Author Response

Reviewer #3 (Public Review):

The only substantial point I raise relates to the sexual selection (mate choice) part of the work. While it has no major effect on the overall conclusion, I think their interpretation needs to be reconsidered.

When reporting the results of mate choice experiment (L219ff), the authors state that males of wild and Klara type preferred wild-type females, because 75% of laid eggs belonged to wild-type females. However, another possibility is that Klara females had reduced fecundity, and the lower share of eggs had nothing to do with mate choice. In the same way, "90% of eggs were fertilized by wild-type males" (L223) is used to conclude that they were preferred by females (active mate choice). However, male success in N. furzeri is largely driven by male dominance (and not female mate choice) and it is more likely (and more precise to state) that wild-type males were more successful in male-male competition for access to females (and fertilize their eggs). This is especially so because wild-type males were larger (L. 322) and body size plays a major role in establishing dominance between N. furzeri males. This is then also pertaining to interpretation in discussion (L 318).

Concerning fecundity, we analyzed quantity and quality of eggs obtained from either klara or wild type breeding groups. As shown in Figure 3A we did not observe differences between klara and wild type fish. Thus, we conclude that fecundity is not reduced in klara females. Regarding males, we did not observe a size difference between the klara and wild type animals in this experiment (Fig. 3C), however, weight was different. As noted by the reviewer, this might influence male dominance and breeding success. We have been more explicit on this in the discussion of the revised version.

Author Response

Reviewer #1 (Public Review):

This paper presents the results of two fragment screens of PTP1B using room-temperature (RT) crystallography, and compares these results with a previously published fragment screen of PTP1b using cryo-temperature crystallography. The RT screen identified fewer fragment hits and lower occupancy compared to the cryo screen, consistent with prior publications on other proteins. The authors attempted to identify additional hits by applying two additional layers of data processing, which resulted in a doubling in the number of possible hits in one of the screens. Because I am not an expert in panDDA modeling, however, I am unable to evaluate the reproducibility and potential potency of these fragment hits as protein binders or their potential use as starting points for follow-up chemistry.

The fragment library used in this study was larger than those used in previously published RT crystallography experiments. Among the cryo hits that bound in RT, most fragments bound in the same manner as they did in cryo, while some bound in altered orientations or conformations, and two bound at different locations in RT compared to cryo. This level of variability is not surprising. However, one fragment was observed to bind covalently to lysines in RT, even though it showed no density in the cryo crystallization attempt. It is unclear from the provided information whether this fragment decayed during storage or if the higher temperatures accelerated the covalent chemistry. The authors also observed temperature-dependent changes in the solvation shell, and modifications to the protein structure upon fragment binding, including a distal modification.

We thank the reviewer for the thorough summary of our manuscript.

Regarding reproducibility of fragment hits, cryo structures are more variable than RT structures for proteins themselves (Keedy et al., Structure, 2014). Thus the variability of repeated cryo-temperature crystallography experiments is a relevant consideration when comparing cryo to RT structures for protein-ligand interactions. However, to our knowledge, no published papers have explored this issue. Our previous cryo fragment screen (Keedy, Hill, et al., eLife, 2018), as with many others, was focused on breadth (many fragments), not depth (replicates). Unpublished work by some of the authors of the present study suggests that fragment poses are robust in replicate cryo experiments; however, future studies focused on fragment reproducibility in terms of binding occupancy, pose, and site at cryo temperature would be useful contributions to the field.

Regarding follow-up chemistry, there is growing evidence from multiple successful fragment-based inhibitor design studies (COVID Moonshot Consortium et al., bioRxiv, 2022; Gahbauer, Correy, et al., PNAS, 2023; etc.) that, although fragments usually bind too weakly to impact function on their own, they offer rich information to seed the design of high-affinity, potent functional modulators of proteins. As our study is the first to report many structures of fragments bound to proteins at RT, we cannot yet comment as to whether they offer unique advantages over cryo fragments in downstream fragment-based drug design efforts, but this is an open area for future study.

Regarding the covalent lysine binder, we agree with the reviewer on this point; our manuscript includes a note to this effect. Unfortunately we were unable to obtain the original fragment sample for mass spectrometry analysis. Returning to the point above about follow-up chemistry, the path forward for this fragment hit is promising and clear, and includes confirming chemistry using the original nominal compound vs. what is observed in the electron density, fragment linking and/or expansion, functional assays, and structural biology, all hopefully leading to a potent covalent inhibitor of wildtype PTP1B.

The current version of the paper is somewhat repetitive in its presentation of the results and could be clearer in its presentation of the variations and comparisons of the two different protocols. It would be helpful to have a more concise summary of the differences between the two protocols in the current paper, as well as a discussion of how they compare to the protocol used in the previously published cryo-temperature fragment screen.

We agree that it would be helpful to cut down on any redundant text and more straightforwardly compare/contrast the different room-temperature screen methods vs. the previous cryo-temperature screen method. To address this suggestion, we deleted the Discussion paragraph about the strengths and weaknesses of the two methods relative to serial approaches, deleted the text in the Introduction that introduces the two screens, and placed new text at the start of the Results section in the subsection titled “Two crystallographic fragment screens at room temperature” to provide a concise summary in one location of the manuscript.

While I appreciate the speculative nature of the discussion at the end of the paper, the evidence presented by the authors does not instil confidence that these results will correspond to meaningful binders that could be used to train future machine learning models. However, depending on the intended use, it may be acceptable to train ML models to predict expected densities under typical experimental conditions.

Indeed, this part of the Discussion is speculative, and seeks to place our results into a possible broader context. The definition of “meaningful binders” in the context of fragment screening is a difficult one. As noted above in response to the comment about follow-up chemistry, one important measure of meaningfulfulness is the ability to successfully seed structure-based design of analogs that have potent functional effects, and many fragments do meet this definition. Regarding potential applications to machine learning, we agree it is not self-evident that structural data for small-molecule fragments will be readily translatable to AI/ML methods aimed at larger compounds. The reviewer’s point about predicting densities is an intriguing one, and is in line with the fragment screening ethos, including existing experimental as well as computational (e.g. Greisman, Willmore, Yeh*, et al., bioRxiv, 2022) approaches to mapping ligandable surface sites and regions. The number of RT structures we report here is high relative to most crystallography studies, but still is likely insufficient to explore questions about AI/ML training, and at any rate would be beyond the scope of the current report. However, it seems equally true that AI/ML methods trained on structures based on data from nonphysiological cryogenic conditions, with associated structural artifacts, may have some (previously unrecognized) limitations, and thus RT crystal structures can play a useful role in AI/ML training sets in the future. We have added new text to the Discussion paragraph in question to convey these points.

Reviewer #2 (Public Review):

The authors set out to understand how a room-temperature X-Ray crystallography-based chemical-fragment screen against a drug target may differ from a cryo screen. They carried out two room-temperature screens and compared the results with that of a cryo screen they previously performed. With a substantial set of crystallographic evidence they showed that the modes of protein-fragment binding are affected by temperature. The conclusion of the work is compelling. It suggests that temperature provides another dimension in X-ray crystallography-based fragment screening. In a practical sense, it suggests that room-temperature fragment screen is a promising new avenue for hit identification in drug discovery and for obtaining insights into the fragment binding. Room-temperature screening carries unique advantage over cryo screening. This work is confirmative to the notion, which seems not yet universally considered, that very weak protein-small molecule binding may be inherently fluid structurally, and that crystal structures of such weak binding, especially cryo structures, cannot be taken for granted without cross validation.

We thank the reviewer for their clear summary and positive comments about our manuscript.

Author Response

Reviewer #2 (Public Review):

In this study, The authors developed a mouse model to specifically investigate whether GC B cells that present nuclear protein (NucPr) could be specifically suppressed by Tfr cells. Most current mouse models that have been used in investigating Tfr functions are based on the overall readout of autoantibody production in the scenario of loss-of-function of Tfr cells. The proposed model of gain-of-function of Tfr cells is novel and valuable.

The authors mainly compared two boosting immunizations by Strepatividin (SA) alone or SA-conjugated with nuclear proteins (SA-NucPr) and demonstrated SA-NucPr boosting immunization was able to expand Tfr cells, suppress overall and SA-specific GC/memory/plasma cell responses. The results are mostly convincing.

One major concern is the conditions and controls used in the study. The control group (SA boosting immunization) would have enhanced T and B cell responses by this boosting. Unfortunately, there was no non-boosting control group so the level was unclear. It is therefore to strictly match such boosting condition in the SA-NucPr group. Notably, both SA and SA-NucPr were used at 10ug for boosting immunization. Considering NucPr were comparable or much larger (Nucleosome, about 200KDa) than SA (about 60KDa), the dose of SA in the SA-NucPr group was far less than that in the SA group. Due to this cavity, it is difficult to judge the difference between two groups was due to less SA boosting immunization or NucPr-induced Tfr function. This was a fundamental issue weakens the conclusion.

The single cell analyses clearly demonstrated the expansion of Tfr clones. It remains unclear why other Treg populations other than Tfr cells were not expanded? The Treg cells in the CXCR5intPD-1int population were recently activated and should be able to respond to the boosting immunization. On an alternative explanation, the changes in Tfr cells could be indirectly driven by the changes in Tfh cells. For example, Tfh can produce IL-21 and restrict Tfr expansion (Jandl C, et al.2017). This could be the case of the reduction in Tfr cells in the SA-OVA group as compared to the SA group.

As the reviewer, we were surprised not to detect significant increase in the levels of CXCR5intPD-1int Tregs in the original experiment after the boosting with SA-NucPrs(Fig.1). Our interpretation of this result was that the fraction of NucPr-specific CXCR5intPD-1int Tregs was small as compared to the total CXCR5intPD-1int Tregs and proliferation of this small fraction of cells would not be detectable by flow cytometry analysis of the total CXCR5intPD-1int Tregs numbers. Alternatively, the observed rapid accumulation of Tfrs was due to proliferation of the NucPr-specific Tfrs that may be abundant after a standard immunization with foreign antigen.

In single cell analysis we have used only presorted CXCR5highPD1high follicular T cells so majority of CXCR5intPD-1int Treg population was excluded from the analysis.

Author Response

Reviewer #1 (Public Review):

The authors optimize a live cell imaging method based on the detection of FAD/NAD(P)H adopted from the fast-growing field of live metabolic imaging. They build upon a method described by KreiB et al 2020 that used metabolic ratio and collagen fiber second harmonic generation imaging. They follow by combining metabolic imaging with morphologic measurements to train a machine-learning model that is able to identify cell types accurately. Upon visualization, authors detected structures hypothesized and then proven to resemble the "goblet cell associated antigen passages" previously studied in intestinal epithelia.

STRENGTHS

• The manuscript is succinct, well written, and overall done rigorously.

• The optimization of the method at multiple levels to the point of identifying both common and rare cell types is impressive.

• Describes the elegant implementation of a sorely needed method in epithelial biology.

• Provides an approach to studying the cholinergic response in epithelial cells, a poorly understood phenomenon despite broad clinical use for diagnosis and treatment.

WEAKNESSES

A) For what is in large part a methods-development paper, the methods are not explained or shared in a manner that facilitates reproducibility. For example:

A.1.) The training and validation datasets seem to come from the same sample (or the source is not clearly described). Therefore, it is not clear whether the "96% accuracy" refers to accuracy within the sample measured, or whether it can extrapolate to other samples.

In order to avoid any confusion, we further clarify that the machine learning training and validation data sets come from the same sample. We had split the total data set into 2 separate subsets for this purpose. This has been laid out in the text as follows:

“In order to assess the performance of machine learning algorithms designed to distinguish cell types, we divided our data set into training and testing subsets. We utilized 75% of the total cells (154 cells) for machine learning training, leaving 25% (52 cells) for subsequent validation.”

A.2.) It is unclear whether the model needs to be re-trained within each new sample measured, or if it's applicable to others. This has implications for method adoption by others. Either way is useful but needs to be clarified.

This is a very interesting point and one that we further clarify in the Discussion noting that in both disease and non-diseased states the model needs to be re-trained in each particular experimental regime.

A.3.) Code was only listed in a PDF file, which makes reproducing the analysis very cumbersome.

We hope that all can utilize the code made for this methodology and have uploaded it to a publicly available GitHub account:

https://github.com/vss11/Label-free-autofluorescence

B) Whereas the optimization to improve cell type detection is very well described, the implementability of the approach could benefit from exploration (using the data already obtained) of the minimal set of measurements needed to identify cell types. For example, is the FAD/NAD(P)H ratio necessary? Or could just morphologic measurements achieve the same goal?

This is an excellent point, and we appreciate the Reviewer’s suggestion for this analysis. We have added Figure 3 Supplement 5 where we perform modeling without autofluorescence data. This analysis reveals a dramatic reduction in accuracy with a Matthew’s correlation coefficient ranging from 0.66 to 0.78. This provides additional justification for the use of autofluorescence for cell type identification. Morphologic measurements are not sufficient for cell type identification alone.

We also have determined the relative contribution of each characteristic to the cell type identification by the Xgboost algorithm in Figure 3 Supplement 4, which shows that autofluorescence signatures are amongst the top contributing characteristics to cell type identification by machine learning.

C) Whereas the conclusions are overall supported by the data, need small adjustments in some cases:

C.1.) For example, P3L80: Claims autofluorescence imaging is more specific than "functional markers", however, this is done in the setting of a very specific intervention that massively affects a protein often used as a secretory cell marker (CCSP aka SCGB1A1), which is known to be secreted (and depleted) in secretory cells upon stimulation.

We agree with the Reviewer that secretory cell identification is a prime example where autofluorescence imaging may be superior to conventional staining, specifically due to the point the Reviewer makes regarding CCSP secretion. We discuss this concept in the Discussion while giving examples of CCSP staining being reduced in asthma, COPD, and smokers. It could be that these cells are missed due to depletion of CCSP. Indeed, we clarify that our methodological approach may be less affected by the loss of the category of specific markers that change with cell state. There are, of course, caveats with utilizing this approach in disease states, and we elaborate on this further below and add this point to the discussion.

C.2.) Relatedly, it is unclear how the method's accuracy would be affected in conditions that affect redox/metabolic state; the approach may be highly affected in inflammation and injury, for example.

As suggested by the Reviewer, we re-analyzed the data after Antimycin A + Rotenone and FCCP to determine if autofluorescence ratio is sufficiently different to identify ciliated and secretory cells and included this data in Figure 2 Supplement 1. This is an example where the redox/metabolic state is indeed altered. Though the autofluorescence ratio is affected, it is still useful for cell type identification after intervention as the ciliated and secretory cells have statistically different ratios.

However, different disease states, particularly infection and inflammation may result in a more profound effect on autofluorescence signatures. For instance, previous work by Dilipkumar et. al, 2019 found changes in autofluorescence over days in repeated measurements in a mouse model of inflammatory bowel disease. Therefore, it is likely that the cell type identification methodology will need to be re-optimized for different experiments and diseased tissues. We include commentary to this effect in the discussion.

D) The data used to describe "SAPs" is very cursory.

To further elaborate on our description of SAPs we have included the following:

1) SAP formation occurs in secretory cells in both stimulated and unstimulated conditions. We performed additional analysis of Figure 4C and determined that SAP formation does occur at baseline prior to stimulation in 9% of secretory cells. Methacholine addition results in 78% of secretory cells forming SAPs (Figure 4 Supplement 1). We have added Figure 5C to demonstrate that SAP formation occurs in the absence of stimulation and is enhanced after methacholine stimulation.

2) We demonstrate that SAPs can uptake both FITC-dextran and FITC-ovalbumin in Figure 5E, and Figure 5 Supplement 2. We also now show that immune cells (CD11c antigen presenting cells) associate with SAPs containing FITC-dextran and FITC-ovalbumin in Figure 5E and Figure 5 Supplement 2. We have expanded the Discussion of SAPs.

3) We now show 3 video examples and an XZ optical cross section of ALI that demonstrate uptake and secretion of FITC-dextran in Figure 5 Supplemental Videos 1-3 and Figure 5 Supplement 1.

D.1.) Unclear if FITC dextran uptake occurs in other cells too, or in secretory cells prior to methacholine stimulation, or induced nonspecifically due to epithelia manipulation. Secretory and goblet cells are very sensitive to stimulation and often considered minimal, for example, see the paper by Abdullah et al DOI:10.1007/978-1-61779-513-8_16 in which extreme care had to be applied to prevent any secretion at all.

Our autofluorescence methodology revealed the formation of “voids” of autofluorescence forming in secretory cells and we focused our experiments on this phenomenon. Based on the reviewer question, we generated Figure 5C to better characterize SAP formation. Figure 5C illustrates that SAP formation occurs in both unstimulated and methacholine stimulated conditions, but is dramatically increased following methacholine stimulation. This is analogous to the behavior of GAPs in the intestine (Knoop et al., 2015). Furthermore, we have reanalyzed Figure 4C to identify SAPs prior to stimulation and found that these structures are present in 9% of secretory cells. After methacholine stimulation this percentage increases to 78%.

D.2.) A single image is provided for the SAP timeline (Figure 5C), which appears to be the same cell shown in the supplementary video.

We now provide numerous example videos and optical XZ cross section of ALI demonstrating SAP uptake and secretion in Supplementary Videos 1-3 and Figure 5 Supplement 1.

IMPACT AND UTILITY

This is well-done work with high potential for widespread adoption within the epithelial biology community, particularly if the methods and code are shared in better detail.

We indeed hope that this methodology can be utilized by others. We have posted analysis code, raw data, MATLAB algorithm, and other necessary files onto a publicly available GitHub link. https://github.com/vss11/Label-free-autofluorescence

Reviewer #2 (Public Review):

Shah and colleagues tackle a significant impediment to exploiting tissue culture systems that enable prospective ex vivo experimentation in real-time. Namely, the ability to identify and track dynamic and coordinated activities of multiple composite cell types in response to experimental perturbations. They develop a clever label-free approach that collects biologically-encoded autofluorescence of epithelial cells by 2-photon imaging of mouse tracheal explant culture over 2 days. They report the ability to distinguish 7 cell types simultaneously, including rare ones, by developing a machine-learning approach using a combination of fluorescence and cytologic features. Their algorithm demonstrates high accuracy by Mathew's Correlation Coefficient when applied to a test set. Lastly, they show the ability of their approach to visualize the dynamic uptake and expulsion of fluorescently-tagged dextran by individual secretory cells. Overall, the results are intriguing and may be very useful for specific applications.

We thank the reviewers for their assessment and indeed hope that the methodology is useful and the discovery of the dynamics of SAP formation have important implications for airway mucosal immunology.

Author Response

Reviewer #1 (Public Review):

Animal colour evolution is hard to study because colour variation is extremely complex. Colours can vary from dark to light, in their level of saturation, in their hue, and on top of that different parts of the body can have different colours as well, as can males and females. The consequence of this is that the colour phenotype of a species is highly dimensional, making statistical analyses challenging.

Herein the authors explore how colour complexity and island versus mainland dwelling affect the rates of colour evolution in a colourful clade of birds: the kingfishers. Island-dwelling has been shown before to lead to less complex colour patterns and darker coloration in birds across the world, and the authors hypothesise that lower plumage complexity should lead to lower evolutionary rates. In this paper, the authors explore a variety of different and novel statistical approaches in detail to establish the mechanism behind these associations.

There are three main findings: (1) rates of colour evolution are higher for species that have more complex colour phenotypes (e.g. multiple different colour patches), (2) rates of colour evolution are higher on island kingfishers, but (3) this is not because island kingfishers have a higher level of plumage complexity than their mainland counterparts.

I think that the application of these multivariate methods to the study of colour evolution and the results could pave the way for new studies on colour evolution.

We appreciate this positive comment about our manuscript.

I do, however, have a set of suggestions that should hopefully improve the robustness of results and clarity of the paper as detailed below:

1) The two main hypotheses tested linking plumage complexity and island-dwelling to rates of colour evolution seem rather disjointed in the introduction. This section should integrate these two aspects better justifying why you are testing them in the same paper. In my opinion, the main topic of the paper is colour evolution, not island-mainland comparisons. I would suggest starting with colours and the challenges associated with the study of colour evolution and then introducing other relevant aspects.

We implemented this suggestion by reorganizing the introduction to introduce color/and challenges with studying it (para 1), then we discuss plumage complexity (para 2). We follow this with a paragraph about the importance of islands in testing evolutionary hypotheses (para 3), and onto kingfishers as a model system (para 4) and our hypothesis/predictions (para 5).

2) Title: the title refers to both complex plumage and island-dwelling, but the potential effects of complexity should apply regardless of being an island or mainland-dwelling species, am I right? Consider dropping the reference to islands in the title.

We removed “island” from the title.

3) The results encompass a large variety of statistical results some closely related to the main hypothesis (eg island/mainland differences) tested and others that seem more tangential (differences between body parts, sexes). Moreover, quite a few different approaches are used. I think that it would be good to be a bit more selective and concentrate the paper on the main hypotheses, in particular, because many results are not mentioned or discussed again outside the Results section.

We removed analyses that we felt were distracting from our main point (e.g., MCMCglmm) and streamlined our approach to use PGLS methods for both rates (phylolm) and multivariate color patterns (d-PGLS). The relevance of sex differences in coloration is also made more clear, as we added details about how we tested for a relationship between male and female coloration and that we use this strong correlation as a justification for averaging color by species (e.g., see lines 369-375).

4) Related to the previous section, the variety of analytical approaches used is a bit bewildering and for the reader, it is unclear why different options were used in different sections. Again, streamlining would be highly desirable, and given the novel nature of the analytical approach (as far as I know, many analytical approaches are applied for the first time to study colour evolution) it would be good to properly explain them to the reader, highlighting their strengths and weaknesses.

We appreciate the suggestion and have now included a workflow diagram, as suggested (see Figure 1). We further added considerable detail to the Methods (old length = 502 words, new length = 1355 words) and mention caveats of the approaches we have taken (e.g., line 308: “We used photosensitivity data for the blue tit (Hart et al., 2000) due to the limited availability of sensitivity data for other avian species”).

