX6_Veronica) that is catalogued as a cCRE in ENCODE (EH38E1530321)". UCNE names are particularly important, because they are systematically used as identifiers in the supplemental Tables and thus would enable the reader to easily find additional information about the element mentioned in the main text.
We have now explicitly included all corresponding identifiers throughout the text.
2) I also recommend inclusion of the UCNE, Vista and ENCODE cCRE tracks in all genome browser screen shots. The UCNE track is currently included only in Figure 1. Vista and ENCODE cCRE tracks are missing in all browser views.
We have now included UCNE, VISTA, and ENCODE cCREs tracks in the main genome browser figures (Figures 1 and 4).
3) Supplementary Table 6: It would be useful to indicate for each variant, the type of ophthalmological disorder (Table S5, column C) it is associated with.
We agree this is a relevant point. However, due to limitations in the (bulk) export of clinical information from the protected Research Environment of Genomics England, inclusion of this type of information is not feasible.
4) Fig S2 and supplementary Table 3 are not referred to in the main Text.
We have corrected this and updated the figure and table accordingly.
5) Supplementary Table 8: The Table caption should be expanded.
The contents of each column should be explained. For instance, column F: what means Homo_sapiens|M01298_1.94d|Zoo_01|2337? Where does this information come from, what data and software resources were used?
We have expanded the caption of this table to clarify this output, which is derived from the QBiC-Pred tool, a software used for predicting quantitative TF binding changes due to nucleotide variants.
6) Line 401 probable typo: 103-105 days (103-125?)
Indeed, this typo has now been corrected.
Reviewer #3:
1) Given that UCNE only accounts for a small fraction of gene regulatory elements, this study is likely with low sensitivity in terms of identifying potential regulatory mutations. Although one would expect that variants in UCNE are more likely to be pathogenic, it is hard to extrapolate from the results to assess the contribution of gene regulatory variant to the disease.
We agree that restricting our analysis to these particular regions is one of the limitations of the study, as stated in the Discussion section (line 304-311). However, one of our main aims was to provide a strategy to reduce the search space for pathogenic variants with a potential regulatory effect. Given the substantial body of literature supporting a regulatory role for these regions and, particularly, the availability of already-existing functional data, we considered that this set of regions could represent a suitable target for such analysis. Indeed, the features evaluated, and the methods presented in this study could be extrapolated and applied in other settings involving other candidate regulatory regions and/or tissues of interest, and their associated disease-phenotypes, for which, in any case, the overall contribution of regulatory pathogenic variation to disease might vary greatly.
2) I am wondering how many UCNE overlaps with open chromatin regions specific to the fetal retina and how many UCNE overlaps with adult only. Are UCNEs enriched for developmental genes? If so, how many patients are due to developmental defect?
We have now integrated into our analysis epigenetic measurements in adult retina, in particular the candidate cis-regulatory elements nominated by Cherry et al. PNAS 2020 (PMID: 32265282) based on accessible chromatin and enrichment for active enhancer-related histone modifications in adult human retina. Details about the integration of this dataset as well as the results and their corresponding interpretation have been incorporated in the Methods (lines 372-374), Results (115-117; Supplementary Table 2), and Discussion (lines 271-273) sections accordingly. In particular, out of the 111 UCNEs identified to display the active enhancer mark H3K27ac, 33 were found to maintain this signature at adult stage. Regarding the specific question from the reviewer, the majority of UCNEs overlapping with open chromatin regions are specific to the fetal retina (1,346), with only 7 UCNEs overlapping with open chromatin regions exclusively in adult state. This indeed further supports the major role of the identified candidate cis-regulatory UCNEs in the regulation of developmental gene expression programs, which was already suggested by the Gene Ontology enrichment analysis performed using the set of UCNE target genes as input (Figure 2C; new Supplementary Table 3). As far as the number of patients with development defects that were included in this study, these included: corneal abnormalities (n=62), Leber congenital amaurosis (n=142), ocular coloboma (n=111), developmental foveal and macular dystrophy (n=230), developmental glaucoma (n=94), anophthalmia or microphthalmia (n=120).
3) I am wondering if the 431 ultrarare variants found in the UCNEs is higher than expected. This can be tested by comparing patients without visual disorders.
