- Jul 2020
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Reviewer #1:
This is a very ambitious and interesting study that uses a state-of-the art combination of multiple methods to provide new insights into functional network interactions during motor learning. However, I have several major concerns against the design and analyses that may have contributed to the overall very weak effects that are reported (mainly null effects in standard measures at the behavioural and neural network level). I also think that some of the conclusions are not justified given the partly non-significant and overall weak effects.
1) My main concern is that no baseline stimulation condition (sham TBS) was included. The authors address this in the discussion but I cannot agree with their argumentation. Without a baseline, it is impossible to assess whether each stimulation protocol had a significant impact on the outcome measures. For instance, it would be plausible that both protocols had opposite effects (which is also hypothesized by the authors) which were, however, only slightly or not significant from baseline. If cTBS slightly decreases connectivity and iTBS slightly increases it, this could result in a difference between both protocols that might not be observed when contrasting each protocol against baseline. Put differently, how do we know that these changes are meaningful and significantly different from zero (baseline)? I think this is especially important in the present study since the overall effects are weak and there is no significant modulation of behavior - so the functional / behavioral relevance of the observed modulation remains unclear. I think that without the inclusion of a baseline (sham), it is very hard to interpret the data.
2) Another main concern is that the reported effects are very weak and not properly corrected for multiple comparisons. I don't think that it is justified to apply small volume corrections for large-scale network effects and it seems that some of the results are at threshold. Given the weak effects in these analyses, in combination with the absence of any modulation in the "standard" analyses (fMRI, connectivity, behaviour, MRS only significant in exploratory post-hoc tests which are not well justified), I am not sure if the reported results are really reflecting any stimulation-induced modulations at all or mainly show some noise added by the TMS protocols. This of course affects the conclusions that can be drawn from the study.
3) There are a number of issues with the design that might have contributed to the weak findings. These include data loss (e.g. no MRS data for the hippocampal voxel) and somehow arbitrary sample sizes that are not well justified. I am also not sure why cortical excitability measures (MEPs) were performed after TBS because this is of minor importance and delayed the start of the fMRI sessions. Given that TBS effects are expected to decrease over time, I am not sure if this was necessary. Was the potential change of the TBS effects across session taken into account (e.g., by using a parametric modulation of the TMS effect)?
4) Given the overall weak effects the conclusions should be toned down. The discussion would further benefit from including additional work that demonstrated changes in remote subcortical regions and effective connectivity after TMS over a frontal area (e.g. Herz et al., J Neurosci 2014).
5) There is no modulatory effect of TBS on behaviour, which is surprising in light of previous neurostimulation studies on motor learning. I think the way this is sold in the discussion is a bit odd. I guess that initially, one would have expected a behavioural modulation that should ideally be correlated with any TBS induced changes in functional connectivity (or with the MRS data). If not, how would you be able to claim behavioural relevance? In the discussion, the absence of a behavioural modulation is sold as an advantage, I think this is not justified and should be toned down. Moreover, since the authors speculate about potential influences of TBS on motor consolidation, I was wondering if consolidation was assessed (which seems to be a relevant parameter here)?
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Preprint Review
This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.
Summary:
The main concerns with the study were three-fold. First, the absence of a control group makes it hard to draw conclusions about the effects of inhibitory and excitatory TBS protocols separately, limiting the appeal of the study. Secondly, the control for multiple statistical tests is not adequate - conducting 87 independent "small volume corrected" tests will lead to an inflated family-wise error rate. Finally, all reviewers were unanimous in judging the effects to be somewhat interesting, but rather weak - with the absence of an effect on behaviour and simpler standard fMRI and connectivity measures being as remarkable as the positive effects reported.
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Reviewer #3:
Short-summary:
Children (wide range) and adults participated in 3 MEG-experiments with auditory oddball paradigms varying in task demands. The focus is on the child N250m. Results show that although the N250m was not attenuated by task differences, increased activation in the left hemisphere (for at least the standard stimulus in the gng-task) was associated with better performances in inhibition tasks. Since the N250m in children was mainly located in the temporal cortices, whereas activation in adults was present in other areas, notable the ACG, this suggests that children differ from adults in the mechanisms required for cognitive control, and rely longer on sensori-motor areas.
Positive: well-written, great pictures.
Major comments:
1) The proposed link between auditory skills and inhibition is poorly explained and requires more elaboration. They want to relate auditory processing to " cognitive skills" (line 22, 77). Yet cognitive skills are a broad construct, encompassing many skills and abilities, including for instance reading, but also executive functions, which includes inhibition (Miyake et al., 2000) . Actually why zoom in on inhibition? Is it as one of the three components of executive function (Miyake et al., 2000) or is it viewed as part of self-regulation (Nigg, 2017)? You refer to cognitive control (46/47)- which suggests the latter explanation - but this remains unclear. It would help the reader to use consistent terminology (e.g., inhibition, cognitive control, executive control, inhibitory control are used now), or to highlight the links between the related concepts. Moreover, there are many paradigms to test inhibition, why did you chose the one you chose (Littman & Takacs, 2017)? And you also examine the behavioral performance sin the inhibition-MEG task (line 303)? Why the additional focus on attention in the results?
2) Your summary of the two child components (N1m and N250m) predicts different findings with the relationships with inhibition. That is, the link between the child N1m and inhibition should be smaller/non-existent, while it is mainly in the N250m that you expect it. While your results prove evidence towards the latter, your analyses do not concern the first component. It would be stronger if you do.
3) The logic of having three different auditory-listening tasks, and one behavioral outcome measure was not entirely clear to me. It seems that the paper is addressing various aims (e.g., replicate earlier work showing that the N250m is unaffected by task parameters in children while it is only present in adults for active tasks; another is linking the N250 m across all three tasks, or only for one of the three, if so, which? related to inhibition, but only for children, or also adults?) it would help to be explicit about this. For instance, there is an MEG-inhibition task and a behavioral task. Were you going to relate performances to both?
4) Why is age a significant predictor in explaining SSRT performance when adding left hemisphere OB-deviant, but not when adding LH GNG-S? Moreover, isn't it surprising that increase in activation yields better SSRT scores when this component disappears in adults?
5) From an association one cannot infer causal relationships - such limitations need to be discussed in more detail. Results do not allow for concluding that a sustained response ' aid inhibitory performance' (line 624).
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Reviewer #2:
In this study the authors sought to explore the relationship between a child-specific auditory evoked response (N250) and cognitive control, using a classic auditory oddball paradigm. Here the cognitive control was manipulated by varying the tasks that participants were instructed to do while listening to the oddball tone sequence: (a) ignore all sounds ("passive listening"), (b) respond to the standard tones ("go/no-go") and respond to the deviant tone (which was called "oddball task" in this study).
Using the combined MEG and EEG as well as MRI, they reported an association between the strength of N250 in the left hemisphere and the behavioural performance in the go/no-go task and a separate inhibition task. Based on this observation as well as the fact that N250 was only visually observed in the children's brain response, the authors claimed that when doing sound involved cognitive tasks, different neural mechanisms were employed by adults and children. Considering the difficulty in operating a MEG and EEG combined experiment on children (6-14 years), the large sample size (n=78) is very, very impressive and the task was carefully embedded in a children-friendly game, which showed that the authors did a lot of work for this project.
However, it is hard to generalise such a conclusion based on the current paradigm and the results reported here. I also found it's a bit hard to follow the logic in the original manuscript, not because of the language but I think it might need more thorough revision to better explain what exactly the authors hypothesize, why use this specific paradigm, and why analyse the data in these ways.
Major comments:
1) While the task "press a button to standard tone" is called go/no-go task, the task "press a button to deviant tone" is called an oddball task. Why is the latter not a go/no-go task? It's definitely ok to have different names for different blocks, and also to analyse the data in these two blocks separately to double check. However, I would not expect fundamental difference in these two blocks. (However, as mentioned later, the authors reported divergence between these two tasks).
2) The main finding "Left hemisphere auditory responses at 250ms predicts behavioural performance on inhibition tasks" was based on the observation that, the brain activity in the left hemisphere (independent of the task) was negatively correlated with the within-individual variance in RT and the error rate in the go/no-go task (where subjects were instructed to respond to the standard tones) and the reaction time in a separate inhibition task done outside of the scanner. Why smaller within-individual variance in RT means better performance? Someone could be consistently very slow and this should not be a better performer. I found this is confusing, especially that based on Table 3, there was no correlation between RT and the brain response strength.
3) The scatterplots in Figure 7 shows the correlation reported in Table 3. However, the correlation seems to be largely driven by a few outliers: about 5 subjects whose source amplitude was much more negative than the rest of the population. Why did these subjects have a particularly strong source amplitude? After excluding these five subjects' data, will the correlation remain significant?
4) This point is related to points (1) and (2). In table 3, left hemisphere response during passive listening was strongly correlated with ICV, ERR and SSRT, but in table 6, there were no more correlations for passive listening. Can the authors explain why there is a difference between two passive listening sessions which in theory should be the same? Again, if there is no difference between "press a button to standard tone" and "press a button to deviant tone" and the correlation observed in table 3 was robust, then we would expect to see the significant correlations in table 6 for ICV, ERR too, but it is not true. These together make the finding less convincing. If there is any misunderstanding, the authors still need to justify these clearly in the manuscript and further analysis would be helpful.
5) Interpretation of the result: Even if the association between the amplitude of N250 and the behavioural performance is proven robust and true, this doesn't mean that this activity "aid the inhibitory performance in children" (line 624). Correlation does not imply causation. The authors need to provide more direct evidence to support such a claim or consider re-wording.
6) Design of the task: a. The block order: Why was the task order fixed for all participants? b. It is unclear why the passive listening block has a different number of trials (300) compared to the other two active blocks (360).
7) Sample size: Although the large sample size used in this study is very impressive, it is unclear how this sample size was determined and why the sample size of two groups (children vs adults) was so different (children n=78, adults n=16). It is crucial to justify this difference in this study because the motivation of this study is based on the hypothesis that N250 is present in children but not in the adult.
8) The unequal number of samples in statistical analysis: Related to the last two points, it is unclear whether the number of trials/participants was equalised before running the statistical analysis in this study.
9) Handedness: As the authors themselves mentioned in the discussion, the effect in left hemisphere observed here could be related to the handedness. Then the authors should also report the handedness amongst the participants.
10) Analysis: It is generally unclear why the children's data were divided into two groups: above or below 10 years old. The authors need to explain their rationale behind this clearly before doing so.
11) Figure 3: Each sub-figure includes 6 different conditions and makes it very hard to visualise. Please consider plotting the results in pairs for comparison, also show error-bar and run proper time-series statistics on the result. Also, it is unclear which channels are selected based on the black and white cap image at the centre. Please visualise it differently, for example, colours.
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Reviewer #1:
This study by van Bijnen et al. used MEG/EEG recordings to examine the behavioral relevance of auditory processing in children, with a specific focus on an auditory cortical component around 250 ms, which only occurs in children but not in adults. They demonstrate that this component, particularly in the left auditory regions, covaries with several behavioral measurements in children. They conclude that the results suggest a shift in cognitive control function from sensorimotor regions in children to prefrontal involvements in adults.
The study addresses an important question in both auditory neuroscience and developmental science and is carefully performed. The modified design for children is interesting. However, I am not quite convinced that the findings constitute a great breakthrough. Moreover, I have major concerns about the correlation analysis that would support the central claim in the paper, that is, the attentional inhibition relevance of the M250 component in the left auditory cortex.
Major:
1) The child-specific M250 component occurring in the auditory cortex is interesting, but as mentioned throughout the paper, this is a well established observation. This study provides some evidence for the behavioral correlates of the component, but I am not convinced that the correlations supports the unique function of the component in attentional inhibition and its development trajectory from children to adults. First, the author only focused on the M250 component and calculated its correlation to behavior and thus could not exclude that other components might also be involved in the process. I would suggest the authors to do the correlation throughout the time course to thoroughly seek the behavioral-related components. Second, even for the passive listening conditions when inhibition is not required, the M250 also correlated with some behavioral measurements in certain task (Table 3) ? Third, only the to-be-inhibited sound was analyzed so how could the results support that the component only correlates with attentional inhibition? I understand that the authors might want to avoid the motor confounding factors associated with attended sound, but without comparison or control analysis, the conclusion could not firmly hold. Finally, behavioral relevance analysis was only done on children but not adults.
2) The authors calculated correlations between the M250 component with a series of behavioral measurements. Is there any way to do multiple comparison correction? Moreover, the results are inconsistent across different comparisons (e.g., Table 3, Table 6). For example, several behavioral indexes correlated with neural component for both PL and GN conditions (Table 3) while only SSRT showed correlation with the component under OB condition. I would suggest a more fair analysis by performing a GLM analysis using all the behavioral measurements (not just select factors that showed significant partial correlation which I think is double dipping to some extent, e.g., table 4, 7) as predictors.
3) To support the developmental trajectory of the M250 component, the authors could also perform the behavioral correlation for adults and compare it to that for children, even the behavioral-relevant component might be different in time and occur in distinct brain regions.
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Preprint Review
This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.
Summary:
All the reviewers acknowledged the importance of the question the paper aims to address, the big sample size, and the interesting experimental design for children. The paper is also clear and well written. However, the reviewers raised several critical concerns that question the main conclusions, including the interpretation of the results (i.e., relation to inhibition in cognitive control), inconsistency across experiments (i.e., differences in the results between the two experiments), and analysis details (i.e., behavioral-neural correlations). The paper could also be improved by a reframing in which the hypotheses and rationale behind the experiments are better explained.
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Reviewer #3:
Summary:
The authors evaluate functional implications of two B9D2 missense variants identified in an individual with Joubert syndrome, by engineering the variants into a C. elegans model system. Few studies have evaluated the functional consequences of patient variants in model systems (rather than null alleles). Overall, the experiments are elegant and rigorous. The functional defects evaluated include decreased and altered localization of the variant proteins at the TZ, altered TZ function, altered cilium function (dye filling and behavioral assays), and reduced TZ protein localization, especially for TMEM216. The functional effects of homozygous null, homozygous missense variants, and compound heterozygous missense variants are compared. While most of the conclusions are well-supported, the work does not connect the functional consequences in C. elegans to phenotypic severity in humans, a critical validation of methods to test pathogenicity of human variants.
Major comment:
The authors introduce the concept of using C. elegans for genotype-phenotype correlations in the Abstract and Introduction, but do not interrogate an allelic series from humans with more and less severe phenotypes. The claims of genotype-phenotype correlation could be de-emphasized (eliminated? restricted to C. elegans), or the work could be strengthened by including more of an allelic series including variants predicted to be more deleterious (p.Ser101Arg identified in families segregating a Meckel syndrome phenotype. and p.His5Gln identified in a family segregating a possible Meckel syndrome phenotype), less deleterious (the p.Leu36Pro variant in a possibly less severely affected person with JBTS, also published in the Bachmann-Gagescu paper), and benign (i.e. common variants that are found homozygous in population databases like gnomAD and are unlikely to impair B9D2 function). It seems that this would be a lot of additional work; however, the Discussion highlights "it should be possible to generate hundreds of alleles in a relatively short time frame at relatively low cost and manpower compared to other multicellular systems. The workflow to generate and characterize ciliopathy associated variants described here can also be extended to other conserved cilia genes and ciliopathies."
Other comments:
1) Important considerations for data presentation and statistical analysis:
-Use dot (or violin) plots rather than bar graphs to show data structure for length, intensity, and other measurements (see PMID 32346721). For the curves of linescan intensities, it would be helpful to include supplemental figures with all of individual curves to see their shapes and variability.
-t-tests on all data points together may over estimate statistical significance; consider whether it would be more appropriate to compare mean measurements for each animal (or median if the data are not normally distributed). At a minimum, list the number of cilia and the number of animals for each experiment.
2) Could the lower levels of mutant protein in the TZ be due to lower levels of total mutant protein? Although there is no MKSR-2 antibody, this could be evaluated by Western blots of mNG::MKSR-2/mNG::MKSR-2(P74S) and mNG::MKSR-2/mNG::MKSR-2(G155S) animals.
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Reviewer #2:
In this manuscript, the authors analyzed the function of two pathogenic missense variants (P74S, G155S) of Joubert Syndrome protein B9D2/ mksr-2 using a C. elegans model. The data shows that both P74S and G155S mutations change the distribution of MKSR-2 on TZ and disrupt the structure and function of cilia in C. elegans, indicating that both mutations are pathogenic.
Characterizing the function of pathogenic mutations associated with ciliopathies is important for us to understand the function of ciliopathy genes and the pathogenesis of ciliopathies, therefore, the topic of the manuscript is very important and interesting.
The manuscript is well organized, and the data is of high experimental quality. However, there is a lack of new insights about the function of MKSR-2 protein or the formation of TZ.
Major concerns:
What are the possible mechanisms by which P74S and G155S mutations affect the function of MKSR-2? Do these mutations affect the interaction between MKSR-2 with other TZ proteins? I do think some (even a little) new insights into the function of MKSR-2 are needed.
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Reviewer #1:
The experiments are elegant, take advantage of the strengths of the model and the conclusions are mostly supported by the results, even if the discussion should address potential limitations a little more. Overall, this is thorough work of potential high impact.
Major comments:
1) The authors test the localization of the mutant B9D2 protein at the base of the cilium, show decreased fluorescent signal and conclude that the patient mutations affect the TZ localization of the protein. It seems important to me to also demonstrate that the overall protein stability is not affected by measuring the protein levels by western blot if possible. The competition assay between wildtype and mutant alleles with and without transgene somewhat supports the presence of product from the mutant alleles, but an objective measure of the amount would further strengthen their point. (One could imagine that the G155S mutation leads to decreased protein stability with increased degradation and this may explain why it is more similar to the knock-out than the P74S allele?).
2) One major claim made by the authors is that the experiments allow to classify the severity of the effect caused by the individual mutations, showing that the G155S is more severe than P74S. What I find puzzling, is that the ultrastructural consequences on the TZ appear to be similar in both mutants, whereas the TZ gating function is affected only in the G155S mutant. How do the authors explain this discrepancy? If the morphology of the gate is affected similarly, why is the function not affected similarly? Maybe some quantification of the ultrastructural defects would show that the ZT is more disrupted in the G155S mutant?
3) A more detailed discussion of the differences between C.elegans and mammalian cilia appears necessary to me, since these difference may prove to limit the applicability of the proposed assays (for example differences in the basal body of C.elegans may limit this approach for basal body resident proteins? Even for the TZ, in humans mutations in only on TZ component cause phenotypes, while in worms, double mutants are necessary for most genes, suggesting differences in the function of the individual proteins or the structure of the TZ). Beyond the species differences, evidence is appearing for cell-type specific roles of ciliary proteins, so that results from one type of cilium (including those shown here in worms), do not necessarily guarantee that this holds true in all cilia types, which would limit the interpretation for patients with many different cilia types. This being said, I still support the relevance of the current work, but just think that these potential limitations should be mentioned in the discussion in a more detailed manner.
4) Figure 2D: the curve for the P74S mutant overlays with the WT curve with respect to height (fluorescence intensity) and length (x-axis). Does this not contradict panels A-C where the signal is weaker and shorter?
5) Figure 6C: my impression from the graphs is that nphp4 and cep290 are just as much affected as mks14 and mks6 in both P74S and G155S mutants? The text does not mention that mksr2 mutants have any effect on nphp4, cep290 or mks5 whereas the graphs do show a mild effect? Wouldn't this contradict the model of how the TZ is built? Figure 6D again seems to show a different result than Figure 6C (mainly for mks6)?
6) Statistics: correction for multiple testing should be performed everywhere (no pair-wise t-tests).
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Preprint Review
This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.
Summary:
The manuscript by Lange et al describes how C. elegans can be used to generate functional assays to interpret the significance of missense variants in known human ciliopathy genes. This work thus aims at being a proof-of-principle for a way to address the major problem of VUSs (variants of unknown significance) faced by human geneticists today and is therefore of high relevance to the field (even if no major novel biological insights with respect to ciliary biology are described). The reviewers agreed that characterizing the function of pathogenic mutations associated with ciliopathies is important for us to understand the function of ciliopathy genes and the pathogenesis of ciliopathies. The manuscript is well organized and the data are of high experimental quality.
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Author Response:
All three reviewers agreed that establishing a link between a proteasome activator and heterochromatin stability was novel and intriguing. However, limited insight into the PA28-gamma mechanism of action (or possibly a new heterochromatin compaction mechanism) dampened reviewer enthusiasm. The reviewers offered many suggestions, including additional experiments, new controls, and structural changes to the Discussion, that we hope you find useful.
We would like to thank the reviewers for their suggestions and comments, which we will take into account to improve our manuscript as much as possible. As stressed by reviewers, our manuscript highlights a new and unexpected function of a proteasome regulator, PA28γ, in the regulation of heterochromatin compaction. We also provide evidences that this unexpected function of PA28γ is independent of its proteasome regulatory function. Moreover, we can show that PA28γ is required at least for proper maintenance of heterochromatin regions dependent on HP1 proteins, thereby providing a clear insight into the PA28γ mechanism of action on chromatin.
Reviewer #1:
In the manuscript entitled, "The 20S proteasome activator PA28γ controls the compaction of chromatin," Fesquet et al. establish a functional link between PA28γ and chromatin compaction in human cells. Previous work established a role for PA28γ in DNA repair and in chromosome stability through mitotic checkpoint regulation; however, a role, if any, for PA28γ in heterochromatin establishment/maintenance was not known. The authors use an elegant LacO-GFP system combined with PA28γ knockdown to support the possibility that this nuclear activator contributes to DNA packaging of repetitive DNA. A nucleosome proximity assay offers additional support that the most compacted chromatin is most sensitive to loss of PA28γ. Using a truncated version of PA28γ, the authors show that this chromatin function appears to be independent of its interaction with the 20S proteasome. ChIP-qPCR suggests that PA28γ binds repetitive DNA and ChIP-qPCR of PA28γ knockdown cells lose H3K9me and H4K20me, two silent heterochromatin marks. In addition to these data, the authors also attempt to establish that PA28γ and HP1β may work together to support heterochromatin formation/maintenance. The manuscript reports several intriguing pieces of data that have the potential to open new areas of inquiry into proteasome components and accessory factors in chromatin organization and remodeling. The potency of these key experiments, however, were diluted by unconvincing co-localization assays, poorly controlled PLA and ChIP-qPCR assays, and a highly speculative Discussion. Moreover, key controls were missing for several experiments (detailed below) that would have otherwise established the heterochromatin-specificity of PA28γ. Finally, important potential functional consequences of heterochromatin disruption, including chromosome segregation defects, transposable element proliferation, and accumulation of DNA damage, were not addressed while there was a focus instead on cell cycle without clear interpretations.
Major Comments:
1) Figure 2: The co-localization experiments were unconvincing - HP1β and PA28γ foci decorate most of the nucleus, making inferences about significant overlap difficult to grasp.
We fully agree with this criticism for Fig. 2A, in which classical indirect immunofluorescence (IF) and widefield microscopy were used. This is why in Fig. 2B, we set up a pre-extraction protocol of cells with a Triton X100 treatment, to remove almost all soluble proteins before cells fixation and IF. Then images were acquired as Z-stacks with an Airyscan confocal microscope, followed by a 3D reconstruction and analysis of the co-localization between PA28γ and PA28γ using Imaris co-localization software. This experiment highlighted that only a fraction of both endogenous proteins co-localize in the nucleus. Furthermore, we noted that the number of co-localization sites evidenced in Fig 2B (~32) was in the same range than the number of dots (~37) detected by another approach (is-PLA), suggesting that we can be confident with these results.
I also found the significance of the PLA assays difficult to discern. When both factors are so abundant in the nucleus, it seems inevitable to observe loss of proximity when one 'partner' is depleted. How do these data demonstrate the specificity of this potential proximity? A clearer explanation would be helpful.
PLA technique imposes numerous constraints to obtain a signal (i.e. distance less than 30-40 nm between the two epitopes, formation of a closed circular DNA template). As a control, we verified that the is-PLA approach gives specific signals between PA28γ and the 20S proteasome. Like PA28γ, the 20S proteasome is very abundant in the nucleus but only a small fraction of PA28γ interacts with the 20S proteasome by Co-IP (Jonik-Nowak, 2018). Consistent with this, less than 60 distinct PLA spots were detected in the nucleus between PA28γ and the 20S proteasome rather than a global nuclear PLA labeling suggesting that it is probably not the abundance of each protein tested that is responsible for PLA signal but rather, as suggested by this kind of techniques, their ability to interact.
Note that the PIP30 data were a distraction from the main thread - I recommend removing or explaining more clearly.
PIP30 is currently the only known regulator of PA28γ, for which we have previously shown a critical role in PA28γ interaction with different partners and localization (Jonik-Nowak, 2018). This is why we examined the potential requirement of PIP30 in this new chromatin regulatory function of PA28γ.
2) The ChIP-qPCR data were certainly exciting but the absence of a negative control locus made me wonder how specific this result was to DNA repeats.
As a control, we already used cyclin E2 promoter in our ChIP-qPCR for PA28γ. This led us to show that the detection of PPA28γ on heterochromatic repetitive DNA sequences is enriched by a factor of 2-3 as compared to this euchromatic loci bound by PA28γ. In a modified version of this manuscript, we will add other control loci located in euchromatin. We will also test these new loci as negative controls in ChIP-qPCR for H3K9me3 and H4K20me3.
3) The LacO-GFP data are really cool. Why didn't the authors not attempt to rescue compaction with a PA28γ transgene as was done for the FLIM-FRET?
Since, we could not obtain stable U20S-LacO-KO-PA28γ and -KO/KI-PA28γ cell lines, we decided to analyze the impact of PA28γ absence, using siRNA approaches. As it was shown that overexpression of PA28γ is sufficient to cause a disruption of Cajal Bodies (Cioce et al., 2006) and a decrease in the number of PML bodies (Zannini et al., 2009), and we also noticed in FRET-FLIM experiments that PA28γ expression level is critical for chromatin compaction, it is difficult to consider to overexpress a RNAi-resistant PA28γ protein in order to rescue the effect of the depleted endogenous protein.
4) Cell cycle data would be much more interesting if the authors set up a priori predictions based on Figures 1-5.
We agree with the comment, and we will correct it in the modified version of the manuscript.
5) The absence of any report of PA28γ KD/KO on genome instability was surprising.
As indicated in the manuscript, the potential effect of PA28γ depletion on genome stability has already been reported in the literature showing an increase in chromosomal instability (Zannini, 2008).
Loss of heterochromatin integrity is expected to compromise chromosome transmission/transposable element expression or insertions. Do the repeats to which PA28γ localizes upregulate upon PA28γ KD or KO? Does DNA damage signaling increase at the loci? These functional consequences would be rather more explicable that the S-phase result reported.
We did not detect any upregulation at these specific loci by RT-qPCR experiment using KO-PA28γ U2OS cells. Concerning the potential accumulation of DNA damage signaling at these loci in the absence of PA28γ, we have not studied this aspect because PA28γ depletion was reported to induce only a marked delay in DSB repair and not a DNA damage accumulation (Levy-Barda, 2011).
6) The histone mark ChIP-qPCR, like the PA28γ ChIP-qPCR, lacks a negative control locus/loci, again undermining the inference of specificity of PA28γ on heterochromatin.
We agree and these different control loci would be added in the modified version.
7) The LLPS paragraph in the discussion was weak - consider removing.
Yes, potentially. To be defined in the context of the modified version.
8) The speculation of 20S into foci does not add and, to my mind, detracts from the focus of the Discussion.
In the modified version of the manuscript, we will focus our study on endogenous proteins and therefore this aspect of the discussion concerning the 20S proteasome, and related to the overexpression of alpha4, will no longer be discussed.
Reviewer #2:
In this manuscript, Fesquet and colleagues describe an important role of the proteasome activator PA28-gamma in the compaction of chromatin. The authors first demonstrate that PA28-gamma colocalizes HP1-beta at nuclear foci induced by the ectopic expression of alpha-4 subunit of the 20S proteasome. They further show that a fraction of PA28-gamma colocalizes also with HP1-beta in cells without ectopic expression of the alpha-4. The authors then show that PA28-gamma is associated with heterochromatic regions and is required for the compaction of lacO array integrated at a pericentromeric region. They also performed the quantitative FLIM-FRET and demonstrate that PA28-gamma controls chromatin compaction in living cells, independently of its interaction with 20S proteasome. Finally, the authors show that PA29-gamma depletion leads to a decrease of heterochromatin marks, H3K9me3 and H4K20me3, at representative heterochromatic regions. From these findings they conclude that PA28-gamma contributes to chromatin compaction and heterochromatin formation.
Although PA28-gamma has been identified as an alternative component associated with 20S proteasome, its physiological roles remain obscure. The present study demonstrates that PA28-gamma is involved in chromatin compaction and heterochromatin formation. The results presented are in most cases of high quality and convincingly controlled. I have the following concerns that should be addressed by the authors.
Major points: 1) For the localization study (Fig. 1), the authors first show the colocalization of alpha-4, PA28-gamma, and HP1-beta in the nuclear foci induced by ectopic expression of alpha-4-GFP. While the authors point out the similarity of cell-cycle dependent patterns between the alpa-4 induced foci and HP1-beta foci (lines 135-138), this argument seems to be poorly reasoned.
We omitted to mention that we also tested the potential co-localization of alpha-4-GFP with different proteins associated with nuclear foci (SC35, PML PCNA, γH2AX) or BrdU-labelled replication foci without success, before to find a correlation with the accumulation of newly synthesized GFP-HP1β in nuclear foci.
The authors previously showed that ectopically expressed CFP-tagged alpha-7, another core component of 20S, accumulates into discrete nuclear foci, and the foci are colocalized with SC35, a well-characterized member of nuclear speckle (Baldin et al. MCB 2008). Considering that both alpha-4 and alpha-7 are core components of 20S proteasome, it is highly likely that the alpha-4-GFP- accumulating nuclear foci are corresponding to the nuclear speckles. If so, HP1-beta foci should be distinct from that of alpha-4-GFP foci. The authors should test the relationship between alpha-4-GFP foci and nuclear speckles, and if this would be the case, it might be better to omit the colocalization data using cells expressing alpha-4-GFP (Fig. 1) and start by potential colocalization of PA28-gamma and HP1-beta in cells without expressing alpha-4-GFP (Fig. 2).
As mentioned above alpha4-GFP did not co-localize with SC35, a marker of the nuclear speckles. When different alpha subunits of the 20S proteasome are overexpressed, only alpha7 and alpha4 show an accumulation in specific nuclear foci. This remains unclear but a possible explanation could be an alternative composition of alpha subunits in the 20S as previously reported for alpha4 (Padmanabhan A. et al., Assemnbly of an evolutionarily conserved alternative proteasome isoform in human cells, , 2016, Cell Reports). As this part of our study appears to confuse readers and to dilute the essential message of the manuscript, we are considering to exclude these data in the modified version.
2) Although the functional link between PA28-gamma and chromatin compaction seems quite interesting, it remains unclear how it contributes to the establishment of repressive histone marks such as H3K9me3 and H4K20me3. While the authors clearly show that 20S-binding-deficient PA28-gamma mutant (PA28-gamma ∆C) could restore the chromatin compaction defect caused by PA28-gamma KO, it is also possible that PA28-gamma controls the stability of factors involved in heterochromatin assembly. To exclude this possibility the authors should test whether PA28-gamma KD/KD does not affect the protein levels of core histone modifying enzymes and HP1 proteins by immunoblotting.
During this study we performed numerous immunoblots using anti-HP1 antibodies and we did not observe any significant variation of these proteins in KO-PA28γ cells. Furthermore, in an atempt to identify proteins whose stability could be controlled directly or indirectly by PA28γ, we performed a SILAC-based quantitative proteomic analysis comparing nuclear extracts from U2OS or HeLa wild type cells to U2OS- or HeLa KO-PA28γ cells. Under the tested conditions, we could not identify variation of the amount of factors involved in chromatin assembly, suggesting that the impact of PA28γ on chromatin organization is not driven by changes in the level of the important histone-modifying enzymes, nor core components of chromatin such as HP1 proteins.
Reviewer #3:
This manuscript explores the localization and function of a previously studied proteasome activator, PA28gamma. This protein is a nuclear activator of the 20S proteasome and is widely conserved during evolution, although largely absent in fungi. The authors report that (1) subunits of the 20S proteasome (alpha4 and alpha6) and GFP-tagged or endogenous PA28gamma colocalize with each other and with HP1beta in the nucleus, with HP1beta required for the localization of PA28gamma to nuclear foci, (2) depletion of PA28gamma results in decompaction of pericentromeric heterochromatin, and (3) use a FLIM-FRET based microscopy assay to show a broad role for PA28gamma in chromatin compaction, a function that PA28gamma shares with HP1beta. They also show that the C terminus of PA28gamma, which is required for its interaction with the 20S proteasome, is not required for its subnuclear localization or compaction functions, and that PA28gamma KO cells have reduced levels of H3K9me3 and H4K20me3 heterochromatin-associated histone modifications.
The identification of a role for PA28gamma in heterochromatin compaction and heterochromatin maintenance is interesting and raises intriguing possibilities about the role of this protein and the 20S proteasome in heterochromatic domains. The study is largely descriptive and does not provide new mechanistic insight into heterochromatin or PA28gamma. Although the experiments in the paper are of high quality and well-executed, they basically amount to identification of a new factor that affects heterochromatin stability. The fact that PA28gamma is a proteasome activator provides no mechanistic insight since the 20S proteasome does not seem to be required for the heterochromatin compaction function of PA28gamma.
The following suggestions may be helpful to the authors in preparing their manuscript for publication (in order of appearance).
1) The IP experiments in Figure 1D should be performed in the presence of nuclease (DNase/RNase A or benzonase) to test whether the interactions are bridged by RNA or DNA.
We actually tried several times to perform this IP experiments using notably benzonase. However, despite several attempts under various conditions, we could not obtain a clear and consistent answer to this question.
2) Figure 2. What percentage of PA28gamma and HP1beta foci overlap in the absence of alpha4 overexpression?
As indicated in the text, on average of 32 spot of co-localization between the two proteins were detected in Figure 2B and on average of 37 spots in is-PLA experiment (Figure 2C).
3) Figure 3. Does decompaction result in loss of silencing of heterochromatin targets such as HERV-K, LINE1, alpha satellite etc? Ideally, the authors should perform RNA-seq to provide a more complete picture of changes in gene expression as a result of PA28gamma depletion.
RT-qPCRs were performed on heterochromatin loci used for ChIP (HERV-K, L1 Line, SatII and alpha-sat) and no significant variation was observed. In order to determine whether the absence of PA28γ could affect gene expression, we performed a trancriptomic analysis using Affymetrix® Human Gene 2.1 ST Array Strip comparing mRNA expression in U2OS -WT and KO-PA28γ cells. This experiment revealed only very little variation between the two samples tested: 11 genes were up in KO-PA28γ (MFAP5 (Microfibrillar-associated protein 5), GLIPR1 (Glioma pathogenesis-related protein 1) and 9 that are still unannotated), and only 2 genes were significantly down: PSME3 (PA28γ) and MAGE-C1(Melanoma-associated antigen C1). These experiences led us to consider that PA28γ probably does not directly affect the level of transcription.
4) Based on experiments with PA28gamma-deltaC, which does not interact with the 20S proteasome, the authors conclude that the 20S proteasome is not required for the PA28gamma-mediated chromatin compaction. Although their IP data (Figure 4E) seem persuasive, a more convincing experiment would be to also perform the FRET assay for compaction with knockdown of subunits of the proteasome.
Knockdown of 20S proteasome subunits was not performed since in that condition all the proteasome family will be affected, and we already know that depletion of these proteins has several and pleiotropic effects (i.e. cell cycle progression), which could indirectly affect chromatin compaction.
5) Figure 6. It is critical that the effects on histone modifications are evaluated using siRNA KD (or other transient KD methods) of PA28g to complement the KO results. PA28gamma KOs have many defects including genome instability and aneuploidy that may affect K9me3 and K20me3 indirectly.
This is indeed a hypothesis that cannot be ruled out. But considering that these modifications (H3K9me3, H4K20me1/3) are crucial for the establishment of chromatin compaction and that the elimination of PA28γ (siRNA treatment) induces chromatin decompaction within 48h, it is reasonable to consider that the variation of these marks does not result from genome instability.
6) In general, the manuscript would benefit from the addition of genome-wide approaches such as ChIP-seq to gain broader insight into PA28gamma localization and general compaction functions.
We agree that the mapping of PA28γ distribution on non-repeated DNA sequences will be useful for the subsequent studies of PA28γ functions in DNA–related processes such as gene regulation. However, because of the difficulty to map HP1 proteins and heterochromatin regions by ChIP-seq, we do not believe that this approach will necessarily reinforce the current message of this first manuscript on the role of PA28γ in the regulation of heterochromatin compaction.
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Reviewer #3:
This manuscript explores the localization and function of a previously studied proteasome activator, PA28gamma. This protein is a nuclear activator of the 20S proteasome and is widely conserved during evolution, although largely absent in fungi. The authors report that (1) subunits of the 20S proteasome (alpha4 and alpha6) and GFP-tagged or endogenous PA28gamma colocalize with each other and with HP1beta in the nucleus, with HP1beta required for the localization of PA28gamma to nuclear foci, (2) depletion of PA28gamma results in decompaction of pericentromeric heterochromatin, and (3) use a FLIM-FRET based microscopy assay to show a broad role for PA28gamma in chromatin compaction, a function that PA28gamma shares with HP1beta. They also show that the C terminus of PA28gamma, which is required for its interaction with the 20S proteasome, is not required for its subnuclear localization or compaction functions, and that PA28gamma KO cells have reduced levels of H3K9me3 and H4K20me3 heterochromatin-associated histone modifications.
The identification of a role for PA28gamma in heterochromatin compaction and heterochromatin maintenance is interesting and raises intriguing possibilities about the role of this protein and the 20S proteasome in heterochromatic domains. The study is largely descriptive and does not provide new mechanistic insight into heterochromatin or PA28gamma. Although the experiments in the paper are of high quality and well-executed, they basically amount to identification of a new factor that affects heterochromatin stability. The fact that PA28gamma is a proteasome activator provides no mechanistic insight since the 20S proteasome does not seem to be required for the heterochromatin compaction function of PA28gamma.
The following suggestions may be helpful to the authors in preparing their manuscript for publication (in order of appearance).
1) The IP experiments in Figure 1D should be performed in the presence of nuclease (DNase/RNase A or benzonase) to test whether the interactions are bridged by RNA or DNA.
2) Figure 2. What percentage of PA28gamma and HP1beta foci overlap in the absence of alpha4 overexpression?
3) Figure 3. Does decompaction result in loss of silencing of heterochromatin targets such as HERV-K, LINE1, alpha satellite etc? Ideally, the authors should perform RNA-seq to provide a more complete picture of changes in gene expression as a result of PA28gamma depletion.
4) Based on experiments with PA28gamma-deltaC, which does not interact with the 20S proteasome, the authors conclude that the 20S proteasome is not required for the PA28gamma-mediated chromatin compaction. Although their IP data (Figure 4E) seem persuasive, a more convincing experiment would be to also perform the FRET assay for compaction with knockdown of subunits of the proteasome.
5) Figure 6. It is critical that the effects on histone modifications are evaluated using siRNA KD (or other transient KD methods) of PA28g to complement the KO results. PA28gamma KOs have many defects including genome instability and aneuploidy that may affect K9me3 and K20me3 indirectly.
6) In general, the manuscript would benefit from the addition of genome-wide approaches such as ChIP-seq to gain broader insight into PA28gamma localization and general compaction functions.
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Reviewer #2:
In this manuscript, Fesquet and colleagues describe an important role of the proteasome activator PA28-gamma in the compaction of chromatin. The authors first demonstrate that PA28-gamma colocalizes HP1-beta at nuclear foci induced by the ectopic expression of alpha-4 subunit of the 20S proteasome. They further show that a fraction of PA28-gamma colocalizes also with HP1-beta in cells without ectopic expression of the alpha-4. The authors then show that PA28-gamma is associated with heterochromatic regions and is required for the compaction of lacO array integrated at a pericentromeric region. They also performed the quantitative FLIM-FRET and demonstrate that PA28-gamma controls chromatin compaction in living cells, independently of its interaction with 20S proteasome. Finally, the authors show that PA29-gamma depletion leads to a decrease of heterochromatin marks, H3K9me3 and H4K20me3, at representative heterochromatic regions. From these findings they conclude that PA28-gamma contributes to chromatin compaction and heterochromatin formation.
Although PA28-gamma has been identified as an alternative component associated with 20S proteasome, its physiological roles remain obscure. The present study demonstrates that PA28-gamma is involved in chromatin compaction and heterochromatin formation. The results presented are in most cases of high quality and convincingly controlled. I have the following concerns that should be addressed by the authors.
Major points:
1) For the localization study (Fig. 1), the authors first show the colocalization of alpha-4, PA28-gamma, and HP1-beta in the nuclear foci induced by ectopic expression of alpha-4-GFP. While the authors point out the similarity of cell-cycle dependent patterns between the alpa-4 induced foci and HP1-beta foci (lines 135-138), this argument seems to be poorly reasoned. The authors previously showed that ectopically expressed CFP-tagged alpha-7, another core component of 20S, accumulates into discrete nuclear foci, and the foci are colocalized with SC35, a well-characterized member of nuclear speckle (Baldin et al. MCB 2008). Considering that both alpha-4 and alpha-7 are core components of 20S proteasome, it is highly likely that the alpha-4-GFP- accumulating nuclear foci are corresponding to the nuclear speckles. If so, HP1-beta foci should be distinct from that of alpha-4-GFP foci. The authors should test the relationship between alpha-4-GFP foci and nuclear speckles, and if this would be the case, it might be better to omit the colocalization data using cells expressing alpha-4-GFP (Fig. 1) and start by potential colocalization of PA28-gamma and HP1-beta in cells without expressing alpha-4-GFP (Fig. 2).
2) Although the functional link between PA28-gamma and chromatin compaction seems quite interesting, it remains unclear how it contributes to the establishment of repressive histone marks such as H3K9me3 and H4K20me3. While the authors clearly show that 20S-binding-deficient PA28-gamma mutant (PA28-gamma ∆C) could restore the chromatin compaction defect caused by PA28-gamma KO, it is also possible that PA28-gamma controls the stability of factors involved in heterochromatin assembly. To exclude this possibility the authors should test whether PA28-gamma KD/KD does not affect the protein levels of core histone modifying enzymes and HP1 proteins by immunoblotting.
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Reviewer #1:
In the manuscript entitled, "The 20S proteasome activator PA28γ controls the compaction of chromatin," Fesquet et al. establish a functional link between PA28γ and chromatin compaction in human cells. Previous work established a role for PA28γ in DNA repair and in chromosome stability through mitotic checkpoint regulation; however, a role, if any, for PA28γ in heterochromatin establishment/maintenance was not known. The authors use an elegant LacO-GFP system combined with PA28γ knockdown to support the possibility that this nuclear activator contributes to DNA packaging of repetitive DNA. A nucleosome proximity assay offers additional support that the most compacted chromatin is most sensitive to loss of PA28γ. Using a truncated version of PA28γ, the authors show that this chromatin function appears to be independent of its interaction with the 20S proteasome. ChIP-qPCR suggests that PA28γ binds repetitive DNA and ChIP-qPCR of PA28γ knockdown cells lose H3K9me and H4K20me, two silent heterochromatin marks. In addition to these data, the authors also attempt to establish that PA28γ and HP1β may work together to support heterochromatin formation/maintenance. The manuscript reports several intriguing pieces of data that have the potential to open new areas of inquiry into proteasome components and accessory factors in chromatin organization and remodeling. The potency of these key experiments, however, were diluted by unconvincing co-localization assays, poorly controlled PLA and ChIP-qPCR assays, and a highly speculative Discussion. Moreover, key controls were missing for several experiments (detailed below) that would have otherwise established the heterochromatin-specificity of PA28γ. Finally, important potential functional consequences of heterochromatin disruption, including chromosome segregation defects, transposable element proliferation, and accumulation of DNA damage, were not addressed while there was a focus instead on cell cycle without clear interpretations.
Major Comments:
1) Figure 2: The co-localization experiments were unconvincing - HP1β and PA28γ foci decorate most of the nucleus, making inferences about significant overlap difficult to grasp. I also found the significance of the PLA assays difficult to discern. When both factors are so abundant in the nucleus, it seems inevitable to observe loss of proximity when one 'partner' is depleted. How do these data demonstrate the specificity of this potential proximity? A clearer explanation would be helpful. Note that the PIP30 data were a distraction from the main thread - I recommend removing or explaining more clearly.
2) The ChIP-qPCR data were certainly exciting but the absence of a negative control locus made me wonder how specific this result was to DNA repeats.
3) The LacO-GFP data are really cool. Why didn't the authors not attempt to rescue compaction with a PA28γ transgene as was done for the FLIM-FRET?
4) Cell cycle data would be much more interesting if the authors set up a priori predictions based on Figures 1-5.
5) The absence of any report of PA28γ KD/KO on genome instability was surprising. Loss of heterochromatin integrity is expected to compromise chromosome transmission/transposable element expression or insertions. Do the repeats to which PA28γ localizes upregulate upon PA28γ KD or KO? Does DNA damage signaling increase at the loci? These functional consequences would be rather more explicable that the S-phase result reported.
6) The histone mark ChIP-qPCR, like the PA28γ ChIP-qPCR, lacks a negative control locus/loci, again undermining the inference of specificity of PA28γ on heterochromatin.
7) The LLPS paragraph in the discussion was weak - consider removing.
8) The speculation of 20S into foci does not add and, to my mind, detracts from the focus of the Discussion.
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Preprint Review
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Summary:
All three reviewers agreed that establishing a link between a proteasome activator and heterochromatin stability was novel and intriguing. However, limited insight into the PA28-gamma mechanism of action (or possibly a new heterochromatin compaction mechanism) dampened reviewer enthusiasm. The reviewers offered many suggestions, including additional experiments, new controls, and structural changes to the Discussion, that we hope you find useful.
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Reviewer #3:
The paper "Morphology and local connectivity of the plis de passage in the superior temporal sulcus" is an interesting and thoughtful paper which uses modern tractography methods quite skillfully to examine whether the finer features of gyrification are related to connectivity patterns. This is an understudied area of research in human MRI because it deals with inter-individual variability, and so is time consuming, not well-suited to existing analysis pipelines, and requires a high level of neuroanatomical expertise. The methods, which included impressive inter-rater reliability and a nice control condition, were well-suited to the question. I also very much appreciated the discussion section, which covered anatomical, historical, evolutionary, and developmental considerations quite effectively and with clarity of language. Overall, the paper is well thought out and executed and a joy to read!
Below are some comments that could improve clarity for the reader:
-Figure 2 is a bit difficult to understand - to clarify that a and b are from the first brain, and b and c are from the second brain, the authors might consider labeling them or putting them in boxes. It might be helpful to add some arrows to help illustrate the "pinching" they describe.
-End of results, p. 19, final paragraph. The authors write "Second, there was generally an increasing number of U-shape fibers from the anterior to the posterior part of the STS." I don't think the authors tested this, so I would rephrase this to say "on visual inspection, it appears that there was an increase in...". I would also replace the word "fibers" with "streamlines".
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Reviewer #2:
A new characterization of the "plis de passage"(PP) is proposed. The interest of this new definition is demonstrated in a cortical area where a huge amount of variability exists, hence it is very difficult to study. The results shown are convincing. The new connection established between PP and U-fibers contributes to the understanding of the link between gross anatomy and connectivity.
Several questions for clarification:
1) The distribution of the number of PP in STS is given in the results. Did you try to match the PP across the subjects, to try to define a stable model? In terms of the location of the PPs, is a model possible or their positions span the whole main branch of STS in a continuum? Did you try to study the relationships between PP and sulcal pits?
2) Did you try to clarify whether all dense clusters of U-fibers correspond to PP across subjects? Due to the random selection of the extremities of the control PPs, such clusters with different trajectories (not necessarily facing each other) could be missed by your procedure in controls?
3) Is there a link found between the superficiality of a PP and the extent of the shift of the two extremities along the sulcus (the S and C shapes)?
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Reviewer #1:
The study aims to improve the anatomical characterisation of STS plis de passage (PPs). Morphologically, the authors use the geometry of the surrounding surface to reveal deep PPs, which might be buried. Structurally, they explore associations with short-range u-shape connectivity across the two banks of the STS. This methodological advancements follow from previous work on the central sulcus (e.g. Zlatkina et al., 2016, European JNS; Catani et al., 2012, Cortex). The authors provide detailed characterisation of these anatomical features in 90 individuals from the HCP dataset, and focus their analysis in differences across the two hemispheres. But the study stops short of showing how this impacts functional organisation or behaviour. Overall, the methodological advancement offered here is incremental relative to other studies, and very little insight is provided about the impact of these morphological features and their variations on STS functional organisation. Considering the HCP offers high quality functional data, including tasks specifically relating to STS function, as well other highly related data (e.g. twins), I thought the present manuscript missed numerous precious opportunities to leverage the present findings into more significant impact and innovation.
Major Comments:
1) The abstract and introduction highlight the importance of studying inter-individual variability in PPs. But the results do not address this variability, as all the results are dedicated to inter-hemispheric differences. What is the significance of inter-individual structural variance, and how is it informed by experience (the introduction suggests that these folding patterns are determined in utero?)? For example, are they more similar in twins? Is there any clear evidence that it determines functional organisation and behaviour? Clinical symptoms? Also, are the inter-individual variations observed specific to the STS? Or do they reflect 'trait' like foldiness of the cortex? None of these questions are explored empirically, and as such, the present findings offer very little advancement to our understanding of STS function and functionality.
2) I'm not sufficiently qualified to determine, but I have to wonder if the 'control PPs' are suitable, considering they are much smaller than the 'true PPs'? Considering the probabilistic nature of the analysis and the fact that the experimenters were not blinded as to which aspect of the sulcus was 'true' or 'control', some more consideration should be given to the appropriateness of this control.
3) The u-shape analysis requires histological confirmation, as demonstrated by Catani et al. for the central sulcus.
4) Nonsignificant results (e.g. between hemispheres) require further consideration - are the two hemispheres truly similar, or is the study underpowered to find such differences? Bayesian statistics can inform this question.
5) I found the discussion to be overly speculative, and in particular the part relating to functional implications to be overly speculative, considering the very modest innovative contribution the current study offered.
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Preprint Review
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Summary:
The reviewers very much appreciated the careful analysis, including very laborious manual segmentation. They agreed that the study provides a new level of detail of STS morphology that hasn’t been available to date. They also agreed that this characterisation has potential to support future research focusing on the inter-individual variability that is so common in this brain region. However, the study has not yet delivered on this promise, as the analysis is focused on inter-hemispheric differences across the group, without illuminating the impact of inter-individual morphological variability on the area's functional organisation or function.
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Reviewer #3:
This is a very interesting paper that describes a novel zebrafish cardiac phenotyping pipeline consisting of high frequency echocardiography, cardiac magnetic resonance imaging (CMR) and micro-computed tomography (micro-CT). The work presented provides proof-of-principle that this suite of elegant techniques provides high resolution images of the adult fish heart. The concerns raised relate mainly to the adoption of this pipeline as a routine phenotyping method and the extent to which the data presented can be considered as a reference set for the field.
Pulsed wave Doppler tracings obtained from high frequency echocardiography are noted for their clarity and reproducibility. The authors took advantage of this, together with color Doppler, to identify abnormal blood flow jets in the alk5 mutant fish. These findings nicely show that echocardiography is a useful screening tool for evaluating mutants with unknown phenotypes or for those in which structural defects are anticipated.
Echocardiography is an established method for quantification of ventricular size and function in adult zebrafish when performed by experienced operators. As noted by the authors, echocardiographic images from a single fish can be collected within minutes and this technique can be used to evaluate large numbers of fish. The real question here is when to implement the complete pipeline and in which situations echocardiography (or one of the other techniques) might be adequate. The authors might consider making some recommendations about this. For example, echocardiography would be sufficient to evaluate adult fish that are expected to have cardiomyopathy phenotypes and completion of the extended imaging pipeline may not be necessary. Procedural tolerance, potential for serial assessment, throughput and cost also need to be considered, especially when substantial numbers of fish need to be evaluated. In order to better compare echocardiography and CMR for assessment of ventricular size and contractile function, echo data for these parameters needs to be included and a comparative analysis undertaken.
The wide range of heart rates in the echo data (58-143 bpm) is a concern and suggests that anesthetic and environmental factors are contributing to variability. The lower value of 58 bpm is notably unphysiological. These extremes of heart rate would confound cardiac assessment, particularly for ventricular size. The causes of these heart rate differences need to be identified, and at the very minimum, greater numbers of fish would need to be studied to be able to identify any biological differences between groups.
A major limitation is the relatively small numbers of fish that have been included in this study. Although looking at 10 WT fish and 12 mutants was sufficient to demonstrate the utility of these imaging methods, there was considerable variability for many of the cardiac parameters measured and the number of WT fish, in particular, is far too small to be robust as a reference data set. If this is an important goal of the paper, then more male and female WT fish of different ages need to be studied. Data also need to be provided for reproducibility, and inter- and intra-observer variability for measurement of cardiac parameters using the different methods.
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Reviewer #2:
In this manuscript, the authors conducted phenotypic studies of a zebrafish adult alk5a/tgfr1 mutant by integrating different technologies, including echocardiography, MRI and microCT. They selected 10 WT and 12 alk5a mutants for their studies, and identified some mild phenotypes in OFT. They conducted correlation analysis among different parameters, and then selected fish with more severe phenotypes for further morphological characterization. The strength of the manuscript is optimization of novel technologies including MRI and microCT for cardiac studies, and their integration. However, there are some notable concerns as described below.
Major concerns:
1) There is excessively high variation in almost all parameters among different fish in the same group. For example, heart rate ranges from 58-143 bpm. It appears that adult zebrafish naturally exhibit high phenotypic variation in cardiac functions. However, the authors need to more carefully control their experimental conditions before reaching this conclusion. It has been reported that anesthesia and water temperature might affect cardiac functions in this animal model.
2) The experiments were not designed to deal with the excessively high variation. Fish from three different ages are phenotyped together as a single group, and the size of the group is small. This is a main weakness of the manuscript.
3) Fig. 3-figure supplement 1: contraction of the ventricle appears rather weak (difference between F' vs F" is small). Can you calculate ejection fraction? Is the EF significantly lower than EF in wild type fish that were obtained from high frequency echo or other technologies? Low EF might indicate that the fish is far from normal physiological condition, suggesting that the technology is premature for assessing cardiac function. Moreover, there is a huge difference in heart size between WT and mutant fish (F' vs G').
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Reviewer #1:
This study by Benisimo-Brito and colleagues describes a comprehensive integration of functional imaging approaches for adult zebrafish cardiovascular phenotyping. The authors describe combined use of echocardiography, MRI and (ex vivo) micro-CT with light- and transmission electron microscopy to study alk5a-mutant zebrafish. They were able to identify multiple altered phenotypic parameters including abnormal hemodynamics (retrograde blood flow), compromised functional output, and morphological defects, including expanded outflow tract and altered atria and aortae. The authors were also able to nicely correlate the extent of morphological defects with function, across a highly variable range in severity of phenotypes.
This is an informative and elegant use of combined imaging platforms to study adult zebrafish; which has thus far been very challenging, given their opaque nature and the need for specialised adaptation of available clinical modalities. That said, use of some of these platforms has been applied previously for imaging adult zebrafish; for example, echocardiography and MRI (Gonzalez-Rosa et al., 2014; Koth et al., 2017) and micro-CT (most recently, Ding et al., 2019). The authors acknowledge this, but it remains the case that the technical novelty, as applied to functional cardiovascular imaging, is compromised. Instead, the strength here is in the combined, integrated use of multiple platforms. This study on the whole provides a very nice proof-of-principle, but it is unclear how this will be widely adopted by zebrafish laboratories elsewhere, given the need for significant high-end imaging facilities and appropriate in-house expertise. Moreover, the methodologies to adapt the platforms for zebrafish studies are not sufficiently well described herein to enable others to readily adopt.
Other specific comments:
1) The statement on page 8 needs qualifying. MRI has been used previously beyond generating static images in adult zebrafish: Koth et al., (2017) documented longitudinal imaging of live adult zebrafish during heart regeneration.
2) The monitoring of heart rate using self-gating (Figure 3, figure supplement 1C-C') is a nice addition - did the authors explore the use of telemetry probes to record the ECG, as this would be a novel addition to what has gone before?
3) Regarding the correlation analysis on page 10 the authors note 32 parameters. What are the prospects for applying machine learning/AI (eg. automated image analysis algorithms) here to enhance the number of parameters that can be recovered? This in turn would increase the depth of phenotyping and further inform the phenotypic-functional association.
4) The inherent variability of phenotypes between individuals is potentially a significant issue for basic studies, despite mapping to human variation in disease progression/outcome. Given the assumed relatively in-bred nature of the mutant background, why is there such variability and does this reflect on the sensitivity of the imaging? The authors note age and body size (page 11) as influencing variation; if they were to image fish of the same age (and sex) and within a narrow body size range is this variability reduced?
5) As under the general comment above, the methodology is insufficient in places for others to adopt the described imaging platform(s); for example, under echocardiography (page 19) the authors loosely describe a "bed made of modelling clay"- more details are required here and elsewhere to facilitate others utilising similar platforms.
6) Under the MRI procedure the authors decided to analyze specimens in a container without water flow, to reduce the imaging time to less than 20 minutes and consequently to maintain survival. This relatively short imaging time is reflected in the low resolution and somewhat suboptimal images shown in Figure 3C-D. Moreover, in the absence of water flow and gill perfusion it is unclear how any functional parameters obtained are physiologically meaningful? This approach renders the use of MRI more for 3D live imaging than for interrogating function. In the previous MRI study, by Koth and co-workers (2017), live adult zebrafish were placed under anaesthesia and physiological conditions, i.e. upright in water and with gills suitably perfused. This enabled imaging for several hours and with a 100% recovery rate and consequently, the resolution and image quality were higher and the functional parameters more physiologically relevant. The current MRI approach ought to be at least comparable in terms of quality of outputs as that which has gone before.
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Preprint Review
This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.
Summary:
The reviews highlight the value of imaging adult zebrafish to evaluate cardiovascular structure and function. However, they point out that each of the three imaging technologies has been reported before, and suggest that the manuscript would be strengthened by a more critical comparison between the imaging modalities. The reviewers also raised concerns about the value of the data as a reference for cardiac function parameters, given the small numbers of WT fish, variability in WT fish, and the lack of data for reproducibility, and inter- and intra-observer variability for the various cardiac parameters. Lastly, they felt that the platforms are highly technical and require significant resource and specialist insight into adaptation for use on zebrafish, thus making it unclear how it will be applicable more broadly and within other laboratories in the field.
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Reviewer #3:
The article by Youssef G et al, focused on developing a Machine Learning system to use immunofluorescence data to detect metastatic cells in tumor stroma, which might be responsible for metastasis in case of OSCC. To detect single cells in the transition of EMT to MET they focused on EMT-Stem cells rather than only EMT phenotypes. They have shown that retention of epithelial marker EpCAM and stem cell marker CD24 and upregulation mesenchymal marker Vimentin can identify disseminating EMT stem cells in the tumor stroma. It is very well presented, well written and has high implication.
Comments to improve:
1) Strongly recommended to add the distribution of tumor status vs. proposed marker expression pattern. That is to show the distribution of EpCAM, CD24, Vimentin +/- in metastatic vs. other tumor status as mentioned in Supplementary figure 2. This might help you to establish these markers combination to follow a pattern in disease progression.
2) In all cell and tissue images add the scale.
3) For figure 3f, show enlarged picture of the single cell staining on the inset or add a separate panel to show only single cell staining.
4) Figure 4, the panel name or the font is too small to read, enlarge the font size (a, b, c, d, f).
5) Same problem with figure 6a, font size too small. In addition, in the heat maps, is it possible to add cluster names horizontally? Also for figure 6c, the cluster names are too small.
6) The EMT sub-populations are not associated with a spectrum of epithelial/mesenchymal genes expression (supplementary figure 5). The explanation is not very clear.
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Reviewer #2:
The authors tackle the important and intractable question of the mismatch between the primacy of EMT in cell culture studies versus the rarity with which EMT is morphologically apparent in resected tumour tissues.
The early part of the study is convincing and well conducted, with identification of subpopulations of EMT cells with the ability to undergo MET, and associated marker profiles in flow cytometry.
They then develop an impressive multiplex assay for the identification of cells with the same profile in resected tumour material- a really promising approach bringing molecular findings into the context of primary tumour tissue.
The major issue that I have is in the application of this assay to tissues, and the subsequent AI analysis. Only one example of the putative invading population is shown (Fig 4C) and the stromal 'infiltrative' subpopulation is adjacent to a very flat and 'pushing' tumour/stroma boundary, with no apparent budding into the stroma. This would need to be addressed with several more examples and high-magnification H&E images. Furthermore, this is a major claim- namely that occult infiltrating EMT cells are commonly encountered in peritumoural stroma but can only be differentiated from somatic stroma by multiplex immunofluorescence- and it needs major evidence to back it up. What do these cells look like on H&E? Are they mesenchymal in their appearances on H&E? Can they be conclusively differentiated from other stromal constituents (eg myofibroblasts, plasma cells) immunohistochemically and/or morphologically? It could be that the power to predict metastatic status power is related to somatic stromal factors rather than EMT.
The AI prediction of metastatic status is compelling, but this fundamental point would need to be persuasively addressed in order to support the author's major claims. I do not feel qualified to comment upon the AI strategies used later in the study.
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Reviewer #1:
This manuscript follows previous studies describing the existence of a subpopulation of mesenchymal-like cells (expressing Vimentin) that also express EpCAM and/or CD24 concomitant with the ability to undergo MET. These subpopulations appear to exist within oral squamous cell carcinoma (OSCC) cell lines and within primary tissues. The paper demonstrates that CD24 expression is requisite for plasticity and suggests that the presence of CD24+/EpCAM+/VIM+ cells in the stroma of OSCC tumors may be indicative of metastasis. Some whole genome transcriptome analysis was also done to determine differences between bulk, EMT restricted and EMT stem populations. Overall, the notion that specific cells have the plasticity needed to move between epithelial and mesenchymal states is intriguing, and the presumption that these cells contribute to metastasis seems logical. However, the work is still rather preliminary. Accordingly, it is difficult to make solid conclusions regarding the prognostic utility of this state or of what may regulate it.
Major comments:
The study uses a very small sample size (24 patients) for the test and validation cohorts. The study should be expanded to use a different set of patient samples for test and validation sets. Moreover, the utility of the stem-EMT signature should be tested using multivariate analyses.
In figure 4, it looks like CD24 is positive in the bulk of tumors (regardless of stage) and in skin. Is this specific? Also, there appear to be VIMENTIN/EPCAM/CD24 positive cells in the bulk of non-metastatic tumours. Can this be seen using sequencing? Overall, the images as presented are not overly convincing.
EMT stem versus restricted signatures should be validated using additional models. Also, greater evidence is required to determine how these cell fractions may differ. Are they sitting in different epigenetic states? Can trajectories be detected in human cancers, using single cell sequencing, for example? Finally, do they have different metastatic potentials?
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Preprint Review
This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.
Summary:
This manuscript was reviewed by experts in the areas of cancer stem like cells, EMT events and pathology. Overall, all of the reviewers were intrigued by the concepts underlying this paper. However, it seems that the work is validating the existence of an EMT-stem like population, whilst also attempting to formulate a clinical prognostic application for the existence of these cells. The function of these cells as metastatic drivers requires further exploration. Moreover, the pathological assessments must be improved upon. We hope that these comments are helpful.
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Reviewer #3:
General assessment:
Futai and colleagues present an extremely elegant study in hippocampal CA1 slice cultures which combines cell type-specific expression of inhibitory synapse markers and conditional deletion of neurexins (Nrxn), knockdown of neuroligin3 (Nlgn3) and rescue experiments with defined splice variants of Nrxn by biolistic transfection with paired patch-clamp recordings. They find that synaptic transmission between inhibitory cholecystokinin(&VGT3)-positive interneurons (VGT3+) and CA1 pyramidal neurons depends specifically on the combination of presynaptic Nrxn1α with insert in splice site #4 and postsynaptic Nlgn3 without A1&A2 splice inserts.
Major concerns:
1.) The role of neurexins for transmission at the VGT3+ interneuron-to-CA1-pyramidal cell synapse remains unclear: The authors claim that Nrxn are important for the transmission at the VGT3+ synapse. However, I do not see the necessary experiment to substantiate such a general claim, for example, by comparing VGT3+synapses of control/undeleted to deleted NrxnTKO slices. Figure 5 rather shows that Nrxn is required to mediate the effect of overexpression of transfected Nlgn3Δ in CA1 neurons but this might be due to the overexpression itself. Thus, this effect would be more convincing if lack of Nrxn at VGT3+ synapses caused the opposite result on uIPSCs.
2.) The idea of "A Specific Neuroligin3-αNeurexin1 Code ..." implies to most readers that a direct interaction between these two molecules is involved because numerous biochemical papers in the field have used the term splice code to refer to a hierarchy of binding affinities between Nrxn and Nlgn variants. However, such preferential binding of αNrxn1+AS4 to Nlgn3Δ is neither shown in the manuscript, nor is it likely: The authors report that „αNrxn1+AS4 and βNrxn3-AS4 are the unique Nrxn genes expressed in VGT3+ neurons compared with PV+ and Sst+ neurons" (Figure 6 & 7) but demonstrate that of these two isoforms, only αNrxn1+AS4 transfected into VGT3+ interneurons mediated the effect of Nlgn3Δ overexpressed in pyramidal neurons (Figure 8). If binding or direct physical interaction was involved in the mechanism, βNrxn3-AS4 should have performed better than αNrxn1+AS4 because both the LNS5-EFG-LNS6 cassette and the insert in AS4 reduced the affinity. The surprise is shared by the authors themselves in the last paragraph of the Results section (p.12). At least to me, it appears that additional pre- or postsynaptic molecules, or a different mechanism altogether, are involved in mediating the effect of αNrxn1+AS4&Nlgn3Δ on VGT3+ synapses.
3.) To actually prove the specificity of the impact of Nlgn3Δ splice variant on inhibitory transmission from VGT3+ interneurons (Figure 2), an important control is missing: Another Nlgn3 variant, in which the A inserts are present, should be tested in the overexpression experiment. I do acknowledge that the authors compared different Nlgn3 variants in a recent paper (Uchigashima et al., 2020, JBC) in a related setting but no data exist for the proposed specificity of the Nlgn3Δ splice variant at VGT3+ synapses as far as I can see.
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Reviewer #2:
Motokazu et. al., identified a specific Nlgn-Nrxn pair that regulates GABAergic synapse function in a subset of interneurons. This is a really interesting study, in which they use complicated techniques to dissect NLGN3 and NRXN function. The authors performed elaborate experiments from a single cell level to a tissue level that support their conclusions. Overall the data appear of high quality and reliable, but the study would benefit from some clarification of text and figures.
1) They are doing overexpression experiments on a WT background, so it's impossible to know if this effect is from homodimers of NLGN3 or heterodimers of NLGN3 with either NLGN1 or NLGN2. Perhaps the authors could discuss this caveat in the manuscript.
2) The authors see an increase in IPSCs when o/e NLGN3 in pyramidal neurons when Sst+ neurons were stimulated using optogenetics, whereas Horn and Nicoll did not see any changes. Horn and Nicoll used NLGN3A2 (including A2 insert) and in this study the construct doesn't have A1 or A2 insert. Perhaps they can discuss if the A2 insert can potentially be the culprit for the discrepancy if this is a potential confounding factor.
Additional comments:
1) In Fig. 1, please indicate Fig. 1C in the legends and make a box for the enlarged region in the lower magnificent image. It would be better to take out the busy alphabetic labels (E1, E2, E3, etc.).
2) Please increase text font size for the sample traces in all figures.
3) Authors showed quantitative graphs showing connectivity but definition of the connectivity is not well explained. More detailed explanation for how they quantified the connectivity should be addressed in methods.
4) In Fig. 5A, it would be more accurate to normalize KO Nrxns to WT Nrxns (set WT Nrnx as 100 %). The current Fig. 5A graph looks like Nrxn3 is not dominant in WT mice (~ 0.1 %) but the Fig. 7B graph shows Nrxn3 is dominant. Is there a discrepancy or perhaps add an explanation?
5) In Fig. 6J, the graphs for βNrxn1, αNrxn2, and βNrxn2 can go together with the image data in Figure S3.
6) Authors should explain and provide more information about the parameters of the Fig. 7A plot in the main text and legends. Correct the missed indication of Fig. 2B in the results text.
7) What if presynaptic αNrxn1+AS4 couples with Nlgn3 KD or NlgnTKO condition? What would be the expectation?
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Reviewer #1:
Gaining insights into synapse-type specific regulatory mechanisms is of significant general interest. Yet, substantial concerns need to be addressed to improve this study.
Major points:
1) The authors state that Nlgn3 is particularly enriched at VGT3+ synapses. This is based on the colocalization of immunolabeling for Nlgn3 and each of the markers for three interneurons types as well as with the general inhibitory synapse marker VIAAT in Figure 1. If the intensity of marker labeling is not similar across interneuron types and if imaged fields are not comparable, the use of Nlgn3 co-labeling to assess a preferential localization is compromised. This concern is apparent e.g. in the example image for SST+ synapses with its weak labeling (Fig 1J) and needs to be addressed.
2) The data in Figure 5 support that the lack of presynaptic Nrxns 1/2/3 abolishes the potentiation effect of VGT3+ inhibitory synaptic transmission by Nlgn3Δ. To interpret these data, the authors also need to show whether the Nrxn triple KO in VGT3+ cells affects the amplitude of uIPSCs in postsynaptic control neurons expressing Nlgns at endogenous level.
3) The physiological findings are based on paired recordings where genetically labeled VGT3+ interneurons are stimulated. These cells are sparse and heterogeneously distributed in CA1 (Pelkey et al., Physiological Reviews 2017). Given the issues with Cre driver lines, a more thorough analysis is needed to establish that bona fide VGT3+ interneurons contribute to the reported findings. The scattered distribution of the individual RFP+ cells in single-cell RNAseq data (Fig 7a) adds to this concern about cell identity. The only relevant evidence presented is the IHC analysis in Fig S1A-C but it does not include probes for other interneuron types in the CA1 that shows the specificity of the VGT3+ label.
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Preprint Review
This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.
Summary
This study by Uchigashima et al. investigates to what extent Neurexin-Neuroligin interactions define synapse functions in an inhibitory microcircuit in the hippocampal CA1. The authors propose that Neuroligin-3 and Neurexin-1α regulate inhibitory synaptic transmission at synapses formed by vGluT3-positive (VGT3+) interneurons on CA1 hippocampal pyramidal neurons. This is based on (1) immunohistochemical data that localize Nlgn3 to synapses of VGT3+ interneurons, (2) the regulation of unitary inhibitory postsynaptic currents by Nlgn3 overexpression or knockdown in postsynaptic pyramidal neurons in organotypic slices from VGT3+ reporter mice, and (3) the finding that Nrxn deletion in VGT3+ interneurons prevents the effect of Nlgn3 overexpression in postsynaptic neurons. Single-cell RNA sequencing and in situ hybridization are presented to show that Nrxn1α and Nrxn3beta mRNAs are prominent Nrxn isoforms in VGT3+ interneurons, and Nrxn1α SS4 rescued Nrxn deletion effects. With some additional critical controls and a more careful interpretation of the presumed mechanism, the manuscript would make a highly interesting contribution to the field of synapse specification by synaptic cell adhesion molecules.
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Author Response
Reviewer #1:
The manuscript by Mitchell et al. finds that the NAIP-NLRC4 inflammasome in mice is a critical host factor that controls intestinal infection with the human specific bacterial pathogen Shigella flexneri. The work suggests that Shigella is actively suppressing the human NAIP-NLRC4 inflammasome possibly using an T3SS effector protein, which does not recognize its substrate in mouse cells. The authors use this information to determine that B6 mice lacking the NAIP or NLRC4 inflammasome components are susceptible to Shigella infection and observe disease symptoms similar to Shigellosis in humans. In addition, 129 mice exhibit additional disease symptoms, and the authors suggest that loss of Caspase-11 in 129 mice is responsible for this phenotype.
The strengths of this manuscript include the introduction of a new mouse model that mimics Shigellosis, the demonstration that NAIP/NLRC4 activation is important for epithelial cell defense, and the potential of these findings to clarify aspects of human infectious disease caused by this pathogen. The manuscript is well presented, and the experiments are conducted with a high degree of rigor. Overall, this is an important contribution to the Shigella field and also has significant implications on our understanding of inflammasomes in host defense against pathogens.
Response: We thank the Reviewer for recognizing the impact and rigor of our work.
There are some weaknesses that should be addressed. Experimentally, it has not been directly demonstrated that IECs from NLRC4-/- mice undergo cell death (using biochemical markers). This is a critical aspect of the model.
Response: Prior work in the field (e.g., Sellin et al, 2014; Rauch et al, 2017) has already established that inflammasome activation in IECs results in their death and expulsion from the intestinal epithelium. We are currently working on showing this also occurs with Shigella but we have no reason to doubt that it does; our preliminary data indicate that Shigella-infected propidium iodide (PI)-positive cells are expelled from IEC monolayer cultures in an NLRC4-dependent manner. We intend to provide these data in a revised version of the manuscript.
In addition, it would be useful for the authors to evaluate bacterial burden over the time course in Figure 6. Although this is not absolutely necessary to support the manuscript conclusions, this information would greatly benefit the community that intends to use these mice in the future.
Response: This is indeed an experiment we plan to complete in the future. At present we are constrained by the numbers of available mice. We agree with the reviewer that the timecourse is not essential to establish the main conclusions of the present manuscript, and have thus prioritized other experiments.
There are also some discussion points about the mouse model that would enhance the overall impact of the work. For example, a more in depth discussion about the differences between human Shigella infection and the new model would be helpful. It is important to emphasize that the mouse model requires a much greater inoculum of the pathogen to induce disease and requires microbiota-deficiency to be effective. What are the implications of this finding on our understanding of human disease?
Response: Although it is often (correctly) stated that as few as 10-100 bacteria can infect humans with Shigella, there is actually considerable heterogeneity in the infectious dose. DuPont et al 1989 summarizes several human challenge studies in their Table 1, which shows that while 25-39% of humans exhibit symptoms after low dose infection (<200 CFU), 36-44% of humans are resistant to high doses (10^4-10^8 CFU). Therefore we do not consider the infectious dose in our mouse model to be out of the range of what is ‘normal’ in humans. Indeed, our new model may help us understand some of the factors that confer resistance to certain humans. We used a dose of 5x10^7 in our manuscript to ensure reproducible infection of all mice. However, in limited studies, we have observed disease in oral route infected, antibiotic pre-treated NAIP–NLRC4-deficient mice with 10^6 CFU (4/4 mice) and 10^5 CFU (2/3 mice). We are currently repeating these experiments, which we intend to include in a revised manuscript. We also agree with the reviewer that the infectious dose in humans vs. mice merits more discussion in a revised manuscript.
In lines 274-285 the authors present an either/or scenario in which either macrophage pyroptosis is required for IEC infection or inhibition of NAIP/NRLC4 pyroptosis in IECs is required for IEC infection. However, these scenarios are not mutually exclusive. For example, it is plausible that the extremely low burdens of Shigella required to infect humans (<100 CFUs) is due to the pathogen initially crossing the epithelial barrier (e.g. through M-cells) to infect macrophage, and then re-infection of IECs after macrophage pyroptosis. In this scenario, the NAIP/NLRC4 inflammasome could prevent further expansion of bacterial in IECs by eliminating the cell-to-cell spread that have been described by others. Importantly, the macrophage lifecycle stage may not be necessary in mice in which the microbiota has been removed and Shigella is delivered at a very high inoculum. While, additional ideas could be, and should be, put forth since the mouse model provides new insights or challenges an existing dogma in the field.
Response: We do clearly state in our manuscript (line 277) that our results do not directly address the question of whether Shigella might benefit from inflammasome activation in macrophages. In a revised version of the manuscript we will further expand on the discussion of the role of inflammasomes in macrophages and IECs to acknowledge multiple, non-mutually exclusive scenarios.
Reviewer #2:
Mitchell et al explore the role of NLRC4 in defending against Shigella infection by demonstrating that NLRC4 contributes to resistance to shigellosis in mice. Using in vitro assays, they first show that mouse but not human macrophages undergo NLRC4-mediated pyroptosis in response to Shigella infection despite an ability for both species to successfully detect Shigella NLRC4 agonists. They then demonstrate that C57BL/6 background mice, which normally resist shigellosis, become susceptible to infection when deficient in NAIPs or NLRC4. In parallel, 129 background mice develop more significant infection including intestinal bleeding. Furthermore, using a mouse line in which NLRC4 expression is restricted to intestinal epithelial cells (IECs), they show that IEC expression of NLRC4 is sufficient to resist shigellosis. Finally, using a known attenuated Shigella mutant, they demonstrate that their shigellosis model can mimic kinetics seen in humans.
Mitchell et al convincingly demonstrate both the importance of NLRC4 in protecting mice against Shigella and the utility of their mouse model for studying Shigella infections, both of which are significant and will push the Shigella field forward. There are mechanistic questions to be addressed in future studies beyond the current manuscript, attesting to the importance of the paper in opening up new areas in the field of research. In some places, the authors draw conclusions that reach beyond what is proven in the data, which should be addressed in text edits to the manuscript. In summary, this article presents an important new model for Shigella infection. The impact of the manuscript is the development of a mouse model with which to study Shigella infection in vivo.
Response: We thank the Reviewer for emphasizing the importance of our new shigellosis model for the field. We have addressed their comments below.
Major comments:
Many questions remain concerning why NLRC4-deficient THP1 cells still undergo pyroptosis. The authors provide evidence that Shigella activates PYRIN and/or AIM2 inflammasomes in humans, and that somehow mouse macrophages would fail to have this same detection. At face value, the data would suggest that humans are able to detect Shigella by Pyrin and AIM2, but for some reason these two inflammasomes are insufficient, and instead NLRC4 is required for in vivo defense. Then in mice, it would imply that everything is flipped - for some reason detection by Pyrin and AIM2 is not important, but now the bacteria can be detected by NLRC4 and this is important. The NLRC4 focused conclusions are consistent with the in vivo data, that NLRC4 in humans fails to detect, but NLRC4 in mice succeeds in detecting Shigella. However, the data that Pyrin and AIM2 in human cells successfully detect Shigella are inconsistent with the overall conclusions of the paper. I suspect that this is an artifact of THP1 cells, and that the in vivo situation in humans is that these two inflammasomes will fail to detect Shigella. There is published precedent from other infections where in vitro detection belies in vivo lack of detection (e.g. Listeria is detected by AIM2 in vitro, but probably not in vivo). It may be difficult to make direct comparisons between how inflammasomes act in THP1 cells as compared to BMMs, due to artifacts arising from the different origins and passage levels of the two cell types. It may be that the inflammasomes response is most important in IECs, as proposed by the authors, and that IECs may not express Pyrin or AIM2. There is evidence from publicly available IEC transcriptional profiles that IECs do not express Pyrin (Mefv) (Reikvam, doi: 10.1371/journal.pone.0017996), although this profile does show Aim2 expression in IEC. It is my understanding that BMMs do not express Pyrin unless they are strongly stimulated with some TLR agonist. As it stands, the in vitro data appear to contradict one of the main conclusions of the paper, because it would seem that human Pyrin and AIM2 inflammasomes can detect Shigella, and so these should compensate for NLRC4. The explanation as to why Pyrin and AIM2 are insufficient to compensate for NLRC4 evasion in human infection should be addressed at least in discussions of the data to explain the apparent discrepancy.
Response: The reviewer states that our claim that human PYRIN and AIM2 inflammasomes can detect Shigella in THP1 cells is “inconsistent” with the overall conclusion of our paper, which is that the NLRC4 inflammasome provides necessary defense of mouse intestinal epithelial cells. We do not agree that there is an inconsistency and indeed many of the points the reviewer makes in their comments fit with our view, so perhaps there is less disagreement than it might seem.
As the reviewer discusses, differences in inflammsome expression in humans vs. mice, and in IECs vs. macrophages vs. THP1 cells, and the kinetics of inflammasome responses, as well as several other factors, can easily account for the results we obtain. It appears that PYRIN is not well expressed in mouse IECs (Price et al. 2016), at least not uniformly at levels in all cells that are sufficient to confer protection. AIM2 is expressed in colonic IECs (Price et al. 2016), but it is not clear that it would be engaged in every infected IEC. For example, AIM2 detects bacterial DNA, which might only be released if the Shigella bacteria lysed in the cytosol. As noted by the reviewer, this may be a relatively rare event, as previously documented for AIM2 activation by Listeria-infected macrophages (Sauer JD et al, 2010). AIM2 activation may also be kinetically delayed in IECs. It appears instead that NLRC4 is the main inflammasome that can respond to Shigella in mouse IECs; thus loss of NLRC4 is sufficient to lead to susceptibility of mice. It remains possible that there is some functional AIM2 or PYRIN (or CASP11 or NLRP1B) in mouse IECs; thus, the further removal of these inflammasomes might lead to even greater susceptibility. Alternatively, a low level of activation mediated by these additional inflammasomes (perhaps in macrophages instead of in IECs) might even be necessary to produce the inflammation that causes disease symptoms.
In humans, consistent with our data in Fig. 1, we propose that the NLRC4 inflammasome is antagonized or otherwise evaded by Shigella. The reviewer wonders why PYRIN or AIM2 cannot compensate for NLRC4, and is suspicious that the activation of PYRIN/AIM2 we observe in THP1 cells is not representative of what would occur in vivo. Certainly we agree that THP1 cells are non-physiological and we do not attempt to make claims in the manuscript that our observation of AIM2/PYRIN activity in these cells means anything for human shigellosis.
The reviewer states: “the in vitro data [in THP1 cells] appear to contradict one of the main conclusions of the paper, because it would seem that human Pyrin and AIM2 inflammasomes can detect Shigella, and so these should compensate for NLRC4.” For all the reasons discussed above, we do not agree there is a contradiction. There are many reasons why PYRIN and AIM2 might function in THP1 cells (and possibly even human macrophages) but would not compensate for NLRC4 in IECs.
In sum, we agree that there is more to learn about which inflammasomes, if any, are activated by Shigella in human IECs, but given the many uncertainties, we do not feel it is fair to say that our results are internally contradictory. We will endeavor to discuss some of these points in a revised manuscript.
Reviewer #3:
Mitchell et al describe the development of a mouse model for shigella gastroenteritis, the lack of which has been a serious impediment to Shigella research. They identified a difference in recognition of shigella between human and mouse Naip/NLRC4 which contributes to the resistance of mice to Shigella gastroenteritis. They suggest that Shigella specifically inhibits human Naip/NLRC4 activation and that the difference between mice and human susceptibility to infection is due to differential inhibition. This was confirmed by the ability of NLRC4-/- mice can recapitulate human infection. Furthermore they show that it is inhibition of NAIP-NLRC4 in IEC that is required for infection to occur. This manuscript therefore describes a number of important findings and uses these to develop a very useful animal model of shigellosis.
We are grateful for the Reviewer’s comments and suggestions, and provide point-by-point responses below:
I have three suggestions that I believe would improve the manuscript:
1) Determine the inflammasome that causes cell death in Shigella-infected THP1's. WT Shigella infection did not induce pyroptosis of colchicine-treated (PYRIN inhibitor) AIM2-/- THP1 cells, indicating one or both of these inflammasomes is responsible for the cell death observed in shigella infected THP1 cells. Why not test these separately to determine which?
Response: We have now made AIM2/MEFV–/– THP-1 cells. Our preliminary finding is that cell death and IL-1B levels in these cells are impaired in response to Shigella infection. We intend to include these data in a revised manuscript.
2) Markers of inflammation during disease. Clinical features of the disease (diarrhoea, weight, CFU/organ, fecal blood) are described well. But since Shigellosis is an inflammatory disease, it would have been nice to have seen some inflammatory molecules/cytokine levels measured, in addition to clinical features. The authors did measure levels of MPO, but that was as a marker for neutrophil recruitment.
Response: We agree that additional readouts of inflammatory disease are warranted. We are planning to repeat our experiments and measure cytokines in the blood. We intend to provide these data in a revised manuscript.
3) Further refinement of the mouse model. The authors present the inhibition of human NAIP/NLRC4 as the main factor that affects the difference in infection between humans and mice but a high innolcum (5 x 10(7) cfu/mouse compared to approx. 100 cfu for humans) is still required in addition to streptomycin treatment. It is not discussed whether any refinement of these procedures was attempted or why such a high inoculum and streptomycin treatment is still required. Presumably microbiota differences in addition to naip-/nlrc4 is an important species specific determinant of infection, hence the streptomycin treatment. Why is such a high innoculum required?
Response: this comment is similar to one of the comments of Reviewer 1. As we state above, it is actually not entirely clear that the infectious dose for humans is consistently ~100 CFU. Indeed, there appears to be great variation, with some humans exhibiting resistance to doses more than 10^5 CFU. Although we used high inoculums in our experiments, this was just to ensure consistent infection of all mice. Preliminary experiments in which we reduce the dose suggests that, like some humans, some mice are also susceptible to lower doses (e.g., 10^5 CFU). Thus our model exhibits an infectious dose within the range of what is observed in humans and we do not feel there is a large discrepancy here, though it appears that we do not recapitulate the extreme susceptibility seen in some humans. We don’t find this particularly surprising as Shigella is a human-specific pathogen and it is likely that at least some of its virulence factors may not work well in mice. Instead, we think what is most surprising is that loss of one host defense component (NLRC4) is sufficient to produce disease symptoms that are strikingly similar to what is seen in humans. We acknowledge that one difference is the need for streptomycin in our model. Clearly this suggests, as the reviewer states, that the microbiota can influence susceptibility. This is a well-described phenomenon with many enteric pathogens and it will be of interest in future studies to determine what components of the microbiota afford protection in our model.
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Reviewer #3:
Mitchell et al describe the development of a mouse model for shigella gastroenteritis, the lack of which has been a serious impediment to Shigella research. They identified a difference in recognition of shigella between human and mouse Naip/NLRC4 which contributes to the resistance of mice to Shigella gastroenteritis. They suggest that Shigella specifically inhibits human Naip/NLRC4 activation and that the difference between mice and human susceptibility to infection is due to differential inhibition. This was confirmed by the ability of NLRC4-/- mice can recapitulate human infection. Furthermore they show that it is inhibition of NAIP-NLRC4 in IEC that is required for infection to occur. This manuscript therefore describes a number of important findings and uses these to develop a very useful animal model of shigellosis.
I have three suggestions that I believe would improve the manuscript:
1) Determine the inflammasome that causes cell death in Shigella-infected THP1's. WT Shigella infection did not induce pyroptosis of colchicine-treated (PYRIN inhibitor) AIM2-/- THP1 cells, indicating one or both of these inflammasomes is responsible for the cell death observed in shigella infected THP1 cells. Why not test these separately to determine which?
2) Markers of inflammation during disease. Clinical features of the disease (diarrhoea, weight, CFU/organ, fecal blood) are described well. But since Shigellosis is an inflammatory disease, it would have been nice to have seen some inflammatory molecules/cytokine levels measured, in addition to clinical features. The authors did measure levels of MPO, but that was as a marker for neutrophil recruitment.
3) Further refinement of the mouse model. The authors present the inhibition of human NAIP/NLRC4 as the main factor that affects the difference in infection between humans and mice but a high innolcum (5 x 10(7) cfu/mouse compared to approx. 100 cfu for humans) is still required in addition to streptomycin treatment. It is not discussed whether any refinement of these procedures was attempted or why such a high inoculum and streptomycin treatment is still required. Presumably microbiota differences in addition to naip-/nlrc4 is an important species specific determinant of infection, hence the streptomycin treatment. Why is such a high innoculum required?
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Reviewer #2:
Mitchell et al explore the role of NLRC4 in defending against Shigella infection by demonstrating that NLRC4 contributes to resistance to shigellosis in mice. Using in vitro assays, they first show that mouse but not human macrophages undergo NLRC4-mediated pyroptosis in response to Shigella infection despite an ability for both species to successfully detect Shigella NLRC4 agonists. They then demonstrate that C57BL/6 background mice, which normally resist shigellosis, become susceptible to infection when deficient in NAIPs or NLRC4. In parallel, 129 background mice develop more significant infection including intestinal bleeding. Furthermore, using a mouse line in which NLRC4 expression is restricted to intestinal epithelial cells (IECs), they show that IEC expression of NLRC4 is sufficient to resist shigellosis. Finally, using a known attenuated Shigella mutant, they demonstrate that their shigellosis model can mimic kinetics seen in humans.
Mitchell et al convincingly demonstrate both the importance of NLRC4 in protecting mice against Shigella and the utility of their mouse model for studying Shigella infections, both of which are significant and will push the Shigella field forward. There are mechanistic questions to be addressed in future studies beyond the current manuscript, attesting to the importance of the paper in opening up new areas in the field of research. In some places, the authors draw conclusions that reach beyond what is proven in the data, which should be addressed in text edits to the manuscript. In summary, this article presents an important new model for Shigella infection. The impact of the manuscript is the development of a mouse model with which to study Shigella infection in vivo.
Major comments:
Many questions remain concerning why NLRC4-deficient THP1 cells still undergo pyroptosis. The authors provide evidence that Shigella activates PYRIN and/or AIM2 inflammasomes in humans, and that somehow mouse macrophages would fail to have this same detection. At face value, the data would suggest that humans are able to detect Shigella by Pyrin and AIM2, but for some reason these two inflammasomes are insufficient, and instead NLRC4 is required for in vivo defense. Then in mice, it would imply that everything is flipped - for some reason detection by Pyrin and AIM2 is not important, but now the bacteria can be detected by NLRC4 and this is important. The NLRC4 focused conclusions are consistent with the in vivo data, that NLRC4 in humans fails to detect, but NLRC4 in mice succeeds in detecting Shigella. However, the data that Pyrin and AIM2 in human cells successfully detect Shigella are inconsistent with the overall conclusions of the paper. I suspect that this is an artifact of THP1 cells, and that the in vivo situation in humans is that these two inflammasomes will fail to detect Shigella. There is published precedent from other infections where in vitro detection belies in vivo lack of detection (e.g. Listeria is detected by AIM2 in vitro, but probably not in vivo). It may be difficult to make direct comparisons between how inflammasomes act in THP1 cells as compared to BMMs, due to artifacts arising from the different origins and passage levels of the two cell types. It may be that the inflammasomes response is most important in IECs, as proposed by the authors, and that IECs may not express Pyrin or AIM2. There is evidence from publicly available IEC transcriptional profiles that IECs do not express Pyrin (Mefv) (Reikvam, doi: 10.1371/journal.pone.0017996), although this profile does show Aim2 expression in IEC. It is my understanding that BMMs do not express Pyrin unless they are strongly stimulated with some TLR agonist. As it stands, the in vitro data appear to contradict one of the main conclusions of the paper, because it would seem that human Pyrin and AIM2 inflammasomes can detect Shigella, and so these should compensate for NLRC4. The explanation as to why Pyrin and AIM2 are insufficient to compensate for NLRC4 evasion in human infection should be addressed at least in discussions of the data to explain the apparent discrepancy.
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Reviewer #1:
The manuscript by Mitchell et al. finds that the NAIP-NLRC4 inflammasome in mice is a critical host factor that controls intestinal infection with the human specific bacterial pathogen Shigella flexneri. The work suggests that Shigella is actively suppressing the human NAIP-NLRC4 inflammasome possibly using an T3SS effector protein, which does not recognize its substrate in mouse cells. The authors use this information to determine that B6 mice lacking the NAIP or NLRC4 inflammasome components are susceptible to Shigella infection and observe disease symptoms similar to Shigellosis in humans. In addition, 129 mice exhibit additional disease symptoms, and the authors suggest that loss of Caspase-11 in 129 mice is responsible for this phenotype.
The strengths of this manuscript include the introduction of a new mouse model that mimics Shigellosis, the demonstration that NAIP/NLRC4 activation is important for epithelial cell defense, and the potential of these findings to clarify aspects of human infectious disease caused by this pathogen. The manuscript is well presented, and the experiments are conducted with a high degree of rigor. Overall, this is an important contribution to the Shigella field and also has significant implications on our understanding of inflammasomes in host defense against pathogens.
There are some weaknesses that should be addressed. Experimentally, it has not been directly demonstrated that IECs from NLRC4-/- mice undergo cell death (using biochemical markers). This is a critical aspect of the model. In addition, it would be useful for the authors to evaluate bacterial burden over the time course in Figure 6. Although this is not absolutely necessary to support the manuscript conclusions, this information would greatly benefit the community that intends to use these mice in the future.