5) The Results section contains quite a bit of discussion (and methods) despite there being a separate Discussion section. I suggest either separating them better or joining them completely.

We appreciate this. We were following other eLife articles that include more discussion within the Results, therefore we would prefer to leave these aspects in place. However, we did move a considerable amount of information from the Results section to the Methods section. In addition, we also reorganized the Results to better match the logical flow of the Introduction. The end result, we hope, is a Results section that is considerably more streamlined.

6) The main analyses of colour evolutionary rates only include chromatic aspects of colour variation. Why was achromatic variation (i.e. light to dark variation) not included in the analyses? I think that such variation is an important part of the perceived colour (e.g. depending on their lightness the same spectral shape could be perceived as yellow or green, black or grey or white). I realize that this omission is not uncommon and I have done so myself in the past, but I think that in this case, it is highly relevant to include it in the analyses (also because previous work suggests that island birds are darker than their mainland counterparts). This should be possible, as achromatic variation may be estimated using double cone quantum catches (Siddiqi et al., 2004) and the appropriate noise-to-signal ratios (Olsson et al., 2018). Adding one extra dimension per plumage patch should not pose substantial computational difficulties, I think.

We incorporated this suggestion and we have now fully integrated achromatic color variation into all of our analyses. These new analyses let us compare results to previous work showing that island birds are darker than mainland counterparts. We further discuss the caveats of chromatic and achromatic channels (e.g., lines 313-317: “Although it is possible, in theory, to combine chromatic and achromatic channels of color variation in a single analysis (Pike, 2012), we opted to analyze them separately, as these different channels are likely under different selection pressures (Osorio and Vorobyev, 2005).”).

7) The methods need to be much better explained. Currently, some methods are explained in the main text and some in the methods section. All methods should be explained in detail in the methods section and I suggest that it would be better to use a more traditional manuscript structure with Methods before Results (IMRaD), to avoid repetition (provided this is allowed by the journal). Whenever relevant the authors need to explain the choice of alternative approaches. Many functions used have different arguments that affect the outcome of the analyses, these need to be properly explained and justified. In general, most readers will not check the R script, and the methods should be understandable to readers that are not familiar with R. This is particularly important because I think that the methodological approach used will be one of the main attractions of the manuscript, and other researchers should be able to implement it on their own data with ease. Judging from the R script, there are quite a few analyses that were not reported in the manuscript (e.g. multivariate evolutionary rates being higher in forest species). This should be fixed/clarified.

We clarified several methodological details in the manuscript (e.g., added package versions throughout, mention the permutation option used for compare.evol.rates, cited RPANDA) and modified the Methods section considerably to make logical connections among the sections. We also checked and cleaned up the R markdown file to ensure the analyses were in sync with the manuscript analyses.

Reviewer #2 (Public Review):

In "Complex plumages spur rapid color diversification in island kingfishers (Aves: Alcedinidae)", Eliason et al. link intraspecific plumage complexity with interspecific rates of plumage evolution. They demonstrate a correlation here and link this with the distinction between island and mainland taxa to create a compelling manuscript of general interest on drivers of phenotypic divergence and convergence in different settings.

This will be a fantastic contribution to the literature on the evolution of plumage color and pattern and to our understanding of phenotypic divergence between mainland and island taxa. A few key revisions can help it get there. This paper needs to get, fairly quickly, up to a point where the difference between plumage complexity and color divergence is defined carefully. That should include hammering home that one is an intraspecific measure, while one is an interspecific measure. It took me three reads of the paper to be able to say this with confidence. Leading with that point will greatly improve the paper if that point gets forgotten then the premise of the paper feels very circular.

We hope our considerable modifications throughout–including explicitly mentioning that complexity is an intraspecific measure whereas rates are interspecific (e.g., see lines 65, 140, 170, 667)–have made the premise of the paper more clear. We also added a new workflow figure (Figure 1) that includes example species pairs showing cases in which intraspecific plumage complexity and interspecific color divergence could show a negative relationship, rather than a positive one as we predict in the manuscript. We discuss this detail in lines 159-161 (“However, this is not necessarily the case, as there are examples within kingfishers that show simple plumages yet high color divergence, as well as complex plumages with little evolutionary divergence (Figure 1B).”).

Also importantly, somewhere early on a hypothesized causal pathway by which insularity, plumage complexity, and color divergence interact needs to be laid out. The analyses that currently follow are good ones, and not wrong, but it's challenging to assess whether they are the right ones to run because I'm not following the authors' reasoning very well here. I think it's possible a more holistic analysis could be done here, but I'll refrain from any such suggestions until I better get what the authors are trying to link.

We overhauled the Introduction. This included adding lines that connect the ideas of complexity and insularity (lines 65-58: “intraspecific plumage complexity (i.e., the degree of variably colored patches across a bird's body) could be a key innovation that drives rates of color evolution in birds and should be considered alongside ecological and geographic hypotheses.”) and insularity and color divergence (lines 69-85). We also rethought the analyses and now include PGLS analyses using tip-based rates that allow us to account for both insularity and complexity in the same analysis.

We also need something near the top that tells us a bit more about the biogeography of kingfishers. Are kingfisher species always allopatric? I know the answer is no, but not all readers will. What I know less well though is whether your insular species are usually allopatric. I suspect the answer is yes, but I don't actually know.

Great point. We have added details to the manuscript to clarify this (e.g., line 214: “The number of sympatric lineages ranged from 1–9 on islands, and 6–38 for mainland taxa.”).

In short, how do the authors think allopatry/sympatry/opportunity for competition link to mainland vs. island link to plumage complexity? And rates of color evolution? Make this clear upfront.

We believe our revised introduction makes these connections much clearer.

Author Response

Reviewer #2 (Public Review):

The molecular characteristics of OCNs in normal or ototoxic conditions are poorly understood before. The strength of this study is that it provides the first single-cell RNA-seq database of OCNs as well as surrounding facial branchial motor neurons. By thoroughly analyzing the database, they found high heterogeneities within OCN populations and identified distinct markers that are enriched in different OCN subtypes. Furthermore, a few previously unknown neuropeptides are revealed, including Npy which is more enriched in the LOC-2 located on the medial side. They also found that neuropeptide expression levels and distributions are subjected to hearing experience and noise exposure. On the other hand, the weakness of the study is that the numbers of single-cell RNA-seq are not sufficient, and may underscore the MOC heterogeneity (Figure 3A). Moreover, the physiological functions of the LOC-2 are not revealed in this study, and no specific markers in one OCN subtype are identified that can predict the morphological or projecting axon features. Those might be addressed in the following studies.

We agree that this study does not allow us to make conclusions about MOC heterogeneity or LOC2 functions. These are certainly interesting avenues to pursue in the future.

Author Response

Reviewer #3 (Public Review):

Although initially discovered as axon guidance molecules in the nervous system, Semaphorins, signaling through their receptors the Neuropilins and Plexins, regulate a variety of cell-cell signaling events in a variety of cell types. In addition, cells often express multiple Semas and receptors. Thus, one important question that has yet to be adequately understood about these important signaling proteins is: how does specificity of function arise from a ubiquitously expressed signaling family?

This study addresses that important question by investigating the role of cysteine palmitoylation on the localization and function of the Neuropilin-2 (Nrp-2) receptor. It was already known that Sema3F signaling through a complex of Nrp-2 and Plexin-A3 regulates pruning of dendritic spines in cortical neurons while Sema3A signals through Nrp-1/PlexA4 to regulate dendritic arborization. The major finding of this study which is well-supported by the data is that palmitoylation of Nrp-2 regulates its cell surface clustering and dendritic spine pruning activity in cortical neurons. Interestingly, palmitoylation of Nrp-1 at homologous residue does not appear to regulate its localization or known neuronal function.

A clear strength of this manuscript is the many techniques that are utilized to examine the question: this study represents a tour de force of biochemical, molecular, genetic, pharmacological and cell biological assays performed both in vitro and in vivo. The authors carefully dissect the function of distinct palmitoylated cysteine residues on Nrp-2 localization and function, concluding that palmitoylation of juxtamembrane cysteines predominates over C-terminal palmityolyation for the Nrp-2 dependent processes assayed in this study. The authors also demonstrate that a specific palmityl transferase (DHHC15) acts on Nrp-2 but not Nrp-1 and is required for Nrp-2 clustering and dendritic spine pruning. These findings are important because they demonstrate one mechanism by which different signaling pathways, even from a related family of proteins, can achieve signaling specificity in the cell.

A minor weakness of the paper is that one would like to see a connection between palmitoylation-dependent cell membrane clustering of Nrp-2 on the cell surface and Nrp-2 regulation of dendritic spine pruning. Although the two phenotypes frequently correlate in the data presented, there are a few notable exceptions: e.g. Nrp-2TCS forms larger clusters in cortical neurons while Nrp-2FullCS is diffuse on the cell surface; both mutants affect spine pruning. In the future, it would also be interesting to know if increased clustering of Nrp-2 was observed at spines that were eliminated, for example. Nonetheless this manuscript represents an important advance in our understanding of synaptic pruning and cellular mechanisms that constrain protein surface localization and signaling pathways.

We agree that the reviewer’s comment on the need to show a direct association between palmitoylation-dependent Nrp-2 clustering on the cell surface and Nrp-2 regulation of dendritic spine pruning is very important. This underscores the need to develop new robust tools that can directly and specifically address the effects of palmitoylation on protein localization and neuronal morphology. For example, an antibody that is specific for palmitoylated Nrp-2, perhaps including site-specific Nrp-2 palmitoylation, would allow for direct visualization of palmitoylated protein localization at subcellular resolution, and if coupled with in vivo imaging, could help address questions related to spine dynamics with respect to Nrp-2 expression and palmitoylation. However, at present we consider this approach an important future direction.

Regarding the Nrp-2 mutants TCS and Full CS, our experiments suggest the existence of a threshold for protein mislocalization beyond which Nrp-2 loses its function. In other words, the defect in protein localization imparted by the mutation of the three juxtamembrane cysteines (TCS Nrp-2 mutant) seems to be sufficient to cause Nrp-2 dysfunction. In addition, as noted above (Reviewer #1), the protein clustering assay is a useful but a more general localization assay; more sophisticated assays need to be developed to investigate palmitoylated proteins when they are mislocalized upon site-specific depalmitoylation, which could provide a more accurate association between a protein’s localization and function.

The reviewer’s idea to look at the localization of Nrp-2 at dendritic spines and correlate this with the fate of spines during postnatal development, including relating to spine maintenance vs elimination, is an excellent suggestion that could link directly Nrp-2 to spine dynamics. To address this, however, again new assays with exogenous Nrp-2 expression will need to be developed, but with very low levels of protein expression to avoid saturation of spines with exogenous tagged-Nrp-2 protein and preserve functional specificity for spine regulation. Alternatively, robust in vivo tagging of ndogenous Nrp-2 protein using CRISPR approaches also provide another avenue to achieve this goal—of note, we are trying this approach but, thus far, we have not been successful in achieving labeling that is robust enough for such experiments.

Author Response

Reviewer #1 (Public Review):

The current study melds computational and docking methods with functional measurements in a systematic approach: first, they analyze the mechanism of inhibitor binding to EAAT2; second, they mutate ASCT to resemble EAAT and show that the general binding pocket and inhibition mechanism are conserved; third, they perform an in silico screen to identify compounds that bind to the WT ASCT binding pocket; fourth, they perform electrophysiological assays showing that this novel compound allosterically modulates ASCT function. This is a complete and comprehensive study with extensive experimental support for the major conclusions. The authors identify an allosteric ASCT inhibitor, and although only partial inhibition is achieved, this study serves as proof-of-concept that this site can be targeted in diverse SLC-1 transporters as an allosteric inhibitory site.

We would like to thank Reviewer #1 for the encouraging comments.

Reviewer #2 (Public Review):

This study set out to explore the nature of a previously described non-competitive and selective inhibitor of the human glutamate transporter, EAAT1 and to explore if this mechanism was conserved across the glutamate transporter family. The non-competitive nature of UCHPH-101 inhibition of EAAT1 has previously been demonstrated with both functional analysis and structures of EAAT1. Here, the authors use detailed electrophysiology analysis to confirm this mechanism of inhibition and to demonstrate that the inhibitor slows the steps of the transport cycle associated with substrate translocation, rather than substrate or sodium ion binding. These findings agree with previous studies that have shown that the compound binds at the interface of the transport and scaffold domains in EAAT1, two domains that are required to move relative to each other for the transport process to occur. UCPH-101 also prevents the transporter from entering an anion-conducting state, which agrees with a recent structure and MD simulations of EAAT1 that demonstrate movements of the transport domain relative to the scaffold domain are required for the EAAT1 to move into the anion-conducting state and support the mechanism of UCPH-101 inhibition confirmed in this study (PMID: 35192345; PMID: 33597752).

While UCPH-101 has been shown to be selective for EAAT1 over other human glutamate transporter subtypes (notably EAAT2 and EAAT3), Dong et al., show that this inhibitor can also reduce transport by another member of the SLC1A family, a neutral amino acid exchanger, ASCT2. Using MD simulations and functional analysis, they show that UCPH-101 acts as a partial, low-affinity inhibitor of ASCT2 and identify two amino acid residues in the binding site that appear to be responsible for the different affinities for EAAT1 and ASCT2. Indeed, when these two residues are changed to the corresponding residues in EAAT1, UCPH-101 becomes a full inhibitor of ASCT2 with an increased affinity.

ASCT2 is a neutral amino acid transporter that can transport glutamine and it is known to be upregulated in several cancers. Thus, finding new compounds and novel ways to inhibit ASCT2 is worthy of investigation. In the last section of this study, the authors conduct a virtual screen of 3.8 million compounds to identify other compounds that could bind to this allosteric site in ASCT2. One compound was identified, and while it had relative low affinity it provides the basis for further exploration of this site.

We would like to thank Reviewer #2 for the thoughtful comments.

Reviewer #3 (Public Review):

Using whole-cell patch-clamp measurements, the authors nicely elaborate the competitive inhibition mechanism of UCPH-101 on EAAT1, concluding that it blocks conformational changes during transmembrane translocation, without inhibiting Na+/glutamate binding. The authors demonstrate that UCPH-101 binds to ASCT2 with strongly reduced affinity. Informed by sequence comparison between EAAT1 and ASCT2, the authors identify a pair of mutations, which makes the putative allosteric-binding pocket (which has been identified by crystallography earlier) in ASCT2 more similar to EAAT1 and restores the inhibitory effect of UCPH-101 in ASCT2. Overall, the electrophysiological experiments appear sound and convincing.

We appreciate the kind words.

Furthermore, using virtual screening against the UCPH-101 binding pocket in ASCT2, the authors identified a novel (non-UCPH-101-like) compound #302 that they experimentally demonstrate to also inhibit ASCT-2. However, the study lacks a detailed investigation of the inhibition mechanism of this compound and it remains unclear if #302 also mediates allosteric inhibition as the authors propose. Furthermore, the study lacks any experimental verification of the assumed binding site of #302.

We agree. Therefore, we have now added more detailed experiments on compound #302 inhibition mechanism, confirming allosteric inhibition (new Fig. G and I).

In addition, the study includes molecular-dynamics (MD) simulations on interactions of UCPH101 with EAAT1 and ASCT2. These simulations intend to support the interpretations of the electrophysiological experiments, i.e., relatively tight interactions of UCPH-101 with EAAT1 and weaker binding to ASCT2, which can be restored using two point-mutations in ASCT-2. Unfortunately, this is a relatively weak part of the study. Due to the lack of any convergence analysis, the statistical significance of the drawn conclusions remains unclear. Furthermore, since it is not reported how UCPH-101 has been parameterized, the chemical accuracy of these models is unclear.

We now add information on the UCPH-101 parametrization protocol, and we have extended the time of MD simulations. Also, we have created additional trajectories for the atom distances between amino acid substrate and ASCT2 side chain in the substrate binding site, providing another data point on convergence in the substrate binding site, which should be unaffected by UCPH-101 binding, according to the experimental data.

Author Response

Reviewer #1 (Public Review):

In this study, the protein composition of exocytotic sites in dopaminergic neurons is investigated. While extensive data are available for both glutamatergic and GABA-ergic synapses, it is far less clear which of the known proteins (particularly proteins localized to the active zone) are also required for dopamine release, and whether proteins are involved that are not found in "classical" synapses. The approach used here uses proximity ligation to tag proteins close to synaptic release sites by using three presynaptic proteins (ELKS, RIM, and the beta4-subunit of the voltage-gated calcium channel) as "baits". Fusion proteins containing BirA were selectively expressed in striatal dopaminergic neurons, followed by in-vivo biotin labelling, isolation of biotinylated proteins and proteomics, using proteins labelled after expression of a soluble BirAconstruct in dopaminergic neurons as reference. As controls, the same experiments were performed in KO-mouse lines in which the presynaptic scaffolding protein RIM or the calcium sensor synaptotagmin 1 were selectively deleted in dopaminergic neurons. To control for specificity, the proteomes were compared with those obtained by expressing a soluble BirA construct. The authors found selective enrichments of synaptic and other proteins that were disrupted in RIM but not Syt1 KO animals, with some overlap between the different baits, thus providing a novel and useful dataset to better understand the composition of dopaminergic release sites.

Technically, the work is clearly state-of-the-art, cutting-edge, and of high quality, and I have no suggestions for experimental improvements.

We thank the reviewer for this summary and for pointing out the high quality of the work.

On the other hand, the data also show the limitations of the approach, and I suggest that the authors discuss these limitations in more detail. The problem is that there is very likely to be a lot of non-specific noise (for multiple reasons) and thus the enriched proteins certainly represent candidates for the interactome in the presynaptic network, but without further corroboration it cannot be claimed that as a whole they all belong to the proteome of the release site.

We fully agree with the reviewer. Most importantly, we have changed the final section from “Conclusions” to “Summary of conclusions and limitations” (lines 501-518) to summarize the limitations with equal weight to the conclusions. In the revised manuscript, we also included many specific additional points in this respect throughout the discussion and the results: many hits could be noise (lines 458, 478-479), thresholding affects the inclusion of proteins in the release site dataset (lines 208-215), the seven-day time window could deliver interactors from the soma to the synapse (lines 493-495), specific oddities (for example histones, lines 482-485), iBioID does not deliver an interactome per se but is simply based on proximity (lines 505-508), and several more. We also clearly state that each specific hit needs follow-up studies (lines 501-503: ” Each protein will require validation through morphological and functional characterization before an unequivocal assignment to dopamine release sites is possible.”), and a similar statement was added on lines 374-375.

Reviewer #2 (Public Review):

The Kaiser lab has been on the forefront in understanding the mechanism of dopamine release in central mammalian neurons. assessing dopamine neuron function has been quite difficult due to the limited experimental access to these neurons. Dopamine neurons possess a number of unique functional roles and participate in several pathophysiological conditions, making them an important target of basic research. This study here has been designed to describe the proteome of the dopamine release apparatus using proximity biotin labeling via active zone protein domains fused to BirA, to test in which ways its proteome composition is similar or different to other central nerve terminals. The control experiments demonstrating proper localization as well as specificity of biotinylation are very solid, yielding in a highly enriched and well characterized proteome data base. Several new proteins were identified and the data base will very likely be a very useful resource for future analysis of the protein composition of synapse and their function at dopamine and other synapses.

We thank the reviewer for this positive assessment of our work.

Major comment:

The authors find that loss of RIM leads to major reduction in the number of synaptically enriched proteins, while they did not see this loss of number of enriched proteins in the Syt1-KO's, arguing for undisrupted synaptome. Maybe I missed this, but which fraction of proteins and synaptic proteins are than co-detected both in the Syt1 and control conditions when comparing the Venn diagrams of Fig2 and Fig 3 Suppl. 2? This analysis may provide an estimate of the reliability of the method across experimental conditions.

We thank the reviewer for proposing to be clear in the comparison of the control and Syt-1 cKODA data. A direct comparison of hit numbers is included on lines 323-324, with 37% overlap between control and Syt-1 cKODA (vs. 15% between control and RIM cKODA). A direct mapping of this overlap is included in Fig. 4E. We think that this direct comparison is complicated by a number of factors, as outlined below, and did our best to include these complications in the discussion, including the last section (lines 501-518).

First, to assess overall similarity, the initial comparison should be to assess axonal proteins identified in the BirA-tdTomato samples. These datasets are quite similar, with 671 (control) and 793 (Syt-1 cKODA) proteins detected, and a high overlap of 601 proteins. We think that this indicates that the experiment per se is quite reproducible. The comparison of the release site proteome between control and Syt-1 cKODA is more complicated. We think that the main point of this comparison is that the overall number of hits is quite similar, with 450 hits in the Syt-1 cKODA proteome and 527 hits in the control proteome, and we now show that this similarity holds across multiple thresholds (lines 298-301; ≥ 1.5: Syt-1 cKODA 602 hits, control 991, ≥ 2.0: 450/527, ≥ 2.5: 252/348). Detailed analyses of overlap reveals that known active zone proteins such as Bassoon, CaV2 channels, RIMs, and ELKS proteins are present in both proteomes, but the overlap is partial and incomplete with 191 proteins found in both proteomes. As discussed throughout and summarized on lines 501-518, the reasons for this partial overlap may be manifold. Trivially, it could be explained by noise or non-saturation (“incompleteness”) of the proteome. We also think that the Syt-1 proteome is not expected to be identical because there is a strong release deficit in these mice. If Syt-1 has a dopamine vesicle docking function (which it does at conventional synapses [4]), this could influence the proteome. We note that protein functions in the dopamine axon are not well established, but inferred from studies of classical synapses.

We have scrutinized the manuscript to not express that the control and Syt-1 cKODA proteomes are identical; we know they are not and discuss the example of α-synuclein specifically (Fig. 6, lines 347-362). Rather, the striking part is that the extent of the proteomes with high hit number, much higher than RIM cKODA, are similar. Specific hits have to be assessed in a detailed way, one hit at a time, in future studies, as expressed unequivocally on lines 501-503).