Although it is indeed a relevant point, retrieving sequencing data from patients without visual disorders is not feasible for us. As it is a restricted research environment, we are only allowed to query sequencing data corresponding to participants enrolled within the framework of our specific sub-domain, namely “Hearing and Sight”, and therefore evaluating additional patients from other sub-domains is not doable. Based on previous studies and our observations, common variants are precisely the ones depleted within UCNEs, while ultrarare variation seems to occur at levels comparable to those observed elsewhere in the genome. Therefore, it is reasonable to speculate that this amount of ultrarare variants is not higher than expected as compared to patients without visual disorders. To further demonstrate the high intolerance of UCNEs to common variation, we have included an evaluation of the distribution of genome-wide residual variation intolerance score (gwRVIS) values compared to a set of randomly selected genomic regions in a human reference cohort (lines 173-178; 487-495; Fig. 3C). Additionally, to address this question further, we have also generated allele frequency distributions for all variants retrieved within a selection of 25 disease-gene associated loci and their corresponding UCNEs in order to assess the specificity of the overlap between rare variants and UCNE regions (lines 181-182, 496-501; Figure S2).
4) It seems that the ultrarare variants listed in sup table 6 are more abundant in a small number of genes. Is this due to the number/size of UCNEs is larger in these genes?
Indeed, the clustering of UCNEs in genomic regions containing genes coding for transcription factors and developmental regulators (e.g., OTX2, PAX6, ZEB2) seems to be one of their intrinsic properties, hence the observed enrichment for a small number of genes. One reason can be that these neighboring UCNEs cooperate to achieve higher degrees of tissue-specific regulatory accuracy needed for these genes.
5) The variant in the putative Pax6 gene regulatory element is intriguing. It would be much more informative if the enhancer with and without the variant is tested in parallel in fish.
That is a very relevant point. We have now tested the effect of this SNV in the UCNE on the reported expression pattern using the same experimental setup that we used for testing the wild-type construct, namely transgenic enhancer zebrafish assays. However, we could not obtain conclusive results, most likely due to the limitations posed by testing these regions outside their native genomic context. We have incorporated the details, results, and interpretation of the assays performed mutant construct in the Methods (lines 525-527; Supplementary Table 12), Results (lines 235-238; Supplementary Table 10), and Discussion (lines 350-353) sections.
6) (optional) it would be quite interesting to check the phenotype in fish or mice with the element repressed via technique such as CRISPRi.
Indeed, we fully agree that CRISPR-based techniques would be the ideal experimental approaches to further validate the functionality of the PAX6-associated UCNE and the identified variant in their native genomic context. Conducting these detailed and focused mechanistic studies is, however, beyond the scope of the present work, for which the main focus was the identification and functional annotation of ultraconserved cCREs.
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Referee #3
Evidence, reproducibility and clarity
In the report, the authors combined bulk and single cell multi-omics data from the retina to identify ultraconserved noncoding elements (UCNE) that might function as regulatory elements based on chromatin openness, DNAse sensitive regions, and histone marks. The candidate UCNEs are intersected with whole genome sequencing data from 3.220 patients with rare eye disease to identify potential rare variants that might affect the activity of UNCE. The goal of the project is intriguing.
My comments are listed below:
- Given that UCNE only accounts for a small fraction of gene regulatory elements, this study is likely with low sensitivity in terms of identifying potential regulatory mutations. Although one would expect that variants in UCNE are more likely to be pathogenic, it is hard to extrapolate from the results to assess the contribution of gene regulatory variant to the disease.
- I am wondering how many UCNE overlaps with open chromatin regions specific to the fetal retina and how many UCNE overlaps with adult only. Are UCNEs enriched for developmental genes? If so, how many patients are due to developmental defect?
- I am wondering if the 431 ultrarare variants found in the UCNEs is higher than expected. This can be tested by comparing patients without visual disorders.
- It seems that the ultrarare variants listed in sup table 6 are more abundant in a small number of genes. Is this due to the number/size of UCNEs is larger in these genes?
- The variant in the putative Pax6 gene regulatory element is intriguing. It would be much more informative if the enhancer with and without the variant is tested in parallel in fish.
- (optional) it would be quite interesting to check the phenotype in fish or mice with the element repressed via technique such as CRISPRi.
Significance
Given that UCNE only accounts for a small fraction of gene regulatory elements, this study is likely with low sensitivity in terms of identifying potential regulatory mutations. Although one would expect that variants in UCNE are more likely to be pathogenic, it is hard to extrapolate from the results to assess the contribution of gene regulatory variant to the disease.
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