There are also some discussion points about the mouse model that would enhance the overall impact of the work. For example, a more in depth discussion about the differences between human Shigella infection and the new model would be helpful. It is important to emphasize that the mouse model requires a much greater inoculum of the pathogen to induce disease and requires microbiota-deficiency to be effective. What are the implications of this finding on our understanding of human disease? In lines 274-285 the authors present an either/or scenario in which either macrophage pyroptosis is required for IEC infection or inhibition of NAIP/NRLC4 pyroptosis in IECs is required for IEC infection. However, these scenarios are not mutually exclusive. For example, it is plausible that the extremely low burdens of Shigella required to infect humans (<100 CFUs) is due to the pathogen initially crossing the epithelial barrier (e.g. through M-cells) to infect macrophage, and then re-infection of IECs after macrophage pyroptosis. In this scenario, the NAIP/NLRC4 inflammasome could prevent further expansion of bacterial in IECs by eliminating the cell-to-cell spread that have been described by others. Importantly, the macrophage lifecycle stage may not be necessary in mice in which the microbiota has been removed and Shigella is delivered at a very high inoculum. While, additional ideas could be, and should be, put forth since the mouse model provides new insights or challenges an existing dogma in the field.
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Preprint Review
This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.
Summary
In this manuscript, the authors introduce a new mouse model of Shigellosis, provide evidence for NAIP/NLRC4 activation as being important for epithelial cell defense, and apply these findings to observations made in humans infected by this pathogen. These are important findings and provide an opportunity to further advance the field in ways not previously possible. However, there are areas where the in vitro and in vivo data presented contradict each other, and there are inconsistencies with previously published work by the authors. In addition, with the development of the new mouse model being a major highlight of this manuscript, significantly more detail and discussion must be added to explain this mouse model.
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www.biorxiv.org www.biorxiv.org
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Reviewer #3:
Luo and colleagues use a combination of viral tracing and targeted neuronal manipulation tools to dissect the role of GABAergic retinal ganglion cells (RGC) in mediating aversive responses to looming visual stimuli. This paper reports the existence of a population of GABAergic RGCs projecting to the superior colliculus (SC). These conclusions were based on a set of retrograde and anterograde tracing experiments in two mouse lines that putatively label GABAergic neurons (vGAT-Cre and GAD2-Cre). Targeted ablation of the GABAergic RGCs compromised looming-triggered escape behavior, which suggests a possible involvement of the superior colliculus projecting GABAergic RGCs in mediating the looming-evoked flight response. Although these findings could provide important insights into the neural circuitry that mediates aversive behaviors, tracking the origin of these circuits back to the retina, we have major concerns with the paper in its current format.
The current data set raises three major concerns that need to be addressed.
-First, the specificity of the labelling strategies is insufficient to make the current claims. The viral tools used to kill or manipulate RGCs are not specific for either RGCs (shRNA experiment), or SC-projecting RGCs (DTA experiments), and hence do not support the pathway specific claims in the manuscript. Also, the presented data raises questions as to whether all labeled neurons in these mouse lines are functionally inhibitory in the adult retinal.
-Second, the behavioral tests performed do not check for effects of killing GABAergic RTGCs on thalamic dependent visual processing.
-Third, the presentation of the data makes it very difficult for the reader to ascertain the claims made by the authors. In addition, the writing needs major revision.
Major concerns:
1) Lack of specificity of the labelling strategy. The authors claim that GABAergic RGCs mediate escape responses via GABA release in the SC. The current data does not support this claim since none of the used tools is specific enough. To maintain these claims, the authors need to either test if the SC-projecting GABAergic RGCs have no (or minimal) collateral projections to other targets or if the ganglion cells that collaterally project to other brain areas are not involved in the aversive behavior being tested. Further, it is necessary to narrow down the cell populations affected by their manipulations and to show that GABA-release by RGCs is involved in the tested escape behavior.
a. We are concerned that the mouse lines used, in particular the vGAT line, do not label cells that are functionally GABAergic in an adult animal. The gene coding for vGAT, Slc32al, is found in amacrine cells, but not in RGCs (Siegert et al. 2011). In Figure 1, the GABAergic positive cell bodies are not confirmed as being RGCs, i.e. having an axon. In Figure S2 the authors do not differentiate between GAD2 and vGAT.
b. The viral tools used for ablating SC-projecting GABAergic neurons are not specific for the SC, but will also label very common collaterals to brain targets other than the SC including the thalamus (e.g. Ellis et al., 2016).
c. The shRNA approach to knock down vGAT expression removes vGAT from all types of vGAT-expressing retinal neurons, not only RGCs. It is very likely that vGAT-expressing amacrine cells are affected by this manipulation.
d. Please remove the claims that the behavior is mediated through the PBGN pathway. The transsynaptic HSV approach to label multisynaptic targets of GABAergic RGCs does not support this claim. It is unknown whether the axon terminals in the PBGN labelled in these experiments stem from the SC. Also, other SC-targets that are known to mediate escape, such as the PAG (Evans et al. 2018), are also labelled in these experiments.
2) Inadequate test of visual circuitry function. The approaches used by the authors to test for different aspects of visual processing focus on a few known reflex pathways, but do not directly test for thalamic/cortical dependent visual behaviors, i.e. image formation. This needs to be changed in the description of the tests and the interpretation of the results. The description in the figure itself is good, but the conclusions drawn are misleading. The authors use four different approaches to test for visual function and draw the following conclusions:
a. Looming-evoked escape is decreased in two experimental conditions where vGAT-expressing neurons are impaired. The authors conclude that GABAergic RGCs projecting to the SC mediate escape. As described in point 1, these experiments are not specific enough to make those claims.
b. Electroretinograms (ERG) have unchanged a- and b- waves, which leads to the claim that GABAergic RGCs are not necessary for normal retina function. This is misleading as the largest contributors to an ERG signal are the photoreceptors and bipolar cells (e.g. Smith, Wang ... and Trembley, 2014).
c. Optokinetic reflex is unchanged after vGAT ablation. The authors conclude from these experiments that image formation is independent of GABAergic RGCs. This approach only tests for very specific retinal projections, probably to the AOS, but does not test for the function of the LGN-cortex projections, which form the major image formation pathway.
3) Insufficient data presentation. The currently presented histological sections are not sufficient to draw the same conclusions as the authors. In addition, several of the experiments lack clear quantifications to back up the claims in the text. The authors need to provide complete and readable pictures, and add quantifications to support their claims.
a. The role of PV+ SC neurons in the present study is unclear. The authors do not provide adequate evidence that GABAergic RGC-recipient neurons in the colliculus are PV+ (line 196). The referenced figure (Fig. 4B) shows only a single example of a staining. Quantification of the % of labeled neurons that are PV positive is required to strengthen this claim.
b. Please provide complete images of the histology at a readable size for all figures and add outlines of brain areas where necessary. Importantly, in Fig. 1G it looks as if the RGC axon terminals are only present in the intermediate layers of the medial SC. Since it is claimed that the GABAergic RGCs include many different types, one would expect axon terminals across all depths of the superficial SC.
c. The re-analysis of the published scRNA results from Rheaume et al 2018 should be made clear in the manuscript. Currently there is no information in the main text about the underlying data set or analysis.
d. It is not clear in the text what the differences/similarities are between vGAT- and GAD2-Cre mouselines. Please make clear in the narrative and conclusion how each mousseline was used.
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Reviewer #2:
In this study, Luo et al. report that GABAergic retinal ganglion cells projecting to the superior colliculus (spgRGCs) drive defensive responses to looming. Previous studies demonstrated that neurons in the superior colliculus (SC) mediate behavioral responses to looming. Most of the >40 ganglion cell types in mice project to SC. Which of these convey the relevant looming signals from the retina is unclear. Most ganglion cells express VGLUT2 and release glutamate from their axon terminals, but anatomical evidence suggested that a subset of ganglion cells may contain GABA. Furthermore, a recent study (Sonoda et al., 2020) showed that some intrinsically photosensitive retinal ganglion cells are labeled in Gad2-Cre mice and reduce the light sensitivity of photoentrainment and pupillary light responses.
Here, Luo et al. find that intravitreal injections of Cre-dependent adeno-associated virus (AAVs) reporters in Vgat-Cre and Gad2-Cre transgenic mice label ganglion cell axons in many retinorecipient brain areas, including SC. The authors reanalyze a published single-cell RNA sequencing dataset (Rheaume et al., 2018), which suggest that subsets of ganglion cells of most types express Gad2. Using optogenetics, the authors confirm that activation of Gad2-Cre- and Vgat-Cre-positive ganglion cells elicit inhibitory postsynaptic currents (IPSCs) in SC neurons. Based on retrograde tracing, the authors suggest that these ganglion cells belong to a variety of types. Next, Luo et al. demonstrate that the deletion of spgRGCs abolishes defensive responses to looming stimuli and looming-evoked cFos expression in SC. The authors illustrate the selectivity of these effects by showing that electroretinograms, optomotor responses, and pupillary light responses are unaffected by spgRGC deletion. Finally, the authors use an AAV-based shRNA strategy to knock down Vgat in the retina and show that this abolishes looming responses.
Overall, this study reports a surprising and potentially transformative finding (i.e., that a small subset of many RGC types uses GABA to drive looming responses in SC and behavior). The authors leverage a wide range of techniques to study spgRGCs, but some results and interpretations are confusing, and some conclusions are insufficiently supported evidence.
Specific comments:
1) The Vgat knockdown experiments are critical to show that GABAergic transmission matters. The current strategy targets all Vgat-expressing neurons in the retina. The vast majority of these are amacrine cells. Silencing amacrine cells will likely have widespread effects among ganglion cells. The authors should use a dual AAV strategy similar to the one they employed for DTA to restrict Vgat-shRNA expression to spgRGCs and show that ganglion cells' responses to looming are unchanged.
2) Previous studies show that activation of SC neurons (particularly PV+cells) promotes defensive responses to looming, and the cFos labeling in this study suggest that spgRGCs activate SC neurons. Yet, optogenetics experiments indicate that spgRGCs elicit IPSCs in SC neurons. These findings seem at odds. Although the authors show that some spgRGCs elicit a mixture of EPSCs and IPSCs, the Vgat knockdown experiments suggest that the GABAergic transmission mediates looming signals and elicits behavioral responses. The authors should characterize looming responses in SC by electrophysiology or optical recordings (as they have done in previous studies) to clarify the contributions of spgRGCs.
3) Details of the cFos experiments were missing. The authors should compare cFos labeling and changes in cFos labeling after spgRGC ablation between looming and other visual stimuli, to discern the specificity of these effects.
4) The characterization of spgRGC types is superficial. The authors should show patch-clamp recordings from a small number of RGCs, which seem to encompass a variety of types. The authors should record light responses characterization (incl. responses to looming stimuli) and reconstruct the morphology of a larger number of ganglion cells to classify types in line with other studies (e.g., Bae et al., 2018, Reinhard et al., 2019).
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Reviewer #1:
The goal of this study is to identify the retinal ganglion cells that mediate the flight response of mice to a looming stimulus. The candidate they focus on are a subset of RGCs that release GABA at synapses with a particular subtype of neuron in the SC that was previously implicated in this behavior.
The impact of the paper is limited for two main reasons. First, there was a paper using a similar mouse line that was just published (Sonoda et al, Science 2020) that revealed that GABAergic intrinsically photosensitive RGCs shape several of the non-vision forming behaviors in mice (such as photoentrainment of circadian rhythms and the pupillary light reflex). The authors may not have been aware of this work when they submitted this paper but now that it is published, the authors need to more rigorously compare their results to that study.
Second, it is not at all clear that the authors have identified a subtype of RGCs that mediate the looming responses. Rather their data (particularly Figure 3) seems to argue that a lot of different RGC subtypes have GABA. So the model is that the 13% of multiple RGC subtypes project to PV cells in the SC and together they mediate the looming response?
Finally, there is a lack of quantitative description of many of the experiments that undermines many of the conclusions the authors want to make. These are described explicitly below.
1) The authors make use of both a GAD2-Cre and vGAT-Cre to label cells in the retina, apparently the results from the two lines are combined throughout the paper. The authors need to verify that the same cells are labeled for each line.
2) In Figure 1, the authors show beautiful images of their labeling but the quantification of RGCs that express GABA is not described. In the mouse retina 50% of the cells in the ganglion cell layer are amacrine cells. They do show some labeling in the optic nerve so clearly some RGCs are labeled but there is no way to know how many. Rather the authors rely on a re-analysis of an RNA-seq data set to estimate that 13% of RGCs across all subtypes! Of course, protein levels and not transcripts are what matter for this sty so at least a co-stain for a RGC marker would strengthen the finding. This would also resolve the issue brought up in point 1 above.
3) Figure 2. The authors use optogenetics to characterize the synaptic connections with target neurons in the SC. Again, there is a surprising lack of quantification. In Figure 2C they show light activation ChR induces an inward current but they don't say how many times they do that experiment. Most experiments are done in TTX + glutamate receptor blockers to isolate GABAergic currents but there is a subset in which the igluR blockers are absent and excitatory currents were detected. The authors need to clarify in what percent of neurons did they see GABAergic currents and in what percentage excitatory currents and in what percentage did they see both? Basically, the authors need to clarify if these RGCs are releasing both GABA and glutamate. This is critical for interpreting the experiments in which they kill this population of neurons and inhibit the behavior.
4) Figure 3. The authors use retrograde labeling to begin to identify the GABAergic RGC subtypes that project to SC. Again, the quantification is lacking - what percent of SC projecting cells were positive for GABA? It is really a confusing result because they find an array of RGC subtypes that seem to express GABA. Hence it does not appear that one type of RGC projects to SC but rather all types - but just the subset that release GABA along with glutamate. 5) Figure 4AB appears quite impressive but the logic is not clear. Does Herpes simplex virus work as an anterograde transsynaptic virus? More explanation is required. Again, there is no quantification of the results - just examples of single neurons found in several retinorecipient brain regions.
(Note: Figures 4C-G are impressive - killing this subpopulation of cells seems to eliminate the response to looming stimuli while other visually guided behaviors are retained.)
6) Figure 5H - this is a difference with the Sonoda paper - they find an effect on PLR when they reduce GABA release from ipRGCs.
7) Figure 6 - the authors use RNAi to reduce vGAT expression in spgRGCs and this also impacts behaviors. There are many controls that need to be done here primarily showing the glutamate release is normal. Otherwise this could just be a synaptic transmission deficit.
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Preprint Review
This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 2 of the manuscript.
Summary:
This study reports that a small subset of different RGC neuron types that release GABA at synapses with a particular class of neurons on the super colliculus mediate the looming responses in mice. This result is potentially highly significant and as noted by one reviewer, potentially transformative in our thinking of how RGC cell types mediate behaviors. However, all three reviewers agree that many of the conclusions are insufficiently supported by the evidence. The reviewers offer many details for necessary experiments and clarifications that need to be made in order for the authors to be able to reach their conclusion.
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Reviewer #3:
The bHLH transcription factor Atoh7 has been studied as a critical regulator of retinal ganglion cell (RGC) generation in several species but, as the authors detail in the Introduction, it is not clear how or at what stage it acts. For example, some (most) data suggest it is required to specify the RGC fate in precursors, while other data suggest it may be required for RGC differentiation and/or survival following their generation. Here, Brodie-Kommit et al. use a well-established method to distinguish these possibilities, deleting Atoh7 in the absence of Bax, a powerful proapoptotic gene. Both naturally occurring and genetically-provoked apoptosis of many neuronal types, including RGCs, have been shown to be prevented in Bax mutants, which are viable and generally healthy.
The authors show that RGCs survive and are functionally normal in the absence of both Atoh7 and Bax, but few axons leave the retina, so of course light-induced behaviors are greatly decreased. Single cell RNAseq demonstrates a delay in RGC differentiation in the absence of Atoh7 (with or without Bax) and a variety of gene expression changes.
As expected for a paper from two superb laboratories, the work is done to the highest standard and uses the best available methods. The result that blocking apoptosis rescues RGCs in the absence of Atoh7 is important and should help resolve controversies about its role, providing a strong argument against what is likely still the best-accepted model.
On the other hand, the paper does not go far beyond that simple result: it shows what Atoh7 does not do, but not what it does do, either to the RGCs that express it or to the RGCs that do not express it but nonetheless require it for survival. The physiological and histological data largely back up the survival result; the behavioral defects are sort of trivial once one knows that RGC axons fail to reach the brain; and the RNAseq data do not lead to substantial novel insights that shed light on either the presumably cell-autonomous or the clearly cell-nonautonomous mechanisms.
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Reviewer #2:
In the present manuscript the authors reveal that RGC differentiation is largely rescued in the absence of Atoh7 when the pro apoptotic gene Bax is also removed in the developing retina. These rescued RGCs show some proper physiological responses but fail to develop proper connections to the brain. Retina vasculature is also affected by the absence of Atoh7 even when RGCs are "rescued". Finally by single cell analysis they reveal that Atho7 is required for proper timing of RGC differentiation but the expression of major markers for RGC can be independent from Aoh7 transcriptional activity. The paper is based on a series of very elegant genetic experiments and the single cell analysis is particularly illuminating in this context.
Major Points:
Cell death is only one of the RPC possible fates in the absence of Atoh7. Indeed the author and a vast amount of literature showed that in the absence of Atoh7 more adopt photoreceptor precursor fate among others. Is the block of apoptosis by Bax inactivation reducing this "ectopic differentiation phenotype" in addition to RGC fate restoration?
Linked to the previous point, does the single cell data reveal why some progenitors die in the absence of Atho7 while others change fate?
The authors should discuss this point in more detail.
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Reviewer #1:
This manuscript challenges the notion that the transcription factor Atoh7 is required to confer neurogenic retinal progenitors the competence of generating retinal ganglion cells (RGCs), the first-born neurons of the retina. This idea is based on the evidence that Atoh7 inactivation in mice causes the loss of the majority of RGCs. Here the authors have generated a Atoh7Cre/Cre;Bax-/- mouse line to ask what happens if apoptotic cell death is prevented in Atoh7 null mice. Using a number of RGC markers, they show that in the adult retina a large number of RGCs are no longer lost and are functionally connected with other retinal cell types as the retinas generate light driven photic responses. However, the RGCs of the Atoh7Cre/Cre;Bax-/- mice cannot connect with brain targets as the axons (when present) do not exit the optic disk but grow in a disorganized manner within the fibre layer. As an additional feature, the hyoloid artery does not regress. In Atoh7Cre/Cre;Bax-/- embryos, RGC generation is delayed as determined by analysis of single cell RNA-seq. The authors conclude that Atoh7 is required for RGC survival but dispensable for their specification.
This is an interesting study that adds up to the existing literature related to the role of Atoh7 in RGC generation/differentiation. However, the conclusion seems rather stretched: do the cells generated in the absence of Atoh7 and Bax really have a (full) RGC identity as claimed in the title? Is the specification of ALL RGC really independent of Atoh7? Conclusions should be toned down and alternative interpretations should be offered. Indeed, preventing apoptosis does rescue the full number of RGCs (see for example melanopsin positive cells). The lack of Isl1 in Fig. 4 and the low number of Brn3a+ cells in Fig. S6 is rather striking and suggests more than a delay. Thus, at least a subset of RGCs seems to require Atoh7, likely early born RGCs. There are several studies indicating that RGCs secrete factors that regulate their own number (GDF11, Kim et al., 2005, Science, as an example). Lack of this feed-back at early stages may favour the generation of RGCs that are not full Atoh7-dependent, creating an imbalance between Atoh7-dependent (early) and Atoh7-independent (late) RGCs.
The second problem that remains unanswered is related to the "identity" of the RGCs present in Atoh7Cre/Cre;Bax-/- embryos. Are they really bona fide RGCs? These cells cannot connect properly with their brain target nor secrete the putative factors needed to induce hyaloid artery regression. These defects could perhaps be explained by asynchrony (cells are generated late to read the axon guidance cues, for examples) but they may also be interpreted as lack of full identity.
The authors need to consider these possibilities and further address the related points below:
1) The difference in cell number detected with RBPMS and Isl1 is puzzling (Fig. 1). Isl1 recognises RGCs but also amacrine cells, which should be increased in absence of Atoh7. How do the authors explain that Isl1+ cells are less than the RBPMS+ ones in Atoh7Cre/Cre;Bax-/- mice.
2) The sentence "Brn3b-positive ipRGCs differentiate normally in the absence of Atoh7" is an overstatement. Only 35% of them do, the others are presumably lost. Furthermore, the presence of a cell specific marker does not ensure that the cells are fully differentiated.
3) Line 435. Presumably a sentence describing the response of RGC in Atoh7Cre/Cre;Bax-/- is missing.
4) Lines 506-509. Failed vasculature regression: the authors state "...implies that Brn3b and Isl1 may activate expression of secreted factors that drive vascular regression". If this is the case why in Atoh7Cre/Cre;Bax-/- retinas the hyaloid artery is still present? The retinas do express levels of Isl1 and Brn3b so that these factors should be present.
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Preprint Review
This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.
Summary:
The three reviewers agree that the study is very elegant and well performed. However, they also find that the conclusions are rather stretched and there is no clear demonstration of what Atoh7 is needed for. Major concerns relate to the real identity of the rescued cells and the claimed independence of RGC specification from Atoh7. Unfortunately, the RNAseq data do not illuminate this issue or solve the cell-autonomous and non cell-non-autonomous mechanisms that are at the basis of the present observations.
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www.biorxiv.org www.biorxiv.org
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Reviewer #2:
The focus of this paper is the zinc metalloprotease ADAMTS-5. This protein has received attention as a therapeutic target for the treatment of degenerative joint diseases such as osteoarthritis. The primary effort is devoted to the development of non-zinc chelating exosite type inhibitors. The authors have previously identified exosites in hypervariable loops that are required for or proteolysis of both aggrecan and versican. Targeting these sites with the hope of selectivity is certainly a good approach. The authors used the glycosaminoglycan (GAG) feature of substrates to build in exosite affinity. To this end the authors probed ADAMTS-5 with a small library of GAG-mimetic glycoconjugated arylsulfonamides.
With some minimal SAR, the authors were able to achieve some selectivity of ADAMTS-5 over ADAMTS-4 and some increase in potency over other inhibitors they have developed. They report IC50 values with the most potent (molecule 4b) at 9.4±2.8 µM. Some further SAR to more fully understand exosite binding (4b did not inhibit a peptide cleavage assay) did not lead to a more potent inhibitor. Further characterization of 4b inhibitory activity was carried out looking at synergism with a known zinc chelating inhibitor and some molecular docking studies. The docking studies led to experiments mutating residues that were thought to involve inhibitor binding. The results largely supported the in silico predictions.
Overall the reported results advance the idea that selective inhibitors of ADAMTS enzyme that are not dependent on zinc coordination are possible; however, in the absence of more detailed studies of inhibition in cells and potentially in animals it is not possible to say how important and influential molecules such as those described here will be on sorting out complicated in vivo physiology. The potency reported for 4b suggests significant optimization would be needed before in vivo significance could be assessed.
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Reviewer #1:
This study pursues the development of ADAMTS-5 protease inhibitors by screening compounds linking a glycan (GlcNAc) with an arylsulfonamide, using click chemistry as the tether contains a triazene. ADAMTS-5 is a metalloprotease that has been implicated as a drug target for osteoarthritis. In prior work, this lab has identified exosites in ADAMTS-5 that can contribute to substrate recognition and processing. Here they identify a hybrid compound, 4b, that can block the protease activity of ADAMTS-5 with 9 µM potency. Using docking, they implicate several Lys residues that might confer interaction and show that potency of 4b is reduced with ADAMTS-5 mutants. Overall, compound 4b may bind as predicted although no additional experimental structural studies are performed to validate binding mode. While the study is a solid but limited medicinal chemistry effort, it is not felt that this manuscript will be of broad interest.
-The compound 4b potency is still rather weak relative to other previously published agents which show sub-µM potency. 1) Biochem J paper (2016) from this lab and BBRC (2016) from a Japanese lab reported antibodies that blocked ADAMTS-5 in the low nM range and worked in human chondrocytes. 2) Thiazolidine-diones (sub µM) were reported as cell active ADAMTS-5 inhibitors (Eur J Med Chem 2014). 3) Acylthio-semicarbazides are sub-µM ADAMTS-5 inhibitors (Eur J Med Chem 2013) although also target ADAMTS-4 more weakly, and showed selectivity against other metallohydrolases.
-Compound 4b was not used in a cell-based let alone animal model to analyze its pharmacological effects or promise. It is thus not clear how compound 4b stacks up to earlier agents. Compound 4b is a rather large compound for advancing clinically.
-No new insight into ADAMTS-5 biological function was gained here.
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Reply to the reviewers
INITIAL RESPONSE TO REVIEWERS / REVISION PLAN
We are grateful to the three reviewers for reviewing our manuscript and providing their comments which helped to improve further the quality of the current study. We attach an initial revised version of the manuscript with changes corresponding to reviewers’ comments being highlighted. We now provide:
- 18 new main figure panels (Fig.1E, Figs.2D-F, Figs.3E-F, Figs.4B,C,E, Figs.6B-F, Figs.7B,D,E,F),
- 9 new supplementary figures, and
- 13 new supplementary tables, that correspond to the points raised by the reviewers. In this initial response to reviewers and revision plan we have already performed the bioinformatics analysis and the majority of new wet lab experiments requested by the reviewers, while we are still awaiting only for the results of three sets of wet lab experiments (RIP-seq, additional protein/RT-qPCR confirmations and B2 incubations with other proteins), which, due to their nature, take longer. We have also revised the main text accordingly with only a number of updates (regarding some methods of experiments currently in progress and the respective discussion) still missing.
In detail:
REVIEWER 1
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
B2 RNAs, encoded from SINE B2 elements has been directly implicated in stress response by its inherent ability to bind RNA Pol II and suppress stress response genes (SRG) in homeostatic conditions. However, upon stimuli, B2 RNAs are cleaved and degraded, resulting in the release of RNA pol II and upregulation of SRGs. Previous work from the senior author identified PRC2 component EZH2 to be the B2 RNA processing factor, cleaving B2, and releasing POL2. SRGs are upregulated upon stress, for example in age-associated neuropathologies like Alzheimer's disease (AD). Considering that the hippocampus is a primary target of amyloid pathologies as well as since SRGs are suggested to be key for the function of a healthy hippocampus, the authors set to understand the role of B2 RNAs that are linked to SRG regulation in the mouse hippocampus with amyloid pathology. They use disease-relevant in vivo and in vitro models combined with unbiased RNA seq data analysis for this endeavor, which indicates the potential relevance of B2 RNAs in APP mediated neuronal pathologies in mice as well as identifies Hsf1 as the factor cleaving B2 RNAs in the hippocampus.
This reviewer generally remarks that “The work is interesting and identification of Hsf1 as the processing factor for B2 RNAs in the hippocampus is significant. I would like to credit the authors for their elegant in vivo experimental design in Figure 2.”
We appreciate the encouraging comments made by this reviewer.
General comment: The reviewer finds “some of the conclusions to be overstated” and has brought a number of concerns to our attention. Indeed, we agree that provision of additional data and details is needed to avoid any confusion about the gene pathways to which our findings apply. In the initial manuscript, (Figures 2 D, F and 6 D, F), we presented the gene expression levels of all B2 RNA regulated SRGs identified in our previous study (Zovoilis et al, Cell 2016), referred as B2 RNA regulated SRGs or B2-SRGs throughout the manuscript. To this end, we performed the respective statistical tests between the different conditions considering these genes, in order to show the transcription dynamics of these genes in either amyloid beta pathology (APP mice /Figs. 2D, F) or amyloid beta toxicity (HT22 cells / Figs. 6D, F). Since we were not looking for new candidate genes upregulated in APP mice or in our HT22 cell culture system, we did not narrow our analysis only to genes delivered by a general-purpose differential gene expression approach such as DESeq but tested all B2-SRGs. However, based on the reviewer’s comments below, we realize that the paper would benefit by presenting in the main figures only those B2 RNA regulated SRGs that overlap with differentially expressed genes identified by DEseq in each experimental system. This will help to avoid confusion and any misunderstanding that all B2 RNA regulated genes are equally affected in our system, which is not the case and would be an overstatement. We are now presenting in new Figure 2 (2E, 2F) only those B2-SRGs that overlap with upregulated genes identified by DESeq in 6m old APP mice (listed in new Suppl. Table 5) and in new Figure 7 (7D, F) we are now presenting only those B2-SRGs that overlap with upregulated genes identified by DESeq in HT22 cells treated with amyloid beta (listed in new Suppl. Table 11). The conclusions drawn by the new figures remain the same as with the old ones and we believe that this new way of presentation of this data will prevent confusion and potential over-statements. We thank the reviewer for bringing this to our attention. Based also on this reviewer’s minor point 3, we recommend that the old figures that included all B2-SRGs (and not only the differentially expressed ones identified by DESeq) are moved to the Supplement as new Supplementary Figures 1 and 7, respectively, so that readers can still get a view of all the data and the transcription dynamics of all B2-SRGs, while we provide both in text and the supplement an explanation about the value as well as limitations of these figures.
**Major comments:**
Major point 1. The reviewer asks: “In figure 1, the authors indicate a strong connection between B2 RNA regulated SRGs and learning and memory. In figure 2, they identify the SRGs in the hippocampus, please provide a direct comparison of learning and memory associated SRGs and the SRGs they identify in figure 2 that are significantly upregulated in APP mice in 6 months.”
In the revised version of the manuscript we now provide: i) As a new figure panel (lower panel in new Fig.1E), the number of B2 RNA regulated SRGs that are associated with learning based on our Peleg et al, Science 2010 paper and as a new Supplementary Table 3, the exact list of these genes. ii) As a new Supplementary Table 4, the list of all genes that are significantly upregulated in APP mice (6 months). iii) As a new Supplementary Table 5, the list of those genes upregulated in amyloid pathology (APP 6 months) that are B2-SRGs (expression levels of these genes are presented in new Figure 2E,F). Per reviewer’s question, we now provide as a new Supplementary Table 6, the list of B2 RNA regulated SRGs that are both learning associated genes and upregulated in 6 month old APP mice. In the text (first two sections of the results), we provide direct comparisons of the number of genes in each category and their overlap.
Major point 2. The reviewer asks: “To better understand the data in the context of hippocampal function, please include functional annotation of SRGs they identified in Figure 2F as they do it in Figure 1 (desirably for each time point, at least for 6M). How many of the SRGs they identify in Figure 1 are part of Figure 2F? Please include functional annotation of significantly upregulated B2 regulated SRGs in Fig2 and compare them with that of Figure 1.”
The number of B2 RNA regulated SRGs in Figure 1 that are part of Figure 2 (in particular Figs.2E,F) is now presented in the new Supplementary Table 5 and also in the text. We now provide as a new Supplementary Table 7 the functional annotation of these genes (see also general comment for this reviewer) and discuss the findings in the text.
We recommend to include only the 6M old mice as this is the time point in which B2 RNA processing was found to differ between WT and APP mice. However, if the reviewer thinks that this is necessary we will add also differential expression lists of other ages as additional supplementary tables.
Major point 3. The reviewer asks: “In figure 3, the authors report that the B2 processing rates are high at the 6M time point at in hippocampi of the APP mice. Please include the levels of unprocessed and processed B2 RNAs in these samples along with this figure, without which it is difficult to gauge the significance of its correlation with SRGs in Figure 2.”
We now provide as new figure panels 3E and 3F the levels of processed B2 RNA fragments and unprocessed (full length) B2 RNAs in these samples, respectively, along with the processing ratio which is now labeled as subfigure 3G.
Major point 4. The reviewer asks: “What is the % of B2 regulated SRGs that are hsf1 bound in Figure 4C? What is there dynamics in the wild type and APP hippocampi?”.
Old Figure 4C is now Figure 4A. The exact number of B2 RNA regulated SRGs that are close to Hsf1 binding sites is now presented as a new figure (Figure 4C) and discussed in the text. A list of these genes is provided as new Supplementary Table 8. For genes that are upregulated in APP mice compared to wild type, the difference in Hsf1 binding dynamics between B2 RNA regulated and not regulated genes is now presented as Suppl. Figure 4D.
Major point 5. The reviewer asks: “What is the distribution of Hsf1 binding sites on (a) non-B2 regulated SRGs and (b) non-SRG genes in hippocampi?”.
This point is related with point 4. We now present a new panel (Fig. 4B) for non B2 RNA regulated genes (listed in Suppl. Table 13) along with the distribution we have in the initial manuscript for all B2 RNA regulated SRGs (now presented as Fig. 4A). The direct comparison of these genes is presented in the new Suppl Figure 4C together with a similar comparison only for genes upregulated in APP mice (Suppl. Fig.4D)
Major point 6. The reviewer notes: “In Figure 4D, the 3months old Wt HSF1 levels are high, yet B2 processing (Figure 3E) is low. Please comment.”
The reviewer’s comment made us realize that we should include a plot that describes the correlation between Hsf1 levels and B2 RNA processing ration across all sequenced samples. This should reveal whether differences such as those observed by the reviewer affect our conclusion regarding the relationship between these two parameters. We now provide this in the new Supplementary Figure 6D, where we found a strong positive correlation between Hsf1 levels and B2 RNA processing ratio. We thank the reviewer for this comment which helped us to substantiate further this relationship.
Major point 7. The reviewer notes: While the authors show in vitro cleavage of B2 RNA by Hsf1, the experiment lacks controls to be conclusive. At least, please include a similar size protein as HSF1 with no-known RNA binding activity and a similar size protein with RNA binding activity as controls in 5A. Please justify the use of PNK as the control protein. Please include the use domain-based deletions of Hsf1 to map the region of HSF1 that is binding and potentially cleaving the B2 RNA. Please include an RNA of similar size and Antisense-B2 RNA to show the specificity of the Hsf1 based cleavage of B2 RNA. Without these controls, the conclusions in Figure 5 cannot be substantiated.
The endogenous ribozyme activity of B2 RNA compared to other control RNAs has already been shown in two previous works but we will also include the relative controls here by providing control incubations with other RNAs. We will also include the incubations with additional control proteins as suggested by the reviewer. We are currently performing these experiments and will include them in the revised version. PNK is used as a control protein because it is an RNA binding protein that is used in the construction of our short RNA libraries and we wanted show that short RNA seq data are free of such confounding factors that could potentially generate artificial fragments. We now include this information in the text.
We feel that the application of domain based deletions for Hsf1, while it would add additional information on the exact biochemistry underlying B2 RNA processing though Hsf1, is beyond the scope of this manuscript. In the current manuscript we are just focusing on the fact that Hsf1 can accelerate B2 RNA processing in vitro and not on the mechanism how this happens. This should be addressed in our opinion on a separate manuscript.
Major point 8. The reviewer asks: “The authors should show that the incubated APP peptides are taken up by the cells (experiments in Figure 5F and Figure 6).” These figures are now labelled as Fig.6C and Figure 7, respectively. That’s a very interesting point and we thank the reviewer for this comment. Multiple studies have shown that toxicity after incubation by amyloid beta is mediated mainly by cell surface receptors, which through cell signalling leads to the response to cellular toxicity that induces stress genes such as Hsf1. Nevertheless, APP peptides may enter the cell, and the reviewer’s questions raised the possibility that oligomers entering the cell could have a direct impact on the stability of the B2 RNA. In that case, providing evidence that the amyloid enters the cell would be important if we had indications that amyloid beta interacts directly with B2 RNA. We did test this and we found no direct effect of amyloid beta on B2 RNA, so the processing in our case is not induced by oligomers that may have entered the cell. We were planning to present this information in a different manuscript, but if the reviewer or editor thinks that it would be beneficial for the paper, we could present this as supplement figure that shows that amyloid beta incubations with B2 RNA do not induce further processing beyond what Hsf1 causes. For the moment we just present this below:
Major point 9. The reviewer asks: “Please provide the list, functional annotation, and % of the SRGs upregulated upon incubation with APP in HT22 cells in comparison to 6month old APP mice. Comment on learning-related Genes.”
In the revised version, we now provide and mention in the text the following data: i) a list of genes upregulated in HT22 cells during amyloid toxicity upon incubation with amyloid beta (new Suppl. Table 9), ii) a list of genes according to point (i) that are common with genes upregulated in APP mice (new Suppl. Table 10), iii) the list and number of B2-SRGs that are upregulated in HT22 cells during amyloid toxicity (the reviewer’s question) (new Suppl. Table 10). We mention in the text the gene numbers and also the genes that are common in all three lists. iv) Functional annotation of genes of point (iii) (new Suppl. Table 12),
We also mention in the text the limitations of our comparisons between the in vivo model of amyloid pathology (APP mice) and the in vitro cell culture model of amyloid toxicity (HT 22 cells) and we clarify that the cell culture model is used just as a simulation of the effect of amyloid beta in gene pathways associated with response to cellular stress and the role of Hsf1 on B2 RNA processing.
Major point 10. The reviewer asks: “The authors should show the efficient downregulation of Hsf1 (protein) upon anti-Hsf1 LNA transfection.”
In the revised version, in addition to the RNA-seq data we provide a second confirmation at the mRNA level with an independent method (RT-qPCR) in new figures 4E and 7B (lower panel). We are currently performing the protein extractions and will provide a WB or an Elisa in the revised version.
Major point 11. The reviewer asks: “Please present the total B2 RNA levels for conditions in Figure 6C.”
We now provide as new supplementary figure (Suppl. Fig. 6B and C) the levels of processed B2 RNA fragments and the total levels of unprocessed full length B2 RNAs of these samples that relate to old Figure 6C (now labeled as Fig.7C)
Major point 12. The reviewer notes: “Hsf1 levels are not significantly downregulated in Control cells which were inoculated with the reverse APP peptide. Please comment.”
We assume that the reviewer here refers to the lack of reduction in Hsf1 levels in the cells inoculated with the reverse peptide and the anti-Hsf1 LNA. Indeed, this lack of reduction is confirmed also by the new qPCR we performed (new Figure 7B, lower panel, R-ctrl vs R-anti-Hsf1). This should likely be attributed to compensation during non-stress conditions. In contrast, under stress conditions, Hsf1 is heavily used in stress response, which could explain the differences we see as cellular needs surpass the available Hsf1 transcripts due to degradation by the LNA. This is also supported by the new RT-qPCR experiments we have performed for B2-SRGs (new Figure 7E). In agreement with what is known for stress response genes such as immediately early genes (for example FosB), levels of these genes are minimal in both R-ctrl and R-anti-Hsf1 conditions and only become activated during stress response. We now discuss this in the text of the revised manuscript.
Major point 13. The reviewer asks: “Please compare and contrast the % of genes, the overlap, and the functional distinctions in 6F to that of 5G and Figure1. What are the genes that are common between Figure1, and that are specifically upregulated upon Anti-Hsf1 LNA transfection along with 1-42 APP. What is % of the occurrence of B2 binding sites in those genes? What are their functional annotations and what is their connection to learning, memory, and cell survival?”
Old Figure 6F is now Figure 7F, while old Figure 5G is now Figure 6C. This point is discussed in the response to points 1 and 9 of this reviewer. In summary, genes upregulated in our amyloid toxicity model included 25 B2-SRGs (new Suppl. Table 11). When testing for enriched terms in these 25 genes, biological processes related with apoptosis, such as regulation of apoptotic process and programmed cell death were at the top of the list (new Suppl. Table 12) and included, among others, genes such as FosB and Mitf that have been connected with Alzheimer’s disease. Out of the 25 genes that are up-regulated in both mice and our cell culture system, six are B2-SRGs (4932438A13Rik, Fosb, Pag1, Ptprs, Sema5a, and Sgms1) and include a well-known immediate early gene (Fosb), genes associated with sensitivity to amyloid toxicity (Pag1, Sema5a, Sgms1, Fosb), as well as genes associated with p53 (Ptprs, Fosb). All these genes get upregulated in amyloid toxicity (42-Ctrl vs R-Ctrl) but are not upregulated when Hsf1 LNA is applied (42-anti-Hsf1 vs R-anti-Hsf1, no significant difference). This information is now included in the text.
**Minor.**
1 . Please include TPM/ FPKM values for hippocampal markers as control in Figure 2 to do justice to the hippocampus specific RNA seq conducted by the Authors.
To our understanding, the reviewer here suggests the testing of well-known hippocampal markers in our mouse data as controls to confirm that they are indeed hippocampus specific. We have selected as reference markers, the genes employed by the Allen Brain Atlas RNA-sequencing project and we provide a comparison of their data in hippocampal cells with our data from mouse hippocampus. This is now presented as new Supplementary Figure 2.
2 . In figure 2D the authors show that B2 RNA regulated SRGs in the 3 months' wild type mice are significantly high. P53 has been reported to be high in young wild types hippocampus, but not SRGs in my opinion. The authors should comment on this.
Old Figure 2D is now Figure 2E. We now mention the reviewer’s comment particularly in the discussion and cite a landmark review article in Neuron journal by Michael Greenberg regarding the role of stress response genes, such as FosB, early during development. As to prevent any confusion, we have also replaced SRGs with B2-SRGs since we tested only B2-SRGS in our study.
3 . In figure 2F, under the 6m APP condition, the replicate 3 looks substantially different from the other replicate. This can significantly impact the analysis and conclusions made. Either remove that replicate and present the analysis without it or please provide a valid explanation. To make the data more valid, please provide hierarchical clustering of the entire data, the non-B2 regulated genes and the B2 regulated SRGs.
We now provide in the new Supplementary Figure 9C a PCA plot, which includes 6m APP mice vs. their WT counterparts and HT22 cells, and shows that this variability is within the biological replicate variability we can expect in these models. To substantiate this further, we have constructed the correlation matrix of the RNA-seq data of both WT and APP 6 month old mice in the new Supplementary Figure 9D. As shown in this matrix, all APP mice clearly correlate with each other and not with their WT counterparts.
In the initial manuscript the heatmaps of former Figure 2 were indeed provided with hierarchical clustering of the entire data and also included non-B2 RNA regulated genes. This data is included now as Supplementary figure 2.
In Figure 2C RNA seq data is represented in TPM while its FPKM in Figure 2D.
Figure 2D is now Figure 2E, while Figure 2C remains labelled with the same number. Given that TPM already includes scaling of the data, it is unsuitable for the averaging of the gene expression levels of multiple genes (B2-SRGs) used in the boxplots of Figure 2. This does not apply in the case of single genes as in Fig 2C (p53) or in the heatmap where each gene is presented in a separate row. This explanation is now included in the methods section.
Figure 2: the number of replicates in the case of 3-month-old wild types only 2. Please specifically denote it and comment why only 2 replicates are provided.
During the hippocampal RNA extractions, the RNA of one of the three 3m old mice had very low RIN scores, which could be a confounding factor for the short-RNA-seq. As this happened some months after the hippocampal extractions, we did not have any other 3 month mice of the same cohort used for the behavioral and IHC studies. Thus, we decided to include only two replicates in this condition. Since the results presented in the current study focus mainly on 6 month old mice, we expect the impact to be minimal. We include this note in the methods section.
4 . Considering that p53 and SRGs are significantly upregulated in 6months in the APP model, it would be great if (allowing that these samples are still available) the authors can include a staining for apoptotic markers, for example, Active Casp3 or similar. This will allow us to better gauge the gene expression changes presented by the authors especially regarding SRGs.
Unfortunately, we do not have these slides but in the revised version we will provide qPCR data for some of these markers.
5 . Under subheading: Hsf1 accelerates B2 RNA processing, 3rd paragraph when the authors comment on known hsf1 binding sites on SRG genes, please correct from: Increased Hsf1-binding was found.... "To the increased number of hsf1 binding sites were found", unless the authors would like to show increased Hsf1 binding by performing CHIP-seq for Hsf1 in the hippocampus at least at the 6-month time point between Wt and APP mice.
We have changed the text accordingly.
Reviewer #1 (Significance (Required)):
B2 RNAs, encoded from SINE B2 elements has been directly implicated in stress response by its inherent ability to bind RNA Pol II and suppress stress response genes (SRG) in homeostatic conditions. However, upon stimuli, B2 RNAs are cleaved and degraded, resulting in the release of RNA pol II and upregulation of SRGs. Previous work from the senior author identified PRC2 component EZH2 to be the B2 RNA processing factor, cleaving B2, and releasing POL2. SRGs are upregulated upon stress, for example in age-associated neuropathologies like Alzheimer's disease (AD). Considering that the hippocampus is a primary target of amyloid pathologies as well as since SRGs are suggested to be key for the function of a healthy hippocampus, the authors set to understand the role of B2 RNAs that are linked to SRG regulation in the mouse hippocampus with amyloid pathology. They use disease-relevant in vivo and in vitro models combined with unbiased RNA seq data analysis for this endeavor, which indicates the potential relevance of B2 RNAs in APP mediated neuronal pathologies in mice as well as identifies Hsf1 as the factor cleaving B2 RNAs in the hippocampus.
The work is interesting and identification of Hsf1 as the processing factor for B2 RNAs in the hippocampus is significant. I would like to credit the authors for their elegant in vivo experimental design in Figure 2.
REVIEWER 2
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
**Summary:**
This manuscript follows from previous work by the corresponding author showing that SINE-encoded B2 RNAs function as regulators of the expression of stress response genes (SRGs). Specifically, stimulus triggers the processing of repressive B2 RNAs that are bound at the SRGs, thereby activating SRG transcription. In this work, the authors investigate whether a similar mechanism might be controlling the expression of genes in models of amyloid beta neuropathology (i.e. mouse hippocampi from an amyloid precursor protein knock-in mouse model, and a cell culture model of amyloid beta toxicity). They performed RNA-seq in these models. Their data show a correlation between the progression of amyloid pathology, expression of genes thought to be regulated by B2 RNA, and the processing of B2 RNA. In addition, they show biochemical data supporting a role for Hsf1 in enhancing the processing of B2 RNA. Knockdown of Hsf1 also reduced B2 RNA processing and the expression of SRGs.
**Major comments:**
Major point 1. The reviewer asks: “In the RNA-seq data one cannot distinguish between Pol III transcribed B2 RNA and Pol II transcribed B2 RNA (typically embedded within introns and UTRs of mRNAs). The models they present, and the structures they show, clearly imply regulation by Pol III transcribed B2 RNA. However, there is no way to know that the short B2 RNAs they sequence aren't coming from degraded mRNAs. This needs to addressed. Minimally, in writing as a caveat of their model. Ideally, it would be addressed experimentally.”
That’s a very interesting point, as it implies that the regulatory role of B2 RNAs may extend from PolIII transcribed B2 RNAs into B2 RNAs embedded into mRNAs (likely nascent ones) that may be also under the same endogenous ribozyme activity of this sequence, suppress PolII and are processed in response to stimuli. The RNA RIN values of our samples were pretty high except one 3m old mouse sample which was for this reason excluded from further analysis. Moreover, during the library construction shorter and longer RNAs have been separated. Thus, any generation of B2 RNA fragment that may have originated from mRNA should be biologically but not technically related and must have happened in the cell before our RNA extraction. To address this point, we now provide a new supplementary figure (Suppl. Figure 8), where we have separated the B2 elements against which we map the RNA fragments into two categories, those that fall within exonic/genic regions and those outside of these regions. Although B2 RNAs are produced by multiple copies in the genome, each copy does harbor multiple SNPs, insertions and deletions, which means that each B2 RNA fragment is mapped to a specific set of B2 elements and not to all of them. In other words, despite multiple mapping a level of spatial specificity is maintained. If the B2 RNAs we map were coming exclusively from either only Pol III B2 elements or mRNA embedded B2 elements, we would expect at least some difference in the distribution of fragments between B2 elements of these two categories, as the second one overlaps with mRNAs. As shown in the new supplementary figure 8, the fact that distribution models are very similar between the two categories indeed supports the hypothesis that both types of B2 elements may contribute to B2 RNA processing. Most importantly, the profile of B2 RNAs in genic regions shows that B2 RNA processing is not random but follows the same processing rules as B2 RNAs from Pol III promoters. Given the limitations posed by the repetitive nature of B2 RNAs, it remains difficult though to provide an exact number regarding the portion of B2 RNA fragments produced by each category and this is clearly noted in our revised discussion part. However, even the indication that B2 RNAs embedded in mRNAs may also play an important role in our model provides a new perspective that should be investigated further in future studies.
Major point 2. The reviewer asks: “The direct regulation of SRGs by B2 RNA was not shown in their model systems for amyloid beta neuropathology. Rather, the authors' used the genes identified in their prior studies as B2 RNA-regulated, which I believe were in the NIH3T3 cell line. Given that transcription is highly cell-type specific, these genes might not be regulated by B2 RNA in mouse hippocampi or their cell culture model, despite the correlations shown. This needs to be addressed. Ideally, a targeted approach to show that transcription of even a couple genes in their system is indeed regulated by B2 RNA would provide stronger support for their conclusions.”
We agree with the reviewer and we now provide a new figure (Fig.6D-F) with the targeted approach that this reviewer proposed. In particular, we have tested whether fragmentation of full length B2 RNAs is in connection with activation of target genes also in our biological system (HT22 cells) as it did in NIH/3T3 cells in our Cell paper. We now show in new Figure 6 that this is indeed the case.
Major point 3. The reviewer proposes a number of additional information that needs to be provided: “The following bioinformatics analyses would strengthen their conclusions. This should be straightforward to do because it involves data they already have, and perhaps analyses they have already have performed.”
a. Regarding the plot in Figure 3A (lower panel). The same plot should be shown for the 3m old and the 12m old APP mice (i.e. not just the 6m data). This would show the specificity of processing B2 RNA and that it indeed correlates with disease progression.
We now provide this plot as new supplementary figure (Suppl. Figure 3). It shows that increased B2 RNA processing coincides only with the active neurodegeneration phase at 6 months and not the terminal stage.
b. Regarding the plots of B2 RNA processing rate. This value could increase either due to more short RNAs or less full length RNA. Which is it for the 3m, 6m, and 12m APP mice? Showing the short and long B2 RNAs as boxplots (as opposed to only the processing rate) would address this and also provide additional insight into the regulation involved. The same applies to the data in Figure 6. (As an aside... do the authors mean processing ratio as opposed to rate? I'm not clear where the time component is coming into play to call this a rate.)
Old Figure 6 is now Figure 7. We now provide all these figures that show that increase in processing ratio at 6 months is mainly due to increase in the processed fragments and not a decrease in full length B2 RNAs. For APP mice these are new Figures 3E and F, and for HT22 cells , these are new Supp. Figures 6B and C.
c. The random genes in Figures 2E and 6E are plotted as heat maps, but statistical significance is hard to see. What do boxplots of the random genes look like, and is the significant difference between 6m old APP and 6m old WT then lost?
Old Figure 2E is now new Suppl. Figure 1C, while old Figure 6E is now new Suppl. Figure 7C. We now provide these boxplots in new supplementary figures 1B and 7B.
Major point 4. The reviewer comments: “ It is interesting that B2 RNA self-processing is enhanced by both Ezh2 and also Hsf1. It would strengthen the data to perform a control with a protein prepared more similarly to the Hsf1 (rather than PNK) to confirm that the enhanced B2 RNA breakdown is indeed attributable to Hsf1 and not a contaminant in the protein prep. Similarly, the authors should provide information on which RNA was added as the negative control for Hsf1-stimulated breakdown (i.e. the ~80 nt RNA).”
This point is also discussed in Reviewer 1 point 7. The ribozyme endogenous activity of B2 RNA has been shown already in two previous studies that performed incubations with control RNAs and proteins. We are currently preparing and will provide these additional incubations as anew supplementary figure in the revised manuscript.
**Minor comments:**
1 . Regarding the GO analyses in Figure 1 (panels B, C, and D). I wasn't clear whether the authors are showing all statistically enriched terms, or only those relevant to neuronal processes and learning. I recommend showing a supplemental table with all terms that have an adjusted p value below a specified cut-off (e.g. 0.05).
The statistical threshold used was an EASE score of 0.05 and all presented terms were above this threshold. In the initial manuscript we filtered only the top 5 terms in tissue enrichment and the top 10 terms for GO Biol process and Cell Compartment that had passed the threshold. We now provide all the terms that passed the threshold as a new Supplementary Table 2, including gene counts, exact gene numbers and related statistics.
2 . The authors show several figures that are not new data (2B, 4A, 4B, Suppl. Fig 1 and 2). I think it would be more clear if these data were summarized and referenced in the results, rather than shown.
Old Suppl. Fig1 and 2 that were results of previous studies or web resources directly available (such as Human Protein Atlas) have been now removed and they are now just referenced in the text. Old Figures 4A and 4B have been removed from the main figures but may be helpful to the readers if they are still available in the Supplement (currently as Suppl. Figure 4A and B), as not all users are familiar with the RNA-seq browsing tools of Allen Brain Atlas resources. Regarding figure 2B that contains data from our previous study on this exact cohort of mice: If the reviewer and the editor agree we recommend that it remains in the main figure (with the appropriate image credit citations), as it provides in an efficient way the clear connection between amyloid load and our results at the molecular level, and, most importantly, it clearly draws a line in amyloid pathology progression between 3m old and 6m old, that agrees with our findings in the RNA-seq data of these mice.
3 . In Figure 3A the schematic shows that B2 is 155 nt, the plots in Figures 3A,B,C show B2 RNA is 120 nt, and Figure 5 shows the RNA is 188 nt. Can the authors please clarify these differences?
The full length of B2 consensus sequence is 188nt and this is the one we use for the in vitro experiments. However, the structure of the B2 RNA has been resolved only for the first 155nt by the Kugel lab, and this is the only publicly available structure that we can reference in our figures. For the mapping of 5’ends of short fragments in Fig.3A we have used the same range tested in our Cell paper to maintain consistency of the results. The reason why this 120nt threshold was selected in the Cell paper was to exclude artifacts from short RNAs mapping partially in our metagene as well as downstream of those B2 elements that are shorter from the consensus sequence. We now explain in methods section these differences.
4 . In the Methods section, the sequence of the g block template didn't contain the T7 promoter sequence that was used as the forward primer for PCR amplification?
We have now included this sequence in lower case.
5 . In Figure 6B, why were Hsf1 levels not decreased in the R treated cells after treatment with the LNA?
Old Figure 6B is now new Figure 7B. Please see response to Reviewer 1, major point 12.
Reviewer #2 (Significance (Required)):
Finally, this reviewer generally remarks that “The models presented for the regulation of stress response genes (SRGs) in amyloid beta neuropathologies are compelling. As are the correlations they found between the progression of amyloid pathology, expression of genes thought to be regulated by B2 RNA, and the processing of B2 RNA. This is a unique direction of research for brain disease and represents an interesting conceptual advance. Most prior studies in this area use common model cell lines, and this lab seems well-positioned to unravel the proposed molecular mechanisms in neuronal systems.”
We appreciate the encouraging comments made by this reviewer.
REVIEWER 3
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
This manuscript describes a regulatory mechanism involving Hsf1 and B2 RNAs in the control of stress response genes (SRGs) during amyloid induced toxicity. In particular Hsf1, upregulated in 6m old APP mice and in HT22 cells treated with beta amyloid peptides, is shown to stimulate the B2 RNA destabilization leading to SRGs activation. While in healthy cells this upregulation can be reverted once the stimulus is removed, the pathological condition fuels the circuitry leading to p53 upregulation and neuronal cell death. The authors previously described the same mechanism acting during cellular heath shock response but in this case the protein identified as trigger of B2 RNA destabilization and SRGs activation was EZH2 (Zovoilis et al, 2016).
This reviewer generally remarks that “Indeed, the first part of the manuscript describes additional analyses of the previous data that prompts further investigation on the potential role of B2 RNA in AD condition. Nevertheless, it is not clear how the prior findings obtained in not biologically related cellular models might be used to obtain helpful indication of B2 RNA neuronal activity.”
We thank the reviewer for this comment. Indeed, the current study’s main aim was to expand the findings of our previous work on the role of B2 RNA in cellular response to thermal stress in NIH/3T3 cells to other types of cellular response to stress, in our case to amyloid toxicity and the resulting amyloid pathology in neural cells. Response to thermal stress (Heat Shock) has been used for years as a basic study model for cellular response to stress. Proteins and gene pathways initially identified in heat shock have been subsequently shown to play identical pro-survival roles in other biological systems and there are studies showing the role of Hsf1, heat shock related proteins and cell stress response pathways in neural cells and the mammalian brain (we will provide these references in the revised version). For example, pathways such as the MAPK pathway and early response genes, that constitute the basis of response to heat shock, have been shown in studies by us and others to be activated and play a critical role in hippocampal function. Thus, examining the role of B2 RNA in the context of neural response to stress constituted a natural continuation of our previous study in NIH/3T3 cells. The fact that the list of B2 RNA regulated SRGs was found to be highly enriched in neuronal tissue terms and cellular compartments related to neuronal functions plainly confirms the close relationship among cellular response pathways in the two biological systems. Due to these facts we were compelled to investigate in more detail our previous findings also in a neural cell model. However, as discussed in point 2 of Reviewer 2, the initial manuscript did not confirm the direct control of B2 RNA on expression of target genes also in our cellular model. This information is now part of the new figure 6 and we thank both reviewers for bringing this to our attention.
The reviewer also remarks that “The research fields of non coding RNAs and neurodegeneration are attractive and challenging and, in my opinion, the molecular circuitry involving B2 RNAs might add important insights for understanding beta amyloid toxicity and neuronal death; however, the data provided are not in the shape making the manuscript suitable for publication: some controls are missing, the way the experiments are presented is not easy to follow and more importantly the authors does not provide any data (tables or lists) of the NGS experiments and the study lacks validation of them. Therefore, in my opinion the manuscript needs a profound revision before to be considered for publication in Review Commons.”
Based on this reviewer’s and the other reviewers’ suggestions we now provide additional controls, detailed tables and gene lists, and qPCR validation of these results. We have also substantially revised the text in the first section of the results and beginning of the discussion, to make our rational for testing B2-SRGs more clear and easier to follow.
**major concerns:**
Major point 1. The reviewer asks: “The first paragraph of the Results is entirely dedicated to re-analyze the data previously published by the same group (Zovoilis et al., 2016). However, this is not adequately explained. In line with this, the table 1 is not required since the data are already provided by Zovoilis et al., 2016, unless the authors handled the data using additional new criteria that have to be explained.”
We now explain our rational for using this data in more detail in the text. Please see also response to the general comment of this reviewer and response to the next point.
In the Zovoilis et al (2016) study, the data presented did not include the list of regulated genes in a direct way but as part of the annotation of the B2 CHART peaks. This may pose difficulty to non-experts to extract the gene list from that data and we thought to include them as separate gene list here so that readers can directly use it for their analysis. Nevertheless, if the reviewer or the editor think that the list is redundant, we can surely omit it.
In addition, the reviewer comments: “Moreover, Zovoilis and colleagues (2016) focused on SRGs regulated upon heat shock and using NIH/3T3 and HeLa cell lines, therefore, it is difficult to me understand how, searching for "cellular function connected with B2 RNA regulated SRGs", the list resulted enriched of neuronal tissue terms or cellular compartments related to neuronal functions. Please clarify this point since the following analyses are based on these findings.”
Neural pathologies, such as amyloid pathology in brain, are often connected with cellular stress due to proteotoxicity. The ability of neural cells to respond to proteotoxicity challenges is connected with various molecular mechanisms, including stress related proteins that were firstly described in the context of heat shock. Thus, both contexts (heat shock and amyloid toxicity) refer to cellular response to stress, which explains why genes identified to be regulated during stress response in NIH/3T3 cells constitute part of the basic stress response toolbox that neural cells have also been described to possess. We have now modified the text accordingly to make our rational more clear.
Major point 2. The reviewer comments: “In Figure 1F there is no arrow indicating that some of the SRGs regulate directly miR-34 as stated in the main text. Moreover, it is more appropriate to replace SRGs with learning‐associated genes both in the figure and in text (2nd paragraph of the results) since Zovoilis and colleagues focused on them. Finally, they did not show in their manuscript the rescue of p53 expression mediated by mir-34; indeed, for miR-34-p53 regulatory axis Zovoilis and colleagues referred to Peleg et al, 2010 and Yamakuchi & Lowenstein, 2009. Please fix all these concerns.”
We have restructured the figure as suggested by the reviewer and made clear the distinction between learning genes and B2 RNA regulated SRGs (B2-SRGs) from the two different studies. In connection with point 1 of Reviewer 1, we believe that new Figure 1E, that includes the exact number of B2-SRGs that are learning associated, will represent more efficiently and accurately the data. We have also corrected in the text the citation regarding miR-34c and p53 in both the introduction and first section of the results (last paragraph).
-The Fig.1A and Fig.1F are wrongly indicated at the end of the sentence "....levels of these genes are normally downregulated in 6m and 12m old mice compared to 3m old mice (p=0.02 and p=0.04, respectively)"; please correct this point.
The error has been corrected.
Major point 3. The reviewer comments regarding Figure 2:
a) Since three mice for each condition have been used for the RNA seq analyses, please provide a blot with the Principal Component Analysis (PCA).
Please see also response to minor point 3 of Reviewer 1. We provide the PCA plots for WT and APP mice in the new Supplementary Figure 9 and we also provide a comparison of the six month old mice with the HT cell samples as well as a correlation matrix for 6 month old mice in the same figure.
b) Fig 2F comes first of Fig 2E in the text, however, I suggest to move this latter to supplementary material.
Old figure 2E has now been moved to supplementary material as new Supplementary Figure 2C and we also provide in a boxplot the exact gene expression levels as new Supplementary Figure 2B.
c) In general, this study lacks validation of the RNA-seq results. Western blot and/or qRTR-PCR to verify the variation of p53 and of some selected SRGs have to be provided.
In the current revised version we already provide qPCRs for p53 and Hsf1 in APP mice and we will include additional genes in the final version.
d) It is also not clear how the authors defined SRGs in the hippocampus: do they correspond to learning‐associated genes described by in Zovoilis et al, 2011 or to B2 RNA H/S regulated genes by Zovoilis et al, 2016?
The way we presented B2 RNA SRGs in the results with regard to learning associated genes was indeed unclear. We now present the distinction between the two gene categories and their relationship as a new Fig.1E panel and we also provide detailed gene lists of common genes and the exact numbers (please see also response to Review 1, major point 1).
-APP 12 month old mice show the sever phenotype of the terminal AD-like pathology, however this does not correlate with significant SRGs and B2 processing increase. Can the author make a comment on this?
That’s a very important point and we thank the reviewer for raising this point. We now comment on this in the discussion part explaining how our findings are characteristic of the initial active neurodegeneration phase of amyloid pathology rather than more terminal stages.
Major point 4: The reviewer comments regarding Figure 5:
a) a gel with no-protein control for the time course of panel B was cited in the text but missing among the panels. Moreover, the time course shown in the graph in 5C does not correspond to the one in 5B.
Indeed, the no-protein control time line should refer only to panel C and not to B, we have now corrected the text. Nevertheless, we now present in the new Supplementary Fig. 5 the gels, based on which the graph in panel C was calculated, including also the gel with no protein timeline. The time course shown in the initial 5C had been mislabeled. It has now been corrected. We apologize for this and we thank the reviewer for bringing this to our attention.
b) 5G indicates that four samples for each condition have been analysed by RNA-seq, since they do not seem to be homogeneous please provide a PCA analysis together with the validation by qRT-PCR of a selected group of deregulated genes.
Old Figure 5G is new Figure 6C. PCA analysis for these samples is now provided in Supplementary Figure 9 and qPCR validation of a number of these genes is provided in new Fig. 7E.
Moreover, it is not clear whether all the genes shown in the heatmap or a number of them, as stated in the text, were found upregulated in 6m old APP mice. Please clarify this point and modify the figure and the text accordingly. A Venn diagram showing the overlap between genes upregulated in 42vsR treatment and those upregulated in 6m old APP mice might help the comprehension of the experiment.
Please see response to Reviewer 1, point 9. We now provide as new supplementary tables the exact overlapping lists and mention these numbers in the text.
Major point 5: The reviewer comments regarding Figure 6 (now labeled as Fig.7):
a) The evaluation of the levels of Hsf1 mRNA and protein upon LNA transfection is missing for both R and 42 treated HT22 cells. From TPM in panel B, Hsf1 downregulation seems to have been more effective in 42 than in R condition. This would mess up the interpretation of the data.
We now provide qPCR data for Hsf1 gene expression levels which confirm the ones from the RNAseq. The reason why Hsf1 downregulation seems not to affect the R condition is discussed in our response to Reviewer 1, major point 12, and the respective explanation is provided in the revised text.
b) Again, in this case any validation of the RNA seq data is provided (any B2 regulated SRGs).
Now, we provide qPCR data for these genes in Fig.7B and new Fig.7E
c) Panels E and F should be swapped or panel E moved to supplementary material.
Panel E is now moved to supplementary material as new Suppl. Figure 7C.
Major point 6. The reviewer comments: “In a previous paper the authors discovered B2 RNAs as a class of transcripts bound to EZH2 and this interaction leads to B2 RNA destabilization in heath shock (H/S) condition. The authors also conclude that the genes controlled by B2 RNAs may not overlap with the ones controlled by Hsf1 during H/S. The author should make a comment on this explaining why during H/S B2 RNAs work independently from Hsf1 and on different target SRGs while, during beta amyloid stress ,the two act together on the same SRGs. Moreover, as shown for EZH2, Hsf1-RIP experiment should be performed in order to confirm the direct involvement of Hsf1 in the SRGs-B2 destabilization.”
In the last two paragraphs of our discussion we indicate that B2 RNA regulation is a new process implicated in the response to stress in amyloid pathology but certainly not the only one. We have revised the text in this part accordingly in the revised version to prevent any confusion. We are currently performing a series of RIP-seq experiments with various antibodies. As, to our knowledge, there is no prior published study performing RIP-seq or CLIP-seq for any tissue using Hsf1 antibodies, the success of this experiment is not guaranteed and depends on the existence of appropriate antibodies.
Major point 7. The reviewer comments: “There is any table listing the results of the RNA seq experiments performed in this paper: control vs APP 3-6-12 m old mice and in R vs 42 treated HT22 cells in presence or absence of LNA against Hsf1. Please provide these data.”
We now provide these lists as new supplementary tables. Please see response to major points 1 and 9 of reviewer 1.
Major point 8. The reviewer comments: “In the discussion the authors claim that healthy cells are able to restore the expression of Hsf1, SRGs and B2 RNA upon removal of the stress. Since there are evidence for the rescue of SRGs and B2 RNA expression post H/S, no data are available for Hsf1, SRGs and B2 RNA upon the removal of 1-42 beta amyloid peptide. This might be a nice information to add to the manuscript.”
This would indeed substantiate further our results in our HT22 cell model. We have now performed this experiment, in which HT-22 cells were removed from the amyloid 42 (and the respective R peptide control) and left to recover for 12 hours before estimating through RT-qPCR the Hsf1 levels ( see graph below, REC corresponds to recovered HT-22 cells). Hsf1 levels in 42-REC have returned to the same levels as in R, p We currently perform the RT-qPCRs of these samples also for B2-SRGs and will include them in the final version as a supplementary figure.
**Minor criticisms:**
-In the introduction the reference Yamakuchi M and Lowenstein CJ, (2009) MiR‐34, SIRT1 and p53: the feedback loop. Cell Cycle, should be added in the sentence: "In contrast, hippocampi of mouse models of amyloid pathology and post- mortem brains of human patients of AD.....and neural death (Zovoilis et al., 2011)."
We have now changed the text at that point accordingly and also updated the legend of Figure 1F that also refers to this same study.
-Authors refer to Hernandez et al., 2020 to state that B2 self cleavage is stimulated by some proteins however, Hernandez and colleagues studied only the effect of EZH2 protein. Please rephrase the sentence accordingly.
Text has been modified accordingly.
-Indicate a reference for the sentence: "......Ezh2, was reported as being responsible for the B2 RNA accelerated destabilization and processing during response to stress."
The respective citation was added.
-The format of many references is not consistent and has to be revised.
We have switched to the Vancouver style. Some references in the legend and methods sections are referred independently from EndNote in case these text sections have to be moved to supplement in the final version in order to not create inconsistencies with endnote.
Reviewer #3 (Significance (Required)):
Finally, this reviewer generally remarks that “The research fields of non coding RNAs and neurodegeneration are attractive and challenging and, in my opinion, the molecular circuitry involving B2 RNAs might add important insights for understanding beta amyloid toxicity and neuronal death.
However, this manuscript does not really add technical advances since the authors employed experimental approaches and bioinformatic analyses previously published by Zovoilis and colleagues in 2011 and 2016.”
Our aim in the current manuscript was not to introduce a new method or experimental approach but rather to study the mechanisms behind B2 RNA regulation of gene expression in neural cells and particularly in amyloid pathology. Nevertheless, the current study constitutes the first reported short-RNA seq in this tissue and offers for the first time the ability to study B2 RNA processing in this tissue which is not possible with standard small and long RNA-seq.
The reported findings might of interest of an audience of experts in non coding RNAs and neurodegeneration. The area of my expertise almost regards the biology of non coding RNAs from biogenesis to function manly focusing on neuronal and muscular systems both in physiological and pathological conditions.
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Referee #3
Evidence, reproducibility and clarity
This manuscript describes a regulatory mechanism involving Hsf1 and B2 RNAs in the control of stress response genes (SRGs) during amyloid induced toxicity. In particular Hsf1, upregulated in 6m old APP mice and in HT22 cells treated with beta amyloid peptides, is shown to stimulate the B2 RNA destabilization leading to SRGs activation. While in healthy cells this upregulation can be reverted once the stimulus is removed, the pathological condition fuels the circuitry leading to p53 upregulation and neuronal cell death. The authors previously described the same mechanism acting during cellular heath shock response but in this case the protein identified as trigger of B2 RNA destabilization and SRGs activation was EZH2 (Zovoilis et al, 2016). Indeed, the first part of the manuscript describes additional analyses of the previous data that prompts further investigation on the potential role of B2 RNA in AD condition. Nevertheless, it is not clear how the prior findings obtained in not biologically related cellular models might be used to obtain helpful indication of B2 RNA neuronal activity. The research fields of non coding RNAs and neurodegeneration are attractive and challenging and, in my opinion, the molecular circuitry involving B2 RNAs might add important insights for understanding beta amyloid toxicity and neuronal death; however, the data provided are not in the shape making the manuscript suitable for publication: some controls are missing, the way the experiments are presented is not easy to follow and more importantly the authors does not provide any data (tables or lists) of the NGS experiments and the study lacks validation of them. Therefore, in my opinion the manuscript needs a profound revision before to be considered for publication in Review Commons.
major concerns:
-The first paragraph of the Results is entirely dedicated to re-analyze the data previously published by the same group (Zovoilis et al., 2016). However, this is not adequately explained. In line with this, the table 1 is not required since the data are already provided by Zovoilis et al., 2016, unless the authors handled the data using additional new criteria that have to be explained. Moreover, Zovoilis and colleagues (2016) focused on SRGs regulated upon heat shock and using NIH/3T3 and HeLa cell lines, therefore, it is difficult to me understand how, searching for "cellular function connected with B2 RNA regulated SRGs", the list resulted enriched of neuronal tissue terms or cellular compartments related to neuronal functions. Please clarify this point since the following analyses are based on these findings.
-In Figure 1F there is no arrow indicating that some of the SRGs regulate directly miR-34 as stated in the main text. Moreover, it is more appropriate to replace SRGs with learning‐associated genes both in the figure and in text (2nd paragraph of the results) since Zovoilis and colleagues focused on them. Finally, they did not show in their manuscript the rescue of p53 expression mediated by mir-34; indeed, for miR-34-p53 regulatory axis Zovoilis and colleagues referred to Peleg et al, 2010 and Yamakuchi & Lowenstein, 2009. Please fix all these concerns.
-The Fig.1A and Fig.1F are wrongly indicated at the end of the sentence "....levels of these genes are normally downregulated in 6m and 12m old mice compared to 3m old mice (p=0.02 and p=0.04, respectively)"; please correct this point.
-Figure 2:
a) Since three mice for each condition have been used for the RNA seq analyses, please provide a blot with the Principal Component Analysis (PCA).
b) Fig 2F comes first of Fig 2E in the text, however, I suggest to move this latter to supplementary material.
c) In general, this study lacks validation of the RNA-seq results. Western blot and/or qRTR-PCR to verify the variation of p53 and of some selected SRGs have to be provided.
d) It is also not clear how the authors defined SRGs in the hippocampus: do they correspond to learning‐associated genes described by in Zovoilis et al, 2011 or to B2 RNA H/S regulated genes by Zovoilis et al, 2016?
-APP 12 month old mice show the sever phenotype of the terminal AD-like pathology, however this does not correlate with significant SRGs and B2 processing increase. Can the author make a comment on this?
-Figure 5:
a) a gel with no-protein control for the time course of panel B was cited in the text but missing among the panels. Moreover, the time course shown in the graph in 5C does not correspond to the one in 5B.
b) 5G indicates that four samples for each condition have been analysed by RNA-seq, since they do not seem to be homogeneous please provide a PCA analysis together with the validation by qRT-PCR of a selected group of deregulated genes. Moreover, it is not clear whether all the genes shown in the heatmap or a number of them, as stated in the text, were found upregulated in 6m old APP mice. Please clarify this point and modify the figure and the text accordingly. A Venn diagram showing the overlap between genes upregulated in 42vsR treatment and those upregulated in 6m old APP mice might help the comprehension of the experiment.
-Figure 6:
a) The evaluation of the levels of Hsf1 mRNA and protein upon LNA transfection is missing for both R and 42 treated HT22 cells. From TPM in panel B, Hsf1 downregulation seems to have been more effective in 42 than in R condition. This would mess up the interpretation of the data.
b) Again, in this case any validation of the RNA seq data is provided (any B2 regulated SRGs).
c) Panels E and F should be swapped or panel E moved to supplementary material.
-In a previous paper the authors discovered B2 RNAs as a class of transcripts bound to EZH2 and this interaction leads to B2 RNA destabilization in heath shock (H/S) condition. The authors also conclude that the genes controlled by B2 RNAs may not overlap with the ones controlled by Hsf1 during H/S. The author should make a comment on this explaining why during H/S B2 RNAs work independently from Hsf1 and on different target SRGs while, during beta amyloid stress ,the two act together on the same SRGs. Moreover, as shown for EZH2, Hsf1-RIP experiment should be performed in order to confirm the direct involvement of Hsf1 in the SRGs-B2 destabilization.
-There is any table listing the results of the RNA seq experiments performed in this paper: control vs APP 3-6-12 m old mice and in R vs 42 treated HT22 cells in presence or absence of LNA against Hsf1. Please provide these data.
-In the discussion the authors claim that healthy cells are able to restore the expression of Hsf1, SRGs and B2 RNA upon removal of the stress. Since there are evidence for the rescue of SRGs and B2 RNA expression post H/S, no data are available for Hsf1, SRGs and B2 RNA upon the removal of 1-42 beta amyloid peptide. This might be a nice information to add to the manuscript.
Minor criticisms:
-In the introduction the reference Yamakuchi M and Lowenstein CJ, (2009) MiR‐34, SIRT1 and p53: the feedback loop. Cell Cycle, should be added in the sentence: "In contrast, hippocampi of mouse models of amyloid pathology and post- mortem brains of human patients of AD.....and neural death (Zovoilis et al., 2011)."
-Authors refer to Hernandez et al., 2020 to state that B2 self cleavage is stimulated by some proteins however, Hernandez and colleagues studied only the effect of EZH2 protein. Please rephrase the sentence accordingly.
-Indicate a reference for the sentence: "......Ezh2, was reported as being responsible for the B2 RNA accelerated destabilization and processing during response to stress."
-The format of many references is not consistent and has to be revised.
Significance
The research fields of non coding RNAs and neurodegeneration are attractive and challenging and, in my opinion, the molecular circuitry involving B2 RNAs might add important insights for understanding beta amyloid toxicity and neuronal death. However, this manuscript does not really add technical advances since the authors employed experimental approaches and bioinformatic analyses previously published by Zovoilis and colleagues in 2011 and 2016.
The reported findings might of interest of an audience of experts in non coding RNAs and neurodegeneration.
The area of my expertise almost regards the biology of non coding RNAs from biogenesis to function manly focusing on neuronal and muscular systems both in physiological and pathological conditions.
-
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Referee #2
Evidence, reproducibility and clarity
Summary:
This manuscript follows from previous work by the corresponding author showing that SINE-encoded B2 RNAs function as regulators of the expression of stress response genes (SRGs). Specifically, stimulus triggers the processing of repressive B2 RNAs that are bound at the SRGs, thereby activating SRG transcription. In this work, the authors investigate whether a similar mechanism might be controlling the expression of genes in models of amyloid beta neuropathology (i.e. mouse hippocampi from an amyloid precursor protein knock-in mouse model, and a cell culture model of amyloid beta toxicity). They performed RNA-seq in these models. Their data show a correlation between the progression of amyloid pathology, expression of genes thought to be regulated by B2 RNA, and the processing of B2 RNA. In addition, they show biochemical data supporting a role for Hsf1 in enhancing the processing of B2 RNA. Knockdown of Hsf1 also reduced B2 RNA processing and the expression of SRGs.
Major comments:
1 . In the RNA-seq data one cannot distinguish between Pol III transcribed B2 RNA and Pol II transcribed B2 RNA (typically embedded within introns and UTRs of mRNAs). The models they present, and the structures they show, clearly imply regulation by Pol III transcribed B2 RNA. However, there is no way to know that the short B2 RNAs they sequence aren't coming from degraded mRNAs. This needs to addressed. Minimally, in writing as a caveat of their model. Ideally, it would be addressed experimentally.
2 . The direct regulation of SRGs by B2 RNA was not shown in their model systems for amyloid beta neuropathology. Rather, the authors' used the genes identified in their prior studies as B2 RNA-regulated, which I believe were in the NIH3T3 cell line. Given that transcription is highly cell-type specific, these genes might not be regulated by B2 RNA in mouse hippocampi or their cell culture model, despite the correlations shown. This needs to be addressed. Ideally, a targeted approach to show that transcription of even a couple genes in their system is indeed regulated by B2 RNA would provide stronger support for their conclusions.
3 . The following bioinformatics analyses would strengthen their conclusions. This should be straightforward to do because it involves data they already have, and perhaps analyses they have already have performed.
a. Regarding the plot in Figure 3A (lower panel). The same plot should be shown for the 3m old and the 12m old APP mice (i.e. not just the 6m data). This would show the specificity of processing B2 RNA and that it indeed correlates with disease progression.
b. Regarding the plots of B2 RNA processing rate. This value could increase either due to more short RNAs or less full length RNA. Which is it for the 3m, 6m, and 12m APP mice? Showing the short and long B2 RNAs as boxplots (as opposed to only the processing rate) would address this and also provide additional insight into the regulation involved. The same applies to the data in Figure 6. (As an aside... do the authors mean processing ratio as opposed to rate? I'm not clear where the time component is coming into play to call this a rate.)
c. The random genes in Figures 2E and 6E are plotted as heat maps, but statistical significance is hard to see. What do boxplots of the random genes look like, and is the significant difference between 6m old APP and 6m old WT then lost?
4 . It is interesting that B2 RNA self-processing is enhanced by both Ezh2 and also Hsf1. It would strengthen the data to perform a control with a protein prepared more similarly to the Hsf1 (rather than PNK) to confirm that the enhanced B2 RNA breakdown is indeed attributable to Hsf1 and not a contaminant in the protein prep. Similarly, the authors should provide information on which RNA was added as the negative control for Hsf1-stimulated breakdown (i.e. the ~80 nt RNA).
Minor comments:
1 . Regarding the GO analyses in Figure 1 (panels B, C, and D). I wasn't clear whether the authors are showing all statistically enriched terms, or only those relevant to neuronal processes and learning. I recommend showing a supplemental table with all terms that have an adjusted p value below a specified cut-off (e.g. 0.05).
2 . The authors show several figures that are not new data (2B, 4A, 4B, Suppl. Fig 1 and 2). I think it would be more clear if these data were summarized and referenced in the results, rather than shown.
3 . In Figure 3A the schematic shows that B2 is 155 nt, the plots in Figures 3A,B,C show B2 RNA is 120 nt, and Figure 5 shows the RNA is 188 nt. Can the authors please clarify these differences?
4 . In the Methods section, the sequence of the g block template didn't contain the T7 promoter sequence that was used as the forward primer for PCR amplification?
5 . In Figure 6B, why were Hsf1 levels not decreased in the R treated cells after treatment with the LNA?
Significance
The models presented for the regulation of stress response genes (SRGs) in amyloid beta neuropathologies are compelling. As are the correlations they found between the progression of amyloid pathology, expression of genes thought to be regulated by B2 RNA, and the processing of B2 RNA. This is a unique direction of research for brain disease and represents an interesting conceptual advance. Most prior studies in this area use common model cell lines, and this lab seems well-positioned to unravel the proposed molecular mechanisms in neuronal systems.
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Referee #1
Evidence, reproducibility and clarity
B2 RNAs, encoded from SINE B2 elements has been directly implicated in stress response by its inherent ability to bind RNA Pol II and suppress stress response genes (SRG) in homeostatic conditions. However, upon stimuli, B2 RNAs are cleaved and degraded, resulting in the release of RNA pol II and upregulation of SRGs. Previous work from the senior author identified PRC2 component EZH2 to be the B2 RNA processing factor, cleaving B2, and releasing POL2. SRGs are upregulated upon stress, for example in age-associated neuropathologies like Alzheimer's disease (AD). Considering that the hippocampus is a primary target of amyloid pathologies as well as since SRGs are suggested to be key for the function of a healthy hippocampus, the authors set to understand the role of B2 RNAs that are linked to SRG regulation in the mouse hippocampus with amyloid pathology. They use disease-relevant in vivo and in vitro models combined with unbiased RNA seq data analysis for this endeavor, which indicates the potential relevance of B2 RNAs in APP mediated neuronal pathologies in mice as well as identifies Hsf1 as the factor cleaving B2 RNAs in the hippocampus. The work is interesting and identification of Hsf1 as the processing factor for B2 RNAs in the hippocampus is significant. I would like to credit the authors for their elegant in vivo experimental design in Figure 2. However, I find some of the conclusions to be overstated and I would like to bring the following concerns I have to your attention:
Major comments:
1 . In figure 1, the authors indicate a strong connection between B2 RNA regulated SRGs and learning and memory. In figure 2, they identify the SRGs in the hippocampus, please provide a direct comparison of learning and memory associated SRGs and the SRGs they identify in figure 2 that are significantly upregulated in APP mice in 6 months.
2 . To better understand the data in the context of hippocampal function, please include functional annotation of SRGs they identified in Figure 2F as they do it in Figure 1 (desirably for each time point, at least for 6M). How many of the SRGs they identify in Figure 1 are part of Figure 2F? Please include functional annotation of significantly upregulated B2 regulated SRGs in Fig2 and compare them with that of Figure 1.
3 . In figure 3, the authors report that the B2 processing rates are high at the 6M time point at in hippocampi of the APP mice. Please include the levels of unprocessed and processed B2 RNAs in these samples along with this figure, without which it is difficult to gauge the significance of its correlation with SRGs in Figure 2.
4 . What is the % of B2 regulated SRGs that are hsf1 bound in Figure 4C? What is there dynamics in the wild type and APP hippocampi?
5 . What is the distribution of Hsf1 binding sites on (a) non-B2 regulated SRGs and (b) non-SRG genes in hippocampi?
6 . In Figure 4D, the 3months old Wt HSF1 levels are high, yet B2 processing (Figure 3E) is low. Please comment.
7 . While the authors show in vitro cleavage of B2 RNA by Hsf1, the experiment lacks controls to be conclusive. At least, please include a similar size protein as HSF1 with no-known RNA binding activity and a similar size protein with RNA binding activity as controls in 5A. Please justify the use of PNK as the control protein. Please include the use domain-based deletions of Hsf1 to map the region of HSF1 that is binding and potentially cleaving the B2 RNA. Please include an RNA of similar size and Antisense-B2 RNA to show the specificity of the Hsf1 based cleavage of B2 RNA. Without these controls, the conclusions in Figure 5 cannot be substantiated.
8 . The authors should show that the incubated APP peptides are taken up by the cells (experiments in Figure 5F and Figure 6).
9 . Please provide the list, functional annotation, and % of the SRGs upregulated upon incubation with APP in HT22 cells in comparison to 6month old APP mice. Comment on learning-related Genes.
10 . The authors should show the efficient downregulation of Hsf1 (protein) upon anti-Hsf1 LNA transfection.
11 . Please present the total B2 RNA levels for conditions in Figure 6C.
12 . Hsf1 levels are not significantly downregulated in Control cells which were inoculated with the reverse APP peptide. Please comment.
13 . Please compare and contrast the % of genes, the overlap, and the functional distinctions in 6F to that of 5G and Figure1. What are the genes that are common between Figure1, and that are specifically upregulated upon Anti-Hsf1 LNA transfection along with 1-42 APP. What is % of the occurrence of B2 binding sites in those genes? What are their functional annotations and what is their connection to learning, memory, and cell survival?
Minor.
1 . Please include TPM/ FPKM values for hippocampal markers as control in Figure 2 to do justice to the hippocampus specific RNA seq conducted by the Authors.
2 . In figure 2D the authors show that B2 RNA regulated SRGs in the 3 months' wild type mice are significantly high. P53 has been reported to be high in young wild types hippocampus, but not SRGs in my opinion. The authors should comment on this.
3 . In figure 2F, under the 6m APP condition, the replicate 3 looks substantially different from the other replicate. This can significantly impact the analysis and conclusions made. Either remove that replicate and present the analysis without it or please provide a valid explanation. To make the data more valid, please provide hierarchical clustering of the entire data, the non-B2 regulated genes and the B2 regulated SRGs. In Figure 2C RNA seq data is represented in TPM while its FPKM in Figure 2D. Figure 2: the number of replicates in the case of 3-month-old wild types only 2. Please specifically denote it and comment why only 2 replicates are provided
4 . Considering that p53 and SRGs are significantly upregulated in 6months in the APP model, it would be great if (allowing that these samples are still available) the authors can include a staining for apoptotic markers, for example, Active Casp3 or similar. This will allow us to better gauge the gene expression changes presented by the authors especially regarding SRGs.
5 . Under subheading: Hsf1 accelerates B2 RNA processing, 3rd paragraph when the authors comment on known hsf1 binding sites on SRG genes, please correct from: Increased Hsf1-binding was found.... "To the increased number of hsf1 binding sites were found", unless the authors would like to show increased Hsf1 binding by performing CHIP-seq for Hsf1 in the hippocampus at least at the 6-month time point between Wt and APP mice.
Significance
B2 RNAs, encoded from SINE B2 elements has been directly implicated in stress response by its inherent ability to bind RNA Pol II and suppress stress response genes (SRG) in homeostatic conditions. However, upon stimuli, B2 RNAs are cleaved and degraded, resulting in the release of RNA pol II and upregulation of SRGs. Previous work from the senior author identified PRC2 component EZH2 to be the B2 RNA processing factor, cleaving B2, and releasing POL2. SRGs are upregulated upon stress, for example in age-associated neuropathologies like Alzheimer's disease (AD). Considering that the hippocampus is a primary target of amyloid pathologies as well as since SRGs are suggested to be key for the function of a healthy hippocampus, the authors set to understand the role of B2 RNAs that are linked to SRG regulation in the mouse hippocampus with amyloid pathology. They use disease-relevant in vivo and in vitro models combined with unbiased RNA seq data analysis for this endeavor, which indicates the potential relevance of B2 RNAs in APP mediated neuronal pathologies in mice as well as identifies Hsf1 as the factor cleaving B2 RNAs in the hippocampus.
The work is interesting and identification of Hsf1 as the processing factor for B2 RNAs in the hippocampus is significant. I would like to credit the authors for their elegant in vivo experimental design in Figure 2.
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Reply to the reviewers
We thank the reviewers for their useful suggestions to improve the manuscript and their support for publication. We have addressed all the comments that have been raised and carried out the suggested additional analyses, resulting in a significantly improved revised version of the manuscript. We provide hereafter a detailed point-by-point response to all questions and comments of the three reviewers.
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Centriole structure has been an attractive but challenging research topic for years. Pierre Gonczy's group has been working on its structure using cryo-electron tomography (cryo-ET). While the axoneme, which has longitudinal periodicity, was analyzed by several groups by cryo-ET for more than a decade, cryo-ET study on the centriole suffers from poor signal to noise ratio due to its limited length and thus fewer periodicity. They chose the centriole of flagellate Trichonympha, which have exceptionally long centrioles and thus offer opportunity of relatively straightforward sub-tomogram averaging. Their approach has been successful, and they revealed intermediate resolution structure of the cartwheel, key of 9-fold symmetry formation, and it's joint to triplet microtubules (Guichard et al. 2012, 2013, 2018).
In this work, they employed modern state-of-art cryo-ET technique, such as direct electron detection and 3D image classification to upgrade our knowledge of centriole structure. In their past works, the central hub of the cartwheel, made of SAS-6 protein forming 9-fold complex, was described as an 8nm periodic object. With improved spatial resolution, they provided further detail with clear polarity, which will deepen our thought about the initial stage of ciliogenesis. They also compared two Trichonympha species (spp and agilis) as well as another flagellate, Teranympha mirabilis, and extended their intriguing evolutional and mechanical hypotheses based on structural differences.
Despite improved spatial resolution, it is still not possible to identify proteins in the cryo-ET map (cellular cryo-ET will not reach such high resolution in the near future). Therefore, this work is rather geometrically descriptive, which will inspire molecular biologists to identify molecules by other methods. Nevertheless, this work demonstrated capability of cellular cryo-ET, especially analysis of structural heterogeneity. Thus, while biological topics handled are rather specialized for cilia from flagellate, this work will attract attention of any biologist interested in molecular structure in vivo. It is worth for publication in a high Journal after addressing the points below. This reviewer believes that the authors can address these points easily with additional analysis.
We are grateful to the reviewer for the favorable evaluation and the many valuable suggestions, in particular concerning the processing pipeline, which we addressed by additional analyses, as detailed below.
Major points:
- Entire scheme A graphic diagram of the entire cartwheel area, summarizing this work, is necessary for the readers' understanding (similar to Fig.6 of the other manuscript, Klena et al.).
We thank the reviewer for this interesting suggestion, which we fully adhere to. As a result, we have generated a graphical summary of the work, which is shown in the new Figure panels 6B-F. Moreover, Figure 6A provides an evolutionary perspective regarding the presence of the CID and of what is now referred to as the fCID (filamentous CID, previously: FLS, see response to reviewer 3). This also helps to link our findings with the companion manuscript by Klena et al. This new Figure 6 is referred to extensively in the discussion of the revised manuscript (pages 13-16).
Then average scheme should be shown in more detail, especially assumption of periodicity, Materials and Methods. The cartwheel hub was averaged with 25nm periodicity (as discussed below). Was the pinhead averaged with 16nm (as detected by FFT in Fig.S2L)? How about the triplet?
This reviewer is not completely sure if the longitudinal averaging strategy is justifiable. Since periodicity of each domain is not trivial, logically the initial average must be done with the size of least common multiple (or larger). It is likely 96nm, assuming 25nm of the central hub is 3 times of microtubule periodicity and 16nm of the pinhead is twice of MT. 96nm average should be possible with a long cartwheel in this work. Alternative, in case periodicity is independent of MT and thus there is no least common multiple, is random picking and classification mentioned in "4. Periodicity". This should also be possible, since they can pick enough number of particles from long cartwheels.
We apologize that the initial version of the manuscript was not sufficiently clear regarding the averaging pipeline that was pursued. To rectify this, we now provide a new Figure S1B to graphically explain the approach followed for STA. As depicted in this figure panel, the step size for sub-volume extraction was 25 nm both centrally and peripherally. This step size was selected because it corresponds to ~3x the major periodicity of ~8.5 nm observed in the power spectra of the sub-volumes. The 25 nm step size is larger than that previously used (i.e. 17 nm in Guichard et al. 2013), in order to identify potential features with larger periodicities. The fact that the step size was of 25 nm in all cases is now mentioned explicitly in the Materials and Methods section of the revised manuscript (line 649).
We agree with the reviewer that 96 nm averaging is possible given the long cartwheel analyzed here, and such a piece of data was in fact included in the original submission, although with a different purpose. Indeed, we carried out STA using ~(100 nm)3 sub-volumes (with binning 3 to reduce computational time), the results of which are reported in Figure S7 (previously Fig. S6). For the purpose of this analysis, we focused on the lateral organization of the cartwheel, but did not use this dataset to explore other periodicities because of the limitations inherent to a binning 3 data set.