Reviewer #3 (Public Review):

In this study Kershberg et al use three novel in vivo biotin-identification (iBioID) approaches in mice to isolate and identify proteins of axonal dopamine release sites. By dissecting the striatum, where dopamine axons are, from the substantia nigra and VTA, where dopamine somata are, the authors selectively analyzed axonal compartments. Perturbation studies were designed by crossing the iBioID lines with null mutant mice. Combining the data from these three independent iBioID approaches and the fact that axonal compartments are separated from somata provides a precise and valuable description of the protein composition of these release sites, with many new proteins not previously associated with synaptic release sites. These data are a valuable resource for future experiments on dopamine release mechanisms in the CNS and the organization of the release sites. The BirA (BioID) tags are carefully positioned in three target proteins not to affect their localization/function. Data analysis and visualization are excellent. Combining the new iBioID approaches with existing null mutant mice produces powerful perturbation experiments that lead and strong conclusions on the central role of RIM1 as central organizers of dopamine release sites and unexpected (and unexplained) new findings on how RIM1 and synaptotagmin1 are both required for the accumulation of alpha-synuclein at dopamine release sites.

We thank the reviewer for assessing our paper, for summarizing our main findings, and for expressing genuine enthusiasm for the approach and the outcomes.

It is not entirely clear how certain decisions made by the authors on data thresholds may affect the overall picture emerging from their analyses. This is a purely hypothesis-generating study. The authors made little efforts to define expectations and compare their results to these. Consequently, there is little guidance on how to interpret the data and how decisions made by the authors affect the overall conclusions. For instance, the collection of proteins tagged by all three tagging strategies (Fig 2) is expected to contain all known components of dopamine release sites (not at all the case), and maybe also synaptic vesicles (2 TM components detected, but not the most well-known components like vSNAREs and H+/DA-transporters), and endocytic machinery (only 2 endophilin orthologs detected). Whether or not a more complete collection the components of release sites, synaptic vesicles or endocytic machinery are observed might depend on two hard thresholds applied in this study: (a) "Hits" (depicted in Fig 2) were defined as proteins enriched {greater than or equal to} 2-fold (line 178) and peptides not detected in the negative control (soluble BirA) were defined as 0.5 (line 175). How crucial are these two decisions? It would be great to know if the overall conclusions change if these decisions were made differently.

We agree with the reviewer that the thresholding decisions are important and have now better incorporated the rationale for these decisions in the manuscript.

Two-fold enrichment threshold. As outlined in the response to point 1 in the editorial decision letter, we now include figure supplements to illustrate the composition of the control proteome if we apply 1.5- or 2.5-fold enrichment thresholds (Fig. 2 – figure supplements 1 and 2) instead of the 2.0-fold threshold used in Fig. 2. This leads to more or less hits (991 and 348, respectively) compared to the 2.0-fold threshold (527 hits). It is noteworthy that the SynGO-overlap is the highest with the 2.0 threshold (37% vs. 31% at 1.5 and 33% at 2.5, Fig. 2 – figure supplement 3), justifying this threshold experimentally in addition to what was done in previous work [1,2]. These data are now described on lines 208-215 of the manuscript. When we apply these different thresholds to RIM and Syt-1 cKODA datasets, the finding that RIM ablation disrupts release site assembly persists. The following hit numbers were observed in the mutants at the 1.5, 2.0 and 2.5 enrichment thresholds, respectively: RIM cKODA 268, 198 and 82 hits; Syt cKODA 602, 450 and 252 hits. Hence, the extent of the release site proteome remains much smaller after RIM ablation independent of the enrichment threshold, bolstering the conclusion that RIM is an important scaffold for these release sites. This is included in the revised manuscript on lines 298301.

Undetected peptides in BirA-tdTomato. We did not express this well enough in the manuscript. The undetected proteins were set to 0.5 such that a protein that was detected with a specific bait but not with BirA-tdTomato could be illustrated with a specific circle size, not to determine inclusion in the analyses. If the average peptide count across repeats with a specific bait was 1, this resulted in inclusion in Fig. 2 and consecutive analyses with the smallest circle size. Hence, this decision was made to define circle size. It did not affect inclusion in Fig. 2 beyond the following two points. If one were to further decrease it, this might result in including peptides that only appeared once as a single peptide for some of the experiments, which we wanted to avoid. If one would set it higher (to 1), this artificial threshold would be equal to proteins that were actually detected experimentally multiple times, which we wanted to avoid as well. We have now clarified this on lines 165-167 and lines 1119-1121.

Expected proteins. In general, interpreting our dataset with a strong prior of expected proteins is difficult. The literature on release site proteins specifically characterized for dopamine is limited. We have found Bassoon, RIM, ELKS and Munc13 to be present using 3D-SIM superresolution microscopy [5,6], and we indeed found these proteins in the data as discussed on lines 227-232 and lines 423-445 in the revised manuscript. The prediction for vesicular and endocytic proteins is complicated. Release sites are sparse [5,7], and vesicle clusters are widespread in the dopamine axon, in some cases filling most of the axon (for example, see extended vesicle clusters filling much of the dopamine axon in Fig. 7E of [5]). Furthermore, docking in dopamine axons has not been characterized, and it is unclear how frequently vesicles are docked. Hence, it is not clear whether vesicular proteins should be concentrated at release sites compared to the rest of the axon (the BirA-tdTomato proteome we use for normalization). Similar points can be made for proteins for endocytosis and recycling of dopamine vesicles. Within the dopamine system, it is unclear whether the recycling pathway is close to the exocytic sites. One consistent finding across functional studies is that depletion after activity is unusually long-lasting in the dopamine system, for tens of seconds, even after only mild stimulation [5,8–13]. Hence, endocytosis and RRP replenishment might be very slow in these axons. It is not certain that endocytic factors are predeployed to the plasma membrane, and if they are, it is unclear how close to release sites they would be. As such, we agree with the reviewer that the proteome we describe is a hypothesisgenerator. With the limited knowledge on dopamine release, predictions beyond the previously characterized proteins in dopamine axons are difficult to make.

We thank the reviewer for suggesting to include a better analysis of different thresholds and for giving us the opportunity to clarify the other points that were raised.

Given the good separation of the axonal compartment from the somata (one of the real experimental strengths of this study), it is completely unexpected to find two histones being enriched with all three tagging strategies (Hist1h1d and 1h4a). This should be mentioned and discussed.

We agree with the reviewer and have addressed this point in the manuscript. This could either reflect noise, or there could be more specific reasons behind it. The manuscript now states on lines 482-485: “It is surprising that Hist1h1d and Hist1h4a, genes encoding for the histone proteins H1.3 and H4, were robustly enriched (Fig. 2A). These hits might be entirely unspecific, or their co-purification could be due to biotinylation of H1 and H4 proteins (Stanley et al., 2001). It is also possible that there are unidentified synaptic functions of some of the unexpected proteins.” Ultimately, we do not know why these proteins are enriched, and we state clearly in the section “Summary of conclusions and limitations” that each new hit has to be validated in future studies (lines 501-503).

It would also help to compare the data more systematically to a previous study that attempted to define release sites (albeit not dopamine release sites) using a different methodology (biochemical purification): Boyken et al (only mentioned in relation to Nptn, but other proteins are observed in both studies too, e.g. Cend1).

We agree with the reviewer that Boyken et al, 2013 [14] is an important resource for our paper and for the assessment of the proteomic composition of release sites. We have now introduced links and citations to this paper multiple times (for example, on lines 231, 241, 430, 443, 481) and have expanded the discussion of overlap between these proteomes, including on Cend1 (lines 479482).

We think that a systematic comparison with Boyken et al, 2013 [14] is complicated because (1) so little is known about dopamine release mechanics and (2) because the approach is very different between the two papers. In respect to (1), most prominently, it is not certain how frequently vesicles are docked in the dopamine axon. Only ~25% of the varicosities contain these release sites, and vesicle docking has not been characterized in striatal dopamine axons to the best of our knowledge. Hence, how a docking site at a classical synapse compares to a dopamine release site remains unclear at the outset. For point (2), the key difference is that “within dataset normalizations” are very different in these two studies. In our iBioID dataset, we normalize to soluble proteins defined as proximity to BirA-tdTomato. In ref. [14], the authors express enrichment over “light”, regular synaptic vesicles purified with the same approach. This has a major impact on the proteome that strongly influences a direct comparison of hits, because there are large differences in the normalization. While each normalization makes sense for the respective paper, it complicates direct comparison.

With these points in mind, we have compared hits across both datasets class-by-class. For some classes, the datasets have reasonable overlap for ≥ 2-fold enriched proteins: for example for active zone proteins (3 of 7 hits in [14] appear in our control proteome) and adhesion and cell surface proteins (8 of 18). For other classes, the overlap is limited: for example for nucleotide metabolism/protein synthesis (0 of 16 hits in [14] appear in our dataset) and cytoskeletal proteins (5 of 29). We hope the reviewer agrees, that given these factors, the analyses and discussion needed for a systematic comparison goes beyond the scope of our paper. We have instead added a number of references to Boyken et al., 2013 [14], as outlined above, when direct comparison is meaningful.

Author Response

Reviewer #2 (Public Review):

In this paper, Xiao et al. suggest that PASK is a driver for stem cell differentiation by translocating from the cytosol to the nucleus. This phenomenon is dependent on the acetylation of PASK mediated by CBP/EP300, which is driven by glutamine metabolism. Furthermore, this study showed that PASK interferes/weakens the Wdr5-APC/C interaction, where PASK interacts with Wdr5, resulting in repression of Pax7, leading to stem cell differentiation.

There exist huge interest in maintaining adult stem cells and ES cells in their pluripotent form and the work painstakingly perform several experiments to present that PASK is a good target to achieve that goal.

However, the work on the paper relies mostly on data from C2C12 cells as adult muscle stem cell models, in vivo experimental data, and primary myoblasts from mice. Using these models makes the story contextual in muscle stem cells. Authors have not tried to extrapolate similar claims in other adult stem cell models. This severely restricts the claim to muscle stem cells even though PASK is required for the onset of embryonic and adult stem cell differentiation in general. Their work could be much strengthened if it is also tried on mesenchymal stem cells as these cells are also as metabolically active as muscle cells.

We thank reviewers for their enthusiasm for our studies using PASKi. We have previously shown that PASKi prevented differentiation of 10T1/2 cells into adipogenic lineage (Kikani et al, Elife, 2016). We used stem cells from embryonic (ESC) and adult (MuSCs) origin to show broad application of PASKi in preserving self-renewal independent of stem cell origin. We believe that PASK function to be conversed across different stem cell paradigms; and our results in this manuscript would provide framework to further study PASK in other stem cell paradigms.

Reviewer #3 (Public Review):

This manuscript entitled "PASK relays metabolic signals to mitotic Wdr5-APC/C complex to drive exit from selfrenewal" by Xiao et al presents an interesting story on the role of PASK in the control of muscle stem cell fate by controlling the decision between self-renewal and differentiation. While the biochemistry presented is fairly compelling, the experiments revolving around the myogenic cells are lacking in quality and data.

Major concerns:

1) The isolation method used by this group to isolate muscle stem cells is inappropriate for the experiments used and may contribute to the misinterpretation of some of the results. It is simply a preplating method that results in a very heterogenous cell population in terms of cell type, comprised of numerous fibroblasts. While preplating can be used to isolate muscle stem cells and culture them as myoblasts, it takes days of growth and multiple rounds of passaging that are not used in this paper in order to get a more pure population of myogenic cells. This would also explain the high number of Pax7 negative cells in their primary myoblast experiments (~50% in some conditions) as they are most likely fibroblasts, which the authors could show by staining for fibroblast markers. The increase in Pax7 cells in certain conditions could also simply be due to the loss of contaminating cell types due to the treatment. Every single experiment that was performed on myoblasts must be redone using a more appropriate cell isolation method (i.e. FACS) or by culturing these isolated cells for a much longer period of time to eventually get a more pure cell population. As it stands, none of the data from the primary myoblast experiments are trustworthy.

We agree – and thus, we have reproduced our results using two different methods of purifying MuSCs from mice, as indicated above. We took care to stain each isolation method with vimentin (a marker for fibroblasts) to ensure the purity of our preparation. Data are included in the Essential revisions section.

2) The authors possess a genetic mouse model where PASK is knocked out. However, the mouse model is never described and the paper that is referenced also does not describe it. Please detail your mouse model.

3) The majority of experiments are performed on C2C12 cells. While C2C12s are adequate for biochemistry and proof of concepts, when it comes to biological significance primary myoblasts should be used. While the authors try to explain this use by claiming that primary myoblasts undergo precocious differentiation that can be avoided by using an appropriate growth media (F10, 20% FBS, 1% P/S, 5ng/mL of bFGF).

Kindly see the response for this comment in the Essential revision section.

4) The authors possess a genetic mouse model, yet performed RNA-Seq on C2C12 myoblasts that were either untreated or treated with a PASK inhibitor. It would be much more informative and valuable to sequence the primary myoblasts from WT and PASK KO mice, thereby providing a more biologically relevant model.

We used C2C12 for several reasons for initial transcriptome analysis using PASKi and validated the results from that analysis in primary myoblasts. (1) C2C12 cells are an excellent model for performing biochemical pathway characterization, including discovering new substrate targets for PASK, finding PASK interacting partners, and measuring the biochemical activity of PASK under various conditions. Thus, it would form the basis for a longer-term study of the signaling functions of PASK in one cell system (myoblasts), which can be validated and compared with the primary cell system. (2) PASKi treatment can acutely inhibit PASK catalytic activity without the genetic loss of its protein level. For many enzymatic proteins, catalytic inhibition could have a different biological effect compared with genetic loss of protein (Weiss et al.; Nat Chem Biol. 2007 Dec; 3(12): 739–744.). Thus, we chose the PASKi and C2C12 myoblasts system to study the kinase activitydependent effect on the myoblast transcriptome. However, throughout the manuscript, we used PASKi, PASK siRNA, and PASKKO primary cells to cross-validate all our data. We believe the conditional loss of PASK in MuSCs specific manner will be a great model to repeat the RNA-seq analysis in the future and compare the data obtained with PASKi in cultured myoblasts.

5) The KO mouse model is rarely used and the cells isolated from it would be very useful in determining the biological role of PASK in muscle cells. The authors should isolate WT and KO cells and perform basic muscle functional experiments such as EDU incorporation for proliferation, and fusion index for differentiation to see whether the loss of PASK has an effect on these cells.

We have published the characterization of myogenesis phenotype of PASKKO model in our previous manuscript (Kikani et al, 2016). Thus, we erred by not redoing those experiment in the previous version. We have now reproduced those results and markedly extended the chacterization of PASKKO cells in vitro, including BrdU incorporation, myogenesis, Pax7 heterogeneity, Myogenin expression and PASK subcellular distribution using WT cells. We have also characterized regeneration phenotype of PASKKO mice. We thank the reviewer for helping strengthen the biological context of our manuscript.

6) The authors never look at quiescent muscle stem cells and early activated muscle stem cells in terms of PASK protein expression and dynamics. The authors should isolate EDL myofibers and stain for PASK and PAX7 at 0, 24, 48, and 72-hour post isolation. This would allow the authors to quantify the changes in PASK expression and cell localization, as well as confirm the number of muscle stem cells in WT and KO mice, during quiescence and during the process of muscle stem cell activation, proliferation, and differentiation in a near in vivo context.

As described in Figure 1-figure supplement 2A, PASK is not expressed in quiescent MuSCs. Therefore, we do not anticipate a functional role of PASK in initial activation of QSC. We do not propose that PASK plays a role in the maintenance of the QSC state or the exit and initial activation of MuSCs following muscle injury. PASK is transcriptionally activated in proliferating myoblasts during regeneration (Kikani et al, elife 2016) and upon isolation of MuSCs (Figure S1D). Therefore, we specifically focus on studying the biochemical functional role of PASK signaling in activated (proliferating) myoblasts isolated from mice or during early regeneration. We have ongoing studies examining the precise temporal kinetics of PASK transcription regulation in Pax7+ MuSCs as they are activated, and to identify its upstream transcriptional regulators. However, we respectfully suggest that these avenues are outside of the purview of this current manuscript that specifically explores the metabolic pathway that establishes progenitor population from activated myoblasts.

7) Contrary to their claim, MyoD is not a stemness/self-renewal gene.

We agree, and have corrected the text.

8) The authors state that PASK is necessary for exit from self-renewal and establishment of a progenitor population, but this is a vast overstatement. In the genetic KO mouse model, the mice are able to regenerate their muscle after injury, therefore PASK cannot be a necessary protein for the formation of progenitor cells.

During the muscle regeneration, we observed a significant inhibition of the early regenerative response in PASKKO mice, marked by severely reduced levels of eMHC. Concomittantly, we observed increased numbers of Pax7+ MuSCs at Day 5 of regeneration compared with WT muscles. We have extensively shown requirement of PASK for myogenin induction in vitro and in vivo (Kikani et al, 2016, Kikani et al, 2019). Based on these evidence, we propose that PASK is necessary for the exit from Pax7+ self-renewing stem cells and generation of Myog+ committed progenitor populations.

9) In numerous figure panels, the y-axis represents the # of cells, rather than a percentage or ratio. This is uninformative as the number of cells will never be the same between conditions and experiments. These panels need to be replaced with a more appropriate y-axis.

We have updated the axes to % cells where appropriate.

Author Response

Reviewer #1 (Public Review):

Doostani et al. present work in which they use fMRI to explore the role of normalization in V1, LO, PFs, EBA, and PPA. The goal of the manuscript is to provide experimental evidence of divisive normalization of neural responses in the human brain. The manuscript is well written and clear in its intentions; however, it is not comprehensive and limited in its interpretation. The manuscript is limited to two simple figures that support its concussions. There is no report of behavior, so there is no way to know whether participants followed instructions. This is important as the study focuses on object-based attention and the analysis depends on the task manipulation. The manuscript does not show any clear progression towards the conclusions and this makes it difficult to assess its scientific quality and the claims that it makes.

Strengths:

The intentions of the paper are clear and the design of the experiment itself is simple to follow. The paper presents some evidence for normalization in V1, LO, PFs, EBA, and PPA. The presented study has laid the foundation for a piece of work that could have importance for the field once it is fleshed out.

Weakness:

The paper claims that it provides compelling evidence for normalization in the human brain. Very broadly, the presented data support this conclusion; for the most part, the normalization model is better than the weighted sum model and a weighted average model. However, the paper is limited in how it works its way up to this conclusion. There is no interpretation of how the data should look based on expectations, just how it does look, and how/why the normalization model is most similar to the data. The paper shows a bias in focusing on visualization of the 'best' data/areas that support the conclusions whereas the data that are not as clear are minimized, yet the conclusions seem to lump all the areas in together and any nuanced differences are not recognized. It is surprising that the manuscript does not present illustrative examples of BOLD series from voxel responses across conditions given that it is stated that it is modeling responses to single voxels; these responses need to be provided for the readers to get some sense of data quality. There are also issues regarding the statistics; the statistics in the paper are not explicitly stated, and from what information is provided (multiple t-tests?), they seem to be incorrect. Last, but not least, there is no report of behavior, so it is not possible to assess the success of the attentional manipulation.

We appreciate the reviewer’s feedback on providing more information so that the scientific quality of our work can be assessed. We have now added a new figure including BOLD responses in different conditions, as well as how we expected the data to look and the interpretations. To provide extra evidence for data quality and reliability, we have included BOLD responses of different conditions for odd and even runs separately in the supplementary information.

In order to avoid any bias in presentation, we have now visualized the results from all areas with the same size and in a more logical order. However, we have also modified all results to include only those voxels in each ROI that were active for the stimuli presented in the main task based on the comment of one of the reviewers. According to the current results, there is no difference in the efficiency of the normalization model in different regions, which we have reported in the results section.

Regarding the statistics, we have corrected the problem. We have performed ANOVA tests, have corrected all results for multiple comparisons, and have added a statistics subsection in the methods section to explicitly explain the statistics.

Finally, we have added the report of the reaction time and accuracy in the results section and the supplementary information. As stated, average performance was above 86% in all conditions, confirming that the participants correctly followed the instructions and that the attentional manipulation was successful.

We hope that the reviewer would find the manuscript improved and that the new analyses, figures, and discussions would address the reviewer’s concerns.

Reviewer #2 (Public Review):

My main concern is in regards to the interpretation of these results has to do with the sparseness of data available to fit with the models. The authors pit two linear models against a nonlinear (normalization) model. The predictions for weighted average and summed models are both linear models doomed to poorly match the fMRI data, particularly in contrast to the nonlinear model. So, while I appreciate the verification that responses to multiple stimuli don't add up or average each other, the model comparisons seem less interesting in this light. This is particularly salient of an issue because the model testing endeavor seems rather unconstrained. A 'true' test of the model would likely need a whole range of contrasts tested for one (or both) of the stimuli, Otherwise, as it stands we simply have a parameter (sigma) that instantly gives more wiggle room than the other models. It would be fairer to pit this normalization model against other nonlinear models. Indeed, this has been already been done in previous work by Kendrick Kay, Jon Winawer and Serge Dumoulin's groups. So far, may concern above has only been in regards to the "unattended" data. But the same issue of course extends to the attended conditions. I think the authors need to either acknowledge the limits of this approach to testing the model or introduce some other frameworks.

We thank the reviewer for their feedback. We have taken two approaches to answer this concern. First, we have included simulations of neural population responses to attended and unattended stimuli. The results demonstrate that with our cross-validation approach, the normalization model is only a better fit if the computation performed at the neural level for multiple-stimulus responses is divisive normalization. Otherwise, the weighted sum or the weighted average models are better fits to the population response when the neurons respectively sum or average responses. These results suggest that the normalization model provides a better fit to the data because the underlying computation performed by the neurons is divisive normalization, not because of the model’s non-linearity.