- Classification*
The authors analyzed structural heterogeneity inside the cartwheel hub, employing reference-free classification by Relion software. The program reveals multiple coexisting structures - two from Trichonympha agilis and three from Teranympha, respectively. Whereas this is an exciting finding and shows future research direction of this field, interpretation of this classification must be done carefully. ** It is puzzling that major (55%) population of T. agilis shows more ambiguous features than the minor population (45%), while spatial resolutions by FSC are not so different - for example, Fig.2H vs Fig.S5C. In case of Teranympha, it is even more drastic - Fig.4D (major class) seems blurred along the centriolar axis, compared to Fig. 4E (minor class). This reviewer is afraid that these "major" classes might contain more than one structure and after subaveraging be blurred in detailed features. The apparent good spatial resolution could be explained, when two structures coexist and subtomograms are aligned within each subclass. Probably lower resolution at the spoke region of the major class (Fig.S2A) than that of the minor class (Fig.S2D) is a sign of heterogeneity within this class. Another risk could be subtomograms with poorer S/N being categorized to one class (due to lack of feature to be properly classified). Fig.S5F (black dots localized in one tomogram) raised this concern.
The following investigation will help to solve this issue. 1. Extract and re-classify subtomograms belonging to the major population. 2. Direct observation of tomograms. The authors could plot two classes of Teranympha (as they did for T. agilis in Fig.S5) and find features of the cylindrical cartwheel hub in two conformations (as shown Fig.4DE). Since such a feature was directly observed in tomograms from the other manuscript (left panels of Fig.S6AC in Klena et al.), it should be possible in this work as well.
We agree with the reviewer that the interpretation of the classification must be done with care, and share her/his interest in better understanding the structural variability between cartwheels classes in T. agilis and T. mirabilis. Although poor S/N may in theory result in erroneous joint classifications, we note that all maps in the original submission stemmed from extensive focused 3D classification, which removed defective and spurious sub-volumes, nevertheless defining distinct classes in the cases reported. Obviously, however, we cannot exclude that much larger data sets and future software advances may lead to the identification of additional features that would allow further sub-classes to be identified.
Regardless, we followed the two suggestions the reviewer offered to us and have (1) extracted and re-classified sub-tomograms belonging to the major populations and (2) undertaken a direct observation of tomograms. These two points are developed in turn below.
(1) We have performed a further round of classification of the major populations in T. agilis (55 % class) and T. mirabilis (64 % class), to assess whether additional sub-classes might be identified and thus help further improve the quality of the central cartwheel map. However, this additional round did not yield new sub-classes nor notable improvement in the map quality as judged by visual inspections. We show in Rebuttal Figure 1 a comparison in each case of the original STA and the corresponding STA upon such re-classification. Importantly, all conclusions spelled out in the original submission hold upon further re-classification, indicating that the initial classification converged to the best map quality based on the current data set and available computational resources.
(2) We have followed the suggestion of the reviewer and now show raw tomograms to confirm that the classes correspond to bona fide structures and not to processing artefacts (new Figures S1C-F). The resulting new Figure S1D for instance shows that the striking variations observed between classes in the T. agilis STA are also visible in the raw tomogram. The more subtle variations among T. mirabilis classes are more difficult to observe in the raw tomogram, but inherent variations that reflect the presence of two classes are nevertheless observed.
Furthermore, following the reviewer’s suggestion, we now mapped the distribution of the two T. mirabilis cartwheel classes onto tomograms, revealing that both classes can occur next to each other within the same centriole (new Figure S8E).
- Periodicity mismatch*
In Fig. 2CD, periodicity of CID has discrepancy from that of the stacked SAS-6 ring (8.5nm and 8.0nm). Do the authors think this is a significant difference or within an error? The same question can occur to other subtomogram averages. It would be nice to show errors as shown in their other manuscript (Fig.3C of Klena et al.) and clarify their idea. If it is systematic difference of periodicity between the stacked ring and CID, this shift will be accumulated through the entire cartwheel region - after 100nm, 8.5nm/8.0nm difference can be accumulated to ~6nm, which should change the entire view of the subtomogram - and the main factor to be classified (periodicity mismatch). This artifact (or influence) should be removed (or separately evaluated) by masking CID (out and in) and run classification separately. By clarifying this, the quality of the major subaverages (mentioned in the previous paragraph) could be improved.
The reviewer wonders whether there might be a periodicity discrepancy within one map, for instance between CID and spokes in the T. spp. cartwheel map (Fig. 2C and Fig. 2D). Here, the periodicity determined from the STA maps is 8.5 ± 0.2 nm (SD, N=4) for the CID and 8.0 ± 1.5 nm (SD, N=2) for the spokes. Based on these standard deviations, there is indeed no significant difference between the two, and thus no periodicity discrepancy. The same applies for measurements in T. agilis and T. mirabilis. The SDs were reported already in the figure legends of the original submission, and we would prefer to leave them there if possible and not mention them in the figures, which are pretty busy as is. We apologize if this was not clear enough in the initial manuscript. Likewise, one may wonder whether there might be periodicity discrepancies between structures from distinct maps, for instance between CID and A-links from T. spp. (Fig. 2C and Fig. 3D). Again, the measurements are within error, since the distance between adjacent CIDs is 8.5 ± 0.2 nm (N=4) and between adjacent A-links 8.4 ± 0.4 nm (N=6); a similar conclusion applies for the corresponding measurement comparisons in T. agilis and T. mirabilis. The figure legends have been altered in the revised manuscript to spell out that there are no significant differences between periodicities (lines 856-858).
Furthermore, we would like to stress that, by definition, STA value are average distances. For instance, in the case of T. spp., the central cartwheel STA was obtained from 511 sub-volumes, and thus the reported N=2 represents the average distance from 511 sub-volumes. Since this is an average, errors can therefore not accumulate over longer distances. This point has also been clarified in the figure legends (line 856-858).
- Periodicity*
They averaged subtomograms extracted with spacing of 252A with initial average as the first template (p.18 Line22). This means they assumed 25nm periodicity from the beginning and excluded different or larger unit size (if they take search range wide, they could detect difference periodicity, but will still be biased by initially assumed 25nm). 25nm average allowed them to see more detail than before (when they assumed 8nm periodicity), but there is still a risk of bias from references. To avoid this risk, this reviewer would propose classification of randomly extracted (but of course along the cylindrical hub or along the triplet microtubules, so one-dimensionally random picking) subtomograms. This experiment will end up with multiple sub-averages, which are 25nm (or multiple times of that) shifted from each other. Then it will prove their assumption.
We agree with the reviewer that in theory the choice of periodicity could introduce a bias. This is why we have chosen a larger step size than in our initial work, corresponding to ~3x the major periodicity of ~8.5 nm observed in the power spectrum of the sub-volumes, as mentioned above. Regardless, following the reviewer’s suggestion, we have now explored other types of periodicities by re-analyzing the dataset through extraction of non-overlapping sub-volumes along the proximal-distal centriole axis. In doing so, we randomized the starting position of the first box between tomograms, reaching the same goal as with random picking but maximizing the number of sub-volumes. We carried out this analysis for all T. spp., T. agilis and T. mirabilis cartwheel classes, and found no notable differences that would affect the conclusions of the manuscript compared to the initial overlapping sub-volume classification, albeit generally with a noisier STA due to the lower number of sub-volumes. A comparison of the two approaches is provided in Rebuttal Figure 2. Moreover, all the points regarding the choice of periodicity have been further clarified in the expanded Materials and Methods section (pages 19-21).
Minor points:
They discussed difference of stacked SAS-6 rings in the cartwheel from various species. How much is the sequence difference of SAS-6 among these species?
Unfortunately, no genomic or transcriptomic data has been published for the species investigated here, although the sparse molecular data available from small subunit rRNA sequences allows one to establish an overall molecular phylogeny. We previously identified a SAS-6 homologue in T. agilis (Guichard et al. 2013), which shares 20 % identity and 45 % similarity with C. reinhardtii SAS-6. Despite low sequence conservation, the structural conservation of SAS-6 is predicted to be high between the two organisms (Guichard et al. 2013). We apologize if these points were not expressed sufficiently clearly in the initial rendition and have adapted the wording in the revised manuscript (lines 325-332).
Are the authors sure that CID is nine-fold symmetric? It is not trivial.
We thank the reviewer for bringing up this interesting point. We have applied 9-fold symmetrization to the entire central cartwheel comprising spokes, hub and CID/ fCID, a choice guided by the apparent 9-fold symmetry of the spokes and peripheral element. We investigated the impact of symmetrization on the CID by relaxing symmetry from C9 to C1 during refinement, but did not observe a difference, and thus continued with C9 symmetry, which improves map resolution by S/N ratio enhancement and additional missing wedge compensation. In addition, we have also analyzed the CID without symmetrization, as reported in Figure S7 (previously: Fig. S6). Note that these maps were generated with larger sub-volumes centered on the spokes to comprise hub, spokes and microtubule triplets, explaining the resulting lower resolution, as the missing wedge is not compensated. Despite these limitations, however, the unsymmetrized CID shown in Figure S7A and S7E resembles the one in the symmetrized maps of Figure 2, indicating that the CID indeed exhibits 9-fold radial symmetry. That this is the case is spelled out explicitly in the revised manuscript (lines 1145-1147).
Fig.1C: Another cross-section from the distal region will be helpful. A longer scale bar is better for readers' understanding.
We understand that the reviewer is curious about the distal region, and cross-section views of resin-embedded sections from T. agilis are available and could be provided if necessary. However, given that the focus of the manuscript is strictly on the cartwheel-bearing proximal region, we felt that featuring the distal region in detail would break the narrative. Therefore, we suggest to keep Figure 1 as in the original manuscript. Following the reviewer’s suggestion, we increased the size of the scale bars from 10 nm to 20 nm in Figure 1C as well as in the corresponding Figure S8C.
Fig.S6F: It would be informative if the subclasses (25% and 20%) are distinguished in this mapping.
As per the reviewer’s request, we provide in Rebuttal Figure 3 a side-by-side comparison of the T. agilis 25 % and 20 % classes centered on the spokes, which are noisier than the composite 45 % class due to the lower number of sub-volumes in each sub-class. Given that there are no notable differences between the two maps that would affect any of the conclusions of the manuscript, we feel it is best to keep what is now Figure S7F (previously: Fig. S6F) unchanged in the revised manuscript.
A figure to explain the classification scheme will help readers understand. How many subtomograms did classification started? Were the 45% class classified into two (25% and 20%) groups by two-step classification or at once (the entire subtomograms were classified into three groups directly?
We thank the reviewer for this useful suggestion. As a result, we have generated a new Supplemental Figure S1G-J that provides a graphical overview of the classification scheme, together with sub-volume numbers for all deposited maps, thus nicely complementing Table S1.
Reviewer #1 (Significance (Required)):
Nevertheless, this work demonstrated capability of cellular cryo-ET, especially analysis of structural heterogeneity. Thus, while biological topics handled are rather specialized for cilia from flagellate, this work will attract attention of any biologist interested in molecular structure in vivo. It is worth for publication in a high journal after addressing the points above. This reviewer believes that the authors can address these points easily with additional analysis.
We reiterate our thanks to this reviewer for her/his favorable evaluation and detailed suggestions, which enabled us to generate a strengthened manuscript.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
Here, Nazarov and colleagues report sub-tomogram average (STA) maps of centrioles with 16 to 40 Å resolution from Trichonympha spp., Trichonympha agilis, and Teranympha mirabilis. Even though the authors have previously described the centriole architecture of T. spp, these STA maps of higher resolution revealed new features of centrioles, like polarized Cartwheel Inner Density (CID) and the pinhead. They also observed Filament-like structure (FLS) from T. mirabilis which seems to correspond to the CID from other species. Interestingly, they suggest that one and two SASS6 rings are stacked in an alternative fashion to make the central hub in T. mirabilis (Figure 5). The following issue should be addressed:
Major points
- Figure 4E. Authors mentioned in the manuscript that "We observed that every other double hub units in the 36% T. mirabilis class appears to exhibit a slight tilt angle relative to the vertical axis". When I see the other side, it does not seem to be tilted. Could the authors explain this?*
We apologize that this aspect was not explained in sufficient detail. The left and right sides of the hub indeed appeared different in transverse views across the cartwheel center (previous Fig. 4E). This was because the area we selected in the original submission was centered on one emanating spoke. Due to the 9-fold symmetry one spoke density was selected on the right side, while the region between two spokes was displayed on the left side (as was illustrated by the slice across the center in previous Figure 4A; dashed rectangles in 4.0 nm panel). We have now selected a larger area to include spokes from both sides of the hub and thus better visualize this offset as shown in the modified Figure 4D-E.
Reviewer #2 (Significance (Required)):
I believe these results are of interest for all centrosome researchers and would like to recommend this manuscript be published in the EMBO journal which is affiliated with the Review Commons.
We thank the reviewer for the recommendation to submit the revised manuscript to EMBO Journal, which we have followed.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
In this manuscript Nazrov et al., use cryo-electron tomography (CET) to analyse the structure of the centriole cartwheel. The Gonczy lab have previously generated a ground-breaking structure of the cartwheel from Trichonympha spp (T. spp.) (Guichard et al., Science, 2012; Guichard et al., Curr. Biol., 2013). This work is a direct continuation of those studies but using modern technology to get higher resolution images of the T. spp. cartwheel and comparing this to the cartwheel from Trichonympha agilis and from another distantly related flagellate Teranympha mirabilis.
The data is generally well presented and of high quality. I am not an expert in CET, so it would be advisable to get the opinion from a reviewer who is, but the Gonczy lab are experienced in these techniques so I would not anticipate any problems. I have to admit that the title of the paper did not excite me, and I expected this to be a very worthy, but incremental study. It was a pleasure to find out that the extra detail provided by the increased resolution has revealed several new and unexpected features that have important implications for our understanding of cartwheel assembly and function. Most important are the potential asymmetry of the cartwheel hub, apparent variations in the packing mechanism of the stacked rings (even within the same cartwheel), and the potential offsetting of ring stacking. These findings will be of great interest to the field, and so I am strongly supportive of publication in The EMBO Journal. I have only a few points that I think the authors should consider.
We thank the reviewer for this positive feedback and the recommendation to submit to EMBO Journal, which we hereby follow.
Prompted by the comment of the reviewer, we revised the title to make it more informative and appealing to readers: “Novel features of centriole polarity and cartwheel stacking revealed by cryo-tomography”.
- Nazarov et al., conclude that the cartwheel structure is intrinsically asymmetric. This is most convincingly based on the displacement of the CID within the hub, but they state that the Discussion that the potential offset between the Sas-6 double rings generates an inherently polar structure. I didn't understand why this is the case. Looking at Fig.S9A,B I can see that the offset in B could tilt to the left (as shown here) or to the right (if the structure was flipped by 180o). But I couldn't see how this makes this structure polar in the sense that a molecule coming into dock with the structure could only bind to one side of the offset structure shown in B, but to both sides of the aligned structure shown in A. I think this needs to be explained better, as it is crucial to understand where any potential polarity in the cartwheel structure comes from.*
We apologize for not having been sufficiently clear about how two SAS-6 rings with an offset could impart organelle polarity. The reviewer is correct that an offset between superimposed rings alone is not sufficient to generate polarity at a larger scale. The important point we would like to stress, however, is that we discovered concerted polarity in multiple locations, from the central hub to the peripheral elements as illustrated in Fig. S7C-D, S7G-H, S7K-L and S7O-P (previously: Fig. S6). Prompted by the reviewer’s comment, we now better emphasize the asymmetric tilt angles of merging spokes, as highlighted also in the improved Figure S7. This asymmetric spoke tilt angle allows one to discriminate the proximal and distal side of a double SAS-6 ring, which is now explained better in the text (lines 259-263 & 502-510).
- Related to this last point, in a co-submitted paper Klena et al. do not report such an asymmetry in the hub structures they have solved from several different species (neither in the tilting of the hub, or the displacement of the CID). I think it would be worth both sets of authors commenting on this point.*
We agree that comparing and contrasting the results of the two companion manuscripts is important and we have updated the text as a consequence in several places (lines 444, 467, 507, 536, 985, 1000). We know from our previous work (Guichard et al. 2013) that the asymmetry of the hub and spoke is not visible at lower resolution. In the accompanying manuscript by Klena et al., no offset in the hub or asymmetric CID localization is reported, probably due to lower resolution and differences between species.
- The authors data strongly suggests that the T. ag. and Te. mir. hubs are composed of a mixture of single and double Sas-6 rings. In contrast, the T. spp. cartwheel only has a single class of rings, but it wasn't absolutely clear if the authors think this comprises a single or double ring. In the text it is presented as though the elongation of the hub densities in the vertical direction is a new feature of the T. ag cartwheel (Fig.2H,I), but to me it looks as though this is also apparent in the T. spp. cartwheel (Fig.2C,D). The authors should address this directly and, if they believe that T. spp. has a double ring, they should comment on whether this more regular structure seems to have offset rings. If not, then the offset rings are unlikely to be the source of asymmetry that leads to the asymmetric displacement of the CID. Finally, if the authors think these are double rings, they should also be clear that they would now slightly re-interpret their original T. spp. cartwheel model (Figure 2, Guichard et al., Curr. Biol.). There is no embarrassment in this-a higher resolution structure has simply revealed more detail.*
We apologize if the conclusions drawn about T. spp. cartwheel hubs were not sufficiently clearly expressed. Like the reviewer, we think that elongated hub elements are also discernible in T. spp., something that is also illustrated by the intensity plot profile in Figure 2C (double peaks on light blue line). These points are spelled out more explicitly in the revised manuscript (lines 177-179). In addition, to emphasize the conservation of the double hub units in both Trichonympha species, we have likewise adapted the text for T. agilis (lines 198-201).
As for the offset observed within T. spp. spoke densities in Figure S10H, we interpret this as evidence for an offset of the double ring at the level of the hub, although we have not observed such offset in T. spp. for reasons that are unclear. The fact that this revises our previous interpretation based on a lower resolution map of T. spp. was already mentioned in the initial submission but is now better emphasized (lines 171-172 & 179-181).
- The authors conclude that T. mirabilis cartwheels lack a CID and instead have a filament-like structure (FLS). I wonder whether it is more likely that the FLS is really a highly derived CID that appears to be structurally distinct when analysed in this way, but that will ultimately have a similar molecular composition. This situation might be analogous to the central tube in C. elegans, which by EM appears to be distinct from the central cartwheel seen in most other species, but is of course still composed of Sas-6. This historical tube/cartwheel nomenclature is now cumbersome to deal with, so perhaps it would be better to be cautious and not give the T. mirabilis structure a completely new name-how about "unusual CID" (uCID).*
We share the view that the CID and the “FLS” –the term used in the initial submission- may have a related molecular composition and function, as we had also speculated in the discussion of the original submission. Following the reviewer’s suggestion, and in an effort to have a more uniform nomenclature, we propose to dub the T. mirabilis structure “filamentous CID” (fCID). This highlights better the similar location of these two entities and their potential shared function, while stressing the filamentous nature of the fCID. We further emphasize this point by providing the new Figure 6A to compare the presence of the two entities in select species. The discussion has also been adapted accordingly (pages 13-14).
Rebuttal Figure Legends
Rebuttal Figure 1: Re-classification of major classes
(A-D) Transverse (top) and longitudinal (bottom) views of T. agilis (A, B) and T. mirabilis (C, D) central cartwheel 3D maps. The final major classes reported in the manuscript (A: 55 % class, C: 64 % class) were subjected to re-classification, which again yielded one major class in each case, with no notable improvement (B, D).
Rebuttal Figure 2: Reclassification with non-overlapping sub-volumes
(A-F) Transverse (top) and longitudinal (bottom) views of T. spp. (A, B) T. agilis (C, D) and T. mirabilis (E, F) central cartwheel 3D maps. The final maps reported in the manuscript (A, C, E) were generated with a 25 nm step size, yielding overlapping sub-volumes, whereas the maps in (B, D, F) were generated from non-overlapping sub-volumes, with no notable differences between the two that would affect the conclusions of the manuscript.
Rebuttal Figure 3: Polar centriolar cartwheel upon sub-classification
(A-C) 3D transverse views of non-symmetrized STA centered on the spokes to jointly show the central cartwheel and peripheral elements in the T. agilis 45 % class (A), as well as separately in the 25 % class (B) and 20% class (C). No notable differences are apparent following such re-classification, apart from the output being noisier due to the lower number of sub-volumes in each sub-class.
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Referee #3
Evidence, reproducibility and clarity
In this manuscript Nazrov et al., use cryo-electron tomography (CET) to analyse the structure of the centriole cartwheel. The Gonczy lab have previously generated a ground-breaking structure of the cartwheel from Trichonympha spp (T. spp.) (Guichard et al., Science, 2012; Guichard et al., Curr. Biol., 2013). This work is a direct continuation of those studies but using modern technology to get higher resolution images of the T. spp. cartwheel, and comparing this to the cartwheel from Triconympha agilis and from another distantly related flagellate Tetranympha mirabilis.
The data is generally well presented and of high quality. I am not an expert in CET, so it would be advisable to get the opinion from a reviewer who is, but the Gonczy lab are experienced in these techniques so I would not anticipate any problems. I have to admit that the title of the paper did not excite me, and I expected this to be a very worthy, but incremental study. It was a pleasure to find out that the extra detail provided by the increased resolution has revealed several new and unexpected features that have important implications for our understanding of cartwheel assembly and function. Most important are the potential asymmetry of the cartwheel hub, apparent variations in the packing mechanism of the stacked rings (even within the same cartwheel), and the potential offsetting of ring stacking. These findings will be of great interest to the field, and so I am strongly supportive of publication in The EMBO Journal. I have only a few points that I think the authors should consider.
Nazarov et al., conclude that the cartwheel structure is intrinsically asymmetric. This is most convincingly based on the displacement of the CID within the hub, but they state that the Discussion that the potential offset between the Sas-6 double rings generates an inherently polar structure. I didn't understand why this is the case. Looking at Fig.S9A,B I can see that the offset in B could tilt to the left (as shown here) or to the right (if the structure was flipped by 180o). But I couldn't see how this makes this structure polar in the sense that a molecule coming into dock with the structure could only bind to one side of the offset structure shown in B, but to both sides of the aligned structure shown in A. I think this needs to be explained better, as it is crucial to understand where any potential polarity in the cartwheel structure comes from.
Related to this last point, in a co-submitted paper Klena et al. do not report such an asymmetry in the hub structures they have solved from several different species (neither in the tilting of the hub, or the displacement of the CID). I think it would be worth both sets of authors commenting on this point.
The authors data strongly suggests that the T. agg. and Te. mir. hubs are composed of a mixture of single and double Sas-6 rings. In contrast, the T. spp. cartwheel only has a single class of rings, but it wasn't absolutely clear if the authors think this comprises a single or double ring. In the text it is presented as though the elongation of the hub densities in the vertical direction is a new feature of the T. agg cartwheel (Fig.2H,I), but to me it looks as though this is also apparent in the T. spp. cartwheel (Fig.2C,D). The authors should address this directly and, if they believe that T. spp. has a double ring, they should comment on whether this more regular structure seems to have offset rings. If not, then the offset rings are unlikely to be the source of asymmetry that leads to the asymmetric displacement of the CID. Finally, if the authors think these are double rings, they should also be clear that they would now slightly re-interpret their original T. spp. cartwheel model (Figure 2, Guichard et al., Curr. Biol.). There is no embarrassment in this-a higher resolution structure has simply revealed more detail.
The authors conclude that T. mirabilis cartwheels lack a CID and instead have a filament-like structure (FLS). I wonder whether it is more likely that the FLS is really a highly derived CID that appears to be structurally distinct when analysed in this way, but that will ultimately have a similar molecular composition. This situation might be analogous to the central tube in C. elegans, which by EM appears to be distinct from the central cartwheel seen in most other species, but is of course still composed of Sas-6. This historical tube/cartwheel nomenclature is now cumbersome to deal with, so perhaps it would be better to be cautious and not give the T. mirabilis structure a completely new name-how about "unusual CID" (uCID).
Significance
see above
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Referee #2
Evidence, reproducibility and clarity
Here, Nazarov and colleagues report sub-tomogram average (STA) maps of centrioles with 16 to 40 Å resolution from Trichonympha spp., Trichonympha agilis, and Teranympha mirabilis. Even though the authors have previously described the centriole architecture of T. spp, these STA maps of higher resolution revealed new features of centrioles, like polarized Cartwheel Inner Density (CID) and the pinhead. They also observed Filament-like structure (FLS) from T. mirabilis which seems to correspond to the CID from other species. Interestingly, they suggest that one and two SASS6 rings are stacked in an alternative fashion to make the central hub in T. mmirabilis (Figure 5). The following issue should be addressed:
Major points
- Figure 4E. Authors mentioned in the manuscript that "We observed that every other double hub units in the 36% T. mirabilis class appears to exhibit a slight tilt angle relative to the vertical axis". When I see the other side, it does not seem to be tilted. Could the authors explain this?
Minor Points
- Page 11, I think Fig. 9G indicates Fig. S9G.
Significance
I believe these results are of interest for all centrosome researchers, and would like to recommend this manuscript be published in the EMBO journal which is affiliated with the Review Commons.
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Referee #1
Evidence, reproducibility and clarity
Centriole structure has been an attractive but challenging research topic for years. Pierre Gonczy's group has been working on its structure using cryo-electron tomography (cryo-ET). While the axoneme, which has longitudinal periodicity, was analyzed by several groups by cryo-ET for more than a decade, cryo-ET study on the centriole suffers from poor signal to noise ratio due to its limited length and thus fewer periodicity. They chose the centriole of flagellate Trichonympha, which have exceptionally long centrioles and thus offer opportunity of relatively straightforward subtomogram averaging. Their approach has been successful and they revealed intermediate resolution structure of the cartwheel, key of 9-fold symmetry formation, and it's joint to triplet microtubules (Guichard et al. 2012, 2013, 2018). In this work, they employed modern state-of-art cryo-ET technique, such as direct electron detection and 3D image classification to upgrade our knowledge of centriole structure. In their past works, the central hub of the cartwheel, made of SAS-6 protein forming 9-fold complex, was described as an 8nm periodic object. With improved spatial resolution, they provided further detail with clear polarity, which will deepen our thought about the initial stage of ciliogenesis. They also compared two Trichonympha species (spp and agilis) as well as another flagellate, Teranympha micabilis, and extended their intriguing evolutional and mechanical hypotheses based on structural differences. Despite improved spatial resolution, it is still not possible to identify proteins in the cryo-ET map (cellular cryo-ET will not reach such high resolution in the near future). Therefore this work is rather geometrically descriptive, which will inspire molecular biologists to identify molecules by other methods. Nevertheless this work demonstrated capability of cellular cryo-ET, especially analysis of structural heterogeneity. Thus, while biological topics handled are rather specialized for cilia from flagellate, this work will attract attention of any biologist interested in molecular structure in vivo. It is worth for publication in a high Journal after addressing the points below. This reviewer believes that the authors can address these points easily with additional analysis.
Major points:
Entire scheme A graphic diagram of the entire cartwheel area, summarizing this work, is necessary for the readers' understanding (similar to Fig.6 of the other manuscript, Klena et al.). Then average scheme should be shown in more detail, especially assumption of periodicity, Materials and Methods. The cartwheel hub was averaged with 25nm periodicity (as discussed below). Was the pinhead averaged with 16nm (as detected by FFT in Fig.S2L)? How about the triplet? This reviewer is not completely sure if the longitudinal averaging strategy is justifiable. Since periodicity of each domain is not trivial, logically the initial average must be done with the size of least common multiple (or larger). It is likely 96nm, assuming 25nm of the central hub is 3 times of microtubule periodicity and 16nm of the pinhead is twice of MT. 96nm average should be possible with a long cartwheel in this work. Alternative, in case periodicity is independent of MT and thus there is no least common multiple, is random picking and classification mentioned in "4. Periodicity". This should also be possible, since they can pick enough number of particles from long cartwheels.
Classification The authors analyzed structural heterogeneity inside the cartwheel hub, employing reference-free classification by Relion software. The program reveals multiple coexisting structures - two from Trichonympha agilis and three from Teranympha, respectively. Whereas this is an exciting finding and shows future research direction of this field, interpretation of this classification must be done carefully. It is puzzling that major (55%) population of T. agilis shows more ambiguous features than the minor population (45%), while spatial resolutions by FSC are not so different - for example, Fig.2H vs Fig.S5C. In case of Teranympha, it is even more drastic - Fig.4D (major class) seems blurred along the centriolar axis, compared to Fig. 4E (minor class). This reviewer is afraid that these "major" classes might contain more than one structure and after subaveraging be blurred in detailed features. The apparent good spatial resolution could be explained, when two structures coexist and subtomograms are aligned within each subclass. Probably lower resolution at the spoke region of the major class (Fig.S2A) than that of the minor class (Fig.S2D) is a sign of heterogeneity within this class. Another risk could be subtomograms with poorer S/N being categorized to one class (due to lack of feature to be properly classified). Fig.S5F (black dots localized in one tomogram) raised this concern. The following investigation will help to solve this issue. 1. Extract and re-classify subtomograms belonging to the major population. 2. Direct observation of tomograms. The authors could plot two classes of Teranympha (as they did for T. agilis in Fig.S5) and find features of the cylindrical cartwheel hub in two conformations (as shown Fig.4DE). Since such a feature was directly observed in tomograms from the other manuscript (left panels of Fig.S6AC in Klena et al.), it should be possible in this work as well.
Periodicity mismatch In Fig. 2CD, periodicity of CID has discrepancy from that of the stacked SAS-6 ring (8.5nm and 8.0nm). Do the authors think this is a significant difference or within an error? The same question can occur to other subtomogram averages. It would be nice to show errors as shown in their other manuscript (Fig.3C of Klena et al.) and clarify their idea. If it is systematic difference of periodicity between the stacked ring and CID, this shift will be accumulated through the entire cartwheel region - after 100nm, 8.5nm/8.0nm difference can be accumulated to ~6nm, which should change the entire view of the subtomogram - and the main factor to be classified (periodicity mismatch). This artifact (or influence) should be removed (or separately evaluated) by masking CID (out and in) and run classification separately. By clarifying this, the quality of the major subaverages (mentioned in the previous paragraph) could be improved.
Periodicity They averaged subtomograms extracted with spacing of 252A with initial average as the first template (p.18 Line22). This means they assumed 25nm periodicity from the beginning and excluded different or larger unit size (if they take search range wide, they could detect difference periodicity, but will still be biased by initially assumed 25nm). 25nm average allowed them to see more detail than before (when they assumed 8nm periodicity), but there is still a risk of bias from references. To avoid this risk, this reviewer would propose classification of randomly extracted (but of course along the cylindrical hub or along the triplet microtubules, so one-dimensionally random picking) subtomograms. This experiment will end up with multiple subaverages, which are 25nm (or multiple times of that) shifted from each other. Then it will prove their assumption.
Minor points: They discussed difference of stacked SAS-6 rings in the cartwheel from various species. How much is the sequence difference of SAS-6 among these species? Are the authors sure that CID is nine-fold symmetric? It is not trivial. p.7 Line21 "Fig.S1D-O": D-L p.8 Line1: It would be nice if more detailed description about MIPs, correlating to recent high resolution works from Bui and Brown labs. p.9 Line6 "Focused 3D classification...": This sentence is unclear. p.18 5 lines from bottom "S6C, S6F": How can these panels be power spectra to measure spacing? Typo? Fig.1C: Another cross-section from the distal region will be helpful. A longer scale bar is better for readers' understanding. p.29 Line6: pin -> pink Fig.S6F: It would be informative if the subclasses (25% and 20%) are distinguished in this mapping. A figure to explain the classification scheme will help readers understand. How many subtomograms did classification started? Were the 45% class classified into two (25% and 20%) groups by two-step classification or at once (the entire subtomograms were classified into three groups directly?
Significance
Nevertheless this work demonstrated capability of cellular cryo-ET, especially analysis of structural heterogeneity. Thus, while biological topics handled are rather specialized for cilia from flagellate, this work will attract attention of any biologist interested in molecular structure in vivo. It is worth for publication in a high journal after addressing the points above. This reviewer believes that the authors can address these points easily with additional analysis.
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Author Response
Reviewer #1:
In this manuscript, Cobb and colleagues report on the biochemical and functional characterization of redox active ER proteins in the malaria parasite Plasmodium falciparum. They studied a protein called PfJ2, which contains HSP40 J and Trx domains and is homologous to human ERdj5. Using the TetR-PfDOZI aptamer system to tag PfJ2 and conditionally regulate its expression, they show that PfJ2 is localized in the parasite ER and is essential for parasite growth during the asexual blood stages. Using co-immunoprecipitation combined with mass spectrometry, they identify partner proteins of PfJ2 including other ER proteins such as PDI and BIP. Using a chemical biology approach based on DVSF crosslinker, they document the redox activity of PfJ2 and identify redox substrates of PfJ2, which include PDI8 and PDI11 protein disulfide isomerases. They further functionally characterized PDI8 and PDI11 using the glmS ribozyme for conditional knockdown. These experiments confirm that PDI8 and PDI11 are partners of PfJ2 and show that knockdown of PDI8 impairs parasite blood stage growth. Finally, the authors show that inhibitors of human PDI inhibit parasite growth (at best in the micromolar range) and block the redox activity of PfJ2 and parasite PDI.
This is an interesting study combining genetic and chemical biology approaches to investigate an understudied compartment of the malaria parasite. The manuscript is clearly written and the work technically sound. In summary, this study illustrates that ER redox proteins in the malaria parasite perform similar functions as in other organisms. The main limitation of this study is that evidence showing that redox ER parasite proteins are druggable is rather weak. PfJ2 is very similar to human ERdj5 in terms of active redox site and function, and the authors used inhibitors that are active on human PDI. It thus remains uncertain whether an antimalarial strategy targeting such conserved pathways is achievable.
RESPONSE: We thank the reviewer for their appreciation of our work. While PfJ2 shares some similarity to human ERdJ5, we disagree that they are functionally similar. Our data show that, unlike ERdJ5, PfJ2 substrates are primarily other redox chaperones. In terms of the redox active site, our data clearly identifies a pathway that is targeted by a small molecule inhibitor. There is a lot of precedence for targeting conserved pathways as an antimalarial strategy. For example, anti-translational and anti-proteasomal inhibitors are being widely studied for their potency as antimalarials (Baragana et al 2015 Nature; Li et al 2016 Nature; Wong et al 2017 Nat. Microbiol.; Kirkman et al 2018 PNAS; Stokes et al 2019 PLoS Path.), several proteases (with conserved active sites) are well known antimalarial targets (Sleebs et al 2014 PLoS Biol.; Nasamu et al 2017 Science; Pino et al 2017 Science; Favuzza et al 2020 Cell Host Microbe), and effective inhibitors targeting a parasite chaperone has been repurposed for antimalarial drug development (Lu et al 2020 PNAS). We thank the reviewer for recognizing that there is a long road ahead of us to develop a more specific inhibitor for PfJ2, however, that is beyond the scope of this study.
In addition, a number of specific points should be addressed to improve the quality of the manuscript:
Although PDI8 and PDI11 gene edition were performed in the PfJ2apt line, the authors did not attempt to knockdown both PfJ2 and PDI8/11 simultaneously (because PfJ2 is essential). Therefore, referring to "double conditional mutants" is misleading.
RESPONSE: We are open to alternative ways to refer to these mutants. Since we have orthogonal systems for knockdown of two proteins, we refer to these as double conditional mutants.
The authors should provide details on the parasite lysis conditions used for the co-IP experiments to identify interacting proteins (Table 1) and redox partners (figure 3). In their proteomic analysis, the authors considered proteins with a 5-fold increase in the specific versus control conditions. A more stringent analysis would retain only proteins identified exclusively in the modified J2apt line.
RESPONSE: We will include this in a new version. We agree that a more stringent analysis would lead to fewer proteins being identified, however, it also runs the risk of missing real interactors. We chose to use a 5-fold cutoff based on previously published work (Boucher et al 2018 PLoS Biol; Florentin et al 2020 PNAS).
In figure 6, the authors should probe the blots for a control protein that is not co-immunoprecipitated with PfJ2 or PDI8. In Supplementary fig 4, control untreated parasites should be analyzed in parallel to GlcN-treated parasites.
RESPONSE: We will do this once our labs reopen after the pandemic.
The partial reduction of protein levels (Fig S4) shows that the glmS system is not very efficient here, which might explain why there is no phenotype in the PDI11 mutant (Fig5B). This questions the conclusion that PDI11 is dispensable.
RESPONSE: We agree and we state that “These results...suggest that PfPDI11 may be dispensable... conclusions are supported by a genome-wide essentiality screen performed in P. falciparum” (Lines 319-322). We will add more discussion to explain this result.
Reviewer #2:
The claim here is of having discovered a druggable cellular process in P. falciparum, one that opens the door to therapeutic intervention in the most deadly form of malaria.
The study commences with a focus on what appears to be the Pf homologue of a eukaryotic protein disulphide isomerase, known to many as ERdJ5 and referred to here as PfJ2. Its cellular contingents were identified by cross-linking and pull down, it’s (predicted) thiol reactivity explored with agents that react with reduced thiols and it’s functional importance to parasite fitness (in the lab) explored by gene knockout. These experiments provide evidence that PfJ2 and it’s associated Pf PDIs engage in thiol redox chemistry in the ER of the parasite and that integrity of this biochemical process is important to viability of the parasite.
Lacking all expertise in molecular parasitology, this reader is unable to judge the specific significance of these findings to the field nor indeed the extent to which these are hard-won discoveries.
RESPONSE: We are gratified to note that the reviewer is cognizant of their limitations and their ability to judge the significance of this work.
However, from the perspective of the fundamentals of ER redox chemistry the findings represent a modest advance, showing that what is true of yeast and mammals is also true of Apicomplexa. The important mystery related to the juxtaposition of a J-domain and thioredoxin domains in PfJ2, remains.
The most important claim however is the one with translational potential, namely that one might be able to discover (electrophilic) compounds that, despite the monotony of shared chemical features of thiol chemistry, will nonetheless possess sufficient specificity towards this or that malarial protein to be converted one day to a useful drug. However, in regards to this important point the authors offer very little in the way of evidence how and if this might be achieved.
RESPONSE: We disagree. The work does not reconfirm the ‘fundamentals of ER redox’ chemistry. There is no work, in any system, that has shown that PfJ2-like proteins act as reductases for PDIs. In fact, as we state in the paper, in other model systems, there is a lot of redundancy built in the ER redox systems and PfJ2-like proteins work with specific clients like SERCA pumps or LDL receptor. Thioredoxin domain proteins in the ER of other eukaryotes have not been shown to work with each other or other chaperones. Furthermore, our data actually does suggest a reason why the J-domain is juxtaposed to thioredoxin domains. It recruits BiP to the mixed disulfides formed by PDIs. This insight would not have been possible in other systems because of the redundant redox mechanisms. In terms of the translational aspect, this work identifies an essential, pathway and a starting point for developing better inhibitors. As the reviewer may be aware, once a starting drug-like molecule has been identified, one has to embark on a medicinal chemistry program to develop more potent inhibitor. However, this is beyond the scope of this manuscript.
Therefore, the main conclusions to draw from this paper are that ER-localised thiol chemistry is also important in malaria parasites and that, assuming one were able to explore localised context-specific features of thiol reactivity in malarial proteins, it may one day be possible to develop anti-malarial drugs that exploit this as a mechanism of action. The generic nature of these considerations limits the significance of the conclusions one might draw from this paper.
RESPONSE: We are disappointed that we were unable to satisfy the reviewer’s need for ‘a giant leap for mankind’ insights.
Reviewer #3:
This paper describes redox-active proteins in the ER of malaria parasites. The authors start out with PfJ2, a J- and Trx-domain containing protein. They find that it is an essential ER protein that interacts with other chaperone and Trx domain proteins. Using a crosslinker with specificity for redox-active cysteines they identify PfPDI8 and PfPDI11 as redox-partners that together may aid folding of other proteins in the secretory pathway. Finally the authors use inhibitors that act on human PDIs and show that they inhibit parasite growth, albeit at rather high concentrations. This may be fortunate as this suggests different specificities for host and parasite PDIs. However, it also means that from this work it is difficult to judge if the parasite PDIs can be specifically targeted.
RESPONSE: We thank the reviewer for recognizing the important insights gained from this work. We agree that the specific inhibitor identified is not an ideal antimalarial. There is a lot of precedence in the field for antimalarial inhibitors that target conserved mechanisms such as protein translation (Baragana et al 2015 Nature; Wong et al 2017 Nat. Microbiol.), aspartic proteases (Sleebs et al 2014 PLoS Biol.; Nasamu et al 2017 Science; Pino et al 2017 Science; Favuzza et al 2020 Cell Host Microbe), the proteasome (Li et al 2016 Nature; Kirkman et al 2018 PNAS; Stokes et al 2019 PLoS Path.), the TRiC chaperone complex (Lu et al 2020 PNAS) etc. We are starting a medicinal chemistry program to identify more potent inhibitors of these redox chaperones. However, that is beyond the scope of this paper.
This is an interesting paper and rightly emphasises that it addresses a much understudied process and organelle in the parasite. The DVSF-crosslinking and the knockdown cell lines are highlights (although the knockdown cell lines were not fully exploited). The paper covers a lot of ground. However, this comes at the cost of depth. The actual function of the studied proteins on folding of other proteins and on the state of the ER was not evaluated and it is also not clear if the human PDI inhibitors indeed target the parasite enzymes. The high concentrations of inhibitors needed to show an effect on DVSF-crosslinking might indicate a secondary effect due to loss of parasite viability. As a result it is not fully clear if the studied proteins are indeed critical for folding of relevant substrates and if this process is druggable. More work is needed to support the main conclusions of the paper.
RESPONSE: We thank the reviewer for appreciating the diverse toolsets used here to gain important insights into the ER of malaria-causing parasites. Due to the short time-frame of the DVSF-crosslinking experiment (30 mins vs 48h life cycle), we are able to conclude that the effect of the drug is not secondary. A new version will clarify this.
Major points:
1) The authors describe conditional knockdown lines and find that PfJ2 and PfPDI8 are essential but these lines are not further exploited for functional studies. Did the knock downs have any effect on proteins they mention as potential substrates (Table 1)? Did it affect the state/morphology of the ER? Did knock down of PfPDI8 remove/shift one of the PfJ2 bands after DVSF-crosslinking, as would be expected? Is there an effect on BiP? A general folding problem in the ER with such a lethal phenotype might have profound effects on the morphology of the organelles receiving protein from the ER. What happens to other cellular markers after knock down of these proteins? Were the knock down cells analysed by EM? Was there an effect on protein export? As it stands the knock down data does not show a role of the complex in the folding of any type of substrate and the function in oxidative folding, as indicated in the title, remains tentative.
RESPONSE: The morphology of the ER is difficult to address due the fact that in these lifecycle stages the ER is quite condensed. Further, the ER is not clearly identifiable via EM. The knockdown of PDI8 is not complete, therefore, it is not possible to perform the suggested experiment as we will always see the residual PDI8 crosslinked with PfJ2. We are not sure what or if there’s any effect on BiP upon knockdown of PfJ2. BiP does not crosslink with PfJ2 and its expression levels do not change. We are not sure what other effect the reviewer expects on BiP. The co-IP data show that BiP is part of a complex with PfJ2 and PDI8, this complex has not been previously observed in the ER of any organism. Since the parasites die during the trophozoite/early schizont stages, several of these organelles such as Rhoptries, micronemes etc probably do not form. Once the lab reopens after the pandemic, we will test for the presence of these organelles via immunofluorescence microscopy as well as EM. Similar experiments could show an effect on protein export. However, since we didn’t identify any exported proteins to be putative substrates of PfJ2 (despite the expectation that chaperones are sticky and bind everything), and therefore, any effect we observe is likely to be indirect. Given the published data establishing the function of PDIs as oxidative folding chaperones, their high degree of conservation, and in vitro characterization, we conclude that they function in oxidative folding. Furthermore, we show that PfJ2 regulates the function of Plasmodium PDIs as well as recruits BiP to the mixed disulfide complex. BiP is a highly conserved chaperone that has clear function in protein folding. Based on this and the data presented here, we conclude that PfJ2 functions as a regulator of oxidative folding in P. falciparum.
2) While I like the idea to use established commercial drugs as novel potential antimalarials, those used here are specific for non-infectious human diseases and target the host which is not a desirable property. Considering this, their rather low activity against the parasite can be taken as a positive result. However, the low activity is less convincing to establish the folding pathway in the parasite ER as a drug target. Beside the issue that it is unclear if indeed oxidative folding is the essential function of the PfJ2 complex (see previous point), the data in Fig. 7 does not clearly establish that this function is targeted by the inhibitors used. The effect is only seen at concentrations of 5xIC50. It is possible that this severely reduced viability which could be a non-specific reason for the lack of DVSF-crosslinked products. This needs to be examined in more depth. For instance, is the crosslink still seen after equivalent treatment of cultures with 5xIC50 of other unrelated drugs? Were other, unrelated processes unaffected? What was the effect of exposure to the drug on the ER and parasite morphology? Was the appropriate parasite stage affected? Can it be tested how fast exposure to 5xIC50 of the drug kills the parasites (at least morphologically, but preferably also by more specific means)?
RESPONSE: We agree that the drugs identified here are not ideal antimalarials but rather they are starting molecules for a larger medicinal chemistry program, that is beyond the scope of this manuscript. While we see significant loss of DVSF crosslinking (for PfJ2) even at the IC50, the relationship between protein activity inhibition and parasite death isn’t always linear. We are testing analogs of 16F16 to identify more potent inhibitors of these proteins. We thank the reviewer for the suggested experiments, and when the pandemic is no long limiting access to the lab, we will perform some of these.
3) While generally sound, a few experiments would have benefitted from more controls. A reducing sample from the same parasites for Fig. S7 (loaded a couple of lanes away to avoid interference of the reducing agent) would have been nice for comparison to show specificity of the higher molecular weight adducts. Detection of a control protein not expected to co-purify (for instance a cytosolic protein or a membrane-bound protein to control for residual parasite material) would have been appropriate for the co-immunoprecipitations (e.g. Fig. 6A,D, Fig. S9).
RESPONSE: We show that there are no non-specific bands for PDI11, because when we mutate the cysteines, we do not observe any cross-linking. We will include the control proteins for the co-IPs, they were not included for the sake of clarity.
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Reviewer #3:
This paper describes redox-active proteins in the ER of malaria parasites. The authors start out with PfJ2, a J- and Trx-domain containing protein. They find that it is an essential ER protein that interacts with other chaperone and Trx domain proteins. Using a crosslinker with specificity for redox-active cysteines they identify PfPDI8 and PfPDI11 as redox-partners that together may aid folding of other proteins in the secretory pathway. Finally the authors use inhibitors that act on human PDIs and show that they inhibit parasite growth, albeit at rather high concentrations. This may be fortunate as this suggests different specificities for host and parasite PDIs. However, it also means that from this work it is difficult to judge if the parasite PDIs can be specifically targeted.
This is an interesting paper and rightly emphasises that it addresses a much understudied process and organelle in the parasite. The DVSF-crosslinking and the knockdown cell lines are highlights (although the knockdown cell lines were not fully exploited). The paper covers a lot of ground. However, this comes at the cost of depth. The actual function of the studied proteins on folding of other proteins and on the state of the ER was not evaluated and it is also not clear if the human PDI inhibitors indeed target the parasite enzymes. The high concentrations of inhibitors needed to show an effect on DVSF-crosslinking might indicate a secondary effect due to loss of parasite viability. As a result it is not fully clear if the studied proteins are indeed critical for folding of relevant substrates and if this process is druggable. More work is needed to support the main conclusions of the paper.
Major points:
1) The authors describe conditional knockdown lines and find that PfJ2 and PfPDI8 are essential but these lines are not further exploited for functional studies. Did the knock downs have any effect on proteins they mention as potential substrates (Table 1)? Did it affect the state/morphology of the ER? Did knock down of PfPDI8 remove/shift one of the PfJ2 bands after DVSF-crosslinking, as would be expected? Is there an effect on BiP? A general folding problem in the ER with such a lethal phenotype might have profound effects on the morphology of the organelles receiving protein from the ER. What happens to other cellular markers after knock down of these proteins? Were the knock down cells analysed by EM? Was there an effect on protein export? As it stands the knock down data does not show a role of the complex in the folding of any type of substrate and the function in oxidative folding, as indicated in the title, remains tentative.
2) While I like the idea to use established commercial drugs as novel potential antimalarials, those used here are specific for non-infectious human diseases and target the host which is not a desirable property. Considering this, their rather low activity against the parasite can be taken as a positive result. However, the low activity is less convincing to establish the folding pathway in the parasite ER as a drug target. Beside the issue that it is unclear if indeed oxidative folding is the essential function of the PfJ2 complex (see previous point), the data in Fig. 7 does not clearly establish that this function is targeted by the inhibitors used. The effect is only seen at concentrations of 5xIC50. It is possible that this severely reduced viability which could be a non-specific reason for the lack of DVSF-crosslinked products. This needs to be examined in more depth. For instance, is the crosslink still seen after equivalent treatment of cultures with 5xIC50 of other unrelated drugs? Were other, unrelated processes unaffected? What was the effect of exposure to the drug on the ER and parasite morphology? Was the appropriate parasite stage affected? Can it be tested how fast exposure to 5xIC50 of the drug kills the parasites (at least morphologically, but preferably also by more specific means)?
3) While generally sound, a few experiments would have benefitted from more controls. A reducing sample from the same parasites for Fig. S7 (loaded a couple of lanes away to avoid interference of the reducing agent) would have been nice for comparison to show specificity of the higher molecular weight adducts. Detection of a control protein not expected to co-purify (for instance a cytosolic protein or a membrane-bound protein to control for residual parasite material) would have been appropriate for the co-immunoprecipitations (e.g. Fig. 6A,D, Fig. S9).
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Reviewer #2:
The claim here is of having discovered a druggable cellular process in P. falciparum, one that opens the door to therapeutic intervention in the most deadly form of malaria.
The study commences with a focus on what appears to be the Pf homologue of a eukaryotic protein disulphide isomerase, known to many as ERdJ5 and referred to here as PfJ2. Its cellular contingents were identified by cross-linking and pull down, it’s (predicted) thiol reactivity explored with agents that react with reduced thiols and it’s functional importance to parasite fitness (in the lab) explored by gene knockout. These experiments provide evidence that PfJ2 and it’s associated Pf PDIs engage in thiol redox chemistry in the ER of the parasite and that integrity of this biochemical process is important to viability of the parasite.
Lacking all expertise in molecular parasitology, this reader is unable to judge the specific significance of these findings to the field nor indeed the extent to which these are hard-won discoveries. However, from the perspective of the fundamentals of ER redox chemistry the findings represent a modest advance, showing that what is true of yeast and mammals is also true of Apicomplexa. The important mystery related to the juxtaposition of a J-domain and thioredoxin domains in PfJ2, remains.
The most important claim however is the one with translational potential, namely that one might be able to discover (electrophilic) compounds that, despite the monotony of shared chemical features of thiol chemistry, will nonetheless possess sufficient specificity towards this or that malarial protein to be converted one day to a useful drug. However, in regards to this important point the authors offer very little in the way of evidence how and if this might be achieved.
Therefore, the main conclusions to draw from this paper are that ER-localised thiol chemistry is also important in malaria parasites and that, assuming one were able to explore localised context-specific features of thiol reactivity in malarial proteins, it may one day be possible to develop anti-malarial drugs that exploit this as a mechanism of action. The generic nature of these considerations limits the significance of the conclusions one might draw from this paper.
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Reviewer #1:
In this manuscript, Cobb and colleagues report on the biochemical and functional characterization of redox active ER proteins in the malaria parasite Plasmodium falciparum. They studied a protein called PfJ2, which contains HSP40 J and Trx domains and is homologous to human ERdj5. Using the TetR-PfDOZI aptamer system to tag PfJ2 and conditionally regulate its expression, they show that PfJ2 is localized in the parasite ER and is essential for parasite growth during the asexual blood stages. Using co-immunoprecipitation combined with mass spectrometry, they identify partner proteins of PfJ2 including other ER proteins such as PDI and BIP. Using a chemical biology approach based on DVSF crosslinker, they document the redox activity of PfJ2 and identify redox substrates of PfJ2, which include PDI8 and PDI11 protein disulfide isomerases. They further functionally characterized PDI8 and PDI11 using the glmS ribozyme for conditional knockdown. These experiments confirm that PDI8 and PDI11 are partners of PfJ2 and show that knockdown of PDI8 impairs parasite blood stage growth. Finally, the authors show that inhibitors of human PDI inhibit parasite growth (at best in the micromolar range) and block the redox activity of PfJ2 and parasite PDI.
This is an interesting study combining genetic and chemical biology approaches to investigate an understudied compartment of the malaria parasite. The manuscript is clearly written and the work technically sound. In summary, this study illustrates that ER redox proteins in the malaria parasite perform similar functions as in other organisms. The main limitation of this study is that evidence showing that redox ER parasite proteins are druggable is rather weak. PfJ2 is very similar to human ERdj5 in terms of active redox site and function, and the authors used inhibitors that are active on human PDI. It thus remains uncertain whether an antimalarial strategy targeting such conserved pathways is achievable.
In addition, a number of specific points should be addressed to improve the quality of the manuscript:
Although PDI8 and PDI11 gene edition were performed in the PfJ2apt line, the authors did not attempt to knockdown both PfJ2 and PDI8/11 simultaneously (because PfJ2 is essential). Therefore, referring to "double conditional mutants" is misleading.
The authors should provide details on the parasite lysis conditions used for the co-IP experiments to identify interacting proteins (Table 1) and redox partners (figure 3). In their proteomic analysis, the authors considered proteins with a 5-fold increase in the specific versus control conditions. A more stringent analysis would retain only proteins identified exclusively in the modified J2apt line.
In figure 6, the authors should probe the blots for a control protein that is not co-immunoprecipitated with PfJ2 or PDI8.
In Supplementary fig 4, control untreated parasites should be analyzed in parallel to GlcN-treated parasites.
The partial reduction of protein levels (Fig S4) shows that the glmS system is not very efficient here, which might explain why there is no phenotype in the PDI11 mutant (Fig5B). This questions the conclusion that PDI11 is dispensable.
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Reviewer #3:
In this manuscript, Bohr et al examine how the pluripotent stem cell system of planarians responds to organ-specific damage. If and how the differentiation of specific cell types is dynamically regulated is a conceptually fascinating problem in planarians and in general stem cell research. The authors address this problem by comparing the stem cell response between a single-organ amputation (the pharynx) versus broad tissue loss (decapitation). Their findings indicate that only the removal of the pharynx triggers the increased differentiation of pharyngeal cell types, while the loss of non-pharyngeal tissues upregulates the differentiation of progenitors of multiple organs, but not the pharynx. Further, the authors implicate temporally restricted ERK signaling as a regulatory component in the differentiation of pharyngeal cell types. These observations are also important because they contrast with the previously proposed "target blind" model (LoCascio et al., 2017) that posits the differentiation of different cell types at constant relative proportions, with the rate of stem cell divisions as global production rate regulator. In contrast, the observations by Bohr et al provide further evidence for more flexibility and specificity within the planarian stem cell system ("target consciousness") in the sense of lineage-specific adjustments in the production rates of specific cell types.
That said, the manuscript generally suffers from an overly narrow focus. Important questions remain regarding the specificity of the stem cell response to pharynx amputation and multiple experiments lack important controls (see below). Moreover, the authors have overlooked that a "target conscious" progenitor response has already been demonstrated by the selective proliferation of protonephridial markers expressing neoblasts in response to protonephridial damage by RNAi (Vu et al., 2015).
Major points:
1) Specificity of the stem cell response:
The central premise of the paper is the selective amplification of pharyngeal progenitors in response to pharynx amputation. The authors conclude this based on i) an increase in the absolute number of foxA+/piwi-1+ cells in a specific area, while ii) de-capitation has no effect on the absolute number of foxA+/piwi-1+ cells in the same area. This approach is an insufficient demonstration of specificity, as the known phenomenon of wound-induced stem cell activation might also change the absolute number of specific neoblast subclasses and might do so in an injury-dependent manner.
To account for this important caveat, the authors need to i) quantify RELATIVE proportions of foxA+/piwi-1+ cells out of total piwi cells (or of total H3P+ cells) and ii) they need to include other organ progenitors in the initial analysis. The latter is also critical because the pharynx is a complex organ comprising descendants of multiple lineages (e.g., muscle, neurons, epidermis) and it is not clear whether the foxA+/piwi-1+ cells indeed serve as a singular origin of all constituting lineages (as assumed by the authors), or if they only provide a subset of pharyngeal cells with a rate-limiting role in pharynx assembly (e.g., pharyngeal muscle). In the face of such uncertainty, iii) the quantification of new cell incorporation into the pharynx versus other tissues via BrdU labeling would be necessary to address this caveat and to provide a global perspective on the specificity of the response independent of incompletely characterized marker genes.
In addition, the following experimental design problems need to be addressed or better documented, including:
• The authors provide insufficient methodological detail on progenitor quantifications, even though the entire manuscript rests on this assay. What are their criteria for scoring a piwi-1+ cell as double-positive for the often weak and noisy lineage labels? If done "by eye", was double-blind scoring used? Were all cells in a given Z-stack counted or only specific planes? If the latter, by which criteria were image planes selected for quantification? How were the specific specimens out of an experimental cohort selected for imaging/quantification? Though not necessarily a unique shortcoming of this particular study, these points simply need to be adequately addressed in order to rigorously support quantitative differences between experimental conditions (e.g., specificity).
• The authors appear not to distinguish at all between technical replicates (e.g., multiple specimens within an experimental cohort) versus biological replicates (independent experimental cohorts). This is significant, because i), the use of the standard error of the mean (SEM) that the authors employ throughout is not really an appropriate measure for a single biological replicate with 3 animals - the standard deviation (SD) would seem a more appropriate measure in this context (SD). ii), the number of worms quantified for each experiment is generally low (n=3 animals in Fig. 1, 3, 5, 6; n=5 animals in the rest of the figures) given the observed variability in the data (e.g., ~25-30 foxA+/piwi-1+ cells 3d after pharynx amputation in Fig. 1E versus 50 cells in figure 1H). Similarly, for kinetic experiments as in Fig. 1H or 2C, it is simply crucial to ensure that the error bars include the variation in response dynamics between multiple replicates due to the drift in the baseline fraction of H3P+ cells or varying staining efficiencies (e.g., different batches of animals on different days), rather than the technical variation in a single experimental cohort only. Please address these concerns by adding more specimens and a thorough description of the experimental design.
2) Timeline of pharynx regeneration: The pharynx regeneration timeline and associated events that the authors present are insufficiently supported by experimental data. The conclusion in line 215 "that proliferation in a critical window of 1 to 2 days after pharynx amputation produces a population of progenitors that are likely essential for pharynx regeneration" rests i) on the diagnosis of a "proliferative peak of FoxA+ stem cells that occurs after pharynx amputation (Figure 2C)" (line 202). However, rather than a "proliferative peak", Fig. 2C shows a broad "proliferative plateau" of FoxA+ stem cells between 6h and 3 days after amputation. Similarly, the foxA+/H3P+ quantification after pharynx amputation in Fig. 4G also displays a lack of a peak of foxA+/H3P+ from 1d to 2d after amputation. ii), the associated nocodazole experiments suffer from the fact that the authors did not quantify the impact of the drug on the abundance of foxA+/piwi-1+ cells during treatment intervals from 0-1d and 2-3d after pharynx amputation. Therefore, the authors cannot rule out that nocodazole treatment might have similar effects on the abundance of foxA+/piwi-1+ cells throughout the 1-3 d post-amputation time interval, with the more severe organ-level phenotype of the 1-2d treatment window being caused by some other effect of the drug (e.g., on the differentiation of another rate-limiting cell type for pharynx regeneration or, conceivably, inhibition of priming neuronal activity ). Similar concerns apply regarding the statement in line 242, "... a window 1-2 days after amputation in which activation of ERK signaling is important for pharynx regeneration." Here, i) the quantification of the end-stage phenotype of drug treatment during the 1-2d time interval (regain of feeding ability) is missing. ii) Similarly, the examination of the consequences of PD treatment on foxA+ expression in piwi-1+ cells in panel 4D-H employs drug soaking for 3 days, yet the corresponding end-stage phenotype of 3-day drug treatment is not shown. iii) the implications of ERK in pharynx regeneration are tentative. Even though the PD compound is initially correctly introduced as "MEK inhibitor", the authors subsequently switch to the factually wrong "ERK inhibitor" designation (e.g., line 358). Further, additional experimental evidence for the assumed Erk inhibition as the cause of the observed phenotypes would be desirable to rigorously support the conclusion.
These caveats need to be addressed if a cell biological timeline is to remain part of this manuscript.
3) Integration with the existing literature:
The authors need to better integrate their findings with the literature. First, they need to cite the findings of Vu et al, which explicitly demonstrated a specific increase in the fractional abundance of piwi-1+/protonephridial marker+ cells in response to RNAi-mediated damage to protonephridia (Vu et al., 2015). As such, this study already demonstrates the main point of Bohr et al., namely that the planarian stem cell system is capable of "target conscious" progenitor provision. At the very least, the authors should credit these results as additional evidence for their model. A further finding that they should discuss is the demonstration by LoCascio et al (LoCascio et al., 2017) that flank region cut-outs cause a significant increase in pharynx cell incorporation over baseline, despite the absence of injury to the pharynx. How do the authors reconcile the discrepancy between these data and their own? In general, the discussion would benefit greatly from a more explicit comparison between the "target blind" model versus their data, as well as a broader perspective on the regulation of stem cell homeostasis.
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Reviewer #2:
Bohr, Shiroor, and Adler investigate how stem cells respond to the loss of specific tissues in planarians. The planarian stem cell population (neoblasts) are distributed throughout the planarian body and include pluripotent stem cells and a wide range of lineage-committed progenitor cells. How this heterogenous pool of cells behave post-injury or amputation is incompletely understood. The discovery of markers to label stem cell progeny have opened the door to investigate how stem cells respond to tissue loss. However, the anatomy of planarians makes it difficult to surgically remove or damage specific organs. The PI of this study developed an assay to remove the pharynx by "chemical amputation" to study the mechanisms underlying regeneration of this organ without drastically perturbing or injuring other tissues. Using this approach, this paper investigates how a well-defined population of FoxA+ progenitors respond to pharynx removal at early time-points during the regeneration. Their data suggest that stem cells are able to detect loss of the pharynx and respond by generating significantly more cells fated to become pharynx, whereas amputation of non-pharyngeal tissues does not have an obvious effect on pharynx progenitor specification dynamics. In addition, using pharmacological treatments, the authors show that cell proliferation and ERK signaling are required for the expansion of pharynx progenitors and cell differentiation. In contrast, other cell types in the planarian eye do not appear to require proliferation or ERK signaling, suggesting that stem cell responses "target blind" as suggested in a previous study, but are rather tuned to specific missing tissues.
This work has the potential to make a significant contribution to the field by advancing our understanding of how the planarian heterogenous stem cell population responds to the loss of a specific organ. However, the report is preliminary as presented. It appears that the authors performed many experiments a single time. In addition, description of the methods is insufficient. The authors need to chiefly demonstrate the reproducibility of the data and robustness of the observations.
Concerns:
1) The authors need to replicate experiments to increase the sample size for most experiments.
2) Details for imaging and quantification should be explicitly stated in the methods, and the reported cell count numbers should be normalized as appropriate for each set of experiments.
3) Although the authors mention "lineage-tracing" experiments (see minor comments), they do not perform DNA analog pulse-chase experiments to analyze a temporal progression and spatial localization of stem cells to FoxA+ progenitors after pharynx removal. The authors rely on PH3-staining in conjunction with FoxA, and supplementary experiments using the pluripotent stem cell marker tgs-1 (which was only examined at 1 dpa). Could the authors clarify what they think FoxA+ stem cells represent? Are these self-renewing pluripotent stem cells or lineage-committed progenitors? Can the authors get some insight by scanning their images of PH3+ cells expressing FoxA visibly undergoing metaphase? Are daughter cells uniformly FoxA+ as reflected in the model? At least in one of the cells shown in the nocodazole treated controls it appears that both daughter cells express FoxA (Figure 3). I suggest showing some higher magnification images to support the interpretations/conclusions. Others have posited (e.g., Rink, Chapter 2 of Planarian Regeneration: Methods and Protocols), that not every dividing cell may be a long-term self-renewing stem cell and whether transient amplifying cells exist or contribute to regeneration in planarians is unknown. Adler and Sánchez Alvarado (2015) discuss the role of transient states and how the transcriptional profiles change in response to regeneration. It wasn't clear to me how the authors think about these cells based on the limited number of experiments and analysis, and there are a few places where the terminology is inconsistent, especially in reference to proliferating ovo+ progenitors (P. 14). The authors need to be clear, and it might be helpful to illustrate their model in one of the early figures or to include it in the final model, which omits tgs-1 due to the limited number of experiments performed with this marker gene (Figure 7).
4) The pharynx is complex and there is no data to assess what the contribution of other progenitor populations might be. I don't think or think it is unlikely that FoxA+ progenitors are solely responsible for reconstructing the pharynx. The authors should examine how other progenitor populations behave during the process of pharynx regeneration by extending the timeline of progenitor cell analysis. This would reveal if there is fluctuation in progenitor dynamics as animals regenerate the pharynx or re-scale proportions after pharynx regeneration. For example, can the authors test if they are able to detect a contribution of neural progenitors to regeneration of the pharyngeal nervous system? And if so, when during the regeneration process does it take place in the context of their study?
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Reviewer #1:
This manuscript explores how planarian stem cells respond to the loss of a specific organ: the pharynx. The previously proposed "target-blind" model of planarian regeneration (LoCascio et al. 2017) posited that stem cells do not respond directly to missing tissues, but rather replace missing cell types based on their normal rates of homeostatic turnover. In contrast, the authors of this manuscript suggest that planarian stem cells can sense and respond to the loss of specific missing tissues, using the pharynx as a case study. The authors conclude that planarians may use more than one mode of regeneration, depending upon the target being regenerated (eye vs. pharynx).
The question explored in this paper is of fundamental importance, and providing an alternative model by which planarian stem cells regenerate missing tissues should be of interest to a broad readership. Unfortunately, in its current form, the manuscript presents enticing preliminary findings, rather than robust experimental observations. Because the authors are attempting to refute a previously published model, it is critical that the data are clear and convincing. If not, these findings could be summarily dismissed without appropriate debate. If the authors can demonstrate that their results are robust across larger samples sizes and experimental replication, and address the major issues listed below, this manuscript would represent a significant contribution to our understanding of planarian regeneration.
Major Issues:
1) Throughout the manuscript, experiments were either not repeated, or the number of biological replicates was not reported. In most cases, it appears that experiments were done only once (with the exception of the drug treatments). Numbers of biological replicates and sample sizes should be explicitly stated and the data from different replicates reported for Figs. 1D-G, 2B-D, 3C, 3E-F, 4B, 4D-H, 5C-D, and 6A-E.
2) The authors do not sufficiently describe their methods for imaging and quantifying cells (Figs. 1E, 1G, 2C-D, 3F, 4E-H, 5D, 6B). The size of the area covered to collect these data is unclear. High-magnification images are shown: are these the areas that were imaged? If so, their results could be biased by choosing small regions of interest. Ideally, the authors should quantify more than one region per animal. Also, they do not describe the depth of the z-stacks collected or how these stacks were normalized/standardized across conditions. All their conclusions hinge on the quantification of progenitor populations in response to different amputation paradigms or chemical treatments, so the standards for imaging and quantification must be clearly reported.
3) Inappropriate statistical tests were used throughout. The use of multiple t-tests amplifies the chance of a Type I error and is especially problematic when up to 7 comparisons were made! The authors should use one-way ANOVA with multiple comparison corrections for all experiments with more than two groups.
4) Figs. 1D-E show that upon pharynx amputation but not head amputation, FoxA+ piwi+ pharynx progenitors increase. These data suffer from the quantification issues highlighted above: how the data were quantified is not sufficiently described, only 3 data points were taken (one per animal), the experiment appears to have been performed only one time, and the wrong statistical test was used. Rather than reporting the number of FoxA+ piwi-1+ cells counted, the authors should quantify the total number of doubly positive cells as a percentage of piwi-1+ cells, as was previously published (Adler et al. 2014). The authors also fail to specify whether the change observed between "3 dpa phx" and "3 dpa head" is significant, which is a material point.
5) Fig. 2D also suffers from the inadequate quantification practices described above. Ideally, FoxA+ cells should be quantified as a percentage of the H3P+ cells observed.
6) The authors use "stem cell", "progenitor", "stem cell progenitor", and "progenitor stem cells" in a mixed and confusing way throughout the paper. For example, in lines 174-175 the authors state that "proliferation of FoxA+ stem cells precedes the increase in pharynx progenitors." This refers to FoxA+ H3P+ cells vs. FoxA+ piwi-1+ cells, but the only difference is that the former are stem cells in the act of mitosis. Is a distinction being made? Elsewhere in the paper, FoxA+ piwi+ cells are referred to as stem cells. The terminology used needs more clarity and consistency.
Along these lines, what is a FoxA+ PSC (Line 168)? Are the authors suggesting that FoxA is a pluripotency marker? Or are the authors saying that tgs-1 has a more expansive expression pattern and is co-expressed with progenitor markers? If double FISH was performed with the progenitor markers reported (ovo, myoD, gata-4/5/6, six-1/2, and pax6), would they all overlap with tgs-1? These experiments need to be performed to make any claims about FoxA expression in the context of pluripotency.
7) In Fig. 3C, how does pharynx regeneration occur over such a long period of time after nocodazole treatment in the 1-2 day window? Does target-blind regeneration occur once this window is missed? The authors should repeat analysis of FoxA+ progenitors at later time points in this condition, and/or show rates of BrdU incorporation into the pharynx with and without nocodazole treatment in this window.
8) The use of the inhibitor PD in Fig. 4 is problematic. No data are shown to verify that phosphorylation of ERK was inhibited in these experiments. A citation of previous use is not sufficient. The effect of PD on WT uncut animals with regards to FoxA+ cells is not shown and is a necessary control. To address questions of drug specificity, the authors should corroborate their findings with a second inhibitor of ERK signaling like U0126, which has already been shown to work in planarians (Owlarn et al. 2017).
9) The conclusion that ERK signaling functions in regulating differentiation but not proliferation is premature (line 249-264). Figs. 4E-H should be quantified as percentages of piwi-1+ and H3P+ cells, especially since there is decreased proliferation overall with PD treatment.
10) The use of head fragments to compare eye regeneration vs. pharynx regeneration is inappropriate. Previous studies have already shown that the absence of eyes is not required to induce ovo+ progenitor amplification (LoCascio et al. 2017). Thus, this is not a surprising result (line 343-345) and the authors are mis-citing previous observations (in the earlier work, head fragments were never described). The region where ovo+ cells was quantified in Fig. 6A is not justified or explained. The yellow box is placed in a medial region where ovo+ cells do not normally reside. The authors should image within the laterally positioned ovo+ streams that have been previously described (Lapan & Reddien 2012; LoCascio et al. 2017).
11) Rather than using head fragments, the authors should repeat the flank resection experiments shown previously (LoCascio et al. 2017). This previous study showed that increased BrdU incorporation into the pharynx occurred following flank resection even though the pharynx was present. That result may have been 1) an artifact of increased BrdU staining due to stimulation of proliferation upon injury, 2) caused by unintentional damage to cells associated with the pharynx, or 3) a response to the loss of FoxA+ progenitor populations that surround the pharynx rather than the loss of the differentiated organ. The authors have the opportunity to revisit this published observation by quantifying the FoxA+ progenitor response during flank resection +/- pharynx. Without these data, this story is incomplete and therefore the conclusion of a targeted regeneration response is not yet convincing.
12) The negative results that proliferation and ERK signaling are dispensable for eye regeneration in Fig. 6 are weak and unconvincing. The regenerated eyes appear smaller; this should be quantified (number of PRNs per eye). If the small pharynges that form in Figs. 3D and 4C are considered a deleterious phenotype, why is the same standard not applied to the eye? Also, existing eye progenitors could have been sufficient for eye regeneration in these drug treatments. Furthermore, eyes did not regenerate after nocodazole treatment 50% of the time. Is it not more likely that the observations reported are dosage and timing artifacts? How has proliferation been affected? These observations do not live up to the claims made.
13) The authors claim that ovo+ cells are not proliferative (H3P+) even in cases where there is eye progenitor amplification (head amputation), but the data are not shown (line 321). They should be. Indeed, previous publications have never shown that ovo+ cells proliferate. This might mean that there are proliferating eye progenitors that precede expression of ovo. The authors should discuss this alternative.
14) The authors claim that eye regeneration does not require proliferation or ERK signaling but pharynx regeneration does. This conclusion hinges on the gross observation that eyes can regenerate in the presence of nocodazole and PD (see point 12 above). These data are coarse and the interpretations are unconvincing. Instead, their model can be directly tested using BrdU pulse-chase experiments. According to the authors' model, one would predict that following pharynx amputation, the rate of incorporation of BrdU+ cells into the regenerating pharynx should be higher than in uninjured controls. Conversely, the rate of BrdU incorporation into the regenerating eye should remain unchanged between injured (i.e. eye-resected) and control animals (LoCascio et al. 2017). Once the authors have established the prediction above, they have the opportunity to show the effects of nocodazole, PD, and U0126 on BrdU incorporation in the regenerating eye vs pharynx following eye resection and pharynx amputation in the same animal. This way, the authors can directly test the requirement for proliferation and/or ERK signaling in both tissues.
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Preprint Review
This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.
Summary
All three reviewers recognized the potential significance of this work, but shared concerns about sample sizes, lack of biological replicates, and insufficient details about cell quantification.
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Reply to the reviewers
Note from the authors (AU): This manuscript has been reviewed by subject experts for Review Commons. The authors would like to thank the reviewers for their comments to the manuscript, and the editor for patience with our response. Our reponse was delayed due to the COVID-19 lock-down situation in our institution. Now we are pleased to provide the following point-by-point response, as detailed below.
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
The manuscript by Suomalainen et al. describes a fluorescence-based approach combined with high-resolution confocal microscopy to study the heterogeneity of adenovirus infection in a population of human cells. The main focus of the authors is the detection of viral transcripts in infected cells, how this correlates with viral genomes, the cell state, and how it varies between different cells in a single population. The paper is generally well written and easy to read, with a few typos, although I found parts of it to be somewhat length and repetitive. Particularly the results section could be pruned somewhat for readability and clarity. The major limitation of the study as it stands is it's overall impact and novelty, which limits journal selection somewhat. A very similar study was recently published, which the authors cite (Krzywkowski et al, 2017). Nevertheless, I think the study design is rigorous and well executed, but I do have some specific comments which may enhance it's overall impact and novelty.
**Major:**
Results "Visualization of AdV-C5..." section:
Why not also look at normal cells that can be synchronized? Cancer cells, such as A549 will by definition be highly heterogenous and at all phases of the cell cycle. Primary non-transformed cells can easily be synchronized by contact inhibition and are much more physiologically relevant.
AU: In the current manuscript, we concentrated on the early phases of the AdV-C5 infection, on the question how virus gene expression is initiated and whether the cell cycle phase of the host cell impacts the initiation of virus gene expression. Answering these questions requires use of cells that express good amount of virus receptors so that viruses efficiently bind to the cells and infections can be synchronized so that extended time does not elapse between virus addition and accumulation of E1A transcripts; extended time between these two steps would make interpretation of the results more complex since cells could have progressed from one cell cycle stage to another during the experiment. Furthermore, having cells at all phases of the cell cycle is actually a benefit since then the experiment can be carried out under an “unperturbed” condition; all cell cycle synchronization methods have pleiotropic effects on the cells.
It is true that primary non-transformed cells are physiologically more relevant than cancer cells, but primary cells have issues with donor-to-donor variability and many primary cells express rather low amounts of AdV-C5 receptors, so synchronized infections in these cells are not possible. Furthermore, the extended cell morphology of many normal fibroblast cell lines and the tendency of cell extensions from neighboring cells to overlap makes fluorescent images of these cells incompatible for automated cell segmentation.
Here, we provide data also from HDF-TERT cells (nontransformed human diploid fibroblasts immortalized by human telomerase expression) to show that two of our key findings from A549 cells are not artefacts of cancer cells. This is, that akin to A549 cells, the infected HDF-TERT cells accumulate high number of E1A transcripts (Fig.1C), and also in these cells nuclear vDNA numbers do not predict the cytoplasmic E1A transcript counts during early phases of infection (S2C Fig). However, since HDF-TERT cells are rather inefficiently infected by AdV-C5, correlation of early E1A transcript accumulation to the cell cycle phase of the host cell could not been done in these cells. We have been unable to identify primary or normal immortalized cells that would be easily available and efficiently infected by AdV-C5 (synchronized infection with short time elapsed between virus addition and accumulation of E1A transcripts).
"The virus particles bound..." - Can the spatial resolution of a confocal microscope truly differentiate individual particles that are sub-wavelength in size? What about the sensitivity for single particles? Some sort of experiment to show that single particles can be detected should be performed and shown to assure the readers that this is in fact possible. Furthermore, even when based on the particle to pfu ratio, the MOI would still be nearly 2000pfu/cell, so the actual number of observed particles is an order of magnitude lower than what was applied to the cells.
AU: The fluorescence signal from individual fluorophore-tagged AdV or anti-hexon antibody-decorated particle is bright enough to be picked up by PMT or HyD detectors of the current confocal laser scanning microscopes. In fact, tracking fluorophore-tagged particles of the size of AdV has been a standard microscopy procedure since late 1990’s.
Because the Reviewers were questioning the apparently high multiplicity of infection used in the experiments, we clarify the difference between “standard” MOI estimations and our infection set-up. First of all, as described in Material and Methods, we estimated the number of physical virus particles in our virus preparations using A260 measurements (J.A. Sweeney et al., Virol. 2002, doi: 10.1006/viro.2002.1406). This method, like all other methods used to estimate virus particle numbers, is likely not 100% reliable.
Second, we incubated the virus inoculum with cells only for 60 min, after which the unbound viruses were washed away. During this short incubation time only a small fraction of input virus particles bind to cells, and indeed as shown in Fig.1A, a theoretical MOI of 54400 physical virus particles/cell or 13600 physical virus particles/cell yielded Median of 75 and 26 bound virus particles per cell, respectively. Interpretation of the results from the cell cycle assays required that there was a relatively short time between infection and analysis so that cells in a large scale did not change their cell cycle status during the experiment. This required use of a rather high MOI. Furthermore, for collection of a large data set, it is convenient that every cell is infected.
Third, what exactly does one pfu mean in terms of physical adenovirus particles? There is no clear answer to this, since several parameters affect the pfu. In which cells was the titration carried out? How long was the input virus inoculum incubated with the cells? How many of the virus particles entering the cell actually established an infection? And, as described in A. Yakimovich et al. (J. Virol. 2012, DOI: 10.1128/JVI.01102-12), only a fraction of infected cells produce a plaque. The majority of papers stating that x pfu/cell was used for infection, usually incubate the cells with the virus inoculum for several hours at 37°C, and never make any attempts to estimate exactly how many virus particles entered into the cells.
Fig. 4 - I am not certain that the observed difference is significant, at least looking at it, beyond the width difference of the peaks, highest expression for both is largely in G1. It would be nice to see this using a western blot of cell cycle sorted cells, which can easily be accomplished using FACS.
AU: In the highest GFP expression bin, CMV-eGFP expressing cells have 43% cells in G1 and 50% in S/G2/M. In comparison, E1A-GFP expressing cells have 58% cells in G1 and 35% in S/G2/M. The difference in G1 cells in the highest eGFP bin is statistically significant (p Page 15, 2nd paragraph. It would be valuable and informative to determine whether there is heterogeneity in histone association with these different vDNAs and whether these histones exhibit divergent modifications (enabling or restricting transcription). Same as above. I am rather surprised that the DBP signal did not correlate well with vDNA signal, particularly for the larger replication centers. How can this be reconciled? Was there an increase in overall vDNA signal later in infection? It is important to know this as it determines whether the observed vDNA signal is real or could be caused by viral RNA or other background causes (non-infected controls notwithstanding). Can the signal be detected with inactivated viruses (via UV for example?)
AU: Whether histone modifications impact the transcriptional output of adenovirus genomes early in infection is indeed an intriguing question, but unfortunately this is very challenging, if not impossible, to study at single-cell / single vDNA level with the existing technology. Techniques for single-cell measurements of chromatin states are still in infancy, although some notable advancements in this field were reported in 2019 (e.g. K. Grosselin et al. Nature Genetics, DOI: https://doi.org/10.1038/s41588-019-0424-9 and S. Ai et al. Nature Cell Biology, DOI: https://doi.org/10.1038/s41556-019-0383-5).
Furthermore, current literature offers a confused picture as to when exactly protein VII on incoming virus genomes is replaced by histones (reviewed in the reference 39, Giberson et al.). Of note, the vast majority of incoming nuclear vDNA molecules scored protein VII-positive with anti-VII staining under the experimental conditions used for the Fig. 2C data. However, we did not include these results into the manuscript because VII-positive signal on vDNAs does not exclude these vDNAs having histones on certain parts of the genome.
The Reviewer wonders why the DBP signal in Fig.6C does not correlate with vDNA signal. There is no discrepancy here because DBP signal in the figure is a proxy for replicating vDNA whereas the click vDNA signal reports incoming vDNA. The one DBP spot without an associated click vDNA signal could be due to a replication center originated from a replicated viral genome, not from incoming viral genome. The figure shows that incoming vDNAs within the same nucleus initiate replication asynchronously.
Page 18, 1st paragraph. It would be interesting to determine whether there was association between pol II and those genomes that showed no E1A, similarly to the histone suggestion. What about things like viral chromatin organization? Soriano et al. 2019 showed how E1A and E4orf3 work in tandem to alter viral chromatin organization by varying histone loading on the viral genome.
AU: This again would be technically very challenging to show. We actually tried to visualize active transcription using an antibody against RNA polymerase II CTD repeat YSPTSPS (phosphor S5), azide-alexa fluor488 and anti-alexa fluor488 antibody to mark EdC-labeled incoming vDNAs and proximity ligation assay for signal amplification. However, this method was not sensitive enough to detect RNA polymerase II association with individual viral genomes. We only detected the proximity ligation signal in replication centers when replicated viral genomes were tagged with EdC.
Fig. 2. Can you really say that a single dot correlates with a single transcript? Has that been validated in any way?
AU: Signal amplification with branched DNA technology leads to binding of a large number of fluorescent probes to a mRNA and thus enables detection of single nucleic acid molecules. This has been validated e.g. in A.N. Player et al. 2001. J. Histochem. Cytochem (https://doi.org/10.1177/002215540104900507) and N. Battich et al. 2013. Nature Methods (https://doi.org/10.1038/nmeth.2657).
**Minor:**
Page 5, last paragraph. "Transcirpts from the viral late transcription unit,..." This is not correct as recently shown by Crisostomo et al, 2019.
AU: The data in Crisostomo et al. paper suggest that some late gene expression can occur before vDNA replication, but an abundant accumulation of late transcripts coincides with onset of vDNA replication. However, the Crisostomo et al. study did not test what the levels of late gene transcripts are if the vDNA replication was inhibited. But to acknowledge the possibility that there might be some level of late gene transcription prior to replication of the viral genomes, the sentence is modified as follows: “Transcripts from the viral late transcription unit, amongst them mRNAs for the viral structural proteins, vastly increase in abundance concomitant with the onset of vDNA replication”. Furthermore, we have added the Crisostomo et al. reference here as well.
Page 10, "... because AdvV-infected cells are less well adherent..." This is not strictly true as loss of attachment only occurs later on in infection. It would be helpful to have statistical significance indicated directly in the figures.
AU: Although clearly visible cell rounding indeed occurs only late in infection, also during early stages of infection the HAdV-C5-infected cells are less adherent than non-infected cells. In many assays this is not obvious, but the RNA FISH staining procedure includes several incubation and washing steps in rather harsh buffers, and we observed random, sometimes considerable, cell loss with infected cultures but not with non-infected cultures.
In the revised manuscript we have included the statistical significance P values both into the main text and the figure legends, but not to the figures directly, because the P values were generated with different statistical tests and P values should not be shown/mentioned without stating which statistical test was used. However, we noticed that we had in some cases omitted to mention what was the number of pairs analyzed in some of the Spearman’s correlation tests. This has now been corrected in the revised manuscript.
The very high MOIs used are concerning, could these have negative effects on the cell viability or overall state?
AU: We refer to our explanation above about the theoretical MOI and the actual MOI. Furthermore, in the experiment described in Fig.2C (correlation of E1A transcripts per cell vs. viral genomes per cell), 42% of analyzed cells had ≤ 5 viral genomes/cell and 27.5% of analyzed cells had between 6-10 viral genomes per cell; these are not high numbers. We also provide controls that the EdC-labeled genomes are detected with good efficiency. Hence the EdC-labeled genomes per cell are a good estimate of the numbers of virus particles that indeed entered into the cells.
There are a few typos and such that should be corrected. AU: We have tried to find and correct the typos.
Reviewer #1 (Significance (Required)):
As I stated above, the work is interesting and significant, to a degree. The major limitation is that the novelty is low as a paper published in 2017 (cited by the authors) used a very similar approach to investigate a similar problem. In addition, there are multiple other recent papers looking at cell populations in the context of adenovirus infection, and whether a single cell or population based approach is better is unclear. This is something the authors might want to strengthen prior to submission.
AU: In the current study, we focused on the early phase of HAdV-C5 infection, on how viral gene expression is initiated and how individual nuclear viral genomes proceed to a replicative phase. The Krzywkowski et al. 2017 J. Virol. Paper that the reviewer refers to used padlock probe-based rolling circle amplification technique to simultaneously detect HAdV-C5 genomes and viral mRNAs in individual infected cells.
The shortcoming of this method is inferior sensitivity compared to the branched DNA technology-based method used by us in the current study. Krzywkowski et al. were able to pick up signals from virus mRNAs and virus genome only relatively late in the infection, i.e. at the time when incoming genomes were expected to have multiplied by replication. Thus the study by Krzywkowski et al. was unable to provide information for the questions addressed in our study, i.e. do the levels of E1A transcripts early in infection correlate with viral vDNA counts in the nucleus and is there variability in the transcription output from individual vDNAs within the same nucleus, or variability in how individual vDNAs within the same nucleus proceed into the replication phase. We hence do provide novel information, and do not consider this as a limitation of our paper.
We emphasize that population assays are done to attempt to understand molecular basis of a phenomenon by correlations. Instead, deep molecular insights require to-the-point-assays, in the case of transcription, single-molecule live cell assays at the level of single genes. Technically, we (and also the field) are not quite there yet.
Regardless, our study is a first step towards understanding transcription output of nuclear HAdV-genome at single-cell, single-genome levels. It has revealed insight that was not apparent from population assays. It is clear that the next step will be time-resolved live cell assays with simultaneous detection of transcription output, genome detection and transcription factor clustering on the genomic loci. With current technology the simultaneous detection of all these events is challenging, and requires the development of further technology.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
The authors show heterogeneity of AdV-C5 mRNA transcript quantity and dynamics in different cell types, which is regulated by the cell cycle phase and does not correlate to incoming viral DNA, using single molecule RNA FISH technologies and detection of incoming viral DNA by EdC labeling.
**Major Comments:**
The authors change the MOI used in their experiments (7 different MOIs are used throughout the paper) in a manner that appears randomly and without explanation. (54400 for Figure 1A, 1B, 3B, S3B; 37500 for Figure 1C; 23440 for Figure 2A, 2C, S5A; 13600 for Figure 1A, 1D; 36250 for Figure 3C, S3D; 11200 for Figure 4B; 23400 for Figure 6B). The authors should provide explanation, why these changes in MOIs are necessary.
AU: The MOIs given are theoretical MOIs, and essentially all figures indicate what was the actual MOI, that is, the real number of virus particles entering into the cells. This is beyond what is commonly provided in virology. It is essential, however, since MOI differs between different cell types. Therefore, we prefer to use the actual MOI as shown in Fig.1A, or we indicate the number of vDNAs that were delivered to the cells of interest.
Variable MOIs had to be used to ensure that different cell lines received comparable numbers of virions, in particular virus particle binding to and entering into the cells. Infection kinetics are different in different types of cells, but can be tuned by MOIs used. Furthermore, different virus preparations were used in the experiments and we performed analyses at different stages of the infection cycle. Due to all these different facettes provided by our experiments, it was impossible to choose one standard (theoretical) MOI for all the experiments.
The authors use mean fluorescence intensity of E1A probes per cell as estimate for viral transcript abundance for some of their experiments (Figure 1D, E, 3B), and count E1A punctae as measure for E1A transcripts in other experiments (Figure 2C, 3C, 5), without showing data, that these measures correlate. Problematic is hereby, that not all E1A punctae have the same signal intensity, as can be seen in Figure S1, which makes the estimation of the correlation of E1A punctae (= number of transcripts) and fluorescence intensity difficult. The authors should provide both (E1A punctae counts and estimation via fluorescence intensity) for at least one experiment, to prove, that the estimation of E1A transcript levels via fluorescence intensity is feasible.
AU: The quantification method had to be adjusted to the number of virus transcripts in the cell at the time of analysis. The best quantification method is segmentation and counting the individual fluorescent puncta per cell, but, as stated in the manuscript, this method does not accurately quantify the mRNA puncta from maximum projections of confocal or widefield image stacks when the number of puncta per cell exceeds ~ 200.
On the other hand, as shown in the quantification below, mean fluorescence intensity measurements per cell do not of course distinguish between cells having one vs. two mRNA puncta. Yet, as shown in the figure below, a relatively good correlation between puncta counting and fluorescence intensity measurements is achieved when cells have ≥ 10 transcripts per cell. Subsets of randomly picked images of the Fig.2C/Fig.5 dataset were included into the analysis (rs is Spearman’s correlation rank coefficient, approximate P p.15: "The nuclear E1A signals in AraC-treated cells were resistant to RNase A, but they were dampened by treatment with S1 nuclease (S6B Fig)." The authors make this statement based on (i) two completely different timepoints (12 h.p.i. for RNaseA treatment, 24.5 h.p.i. for S1 nuclease treatment) and (ii) in different clones of the A549 cells as stated in the methods section on p.21 (Two different clones of human lung epithelial carcinoma A549 cells were used in the study: our laboratory's old A549 clone (experiments shown in Fig. 1, Fig. 3B and S1 Fig., S3B and S3C Fig., S6A and S6B Fig., RNase A treatment) and A549 from American Type Culture Collection (ATCC, experiments shown in Fig. 2 and Fig. 5, Fig. 6, S2B Fig., S4 Fig., S5 Fig., and S6B Fig. S1 nuclease-treatment)). This makes it difficult to interpret, if the data is due to differences in the timepoints or cell types, or if it is due to binding of the E1A probe to single stranded vDNA.
AU: This is a fair criticism, thank you. We have replaced the RNase A figure S6B in the revised manuscript. A new RNase A experiment was repeated in ATCC A549 cells using the same infections conditions as with the S1 nuclease-treated cells.
**Minor Comments:**
p.4: "AdV are non-enveloped, double-stranded DNA viruses that cause mild respiratory infections in immuno-competent hosts, and establish persistent infections, which can develop into life-threatening infections if the host becomes immuno-compromised [reviewed in 6]." Not all AdV cause respiratory diseases, the disease outcome of human AdV depends on the site of primary infection, which differs between the different AdV types.
AU: We have modified the text as follows: AdV are non-enveloped, double-stranded DNA viruses that cause mild respiratory, gastrointestinal or ocular infections…
p.7: The authors state, that "At the 17 h time point, about half of the cells had high numbers of protein VI transcripts, and most of them very high numbers of E1A transcripts.", however, the picture shown in Figure 1F shows a different phenotype, with low transcript levels of VI in E1A high cells and high transcript levels of VI in E1A low cells.
AU: This was perhaps a bit difficult to see in the overlay images since one has to distinguish between green and yellowish green. We have provided the individual channels along the overlay picture in Fig. S1D, and now it is clear that at 17h pi cells with high numbers of VI transcripts have also high numbers of E1A transcripts.
p.8: "This nuclear E1A signal is due to binding of the E1A probe to single-stranded vDNA in the replication centers (see below)." The authors should state here, that due to the binding of the probes to the single stranded vDNA in the replication centers, the nucleus was excluded from the analysis for Figure 1F in late timepoints.
AU: We have modified the text according to the Reviewer’s suggestion. The text is now as follows: ‘Due to further studies (see below), we assume that this nuclear E1A signal represents binding of the E1A probe to single-stranded vDNA in the replication centers. Accordingly, the nuclear area was excluded when quantifying the viral transcripts per cell in late timepoints (Fig. 1F).’
Due to this time point the author cannot state that the E1A staining seen (Fig. 1F; indicated with white arrows) are replication centers; this is just an assumption, since there is no evidence in Fig 1 the author cannot be sure; the author should change the text: "taking the following experiments into account...", "due to further studies (see below)..... we assume that..."
AU: We have modified the text according to the Reviewer’s suggestion; see also the previous comment above.
p.8: The authors should mention the figure they refer to, since there is no E1B-55K staining in Fig. 1F
AU: The text has been modified as follows: Whereas other time points showed relatively few E1A, E1B-55K or VI puncta over the nuclear area (Fig. 1B, 1F, S1A Fig.), clustered nuclear E1A signals were apparent at 23 h.
p.9: Which test was used to calculate the additional p-values?
AU: As stated in the Material and Methods section or the figure legends, the p-values were calculated either by a permutation test using custom-programmed R-script (the code has been deposited on Mendeley Data along with other data associated with this manuscript), or by Kolmogorov-Smirnov test using GraphPad Prism. GraphPad Prism was also used to calculate Spearman’s correlation coefficients and the associated approximate p values. In the revised manuscript, we have added the following sentense into the Material and Methods section / Statistical analyses: Spearman’s correlation tests were done using GraphPad Prism.
p.10: For the experiment for the correlation of viral genomes per cell and E1A transcripts in HDF-TERT cells (Figure S2C), the MOI is missing in the description of the results, as well as in the corresponding figure legends.
AU: We have indicated the theoretical MOI (~ 4800 virus particles per cell) in the figure legend and in the Material and Methods section. The actual MOI, i.e. the actual number of virus particles entering into the cells, could not be determined due to the long (15 h) incubation time of virus inoculum with the cells, which in turn was required because these cells bind AdV-C5 rather inefficiently. However, between 1 and 32 EdC-labeled virus genomes were detected per cell nucleus at 22 h pi.
11: calculation of correlation? rs? Why does the author combine S and G2/M phase? Fig. S3A show different values for the phases
AU: rs is the abbreviation for Spearman’s correlation coefficient, and, as indicated in the Material and Methods, we used GraphPad Prism to calculate the Spearman’s correlation coefficients.
Different methods to estimate cell cycle stages. DNA content method cannot separate S and G2/M with great confidence, whereas Kusabira Orange-hCdt1 and Azami-Green-hGeminin expressions in HeLa-Fucci cells allow more fine-tuned assessment of the cell cycle phases.
p.11: "Thus, the total intensity of nuclear DAPI signal can be used to accurately assign G1 vs S/G2/M stage to cells." The authors should also here refer to other papers, which showed that this correlation is feasible, as they did in the methods section (67. Roukos V, Pegoraro G, Voss TC, Misteli T. Cell cycle staging of individual cells by fluorescence microscopy. Nature protocols. 2015;10(2):334-48. Epub 2015/01/31. doi: 10.1038/nprot.2015.016. PubMed PMID: 25633629; PubMed Central PMCID:PMCPMC6318798.), and maybe also refer to a newer paper which deals with this technique: Ferro, A., Mestre, T., Carneiro, P. et al. Blue intensity matters for cell cycle profiling in fluorescence DAPI-stained images. Lab Invest 97, 615-625 (2017). https://doi.org/10.1038/labinvest.2017.13
AU: The integrated nuclear DAPI signal intensity is indeed a widely used method to assign cell-cycle stage to individual cells. We have added the second reference suggested by the Reviewer to the reference list for this method.
p.11: "Furthermore, when focusing on the highest E1A expressing cells, i.e. the cells with mean cytoplasmic E1A intensities larger than 1.5 × interquartile range from the 75th percentile, 71.9% of these cells were found to be in the G1 phase of cell cycle, whereas only 55.8% of cells in the total sampled cell population were G1 cells." The authors do not provide any reference to a figure within the manuscript or the supplements, which contains these data. Are these data not shown in the manuscript?
AU: These values are calculated from the data shown in Fig.3B. The source data supporting findings of this study (maximum projection images, excel files of the CellProfiler and Knime workflows) have now been deposited to Mendeley Data as stated in the Material and Methods / Data availability section of the revised manuscript and listed in Supplementary tables.
p.12: punctuation mistake; . instead of , To enrich G1 cells. AdV-C-5 (moi ~ 36250) was added. Why does the author switch between signal intensities and counting E1A puncta per cell (limited to 200) in the different experiments to illustrate accumulation of E1A transcripts?
AU: The same answer as above: the quantification method had to be adjusted to the number of virus transcripts in the cell at the time of analysis. The best quantification method is segmentation and counting the individual fluorescent puncta per cell, but, as stated in the manuscript, this method does not accurately quantify the mRNA puncta from maximum projections of confocal or widefield image stacks when the number of puncta per cell exceeds ~ 200. On the other hand, as shown in the quantification in the new S1C Fig., mean fluorescence intensity measurements per cell do not of course distinquish between cells having one vs. two mRNA puncta, but a relatively good correlation between puncta counting and fluorescence intensity measurements is achieved when cells have ≥ 10 transcripts per cell.
p.14: "For E1A (or E1B-55K), we did not detect transcriptional bursts with bDNA-FISH probes on nuclear vDNAs, either prior to or after accumulation of viral transcripts in the cell cytoplasm." The authors do not provide any reference to a figure within the manuscript or the supplements, which contains these data. Are these data not shown in the manuscript?
AU: This statement is based on hundreds of images we have analyzed during the course of the study. It is impossible to show all of these images, so in principle, this is “data not shown”. We have modified the text as follows: With hundreds of images analyzed, we never unambiguously detected transcriptional bursts with E1A (or E1B-55K) bDNA-FISH probes on nuclear vDNAs, either prior to or after accumulation of viral transcripts in the cell cytoplasm.
p.14: space between number and %
AU: Thank you for pointing this out. It has been corrected.
p.15: "This is was also seen in AdV-C5-EdC-infected cells" should be changed to "This was also seen in AdV-C5-EdC-infected cells"
AU: Thank you for pointing this out. It has been corrected.
Fig. 1B:
−figure legend does not indicate how cells were staine −also no description in the continuous text −which E1A transcripts are stained? all? 12S? 13S?
AU: The first sentence in Results section states that “We used fluorescent in situ hybridization (FISH) with probes targeting E1A, E1B-55K and protein VI transcripts followed by branched DNA (bDNA) signal amplification to visualize the appearance and abundance of viral transcripts in AdV-C5-infected A549 lung carcinoma cells.” Furthermore, the legend to Figure 1 starts with the title “Visualization of AdV-C5 E1A, E1B-55K and protein VI transcripts in infected cells by bDNA-FISH technique”, and the legend to Fig.1B mentions that “cells were stained with probes against E1A and E1B-55K mRNAs or E1A and protein VI mRNAs”. We are of the opinion that this is enough information to understand the figures.
The main text to Fig.1 also states that “The E1A probes covered the entire E1A primary transcript region and thus all E1A splice variants. The temporal control of E1A primary transcript splicing and E1A mRNA stability give rise predominantly to 13S and 12S E1A mRNAs at 5 h pi (references)”.
Fig. 1D: −difference in accumulation of viral transcripts is not that visible as in IF staining (Fig. 1B; Fig. 1S);
Fig. 1 or S1 Fig. do not show IF staining but signals from FISH.
−graph does not show any difference between E1A and E1B-55K
AU: The y-axes values in Fig.1D graph are arbitrary units and thus E1A and E1B-55K graphs are not directly comparable to each other. We have included into the revised manuscript S1B Fig., which shows quantification of E1A and E1B-55K fluorescent puncta per cell at the 5 h pi; the difference between E1A and E1B-55K was statistically significant.
Fig. 1F: −figure legend does not fit with labelling of IF images and continuous text −description says 22 h, while IF labeling and text (p. 7, last lane) mentions 23 h pi
AU: The figure annotations state the time of analyses as total time after virus addition to cells, whereas text stated the time of analyses as x h post virus removal since we wanted to stress that the input virus was incubated only for 1 h with the cells. However, Reviewers found this confusing, so we have changed the text in the revised manuscript so that time of analysis is stated as total time after virus addition to cells (as in the figure annotations). Only in the Material and Methods section we maintain the original 1 h + x h statement for the time of analysis.
Fig. 2A: −figure legend: lane 5 Punctuation wrong: azide-Alexa Fluor488. Alexa Fluor647
AU: Thank you for pointing this out. It has been corrected.
Fig. 4A: −difficulties to understand −author stated that promoter-driven EGFP expression is clearly dominated by G1 cells for E1A and by S/G2/M cells for CMV, however this is not clearly visible in the graph −no severe differences visible between CMV-eGFP and E1A-eGFP −author should include numbers for quantification and statistical calculations to illustrate the differences
AU: In the highest GFP expression bin, CMV-eGFP expressing cells have 43% cells in G1 and 50% in S/G2/M (n=2149). In comparison, E1A-GFP expressing cells have 58% cells in G1 and 35% in S/G2/M (n=2258). The difference in G1 cells in the highest eGFP bin is statistically significant (p
Fig. 4B: −amount of E1A protein levels calculated via IF (signal intensities) −immunofluorescence is not a suitable tool for protein quantification
AU: It is true that not all antibodies are suitable for IF (or for Western blot), and we cannot be certain that the monoclonal anti-E1A antibody used by us detects all E1A forms with different post-translational modifications with equal efficiency. However, IF is a widely accepted method to estimate protein levels in the cell, especially if the proteins like E1A accumulate in the nucleus (makes segmentation of the signal easy) and give a rather uniform nuclear staining pattern.
Fig. 5: −in A. it is stated, that E1A bDNA -FISH is not suitable, since it is too short to be detectable. However, in B E1A bDNA-FISH is used. is there a difference? −according to the method part just one E1A mRNA was used for the assays, why is it then not possible to use that one in Fig. 5A? −explanation of the procedure and the experiment is very confusing
AU: The Reviewer probably refers to Fig.6 here, not to Fig.5. The E1A introns are short (about 100 bases) and cannot be picked up with bDNA FISH probes. In Fig. 6B we were using the E1A bDNA-FISH probes, which were made against the AdV-C5 genome map positions 551-1630 to detect vDNA single strands of the E1A region and these single strands were long enough to be picked out by our E1A probes.
Fig. S6B: −authors want to show that it is RNase-insensitive, but S1 nuclease-sensitive
−two different A549 cell clones and two different time points are used for the treatments → not compareable to each other
AU: This is a fair criticism. We have replaced the RNase A figure in S6B Fig. in the revised manuscript. The new RNase A experiment was carried out in ATCC A549 cells using the same infections conditions as with the S1 nuclease-treated cells.
Material and Methods: −headings do not indicate which methods are explained −no clear structure AU: We have made minor changes to the headings of Material and Methods section. We have first explained in detail the bDNA-FISH method, but otherwise the order is according to the order of the figures.
Reviewer #2 (Significance (Required)):
highly significant manuscript very important for the virology field
my research topics are human adenoviruses and their replication cycle
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
**Summary:** Soumalainen et al have studied adenovirus viral gene expression and replication at a single-cell level. They explore the extent of correlation between incoming genome copy number and early gene expression and progression into the late phase, revealing substantial variation between cells in the numbers of E1A transcripts (the first gene expressed upon infection) that is not explained by differences in the numbers of viral genome templates in the cells. They also explore the relevance of cell cycle stage to this variability and show a positive correlation between G1 cell cycle stage and higher levels of gene activity, which explains at least part of the variation. To form these conclusions they have applied new methods to visualise and quantify single molecules of nucleic acid in single cells. The experiments are all carefully and fully described with full detail of materials. Overall the manuscript is well written and easy to follow.
**Major comments:**
All of the experiments appear to be done with rigour and their results reported with due regard to statistical significance etc. My major concern though is that they have been done, perhaps out of necessity to get detectable signals, at very high multiplicities of infection. A well-accepted standard to achieve infection of all cells in a culture is an MOI of 10 infectious units per cell. Even this is acknowledged not to represent the biology of natural infection and it is striking that, where technically feasible, lower MOI studies are more revealing of how a virus actually works. Here, the authors have used counts of particles rather than infectious units to determine MOI and for Ad5, the particle/pfu ratio is typically 20-100. Their MOIs though are 13,000 - 50,000 per cell, implying an infectious MOI of at least 130 for their A549 experiments, which are known to be readily infected by Ad5 from other work.
AU: Unlike common experiments done by others, we used a synchronized infection and removed the input virus after 1h incubation at 37°C. This type of infection initiation requires high input virus amounts, as opposed to studies in which the virus inoculum is incubated with cells for several hours/days, as is typically done in studies determining the infectious or plaque forming units in virus inoculum. Hence, the MOI used by others involved incubation of inoculum with cells over extended periods of time, and they cannot be compared to our pulsed infection conditions.
Although the calculated theoretical MOIs (physical particles/cell) were high in our experiments, only 0.1% – 0.2% of input virus particles bound to cells during the 1h incubation period (Fig. 1 A; this estimation is based on the ratios between Median values for the number of cell-associated viruses vs input virus numbers).
Furthermore, in the experiment described in Fig.2C (correlation of E1A transcripts per cell vs. viral genomes per cell), 42% of analyzed cells had ≤ 5 viral genomes/cell and 27.5% of analyzed cells had between 6-10 viral genomes per cell. Please note, that these are not high numbers.
The input virus amounts used were selected this way, because we aimed at getting a broader view of how virus transcription at early phases of infection responds to a varying number of virus genomes delivered to the nucleus. Therefore, we did not limit the analyses to a situation with 1 or less than 1 virus particles/genomes per cell.
In addition, the analyses of how cell cycle phase impacts the initiation of virus gene expression requires a relatively short time between virus inoculation and time point of analysis (i.e. a rather high MOI). Otherwise, as also pointed out by the Reviewer, the cells could have experienced more than one cell cycle phase during the duration of the experiment. Furthermore, although the initial natural infection probably starts with a very low MOI, the second round of infection is a high MOI infection due to a large number of progeny virus particles released from an infected cell.
Surprisingly, the authors do not see intracellular vDNA copy numbers that are fully reflective of this high MOI, with median intracellular vDNA of 75 /cell at the highest MOI. The authors should consider how the population distribution of vDNA /cell does or does not fit the predicted Poisson distribution. Nonetheless, at these high copy numbers / cell, there must surely be a risk that the variation in gene expression activity arises stochastically, out of competition between genomes for essential transcription factors. Given that multiple cellular factors are each required for E1A transcription, high genome copy numbers could actually inhibit E1A expression relative to cells with more modest copy numbers because limited supplies of individual factors are recruited to different viral genome copies.
AU: The “discrepancy” between theoretical MOI and the actual observed number of cell-associated virus particles or cell-associated virus genomes is explained above. Furthermore, we would like to point out that we have directly estimated the number of virus particles bound to cells with the input virus amounts used, something that is usually not done in other studies.
It is indeed theoretically possible that high nuclear genome numbers could lead to inhibition of transcription due to competition for limiting essential host factors. However, if we included only cells with ≤4 vDNA molecules per nucleus into the analysis (total number of cells analyzed was 258), then Spearman’s correlation coefficient for vDNA per nucleus vs E1A mRNAs per cell was 0.186 (p=0.0027). Thus, this would not support the notion that cells with moderate nuclear vDNA copy numbers would have a better correlation between the nuclear vDNA copies vs E1A mRNA counts per cell.
The vDNA/cell in Fig.2C does not fit predicted Poisson distribution, var/mean=9.129.
It is important for the analysis of correlation of gene expression with cell cycle that the virus has not, at the time point analysed, already perturbed the cell cycle (a well-known effect of infection) which the authors document in Suppl Fig3B. To my eye, the G1 peak in infected cells is somewhat narrower than in the control while the S/G2 bump is a little greater. The % of cells in each of the two gates needs to be shown to support the conclusion.
AU: In non-infected sample G1= 54.63% and S/G2/M = 45.37%, in infected cells G1= 51.4% and S/G2/M= 48.6%. We have added this information into the S3B Fig.
Turning to the experiments documenting a correlation between E1A expression and cell cycle stage, the authors interpret their findings in terms of the stage the cells are at when the analysis was done (G1 stage cells have more E1A transcripts). The key experiment (Fig 3B) is analysed at only 4 h pi, so substantial progression from G2/M back to G1 after virus addition can probably be discounted, but the point should be discussed. The authors also use release from G1 in another cell line to support their argument that G1 supports higher levels of E1A expression (Fig 3C). Here, they elect to exclude all cells with fewer than 50 E1A transcripts from their analysis. The reason for this is completely obscure and isn't obviously justified; conceivably it could bias the outcome of the experiment. At minimum, this decision needs to be carefully explained; ideally, the full data set should be used.
AU: Fig.3B: As suggested by the Reviewer, we have added to the main text the following explanation: “We used a high MOI infection (median 75 cell-associated virus particles, Fig. 1A) in order to achieve a rapid onset of E1A expression so that the time between virus addition and analysis was short. Thus, it is not expected that a substantial number of cells would have changed their cell cycle status during the experiment.”
Fig.3C: We show the results also from the full data set of infected cells, i.e., cells with ≥ 1 E1A puncta in S3D Fig. We excluded the cells without zero E1A puncta because with these cells it is impossible to know whether they received no virus or whether E1A transcription had not yet started. Permutation test indicated that the difference between the starved+starved and starved+FCS is statistically significant even in this case. Because both samples are dominated by cells with low E1A counts, we log-transformed the E1A values for the box plot figure.
The authors note the highest level of E1A activity (as opposed to RNA) was in G1/S cells and suggest that high E1A cells advance preferentially into S. Whilst in line with the literature that E1A promotes progression into S, an alternative explanation is simply that there is a time lag between RNA accumulation and protein accumulation, during which progression through the cycle would be expected.
AU: This is a valid point, and we have modified the text as follows: “… which could reflect the advancement of high E1A expressing cells into S-phase. However, considering the time between virus addition and analysis (10.5 h), we cannot exclude the possibility that the observed G1/S preference is at least partly due to time-dependent progression of G1 cells to G1/S.”
**Minor comments:** Fig 1 and elsewhere. Given that the 1 h incubations with virus were done at 37 C, the convention would be to include this period in the time post-infection at which harvest / fix time points are quoted. There is inconsistency between text and legend with 12 h pi being sometimes represented as 11 h after virus removal; this is an unnecessary confusion.
AU: We have modified the text so that hours pi always include the 1h incubation with the input virus. Only in the Material and Methods section we kept the original 1h virus binding – fixing at xh post virus removal.
Results description prior to the ref to Fig 1B: unclear what this is supposed to mean.
AU: We have now slightly modified the first paragraph of the Results section. We mention the benefits of the bDNA signal amplification method and explain the experimental set up, i.e. that the input virus was incubated with the cells only for 1h. We also justify why we used a short incubation for the virus inoculum.
Fig 4A: provide % of cells in each gate in each histogram.
AU: In the highest GFP expression bin, CMV-eGFP expressing cells have 43% of cells in G1 and 50% in S/G2/M. In comparison, E1A-GFP expressing cells have 58% of cells in G1 and 35% in S/G2/M. This has been added to the figure, and it is also mentioned in the main text. Furthermore, we added to the text the results from Two Proportion Z-test to show that the proportion difference of G1 cells in the highest bin was statistically significant (p
Fig 5: bottom right panel x axis label is wrong
AU: Thank you for pointing out this. This has been corrected.
In the presentation of Fig 6, it would be much clearer for the reader if the detected replication foci (ss DNA detected as E1A puncta) were referred to as something other than E1A puncta. There is too much scope for confusion with the earlier experiments in which E1A RNA was detected.
AU: We agree. In the revised manuscript, we refer to these puncta in the text as E1A ssDNA-foci.
Reviewer #3 (Significance (Required)):
The study represents the application of state of the art single-molecule visualization techniques to an as yet not understood aspect of virus infection. That said, there is prior experimentation in this area, which the authors fully acknowledge and build upon. The new work is largely descriptive, in that it reveals very clearly the discrepancy between genome copy number and amounts of mRNA without seeking to explain these, beyond the cell cycle analysis. Whilst there is a better correlation between vDNA number and transcript once the data are stratified by cell cycle stage, it is still not strong (Fig 5), indicating that other substantial contributing factors remain to be described.
The work will be of interest certainly to adenovirologists, but also to others who study virus infections - particularly nuclear-replicating DNA viruses such as herpesviruses - where similar considerations are likely to apply.
Expertise: adenovirus; gene expression; virus-host interactions; molecular biology
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Referee #3
Evidence, reproducibility and clarity
Summary: Soumalainen et al have studied adenovirus viral gene expression and replication at a single-cell level. They explore the extent of correlation between incoming genome copy number and early gene expression and progression into the late phase, revealing substantial variation between cells in the numbers of E1A transcripts (the first gene expressed upon infection) that is not explained by differences in the numbers of viral genome templates in the cells. They also explore the relevance of cell cycle stage to this variability and show a positive correlation between G1 cell cycle stage and higher levels of gene activity, which explains at least part of the variation. To form these conclusions they have applied new methods to visualise and quantify single molecules of nucleic acid in single cells. The experiments are all carefully and fully described with full detail of materials. Overall the manuscript is well written and easy to follow.
Major comments:
All of the experiments appear to be done with rigour and their results reported with due regard to statistical significance etc. My major concern though is that they have been done, perhaps out of necessity to get detectable signals, at very high multiplicities of infection. A well-accepted standard to achieve infection of all cells in a culture is an MOI of 10 infectious units per cell. Even this is acknowledged not to represent the biology of natural infection and it is striking that, where technically feasible, lower MOI studies are more revealing of how a virus actually works. Here, the authors have used counts of particles rather than infectious units to determine MOI and for Ad5, the particle/pfu ratio is typically 20-100. Their MOIs though are 13,000 - 50,000 per cell, implying an infectious MOI of at least 130 for their A549 experiments, which are known to be readily infected by Ad5 from other work.
Surprisingly, the authors do not see intracellular vDNA copy numbers that are fully reflective of this high MOI, with median intracellular vDNA of 75 /cell at the highest MOI. The authors should consider how the population distribution of vDNA /cell does or does not fit the predicted Poisson distribution. Nonetheless, at these high copy numbers / cell, there must surely be a risk that the variation in gene expression activity arises stochastically, out of competition between genomes for essential transcription factors. Given that multiple cellular factors are each required for E1A transcription, high genome copy numbers could actually inhibit E1A expression relative to cells with more modest copy numbers because limited supplies of individual factors are recruited to different viral genome copies. It is important for the analysis of correlation of gene expression with cell cycle that the virus has not, at the time point analysed, already perturbed the cell cycle (a well-known effect of infection) which the authors document in Suppl Fig3B. To my eye, the G1 peak in infected cells is somewhat narrower than in the control while the S/G2 bump is a little greater. The % of cells in each of the two gates needs to be shown to support the conclusion.
Turning to the experiments documenting a correlation between E1A expression and cell cycle stage, the authors interpret their findings in terms of the stage the cells are at when the analysis was done (G1 stage cells have more E1A transcripts). The key experiment (Fig 3B) is analysed at only 4 h pi, so substantial progression from G2/M back to G1 after virus addition can probably be discounted, but the point should be discussed. The authors also use release from G1 in another cell line to support their argument that G1 supports higher levels of E1A expression (Fig 3C). Here, they elect to exclude all cells with fewer than 50 E1A transcripts from their analysis. The reason for this is completely obscure and isn't obviously justified; conceivably it could bias the outcome of the experiment. At minimum, this decision needs to be carefully explained; ideally, the full data set should be used.
The authors note the highest level of E1A activity (as opposed to RNA) was in G1/S cells and suggest that high E1A cells advance preferentially into S. Whilst in line with the literature that E1A promotes progression into S, an alternative explanation is simply that there is a time lag between RNA accumulation and protein accumulation, during which progression through the cycle would be expected.
Minor comments:
Fig 1 and elsewhere. Given that the 1 h incubations with virus were done at 37 C, the convention would be to include this period in the time post-infection at which harvest / fix time points are quoted. There is inconsistency between text and legend with 12 h pi being sometimes represented as 11 h after virus removal; this is an unnecessary confusion.
Results description prior to the ref to Fig 1B: unclear what this is supposed to mean.
Fig 4A: provide % of cells in each gate in each histogram.
Fig 5: bottom right panel x axis label is wrong
In the presentation of Fig 6, it would be much clearer for the reader if the detected replication foci (ss DNA detected as E1A puncta) were referred to as something other than E1A puncta. There is too much scope for confusion with the earlier experiments in which E1A RNA was detected.
Significance
The study represents the application of state of the art single-molecule visualization techniques to an as yet not understood aspect of virus infection. That said, there is prior experimentation in this area, which the authors fully acknowledge and build upon. The new work is largely descriptive, in that it reveals very clearly the discrepancy between genome copy number and amounts of mRNA without seeking to explain these, beyond the cell cycle analysis. Whilst there is a better correlation between vDNA number and transcript once the data are stratified by cell cycle stage, it is still not strong (Fig 5), indicating that other substantial contributing factors remain to be described.
The work will be of interest certainly to adenovirologists, but also to others who study virus infections - particularly nuclear-replicating DNA viruses such as herpesviruses - where similar considerations are likely to apply.
Expertise: adenovirus; gene expression; virus-host interactions; molecular biology
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Referee #2
Evidence, reproducibility and clarity
The authors show heterogeneity of AdV-C5 mRNA transcript quantity and dynamics in different cell types, which is regulated by the cell cycle phase and does not correlate to incoming viral DNA, using single molecule RNA FISH technologies and detection of incoming viral DNA by EdC labeling.
Major Comments:
The authors change the MOI used in their experiments (7 different MOIs are used throughout the paper) in a manner that appears randomly and without explanation. (54400 for Figure 1A, 1B, 3B, S3B; 37500 for Figure 1C; 23440 for Figure 2A, 2C, S5A; 13600 for Figure 1A, 1D; 36250 for Figure 3C, S3D; 11200 for Figure 4B; 23400 for Figure 6B). The authors should provide explanation, why these changes in MOIs are necessary. The authors use mean fluorescence intensity of E1A probes per cell as estimate for viral transcript abundance for some of their experiments (Figure 1D, E, 3B), and count E1A punctae as measure for E1A transcripts in other experiments (Figure 2C, 3C, 5), without showing data, that these measures correlate. Problematic is hereby, that not all E1A punctae have the same signal intensity, as can be seen in Figure S1, which makes the estimation of the correlation of E1A punctae (= number of transcripts) and fluorescence intensity difficult. The authors should provide both (E1A punctae counts and estimation via fluorescence intensity) for at least one experiment, to prove, that the estimation of E1A transcript levels via fluorescence intensity is feasible. p.15: "The nuclear E1A signals in AraC-treated cells were resistant to RNase A, but they were dampened by treatment with S1 nuclease (S6B Fig)." The authors make this statement based on (i) two completely different timepoints (12 h.p.i. for RNaseA treatment, 24.5 h.p.i. for S1 nuclease treatment) and (ii) in different clones of the A549 cells as stated in the methods section on p.21 (Two different clones of human lung epithelial carcinoma A549 cells were used in the study: our laboratory's old A549 clone (experiments shown in Fig. 1, Fig. 3B and S1 Fig., S3B and S3C Fig., S6A and S6B Fig., RNase A treatment) and A549 from American Type Culture Collection (ATCC, experiments shown in Fig. 2 and Fig. 5, Fig. 6, S2B Fig., S4 Fig., S5 Fig., and S6B Fig. S1 nuclease-treatment)). This makes it difficult to interpret, if the data is due to differences in the timepoints or cell types, or if it is due to binding of the E1A probe to single stranded vDNA.
Minor Comments:
p.4: "AdV are non-enveloped, double-stranded DNA viruses that cause mild respiratory infections in immuno-competent hosts, and establish persistent infections, which can develop into life-threatening infections if the host becomes immuno-compromised [reviewed in 6]." Not all AdV cause respiratory diseases, the disease outcome of human AdV depends on the site of primary infection, which differs between the different AdV types.
p.7: The authors state, that "At the 17 h time point, about half of the cells had high numbers of protein VI transcripts, and most of them very high numbers of E1A transcripts.", however, the picture shown in Figure 1F shows a different phenotype, with low transcript levels of VI in E1A high cells and high transcript levels of VI in E1A low cells.
p.8: "This nuclear E1A signal is due to binding of the E1A probe to single-stranded vDNA in the replication centers (see below)." The authors should state here, that due to the binding of the probes to the single stranded vDNA in the replication centers, the nucleus was excluded from the analysis for Figure 1F in late timepoints. Due to this time point the author cannot state that the E1A staining seen (Fig. 1F; indicated with white arrows) are replication centers; this is just an assumption, since there is no evidence in Fig 1 the author cannot be sure; the author should change the text: "taking the following experiments into account...", "due to further studies (see below)..... we assume that..." p.8: The authors should mention the figure they refer to, since there is no E1B-55K staining in Fig. 1F
p.9: Which test was used to calculate the additional p-values?
p.10: For the experiment for the correlation of viral genomes per cell and E1A transcripts in HDF-TERT cells (Figure S2C), the MOI is missing in the description of the results, as well as in the corresponding figure legends.
p. 11: calculation of correlation? rs? Why does the author combine S and G2/M phase? Fig. S3A show different values for the phases
p.11: "Thus, the total intensity of nuclear DAPI signal can be used to accurately assign G1 vs S/G2/M stage to cells." The authors should also here refer to other papers, which showed that this correlation is feasible, as they did in the methods section (67. Roukos V, Pegoraro G, Voss TC, Misteli T. Cell cycle staging of individual cells by fluorescence microscopy. Nature protocols. 2015;10(2):334-48. Epub 2015/01/31. doi: 10.1038/nprot.2015.016. PubMed PMID: 25633629; PubMed Central PMCID:PMCPMC6318798.), and maybe also refer to a newer paper which deals with this technique: Ferro, A., Mestre, T., Carneiro, P. et al. Blue intensity matters for cell cycle profiling in fluorescence DAPI-stained images. Lab Invest 97, 615-625 (2017). https://doi.org/10.1038/labinvest.2017.13
p.11: "Furthermore, when focusing on the highest E1A expressing cells, i.e. the cells with mean cytoplasmic E1A intensities larger than 1.5 × interquartile range from the 75th percentile, 71.9% of these cells were found to be in the G1 phase of cell cycle, whereas only 55.8% of cells in the total sampled cell population were G1 cells." The authors do not provide any reference to a figure within the manuscript or the supplements, which contains these data. Are these data not shown in the manuscript?
p.12: punctuation mistake; . instead of , To enrich G1 cells. AdV-C-5 (moi ~ 36250) was added. Why does the author switch between signal intensities and counting E1A puncta per cell (limited to 200) in the different experiments to illustrate accumulation of E1A transcripts?
p.14: "For E1A (or E1B-55K), we did not detect transcriptional bursts with bDNA-FISH probes on nuclear vDNAs, either prior to or after accumulation of viral transcripts in the cell cytoplasm." The authors do not provide any reference to a figure within the manuscript or the supplements, which contains these data. Are these data not shown in the manuscript?
p.14: space between number and %
p.15: "This is was also seen in AdV-C5-EdC-infected cells" should be changed to "This was also seen in AdV-C5-EdC-infected cells"
Fig. 1B:
−figure legend does not indicate how cells were staine
−also no description in the continuous text
−which E1A transcripts are stained? all? 12S? 13S?
Fig. 1D:
−difference in accumulation of viral transcripts is not that visible as in IF staining (Fig. 1B; Fig. 1S);
−graph does not show any difference between E1A and E1B-55K
Fig. 1F:
−figure legend does not fit with labelling of IF images and continuous text
−description says 22 h, while IF labeling and text (p. 7, last lane) mentions 23 h pi
Fig. 2A:
−figure legend: lane 5 Punctuation wrong: azide-Alexa Fluor488. Alexa Fluor647
Fig. 4A:
−difficulties to understand
−author stated that promoter-driven EGFP expression is clearly dominated by G1 cells for E1A and by S/G2/M cells for CMV, however this is not clearly visible in the graph
−no severe differences visible between CMV-eGFP and E1A-eGFP
−author should include numbers for quantification and statistical calculations to illustrate the differences
Fig. 4B:
−amount of E1A protein levels calculated via IF (signal intensities)
−immunofluorescence is not a suitable tool for protein quantification
Fig. 5:
−in A. it is stated, that E1A bDNA -FISH is not suitable, since it is too short to be detectable. However, in B E1A bDNA-FISH is used. is there a difference?
−according to the method part just one E1A mRNA was used for the assays, why is it then not possible to use that one in Fig. 5A?
−explanation of the procedure and the experiment is very confusing
Fig. S6B:
−authors want to show that it is RNase-insensitive, but S1 nuclease-sensitive
−two different A549 cell clones and two different time points are used for the treatments → not compareable to each other
Material and Methods:
−headings do not indicate which methods are explained
−no clear structure
Significance
highly significant manuscript very important for the virology field
my research topics are human adenoviruses and their replication cycle
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Referee #1
Evidence, reproducibility and clarity
The manuscript by Suomalainen et al. describes a fluorescence-based approach combined with high-resolution confocal microscopy to study the heterogeneity of adenovirus infection in a population of human cells. The main focus of the authors is the detection of viral transcripts in infected cells, how this correlates with viral genomes, the cell state, and how it varies between different cells in a single population. The paper is generally well written and easy to read, with a few typos, although I found parts of it to be somewhat length and repetitive. Particularly the results section could be pruned somewhat for readability and clarity. The major limitation of the study as it stands is it's overall impact and novelty, which limits journal selection somewhat. A very similar study was recently published, which the authors cite (Krzywkowski et al, 2017). Nevertheless, I think the study design is rigorous and well executed, but I do have some specific comments which may enhance it's overall impact and novelty.
Major: Results "Visualization of AdV-C5..." section:
Why not also look at normal cells that can be synchronized? Cancer cells, such as A549 will by definition be highly heterogenous and at all phases of the cell cycle. Primary non-transformed cells can easily be synchronized by contact inhibition and are much more physiologically relevant. "The virus particles bound..." - Can the spatial resolution of a confocal microscope truly differentiate individual particles that are sub-wavelength in size? What about the sensitivity for single particles? Some sort of experiment to show that single particles can be detected should be performed and shown to assure the readers that this is in fact possible. Furthermore, even when based on the particle to pfu ratio, the MOI would still be nearly 2000pfu/cell, so the actual number of observed particles is an order of magnitude lower than what was applied to the cells.
Fig. 4 - I am not certain that the observed difference is significant, at least looking at it, beyond the width difference of the peaks, highest expression for both is largely in G1. It would be nice to see this using a western blot of cell cycle sorted cells, which can easily be accomplished using FACS. Page 15, 2nd paragraph. It would be valuable and informative to determine whether there is heterogeneity in histone association with these different vDNAs and whether these histones exhibit divergent modifications (enabling or restricting transcription). Same as above. I am rather surprised that the DBP signal did not correlate well with vDNA signal, particularly for the larger replication centers. How can this be reconciled? Was there an increase in overall vDNA signal later in infection? It is important to know this as it determines whether the observed vDNA signal is real or could be caused by viral RNA or other background causes (non-infected controls notwithstanding). Can the signal be detected with inactivated viruses (via UV for example?)
Page 18, 1st paragraph. It would be interesting to determine whether there was association between pol II and those genomes that showed no E1A, similarly to the histone suggestion. What about things like viral chromatin organization? Soriano et al. 2019 showed how E1A and E4orf3 work in tandem to alter viral chromatin organization by varying histone loading on the viral genome. Fig. 2. Can you really say that a single dot correlates with a single transcript? Has that been validated in any way?
Minor:
Page 5, last paragraph. "Transcirpts from the viral late transcription unit,..." This is not correct as recently shown by Crisostomo et al, 2019.
Page 10, "... because AdvV-infected cells are less well adherent..." This is not strictly true as loss of attachment only occurs later on in infection. It would be helpful to have statistical significance indicated directly in the figures.
The very high MOIs used are concerning, could these have negative effects on the cell viability or overall state?
There are a few typos and such that should be corrected.
Significance
As I stated above, the work is interesting and significant, to a degree. The major limitation is that the novelty is low as a paper published in 2017 (cited by the authors) used a very similar approach to investigate a similar problem. In addition, there are multiple other recent papers looking at cell populations in the context of adenovirus infection, and whether a single cell or population based approach is better is unclear. This is something the authors might want to strengthen prior to submission.
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Reply to the reviewers
First of all, we thank all reviewers for their constructive suggestions and comments.
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
This group has been at the forefront recently of using imaging technologies to understand how chromosome segregation is coordinated in mammalian oocytes, and why errors occur. In the current paper they examine the dynamics of microtubule organising centres (which effectively replace centrioles/centrosomes in oocytes) in MI. The imaging of oocytes in this paper is beautiful. The major findings are (1) that MTOCs that are supposed to be at the spindle pole sometimes end up at the spindle equator, and this is documented very beautifully and (2) the correct positioning of MTOCs at the spindle pole appears to require kinetochore microtubules, as indicated by experiments manipulating the kinetochore component NDC80.
We appreciate the reviewer’s comment and clear description of our study.
**Major Comments**
As such the major claims of the paper are basically well supported. However, the analyses are is almost entirely restricted to prometaphase/metaphase, and the conclusions are relatively limited. The salient omission is any analysis of MTOC/chromosome relationship during anaphase. Were the paper to be extended to determine whether the lingering of MTOCs at the spindle equator is related to chromosome segregation error, that would increase the reach and importance of the work substantially. Specifically:
Can tracking experiments be performed to determine whether the chromosome that shows movement similarities to the errant MTOC is more/less likely to missegregate? Complete tracking as these authors are expert at should achieve this, or photo-labelling the desired chromosome.
Thank you for your comment. In our experimental system, oocytes rarely exhibit chromosome segregation errors (
Can the position of MTOCs (proportion that linger at the equator) be manipulated in the absence of other defects to determine whether this increases errors (lagging at anaphase, metaphase-II chromosome counting spreads)?
We agree with the reviewer that a specific manipulation of MTOC positions is exactly what we would need to investigate the significance of central MTOCs. Unfortunately, there are currently no tools available to specifically manipulate MTOC positions without other defects. Therefore, the significance of central MTOCs is currently unclear. In the revised manuscript, we will state these points in Discussion.
The above analysis would have to be well supported by controls showing that these constructs are having no impact on normal anaphase (proportion of oocytes completing meiosis-I, likelihood of lagging chromosomes etc).
Thank you for the comment. As we answered above, control oocytes rarely exhibit chromosome segregation errors or lagging chromosomes (
Related to the above, though I appreciate a fixed metaphase image of MTOC immunofluorescence is presented, the paper is about the dynamics of MTOCs and thus nonetheless relies heavily on the live imaging of cep192. The core results should be confirmed using another (substantially different) MTOC probe. *This final comment applies to the current metaphase data, regardless of whether the study is ultimately extended*
Thank you for the suggestion. We will confirm the dynamics of MTOCs at metaphase with mEGFP-Cdk5Rap2, another established marker of MTOCs.
Reviewer #1 (Significance (Required)):
As explained above, as presented this paper is largely scientifically sound, but far more limited in scope than this groups other recent papers. As explained above, the paper would be made more impactful and the readership broadened if a relationship between MTOC position/movement and segregation problems were established. Or on the other hand if it were established why some MTOCs sometimes linger at the spindle equator. Whilst to my knowledge this is the first time that equator MTOCs have been documented so carefully, oocyte cell biologists may not find the core observation that MTOCs are occasionally at the spindle equator extremely surprising.
Thank you for your helpful suggestions. Due to lack of tools to specifically manipulate MTOC positions, we are unfortunately not able to directly address whether MTOC position/movement contributes to chromosome segregation problems. On the other hand, we are currently investigating to answer your important question ‘why some MTOCs sometimes linger at the spindle equator’. We speculate that MTOCs become central due to unstable kinetochore-microtubule attachments, which are predominantly observed at early metaphase in normal oocytes. To test this idea, we are currently investigating whether the appearance of central MTOCs are prevented by forced stabilization of kinetochore-microtubule attachments with Ndc80-9A. Our pilot analysis thus far supports this idea. In light of your suggestions, we will incorporate the results into the revised manuscript.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
I am commenting on the work of Courtois et al. as an expert in the biochemistry of spindle formation with a focus on acentriolar assembly.
First and foremost, this a technically excellent study with a number of very interesting and well-documented observations, which are highly relevant for our understanding of the mechanisms of acentriolar spindle formation in the mouse oocyte model. In principle, the manuscript is in a very mature state. However, my major concern at this point would be that there is a break in the story. It starts describing the (very interesting) observation of "central MTOCs". After thoroughly investigating how these behave, the authors stop and look at overall MTOCs distribution after loss of stable MT-kinetochore interactions based on oocytes expressing the Ndc80_9D mutant instead of wt Ndc80. The two parts are experimentally and conceptually not well connected.
We appreciate your comments on our techniques and novel observations in this study, and thank you for your helpful suggestions.
Answering the following questions may help to further develop the paper:
If I understand the arguments correctly, central MTOCs are an "accident" on the way to complete meiosis I spindle formation, which will eventually be corrected and all MTOCs clustered at the poles. Thus, they may serve as an assay for spindle assembly fidelity and kinetics (?). At this point, the reader is left with the observation without efforts to explain the meaning of this observation, ideally experimentally, or at least in a valid discussion.
Thank you for your thoughtful comment. We agree that we should clearly explain our view on central MTOCs. We indeed speculate that central MTOCs are an “accident” due to unstable kinetochore-microtubule attachments, which are normally pronounced at early metaphase.
We will revise the manuscript as follows: (1) Following the section for the observation of central MTOCs, we will state our hypothesis that central MTOCs may appear due to unstable kinetochore–microtubule attachments. (2) We will introduce our experiment of the manipulation of kinetochore–microtubule attachment stability as a test for our hypothesis. (3) We will present new results of our analysis for the effects of kinetochore–microtubule attachment stability on the appearance of central MTOCs (please see below).
Enthusiasm for the technically excellent experiments using the Ndc80 variants are somewhat reduced as conclusions from these experiments are published in the parallel paper of the same laboratory (Yoshida et al.). Due to my opinion, it may thus be even more important to connect these observations with the first part described central MTOCs and to clarify their significance.
Thank you for the important suggestion.
First, we agree that we should connect our observations of central MTOCs to the phenotypes of Ndc80 manipulations. To do this, we will reanalyze our dataset to quantify the effects of Ndc80 manipulations on central MTOCs. Our pilot analysis thus far suggests that the forced stabilization of kinetochore–microtubule attachments by Ndc80-9A reduces the appearance of central MTOCs. This would support our idea that central MTOCs appear due to unstable kinetochore–microtubule attachments.
Second, we agree with the reviewer that experimental clarification of the significance of central MTOCs would be nice. However, as outlined above, we unfortunately have no tool to directly address the significance of MTOC positioning in the fidelity of spindle assembly and chromosome segregation. Although we assume that MTOC positioning is critical for spindle assembly fidelity, as generally thought based on previous studies (Breuer et al., 2010; Clift and Schuh, 2015; Schuh and Ellenberg, 2007), the significance of MTOC positioning in spindle assembly remains uncertain, as you (and also the reviewer 1) point out. We will discuss these points in the revised manuscript.
Shown if in Fig. 3B but not fully explained: How does the distribution of what is defined as central MTOCs behave in Ndc80_wt and Ndc80_9A mutant oocytes? Do the variants differ, i.e. are there fewer, or less persistent central MTOCs in the 9A mutant? Would they differ in kinetics of appearance and "rescue" to the poles?
Thank you for the question. As outlined above, we will reanalyze our dataset to quantify the effects of Ndc80-9A on the behavior of central MTOCs. Our pilot analysis suggests that the forced stabilization of kinetochore–microtubule attachments suppresses the appearance of central MTOCs.
Similarly: is there a correlation of central MTOC appearance, Ndc80 phosphorylation/stability of kinetochore attachment and Anaphase I onset? The authors mention that oocytes expressing the 9A mutant go faster into Anaphase.
Thank you for this comment. First, we will investigate whether the levels of Ndc80 phosphorylation at kinetochores has any correlations to the distance to central MTOCs. Second, we will address whether microtubules connect kinetochores to central MTOCs. Third, we will perform the tracking of chromosomes that showed correlated motions to closely positioned MTOCs until anaphase onset.
The observation that "central MTOCs exhibited correlated motions with closely positioned kinetochores" is poorly defined, yet an important observation. Does this mean some sort of short k-fibers remain to connect central MTOCs and kinetochores? Wouldn't one expect that the loss of stable end-on-attachment causes MTOCs to become central? How does this fit into a/the model?
We believe these concerns will be addressed by the experiments/analyses proposed above. First, we will check if central MTOCs are connected to kinetochores by microtubules. Second, we indeed speculate that loss of stable kinetochore-microtubule attachment allows MTOCs to become central. We will test this idea by quantifying the appearance of central MTOCs in Ndc80-9A-expressing oocytes.
Along the same lines: The authors hype their conclusion that kinetochores dominate meiosis I spindle formation based on the observation that loss of kinetochore functions results in less well-organized spindle poles and worse MTOC "confinement". This may mean that kinetochores, together with MTOCs, maintain stable k-fibers in meiosis, as shown here and in Yoshida et al. When one or the other end of k-fibers is destabilized (loss of end-on-attachment, loss of MTOC attachment), the fibers collapse and the remaining minus-or-plus-end associated structure loses its destination. We then see central MTOCs and/or kinetochores at poles. In this respect, the interpretation / discussion should be less "kinetochore-centered".
We agree with your thoughtful comment that the regulations of minus-ends (e.g. MTOCs) and of plus-ends (e.g. kinetochores) are equally relevant for spindle bipolarization. We will tone down our kinetochore-centered view in the Abstract and Discussion and revise them into more balanced statements.
Is there any way to determine the efficiency of Ndc80 knockdown in the gene replacement respective experiment? I share the view of the authors that their method may be more efficient and may explain apparent discrepancies to previous studies on Ndc80-9A (Guy and Homer, 2013) with more dramatic effects on spindle geometry. However, at that point, this remains speculative. For instance, one may also speculate vice versa that the ko strategy used here is less efficient in a maternally dominated system and leaves behind more wt Ndc80, which better compensates defects seen in the 9A mutant.
Our gene deletion strategy (Zp3-Cre Ndc80f/f) resulted in >90% depletion of the Ndc80 protein (estimated by Western blot; Supplementary Figure 1c in Yoshida et al, Nat Commun 2020). On the other hand, Gui and Homer report that their morpholino-based depletion strategy resulted in 60–70% depletion of the Ndc80 protein (estimated by Western blot; Figure 1B in Gui and Homer, Dev Cell 2013). Thus, the depletion was more efficient in our experimental system. We will add this information in the manuscript.
Reviewer #2 (Significance (Required)):
Courtois et al present data on mechanisms governing spindle assembly in mouse oocytes. Mouse oocytes serve as model system for spindle formation in the absence of centriole-based MTOCs. At the onset of meiosis I, numerous MTOCs form, which shape a mass ("ball") of MT nucleated around chromatin into a bipolar structure. Accumulating evidence indicates that kinetochores play an important role in acentriolar spindle formation in mouse oocytes, yet the mechanisms behind kinetochore action remains unclear.
Here, Courtois et al. analyze spindle formation in live mouse oocytes using 3D-time-lapse imaging. They use fluorescently tagged Cep192 to track MTOCs and Histone H2B or CENP-C to visualize chromatin or kinetochores. In the first part, the authors deal with the appearance of "central MTOCs", i.e. aggregates of centrosomal protein(s) that, apparently, fail to remain stably integrated into the spindle pole clusters on MTOCs during spindle formation. The authors convincingly demonstrate that these central MTOCs can be seen in the majority of spindles investigated. They demonstrate that central MTOCs generally come from positions at poles from where they "fall back" towards chromosomes. Central MTOCs may even cross the spindle and end up at opposite poles from where they originated from. Interestingly, central MTOCs are often found next to kinetochores.
In the second part, the authors focus on the role of kinetochores and their stable MT attachment for spindle formation in general and bipolarity/pole organization in particular. The same lab has published data on the role of kinetochores in meiosis I spindle very recently (Yoshida et al. Nat Comm, 2020). Here, they successfully exploit Ndc80 phospho-mutants to compare MTOC distribution in oocytes with reduced or increased end-on-attachment. The data show that stable end-on attachment determines stable MTOC clustering at spindle poles and governs the maintenance of bipolarity and spindle length.
Thank you for your clear description of our study.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
In order to assemble a bipolar structure, acentrosomal spindles relay on multiple non-centrosomal pathways. Mouse oocytes specifically build bipolar spindles by sorting and clustering of microtubule organizing centers (MTOCs). While microtubule cross-linkers, spindle motors and microtubule nucleators are involved; the role of kinetochores and kinetochore-microtubule attachments in meiotic spindle assembly and maintenance has not been thoroughly tested. Using an impressive combination of live cell imaging and semi-automated image analysis, Courtois et al. quantified MTOC behavior in bipolar mouse oocyte spindles and found an ongoing MTOC sorting in metaphase and instances of MTOC-kinetochore associations. The authors further employed an elegant genetic system to replace NDC80 in maturing oocytes with a mutant almost completely unable to form stable microtubule-kinetochore attachments. The data show lack of MTOC confinement at the spindle poles and increased spindle elongation while maintaining spindle bipolarity. The authors concluded that stable kinetochore-microtubule attachments are required to confine MTOCs at the poles, which in turn sets an optimal spindle length. Overall, the data are of very high quality and clearly presented, the manuscript is easy to follow, and the methods are comprehensively described. One concern is the lack of mechanistic link between the natural metaphase MTOC sorting (Fig. 1-2) and massive MTOC rearrangements observed with the NDC80-9D mutant (Fig. 3). A second concern is that deficient MTOC confinements and spindle elongation observed with the 9D mutant could be due unaligned chromosomes rather than lack of stable kinetochore-microtubule attachments, which is the authors' interpretation.
**Major Points:**
1) Massive MTOC rearrangements (Supplementary Video 6) are reminiscent of spindle assembly defects or spindle collapse. Since these spindles do not reach a normal metaphase and seem to change shape (Supplementary Video 6; 11:10), it is difficult to differentiate between spindle assembly and spindle maintenance defects. Is there a difference in the timing of bipolar spindle assembly for NDC80-9D vs WT? If so, one interpretation is that stable attachments not only ensure MTOC confinement but also contribute to bipolar spindle assembly.
We apologize for the lack of explanation for the spindle dynamics seen in Supplementary Video 6, 11:10. At this time point, the spindle rotated in 3D, which appeared as if the spindle collapsed in the z-projection movie. We will add this explanation into the legend.
Our quantitative analysis of spindle shape in 3D indicated no increased collapse in Ndc80-9D, based on the signals of the spindle marker EGFP-Map4. Moreover, we observed no detectable difference in the timing of the onset of bipolar spindle assembly, as long as we define it with EGFP-Map4 signals. These results are shown in Figure 4B.
2) Fig. 1-2 vs Fig. 3 - It is not clear how the discrete MTOC sorting phenotype presented in Fig. 1-2 relates to the massive MTOC collapse shown in Fig. 3. The natural MTOC sorting and MTOC-kinetochore associations seem to be happening within the bipolar structure confined by the polar MTOCs. The MTOC rearrangements (e.g., Supplementary Video 6) are much more drastic, reminiscent of a spindle collapse. To make a mechanistic link between the phenotypes, it would be useful to use an intermediate NCD80 mutant (ex. NDC80-4D; Zaytsev et al., 2014 JCB) that may support chromosome alignment and maintenance of the canonical bipolar spindle structure, but still show effects on MTOC sorting.
Thank you for your nice suggestion. We will test Ndc80-4D. The construct is ready.
3) Fig. 4 - The authors should provide evidence that unstable kinetochore-microtubule attachments, rather than chromosome-derived signals of misaligned chromosomes (e.g., from Ran or Aurora B), limit spindle elongation. For example, the authors could measure spindle elongation in oocytes with misaligned chromosomes but stable attachments: for example, NDC80-9A oocytes released from an Eg5 inhibition block should carry a number of polar chromosomes with stable attachments. The expectation would be that such spindles form with confined MTOCs and do not elongate as much as NDC80-9D expressing oocytes.
Thank you for this important suggestion. Following your suggestion, we have conducted a pilot experiment using monastrol washout. However, unfortunately, we did not observe increased chromosome misalignment in Ndc80-9A. We will play around experimental conditions.
Moreover, we propose to perform an additional experiment. We will use cohesin depletion with Rec8 TRIM-Away, which will produce chromosome misalignment and reduce kinetochore-microtubule attachment stability. We expect that these oocytes exhibit excessive spindle elongation. Then, we ask if Ndc80-9A, which would force to stabilize kinetochore-microtubule attachment (but fail to align chromosomes due to loss of chromosome cohesion), can suppress excessive spindle elongation.
These experiments will allow us to address direct contribution of kinetochore-microtubule attachment to proper spindle elongation. However, in our opinion, regardless of the results, we cannot exclude the possibility that chromosome alignment contributes to proper spindle elongation, which is indeed an intriguing hypothesis. We will discuss these possibilities in Discussion.
4) Figure 5D - The authors' model suggests that MTOCs are confined due to their connection to stably attached k-fibers. It would be useful to speculate on the molecular mechanism behind the confinement. Does a maximal k-fiber length restrict the elongation, or is there a pulling force exerted by the kinetochores?
Thank you for your thoughtful suggestion. As the reviewer suggests, we speculate that the length of k-fibers is critical for restricting MTOC position and spindle elongation. K-fibers may prevent excessive spindle elongation by anchoring MTOCs at their minus ends. Alternatively, k-fibers may act as a platform that inactivates spindle bipolarizers. We will discuss these possibilities in our revised manuscript.
5) Discussion - Lines 203-204 - "The findings of this study, together with recent studies, suggest a model for how kinetochore-microtubule attachments contribute to acentrosomal spindle assembly (Figure 5D)". - Throughout the paper the authors underscore that biopolar spindles do assembly with the NDC80-9D mutant. The authors should clarify whether spindle assembly is affected by the NDC80-9D mutant or not?
Thank you for your comment. We agree with the reviewer that we should clearly state our conclusion based on the phenotype of the Ndc80-9D mutant. Our conclusion is that stable kinetochore-microtubule attachment fine-tunes bipolar spindle assembly. If oocytes lack stable attachments, they can form a bipolar-shaped spindle composed of microtubule arrays that are largely bipolar, but the spindle becomes too much elongated and lacks MTOCs at its poles. We will explicitly state these ideas in our revised manuscript.
**Minor Points:**
1) Introduction - Lines 38-44 - The authors should cite the role of the Augmin complex in acentrosomal spindle assembly (Watanabe et al., 2016 Cell Reports).
Thank you for your excellent suggestion. We will cite this relevant paper.
2) Results - Lines 55-56 - "However, the precise manipulation of the stability of kinetochore-microtubule attachments has not been tested" - Gui et Homer 2013 studied the outcome of NDC80 depletion and tested the NDC80-9A mutant in the context of oocyte spindle assembly. Although, as the authors point out in the Discussion section, there might be differences in the experimental design that lead to different conclusions, it is not entirely accurate that precise manipulations of attachments stability have not been tested. A different wording (e.g., "has not been comprehensively tested") may be better.
Thank you for your suggestion. We agree that “has not been comprehensively tested” fits better.
3) Results - Lines 162-164 - "Ndc80-9D-expressing oocytes had no significant delay in the onset of spindle elongation, but had significantly faster kinetics of elongation compared to Ndc80-WT- and Ndc80-9D-expressing oocytes" - The authors probably meant "... Ndc80-9A expressing oocytes."
Thank you for pointing out this mistake. We will correct it.
4) Discussion - Lines 239-242 - "... microtubule nucleation in later stages may not be determined by MTOCs but are largely attributed to nucleation within the spindle, as observed by microtubule plus-end tracking in bipolar-shaped spindles (Supplementary Figure 4)." - Strictly speaking, EB3 comets indicate microtubule polymerization rather than nucleation. Microtubule nucleation within the spindle is, however, supported by studies of the Augmin complex (e.g., Watanabe et al., 2016 Cell Rep).
Thank you for your comment. We will correct our wording for EB3 comets and discuss that microtubule nucleation within the spindle is shown in Watanabe et al., 2016 Cell Rep.
5) Discussion - Lines 257-260 - "The lagging MTOCs can be positioned close to kinetochores on bi-oriented chromosomes, underscoring the importance of active error corrections of kinetochore-microtubule attachments during metaphase (Lane and Jones, 2014; Yoshida et al., 2015)." - The reasoning here is not clear. Does the number/persistence of lagging MTOCs correlate with chromosome mis-alignment or with the efficiency/timing of chromosome alignment in WT cells?
We apologize that our discussion was not clear. Previous studies (Lane and Jones, 2014; Yoshida et al., 2015) show that kinetochore-microtubule attachment errors are found on aligned chromosomes during metaphase and must be corrected until anaphase onset in oocytes. We speculate that lagging (or central) MTOCs may be a source of such kinetochore-microtubule attachment errors, although we cannot directly test this hypothesis due to lack of tools to specifically manipulate MTOC positions. We will discuss these points in Discussion.
To check if central MTOCs are correlated with chromosome misalignment, we will perform the tracking of chromosomes that were closely positioned to lagging MTOCs.
6) Discussion - Line 266 - "Yoshida et al., 2020" - This article is cited elsewhere in the text as "Yoshida et al., in press".
Thank you for pointing out these mistakes. We will correct them.
Reviewer #3 (Significance (Required)):
Courtois et al., have found a new mechanism contributing to acentrosomal spindle assembly in mouse oocytes. Although kinetochore-dependent spindle assembly occurs in mitotic cells (e.g., Toso et al., 2009 JCB), only the recent work from the Kitajima lab (Yoshida et al., 2020 Nat Comm; this manuscript) showed that kinetochores also impact acentrosomal spindle assembly in meiosis. The genetic model presented here brings a significant technical advance in dissecting relative contributions of spindle assembly pathways in mouse oocytes (ex. Schuh and Ellenberg 2007 Cell; Watanabe et al., 2016 Cell Rep; Drutovic et al., 2020 EMBO J) and complements current methods used to study meiotic error-correction (e.g., Chmatal et al., 2015 Curr Biol, Yoshida et al., 2015 Dev Cell; Vallot et al., 2018 Curr Biol and many others). This model expands an existing toolbox of techniques allowing complete elimination of the endogenous protein specifically in mature mouse oocytes (Clift et al., 2017 Cell; Clift et al., 2018 Nat Protocols), which is a difficult feat due to a limited capacity of ex-vivo culture (Pfender et al., 2015 Nature). Therefore, the work presented in this manuscript may encourage other researchers to establish similar systems for oocyte-specific manipulations, which will allow more precise insight into oocyte biology.
Expertise keywords: spindle dynamics, chromosome segregation, mitosis, meiosis
We appreciate your comments. Additional experiments following on your constructive comments will further improve our manuscript.
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Referee #3
Evidence, reproducibility and clarity
In order to assemble a bipolar structure, acentrosomal spindles relay on multiple non-centrosomal pathways. Mouse oocytes specifically build bipolar spindles by sorting and clustering of microtubule organizing centers (MTOCs). While microtubule cross-linkers, spindle motors and microtubule nucleators are involved; the role of kinetochores and kinetochore-microtubule attachments in meiotic spindle assembly and maintenance has not been thoroughly tested. Using an impressive combination of live cell imaging and semi-automated image analysis, Courtois et al. quantified MTOC behavior in bipolar mouse oocyte spindles and found an ongoing MTOC sorting in metaphase and instances of MTOC-kinetochore associations. The authors further employed an elegant genetic system to replace NDC80 in maturing oocytes with a mutant almost completely unable to form stable microtubule-kinetochore attachments. The data show lack of MTOC confinement at the spindle poles and increased spindle elongation while maintaining spindle bipolarity. The authors concluded that stable kinetochore-microtubule attachments are required to confine MTOCs at the poles, which in turn sets an optimal spindle length. Overall, the data are of very high quality and clearly presented, the manuscript is easy to follow, and the methods are comprehensively described. One concern is the lack of mechanistic link between the natural metaphase MTOC sorting (Fig. 1-2) and massive MTOC rearrangements observed with the NDC80-9D mutant (Fig. 3). A second concern is that deficient MTOC confinements and spindle elongation observed with the 9D mutant could be due unaligned chromosomes rather than lack of stable kinetochore-microtubule attachments, which is the authors' interpretation.
Major Points:
1) Massive MTOC rearrangements (Supplementary Video 6) are reminiscent of spindle assembly defects or spindle collapse. Since these spindles do not reach a normal metaphase and seem to change shape (Supplementary Video 6; 11:10), it is difficult to differentiate between spindle assembly and spindle maintenance defects. Is there a difference in the timing of bipolar spindle assembly for NDC80-9D vs WT? If so, one interpretation is that stable attachments not only ensure MTOC confinement but also contribute to bipolar spindle assembly.
2) Fig. 1-2 vs Fig. 3 - It is not clear how the discrete MTOC sorting phenotype presented in Fig. 1-2 relates to the massive MTOC collapse shown in Fig. 3. The natural MTOC sorting and MTOC-kinetochore associations seem to be happening within the bipolar structure confined by the polar MTOCs. The MTOC rearrangements (e.g., Supplementary Video 6) are much more drastic, reminiscent of a spindle collapse. To make a mechanistic link between the phenotypes, it would be useful to use an intermediate NCD80 mutant (ex. NDC80-4D; Zaytsev et al., 2014 JCB) that may support chromosome alignment and maintenance of the canonical bipolar spindle structure, but still show effects on MTOC sorting.
3) Fig. 4 - The authors should provide evidence that unstable kinetochore-microtubule attachments, rather than chromosome-derived signals of misaligned chromosomes (e.g., from Ran or Aurora B), limit spindle elongation. For example, the authors could measure spindle elongation in oocytes with misaligned chromosomes but stable attachments: for example, NDC80-9A oocytes released from an Eg5 inhibition block should carry a number of polar chromosomes with stable attachments. The expectation would be that such spindles form with confined MTOCs and do not elongate as much as NDC80-9D expressing oocytes.
4) Figure 5D - The authors' model suggests that MTOCs are confined due to their connection to stably attached k-fibers. It would be useful to speculate on the molecular mechanism behind the confinement. Does a maximal k-fiber length restrict the elongation, or is there a pulling force exerted by the kinetochores?
5) Discussion - Lines 203-204 - "The findings of this study, together with recent studies, suggest a model for how kinetochore-microtubule attachments contribute to acentrosomal spindle assembly (Figure 5D)". - Throughout the paper the authors underscore that biopolar spindles do assembly with the NDC80-9D mutant. The authors should clarify whether spindle assembly is affected by the NDC80-9D mutant or not?
Minor Points:
1) Introduction - Lines 38-44 - The authors should cite the role of the Augmin complex in acentrosomal spindle assembly (Watanabe et al., 2016 Cell Reports).
2) Results - Lines 55-56 - "However, the precise manipulation of the stability of kinetochore-microtubule attachments has not been tested" - Gui et Homer 2013 studied the outcome of NDC80 depletion and tested the NDC80-9A mutant in the context of oocyte spindle assembly. Although, as the authors point out in the Discussion section, there might be differences in the experimental design that lead to different conclusions, it is not entirely accurate that precise manipulations of attachments stability have not been tested. A different wording (e.g., "has not been comprehensively tested") may be better.
3) Results - Lines 162-164 - "Ndc80-9D-expressing oocytes had no significant delay in the onset of spindle elongation, but had significantly faster kinetics of elongation compared to Ndc80-WT- and Ndc80-9D-expressing oocytes" - The authors probably meant "... Ndc80-9A expressing oocytes."
4) Discussion - Lines 239-242 - "... microtubule nucleation in later stages may not be determined by MTOCs but are largely attributed to nucleation within the spindle, as observed by microtubule plus-end tracking in bipolar-shaped spindles (Supplementary Figure 4)." - Strictly speaking, EB3 comets indicate microtubule polymerization rather than nucleation. Microtubule nucleation within the spindle is, however, supported by studies of the Augmin complex (e.g., Watanabe et al., 2016 Cell Rep).
5) Discussion - Lines 257-260 - "The lagging MTOCs can be positioned close to kinetochores on bi-oriented chromosomes, underscoring the importance of active error corrections of kinetochore-microtubule attachments during metaphase (Lane and Jones, 2014; Yoshida et al., 2015)." - The reasoning here is not clear. Does the number/persistence of lagging MTOCs correlate with chromosome mis-alignment or with the efficiency/timing of chromosome alignment in WT cells?
6) Discussion - Line 266 - "Yoshida et al., 2020" - This article is cited elsewhere in the text as "Yoshida et al., in press".
Significance
Courtois et al., have found a new mechanism contributing to acentrosomal spindle assembly in mouse oocytes. Although kinetochore-dependent spindle assembly occurs in mitotic cells (e.g., Toso et al., 2009 JCB), only the recent work from the Kitajima lab (Yoshida et al., 2020 Nat Comm; this manuscript) showed that kinetochores also impact acentrosomal spindle assembly in meiosis. The genetic model presented here brings a significant technical advance in dissecting relative contributions of spindle assembly pathways in mouse oocytes (ex. Schuh and Ellenberg 2007 Cell; Watanabe et al., 2016 Cell Rep; Drutovic et al., 2020 EMBO J) and complements current methods used to study meiotic error-correction (e.g., Chmatal et al., 2015 Curr Biol, Yoshida et al., 2015 Dev Cell; Vallot et al., 2018 Curr Biol and many others). This model expands an existing toolbox of techniques allowing complete elimination of the endogenous protein specifically in mature mouse oocytes (Clift et al., 2017 Cell; Clift et al., 2018 Nat Protocols), which is a difficult feat due to a limited capacity of ex-vivo culture (Pfender et al., 2015 Nature). Therefore, the work presented in this manuscript may encourage other researchers to establish similar systems for oocyte-specific manipulations, which will allow more precise insight into oocyte biology.
Expertise keywords: spindle dynamics, chromosome segregation, mitosis, meiosis
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Referee #2
Evidence, reproducibility and clarity
I am commenting on the work of Courtois et al. as an expert in the biochemistry of spindle formation with a focus on acentriolar assembly.
First and foremost, this a technically excellent study with a number of very interesting and well-documented observations, which are highly relevant for our understanding of the mechanisms of acentriolar spindle formation in the mouse oocyte model. In principle, the manuscript is in a very mature state. However, my major concern at this point would be that there is a break in the story. It starts describing the (very interesting) observation of "central MTOCs". After thoroughly investigating how these behave, the authors stop and look at overall MTOCs distribution after loss of stable MT-kinetochore interactions based on oocytes expressing the Ndc80_9D mutant instead of wt Ndc80. The two parts are experimentally and conceptually not well connected.
Answering the following questions may help to further develop the paper:
- If I understand the arguments correctly, central MTOCs are an "accident" on the way to complete meiosis I spindle formation, which will eventually be corrected and all MTOCs clustered at the poles. Thus, they may serve as an assay for spindle assembly fidelity and kinetics (?). At this point, the reader is left with the observation without efforts to explain the meaning of this observation, ideally experimentally, or at least in a valid discussion.
- Enthusiasm for the technically excellent experiments using the Ndc80 variants are somewhat reduced as conclusions from these experiments are published in the parallel paper of the same laboratory (Yoshida et al.). Due to my opinion, it may thus be even more important to connect these observations with the first part described central MTOCs and to clarify their significance.
- Shown if in Fig. 3B but not fully explained: How does the distribution of what is defined as central MTOCs behave in Ndc80_wt and Ndc80_9A mutant oocytes? Do the variants differ, i.e. are there fewer, or less persistent central MTOCs in the 9A mutant? Would they differ in kinetics of appearance and "rescue" to the poles?
- Similarly: is there a correlation of central MTOC appearance, Ndc80 phosphorylation/stability of kinetochore attachment and Anaphase I onset? The authors mention that oocytes expressing the 9A mutant go faster into Anaphase.
- The observation that "central MTOCs exhibited correlated motions with closely positioned kinetochores" is poorly defined, yet an important observation. Does this mean some sort of short k-fibers remain to connect central MTOCs and kinetochores? Wouldn't one expect that the loss of stable end-on-attachment causes MTOCs to become central? How does this fit into a/the model?
- Along the same lines: The authors hype their conclusion that kinetochores dominate meiosis I spindle formation based on the observation that loss of kinetochore functions results in less well-organized spindle poles and worse MTOC "confinement". This may mean that kinetochores, together with MTOCs, maintain stable k-fibers in meiosis, as shown here and in Yoshida et al. When one or the other end of k-fibers is destabilized (loss of end-on-attachment, loss of MTOC attachment), the fibers collapse and the remaining minus-or-plus-end associated structure loses its destination. We then see central MTOCs and/or kinetochores at poles. In this respect, the interpretation / discussion should be less "kinetochore-centered".
- Is there any way to determine the efficiency of Ndc80 knockdown in the gene replacement respective experiment? I share the view of the authors that their method may be more efficient and may explain apparent discrepancies to previous studies on Ndc80-9A (Guy and Homer, 2013) with more dramatic effects on spindle geometry. However, at that point, this remains speculative. For instance, one may also speculate vice versa that the ko strategy used here is less efficient in a maternally dominated system and leaves behind more wt Ndc80, which better compensates defects seen in the 9A mutant.
Significance
Courtois et al present data on mechanisms governing spindle assembly in mouse oocytes. Mouse oocytes serve as model system for spindle formation in the absence of centriole-based MTOCs. At the onset of meiosis I, numerous MTOCs form, which shape a mass ("ball") of MT nucleated around chromatin into a bipolar structure. Accumulating evidence indicates that kinetochores play an important role in acentriolar spindle formation in mouse oocytes, yet the mechanisms behind kinetochore action remains unclear.
Here, Courtois et al. analyze spindle formation in live mouse oocytes using 3D-time-lapse imaging. They use fluorescently tagged Cep192 to track MTOCs and Histone H2B or CENP-C to visualize chromatin or kinetochores. In the first part, the authors deal with the appearance of "central MTOCs", i.e. aggregates of centrosomal protein(s) that, apparently, fail to remain stably integrated into the spindle pole clusters on MTOCs during spindle formation. The authors convincingly demonstrate that these central MTOCs can be seen in the majority of spindles investigated. They demonstrate that central MTOCs generally come from positions at poles from where they "fall back" towards chromosomes. Central MTOCs may even cross the spindle and end up at opposite poles from where they originated from. Interestingly, central MTOCs are often found next to kinetochores.
In the second part, the authors focus on the role of kinetochores and their stable MT attachment for spindle formation in general and bipolarity/pole organization in particular. The same lab has published data on the role of kinetochores in meiosis I spindle very recently (Yoshida et al. Nat Comm, 2020). Here, they successfully exploit Ndc80 phospho-mutants to compare MTOC distribution in oocytes with reduced or increased end-on-attachment. The data show that stable end-on attachment determines stable MTOC clustering at spindle poles and governs the maintenance of bipolarity and spindle length.
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Referee #1
Evidence, reproducibility and clarity
This group has been at the forefront recently of using imaging technologies to understand how chromosome segregation is coordinated in mammalian oocytes, and why errors occur. In the current paper they examine the dynamics of microtubule organising centres (which effectively replace centrioles/centrosomes in oocytes) in MI. The imaging of oocytes in this paper is beautiful. The major findings are (1) that MTOCs that are supposed to be at the spindle pole sometimes end up at the spindle equator, and this is documented very beautifully and (2) the correct positioning of MTOCs at the spindle pole appears to require kinetochore microtubules, as indicated by experiments manipulating the kinetochore component NDC80.
Major Comments
As such the major claims of the paper are basically well supported. However, the analyses are is almost entirely restricted to prometaphase/metaphase, and the conclusions are relatively limited. The salient omission is any analysis of MTOC/chromosome relationship during anaphase. Were the paper to be extended to determine whether the lingering of MTOCs at the spindle equator is related to chromosome segregation error, that would increase the reach and importance of the work substantially. Specifically:
- Can tracking experiments be performed to determine whether the chromosome that shows movement similarities to the errant MTOC is more/less likely to missegregate? Complete tracking as these authors are expert at should achieve this, or photo-labelling the desired chromosome.
- Can the position of MTOCs (proportion that linger at the equator) be manipulated in the absence of other defects to determine whether this increases errors (lagging at anaphase, metaphase-II chromosome counting spreads)?
- The above analysis would have to be well supported by controls showing that these constructs are having no impact on normal anaphase (proportion of oocytes completing meiosis-I, likelihood of lagging chromosomes etc).
- Related to the above, though I appreciate a fixed metaphase image of MTOC immunofluorescence is presented, the paper is about the dynamics of MTOCs and thus nonetheless relies heavily on the live imaging of cep192. The core results should be confirmed using another (substantially different) MTOC probe. This final comment applies to the current metaphase data, regardless of whether the study is ultimately extended
Significance
As explained above, as presented this paper is largely scientifically sound, but far more limited in scope than this groups other recent papers. As explained above, the paper would be made more impactful and the readership broadened if a relationship between MTOC position/movement and segregation problems were established. Or on the other hand if it were established why some MTOCs sometimes linger at the spindle equator. Whilst to my knowledge this is the first time that equator MTOCs have been documented so carefully, oocyte cell biologists may not find the core observation that MTOCs are occasionally at the spindle equator extremely surprising.
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Reply to the reviewers
We would like to thank Reviewer #1 and #2 for the evaluation of our research and comments to our manuscript. Their comments are highly appreciated and addressed as described below.
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
**Summary:**
*Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).*
Here Ha et al. has further developed their Pumilio RNA tagging methodology for the isolation of UV-crosslinked proteins that are suggested to associate with Xist RNA in mouse embryonic stem cells (mESCs). Within this study the authors claim to have found the Lupus antigen RNA binding protein (La) as a novel Xist interacting partner that influences the efficacy of X-chromosome inactivation (XCI). The authors use a number of different techniques such as qPCR, fluorescent imaging, ATAC-SEQ and SHAPE to show aberration of XCI upon La shRNA knockdown. However, this study has significant flaws in the efficient isolation and validation of Xist associated proteins using their FLAG-out methodology. Furthermore, later experiments predominantly focus on cell death/survival assays, which is somewhat troubling given the essential roles La plays in processes such as cell differentiation and proliferation, ribosome biogenesis, transcriptional control and tRNA maturation. I feel the authors need to robustly address the potential effects La knockdown may be having on their mESCs.
Reviewer #1 did not fully understand the basic designs of the experimental systems (FLAG-out and iXist), and completely rejected these experimental systems. Reviewer #1 also ignored the majority of the functional analysis on the candidate protein, Ssb. These issues cannot be addressed by additional experiments
**Major comments:**
*-Are the key conclusions convincing?*
My major concern is in their Xist RNA purification.
First of all, I couldn't find any data on proving the enrichment of Xist RNA itself in their Pumilio pull-down experiment. It would have been useful to show Xist RNA enrichment before benzonase step. Secondly, it is hard to imagine the protocol would successfully isolated Xist RNA-protein complexes from the cell. An earlier report by Clemson et al., (J Cell Biol., 1996) has shown that majority of Xist RNA is still stuck in the nucleus after nuclear matrix prep protocol using detergent, which is not so different from the authors' protocol. Moreover, the authors used UV crosslink, which would have made even harder to purify Xist RNA without sonication. Thirdly, as the tag is located on 5' of Xist RNA, it is rather surprising to see that Spen is not detected in their pulldown. Spen is one of the main functional interactors with Xist, robustly detected by several previous reports. Similarly, other high-affinity binders of Xist such as hnRNP-K and Ciz1 were also lacking from this screen. Finally, the peptides found associated with FLAG-out Xist are extremely low in comparison with other data using glutaraldehyde or formaldehyde crosslinking. For example, HnRNP-M found in Chu et al 2015 has 1120 peptide counts in differentiated cells. The authors here use HnRNP-M as a baseline for specific interactions and show a total of 6 peptide counts in Xist expressing cells and 5 in i-Empty cells (Supplementary excel sheet 1). Similarly, the La protein of interest in this study has 8 counts in i-FLAG-Xist and 6 counts in i-Empty. I struggle to see how this result indicate specific Xist binding. Worryingly this is the starting rationale for the rest of their experiments, it is hard to therefore accept the rest of their conclusions either.
We have detected Xist RNA after Pumilio pull-down, and added the data in the revised manuscript (Figure S1). The enrichment of Xist RNA by Pumilio pull-down is about 75-fold, comparable to the enrichment reported by Minajigi et al.
Two out of three previous studies used similar protocols to prep cell lysates for co-IP, including UV cross-linking and detergent (McHugh et al. 2015 and Minajigi et al. 2015). The major difference between their protocols and ours is the co-IP step. They used antisense oligos to pull-down Xist RNA-protein complex, while we take advantage of the specific interaction between PUF and PBS to pull-down Xist RNA-protein complex. With the data in Figure S1, we are confident that our strategy is successful in isolating Xist RNA
For systematic identification of Xist binding proteins, each method has its own strength and weakness. As we described in the introduction, only 4 proteins were commonly identified by all three studies to systematically identify Xist binding proteins. There is no doubt that our method also missed some authentic Xist binding proteins (false negative) and identified some false positive candidates. Thus, we have to be careful in balancing between the false negative and false positive calls. The reason that we applied the ranking gain to identify Xist binding protein candidates, is to minimize the false negative rate. Meanwhile, we compared our Xist binding protein candidate list with previous identified Xist-binding proteins to enhance the confidence in our candidate lists.
Regardless the strength and weakness of our method, Ssb is also an Xist-binding protein identified by another study (Chu et al. 2015). More importantly, we have provided experimental validation to confirm Ssb’s involvement in XCI and extensive functional analysis to reveal the protein’s mechanistic role in XCI.
The other key conclusion the authors make is from the use of numerous cell death/survival assays for both male and female cell lines. This is extremely troubling in the context of assessing their target protein La. La is involved in multiple RNA maturation events of rRNAs, tRNAs and other polIII transcripts. Furthermore, La has been implicated in binding to the mRNA for Cyclin D1 in both human cells and mouse fibroblasts (NIH/3T3 - male) which show a significant effect on cell proliferation upon siRNA knockdown https://www.nature.com/articles/onc2010425. This, along with the observation that La knock-out blastocysts fail to develop any mice or ES cell lines (male or female) show the effect observed in the authors results is most likely not X-linked cell death https://mcb.asm.org/content/mcb/26/4/1445.full.pdf. The authors need to show that their shRNA KD isn't affecting the proliferation and general fitness of their mESC lines.
The cell death/survival assay was specially designed for analyzing the defect of XCI. The cell death of iXist ESCs upon adding Dox is due to the induction of Xist, which consequently initiates the silencing of the only X chromosome in male cells. Knockdown of genes involved in XCI compromises XCI, thus allowing cell survival. Given the diverse functions of Ssb in cell differentiation and proliferation, ribosome biogenesis, transcriptional control and tRNA maturation, one would expect slow growth and/or cell death of Ssb knockdown cells. Indeed, the result is consistent with our expectation (Figure 2C, without Dox). Nevertheless, more Ssb knockdown cells survive in the presence of Dox, compared with control cells (Figure 2C-E, with Dox), suggesting that Ssb plays an important role in XCI.
*- Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?*
As discussed above, I feel the authors have not clearly demonstrated Xist specific protein enrichment and haven't proven X-linked cell death. Due to the lack of necessary control experiments as discussed below, I feel the notion that La is involved directly in XCI as an RNA chaperone is currently preliminary/speculative.
The FLAG-out experiment just provided an initial point for the study. We have demonstrated the interaction between Xist and Ssb by RIP. And, Ssb knockdown antagonizes the lethal effect of induced XCI in male cells, allowing more cell to survive. This is contradictory to the diverse house-keeping functions of Ssb, which should lead to slow proliferation or cell death. Therefore, the data here (Figure 2C-E) should suggest a role of Ssb in XCI. In addition, we showed that knockdown of Ssb compromises the silencing of X-linked genes (Figure 2F, 2G, and 3E), the compaction of X chromosome (Figure 3D), Xist cloud formation (Figure 4), epigenetic modifications on Xi (Figure 5), Xist RNA folding (Figure 6F-I), and Xist RNA stability (Figure 7C and D). All these data indicate that Ssb is involved in XCI by regulating Xist RNA folding.
*- 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.*
I would suggest them to show RT-qPCR results of Xist RNA enrichment from the sample after flagIP before benzonase treatment.
We have the data, and added it to Figure S1.
Also, it would have been more convincing if their negative control construct (i-Empty) would contain 25 copies of PBSb RNA at least.
This is a good alternative design of the negative control. Using i-Empty expressing 25 copies of PBSb RNA will allow us subtract the background causing by proteins binding to PBSb RNA. Yet, as discussed above, regardless how we improve the experimental setting, we cannot completely avoid the issue of false positive and false negative. Our goal of the FLAG-out experiment is to generate a list of Xist binding protein candidates, and their binding to Xist and their functions in XCI should be validated by additional experiments. With our current experimental setting, a list of Xist binding protein candidates has been generated, and we have validated the role of Ssb in XCI with subsequent experiments.
In Fig1b, the total amount of proteins loaded on the gel is not equivalent between two lanes. The gel should show equivalent amounts of proteins on the gel. It looks like if the negative control sample had been loaded at the same amount as the one with Xist, the band pattern wouldn't be distinguishable between the two samples. Furthermore, as these samples were used in the following mass spectrometry screen it may suggest that the minimal increase in peptide counts observed in the iXist FLAG-out were due to an increased amount of sample being loaded? No controls are conducted to account for this.
IP samples of i-Empty and i-FLAG-Xist were loaded in the gel in Figure 1b. It is expected that IP sample of i-FLAG-Xist should pull down more proteins than IP samples of i-Empty. The FLAG-PUFb bands (the strongest band in each lane) are about the same amount in two samples, indicating roughly equal amount of loading. After normalization of gel loading according to the FLAG-PUFb bands, the upper part of the i-FLAG-Xist lane showed some unique bands.
For mass spectrometry analysis, the loading of two samples are independent, therefore, to compare the absolute amount of each protein between the two samples does not always provide valuable information. Yet, the relative amount of different proteins within one sample is not affected by the loading amount, thus, more informative. Therefore, we used the ranking information to estimate the relative amount of different proteins in each sample and used the ranking gain to further identify protein candidates.
The authors quantify cell death in figures 2C - E. It seems clear that shSsb 1 and 2 have an effect on cell count even in the absence of Dox. The rescue effect seen upon Dox addition is minimal when compared to Empty + Dox 2D. The authors ∆A-iXist line with and without Ssb KD/Dox would be an informative control on whether the increase in cell survival that they see is X-linked.
As the reviewer pointed out earlier, Ssb plays multiple roles in cellular processes. Inevitably, KD of Ssb leads to slow growth and/or cell death with or without Dox. Thus, it is less meaningful to compare the surviving cell counts in Figure 2D. Rather, the survival rate (Figure 2E) reflects the rescuing effect more precisely. Shown in Figure 2E, both shSsb 1 and 2 increase the survival rate significantly, compared with Empty control.
Moreover, the data in Figure 3B and C demonstrated that Ssb KD compromises the survival of female differentiating cells, but not the survival of male differentiating cells, also indicating a role of Ssb in XCI. With these experiments, it should be sufficient to conclude that Ssb KD affects X-linked cell death/survival in both iXist male ESCs and WT female differentiating cells
The qPCR results used to validate silencing defects show minor changes in expression and also don't show significant silencing of X-linked genes sufficient for cell death. Could this be because only ~ 50 - 60% of Male iXist cells seem to be expressing in the movies and that this will have an effect on the observed qPCR results? Furthermore, it seems counterintuitive that expression in the Empty male cells increases in 48h compared to 14h. Is this due to cell death and positive selection of cells less able to silence their X-chromosome? How would these data look in the female XX line? How would the data look in a ∆A-iXist line in the presence and absence of shSsb/Dox?
First, high-quality live-cell imaging can only be carried out for 2 hours with 2-min time interval. The movies are meant to show the onset of Xist RNA signals. Therefore, they were taken one hour after Dox treatment (figure legend of Figure 4B-D). After overnight Dox treatment, Xist clouds can be seen in majority of cells.
Second, in Fig. 2F-G, we did not include uninduced iXist male ESCs. Therefore, it is impossible to judge whether induction of Xist in this male ESC line results in Xist-dependent silencing at 14 and 48 hr. However, in our previous publication (Li et al., JMB, 2018, 430: 2734-2746), it has been shown that Gpc4, Hprt, Mecp2, G418, and TomatoRed are silenced (4- to 16-fold reduction) at 24 and 48 hours after Dox induction.
Third, the qRT-PCR results in 14 h and in 48 h are not normalized to the same internal control. Thus, they are not directly comparable.
Confusingly, the male line in Fig 3C shows a drop in live cell count at day 6 of differentiation? Surely given their previous results in Fig 2 the Ssb KD should increase cell viability with +Dox? Ssb KD seems to have an adverse effect on ES cells during extended differentiation protocols. In Figure S1 the authors show ~ 8 - 10% survival of male lines during differentiation. Could the recombination of the Xist sequence around the loxP sites enable the cells to outcompete the dead cells? How would iEmpty and ∆A-iXist cells compare here? Have the differentiated cells been tested for their expression of Xist? Additionally, how are there similar live cell counts for male vs female lines when ~90% of male cells die during differentiation? Were more cells plated at day 4? If so, this would bias the competition of male cell survival and therefore make the male line an inappropriate control.
Given the essential role of La during development a control is needed to prove that this death is X-linked in the female 3F1 line. For example, an XO cell line retaining the Cast allele and shSsb expression could show the amount of death caused from shSsb alone independent of X-linked cell death.
The reviewer completely misunderstood the experiment. The severe cell death specifically observed in female differentiating ESCs is a strong evidence showing Ssb is involved in XCI (Figure 3).
The male ESCs in Figure 3C is a WT ESC line without the inducible Xist transgene, in which no XCI occurs upon differentiation. It is completely different from iXist male ESCs with Dox, in which forced Xist induction leads to XCI. Thus, the diverse functions of Ssb might contribute to the slight decrease in live cell count of wild type male cells at day 6 of differentiation.
Figure S2 shows the differentiation of iXist male ESCs with or without Dox. As explained above, forced Xist induction silences the only X chromosome in male cells, resulting in cell death. In addition, XCI occurs more efficiently in differentiation condition (Figure S2) than in pluripotent status (Figure 2C)
During differentiation, female ESCs silence one X chromosome, and the other X chromosome remains active. KD of Ssb compromises XCI, and two X chromosomes in some female differentiating cells maintain active, leading to cell death. The reviewer is correct that we need a control to rule out that the essential role of Ssb during development affects cell survival and death. An XO cell line can be used as a control. Similarly, a male cell line (XY) is also a good control. We already included a male cell line as a control in Figure 3B and 3C.
If I understood correctly, the RNA FISH used dsDNA probes ("Sx9") against 40 kb of the X-inactivation centre (Xic). Surely Tsix or other Xic transcripts will also be visible? Can the authors use their RNA FISH to determine the XX or XO status of their cells? In Figure S5 a number of cells appear to show a single pinpoint of transcription. This could either be low levels of Xist transcripts or Xic transcription from an XO line in which the 129 chromosome is missing. It would be best to solely quantify cells which have two x chromosomes and if a significant amount of X chromosomes have been kicked out, this should be discussed and controlled for.
This is a valid concern, but this concern can be adequately addressed with the available data in the manuscript.
First, if the female Ssb KD cell line is an “XO” cell line, in which the X129 allele is “kicked out”, the RNA allelotyping results should show an absolute “silencing” of the X129 allele. However, in complete contrast to this notion, RNA allelotyping detected “more” RNA transcripts from X129, showing the chromosome-wide XCI defects (Figure 3D).
Second, overexpression of Ssb in Ssb KD female cells restores the Xist clouds and the polycomb marks (Figure S8), suggesting that the Ssb KD female cells are XX, but not XO.
Third, the severe cell death specifically occurred in female Ssb KD lines is also against the “XO” argument (Figure 3B&C).
In Fig6, the authors generated a number of Ssb constructs for a rescue assay. However, these results complicate the matter and raise more questions than they address. It seems odd that the ∆RRM1 does not rescue based on comparison with their putative negative control, ∆NLS. However, the ∆RRM1 + 2 and ∆LAM do rescue the phenotype better than the full length Ssb? This makes no logical sense and highlights the inherent variation in cell viability these generated cell lines seem to show.
Following on from this, figure S7 quantifies the GFP tag mRNA levels, depicting all ∆RRM mutants with expression below ~30%? How can ∆RRM1 or 2 be rescuing in this scenario? Have these lines been tested for their XX or XO status? The loss of an X chromosome would lead to a rescue of the cell death phenotype, which is a process known to occur in XX lines that have been cultured for extended periods of time. Could it also be that the cell lines derived are more or less sensitive to exogenous shRNA expression? Also, further validation is needed to assess the efficiency of KD in these lines as theoretically most of these constructs will be targeted by shRNA? What is the endogenous Ssb expression level in these lines? Where in the mRNA sequence are the shRNAs targeted to? Does this make sense on the relative expression levels of ∆RRM1/2 for example? Further testing of GFP expression could also be assessed by quantitative western blot of GFP or even visualised in their RNA FISH/IF samples (Figure S8), currently neither are shown. In addition, some kind of information of stability of each Ssb protein constructs has not been demonstrated.
Our shRNA targets the LAM domain, so the expression of ∆LAM is not affected by the shRNA. The reviewer is correct that the detected GFP expression levels of ∆RRM1 and ∆RRM2 are too low to be conclusive. We have removed the data point of ∆RRM1 and ∆RRM2. Meanwhile, it is clear that ∆RRM1&2 has a better rescuing effect than ∆NLS, when ∆RRM1&2 and ∆NLS are expressed at similar levels. Ssb is a well known RNA chaperone/RNA helicase. Identifying Ssb is an Xist-binding protein already suggests the functional role of Ssb in XCI. The data of the plasmid rescue experiments further suggests that Ssb is involved in XCI as a RNA chaperone/RNA helicase.
As for the Western blot and GFP fluorescence (IF), we have tried both. Neither of them detected GFP signal, reflecting the low expression level of these GFP fusion proteins. As the reviewers pointed out that the shSsb is not targeting the 5’ or 3’-UTR region, therefore, interfering the exogenous Ssb as well. This might be a reason for the low expression of these GFP fusion proteins.
For the data shown in Figure 7A and B the authors quantify the % of cells with Xist signal. The authors have already shown a defect in Xist visualisation in Ssb KD. Surely it is plausible to assume a faster loss of Xist signal below background in weaker expressing cells. A more appropriate quantification would be the % loss of Xist signal per cell over time.
With Figure 7C and D, the samples have been treated with actinomycin D which globally affects the transcription of cells even the PolIII associated genes Ssb is needed to mature. This treatment could have an added effect on cell mortality and function. Data confirming that actinomycin D doesn't affect the cells disproportionately is needed. The difference in half-life could be attributed to such a treatment.
We agree with the reviewer that monitoring Xist signal loss per cell would be a better way to analyze the data. However, in Xist signal loss experiment, snapshot images were taken at four time points (1h, 2h, 3h and 4h). This is not a time-lapse imaging. High-quality time-lapse imaging can only be done within a 2-hour time period with 2-min time interval. Therefore, cell-tracking cannot be done in this experiment. In addition, even though Ssb KD slows down the formation of Xist cloud within the early phase (3 hours) of Xist induction (Figure 4), prolonged (overnight) Xist induction leads to Xist cloud formation in a significant fraction of Ssb KD cells, and the Xist cloud signals are about the same in WT and Ssb KD cells (Figure 7A, 0 h). Similarly, qRT-PCR also revealed that Xist RNA are at the same level in WT and Ssb KD cells (Figure 7C, 0 h). These data argue against that a faster loss of Xist signal in Ssb KD cells is due to weaker initial Xist signal.
Actinomycin D was added at the last 11 hours of the experiment. During this period, no obvious adverse effects on cells were observed.
In summarising the authors claim that La binds Xist to facilitate folding and appropriate spreading of Xist along the X-chromosome. No direct interaction has been shown, CLIP-seq data would resolve this, however I do understand this is a challenging technique. The authors have instead opted for RIP followed by qPCR (Figure S2). However, this process has a greater potential for non-specific recovery of RNAs via indirect binding. Furthermore, qPCR may also amplify the relative abundance of the RNA detected. As multiple nucleolar proteins came down in the mass spec screen and FLAG-Ssb is being over expressed, it is plausible to assume some transient Xist interactions may arise from nucleolar association at which La will be in high abundance. Positive and negative nuclear RNA controls (e.g. 7SK and U1 snRNA respectively) could be used so to determine the amount of non-specific Protein-RNA interactions in their RIP pull downs. Cytoplasmic actin is not an appropriate control as it is cytosolic.