In a second approach, we tested a nonlinear model, which was a generalization of the weighted sum and the weighted average models with an extra saturation parameter (with even more parameters than the normalization model). The results demonstrated that this model was also a worse fit than the normalization model.

Regarding the reviewer’s comment on testing for a range of contrasts, as we have emphasized now in the discussion, here, we have used single-, multiple-, attended- and unattended-stimulus conditions to explore the change in response and how the normalization model accounts for the observed changes in different conditions. While testing for a range of contrasts would also be interesting, it would need a multi-session fMRI experiment to test for a range of contrasts with isolated and paired stimulus conditions in the presence and absence of attention. Moreover, the role of contrast in normalization has been investigated in previous studies, and here we added to the existing literature by exploring responses to multiple objects, and investigating the role of attention. Finally, since the design of our experiment includes presenting superimposed stimuli, the range of contrasts we can use is limited. Low-contrast superimposed stimuli cannot be easily distinguished, and high-contrast stimuli block each other.

We hope that the reviewer would find the manuscript improved and that the new models, simulations, analyses, and discussions would address the reviewer’s concerns.

Reviewer #3 (Public Review):

In this paper, the authors model brain responses for visual objects and the effect of attention on these brain responses. The authors compare three models that have been studied in the literature to account for the effect of attention on brain responses to multiple stimuli: a normalization model, a weighted average model, and a weighted sum model.

The authors presented human volunteers with images of houses and bodies, presented in isolation or together, and measured fMRI brain activity. The authors fit the fMRI data to the predictions of these three models, and argue that the normalization model best accounts for the data.

The strengths of this study include a relatively large number of participants (N=19), and data collected in a variety of different visual brain regions. The blocked design paradigm and the large number of fMRI runs enhance the quality of the dataset.

Regarding the interpretation of the findings, there are a few points that should be considered: 1) The different models that are being studied have different numbers of free parameters. The normalization model has the highest number of free parameters, and it turns out to fit the data the best. Thus, the main finding could be due to the larger number of parameters in the model. The more parameters a model has, the higher "capacity" it has to potentially fit a dataset. 2) In the abstract, the authors claim that the normalization model best fits the data. However, on closer inspection, this does not appear to be the case systematically in all conditions, but rather more so in the attended conditions. In some of the other conditions, the weighted average model also appears to provide a reasonable fit, suggesting that the normalization model may be particularly relevant to modeling the effects of attention. 3) In the primary results, the data are collapsed across five different conditions (isolated/attended for preferred and null stimuli), making it difficult to determine how each model fares in each condition. It would be helpful to provide data separately for the different conditions.

We thank the reviewer for their feedback.

Regarding the reviewer’s concern about the number of free parameters, we have introduced a simulation approach, demonstrating that with our cross-validation approach, a model with a higher number of parameters is not a good fit when the underlying neural computation does not match the computation performed by the model. Moreover, we have now included another nonlinear model with 5 parameters that performs worse than the normalization model. Besides, we have used the AIC measure in addition to cross-validation for model comparison, and the AIC measure confirms the previous results.

Regarding the difference in the efficiency of the normalization model across conditions, after selecting the voxels that were active during the main task in each ROI (done according to the suggestion of one of the reviewers to compensate for the difference in size of localizer and task stimuli), we observed that the normalization model was a better fit for both attended and unattended conditions. However, since the weighted average model results were also close to the data in unattended conditions, we have discussed the unattended condition separately and have discussed the relevance of our results to previous reports of multiple-stimulus responses in the absence of attention.

Finally, concerning model comparison for different conditions, we have calculated the models’ goodness of fit across conditions for each voxel. The reason for calculating the goodness of fit in this manner was to evaluate model fits based on their ability in predicting response changes with the addition of a second stimulus and with the shifts of attention. Since correlation is blind to a systematic error in prediction for all voxels in a condition, calculating the goodness of fit across voxels would lead to misinterpretation. We have now included a figure in the supplementary information illustrating the method we used for calculating the goodness of fit.

We hope that the reviewer would find the manuscript improved and that the new analyses, simulations, figures, and discussions would address the reviewer’s concerns.

Author Response

Reviewer #1 (Public Review):

In this manuscript, Braet et al provide a rigorous analysis of SARS-CoV-2 spike protein dynamics using hydrogen/deuterium exchange mass spectrometry. Their findings reveal an interesting increase in the dynamics of the N-terminal domain that progressed with the emergence of new variants. In addition, the authors also observe an increase in the stabilization of the spike trimeric core, which they identify originates from the early D614G mutation.

Overall this is a timely and interesting exploration of spike protein dynamics, which have so far remained largely unexplored in the literature.

What I find a bit missing in this manuscript is a link between how the identified changes in protein dynamics lead to increased viral fitness. While there are some possibilities listed in the discussion, I think these should be elaborated upon further. In addition, it should also be discussed how understanding the changes in the spike protein dynamics could have implications for the development of small molecule inhibitors for the virus.

We have included information in the introduction and conclusion to make the connection more clearly between our observations, function, and viral fitness of spike protein. We have also connected specific mutations to observed function. We have re-organized the discussion for increased clarity and to improve the correlation of our observations to viral fitness.

Reviewer #2 (Public Review):

The study systematically looks at dynamic differences across variants longitudinally and the authors appropriately only limit their analyses to peptides that are conserved across the different variants.

There are some concerns listed below, particularly related to the ensemble heterogeneity that is reported and need considerable revision.

1) The authors explain that cold-temperature treatment of the S trimer ectodomain constructs has been shown to lead to instability and heterogeneity. They also show this with a comparison of untreated vs. 3-hour 37 ℃ treated samples. I'm confused as to why "During automated HDXMS experiments protein samples were stored at 0 ℃". Will this not cause issues in protein heterogeneity, where the longer the protein sits at 0 ℃ the more potential heterogeneity there will be, and thus greatly confound the analysis?

We thank the reviewer for highlighting this point. We have carefully examined and reevaluated our analysis of both wild -type and variant spike HDXMS. During automated HDXMS experiments, protein samples are indeed maintained at 0 ℃, in between runs and replicates for fixed periods of time (4 h per replicate). In the case of WT S, we did observe conformational heterogeneity between replicates (Figure 2- figure supplement 6), as correctly pointed out by the reviewer. We have repeated analysis of WT S without 0 ℃ incubation in automated HDXMS experiments. In the revised manuscript, Figure 2 shows the more homogenous conformation of WT S, when not incubated at 0 ℃ in between replicates. Extension of these analyses to D614G (Figure 2- figure supplement 7) and all subsequent variants that each contain D614G, showed almost no conformational heterogeneity.

We have included a detailed description (lines 237-244) of the revised manuscript to describe in greater detail effects of 0 ℃ incubation on HDXMS of WT S.

Our results revealed that WT S was more sensitive to cold denaturation as described previously [Costello et al. 2021] where the reported half-life for conformational transitions after 0 ℃ incubation was 17 hours. We had not anticipated conformational heterogeneity revealed by deuterium exchange when using an automated HDXMS setup. Upon further review, we see a significant ensemble shift in trimer stalk peptides for the second and third replicates which sat at 0 ℃ for 4 and 8 hours respectively. This is only observed in WT but not any of the other variant S samples. We thank the reviewer for pointing this out and strengthening our conclusions.

2) The authors presume that the bimodal spectra that are observed reflect EX1 kinetics, however, there can be multiple reasons for an apparent bimodal distribution in the spectra. I agree that some of the spectra indicate that more than a single species is present, but what the two populations represent is murky. In Figure 2D, the apparent size of the highly deuterated population gets larger going from the 60 sec to the 600-sec spectra, as expected for an EX1 transition. However, in Figure 3D the WT highly deuterated population gets smaller going from the 60-sec to the 600-sec spectra. Were bimodal examples observed beyond those shown in Figure 2?

We agree with the reviewer. The appearance of bimodal spectra in deuterium exchange of S protein peptides in WT S are not a result of EX1 kinetics alone. We have revised the explanation for the presence of the bimodal spectra. These are largely a consequence of automated HDXMS workflows, that included 0° C incubations for short periods of time in between replicates. We report new experiments where we have eliminated 0 °C incubations by incubating at 20 °C between replicates and observed a lot lower conformational heterogeneity.

Consequently, the shifts in bimodal spectra in figure 3D for WT S are also likely a consequence of automated HDX MS experiments with 0 ℃ incubation. We have carried out new experiments without 0 ℃ incubation, and these are shown in a revised figure 3. Even without 0 ℃ incubation, we do see bimodal spectra for certain peptides [figure 2 – S5]. These reflect an ensemble of prefusion and splayed conformations of WT S. Lack of baseline resolution precludes application of HDexaminer to resolve spectral envelopes quantitatively.

3) How were the spectra that appeared broadened analyzed? There is no description of this in the methods, and the only data shown for this is in table 1. The left/right percentages are reported without any description of how they were obtained. Are these solely from a single spectrum? The most alarming issue is that Table 1B reports 9.4% for the right population of the 988-998 peptide, but the corresponding spectra in Figure 3D doesn't seem to have any highly deuterated population at all.

We agree with the reviewer. We have removed HD examiner analysis of spectral broadening. Some of the spectral broadening was a consequence of 0 ℃ incubation in automated HDX analyses. These have been revised in new supplemental figures for wild -type HDX MS. Baseline resolution precludes effective quantitation of spectral envelopes, Figure 2-figure supplement 5 highlights qualitatively the spectral broadening for the reader’s benefit.

4) The authors state on page 12: "Replicate analysis of stabilized S trimers with incubation at 4C prior to deuterium exchange (see methods) showed a time-dependent reversal of stabilization as reported previously (Costello et al., 2022), most evident at the same peptides." Is this data shown anywhere? If not then it should be included somewhere, possibly in table 1 as I would expect the cold treatment to offset the left/right population sizes.

We note that this statement was misleading and have revised the text. The time-dependent reversal of stabilization has previously been described (Costello et al., 2022 paper) and is not part of this study.

5) The authors state that peptide 899-913 'exhibits a slow conformational interconversion (time scale ~ 15-30 min)'. Where did this estimated rate come from? From the data shown and the limited number of time points, I don't think there is sufficient sampling of this conformational transition to really narrow down the exact timescale, especially since the ratio of left/right populations is so dependent on the pre-treatment of the sample prior to deuterium exchange. (See 1st comment)

We thank the reviewer. The heterogeneity in deuterium exchange is attributable to the variable 0 °C incubation times in our automated HDXMS workflow. We have removed any explanations of conformational interconversion occurring in our experimental timescales.

6) The woods plots presented in the Supporting information: (Figures 2-S4, 2-S5, 3-S4, 4-S2, 5-S2, 6-S2) are not conventional Woods plots. Normally the plots would indicate a global threshold for what is deemed to be significant based on the overall error in the dataset. From what I gather the authors used error within an individual peptide to establish significance for each specific peptide, which would be okay, but the authors don't describe the number of replicates or how the p-value was calculated. I would strongly recommend that the authors instead rely on a hybrid significance testing approach, as described recently: (PMID 31099554). What's really alarming with the current approach is that several of the Woods plots shown have data points found to be significantly different that are right at zero on the y-axis.

We thank the reviewer. We have replaced all of the Woods plots with volcano plots. We have now applied a hybrid significance testing approach as recommended by the reviewer.

7) Table 1: The summary of the peptides with observed bimodal behavior should include data from the replicates, particularly for assessment of how consistent the left/right population sizes are across replicates. Instead of just a percentage, the table should report an average and the standard deviation from the replicate measurements. Furthermore, the table should also include peptides that are overlapping with those presented. Based on Figure 2-figure supplement 1, there are at least two other peptides that cover the 899-913 region. These additional peptides should show a similar trend with bimodal profiles and will be important for showing how reproducible the apparent EX1 kinetics are in the dataset.

All available replicates and overlapping peptides should be analyzed to ensure that these percentages reported are consistent across the data. It is also odd that the authors choose to use the 3+ charge state of the WT, but the 2+ for the D614G mutant. If both charge states were present, then both of them should be analyzed to ensure the population distributions are consistent within different charge states.

We thank the reviewers for their suggestion. We have removed Table 1 since bimodal spectra are not resolvable for quantitation as described previously. We instead show spectra of overlapping peptides in these regions for interpretation by the reader.

We show charge states that provide highest intensity for the peptides (Figure 2-figure supplement 5, Figure 3-figure supplement 3, Figure 4-figure supplement 3, Figure 5-figure supplement 3, Figure 6-figure supplement 3).

8) The method for calculating p-values used to assess the significance of a difference in observed deuterium uptake is not described. The manuscript mentions technical replicates, but no specific information as to how many replicates were collected for each time point. These details should be included as they are also part of the summary table that is recommended for the publication of HDX data.

We have utilized hybrid significance testing as suggested by the reviewers to determine significance as outlined by Hageman et al. We have included this in table S3 and in the text.

Author Response

Reviewer #1 (Public Review):

Major points:

1) How STC1 controls changes in MSCs' ability for hampering CAR-T cell-mediated anti-tumor responses is unclear.

In this study, we demonstrated that the presence of STC1 is critical for MSCs to exert their immunosuppressive role by inhibiting cytotoxic T cell subsets, activating key immune suppressive/escape related molecules such as IDO and PD-L1, and crosstalking with macrophages in the TME. These immunosuppressive functions of MSC could be significantly hampered when the STC1 gene was knockdown. Considering that staniocalcin-1 is glycoprotein hormone that is secreted into the extracellular matrix in a paracrine manner, we would conclude that the role of STC-1 is not to alter the function of MSCs intracellularly. Rather, it facilitates the immunosuppressive capabilities of MSCs through extracellular secretion into the TME as a pleiotropic factor, thus impacting the functioning of T cells, cancer cells and other immune cells.

The reviewer's question is well taken, and we have added the points mentioned above to the Discussion section to ensure a more comprehensive conclusion. Moreover, a recent study published in Cancer Cell, which was suggested by the other reviewer, is consistent with our results. It has provided further mechanistic information on how stanniocalcin-1 impacts immunotherapy efficacy and T cell activation. The reference has been cited and discussed as shown below.

"In this model, activated macrophages or stress signals during CAR-T therapy may prompt MSCs to secret staniocalcin-1 into the extracellular matrix of TME, serving as a pleiotropic factor to negatively impact the function of T cells and stimulate the expression of molecules that inactivate immune responses, ultimately providing an immunosuppressive effect of MSC." (page 22, highlighted). "In line with our study, it was recently reported that stanniocalcin-1 negatively correlates with immunotherapy efficacy and T cell activation by trapping calreticulin, which abrogates membrane calreticulin-directed antigen presentation function and phagocytosis [50]." (Page 20, highlighted)

2) Is ROS important? It is not tested directly.

ROS plays an important role during immune response, which are released by neutrophils and macrophages. Not only do they act as key mediators of the adaptive immune response, but they also have the ability to modulate the activation of B-cells and T-cells. In our study, we suggest that ROS may be involved in NLRP3 inflammasome activation and the expression and secretion of STC1. Although we did not pursue this line of inquiry further as it was beyond the scope of our paper, we have included additional relevant research in Discussion and a reference is provided.

"It has been proved that the expression and secretion of STC1 in multiple cell lines can be stimulated by external stimuli, including cytokines and oxidative stress [26]." (Page 21, highlighted)

3) The changes in CD8 and Treg are not convincing. Moreover, it is not tested how these changes can be elicited by the presence of MSCs.

We have included additional in vivo data to assess the levels of Treg cells and CD8+ in this revised manuscript. This not only confirms the alterations of CD8 and Treg, but also offers additional line of evidence to further analyze the influence of MSCs on CAR-T in vivo. The findings are presented in Figure 4B, and the corresponding discussion can be found on Page 17 (highlighted).

Reviewer #2 (Public Review):

Major points:

1) STC-1 is expressed and secreted by many human cancer cells. This should be discussed in the introduction or discussion with more inter-related background info on both its regulation in cancer cells and secretion pattern into TME. It is important because you state that the STC-1 secreted by MSC has such strong functions, then how about those produced and secreted by cancer cells? Are those also stimulated by macrophages or other components in TME? Do they have possible functions in helping cancer cell to escape the immune surveillance mechanisms?

Thanks for the suggestion. We have added more details about the regulation and secretion of STC-1 in cancer cells (see below). The information is added to both the introduction and discussion (highlighted on pages 4 and 21), and all the above questions are addressed.

"It was proved that STC1 is involved in several oxidative and cancer-related signaling pathways such as NF-κB, ERK, and JNK pathways [26,27]. The expression and secretion of STC1 in cancer tissue can be stimulated by external stimulus including external cytokines and oxidative stress [26]. Under hypoxia conditions, STC1 could be modulated by HIF-1 to facilitate the reprogramming of tumor metabolism from oxidative to glycolytic metabolism [28]. STC1 was also reported to participate in the process of epithelial-to-mesenchymal transition (EMT), which is associated with tumor invasion and the reshape the tumor microenvironment, as well as increasing therapy resistance [29]." (Page 4)

"It has been proved that the expression and secretion of STC1 in multiple cell lines can be stimulated by external stimuli including cytokines and oxidative stress [26]." (Page 21)

2) In Figure 4B, using a single marker of IL-1β to show the immune suppressive capability of MSC in vivo is not sufficient, staining for CD4+ and CD8+ should also be included to demonstrate whether MSC could modulate T cell compositions, which can give more direct evidence about MSC's impacts on CAR-T cell.

The above experiments were done as suggested, and the data were presented in figure 4B. Explanations of the results are shown on page 17 Results section and page 21 Discussion section (highlighted).

3) One of the major risks associated with CAR-T therapy is an excessive immune response that causes cytokine release syndrome. MSCs have been used in clinics as a way to suppress immune response including post-CAR-T. What does the author think about using MSC with STC-1 knockout? Can it still help reduce toxicity while maintaining CAR-T efficacy? This might be a potential application.

This is definitely an interesting idea. Based on the data presented in the current study, it is clear that knockdown of STC-1 would abrogate the immune-suppressive impact of MSC, and therefore affect CAR-T efficacy. However, whether the presence of MSC can help reduce cytokine release syndrome when losing the function of STC-1 requires further study. We agree with the reviewer, and we had briefly discussed this possibility at the very end of the discussion as shown below (Page 22, highlighted).

"… the findings we presented here are no doubt that would have potential clinical applications toward improving the efficiency of CAR-T therapy as well as reducing the excessive toxicity by modulating the level of STC1 in TME".

4) There was a recent study published in Cancer Cell (Lin et al. Stanniocalcin 1 is a phagocytosis checkpoint driving tumor immune resistance. 2021), and they also reported that STC1 negatively correlates with immunotherapy efficacy and patient survival. It should be cited, and in fact, it provided support to the authors' present study with completely different experimental settings.

Thanks for providing this important information. It is an excellent study and consistent with our findings. The reference was added and discussed on page 20 (highlighted) as shown below.

"In line with our study, it was recently reported that stanniocalcin-1 negatively correlates with immunotherapy efficacy and T cell activation by trapping calreticulin, which abrogates membrane calreticulin-directed antigen presentation function and phagocytosis [50]"

Author Response

Reviewer #1 (Public Review):

This theoretical (computational modelling) study explores a mechanism that may underlie beta (13-30Hz) oscillations in the primate motor cortex. The authors conjecture that traveling beta oscillation bursts emerge following dephasing of intracortical dynamics by extracortical inputs. This is a well written and illustrated manuscript that addressed issues that are both of fundamental and translational importance.

We are pleased by the reviewer’s judgement about the importance of the question that we consider and about the presentation of our manuscript.

Unfortunately, existing work in the field is not well considered and related to the present work. The rationale of the model network follows closely the description in Sherman et al (2016). The relation (difference/advance) to this published and available model needs to be explicitly made clear. Does the Sherman model lack emerging physiological features that the new proposed model exhibits?

We view the work of Sherman et al (2016) and ours as complementary. Sherman et al propose a model of a single E-I module, using the terminology of our manuscript, that is much more detailed than ours since it approximately accounts for the layered structure of the cortex using two layers of multi-compartment spiking neurons, each comprising 100 excitatory neurons and 35 inhibitory neurons. This allows a detailed comparison of the model with local MEG signals. We used a much simpler description and only describe the population behavior of local E and I neurons populations in each module. However, contrary to Sherman’s model, this allows us to address the spatial aspect of beta oscillations which is the main target of our work. Our simple description of a local E-I module allows us to consider several hundred E-I modules with a spatially-structured connectivity and to analyze the spatio-temporal characteristics of beta activity. We have now described the relation of our work with Sherman et al (2019) in the discussion section (lines 540-547).

The authors may also note the stability analysis in: Yaqian Chen et al., “Emergence of Beta Oscillations of a Resonance Model for Parkinson’s Disease”, Neural Plasticity, vol. 2020, https://doi.org/10.1155/2020/8824760

We thank the reviewer for pointing out this paper that had escaped our notice. It presents the stability analysis of a single E-I module with propagation delay (and instantaneous synapses). At the mathematical level, the analysis brings little as compared to the much older article of Geisler et al., J Neurophys (2005) that we cite. However, the model specifically proposes to describe beta oscillations in the motor cortex as arising from the interaction between excitatory and inhibitory neurons, as we do. Therefore, we included this reference as well as a reference to the previous work of Pavlides et al., PLoS Comp Biol (2015) where the model was developed.

The model-based analysis of the traveling nature of the beta frequency bursts appears to be the most original component of the manuscript. Unfortunately, this is also the least worked out component. The phase velocity analysis is limited by the small number (10 x 10) of modeled (and experimentally recorded) sites and this needs to be acknowledged.How were border effects treated in the model and which are they?

We thank the reviewer for these points which gave us the opportunity to clarify them and improve our manuscript. As described in Methods: Simulations (line 847 and seq.) and shown in Fig. S2 (Fig. S10 in the original submission), we actually simulated our model on a 24 × 24 grid and did all our measurements in a central 10×10 grid to take into account that the electrode covers only part of the motor cortex. In addition to minimize border effects, we added on each side of the 24×24 grid two rows of E-I modules kept at their (non-oscillating) fixed points of stationary activity, as depicted in Fig. S2. In order to address the concern of the reviewer, and to check that indeed border effects had a minimal impact on our results, we have performed a new set of simulations on a 24×24 grid with periodic boundary conditions. The results are shown in the new supplementary Fig. S9 and are indistinguishable from those reported in the main text and figures. In particular, the proportion of the different wave types and the wave speeds are unaffected by this change of boundary conditions. A paragraph has been added in the revised version (lines 371-378) to discuss this point.

How much of the phase velocities are due to unsynchronized random fluctuations? At least an analysis of shuffled LFPs needs to be performed.

The phase velocities are indeed due to unsynchronized random fluctuations (coming from the finite number of neurons in each of our modules as well as, and more importantly, from the uncorrelated local external inputs). In order to check that the spatial-structure of connectivity was important, we followed the suggestion of the reviewer and also performed a new set of simulations to provide a further test. As proposed by the reviewer, after performing the simulations we shuffled in space the signal of the different electrodes and also did a parallel analysis where we shuffled the signal from different electrodes in the recording. We then reclassified the shuffled simulations/recordings in exactly the same way as the original ones. As shown in the new additional Fig. S16, this resulted in the full elimination of time frames classified as “planar waves” both in the model and in the experimental recordings. Additionally, it little modified the proportion of “synchronized” or “random” episodes which is intuitively understandable since shuffling does not change the nature of these states. In order to further assess the impact of connections between modules, we also decided to suppress them, namely to put their range l to zero. In order to avoid modifying the working point of a local module by this manipulation, we focused on the case without propagation delay. Without long-range connection, the local dynamics of each module is little modified. However, as shown in the new Fig. S18a, synchronization between neighboring modules is strongly decreased and the proportion of the different wave types is entirely changed: synchronized states and planar waves disappear and are replaced by random states. These results are described in two new paragraphs (lines 401-414 and lines 431-435).

Is there a relationship between the localizations of the non-global external input and the starting sites of the traveling waves?

This is also an interesting question that parallels some asked by the other reviewers and which we did our best to address. As described in the “Essential revisions” point 5) above, we aligned all “planar wave events” in space and time with the help of the spatio-temporal phase maps of the oscillations. We did find that planar waves were preceded by an increase in the global synchronization index σp, both in simulations and in experiments. In simulations this increase also corresponded to a shift of the global inputs away from their mean, as depicted in the new Fig. 4 in the main manuscript. However, no significant average spatio-temporal profile of the local inputs emerged when we used these temporal alignments. This is presumably due to the large variability of local inputs that can give rise to planar waves. We have described these results in the new section “Properties of planar waves and characteristics of their inputs”.

In summary, this work could benefit from a widening of its scope to eventually inspire new experimental research questions. While the model is constructed well, there is insufficient evidence to conclude that the presented model advances over another published model (e.g. Sherman et al., 2016).

As described in the “Essential revisions” and the discussion section of the manuscript, our work highlights a number of questions that can (and hopefully will) inspire new experimental research. We also hope that we have clarified above that our model complements Sherman et al.’s model and advances it as far as the spatial aspects of beta oscillations in motor cortex are concerned.

Reviewer #2 (Public Review):

Kang et. al., model the cortical dynamics, specifically distributions of beta burst durations and proportion of different kind of spatial waves using a firing rate model with local E-I connections and long range and distance dependent excitatory connections. The model also predicts that the observed cortical activity may be a result of non stationary external input (correlated at short time scales) and a combination of two sources of input, global and local. Overall, the manuscript is very clear, concise and well written. The modeling work is comprehensive and makes interesting and testable predictions about the mechanism of beta bursts and waves in the cortical activity. There are just a few minor typos and curiosities if they can be addressed by the model. Notwithstanding, the study is a valuable contribution towards developing data driven firing rate.

We really appreciate the positive comments of the reviewer and thank her/him for them. We have done our best to correct the typos and to address the questions raised by the reviewer.

1) The model beautifully reproduces the proportion of different kind of waves that can be seen in the data (Fig 3), however the manuscript does not comment on when would a planar/random wave appear for a given set of parameters (eg. fixed v ext, tau ext, c) from the mechanistic point of view. If these spatio-temporal activities are functional in nature, their occurrence is unlikely to be just stochastic and a strong computational model like this one would be a perfect substrate to ask this question. Is it possible to characterize what aspects of the global/local input fluctuations or interaction of input fluctuations with the network lead to a specific kind of spatio-temporal activity, even if just empirically ?

This is an important question that parallels some asked by the other reviewers and which we did our best to address. As described in the “Essential revisions” paragraph above, we aligned all “planar wave events” either in phase or at their starting time points. We did find that planar waves were preceded by an increase in the global synchronization index σp, both in simulations and in experiments. In simulations this increase also corresponded to a shift of the global inputs away from their mean, as depicted in the new Fig. 4 in the main manuscript. When we used the same alignment to average spatio-temporal local inputs, we did not see the emergence of any significant patterns. This presumably reflects the high variability of local inputs able to produce a planar wave.

Do different waves appear in the same trial simulation or does the same wave type persist over the whole trial? If former, are the transition probabilities between the different wave types uniform, i.e probability of a planar wave to transit into a synchronized wave equal to the probability of a random wave into synchronized wave?

In the same trial simulation, different types of waves indeed successively appear. The curiosity of the reviewer led us to investigate this interesting point. Since time frames classified as random or synchronized are much more numerous than the planar (and radial) wave ones, it is much more probable that a planar wave transits into a synchronized or a random pattern than the reverse process (i.e., synchronized and random patterns preferentially transit into each other). Nonetheless, we considered questions related to the one of the reviewer. What are the states preceding a planar wave event? Given that a planar wave episode is preceded by a random (or synchronous) episode, is it more likely to be followed by a random or by a synchronous event? We actually find that the entry state is prominently a synchronized state. Furthermore, when the entry state is synchronized, the exit state is also synchronized much more often than would be expected by chance. This shows that most often, planar waves are created from an underlying synchronized persistent state. This has been described in the revised manuscript (lines 443-451).

2) Denker et al 2018, also reports a strong relationship between the spatial wave category, beta burst amplitude, the beta burst duration and the velocity (Fig 6E - Denker et. al), eg synchronized waves are fastest with the highest beta amplitude and duration. Was this also observed in the model ?

We had long exchanges with Michael Denker about his analysis since there are some differences between his code and what is described in Denker et al. (2017), possibly because of several typos in the Method section of Denker et al (2017). We have checked that the results of our code agree with his but there are some differences with the results obtained on the available datasets and those reported in Denker et al from other data sets. We have now provided the detailed statistics of the different wave types as obtained by our analysis in the simulation of model SN (Fig. S9) and SN’ (Fig. S11) and in the recordings for monkey L (Fig. S10) and monkey N (Fig. S12). In the recording data, the amplitude and speed of the synchronized and planar waves are comparable and higher than in the radial and random wave types. The duration of synchronized events is longer than the one of planar waves and of the other waves types. Comparable results are obtained in the simulations with nonetheless a few differences: the mean amplitude of planar waves is somewhat larger than those of synchronized states, the hierarchy of duration in the different states is respected but the duration themselves are longer in the simulations than in the recordings (about 40 % for the planar waves and almost two times longer for the synchronized states). We attribute these differences to the fact synchronization is slightly less effective in the recordings than in the model. Long synchronization episodes in the recordings are often cut-off by a few time frames where the synchronization index goes below the threshold value for a synchronized pattern. This happens rarely enough not to affect much the global statistics of the different states but it as a much more visible effect on the measured duration of the synchronized states.

Reviewer #3 (Public Review):

In this manuscript, the authors consider a rate model with recurrently connections excitatory-inhibitory (E-I) modules coupled by distance-dependent excitatory connections. The rate-based formulation with adaptive threshold has been previously shown to agree well with simulations of spiking neurons, and simplifies both analytical analysis and simulations of the model. The cycles of beta oscillations are driven by fluctuating external inputs, and traveling waves emerge from the dephasing by external inputs. The authors constrain the parameters of external inputs so that the model reproduces the power spectral density of LFPs, the correlation of LFPs from different channels and the velocity of propagation of traveling waves. They propose that external inputs are a combination of spatially homogeneous inputs and more localized ones. A very interesting finding is that wave propagation speed is on the order of 30 cm/s in their model which is consistent with the data but does not depend on propagation delays across E-I modules which may suggest that propagation speed is not a consequence of unmylenated axons as has been suggested by others. Overall, the analysis looks solid, and we found no inconsistency in their mathematical analysis.

We thank the reviewer for his comments and for his expert review.

However, we think that the authors should discuss more thoroughly how their modeling assumptions affect their result, especially because they use a simple rate-based model for both theory and simulations, and a very simplified proxy for the LFPs.

In the revised manuscript, we have performed additional simulations to test different modeling assumptions as suggested by the reviewer and discussed further below.

The authors introduce anisotropy in the connectivity to explain the findings of Rubino et al. (2006), showing that motor cortical traveling waves propagate preferentially along a specific axis. They introduce anisotropy in the connectivity by imposing that the long range excitatory connections be twice as long along a given axis, and they observe waves propagating along the orthogonal axis, where the connectivity is shorter range. Referring specifically to the direction of propagation found by Rubino et al, could the authors argue why we should expect longer range connections along the orthogonal axis? In fact, Gatter and Powell (1978, Brain) documented a preponderance of horizontal axons in layers 2/3 and 5 of motor cortex in non-human primates that were more spatially extensive along the rostro-caudal dimension as compared with the medio-lateral dimension, and Rubino et al. (2006) showed the dominant propagation direction was along the rostro-caudal axis. This is inconsistent with the modeling work presented in the current manuscript.

This is an important comment and we thank the reviewer for pointing out these data in Gatter and Powell (1978). Since the experimental data show that planar wave propagation directions are anisotropically distributed, we have tried and investigated what the underlying mechanism of this anisotropy could be in the framework of our model. Anisotropy in connectivity is an obvious possibility. Given our result, and the data of Gatter and Powell, it appears however that it is not the underlying cause of the observed anisotropy direction in the motor cortex (in the framework of our model). We have thus investigated another possibility, namely that the local external inputs are anisotropically targeting the motor cortex, being more spread out along a given axis (lines 510-529 and new Fig. 5g-l). We find that planar waves propagate preferentially along the orthogonal axis. This leads us to conclude that the observed propagation anisotropy could be of consequence of the external input being more spread out along the medio-lateral axis. Data addressing this issue could be obtained using retroviral tracing techniques.

The clarity and significance of the work would greatly improve if the authors discussed more thoroughly how their modeling assumptions affect their result. In particular, the prediction that external inputs are a combination of local and global ones relies on fitting the model to the correlation between LFPs at distant channels. The authors note that when the model parameter c=1, LFPs from distant channels are much more correlated than in the data, and thus have to include the presence of local inputs. We wonder whether the strong correlation between distant LFPs would be lower in a more biologically realistic model, for example a spiking model with sparse connectivity and a spiking external population, where all connections are distant dependent. While the analysis of such a model is beyond the scope of the present work, it would be helpful if the authors discussed if their prediction on the structure of external inputs would still hold in a more realistic model.

This is a legitimate question that we indeed asked ourselves. In a previous work with a simpler chain model, we only considered finite size fluctuations. We found good agreement between our simplified description of finite size fluctuations and simulations of a spiking network with fully connected modules and sparse distance-dependent connectivity. This leads us to believe that our description of finite-size fluctuations is reliable in this setting. Assuming that it is the case, we find that with 104 neurons or more per module finite size noise is not strong enough to replace our local external inputs. Even with 2000 neurons per modules the intrinsic fluctuations the network is very synchronized (new Fig. S15e-g). With 200 neurons per module, the intrinsic fluctuations are strong enough to replace the fluctuating local inputs (Fig. S15a-d) but this is quite a low number. Our description of local noise would have to underestimate the fluctuation in a more sparsely connected network by a significant amount for agreement with the data to be obtained without local inputs. Moreover, it seems to us quite plausible that different regions of motor cortex receive different inputs but, of course, this can only settled by further experiments. Together with the new Fig. S15, we have added a paragraph to address this question in the manuscript (lines 379-400).

Author Response

Reviewer #2 (Public Review):

Weaknesses (major)

1) Adding control groups (sham stimulation) to Experiment 5 and Experiment 8 would be needed to increase confidence that NITESGON's memory-enhancing effects do not depend on sleep but do depend on dopamine receptor activity.

Thank you for highlighting this major weakness within our research; we will be sure to include control groups in future research if we conduct replication studies. Additionally, upon review of your comment, we have addressed the lack of control/sham groups in Experiment 5 and 8 in the Discussion section when acknowledging the limitations of the research.

Please see the newly added text from the Discussion section on pages 21-22 below:

“Moreover, it must also be acknowledged that Experiments 5 and 8 did not include a control-sham stimulation group, thus limiting the interpretation of these two experimental findings. Control-sham stimulation groups would increase our confidence in our findings that NITESGON’s memory-enhancing effects depend not on sleep but on DA receptor activity.”

2) Task order in the interference study in Experiment 4 was randomized during the first visit for task training as well as during the memory test, however, the word-association and spatial navigation tasks used in Experiments 3 and 4 were not counterbalanced during training or memory testing. Thus, the authors cannot rule out the possibility of order effects.

Upon reading your comment and reviewing the paper, we have decided to add a limitations paragraph to the paper which highlights the concern of Experiments 3 and 4 not being counterbalanced during training or memory testing. Additionally, the new section provides an explanation of how not counterbalancing Experiments 3 and 4 introduced the possibility of order effects being present in the results.

Please see the new addition from the Discussion section on page 21 below:

“When interpreting the current findings, it must be considered that some limitations exist within the research; limitations on experimental design are noted below, followed by a discussion of utilizing indirect proxy measures. The task order for Experiment 4 was randomized during the first visit for training and the recall-only memory test 7-days later; however, the word association and spatial navigation task used in Experiments 2 and 3 were not counterbalanced; therefore, the findings of Experiments 2 and 3 could have been impacted by a potential order effect.”

3) It is unclear how Experiment 3 and Experiment 4 differ. Percent of words recalled is the measure of memory performance, however, there is not a clear measure of interference in Experiment 4 (i.e., words recalled during Memory task II that were from Memory task I).

Thank you for highlighting the difficulty in distinguishing the differences between Experiment 3 and Experiment 4. To clarify what the differences are between Experiment 3 and Experiment 4, we explained in Experiment 4’s introductory paragraph that the object-location task used in Experiment 3 was replaced with a Japanese-English verbal associative learning task in Experiment 4.

Please see the paragraph from the Experiment 4 subsection on page 10 below:

“Experiments 2 and 3 revealed both retroactive and proactive memory effects 7-days after initial learning of the two tasks. To further explore if NITESGON is linked to behavioral tagging and evaluate if interference impacts NITESGON as the strong stimulus, Experiment 4 removed the object-location task used in Experiments 2 and 3 and replaced it with a Japanese-English verbal associative learning task similar to the Swahili-English verbal associative task. Considering how memory formation and persistence are susceptible to interference occurring pre-and post-encoding(37-39) and are heavily influenced by commonality amongst the learned and intervening stimuli(40); it is believed that conducting two consecutive, like-minded word-association (i.e., Swahili-English and Japanese-English) tasks will result in one’s consolidation process interfering with that of the other(41). Considering how our previous experiments suggest the effect obtained by NITESGON improves the consolidation of information via behavioral tagging, it is possible that NITESGON on the first task might help reduce the overall interference effect on the second task.”

Additionally, we explained in further detail that comparing the percentage of correctly recalled word pairs on the second task 7-days after learning from the percentage of correctly recalled word pairs on the first task 7-days after learning was done to measure for an interference effect.

Please see the adapted text from the Experiment 4 subsection on page 11 below:

“Upon assessment for a potential interference effect, the active group displayed no significant difference in how many words participants were able to recall between the first and the second task (difference: .76 4.93) (F = .29, p = .60), whereas the sham group demonstrated the first task rendered an interference effect on the second task (difference: 5.16 5.99) (F = 14.11, p = .001).”

Lastly, in the methods section describing how the interference effect was calculated was changed. The newly edited text better explains that the percentage of words pairs learned were subtracted from one another to measure the significance of interference one may have potentially had on the other.

Please see the amended text in the Methods section on page 38 below:

“In addition, an interference effect was calculated by subtracting the percentage of correctly recalled word pairs on the second task 7-days after learning from the percentage of correctly recalled word pairs on the first task 7-days after learning. This number gave a proxy of interference.”

4) In Experiment 5 the learning and test phases for the two sleep groups were conducted at different times of day (sleep group: training at 8pm and testing the next morning at 8am, sleep deprivation group: training at 8am and testing at 8pm) which introduces the possibility of circadian effects between the two groups. Additionally, the memory test occurred at the 12h point for this experiment instead of the 7-day point. Therefore, the authors' conclusions are not addressed by this experiment, and it remains unclear whether the 7-day long-term memory effects of NITESGON are sleep-dependent.

Upon reading your comment and reviewing the paper, we have decided to add a limitations paragraph to the paper which highlights the two sleep groups being conducted at different times of day and the memory test occurring at the 12-hour point as opposed to 7-days after initial learning. In addition to acknowledging these limitations, we have also provided explanations regarding what potential effects are introduced by having the sleep groups learn and test at different times of day, such as circadian effects between the two groups, and the memory tests occurring at 12-hours rather than 7-days after initial learning.

Please see the new addition from the Discussion section on page 21 below:

“Additionally, in Experiment 5, the learning and test phases for the two groups were conducted at different times of day (i.e., sleep group: training at 8 p.m. and testing at 8 a.m., sleep deprivation group: training at 8 a.m. and testing at 8 p.m.), thus introducing the potential for circadian effects between the two groups. Furthermore, the recall-only memory testing occurred at the 12-hour point rather than 7-days later, allowing us to conclude that the observed effect seen 12-hours later was not affected by sleep; however, it remains unclear whether the 7-day long-term memory effects of NITESGON are sleep-dependent.”

Weaknesses (minor)

1) Salivary amylase is being used as a proxy of noradrenergic activity; however, salivary amylase levels increase with stress as well, which impacts memory performance. It would be helpful if the authors addressed this and whether they measured other physiological indicators of stress/sympathetic nervous system activation.

Upon review of your comment, we have edited the paper so that it includes text in the Discussion section that brings attention to the fact that stress can enhance salivary amylase and advises readers that this should be considered when interpreting results. We also add an additional measure which measure pupil size, a measure well-know for sympathetic measure. In addition we add also a VAS score to ask people about their stress levels.

Please see the added new addition from page 22 below.

“Although the use of indirect proxy measures, such as sAA for NA activity and sEBR for DA activity, enabled the tracking of LC-NA activity changes from baseline measurements and demonstrated the potential of an LC-DA relationship, caution must be advised when interpreting results considering these proxy measures are affiliated with limitations, such as being substantially variable, as well as the potential of other brain regions and monoamine neurotransmitters being associated with changes seen in sAA concentration levels(80), an enzyme that is provoked by both central parasympathetic and sympathetic nervous system activation, including acute stress responses(81). Additionally, although sEBR has been increasingly linked to DA, it has been defined as a more viable measure of striatal DA activity(52, 82). At the same time, some evidence suggests that sEBR and DA levels may be unrelated(83, 84), thus requiring further validation as a behavioral proxy measure.”

2) Insufficient details of how the blinding experiment was conducted make it difficult to determine whether participants had awareness or subjective responses during the NITESGON stimulation. Adding physiological indicators of heart rate, skin conductance, and respiration would provide a better indicator of a sympathetic nervous system response. Additionally, a series of randomized stimulation and sham trials delivered to the participant would provide a more objective measure of the detectability of the stimulation.

Thank you for your comment regarding the portion of the experiments that were included to determine the efficacy of the measures taken to ensure the experiments were well blinded. After reviewing the comment and reading over the paper, we were concerned that it was not clear enough to the reader that the efficacy of blinding was determined by having each participant of every experiment complete the same single-answer questionnaire after all NITESGON and testing had been experienced. Therefore, we edited the wording below to elucidate that there was not an individual blinding experiment but that there was a questionnaire for every participant in every experiment to help determine the efficacy of blinding for each experiment and the research.

Please see the text from the Blinding section on pages 17-18 below:

“Blinding. To determine if the stimulation was well blinded, all participants in Experiments 1-7 were asked to guess if they thought they were placed in the active or control group (i.e., what stimulation participants received compared to what participants expected). Our findings demonstrated that participants could not accurately determine if they were assigned to the active or sham NITESGON group in each experiment, suggesting that our sham protocol is reliable and well-blinded (see fig. 8).”

Additionally, please see the text in the Methods section that has been reworded to clarify how the questionnaire of blinding was conducted on page 47 below:

“Blinding: To determine if the stimulation for all experiments was well blinded, all participants who participated in Experiments 1-7 were asked to complete a single-response questionnaire after the conclusion of the NITESGON procedure. Here, participants were asked to guess if they thought they were placed in the active or control group. A χ2 analysis was used to determine if there was a difference between what stimulation participants received compared to what participants expected.”

3) It would be appreciated if the authors could speak to the possible role of the amygdala in the memory-enhancing effects of NITESGON, as this region is a well-known modulator of many types of memory consolidation and is implicated in noradrenergic-related memory enhancement.

Upon consideration of your comment, we added text providing the reader with insight into how NITESGON has activated the amygdala in previous research, similar to the VTA in the current study, and how the LC and amygdala were shown to be activated during emotionally arousing stimuli in another study. Furthermore, we have acknowledged that the amygdala is understood to have modulatory implications in long term memory and how future investigations are needed to establish the amygdala’s role with NITESGON.