We have to clarify one point that the mass spec screen analyzed samples pulled down by FLAG-PUFb, but not FLAG-Ssb.
We did not intend to distinguish whether Ssb directly binds Xist or is just associated with Xist. RIP followed by qPCR is sufficient to prove the association between Ssb and Xist RNA.
We can include nuclear RNA as controls, if the reviewer regards RIP as a valid method to show protein and RNA association
Other than this the authors may want to probe (via IF) for the presence of La accumulation on the X? Many other know factors such as Ciz1, hnrnpK and PRC1/2 complexes show clear accumulation on the X. If I understand correctly, there are many La antibodies on the market and endogenous levels on the X could be assessed. These antibodies may be useful in IP's and pull downs also.
Many XCI factors play extensive roles in the cell and are not clearly enriched on Xi, including Spen (Moindrot et al. 2015). We have tried the immunostaining and did not detect Ssb’s enrichment on Xi. Ssb shows a general distribution in the nucleus without a clear enrichment on Xi (data not shown).
*-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.*
The experiments suggested above are centrally focussed on the cell lines that are currently in the authors possession with maybe exceptions with the ∆A-iXist-shSsb line suggested. However, this should be reasonably quick to obtain given their previous work for this paper. Most experiments suggested will focus on the validation of karyotype, Xist expression, rescue construct expression, further RNA FISH classification and repeating more appropriate positive and negative controls for a number of experiments. In theory this can be obtained relatively simply and quickly from current resources. But with the sheer volume of further experiments that are required here, this may take a significant amount of time.
One vital improvement needed is the replication of mass spec data and the validation of Xist specific recovery and protein enrichment. As it stands this manuscript seems to not have any replicates of the FLAG-out methodology and mass spec data. This is troubling given the poor recovery and specificity of the protein samples obtained. Repeating these experiments would be costly in time and also financially. As it stands, I feel this is essential to conclusively validate their target of interest.
*- Are the data and the methods presented in such a way that they can be reproduced?*
The data is presented relatively well, however, it would be beneficial if deailed methods were in the main text and not in a supplementary file. Similarly, more information about the process of differentiation and how cell death/survival was quantified and validated is needed.
The reviewer rejected the basic design of the experimental system and ignored the majority of the functional analysis data. No additional experiment can address these issues
We can include more information in the main text, regarding Ssb. However, there is limited space for the main text, various depending on the journals. Meanwhile, the current citation on Ssb is adequate to emphasize that Ssb is a versatile RNA binding protein involved in a variety of fundamental RNA processing events in the cell.
*- Are the experiments adequately replicated and statistical analysis adequate?*
In the most part yes, however there seems to be no replicates of the FLAG-out mass spec screen which is worrying given the minimal specificity observed in the current data.
As we mentioned above, the FLAG-out experiment only serves as a starting point to generate a list of Xist binding protein candidates. Rather than repeating the FLAG-out experiment, we compared the result of FLAG-out to previously published lists of Xist binding protein candidates. More importantly, additional experiments are carried out to validate the Xist binding proteins identified by FLAG-out.
**Minor comments:**
*- Specific experimental issues that are easily addressable.*
Unfortunately, the majority of experimental issues need to be addressed with more robust data which are highlighted above. However, some image analysis, quantification and classification can be amended relatively easily. For example, the live-cell imaging data should be quantified as loss of signal as discussed and RNA FISH should be used to classify XX positive cells and the XO cells can be discarded from analysis.
We have addressed these issue in the previous sections of this rebuttal.
*- Are prior studies referenced appropriately?*
Most papers regarding Xist pull down and biology are discussed and referenced appropriately. However, the role in which La plays during development and its aberrant affects upon KD are seemingly downplayed. I would like to see more discussion of potential defects that could be caused due to globally altering cellular RNA folding.
We have tried to cite key references about Ssb in development and RNA folding. Due to length limitation, we cannot cite all references in the topic. If necessary, we could discuss the possibility of indirect effect of Ssb KD on XCI through globally altering cellular RNA folding.
*- Are the text and figures clear and accurate?*
For the most part, lots of the figures are clear and accurate. Apart from these exceptions.
1.The Y-axis of Figure 2D is confusing. What does 0.3 as a "sum of area" equate to? 30% of the area was ES cells? This doesn't look to be the case from Fig 2C. Also, how does the intensity of the signal compare? The area may not be a good quantification due to ES cells growing in colonies.
We have revised the Y-axis labelling of Figure 2D to “sum of area cm2”. Thus, “0.3” means that the area of ESCs is 0.3 cm2. ALPP is highly expressed on ES cell surface. ALPP stain usually produce saturated stains on ES cell colonies. Thoroughly stained ES cell colonies, big and small, show similar signal intensity levels. To analyze the “total signal intensity” will be not much different from “sum of area”.
2.In the Movies S1-7 there are boxes around certain cells and marked with "Figure 5a - c". This seems to be incorrect as figure 5 is currently the IF staining of polycomb marks. I assume this is in relation to Figure 4b-d?
We have corrected the labelling mistakes.
3.Similarly, in Movies S1-7, the intensities of Xist foci seem by eye to be similar. In the paper it is claimed that the Xist clouds that do form are lower in intensity. Are the Movies depicting the same range of pixel intensities? If not, this should be amended. Similarly, figure 7 seems to show relatively equivalent RNA signal at 0 h?
All the images were collected using a fixed standard of the microscope and camera setting, and these movies depict the same range of pixel intensities. Movies S1-S3 are WT control, and Movies S4-S7 are Ssb KD cells. The Xist cloud signals are weaker in Movie S4-S7 (also quantified in Figure 4E). For the Xist cloud signal, not only the intensity, but also the area of Xist cloud, have to be taken into account.
The 0 h in Figure 7 is after overnight Dox treatment, and different from the time point in Movies S1-7 (maximum 3 hour Dox treatment, figure legend of Figure 4B-D). The discrepancy can be explained by that knockdown of Ssb only slows down the formation of Xist clouds. After overnight forced expression, the Xist RNA still shows an accumulation in the cells. Figure 7 shows the forced accumulation of Xist RNA after prolonged Dox treatment disappears faster after Dox withdraw.
4.In figure 4A the data is from female XX cells, this should be highlighted to limit confusion with the male iXist data shown below in 4B-E. It would also be helpful to have the male/female icons (as in figure 3B), for each figure that has images of cells. Currently Figure 4, 5, 7, S5 and S8 are lacking these icons.
We have revised the labelling on Figure 3, 4, 5, 7 S6 and S9 (S5 and S8 before revision).
5.No explanation of the Flag-Ssb expression is given for figure S2. Furthermore, is it really necessary to express Flag-Ssb? There are reasonably good antibodies out there for Ssb as this was how it was originally found in Systemic Lupus patients. Also, no data showing the amount of Ssb being overexpressed is shown. This may have big implication to the validity of the RIP-qPCR analysis.
We could perform qRT-PCR to quantify the overexpression level of Flag-Ssb. If required, we could use Ssb antibody to do Western blot to show the amount of Flag-Ssb protein.
*- Do you have suggestions that would help the authors improve the presentation of their data and conclusions?
Most of the data is presented reasonably well, but the robustness of the data somewhat retracts from their conclusions. I feel the certainty of their conclusion regarding Xist specific La binding and RNA chaperone activity is still presumptive and should be rewritten unless more robust data can confirm Xist interaction. I would also suggest deciding on the nomenclature for the protein of interest and use either La or Ssb, the continued use of both through the figures and text can get a little confusing to the reader.
In the current literatures, Ssb seems to be commonly used as a gene name and La is used as a protein name. We have revised the manuscript to use one name “Ssb” to describe both the gene and the protein.
Reviewer #1 (Significance (Required)):
*- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.*
It was a good trial to use PBSb-PUFb system to purify Xist RNA binding proteins, compared to previous reports had used anti-sense oligo purification using complementary sequence to Xist RNA sequences. But currently the purification still needs further validation and repeats to confirm its use. A potential complementary technique could be to isolate Xist directly by using biotinylated probes against the PBSb sequence.
The authors further claim the identification of a novel Xist RNA chaperone (La/Ssb) which they say facilitates XCI progression. This would be a novel finding in the field; however, the data is currently not robust enough to support this
*- Place the work in the context of the existing literature (provide references, where appropriate).*
This work has focused on the development of a milder methodology for purifying Xist RNA during XCI. Others have published similar methodologies predominantly focusing on purifying Xist RNA directly with biotinylated probes (McHugh et al. 2015; Minaji et al. 2015; and Chu et al. 2015). Although this method boasts a milder purification method, it seems to be low yielding in Xist specific proteins. Others have shown a more robust identification of bona fide Xist binding proteins which are currently missing in this manuscript. A recent preprint from the Plath lab has identified new factors involved in XCI during differentiation and their tethering/rescue experiments are far more convincing than the ones shown in this manuscript https://www.biorxiv.org/content/10.1101/2020.03.09.979369v1. The candidate protein Ha et al. have identified has multiple roles in developing cells and has shown to be important during mouse development. However, Ha et al do not robustly show that the knockdown of Ssb causes X-linked cell mortality. Alternatively, as would be presumed from Ssb's essential role in many housekeeping short non-coding RNAs, the cell death seems more ubiquitous upon shRNA KD. Therefore, the link the authors are making here are relatively weak.
Ssb KD rescues cell death caused by forced induction of Xist in male ESCs. In addition, Ssb KD leads to cell death in differentiating female ESCs, while it has a negligible effect on cell death in differentiating male ESCs. These data clearly demonstrated X-linked cell survival/mortality by Ssb KD.
Plath lab’s work is different from ours. In their manuscript, the authors report the observation of a protein condensation which is assembled by Xist but sustains in absence of Xist. TDP-43 (a.k.a. Tardbp) happens to be one protein factor involved in the protein condensation and also one candidate protein selected for further validation in our study. In our study, Tardbp KD did not rescue cell death caused by induced XCI in male cells. Thus, Tardbp is not further studied. In the manuscript, we have discussed the possibility that low efficiency of knockdown and redundancy might contribute to the failure in validation of Tardbp
*- State what audience might be interested in and influenced by the reported findings.*
The audience may be interested in the novel technique and the finding of a novel Xist binding protein.
*- 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.*
RNA biochemistry and developmental biology
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
**Summary:**
This manuscript describes a novel "FLAG-out" system, where the authors sought to identify Xist RNA binding proteins. The authors focused on a specific protein found in their screen and also identified in several other screens for Xist RNA binding proteins, Ssb/La, and further characterize the role of this protein in XCI. This manuscript describes the loss of Ssb/La and suggest that it predominately impacts the canonical 'cloud' formation of Xist RNA on the X chromosome during XCI initiation. Further, they determine that loss of Ssb/La decreases Xist RNA half-life and alters folding of Xist RNA transcripts. Based on their findings, the authors propose that Ssb/La functions to directly bind and fold Xist RNA transcripts in a manner that stabilizes Xist RNA, allowing for proper 'cloud' formation and successful initiation of XCI.
**Major comments:**
The authors made an interesting findings that the SLE-relevant autoantigen Ssb/La stabilizes Xist RNA transcripts, and there is some evidence that this occurs by binding and maintaining proper folding of Xist RNA. Despite these intriguing observations, there are many parts of the manuscript that need to be addressed in order to support the authors main conclusions.
The most troubling aspect of this manuscript is the persistent use of an artificial XCI system in male cells to draw strong conclusions about the function of Ssb in XCI. This issue is prevalent throughout the manuscript, and I question why the authors chose to perform most of their experiments in male cells when the same experiments can be (and have previously been by other groups) performed in female cells. Using male ESCs and then making conclusions for XCI, which is a female-specific process, is a major concern.
In addition to iXist male ESC line, many experiments, such as cell death/survival (Figure 3B, C), allelotype (Figure 3E), Xist could formation (Figure 4A), H3K27me3 and H2AK119ub IF (Figure 5), were performed in female ESC. We chose to do SHAPE and Xist RNA stability assays in iXist male ESC line, because the onset of XCI is much more synchronized in this system. Moreover, in female cells, Xa causes additional layers of complication/noise in the ATAC-sequencing which may not be fully cleared up by data analysis. On the other hand, inducible Xist expression in male ESCs can be used as an experimental system to recapitulate the silencing step of XCI (Ha et al. 2018; Wutz et al. 2002).
Out of the 138 identified binding proteins, the authors chose to only validate three: Mybbp1a, Tardbp, and Ssb/La. The logic for choosing these candidates is weak, and the authors are only able to validate 1 out of 3 of these proteins.
In theory, all candidate proteins in the list are possibly involved in XCI. There is no method which can help to make accurate prediction. We did not follow a clear-cut logic in selecting candidates for validation, but we do consider the candidate gene’s knockout phenotype, “early embryonic lethality”, as a phenotype consistent with a critical role of the candidate gene in XCI. Meanwhile, in the manuscript, we have discussed why we chose the three proteins for validation as the following:
“……From the candidate proteins, we shortlisted three proteins for individual validation. Myb-binding protein 1A (Mybbp1a, Q7TPV4) and TAR DNA-binding protein 43 (Tardbp, Q921F2) were selected because they are known transcription repressors (11, 12). The Lupus autoantigen La (P32067, encoding-gene name: Ssb) was selected because systemic lupus erythematosus (SLE) is an autoimmune disease characterized by a strikingly high female to male ratios of 9:1 (13). Moreover, its autoimmune antigen La is a ubiquitous and versatile RNA-binding protein and a known RNA chaperone (14). All the three selected candidates have also been identified as Xist-binding proteins in previous studies (2, 4). Moreover, the knockout of these three genes all lead to early embryonic death. Tardbp knockout causes embryonic lethality at the blastocyst implantation stage (15). Mybbp1a and Ssb knockout affect blastocyst formation (16, 17). Early embryonic lethality is a mutant phenotype consistent with a critical role of the mutated gene in XCI (1)** ……”
We used cell death/survival assay to further validate the role of Xist binding protein candidates in XCI. This is a stringent assay. It requires not only that Xist binding protein candidates bind to Xist, but also that the candidates have to be functionally important in XCI.
Indeed, it has been demonstrated by Plath lab (the BioRxix manuscript mentioned by reviewer 1) that Tardbp (also named TDP-43), together with other RBPs, bind to the E repeat of Xist to form a condensate and create an Xi-domain. Yet, Tardbp KD did not rescue cell death caused by forced XCI in male cells in our studies. Thus, only 1 out of 3 of these candidates is validated and further studied. In the manuscript, we also discussed that low efficiency of knockdown and redundancy might contribute to the failure in validation of Tardbp and Mybbp1a.
Use of the cell death assay is not strong enough to "confirm that La is involved in induced XCI" as stated by the authors. This is a huge overstatement.
Given the diverse functions of Ssb in cell differentiation and proliferation, ribosome biogenesis, transcriptional control and tRNA maturation, one would expect less surviving Ssb knockdown cells. In contrast, more Ssb knockdown cells survives in the presence of Dox, suggesting that Ssb plays an important role in XCI. Considering the reviewer’s comment, we revised the sentence to “further suggest that Ssb is involved in induced XCI”.
While the authors observed differences in X-linked gene expression after Ssb KD, they did not examine expression of these genes in after KD of either Mybbp1a or Tardbp. Are the changes observed in these genes specific to Ssb KD? Or could there still be alterations of X-linked gene expression in the non-validated KDs? This experiment should be performed and included in the manuscript, either within Fig 2 or in the supplemental. As well, inclusion of a well characterized positive control, for example Hnrnpu, as comparison to Ssb should be included.
Mybbp1a and Tardbp were not validated by the cell death assay. Thus, compared with Ssb, Mybbp1a and Tardbp are less important for XCI functionally. We only focused on Ssb in the subsequent studies. Mybbp1a and Tardbp KD could be additional negative controls. Yet, we have used empty vector as a negative control. We do not need so many controls.
As mentioned, Tardbp indeed binds to Xist RNA. It is very likely that Tardbp KD might alter some X-linked gene expression. This rules out Tardbp KD as a good negative control.
If we do not see any effect of Ssb KD on X-linked gene expression, a positive control is absolutely required. However, we have detected that Ssb KD compromises the silencing of several X-linked gene. A positive control might not be essential.
The authors perform RIP to validate the interaction of Ssb with Xist, but this is performed in male ES cells with induced Xist RNA and with FLAG-tagged Ssb. Aside from these cells being male, in this system Xist RNA expression is much higher than would be found endogenously. RIP should have been done in female differentiated ESCs if there is in fact a role for XCI.
The authors need to include more details in the methods section to explain how the FLAG-Ssb is expressed in these cells, and why the authors chose to use a tagged contrast over endogenous Ssb. Due to these issues the result from this experiment is essentially meaningless and is not convincing of Ssb interaction with Xist RNA. There is no reason RIP cannot be performed in female cells, and the authors should repeat this experiment in the relevant experimental condition. As well, if a validated Ssb antibody exists the authors should perform RIP using the endogenous protein.
If required, we could try to perform RIP and/or CLIP using Ssb antibody in female cells.
The authors state in Fig 3A-C that the results of the cell death and differentiation experiments "...support a functional role of La in XCI". The authors state earlier that Ssb is a ubiquitous protein that is embryonic lethal (in both female and males). Based on this, the cell death results shown do not support a functional role of La in XCI as the Ssb KD could be having an indirect affect due to its other developmental functions. This manuscript lacks a direct functional link between Ssb and XCI; more data is necessary.
Given the diverse functions of Ssb in cell differentiation and proliferation, ribosome biogenesis, transcriptional control and tRNA maturation, one would expect less surviving Ssb knockdown cells. In contrast, more Ssb knockdown cells survives in the presence of Dox, suggesting that Ssb plays an important role in XCI.
For the data in Fig 3A-C, Ssb KD causes the death of female differentiating cells, but not male differentiating cells. Therefore, it rules out that the death of female cells is due to the general function of Ssb. Rather, the specific role of Ssb in XCI contributes to the female specific cell death.
In Fig 3D, the authors perform ATAC-seq in inducible male ES cells. The authors claim that the extremely slight reduction in chromatin compaction of the Ssb KD compared to control iXist "directly connect La to the heterochromatinization of Xi, supporting a functional role of La in XCI". This is also an overstatement based on the minimal, and possibly indirect, change in compaction. The positive control i-detaA-Xist sample has significantly less compaction (and thus significantly higher compaction defect) than the Ssb KD again disputing the claim stated above. It is unclear why performing ATAC-seq is even necessary, as Ssb isn't stated to have a function in regulating chromatin architecture. In addition, why the authors performed ATAC-seq in the artificial male XCI system and not in the F1 female cells, and the N of the experiment is unclear. If the authors want to include the ATAC-seq in further revisions it should be repeated n=3 in the female system.
The male induced XCI system provides a more synchronized onset of XCI. More importantly, in the male induced XCI system, only one X chromosome exists, avoiding the interference from the active X chromosome in female cells. If ATAC-seq was performed in female cells, only loci with SNPs can be distinguished. The sequencing reads from Xa will create additional layers of complication/noise which may not be cleared up fully by data analysis
“i-delat-Xist” is a positive control to show the experimental system works. It is not justified to compare the chromatin accessibility of the mutant, which is only a Ssb “knockdown” mutant, and the control “i-delat-Xist”, in which the Repeat A is “deleted”. We admit that ATAC-Seq results did not reveal a drastic difference in chromatin accessibility between the wild type sample and the mutant sample. However, as what we discussed in the manuscript, clear difference can still be seen at the 14 h time point. This is shown clearly by the heatmap (Fig. 3E) and the sequencing coverage profile (Fig. S4A).
In Fig 6, the authors state in their methods that "The shRNA construct, which worked efficiently against Ssb, was not designed against the 3' UTR of the RNA. Therefore, the shRNA is against some of the rescue plasmid constructs. Nonetheless, transfecting the Ssb knockdown cells with the rescue plasmids should compensate the effect of Ssb knockdown and serve as a rescue assay to study the functional domains of La.". This is troubling and seems like a major experimental issue; the specific rescue constructs that may be impacted by this issue are not stated and should be explicitly mentioned. This becomes more confusing when examining the data from rescue experiments.
We pointed out this issue in the original manuscript. We agree that the experiment was not perfectly designed. In the revision, we added in the information on the shRNA target site. Our shRNA targets the LAM domain, so the expression of ∆LAM is not affected by the shRNA. We agree that the detected GFP expression levels of ∆RRM1 and ∆RRM2 are too low to be conclusive. In the revision, we have removed the data point of ∆RRM1 and ∆RRM2. Meanwhile, it is clear that ∆RRM1&2 has a better rescuing effect than ∆NLS, when ∆RRM1&2 and ∆NLS are expressed at similar levels. Ssb is a well-known RNA chaperone/RNA helicase. Identifying Ssb is an Xist-binding protein already suggests the functional role of Ssb in XCI. The data of the plasmid rescue experiments further suggests that Ssb is involved in XCI as a RNA chaperone/RNA helicase.
If it is necessary, we could redo this experiments using a shSsb targeting 3’-UTR or expressing GFP-Ssb immune to shSsb.
In Figure S7, the expression of the rescue constructs deltaRRM1 and deltaRRM2 is extremely low, yet the authors observe a rescue of the cloud phenotype (fig 6D) from those constructs that reaches almost the level of full length Ssb. This is confusing, and the authors need to address this by performing a western blot to show the protein levels of these rescue constructs and discuss further how such a low level of expression can show a rescue phenotype. The results would also be stronger if the authors examined H3K27me3 and H2AK119ub1 enrichment since they observed decreased overlap of these marks with Xist RNA after Ssb KD. Finally, the authors state that "...all three RNA-binding domains are required for the functionality of La in XCI..." however I have trouble coming to this conclusion based on the above issues. As well, if the authors want to support direct function, they should repeat the RIP experiments with these rescues constructs to show that the domains capable of rescue can still bind to Xist RNA.
Reviewer 1 raised similar concerns. In Figure 6C, the live cell counts of ∆RRM1 and ∆NLS are about the same. It might be due to the low expression level of ∆RRM1 (Figure S7). It is clear that ∆RRM1&2 has a better rescuing effect than ∆NLS, when ∆RRM1&2 and ∆NLS are expressed as similar levels. To make the data more straight forward, we removed the data point of ∆RRM1 and ∆RRM2, because of their low expression levels.
As for the Western blot and GFP fluorescence (IF), we have tried both. Neither of them detected GFP signal, reflecting the low expression level of these GFP fusion proteins. The shSsb is not targeting the 5’ or 3’-UTR region, therefore interfering the exogenous Ssb as well. This might be a reason for the low expression of these GFP fusion proteins. If it is necessary, we could redo this experiments using a shSsb targeting 3’–UTR or expressing GFP-Ssb immune to shSsb.
We deleted the sentence "all three RNA-binding domains are required for the functionality of La in XCI".
**Minor comments:**
The authors may want to consider better highlighting the strengths of their "FLAG-out" system. As written, is it difficult to tell how this system sets them apart from the previously published studies referenced in the text, especially as some of these studies used similar crosslinking conditions and cell types. Additionally, the logic and questions the authors pose in the introduction as to why they performed this project are too general and not very strong. For example, the authors mention how might protein machinery may assemble on Xist RNA, and how might Xist RNA may spread on the X chromosome. However neither of these topics are actually addressed in their experiments or discussion. These are interesting questions, but the authors should either discuss them further within the context of their results or take these questions out. It would also be helpful if the authors could better label Figure 4, as it is unclear in the figure itself that Fig 4A is in reference to female cells, but remaining panels are in male cells.
The inducible XCI in male cells is a valid system to recapitulate the silencing step of XCI. It also provides unique advantages in many experiments, such as ATAC-seq. Meanwhile, we did perform extensive functional analysis on the endogenous XCI process using female cells. However, we do realize that presenting the data of induced XCI in male cells together with the data from female cells is confusing to many readers. We have revised the labelling on Figure 3, 4, 5, 7 S6 and S9 (S5 and S8 before revision).
To understand “how the protein machinery is assembled by Xist” and “how Xist spreads along its host chromosome territory” are not specifically the initial aims of this study. We removed the sentences from the introduction section. However, we believe Ssb may provide clues for the future studies to fully address these questions, and we did provide the following thoughts in the discussion section:
“……Secondly, as Ssb is able to utilize ATP to unwind RNA-RNA and RNA-DNA duplex, it may play a more active role in controlling the structural dynamics of Xist in living cells (14, 23). These structural dynamics may be important for recruiting proteins onto the RNA and spreading of the RNA along its host chromosome territory……”
Reviewer #2 (Significance (Required)):
I am not convinced the this manuscript, as written, has sufficient novelty. Ssb/La has been previously identified to be an Xist RNA binding protein with older/different approaches. However, there are some interesting observations in this manuscript. Major revisions are necessary.
We agree with the reviewer that identification of Ssb as an Xist RNA binding protein is not novel. The novelty of our discovery lies in: 1) we developed a new method for isolating lincRNA associated proteins; 2) we confirmed that Ssb is an important player involved in XCI; 3) we showed that Ssb regulates the folding of Xist RNA, consequently the stability of Xist and the formation of Xist cloud.
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Referee #2
Evidence, reproducibility and clarity
Summary:
This manuscript describes a novel "FLAG-out" system, where the authors sought to identify Xist RNA binding proteins. The authors focused on a specific protein found in their screen and also identified in several other screens for Xist RNA binding proteins, Ssb/La, and further characterize the role of this protein in XCI. This manuscript describes the loss of Ssb/La and suggest that it predominately impacts the canonical 'cloud' formation of Xist RNA on the X chromosome during XCI initiation. Further, they determine that loss of Ssb/La decreases Xist RNA half-life and alters folding of Xist RNA transcripts. Based on their findings, the authors propose that Ssb/La functions to directly bind and fold Xist RNA transcripts in a manner that stabilizes Xist RNA, allowing for proper 'cloud' formation and successful initiation of XCI.
Major comments:
The authors made an interesting findings that the SLE-relevant autoantigen Ssb/La stabilizes Xist RNA transcripts, and there is some evidence that this occurs by binding and maintaining proper folding of Xist RNA. Despite these intriguing observations, there are many parts of the manuscript that need to be addressed in order to support the authors main conclusions.
• The most troubling aspect of this manuscript is the persistent use of an artificial XCI system in male cells to draw strong conclusions about the function of Ssb in XCI. This issue is prevalent throughout the manuscript, and I question why the authors chose to perform most of their experiments in male cells when the same experiments can be (and have previously been by other groups) performed in female cells. Using male ESCs and then making conclusions for XCI, which is a female-specific process, is a major concern.
• Out of the 138 identified binding proteins, the authors chose to only validate three: Mybbp1a, Tardbp, and Ssb/La. The logic for choosing these candidates is weak, and the authors are only able to validate 1 out of 3 of these proteins.
• Use of the cell death assay is not strong enough to "confirm that La is involved in induced XCI" as stated by the authors. This is a huge overstatement.
• While the authors observed differences in X-linked gene expression after Ssb KD, they did not examine expression of these genes in after KD of either Mybbp1a or Tardbp. Are the changes observed in these genes specific to Ssb KD? Or could there still be alterations of X-linked gene expression in the non-validated KDs? This experiment should be performed and included in the manuscript, either within Fig 2 or in the supplemental. As well, inclusion of a well characterized positive control, for example Hnrnpu, as comparison to Ssb should be included.
• The authors perform RIP to validate the interaction of Ssb with Xist, but this is performed in male ES cells with induced Xist RNA and with FLAG-tagged Ssb. Aside from these cells being male, in this system Xist RNA expression is much higher than would be found endogenously. RIP should have been done in female differentiated ESCs if there is in fact a role for XCI.
• The authors need to include more details in the methods section to explain how the FLAG-Ssb is expressed in these cells, and why the authors chose to use a tagged contrast over endogenous Ssb. Due to these issues the result from this experiment is essentially meaningless and is not convincing of Ssb interaction with Xist RNA. There is no reason RIP cannot be performed in female cells, and the authors should repeat this experiment in the relevant experimental condition. As well, if a validated Ssb antibody exists the authors should perform RIP using the endogenous protein.
• The authors state in Fig 3A-C that the results of the cell death and differentiation experiments "...support a functional role of La in XCI". The authors state earlier that Ssb is a ubiquitous protein that is embryonic lethal (in both female and males). Based on this, the cell death results shown do not support a functional role of La in XCI as the Ssb KD could be having an indirect affect due to its other developmental functions. This manuscript lacks a direct functional link between Ssb and XCI; more data is necessary.
• In Fig 3D, the authors perform ATAC-seq in inducible male ES cells. The authors claim that the extremely slight reduction in chromatin compaction of the Ssb KD compared to control iXist "directly connect La to the heterochromatinization of Xi, supporting a functional role of La in XCI". This is also an overstatement based on the minimal, and possibly indirect, change in compaction. The positive control i-detaA-Xist sample has significantly less compaction (and thus significantly higher compaction defect) than the Ssb KD again disputing the claim stated above. It is unclear why performing ATAC-seq is even necessary, as Ssb isn't stated to have a function in regulating chromatin architecture. In addition, why the authors performed ATAC-seq in the artificial male XCI system and not in the F1 female cells, and the N of the experiment is unclear. If the authors want to include the ATAC-seq in further revisions it should be repeated n=3 in the female system.
• In Fig 6, the authors state in their methods that "The shRNA construct, which worked efficiently against Ssb, was not designed against the 3' UTR of the RNA. Therefore, the shRNA is against some of the rescue plasmid constructs. Nonetheless, transfecting the Ssb knockdown cells with the rescue plasmids should compensate the effect of Ssb knockdown and serve as a rescue assay to study the functional domains of La.". This is troubling and seems like a major experimental issue; the specific rescue constructs that may be impacted by this issue are not stated and should be explicitly mentioned. This becomes more confusing when examining the data from rescue experiments.
• In Figure S7, the expression of the rescue constructs deltaRRM1 and deltaRRM2 is extremely low, yet the authors observe a rescue of the cloud phenotype (fig 6D) from those constructs that reaches almost the level of full length Ssb. This is confusing, and the authors need to address this by performing a western blot to show the protein levels of these rescue constructs and discuss further how such a low level of expression can show a rescue phenotype. The results would also be stronger if the authors examined H3K27me3 and H2AK119ub1 enrichment since they observed decreased overlap of these marks with Xist RNA after Ssb KD. Finally, the authors state that "...all three RNA-binding domains are required for the functionality of La in XCI..." however I have trouble coming to this conclusion based on the above issues. As well, if the authors want to support direct function, they should repeat the RIP experiments with these rescues constructs to show that the domains capable of rescue can still bind to Xist RNA.
Minor comments:
The authors may want to consider better highlighting the strengths of their "FLAG-out" system. As written, is it difficult to tell how this system sets them apart from the previously published studies referenced in the text, especially as some of these studies used similar crosslinking conditions and cell types. Additionally, the logic and questions the authors pose in the introduction as to why they performed this project are too general and not very strong. For example, the authors mention how might protein machinery may assemble on Xist RNA, and how might Xist RNA may spread on the X chromosome. However neither of these topics are actually addressed in their experiments or discussion. These are interesting questions, but the authors should either discuss them further within the context of their results or take these questions out. It would also be helpful if the authors could better label Figure 4, as it is unclear in the figure itself that Fig 4A is in reference to female cells, but remaining panels are in male cells.
Significance
I am not convinced the this manuscript, as written, has sufficient novelty. Ssb/La has been previously identified to be an Xist RNA binding protein with older/different approaches. However, there are some interesting observations in this manuscript. Major revisions are necessary.
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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).
Here Ha et al. has further developed their Pumilio RNA tagging methodology for the isolation of UV-crosslinked proteins that are suggested to associate with Xist RNA in mouse embryonic stem cells (mESCs). Within this study the authors claim to have found the Lupus antigen RNA binding protein (La) as a novel Xist interacting partner that influences the efficacy of X-chromosome inactivation (XCI). The authors use a number of different techniques such as qPCR, fluorescent imaging, ATAC-SEQ and SHAPE to show aberration of XCI upon La shRNA knockdown. However, this study has significant flaws in the efficient isolation and validation of Xist associated proteins using their FLAG-out methodology. Furthermore, later experiments predominantly focus on cell death/survival assays, which is somewhat troubling given the essential roles La plays in processes such as cell differentiation and proliferation, ribosome biogenesis, transcriptional control and tRNA maturation. I feel the authors need to robustly address the potential effects La knockdown may be having on their mESCs.
Major comments:
-Are the key conclusions convincing?
My major concern is in their Xist RNA purification. First of all, I couldn't find any data on proving the enrichment of Xist RNA itself in their Pumilio pull-down experiment. It would have been useful to show Xist RNA enrichment before benzonase step. Secondly, it is hard to imagine the protocol would successfully isolated Xist RNA-protein complexes from the cell. An earlier report by Clemson et al., (J Cell Biol., 1996) has shown that majority of Xist RNA is still stuck in the nucleus after nuclear matrix prep protocol using detergent, which is not so different from the authors' protocol. Moreover, the authors used UV crosslink, which would have made even harder to purify Xist RNA without sonication. Thirdly, as the tag is located on 5' of Xist RNA, it is rather surprising to see that Spen is not detected in their pulldown. Spen is one of the main functional interactors with Xist, robustly detected by several previous reports. Similarly, other high-affinity binders of Xist such as hnRNP-K and Ciz1 were also lacking from this screen. Finally, the peptides found associated with FLAG-out Xist are extremely low in comparison with other data using glutaraldehyde or formaldehyde crosslinking. For example, HnRNP-M found in Chu et al 2015 has 1120 peptide counts in differentiated cells. The authors here use HnRNP-M as a baseline for specific interactions and show a total of 6 peptide counts in Xist expressing cells and 5 in i-Empty cells (Supplementary excel sheet 1). Similarly, the La protein of interest in this study has 8 counts in i-FLAG-Xist and 6 counts in i-Empty. I struggle to see how this result indicate specific Xist binding. Worryingly this is the starting rationale for the rest of their experiments, it is hard to therefore accept the rest of their conclusions either.
The other key conclusion the authors make is from the use of numerous cell death/survival assays for both male and female cell lines. This is extremely troubling in the context of assessing their target protein La. La is involved in multiple RNA maturation events of rRNAs, tRNAs and other polIII transcripts. Furthermore, La has been implicated in binding to the mRNA for Cyclin D1 in both human cells and mouse fibroblasts (NIH/3T3 - male) which show a significant effect on cell proliferation upon siRNA knockdown https://www.nature.com/articles/onc2010425. This, along with the observation that La knock-out blastocysts fail to develop any mice or ES cell lines (male or female) show the effect observed in the authors results is most likely not X-linked cell death https://mcb.asm.org/content/mcb/26/4/1445.full.pdf. The authors need to show that their shRNA KD isn't affecting the proliferation and general fitness of their mESC lines.
- Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?
As discussed above, I feel the authors have not clearly demonstrated Xist specific protein enrichment and haven't proven X-linked cell death. Due to the lack of necessary control experiments as discussed below, I feel the notion that La is involved directly in XCI as an RNA chaperone is currently preliminary/speculative.
- 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.
I would suggest them to show RT-qPCR results of Xist RNA enrichment from the sample after flagIP before benzonase treatment.
Also, it would have been more convincing if their negative control construct (i-Empty) would contain 25 copies of PBSb RNA at least.
In Fig1b, the total amount of proteins loaded on the gel is not equivalent between two lanes. The gel should show equivalent amounts of proteins on the gel. It looks like if the negative control sample had been loaded at the same amount as the one with Xist, the band pattern wouldn't be distinguishable between the two samples. Furthermore, as these samples were used in the following mass spectrometry screen it may suggest that the minimal increase in peptide counts observed in the iXist FLAG-out were due to an increased amount of sample being loaded? No controls are conducted to account for this.
The authors quantify cell death in figures 2C - E. It seems clear that shSsb 1 and 2 have an effect on cell count even in the absence of Dox. The rescue effect seen upon Dox addition is minimal when compared to Empty + Dox 2D. The authors ∆A-iXist line with and without Ssb KD/Dox would be an informative control on whether the increase in cell survival that they see is X-linked.
The qPCR results used to validate silencing defects show minor changes in expression and also don't show significant silencing of X-linked genes sufficient for cell death. Could this be because only ~ 50 - 60% of Male iXist cells seem to be expressing in the movies and that this will have an effect on the observed qPCR results? Furthermore, it seems counterintuitive that expression in the Empty male cells increases in 48h compared to 14h. Is this due to cell death and positive selection of cells less able to silence their X-chromosome? How would these data look in the female XX line? How would the data look in a ∆A-iXist line in the presence and absence of shSsb/Dox?
Confusingly, the male line in Fig 3C shows a drop in live cell count at day 6 of differentiation? Surely given their previous results in Fig 2 the Ssb KD should increase cell viability with +Dox? Ssb KD seems to have an adverse effect on ES cells during extended differentiation protocols. In Figure S1 the authors show ~ 8 - 10% survival of male lines during differentiation. Could the recombination of the Xist sequence around the loxP sites enable the cells to outcompete the dead cells? How would iEmpty and ∆A-iXist cells compare here? Have the differentiated cells been tested for their expression of Xist? Additionally, how are there similar live cell counts for male vs female lines when ~90% of male cells die during differentiation? Were more cells plated at day 4? If so, this would bias the competition of male cell survival and therefore make the male line an inappropriate control. Given the essential role of La during development a control is needed to prove that this death is X-linked in the female 3F1 line. For example, an XO cell line retaining the Cast allele and shSsb expression could show the amount of death caused from shSsb alone independent of X-linked cell death.
If I understood correctly, the RNA FISH used dsDNA probes ("Sx9") against 40 kb of the X-inactivation centre (Xic). Surely Tsix or other Xic transcripts will also be visible? Can the authors use their RNA FISH to determine the XX or XO status of their cells? In Figure S5 a number of cells appear to show a single pinpoint of transcription. This could either be low levels of Xist transcripts or Xic transcription from an XO line in which the 129 chromosome is missing. It would be best to solely quantify cells which have two x chromosomes and if a significant amount of X chromosomes have been kicked out, this should be discussed and controlled for.
In Fig6, the authors generated a number of Ssb constructs for a rescue assay. However, these results complicate the matter and raise more questions than they address. It seems odd that the ∆RRM1 does not rescue based on comparison with their putative negative control, ∆NLS. However, the ∆RRM1 + 2 and ∆LAM do rescue the phenotype better than the full length Ssb? This makes no logical sense and highlights the inherent variation in cell viability these generated cell lines seem to show. Following on from this, figure S7 quantifies the GFP tag mRNA levels, depicting all ∆RRM mutants with expression below ~30%? How can ∆RRM1 or 2 be rescuing in this scenario? Have these lines been tested for their XX or XO status? The loss of an X chromosome would lead to a rescue of the cell death phenotype, which is a process known to occur in XX lines that have been cultured for extended periods of time. Could it also be that the cell lines derived are more or less sensitive to exogenous shRNA expression? Also, further validation is needed to assess the efficiency of KD in these lines as theoretically most of these constructs will be targeted by shRNA? What is the endogenous Ssb expression level in these lines? Where in the mRNA sequence are the shRNAs targeted to? Does this make sense on the relative expression levels of ∆RRM1/2 for example? Further testing of GFP expression could also be assessed by quantitative western blot of GFP or even visualised in their RNA FISH/IF samples (Figure S8), currently neither are shown. In addition, some kind of information of stability of each Ssb protein constructs has not been demonstrated.
For the data shown in Figure 7A and B the authors quantify the % of cells with Xist signal. The authors have already shown a defect in Xist visualisation in Ssb KD. Surely it is plausible to assume a faster loss of Xist signal below background in weaker expressing cells. A more appropriate quantification would be the % loss of Xist signal per cell over time.
With Figure 7C and D, the samples have been treated with actinomycin D which globally affects the transcription of cells even the PolIII associated genes Ssb is needed to mature. This treatment could have an added effect on cell mortality and function. Data confirming that actinomycin D doesn't affect the cells disproportionately is needed. The difference in half-life could be attributed to such a treatment.
In summarising the authors claim that La binds Xist to facilitate folding and appropriate spreading of Xist along the X-chromosome. No direct interaction has been shown, CLIP-seq data would resolve this, however I do understand this is a challenging technique. The authors have instead opted for RIP followed by qPCR (Figure S2). However, this process has a greater potential for non-specific recovery of RNAs via indirect binding. Furthermore, qPCR may also amplify the relative abundance of the RNA detected. As multiple nucleolar proteins came down in the mass spec screen and FLAG-Ssb is being over expressed, it is plausible to assume some transient Xist interactions may arise from nucleolar association at which La will be in high abundance. Positive and negative nuclear RNA controls (e.g. 7SK and U1 snRNA respectively) could be used so to determine the amount of non-specific Protein-RNA interactions in their RIP pull downs. Cytoplasmic actin is not an appropriate control as it is cytosolic.
Other than this the authors may want to probe (via IF) for the presence of La accumulation on the X? Many other know factors such as Ciz1, hnrnpK and PRC1/2 complexes show clear accumulation on the X. If I understand correctly, there are many La antibodies on the market and endogenous levels on the X could be assessed. These antibodies may be useful in IP's and pull downs also.
-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.
The experiments suggested above are centrally focussed on the cell lines that are currently in the authors possession with maybe exceptions with the ∆A-iXist-shSsb line suggested. However, this should be reasonably quick to obtain given their previous work for this paper. Most experiments suggested will focus on the validation of karyotype, Xist expression, rescue construct expression, further RNA FISH classification and repeating more appropriate positive and negative controls for a number of experiments. In theory this can be obtained relatively simply and quickly from current resources. But with the sheer volume of further experiments that are required here, this may take a significant amount of time. One vital improvement needed is the replication of mass spec data and the validation of Xist specific recovery and protein enrichment. As it stands this manuscript seems to not have any replicates of the FLAG-out methodology and mass spec data. This is troubling given the poor recovery and specificity of the protein samples obtained. Repeating these experiments would be costly in time and also financially. As it stands, I feel this is essential to conclusively validate their target of interest.
- Are the data and the methods presented in such a way that they can be reproduced?
The data is presented relatively well, however, it would be beneficial if deailed methods were in the main text and not in a supplementary file. Similarly, more information about the process of differentiation and how cell death/survival was quantified and validated is needed.
- Are the experiments adequately replicated and statistical analysis adequate?
In the most part yes, however there seems to be no replicates of the FLAG-out mass spec screen which is worrying given the minimal specificity observed in the current data.
Minor comments:
- Specific experimental issues that are easily addressable.
Unfortunately, the majority of experimental issues need to be addressed with more robust data which are highlighted above. However, some image analysis, quantification and classification can be amended relatively easily. For example, the live-cell imaging data should be quantified as loss of signal as discussed and RNA FISH should be used to classify XX positive cells and the XO cells can be discarded from analysis.
- Are prior studies referenced appropriately?
Most papers regarding Xist pull down and biology are discussed and referenced appropriately. However, the role in which La plays during development and its aberrant affects upon KD are seemingly downplayed. I would like to see more discussion of potential defects that could be caused due to globally altering cellular RNA folding.
- Are the text and figures clear and accurate?
For the most part, lots of the figures are clear and accurate. Apart from these exceptions.
1.The Y-axis of Figure 2D is confusing. What does 0.3 as a "sum of area" equate to? 30% of the area was ES cells? This doesn't look to be the case from Fig 2C. Also, how does the intensity of the signal compare? The area may not be a good quantification due to ES cells growing in colonies.
2.In the Movies S1-7 there are boxes around certain cells and marked with "Figure 5a - c". This seems to be incorrect as figure 5 is currently the IF staining of polycomb marks. I assume this is in relation to Figure 4b-d?
3.Similarly, in Movies S1-7, the intensities of Xist foci seem by eye to be similar. In the paper it is claimed that the Xist clouds that do form are lower in intensity. Are the Movies depicting the same range of pixel intensities? If not, this should be amended. Similarly, figure 7 seems to show relatively equivalent RNA signal at 0 h?
4.In figure 4A the data is from female XX cells, this should be highlighted to limit confusion with the male iXist data shown below in 4B-E. It would also be helpful to have the male/female icons (as in figure 3B), for each figure that has images of cells. Currently Figure 4, 5, 7, S5 and S8 are lacking these icons.
5.No explanation of the Flag-Ssb expression is given for figure S2. Furthermore, is it really necessary to express Flag-Ssb? There are reasonably good antibodies out there for Ssb as this was how it was originally found in Systemic Lupus patients. Also, no data showing the amount of Ssb being overexpressed is shown. This may have big implication to the validity of the RIP-qPCR analysis.
- Do you have suggestions that would help the authors improve the presentation of their data and conclusions?
Most of the data is presented reasonably well, but the robustness of the data somewhat retracts from their conclusions. I feel the certainty of their conclusion regarding Xist specific La binding and RNA chaperone activity is still presumptive and should be rewritten unless more robust data can confirm Xist interaction. I would also suggest deciding on the nomenclature for the protein of interest and use either La or Ssb, the continued use of both through the figures and text can get a little confusing to the reader.
Significance
- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.
It was a good trial to use PBSb-PUFb system to purify Xist RNA binding proteins, compared to previous reports had used anti-sense oligo purification using complementary sequence to Xist RNA sequences. But currently the purification still needs further validation and repeats to confirm its use. A potential complementary technique could be to isolate Xist directly by using biotinylated probes against the PBSb sequence. The authors further claim the identification of a novel Xist RNA chaperone (La/Ssb) which they say facilitates XCI progression. This would be a novel finding in the field; however, the data is currently not robust enough to support this.
- Place the work in the context of the existing literature (provide references, where appropriate).
This work has focused on the development of a milder methodology for purifying Xist RNA during XCI. Others have published similar methodologies predominantly focusing on purifying Xist RNA directly with biotinylated probes (McHugh et al. Minaji et al and Chu et al.). Although this method boasts a milder purification method, it seems to be low yielding in Xist specific proteins. Others have shown a more robust identification of bona fide Xist binding proteins which are currently missing in this manuscript. A recent preprint from the Plath lab has identified new factors involved in XCI during differentiation and their tethering/rescue experiments are far more convincing than the ones shown in this manuscript https://www.biorxiv.org/content/10.1101/2020.03.09.979369v1. The candidate protein Ha et al have identified has multiple roles in developing cells and has shown to be important during mouse development. However, Ha et al do not robustly show that the knockdown of Ssb causes X-linked cell mortality. Alternatively, as would be presumed from Ssb's essential role in many housekeeping short non-coding RNAs, the cell death seems more ubiquitous upon shRNA KD. Therefore, the link the authors are making here are relatively weak.
- State what audience might be interested in and influenced by the reported findings.
The audience may be interested in the novel technique and the finding of a novel Xist binding protein.
- 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.
RNA biochemistry and developmental biology
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Reply to the reviewers
General comments
We thank all three reviewers for providing their thoughtful and insightful review comments of our manuscript. We appreciate that the reviewers recognized the significance and impact of our work - “Very little imaging has been done on CAR synapses and to our knowledge this is the first live cell imaging study describing CAR microclustsers” (Reviewer 2); “This is an evolving field and little is known to date. Hence, this study could represent an insightful and important advance to the field” (Reviewer 3). A broad audience from both basic and clinical research sides will be interested in this work: “_This study will have a broad audience. Both scientists that study basic T cell signaling as well as clinicians that use CAR Ts will be interested in this study” (_Reviewer 2); “Audience is to both basic immunologist and cancer biologists” (Reviewer 3).
Meanwhile, we understand that the reviewers have raised a few major and minor issues, which we attempted to address. Most importantly, as suggested by both reviewer 1 and 3, we performed new experiments showing that LAT is not required for microcluster formation of the 1st generation of CAR (new Fig 4 and EV5). This finding suggests that the CAR-independent signaling is due to the intrinsic CAR architecture, and is not dependent on the co-signaling domains of CD28 and 4-1BB.
With the successful solutions to other issues, we believe the manuscript has been significantly improved and is ready for publication. Below we will provide point-to-point responses to each reviewer’s comments.
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
The authors compare the TCR alone to a CAR that contains signaling modules from three receptors- TCR, CD28 and 41BB. The data quality if good and the experiments done are. The difference is quite clear, and I would even like to see a little more of the evidence related to failure of the TCR system.
We appreciate the general positive comment of this reviewer.
More specifically:
Su and colleagues show that a third generation CAR with TCR zeta, CD28 and 41BB signal transduction pathways can activate a T cell for microcluster formation and Gads/SLP-76 recruitment, but not IL-2 production, without LAT. This is surprising because LAT is generally considered, as is up held here, as an essential adapter protein for T cell activation. However, this is not a "fair" experiment as the CAR has sequences from TCR, and two co-stimulatory receptor- CD28 and 41BB. It would be important and very straight-forward to test first and second generation CARs to determine if LAT independence is a function of the CAR architecture itself, or the additional costimulatory sequences. If it turns out that a first generation CAR with only TCR sequences can trigger LAT independent clustering and SLP-76 recruitment then the comparison would be fair and no additional experiment would be needed to make the point that the CAR architecture is intrinsically LAT independent. If the CD28 and/or 41BB sequences are needed for LAT independence then the fair comparison would be to co-crosslink TCR, CD28 and 41BB (an inducible costimulator such that anti-CD27 might be substituted to have a constitutively expressed receptor with this similar motifs) should be cross-linked with the TCR to make this a fair comparison between the two architectures.
We agree with the reviewer that it is critical to make a “fair” comparison between TCR and CAR by testing the 1st generation CAR, which only contains the TCR/CD3z domain. Our new data showed that LAT is not required for microcluster and synapse formation of the 1st generation of CAR, in both Jurkat and primary T cells (new Fig 4 and EV5). This result is similar to our previously reported result from the 3rd generation CAR, although the 1st generation CAR induced less IL-2 production and CD69 expression in LAT null cells than the 3rd generation CAR did (new Fig 6). This suggests that the LAT-independent signaling is intrinsic to the CAR architecture, as the reviewer suggested. The co-signaling domains from CD28 and 4-1BB contribute to, but are not required for bypassing LAT to transduce the CAR signaling.
The authors may want to cite work from Vignali and colleagues that even the TCR has two signaling modules- the classical ZAP-70/LAT module that is responsible to IL-2 and a Vav/Notch dependent module that controls proliferation. Its not clear to me that the issue raised about distinct signaling by CARs is completely parallel to this, but its interesting that Vignali also associated the classical TCR signaling pathway as responsible for IL-2 with an alterive pathways that uses the same ITAMs to control distinct functions. See Guy CS, Vignali KM, Temirov J, Bettini ML, Overacre AE, Smeltzer M, Zhang H, Huppa JB, Tsai YH, Lobry C, Xie J, Dempsey PJ, Crawford HC, Aifantis I, Davis MM, Vignali DA. Distinct TCR signaling pathways drive proliferation and cytokine production in T cells. Nat Immunol. 2013;14(3):262-70.
We appreciate the reviewer’s mentioning this paper from Vignali’s group. It provides insights into understanding LAT-independent signaling in CAR T cells. We cited this paper and added a discussion about the mechanism of LAT-independent signaling.
I would be very interested to see a movie of the LAT deficient T cells interacting with the anti-CD3 coated bilayers in Figure 2A. Since OKT3 has a high affinity for CD3 and is coated on the surface at a density that should engage anti-CD3 I'm surprised there is no clustering even simply based on mass action. The result looks almost like a dominant negative effect of LAT deficiency on a high affinity extracellular interaction. It would be interesting to see how this interface evolves or if there is anti-adhesive behavior that emerges.
We now presented a movie showing the detailed process of LAT deficient GFP-CAR T cells landing on the bilayers coated with OKT3 (new Movie EV5), in which the bright field images delineate the locations of the cells, the OKT3 signal marks TCR, and the GFP signal marks CAR proteins on the plasma membranes. No TCR clusters (as indicated by OKT3) were formed during the landing process. We think the binding of bilayer-presented OKT3 to TCR is not sufficient to trigger TCR microclusters. However, TCR microclusters could form in LAT-deficient cells if OKT3 is presented by glass surface. This point is raised by reviewer 2. We added a discussion on the difference between bilayer and glass-presented OKT3 in inducing microcluster formation.
Reviewer #1 (Significance (Required)):
While it interesting that the CAR is LAT independent, its obvious that the signalling networks are different as the CAR has two sets of motifs that are absent in the TCR, so the experiments as presented are not that insightful about the specific nature of the differences that lead to the different outcomes. At present its not a particularly well controlled experiment as the third gen CAR is changing too many things in relation to the TCR for the experiment to be interpreted. It would be easy to address this is a revised manuscript. To publish as is the discussion would need to acknowledge these limitations. The work is preliminary as science, but it might be useful to T cell engineering field to have this information as a preliminary report, which might be an argument for adding discussion of limitations, but going forward without more detailed analysis of mechanism.
This is an excellent point and we have addressed it. See our response above on the new data of the 1st generation CAR.
Reviewer #2 (Evidence, reproducibility and clarity):
Summary:
Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).
In this study, the authors have interrogated CAR signaling by imaging CD19-CAR microclusters as well as T cell signaling molecules recruited to CAR microclusters. They report differences spatial assembly between CAR and TCR microclusters that form on a lipid bilayer containing ligand. They also report that LAT is not required for CAR microcluster formation, recruitment of downstream signaling molecules or IL-2 production in Jurkat cells, while in primary T cells IL-2 production by CARs show more of a LAT dependence. From these observations, they conclude that CAR T cells have a rewired signaling pathway as compared to T cells that signal through the TCR.
Major comments:
- Are the key conclusions convincing?
The conclusions made by the authors about CAR microclusters are convincing. However, the conclusion that there is a "rewired signaling network" different from TCR microclusters needs to be more convincingly demonstrated in side-by-side comparisons of TCR and CAR microclusters and synapses.
- One of the key conclusions in this study is that CAR microclusters form in the absence of LAT, but TCR microclusters require LAT (in JCam2.5 cells in Fig. 2 and primary T cells in Fig. 4B). The requirement of LAT for formation of TCR microclusters is surprising, given multiple reports (one of which the authors have cited) that TCRz and ZAP70 clusters form normally in the absence of LAT (pZAP microclusters form normally in JCam2.5 cells Barda-Saad Nature Immunology 2005 Figure 1; TCRz clusters form normally in LAT CRISPR KO Jurkat cells Yi et al., Nature Communications, 2019 Figure 5). The authors should carefully evaluate TCRz and ZAP70 clusters (that form upstream of LAT) in their assays.
We thank the reviewer for raising this excellent point. LAT-independent TCR clusters were reported in the two papers mentioned by the reviewer, which we think is convincing. However, there is a key difference in the experimental settings between these two papers and ours. We use supported lipid bilayer to present MOBILE TCR-activating antibody to activate T cells, whereas these two papers used IMMOBILE TCR-activating antibody attached to the cover glass. We reasoned that the mobile surface of supported lipid bilayer more closely mimics the antigen-presenting cell surface where antigens are mobile on the membrane. We added a new discussion about the difference between supported lipid bilayer and cover glass-based activation.
We agree with the reviewer on the careful evaluation of TCR and ZAP70 clusters. We had showed the data of TCR clusters as marked by TCR-interacting OKT3 (Fig 3A). We performed new experiments on ZAP70 clusters (new Fig EV3). Our data suggest that, similar to TCR clusters, ZAP70 clusters are not formed in LAT-deficient T cells, if activated by OKT3, but are formed if activated by CD19.
- The authors make major conclusions about LAT dependence and independence of TCR and CAR microclusters respectively, by using JCam2.5 Jurkat cells and CRISPR/Cas9 edited primary cells. Of relevance to this conclusion, differences in the phosphorylation status of ZAP70 and SLP76 have been described between JCam2.5 cells lacking LAT (in which LAT was found to be deleted by gamma radiation) and J.LAT cells (in which LAT was specifically deleted by CRISPR/Cas9 in Lo et al Nature Immunology 2018). Of importance, pZAP and pSLP76 appeared fairly intact in J.LAT cells, but absent in JCam2.5 cells (Lo et al., Nat Immunol. 2018, Supp Fig 2). Therefore, the authors should evaluate TCRz, ZAP70, Gads and SLP76 in TCR and CAR microclusters in J.LAT cells. This may partly explain the discrepancy in LAT requirement for IL-2 production in JCam2.5 cells and primary cells with LAT CRISPRed out.
Jcam2.5 is a classical well-characterized LAT-deficient cell line that has been continuously used in the T cell signaling field (Barda-Saad Nature Immunology 2005, Rouquette-Jazdanian A, Mol. Cell, 2012; Balagopalan L, J Imm. 2013; Carpier J, J Exp Med, 2018; Zucchetti A, Nat. Comm. 2019). We agreed with the concern that the reviewer raised on the absence of pZAP70 and pSLP76 in JCam2.5 cells. As the reviewer suggested, we obtained J.LAT, which is LAT null but has intact pZAP70 and pSLP76. We introduced CAR into J.LAT and the wild-type control and performed the clustering assay as we did for Jcam2.5. Our results showed that, similar to Jcam2.5, CAR forms robust microclusters in J.LAT cells (new Fig EV2). More importantly, we presented data confirming the LAT-independent CAR clustering, SLP76 phosphorylation, and IL-2 production in human primary T cells (Fig 7). Therefore, the data from three independent cell sources support our conclusion on LAT-independent CAR signal transduction.
- Since the authors are reporting differences between CAR synapses and TCR synapses, the authors should show side by side comparison of CAR and TCR synapses in Figure 1F.
We focused on characterizing CAR synapse in this manuscript and did not make any conclusion on the difference between TCR and CAR synapse. We are cautious about comparing CAR synapse to TCR synapse for technical reasons: it is critical to use antigen-specific TCRs (e.g. mouse OTI as a common model) to study the TCR synapse pattern so that the study will be physiologically relevant. However, we use human T cell line and human primary T cells for the CAR study. The technical barrier to introduce an antigen-specific TCR complex into these cells, and to activate these cells by purified peptide-MHC complex, is very high. And the result is interesting, but beyond the scope of the current work.
- The authors should evaluate Gads microcluster formation in response to TCR stimulation via OKT3 (in Figure 4A). Given that it has been reported that TCRz, Grb2 and c-Cbl are recruited to microclusters in Jurkat cells lacking LAT by CRISPR deletion (Yi et al., Nature Communications, 2019), it is important to establish the differences between TCR microclusters and CAR microclusters in side by side comparisons in their assay system.
As the reviewer suggested, we evaluated Gads microcluster formation with TCR stimulation and found that Gads did not form microclusters in LAT-deficient cells (new Fig 5A). Because we only made conclusions on the Gads-SLP76 pathway, we think investigating Grb2 and c-Cbl microcluster, though interesting, is beyond the scope of this manuscript.
- Similar to the comment about Gads above, the authors should evaluate pSLP76 microcluster formation in response to TCR stimulation via OKT3 in primary T cells lacking LAT in Figure 4C, i.e. side by side comparisons of pSLP76 in TCR and CAR synapses (with and without LAT) should be shown.
We totally agree and performed new experiment on pSLP76 in human primary T cells. Our data suggested that, similar to Jurkat, pSLP76 microclusters remain intact in LAT null primary cells (new Fig 7D and 7E).
- Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?
- The data shown in Figure 3C shows a reduction in conjugate formation from 80% (WT) to 30% (LAT -). This is a severe reduction and does not support the authors' claim in the corresponding Figure legend that "LAT is dispensable for cell conjugate formation between Jurkat T cells expressing CAR and Raji B cells" and the Abstract that "LAT.....is not required for....immunological synapse formation". Statistical analysis for variance should be shown here.
We agree with the reviewer’s judgement. This cell conjugation analysis was performed using Jcam2.5 cells. As pointed by the reviewer, Jcam2.5 has additional defects in ZAP70 and SLP76 in addition to the lack of LAT. Therefore, we performed the same analysis again using J.LAT cells, which was recommended by the reviewer. Our new data showed that J.LAT cells form conjugates with Raji B cells in a similar rate as the wild-type cells do, as evaluated by statistical analysis (new Fig 6A). Therefore, we think these new data support the claim that LAT is dispensable for cell conjugate formation.
- In a similar vein, based on data from Movie S5 (where in a single cell, CAR microclusters translocate from cell periphery to center), and Figure 3C where (as described above in point 1) conjugate formation appears to be severely reduced, the authors conclude in the Results and Abstract that "LAT....is not required for actin remodeling following CAR activation". This conclusion is not supported by the data and the authors should remove this claim. Alternatively, actin polymerization in CAR expressing cells (that are LAT sufficient and deficient) can be easily evaluated using phalloidin or F-Tractin.
As suggested by the reviewer, we evaluated actin polymerization in TCR or CAR stimulated cells using a filamentous actin reporter F-tractin. Our data showed that LAT is required for TCR-induced but not CAR-induced actin polymerization (new Fig 5C). Therefore, our results support the claim that LAT is not required for actin remodeling following CAR activation.
- Would additional experiments be essential to support the claims of the paper?<br> Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.
Yes. Please see major comments above.
- 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. It should take 3 months to complete these experiments, since reagents and experimental systems to do these experiments already exist.
- Are the data and the methods presented in such a way that they can be reproduced?<br> Yes. Methods are clearly explained.
We appreciate the reviewer’s recognition of the clarity of the methods part.
- Are the experiments adequately replicated and statistical analysis adequate?
There is no statistical analysis to evaluate differences between samples in Figures 3 and 4. These must be included.
We now added statistical analysis in Fig 5B and 6A (old figure 3 and 4).
Minor comments:
- Specific experimental issues that are easily addressable.
Please see Major Comments above. We believe that the recommended experiments are not difficult to execute since reagents exist and experimental systems are already set up.
- Are prior studies referenced appropriately?
Authors reference 13 and 14 for the following sentence in Results section 2: "Deletion or mutation of LAT impairs formation of T cell microclusters". However, in Reference 14 Barda-Saad et al., actually show that pZAP clusters are intact in JCam2.5 cells lacking LAT. Perhaps authors should clarify that LAT (and downstream signaling molecule) microclusters are impaired when LAT is deleted or mutated.
As the reviewer suggested, we now clarified that clustering of LAT downstream binding partners is impaired when citing reference (Barda-Saad et al).
- Are the text and figures clear and accurate?
Yes. But would be helpful if authors specify what "control" is in Fig. 3B and C. In Figure 3B it is lipid bilayers without CD19, while in 3C it is K562 cells that do not express CD19.
We now specified “control” in the figure.
- Do you have suggestions that would help the authors improve the presentation of their data and conclusions?
Would be helpful if authors specify in every Figure or at least Figure legend the experimental bilayer system/ligand used, since they use both OKT3 and CD19 as ligands in the paper.
We now specified the ligand in the figure or legend.
Reviewer #2 (Significance):
- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.
If CAR microclusters and synapses are appropriately compared in a side by side comparison with TCR microclusters and synapses (as described in comments above), this study will be a conceptual advance in the field of CAR signaling. CAR microclusters have not been studied previously.
- Place the work in the context of the existing literature (provide references, where appropriate).
Very little imaging has been done on CAR synapses and to our knowledge this is the first live cell imaging study describing CAR microclusters.
We appreciate this reviewer’s comment on our work as a conceptual advance in understanding CAR signaling.
- State what audience might be interested in and influenced by the reported findings.<br> This study will have a broad audience. Both scientists that study basic T cell signaling as well as clinicians that use CAR Ts will be interested in this study.
We appreciate this reviewer’s recognition of the broad audience of this manuscript.
- 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.
T cell signaling and imaging of proximal T cell signaling responses.
Reviewer #3 (Evidence, reproducibility and clarity):
This manuscript by Dong and colleagues characterizes the molecular requirements and consequences of engaging a third-generation chimeric antigen receptor (CAR) directed to CD19. Utilizing a biological system of JCaM2.5, a Jurkat T cell mutant with dramatically low levels of LAT, expressing a CAR directed to CD19 fused to the cytoplasmic tails of CD28, 4-1BB and CD3z that is activated by CD19/ICAM1 reconstituted lipid bilayers, the authors demonstrate LAT is not required for microcluster formation, immunologic synapse formation or recruitment of GADS and pSLP76 to the plasma membrane. In contrast, LAT was required for anti-CD3 mediated microcluster formation and pSLP76 recruitment to the plasma membrane. However, LAT does appear to contribute to efficient synapse formation, PIP2 hydrolysis and IL-2 secretion when CAR+ JCaM2.5 or primary T cells are presented with Raji B cells, respectively. These data provide intriguing insights into the molecular requirements for third-generation CAR-T cell functions. The authors have developed quite a nice system to understand the molecular contributions for CAR-T function. A few suggestions are provided here to further enhance the accuracy and significance of the findings:
- The authors can address whether the LAT-independent effects are due to the attributes of third generation CAR-Ts with inclusion of CD28 and 4-1BB cytoplasmic domains or whether these differences are intrinsic to all CAR-Ts (e.g., first and second generation CARs).
This is an excellent point. We have included new data showing LAT-independent cluster formation of the 1st generation CAR in both Jurkat and primary T cells (new Fig 4 and EV5). Therefore, we favor the second possibility as pointed by the reviewer that LAT-independent effects are intrinsic to CAR architecture.
- Since a first-generation CAR-T forms non-conventional synapses (Davenport, et al., PNAS 2018), the authors should consider more detailed kinetic analysis to understand the formation and dissolution of the constituents of the synapse with their third generation CAR. This should include measurements of the duration of microcluster and synapse formation as well as further analysis of c- and p-SMAC constituents (e.g., LFA-1, TALIN, LCK and pSLP76) over time.
We agree with the reviewer on a more detailed characterization of the CAR synapse. We measured the duration of the unstable CAR synapse and time from cell landing to the start of retrograde flow (new Fig 2C). We also determined the localization of CD45, a marker for d-SMAC (new Fig 2D). We found that the formation of dSMAC is also not common in CAR T synapse, strengthening our conclusion that CAR forms non-typical immunological synapse.
- The authors utilize two different activation platforms. While using CD19/ICAM1 reconstituted bilayers, CAR+ JCaM2.5 or CAR+ primary T cells demonstrate no differences compared to wildtype JCaM2.5 cells in the parameters studied. However, when using Raji B cells, the CAR+ JCaM2.5 cells or CAR+ primary T cells demonstrate a more intermediate phenotype with respect to cell conjugate formation (Figure 3C) and IL-2 production (Figure 4D). The authors should analyze whether the differences attributed to the different outcomes may be due to the stimulation mode. For example, is c-SMAC assembly and GADS or pSLP76 recruitment to the plasma membrane still LAT-independent when activated with Raji B cells?
As the reviewer suggested, we examined c-SMAC assembly in Raji B cells conjugated with CAR T cells. We found that the majority of CAR do not form cSMAC (new Fig EV4), which is consistent with the result from the bilayer activation system. Since both Gads and SLP76 are cytosolic proteins, they keep largely in the cytosolic pool which obscures their recruitment and clustering on the plasma membrane when imaged by confocal microscopy at the cross-section of cell-cell synapse.
- The authors should consider whether CAR expression level affects their observations. For example, do lower levels of CAR expression make the system LAT-dependent? Further, what is the level of the CAR relative to endogenous TCR expression on their primary T cells.
We agree with the reviewer that it is informative to determine if LAT-independent signaling is dose dependent. We tried to measure the CAR concentration relative to the endogenous TCR/CD3z. By western blot using two different antibodies against CD3z, we detected TCR/CD3z expression, but found no bands corresponding to CAR. We believe this reflects a low expression of CAR in our system, which is confirmed by FACS. The general low expression of CAR makes it challenging to sort an even lower CAR-expressing population. Therefore, we sought alternative ways to determine the dose-dependence; we titrated the CD19 concentrations on the bilayer. As shown in the new Figure EV1, CAR formed microclusters similarly in the wild-type versus LAT-deficient cells in a wide range of CD19 concentration. Therefore, we conclude that the LAT-independent cluster formation is robust at low antigen density as well.
Minor comment:
- Since JCaM2.5 has differences when compared to the parental Jurkat E6.1 T cell line, the authors should utilize JCaM2.5 reconstituted with wildtype LAT as a comparator.<br> Agreeing with this reviewer, we recognized that Jcam2.5 was generated by mutagenesis which may result in protein expression difference for genes besides Lat. As suggested by reviewer1, we used J.LAT, a genuine LAT knockout cell line that is generated by CRISPR-mediated gene targeting, to perform the clustering assay (new Fig EV2). Our results showed that, similar to Jcam2.5, CAR but not the TCR formed microclusters in J.LAT cells.
Reviewer #3 (Significance):
The mechanism(s) by which CAR-Ts function is of high significance from both scientific and clinical viewpoints. From a scientific viewpoint, it provides important basic mechanistic information of how T cells are being activated to kill tumor cells. By understanding the molecular requirements, additional generations of CARs can be designed to provide greater efficacy, overcome resistance and possibly less toxicity.
This is an evolving field and little is known to date. Hence, this study could represent an insightful and important advance to the field.
Audience is to both basic immunologist and cancer biologists.
We appreciate this reviewer’s comments on the high significance of our work to the field of both basic immunology and clinical application.
My expertise is in T cell signaling, T cell biology and immunotherapy.
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Referee #3
Evidence, reproducibility and clarity
This manuscript by Dong and colleagues characterizes the molecular requirements and consequences of engaging a third-generation chimeric antigen receptor (CAR) directed to CD19. Utilizing a biological system of JCaM2.5, a Jurkat T cell mutant with dramatically low levels of LAT, expressing a CAR directed to CD19 fused to the cytoplasmic tails of CD28, 4-1BB and CD3 that is activated by CD19/ICAM1 reconstituted lipid bilayers, the authors demonstrate LAT is not required for microcluster formation, immunologic synapse formation or recruitment of GADS and pSLP76 to the plasma membrane. In contrast, LAT was required for anti-CD3 mediated microcluster formation and pSLP76 recruitment to the plasma membrane. However, LAT does appear to contribute to efficient synapse formation, PIP2 hydrolysis and IL-2 secretion when CAR+ JCaM2.5 or primary T cells are presented with Raji B cells, respectively. These data provide intriguing insights into the molecular requirements for third-generation CAR-T cell functions.
The authors have developed quite a nice system to understand the molecular contributions for CAR-T function. A few suggestions are provided here to further enhance the accuracy and significance of the findings:
- The authors can address whether the LAT-independent effects are due to the attributes of third generation CAR-Ts with inclusion of CD28 and 4-1BB cytoplasmic domains or whether these differences are intrinsic to all CAR-Ts (e.g., first and second generation CARs).
- Since a first-generation CAR-T forms non-conventional synapses (Davenport, et al., PNAS 2018), the authors should consider more detailed kinetic analysis to understand the formation and dissolution of the constituents of the synapse with their third generation CAR. This should include measurements of the duration of microcluster and synapse formation as well as further analysis of c- and p-SMAC constituents (e.g., LFA-1, TALIN, LCK and pSLP76) over time.
- The authors utilize two different activation platforms. While using CD19/ICAM1 reconstituted bilayers, CAR+ JCaM2.5 or CAR+ primary T cells demonstrate no differences compared to wildtype JCaM2.5 cells in the parameters studied. However, when using Raji B cells, the CAR+ JCaM2.5 cells or CAR+ primary T cells demonstrate a more intermediate phenotype with respect to cell conjugate formation (Figure 3C) and IL-2 production (Figure 4D). The authors should analyze whether the differences attributed to the different outcomes may be due to the stimulation mode. For example, is c-SMAC assembly and GADS or pSLP76 recruitment to the plasma membrane still LAT-independent when activated with Raji B cells?
- The authors should consider whether CAR expression level affects their observations. For example, do lower levels of CAR expression make the system LAT-dependent? Further, what is the level of the CAR relative to endogenous TCR expression on their primary T cells.
Minor comment:
- Since JCaM2.5 has differences when compared to the parental Jurkat E6.1 T cell line, the authors should utilize JCaM2.5 reconstituted with wildtype LAT as a comparator.
Significance (Required)
The mechanism(s) by which CAR-Ts function is of high significance from both scientific and clinical viewpoints. From a scientific viewpoint, it provides important basic mechanistic information of how T cells are being activated to kill tumor cells. By understanding the molecular requirements, additional generations of CARs can be designed to provide greater efficacy, overcome resistance and possibly less toxicity.
This is an evolving field and little is known to date. Hence, this study could represent an insightful and important advance to the field.
Audience is to both basic immunologist and cancer biologists.
My expertise is in T cell signaling, T cell biology and immunotherapy.
-
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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).
In this study, the authors have interrogated CAR signaling by imaging CD19-CAR microclusters as well as T cell signaling molecules recruited to CAR microclusters. They report differences spatial assembly between CAR and TCR microclusters that form on a lipid bilayer containing ligand. They also report that LAT is not required for CAR microcluster formation, recruitment of downstream signaling molecules or IL-2 production in Jurkat cells, while in primary T cells IL-2 production by CARs show more of a LAT dependence. From these observations, they conclude that CAR T cells have a rewired signaling pathway as compared to T cells that signal through the TCR.
Major comments:
Are the key conclusions convincing?
The conclusions made by the authors about CAR microclusters are convincing. However, the conclusion that there is a "rewired signaling network" different from TCR microclusters needs to be more convincingly demonstrated in side-by-side comparisons of TCR and CAR microclusters and synapses.
- One of the key conclusions in this study is that CAR microclusters form in the absence of LAT, but TCR microclusters require LAT (in JCam2.5 cells in Fig. 2 and primary T cells in Fig. 4B). The requirement of LAT for formation of TCR microclusters is surprising, given multiple reports (one of which the authors have cited) that TCR and ZAP70 clusters form normally in the absence of LAT (pZAP microclusters form normally in JCam2.5 cells Barda-Saad Nature Immunology 2005 Figure 1; TCR clusters form normally in LAT CRISPR KO Jurkat cells Yi et al., Nature Communications, 2019 Figure 5). The authors should carefully evaluate TCR and ZAP70 clusters (that form upstream of LAT) in their assays.
- The authors make major conclusions about LAT dependence and independence of TCR and CAR microclusters respectively, by using JCam2.5 Jurkat cells and CRISPR/Cas9 edited primary cells. Of relevance to this conclusion, differences in the phosphorylation status of ZAP70 and SLP76 have been described between JCam2.5 cells lacking LAT (in which LAT was found to be deleted by gamma radiation) and J.LAT cells (in which LAT was specifically deleted by CRISPR/Cas9 in Lo et al Nature Immunology 2018). Of importance, pZAP and pSLP76 appeared fairly intact in J.LAT cells, but absent in JCam2.5 cells (Lo et al., Nat Immunol. 2018, Supp Fig 2). Therefore, the authors should evaluate TCR, ZAP70, Gads and SLP76 in TCR and CAR microclusters in J.LAT cells. This may partly explain the discrepancy in LAT requirement for IL-2 production in JCam2.5 cells and primary cells with LAT CRISPRed out.
- Since the authors are reporting differences between CAR synapses and TCR synapses, the authors should show side by side comparison of CAR and TCR synapses in Figure 1F.
- The authors should evaluate Gads microcluster formation in response to TCR stimulation via OKT3 (in Figure 4A). Given that it has been reported that TCR, Grb2 and c-Cbl are recruited to microclusters in Jurkat cells lacking LAT by CRISPR deletion (Yi et al., Nature Communications, 2019), it is important to establish the differences between TCR microclusters and CAR microclusters in side by side comparisons in their assay system.
- Similar to the comment about Gads above, the authors should evaluate pSLP76 microcluster formation in response to TCR stimulation via OKT3 in primary T cells lacking LAT in Figure 4C, i.e. side by side comparisons of pSLP76 in TCR and CAR synapses (with and without LAT) should be shown.
Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?
- The data shown in Figure 3C shows a reduction in conjugate formation from 80% (WT) to 30% (LAT -). This is a severe reduction and does not support the authors' claim in the corresponding Figure legend that "LAT is dispensable for cell conjugate formation between Jurkat T cells expressing CAR and Raji B cells" and the Abstract that "LAT.....is not required for....immunological synapse formation". Statistical analysis for variance should be shown here.
- In a similar vein, based on data from Movie S5 (where in a single cell, CAR microclusters translocate from cell periphery to center), and Figure 3C where (as described above in point 1) conjugate formation appears to be severely reduced, the authors conclude in the Results and Abstract that "LAT....is not required for actin remodeling following CAR activation". This conclusion is not supported by the data and the authors should remove this claim. Alternatively, actin polymerization in CAR expressing cells (that are LAT sufficient and deficient) can be easily evaluated using phalloidin or F-Tractin.
Would additional experiments be essential to support the claims of the paper?<br> Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.
Yes. Please see major comments above.
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. It should take 3 months to complete these experiments, since reagents and experimental systems to do these experiments already exist.
Are the data and the methods presented in such a way that they can be reproduced?
Yes. Methods are clearly explained.
Are the experiments adequately replicated and statistical analysis adequate?
There is no statistical analysis to evaluate differences between samples in Figures 3 and 4. These must be included.
Minor comments:
Specific experimental issues that are easily addressable.
Please see Major Comments above. We believe that the recommended experiments are not difficult to execute since reagents exist and experimental systems are already set up.
Are prior studies referenced appropriately?
Authors reference 13 and 14 for the following sentence in Results section 2: "Deletion or mutation of LAT impairs formation of T cell microclusters". However, in Reference 14 Barda-Saad et al., actually show that pZAP clusters are intact in JCam2.5 cells lacking LAT. Perhaps authors should clarify that LAT (and downstream signaling molecule) microclusters are impaired when LAT is deleted or mutated.
Are the text and figures clear and accurate?
Yes. But would be helpful if authors specify what "control" is in Fig. 3B and C. In Figure 3B it is lipid bilayers without CD19, while in 3C it is K562 cells that do not express CD19.
Do you have suggestions that would help the authors improve the presentation of their data and conclusions?<br> Would be helpful if authors specify in every Figure or at least Figure legend the experimental bilayer system/ligand used, since they use both OKT3 and CD19 as ligands in the paper.
Significance (Required)
Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.
If CAR microclusters and synapses are appropriately compared in a side by side comparison with TCR microclusters and synapses (as described in comments above), this study will be a conceptual advance in the field of CAR signaling. CAR microclusters have not been studied previously.
Place the work in the context of the existing literature (provide references, where appropriate).
Very little imaging has been done on CAR synapses and to our knowledge this is the first live cell imaging study describing CAR microclusters.
State what audience might be interested in and influenced by the reported findings.
This study will have a broad audience. Both scientists that study basic T cell signaling as well as clinicians that use CAR Ts will be interested in this study.
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.
T cell signaling and imaging of proximal T cell signaling responses.
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Referee #1
Evidence, reproducibility and clarity
The authors compare the TCR alone to a CAR that contains signaling modules from three receptors- TCR, CD28 and 41BB. The data quality if good and the experiments done are. The difference is quite clear, and I would even like to see a little more of the evidence related to failure of the TCR system.
More specifically:
Su and colleagues show that a third generation CAR with TCR zeta, CD28 and 41BB signal transduction pathways can activate a T cell for microcluster formation and Gads/SLP-76 recruitment, but not IL-2 production, without LAT. This is surprising because LAT is generally considered, as is up held here, as an essential adapter protein for T cell activation. However, this is not a "fair" experiment as the CAR has sequences from TCR, and two co-stimulatory receptor- CD28 and 41BB. It would be important and very straight-forward to test first and second generation CARs to determine if LAT independence is a function of the CAR architecture itself, or the additional costimulatory sequences. If it turns out that a first generation CAR with only TCR sequences can trigger LAT independent clustering and SLP-76 recruitment then the comparison would be fair and no additional experiment would be needed to make the point that the CAR architecture is intrinsically LAT independent. If the CD28 and/or 41BB sequences are needed for LAT independence then the fair comparison would be to co-crosslink TCR, CD28 and 41BB (an inducible costimulator such that anti-CD27 might be substituted to have a constitutively expressed receptor with this similar motifs) should be cross-linked with the TCR to make this a fair comparison between the two architectures.
The authors may want to cite work from Vignali and colleagues that even the TCR has two signaling modules- the classical ZAP-70/LAT module that is responsible to IL-2 and a Vav/Notch dependent module that controls proliferation. Its not clear to me that the issue raised about distinct signaling by CARs is completely parallel to this, but its interesting that Vignali also associated the classical TCR signaling pathway as responsible for IL-2 with an alterive pathways that uses the same ITAMs to control distinct functions. See Guy CS, Vignali KM, Temirov J, Bettini ML, Overacre AE, Smeltzer M, Zhang H, Huppa JB, Tsai YH, Lobry C, Xie J, Dempsey PJ, Crawford HC, Aifantis I, Davis MM, Vignali DA. Distinct TCR signaling pathways drive proliferation and cytokine production in T cells. Nat Immunol. 2013;14(3):262-70.
I would be very interested to see a movie of the LAT deficient T cells interacting with the anti-CD3 coated bilayers in Figure 2A. Since OKT3 has a high affinity for CD3 and is coated on the suface at a density that should engage anti-CD3 I'm surprised there is no clustering even simply based on mass action. The result looks almost like a dominant negative effect of LAT deficiency on a high affinity extracellular interaction. It would be interesting to see how this interface evolves or if there is anti-adhesive behavior that emerges.
Significance
While it interesting that the CAR is LAT independent, its obvious that the signalling networks are different as the CAR has two sets of motifs that are absent in the TCR, so the experiments as presented are not that insightful about the specific nature of the differences that lead to the different outcomes. At present its not a particularly well controlled experiment as the third gen CAR is changing too many things in relation to the TCR for the experiment to be interpreted. It would be easy to address this is a revised manuscript. To publish as is the discussion would need to acknowledge these limitations. The work is preliminary as science, but it might be useful to T cell engineering field to have this information as a preliminary report, which might be an argument for adding discussion of limitations, but going forward without more detailed analysis of mechanism.
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Reply to the reviewers
Rebuttal to reviewers ReviewCommons manuscript # RC-2020-00281
We would like to thank the reviewers and editors of Review Commons for evaluating our manuscript entitled “Transcriptional comparison of Testicular Adrenal Rest Tumors with fetal and adult tissues” and providing their valuable comments. We have listed the reviewers’ comments along with our response and amendments below.
Board Advice on initial submission:
This seems to be a study mainly relevant to the field of Testicular Adrenal Rest Tumors (TART). It presents the first RNAseq profiling of these tumors in multiple human samples at different stages. This has the potential to advance knowledge in this particular field. It would be less interesting to researchers interested in tissue spatial transcriptomics in general, since the experimental and computational tools are quite standard, but the findings may be important to the TART field.
Response: Indeed, this is the first study using transcriptomics to characterize Testicular Adrenal Rest Tumors, a frequent occurrence in patients with Congenital Adrenal Hyperplasia. It is also the first to find that the reported adrenal and testicular features of these tumors can be found in a single cell. We therefore believe this study is not only of interest to those working in the TART field, but also in development, endocrinology and andrology in general.
Comments Reviewer #1:
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
**Summary:**
The manuscript by Schroder M., et al describes the whole transcriptome of testicular adrenal rest tumors (TART) and shows that TART tissue is characteristically similar to adult adrenal and testicular rather than fetal adrenal and testicular tissues. The authors propose that their previous claim that TART is derived from an undifferentiated pluripotent progenitor is likely untrue and claim that TART likely originates from a mature cell type with both adrenal and testicular characteristics. The authors describe a unique cell type most similar to the adult adrenal, but with variable testis-specific gene expression patterns. The finding of overexpressed genes associated with ECM remodeling is interesting and may provide insight into the natural history of these tumors. A strength of the study is the number of tissue samples since surgery for these rare tumors is usually not performed.
**Major Comments:**
The key conclusions are mostly based on RNA studies, thus their claims are preliminary.
Response: We agree that a major part of our conclusions is based on RNA studies. Although indeed primarily based on transcriptomics, this claim is, in our opinion, not preliminary as the identity of TART cells can definitively be deduced from their expression profile. Our second key conclusion, i.e. that TART cells comprise both adrenal and testicular features within the same, unique, TART specific cell, is based on immunohistochemistry of adrenal and testis-specific enzymes.
In Figure 1/Result p. 3: Authors claim that there were no exclusive HSD17B3 staining cells without CYP11B1, however Figure 1 looks like there are exclusively green (HSD17B) areas (especially TART3). The authors need to address this. It appears as if there are mature Leydig cells. This is important because the presence of Leydig cells would affect the interpretation of the findings.
Response: We do understand the concern of the reviewer. Aspecific background staining for HSD17B3 in TART samples complicated the differentiation between specific and background staining. This can be seen when comparing the staining in HSD17B3-positive (Leydig) cells with the background staining in non-Leydig cells in testis tissue and in a portion of TART cells. In TART, we found that cells with high intensity, specific HSD17B3 staining all also showed CYP11B1 staining, but not vice-versa. However, we do acknowledge that due to this -most likely background- staining, the occurrence of mature Leydig cells in TART cannot be completely excluded based on our results.
Therefore, we have tried to be more careful in our claims in the results section (page 3; TART cells express adrenal- and Leydig cell-specific steroidogenic enzymes paragraph) and we have addressed this in the discussion section (page 5/6):
High background staining for HSD17B3 complicated the differentiation between specific and background staining. For some cells this exclusive HSD17B3 staining might have been specific and therefore, despite that most HSD17B3-positive cells were positive for CYP11B1, the absence of mature Leydig cells in TART could not be guaranteed by these results.
Discussion: authors state that based on their previous observations that fetal Leydig cells have both adrenal and testis developmental potential. It was speculated that TART might have been derived from a totipotent progenitor cell type, but the current study shows that these tumors lack similarities with fetal tissues. Thus, the authors claim that these tumors are not derived from the transdifferentiation of pluripotent cells. However what is the origin of this mature distinct cell type? Is it not possible that this distinctive cell type is derived from a common progenitor since the testis and adrenal gland are derived from the same adrenogonadal primordium? Lack of similarities with fetal tissues at this late stage of development does not necessarily rule out a common progenitor origin.
Response: In this study, we compared the TART transcriptome with fetal tissues, as we hypothesized these might be similar considering the likely progenitor origin of TART cells. However, this was not the case, and we showed that the transcriptomic profile of TART resembles the transcriptomic profile of mature cell types, rather than their fetal counterparts. Therefore, we conclude that the hypothesis that TART arises from progenitor cells is not supported by our data. The reviewer is correct that we did not prove that it is not derived from pluripotent cells. We have therefore added the following text to the discussion:
Although we here find that the transcriptome of TART tissues are clearly distinct from fetal tissues, we did not prove that TART does not originate from fetal Leydig cells. TART being derived from a multipotent progenitor cell is still possible as we initially hypothesized, given the fact that TART is likely already present in utero and its resemblance to both testis and adrenal tissues which derive from a common primordium. Therefore, we were surprised to find TART to be more like adult adrenal and testis tissue, raising the possibility of TART being derived from a ‘mature’ progenitor cell type, i.e. adult stem Leydig cells or adrenal progenitor cells, that under influence of high ACTH levels and/or the localization in the testicular region might differentiate into a distinct cell type that expresses both adrenal- and testis-specific markers. However, this remains to be established.
**Minor Comment:**
In Methods: Was RNA isolated from FFPE sections or frozen tissue?
We agree that this was not clearly mentioned enough in our original manuscript, as both frozen (RNA isolation) and FFPE (IHC) material was used. We have now clarified in the methods section that the RNA was retrieved from frozen tissue samples (page 8; RNA isolation, library preparation, and sequencing paragraph).
Reviewer #1 (Significance (Required)):
This first study of transcriptome analysis of TART provides useful insight into the characteristics of these rare tumors that commonly develop in males with classic CAH. This study provides a foundation for further investigation of the biological pathways contributing to the development of TART, the most common cause of male infertility in CAH. This study is of interest to endocrinologists. Reviewed by a pediatric endocrinologist and molecular biologist - we are not completely aware of the sequencing analysis but are familiar with clustering and enrichment analysis.
Comments Reviewer #3:
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
The manuscript by Schröder et al describes the transcriptome sequencing of TARTs in CAH/CS in order to sort out the origin of TARTs. This is an interesting subject and the manuscript is well-written but I have a few comments that could be addressed.
Some parts of the Results should be in the Methods and some in the Discussion. In the Results only the results should be given.
Response: We agree that we have incorporated some methodological sentences and some concluding remarks in the results sections to, in our opinion, improve the flow of the manuscript. As the manuscript guidelines differ between journals, we have for now decided not to change this. We will do so if this is wanted by the concerning journal.
Normally TARTs are not removed or biopsied, if not by mistake... Thus, most centers would not have tissue samples of TARTs at all. How come you have so many samples available?
Response: We thank the reviewer for highlighting this. As indeed TARTs are not routinely removed, the number of TART tissues included in our dataset is unique. Most of the TART samples were already obtained in 2004 because of reported pain and discomfort and in an attempt to improve semen quality in these patients. Removal of those particular TART samples have led to new insights that removal of longstanding TART did not improve semen parameters, nor parameters of pituitary-gonadal function (Claahsen-van der Grinten et al., 2007). Therefore, to date, the only indication for surgery for the removal of longstanding TART is the relief of pain or discomfort.
Ref 2 and 3 are rather old and similar. Could newer review references be used instead?
Response: We have changed those two references for a more recent review by Dr. Witchel on Congenital Adrenal Hyperplasia, who addresses both statements in a more recent review (Witchel, 2017).
Reviewer #3 (Significance (Required)):
New and significant study. Very interesting for people dealing with CAH patients.
References
Claahsen-van der Grinten, H. L., Otten, B. J., Takahashi, S., Meuleman, E. J. H., Hulsbergen-van de Kaa, C., Sweep, F. C. G. J., & Hermus, A. R. M. M. (2007). Testicular adrenal rest tumors in adult males with congenital adrenal hyperplasia: Evaluation of pituitary-gonadal function before and after successful testis-sparing surgery in eight patients. Journal of Clinical Endocrinology & Metabolism, 92(2), 612-615. doi:10.1210/jc.2006-1311
Witchel, S. F. (2017). Congenital Adrenal Hyperplasia. J Pediatr Adolesc Gynecol, 30(5), 520-534. doi:10.1016/j.jpag.2017.04.001
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Referee #2
Evidence, reproducibility and clarity
The manuscript by Schröder et al describes the transcriptome sequencing of TARTs in CAH/CS in order to sort out the origin of TARTs. This is an interesting subject and the manuscript is well-written but I have a few comments that could be addressed.
- Some parts of the Results should be in the Methods and some in the Discussion. In the Results only the results should be given.
- Normally TARTs are not removed or biopsied, if not by mistake... Thus, most centers would not have tissue samples of TARTs at all. How come you have so many samples available?
- Ref 2 and 3 are rather old and similar. Could newer review references be used instead?
Significance
New and significant study. Very interesting for people dealing with CAH patients.
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Referee #1
Evidence, reproducibility and clarity
Summary:
The manuscript by Schroder M., et al describes the whole transcriptome of testicular adrenal rest tumors (TART) and shows that TART tissue is characteristically similar to adult adrenal and testicular rather than fetal adrenal and testicular tissues. The authors propose that their previous claim that TART is derived from an undifferentiated pluripotent progenitor is likely untrue and claim that TART likely originates from a mature cell type with both adrenal and testicular characteristics. The authors describe a unique cell type most similar to the adult adrenal, but with variable testis-specific gene expression patterns. The finding of overexpressed genes associated with ECM remodeling is interesting and may provide insight into the natural history of these tumors. A strength of the study is the number of tissue samples since surgery for these rare tumors is usually not performed.
Major Comments:
The key conclusions are mostly based on RNA studies, thus their claims are preliminary.
In Figure 1/Result p. 3: Authors claim that there were no exclusive HSD17B3 staining cells without CYP11B1, however Figure 1 looks like there are exclusively green (HSD17B) areas (especially TART3). The authors need to address this. It appears as if there are mature Leydig cells. This is important because the presence of Leydig cells would affect the interpretation of the finidings
Discussion: authors state that based on their previous observations that fetal Leydig cells have both adrenal and testis developmental potential. It was speculated that TART might have been derived from a totipotent progenitor cell type, but the current study shows that these tumors lack similarities with fetal tissues. Thus, the authors claim that these tumors are not derived from the transdifferentiation of pluripotent cells. However what is the origin of this mature distinct cell type? Is it not possible that this distinctive cell type is derived from a common progenitor since the testis and adrenal gland are derived from the same adrenogonadal primordium? Lack of similarities with fetal tissues at this late stage of development does not necessarily rule out a common progenitor origin.
Minor Comment:
In Methods: Was RNA isolated from FFPE sections or frozen tissue?
Significance
This first study of transcriptome analysis of TART provides useful insight into the characteristics of these rare tumors that commonly develop in males with classic CAH. This study provides a foundation for further investigation of the biological pathways contributing to the development of TART, the most common cause of male infertility in CAH. This study is of interest to endocrinologists. Reviewed by a pediatric endocrinologist and molecular biologist - we are not completely aware of the sequencing analysis but are familiar with clustering and enrichment analysis.