Please see the text from the Discussion section on page 20 below:

“Additionally, it is well-known that the amygdala is not the final place of memory storage, but rather has major modulatory influences on the strength of a memory(74). Similar to the VTA in the current study, prior research has shown that the amygdala is activated during NITESGON but ceased post-stimulation; however, NITESGON was not accompanied by a task during the experiment(14). Moreover, a recent fMRI study spotlights the dynamic behavior of the LC during arousal-related memory processing stages whereby emotionally arousing stimuli triggered engagement from the LC and the amygdala during encoding; however, during consolidation and recollection stages, activity shifted to more hippocampal involvement(75). Considering the impact the VTA and amygdala can have on memory, future experimental investigations are needed to establish their role in the memory-enhancing effects of NITESGON.”

Author Response

Reviewer #1 (Public Review):

In this manuscript, Cover et al. examine the role of thalamic neurons of the rostral intralaminar nuclei (rILN) that project to the dorsal striatum (DS) in mice performing a reinforced action sequence task. Using patch-clamp electrophysiology, they find that neurons from the three rILN (CM, PC, and CL) have similar electrophysiological properties. Using fiber photometry recordings of calcium activity from rILN neurons that project to DS, they show that these neurons increase in activity at the first lever press and reward acquisition in mice performing a lever pressing operant task. They additionally demonstrate that this action initiation and reward-related activity exists more generally in mice performing other movements or rewarded tasks. Building on their lab's previous work, the authors further find that by optogenetically activating or inhibiting these rILN-DS neurons, mice will increase or decrease task performance, respectively. Lastly, the authors show that a variety of cortical and subcortical areas have input to rILN-DS neurons suggesting that these neurons might act as an integrator of signals from such areas during task performance.

• The authors beautifully show that the electrophysiological properties of CM, PC, and CL neurons are similar and go on to treat the rILN as one homogenous nucleus for functional fiber photometry recordings and optogenetic stimulations. It seems that these recordings and stimulations were only performed in CL, as indicated in the images (Fig. 2A, 4A). Is this the case, or were CM, PC, and CL neurons sampled? It would be helpful to clarify if DS projecting neurons from all rILN nuclei show the reported action initiation and reward acquisition activity or only CL neurons.

The arrangement of the rILN nuclei presents a technical challenge for experiments attempting to selectively record from or manipulate a single nucleus in this grouping. Based on our findings that the three nuclei do not differ in electrophysiological properties, we approached the in vivo experiments with the intent to target the rILN as a unit. As the reviewer points out, the medial-lateral coordinate for optic fiber placement tended to align above the CL and PC nuclei. However, variability in fiber placement and spread of light within tissue resulted in inclusion of CM activity as well. Given the spread of light through tissue (Shin, et al., 2016; PMID: 27895987), it would be very difficult to confidently determine from histology which photometry recordings were primarily obtained from CL vs PC vs CM neuronal activity. We agree with the reviewer that these three nuclei may differently signal during reward-driven behavior. Our di-synaptic tracing study supports this possibility as it revealed unique afferent connectivity to rILNDS projecting neurons. We now mention this limitation of our approach in the discussion (lines 324 - 330).

• Along similar lines, to what extent of rILN was targeted for optogenetic activation and inhibition? It seems that the authors implanted a total of 4 optic fibers, two on each side (please clarify in methods). What was the reasoning behind this? Please show that only rILN and not PF was activated/inhibited.

We apologize for the confusion in our description of this method. For our optogenetic experiments, we infused viruses at four locations (bilateral striatum and rILN) and implanted only two fibers (bilateral rILN) to selectively target striatally-projecting rILN neurons. We have added clarification on this detail to the methods section.

To prevent inadvertent modulation of Pf neurons, we used virus injection coordinates and volumes that prevented viral spread to the Pf and furthermore implanted the optic fibers in the more rostral regions of the rILN. We histologically confirmed viral expression and fiber placement for all mice and excluded any mice with viral spread to the Pf or off-target fiber placement. We include these criteria for post-hoc exclusion in the methods.

• While AAV1 is becoming a popular tool for transsynaptic labeling, performing confirmatory patch-clamp recordings with optogenetic activation of inputs, would provide better evidence for the synaptic connection between upstream regions, such as ACC and OFC, and rILN neurons.

We agree that electrophysiological confirmation of these inputs to the rILN would complement our tracing study. As our focus for this experiment was to specifically identify inputs that synapse on striatally-projecting rILN neurons, we interrogated putative afferents that were already established to project to the rILN. There are several studies that demonstrate the physiological circuits from some of these afferent projections to the rILN (without di-synaptic specificity), such as the SNr  rILN projection (Rizzi & Tan, 2019; PMID: 31091455).

• In addition, the transsynaptic tracing experiments would benefit from showing the cell count quantifications in CM, PC, and CL. It seems that the authors have already performed this quantification for constructing their diagrams on the right. To make any point about the relative strength of afferent innervation to rILN-DS neurons showing such quantification would be necessary.

Thank you for this suggestion, we now include cell counts for 2 cases per investigated afferent (Supplemental Table S2).

• Why is the injection site for the retrograde cre-dependent tdTomato AAV (Fig. 5 middle left panels) showing expression? Is the cre coming through transsynaptic AAV1 from direct projections of each AAV1 injection site (AAV1 is not supposed to spread across a second synapse)? The diagrams suggest that not all regions (e.g. SUM or SC) have direct projections to DS.

We apologize for this confusion. The tdTomato fluorophore expression observed in the striatum may arise from several possible circuit configurations. To survey just a couple: 1) tdTomato expression in the DS arises from direct projections from the afferent bypassing the thalamus (e.g. ipsilateral ACC→Striatum), which would result in labeled striatal somata (ACC pyramidal neurons delivering AAV1-cre to an MSN, and those local MSN collaterals retrogradely picking up rAAV-DIO-tdtomato) and ACC labeled axon terminals in the DS (ACC interneurons delivering AAV1-cre to DS-projecting ACC pyramidal neurons that pick up rAAV-DIO-tdtomato); 2) terminal projections arising from the labeled rILN neurons shown in the middle-right panels (i.e. ACC→rILN→Striatum).

Reviewer #2 (Public Review):

This manuscript details the role of the rILN to the DS pathway in the onset of operant behavior that promotes the delivery of a reward and in the ultimate acquisition of that reward. The strengths of the paper are in the detailed fiber photometry study that encompasses several behavioral domains that correlate to the signal observed in the rILN to DS pathway. I am especially interested in how the "encoding" shifts across time as the animals refine their behavior both in a temporal sense and in the magnitude of the signal. Further, the authors demonstrate then that this is dependent on action, as they do not observe signals in a Pavlovian behavioral task, but do observe reward-based signals in a "free consumption" task (the strawberry milk). The examination into devaluation also enhances the understanding of this pathway, even though there were no differences between a valued and devalued task. Finally, the authors examine bi-directional optogenetic manipulation of the pathway, and its impact on how the trials are completed, omitted, or incomplete. They find that manipulation alters the % completed trials and regulates trial omission. This paper really does not have any glaring weaknesses to point out, however, the physiological assessment does seem to have a few strong trends and even though the studies are well powered, and included both sexes, sex as a biological variable was not commented on that I could find. My estimation of the data doesn't suggest strong sex differences in any metric measured. Additionally, the data that included projections to the rILN were very interesting, and future studies looking into the physiology of these neurons, and/or how the physiology of these neurons adapt after operant training may be very interesting to understand plasticity within the adaptation across the training from FR1 to FR5 with time limits.

Thank you for your review. We analyzed our data for sex differences but did not identify any significant differences between male and female subjects for any of the experiments.

Public Review

Reviewer #1 (Public Review):

1) “In fact, it is not surprising that the collagen mutants display a detached cuticle, because the extracellular domains of MUP-4 and MUA-3 (the transmembrane receptors of apical hemidesmosomes that are primarily responsible for tethering the epidermis to the cuticle) both contain vWFA collagen-binding domain (Hong et al., JCB 2001; Bersher et al., JCB 2001). Hence loss of certain collagens in the cuticle directly affects cuticle-epidermis attachment due to defective ligand-receptor interactions is a much more plausible explanation.”

We agree with the reviewer that a specific molecular interaction likely mediates the attachment of the cuticle to the epidermis, not only in the area above the hemidesmosomes, but also in the area of the meisosomes. The collagens that potentially associate with MUP-4 and/or MUA-3 in the muscle regions have not been identified, nor in the main epidermal region, where the putative receptor is not known. We have modified the text accordingly.

“Likewise, it is more resonable to propose that lack of certain collagens in the cuticle directly affects cuticle stiffness, rather than working indirectly through epidermal meisosomes.”

We agree with the reviewer that the loss of specific structural components of the cuticle could well affect stiffness directly, especially if the furrows are affected; non-furrow collagen mutants do not show this phenotype. An analogy might be the increased stiffness that corrugation provides. We have modified the text accordingly. Our future research aims precisely at modelling these physical aspects.

2) “VHA-5::GFP does not co-localize with fluorescent markers for MVB, recycling endosomes and autophagolysosomes. By claiming this, the authors made a huge assumption that the overexpressed VHA-5::GFP fusion protein can only possibly associate with four types of organelles (meisosomes, MVB, recycling endosomes and autophagolysosomes) but not any other known or to-be-identified subcellular structures. In addition, a previous study did report that VHA-5 is localized in several other places besides the apical membrane stacks (Liegeois et al., JCB 2006).”

The reviewer cites the Liegeois paper that we mention above, which, in our opinion, and that of reviewer 2 (“VHA-5 is well known to localise to the apical membrane stacks (Liegeois 2006) and could be served as marker of apical membrane structure”), provides extremely strong support for our position. In Liegeois et al., 2006, there is a quantification of immunogold staining that shows that >85% of VHA-5 is found in meisosomes (Fig S5D). By providing the results of co-localisation analyses with 3 cytoplasmic vesicular markers, we simply wanted to illustrate the specificity of the signal to the non-initiated. Importantly, we now provide strong evidence that VHA-5::GFP marker co-localises with apical plasma membrane macrodomains revealed by both a PH domain of PLCδ and a CAAX marker. As our ultrastructural analyses demonstrate that meisosomes are composed by apical membrane folds, this again is wholly consistent with VHA-5 being a bonafide marker of meisosomes.

Reviewer #2 (Public Review):

The reviewer questioned the need to give another name to the “apical membrane stacks”. We made this proposition after consultation with a broad community of researchers in the field. We believe that this simpler name provides a link to an analogous structure in yeast, the eisosome, also at the interface between the aECM and the cell.

The reviewer wrote, “The major problem of this paper is that there is not much new information”, that it was known, for example, that “"furrowless" dpy mutants result in complete disorganization of the epidermis”. In addition to demonstrating that the furrowless Dpy mutants have very particular and specific phenotypes, without affecting the presence of hemidesmosomes (PMID: 33033182), nor different vesicular markers (FIgure 6S2), we would like to point out that reviewer #1 commented, “the work presented by Aggad et al. is rich in novelty”, and Reviewer #3, “The major strengths of the paper are the novelty”. We have re-written and reorganised the text and hope Reviewer #2 appreciates the novelty more in the revised version.

Author Response

Reviewer #2 (Public Review):

Wu Yang et al. investigated how exophers (large vesicles released from neuronal somas) are degraded. They find that the hypodermal skin cells surrounding the neuron break up the exophers into smaller vesicles that are eventually phagocytosed. The neuronal exophers accumulate early phagosomal markers such as F-actin and PIP2, and blocking actin assembly suppressed the formation of smaller vesicles and the clearance of neuronal exophers. They show the smaller vesicles are labeled with various markers for maturing phagosomes, and inhibiting phagosome maturation blocked the breakdown of exophers in to smaller vesicles. Interestingly, they discover that GTPase ARF-6, effector SEC-10/Exocyst, and the phagocytic receptor CED-1 in the hypodermis are required for efficient production of exophers by neurons.

Strength

The study clearly demonstrates that exophers are eliminated via hypodermal cellmediated phagocytosis. Exophers are broken down into smaller vesicles that accumulate phagocytic markers, and inhibiting this process shows that exophers are not resolved. The paper does a thorough examination of various markers and mutants to demonstrate this process.

The hypodermal cells not only engulf these small vesicles, but they also play a role in the formation of exophers. Exopher production is reduced when ARF-6, SEC-10, or CED-1 are knocked down in the hypodermis. This is intriguing because phagocytosis is a critical step in the final elimination of cells, but in this unique situation, it appears that the neuron fails to extrude the exopher without phagocytes.

Weakness

Non-professional phagocytes engulfing cell corpses and many other types of cellular debris (e.g. degenerating axons) have been shown in multiple systems and the observations here are not surprising. Many of the markers used in the study are wellestablished phagocytic markers and do not bring forward a new technological advance.

What's interesting is that the breakdown of exophers into smaller vesicles and eventual clearance follows a different sequence of events than macrophages. Exophers appear to undergo phagosomal fission before interacting with lysosomes. This would be difficult to appreciate by a general reader.

While the paper has strengths, it appears that the message is not clear. The title suggests that the reader will learn about how ARF-6 and CED-1 control exopher extrusion. Although this observation is intriguing and maybe the main point of the paper, there does not appear to be a substantial amount of data to support this claim. The only data to back this up is in the final figure and the majority of the paper is focused on how hypodermal cells phagocytose exophers.

The title has been revised.

To show exopher secretion is dependent on the hypodermal cells-

1) Could authors induce exopher production through other means? And test any involvement of CED-1? For example, authors note exopher production increases under stress conditions including expression of mutant Huntingtin protein. It would be intriguing if loss of CED-1 would be sufficient to block or reduce exopher production in that context and would highlight an exciting role for phagocytic cell types.

We interpreted this question as an inquiry into whether the neuron intrinsic exopher inducer was relevant to reliance on hypodermal interaction for exophergenesis, given our use of aggregating mCherry as the inducer. Unfortunately, our Huntingtin expressor lines now display high levels of transgene silencing, precluding their use in this experiment. To address this concern, we switched to a low toxicity GFP expressing transgene from the Chalfie lab, uIs31[Pmec17::GFP]. We found that arf-6 mutations suppressed exophers in this background as effectively as they did in previous mCherry experiments, indicating that our results are not dependent upon the particular transgene marking the touch neurons, or the specific protein they express (Fig 6E).

2) It is not clear if the CED-1 localization to the exopher is due to CED-1 expression during phagocytosis or is it involved in the extrusion. Perhaps the basal level of CED-1 is important for the extrusion but the strong expression is important for recognition of the exopher.

In the experiments we performed we used a constitutively expressed hypodermisspecific CED-1::GFP to show localization to exophers, so the recruitment of CED1::GFP in hypodermal membranes to the site where the neighboring neuron is producing an exopher is not caused by changes in expression, but rather is more likely to reflects protein recruitment. We now point this out more explicitly in the text. Added text: “Since the hypodermal CED-1DC::GFP we used is constitutively expressed, we attribute the exopher surrounding CED-1DC::GFP signal to CED-1 recruitment by exopher-surface signals."

3) While the data with ttr-52 and anoh-1 alleles is compelling, do we know that exophers actually expose PS? Especially since at a certain point, the exopher is still attached to the neuronal soma. Is PS still exposed by exopher in CED-1 background?

We are also very interested in this. Unfortunately, we have had difficulty obtaining sufficient MFGE8 PS-biosensor expression in the adult to test this question directly.

4) What is the fate of a neuron that is unable to produce exophers? Could one look at lifespan of ALMR neuron in CED-1, ARF-6 or Sec-10 allele (potentially with specificity to hypodermis)?

To address this question we measured the function of the mechanosensory touch neurons, using the classic gentle touch response assay in mCherry expressing animals, comparing controls to arf-6 and ced-1 mutants. For both arf-6 and ced-1 alleles, we found reduced response to gentle touch in older adults (Ad10), indicating a deficit in neuronal function. These results are consistent with exopher production maintaining neuronal health into old age, but interpretation is limited since neither ced-1 or arf-6 act specifically in exophergenesis and therefore also affect the animals in additional ways. Currently, there are no known genetic perturbations that act specifically in exophergenesis, so there is no better approach to do the analysis. We had already published similar results in our 2017 Nature paper that first described exophers, showing that gentle touch response is better preserved in a touch neuron HttQ128::CFP strain that produced a touch neuron exopher than in the same mutant background in which the touch neurons that had not produced an exopher.

Author Response

Reviewer 2 (Public Review):

The authors’ coarse-grained mathematical model is based upon proteome partitioning constraints. Similar models have been developed in the past, although the authors do an excellent job distinguishing their work. The interdependence among growth rate, growth yield, and carbon transport (together with the comparatively few state variables) makes the proposed model an attractive general framework for predictive metabolic engineering and strain optimization in bio-manufacturing.

Strengths:

1) The recognition that the constant biomass concentration (1/beta) can be used to recast the growthrate versus growth yield trade-off in terms of a growth rate versus carbon uptake trade-off (lines 147-155, Eq. 2), and coupling of the growth- and carbon uptake-rates through proteome partitioning, are powerful ideas. They transform the traditional (false) dichotomy of a negative correlation between growth and yield into a feasible space of growth-yield combinations (e.g. Figs 2BC).

2) The authors calibrate the model for E. coli (BW25113) grown in glycerol/glucose, batch/continuousculture (lines 157-164), then apply the model to an impressive variety of E. coli strains. This is not typically done with semi-mechanistic models and elevates the authors’ approach by implying that their model is sufficiently-general so as to apply across strains, yet sufficiently-constrained so as to provide quantitative predictions.

Weaknesses:

1) The tension between generality and constraint leads to some category errors where strain-specific empirical invariants are taken as general strain-independent operating conditions. This happens at least twice: a minor case involving the growth-rate threshold for acetate overflow, and a serious case where the magnitude of the ’housekeeping’ proteome fraction φq is taken to be strain- and condition-independent.

a) (lines 82-86) The growth-rate threshold for the acetate overflow switch in E. coli was observedin ’studies with a single strain in different conditions’ [i.e. different carbon sources in batch]. The interpretation provided in the references cited (lines 83-84) is that the threshold is a manifestation of a tipping point between carbon uptake rate and the costs of energy generation. The carbon uptake rate is implicitly strain-dependent; there is no reasonable expectation that all strains growing in glucose will be fermenting (or all respiring). The conclusion (line 84) that ’the model predicted no correlation between growth rate and acetate secretion rate in the case of different strains growing in the same environment’ is tautological when the carbon uptake rate (vmc) is used by the authors to distinguish among strains. This error is easily fixed by simply changing the wording, but it serves to illustrate how constraints operating at the strain level can be tacitly (and erroneously) applied at the genus level.

The emphasis we put on the comparison between batch growth on glucose of different strains vs batch growth in different environments of a single strain may have been misleading. The point we wanted to make was that the occurrence of fermentation (acetate overflow) during fast growth on glucose is not a necessary consequence of intrinsic physical constraints on metabolism, but the consequence of strain-specific regulatory mechanisms. This is demonstrated by the existence of E. coli strains that do not ferment while growing on glucose, but that have essentially the same metabolic capacities as strains that do. When we started this study, we did expect (perhaps naively) that growth on glucose at a high rate necessarily comes with low yield due to the higher relative acetate overflow, that is, the ratio of the acetate secretion and glucose uptake rates (Supplementary Figure 4 in the revised manuscript).

In the new version of the manuscript, we have modified the analysis of the glucose uptake and acetate secretion data, by plotting them against growth rate and growth yield in separate 2D plots, as suggested by Reviewer 1. This has led to a perspective that is more in line with the comment of this reviewer that the model explores different ways in which a carbon uptake rate can be converted into a growth rate, depending on the selected resource allocation strategy, and that this gives rise to trade-offs between growth rate and growth yield. In the context of this analysis, we do come back to the original point we wanted to make, but phrased differently (and hopefully more clearly this time).

Changes in manuscript: The comparison between batch growth on glucose of different strains and batch growth on different carbon sources of a single strain is less emphasized. We have rewritten the section and rephrased our claims accordingly throughout the paper (notably in the Abstract, Introduction, and Discussion).

b) The second example of this strain-genus confusion is more serious, and perhaps is enough to unravel the model. One of the strengths of the current framework is that although there are four degrees of freedom via the proteome allocation parameters, the model is sufficiently-constrained that the behavior can be meaningfully projected onto lower-dimensional observables like growth rate and yield (e.g. Figs 2BC).

One of the main constraints in the model that allows this meaningful projection is the assumption that the fraction of ’housekeeping’ proteins φq is constant irrespective of strain and growth conditions (line 172) and that these proteins carry flux synthesizing non-protein macromolecules (lines 141-142). Neither of these claims is supported by the references provided.

The ’housekeeping’ fraction φq was inferred in Scott et al. 2010 (line 172) from a nearly-growthmedium-independent maximum in the RNA/protein ratio under translation limitation of strain MG1655. The magnitude of that intercept is highly strain-dependent and can vary nearly 2-fold, especially in ALE strains. Furthermore, subsequent proteomic data (e.g. Hui et al. 2015 cited by the authors) has clarified that this ’housekeeping’ fraction is, for the most part, composed of growth-rate independent offsets in the metabolic proteins.

The origin of these offsets is thought to be related to substrate-saturation (Eqs. 1 and 2 of Dourado et al. 2021 cited by the authors) and consequently, these offsets (and by extension most of φq) carry no flux. Substrate saturation is perhaps at the root of the discrepancy in the Fig. 4 fits that necessitates adjustment of the catalytic constants (line 338). It is not correct to say that ’external substrate concentration S is assumed constant’ (bottom p. 25) therefore the catabolic rate vmc is an environment-dependent [i.e. substrate-concentration-independent] parameter. The ’mc’ proteins include carbon uptake and metabolism (e.g. Fig 1, or Table 2) so that intracellular changes in S could arise from strain differences thereby affecting vmc and the magnitude of the ‘housekeeping’ fraction.