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
The manuscript entitled "Vasohibin-1 mediated tubulin detyrosination selectively regulates secondary sprouting and lymphangiogenesis in the zebrafish trunk" by de Oliveira investigates the function of the carboxylpeptidase Vasohibin during the formation of the zebrafish trunk vasculature and reports a requirement of Vasohibin for secondary sprout formation and in particular the formation the lymphatic vasculature.
Having established the expression of Vasohibin in sorted ECs of 24 hpf embryos, the remaining study addresses the function of Vasohibin in this cell type. It is largely based on the use of a splice-site interfering morpholino. Particular commendable is the analysis, demonstrating that the KD of vash-1 indeed results in a significant reduction of detyrosination in endothelial tubulin. Findings in the vascular system then include: (i) the detection of increased division and hence supernumerous cells occurring selectively in 2nd sprouts from the PCV; (ii) an increased persistence of the initially formed 3 way connections with ISV and artery; (iii) reduced formation of parachordal lymphangioblasts and (iv) a reduced number of somites with a thoracic duct segment; (v) frequent formation of lumenized connections between PLs (where present) and ISV. To demonstrate specificity, the approach was repeated with a different morpholino and defects were partially rescued by MO-insensitive RNA.
Possible additional and relevant information could include data on a vash-1 promotor mutant to independently verify the MO-based functional analysis. Mutants would also allow analysis of further development, are the defects leading to the demise of the fish or is a later regeneration and normalization of the lymphatic vasculature observed?
We agree that a mutant would be desirable to validate the phenotypic analysis of the morpholinos used, and would also allow for further analysis. However, this is not achievable within a reasonnable time frame, especially in the context of current work restrictions.
In addtion to the two splice morpholinos currently used to knockdown vash-1 expression, we will use an ATG morpholino to further investigate our observations and hypothesis regarding the role of vash-1 in lymphatic vessels formation. We will also validate it by westernblot and attempt to rescue it with mRNA.
We have not investigated the phenotype past 4 dpf. We will add investigation of lymphatics and morphology at 5 dpf.
In addition, are other lymphatic vessel beds like the cranial lymphatics affected?
Using the Tg[fli1a:EGFP]y7 line, we have not been able to identify apparent differences in other vascular beds including the cranial lymphatics. However a detailed fine-grained investigation of the cranial vascular bed has not been performed. Given the focus of the present study on the trunk vasculature to understand the mechanisms of vash-1, we feel that a detailed analysis of cranial lymphatics would at this stage be somewhat out of scope.
PLs have been demonstrated to be at least partially guided in their movement by the CXCR4/SDF1 system and SVEP1. Has the expression of these factors been tested in vash-1 KDs?
We have not investigated the potential role of the CXCR4/SDF1 system and SVEP1 in vash-1 regulation of lymphangiogenesis. We will investigate the expression of cxcr4a, cxcl12a, cxcl12b and svep1 by in situ hibridization upon vash-1 knockdown.
With regards to the frequently observed connections of PLs and ISVs in vash-1 morphants, can the proposed lumen formation of these shunts be demonstrated e.g. by injection of Q-dots or microbeads into the circulation?
Although the lumenisation is very clear thanks to the membrane targeted expression of the label in this line, we will further analyse whether these abberant ISV to ISV connection can be perfused by Q-dots injections.
Concerning the mechanisms of these defects, is it possible to analyse the asymmetric cell division leading to 2nd sprouts in greater detail? Is the same number or are more cells sprouting form PCV and can the fli1ep:EGFP-DCX cell line in fixed samples be used to identify the spindle orientation in dividing cells?
We agree with the reviewer and plan to use the Tg[fli1ep:EGFP-DCX] fish line to investigate spindle asymmetry in uninjected embryos, as well as compare the spindle in control MO and vash-1 KD embryos. Vash-1 has been shown to regulate spindle formation in osteosarcoma cells (Liao et al., 2019). We will attempt to clarify whether this function is conserved in endothelial cells and contributes to the control of endothelial cell proliferation during initiation and formation of secondary sprouting.
We also agree that it is important to look at the PCV in the begining of secondary sprouting and will clarify whether the sprouting is initiated by an increased number of cells.
**Minor issues:** Page 5, Mat & Meth, please spell out PTU at its first mention.
This has been corrected accordingly (see page 4).
Page 6 Mat & Meth, Secondary sprout and 3-way connection parameters: The number of nuclei was assessed in each secondary sprouts (del s, singular) just prior...
This has been corrected accordingly (see page 5).
Page 16, 8th line from bottom: Recent work demonstrated that a secondary sprout either contributes (add s) to remodelling a pre-existing ISV into a vein, or forms (add s)a PLs (Geudens et al., 2019).
This has been corrected accordingly (see page 16).
Page 25, Legend to Fig. 2D-G: "...G,G' shows quantification of dTyr signal upon vash-1 KD..." Fig2 G,G' show immunostaining rather than quantification of the dTyr signal, which is shown Fig. 2H-J
This has been corrected accordingly (see page 26).
Fig. 1D / Fig. 2H-J please increase weight of the error intervals and / or change colour for improved visibility
This has been corrected accordingly (Fig. 1D and 2H-J), and we added n.s. to Fig. 1D.
Reviewer #1 (Significance (Required)):
Taken together the manuscript is comprehensively written and the study provides a conclusive analysis of the MO-mediated KD of Vasohibin in zebrafish embryonic development presenting significant novel findings. Known was a generally inhibitory function of Vasohibin on vessel formation and its enzymatic activity as a carboxylpeptidase responsible for tubulin detyrosination, affecting spindle function and mitosis. New is the detailed analysis of the Vasohibin KD on zebrafish trunk vessel formation and the description of a selective impairment of 2nd sprout formation. The manuscript is of interest for vascular biologists.
REFEREES CROSS-COMMENTING
I fully concur with the comments of reviewer #2, all three reviews find that this study is of significant interest to the vascular biology community as the relevance of tubulin detyrosination for developmental angiogenesis has not been investigated. Also all three reviews highlight the potential limitations of the use of splice morpholinos (suggested alternatives include ATG morpholinos and CRIPR mutants), the requirement to provide further evidence for a endothelial cell autonomous defect and the need to clarify some of the data representation.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
**Summary:**
The manuscript by Bastos de Oliveira et al. describes an important investigation of the endothelial tubulin detyrosination during vascular development. Namely, they found detyronised microtubules in secondary sprouts, which is absent in MO-vash-1 treated embryos. The authors use the vash-1 morpholino approach to uncover the developmental consequences of suppressed detyrosination in angiogenesis and lymphangiogenesis in vivo in zebrafish. By a combination of transgenic lines, immunohistochemistry and time-lapse imaging, Bastos de Oliveira et al., have found that Vash-1 is a negative regulator of secondary sprouting in zebrafish. The authors showed that in the absence of Vash-1 more cells are present in the secondary sprouts due to increased cell proliferation; however lymphatic vascular network fails to form. The current manuscript requires additional experimental evidence to support the conclusions. Please see below the major technical concerns and minor comments.
**Major comments:**
-This study is based on analysis of the phenotypes observed in embryos injected with vash-1 morpholino. The authors use two different types of splice morpholinos, perform rescue experiments with RNA, and validate one MO-vash-1 with western blot. Morpholinos are not trivial to work with, and the results are variable hence additional controls need to be included, as following the recommendation put together by the zebrafish community (Stainier et, al., Plos Genetics, 2017). As the severity of the phenotypes comparing MO1 with MO2 is different and MO-vash-1 embryos appear developmentally delayed (Figure 2D-F and 5E-F overall size seem to be affected), additional MO is required, for example, ATG-MO or generation of CRISPR mutant would be favourable. All the morpholino used need to be validated using an antibody, RT-PCR and qPCR. It is essential to carry out the rescue experiments for all the MO used in this study and following the guidelines. Including the dose-response curve, data would be informative.
We agree with the reviewer and the recommendations of the zebrafish community. We will investigate the phenotypes with another KD strategy, such as the ATG-Morpholino suggested by the reviewer. We will also supply more validation of the MO2 including RNA rescue and westernblot (already included in Fig. 5 I).
We added dose-response curves (Supp. Figure 1 E,G) and a developmental morphology assessment for the morpholino 1 (Supp. Figure 1 A,B).
Given our extensive analysis of the effects of vash-1 KD, we believe the embryos in 2F are not developmentally delayed. However, the image in figure 2F does give that impression, and therefore may have triggered the reviewer’s concerns. We double checked and found that due to an oversight, we included a picture from a slightly different region of the trunk in comparision to Fig. 2D. We will add pictures of the same trunk region (Fig.2D-F) as we have done in all other figures. We nonetheless supply a supplementary figure 1 showing and quantifying the development of the analysed vash-1 morphants.
-In addition to EC, the levels of dTyr are lower in MO-vash-1 in neural tube and neurons spanning the trunk (Figrue 2 D-G'). These have been previously shown to be important for secondary sprouting. Is it possible that the observed phenotypes in the secondary sprouting are due to defects in these neurons?
We agree with the reviewer that a potential contribution of altered neuronal differentiation to the vascular phenotype should be clarified. We will assess the morphology of the neurons and their dendrites relevant for pathfinding (Lim et al., 2011) in vash-1 KD embryos, using a pan-neuronal zebrafish line, as well as via immunostaining against alpha-tubulin. Should we find evidence for changes in neuronal cells, we will attempt to clarify a cell autonomous role of vash-1 by transplantation experiments.
-Embryo number used in this study appears to be low especially in figure 3G, 5D, 5G, to conclude draw conclusions from these experiments, the number of embryos used should be higher than 20. Figure 4J please specify how many embryos were used.
We will increase the number of embryos per condition to a minimum of 20 embryos and update the averages in the text for 3G (control: 7 and vash-1 KD: 11 embryos).
In 5D and 5G each point is an embryo and more than 20 embryos per condition were used (in 5D 23-35 embryos per condition, in 5G 60-63 embryos/condition), we corrected the legend 5D and 5G (see page 27) and made it clear that each point in the graph corresponds to one embryo (5D- percentage of PLs associated with veins in each embryo; 5G- percentage of somites with toraxic duct in each embryo).
In 4J, 18 embryos were used for control (about 3 sprouts/embryo– 52 sprouts quantified) and 7 embryos for vash-1* KD condition (about 3 sprouts/embryo – 24 sprouts quantified). We corrected the number of control sprouts in the legend and added the number of embryos to increase clarity (see page 27).
-The authors hypothesise that VASH acts in the sprouting endothelial cells, based on the Q-PCR in Figure 1. However, in this experiment all EC have been sorted thus this remains ambiguous in which cell types vash-1 is expressed. Please provide the expression pattern for vash-1 across the developmental stages the phenotypes are observed.
We agree with the reviewer that it would be beneficial to understand the expression pattern of vash-1 in wild type embryos. We plan to perform in situ hybridization for vash-1 mRNA.
-Throughout the manuscript the authors refer the lymphatic identity, however, there is no evidence in the paper that the identity status has been assessed. To support these claims Prox1 immunohistochemistry or analysis of prox1 expression in the reporter line would be appropriate.
We agree with the reviewer and plan to perform a Prox1 immunostaining (Koltowska et al., 2015) in vash-1 KD embryos at 34-36 hpf (secondary sprouting) to investigate Prox1 levels upon vash-1 KD.
**Minor comments:**
-The authors refer to the literature where overexpression of VASH suppresses the angiogenesis. As the RNA injections were used in rescue experiments, the data of vash-1 RNA injections into the wild-type embryos would be beneficial.
We have injected vash-1 RNA into a control morpholino injected embryos (28 control embryos, 14 Vash-1 RNA injected embryos) and we observed a significant decrease in PLs at 52 hpf (average of -control: 87,5% somites with PLs to 67% somites with PLs in vash-1 RNA embryos). This could be due to a decrease of secondary sprouting, which would be in accordance with the current literature that vash-1 overexpression is anti-angiogenic. We will further investigate and add the results to figure 5. Figure 1. vash-1* mRNA injection leads to a decrease in somites with PLs (preliminary).
-In figures 2J, 3J, 3K, 3N, 4J, 5C, 5D and 5G the N number was set for examples as the number of sprouts, the number of somites with TD, number of ISV. To strengthen the observation in the manuscript quantification of the sprouts, PL, vISVs and lymphatic phenotypes with N set as the number of embryos would be more informative. Indicating the number of embryos used, in the graphs, would be helpful.
We agree with the reviewer and have added embryo numbers in all legends and graphs. In 2J, 3J, 3K, 4J each point is a sprout, a cell division or an ISV, corresponding to the N. We agree that the number of embryos could be more clearly stated, so we added the number of embryos analysed in the figure legend and will add them in the graphs.
In 5C, 5D and 5G each point corresponds to an embryo (clarified in the legend of Fig. 5- see page 27).
Fig. 5C refers to the percentage of somites with PLs in each embryo, 5D refers to percentage of the existing PLs in one embryo connected to a venous ISV, 5G corresponds to percentage of somites with a TD segment in each embryo.
-In Figure 5A, B and D the authors quantify what they refer to as a lumenised connection between the vISVs and PL. In the control image (second star), a somewhat lumenised structure is present, clarification of how the scores were set is missing.
In Fig. 5C we show a quantification of the percentage of somites with PLs per embryo, by counting the PLs identified with an asterisk in Fig. 5A-B. PLs are normally not lumenised, with few exceptions also ocurring in wild-type – see Fig. 4 in (S Isogai et al., 2001).
In Fig. 5D we quantified the proportion of PLs associated/connected with venous ISvs (see Methods section page 6), by 52 hpf in control and vash-1 morphants.
In 5B and 5F,F‘, the arrowheads identify lumenised PLs present in vash-1 KD embryos. We will add a quantification of kdr-l:ras-Cherry positive ISV-to-ISV connections, corresponding to the lumenised endothelial connections, since kdr-l:ras-Cherry signal labels endothelial (and not lymphatic) cells and is particularly strong at the luminal endothelial membrane of the vessel.
-In Figure 3 E and F the authors show the excessive sprouting phenotype between controls and Mo-vash-1. The images presented are taking from different parts of the embryos (middle of the trunk vs plexus region), hampering the comparison between the two groups. The quantification of the phenotypes in both experimental groups should be in the same region of the embryo, as the local difference can occur. It is key to provide representative images to support these observations.
The images presented are representative of the phenotype quantified, and the time-lapses were done in comparable regions of the zebrafish trunk (+- 1-2 somites in both groups due to drift during image aquisition), making the comparison possible.
-Figure 1D the vash-1 expression levels in EC seem very variable in this graph, therefore no conclusion can be drawn from this data, especially as the authors do not provide the p-values.
We added n.s. in the graph, to make it clear that the difference between developmental stages is not significant, potentially due to high biological variability between embryos, as seen in two primer pairs. We believe that presenting this biological variability is of importance to the readers.
We write on page 12 about this result: „During the sprouting phase (24hpf), vash-1 expression was 5-7 times higher in endothelial than in non-ECs, decreasing at 48 hpf (Fig. 1C-D). Although these results are not significant, they were independently confirmed with a second primer set.”. The only conclusion we made from this data is that Vash-1 is dynamically expressed in the zebrafish endothelium during development, as we now added in the discussion (page 14).
-In the introduction, the authors state: 'Although primary and secondary sprouts appear morphologically similar, with tip and stalk cells' - Please provide the reference that supports the claim that secondary sprouts have tip-stalk cells morphology/organisation.
Although many studies have investigated primary and secondary sprouting, identifying both shared as well as distinct molecular regulation, and show morphological details that are apparently similar, a formal claim that secondary sprouts show tip and stalk cell identities and behaviour is hard to find. Given that this is not relevant for the central findings of the work, we modified the sentence and added a reference “Although primary and secondary sprouts appear morphologically similar, with tip and stalk cells” (Sumio Isogai et al., 2003)…” See page 2.
We also updated the discussion for consistency: “Although the cellular mechanisms of primary and secondary sprouting in zebrafish appear very similar, with tip cell selection and guided migration and stalk cell proliferation, secondary sprouting utilises alternative signalling pathways and entails a unique specification step that establishes both venous ISVs and lymphatic structures.” (see page 15)
-The authors refer the increased cell division phenotypes observed in the movies, however, the movie files have not been available to the reviewers.
We will provide the movies.
Reviewer #2 (Significance (Required)):
This is an important study as uncovering the mechanistic details of angiogenic and lymphangiogenic negative regulators is of high value with the potential for therapeutic developments. To date, Vash-1 has been only studied in the context of tumour angiogenesis, vasculature in diabetic nephropathy and pulmonary arterial hypertension, and it remains unclear what is its role during development and how does it regulate vascular network formation. The tyrosination status of microtubule in endothelial cells is understudied. This study revealed, previously uncharacterised detyrosinated microtubules in endothelial cells in vivo. And further dissects how this process might be regulated, brings unique insights into the vascular biology field and beyond. Thus, delving into the cell biological mechanism such as microtubule dynamics and modification in vivo in embryo context is a significant step forward in setting new standards in the field.
I am developmental biologist who has experience in model organisms such as zebrafish and mouse. The main focus of my work is on developmental angiogenesis and lymphangiogenesis.
REFEREES CROSS-COMMENTING
After reading the other reviews comments, it seems that we all agree that this study is of high value to vascular biology field and beyond bringing novel findings.
Importantly the reviewers' comments are in line with each other and have identified several commonalities that should be addressed. Such as: Further validation of Morpholinos, or using alternative methods to replicate the findings. additional evidence that the observed phenotypes are primary due to vash-1 requirement within EC, and not due to the secondary effect in other cells such as CXCR4/SDF1 system and SVEP1, neurons or general delay of the embryos Further evidence of for VASH expression pattern the number of embryos used in the experiments, and how the data is represented.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
Vasohibin-1 (Vash-1) is known to detyrosinate microtubules (MTs) and limit angiogenesis. Using in vivo live imaging and whole mount immunofluorescence staining of zebrafish trunk vasculature, Bastos de Oliveira et al. show that the MT detyrosination role of Vash-1 is conserved in zebrafish and that Vash-1 is essential for limiting venous sprouting and subsequent formation of lymphatics. Their findings suggest a role for MT detyrosination in lympho-venous cell specification.
**Major comments:**
1 . The authors claim that Vash-1 regulates secondary sprouting and lymphangiogenesis by detyrosinating MTs. However, no direct evidence of this link is provided in the manuscript. The authors only separately show that knockdown of vash-1 affects MT detyrosination and secondary sprouting and lymphangiogenesis. They have not shown a causative effect. The authors should therefore qualify the above stated claim as speculative. In other words, the authors should mention that their data only suggests that disruption of MT detyrosination is the underlying cause for aberrant secondary sprouting and lymphangiogenesis in vash-1 KD embryos.
We agree with the reviewer about the lack of evidence to state that the disruption of microtubule detyrosination leads to aberrant secondary sprouting. Although we believe this is the most parsimonius explanation for the secondary sprouts behavioural defects as cell division is disturbed and microtubule detyrosination is implicated in cell division (Barisic et al., 2015), we want to make clear that our data currently only suggest a specific role of microtubule detyrosination in secondary sprouting. Examples of this are page 14 of the discussion „These results suggest that Vash-1-driven microtubule detyrosination limits excessive venous EC sprouting and proliferation during lympho-venous development in zebrafish.” as well as the abstract.
We also corrected the sentence in the discussion (page 14): “In this study, we identified Vash-1-mediated microtubule detyrosination as a cellular mechanism as a novel regulator of EC sprouting from the PCV and the subsequent formation of lymphatic vessels in the zebrafish trunk.”
To avoid any overstatement, we also propose the following title change: Vasohibin-1 mediated tubulin detyrosination selectively regulates secondary sprouting and lymphangiogenesis in the zebrafish trunk.
As detailed in response to comment 2 below, we will however attempt to investigate the direct connection. Depending on the outcome, we will adapt conclusions and title accordingly.
2 . In order to provide more compelling evidence for a direct relationship between MT tyrosination and lymphangiogenesis, the authors could try mutating the carboxypeptidase domain of vash-1 or overexpressing a dominant negative transcript (that contains a mutated carboxypeptidase domain). If this gives the same phenotypes as the vash-1 morphants, it would indicate that the carboxypeptidase activity of Vash-1 (in detyrosinating MTs) is responsible for limiting secondary sprouting and promoting specification of lymphatics. This suggested experiment is fairly realistic in terms of both time and resources. For example, since the authors already have the human vash-1 cDNA cloned, making a dominant negative transcript from this would take around two weeks, imaging and analysis of embryos injected with this mRNA would take another four weeks. Therefore, in total, the suggested experiment would take around 6 weeks. Although the alternative experiment, that is, making a carboxypeptidase domain mutant of vash-1 would be a better choice in terms of reproducibility and long-term use of a stable line, it would admittedly take a relatively larger amount of time. Therefore, the ultimate choice would depend on the authors.
We will investigate this further by cloning and expressing a mutated vash-1 cDNA which translates a validated catalytically dead Vash-1 (Nieuwenhuis et al., 2017). However, this mutant has not been shown to function as dominant negative, so it is unclear whether it can be used as a dominant negative mutant.
3 . Both the data and methods are presented in a way that ensures reproducibility. The statistical analysis is very well done, in that the authors were very prudent in their choice of statistical tests. However, in many figures and subfigures (Fig. 2B, H-J; Fig. 3G, J, K, N; Fig. 4J; Fig. 5J), the number of replicates was not mentioned and instead only the sample size was stated. Whether this was just an oversight or if it should be taken to mean that the analysis was performed on just one replicate is unclear. The authors need to clarify this aspect of their analysis. Further, In Fig. 2H-J, Fig. 3G,J, K, N and Fig. 4J, the total number of data points in control MO vs vash-1 KD seem to be quite different. In other words, there seem to be a lot more data points in one experimental condition than the other. Does this difference fall within the acceptable range? If the authors were to compare a similar number of data points between the two experimental conditions, would the results of the statistical analysis still be the same?
We apreciate this comment and clarified the replicate numbers in the figure legends: Fig. 2B- 3 replicates (page 25), Fig. 2 H-J- quantification is 1 replicate (page 26), Fig. 2 D-G is representative of 3 replicates (page 25). Fig. 3 G,J,K,N – quantification is from 1 replicate (page 26), Fig. 3 B,C,E,F,H,I are representative of 2 experimental replicates (page 26). Fig. 4J – quantification is 1 replicate (page 27), Fig. 4 A-F is representative of 3 replicates (page 27). Fig. 5 J correspondes to 1 replicate (page 28).
We plan to increase replicates and numbers in quantifications shown in Fig. 3 G,J,K,N and Fig. 5 J as they are relevant for the conclusions of the manuscript, and adapt the text.
The quantifications of immunostaining signals are comparable between different samples of the same experiment but technically not easy accross different experiments, due to some variability of the immunostaining. However, the pattern we report in the quantifications and representative pictures is consistentely detected (reduced dTyr signal upon vash-1 KD in Fig 2 D-G; higher dTyr intensity in secondary rather than primary sprouts in Fig. 4 A-F). We added in the legend that the pictures of the embryos in these figures are representative of 3 biological replicates (see page 25 and 27).
We recognise the unequal sample size in control and vash-1 KD groups in Fig. 2H-J, Fig. 3G,J, K, N and Fig. 4J. Generally, the vash-1 KD group shows more variance than the control group (see Fig. 3 J-N, 4J for example), hence the reason why we analysed a higher sample size.
In the planned experiments (repeating quantifications of Fig. 3 J-N), we will analyse a similar number of embryos.
We corrected the figure legend of 2 H-J on the number of ISVs - 108 ISVs from 7 embryos for control and 150 ISVs for vash-1 KD, from 9 embryos (see page 26).
4 . The authors only provide KD data on the function of vash-1 using morpholinos. According to several recent guidelines concerning the use of morpholinos, this is not widely accepted in the zebrafish community as sufficient to provide robust insight into gene function. Please refer for example to the following publication: Guidelines for morpholino use in zebrafish, Stainier et al., PLOS Genetics, 2017. The generation of a vash-1 mutant is a necessary requirement for backing up morpholino KD data. Further, even though the authors state that embryos were selected on the pre-established criteria that they have normal morphology, beating heart, and flowing blood, certain morphological differences between control MO injected and vash-1 KD embryos could be observed in some figures. In Fig. 2D, F and Fig. 5A, B, E, F the vash-1 KD embryos seem smaller (extend of the dorso-ventral axis) than control MO injected embryos. The authors need to provide images showing the overall morphology of morpholino injected embryos and need to provide evidence that morpholino injections do not cause developmental delays.
We agree that a mutant would be desirable to validate the phenotypic analysis of the morpholinos used, and would also allow for further analysis. However, this is not achievable within a reasonnable time frame, especially in the context of current work restrictions. We have added a sentence about the need to confirm the loss of function phenotype with vash-1 mutants in the discussion (see page 14).
In addtion to the two morpholinos currently used to knockdown vash-1 expression, we will use an ATG morpholino to further investigate our observations and hypothesis regarding the role of vash-1 in lymphatic vessels formation. We will also validate it by westernblot and attempt to rescue it with mRNA.
We added a supplementary figure with pictures and quantifications of antero-posterior (Sup. Figure 1 C) and dorso-ventral length (Sup. Figure 1 D) of the analysed control and vash-1 morpholino injected embryos‘ development at 24, 34, 52 and 4dpf which shows no significant developmental delay and morphological defect. There is some occurrence of curvature of the tail at 34-52 hpf.
We added a sentence in the Methods section (pages 10) to clarify the morphant’s morphology and dosage-response curves.
We observe a 1-2 hour developmental delay of both the control and the vash-1 KD embryos compared to uninjected wild-type embryos, which led us to chose the 52 hpf time point to investigate the PLs. In uninjected embryos they are usually developed by 48hpf (Hogan et al., 2009).
Fig. 2 D shows a more anterior region of the zebrafish trunk than Fig. 2F (the tail has a smaller dorso-ventral length)- we will provide more comparable pictures from the same region.
Fig. 5B is slightly tilted – we will provide a picture with the same orientation.
Fig. 5 E and F have a similar length from dorsal aorta to the dorsal longitudinal anastomotic vessel. However, we appreciate a difference in the sub intestinal vascular plexus (SIVP), which is consistently underdeveloped in the vash-1 KD embryos.
Figure 2- vash-1 deficient embryos show underdeveloped intestinal vascular system at 4 dpf.
**Minor comments:**
a. The authors should back their qPCR data for vash-1 expression (Figure 1) by standard mRNA in situ hybridization, given the large degree of variability in vash-1 expression. Do they observe a dynamic expression in the vasculature using this technique?
We agree with the reviewer that an in situ hybridization would be beneficial to understand the expression pattern of vash-1 in wild type embryos. Accordingly, we will look at vash-1 expression by in situ hybridization in WT embryos.
The number of nuclei per sprout in Fig. 3J does not correspond with the number of divisions per sprout presented in Fig. 3K. The authors observe one or two cell divisions per sprout in ctr MO injected embryos (Fig. 3K), however, Fig. 3J shows that the majority of ctr. sprouts contains only one cell. This is even more dramatic for vash-1 MO injected embryos, which can have up to four divisions, therefore should contain six cells. However, the maximum number of cells the authors report is three to four cells. How do these observations go together?
We believe these quantifications are not contradicting. The number of endothelial nuclei was assessed just prior to the connection to the ISV and the cell division quantification was done in a time-lapse from the time of secondary sprout emergence until the resolution of the 3-way connection. It is expected that there are more cell divisions during a longer time frame, as cells migrate dorsally or ventrally out of the sprout.
Fig. 5I and J have the same data points for control MO and vash-1 MO1. Does this mean that both graphs are from the same experiment? If so, the authors could combine the two graphs into one. If the two graphs are not from the same experiment, both would need to have independent controls.
Fig 5 I and J are indeed from the same experiment. They are now combined into one graph (see Fig. 5 J).
d. The percentage of somites with PLs in vash-1 MO1 injected embryos in Fig. 5I is half the value shown in Fig. 5C. Although this kind of variability might be expected in biological samples, perhaps the authors could briefly discuss the issue and its implications on reproducibility in the manuscript so as to have the readers be aware of it, especially since the rescue of the vash-1 morpholino phenotype back to 50% from 25% is the same value the authors observed in the vash-1 KD alone in Fig. 5C. Here the value is 50% for the morpholino injection.
We added a sentence discussing the phenotypic variability in the discussion (see page 16), and we added a dosage response curve for the PLs (Sup. Figure 1 F), showing that embryos injected with the same amount of morpholino show variability in the percentage of somites with PLs at 52hpf. We added a more representative picture of PLs for vash-1 morphant in Fig. 5I ( Y-axis of Fig. 2H and 4J correspond to ratios, which have no units. Nontheless, we added AU/AU to these graphs to make it clearer. We added the bars in Fig. 5D.
It would help to have an inference or conclusion at the end of each results section.
We added one conclusion sentence per results section (see pages 11-14).
Reviewer #3 (Significance (Required)):
Conceptual: As per my knowledge, this is the first study that looks at microtubule modifications in the context of a vertebrate organism past the gastrulation stage, as opposed to similar studies that have been done in cell culture or invertebrates (S. cerevisiae, C. elegans and D. melanogaster). Moreover, this study is one of few that address a novel link between the cytoskeleton and the process of cell fate specification.
Previous studies have separately shown that Vash-1 limits angiogenesis and detyrosinates MTs. The current study combines the two observations in the context of endothelial cells, and hypothesizes that perhaps the function of Vash-1 in limiting angiogenesis and at the same time promoting lymphatic development could be due to its role in MT modification at the molecular level and the consequent effect of this on cell division and/or fate specification at the cellular level. In short, this study aims to connect the long-standing gap in knowledge between cytoskeletal modifications and cell dynamics (in particular, division and specification) in a vertebrate organism. I therefore believe that the current study would be an exciting finding for research communities that study cytoskeletal influence on cellular dynamics and also those in the broad area of vascular biology.
My field of expertise relates to vascular biology, specifically developmental angiogenesis and the behavior of endothelial cells in zebrafish.
References
Barisic, M., Silva E Sousa, R., Tripathy, S. K., Magiera, M. M., Zaytsev, A. V., Pereira, A. L., Janke, C., Grishchuk, E. L., & Maiato, H. (2015). Microtubule detyrosination guides chromosomes during mitosis. Science, 348(6236), 799–803. https://doi.org/10.1126/science.aaa5175
Hogan, B. M., Bos, F. L., Bussmann, J., Witte, M., Chi, N. C., Duckers, H. J., & Schulte-Merker, S. (2009). Ccbe1 is required for embryonic lymphangiogenesis and venous sprouting. Nature Genetics, 41(4), 396–398. https://doi.org/10.1038/ng.321
Isogai, S, Horiguchi, M., & Weinstein, B. M. (2001). The vascular anatomy of the developing zebrafish: an atlas of embryonic and early larval development. Developmental Biology, 230(2), 278–301. https://doi.org/10.1006/dbio.2000.9995
Isogai, Sumio, Lawson, N. D., Torrealday, S., Horiguchi, M., & Weinstein, B. M. (2003). Angiogenic network formation in the developing vertebrate trunk. Development, 130(21), 5281–5290. https://doi.org/10.1242/dev.00733
Kimura, H., Miyashita, H., Suzuki, Y., Kobayashi, M., Watanabe, K., Sonoda, H., Ohta, H., Fujiwara, T., Shimosegawa, T., & Sato, Y. (2009). Distinctive localization and opposed roles of vasohibin-1 and vasohibin-2 in the regulation of angiogenesis. Blood, 113(19), 4810–4818. https://doi.org/10.1182/blood-2008-07-170316
Koltowska, K., Lagendijk, A. K., Pichol-Thievend, C., Fischer, J. C., Francois, M., Ober, E. A., Yap, A. S., & Hogan, B. M. (2015). Vegfc Regulates Bipotential Precursor Division and Prox1 Expression to Promote Lymphatic Identity in Zebrafish. Cell Reports, 13(9), 1828–1841. https://doi.org/10.1016/j.celrep.2015.10.055
Liao, S., Rajendraprasad, G., Wang, N., Eibes, S., Gao, J., Yu, H., Wu, G., Tu, X., Huang, H., Barisic, M., & Xu, C. (2019). Molecular basis of vasohibins-mediated detyrosination and its impact on spindle function and mitosis. Cell Research, June. https://doi.org/10.1038/s41422-019-0187-y
Lim, A. H., Suli, A., Yaniv, K., Weinstein, B., Li, D. Y., & Chien, C. Bin. (2011). Motoneurons are essential for vascular pathfinding. Development, 138(21), 4813. https://doi.org/10.1242/dev.075044
Nicenboim, J., Malkinson, G., Lupo, T., Asaf, L., Sela, Y., Mayseless, O., Gibbs-Bar, L., Senderovich, N., Hashimshony, T., Shin, M., Jerafi-Vider, A., Avraham-Davidi, I., Krupalnik, V., Hofi, R., Almog, G., Astin, J. W., Golani, O., Ben-Dor, S., Crosier, P. S., … Yaniv, K. (2015). Lymphatic vessels arise from specialized angioblasts within a venous niche. Nature, 522(7554), 56–61. https://doi.org/10.1038/nature14425
Nieuwenhuis, J., Adamopoulos, A., Bleijerveld, O. B., Mazouzi, A., Stickel, E., Celie, P., Altelaar, M., Knipscheer, P., Perrakis, A., Blomen, V. A., & Brummelkamp, T. R. (2017). Vasohibins encode tubulin detyrosinating activity. Science, 358(6369), 1453–1456. https://doi.org/10.1126/science.aao5676
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Referee #3
Evidence, reproducibility and clarity
Vasohibin-1 (Vash-1) is known to detyrosinate microtubules (MTs) and limit angiogenesis. Using in vivo live imaging and whole mount immunofluorescence staining of zebrafish trunk vasculature, Bastos de Oliveira et al. show that the MT detyrosination role of Vash-1 is conserved in zebrafish and that Vash-1 is essential for limiting venous sprouting and subsequent formation of lymphatics. Their findings suggest a role for MT detyrosination in lympho-venous cell specification.
Major comments:
1 . The authors claim that Vash-1 regulates secondary sprouting and lymphangiogenesis by detyrosinating MTs. However, no direct evidence of this link is provided in the manuscript. The authors only separately show that knockdown of vash-1 affects MT detyrosination and secondary sprouting and lymphangiogenesis. They have not shown a causative effect. The authors should therefore qualify the above stated claim as speculative. In other words, the authors should mention that their data only suggests that disruption of MT detyrosination is the underlying cause for aberrant secondary sprouting and lymphangiogenesis in vash-1 KD embryos.
2 . In order to provide more compelling evidence for a direct relationship between MT tyrosination and lymphangiogenesis, the authors could try mutating the carboxypeptidase domain of vash-1 or overexpressing a dominant negative transcript (that contains a mutated carboxypeptidase domain). If this gives the same phenotypes as the vash-1 morphants, it would indicate that the carboxypeptidase activity of Vash-1 (in detyrosinating MTs) is responsible for limiting secondary sprouting and promoting specification of lymphatics. This suggested experiment is fairly realistic in terms of both time and resources. For example, since the authors already have the human vash-1 cDNA cloned, making a dominant negative transcript from this would take around two weeks, imaging and analysis of embryos injected with this mRNA would take another four weeks. Therefore, in total, the suggested experiment would take around 6 weeks. Although the alternative experiment, that is, making a carboxypeptidase domain mutant of vash-1 would be a better choice in terms of reproducibility and long-term use of a stable line, it would admittedly take a relatively larger amount of time. Therefore, the ultimate choice would depend on the authors.
3 . Both the data and methods are presented in a way that ensures reproducibility. The statistical analysis is very well done, in that the authors were very prudent in their choice of statistical tests. However, in many figures and subfigures (Fig. 2B, H-J; Fig. 3G, J, K, N; Fig. 4J; Fig. 5J), the number of replicates was not mentioned and instead only the sample size was stated. Whether this was just an oversight or if it should be taken to mean that the analysis was performed on just one replicate is unclear. The authors need to clarify this aspect of their analysis. Further, In Fig. 2H-J, Fig. 3G,J, K, N and Fig. 4J, the total number of data points in control MO vs vash-1 KD seem to be quite different. In other words, there seem to be a lot more data points in one experimental condition than the other. Does this difference fall within the acceptable range? If the authors were to compare a similar number of data points between the two experimental conditions, would the results of the statistical analysis still be the same?
4 . The authors only provide KD data on the function of vash-1 using morpholinos. According to several recent guidelines concerning the use of morpholinos, this is not widely accepted in the zebrafish community as sufficient to provide robust insight into gene function. Please refer for example to the following publication: Guidelines for morpholino use in zebrafish, Stainier et al., PLOS Genetics, 2017. The generation of a vash-1 mutant is a necessary requirement for backing up morpholino KD data. Further, even though the authors state that embryos were selected on the pre-established criteria that they have normal morphology, beating heart, and flowing blood, certain morphological differences between control MO injected and vash-1 KD embryos could be observed in some figures. In Fig. 2D, F and Fig. 5A, B, E, F the vash-1 KD embryos seem smaller (extend of the dorso-ventral axis) than control MO injected embryos. The authors need to provide images showing the overall morphology of morpholino injected embryos and need to provide evidence that morpholino injections do not cause developmental delays.
Minor comments:
a. The authors should back their qPCR data for vash-1 expression (Figure 1) by standard mRNA in situ hybridization, given the large degree of variability in vash-1 expression. Do they observe a dynamic expression in the vasculature using this technique?
b. The number of nuclei per sprout in Fig. 3J does not correspond with the number of divisions per sprout presented in Fig. 3K. The authors observe one or two cell divisions per sprout in ctr MO injected embryos (Fig. 3K), however, Fig. 3J shows that the majority of ctr. sprouts contains only one cell. This is even more dramatic for vash-1 MO injected embryos, which can have up to four divisions, therefore should contain six cells. However, the maximum number of cells the authors report is three to four cells. How do these observations go together?
c. Fig. 5I and J have the same data points for control MO and vash-1 MO1. Does this mean that both graphs are from the same experiment? If so, the authors could combine the two graphs into one. If the two graphs are not from the same experiment, both would need to have independent controls.
d. The percentage of somites with PLs in vash-1 MO1 injected embryos in Fig. 5I is half the value shown in Fig. 5C. Although this kind of variability might be expected in biological samples, perhaps the authors could briefly discuss the issue and its implications on reproducibility in the manuscript so as to have the readers be aware of it, especially since the rescue of the vash-1 morpholino phenotype back to 50% from 25% is the same value the authors observed in the vash-1 KD alone in Fig. 5C. Here the value is 50% for the morpholino injection.
e. The Y-axis label is missing in Fig. 2H and Fig. 4J. Figure 5D lacks bars showing median and standard deviation.
f. It would help to have an inference or conclusion at the end of each results section.
Significance
Conceptual: As per my knowledge, this is the first study that looks at microtubule modifications in the context of a vertebrate organism past the gastrulation stage, as opposed to similar studies that have been done in cell culture or invertebrates (S. cerevisiae, C. elegans and D. melanogaster). Moreover, this study is one of few that address a novel link between the cytoskeleton and the process of cell fate specification.
Previous studies have separately shown that Vash-1 limits angiogenesis and detyrosinates MTs. The current study combines the two observations in the context of endothelial cells, and hypothesizes that perhaps the function of Vash-1 in limiting angiogenesis and at the same time promoting lymphatic development could be due to its role in MT modification at the molecular level and the consequent effect of this on cell division and/or fate specification at the cellular level. In short, this study aims to connect the long-standing gap in knowledge between cytoskeletal modifications and cell dynamics (in particular, division and specification) in a vertebrate organism. I therefore believe that the current study would be an exciting finding for research communities that study cytoskeletal influence on cellular dynamics and also those in the broad area of vascular biology.
My field of expertise relates to vascular biology, specifically developmental angiogenesis and the behavior of endothelial cells in zebrafish.
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Referee #2
Evidence, reproducibility and clarity
Summary:
The manuscript by Bastos de Oliveira et al. describes an important investigation of the endothelial tubulin detyrosination during vascular development. Namely, they found detyronised microtubules in secondary sprouts, which is absent in MO-vash-1 treated embryos. The authors use the vash-1 morpholino approach to uncover the developmental consequences of suppressed detyrosination in angiogenesis and lymphangiogenesis in vivo in zebrafish. By a combination of transgenic lines, immunohistochemistry and time-lapse imaging, Bastos de Oliveira et al., have found that Vash-1 is a negative regulator of secondary sprouting in zebrafish. The authors showed that in the absence of Vash-1 more cells are present in the secondary sprouts due to increased cell proliferation; however lymphatic vascular network fails to form. The current manuscript requires additional experimental evidence to support the conclusions. Please see below the major technical concerns and minor comments.
Major comments:
-This study is based on analysis of the phenotypes observed in embryos injected with vash-1 morpholino. The authors use two different types of splice morpholinos, perform rescue experiments with RNA, and validate one MO-vash-1 with western blot. Morpholinos are not trivial to work with, and the results are variable hence additional controls need to be included, as following the recommendation put together by the zebrafish community (Stainier et, al., Plos Genetics, 2017). As the severity of the phenotypes comparing MO1 with MO2 is different and MO-vash-1 embryos appear developmentally delayed (Figure 2D-F and 5E-F overall size seem to be affected), additional MO is required, for example, ATG-MO or generation of CRISPR mutant would be favourable. All the morpholino used need to be validated using an antibody, RT-PCR and qPCR. It is essential to carry out the rescue experiments for all the MO used in this study and following the guidelines. Including the dose-response curve, data would be informative.
-In addition to EC, the levels of dTyr are lower in MO-vash-1 in neural tube and neurons spanning the trunk (Figrue 2 D-G'). These have been previously shown to be important for secondary sprouting. Is it possible that the observed phenotypes in the secondary sprouting are due to defects in these neurons?
-Embryo number used in this study appears to be low especially in figure 3G, 5D, 5G, to conclude draw conclusions from these experiments, the number of embryos used should be higher than 20. Figure 4J please specify how many embryos were used.
-The authors hypothesise that VASH acts in the sprouting endothelial cells, based on the Q-PCR in Figure 1. However, in this experiment all EC have been sorted thus this remains ambiguous in which cell types vash-1 is expressed. Please provide the expression pattern for vash-1 across the developmental stages the phenotypes are observed.
-Throughout the manuscript the authors refer the lymphatic identity, however, there is no evidence in the paper that the identity status has been assessed. To support these claims Prox1 immunohistochemistry or analysis of prox1 expression in the reporter line would be appropriate.
Minor comments:
-The authors refer to the literature where overexpression of VASH suppresses the angiogenesis. As the RNA injections were used in rescue experiments, the data of vash-1 RNA injections into the wild-type embryos would be beneficial.
-In figures 2J, 3J, 3K, 3N, 4J, 5C, 5D and 5G the N number was set for examples as the number of sprouts, the number of somites with TD, number of ISV. To strengthen the observation in the manuscript quantification of the sprouts, PL, vISVs and lymphatic phenotypes with N set as the number of embryos would be more informative. Indicating the number of embryos used, in the graphs, would be helpful.
-In Figure 5A, B and D the authors quantify what they refer to as a lumenised connection between the vISVs and PL. In the control image (second star), a somewhat lumenised structure is present, clarification of how the scores were set is missing.
-In Figure 3 E and F the authors show the excessive sprouting phenotype between controls and Mo-vash-1. The images presented are taking from different parts of the embryos (middle of the trunk vs plexus region), hampering the comparison between the two groups. The quantification of the phenotypes in both experimental groups should be in the same region of the embryo, as the local difference can occur. It is key to provide representative images to support these observations.
-Figure 1D the vash-1 expression levels in EC seem very variable in this graph, therefore no conclusion can be drawn from this data, especially as the authors do not provide the p-values.
-In the introduction, the authors state: 'Although primary and secondary sprouts appear morphologically similar, with tip and stalk cells' - Please provide the reference that supports the claim that secondary sprouts have tip-stalk cells morphology/organisation.
-The authors refer the increased cell division phenotypes observed in the movies, however, the movie files have not been available to the reviewers.
Significance
This is an important study as uncovering the mechanistic details of angiogenic and lymphangiogenic negative regulators is of high value with the potential for therapeutic developments. To date, Vash-1 has been only studied in the context of tumour angiogenesis, vasculature in diabetic nephropathy and pulmonary arterial hypertension, and it remains unclear what is its role during development and how does it regulate vascular network formation. The tyrosination status of microtubule in endothelial cells is understudied. This study revealed, previously uncharacterised detyrosinated microtubules in endothelial cells in vivo. And further dissects how this process might be regulated, brings unique insights into the vascular biology field and beyond. Thus, delving into the cell biological mechanism such as microtubule dynamics and modification in vivo in embryo context is a significant step forward in setting new standards in the field.
I am developmental biologist who has experience in model organisms such as zebrafish and mouse. The main focus of my work is on developmental angiogenesis and lymphangiogenesis.
REFEREES CROSS-COMMENTING
After reading the other reviews comments, it seems that we all agree that this study is of high value to vascular biology field and beyond bringing novel findings.
Importantly the reviewers' comments are in line with each other and have identified several commonalities that should be addressed. Such as: Further validation of Morpholinos, or using alternative methods to replicate the findings. additional evidence that the observed phenotypes are primary due to vash-1 requirement within EC, and not due to the secondary effect in other cells such as CXCR4/SDF1 system and SVEP1, neurons or general delay of the embryos Further evidence of for VASH expression pattern the number of embryos used in the experiments, and how the data is represented.
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Referee #1
Evidence, reproducibility and clarity
The manuscript entitled "Vasohibin-1 mediated tubulin detyrosination selectively regulates secondary sprouting and lymphangiogenesis in the zebrafish trunk" by de Oliveira investigates the function of the carboxylpeptidase Vasohibin during the formation of the zebrafish trunk vasculature and reports a requirement of Vasohibin for secondary sprout formation and in particular the formation the lymphatic vasculature.
Having established the expression of Vasohibin in sorted ECs of 24 hpf embryos, the remaining study addresses the function of Vasohibin in this cell type. It is largely based on the use of a splice-site interfering morpholino. Particular commendable is the analysis, demonstrating that the KD of vash-1 indeed results in a significant reduction of detyrosination in endothelial tubulin. Findings in the vascular system then include: (i) the detection of increased division and hence supernumerous cells occurring selectively in 2nd sprouts from the PCV; (ii) an increased persistence of the initially formed 3 way connections with ISV and artery; (iii) reduced formation of parachordal lymphangioblasts and (iv) a reduced number of somites with a thoracic duct segment; (v) frequent formation of lumenized connections between PLs (where present) and ISV. To demonstrate specificity, the approach was repeated with a different morpholino and defects were partially rescued by MO-insensitive RNA.
Possible additional and relevant information could include data on a vash-1 promotor mutant to independently verify the MO-based functional analysis. Mutants would also allow analysis of further development, are the defects leading to the demise of the fish or is a later regeneration and normalization of the lymphatic vasculature observed? In addition, are other lymphatic vessel beds like the cranial lymphatics affected? PLs have been demonstrated to be at least partially guided in their movement by the CXCR4/SDF1 system and SVEP1. Has the expression of these factors been tested in vash-1 KDs? With regards to the frequently observed connections of PLs and ISVs in vash-1 morphants, can the proposed lumen formation of these shunts be demonstrated e.g. by injection of Q-dots or microbeads into the circulation? Concerning the mechanisms of these defects, is it possible to analyse the asymmetric cell division leading to 2nd sprouts in greater detail? Is the same number or are more cells sprouting form PCV and can the fli1ep:EGFP-DCX cell line in fixed samples be used to identify the spindle orientation in dividing cells?
Minor issues: Page 5, Mat & Meth, please spell out PTU at its first mention.
Page 6 Mat & Meth, Secondary sprout and 3-way connection parameters: The number of nuclei was assessed in each secondary sprouts (del s, singular) just prior...
Page 16, 8th line from bottom: Recent work demonstrated that a secondary sprout either contributes (add s) to remodelling a pre-existing ISV into a vein, or forms (add s)a PLs (Geudens et al., 2019).
Page 25, Legend to Fig. 2D-G: "...G,G' shows quantification of dTyr signal upon vash-1 KD..." Fig2 G,G' show immunostaining rather than quantification of the dTyr signal, which is shown Fig. 2H-J
Fig. 1D / Fig. 2H-J please increase weight of the error intervals and / or change colour for improved visibility
Significance
Taken together the manuscript is comprehensively written and the study provides a conclusive analysis of the MO-mediated KD of Vasohibin in zebrafish embryonic development presenting significant novel findings. Known was a generally inhibitory function of Vasohibin on vessel formation and its enzymatic activity as a carboxylpeptidase responsible for tubulin detyrosination, affecting spindle function and mitosis. New is the detailed analysis of the Vasohibin KD on zebrafish trunk vessel formation and the description of a selective impairment of 2nd sprout formation. The manuscript is of interest for vascular biologists.
REFEREES CROSS-COMMENTING
I fully concur with the comments of reviewer #2, all three reviews find that this study is of significant interest to the vascular biology community as the relevance of tubulin detyrosination for developmental angiogenesis has not been investigated. Also all three reviews highlight the potential limitations of the use of splice morpholinos (suggested alternatives include ATG morpholinos and CRIPR mutants), the requirement to provide further evidence for a endothelial cell autonomous defect and the need to clarify some of the data representation.
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Reviewer #3:
Kim et al. studied the roles of GATA1 binding and H3K27ac on chromatin interactions associated with binding of the architectural factor CTCF. The authors focused on a TAD and sub-TAD surrounding the human beta-globin locus. The sub-TAD that is flanked by tissue-invariant CTCF sites that form contacts with each other only in erythroid cells. Loss of GATA1 binding at the beta-globin enhancer (LCR) and perturbation of H3K27ac affects some CTCF associated contacts, both involving the sub-TAD as well as the (tissue-invariant) TAD boundaries.
The mechanisms by which tissue specific contacts are established are of great interest in the field. However, while this report provides some correlative data to support a link between H3K27ac and CTCF contacts, the study in its current form is too preliminary and failed to address alternative explanations.
1) A major concern for this study: the MEL/chr11 cell line might not be a suitable model system. The CTCF/Rad21 binding profiles in the MEL/chr11 seem quite different from normal human erythroid cells. Specifically, the signals of CTCF binding at C4, C5 and C6 are almost at the baseline (Fig2A/B and Fig3D/E) in MEL/chr11. However, those sites should have comparable CTCF binding strength with C3 and C7, (e.g. Fig2E). The diminished CTCF binding at the C4-C6 sites might affect the sub-TAD structures in MEL/chr11 cells.
2) Loss of GATA1 binding at HS2 or HS3 affects GATA1 binding at other sites as well. It is also possible that loss of GATA1 binding impairs binding by other nuclear factors that might be involved in sub-TAD formation. The same holds for histone modifications other than H3K27ac. This should be discussed.
3) Fig.2: the correlation between H3K27ac and CTCF contacts does not hold at all examined sites. For example, H3K27ac is reduced also at C7 and C3 that maintain contacts. Same in Fig.3 where increases in H3K27ac is also increased near CTCF sites whose contacts don't increase.
4) Fig.2: total H3 is reduced at C5 to the same extent as asH3. How was H3K27ac normalized?
5) Why were p300 inhibition experiments done in K562 cells? In Fig2E and Fig4D it seems that the H3K27ac signals are not concordant between MEL/chr11 and K562 cells. For example, in Fig2E, the H3K27ac at C3 site is very strong in the MEL/chr11; However, in Fig4D, the H3k27ac signal at C3 site is near baseline. The H3K27ac signals also suggest that the MEL/chr11 cell line is different from normal human erythroid cells and also the leukemia cell line K562. The different H3K27ac profiles at CTCF binding sites confound the interpretation.
6) Fig1E, the interactions between HS5-3'HS1 (C4-C5) were not significantly changed in the HS3/HS2 mutated MEL/chr11 cells. Actually this well known interaction between HS5-3'HS1 was not even detected by 3C in the WT control MEL/chr11 cells, contrary to previous studies. Again, this suggests that the MEL/chr11 cell line is not an ideal system for this study. The reduced interactions between beta-globin and C5/HS5 could be the result of loss of GATA1 binding but not CTCF. For clarity it would be better to plot the 3C data scale to the actual genomic distance.
7) The authors should make clearer distinctions throughout whether they are considering supposedly tissue-invariant CTCF contacts near the TAD boundaries or the tissue-specific sub-TAD. It appears that both can change upon their perturbations.
8) Data mining: "acetylation level was decreased remarkably in the boundaries of longer interactions (over than 100 Kb)" why is this remarkable? How does contact distance inform the role of H3K27ac? What is the correlation genome wide between H3K27ac and CTCF ChIA PET? This could be done using data sets from a variety of cell types.
9) Fig.6A: GATA1 knock down seems to affect H3K27 broadly and not any more extensively (or even less) at CTCF sites.
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Reviewer #2:
The authors analyzed long and short range chromatin interactions in erythroid cell lines after perturbation of globin associated enhancers, histone acetylation, GATA1 expression, or CBP/p300 expression. The data show that CTCF binding sites are associated with high levels of H3K27 acetylation in the context of short range interactions. Deletion of GATA1 binding sites in the human LCR in hypersensitive sites 2 or 3 caused a reduction in H3K27ac at nearby CTCF sites and impaired the interaction of those sites, without interfering with the binding of CTCF or the cohesin subunit Rad21. Treatment of cells with TSA or inhibition of CBP/p300 or GATA1 expression had a similar effect.
The manuscript provides interesting and novel observations with respect to GATA1 binding and H3K27ac at nearby CTCF sites. However, the mechanism(s) by which GATA1 modulates H3K27ac levels at CTCF sites remain(s) unknown.
Specific comments.
1) One possible interpretation of the results, which has not been addressed by the authors, is that the reduction in histone acetylation levels may modulate the stiffness of chromatin, which may particularly affect short-range interactions.
2) Figure 3: TSA treatment also affects other histone acetylation events. This should be mentioned. Furthermore, the data in Figure 3D are not consistent with data in Figure 1E with respect to the control cells. In Figure 1E it is shown that the anchor C5 interacts with beta and C6, Figure 3C shows no interactions between these elements.
3) Figure 4: There is no description of how the CBP/p300 depleted cells were generated. Was this a single shRNA? If so, it would be important to repeat the experiment using a second shRNA targeting a different region of the RNA to avoid off-target effects. Furthermore, it would be important to show the CBP/p300 binding pattern across the globin locus and the CTCF sites. Does H3K27ac at the CTCF sites correlate with CBP/p300 binding? These data should be available from K562 cells.
4) Figure 6: there is no description of how the GATA1 depleted cells were generated. Again, was this a single shRNA?
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Reviewer #1:
In this study, Kim et al described the GATA1-dependent histone H3K27ac on a subset of CTCF binding sites and CTCF-mediated chromatin interactions in erythroid cells. The authors generated a GATA1 binding site deletion in MEL cells and observed impaired CTCF-mediated interactions around the beta-globin gene cluster. They further modulated H3K27ac by TSA treatment of CBP/p300 knockdown, and noted that altered H3K27ac affected interactions between a subset of CTCF sites. The authors further performed global correlation analysis between public ChIA-PET and H3K27ac in K562 cells with or without GATA1 depletion, and presented evidence supporting a modest effect of H3K27ac at some CTCF sites upon depletion of GATA1 in K562 cells. Based on these findings, the authors concluded that GATA1-dependent H3K27ac mediates erythroid-specific chromatin interactions between CTCF sites.
Overall, this manuscript provides molecular details of the role of GATA1-mediated transcriptional programs in regulating chromatin interactions between CTCF sites. The experiments related to GATA1 binding site mutations were well designed and executed. The authors also make use of existing public datasets to extend the analysis to global scales. However, the current study falls short in providing strong evidence to support several conclusions due to a combination of insufficient data and suboptimal data quality. The overall conclusions that GATA1 regulates chromatin interactions through H3K27ac-mediated effects do not significantly extend our current understanding based on previous studies in the authors' group (Kang et al., 2017 BBA 1860:416) and others (Hsu et al., 2017 Mol Cell 66:102). The lack of replicate experiments for several ChIP-seq studies limited the robustness of relevant conclusions.
In summary, although the current study provides additional insights into the role of GATA1-dependent transcriptional programs in regulating CTCF-mediated chromatin interactions, the overall findings were not developed in sufficient depth in light of previous studies and some conclusions were not sufficiently supported by the evidence.
Major points:
1) The authors previously showed that interactions between CTCF sites associated with sub-TADs are dependent on the binding of GATA1 to LCR enhancers (Kang et al., 2017 BBA 1860:416). In this study, the authors provided further details of the relationship between GATA1 binding, H3K27ac, and CTCF-mediated interactions. These data provide additional insights into the molecular mechanisms of GATA1-mediated effect on chromatin interactions, although they do not add significantly to previous studies from these authors and others (e.g. Hsu et al., 2017 Mol Cell 66:102).
2) To determine the effect of GATA1 binding site KO on CTCF-mediated interactions, the authors performed 3C-qPCR analysis (Fig. 1E). Given the higher resolution and ability to quantitatively contextualize the data obtained from next-gen sequencing based 3C methods, I recommend these be done in the future. Similar for the studies in Figs. 3B-E and 4E.
3) Page 5, line 115-117. It is stated that 'when it is compared to the results from knocking down GATA-1...'; however, no results from GATA-1 knockdown were included for the comparison. These studies would be helpful to determine whether the effect is directly caused by loss of GATA1 binding at the LCR enhancers.
4) It is unclear whether GATA1 site KO also affected H3K27ac signals at other CTCF-bound chromatin regions besides the b-globin gene cluster (C3 to C7). For example, in Fig. 2E, it would be helpful to include C2 and C8 regions in the ChIP-seq track as controls. Additional analyses of global changes of H3K27ac and CTCF binding also will be helpful to determine whether the effect is limited to the b-globin gene cluster.
5) In Fig. 3, to determine the effect of H3K27ac on chromatin interactions, the authors treated the cells with the HDAC inhibitor TSA. A major caveat of these studies is related to the pleiotropic effects of TSA on global H3K27ac and gene expression, thus the indirect effects on chromatin interactions due to altered gene regulation and/or erythroid maturation. This limitation should be discussed.
6) In Fig. 5, it would be important to include analyses by separating CTCF binding sites depending on whether or not they are associated with TAD boundaries. Is there correlation between the range of CTCF-mediated interactions and TAD boundary? These studies will provide a global assessment of the findings based on the beta-globin gene cluster in Figs. 1-4.
7) The effects of GATA1 knockdown on H3K27ac signals at CTCF binding sites at the global or selected regions are marginal (Fig. 6A, C,D, noting the scales of y-axis not started with 0 in Fig. 6C,D). It is unclear whether this is due to suboptimal data quality (see below) or limited effects at a global scale. These results raise questions about the extent to which the GATA1-mediated effect on H3K27ac at CTCF sites and CTCF-mediated interactions in erythroid cells.
8) There are concerns about the quality of several genomic datasets including H3K27ac ChIP-seq that may limit the robustness of relevant analyses. Specifically, the quality of H3K27ac ChIP-seq in K562 cells is suboptimal and no apparent H3K27ac enrichment is noted at the CTCF-binding sites (C3, C4, C6 and C7). Comparing Fig. 2E with Figs. 4D and 6A, the signal-to-noise ratio is highly variable in different experiments. No replicate experiment or quality control analysis was provided.
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Preprint Review
This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.
Summary
This study investigates the relationship between the binding of the transcription factor GATA-1 and histone acetylation on CTCF sites in the same genomic region. Although the reviewers find this work interesting, they raise concerns about the strength of the novel conclusions that can be drawn at this stage. Specifically, the reviewers are concerned about the strength of the data supporting a specific role of GATA1 binding in regulating CTCF-associated sub-TAD interactions (Reviewer #1 and #3), the lack of technical details on several key experiments (Reviewer #1, #2 and #3), the lack of consideration of alternative interpretations on GATA1- and/or H3K27ac-mediated chromatin regulation (Reviewer #2 and #3), and the use of MEL/chr11 cell line as the experimental model system (Reviewer #1 and #3).
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Reviewer #3:
The manuscript by Sahm et al. describes the transcriptomic comparison of breeders and non-breeders in two species of mole rats from genus Fukomys. The remarkable aspect of Fukomys mole rats is that the breeders live significantly longer than workers. The authors produced new breeder couples by pairing animals from different family groups and then compared their transcriptomes to non-breeders of the same age from the original colonies.
There were very few differences identified between breeders and non-breeders. Most transcriptomic changes were confined to gonads and endocrine glands. This is somewhat unsurprising and these organs become active for breeding. The pathways that became activated were related to ribosome biogenesis, protein translation, and MYC signaling, which all reflect physiological activation of these tissues in breeders.
Interestingly, some activation of these signaling pathways was also observed in non-gonadal tissues, which contradicts the common dogma that downregulation of these pathways is associated with lifespan extension. This is a remarkable result that suggests that short-lived model organisms may not correctly reflect signaling effects required for longevity in long-lived species. The authors also point out to higher glucocorticoid levels in workers and speculate that that may be showing Cushing's like syndrome.
Overall, this is an important study of high interest.
Items to address:
1) It is not clear how long were the animals maintained in a breeder status prior to analysis.
2) Figure 3A, the rationale for the comparison of changes in young breeder versus non breeder animals to changes that occur with age over time is unclear.
3) Figure 4, were the changes driven by gonads mainly? How many were non-gonadal? For example, in Figure 2, muscle showed 0 DEGs by status, but in pathway analysis we see upregulation of pathways. Please explain.
4) Table 2: TOR is downregulated in breeders while ribosome processes are upregulated, please explain.
5)The finding of Cushing's syndrome needs more support. Please consider comparing Cushing's related transcriptome changes as a whole to the changes observed in mole rats, otherwise this conclusion may need to be toned down.
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Reviewer #2:
Sahm et al. have analyzed gene expression of 22 samples across 15 tissues of Fukomys mole rat, and with these resources, they try to explain why breeders and non-breeders have different lifespans in those species. Below I highlight key aspects of the approach (and related conclusions, or lack thereof) that I believe represent serious issues. One positive note, again, the questions and data of this paper that I believe are highly interesting and important if the author can pursue it in a more focused and solid way.
1) First, in my opinion, the discussion of the paper is not well synthesized, and the content does not help the reader for the aim of study and of what is discussed in the title (e.g., HPA) and abstract. While the entire discussion fails to build logics between HPA stress and aging in mole rats, it tells the story of intervention and hypothesis testing instead, with the last of the results immediately jumping into comparisons of positive selection genes and DEG (It is unclear why it is relevant even one gene is found under positive selection).
2) Given hundreds of samples have been sequenced in the paper, there is no extensive examination of batch effect, which could ultimately, in its present form, put all the results (e.g., DEG analysis, pathways enrichment, multifactors analysis) and conclusions at high risk. Particularly, the PCA analysis has indicated the species, tissue, and the combination of both variables accounted for 98.4 % of the total variance in the data set--it becomes more important to know how the authors have organized the sequencing strategy.
3) Unsophisticated use of enrichment analysis on pathways likely leads to misleading conclusions - a large portion of the analyses presented use an unreasonable approach to identify genes with expression shift across tissues and species to make conclusions that I believe are largely misleading about genes important for longevity. The authors identify pathways that have overall (gene-wide) changes in gene expression as p values looks like a potential indication of expression shift. While this approach MAY be lucky enough to catch a few of these 'true positives", the VAST majority of what it will identify will be pathways with global accumulated changes (which is what the point would be), but rather small pathways or pathways fully of with wired p values or undetectable expression (and thus perhaps some of the least important genes). Then, the extension of these approaches to pathways further muddies the waters of any discussions. The approaches for detecting affected global pathways have been the subject of a very large body of literature, and there are well developed hierarchical/empirical models for testing these hypotheses that the authors have not referred to. And, of course, it is strongly encouraged that the authors formulate a new model/index to reevaluate pathways analysis as the data and experimental design is unique compared to other studies.
4) Last but not the least, small KEGG set, i.e., KEGG, that with very few genes could also be confusing such whole categories analysis. The author should check this potential bias using random sampling 'pseudo-KEGG set' with the same gene number and/or identify a threshold to filter each true KEGG category considered.
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Reviewer #1:
This is a primarily descriptive study reporting gene expression differences between breeding and non-breeding individuals of two long-lived Fukomys species. These mole rat species are interesting for this type of analysis because it has been previously shown that breeders live ~2-fold longer than non-breeders. Thus, it may be possible to learn about mechanisms that determine longevity by studying differences between breeders and non-breeders. Although the manuscript is primarily descriptive, there is value in observational data sets such as this, which can be hypothesis generating and can spur future, more mechanistic studies. A lot of analyses are presented - with many different comparisons of differentially expressed genes, GO terms, etc. - but they have not yet produced a lot in terms of true biological insight. As it was largely limited to gene expression, it is also a bit one-dimensional. The data will likely be of interest and value to scientists who study the comparative biology of aging and systems biology of aging, but without additional biological insights derived from the data, the manuscript will probably not capture the interest of a broad scientific audience or even the broader gerontology community.
My largest concerns with the content of this manuscript are related to interpretation of the data. While this type of comparative gene expression analysis can generate hypotheses, it cannot strongly support or refute causal relationships without additional experimentation. The authors repeatedly appear to interpret correlative observations with causation and make claims that overreach the data. The title itself is a good example of this where the authors claim "the HPA stress axis shapes aging rates". There is no direct evidence to support this claim or others similar in nature throughout the manuscript.
Another area of overinterpretation is with respect to the relevance of these findings as a test of the validity of prior aging theories or mechanisms. It seems very likely that this is a somewhat unique evolutionary case where upon the switch from non-breeder to breeder there is a dramatic rewiring of physiology at many levels (transcriptional, translational, post-translational, metabolome, epigenome, etc.). Simply because this switch does not match patterns seen in longevity interventions such as caloric restriction, reduced GH/IGF-1 signaling, or mTOR inhibition does not refute or call into question literature describing potential mechanisms for how those interventions act. It likely simply reflects that this is a different path to achieve longevity.
Specific comments:
- The title is problematic as described above.
- The first sentence in the abstract is problematic as it implies causality between sexual activity/reproduction and longevity. Sexual activity/reproduction are associated with life expectancy. It is interesting to consider whether these two things could perhaps be uncoupled in this animal, as has been shown for reduced fecundity and longevity in invertebrate models.
- The phrase "oppose crucial findings" in the abstract is also problematic. First, the word "crucial" does not seem to make sense in this context. What makes them crucial? Second, it is intuitive and expected, indeed perhaps required, that reproduction would be associated with anabolic processes, and this study does not show causality between the observed changes in these processes and longevity, so it does not actually "oppose" the prior findings in genetic models with reduced IGF-1/GH signaling where lifespan is extended.
- I have a problem with the premise that it is possible to "confirm or falsify" results from cross-species or intra-species studies through the type of approach taken here as implied in line 47. Confirming or falsifying results implies something about the quality of the prior data itself. I assume what the authors mean is confirming or falsifying the underlying hypotheses or assumptions. Even that is questionable, however, since the mechanisms by which longevity are determined could simply be different within species versus across species versus cases like this where you have dramatically different life expectancies in breeders versus non-breeders.
- I thought the discussion of the GH/IGF-1 results was fairly balanced, but I would encourage the authors to consider more deeply the within species versus across species observations in the literature. The evidence for reduced GH/IGF-1 increasing lifespan comes from within species studies and appears to hold true from worms to dogs and likely in humans as well, although the correlation between body size and lifespan is a bit more complicated in humans for obvious reasons. Within species, smaller individuals who have reduced GH/IGF-1 tend to live longer - that's a correlation. In worms, flies, and mice a reduction in growth signaling has been shown to be sufficient - and very likely causal - for enhanced longevity through genetic and pharmacological studies. The comment that these interventions are all performed during development is not exactly true - in mice rapamycin at least works in adulthood and even when only given transiently or intermittently in adulthood. Across species, larger species tend to live longer. Perhaps the mechanisms going from non-breeders to breeders more resemble the evolutionary longevity strategies that have been taken at the species level rather than the mechanism that appear to determine longevity within species. Personally, I don't see this as a contradiction or a controversy within the field.
- I find the "short-lived" versus "long-lived" species argument to be overly speculative and arbitrary. Interventions such as CR, mTOR inhibition, reduced IGF-1, etc. extend lifespan at least from worms to mice, which is a >50-fold difference in lifespan. Mice to people is ~30-fold. Compared to mice, worms are shorter-lived (by a fold difference metric) than mice are compared to humans.
- The authors state that breeders and non-breeders have "massively diverging aging rates", but I think it is important to keep in mind that differences in lifespan do not necessarily imply differences in aging rate. Especially going from the long-lived state to the short-lived state. Perhaps there is good evidence that functional and molecular declines and diseases/pathologies of aging are accelerated in the non-breeders and delayed in the breeders, which would support this assertion, but this is unclear from the manuscript.
- The phrase "unilaterally described as harmful" in the conclusion is simply not true. GH/IGF-1 signaling limits lifespan in worms, flies, and mice but none of the papers cited unilaterally claim that it is harmful. In fact, some of those same papers note that high GH/IGF-1 signaling often confers a selective advantage in terms of faster maturation and reproduction. So, at the species level, this is beneficial. Even at the individual level, high GH/IGF-1 may be associated with better outcomes in the wild where predation is a factor. High GF/IGF-1 in these species is detrimental (only?) in the context of aging/longevity in a relatively safe laboratory environment at the individual level.
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Preprint Review
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Summary
Mole-rats live in social colonies that contain two breeders and many non-breeding workers. This study looks at gene expression between breeding and non-breeding individuals of two long-lived mole-rat species in the genus Fukomys. The large lifespan difference between breeders and non-breeders has made these animals an important model system for the study of aging, and the data collection reported in this manuscript is very impressive. The paper is descriptive data, with limited data analyses and experimental support for the conclusions. However, given the scale and rarity of the dataset, it will be a valuable resource for the aging research community once the quality and physiological relevance of the data are demonstrated by additional bioinformatic analyses and experimental validations.
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Reply to the reviewers
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Response to Reviewers
We are grateful to the Reviewers for their thoughtful and helpful assessment of our work. Below we include a point-by-point response to the Reviewers' critiques concerning the interpretation of our results and the power of our system to elucidate key dynamics of fission yeast homology-directed repair (HDR). We appreciate that the Reviewers judged our assay to be a valuable new tool for studying DSB repair in S. pombe. In general, the Reviewers also felt that our data provides new insights into homology search during HDR in fission yeast, including 1) that multiple DSB-donor encounters often precede repair and 2) that the activity of the helicase Rqh1, which dissolves strand invasion structures, alters the kinetics and efficiency of HDR in S. pombe. The Reviewers also raised several concerns with regards to 1) some technical aspects of the experimental approach, 2) the display of the data, and 3) the interpretation of the data. The Reviewers requested additional experiments to address the efficacy of our 5 minute observational time window and the rate of spontaneous damage in the Rqh1 null background, which we are able to provide in a resubmission. We will also clarify experimental details that the Reviewers found confusing in the original text. Lastly, the Reviewers highlighted minor needed figure adjustments that we will incorporate.
Point-by-point Response:
*Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Vines et al adapted a system that has been used in S. cerevisiae to study the homology search and homologous recombination repair events by live cell imaging. The authors utilized a system they set up in a fission yeast strain that has a fluorescently tagged endonuclease induced DSB site and monitored RAD52 focus formation in both haploid and diploid cells. The main findings presented are that multiple strand invasion events occur during DSB repair and the role of Rqh1 in promoting these multiple events. For example, cells with Rqh1 loss either have a single strand invasion event that quickly leads to repair or a very long extensive repair time. Overall the results are intriguing with new insight into DSB repair being presented.*
We appreciate the Reviewer’s recognition that our work provides new insights into homology-directed repair (HDR) in fission yeast.
The manuscript would benefit from having another system to help to support or validate the key findings and/or the use of some mutants to help uncouple the different roles of Rad51 and/or Rqh1.
While we agree with the Reviewer that using orthogonal approaches is always desirable, it is not clear what other experimental platform can address the dynamic events with single cell resolution that underlie our observations here; indeed, this was the motivation behind designing this new approach. However, we will provide additional, detailed context to support our findings in the revised manuscript that highlights how orthogonal experimental strategies (e.g. DSB repair outcome assays) already in the literature (e.g. Hope et al., PNAS, 2006) are consistent with our findings. Importantly, however, there is no other population-based system we are aware of that could demonstrate, for example, that Rqh1 shows two different behaviors in individual cells (repair failure and more rapid repair). See more in response to comment 7, below.
\*Major comment:**
1) In Figure 1C, and also Figure 2D, the RAD52 focus observed does not appear in the same location as the LacO cassette. I assume this is because of the way the images are cropped. It would be nice if the authors are saying that the RAD52 focus co-localizes with the inducible DSB location for this to be more readily apparent in the representative images. *
Co-localization events, indicated with the yellow circles, are assessed within raw 3D data that is then flattened for representation in 2D in the figures. For Figure 1C, the two events in the example cell indeed overlap in 3D space. However, in Figure 2D (cells lacking Rad51) we do not observe any colocalization events in the example (and there are no time points annotated with yellow circles).
2) In Figure 3A, the authors claim that the mean time to repair an endonuclease induced DSB is 50 min +/- 20 min. It is unclear whether or not this experiment is done in a diploid strain.
We apologize if we were not clear. All experiments presented in the manuscript are carried out in diploid cells. What varies is whether there is a lac operator integrated at one copy of Chr II (all experiments except Fig. 2A) or on both copies (only Fig. 2A). This will be clarified in the revised text.
3) In Figure 3, whether or not this experiment represents asynchronous cells can greatly influence the timing of DSB repair, as the cell cycle is a huge contributor to HDR repair.
We agree with the Reviewer - the cell cycle has a critical influence on DSB repair mechanism. The diploid fission yeast in which we induced and observed DSBs are indeed asynchronous. However, in fission yeast, which spend over 80% of their cell in G2, we can assess cell cycle by morphology; cytokinesis coincides with the beginning of G2, which then persists until mitotic entry (which is also very obvious from the nuclear shape as visualized by Rad52-mCherry). Moreover, we previously found that HO endonuclease only induces DSBs during S phase (Leland et al., eLife, 2018). Given this, for individual cells we observe site-specific DSBs beginning in late S and early G2 phases and all of our analysis is done at this phase of the cell cycle. These observations are further validated by the observation that an HO-induced DSB undergoes very high rates of gene conversion in fission yeast (Prudden et al, EMBO J., 2003).
4) In Figure 3D, since a major finding of the paper is that there are multiple invasion events, it would be nice to show some representative images of a few cells where multiple pairings occur.
In Supplementary Figure 2A, we provided an example of a cell with multiple encounters between the DSB and donor. This will be more clearly highlighted in the revised text.
5) It is known from Eric Greene's work that RAD51 mediated homology search can do multiple samplings of 8-9 nucleotide segments. Have the authors considered the area around the DSB site and how many potential pairing sites there might be in this region? Is it possible that having a LAC array with repeated segments might be influencing this the pairing since there would be multiple templates?
We acknowledge that the homology of the region surrounding the DSB is important for faithful recognition of a homologous donor and that there could be many pairing sites surrounding our induced DSB after end resection. Such local sampling, however, would not be discernible due to the resolution of the light microscope (>0.2µm). We will address this noteworthy point during our discussion in the revision. Importantly, we placed the lacO array over 3 kb away from the locus where the HO recognition site is integrated on the homologous chromosome to attempt to avoid exactly the Reviewer’s concern.
6) It would aid the reader if there were some picture schematics of what the authors think is occurring throughout the paper in the Figures. Since this is a results/discussion, this approach would be appropriate in lieu of a model figure at the end (which would also be very nice).
We agree that diagrams would aid in communication of our hypotheses and interpretations, and these will be included in the revision.
7) Since the multiple strand invasion events is a major finding of the paper, it is important to test the hypothesis that multiple strand invasion events are occurring a different way. A few ideas would be to examine Lorraine Symington's work on BIR where she observes multiple template switching events (Smith, CE, Llorente, B, Symington, LS (2007) Nature, 447(7140): 102-105) or something analogous to Wolf Heyer's recent study in Cell on template switching that the authors already cited. Another idea is to try a RAD51 mutant. For example, Doug Bishop's group has created a RAD51 mutant that uncouples the homology search from strand exchange, Rad51-II3A mutant (Cloud, V et al (2012) Science, 337(6099): 1222). Perhaps a mutant like this might be able to further support the key finding here.
While our findings share parallels with the works raised by the Reviewer, we would argue that there is a fundamental difference between BIR-type assays and the one we present here, namely that we are visualizing multiple strand invasion events at the homologous chromosome in a normal, high fidelity repair event rather than multiple strand invasion events during BIR, which frequently result in translocations. Moreover, as the two chromosomes are perfectly homologous in our assay, we cannot leverage sequencing to reveal past strand invasion events that took place during HDR. We also cannot, unfortunately, access multiple simultaneous strand invasion events due to the diffraction limit of the light microscope. We concede that it would be informative to further dissect strand invasion using tools such as the Rad51-II3A mutant described in budding yeast in work referenced above by Reviewer #1 and developed in fission yeast by Sarah Lambert’s group (Ait Saada et al., Mol. Cell, 2017). However, with the present limitations on our laboratory access and the timeline necessary to carry out this experiment, we feel this is currently beyond the scope of this work.
8) It is surprising that Rqh1 doesn't have a role in DNA end resection since this is a conserved function from budding yeast to man. Would similar results to what is observed in Figure 4 be observed in a Dna2 or Exo1 mutant?
We acknowledge that Rqh1 orthologs in other organisms (BLM/Sgs1/etc.) have been shown to contribute to DSB end resection. However, previous work from our group indicates that Rqh1 is entirely dispensable for long-range resection in fission yeast (Leland et al., eLife, 2018). Interestingly, in this work we also demonstrated that it is only upon loss of either the 53BP1/Rad9 orthologue Crb2 or Rev7 that Rqh1 is able to compensate for loss of Exo1. It remains unclear whether this is a peculiarity of fission yeast (perhaps because they rely heavily on HR due to extensive time in G2) or if it is a direct consequence of the long G2 itself. Regardless, we demonstrated that cells lacking Exo1 cannot generate sufficient ssDNA tracts to load visualizable Rad52-mCherry (Leland et al., eLife, 2018). Given this, we cannot address this genetic background in this assay. The essential role for Dna2 in replication has also precluded its analysis.
\*Minor comment:**
1) As mentioned in the first line of the abstract, HDR is generally considered error-free as opposed to a pathway that "can be" error-free. *
We acknowledge that HDR (and more specifically HR) is often error-free, but there are notable exceptions such as when a non-homologous donor is utilized for repair or when the polymerases engaged during repair incorporate errors (work from Haber and colleagues). We will expand and clarify this sentence in the revision.
2) In Figure 2D, it is unclear whether this experiment is done in diploid cells. The rest of the figure is in diploid cells but two LacO cassette are not present past the first frame. Please clarify in the legend and/or figure panel. As mentioned above, this is also confusing in Figure 3.
As above, we monitored repair events in diploid cells only – this will be clarified in the revised text.
*Reviewer #1 (Significance (Required)):
The most important advancement in this paper is that multiple strand invasion events occur during homologous recombination and the role of the Rqh1 in this process. Rqh1 is important protein whose mutation is implicated in human disease such as Bloom syndrome and cancer. In addition, misregulation of double-strand break repair and particularly of Rad51 is associate with cancer. Therefore, understanding the basic mechanisms of how Rad51 mediates double-strand break repair and the role of Rqh1 in this process is critical for understanding fundamental aspects of cancer development. * We appreciate the Reviewer’s assessment of the impact of this work.
*Reviewer #2 (Evidence, reproducibility and clarity (Required)):
In this study, Vines et al developed a microscopy-based assay to determine the kinetics of a site-specific interhomolog repair event, in living fission yeast cells. They detect efficient homology search and homology-directed repair in the system. They also observe that repair is likely to involve multiple site-specific and Rad51-dependent co-localization events between the DSB and donor sequence, suggesting that efficient inter-homologue repair involves multiple strand invasion events. Loss of the RecQ helicase Rqh1 leads to repair through a single strand invasion event. However, failure to repair is more frequent in rqh1 mutants, which could reflect increased strand invasion at non-homologous sites.
Overall, I find the approach to investigate homology search and homology-directed repair using live cell imaging interesting and potentially very informative. The ability to observe the process in living cells, and with high temporal resolution, complements a variety of previous studies that employ more indirect approaches to invoke similar models. In particular, previous work by the Heyer, Lichten and Hunter laboratories, in budding yeast, has established that Sgs1 promotes non-crossover recombination by acting as a quality control in the maturation of HR intermediates. In this sense, while newly described here for fission yeast, it is not unexpected that homology-directed repair involves multiple strand invasion cycles. In my opinion, the strength of the work is the method/approach, rather than the specific conclusions made (even though I think that it is important to know how fission yeast cells perform homology search).*
We thank the Reviewer for their appreciation of the value that cell biology can bring to the study of homology-directed repair. We wholeheartedly agree that this work is consistent with prior work on Sgs1. With regards to multiple strand invasion cycles, while we agree that there may be many in the field who could be unsurprised by this result, we would argue that 1) demonstrating this by direct visualization of individual DNA repair invents has clear inherent value and 2) many studying homology search itself (or who have modeled homology search in silico, for example) do not incorporate multiple strand invasion cycles in their thinking. Thus, we would argue that this work goes beyond a technical feat and will have impact beyond the approach.
*However, for the reasons detailed below, my general impression is that it isn't clear how robust the method is at delivering unambiguous information on the important questions asked:
1) The authors state that they have developed a system to monitor the 'dynamics and kinetics' of an engineered, inter-homologue repair event. With this in mind, I was expecting a more detailed exploration of the process of homology search. For example, what happens at shorter time scales? Is it possible that by imaging at every 5 minutes many of the events are missed? Could the authors be missing very transient events (especially in rqh1 mutants) by using an inappropriate time scale? *
We acknowledge that it would be ideal to observe DSB repair across a range of time scales in our system. For practical reasons we found it most valuable to choose the 5 minute time window since it was most amenable to observing the entire course of repair as often as possible in an asynchronous cell population (see our response to Reviewer #1’s comment 3 above) while mitigating photobleaching. However, we recognize that we sacrificed time resolution between acquired frames in order to do this. Like the Reviewer, we were also concerned that we were missing transient events due to an inappropriate timescale.
To address this, we acquired additional data in WT cells with greater time resolution with a focus on encounter frequency rather than time to repair (as the overall length of the usable movie that we can obtain is shorter). When imaging WT cells with a site-specific DSB at 2 minute intervals (2.5 times more frequently), we do observe a shift (of ~ 1 encounter per 30 minute window) toward more colocalization events with the donor sequence. We also observe, however, that more sampling leads to an increase in random encounters as revealed by similar analysis of the two lacO control strain as described in the manuscript. These data will be included in the revision and suggest that we may be missing some transient encounter events while using 5 minute time points. As noted by Reviewer #2, this could account for repair in the subset of WT and Rqh1-null cells in which we observed no encounters. We will acknowledge these caveats in the revision but would argue that our data support the conclusion that loss of Rqh1 decreases the number and/or lifetime of strand invasion events.
2) Another point relates to the Rad52 signal/foci, which is central to the study. While it is clear to me what the authors consider to be a focus of Rad52, I am not sure how to interpret what has happens when Rad52 is as enriched throughout the entire nucleus as it is in the repair focus in the still before. For example, Figure 1C, 40 min vs 45 min. How do the authors interpret what is being visualised? Similarly, is the level of colocalization at 90 min really reflecting a specific enrichment of Rad52 at the DSB site? Much more of the Rad52 signal is away from the DSB. In other words, are quantitative criteria being used to assign colocalization events?
As described in our Methods and the text, we used specific criteria to define 1) whether DSBs are site-specific and 2) whether they are colocalized with the donor site. In the images indicated as “contrast adjusted” we have scaled each panel time point individually with respect to the pixel intensities (that is, the least and most intense pixels have been set the same value for each). This strategy allows us to convey relatively dim Rad52-mCherry foci, particularly early after DSB end resection. A consequence of this is that the apparent background for panels in which there is not a strong Rad52-mCherry focus will appear higher, while the background will appear relatively less at time points with a strong Rad52-mCherry focus. For this reason we also present the raw image (found above). It is important to emphasize that when we are applying co-localization criteria, we do so within a 3D stack of images to ensure that the Rad52-mCherry signal and lacO array GFP signal coincide. In 2D representation, however, we understand that this may appear less clear.
In the particular case of the colocalization in Figure 1C at 90 minutes that the Reviewer points out, it is more evident in the 3-D Z stacks that the surrounding mCherry signal apart from the colocalization with the lacO array is due to inhomogeneity in the background signal. Another contribution is that the lacO array signal often becomes delocalized during colocalization events (as evident in that 90 minute time point). Although this is an interesting observation, we are still investigating what activity may explain this response. We will address the caveats of our colocalization analysis more fully in the revision.
3) In the system described here, Rad52 foci form in only ~15% of cells. I think it would be important to rationalise this low number in the manuscript. Moreover, G2 Rad52 foci still form at considerable rates in cells without HO. I think it would be important that the authors provide some explanation on what this might reflect.
There are several considerations that we believe contribute to this observation, which we also documented previously in haploid cells (Leland et al., eLife, 2018). First and foremost, this assay is quite different from endpoint assays that involve induction of HO nuclease because we analyze only those events that happen immediately after additional of uracil to elevate HO endonuclease expression under the control of the urg1 promoter. Combined with the efficient repair of any DSB induced by leaky HO expression (taking less than an hour according to our data), we likely miss events that have already taken place or would take place later in other assay systems. Lastly, it is established that nucleosomes can prevent HO cleavage in its intrinsic role in budding yeast (Laurenson and Rine, Microbiol. Rev., 1992; Haber, Ann. Rev. Genet., 1998); we cannot rule out that cleavage at this particular site is less efficient due to intrinsic nucleosome stability. With respect to spontaneous DNA damage, most of this is short-lived and occurs in S-phase, likely due to replication stress, although we occasionally observe long-lived Rad52 foci in a sub-population of cells – this is in line with previous publications (Coulon et al., MBoC, 2006; Lorenz et al. Mol. Cell Biol., 2009; Sanchez et al., Mol. Cell Biol., 2012; Schonbrun et al., J Biol. Chem., 2013). We will provide a greater explanation of the observed induction rate in the revision.
\*Other issues to consider:**
4) In Figure 2D, the overlay does not show any green. It is possible that the green channel was not overlaid with the pink? *
We apologize for this error and very much appreciate the Reviewer noticing that it is missing from the merged image. This will be corrected.
5) In Figure 2D, the unadjusted images for Rad52 are very sharp. Did the authors perform contrast adjustment in the top panels? If so, this should be indicated. My current impression is that the data was duplicated by mistake.
The Rad52-mCherry data in Figure 2D was labelled correctly and not duplicated. Because cells lacking Rad51 accumulate extensively resected DSBs (and therefore abnormally high levels of Rad52 loading), the intensity of Rad52-mCherry is very high. For simplicity we will remove the contrast-adjusted Rad52-mCherry images in the revision.
6) I don't understand why is the time since nuclear division different is every single figure. For simplicity, it would be much better to start every figure at T=0.
We agree with the Reviewer. In the revision we will normalize all kymographs to begin at t=0 with the exception of the Fig. S1D (where we are visualizing the subsequent division).
*Reviewer #2 (Significance (Required)):
see above. Reviewer #3 (Evidence, reproducibility and clarity (Required)):
In this manuscript, the authors describe a system to monitor an inducible site-specific double-strand break (DSB) and the undamaged homologous locus during homology-directed repair in S. pombe cells. The authors show that the Rad52 focus on the induced DSB is more persistent than spontaneous Rad52 foci that form throughout the cell cycle. The persistent Rad52 focus intermittently colocalizes with the donor sequence labeled with LacI-GFP, reflecting multiple strand invasion events, and this colocalization requires the Rad51 recombinase. The authors report that the time to repair is dependent on the number of strand invasion events (colocalization of Rad52 and homolog), and that the initial distance between the induced DSB and the homolog predicts the time to their first contact, but does not predict the time to repair. Lastly, the authors claim that repair in rqh1Δ cells is bimodal, either failing to repair within the experimental time frame, or being more efficient than WT cells (which often involves a single colocalization event).
**These claims are supported by the data:**
1) Rad52 focus on the induced DSB is more persistent than spontaneous Rad52 foci that form throughout the cell cycle.
2) Multiple colocalization events between Rad52 focus and the donor sequence are frequent, and this colocalization is dependent on Rad51, which reflects multiple strand invasion events.
3) rqh1Δ cells have a lower rate of productive repair compared to WT cells. *
The key concern I have for this section is the noise in Rad52 images. For example, in Fig. 1C at 15 minutes, it looks like there is a Rad52 focus both before and after adjustment but the time point is labeled as not having a Rad52 focus. Conversely, in Fig. 2D at 60 minutes, it looks like there isn't a Rad52 focus but the time point is labeled as having a Rad52 focus. How did the authors determine the presence of a Rad52 focus? Additionally, it is difficult to assess colocalization of Rad52 and LacI-GFP in merged images (hard to see Rad52 focus in Fig. 1C merged and LacI-GFP in Fig. 2D merged).
The criteria that we established to indicate a Rad52-mCherry focus (as annotated by a pink circle and as explained in the Methods) is that it persists for at least three frames (>15 minutes). This was chosen because it is a characteristic of the HO-induced DSB but not of spontaneous DNA damage that occurs frequently during S-phase. Indeed, the numerous, small, and short-lived foci at the 15 minute time point in Fig. 1C referred to by the Reviewer occurs just 15 minutes after nuclear division and is perfectly characteristic of replication stress that is independent of HO endonuclease expression. Thus, the pink circles indicate a specific type of Rad52-mCherry focus that is relevant for the assay. We agree that the Rad52-mCherry focus in Fig. 2D at ~60 minutes is poorly visualized in the flattened image, but would like to emphasize that we assess the foci in the true 3D volume. With regards to the merged images, we will adjust the individual signals to make it easier for the reader to assess colocalization in the revision.
\*These claims are supported by weak data:**
1) The initial distance between the induced DSB and donor sequence predicts the time to their first physical encounter (Line 60). *
We agree with the Reviewer that our word choice (“predicts”) suggests a stronger relationship than is supported by the data. However, we also argue that there is nonetheless a meaningful correlation. We believe this is an important point to make because it supports prior work in budding yeast suggesting that relative position affects donor choice preference. We will edit this language in the revised text.
2) Repair efficiency is dictated by the number of strand invasion events (Line 61-62). Figures 3E and 3F technically have positive correlations that support the authors' claims but there is a lot of noise. I think the data needs to be more robust, especially considering the strong wording used to describe the data. A minor comment on Fig. 3F: why is there a data point with 3.5 encounters?
Again, we agree with the Reviewer that our word choice (“dictate”) is too strong given the data and we will edit the text accordingly. We thank the reviewer for noticing the error in Fig. 3F, which will be corrected.
\*These claims are not supported by the data:**
1) In the absence of Rqh1, successful repair requires a single strand invasion event (Line 63). *
We acknowledge that this is too strong a claim to make based on our data and will amend this language in the revision text. Specifically, and as outlined in our response to Reviewer #2 with regards to our imaging frequency, we will revise the manuscript to state that cells lacking Rqh1 are more likely to repair without a visualized colocalization event and/or they possess shorter lived strand invasion events. Importantly, repair outcome assays indicate that cells lacking Rqh1 display elevated gene conversion rates rather than non-HDR-mediated repair (Hope et al., PNAS, 2006). Thus, we do not expect that the lack of colocalization reflects NHEJ but rather our inability to “catch” the colocalization event with the temporal resolution we can achieve.
2) rqh1Δ cells that complete repair are more efficient than WT cells and often involve a single colocalization event (Line 178-179).
As for the above, we agree that our claim that rqh1Δ cells “often” involve a single colocalization event is too strong a claim based on our data. We will amend this language in the revised text.
Fig. 4A shows an example of a rqh1Δ cell with productive repair but without any colocalization with the homolog, which contradicts the statement that successful repair requires a single strand invasion event in the absence of Rqh1. If the authors interpreted the single continuous presence of Rad52 focus during time-lapse as evidence of a single strand invasion event, then it would nullify using multiple colocalization events as evidence for multiple strand invasion events. In other words, the data in Fig. 3D that clearly displays multiple colocalization events in individual cells during repair can no longer be evidence of multiple strand invasion events since those cells all had one continuous presence of Rad52 focus.
We believe that we understand the confusion that the Reviewer is articulating in their comment and apologize that we have not been clearer in explaining our interpretation. For this site-specific DSB to be repaired, we expect that it must either 1) engage with the homologous chromosome to be repaired by HR/BIR or 2) be repaired through an alternative pathway – at this non-repetitive, resected locus this would likely be a microhomology-mediated (alt-) NHEJ mechanism. However, prior analysis of repair outcome in a model of interhomologue repair in the absence of Rqh1 (Hope et al., PNAS, 2006) demonstrates an increase in cross-over HR events rather than end joining events, arguing that interhomologue HR still dominates (and with increased CO to NCO frequency). We interpret the continuous presence of a Rad52 focus to only reflect that a DSB has been subjected to resection and has not yet been repaired. Taking these two points together, within the lifetime of a Rad52-loaded DSB it can either 1) never colocalize with the donor sequence and fail to repair (as in cells lacking Rad51, Fig. 2D-F) or 2) undergo strand invasion (and therefore colocalization) at least one time (but possibly multiple times) to allow for HDR to occur. However, we agree (and must clarify in the revision) that we often infer that at least one strand invasion event has taken place to support successful HDR when we do not capture the event at our experimental time resolution. Based on the additional data at shorter timescales that we will add to the revised manuscript (as outlined in the response to Reviewer 2, point 1), which demonstrates that we may in some cases be undercounting relevant colocalization events that are too brief to be accurately captured with 5 minute time resolution, we think the most parsimonious explanation is that cells lacking Rqh1 spend less time with the DSB and donor sequence colocalized prior to repair. We agree with the Reviewer, however, that we cannot say whether this reflects a shorter duration of interactions and/or a fewer number of interactions. We will therefore revise the manuscript to acknowledge this point.
Regarding the second claim, I think Fig. 4D only shows rqh1Δ cells with successful repair (since the longest repair time is 55 minutes, but it is not clear from the figure legend). It is not shown how many colocalization events these cells had in Fig. 4D, but there are 16 cells in Fig. 4D while there are only 2 cells with a single encounter (shown in Fig. 4F). With these numbers, it seems like rqh1Δ cells that complete repair are more efficient than WT cells but only few of these cells involve a single colocalization event.
The Reviewer is correct, Figure 4D does indeed show only rqh1Δ cells with the site-specific DSB that successfully repair – this will be clarified in the revision text. As described above in our response to Reviewer #2’s comment 1, it may be that we are missing colocalization events in rqh1Δ DSB cells. However, we would argue that our data do support that, for cells lacking Rqh1 that execute repair, there are fewer and/or shorter-lived colocalization events. Again, this will be made clear in the revision.
Also, how often do Rad52 foci form spontaneously in rqh1Δ cells and what is the duration? This data was provided for WT but not for rqh1Δ.
We agree that increased levels of genome instability (and therefore Rad52 foci) would present an issue – and indeed this has prevented us from analyzing some genetic backgrounds. However, we do not observe a significant increase in spontaneous Rad52-mCherry focus formation in rqh1Δ cells. This data will be included in the revision.
All of the data would have been more supported if the homologous chromosome would have been tagged. Such a configuration would really have helped the interpretation of the rqh1∆ data.
We agree that in theory it would be advantageous to have both copies of the chromosome tagged. Indeed, we attempted to leverage a different version of this experimental system with lacO arrays on both copies while inducing a DSB. However, the complexity of monitoring (and keeping the identity clear) for the two copies presented major challenges. Better would be two distinct arrays – an approach that has been used in budding yeast. However, to date many groups, including ours, have been unable to get TetO-TetR arrays to perform well in fission yeast.
* Reviewer #3 (Significance (Required)):
The significance of this work is the conceptual advance in the field of DNA repair. Homology search is an important process in homology-directed repair and is not fully understood. This study reports time-lapse data on the interaction between a DSB and its donor template during repair and provides insight into the kinetics of homology search. The audience for this manuscript is the field of DNA repair, and to a lesser extent, field of live-cell imaging.*
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Referee #3
Evidence, reproducibility and clarity
In this manuscript, the authors describe a system to monitor an inducible site-specific double-strand break (DSB) and the undamaged homologous locus during homology-directed repair in S. pombe cells. The authors show that the Rad52 focus on the induced DSB is more persistent than spontaneous Rad52 foci that form throughout the cell cycle. The persistent Rad52 focus intermittently colocalizes with the donor sequence labeled with LacI-GFP, reflecting multiple strand invasion events, and this colocalization requires the Rad51 recombinase. The authors report that the time to repair is dependent on the number of strand invasion events (colocalization of Rad52 and homolog), and that the initial distance between the induced DSB and the homolog predicts the time to their first contact, but does not predict the time to repair. Lastly, the authors claim that repair in rqh1Δ cells is bimodal, either failing to repair within the experimental time frame, or being more efficient than WT cells (which often involves a single colocalization event).
These claims are supported by the data:
1) Rad52 focus on the induced DSB is more persistent than spontaneous Rad52 foci that form throughout the cell cycle.
2) Multiple colocalization events between Rad52 focus and the donor sequence are frequent, and this colocalization is dependent on Rad51, which reflects multiple strand invasion events.
3) rqh1Δ cells have a lower rate of productive repair compared to WT cells. The key concern I have for this section is the noise in Rad52 images. For example, in Fig. 1C at 15 minutes, it looks like there is a Rad52 focus both before and after adjustment but the time point is labeled as not having a Rad52 focus. Conversely, in Fig. 2D at 60 minutes, it looks like there isn't a Rad52 focus but the time point is labeled as having a Rad52 focus. How did the authors determine the presence of a Rad52 focus? Additionally, it is difficult to assess colocalization of Rad52 and LacI-GFP in merged images (hard to see Rad52 focus in Fig. 1C merged and LacI-GFP in Fig. 2D merged).
These claims are supported by weak data:
1) The initial distance between the induced DSB and donor sequence predicts the time to their first physical encounter (Line 60).
2) Repair efficiency is dictated by the number of strand invasion events (Line 61-62). Figures 3E and 3F technically have positive correlations that support the authors' claims but there is a lot of noise. I think the data needs to be more robust, especially considering the strong wording used to describe the data. A minor comment on Fig. 3F: why is there a data point with 3.5 encounters?
These claims are not supported by the data:
1) In the absence of Rqh1, successful repair requires a single strand invasion event (Line 63).
2) rqh1Δ cells that complete repair are more efficient than WT cells and often involve a single colocalization event (Line 178-179). Fig. 4A shows an example of a rqh1Δ cell with productive repair but without any colocalization with the homolog, which contradicts the statement that successful repair requires a single strand invasion event in the absence of Rqh1. If the authors interpreted the single continuous presence of Rad52 focus during time-lapse as evidence of a single strand invasion event, then it would nullify using multiple colocalization events as evidence for multiple strand invasion events. In other words, the data in Fig. 3D that clearly displays multiple colocalization events in individual cells during repair can no longer be evidence of multiple strand invasion events since those cells all had one continuous presence of Rad52 focus. Regarding the second claim, I think Fig. 4D only shows rqh1Δ cells with successful repair (since the longest repair time is 55 minutes, but it is not clear from the figure legend). It is not shown how many colocalization events these cells had in Fig. 4D, but there are 16 cells in Fig. 4D while there are only 2 cells with a single encounter (shown in Fig. 4F). With these numbers, it seems like rqh1Δ cells that complete repair are more efficient than WT cells but only few of these cells involve a single colocalization event. Also, how often do Rad52 foci form spontaneously in rqh1Δ cells and what is the duration? This data was provided for WT but not for rqh1Δ. All of the data would have been more supported if the homologous chromosome would have been tagged. Such a configuration would really have helped the interpretation of the rqh1∆ data.
Significance
The significance of this work is the conceptual advance in the field of DNA repair. Homology search is an important process in homology-directed repair and is not fully understood. This study reports time-lapse data on the interaction between a DSB and its donor template during repair and provides insight into the kinetics of homology search. The audience for this manuscript is the field of DNA repair, and to a lesser extent, field of live-cell imaging.
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Referee #2
Evidence, reproducibility and clarity
In this study, Vines et al developed a microscopy-based assay to determine the kinetics of a site-specific interhomolog repair event, in living fission yeast cells. They detect efficient homology search and homology-directed repair in the system. They also observe that repair is likely to involve multiple site-specific and Rad51-dependent co-localization events between the DSB and donor sequence, suggesting that efficient inter-homologue repair involves multiple strand invasion events. Loss of the RecQ helicase Rqh1 leads to repair through a single strand invasion event. However, failure to repair is more frequent in rqh1 mutants, which could reflect increased strand invasion at non-homologous sites.
Overall, I find the approach to investigate homology search and homology-directed repair using live cell imaging interesting and potentially very informative. The ability to observe the process in living cells, and with high temporal resolution, complements a variety of previous studies that employ more indirect approaches to invoke similar models. In particular, previous work by the Heyer, Lichten and Hunter laboratories, in budding yeast, has established that Sgs1 promotes non-crossover recombination by acting as a quality control in the maturation of HR intermediates. In this sense, while newly described here for fission yeast, it is not unexpected that homology-directed repair involves multiple strand invasion cycles. In my opinion, the strength of the work is the method/approach, rather than the specific conclusions made (even though I think that it is important to know how fission yeast cells perform homology search). However, for the reasons detailed below, my general impression is that it isn't clear how robust the method is at delivering unambiguous information on the important questions asked:
1) The authors state that they have developed a system to monitor the 'dynamics and kinetics' of an engineered, inter-homologue repair event. With this in mind, I was expecting a more detailed exploration of the process of homology search. For example, what happens at shorter time scales? Is it possible that by imaging at every 5 minutes many of the events are missed? Could the authors be missing very transient events (especially in rqh1 mutants) by using an inappropriate time scale?
2) Another point relates to the Rad52 signal/foci, which is central to the study. While it is clear to me what the authors consider to be a focus of Rad52, I am not sure how to interpret what has happens when Rad52 is as enriched throughout the entire nucleus as it is in the repair focus in the still before. For example, Figure 1C, 40 min vs 45 min. How do the authors interpret what is being visualised? Similarly, is the level of colocalization at 90 min really reflecting a specific enrichment of Rad52 at the DSB site? Much more of the Rad52 signal is away from the DSB. In other words, are quantitative criteria being used to assign colocalization events?
3) In the system described here, Rad52 foci form in only ~15% of cells. I think it would be important to rationalise this low number in the manuscript. Moreover, G2 Rad52 foci still form at considerable rates in cells without HO. I think it would be important that the authors provide some explanation on what this might reflect.
Other issues to consider:
4) In Figure 2D, the overlay does not show any green. It is possible that the green channel was not overlaid with the pink?
5) In Figure 2D, the unadjusted images for Rad52 are very sharp. Did the authors perform contrast adjustment in the top panels? If so, this should be indicated. My current impression is that the data was duplicated by mistake.
6) I don't understand why is the time since nuclear division different is every single figure. For simplicity, it would be much better to start every figure at T=0.
Significance
see above.
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Referee #1
Evidence, reproducibility and clarity
Vines et al adapted a system that has been used in S. cerevisiae to study the homology search and homologous recombination repair events by live cell imaging. The authors utilized a system they set up in a fission yeast strain that has a fluorescently tagged endonuclease induced DSB site and monitored RAD52 focus formation in both haploid and diploid cells. The main findings presented are that multiple strand invasion events occur during DSB repair and the role of Rqh1 in promoting these multiple events. For example, cells with Rqh1 loss either have a single strand invasion event that quickly leads to repair or a very long extensive repair time. Overall the results are intriguing with new insight into DSB repair being presented. The manuscript would benefit from having another system to help to support or validate the key findings and/or the use of some mutants to help uncouple the different roles of Rad51 and/or Rqh1.
Major comment:
1) In Figure 1C, and also Figure 2D, the RAD52 focus observed does not appear in the same location as the LacO cassette. I assume this is because of the way the images are cropped. It would be nice if the authors are saying that the RAD52 focus co-localizes with the inducible DSB location for this to be more readily apparent in the representative images.
2) In Figure 3A, the authors claim that the mean time to repair an endonuclease induced DSB is 50 min +/- 20 min. It is unclear whether or not this experiment is done in a diploid strain.
3) In Figure 3, whether or not this experiment represents asynchronous cells can greatly influence the timing of DSB repair, as the cell cycle is a huge contributor to HDR repair.
4) In Figure 3D, since a major finding of the paper is that there are multiple invasion events, it would be nice to show some representative images of a few cells where multiple pairings occur.
5) It is known from Eric Greene's work that RAD51 mediated homology search can do multiple samplings of 8-9 nucleotide segments. Have the authors considered the area around the DSB site and how many potential pairing sites there might be in this region? Is it possible that having a LAC array with repeated segments might be influencing this the pairing since there would be multiple templates?
6) It would aid the reader if there were some picture schematics of what the authors think is occurring throughout the paper in the Figures. Since this is a results/discussion, this approach would be appropriate in lieu of a model figure at the end (which would also be very nice).
7) Since the multiple strand invasion events is a major finding of the paper, it is important to test the hypothesis that multiple strand invasion events are occurring a different way. A few ideas would be to examine Lorraine Symington's work on BIR where she observes multiple template switching events (Smith, CE, Llorente, B, Symington, LS (2007) Nature, 447(7140): 102-105) or something analogous to Wolf Heyer's recent study in Cell on template switching that the authors already cited. Another idea is to try a RAD51 mutant. For example, Doug Bishop's group has created a RAD51 mutant that uncouples the homology search from strand exchange, Rad51-II3A mutant (Cloud, V et al (2012) Science, 337(6099): 1222). Perhaps a mutant like this might be able to further support the key finding here.
8) It is surprising that Rqh1 doesn't have a role in DNA end resection since this is a conserved function from budding yeast to man. Would similar results to what is observed in Figure 4 be observed in a Dna2 or Exo1 mutant?
Minor comment:
1) As mentioned in the first line of the abstract, HDR is generally considered error-free as opposed to a pathway that "can be" error-free.
2) In Figure 2D, it is unclear whether this experiment is done in diploid cells. The rest of the figure is in diploid cells but two LacO cassette are not present past the first frame. Please clarify in the legend and/or figure panel. As mentioned above, this is also confusing in Figure 3.
Significance
The most important advancement in this paper is that multiple strand invasion events occur during homologous recombination and the role of the Rqh1 in this process. Rqh1 is important protein whose mutation is implicated in human disease such as Bloom syndrome and cancer. In addition, misregulation of double-strand break repair and particularly of Rad51 is associate with cancer. Therefore, understanding the basic mechanisms of how Rad51 mediates double-strand break repair and the role of Rqh1 in this process is critical for understanding fundamental aspects of cancer development.
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Preprint Review
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Summary:
VolcaNoseR is a plotting tool to make volcano plots from transcriptomics and proteomics datasets. It is easy to use, intuitive, interactive and reactive. However, VolcaNoseR does not provide any statistical analysis of the data, and it is therefore of limited applicability as it is likely that a user that is able to handle and analyse this type of data, is likely to be able to produce a scatter plot. The bigger hurdles with this type of analysis that could lead to new biological results are centered around the correct statistical analysis of the data, problems that the software does not address.
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Preprint Review
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Summary:
This manuscript describes the mechanism by which the transcriptional repressor ZBTB18 regulates the expression of sterol regulatory-element binding proteins (SREBP), transcription factors that control the expression of enzymes involved in fatty acid and cholesterol biosynthesis,in glioblastomas. The connection between core transcriptional regulation and tumor metabolism is an area of current interest. This manuscript uses immunoprecipitation and mass spectrometry to identify the well-characterized transcriptional corepressor CTBP2 as a new ZBTB18 interacting protein, which the authors show overlap in binding at many gene promoters. They further show that CTBP2 activates, while ZBTB represses the expression of some SREBP genes. The reciprocal regulation of ZBTB18 and CTBP2 has potential value in understanding the functional regulation of lipid biology. However, reviewers raised substantial concerns with the studies, as described below.
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Reply to the reviewers
We thank the reviewers for their comments and outline below how we plan to address them.
Reviewer #1 (Evidence, reproducibility and clarity (Required)): **Summary:** Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate). The authors here describe a method to modify bacterial artificial chromosomes (BAC) harbouring gene loci from eukaryotes. When wanting to modify a BAC an antibiotic selection cassette is often included alongside the desired mutation/modification to increase the number of successful recombinants in E.coli. Traditionally, this is removed in a second recombination process to leave only the desired modification. The novelty in the procedure described herein is to add a synthetic intron consensus sequence around the selection cassette, which eliminates the need for the subsequent removal of the antibiotic cassette from the BAC before transfection into mammalian cells, saving time and resources. The technique is clever in its simplicity and appears to function for a number of gene loci. The authors validated the correct functioning of the modified BACs for a number of genes using three main assays - transcript level, protein level and localisation. **Major comments:** *Are the key conclusions convincing?* The conclusion that the method described generates functional modified BACs is valid. *Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?* While the method is successfully employed in this study, its efficiency is not quantified in relation to the state-of-the-art as described in the introduction. One assumes it would be more efficient, but this has not been tested empirically in the paper. Does the inclusion of the synthetic intron sequence have an effect on the efficiency of modifying BACs compared to a more typical two-step positive/negative antibiotic selection cassette? *
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This is a good point that we did not directly address. In general, the efficiency is similar to that of integrating any cassette with selectable marker, as has been published (Poser et al 2008), and therefore also higher than the two-step counterselection method, which requires such a cassette integration in the first step alone. We will include new data specifically addressing the efficiency of our new method (see specifics below)
The functionality of this approach rests entirely on the ability of the target cell to correctly splice out the synthetic intron. The authors are aware of this potential problem as highlighted in the lines below, but do not make efforts to explicitly test splicing. On lines 224-225, the authors state "We cannot exclude that a small portion of synthetic introns within individual cells are misspliced". On lines 230-231 it is stated that "mis-spliced mRNAs are probably minimal and degraded by nonsense-mediated decay". On lines 215-217, the authors describe an "investigation of transgenic lines at the single-cell level" that suggests "the synthetic intron is correctly spliced out in all the cells of the population". How do the authors reach this conclusion? U2OS and HeLa cells are considered very "robust" and may not show detectable consequences when stressed with an increased level of nonsense-mediated decay. Further, many genes maintain a high level of expression that buffers them against small changes in transcription/splicing. The synthetic intron might have a bigger impact on more tightly regulated genes, so assessing the splicing rate would be essential if the authors wish to advocate their technique as generally applicable.
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We will assay for splicing efficiency as outlined below.
The ability of the synthetic intron to be removed from final transcripts depends on functioning splicing machinery. The authors might emphasise this issue, as spliceosome mutations are important fields of study and might not be compatible with this method.
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We can add this in the text
The authors used un-directed integration of each BAC under study. Therefore, it is hard to assess what effect the synthetic intron has, as the authors only ever assess the downstream levels of the correctly spliced, translated and localised protein. The authors themselves state that this can lead to clonal variations in expression of up to 2-fold and on line 250 that this variation "could compensate for synthetic intron effects", but make no effort to test this. Again, lines 267-268 highlight the potential dangers of potential effects of the synthetic introns, but do not test these. \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.* If not already performed, a large number of bacterial colonies should be screened for the correct modification and frequency of correct ones reported. This frequency - reported for at least three different modifications - would estimate what sort of efficiency this method provides. The modified region of each BAC should be sequenced and the results reported. The rate of exactly modified clones is important, in case of spontaneous or low fidelity integration of the antibiotic cassette. The percentage of transcripts that have the synthetic intron correctly spliced out should be measured for some of the BAC constructs used in the study. A direct head-to-head comparison of this newer method compared to other techniques, or even the authors' own previous two-step approach is necessary to assess the benefits of this method. Preferably, the experiment would be run in parallel with and without antibiotic selection applied, to show that it drastically improves chances of finding a correct clone. *
We will generate 3 new mutations in BACs and analyze both the efficiency of integration by PCR and accuracy via sequencing. In practice, we have observed that the efficiency is similar to any other cassette integration, such as a GFP tag (Poser et al Nature Methods 2008) or a counterselection cassette (Bird et al Nature Methods 2012) (80-90%). Integrating a mutation via the second step of the counterselection method introduces a further 20% decrease in efficiencies on average.
\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.* Repeating the transformation of the BAC and targeting cassette and assessing the recombination efficiency and sequencing should only require existing reagents and take less than a week or two to complete. Quantitative RT-PCR to assess the percentage of transcripts that have the synthetic intron spliced out would take a little more work. However, this should not be a considerable investment in time or resources for a standard microbiology laboratory and could be completed within a few weeks using modern techniques, such as that described in Londoño et al. 2016. Repeating all the experiments in parallel would be considerable work and would only be strictly necessary if the authors wish to emphasise the benefits of their method over the many others already in wide use. *
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We will use quantitative PCR to estimate the fraction of transcripts that correctly splice out the artificial intron for two clonal cell lines characterized in the study: RNAi-resistant AurA-GFP (Fig 4), and GTSE1-14A (newly introduced; see below). While the exact method described in Londoño et al 2016 will not be applicable due to the larger size of the artificial intron, we believe we can adapt it to detect different splicing events.
\Are the data and the methods presented in such a way that they can be reproduced?* Barring the omission of Table S1, which presumably includes exact information on the BACs modified and sequences used etc., there is sufficient other data and methods to allow the experiments to be repeated. Targeting the ESI procedure to the middle of exons is likely to have a bigger impact for smaller exons as the authors mention on lines 99-100. Making it clear which exon sizes for each gene were successfully targeted in this study would help give some idea of how significant a problem this might be. Perhaps Table S1 contains this information, but it was not provided. It would also help reviewers check the design strategies. *
We apologize for inadvertently failing to upload Table S1 on bioRxiv. It has been uploaded now as part of this submission process. This table indeed contains BAC and target sequence information, including the size of the targeted exon (and the 2 “new” resulting exons). Targeted exons range in size from 138bp to 1537bp, and “new” exons are as small as 48bp.
\Are the experiments adequately replicated and statistical analysis adequate?* The replication and statistically analysis of the data as presented appear adequate. Figure Legends should state the statistic used to generate error bars. *
This will be updated
\*Minor comments:** Specific experimental issues that are easily addressable. Are the promoters used in the vectors described universally functional? For example, is the PGK promoter functional in yeast? *
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The PGK promoter contained in the cassettes is a mammalian promoter, which has also been reported to work in flies.
\Are prior studies referenced appropriately?* The manuscript may benefit from the referencing of BAC modification techniques from a wider variety of groups, such as those using CRISPR-guided recombineering (Pyne et al. 2015). *
We will add citations of more techniques
\Are the text and figures clear and accurate?* The body text is very clear save minor typographical or grammatical errors. Regarding figures, some of the coloured text in Figure 1 is somewhat illegible when printed in grayscale. Line 278 - The acronyms LAP and NLAP are not defined/explained. Antibody section starting Line 282 may fit better next to Western Blot section. Figure 2C - The blot images would benefit from arrows to indicate expected sizes of proteins. Figure 3A - the graph may benefit from a dashed line at 100% to highlight that values are normalised to controls. Figure 4 - The differences between panels B & C are unclear. Figure 4E - The legend could provide a little more detail on cell cycle stage/status of the captured cells. *
All of the above will be addressed accordingly
\Do you have suggestions that would help the authors improve the presentation of their data and conclusions?* Lines 23-27 are somewhat unclear and feel out of context. Perhaps the authors could clarify this as a further advantage of using BACs instead of endogenous gene modifications. *
Thanks for the input, we will clarify this.
While not affecting the factual content of the paper, I would advocate that the authors format the method described in Figure S3 into a more detailed text based layout similar to that seen in a typical Nature Methods article. However, this may depend on the format required by any eventual publishing journal.
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We prefer the graphical protocol, but will discuss whether to add a text protocol with the journal editor.
That all of the work the paper was carried out in human cell lines and using human genes is a further caveat, but the authors admit this in the discussion and one would assume that most mammalian cells would respond similarly in their ability to splice out the synthetic intron. Reviewer #1 (Significance (Required)): \Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.* This work is a formal description of a newer method that could be useful for many of those employing bacterial artificial chromosomes in numerous studies, such as gene regulation. *Place the work in the context of the existing literature (provide references, where appropriate).* This work builds on methodology previously published by the authors - a counter-selection two-step procedure (Bird et al. 2011). It sets out to formally describe a method merely mentioned as "BAC intronization" in a later paper by some of the authors (Zheng et al. 2014). Other alternative one-step procedures are also available, but present a different set of challenges (Lyozin et al. 2014). Some newer approaches, such as those using CRISPR-guided recombineering (Pyne et al. 2015) or systems that combine CRISPR and positive/negative selection cassettes (Wang et al. 2016) may be slightly more efficient, but are also more complex in their design. Bird et al. 2011 DOI: 10/dv776q Pyne et al. 2015 DOI: 10/f7jx92 Wang et al. 2016 DOI: 10/f89db5 Zheng et al. 2014 DOI: 10/f5pkr6 *State what audience might be interested in and influenced by the reported findings.* As a technology paper this work should have interest from a broad field of research. While the use of BACs could sometimes be considered more traditional in light of the explosion in CRISPR-based genome editing capabilities, it is definitely seeing a resurgence as the limitations of CRISPR in modifying large regions of genome become more apparent. Therefore, technologies that accelerate the modification of BACs could prove increasingly useful. As category of audience, all those involved in significant recombineering or gene/genome engineering would potentially benefit. *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.* Synthetic genomics, synthetic biology, cancer cell biology, gene and genome engineering REFEREES CROSS COMMENTING I would agree with reviewer two's assessment that we both view the paper in a similar light. Reviewer #2 (Evidence, reproducibility and clarity (Required)): This is a methods-focused paper that presents a strategy to efficiently introduce mutations into a bacterial artificial transgene using synthetic introns. BAC-based methods have been an effective strategy for introducing trans genes into human cells to achieve near-endogenous expression, including extensive work from these authors. However, generating mutations and changes within the internal coding sequence presents some challenges for how to target these mutations and select for the mutated form. Here, the authors describe a way to overcome this by introducing synthetic introns into an adjacent sequence. This allows them to introduce a selectable marker and conduct the molecular biology without creating complications downstream for the functionality of the protein. This method is carefully described and presented. The authors also provide clear validation by using this to create RNAi-resistant versions of multiple different mitotic factors as well as creating targeted mutants that alter the functional properties of a protein. This work clearly takes advantage of other ongoing studies from these labs (including mutants and cell lines that appear to also have been described elsewhere), but the ability to combine these in a single paper and clearly describe the method provides a helpful advance and validation. Based on the description and data presented, I think that things are clear and carefully validated. As such, I do not have technical comments or concerns and I would be comfortable with this paper appearing in an appropriate journal in its present form. Reviewer #2 (Significance (Required)): This is a solid methods paper, but for considering the nature of the impact and significance of this paper, there are several things to note: 1.The BAC-based method does appear to be a powerful and effective strategy. However, beyond the work of Mitocheck and the authors that are part of this paper, this has not seen widespread adoption. It is possible that this current method may increase its usage due to the value of the targeted mutations within the coding sequence, but at present it is not a broadly used strategy. *
We agree that using BACs as transgenes has not seen widespread adoption as a tool on the broader cell biology community (although certainly beyond members of the Mitocheck consortium). This is likely because many erroneously think that it is a technique for specialist laboratories. We are trying to change this! For reasons outlined below, there is still an increasing desire for conditional analysis of mutated genes under physiological expression/regulation frequently not attainable via directed Cas9-based mutation. A major aim of this paper is thus to further simplify the methods for generating modified BAC transgenes.
2.This BAC-based approach (and also RNAi) are becoming increasingly replaced by the use of CRISPR/Cas9 genome editing. The absence of Cas9-based strategies in this paper limits the potential impact and reach of this paper. The authors do mention the possibility of using a similar synthetic intron strategy for use with Cas9 in the Discussion, and appear to have conducted some experiments. If possible, it would substantially increase the value of this paper if this data and strategy were also included in the Results section (acknowledging that this may still be a work in progress).
While some uses of BAC transgenes are in some cases better replaced by CRISPR/Cas9 techniques (i.e. GFP tagging), there are several occasions where using BACs are preferable: As stated in the text, RNAi-resistant BACs allow for conditional analysis of recessive mutations. Mutations in essential genes that are lethal will prevent growth and recovery of viable cells if integrated into the genome via Cas9. Additionally, deleterious mutations are prone to accumulate suppressive changes in chromosome integrity or gene expression during the procedure of selecting and expanding Cas9-modified cells for analysis, particularly in the genomically instable cancer cell lines frequently employed.
We use both BACs and CRISPR/Cas9 in our lab according to our needs.
We do have an ongoing project to apply this intronization technique to enable more efficient selection of CRISPR/Cas9 integrations. Preliminary results suggest that it works to allow selection of point mutations, but it is still being optimized, including a redesign of the cassette, and is not ready for publication.
3.The method is solid and well-validated, but there are no new results or insights presented in this paper from the work that is described (this is fine, just commenting for considering the right journal fit).
As “biological insights” gained as a result of this technique we had cited a couple studies that made use of the technique already (to functionally analyze a microcephaly-associated mutation in the centriolar protein CPAP at the single cell level in HeLa cells and neural progenitor cells (Zheng et al 2014, Gabirel et al 2016)). As a response to this critique to include “new biology” in this paper, we will add new unpublished data investigating a specific question: Is the cell-cycle-regulated disruption of the EB1-GTSE1 (microtubule plus-end tracking proteins) interaction in mitosis required for chromosome segregation fidelity? We have generated a GTSE1 mutant with 14 phosphosites mutated to alanine using this technique. We will present the effect on chromosome segregation.
REFEREES CROSS COMMENTING It appears that both reviewers are largely on the same page regarding this paper.
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Referee #2
Evidence, reproducibility and clarity
This is a methods-focused paper that presents a strategy to efficiently introduce mutations into a bacterial artificial transgene using synthetic introns. BAC-based methods have been an effective strategy for introducing trans genes into human cells to achieve near-endogenous expression, including extensive work from these authors. However, generating mutations and changes within the internal coding sequence presents some challenges for how to target these mutations and select for the mutated form. Here, the authors describe a way to overcome this by introducing synthetic introns into an adjacent sequence. This allows them to introduce a selectable marker and conduct the molecular biology without creating complications downstream for the functionality of the protein.
This method is carefully described and presented. The authors also provide clear validation by using this to create RNAi-resistant versions of multiple different mitotic factors as well as creating targeted mutants that alter the functional properties of a protein. This work clearly takes advantage of other ongoing studies from these labs (including mutants and cell lines that appear to also have been described elsewhere), but the ability to combine these in a single paper and clearly describe the method provides a helpful advance and validation.
Based on the description and data presented, I think that things are clear and carefully validated. As such, I do not have technical comments or concerns and I would be comfortable with this paper appearing in an appropriate journal in its present form.
Significance
This is a solid methods paper, but for considering the nature of the impact and significance of this paper, there are several things to note:
1.The BAC-based method does appear to be a powerful and effective strategy. However, beyond the work of Mitocheck and the authors that are part of this paper, this has not seen widespread adoption. It is possible that this current method may increase its usage due to the value of the targeted mutations within the coding sequence, but at present it is not a broadly used strategy.
2.This BAC-based approach (and also RNAi) are becoming increasingly replaced by the use of CRISPR/Cas9 genome editing. The absence of Cas9-based strategies in this paper limits the potential impact and reach of this paper. The authors do mention the possibility of using a similar synthetic intron strategy for use with Cas9 in the Discussion, and appear to have conducted some experiments. If possible, it would substantially increase the value of this paper if this data and strategy were also included in the Results section (acknowledging that this may still be a work in progress).
3.The method is solid and well-validated, but there are no new results or insights presented in this paper from the work that is described (this is fine, just commenting for considering the right journal fit).
REFEREES CROSS COMMENTING
It appears that both reviewers are largely on the same page regarding this paper.
-
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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). The authors here describe a method to modify bacterial artificial chromosomes (BAC) harbouring gene loci from eukaryotes. When wanting to modify a BAC an antibiotic selection cassette is often included alongside the desired mutation/modification to increase the number of successful recombinants in E.coli. Traditionally, this is removed in a second recombination process to leave only the desired modification. The novelty in the procedure described herein is to add a synthetic intron consensus sequence around the selection cassette, which eliminates the need for the subsequent removal of the antibiotic cassette from the BAC before transfection into mammalian cells, saving time and resources. The technique is clever in its simplicity and appears to function for a number of gene loci. The authors validated the correct functioning of the modified BACs for a number of genes using three main assays - transcript level, protein level and localisation.
Major comments:
Are the key conclusions convincing?
The conclusion that the method described generates functional modified BACs is valid.
Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?
While the method is successfully employed in this study, its efficiency is not quantified in relation to the state-of-the-art as described in the introduction. One assumes it would be more efficient, but this has not been tested empirically in the paper. Does the inclusion of the synthetic intron sequence have an effect on the efficiency of modifying BACs compared to a more typical two-step positive/negative antibiotic selection cassette? The functionality of this approach rests entirely on the ability of the target cell to correctly splice out the synthetic intron. The authors are aware of this potential problem as highlighted in the lines below, but do not make efforts to explicitly test splicing. On lines 224-225, the authors state "We cannot exclude that a small portion of synthetic introns within individual cells are misspliced". On lines 230-231 it is stated that "mis-spliced mRNAs are probably minimal and degraded by nonsense-mediated decay". On lines 215-217, the authors describe an "investigation of transgenic lines at the single-cell level" that suggests "the synthetic intron is correctly spliced out in all the cells of the population". How do the authors reach this conclusion? U2OS and HeLa cells are considered very "robust" and may not show detectable consequences when stressed with an increased level of nonsense-mediated decay. Further, many genes maintain a high level of expression that buffers them against small changes in transcription/splicing. The synthetic intron might have a bigger impact on more tightly regulated genes, so assessing the splicing rate would be essential if the authors wish to advocate their technique as generally applicable. The ability of the synthetic intron to be removed from final transcripts depends on functioning splicing machinery. The authors might emphasise this issue, as spliceosome mutations are important fields of study and might not be compatible with this method. The authors used un-directed integration of each BAC under study. Therefore, it is hard to assess what effect the synthetic intron has, as the authors only ever assess the downstream levels of the correctly spliced, translated and localised protein. The authors themselves state that this can lead to clonal variations in expression of up to 2-fold and on line 250 that this variation "could compensate for synthetic intron effects", but make no effort to test this. Again, lines 267-268 highlight the potential dangers of potential effects of the synthetic introns, but do not test these.
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.
If not already performed, a large number of bacterial colonies should be screened for the correct modification and frequency of correct ones reported. This frequency - reported for at least three different modifications - would estimate what sort of efficiency this method provides. The modified region of each BAC should be sequenced and the results reported. The rate of exactly modified clones is important, in case of spontaneous or low fidelity integration of the antibiotic cassette. The percentage of transcripts that have the synthetic intron correctly spliced out should be measured for some of the BAC constructs used in the study. A direct head-to-head comparison of this newer method compared to other techniques, or even the authors' own previous two-step approach is necessary to assess the benefits of this method. Preferably, the experiment would be run in parallel with and without antibiotic selection applied, to show that it drastically improves chances of finding a correct clone.
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.
Repeating the transformation of the BAC and targeting cassette and assessing the recombination efficiency and sequencing should only require existing reagents and take less than a week or two to complete. Quantitative RT-PCR to assess the percentage of transcripts that have the synthetic intron spliced out would take a little more work. However, this should not be a considerable investment in time or resources for a standard microbiology laboratory and could be completed within a few weeks using modern techniques, such as that described in Londoño et al. 2016. Repeating all the experiments in parallel would be considerable work and would only be strictly necessary if the authors wish to emphasise the benefits of their method over the many others already in wide use.
Are the data and the methods presented in such a way that they can be reproduced?
Barring the omission of Table S1, which presumably includes exact information on the BACs modified and sequences used etc., there is sufficient other data and methods to allow the experiments to be repeated. Targeting the ESI procedure to the middle of exons is likely to have a bigger impact for smaller exons as the authors mention on lines 99-100. Making it clear which exon sizes for each gene were successfully targeted in this study would help give some idea of how significant a problem this might be. Perhaps Table S1 contains this information, but it was not provided. It would also help reviewers check the design strategies.
Are the experiments adequately replicated and statistical analysis adequate?
The replication and statistically analysis of the data as presented appear adequate. Figure Legends should state the statistic used to generate error bars.
Minor comments:
Specific experimental issues that are easily addressable. Are the promoters used in the vectors described universally functional? For example, is the PGK promoter functional in yeast?
Are prior studies referenced appropriately?
The manuscript may benefit from the referencing of BAC modification techniques from a wider variety of groups, such as those using CRISPR-guided recombineering (Pyne et al. 2015).
Are the text and figures clear and accurate?
The body text is very clear save minor typographical or grammatical errors. Regarding figures, some of the coloured text in Figure 1 is somewhat illegible when printed in grayscale.
Line 278 - The acronyms LAP and NLAP are not defined/explained.
Antibody section starting Line 282 may fit better next to Western Blot section.
Figure 2C - The blot images would benefit from arrows to indicate expected sizes of proteins.
Figure 3A - the graph may benefit from a dashed line at 100% to highlight that values are normalised to controls.
Figure 4 - The differences between panels B & C are unclear.
Figure 4E - The legend could provide a little more detail on cell cycle stage/status of the captured cells.
Do you have suggestions that would help the authors improve the presentation of their data and conclusions?
Lines 23-27 are somewhat unclear and feel out of context. Perhaps the authors could clarify this as a further advantage of using BACs instead of endogenous gene modifications.
While not affecting the factual content of the paper, I would advocate that the authors format the method described in Figure S3 into a more detailed text based layout similar to that seen in a typical Nature Methods article. However, this may depend on the format required by any eventual publishing journal. That all of the work the paper was carried out in human cell lines and using human genes is a further caveat, but the authors admit this in the discussion and one would assume that most mammalian cells would respond similarly in their ability to splice out the synthetic intron.
Significance
Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.
This work is a formal description of a newer method that could be useful for many of those employing bacterial artificial chromosomes in numerous studies, such as gene regulation.
Place the work in the context of the existing literature (provide references, where appropriate).
This work builds on methodology previously published by the authors - a counter-selection two-step procedure (Bird et al. 2011). It sets out to formally describe a method merely mentioned as "BAC intronization" in a later paper by some of the authors (Zheng et al. 2014). Other alternative one-step procedures are also available, but present a different set of challenges (Lyozin et al. 2014). Some newer approaches, such as those using CRISPR-guided recombineering (Pyne et al. 2015) or systems that combine CRISPR and positive/negative selection cassettes (Wang et al. 2016) may be slightly more efficient, but are also more complex in their design.
Bird et al. 2011 DOI: 10/dv776q
Pyne et al. 2015 DOI: 10/f7jx92
Wang et al. 2016 DOI: 10/f89db5
Zheng et al. 2014 DOI: 10/f5pkr6
State what audience might be interested in and influenced by the reported findings.
As a technology paper this work should have interest from a broad field of research. While the use of BACs could sometimes be considered more traditional in light of the explosion in CRISPR-based genome editing capabilities, it is definitely seeing a resurgence as the limitations of CRISPR in modifying large regions of genome become more apparent. Therefore, technologies that accelerate the modification of BACs could prove increasingly useful. As category of audience, all those involved in significant recombineering or gene/genome engineering would potentially benefit.
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.
Synthetic genomics, synthetic biology, cancer cell biology, gene and genome engineering
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- Jun 2020
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Reviewer #3
The authors address an important open question in decision neuroscience: how do perceptual and metacognitive choices arise from the same sensory input? They perform an experiment that asks observers to report both their percept and associated confidence, and then investigate the role of history biases in both perceptual and metacognitive decisions. They find support for (well-established, e.g. https://elifesciences.org/articles/11946 ) second-order theories of metacognition, which state that metacognitive reports arise from more than just the information used to guide first-order perceptual decisions.
The methods and statistics are rigorous, and the authors have thoroughly reviewed the relevant literature on the topic. The mutual information analysis is a nice way to quantify the size of different biasing factors. However, the paper presents only limited mechanistic insight into history biases, decision processes or metacognition. It would help to clarify what the main insights are, and how they change our understanding of decision-making and its underlying computational nature.
Technical suggestions/comments:
-A recent study (https://elifesciences.org/articles/49834 ) shows the importance of correcting for slow fluctuations within a session, to accurately estimate history biases that are due to the previous trial itself. It would be great to see if the results hold after such a correction, to ensure that the effects are not due to slow fluctuations in choice and metacognitive reports within a session.
-The answer to whether perceptual and metacognitive decisions arise from the same or distinct processes may depend strongly on the nature of the confidence report. Would the same conclusions hold if choice and confidence were indicated with the same action (e.g. https://elifesciences.org/articles/12192 )?
-Half of participants base their confidence ratings mostly on confidence history, rather than on evidence. Does this just mean people were not very motivated to do well on the confidence report? Were they rewarded to perform well on both tasks?
-The authors test the population-level prevalence and effect sizes of perceptual and metacognitive history biases? However, with n = 37 their size does not seem large or diverse enough to infer properties of the general population.
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Reviewer #2
In this paper, Benwell and colleagues present a perceptual decision-making task with confidence ratings in human subjects to investigate choice and metacognitive history biases. The statistical analyses are solid and thorough. The mutual information analysis is a valuable complement. The paper is comprehensive, clearly written and the figures are informative. It would help if the authors could emphasise their reasoning across the paper: why selecting this particular paradigm to study history biases, and what is the added value of their findings beyond previous work? Many similar data sets (perceptual decision & confidence) have been published and are open. Moreover, it seems important to better justify their hypotheses and motivate the analyses, particularly the final focus on repeat vs. alternation in a paradigm in which these were not manipulated explicitly by the experimenters.
The authors suggest that history biases are adaptive (building on the stability of real-life environments), whereas at other times, that they are maladaptive ("irrelevant factors such as previous confidence reports"). It would be helpful to explicit the arguments in favour of either interpretation, and to clarify what computational interpretation the present findings favour, if any. There is already a little bit about why it may be advantageous to assume stability. Are there other reasons to think of choice/metacognitive bias as helpful vs. maladaptive? In which contexts? If repeating were a more prevalent bias than alternating in the population, why would this be useful? Relatedly, it would be helpful to further clarify for readers why it is relevant to study choice and confidence history biases, i.e. explain why it is not a simple by-product of experimental designs where experimenters artificially present multiple times very similar decisions.
It is interesting that the authors comment on the prevalence of each result in their sample, instead of simply reporting statistics on group means, to get a better sense of the strength of the findings. However, it is a bit difficult to generalise about "population prevalence" unless larger samples than the current n=37 are used. Because the experimental design overlaps with previous work, most of these analyses could be re-done on other datasets to address discrepancies between the present findings and that of Urai et al. The confidence database (Rahnev et al., 2020) may provide a useful resource for future work (especially for drawing conclusions about population prevalence based on the current sample of 37 subjects).
Technical suggestions/comments:
Could the authors indicate the proportion of errors vs. correct trials on previous repeating vs. alternative trials? If there were more errors on alternating trials, could it be that due to a post error slowing mechanism whereby subjects became more accurate after an alternate, hence the increase in d'? Regarding Fig S1 and the comparison with previous studies, it would be worth discussing the results in relation to a related study in rats (Hermoso, Hyafil et al., 2020 ncomms), for instance examining if the choice bias was overall driven by correct trials and absent after errors (Fig 2A). Finally, in Fig S1 the authors show a choice bias present for both high and low confidence at t-1. Would it be more precise, for concluding about a lack of an influence of confidence, to perform an ordinal regression analysis using the 4 levels of confidence available?
In Fig 1F, 1G, 1H, could the authors perform psychometric and statistical analyses to actually demonstrate the findings that the authors describe, or rephrase that the confirmation of model predictions are qualitative only? (For instance, showing quantitatively that the slopes are different for high and low confidence in Fig. 1F)
In particular, when comparing Fig 1F and 1G, are the patterns for confidence and RTs identical with respect to absolute orientation? If so, is this an issue for interpreting confidence data (supposedly not only a strict reflection of RTs but also incorporating information about accuracy)?
Could the authors comment on (even briefly) the other psychometric parameters (stimulus independent lapse rate and slope)? Why do the stimulus-independent lapses fixed and not fitted with the two other psychometric parameters? Does it change the conclusions if they are fitted? It would be worth checking parameters of the sigmoid psychometric function in Fig S1 (right panel red curve), because the psychometric function looks unusual with an increase at highly positive orientations.
It is reassuring to see that the correlation results in Fig. S4 are reproduced using an alternative metric of metacognitive efficiency (meta-d'/d'). However, could the authors provide this measure for all other analyses based on meta-d'-d'? I am not asking for a detailed breakdown or new figures, but at least in the text specify whether findings are maintained using this alternative metric.
Could the authors argue that here we have a true metacognitive history bias, and not a bias due to low-level effects e.g. motor anchoring, use of scale? (See e.g. Foda, H., Barger, K., Navajas, J., & Bahrami, B. (2017). Domain-general idiosyncratic anchoring of metacognition.)
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Reviewer #1
This preprint by Benwell and colleagues aims at studying 'history' biases during perceptual and metacognitive decision-making. The authors rely on a canonical, well-controlled two-alternative orientation discrimination task with confidence reports on a 4-point scale, tested in 37 human observers. Based on both model-free (mutual information) and model-based (type-2 signal detection theory) analyses, the authors report dissociations between perceptual history biases (related to previous perceptual decisions) and metacognitive history biases (related to previous confidence reports). They interpret these two forms of history biases as 'heuristics' informing type-1 (perceptual) and type-2 (confidence) decisions in the absence of sufficient evidence in the current trial.
History biases have generated a lot of interest over recent years, and the comparison of perceptual and metacognitive history biases in a single behavioral modeling study is interesting. The standard 2-AFC + 4-point confidence paradigm used by the authors is well suited to address the research question outlined by the authors in the introduction. However, I am not entirely satisfied by the definition of metacognitive history biases used by the authors throughout the manuscript – something which is likely to impact some of the dissociations reported by the authors.
Indeed, the authors report no effect of confidence at trial t-1 on perceptual bias at trial t (lines 147-149). However, this apparent dissociation appears unsurprising if the response at trial t-1 is not considered in the analysis (as shown in Figure 4). Indeed, a high confidence report in favor of a Left response at trial t-1 is likely to bias the perceptual response at trial t in the opposite direction to a high confidence report in favor of a Right response at trial t-1. In other words, when considering metacognitive history biases, the authors should consider four (not two) types of trials, based on the previous confidence report (high/low) but also the previous response (Left/Right). A true dissociation between previous confidence and the current response would imply that the perceptual history bias (i.e., the effect of the previous response on the current response) is of the same magnitude following low confidence reports and high confidence reports. It is very important that the authors re-compute the effect of metacognitive history biases in this response-dependent fashion. Indeed, Figure 4 will show no effect (i.e., an apparent dissociation) even if the perceptual history bias is strongly increased following high confidence reports than low confidence reports. The manuscript should always consider the direction of the previous response when assessing metacognitive history biases, and only claim dissociations if these previous response-dependent analyses reveal no interaction between the size of perceptual history biases and the previous confidence report (high vs. low).
Along the same lines, Figure 2C should be modified to plot confidence not as a function of the absolute orientation, but as a function of the orientation signed by the provided response (positive for a stimulus orientation consistent with the provided response, and negative for a stimulus orientation inconsistent with the provided response). It is highly likely that confidence does not scale with the absolute orientation as suggested by Figure 2C, but as a function of the consistency of the stimulus orientation with the provided response (highest confidence for an easy stimulus orientation consistent with the provided response, lowest confidence for an easy stimulus orientation inconsistent with the provided response).
Regarding the use of mutual information (MI) as a metric for comparing all kinds of effects, I wonder whether the authors could explain more extensively their reasoning. I was under the impression that MI is bounded differently for different kinds of variables (e.g., a binary effect is bounded at 1 bit). Comparing binary variables (Left/Right responses) with non-binary variables (1-4 confidence reports) may thus be problematic, unless I have missed something. To validate the use of MI for claiming "sub-optimal metacognitive performance" (lines 193-195) would require to perform sanity checks such as model simulations with optimal metacognitive performance. The goal of these simulations is to show the difference between the pattern of MI obtained for simulations with genuinely optimal metacognitive performance (and matched perceptual performance to the human subjects) and the pattern of MI obtained for human subjects.
In the type-2 SDT modeling, the authors now separate Left and Right responses when computing their abs(meta-c minus c) measure. As mentioned above, I believe that the authors should always split Left and Right responses when assessing metacognitive history biases and claiming dissociations between type-1 (perceptual) and type-2 (metacognitive) processes. As above, also, model simulations with optimal metacognitive performance but no history biases (neither perceptual nor metacognitive) may be useful to validate the analyses carried out by the authors on their behavioral data.
In Figure 4, as in Figure 2C, post high confidence and post low confidence should be further split between post high/low confidence in a Left response and post high/low confidence in a Right response. While a high confidence report in the previous trial may not potentiate perceptual sensitivity (d') on the current trial, it is possible that a high confidence report for a Left response in the previous trial would bias perception in favor of a Left response in the current trial more than a low confidence report for a Left response (and vice versa for a Right response).
Finally, the repeat vs. alternate effect shown in Figure 5 could be driven by at least two very different mechanisms that cannot be teased apart from presented analyses: 1) the reduced orientation sensitivity in repeat trials proposed by the authors, and 2) a non-zero 'repetition lapse' rate (i.e., a fraction of trials in which the subject blindly repeats his/her previous response). To distinguish between these two accounts, it would be very useful to re-plot Figure 5C. Instead of the absolute orientation, one would use as x-axis the orientation signed by the previous response (positive if the current orientation is in the same direction as the previous response, negative otherwise) and plot on the y-axis the fraction of repeat decisions in the current trial. Fitting a sigmoid function to this curve with a non-zero probability of a blind repetition should afford to tell whether the lower apparent sensitivity in repeat trials shown on Figure 5C is actually triggered by blind repetitions – instead of a lower sensitivity to orientation.
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Preprint Review
This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 2 of the manuscript.
Summary
This preprint describes a behavioral study of 'history biases' during perceptual and metacognitive decisions. The preprint reports dissociations between history biases across successive perceptual decisions and history biases across successive confidence reports. The question of how perceptual decisions and confidence reports arise from the same sensory input is important, and the statistical analyses of the behavioral data are rigorous and thorough. In particular, the proposed mutual information analysis appears valuable to quantify and compare different biasing effects. However, despite these methodological strengths, the preprint does not reveal substantive new insights into the computational processes which may give rise to the observed effects. Also, the preprint reads at times more like a collection of findings than a coherent study of a targeted research question. In this respect, it would be useful to emphasize the added value of the findings beyond the extensive previous work on history biases and on the relation between type-1 (perceptual) and type-2 (metacognitive) decisions. An explicit motivation of the performed analyses, particularly the focus on the repetition vs. alternation contrast at the end of the results, would be very useful to clarify the reasoning followed by the authors throughout the study.
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Reply to the reviewers
We thank all reviewers for their comments and suggestions, which will make our manuscript a much better one. Accordingly, we have already made changes to the manuscript (marked in yellow) and we will perform all the experiments requested. Below, we answer the reviewers point by point.
Reviewer #1 (Evidence, reproducibility and clarity (Required)): This study provides solid evidences showing a role for the spectraplakin Short-stop (Shot) in subcellular lumen formation in the Drosophila embryonic and larval trachea. This subcellular morphogenetic process relies on an inward membrane growth that depends on the proper organization of actin and microtubules (MTs) in terminal cells (TCs). Shot depletion leads to a defective or absent lumen while conversely, Shot overexpression promotes excessive branching, independently on the regulation of centrosome numbers previously shown to be important for the regulation of the lumen formation process (Ricolo, D., Deligiannaki, M., Casanova, J. & Araújo, S. J. Centrosome Amplification Increases Single-Cell Branching in Post-mitotic Cells. Current Biology 26, 2805-2813 (2016)). Shot is rather important to regulate the organization of the cytoskeleton by crosslinking MTs and actin. Shot expression in TCs is controlled by the Drosophila Serum Response Factor (DSRF) transcription factor. Finally Shot functionally overlaps with the MT-stabilizing protein Tau to promote lumen morphogenesis. The figures are clear and the questions well addressed with carefully designed and controlled experiments. However, I would have few suggestions that will hopefully make some points clearer. **Major comments:** -Statistical analyses should be added for comparisons of proportions, including Fig. 1E, 1L, Fig. 2G-I, Fig. 6L, Fig. 7K, Fig. 8C-D and Fig. 9G.
We agree with this and have now redone all graphs and revised all quantifications from this study. We have added error bars in all above mentioned graphs and have provided statistical analysis where appropriate. We have also redone all graphics and phenotype reporting, which is done now in relation to total TCs (rather than embryos or GBs and DBs TCs). This was suggested also by reviewer #2 and we agree because this is a more stringent and comparable way of quantifying our results.
-It is not always clear what genotype has been used as the "wt" genotype, as in Fig. S2 or Fig. 3 for example, this should be added to figure legends.
We have now clarified which flies are used as controls in each experiment throughout the paper. We have left wt where flies were wt, and changed all other cases to either the genotype or “control”.
-Live imaging of Shot has been performed with ShotC-GFP, that cannot bind actin. Don't the authors think ShotA-GFP would reflect more accurately Shot endogenous behavior as it interacts both with actin and MTs? It would be better to show this, even if the results shown here tend to be consistent with Shot endogenous localization shown with Shot antibody staining.
We agree and we will analyse movies with both ShotC and ShotA and present them in the revised version.
-It is of course not possible to generate CRISPR mutant flies with mutations in putative DSRF binding sites in a reasonable amount of time, to confirm that Shot transcription is controlled by DSRF. It would thus be nice to reveal shot mRNA expression with in situ hybridization experiments in wt vs. bs embryos. This would confirm that Shot mRNA is downregulated upon DSRF inhibition and rule out a possible indirect effect on Shot protein stability for example.
We believe the presented 3-way approach (in silico, protein quantification and phenotype rescue) is sufficient to show that Shot expression is regulated by DSRF. It is unlikely that we are dealing with protein stability or other issues, because we can rescue the lumen elongation phenotype by solely expressing Shot in TCs. However, we agree it would be nice to show this in an in situ hybridization experiment, and we will try to provide a conclusive one for resubmission. In situ detection methods, however, may not be accurate enough to detect such differences in single-cells.
-In the same figure, it would also be interesting to show what happens to actin and MTs in bs TCs and to which extent their organization is rescued by Shot overexpression.
We are working on this for resubmission. These experiments were frozen by the current COVID-19 pandemic and this is why they were not submitted with the first version.
-UAS-EB1GFP does not seem to be an appropriate control in Figure 9 (A and B) since it can affect MT dynamics (Vitre, B. et al. EB1 regulates microtubule dynamics and tubulin sheet closure in vitro. Nat. Cell Biol. 10, 415-421 (2008)). Why not simply use an UAS-GFP?
We have not detected any notorious larval TC phenotypes by overexpressing UASEB1GFP in TCs. Their branching is comparable to that in previous studies (for example Schotenfeld-Roames, et al Current Biology 2014) and there were no detectable luminal branching phenotypes. However, we agree it is more correct to analyse cells with a plain GFP and have repeated the controls for this experiment using DSRFGAL4UASGFP. This is now shown in figure 9.
-Shot and probably Tau crosslinking activities are important for lumen morphogenesis with a striking increase in the number of embryos without lumen in shot3 and shot3 tauMR22 mutant embryos. The rescue experiments clearly show that Shot binding to both MT and actin is essential for efficient rescue. The same might apply to Tau since it is able to crosslink actin and MTs (Elie, A. et al. Tau co-organizes dynamic microtubule and actin networks. Sci Rep 5, 1-10 (2015)). I believe showing actin and MTs organization in these rescue experiments would be necessary.
We agree and we will provide these experiments upon resubmission.
Second, the overexpression experiments indicate that Shot is able to induce extra lumen formation even when unable to bind actin as shown with the increase in the number of supernumerary lumina (ESLs) under overexpression of ShotC and ShotCtail to a lesser extent. This phenotype is also observed under Tau overexpression. This suggest that not crosslinking anymore but rather making MTs more stable could be sufficient to promote extra lumen formation in a wt context. Stabilising MTs by treatment with Taxol might thus be sufficient to promote ESL formation. I am fully aware of the difficulty of treating Drosophila embryos with drugs, making this experiment hard to do, but I think this dual function of Shot and Tau (crosslinking actin and MTs to promote branching vs. stabilizing MTs leading to excessive branching) should be discussed.
In Figure 2 we show not just that UASShotC is able to induce ESl but also that UAS-ShotCtail containing only the MT binding domain of Shot is enough to induce ESLs in TCs, whereas UAS-deltaCtail is not. We agree Taxol treatment would be a nice experiment to do, however we also think we provide enough evidence that MT stability is enough for ESL whereas de novo lumen formation requires crosslinking of MTs to actin. As advised, we will discuss better both Shot and Tau dual function in ESL generation and de novo lumen formation for resubmission.
**Minor comments:**
We have already addressed most these minor comments in the manuscript (text revised and changes in yellow). And we provide answers to some of the comments below.
-p2 line 1: 'acentrosomal luminal branching points' may be better than 'acentrosomal branching points' to describe the phenotype. -p4, line 16: the reference 23 is not properly inserted (should be after 'closure'). -p5, line 16: Please mention what the abbreviations Bnl and Btn stand for. -p5, line 20: these 80% of TCs cells with defects in subcellular lumen formation should appear on the graph in Fig. 1E (as shown in graph 1L).
We have added shot RNAi results to graph E in figure 1.
-p5, line 26: this 36% value does not seem to correspond to anything on the graph in Fig. 1N. According to the figure legend, 20% of TCs did not elongate at all and the lumen was completely absent (class IV), which is consistent with the result shown in Fig. 1L. Also, I am not sure why only 25 TCs were analysed in Fig. 1N while there are the data to analyse more as shown in Fig. 1E (400 TCs), this would make the graph more representative.
Figure 1 N represents a detail of the different phenotypes present in shot mutant embryos. Whereas for most of the paper we consider only complete lack of TC lumen, here we show the different types of affected TCs and not just the ones with a complete lack of subcellular lumen. We apologise because it was not explained in the original manuscript that types III and IV are the “no lumen” class (they were subdivided into 2 classes because they have different cell enlongation phenotypes). 36% of the total of affected TCs displayed the lack of lumen phenotype (this means a 22,5 % of the total number of TCs, because total affected TCs are 62,5% only). Numbers are similar but not exactly the same because this analysis was done using confocal microscopy and cells analysed one by one in detail, which is not possible using colorimetric methods and only luminal markers. This is also the reason we only analysed 25 TCs in this case. We thank the reviewer for pointing this out and have better described it in the manuscript.
-p6, line 8: ShotA-GFP is indeed a long isoform but is not the full-length Shot, as it does not contain the plakin repeat exon which would add another ~3000aa.
We have corrected this.
-p6, lines 21-23: ShotA-GFP localisation is not shown in FigS1. The authors should refer to Fig. 2. Enlarged areas/arrows might help the reader to better visualise the different localisations of ShotA-GFP and ShotC-GFP.
We thank the reviewer for this request and we will change the figure providing enlarged areas upon resubmission. In this version of the manuscript we have already changed the error in figure referral in the text.
-p7, line 23: Rca1 mutants should be better introduced here.
We have added one sentence of introduction to the Rca1 phenotype.
-p8, line 6: Shot colocalizes/associates with stable MTs and actin would be a more appropriate title for this paragraph.
We thank the reviewer for this alternative, and we have changed this title in the manuscript.
-p16, line 18: 'Shot is able to mediate crosstalk' would be better than 'Shot is able to crosstalk'. -p40, lines 6 and 7: L, M and N should be K', K' and K' respectively. -p41, Fig 10D: It is quite hard to see on the cartoon what the phenotype is for Shot OE.
We will make this clearer for resubmission.
-The following reference shows an important role for Shot in crosslinking actin and MTs during morphogenesis of the Drosophila embryo and should be cited in this manuscript (Booth, A. J. R., Blanchard, G. B., Adams, R. J. & Röper, K. A Dynamic Microtubule Cytoskeleton Directs Medial Actomyosin Function during Tube Formation. Developmental Cell 29, 562-576 (2014)).
We thank the reviewer for pointing this out, because this is of course an important reference known to us, which we forgot to add. We have now added this to the manuscript.
-FigS3. It would be good to add the labels on the figure (ShotC-GFP in green, and MoeRFP/lifeActinRFP in Magenta).
We will do this for resubmission.
Reviewer #1 (Significance (Required)): The findings shown in this manuscript shed an important light on the way subcellular morphogenesis occurs. It was known that both actin and MTs were required in this process, particularly during the formation of Drosophila trachea (JayaNandanan, N., Mathew, R. & Leptin, M. Guidance of subcellular tubulogenesis by actin under the control of a synaptotagmin-like protein and Moesin. Nature Communications 1-10 (2019). doi:10.1038/ncomms4036; Gervais, L. & Casanova, J. In Vivo Coupling of Cell Elongation and Lumen Formation in a Single Cell. Current Biology 20, 359-366 (2010)). This work provides additional molecular insights into the way branching morphogenesis from a single cell occurs in vivo, clearly demonstrating a requirement for actin-MT crosslinking mediated by Shot and Tau. This could be of great interest in the field of branching morphogenesis and lumen formation, not only in invertebrates but also in vertebrates where such a crosslinking might occur in the vasculature, the lung, the kidney or the mammary gland for example (Ochoa-Espinosa, A. & Affolter, M. Branching Morphogenesis: From Cells to Organs and Back. Cold Spring Harb Perspect Biol 4, a008243-a008243 (2012)). *Field of expertise:* morphogenesis, Drosophila, cytoskeleton, microtubules. Reviewer #2 (Evidence, reproducibility and clarity (Required)): **Summary:** The development of branched structures with intracellular lumen is widely observed in single cells of circulatory systems. However the molecular and cellular mechanisms of this complex morphogenesis are largely unknown. In previous study, the authors revealed that centrosome as a microtubule organizing center (MTOC) located at the apical junction contributes subcellular lumen formation in the terminal cells of Drosophila tracheal system. The microtubule bundles organized by MTOC are suggested to serve as trafficking mediators and structural stabilizers for the newly elongated lumen. In this manuscript, they focused on a Drosophila spectraplakin, Shot, which have been reported to crosslink MT minus-ends to actin network, in the subcellular lumen formation. The paper started by description of lumen elongation defect of the tracheal terminal cells in the shot[3] null mutant. The overexpression of full-length and series of truncated form of shot exhibited extra-subcellular lumina (ESL) in TCs, suggesting that Shot is required for the lumen formation in dose dependent manner. They next addressed whether Shot overexpression induces ESL through the supernumerary centrosomes as in Rca1 mutant, however the number of centrosomes was not affected. Moreover, the ESL were sprouted distally from the apical junction, suggesting that Shot operate in different way from the Rca1-dependent microtubule organization. To get mechanistic insight of Shot in the luminal formation, they checked localization of the Shot and found it localized with stable MTs around the nascent lumen and with the F-actin at the tip of the cell during the cell elongation and subcellular lumen formation. In shot[3] mutant, the MT-bundles were no longer localized to apical region and the actin accumulation at the tip of the cell was also reduced. The rescue experiments using several truncated forms of Shot, and well-designed genetic analysis using various shot mutants revealed that both MT binding domain and actin binding domains are needed to develop the lumen. The expression of shot was under the regulation by terminal cell-specific transcription factor bs/DSRF, and the overexpression of shot in bs LOF mutant suppressed its phenotype, indicated that part of the luminal phenotype of bs mutant in terminal cells are due to lower levels of the activity of shot. Finally, they checked whether Tau can compensate the function of shot in the subcellular lumen formation. The lumen elongation defect in shot mutant was suppressed by tau expression, and tau overexpression phenocopied the shot overexpression-induced ESL. Although tau mutant did not show the lumen formation defects, the double mutant of shot and tau exhibited synergistic effect. Shot was also required for subcellular luminal branching at larval stages. Overall, this work highlighted the importance of Shot as a crosslinker between MT and actin that acts in downstream of the FGF signaling-induced bs/DSRF expression for the subcellular lumen formation. An excess of Shot is sufficient for ESL formation from ectopic acentrosomal branching points. Furthermore, the Tau protein can functionally replace Shot in this context. **Major comments:** *- Are the key conclusions convincing?* *- Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?* The conclusions were basically supported by the set of data presented in this article, but following points need to be clarified. The truncated form ShotC lacks only half of calponin domain that are essential for the actin binding, thus it is still possible to bind actin to some extent. Although the actin binding activity is reported as "very weak" in the cited references, the quantitative analysis has not been done. Thus, the interpretation and claims based on the experiments using ShotC should be reviewed carefully.
We agree with the reviewer and will revise all the text for resubmission in order to make this unambiguous. However, we would like to remark that our claims are not only based on UAS-ShotC but also in the shotkakP2 allele, which does not contain one of the calponin domains and in isoforms such UAS-Shot C-tail which do not have any ABD.
Data set in some places seems fragmented. For example, overexpression study of shot constructs (Fig. 2) lacks phenotypic comparison of control (btl Gal4 driven control FP) to compare if phenotypes of shot constructs expression are different from control. Different methods of phenotypic quantification are employed. One was counting embryo number with at least one abnormality among 20 TCs of DB or GB, or the other counting every TC for the presence of lumen/branching conditions. The latter is more stringent measure and is more appropriate for the study of single cell morphogenesis.
We totally agree with the reviewer. We have now revised all quantifications and graphs:
1) We have used btl>GFP as control to all overexpression experiments in embryos and DSRFGAL4UASGFP in control larvae.
2) We have made the paper uniform regarding quantifications, which are now all done in relation to total TCs and not embryos.
For this reason, many of the graphs, figure legends and quantification values in the the manuscript text are now changed.
*- 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.* The all movies were using ShotC isoform which lacks half of the actin binding domain. The truncated isoform is not suitable to observe the localization, especially the colocalization with actin. The movies need to be retaken using full-length Shot at the dosage that does not interfere with normal TC development.
We agree and we will analyse movies with both ShotC and ShotA for resubmission.
Some statements on Moesin and Tau localization sound as if the authors studied Shot interaction with nascent Moe and Tau molecules. This is confusing because fragments of Moe and Tau, but not functional full length proteins, were used.
We will revise the text to make this unambiguous fir resubmission.
*- 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. Because the transgenic fly is already present, we assume it would be done in 4 weeks. However, it would be influnced under social circumstances whether the lab facilities are able to access or not. *- Are the data and the methods presented in such a way that they can be reproduced?* *- Are the experiments adequately replicated and statistical analysis adequate?* The methods provided seem to be sufficient for reproducing the data by competent researchers, and most of the data are solid and the sample numbers are sufficient for the claims. However, the criteria for phenotypic evaluation differs among graphs and figures, that possibly confuse the readers. Standardized measurement methods are desirable. **Minor comments:** *- Specific experimental issues that are easily addressable.* In the rescue experiments shown in Figure 6, only full-length Shot rescued the subcellular lumen formation, but either of truncated Shot did not. The localization study of MT and actin in those conditions will reveal whether proper localizations of actin and MT are critical for the lumen formation.
We are working on this for resubmission. These experiments were stalled by the current COVID-19 pandemic and this is why they were not submitted with the first version. We will provide MT and actin localization for the rescue experiments with ShotA and ShotC.
*- Are prior studies referenced appropriately?* The references are cited appropriately. *- Are the text and figures clear and accurate?* There are several typos: Remodelling -> remodeling, signalling -> signaling. In the figure 2, G and H seem redundant. Scale bars are missing in Fig1 F-K, Fig2 K-L, Fig6 A-I, Fig7 E-J and Fig8 E-J.
We have changed the graphs in figure 2. Typos have been corrected. We will provide errors bars for resubmission.
The author often called shot+ genotype as "wild type". They are transgenic strains with some mutations, and cannot be found in the wild. They should be simply called with genotype or "control" for experiments.
We thank the reviewer for pointing these typos and incoherences with control genotypes. We have partly revise the text and figures and will finish for resubmission.
*- Do you have suggestions that would help the authors improve the presentation of their data and conclusions?* In Figure 4, as the localization of Shot is difficult to see in detail, enlarged insets might help. In addition, the green and cyan in C'-E' is difficult to distinguish.
We will change this for resubmission.
With Figure 5, the authors claimed that Shot LOF leads to disorganized MT-bundles and actin localization. We feel this is an overstatement and the Figure should be backed up with better data, or removed. F-actin and microtubule localizations are highly dynamic and the snapshot pictures are insufficient for demonstrating defective localization. It is also possible that (potential) difference in the marker localization is due to indirect effect of Shot LOF in cell shape.
We agree with the reviewer that fixed samples are not the best to analyse cytoskeletal components, but we observe clear differences in MT bundles and specially in actin localization in shot mutants as compared to controls and we believe it is important to show these results. Cell shape might of course alter the analysis which is why we present 3 different cell shapes in Figure 5. In addition, there are many previous studies where localization of MTs and actin was done in fixed mutant embryos, where cell shape is also affected, and revealed important steps in TC formation (Gervais and Casanova, 2010; JayanNadanan et al. 2014).Nonetheless, we have revised the text in order to avoid overstatements.
Reviewer #2 (Significance (Required)): *- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.* *- Place the work in the context of the existing literature (provide references, where appropriate).* In blood capillary and insect trachea, the branching process of single vessel cells involves sprouting of cell protrusions, followed by the lumen extension from the main vessels. The lumen formation involves assembly of plasma membrane components inside of the cytoplasm. Since the luminal membrane is associated with protein complexes common to apical cell membrane, lumen formation is believed to involve redirection of apical trafficking of membranes to intracellular sites (Sigurbjörnsdóttir, Mathew, Leptin 2014, 10.1038/nrm3871). The authors previously demonstrated that centrosome is an important link of preexisting lumen to de novo lumen formation, leading to the hypothesis that centrosome-derived microtubules organize lumen membrane assembly. *- State what audience might be interested in and influenced by the reported findings.* In this manuscript, the authors addressed this issue by looking at the function of Shot/Plakin that has both microtubule and actin binding activities. Shot is an ideal candidate for linking actin-rich cell protrusions in the leading edge to centrosome- associated lumen tip. Indeed the authors clearly showed that shot is required for lumen extension and overexpressed shot protein associates with intracellular tract rich in microtubules and F-actin. Their findings are definitely a progress in the field of Drosophila tracheal development. Having said that, how Shot links leading edge protrusions and centrosomes, how it is organized into pre-lumen tract, and how it contribute to further assembly of luminal membrane and directed secretion, are not well understood yet. Without clues to those fundamental questions, I believe this paper is most appropriate for experts readers of Drosophila cell biology and tracheal development. Finally I feel that the paper include many data sets and some pictures are not easy to grasp essential points, such as three movies showing localization of overexpressed shot-C, RFP-moesin, and Lifeact. *- Define your field of expertise with a few keywords to help the authors contextualize your point of view.* Drosophila, tracheal cell biology. *- Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.* No Reviewer #3 (Evidence, reproducibility and clarity (Required)): **Summary** In their manuscript entitled "Coordinated crosstalk between microtubules and actin by a spectraplakin regulates lumen formation and branching" Ricolo and Araujo characterize the requirement for Short Stop (Shot) in the formation of subcellular tubes in tracheal terminal cells. The authors examined embryos homozygous for shot3, a presumed null allele of shot. They found an 80% penetrant defect in seamless tube formation or growth. The phenotype resembles that reported for mutations in blistered, which encodes the Drosophila SRF ortholog. The authors find that expression of SRF is not blocked by mutations in shot and later find that bs mutants have decreased levels of shot expression and that shot overexpression can partly suppress the bs tube formation defects. The authors then examine whether the requirement for shot is autonomous to the trachea and find that it is, as pan-tracheal shot RNAi replicates the seamless tube defects. The authors find that overexpression of various Shot isoforms results in the formation of ectopic seamless tubes within terminal cells. Using the various transgenic constructs available for shot, the authors show that the overexpression phenotype is dependent upon the interaction between Shot and microtubules, and is dose-dependent. Previous work had shown that ectopic terminal cell tubes also can arise due to increased centrosome number; the authors show that centrosome number is not altered in shot mutants. Shot has well characterized actin and microtubule binding functions, and the authors show that Shot localization overlaps both with microtubules and with actin, and that both cytoskeletal elements are aberrant in shot mutant cells. In a series of experiments utilizing various shot mutant backgrounds and shot transgenes, the authors identify requirements for both Shot-cytoskeleton interactions in the formation and branching of seamless tubes in terminal cells. Finally, the authors examine the requirement for Tau in the same processes. Tau and Shot had previously been found to work together in neurons, and this seems to be true in terminal cells as well. Tau overexpression induces ectopic seamless tubes and can partially suppress shot loss of function. Embryos mutant for tau showed seamless tube directionality defects, but not lumen formation or branching. Embryos doubly mutant for tau and shot showed a more severe seamless tube defect than shot mutants alone - an increase in terminal cells with no lumen from 22% to 85%. Authors also examined terminal cells in larval stages using dsrf-Gal4 to knockdown shot in terminal cells (rather than pan-tracheal knockdown with breathless). The authors conclude from their studies that Shot, through its interactions with microtubules and the actin cytoskeleton coordinate the outgrowth and branching of subcellular tubes. Overlapping function of Tau and possibly other additional MAPs also act in these processes. The work is largely well done and the conclusions are supported by the data. **Minor concerns:** -If one were to start this work today, crispr knockout and knockins would be preferred. While shot^3 is widely considered a null allele, there are indications that some shot function is still present in shot^3 embryos. This would also be relevant to the penetrance of the defects. The transgenes are useful, but given the dosage effects noted in various of the authors experiments, interpretation of some experiments is complicated as compared to a knockin. For overexpression experiments, landing site constructs would be preferable. I do not mean to suggest that the authors necessarily go this route, but am just pointing out a limitation of the approach.
We agree, but we also think that with the amount of data and tools generated by other labs over recent years, regarding shot function in the nervous system (Voelzmann et al 2017), we are in a position to be able to take the conclusions of this work based on these transgenic and different shot alleles.
-Insight into function at higher resolution than altered microtubule and actin organization would significantly increase the impact. -cell autonomy (line 19, p5) is not the correct term. Pan-tracheal knockdown tests tissue autonomy. Mosaic analysis or terminal cell specific knockdown would address cell autonomy.
We have changed the manuscript accordingly.
-line 14 p6 acting should be actin -dsrf-Gal4 transgenes were made by Mark Metzstein
We have corrected these.
-there also appears to be rescue of the fusion cell defects of shot by Tau overexpression. Authors should comment on this and what it means for the seamless tubulogenesis program in terminal cells vs fusion cells.
We will reanalyse shot rescued with tau embryos focusing on fusion phenotypes and discuss this in the revised version.
Reviewer #3 (Significance (Required)): The findings will be of interest to a broad cell biology community as they provide a conceptual advance and may help to focus future work on seamless tubulogenesis. The authors do a good job of placing the results in the context of previous studies. *Field of expertise:* Drosophila, tracheal tubulogenesis, developmental biology
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Referee #3
Evidence, reproducibility and clarity
Summary
In their manuscript entitled "Coordinated crosstalk between microtubules and actin by a spectraplakin regulates lumen formation and branching" Ricolo and Araujo characterize the requirement for Short Stop (Shot) in the formation of subcellular tubes in tracheal terminal cells.
The authors examined embryos homozygous for shot3, a presumed null allele of shot. They found an 80% penetrant defect in seamless tube formation or growth. The phenotype resembles that reported for mutations in blistered, which encodes the Drosophila SRF ortholog. The authors find that expression of SRF is not blocked by mutations in shot and later find that bs mutants have decreased levels of shot expression and that shot overexpression can partly suppress the bs tube formation defects.
The authors then examine whether the requirement for shot is autonomous to the trachea and find that it is, as pan-tracheal shot RNAi replicates the seamless tube defects.
The authors find that overexpression of various Shot isoforms results in the formation of ectopic seamless tubes within terminal cells. Using the various transgenic constructs available for shot, the authors show that the overexpression phenotype is dependent upon the interaction between Shot and microtubules, and is dose-dependent.
Previous work had shown that ectopic terminal cell tubes also can arise due to increased centrosome number; the authors show that centrosome number is not altered in shot mutants.
Shot has well characterized actin and microtubule binding functions, and the authors show that Shot localization overlaps both with microtubules and with actin, and that both cytoskeletal elements are aberrant in shot mutant cells. In a series of experiments utilizing various shot mutant backgrounds and shot transgenes, the authors identify requirements for both Shot-cytoskeleton interactions in the formation and branching of seamless tubes in terminal cells.
Finally, the authors examine the requirement for Tau in the same processes. Tau and Shot had previously been found to work together in neurons, and this seems to be true in terminal cells as well. Tau overexpression induces ectopic seamless tubes and can partially suppress shot loss of function. Embryos mutant for tau showed seamless tube directionality defects, but not lumen formation or branching. Embryos doubly mutant for tau and shot showed a more severe seamless tube defect than shot mutants alone - an increase in terminal cells with no lumen from 22% to 85%.
Authors also examined terminal cells in larval stages using dsrf-Gal4 to knockdown shot in terminal cells (rather than pan-tracheal knockdown with breathless).
The authors conclude from their studies that Shot, through its interactions with microtubules and the actin cytoskeleton coordinate the outgrowth and branching of subcellular tubes. Overlapping function of Tau and possibly other additional MAPs also act in these processes.
The work is largely well done and the conclusions are supported by the data.
Minor concerns:
-If one were to start this work today, crispr knockout and knockins would be preferred. While shot^3 is widely considered a null allele, there are indications that some shot function is still present in shot^3 embryos. This would also be relevant to the penetrance of the defects. The transgenes are useful, but given the dosage effects noted in various of the authors experiments, interpretation of some experiments is complicated as compared to a knockin. For overexpression experiments, landing site constructs would be preferable. I do not mean to suggest that the authors necessarily go this route, but am just pointing out a limitation of the approach.
-Insight into function at higher resolution than altered microtubule and actin organization would significantly increase the impact.
-cell autonomy (line 19, p5) is not the correct term. Pan-tracheal knockdown tests tissue autonomy. Mosaic analysis or terminal cell specific knockdown would address cell autonomy.
-line 14 p6 acting should be actin
-dsrf-Gal4 transgenes were made by Mark Metzstein
-there also appears to be rescue of the fusion cell defects of shot by Tau overexpression. Authors should comment on this and what it means for the seamless tubulogenesis program in terminal cells vs fusion cells.
Significance
The findings will be of interest to a broad cell biology community as they provide a conceptual advance and may help to focus future work on seamless tubulogenesis. The authors do a good job of placing the results in the context of previous studies.
Field of expertise: Drosophila, tracheal tubulogenesis, developmental biology
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Referee #2
Evidence, reproducibility and clarity
Summary:
The development of branched structures with intracellular lumen is widely observed in single cells of circulatory systems. However the molecular and cellular mechanisms of this complex morphogenesis are largely unknown. In previous study, the authors revealed that centrosome as a microtubule organizing center (MTOC) located at the apical junction contributes subcellular lumen formation in the terminal cells of Drosophila tracheal system. The microtubule bundles organized by MTOC are suggested to serve as trafficking mediators and structural stabilizers for the newly elongated lumen.
In this manuscript, they focused on a Drosophila spectraplakin, Shot, which have been reported to crosslink MT minus-ends to actin network, in the subcellular lumen formation. The paper started by description of lumen elongation defect of the tracheal terminal cells in the shot[3] null mutant. The overexpression of full-length and series of truncated form of shot exhibited extra-subcellular lumina (ESL) in TCs, suggesting that Shot is required for the lumen formation in dose dependent manner. They next addressed whether Shot overexpression induces ESL through the supernumerary centrosomes as in Rca1 mutant, however the number of centrosomes was not affected. Moreover, the ESL were sprouted distally from the apical junction, suggesting that Shot operate in different way from the Rca1-dependent microtubule organization. To get mechanistic insight of Shot in the luminal formation, they checked localization of the Shot and found it localized with stable MTs around the nascent lumen and with the F-actin at the tip of the cell during the cell elongation and subcellular lumen formation. In shot[3] mutant, the MT-bundles were no longer localized to apical region and the actin accumulation at the tip of the cell was also reduced. The rescue experiments using several truncated forms of Shot, and well-designed genetic analysis using various shot mutants revealed that both MT binding domain and actin binding domains are needed to develop the lumen. The expression of shot was under the regulation by terminal cell-specific transcription factor bs/DSRF, and the overexpression of shot in bs LOF mutant suppressed its phenotype, indicated that part of the luminal phenotype of bs mutant in terminal cells are due to lower levels of the activity of shot. Finally, they checked whether Tau can compensate the function of shot in the subcellular lumen formation. The lumen elongation defect in shot mutant was suppressed by tau expression, and tau overexpression phenocopied the shot overexpression-induced ESL. Although tau mutant did not show the lumen formation defects, the double mutant of shot and tau exhibited synergistic effect. Shot was also required for subcellular luminal branching at larval stages.
Overall, this work highlighted the importance of Shot as a crosslinker between MT and actin that acts in downstream of the FGF signaling-induced bs/DSRF expression for the subcellular lumen formation. An excess of Shot is sufficient for ESL formation from ectopic acentrosomal branching points. Furthermore, the Tau protein can functionally replace Shot in this context.
Major comments:
- Are the key conclusions convincing? - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?
The conclusions were basically supported by the set of data presented in this article, but following points need to be clarified.
The truncated form ShotC lacks only half of calponin domain that are essential for the actin binding, thus it is still possible to bind actin to some extent. Although the actin binding activity is reported as "very weak" in the cited references, the quantitative analysis has not been done. Thus, the interpretation and claims based on the experiments using ShotC should be reviewed carefully.
Data set in some places seems fragmented. For example, overexpression study of shot constructs (Fig. 2) lacks phenotypic comparison of control (btl Gal4 driven control FP) to compare if phenotypes of shot constructs expression are different from control. Different methods of phenotypic quantification are employed. One was counting embryo number with at least one abnormality among 20 TCs of DB or GB, or the other counting every TC for the presence of lumen/branching conditions. The latter is more stringent measure and is more appropriate for the study of single cell morphogenesis.
- 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.
The all movies were using ShotC isoform which lacks half of the actin binding domain. The truncated isoform is not suitable to observe the localization, especially the colocalization with actin. The movies need to be retaken using full-length Shot at the dosage that does not interfere with normal TC development.
Some statements on Moesin and Tau localization sound as if the authors studied Shot interaction with nascent Moe and Tau molecules. This is confusing because fragments of Moe and Tau, but not functional full length proteins, were used.
- 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.
Because the transgenic fly is already present, we assume it would be done in 4 weeks. However, it would be influnced under social circumstances whether the lab facilities are able to access or not.
- Are the data and the methods presented in such a way that they can be reproduced? - Are the experiments adequately replicated and statistical analysis adequate?
The methods provided seem to be sufficient for reproducing the data by competent researchers, and most of the data are solid and the sample numbers are sufficient for the claims. However, the criteria for phenotypic evaluation differs among graphs and figures, that possibly confuse the readers. Standardized measurement methods are desirable.
Minor comments:
- Specific experimental issues that are easily addressable.
In the rescue experiments shown in Figure 6, only full-length Shot rescued the subcellular lumen formation, but either of truncated Shot did not. The localization study of MT and actin in those conditions will reveal whether proper localizations of actin and MT are critical for the lumen formation.
- Are prior studies referenced appropriately?
The references are cited appropriately.
- Are the text and figures clear and accurate?
There are several typos: Remodelling -> remodeling, signalling -> signaling. In the figure 2, G and H seem redundant. Scale bars are missing in Fig1 F-K, Fig2 K-L, Fig6 A-I, Fig7 E-J and Fig8 E-J.
The author often called shot+ genotype as "wild type". They are transgenic strains with some mutations, and cannot be found in the wild. They should be simply called with genotype or "control" for experiments.
- Do you have suggestions that would help the authors improve the presentation of their data and conclusions?
In Figure 4, as the localization of Shot is difficult to see in detail, enlarged insets might help. In addition, the green and cyan in C'-E' is difficult to distinguish.
With Figure 5, the authors claimed that Shot LOF leads to disorganized MT-bundles and actin localization. We feel this is an overstatement and the Figure should be backed up with better data, or removed. F-actin and microtubule localizations are highly dynamic and the snapshot pictures are insufficient for demonstrating defective localization. It is also possible that (potential) difference in the marker localization is due to indirect effect of Shot LOF in cell shape.
Significance
- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.
- Place the work in the context of the existing literature (provide references, where appropriate).
In blood capillary and insect trachea, the branching process of single vessel cells involves sprouting of cell protrusions, followed by the lumen extension from the main vessels. The lumen formation involves assembly of plasma membrane components inside of the cytoplasm. Since the luminal membrane is associated with protein complexes common to apical cell membrane, lumen formation is believed to involve redirection of apical trafficking of membranes to intracellular sites (Sigurbjörnsdóttir, Mathew, Leptin 2014, 10.1038/nrm3871). The authors previously demonstrated that centrosome is an important link of preexisting lumen to de novo lumen formation, leading to the hypothesis that centrosome-derived microtubules organize lumen membrane assembly.
- State what audience might be interested in and influenced by the reported findings.
In this manuscript, the authors addressed this issue by looking at the function of Shot/Plakin that has both microtubule and actin binding activities. Shot is an ideal candidate for linking actin-rich cell protrusions in the leading edge to centrosome- associated lumen tip. Indeed the authors clearly showed that shot is required for lumen extension and overexpressed shot protein associates with intracellular tract rich in microtubules and F-actin. Their findings are definitely a progress in the field of Drosophila tracheal development. Having said that, how Shot links leading edge protrusions and centrosomes, how it is organized into pre-lumen tract, and how it contribute to further assembly of luminal membrane and directed secretion, are not well understood yet. Without clues to those fundamental questions, I believe this paper is most appropriate for experts readers of Drosophila cell biology and tracheal development.
Finally I feel that the paper include many data sets and some pictures are not easy to grasp essential points, such as three movies showing localization of overexpressed shot-C, RFP-moesin, and Lifeact.
- Define your field of expertise with a few keywords to help the authors contextualize your point of view.
Drosophila, tracheal cell biology.
- Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.
No
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Referee #1
Evidence, reproducibility and clarity
This study provides solid evidences showing a role for the spectraplakin Short-stop (Shot) in subcellular lumen formation in the Drosophila embryonic and larval trachea. This subcellular morphogenetic process relies on an inward membrane growth that depends on the proper organization of actin and microtubules (MTs) in terminal cells (TCs). Shot depletion leads to a defective or absent lumen while conversely, Shot overexpression promotes excessive branching, independently on the regulation of centrosome numbers previously shown to be important for the regulation of the lumen formation process (Ricolo, D., Deligiannaki, M., Casanova, J. & Araújo, S. J. Centrosome Amplification Increases Single-Cell Branching in Post-mitotic Cells. Current Biology 26, 2805-2813 (2016)). Shot is rather important to regulate the organization of the cytoskeleton by crosslinking MTs and actin. Shot expression in TCs is controlled by the Drosophila Serum Response Factor (DSRF) transcription factor. Finally Shot functionally overlaps with the MT-stabilizing protein Tau to promote lumen morphogenesis.
The figures are clear and the questions well addressed with carefully designed and controlled experiments. However, I would have few suggestions that will hopefully make some points clearer.
Major comments:
-Statistical analyses should be added for comparisons of proportions, including Fig. 1E, 1L, Fig. 2G-I, Fig. 6L, Fig. 7K, Fig. 8C-D and Fig. 9G.
-It is not always clear what genotype has been used as the "wt" genotype, as in Fig. S2 or Fig. 3 for example, this should be added to figure legends.
-Live imaging of Shot has been performed with ShotC-GFP, that cannot bind actin. Don't the authors think ShotA-GFP would reflect more accurately Shot endogenous behavior as it interacts both with actin and MTs? It would be better to show this, even if the results shown here tend to be consistent with Shot endogenous localization shown with Shot antibody staining.
-It is of course not possible to generate CRISPR mutant flies with mutations in putative DSRF binding sites in a reasonable amount of time, to confirm that Shot transcription is controlled by DSRF. It would thus be nice to reveal shot mRNA expression with in situ hybridization experiments in wt vs. bs embryos. This would confirm that Shot mRNA is downregulated upon DSRF inhibition and rule out a possible indirect effect on Shot protein stability for example.
-In the same figure, it would also be interesting to show what happens to actin and MTs in bs TCs and to which extent their organization is rescued by Shot overexpression.
-UAS-EB1GFP does not seem to be an appropriate control in Figure 9 (A and B) since it can affect MT dynamics (Vitre, B. et al. EB1 regulates microtubule dynamics and tubulin sheet closure in vitro. Nat. Cell Biol. 10, 415-421 (2008)). Why not simply use an UAS-GFP?
-Shot and probably Tau crosslinking activities are important for lumen morphogenesis with a striking increase in the number of embryos without lumen in shot3 and shot3 tauMR22 mutant embryos. The rescue experiments clearly show that Shot binding to both MT and actin is essential for efficient rescue. The same might apply to Tau since it is able to crosslink actin and MTs (Elie, A. et al. Tau co-organizes dynamic microtubule and actin networks. Sci Rep 5, 1-10 (2015)). I believe showing actin and MTs organization in these rescue experiments would be necessary.
Second, the overexpression experiments indicate that Shot is able to induce extra lumen formation even when unable to bind actin as shown with the increase in the number of supernumerary lumina (ESLs) under overexpression of ShotC and ShotCtail to a lesser extent. This phenotype is also observed under Tau overexpression. This suggest that not crosslinking anymore but rather making MTs more stable could be sufficient to promote extra lumen formation in a wt context. Stabilising MTs by treatment with Taxol might thus be sufficient to promote ESL formation. I am fully aware of the difficulty of treating Drosophila embryos with drugs, making this experiment hard to do, but I think this dual function of Shot and Tau (crosslinking actin and MTs to promote branching vs. stabilizing MTs leading to excessive branching) should be discussed.
Minor comments:
-p2 line 1: 'acentrosomal luminal branching points' may be better than 'acentrosomal branching points' to describe the phenotype.
-p4, line 16: the reference 23 is not properly inserted (should be after 'closure').
-p5, line 16: Please mention what the abbreviations Bnl and Btn stand for.
-p5, line 20: these 80% of TCs cells with defects in subcellular lumen formation should appear on the graph in Fig. 1E (as shown in graph 1L).
-p5, line 26: this 36% value does not seem to correspond to anything on the graph in Fig. 1N. According to the figure legend, 20% of TCs did not elongate at all and the lumen was completely absent (class IV), which is consistent with the result shown in Fig. 1L.
Also, I am not sure why only 25 TCs were analysed in Fig. 1N while there are the data to analyse more as shown in Fig. 1E (400 TCs), this would make the graph more representative.
-p6, line 8: ShotA-GFP is indeed a long isoform but is not the full-length Shot, as it does not contain the plakin repeat exon which would add another ~3000aa.
-p6, lines 21-23: ShotA-GFP localisation is not shown in FigS1. The authors should refer to Fig. 2. Enlarged areas/arrows might help the reader to better visualise the different localisations of ShotA-GFP and ShotC-GFP.
-p7, line 23: Rca1 mutants should be better introduced here.
-p8, line 6: Shot colocalizes/associates with stable MTs and actin would be a more appropriate title for this paragraph.
-p16, line 18: 'Shot is able to mediate crosstalk' would be better than 'Shot is able to crosstalk'.
-p40, lines 6 and 7: L, M and N should be K', K' and K' respectively.
-p41, Fig 10D: It is quite hard to see on the cartoon what the phenotype is for Shot OE.
-The following reference shows an important role for Shot in crosslinking actin and MTs during morphogenesis of the Drosophila embryo and should be cited in this manuscript (Booth, A. J. R., Blanchard, G. B., Adams, R. J. & Röper, K. A Dynamic Microtubule Cytoskeleton Directs Medial Actomyosin Function during Tube Formation. Developmental Cell 29, 562-576 (2014)).
-FigS3. It would be good to add the labels on the figure (ShotC-GFP in green, and MoeRFP/lifeActinRFP in Magenta).
Significance
The findings shown in this manuscript shed an important light on the way subcellular morphogenesis occurs. It was known that both actin and MTs were required in this process, particularly during the formation of Drosophila trachea (JayaNandanan, N., Mathew, R. & Leptin, M. Guidance of subcellular tubulogenesis by actin under the control of a synaptotagmin-like protein and Moesin. Nature Communications 1-10 (2019). doi:10.1038/ncomms4036; Gervais, L. & Casanova, J. In Vivo Coupling of Cell Elongation and Lumen Formation in a Single Cell. Current Biology 20, 359-366 (2010)). This work provides additional molecular insights into the way branching morphogenesis from a single cell occurs in vivo, clearly demonstrating a requirement for actin-MT crosslinking mediated by Shot and Tau.