It is not clear to me how the predictive power of the model will be affected by relaxing the constant φq assumption and replacing it with the more justifiable assumption that all metabolic proteins contribute some small fraction to φq based upon substrate saturation.

The reviewer criticizes two assumptions made in the construction and analysis of the model: (i) the fraction of housekeeping proteins is constant irrespective of strain and growth conditions, and (ii) the housekeeping proteins carry flux because they synthesize macromolecules other than proteins. Below, we summarize how we have tried to clarify these assumptions and which additional work we have performed to build model variants relaxing the assumptions.

We identified the housekeeping protein category with the Q-sector in the original paper of Scott et al. [13], which was misleading. The Hwa group indeed defines the Q-sector as not carrying flux [7], whereas we do allow this for the housekeeping protein category. Our housekeeping protein category, which we refer to as ”other proteins” or ”residual proteins” (Mu) in the new version of the manuscript, consists of all proteins not labelled as proteins in the categories of ribosomes and translation-affiliated proteins (R), enzymes in central carbon metabolism (Mc), or enzymes in energy metabolism (Mer+Mef). Mu carries flux, because it includes (among other things) the machinery for DNA and RNA synthesis (DNA polymerase, RNA polymerase, ...). When plotting the proteome fraction of this category determined from the data of Schmidt et al. [12], we found that the fraction remains approximately constant over a large range of growth conditions. This motivated the simplifying assumption to keep the proteome fraction for Mu constant in the simulations.

The reviewer is right, however, that this may not be the case when considering a variety of E. coli strains growing on glucose, especially the strains resulting from laboratory evolution experiments. We have therefore redone the simulations while allowing the Mu category to vary, by a percentage corresponding to experimentally-observed variations of this category over the range of growth conditions considered by Schmidt et al. [12] (Supplementary Figure 1). In comparison with the original results, the relaxation of this condition enlarges the attainable range of growth rates by about 10%, but the overall shape of the cloud of rate-yield phenotypes remains the same. These new simulation results are shown in the main figures of the revised manuscript.

In parallel, we have developed a model variant that includes a Q category in the sense of Scott et al., defined by the (growth-rate independent) offsets of the linear relations between growth rate and protein fractions [7]. We have retained an Mu category of other proteins in the model, interpreted as consisting of the growth-rate dependent fraction of other proteins, including the molecular machinery responsible for the synthesis of other macromolecules. Whereas the Mu category carries a flux, this is not the case for the Q category. We have calibrated the model variant from the same data as the original model, and predicted the admissible rate-yield phenotypes. While the cloud of predicted rate-yield phenotypes is slightly displaced in comparison with the reference model, the overall qualitative shape is the same. We explain this robustness by the fact that, despite the different interpretation of the protein categories, the models are structurally very similar and calibrated from data for the same reference strain. This gives rise to different values of the catalytic constants, which compensate for the differences in protein concentrations. Note that more data are needed for the calibration of the model with the Q category, because it requires estimation of the growth-rate-independent proteome fraction for all individual protein categories. In particular, in addition to carbon limitation, conditions of nitrogen and sulfur limitation are necessary [7]. In the absence of such data, additional assumptions need to be made, as we have explained in the new version of the manuscript.

We could not find a discussion of the relation between substrate saturation and growth-rate independent offsets in proteomics data in the paper by Dourado et al. [2]. In the revised version of the manuscript, however, we have exploited their idea to compare substrate saturation for different predicted and observed rate-yield phenotypes. As a prerequisite, this has required a refinement of the estimation of the half-saturation constants during model calibration, for which we have used the dataset of Km values collected by Dourado et al. [2]. The finding that high-rate, high-yield growth comes with high substrate saturation, indicating an efficient utilization of proteomic resources, has been given more emphasis in the revised manuscript. Note that each resource allocation strategy will give rise to a different concentration of metabolites, and therefore to a different level of substrate saturation of the enzymes.

The reviewer is right that the phrase ”the external substrate concentration S is assumed constant” is not correct for batch growth, although it approximately holds for continuous growth in a chemostat. In the case of balanced growth in batch, the external substrate concentration S is much higher than the half-saturation constant ), so that the kinetic equation for the macroreaction can be approximated by vmc = mc es, where es = kmc. In the revised manuscript, we have explicitly distinguished between these two situations (batch and continuous growth). Note that S is not the intracellular, but the extracellular concentration of substrate.

Changes in manuscript: We have better explained the meaning of the residual protein category Mu and corrected the misleading identification of this category with the Q-sector of Scott et al. [13] in the section Coarse-grained model with coupled carbon and energy fluxes and in Appendix 1. In new subsections of Appendix 1 and Appendix 2, we discuss the construction and calibration of a model variant with an additional growth-rate independent protein category corresponding to the Q-sector of Scott et al.. In the Discussion, we explain that the rate-yield predictions obtained from this model and the reference model are essentially the same, indicating the robustness of the model predictions.

We have redone all simulations using a resource allocation parameter for the housekeeping protein fraction Mu that is allowed to vary within experimentally-determined bounds (Coarsegrained model with coupled carbon and energy fluxes and Methods). The bounds are determined from the data of Schmidt et al. [12], as shown in the new Supplementary Figure 1. These simulations also include refined estimates for the half-saturation constants in the metabolic macroreactions.

In the final Results section, Resource allocation strategies enabling fast and efficient growth of Escherichia coli, we develop the point that higher saturation of enzymes and ribosomes is key to high-rate, high-yield growth of E. coli, in agreement with observations from other recent studies [2, 5, 9]. In Appendix 1, we emphasize that S is the extracellular substrate concentration and we distinguish between simplifications of vmc for batch and continuous growth.

Author Response

Reviewer #1 (Public Review):

The authors sought to identify the relationship between social touch experiences and the endogenous release of oxytocin and cortisol. Female participants who received a touch from their romantic partner before a stranger exhibited a blunted hormonal response compared to when the stranger was the first toucher, suggesting that social touch history and context influence subsequent touch experiences. Concurrent fMRI recordings identified key brain networks whose activity corresponded to hormonal changes and self-report.

The strengths of the manuscript are in the power achieved by collecting multi-faceted metrics: plasma hormones across time, BOLD signal, and self-report. The experiment was cleverly designed and nicely counterbalanced. Data analysis was thorough and statistically sophisticated, making the findings and conclusions convincing.

This work sheds new light on potential mechanisms underlying how humans place social experiences in context, demonstrating how oxytocin and cortisol might interact to modulate higher-level processing and contextualizing of familiar vs. stranger encounters.

Thank you very much for this generous evaluation of the study.

Reviewer #2 (Public Review):

To test how oxytocin impacts the brain and the psychological, neural, and hormonal response to touch, the authors tested human females during two counterbalanced fMRI sessions wherein females were stroked on the arm or the palm, by a real-world romantic partner or a stranger, while blood levels of oxytocin and cortisol were collected at multiple time points.

This combination of measures, and the number of hypotheses that could be tested with them, is remarkable - virtually unheard of. This impressive, difficult, and more ecological design than is typical for the field is a major strength of the study, which allowed the authors to test many important hypotheses concurrently and to show contextual effects that could not otherwise be observed. The only potential drawback perhaps is that with such a large design, including many measures, the authors produced so many significant interactions and results that it could be hard for the casual reader to appreciate the importance of each.

The authors supported their hypothesis that oxytocin effects are context-sensitive, as they found a key interaction wherein experiencing the partner first increased oxytocin for the partner relative to when they came first the OT levels were low but then increased if they were preceded by the partner (excepting one timepoint). Cortisol responses (which reflect hormonal stress) were also higher when the stranger came first than when he was preceded by the partner). In addition, touch was experienced more positively on the arm than on the palm, supporting the role of c-fibers in conveying specifically felt responses to warm, tender touch.

These data indicate significant context sensitivity with real-world implications. For example, experiencing warm touch on the arm can make us more receptive to other people in subsequent encounters. Conversely, when strangers try to approach and get close to us "out of the blue" people experience this as stressful, which reduces the pleasantness of the interaction and may reduce trust in the moment...perhaps even subsequently.

This research is critical to the basic science of neurohormonal modulation, given that most of this research occurs in rodents or in simplified studies in humans, usually through intranasal oxytocin administration with unclear impacts on circulating levels in the brain and blood. Oxytocin in particular has suffered from oversimplification as the "love drug" - wherein people assume that it always renders people more loving and trusting. The reality is more complex, as they showed, and these demonstrations are needed to clarify for the field and the public that neurohormones adaptively shift with the context, location, and identity of the social partner in an adaptive way. These results also help us understand the many null effects of oxytocin on trusting strangers in human neuroeconomic studies. In a modern world that is characterized by significant loneliness, interactions with strangers and outsiders, and touch-free digital interactions, our ability to understand the human need for genuine social contact and how it impacts our response to outsiders (welcomed in versus a source of stress) is critical to human health and the wellbeing of individuals and society.

Thank you very much for this nice summary of the study and its implications.

As you pointed out, the design was ambitious and involved a broad range of measures and levels of hypothesis-testing. This presented challenges in reporting the results. In this paper we have tried to provide interpretation of the basic results, such as that social encounters (even in the scanner environment) are sufficient to evoke changes in endogenous oxytocin levels over the course of the experimental session, and that various interactions arise due to an influence of contextual factors such as the familiarity of the person and the recent social interaction history. For the more complex results, such as the nature of relationships between BOLD signal change and the degree of change in individuals’ plasma oxytocin levels, we have tried to outline provisional interpretations.

We hope that the picture will gradually become more filled-in by work from ours and others’ labs—maybe these findings and interpretations will look very different in a few years’ time. We consider this study a starting point for future research into the dynamics and function of human endogenous oxytocin.

Reviewer #3 (Public Review):

In an ambitious, multimodal effort, Handlin, Novembre et al. investigated how the endogenous release of oxytocin and cortisol as well as functional brain activity are modulated by social touch under different contextual circumstances (e.g. palm vs. arm touch, stranger vs. partner touch) in neurotypical female participants.

Using serial sampling of plasma hormone levels in blood during concurrent functional MRI neuroimaging, the authors show that the familiarity of the interactant during social touch not only impacts current hormonal levels but also subsequent hormonal responses in a successive touch interaction. Specifically, endogenous oxytocin levels are significantly heightened (and cortisol levels dampened) during touch from a romantic partner compared to touch from an unfamiliar stranger, at least during the first touch interaction. During the second touch interaction, however, oxytocin levels plummeted when being touched by a stranger following partner touch (although a recovery was made), whereas the normally elevated oxytocin responses to partner touch were dampened when following stranger touch. These results are paralleled by similar familiarity- and order-related effects in neural regions involving the hypothalamus, dorsal raphe, and precuneus.

However, an important distinction to be made is that, although a significant main effect of familiarity was encountered in several brain regions when taking peak plasma oxytocin levels into account, subsequent t-tests showed no activation differences in the BOLD response between partner and stranger touch within the same subjects. Significant interaction maps seem thus mainly driven by between-subject effects at the different time points, which is arguably due to differences between subjects in their initial calibration of neural/hormonal responses, and not session-to-session changes within the same subjects.

A similar comment can be made for the reported covariance between (changes in) maximal oxytocin levels and (changes in) BOLD activity for the hypothalamus.

In an effort to delineate the complex cascade of responses induced by afferent tactile stimulation, the authors report an exploratory regression analysis to identify BOLD activation that precedes the pattern of serial plasma changes in oxytocin levels (looking backwards; i.e. implying changes in brain activation drive changes in hormonal plasma levels). Although the authors are appropriately modest about the significance of the encountered effects, additional control analyses could bring further clarifications about the temporal (e.g., can similar covariations also be found when looking forward) and hormonal specificity (e.g. can similar findings be found for cortisol-variations) of the encountered results. Nevertheless, despite the 'dynamically' covarying relationships between BOLD and max plasma oxytocin levels (i.e. dynamic as in the sense across conditions, not across timepoints), claims about the directionality of this effect (i.e. 'hormonal neuromodulation' vs. 'neural modulation of hormonal levels') remain speculative.

A particular strength of this study is the employment of a "female-first" strategy since experimental data concerning endogenous oxytocin levels in women are sparse. Adequate control analyses are reported to take potential variability due to differences in contraception and phase in the hormonal cycle into account.

Thank you for your attentive reading of the study, and for raising several very important points.

You are right that the BOLD activation maps showing interactions between the change in OT levels and other factors (familiarity, order) reflect differences between subjects in the two runs of the experiment. The effect of familiarity emerged from the full model for the whole group (all participants, whether they started with partner or stranger), as an interaction between the partner/stranger factor and the change in OT. As you point out, this reflects interindividual-level covariation between OT changes and BOLD changes. For example, individuals showing greater OT increase were also more likely to show higher BOLD in certain clusters during partner compared to stranger touch. Similarly, the partner vs stranger contrast showing hypothalamus and Raphe reflects greater OT-BOLD covariance in the stranger first compared to the partner fist groups: in the stranger first group, BOLD was greater the lower the mean OT was across individuals.

The t-tests with OT as covariate further indicate that the interaction was driven by group differences in the second run. As you point out, within groups (partner or stranger first), there was no significant change in the OT-BOLD covariance from the first to the second run, though these relationships were different between groups. We agree with you that this lack of difference in within-group OT-BOLD covariance from the first to the second run is likely because responses in the first run biased responses in the second run—but in different ways depending on whether the partner or the stranger was presented first. Both groups did show a meaningful correlation in mean OT levels between the first and the second run (we have now included this information in the paper).

In general, we agree that it is very important to make clear that, as in many covariation/correlation effects in fMRI studies, the effects are driven by interindividual differences for a given covariant relationship, rather than the within-subject BOLD response increasing or decreasing.

We also agree that it is not possible to determine the direction of modulation from these results. The creation of the temporal OT regressor as “backward-looking” was informed by evidence from animal models for central-to-peripheral effects from hypothalamus to pituitary to bloodstream. We assumed this directionality in the analysis. Given the exploratory nature of this regressor, “looking forward” from temporal OT sample patterns to BOLD patterns with different time intervals would be an equally valid approach. It could reveal activation related to any systematic influence of peripheral OT levels on cortical responses. As the premise of the temporal OT regressor analysis in the present study was any assumed central-to-peripheral modulation, we have kept this as the focus but will explore any specific peripheral-to-central covariation in future work.

We believe that the full causal picture is likely to involve bidirectional modulation: a modulatory loop (or even loops) in which peripheral and central changes influence one another. Unfortunately, it is difficult to address such temporal feedback with the poor time resolution of fMRI.

Author Response

Reviewer #1 (Public Review):

This is one of the most careful analyses of sexual dimorphism in dinosaurs, based on a remarkable assemblage of 61 ornithomimosaur fossils from the Early Cretaceous of western France. The dimorphism is expressed in variations in the shaft curvature and the distal epiphysis width, analysed appropriately here and plausible because these are the kinds of morphological features that vary between males and females among birds and crocodilians, among others.

In the Introduction, it is right to highlight the shortage of convincing cases of demonstrated sexual dimorphism (SD) in dinosaurs. But note the points made by Hone, Saitta and others that SD can exist in many species today without major morphological differences, making it hard to demonstrate in fossils with such types of dimorphism. Also, some proposed statistical tests to ensure that SD has been convincingly demonstrated in fossils are so stringent they would be hard ever to pass (requiring enormous and constant morphological distinctiveness). In other words, we are conditioned not to find SD in dinosaurs, and yet may be massively under-reporting it because of preservation difficulties (of course) but also because of some overly rigorous demands for proof. These issues help argue that the current study is especially valuable because the data set is large (itself a rarity), and 3D bone shape analysis and proper statistical testing have been applied.

We are grateful that Reviewer 1 raised this point regarding the occurrence of many subtle sexual dimorphism among modern populations, and added a sentence in the introduction, to further emphasize the importance of a large dataset composed of coeval organisms.

It's interesting the dinosaur example shows the same two dimorphic traits (femoral obliquity = bicondylar angle; width of distal epiphysis = bicondylar breadth) seen in mammals (MS, lines 117-123), where the femur angle may vary because of the need for broader hips in the female to accommodate the birth canal, and yet dinosaurs laid eggs. These are small dinosaurs, so perhaps their eggs were relatively large in proportion to body size. Perhaps the authors could comment on this. There is some discussion with regard to modern birds at MS lines 187-199.

We agree with comments from Reviewer 1 and we raise the question of egg possibly constraining the pelvic and proximal hindlimb morphology from line 170 to 189 and how it relates to modern archosaurs from line 189 to 202. We also originally intended to discuss how the Kiwi hindlimb morphology accommodates large eggs, but no significant dimorphism was demonstrated in the pelvic and hindlimb morphology of this bird.

Author Response

Reviewer #2 (Public review):

Ansari et al. describe a web-based software for the design of guide RNA (gRNA) sequences and primers for CRISPR-Cas-based identification of single nucleotide variants (SNVs). The use of CRISPR-Cas to rapidly identify specific mutations in both cancer and infection is an evolving field with good potential to play a role in future research and diagnostics.

The software described by Ansari et al. is easy to use for the design of guide RNAs. The most important question is how good the gRNAs that the software suggests are. As such, the manuscript would benefit from better describing the parameters used for the gRNA design as well as including more validation experiments. Clearly, the scope of the manuscript is not about developing different detection methods, but I would argue that performing more wet lab experiments is needed to support the usability of the software.

We thank the reviewer for taking interest in this manuscript and raising an important point about increasing the number of targets for our wet lab experiments. To address this, we have tried to include more supporting data in the updated version of the manuscript.

Reviewer #3 (Public review):

This manuscript by Ansari and coworkers describes CriSNPr, a tool for designing gRNAs for CRISPR-based diagnostics for SNP detection. CriSNPr allows one to design assays to detect human and SARS-CoV-2 mutations, positioning the mismatches for optimal detection based on results from the literature. Designs can be generated for six different CRISPR effector proteins. The authors test their approach by designing assays to detect a single SNV using three different CRISPR effectors. A strength of the manuscript is that the method does appear to work, at least for the E484K mutation, for multiple CRISPR effector proteins.

The weaknesses of this manuscript are the lack of data demonstrating that the method works. There is only one very small experimental demonstration using a single mutation (Figure 4) and some very high-level analyses using two SNP/SNV databases (Figure 5). The authors do not provide any data to answer any basic questions about how well their designs work, how fast and easy it is to run their method, or which designs are predicted to work better than others. These weaknesses ultimately limit the impact of the work on the field, as it is not clear what the benefits of using the author's approach are versus simply applying the rules for the individual CRISPR effector proteins outlined in Figure 1 of the manuscript.

We thank the reviewer for taking interest in this manuscript and appreciate the constructive feedback and suggestions. In the new version of this paper, we've added more data to back up other SNVs with different CRISPR systems and the CriSNPr pipeline for sgRNA design. Even in these datasets, we see that for particular SNVs, the choice of the CRISPR system used might affect the sensitivity of detecting the mutation (Figures 5 and 6). This would be a huge task to do again for multiple targets and targeting systems, which is outside the scope of this study. Importantly, such large datasets are currently missing for the different CRISPRDx systems since we have not come across studies where users have comparatively determined the best methodology for their assay. In our opinion, criSNPr gives users this opportunity by providing a unified platform, and our validation assays show how this can be done in a relatively fast manner.

A stand-alone version of the server is made available for download at https://github.com/asgarhussain/CriSNPr to increase its speed and accessibility for the end user.

Addressing the point of determining which crRNAs work best for a given assay requires a large amount of data on target SNPs for individual Cas systems, which is currently scarce. In the current version of CriSNPr, we have considered prioritizing crRNA mismatch-sensitive positions based on original published studies. For example, for AaCas12b, mismatch positions are ranked as follows: 1&4 > 1&5 > 4&11 > 4&16 > 5&8 > 5&11 > 16&19. Similarly, crRNA mismatch-sensitive positions for individual Cas systems (as shown in Figure 1) have been used to prioritize crRNAs. Improving on these design principles further would require studying the biology of individual Cas:DNA/RNA interactions, which is beyond the scope of this study. However, in the updated version of the CriSNPr, we attempted to improve the scoring algorithm by taking into account off-targets for a crRNA design, and priority is given to the combinatorial positions with the fewest off-targets as well as the weightage of their efficacy.

Author Response:

We would like to thank both reviewers and editors for their time and effort in reviewing our work, and the thoughtful suggestions made.

Reviewer #1 (Public Review):

[…] The experiments are well-designed and carefully conducted. The conclusions of this work are in general well supported by the data. There are a couple of points that need to be addressed or tested.

1) It is unclear how LC phasic stimulation used in this study gates cortical plasticity without altering cellular responses (at least at the calcium imaging level). As the authors mentioned that Polack et al 2013 showed a significant effect of NE blockers in membrane potential and firing rate in V1 layer2/3 neurons during locomotion, it would be useful to test the effect of LC silencing (coupled to mismatch training) on both cellular response and cortical plasticity or applying NE antagonists in V1 in addition to LC optical stimulation. The latter experiment will also address which neuromodulator mediates plasticity, given that LC could co-release other modulators such as dopamine (Takeuchi et al. 2016 and Kempadoo et al. 2016). LC silencing experiment would establish a causal effect more convincingly than the activation experiment.

Regarding the question of how phasic stimulation could alter plasticity without affecting the response sizes or activity in general, we believe there are possibilities supported by previous literature. It has been shown that catecholamines can gate plasticity by acting on eligibility traces at synapses (He et al., 2015; Hong et al., 2022). In addition, all catecholamine receptors are metabotropic and influence intracellular signaling cascades, e.g., via adenylyl cyclase and phospholipases. Catecholamines can gate LTP and LTD via these signaling pathways in vitro (Seol et al., 2007). Both of these influences on plasticity at the molecular level do not necessitate or predict an effect on calcium activity levels. We will expand on this in the discussion of the revised manuscript.