This could be of great interest in the field of branching morphogenesis and lumen formation, not only in invertebrates but also in vertebrates where such a crosslinking might occur in the vasculature, the lung, the kidney or the mammary gland for example (Ochoa-Espinosa, A. & Affolter, M. Branching Morphogenesis: From Cells to Organs and Back. Cold Spring Harb Perspect Biol 4, a008243-a008243 (2012)).
Field of expertise: morphogenesis, Drosophila, cytoskeleton, microtubules.
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Reply to the reviewers
The response to reviewers consists of three parts:
- A summary of the main points from the two reviews, and the authors' response to these points.
- A detailed revision plan for the preprint, taking into account both the main points of the reviews, and other comments made by the reviewers.
- A point-by-point response to the reviewers.
For figure citations, OV = old version, i.e. bioRxiv preprint 2019-826180v2, and NV = new version, i.e. revised and re-submitted version.
1. Summary of main points by the reviewers, and authors’ responses:
- Both reviewers felt that the manuscript was overlong; Reviewer 1 recommended either shortening it or splitting it into two stories, while Reviewer 2 recommended cutting down the text.
- We have considerably shortened the manuscript in accordance with this request (see revision plan below). We had already considered splitting the manuscript into two parts during the drafting stage, and had rejected this possibility as the data are intertwined - the retroactive validation of the dimer interface by the mutagenesis constructs (OV Fig. S3 [NV Fig. S4]) being a good example.
- The revised manuscript features 7 main figures and 13 supplementals.
- Both reviewers felt too much text and figure space was allocated to negative data, specifically the investigation of potential lipid binding by the TbMORN1 protein, and that there should be more focus on the positive parts of the story.
- A key part of shortening the manuscript has been moving most of the negative data on lipid binding into the supplemental figures, and considerably shortening the associated text. This has allowed the main figures and associated text to focus more on the positive elements of the project, while still ensuring publication of all the data.
- The reviewers appear to be in slight disagreement concerning discussion of the data. Reviewer 1 has encouraged more speculation on the physiological role of PE binding, a potential lipid transfer function, a role for calcium ions, the relevance of the observed disulphide bond, and the role of zinc ions in apicomplexan proteins; Reviewer 2 has recommended avoiding excessive speculation or inference.
- Given that both reviewers have agreed that the original manuscript was overlong, we have implemented Reviewer 2's suggestion here and reduced the amount of speculation in the revised text.
- The reviewers agreed that the technical quality of the data was high and that the conclusions drawn were robust.
- We are glad that the reviewers were appreciative of the data quality. For this reason, we were reluctant to remove any of the data from the manuscript and would prefer instead to transfer it to the supplementals. We feel that the negative data still have considerable community value, given that they show that MORN repeats are not automatically lipid binding modules and can thus act as a caveat to other researchers.
2. Detailed revision plan for the preprint:
- We have implemented the reviewers' suggestions and substantially shortened the
manuscript, primarily by trimming the (phospho)lipid-binding section, which contains
a large amount of negative data. The following main figures have been moved into the
supplemental section:
- OV Fig. 2 ("TbMORN1 interacts with phospholipids but not liposomes") has become NV Fig. S2
- OV Fig. 4 ("TbMORN1(2-15) does not bind to liposomes in vitro") has become NV Fig. S6
- OV Fig. 8 ("Conservation and properties of residues in TbMORN1(7- 15)") has become NV Fig. S11
- This has left a total of 7 main figures and 13 supplementals.
- The text associated with the entirety of the lipid-binding part (OV lines 210- 530, OV Figs. 2-6 [NV Figs. 2-4, S2, S6], OV Supplemental Figs. 2-6 [NV Supplemental Figs. S3-S5, S7, S8]) has been condensed. The focus of this section is now on the positive parts of the data: the PE association (OV Fig. 3 [NV Fig. 2]) and the in vivo work (OV Figs. 5, 6 [NV Figs. 3, 4]).
- We have additionally limited the amount of inference and speculation in the manuscript.
3. Point-by-point responses to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity):
MORN (membrane occupation and recognition nexus) repeat proteins are found in prokaryotes and eukaryotes. They feature characteristic repeats in their primary sequence, have been assumed to play a role in lipid binding, but remain poorly characterized on the functional and structural level. This manuscript tries to address both these questions and is organized in major parts. In the first part the authors characterize a putative role of MORN repeat proteins in lipid binding and membrane association. In the second part, the authors use X-ray crystallography to establish the structure of MORN repeat proteins and to investigate the dimerization.
As a cleverly chosen point of departure, they focus their study particularly on MORN1 from Trypanosoma brucei (TbMORN1), which is composed solely on MORN repeats. The structures of MORN repeats (from several species) in part two provide interesting insights into their mode of homotypic interactions and their role as dimerization or oligomerization devices. The lipid binding and membrane association of MORN proteins in the first part remains somewhat confusing and unclear, despite the use of a whole battery of techniques.
We anticipate that the shortening and refocusing of the lipid binding data has addressed this issue.
It is questionably, why the authors invest so many figures and words to inform the reader on negative results.
We have chosen to publicise our negative data in full because, as noted in the manuscript, there is a widespread and erroneous assumption that MORN repeats are lipid binding modules. We feel that publishing these data will allow them to act as a caveat to other researchers working on MORN repeat proteins. We have, however, addressed the reviewer's request in that we have considerably shortened the text associated with these data and have moved the corresponding figures into the supplementals.
The authors suggest that MORN proteins can bind to lipids via their hydrophobic acyl chainswhich is 'very hard to imagine under physiological conditions unless TbMORN1 is a lipid carrier and not a membrane-binding proteins. Unfortunately, a role as lipid carrier has not been rigorously tested.
The reviewer is correct that we have not specifically tested for a function as a lipid carrier protein and although this was only speculation, it has been toned down accordingly.
In this sense the first part remains somewhat immature and incoherent. Furthermore, they suggest based on the lack-of-evidence that MORN proteins do not bind membranes in vivo and in vitro.
We are not clear where this suggestion was made. Our data indicate that TbMORN1 does not directly bind membranes in vivo or in vitro, and we therefore noted that putative lipid binding by other MORN repeat proteins should be viewed with caution. Specifically, we stated in the Discussion (OV lines 955-956) that "the presence of MORN repeats in a protein should not be taken as indicative of lipid binding or lipid membrane binding without experimental evidence". Again, our expectation is that the major changes planned for the data presentation in this section will make it more coherent.
The main issue of this manuscript is, in my view, the way the data were presented.The manuscript is generally well-written, but much too long. The structural work is important and concise.
We have considerably shortened the manuscript as per the reviewer's request, and especially the section on lipid binding.
The first part, however, reports in five separate figures on a lack of membrane binding by a MORN protein and its ability to bind individual lipids. The physiologically relevance of this lipid binding is questionable as acknowledged by the authors.
We have moved two of these figures (OV Figs. 2, 4) into the supplementals section [NV Figs. S2, S6], shortened the associated text, and limited the amount of speculation.
Even though I find it important that the membrane/lipid binding ability of MORN proteins is rigorously tested, I would highly recommend to separate the current manuscript in two independent stories. Alternatively, I would recommend to reduce the first part into a single figure and to remove the most artifactual assays.
We have implemented the second of these two suggestions for the manuscript. We had already considered splitting the manuscript during the drafting stage, but rejected this possibility as the data were too intertwined. Consequently, we have opted to considerably reduce the first part, and moved OV Figs. 2 and 4 into the supplementals [NV Figs. S2, S6]. We would prefer not to remove data altogether as they are likely to have community value even if they are negative and as noted, they are of good quality.
In the current form, the first part and the second part of the manuscript remain somewhat detached from each other. The characterization of the lipid binding/membrane binding properties has a number of substantial weaknesses (e.g. use of quite different, nonphysiological buffers for membrane binding assays; use of deletion mutants for the binding assays, which do not show the full potential of oligomerization). This which makes it hard to read and confuses the reader. Even though I have no reason to doubt the conclusions by the authors, I do not think that all necessary caution has been invested to rule out other possibilities.
We believe that the shortening and refocusing of the manuscript should address these issues. For consideration of the buffer and deletion mutant points, please see responses to Major Points below.
In summary, even though the technical quality of the individual performed assays is high, there are some conceptual issues that make it hard to make a strong case based on a collection of individual, clear datasets. Even though I find the structures of the MORN proteins important, timely, and interesting, I would not recommend this study for publication in its current form. The manuscript would be more fun to read if both of the parts would be shortened substantially and more focused.
We have implemented this suggestion: the manuscript has been considerably shortened (from 20,489/135,073 to 18,555/103,988 characters/words, focused on reducing the negative lipid-binding results).
While I agree that most evidence provided on lipid/membrane binding of TbMORN1 argue against a direct role of MORN proteins in membrane binding, I feel that the experimental approach is not coherent enough. See a few major points of criticism below.
Major Points:
- The authors decide to characterize the membrane binding of a MORN repeat protein using a deletion variant that lacks the N-terminal repeat. However, in Figure 1B they show that the N-terminal repeat is important for the formation of higher-order oligomers. While I fully understand that the presence of the most N-terminal repeat does hamper the structural work, I find it problematic to remove it for the lipid/membrane-binding assays. The formation of higher oligomeric species beyond the dimer, may be important for membrane binding/recruitment (avidity effects).
As we explained in the manuscript, the reason for not using the full-length protein for in vitro work was because it was polydisperse, and that the yields were extremely low. See OV lines 178-179 ("The yields of TbMORN1(1-15) were always very low, making this construct not generally suitable for in vitro assays".) and OV lines 411-414 ("...TbMORN1(1-15), which was polydisperse in vitro and formed large oligomers (Fig. 1B). The membrane-binding activity of these polydisperse oligomers was not possible to test in vitro, as the purification yields of TbMORN1(1-15) were always low."). Consequently, we used the longest construct that was suitable in terms of chemical and oligomeric homogeneity. Using the full-length protein would have had inherent problems with aggregation, and consequently would have compromised the data and derived results. In order to make this clear in the manuscript we edited the sentence mentioned above as follows:
“It was not possible to test the membrane-binding activity of these polydisperse oligomers in vitro however, as the purification yields of TbMORN1(1-15) were always low. As an alternative, the possible membrane association of TbMORN1(1-15) was examined in vivo."
2) (Related to point 1) I do not understand the choice of the buffers used for some of the assays. The use of pH 8.5 and NaCl concentrations of 200 mM are non-physiological.
These were the buffer conditions required to retain the protein in a monodisperse state, suitable for in vitro assays.
For CD spectroscopy, a high ionic strength was obtained by the use of 200 mM NaF. If a high ionic strength is required to prevent the formation of higher oligomers of MORN, it raises the question if the formation of higher oligomers (under physiological conditions) may also contribute to their function.
The oligomers of TbMORN1 may indeed be the most functionally relevant form of TbMORN1 but we do not currently have a means of testing this in vitro, as acknowledged in the text (OV lines 411-414, quoted above). The aim of CD spectroscopy was to assess fold integrity and stability of different constructs; we used buffers as recommended for the CD spectroscopy experiments by Kelly et al, 2005 (doi:10.1016/j.bbapap.2005.06.005) (Table 1 and section 4.2). Furthermore, the CD spectra of TbMORN(1-15) and TbMORN(2-15) (OV Fig. S1E [NV Fig. S1E]) are basically superimposable, suggesting identical secondary structure content at the concentration used for these experiments.
It is unclear, in which buffer the fluorescence anisotropy measurements were performed.
We have provided details on the buffer conditions for the fluorescence anisotropy experiments in the Materials and Methods section, NV page 23, lines 962-963.
The sucrose-loaded vesicles were hydrated in a 20 mM HEPES pH 7.4, 0.3 M Sucrose. The composition of the buffer after the addition of MORN proteins is not clear.
The Materials and Methods are now unambiguous on this point. Please see NV lines 1036- 1046: "6 μM Rhodamine B dihexadecanoyl phosphoethanolamine (Rh-DHPE) was added to all lipid mixtures to facilitate the visualisation of the SLVs. The lipid mixtures were dried under a nitrogen stream, and the lipid films hydrated in 20 mM HEPES pH 7.4; 0.3 M sucrose. The lipid mixtures were subjected to 4 cycles of freezing in liquid nitrogen followed by thawing in a sonicating water bath at RT. The vesicles were pelleted by centrifugation (250,000 × g, 30 min, RT) and resuspended in 20 mM HEPES pH 7.4, 100 mM KCl to a total lipid concentration of 1 mM. SLVs were incubated with 1.5 μM purified TbMORN1(2-15) in gel filtration buffer (20 mM Tris-HCl pH 8.5, 200 mM NaCl, 2% glycerol, 1 mM DTT) at a 1:1 ratio (30 min, RT)." The liposomes were at physiological pH and close to physiological ionic strength.
Despite the use of an impressive array of techniques, this first part of the manuscript remains somewhat immature and incoherent. Due to the use of constructs that have not the full ability to oligomerize (point 1) and due to the inconsistent use of experimental conditions, it is hard to draw firm conclusions from this first part.
Any biochemical study is conducted within the constraints of the choice of construct and the choice of buffer conditions, and the data are valid within those parameters. This applies as much to positive data as to negative data, so we are not clear why the reviewer is placing such emphasis on this point. In the case of the LiMA data, which are the most unbiased and comprehensive dataset in the manuscript, these experiments were well-controlled and there were also domains present that were recruited to membranes under the buffer conditions, allowing us to rule out that the assay conditions were completely unsuitable. Validating negative results should be done as carefully and with as many orthogonal approaches as the validation of positive results. The reviewer acknowledges below that "the data point in the direction that MORN proteins (or at least TbMORN1) does not directly bind to membranes". This is the conclusion that we wanted to communicate.
For example: In Figure 2E TbMORN(2-15) does show some concentration-dependent binding, which -however- is interpreted as background binding. What are the results using this assay (or better: a liposome floatation assay) when using full-length TbMORN(1-15) in a more physiological buffer?
As noted already, it is not possible to use the TbMORN1(1-15) construct for in vitro assays owing to the extremely low yields and polydisperse nature of the protein. The excess fulllength protein was associated with the cytosolic fraction and not the membrane fraction in vivo (OV Fig. 6B [NV Fig. 4B]).
The statement that MORN proteins bind to lipids, but not to liposomes/membranes is -in my view- not sufficiently addressed to make a strong case.
At no point do we suggest that MORN repeat proteins in general bind to lipids and not to liposomes/membranes. On the contrary, and as detailed in the manuscript, we set out to assay the lipid binding activity of TbMORN1, found that it appears to bind to lipids but not to liposomes/membranes, and have therefore cautioned that lipid or liposome/membrane binding of other MORN repeat proteins must be tested experimentally before claims of function are made.
3) The physiological relevance of lipid binding to MORN proteins remains obscure (as also acknowledged by the authors). Does the binding of PE lipids to the MORN protein have a physiological role? Does the binding of fluorescent PI(4,5)P2 point to a physiological role of MORN proteins?
These are interesting questions that we would like to address in future work.
4) In light of recent data from the Chris Stefan lab (PMID: 31402097) a co-incidence detection of PI(4,5)P2, PS, and cholesterol seems possible. Can the authors address this possibility?
Again, the involvement of cholesterol, PS, and PI(4,5)P2 would be interesting questions for subsequent work but are beyond the scope of the present study. We did partially address this issue in our use of PI(4,5)P2, POPC and cholesterol containing liposomes in liposome cosedimentation assays, which showed no binding (OV Fig. S3A [NV Fig. S4A]).
Furthermore, the role of Ca2+ signaling / Ca2+ ions has not been addressed. In light of the important role of Ca2+ for the recognition of PI(4,5)P2 (PMID: 28177616), this point should be addressed.
We carried out liposome pelleting assays in the presence of Ca2+ and Mg2+, and saw no binding by TbMORN1(2-15) in either condition (see data below). These data were not included in the MS because of the insufficient number of technical replicates available.
5) For characterizing the binding of lipids to MORN proteins, the authors use nonphysiological fluorescent and short-chain lipid analogues at concentrations, which are unlikely to occur for endogenous PIPs in the cytosol of cells. Why choosing such an artificial system? Why introducing this system at length, if other -less artifact-prone- assays are available? I would recommend to not feature this assay as prominently as it was in the current study.
Our aim was to stick to using the same fluorophore throughout all the experiments. The choice of short-chain lipids was constrained by what was commercially available with the BODIPY TMR fluorophore. We have implemented the reviewer's suggestion in the manuscript, and the text associated with the fluorescence anisotropy assays has been considerably shortened. We are aware that the chosen concentration of the fluorescent lipids was out of physiological range, but the requirements of the fluorescence anisotropy itself necessitated a compromise. The possible shortcomings of the fluorescence anisotropy assays are, we believe, more than amply compensated by the LiMA data.
6) How would PE find its way to the lipid binding region in MORN? Would it diffuse to the MORN protein via the aqueous phase or would the MORN protein pickup PE form membranes up collision? The authors should address this point, by separating the lipiddepleted MORN protein from donor-vesicles containing PE by a dialysis membrane. If PE would not find its way to the lipid binding site of MORN, this would imply that MORN protein can extract lipids only upon colliding with the membrane. What is the stoichiometry of PE to MORN?
These are all interesting questions that we would like to pursue in subsequent work, but we feel that they are beyond the scope of the present study. Until we have conditions suitable for obtaining high yields and monodisperse populations of the full-length protein, which probably also necessitates developing conditions for controlled oligomerisation, it would be premature to start this. As to how it picks up PE: it is well known that specific lipid binding/chaperoning proteins can deliver their lipid cargo to other proteins. Additionally, proteins that bind lipids use hydrophobic domains to both interact with and sequester fatty acids and/or lipids from membranes. The literature is populated with lots of such examples. https://www.sciencedirect.com/science/article/pii/S0092867416310765.
Despite my critique raised above, I agree with the authors that the data point in the direction that MORN proteins (or at least TbMORN1) does not directly bind to membranes. Their data, however, would still be consistent with a role as lipid transfer protein and a recruitment of MORN proteins to the membrane by other proteins. Have the authors performed any additional experiments in this direction? Also, the potential role of palmitoylation is only mentioned in the discussion (page 22), while palmitoylation would provide a simple means for membrane recruitment.
We are glad that the reviewer concurs with our main conclusion. We agree, as noted in the discussion, that a role as a lipid transfer protein might still be possible, and this is something that we would like to pursue in follow-up work. We have not yet performed any additional experiments in this direction. Concerning palmitoylation, the predictions using the CSS-Palm software were always weak and ambiguous, and in addition the best candidate cysteine residue was Cys351, which is in our structure engaged in the disulphide bond observed in the C2 crystal form. We feel that this is something to keep in mind, but is not yet a strong enough hypothesis to pursue intensively.
Minor Points:
Figure 1B: The authors should provide information on the void volume of the column.
Implemented in the figure legend (7.2 ml).
Page 17, line 696-701: The authors point out that the C2 crystal form is stabilized by two disulfide bridges. The authors should comment on the physiological relevance of these disulfide bridges.
Given the reducing environment of the cytosol, it is an open question as to whether these disulphide bridges exist in vivo. We would prefer not to speculate on this point, as we do not feel it would be productive.
Page 18, line 734-740: The authors should provide data on the potential role of Zn2+ on MORN function in a physiological context. The section describing that the dimer is stabilized by Zn2+ ions (pages 18 and 19) lacks a discussion if Zn2+ are functionally relevant. There is only a beautiful sequence analysis and a discussion of the conservation of the Zn2+ coordinating residues. Can the authors perform Zn2+ titrations and SEC-MALS experiments (or alternatives such as SAXS) to show that Zn2+ indeed affects the oligomeric state of only the PfMORN, but not the other MORN proteins that form alternative dimers?
The known requirement for zinc ions in Plasmodium growth was already noted (OV lines 992- 993, Marvin et al., 2012), and is, we believe, sufficient to address the issue of physiological relevance at this stage. The zinc ions are predicted to affect the architecture of the apicomplexan (Plasmodium, Toxoplasma) MORN1 protein dimers, not their oligomeric state. For PfMORN1, SEC-MALS and SAXS were carried out in 20 mM Tris-HCl pH 7.5, 100 mM NaCl with no zinc present. When EDTA was added, no change in behaviour of the protein was seen by SEC-MALS. When “TPEN”, a strong zinc chelator, was added, the protein precipitated in SEC-MALS experiments.
Reviewer #1 (Significance):
A putative role of MORN proteins in membrane and lipid binding is addressed. The view the MORN proteins bind directly to membranes is challenged. Structures of dimeric MORN proteins provide important insight into the modes of dimerization.
There is a recent structure of MORN proteins (which is referenced by the authors), but I feel that additional structural work is important and justified. The work on membrane vs. lipid binding is important, but not sufficiently addressed in the current manuscript.
We are glad that the reviewer finds the structural work important and justified, although we disagree with the reviewer’s assessment of the lipid binding. As noted in the previous paragraph, our data challenge the assumption that MORN repeat proteins directly bind membranes, and we feel that this alone is a significant conceptual advance.
I would recommend to separate the study in two parts. The audience is likely to confused (or bored) by the lengthy discussion on whether or not MORN proteins bind lipids and or membrane or not.
We would prefer to implement the reviewer's other suggestion, namely that the manuscript is considerably shortened and less focus given to the negative data on lipid binding.
I am not an expert in structural biology, but have a fair understanding of structural biology. I have worked on lipid binding proteins and have a very good understanding of lipid/membrane-binding assays.
Reviewer #2 (Evidence, reproducibility and clarity):
Summary
The manuscript describes an extensive and detailed investigation into the structure and function(s) of MORN domains. It has to be acknowledged that, despite the considerable amount of work reported, the conclusions are rather limited. From a technical viewpoint, the experiments have been appropriately executed and, generally, I concur with the conclusions drawn. However, the manuscript is over-long: in general, I would recommend concentrating on positive conclusions which can be drawn from the data and avoid excessive speculation or inference (some examples given below).
We are glad that the reviewer is satisfied with the technical quality of the work and (in general) the validity of the conclusions. We acknowledge that the original submission was fairly long, and have considerably shortened the revised manuscript and focused more on the positive conclusions in order to implement this suggestion.
Major Comments
There are three general- perhaps rather obvious- points to make. First, there is no particular reason to think that conservation of structure necessarily indicates conservation of a particular function. There seems to be an implicit assumption that MORN domains are associated with a specific, well-defined biological function. Given their diversity, are there particular reasons to think that this is the case?
The reviewer is exactly right that there is an implicit assumption that MORN domains are associated with a specific, well-defined function: specifically, lipid binding. It is this assumption, which has been widely circulated in the almost complete absence of experimental evidence, that we are challenging. We agree that MORN repeats are likely to be capable of multiple functions, and protein-protein interactions are now better supported than protein-lipid interactions.
Second, a strategy which examines the properties of just the recombinant MORN domains in vitro, removed from the context of the whole protein (eg junctophilin) or- importantly- its interacting partners in vivo, has obvious limitations. Frequently a reductionist approach is successful; however, in this case, MORN domains appear to be less tractable to that kind of approach. For all the in vitro binding and structural experiments presented, there is always a concern that the absence of other parts of the relevant MORN-containing protein or its partners could explain failure or inconsistency of in vitro biological activity measurements.
Again, the reviewer is right that there is an inherent contextual limitation to any in vitro work that utilises a single protein, but this is a concern that - by definition - could be raised about any in vitro study utilising a single protein. It should be noted that we have also carried out in vivo experiments using TbMORN1 (OV Figs. 5, 6 [NV Figs. 3, 4]).
Third, the possibility that MORN domains might mediate interactions with other proteins seems to be given little consideration, in spite of the Li et al (2019) paper. An experimental strategy which looked for binding partners (eg by pulldown assay) might have provided more insight.
These data are already in the literature. A previous study by the same team (Morriswood et al., 2013) used proximity-dependent biotin identification to identify candidate binding partners and near neighbours of TbMORN1.
In order to stress this point we added the following sentence in the discussion section, NV pages 18-19, lines 774-778.
“The concluding data presented here suggest that TbMORN1 utilises this oligomerisation capacity to build mesh-like assemblies, which can reach considerable size in vitro (Fig. 7G). These mesh-like assemblies may reflect the endogenous organisation of the protein in vivo, where a number of binding partners have already been identified (Morriswood et al., 2013)”.
Minor Comments
- In the abstract and elsewhere the authors refer to a possible function of MORN domains as 'dimerisation and oligomerisation devices' (line 53). What is the evidence that dimer formation is important for function in vivo?
This is an interesting and important question and one that we would like to address in future work. We did attempt to generate trypanosome cell lines that inducibly expressed monomeric TbMORN1 (the double mutant, where the point mutations were simultaneously introduced in the dimerisation interface in repeats 13 and 14), but no expression of the ectopic protein was ever observed (9 separate clones obtained in 3 independent transfections). This might indicate the importance of the dimeric state in vivo, perhaps hinting that dimerisation is important for protection from degradation. In general, proteins assuming higher oligomeric states in homo- or heteromeric assemblies benefit from increased robustness in the cellular environment and optimised activity by the following means:
- Increased stability by decreasing the surface area/volume ratio
- Simple construction of larger complexes
- Allosteric regulation
- Co-localisation of distinct biological functions
- Substrate channelling
- Protection from aggregation or degradation
Which or which combination of the factors is relevant for TbMORN1 being a functional dimer in vivo is difficult to say at this point.
- Did the authors attempt to co-crystallize TbMORN1(7-15) with PI(4,5)P2?
No. For crystallisation, we used lysine methylated samples, and by doing this we neutralised positively-charged potential binding sites which would have interacted with the negatively charged lipid headgroup. We did not observe any bound lipids in the electron density maps obtained from the crystals.
- Fig 2C: did the authors also estimate binding stoichiometry as well as the equilibrium binding constants for these data? This should be determined by fitting a single binding site model to the data. Other methods (eg ITC) can probably determine this with more accuracy. The value of stoichiometry is sometimes forgotten in such binding measurements- is one ligand bound per monomer or dimer, for example?
We discussed estimation of the binding stoichiometry in the fluorescence anisotropy assays at some length, but the conclusion was that the required experiments would contain too many approximations to provide high-confidence data. We did use ITC and also MST, but did not observe any binding with these assays.
- Lines 674-678 I found it hard to work out whether these constructs harbour the natural C-terminal sequence without truncation or addition of an affinity tag. I think the answer is 'yes' but it was difficult working this out from the details in M&M.
TbMORN1(7-15) crystallisation was with a C-terminal Strep tag; TgMORN1(7-15) and PfMORN1(7-15) had their affinity tags removed by protease treatment prior to crystallisation. We have clarified this point in the M&M, page 29, lines 1189-1192: “Crystallisation of TbMORN1(7-15) (with a C-terminal Strep tag), TgMORN1(7-15) and PfMORN1(7-15) (both with affinity tags removed) was performed at 22 °C using a sitting-drop vapour diffusion technique and micro-dispensing liquid handling robots (Phoenix RE (Art Robbins Instruments) and Mosquito (TTP labtech).”
- Lines 688-694 The PISA interface analysis is useful here in distinguishing crystal contacts from those which persist in solution. The discussion of the results is unclear, however, on this critical point: were the dimer interfaces the only contacts which were significant in the various crystal forms?
Yes, correct. PISA showed that the described dimerisation contacts were the only significant ones in the various crystal forms. Other crystals contacts had typically low P-values and poor ΔG and small “radar” surface in the complexive PISA analysis.
In the case of both TbMORN1 crystal forms and in the case of the TgMORN1 P43212 crystal form we have a dimer in the asymmetric unit, while in the case of the PfMORN1 and TgMORN1 P6222 form we have one molecule in the asymmetric unit, and the dimer is created by the crystallographic twofold axis. In the latter cases the quaternary structure resulting from the symmetry operations was the top-scoring one considering either P-values and/or the number of stabilising interactions buried surface area.
- Lines 754-763 This paragraph seems rather speculative and is a good example where the text could be cut down.
If the line citation is correct, then we disagree with this assessment and would prefer not to implement it. The paragraph in question concerns a detailed and very precise discussion of the side chain interactions that stabilise the V-shaped forms of TgMORN1 and PfMORN1.
- Line 765-788 This section is also rather overdone: such observations are only useful if they are subsequently tested by recording dimer conformation for a representative selection of MORN dimers from different species.
Again, we disagree with the reviewer's assessment of this analysis. The analysis has considerable predictive power and already has some experimental validation via the SAXS observation that PfMORN1 is capable of forming extended dimers in solution (OV Fig. 10C [NV Fig. 7C]).
- Lines 800-801 I don't think this statement is strictly correct. The SAXS data show that PfMORN1(7-15) adopts an extended conformation, with no evidence of the 'V' shaped structure. Related to that point, from what I could glean from the SAXS Methods section, all solution conditions for these experiments were conducted without Zn2+? If some dimer interfaces require Zn2+, should it not be included?
We have clarified this statement. The SAXS experiments were conducted without zinc, and, as we have stressed, the V-shaped form of TgMORN1 and PfMORN1 was only ever observed in the crystals. For PfMORN1, SEC-MALS and SAXS were carried out in 20 mM Tris-HCl pH 7.5, 100 mM NaCl with no zinc present. When EDTA was added, no change in behaviour of the protein was seen by SEC-MALS. When “TPEN”, a strong zinc chelator, was added, the protein precipitated in SEC-MALS experiments.
Reviewer #2 (Significance):
There is certainly value in establishing that MORN domains do not, in vitro, appear to bind to lipid vesicles, and to define their lipid binding capability (although it is rather complex). The crystal structures and SAXS data extend the rather limited structural data on MORN domains. Despite the effort involved, conclusions about likely functions of MORN domains in vivo are rather limited.
We are glad that the reviewer acknowledges the value in challenging the assumption that MORN repeats are lipid binding devices, and that the structural data are important for expanding the knowledge base on this class of repeat motif proteins. In vivo functional work is being actively pursued at present.
My expertise lies in X-ray crystallography and protein biochemistry.
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Referee #2
Evidence, reproducibility and clarity
Summary
The manuscript describes an extensive and detailed investigation into the structure and function(s) of MORN domains. It has to be acknowledged that, despite the considerable amount of work reported, the conclusions are rather limited. From a technical viewpoint, the experiments have been appropriately executed and, generally, I concur with the conclusions drawn. However, the manuscript is over-long: in general, I would recommend concentrating on positive conclusions which can be drawn from the data and avoid excessive speculation or inference (some examples given below).
Major Comments
There are three general- perhaps rather obvious- points to make. First, there is no particular reason to think that conservation of structure necessarily indicates conservation of a particular function. There seems to be an implicit assumption that MORN domains are associated with a specific, well-defined biological function. Given their diversity, are there particular reasons to think that this is the case? Second, a strategy which examines the properties of just the recombinant MORN domains in vitro, removed from the context of the whole protein (eg junctophilin) or- importantly- its interacting partners in vivo, has obvious limitations. Frequently a reductionist approach is successful; however, in this case, MORN domains appear to be less tractable to that kind of approach. For all the in vitro binding and structural experiments presented, there is always a concern that the absence of other parts of the relevant MORN-containing protein or its partners could explain failure or inconsistency of in vitro biological activity measurements. Third, the possibility that MORN domains might mediate interactions with other proteins seems to be given little consideration, in spite of the Li et al (2019) paper. An experimental strategy which looked for binding partners (eg by pulldown assay) might have provided more insight.
Minor Comments
- In the abstract and elsewhere the authors refer to a possible function of MORN domains as 'dimerisation and oligomerisation devices' (line 53). What is the evidence that dimer formation is important for function in vivo?
- Did the authors attempt to co-crystallize TbMORN1(7-15) with PI(4,5)P2?
- Fig 2C: did the authors also estimate binding stoichiometry as well as the equilibrium binding constants for these data? This should be determined by fitting a single binding site model to the data. Other methods (eg ITC) can probably determine this with more accuracy. The value of stoichiometry is sometimes forgotten in such binding measurements- is one ligand bound per monomer or dimer, for example?
- Lines 674-678 I found it hard to work out whether these constructs harbour the natural C-terminal sequence without truncation or addition of an affinity tag. I think the answer is 'yes' but it was difficult working this out from the details in M&M.
- Lines 688-694 The PISA interface analysis is useful here in distinguishing crystal contacts from those which persist in solution. The discussion of the results is unclear, however, on this critical point: were the dimer interfaces the only contacts which were significant in the various crystal forms?
- Lines 754-763 This paragraph seems rather speculative and is a good example where the text could be cut down.
- Line 765-788 This section is also rather overdone: such observations are only useful if they are subsequently tested by recording dimer conformation for a representative selection of MORN dimers from different species.
- Lines 800-801 I don't think this statement is strictly correct. The SAXS data show that PfMORN1(7-15) adopts an extended conformation, with no evidence of the 'V' shaped structure. Related to that point, from what I could glean from the SAXS Methods section, all solution conditions for these experiments were conducted without Zn2+? If some dimer interfaces require Zn2+, should it not be included?
Significance
There is certainly value in establishing that MORN domains do not, in vitro, appear to bind to lipid vesicles, and to define their lipid binding capability (although it is rather complex). The crystal structures and SAXS data extend the rather limited structural data on MORN domains. Despite the effort involved, conclusions about likely functions of MORN domains in vivo are rather limited. My expertise lies in X-ray crystallography and protein biochemistry.
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Referee #1
Evidence, reproducibility and clarity
MORN (membrane occupation and recognition nexus) repeat proteins are found in prokaryotes and eukaryotes. They feature characteristic repeats in their primary sequence, have been assumed to play a role in lipid binding, but remain poorly characterized on the functional and structural level. This manuscript tries to address both these questions and is organized in major parts. In the first part the authors characterize a putative role of MORN repeat proteins in lipid binding and membrane association. In the second part, the authors use X-ray crystallography to establish the structure of MORN repeat proteins and to investigate the dimerization.
As a cleverly chosen point of departure, they focus their study particularly on MORN1 from Trypanosoma brucei (TbMORN1), which is composed solely on MORN repeats. The structures of MORN repeats (from several species) in part two provide interesting insights into their mode of homotypic interactions and their role as dimerization or oligomerization devices. The lipid binding and membrane association of MORN proteins in the first part remains somewhat confusing and unclear, despite the use of a whole battery of techniques. It is questionably, why the authors invest so many figures and words to inform the reader on negative results. The authors suggest that MORN proteins can bind to lipids via their hydrophobic acyl chains- which is 'very hard to imagine under physiological conditions unless TbMORN1 is a lipid carrier and not a membrane-binding proteins.' Unfortunately, a role as lipid carrier has not been rigorously tested. In this sense the first part remains somewhat immature and incoherent. Furthermore, they suggest based on the lack-of-evidence that MORN proteins do not bind membranes in vivo and in vitro.
The main issue of this manuscript is, in my view, the way the data were presented.The manuscript is generally well-written, but much too long. The structural work is important and concise. The first part, however, reports in five separate figures on a lack of membrane binding by a MORN protein and its ability to bind individual lipids. The physiologically relevance of this lipid binding is questionable as acknowledged by the authors. Even though I find it important that the membrane/lipid binding ability of MORN proteins is rigorously tested, I would highly recommend to separate the current manuscript in two independent stories. Alternatively, I would recommend to reduce the first part into a single figure and to remove the most artifactual assays. In the current form, the first part and the second part of the manuscript remain somewhat detached from each other. The characterization of the lipid binding/membrane binding properties has a number of substantial weaknesses (e.g. use of quite different, non-physiological buffers for membrane binding assays; use of deletion mutants for the binding assays, which do not show the full potential of oligomerization). This which makes it hard to read and confuses the reader. Even though I have no reason to doubt the conclusions by the authors, I do not think that all necessary caution has been invested to rule out other possibilities.
In summary, even though the technical quality of the individual performed assays is high, there are some conceptual issues that make it hard to make a strong case based on a collection of individual, clear datasets. Even though I find the structures of the MORN proteins important, timely, and interesting, I would not recommend this study for publication in its current form. The manuscript would be more fun to read if both of the parts would be shortened substantially and more focused. While I agree that most evidence provided on lipid/membrane binding of TbMORN1 argue against a direct role of MORN proteins in membrane binding, I feel that the experimental approach is not coherent enough. See a few major points of criticism below.
Major Points:
1) The authors decide to characterize the membrane binding of a MORN repeat protein using a deletion variant that lacks the N-terminal repeat. However, in Figure 1B they show that the N-terminal repeat is important for the formation of higher-order oligomers. While I fully understand that the presence of the most N-terminal repeat does hamper the structural work, I find it problematic to remove it for the lipid/membrane-binding assays. The formation of higher oligomeric species beyond the dimer, may be important for membrane binding/recruitment (avidity effects).
2) (Related to point 1) I do not understand the choice of the buffers used for some of the assays. The use of pH 8.5 and NaCl concentrations of 200 mM are non-physiological. For CD spectroscopy, a high ionic strength was obtained by the use of 200 mM NaF. If a high ionic strength is required to prevent the formation of higher oligomers of MORN, it raises the question if the formation of higher oligomers (under physiological conditions) may also contribute to their function. It is unclear, in which buffer the fluorescence anisotropy measurements were performed. The sucrose-loaded vesicles were hydrated in a 20 mM HEPES pH 7.4, 0.3 M Sucrose. The composition of the buffer after the addition of MORN proteins is not clear. Despite the use of an impressive array of techniques, this first part of the manuscript remains somewhat immature and incoherent. Due to the use of constructs that have not the full ability to oligomerize (point 1) and due to the inconsistent use of experimental conditions, it is hard to draw firm conclusions from this first part. For example: In Figure 2E TbMORN(2-15) does show some concentration-dependent binding, which -however- is interpreted as background binding. What are the results using this assay (or better: a liposome floatation assay) when using full-length TbMORN(1-15) in a more physiological buffer? The statement that MORN proteins bind to lipids, but not to liposomes/membranes is -in my view- not sufficiently addressed to make a strong case.
3) The physiological relevance of lipid binding to MORN proteins remains obscure (as also acknowledged by the authors). Does the binding of PE lipids to the MORN protein have a physiological role? Does the binding of fluorescent PI(4,5)P2 point to a physiological role of MORN proteins?
4) In light of recent data from the Chris Stefan lab (PMID: 31402097) a co-incidence detection of PI(4,5)P2, PS, and cholesterol seems possible. Can the authors address this possibility? Furthermore, the role of Ca2+ signaling / Ca2+ ions has not been addressed. In light of the important role of Ca2+ for the recognition of PI(4,5)P2 (PMID: 28177616), this point should be addressed.
5) For characterizing the binding of lipids to MORN proteins, the authors use non-physiological fluorescent and short-chain lipid analogues at concentrations, which are unlikely to occur for endogenous PIPs in the cytosol of cells. Why choosing such an artificial system? Why introducing this system at length, if other -less artifact-prone- assays are available? I would recommend to not feature this assay as prominently as it was in the current study.
6) How would PE find its way to the lipid binding region in MORN? Would it diffuse to the MORN protein via the aqueous phase or would the MORN protein pickup PE form membranes up collision? The authors should address this point, by separating the lipid-depleted MORN protein from donor-vesicles containing PE by a dialysis membrane. If PE would not find its way to the lipid binding site of MORN, this would imply that MORN protein can extract lipids only upon colliding with the membrane. What is the stoichiometry of PE to MORN?
Despite my critique raised above, I agree with the authors that the data point in the direction that MORN proteins (or at least TbMORN1) does not directly bind to membranes. Their data, however, would still be consistent with a role as lipid transfer protein and a recruitment of MORN proteins to the membrane by other proteins. Have the authors performed any additional experiments in this direction? Also, the potential role of palmitoylation is only mentioned in the discussion (page 22), while palmitoylation would provide a simple means for membrane recruitment.
Minor Points:
Figure 1B: The authors should provide information on the void volume of the column.
Page 17, line 696-701: The authors point out that the C2 crystal form is stabilized by two disulfide bridges. The authors should comment on the physiological relevance of these disulfide bridges.
Page 18, line 734-740: The authors should provide data on the potential role of Zn2+ on MORN function in a physiological context. The section describing that the dimer is stabilized by Zn2+ ions (pages 18 and 19) lacks a discussion if Zn2+ are functionally relevant. There is only a beautiful sequence analysis and a discussion of the conservation of the Zn2+ coordinating residues. Can the authors perform Zn2+ titrations and SEC-MALS experiments (or alternatives such as SAXS) to show that Zn2+ indeed affects the oligomeric state of only the PfMORN, but not the other MORN proteins that form alternative dimers?
Significance
A putative role of MORN proteins in membrane and lipid binding is addressed. The view the MORN proteins bind directly to membranes is challenged. Structures of dimeric MORN proteins provide important insight into the modes of dimerization.
There is a recent structure of MORN proteins (which is referenced by the authors), but I feel that additional structural work is important and justified. The work on membrane vs. lipid binding is important, but not sufficiently addressed in the current manuscript.
I would recommend to separate the study in two parts. The audience is likely to confused (or bored) by the lengthy discussion on whether or not MORN proteins bind lipids and or membrane or not.
I am not an expert in structural biology, but have a fair understanding of structural biology. I have worked on lipid binding proteins and have a very good understanding of lipid/membrane-binding assays.
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Preprint Review
This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.
Summary
This paper analyzes the evolution of the KRAB-containing zinc finger protein (KZFP) family of proteins. While the reviewers were all interested in the topic, several major concerns came up during review. These include technical limitations of the methods chosen to analyze this challenging protein family (e.g., determination of orthology, selection analysis, and so on), and that new ideas, including claims about non-coding evolution and positive selection, are not convincingly supported by the analysis presented.
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Reply to the reviewers
R-We would like to thank the reviewers for their constructive feedback. We respond to all the reviewers points below. We highlighted major changes introduced to the manuscript in response to both reviewers’ comments in the attached revised version of the manuscript.
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
The work described in this manuscript by Tan and Marques aims to address if the splicing of enhancer-associated long noncoding RNAs (elncRNAs) has a direct impact on enhancer activity or just reflects their cognate's enhancer's high activity.
For this purpose, the authors started by integrating RNA-seq data for human lymphoblastoid cell lines with ENCODE enhancer annotations and ChIP-seq data for enhancer function-associated chromatin modifications to show that multi-exonic elncRNAs are more transcriptionally active than single-exonic elncRNAs and eRNAs. They then show that regions flanking elncRNA splice sites are enriched in splicing-associated sequence elements and that these are under stronger purifying selection (suggesting some functional relevance), both when compared to promoter-associated lncRNAs. They also show the concomitance of cis-disrupted splicing in elncRNAs and drops in expression in their target genes. Finally, they use causal inference analysis of joint seQTLs and joint scQTLs to investigate the causal relationship between splicing of elncRNAs and expression of putative gene targets and chromatin states at elncRNA cognate enhancers, respectively. They conclude that, in both cases, most associations are causally mediated by splicing of elncRNAs and therefore that this contributes to their enhancer activity.
This manuscript is generally well written, targets an original question and potentially sets the seeds for a new exciting line of research on transcriptional regulation, by providing some evidence for the functional relevance of the splicing of elncRNAs. However, the overlooking of some important aspects of the regulation of RNA splicing led to biases in the design of data analyses and in the interpretation of the biological implications of some results that need to be dealt with before the described work can be considered sound enough for publication.
R:We would like to thank the reviewer for taking the time to assess our manuscript and for the constructive comments.
**Essential revisions:**
1.Results, page 10, lines 21-24: The statement that "the impact of SS variants on gene splicing efficiency depends on the total number of alternative transcripts and exons" is not properly substantiated. The four examples given in Figure S3 do not illustrate any dependence or trend. If such dependence is "expected" the underlying concept must be explained, i.e. why the impact of SS variants on splicing efficiency should depend on the number of alternative transcripts and exons. The reader is unrealistically expected to be used to the chosen splicing efficiency metric to intuit its dependence on the number of exons. Moreover, it is not obvious where the dependence on the number of alternative transcripts comes from, particularly given that alternative splicing (e.g. the skipping of a neighbouring exon, if internal) is not profiled.
R-In the previous version of the manuscript, we estimated gene level changes in splicing, which includes all alternative splicing events within a gene. Therefore, the more exons an elncRNA has, the more “diluted” we expect the overall impact of SS variant on elncRNA splicing efficiency to be. After considering the reviewer’s comment, we realized that only splicing events directly impacted by the SS variant should be considered in this analysis.
In the revised version of the manuscript, we considered only alternative splicing events that include the splice donor acceptor site changed by the SS variant, and are therefore a direct consequence, of the SS variant. As suggested by the reviewer, for these splicing events, we report the fold difference in Percentage-Spliced-In (PSI) (estimated by Leafcutter (Li et al. 2018)) between samples that carry reference and alternative alleles at these SS variants. To further illustrate these changes, we now include a diagram, for each SS variant, with differential splicing information and the fold difference in PSI for each affected splicing events (Figure 3B,C, Supplementary Figure S3). In addition, the overall change across all affected splicing events is also plotted in Figure 3D and Supplementary Figure S4.
We have modified this section to account for this and the next comment from the reviewer.
“To estimate the impact of SS variant on splicing efficiency, we calculated the Percentage-Spliced-In (PSI) (Li et al. 2018) per individual and for each elncRNA splicing event involving the splice donor or acceptor site disrupted by the SS variants (Figure 3B,C, Supplementary Figure S3). PSI measures exon inclusion and considers spliced reads spanning exon junctions (Li et al. 2018). We compared the average difference in PSI, as a proxy for change in splicing efficiency, of all affected splicing events between individuals that carry the reference and alternative canonical splice donor/acceptor sites (GT-AG). Alongside decreased exon inclusion, SS variants can also promote exon skipping events (Figure 3B,C, Figure S3). Despite some increase in exon skipping, SS variants are associated with an overall decrease in splicing efficiency (Figure 3D and Supplementary Figure S4).” (Page 10).
Along these same lines, and more importantly, why haven't the authors looked at the possibility that a variant disrupting a splice site would lead to skipping of the neighbouring exon (if internal)? Given how the spliceosome operates (in terms of intron and exon recognition), wouldn't this be the most likely scenario? When calculating the splicing index, are reads spanning junctions between non-consecutive exons considered? Otherwise, not profiling alternative isoforms generated by exon skipping will necessarily bias splicing efficiency quantifications by overlooking fully efficient splicing associated with such isoforms. Similarly, how did the authors make sure that splicing changes did not bias elncRNA expression estimates? How was the effective transcript length determined for the calculation of RPKMs? The authors need to make these methodological clarifications, as well as why exon skipping was not considered as a splicing disruption with potential functional implications. Calculating the percent spliced-in (PSI) for all internal exons would be much informative.
R-Regarding the methodology, what we refer to as splicing efficiency is Percentage-Spliced-In (PSI). We calculated PSI for all, including alternative, splicing events. We now make this clearer throughout the manuscript and in the figure axis/legends.
As detailed in the methods section, to minimize the impact of alternative splicing on gene expression estimates, we quantified expression at the gene, and not at the transcript, level using HTSeq across all annotated exons. This approach allows us to assess elncRNA and target gene expression while masking differences in alternative transcript abundance, which are not relevant in the context of this analysis.
As suggested by the reviewer, instead of considering PSI of all possible splicing events of the gene, in the revised version of the manuscript, we considered only splicing events that are directly impacted by the SS variant. This change does not impact our conclusions, but certainly provides a better understanding of how SS variants impact splicing and we would like to thank the reviewer for raising this point. As predicted by the reviewer and as expected given how the spliceosome operates, exon skipping is a frequent outcome of SS variants. However, the increase in exon skipping is not sufficient to compensate for the decrease in the inclusion of these exons, which is directly impacted by the SS variants. This is demonstrated by the lower overall splicing efficiency for each elncRNAs in individuals that carry SS variants that disrupt canonical splice/donor acceptor sites (Figure 3D and Supplementary Figure S4).
3.All results in panels 3B-F are presented as fold differences. It is actually not clear what those differences refer to. For instance, the grey boxes are the distributions of the fold differences in splicing index / expression between individuals carrying reference alleles and what?
R-The boxplots represent the distribution of the fold difference in PSI or expression for each individual relative to the median PSI or expression in individuals with the reference genotype. As expected, the distribution of log fold difference in either PSI or expression for individuals carrying the reference allele is centered at 0.
We have clarified this in the methods section and figure legends.
4.It is expectable that most joint seQTLs result from variants directly impacting splicing in cis. As the quantification of splicing is noisier than that of expression, a stronger effect is required for the detection of an sQTL than an eQTL. In other words, joint seQTLs are essentially sQTLs. This illustrated by the example in Figure 4A, with the SNP in an intronic region of the elncRNA being associated with strong differences in splicing and tiny (R-We agree with the reviewer that the quantification of splicing is noisier than that of expression. However, and in contrast with the reviewer’s hypothesis, higher “noise” in splicing quantification compared to expression led to weaker associations between splicing and seQTLs, as illustrated in the figure below. This is in line with splicing being measured with higher error rate, which would ultimately lead to smaller detectable sQTL effect than what they would be with perfect measurements. This also demonstrates that since a priori, eQTLs association are stronger, if a bias exists in the causal inference analysis, it should favour detection of non-causal associations. Therefore, our approach is not biased in detecting causal seQTLs.
We agree with the reviewer that this potential bias may be a concern to readers and should be addressed. We have added this analysis to the text (Supplementary Figure S7E) and explained why the causal inference testing approach is not biased in detecting causal seQTLs.
“To assess whether this approach was biased towards the detection of causal seQTLs we compared the slope and adjusted p-value of the associations between the variant and splicing or expression for all causal seQTLs. As illustrated in Supplementary Figure S7E this analysis revealed there is no evidence that stronger sQTLs would favour causal model predictions.” (Page 16).
5.It is not totally clear what message the authors intend to convey with the result of panel 4D. Are they talking about the relative position of the variant to the elncRNA transcript or the target transcript? If the former, shouldn't the known synergy between transcription and 5´ end splicing reflect on elncRNA expression? If the latter, it is not obvious how the result connects to the mentioned synergy.
R:In Figure 4D, we show the relative position, within a transcript, of the exonic splicing junction which is associated with causal seQTLs. The enrichment in associations with splicing junctions located at 5’ end of elncRNAs is consistent with the synergy between 5’ end splicing and transcription. We clarify this in the text:
“Importantly, 90% of seQTL associations that support elncRNA splicing as a mediator of target expression are associated with splicing junctions located at the 5´ end of the transcript, which is consistent with the known synergy between transcription and 5´ end splicing (Furger et al. 2002; Damgaard et al. 2008)(Figure 4D).” (Page 17).
**Proofreading edits:**
R:We would like to thank the reviewer for identifying all the typos listed below. We have corrected them in the revised version of the manuscript.
6.Introduction, page 3, line 13: double "in".
7.Figure S2A, leftmost panel X-axis label: "intrno" instead of "intron".
8.Results, page 10, line 30: remove "of".
9.It is 5´ and 3´(prime) not 5' and 3' (apostrophe).
**Other suggestions:**
10.Violin plots (with included boxplots) would more comprehensively convey the differences in distributions than the chosen notched boxplots.
R-We thank the reviewer for this suggestion. Although we appreciate the added information a violin plot can provide, this also renders, in our opinion, their interpretation less intuitive. Because boxplots are simpler and easier to interpret, after consideration, we decided to continue using these to represent the distribution of the data.
Reviewer #1 (Significance (Required)):
It is hard for me to assess the significance of this work (beyond some evidence for the potential functional relevance of the splicing of elncRNAs) until the aforementioned concerns are addressed but it is of potential interest to the broad RNA research community.
I am a computational biologist with experience in the analysis of high-throughput transcriptomic data and a focus on transcriptional and alternative splicing regulation.
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Reviewer #2 (Evidence, reproducibility and clarity (Required)):
This manuscript addresses an interesting question - whether the splicing of transcriptional enhancer-associated RNAs influences their transcriptional enhancement activity. The analyses appear carefully done, using appropriate datasets and statistical methods. The authors find, for example, that marks of active chromatin are enriched near spliced elncRNAs, that splicing-related motifs of elncRNAs are under selective constraint, and that splicing of elncRNAs is associated with higher elncRNA expression and to very slightly higher expression of target genes.
R:We thank the reviewer for the constructive feedback on our manuscript. We have extended our analysis to address the reviewers concerns that we detail in our response to the comments below.
However, I did not find the main results convincing of the main conclusion for the following reasons:
1.The most direct evidence is shown in Figure 3, where SNPs that occur in 3' splice sites of elncRNA introns are explored, and it is shown that variants predicted to disrupt splicing of elncRNA introns are associated with reduced expression of target but not non-target genes. But the fold difference in expression of target genes is extremely small - a few percent - and is actually less than the fold difference in expression of the elncRNAs themselves (which appears closer to 10%), raising the question of whether elncRNA expression rather than splicing may be more important for activity. Furthermore, the entire analysis has an anecdotal quality, being based on only 4 splice-disrupting elncRNA variants. I did not find the figure at all convincing of the conclusion the authors draw from it.
R:We agree with the reviewer that our analysis is limited by the available genotyping data that is restricted to common genetic variants. Our evolutionary constraint analysis (Figure 2) indicates that variants that disrupt elncRNA splicing are depleted by natural selection and so we expected to identify a relatively small number of elncRNAs (n=4) suitable for this analysis. Despite the anticipated challenges in identifying elncRNA splice site mutations, we nevertheless believe this unbiased natural mutational analysis is analogous to experimentally disrupting splice sites of these 4 elncRNA candidates.
Regarding the strength of impact of splicing on target expression: in the absence of a comparable experiment, we could not anticipate the magnitude of the effect. We acknowledge that previous studies, which sought to completely remove splicing by either deleting all elncRNA introns (Yin et al. 2015) or terminating transcription after its 1st exon (Engreitz et al. 2016), were both associated with significantly stronger impact on elincRNA splicing and target expression than what we report here. The analysis we present here involves single nucleotide polymorphisms and so it is not surprising to have resulted in more moderate impact on overall splicing. Furthermore, whether the differences in the impact on target expression between this and previous analysis is the result of stronger effect of complete removal of splicing or a consequence of the genetic changes introduced remains unclear. The small yet consistent decrease in target expression we observed, even with minimal changes in splicing of an unbiased set of 4 candidates, is in our opinion strong evidence that modulation in elncRNA splicing is sufficient to impact, albeit moderately, target expression.
Importantly, we replicated the impact of decreased splicing on target expression of the 4 elncRNA candidates using 89 samples of Yoruba (YRI) population from the Geuvadis dataset (Supplementary Figure S5). The robustness of the mutational study consistently supports the physiologically relevant effect of elncRNA splicing on cognate enhancer function.
As pointed out by the reviewer, elncRNA SS variants led to stronger impact on the expression of the elncRNAs compared to that of their targets (Figure 3F,H and Supplementary Figure S4), which suggests that target expression regulation is likely a consequence of changes in elncRNA expression as a result of changes in its splicing. This is described as our working model in the discussion section of the manuscript.
2.Figure 4 uses a causal inference approach and involves larger datasets. While causal inference can be a useful tool to identify candidate causal relationships, it does not prove causality, which still requires some sort of experimental perturbation. Thus, I found these results suggestive but still not satisfying to justify, e.g., the title of the paper or claims made in the abstract. As in Figure 3, the specific example shown in Fig. 4A again shows a relatively tiny effect on target gene expression, which again appears to be a few percent at most.
R:For the reasons explained above, we had no expectation that the effect size of the association between elncRNA splicing and target expression would be high. It is nevertheless key that these associations are robust, which would provide reliable support for our hypothesis. To assess this, we used 2 independent datasets to replicate elncRNA target associations with sQTL variants associated with elncRNA splicing: 1) 147 LCL samples from GTEx and 2) 31,684 blood samples from eQTLgen. Using these datasets, we replicated the association between sQTL and target expression for targets of up to 77% of elncRNAs. As expected, replicated associations have significantly higher effect size in both datasets (Supplementary Figure S9) and 1.2 times more associations can be replicated in the eQTLgen blood samples with a larger cohort size. LCL-specific effect of elncRNA splicing likely explains why not all associations are replicated in these blood samples. We report these analyses in the manuscript (Supplementary Figure S9).
We agree with the reviewer that the causal inference analysis is only suggestive per se. However, we would argue that conclusions of the present manuscript do not rely on this analysis alone, but instead on the combined evidence of several experiments, including the natural mutational analysis that is analogous to the experiment the reviewer proposes.
Considering the reviewers concern, we realized that previous version of Figure 4A did not reflect the average strength of the association between seQTL variant and target expression (median=0.319, ranging from 0.16 to 0.81, Rebuttal Figure 1). For this reason, we replaced the previous illustration by a more representative example (Figure 4A).
The text illustrating reproducibility of our results in GTEx and eQTLgen have been added to Page 17 of the manuscript.
“We used two independent datasets to assess the robustness of elncRNA target association with sQTL variants we predict to be associated with the splicing of these elncRNAs in LCLs. Using a smaller cohort of LCLs (n=147 (GTEx Consortium 2013)), we found a significant association in the same direction between sQTL and target expression for targets of 70% of elncRNAs (45% of variants). A larger fraction of associations (77% of elncRNAs and 52% of variants) could be replicated in a larger cohort of blood samples (n=31,684 (Võsa et al. 2018)). The difference in size between these two cohorts is likely to explain the difference in replication rate. The association between elncRNA splicing variants and target expression that were replicated have significantly higher effect size relative to non-replicated associations (Supplementary Figure S9). Furthermore, LCL-specific effect also likely explains why not all associations can be replicated in the large blood cohort.” (Page 17).
3.Figure 2 shows that splicing-related signals are under selective constraint in spliced elncrRNAs, which is convincing. But this does not prove that splicing of elncRNAs is directly related to enhancer activity. It is equally plausible that elncRNA expression directly impacts enhancer activity and that elncRNA splicing is conserved because it boost elncRNA expression, for example.
R:The reviewer is right and the sentence “If splicing of elncRNAs is important for enhancer function, …” does not faithfully describe the conclusions that can be drawn from the analysis reported in Figure 2. This portion of the text now reads: “If splicing of elncRNAs is functionally relevant, one would expect selection to have prevented the accumulation of deleterious mutations in their splicing-associated motifs during evolution” (Page 8). We would like to thank the reviewer for pointing this out.
Other points:
4.Are the ChiP profiles in Figures 1A-E significantly different from each other in a statistical sense? Probably yes, but a specific test should be done.
R:We now added boxplots representing the distribution of read density centered at transcript promoters. Statistical difference in the distribution is also tested. We show this in the revised Figure 1A-E and Supplementary Figure S1B-C.
5.This sentence (p. 10) was hard to follow and should be clarified: "As expected, the impact of SS variants on gene splicing efficiency depends on the total number of alternative transcripts and exons and ranges from 11% to 24% for elncRNA with 6 to 2 number of exons, respectively (Supplementary Figure S3)."
R:We had previously estimated the average amount of change in splicing for all alternative splicing events at each elncRNA candidate. To calculate this, we considered the difference in Percentage-Spliced-In (PSI) for all splicing events and divided this by the total number of considered events. Given that only a subset of events is affected by a splice site variant, the more exons an elncRNA has, the more alternative splicing events are likely to occur and the lower the average impact of a SS variant on overall gene splicing efficiency is expected to be. Following a comment from reviewer 1 (comment 1), we now only consider splicing events directly disrupted by the SS variant. We agree this sentence was not clear and have removed it from the manuscript.
6.Related to point 5 above, Supplementary Figure 3 is somewhat confusing because two splicing change and three expression change plots are shown for each locus, without labels of what each one is, or explanation of what the red and green colors mean.
R:We apologize for the confusion and thank the reviewer for pointing this out. In the figure, we plot the fold difference in elncRNA splicing, target gene splicing, target gene expression, non-target gene expression, and elncRNA expression. elncRNA features are plotted in red and target gene features are plotted in green. We have added labels to clarify the relevant plots (Figure 3D-H, Supplementary Figure S4,5).
**Minor points:**
1.Top of p. 16: "90% of those that support elncRNA splicing as a mediator 3 of target expression are located at the 5' end of the transcript, which is 4 consistent with the known synergy between transcription and 5' end splicing" - a reference is needed
R:We thank the reviewer for pointing this out and we have now added the appropriate reference.
“Importantly, 90% of seQTL associations that support elncRNA splicing as a mediator of target expression are associated with splicing junctions located at the 5´ end of the transcript, which is consistent with the known synergy between transcription and 5´ end splicing (Furger et al. 2002; Damgaard et al. 2008)(Figure 4D).” (Page 17)
2.Figure 5B,C y-axes indicate "fold difference", but scales include negative numbers, which is confusing. Probably should redo the analysis showing log of fold difference.
R:We thank the reviewer for the suggestion. Since the fold difference in Percentage-Spliced-In (PSI) used to estimate the amount of splicing at each exon junction can be of both positive and negative values, we now plot the log modulus transformation (John and Draper, 1980) of the data, which is equivalent to a log transformation while preserving the sign of the data. The analysis has been redone for Figure 3D-H, 5B,C, and Supplementary Figure S4, S5. This change does not impact the conclusions and makes the interpretation of the results more intuitive.
3.p. 20 Describes U1 snRNP as "a protein essential for the 4 recognition of nascent RNA 5' splice site and assembly of the spliceosome". U1 is a large RNA-protein complex, not a protein.
R:We thank the reviewer for pointing this out and this has now been corrected.
“Chromatin-bound lncRNAs have been recently shown to be enriched in U1 small nuclear ribonucleoprotein (snRNP) RNA-protein complex, a protein essential for the recognition of nascent RNA 5´ splice site and assembly of the spliceosome (Yin et al. 2020).” (Page 22)
4.Typo: p. 11, l. 2 missing word (genes): "expression levels of other nearby was unaffected"
R:This has been corrected.
Reviewer #2 (Significance (Required)):
The question addressed is very interesting, given recent work the significance of transcription from enhancers, and work addressing functional relationships between splicing and expression. The work is suggestive of effects of enhancer splicing on expression but I did not find it fully convincing as the effects observed are extremely small, and other explanations are not ruled out, as discussed above.
Prior literature has shown that many active enhancers are transcribed, that enhancer transcription can preced and is positively correlated with target gene expression, and work from both Ulitsky and from the authors indicates that splicing of enhancer-associated lncRNAs is positively correlated with enhancer activity. A variety of studies have also shown that splicing of protein-coding genes generally has a strong positive effect on gene expression. Here, the authors attempt to go further and show that splicing of enhancers causes increased transcriptional enhancement of target genes. A variety of public genotype, expression, chromatin and other types of data are analyzed to address this question. The statistical genetics crowd may find the work of interest, but molecular biologists will not be convinced of the conclusions. My expertise is in computational biology, genomics and RNA biology.
Engreitz JM, Haines JE, Perez EM, Munson G, Chen J, Kane M, McDonel PE, Guttman M, Lander ES. 2016. Local regulation of gene expression by lncRNA promoters, transcription and splicing. Nature 539: 452-455.
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Li YI, Knowles DA, Humphrey J, Barbeira AN, Dickinson SP, Im HK, Pritchard JK. 2018. Annotation-free quantification of RNA splicing using LeafCutter. Nature genetics 50: 151-158.
Yin Y, Yan P, Lu J, Song G, Zhu Y, Li Z, Zhao Y, Shen B, Huang X, Zhu H et al. 2015. Opposing Roles for the lncRNA Haunt and Its Genomic Locus in Regulating HOXA Gene Activation during Embryonic Stem Cell Differentiation. Cell stem cell 16: 504-516.
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Referee #2
Evidence, reproducibility and clarity
This manuscript addresses an interesting question - whether the splicing of transcriptional enhancer-associated RNAs influences their transcriptional enhancement activity. The analyses appear carefully done, using appropriate datasets and statistical methods. The authors find, for example, that marks of active chromatin are enriched near spliced elncRNAs, that splicing-related motifs of elncRNAs are under selective constraint, and that splicing of elncRNAs is associated with higher elncRNA expression and to very slightly higher expression of target genes.
However, I did not find the main results convincing of the main conclusion for the following reasons:
1.The most direct evidence is shown in Figure 3, where SNPs that occur in 3' splice sites of elncRNA introns are explored, and it is shown that variants predicted to disrupt splicing of elncRNA introns are associated with reduced expression of target but not non-target genes. But the fold difference in expression of target genes is extremely small - a few percent - and is actually less than the fold difference in expression of the elncRNAs themselves (which appears closer to 10%), raising the question of whether elncRNA expression rather than splicing may be more important for activity. Furthermore, the entire analysis has an anecdotal quality, being based on only 4 splice-disrupting elncRNA variants. I did not find the figure at all convincing of the conclusion the authors draw from it.
2.Figure 4 uses a causal inference approach and involves larger datasets. While causal inference can be a useful tool to identify candidate causal relationships, it does not prove causality, which still requires some sort of experimental perturbation. Thus, I found these results suggestive but still not satisfying to justify, e.g., the title of the paper or claims made in the abstract. As in Figure 3, the specific example shown in Fig. 4A again shows a relatively tiny effect on target gene expression, which again appears to be a few percent at most.
3.Figure 2 shows that splicing-related signals are under selective constraint in spliced elncrRNAs, which is convincing. But this does not prove that splicing of elncRNAs is directly related to enhancer activity. It is equally plausible that elncRNA expression directly impacts enhancer activity and that elncRNA splicing is conserved because it boost elncRNA expression, for example.
Other points:
4.Are the ChiP profiles in Figures 1A-E significantly different from each other in a statistical sense? Probably yes, but a specific test should be done.
5.This sentence (p. 10) was hard to follow and should be clarified: "As expected, the impact of SS variants on gene splicing efficiency depends on the total number of alternative transcripts and exons and ranges from 11% to 24% for elncRNA with 6 to 2 number of exons, respectively (Supplementary Figure S3)."
6.Related to point 5 above, Supplementary Figure 3 is somewhat confusing because two splicing change and three expression change plots are shown for each locus, without labels of what each one is, or explanation of what the red and green colors mean.
Minor points:
1.Top of p. 16: "90% of those that support elncRNA splicing as a mediator 3 of target expression are located at the 5' end of the transcript, which is 4 consistent with the known synergy between transcription and 5' end splicing" - a reference is needed
2.Figure 5B,C y-axes indicate "fold difference", but scales include negative numbers, which is confusing. Probably should redo the analysis showing log of fold difference.
3.p. 20 Describes U1 snRNP as "a protein essential for the 4 recognition of nascent RNA 5' splice site and assembly of the spliceosome". U1 is a large RNA-protein complex, not a protein.
4.Typo: p. 11, l. 2 missing word (genes): "expression levels of other nearby was unaffected"
Significance
The question addressed is very interesting, given recent work the significance of transcription from enhancers, and work addressing functional relationships between splicing and expression. The work is suggestive of effects of enhancer splicing on expression but I did not find it fully convincing as the effects observed are extremely small, and other explanations are not ruled out, as discussed above.
Prior literature has shown that many active enhancers are transcribed, that enhancer transcription can preced and is positively correlated with target gene expression, and work from both Ulitsky and from the authors indicates that splicing of enhancer-associated lncRNAs is positively correlated with enhancer activity. A variety of studies have also shown that splicing of protein-coding genes generally has a strong positive effect on gene expression. Here, the authors attempt to go further and show that splicing of enhancers causes increased transcriptional enhancement of target genes. A variety of public genotype, expression, chromatin and other types of data are analyzed to address this question. The statistical genetics crowd may find the work of interest, but molecular biologists will not be convinced of the conclusions. My expertise is in computational biology, genomics and RNA biology.
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Referee #1
Evidence, reproducibility and clarity
The work described in this manuscript by Tan and Marques aims to address if the splicing of enhancer-associated long noncoding RNAs (elncRNAs) has a direct impact on enhancer activity or just reflects their cognate's enhancer's high activity.
For this purpose, the authors started by integrating RNA-seq data for human lymphoblastoid cell lines with ENCODE enhancer annotations and ChIP-seq data for enhancer function-associated chromatin modifications to show that multi-exonic elncRNAs are more transcriptionally active than single-exonic elncRNAs and eRNAs. They then show that regions flanking elncRNA splice sites are enriched in splicing-associated sequence elements and that these are under stronger purifying selection (suggesting some functional relevance), both when compared to promoter-associated lncRNAs. They also show the concomitance of cis-disrupted splicing in elncRNAs and drops in expression in their target genes. Finally, they use causal inference analysis of joint seQTLs and joint scQTLs to investigate the causal relationship between splicing of elncRNAs and expression of putative gene targets and chromatin states at elncRNA cognate enhancers, respectively. They conclude that, in both cases, most associations are causally mediated by splicing of elncRNAs and therefore that this contributes to their enhancer activity.
This manuscript is generally well written, targets an original question and potentially sets the seeds for a new exciting line of research on transcriptional regulation, by providing some evidence for the functional relevance of the splicing of elncRNAs. However, the overlooking of some important aspects of the regulation of RNA splicing led to biases in the design of data analyses and in the interpretation of the biological implications of some results that need to be dealt with before the described work can be considered sound enough for publication.
Essential revisions:
1.Results, page 10, lines 21-24: The statement that "the impact of SS variants on gene splicing efficiency depends on the total number of alternative transcripts and exons" is not properly substantiated. The four examples given in Figure S3 do not illustrate any dependence or trend. If such dependence is "expected" the underlying concept must be explained, i.e. why the impact of SS variants on splicing efficiency should depend on the number of alternative transcripts and exons. The reader is unrealistically expected to be used to the chosen splicing efficiency metric to intuit its dependence on the number of exons. Moreover, it is not obvious where the dependence on the number of alternative transcripts comes from, particularly given that alternative splicing (e.g. the skipping of a neighbouring exon, if internal) is not profiled.
2.Along these same lines, and more importantly, why haven't the authors looked at the possibility that a variant disrupting a splice site would lead to skipping of the neighbouring exon (if internal)? Given how the spliceosome operates (in terms of intron and exon recognition), wouldn't this be the most likely scenario? When calculating the splicing index, are reads spanning junctions between non-consecutive exons considered? Otherwise, not profiling alternative isoforms generated by exon skipping will necessarily bias splicing efficiency quantifications by overlooking fully efficient splicing associated with such isoforms. Similarly, how did the authors make sure that splicing changes did not bias elncRNA expression estimates? How was the effective transcript length determined for the calculation of RPKMs? The authors need to make these methodological clarifications, as well as why exon skipping was not considered as a splicing disruption with potential functional implications. Calculating the percent spliced-in (PSI) for all internal exons would be much informative.
3.All results in panels 3B-F are presented as fold differences. It is actually not clear what those differences refer to. For instance, the grey boxes are the distributions of the fold differences in splicing index / expression between individuals carrying reference alleles and what?
4.It is expectable that most joint seQTLs result from variants directly impacting splicing in cis. As the quantification of splicing is noisier than that of expression, a stronger effect is required for the detection of an sQTL than an eQTL. In other words, joint seQTLs are essentially sQTLs. This illustrated by the example in Figure 4A, with the SNP in an intronic region of the elncRNA being associated with strong differences in splicing and tiny (<1%) and barely significant differences in expression. Moreover, current knowledge and reported evidence strongly suggests that cis regulation of splicing is essentially "local", i.e. directly involves the processed sequences and not the interference of neighbouring RNAs. Similarly, to my knowledge there is no evidence suggesting a trend for genes encoding splicing factors being associated to the same eQTL variants as those of their target RNAs. I would therefore predict that most joint seQTLs result from variants within the elncRNA loci directly impacting their splicing. If this is the case, causal inference analysis will naturally be biased towards more strongly linking the variants with elncRNA splicing and thereby suggesting its causal role. The same rationale applies to scQTLs. The authors need to control for that potential bias in their analyses or explain why there is no bias.
5.It is not totally clear what message the authors intend to convey with the result of panel 4D. Are they talking about the relative position of the variant to the elncRNA transcript or the target transcript? If the former, shouldn't the known synergy between transcription and 5´ end splicing reflect on elncRNA expression? If the latter, it is not obvious how the result connects to the mentioned synergy.
Proofreading edits:
6.Introduction, page 3, line 13: double "in".
7.Figure S2A, leftmost panel X-axis label: "intrno" instead of "intron".
8.Results, page 10, line 30: remove "of".
9.It is 5´ and 3´(prime) not 5' and 3' (apostrophe).
Other suggestions:
10.Violin plots (with included boxplots) would more comprehensively convey the differences in distributions than the chosen notched boxplots.
Significance
It is hard for me to assess the significance of this work (beyond some evidence for the potential functional relevance of the splicing of elncRNAs) until the aforementioned concerns are addressed but it is of potential interest to the broad RNA research community.
I am a computational biologist with experience in the analysis of high-throughput transcriptomic data and a focus on transcriptional and alternative splicing regulation.
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Reply to the reviewers
Response to reviewer comment for manuscript RC-2020-00207
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
**Major Comments:**
The authors of the paper start the paper with just one protein narrowed down ie. HRG. The rest of the paper uses affinity based proteomics, antibody validation, GWAS and survival analysis to validate this target and support their claim that HRG is an age associate protein linked to mortality and certain clinical outcomes. How did the authors conclude that HRG was the only target to explore further in this paper? What methods or analysis was done for this? What were the other proteins if any that showed up in these studies?
We appreciate this comment which reveals unclear explanation how the protein was chosen for further analysis. The protein profile obtained using HPA045005 was the top and single hit out of 7258 protein profiles using a threshold of adjusted P-value below 0.01. In other words, only the profile of HRG was statistically significantly associated with age in the screening sample set (N = 156). The results of all protein profiles were attached as Supporting Table 1. Phrases about the alpha level were added to the text to make the threshold clear. Because antibody validation of these exploratory studies requires enormous efforts and time, we could not choose a more liberal and inclusive threshold.
For mortality outcome, it is not clear which class of disease is most strongly associated with increased risk of mortality from elevated HRG levels. If cause-specific mortality exists among the cohorts, could authors provide a more exact breakdown of the type of associated mortality by a disease class?
We thank the reviewer for the question and have now added cause-specific data in the manuscript. Using cause of death data, mortality risk by diseases in circulatory system were compared with the risk by neoplasm and others. ElevatedHPA045005-HRG profiles were found to associate with mortality risk by diseases of the circulatory system (HR = 1.46 per SD, P = 2.80 × 10‑4, ICD-10 code I00-I99). It was larger than the risk by malignant neoplasms (HR = 1.28 per SD, P = 1.73 × 10‑2, ICD-10 code C00-C97). We chose big categories as ICD-10 codes "I" and "C" because the number of events was too small to get enough power in the survival analysis.
Page 4 Section 3 (Results)-
The authors say "We found consistent age-associated trends with HPA045005 across all eight replication sets (Supporting Figure 3)". On examining the supporting figure we noticed that the slope for the set with the largest number of subjects (Set 3 with ~3000 people) is visually negligibly positive (showing weakest age associated trends with HPA045005). Some comments from the authors on why they think the largest data set showed the weakest association.
The plot for each cohort (in Supporting Figure 3) had different ranges in the y-axes. To make those plots comparable, the ranges in the y-axes of the different panels in the figure were modified to be the same for all cohorts. In the new version of the plot, it is easier to notice that there in fact is an increasing trend of the profiles in set 3. As we briefly discussed in Discussion, weaker age-association of the sample set may be due to the set was near to a random sample of population in the age range. Set 1, however, had over-representation of older people by selecting equal number of people in every age-intervals.
From Figure 2 C in the main manuscript one concludes that for HPA045005, binding for CC individuals is ~ 2 times higher than TT individuals. Is it possible the age association showing up for HPA045005 is primarily a function of changing/increase in allele frequency as a function of age?
The authors could consider adding a clarifying plot of Age vs Allele frequency or adding an interaction term of Age and Allele Frequency in the regression and survival analysis to address this question.
As suggested, we now added a test of age association, and average age was compared by genotype. The result was added in Supporting Table 3. The heterozygote (CT) group has slightly higher average age without statistical significance (ANOVA P = 0.096).
It is interesting that the signals were significant with the HPA045005 antibody but not with the BSI037 antibody. This is in spite of the fact that the GWAS for BSI0137 signals had an even stronger hit to the same locus. Can the authors please comment on why the signals from HPA045005 and BSI0137 were not highly correlated with one another and why the better antibody could not replicate the survival analysis results?
We thank the reviewer for the comments. We believe that our text about our findings were not clear enough, though it is a primary finding. We modified the main text to easily distinguish the HPA045005-derived profiles that were influenced by the 204th amino-acid of HRG protein, from the BSI0137-derived profiles influenced by the 493the amino-acid. The signals from those two antibodies were likely obtained by capturing different parts of HRG, which are schematically illustrated in Figure 2D. What we found is that only one binder's profiles, not the other's, had predictive power for mortality risk within about 8.5 years. That suggests some age-dependent changes around the 204th residue of HRG reflected biological aging rather than whole protein level. To make our finding clearer, the two binders were compared in Table 2.
**Minor Comments:**
Figure 1: The authors description of the figure could use more clarification. "For each sample set, the estimated effect from the linear regression model.." estimated effect of what on what? On reading the main text one concludes it is the effect of age on HPA045005. This needs to be clarified in the label.
We agree with the reviewer and have added these words.
Figure 3: The X axis for the Kaplan Meir survival curve is labelled as Age. Survival is usually time to event and time is usually the follow up time. Further clarification for the choice of this label might be helpful.
We clarified the choice of the time scale in the figure legend with a reference, where it was further discussed (Thiébaut & Bénichou, 2004). We chose age as the time scale, seeing age is the strongest risk factor for all-cause mortality, as the suggestion in the reference. We attempted to use follow-up time as the time scale with age adjustment before, which gave us almost the same results but violated the proportionality assumption of COX models.
Figure 3: it would be good to include a table with the number of individuals at risk at the bottom of the plot at defined time intervals. The figure currently compares the bottom and top quartiles of HRP for visual assessment of mortality risk, it would also be informative to include middle quantiles.
The figure was updated accordingly. The risk table was included and the results of the middle group were presented.
Supporting Table 5: The note at the bottom of this table states "standardized HRG values by linear regression and scaling." What does standardization by linear regression mean?
A sentence that explains the standardization was added in the footnote of the table.
Supporting Table 5: It would be useful to understand that HRG carries additional risk beyond known Age and known clinical biomarkers listed in Table 2 (APOA1, APOB, TC, TG, Glucose, LDL). Could authors include a multivariate CoxPH regression with just Age? and with Age + clinical covariates?
The impact of those clinical variables on survival models was examined and the results were added to Supporting Table 6 (which was Table S5). It turned out that the addition of those variables barely changed the results of the model for the HRG profile affected by 202th amino-acid.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
**Summary**
The manuscript by Hong et al. describes the identification and validation of histidine-rich glycoprotein (HRG) as a marker of chronological age and all-cause mortality. HRG was determined using proteomics of serum and plasma samples in 9 different cohorts (total sample size ~4,100). The association with mortality was tested in the largest available cohort (TwinGene), comprising ~3,000 samples. The association with mortality seems to be stronger in women in comparison to men and could not be explained by CRP or diabetes-related traits. The HRG levels determined using an alternative antibody, BSI0137, did not show any association with mortality, indicating that the effect on mortality is likely isoform-dependent. The performed analyses seem to be statistically solid. However, the association with mortality still needs to be replicated in independent studies and the HRG measurement does not yet seem to be ready for standardized high-throughput measurement, which is necessary to make it usable as biomarker.
**Major comments**
- Although the authors have convincingly identified HRG to be associated with chronological age and mortality, it will require quite some additional work (including replication of the observed association with mortality in independent cohorts, testing the predictive ability, and making the measurement standardized and high-throughput) to prove its use as potential biomarker. At the moment, this is not at all discussed in the manuscript. Moreover, there have been some recent large-scale studies that identified biomarkers at the metabolic level that are not at all mentioned by the authors. The authors only refer once to the recent proteomic study by Lehallier in the Introduction, but do not at all discuss their findings in relation to this paper. Last but not least, HRG has already been associated with mortality in a previous study (https://www.ncbi.nlm.nih.gov/pubmed/29303798), but there is no mention of this anywhere in the manuscript. Hence, I think it would be good if the authors perform a thorough literature search to place their findings into context and rewrite their Discussion accordingly.
We appreciate the reviewer's comments on the limitation of our paper. We are aware of the requirement of further investigation on HPA045005-HRG profiles as a biomarker to confirm it with independent cohorts. Instead, we supported our findings with a set of confirmatory analyses; we validated and annotated age-associated profile applying GWAS, sandwich assays, peptide arrays and mass spectrometry. Comparing two antibody profiles, we narrowed down to age-associated region within the protein HRG. The approach and finding, we believe, is novel.
We added some discussion about recent large-scale proteomic studies such as Tanaka et al, 2018 and Lehallier et al, 2019. Unexpectedly, HRG was found not measured in those studies despite of the protein is one of the abundant proteins in blood (Poon et al, 2011). It may reflect challenges in assay development and missing piece in those large studies. The papers lack further investigation for molecular targets, which is common in proteomic papers, and makes it difficult to compare between studies and technologies. In that sense, our approach is different from other proteomic studies, because we invested time and efforts to investigate the molecular target.
We are though thankful for the introduction of the suggested HRG publication, which we did not know about. We concluded that there are substantial differences in the subjects and suggested functions for the protein. Kuroda et al. found HRG as a biomarker for sepsis of ICU patients, while our study was done on the general population. They were measuring HRG protein level, whereas we found one particular region in HRG as a biomarker for all-cause mortality. Hence, we briefly discussed the reference in the paragraph about general information about HRG.
- The authors need to add a Supplementary Table showing the association of all their 7,258 HPA antibodies with chronological age. Although I trust the authors, I can currently not tell if it is indeed correct that only one antibody was significantly associated with age in set 1.
We agree with the reviewer. The table of association test results of all 7258 antibody profiles was attached to the paper as Supporting Table 1. We were also surprised that only one passed a conventional P-value threshold 0.01 after Bonferroni correction. It might be due to the low number of samples in the sample set 1 (N=156), compared to the number of antibodies or tests.
- According to description in the Supporting Information, several samples in set 3-5 were overlapping with set 1 (45 in total). These samples should be removed from datasets 3-5 to make sure that there are no overlapping samples in the meta-analysis. However, I am not sure if the authors have actually done this. For the GWAS the overlapping samples from set 3 could still be included, given that set 1 is not involved in that. The authors could actually use these 45 overlapping samples to provide additional details about the reproducibility of HPA045005 between different measurements, for example by showing a correlation plot.
We agree with the reviewer. Those 45 overlapping samples were excluded in the meta-analysis. As the reviewer's comment, only the data of sample set 3 was used for the GWAS.
We also appreciate the comment regarding reproducibility and acknowledge that there are limitations to the technical performance of our exploratory SBA method. The procedure is tailored to handle large number of antibodies and profile 384 sample in the analysis plates. This setup allowed us to process relatively large number of samples per batch but it might be affected by batch effects. In our study set 3, there were 2999 samples randomized and analyzed in 8 different 384-well plates. The 44 overlapping samples between sets 1 and 3 were added to one of these 8 plates. This resulted in 1-11 samples to be analyzed on the same plate, hence, comparing these 44 with previous assays might be influenced if not dominated by plate effects. We went back to the initial data set generated during 2011/2012 and compared the first data with replicated assays using the same freeze-thawed samples. For HPA045005 we found the data to correlate by r=0.45. The next analyses of these 44 samples were conducted during 2015 using different sample aliquots and preparations as well as different SBAs. The correlation to previous assays was r
- When looking at the effect of the rs9898-stratified analysis (Table S2) it seems that there only is an effect in the presence of the C-allele. Have the authors considered the presence of a potential recessive effect of this variant when looking at mortality?
Average age of the individuals of each genotype of the SNP was compared and added into Supporting Table 3 (which was Table S2). No significant difference between the genotypes was found. As the reviewer noted, the mortality association of the HRG profiles affected by 204th amino-acid in the TT genotype group of rs9898 was milder and did not reach statistical significance. We believe that it is due to substantially smaller sample size and number of deaths in the genetic group. To clarify the difference in numbers, those numbers were added into the Supporting Table 3 (which was Table S2).
- The authors need to discuss in more detail the implications of the difference between the two HRG antibodies in their association with mortality, for example in light of the use of HRG levels as a potential biomarker (i.e. how should one deal with the fact the way the levels are measured influences the outcome).
We appreciated this valuable comment, which clearly reveals that our claim was not explained sufficiently. We modified the main text to distinguish those two antibody profiles more clearly. We also added Figure 2D and changed the structure of Table 2 to highlight the difference between the two antibody profiles.
- Why did the authors put part of their Discussion in the Supplement? This is not common practice. They should either move it to the manuscript or remove it completely.
We moved the discussion in the supplement to main text as the reviewer's suggestion.
Reviewer #2 (Significance (Required)):
The manuscript is clearly written and the analyses seem to be solid. However, although the findings described in the manuscript are interesting for the ageing field, they only provide a small step in the process of the usability of HRG as biomarker, i.e. many validation and follow-up studies will be necessary to prove its value. There have been some recent biomarker studies that have been much more advanced in this respect, which limits the novelty of this manuscript. I therefore feel that this manuscript may be best suitable for a medium-impact ageing-specific journal. My fields of expertise are ageing, genetics, and molecular epidemiology. Given my limited expertise when it comes to proteomics, I was not able to provide detailed comments on the methodology concerning this part.
We thank the reviewer for the honest and constructive assessment of our work and agree with the suggestion to transfer this work to a medium-impact journal covering aspects of ageing research.
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Referee #2
Evidence, reproducibility and clarity
Summary
The manuscript by Hong et al. describes the identification and validation of histidine-rich glycoprotein (HRG) as a marker of chronological age and all-cause mortality. HRG was determined using proteomics of serum and plasma samples in 9 different cohorts (total sample size ~4,100). The association with mortality was tested in the largest available cohort (TwinGene), comprising ~3,000 samples. The association with mortality seems to be stronger in women in comparison to men and could not be explained by CRP or diabetes-related traits. The HRG levels determined using an alternative antibody, BSI0137, did not show any association with mortality, indicating that the effect on mortality is likely isoform-dependent. The performed analyses seem to be statistically solid. However, the association with mortality still needs to be replicated in independent studies and the HRG measurement does not yet seem to be ready for standardized high-throughput measurement, which is necessary to make it usable as biomarker.
Major comments
Although the authors have convincingly identified HRG to be associated with chronological age and mortality, it will require quite some additional work (including replication of the observed association with mortality in independent cohorts, testing the predictive ability, and making the measurement standardized and high-throughput) to prove its use as potential biomarker. At the moment, this is not at all discussed in the manuscript. Moreover, there have been some recent large-scale studies that identified biomarkers at the metabolic level that are not at all mentioned by the authors. The authors only refer once to the recent proteomic study by Lehallier in the Introduction, but do not at all discuss their findings in relation to this paper. Last but not least, HRG has already been associated with mortality in a previous study (https://www.ncbi.nlm.nih.gov/pubmed/29303798), but there is no mention of this anywhere in the manuscript. Hence, I think it would be good if the authors perform a thorough literature search to place their findings into context and rewrite their Discussion accordingly.
The authors need to add a Supplementary Table showing the association of all their 7,258 HPA antibodies with chronological age. Although I trust the authors, I can currently not tell if it is indeed correct that only one antibody was significantly associated with age in set 1.
According to description in the Supporting Information, several samples in set 3-5 were overlapping with set 1 (45 in total). These samples should be removed from datasets 3-5 to make sure that there are no overlapping samples in the meta-analysis. However, I am not sure if the authors have actually done this. For the GWAS the overlapping samples from set 3 could still be included, given that set 1 is not involved in that. The authors could actually use these 45 overlapping samples to provide additional details about the reproducibility of HPA045005 between different measurements, for example by showing a correlation plot.
Minor comments
When looking at the effect of the rs9898-stratified analysis (Table S2) it seems that there only is an effect in the presence of the C-allele. Have the authors considered the presence of a potential recessive effect of this variant when looking at mortality?
The authors need to discuss in more detail the implications of the difference between the two HRG antibodies in their association with mortality, for example in light of the use of HRG levels as a potential biomarker (i.e. how should one deal with the fact the way the levels are measured influences the outcome).
Why did the authors put part of their Discussion in the Supplement? This is not common practice. They should either move it to the manuscript or remove it completely.
Significance
The manuscript is clearly written and the analyses seem to be solid. However, although the findings described in the manuscript are interesting for the ageing field, they only provide a small step in the process of the usability of HRG as biomarker, i.e. many validation and follow-up studies will be necessary to prove its value. There have been some recent biomarker studies that have been much more advanced in this respect, which limits the novelty of this manuscript. I therefore feel that this manuscript may be best suitable for a medium-impact ageing-specific journal.
My fields of expertise are ageing, genetics, and molecular epidemiology. Given my limited expertise when it comes to proteomics, I was not able to provide detailed comments on the methodology concerning this part.
Joris Deelen
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Referee #1
Evidence, reproducibility and clarity
Summary:
The paper applied affinity based proteomics and antibody validation to choose and validate histidine-rich glycoprotein (HRG) as a protein/target of interest. Survival analysis techniques were used to show associations between this protein and certain biomarkers, age and all cause mortality.<br> These results and findings were used to conclude that HRG may serve as a molecular indicator of age and mortality risk.
Major Comments:
The authors of the paper start the paper with just one protein narrowed down ie. HRG. The rest of the paper uses affinity based proteomics, antibody validation, GWAS and survival analysis to validate this target and support their claim that HRG is an age associate protein linked to mortality and certain clinical outcomes. How did the authors conclude that HRG was the only target to explore further in this paper? What methods or analysis was done for this? What were the other proteins if any that showed up in these studies?
For mortality outcome, it is not clear which class of disease is most strongly associated with increased risk of mortality from elevated HRG levels. If cause-specific mortality exists among the cohorts, could authors provide a more exact breakdown of the type of associated mortality by a disease class?
Page 4 Section 3 (Results)-
The authors say "We found consistent age-associated trends with HPA045005 across all eight replication sets (Supporting Figure 3)". On examining the supporting figure we noticed that the slope for the set with the largest number of subjects (Set 3 with ~3000 people) is visually negligibly positive (showing weakest age associated trends with HPA045005). Some comments from the authors on why they think the largest data set showed the weakest association.
From Figure 2 C in the main manuscript one concludes that for HPA045005, binding for CC individuals is ~ 2 times higher than TT individuals. Is it possible the age association showing up for HPA045005 is primarily a function of changing/increase in allele frequency as a function of age? The authors could consider adding a clarifying plot of Age vs Allele frequency or adding an interaction term of Age and Allele Frequency in the regression and survival analysis to address this question.
It is interesting that the signals were significant with the HPA045005 antibody but not with the BSI037 antibody. This is in spite of the fact that the GWAS for BSI0137 signals had an even stronger hit to the same locus. Can the authors please comment on why the signals from HPA045005 and BSI0137 were not highly correlated with one another and why the better antibody could not replicate the survival analysis results?
Minor Comments:
Figure 1: The authors description of the figure could use more clarification. "For each sample set, the estimated effect from the linear regression model.." estimated effect of what on what? On reading the main text one concludes it is the effect of age on HPA045005. This needs to be clarified in the label.
Figure 3: The X axis for the Kaplan Meir survival curve is labelled as Age. Survival is usually time to event and time is usually the follow up time. Further clarification for the choice of this label might be helpful.
Figure 3: it would be good to include a table with the number of individuals at risk at the bottom of the plot at defined time intervals. The figure currently compares the bottom and top quartiles of HRP for visual assessment of mortality risk, it would also be informative to include middle quantiles.
Supporting Table 5: The note at the bottom of this table states "standardized HRG values by linear regression and scaling." What does standardization by linear regression mean?
Supporting Table 5: It would be useful to understand that HRG carries additional risk beyond known Age and known clinical biomarkers listed in Table 2 (APOA1, APOB, TC, TG, Glucose, LDL). Could authors include a multivariate CoxPH regression with just Age? and with Age + clinical covariates?
Significance
The authors have identified a new biomarker for aging and mortality. Understanding the mechanism and pathways involved in HRG homeostasis and how aging causes dysregulation of this HRG could be a topic for further research. Overall, this pathway provides an opportunity of a new molecular target for aging-based drugs and research.
This article should be of interest to researchers interested in the biology of aging and for researchers developing drugs to slow down the process of aging. In addition, it should be of interest to researchers studying the HRG as a biomarker (for example, in sepsis (https://ccforum.biomedcentral.com/articles/10.1186/s13054-018-2127-5, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3437790).
This paper was reviewed by 3 co-reviewers, a senior principal investigator with extensive bioinformatics, metabolomics/proteomics, epidemiological experience, a highly experienced computational biologist with a record of developing and applying methods in bioinformatics and computational biophysics and lastly an computational biologist with a background in applied mathematics and statistical analysis. All three scientists are interested in aging research and understanding how human physiology and biomarkers in specific, change as a function of age.
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