While a loss of function experiment could add additional corroborating evidence that LC output is required for the plasticity seen, we did not perform loss-of-function experiments for three reasons:

1. The effects of artificial activity changes around physiological set point are likely not linear for increases and decreases. The problem with a loss of function experiment here is that neuromodulators like noradrenaline affect general aspects neuronal function. This is apparent in Polack et al., 2013: during the pharmacological blocking experiment, the membrane hyperpolarizes, membrane variance becomes very low, and the cells are effectively silenced (Figure 7 of (Polack et al., 2013)), demonstrating an immediate impact on neuronal function when noradrenaline receptor activation is presumably taken below physiological/waking levels. In light of this, if we reduce LC output/noradrenergic receptor activation and find that plasticity is prevented, this could be the result of a direct influence on the plasticity process, or, the result of a disruption of another aspect of neuronal function, like synaptic transmission or spiking. We would therefore challenge the reviewer’s statement that a loss-of-function experiment would establish a causal effect more convincingly than the gain-of-function experiment that we performed.

2. The loss-of-function experiment is technically more difficult both in implementation and interpretation. Control mice show no sign of plasticity in locomotion modulation index (LMI) on the 10-minute timescale (Figure 4J), thus we would not expect to see any effect when blocking plasticity in this experiment. We would need to use dark-rearing and coupled-training of mice in the VR across development to elicit the relevant plasticity ((Attinger et al., 2017); manuscript Figure 5). We would then need to silence LC activity across days of VR experience to prevent the expected physiological levels of plasticity. Applying NE antagonists in V1 over the entire period of development seems very difficult. This would leave optogenetically silencing axons locally, which in addition to the problems of doing this acutely (Mahn et al., 2016; Raimondo et al., 2012), has not been demonstrated to work chronically over the duration of weeks. Thus, a negative result in this experiment will be difficult to interpret, and likely uninformative: We will not be able to distinguish whether the experimental approach did not work, or whether local LC silencing does nothing to plasticity.

   Note that pharmacologically blocking noradrenaline receptors during LC stimulation in the plasticity experiment is also particularly challenging: they would need to be blocked throughout the entire 15 minute duration of the experiment with no changes in concentration of antagonist between the ‘before’ and ‘after’ phases, since the block itself is likely to affect the response size, as seen in Polack et al., 2013, creating a confound for plasticity-related changes in response size. Thus, we make no claim about which particular neuromodulator released by the LC is causing the plasticity.

3. There are several loss-of-function experiments reported in the literature using different developmental plasticity paradigms alongside pharmacological or genetic knockout approaches. These experiments show that chronic suppression of noradrenergic receptor activity prevents ocular dominance plasticity and auditory plasticity (Kasamatsu and Pettigrew, 1976; Shepard et al., 2015). Almost absent from the literature, however, are convincing gain-of-function plasticity experiments.

Overall, we feel that loss-of-function experiments may be a possible direction for future work but, given the technical difficulty and – in our opinion – limited benefit that these experiments, would provide in light of the evidence already provided for the claims we make, we have chosen not to perform these experiments at this time. Note that we already discuss some of the problems with loss-of-function experiments in the discussion.

2) The cortical responses to NE often exhibit an inverted U-curve, with higher or lower doses of NE showing more inhibitory effects. It is unclear how responses induced by optical LC stimulation compare or interact with the physiological activation of the LC during the mismatch. Since the authors only used one frequency stimulation pattern, some discussion or additional tests with a frequency range would be helpful.

This is correct, we do not know how the artificial activation of LC axons relates to physiological activation, e.g. under mismatch. The stimulation strength is intrinsically consistent in our study in the sense that the stimulation level to test for changes in neuronal activity is similar to that used to probe for plasticity effects. We suspect that the artificial activation results in much stronger LC activity than seen during mismatch responses, given that no sign of the plasticity in LMI seen in high ChrimsonR occurs in low ChrimsonR or control mice (Figure 4J). Note, that our conclusions do not rely on the assumption that the stimulation is matched to physiological levels of activation during the visuomotor mismatches that we assayed. The hypothesis that we put forward is that increasing levels of activation of the LC (reflecting increasing rates or amplitude of prediction errors across the brain) will result in increased levels of plasticity. We know that LC axons can reach levels of activity far higher than that seen during visuomotor mismatches, for instance during air puff responses, which constitute a form of positive prediction error (unexpected tactile input) (Figures 2C and S1C).  The visuomotor mismatches used in this study were only used to demonstrate that LC activity is consistent with prediction error signaling. We will expand on these points in the discussion as suggested.

Reviewer #2 (Public Review):

[…] The study provides very compelling data on a timely and fascinating topic in neuroscience. The authors carefully designed experiments and corresponding controls to exclude any confounding factors in the interpretation of neuronal activity in LC axons and cortical neurons. The quality of the data and the rigor of the analysis are important strengths of the study. I believe this study will have an important contribution to the field of system neuroscience by shedding new light on the role of a key neuromodulator. The results provide strong support for the claims of the study. However, I also believe that some results could have been strengthened by providing additional analyses and experimental controls. These points are discussed below.

Calcium signals in LC axons tend to respond with pupil dilation, air puffs, and locomotion as the authors reported. A more quantitative analysis such as a GLM model could help understand the relative contribution (and temporal relationship) of these variables in explaining calcium signals. This could also help compare signals obtained in the sensory and motor cortical domains. Indeed, the comparison in Figure 2 seems a bit incomplete since only "posterior versus anterior" comparisons have been performed and not within-group comparisons. I believe it is hard to properly assess differences or similarities between calcium signal amplitude measured in different mice and cranial windows as they are subject to important variability (caused by different levels of viral expression for instance). The authors should at the very least provide a full statistical comparison between/within groups through a GLM model that would provide a more systematic quantification.

We will implement an improved analysis in the revised version of the manuscript.

Previous studies using stimulations of the locus coeruleus or local iontophoresis of norepinephrine in sensory cortices have shown robust responses modulations (see McBurney-Lin et al., 2019, https://doi.org/10.1016/j.neubiorev.2019.06.009 for a review). The weak modulations observed in this study seem at odds with these reports. Given that the density of ChrimsonR-expressing axons varies across mice and that there are no direct measurements of their activation (besides pupil dilation), it is difficult to appreciate how they impact the local network. How does the density of ChrimsonR-expressing axons compare to the actual density of LC axons in V1? The authors could further discuss this point.

In terms of estimating the percentage of cortical axons labelled based on our axon density measurements: we refer to cortical LC axonal immunostaining in the literature to make this comparison. In motor cortex, an average axon density of 0.07 µm/µm2 has been reported (Yin et al., 2021), and 0.09 µm/µm2 in prefrontal cortex (Sakakibara et al., 2021). Density of LC axons varies by cortical area, with higher density in motor cortex and medial areas than sensory areas (Agster et al., 2013): V1 axon density is roughly 70% of that in cingulate cortex (adjacent to motor and prefrontal cortices) (Nomura et al., 2014). So, we approximate a maximum average axon density in V1 of approximately 0.056 µm/µm2. Because these published measurements were made from images taken of tissue volumes with larger z-depth (~ 10 µm) than our reported measurements (~ 1 µm), they appear much larger than the ranges reported in our manuscript (0.002 to 0.007 µm/µm2). We repeated the measurements in our data using images of volumes with 10 µm z-depth, and find that the percentage axons labelled in our study in high ChrimsonR-expressing mice ranges between 0.012 to 0.039 µm/µm2. This corresponds to between 20% to 70% of the density we would expect based on previous work. Note that this is a potentially significant underestimate, and therefore should be used as a lower bound: analyses in the literature use images from immunostaining, where the signal to background ratio is very high. In contrast, we did not transcardially perfuse our mice leading to significant background (especially in the pia/L1, where axon density is high - (Agster et al., 2013; Nomura et al., 2014)), and the intensity of the tdTomato is not especially high. We therefore are likely missing some narrow, dim, and superficial fibers in our analysis.

We also can quantify how our variance in axonal labelling affects our results: For the dataset in Figure 3, there doesn’t appear to be any correlation between the level of expression and the effect of stimulating the axons on the mismatch or visual flow responses for each animal (Figure R1: https://imgur.com/gallery/Yl60hnT), while there is a significant correlation between the level of expression and the pupil dilation, consistent with the dataset shown in Figure 4. Thus, even in the most highly expressing mice, there is no clear effect on average response size at the level of the population. We will add these correlations to the revised manuscript.

To our knowledge, there has not yet been any similar experiment reported utilizing local LC axonal optogenetic stimulation while recording cortical responses, so when comparing our results to those in the literature, there are several important methodological differences to keep in mind. The vast majority of the work demonstrating an effect of LC output/noradrenaline on responses in the cortex has been done using unit recordings, and while results are mixed, these have most often demonstrated a suppressive effect on spontaneous and/or evoked activity in the cortex (McBurney-Lin et al., 2019). In contrast to these studies, we do not see a major effect of LC stimulation either on baseline or evoked calcium activity (Figure 3), and, if anything, we see a minor potentiation of transient visual flow onset responses (see also Figure R2). There could be several reasons why our stimulation does not have the same effect as these older studies:

1. Recording location: Unit recordings are often very biased toward highly active neurons (Margrie et al., 2002) and deeper layers of the cortex, while we are imaging from layer 2/3 – a layer notorious for sparse activity. In one of the few papers to record from superficial layers, it was been demonstrated that deeper layers in V1 are affected differently by LC stimulation methods compared to more superficial ones (Sato et al., 1989), with suppression more common in superficial layers. Thus, some differences between our results and those in the majority of the literature could simply be due to recording depth and the sampling bias of unit recordings.

2. Stimulation method: Most previous studies have manipulated LC output/noradrenaline levels by either iontophoretically applying noradrenergic receptor agonists, or by electrically stimulating the LC. Arguably, even though our optogenetic stimulation is still artificial, it represents a more physiologically relevant activation compared to iontophoresis, since the LC releases a number of neuromodulators including dopamine, and these will be released in a more physiological manner in the spatial domain and in terms of neuromodulator concentration. Electrical stimulation of the LC as used by previous studies differs from our optogenetic method in that LC axons will be stimulated across much wider regions of the brain (affecting both the cortex and many of its inputs), and it is not clear whether the cause of cortical response changes is in cortex or subcortical. In addition, electrical LC stimulation is not cell type specific.

3. Temporal features of stimulation: Few previous studies had the same level of temporal control over manipulating LC output that we had using optogenetics. Given that electrical stimulation generates electrical artifacts, coincident stimulation during the stimulus was not used in previous studies. Instead, the LC is often repeatedly or tonically stimulated, sometimes for many seconds, prior to the stimulus being presented. Iontophoresis also does not have the same temporal specificity and will lead to tonically raised receptor activity over a time course determined by washout times.

4. State specificity: Most previous studies have been performed under anesthesia – which is known to impact noradrenaline levels and LC activity (Müller et al., 2011). Thus, the acute effects of LC stimulation are likely not comparable between anesthesia and in the awake animal.

Due to these differences, it is hard to infer why our results differ compared to other papers. The study with the most similar methodology to ours is (Vazey et al., 2018), which used optogenetic stimulation directly into the mouse LC while recording spiking in deep layers of the somatosensory cortex with extracellular electrodes. Like us, they found that phasic optogenetic stimulation alone did not alter baseline spiking activity (Figure 2F of Vazey et al., 2018), and they found that in layers 5 and 6, short latency transient responses to foot touch were potentiated and recruited by simultaneous LC stimulation. While this finding appears more overt than the small modulations we see, it is qualitatively not so dissimilar from our finding that transient responses appear to be slightly potentiated when visual flow begins (Figure R2). Differences in the degree of the effect may be due to differences in the layers recorded, the proportion of the LC recruited, or the fact anesthesia was used in Vazey et al., 2018.

Note that we only used one set of stimulation parameters for optogenetic stimulation, and it is always possible that using different parameters would result in different effects. We will add a discussion on the topic to the revised manuscript.

In the analysis performed in Figure 3, it seems that red light stimulations used to drive ChrimsonR also have an indirect impact on V1 neurons through the retina. Indeed, figure 3D shows a similar response profile for ChrimsonR and control with calcium signals increasing at laser onset (ON response) and offset (OFF response). With that in mind, it is hard to interpret the results shown in Figure 3E-F without seeing the average calcium time course for Control mice. Are the responses following visual flow caused by LC activation or additional visual inputs? The authors should provide additional information to clarify this result.

This is a good point. When we plot the average difference between the stimulus response alone and the optogenetic stimulation + stimulus response, we do indeed find that there is a transient increase in response at the visual flow onset (and the offset of mismatch, which is where visual flow resumes), and this is only seen in ChrimsonR-expressing mice (Figure R2: https://imgur.com/gallery/cqN2Khd). We therefore believe that these enhanced transients at visual flow onset could be due to the effect of ChrimsonR stimulation, and indeed previous studies have shown that LC stimulation can reduce the onset latency and latency jitter of afferent-evoked activity (Devilbiss and Waterhouse, 2004; Lecas, 2004), an effect which could mediate the differences we see. We will add this analysis to the revised manuscript.

Some aspects of the described plasticity process remained unanswered. It is not clear over which time scale the locomotion modulation index changes and how many optogenetic stimulations are necessary or sufficient to saturate this index. Some of these questions could be addressed with the dataset of Figure 3 by measuring this index over different epochs of the imaging session (from early to late) to estimate the dynamics of the ongoing plasticity process (in comparison to control mice). Also, is there any behavioural consequence of plasticity/update of functional representation in V1? If plasticity gated by repeated LC activations reproduced visuomotor responses observed in mice that were exposed to visual stimulation only in the virtual environment, then I would expect to see a change in the locomotion behaviour (such as a change in speed distribution) as a result of the repeated LC stimulation. This would provide more compelling evidence for changes in internal models for visuomotor coupling in relation to its behavioural relevance. An experiment that could confirm the existence of the LC-gated learning process would be to change the gain of the visuomotor coupling and see if mice adapt faster with LC optogenetic activation compared to control mice with no ChrimsonR expression. Authors should discuss how they imagine the behavioural manifestation of this artificially-induced learning process in V1.

Regarding the question of plasticity time course: Unfortunately, owing to the paradigm used in Figure 3, the time course of the plasticity will not be quantifiable from this experiment. This is because in the first 10 minutes, the mouse is in closed loop visuomotor VR experience, undergoing optogenetic stimulation (this is the time period in which we record mismatches). We then shift to the open loop session to quantify the effect of optogenetic stimulation on visual flow responses. Since the plasticity is presumably happening during the closed loop phase, and we have no read-out of the plasticity during this phase (we do not have uncoupled visual flow onsets to quantify LMI in closed loop), it is not possible to track the plasticity over time.

Regarding the behavioral relevance of the plasticity: The type of plasticity we describe here is consistent with predictive, visuomotor plasticity in the form of a learned suppression of responses to self-generated visual feedback during movement. Intuitive purposes of this type of plasticity would be 1) to enable better detection of external moving objects by suppressing the predictable (and therefore redundant) self-generated visual motion and 2) to better detect changes in the geometry of the world (near objects have a larger visuomotor gain that far objects). In our paradigm, we have no intuitive read-out of the mouse’s perception of these things, and it is not clear to us that they would be reflected in locomotion speed, which does not differ between groups (manuscript Figure S5). Instead, we would need to turn to other paradigms for a clear behavioral read-out of predictive forms of sensorimotor learning: for instance, sensorimotor learning paradigms in the VR (such as those used in (Heindorf et al., 2018; Leinweber et al., 2017)), or novel paradigms that reinforce the mouse for detecting changes in the gain of the VR, or moving objects in the VR, using LC stimulation during the learning phase to assess if this improves acquisition. This is certainly a direction for future work. In the case of a positive effect, however, the link between the precise form of plasticity we quantify in this manuscript and the effect on the behavior would remain indirect, so we see this as beyond the scope of the manuscript. We will add a discussion on this topic to the revised manuscript.

Finally, control mice used as a comparison to mice expressing ChrimsonR in Figure 3 were not injected with a control viral vector expressing a fluorescent protein alone. Although it is unlikely that the procedure of injection could cause the results observed, it would have been a better control for the interpretation of the results.

We agree that this indeed would have been a better control. However, we believe that this is fortunately not a major problem for the interpretation of our results for two reasons:

1. The control and ChrimsonR expressing mice do not show major differences in the effect of optogenetic LC stimulation at the level of the calcium responses for all results in Figure 3, with the exception of the locomotion modulation indices (Figure 3I). Therefore, in terms of response size, there is no major effect compared to control animals that could be caused by the injection procedure, apart from marginally increased transient responses to visual flow onset – and, as the reviewer notes, it is difficult to see how the injection procedure would cause this effect.

2. The effect on locomotion modulation index (Figure 3I) was replicated with another set of mice in Figure 4C, for which we did have a form of injected control (‘Low ChrimsonR’), which did not show the same plasticity in locomotion modulation index (Figure 4E). We therefore know that at least the injection itself is not resulting in the plasticity effect seen.

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Author Response

Reviewer #2 (Public Review):

Weaknesses: The authors do not make a direct link between TOR and REPTOR2 signalling. This seems important since REPTOR2 is a novel gene that arose from the duplication of REPTOR.

We have added several experiments to strengthen the connection between TOR and REPTOR2, and determined the effect of co-silencing of TOR and REPTOR2 on autophagy and proportion of the winged morph. Please see the details below in your comments point 3.

Author Response

Reviewer #2 (Public Review):

This paper has collected an impressive data set of the visual response properties of neurons in the visual layers of the mouse superior colliculus. There are 3 main findings of the study. First, the authors identify 24 functional classes of neurons based on the clustering of each neuron's visual response properties. Second, unlike in the retina where each cell type is regularly spaced, functional classes in the superior colliculus appear to cluster near each other. Third, visual representation has a lower dimensionality in the superior colliculus compared to the retina. The dataset has the potential to support the conclusions of the paper, but further analysis is required to make the claims convincing.

Strengths:

The main strength of the paper is its impressive dataset of more than 5000 neurons from the visual layers of the superior colliculus. This data set includes recordings from both an interesting set of genetically labelled classes of cells and from a reasonably large portion of the superior colliculus. This dataset offers the opportunity to support the major claims of the paper. This includes i) the identification of 24 functional classes of neurons, ii) the intriguing possibility that functional classes form local patches within the superior colliculus and iii) that the representation of visual information in the superior colliculus has a lower dimensionality compared to the retina.

Weaknesses:

The weakness of the paper is that its main claims are not adequately supported by the presented data or analysis. First, support for the existence of 24 functional classes is not clear enough. Our major concern is that it is not clear that each class of neurons was distributed across different mice. Are certain cell types overrepresented in individual animals, or do you find examples of each cell type in most animals?

The new Supplementary Figure 7G shows how individual mice contribute to the functional types for all neurons. Further, the new Supplementary Figure 12 shows the receptive field locations derived from recordings in each of the animals.

In addition, it should be made explicit how the responses of each genetically labeled class of neurons are distributed among the 24 functional clusters.

We have added a new Figure 5D to show this.

Second, the analysis of the spatial clustering of functional cell types is not complete. Do the same functional clusters sample the same retinotopic locations in different mice? How are clusters of the functional type distributed in visual space?

Please see our point-by-point responses below to the concerns.

Third, the lower dimensionality of representation in the superior colliculus may be the result of selective projections of retinal ganglion cells, not all retinal ganglion cell types project to the superior colliculus. Please estimate the dimensionality of the visual representation of those retinal ganglion cell types that projects to the superior colliculus.

Certainly part of the dimensionality reduction may come from the incomplete retino-geniculate projection; we have added discussion on this topic.

Author Response

Reviewer #1 (Public Review):

In this manuscript, the authors describe a one-step genome editing method to replace endogenous EB1 with their previously-developed light-sensitive variant, in order to examine the effect of acute and local optogenetic inactivation of EB1 in human neurons. They then attempt to assess the effects of EB1 inactivation on microtubule growth, F-actin dynamics, and growth cone advance and turning. They also perform these experiments in neurons that are lacking EB3, in order to determine whether EB1 can function in a direct and specific way without possible EB3 redundancy.

First, the experiments depicting the methodology are rigorous and compelling. Most previous studies of +TIP function use knockout or knockdown studies in which the proteins are inactivated over many hours or days in non-human systems. This is the first study to acutely and locally inactivate a +TIP in human neurons. While this group previously published the effects of replacing endogenous EB1 with the light-sensitive variant, the novelty in this current study is that they use a one-step gene editing replacement method (using CRISPR/Cas9) along with using human neurons derived from iPSCs. After proving their new experimental system works, the authors next seek to test the effect that acutely inactivating EB1 (alongside chronic EB3 knockdown) has on microtubule dynamics, and they observe a marked reduction in MT growth and MT length. They then seek to investigate whether F-actin dynamics are immediately affected by EB1 inactivation.

While measured F-actin flow rates are not significantly affected, which leads the authors to conclude that EB1 inactivation does not have any immediate effect, the included figures and movies show a different phenotype, which is not discussed. Finally, they examine the effect of EB1 inactivation on growth cone advance and growth cone turning, and find that both are affected. However, the lack of certain controls in these final experiments (specifically for Figures 3, 4, and 5) reduces the strength of their findings.

Thus, the first part of this paper describing the new methodology is very compelling and should be of interest to a wide readership, while the second part describing the functional analysis is mostly solid, with very high-quality imaging data. However, additional analysis and controls would be needed to increase confidence in their conclusions.

1) Analysis of F-actin dynamics is not thorough, and their claim is not completely supported by the data. Figure 3 only depicts F-actin dynamics data from growth cones of π-EB1 EB3-/- i3Neurons and does not [include] control growth cones (to compare dark and light conditions). While their conclusion is that F-actin dynamics are not affected, there do appear to be immediate changes in the F-actin images, other than flow rates. For example, the F-actin bundles do not appear to emanate straight out with the light condition, compared to the dark condition. There also appears to be more F-actin intensity in the transition domain of the growth cone, compared to the dark condition. If the reason is due to the effects of four minutes of blue light exposure, this would be made clear by doing this experiment with control growth cones as well.

In Figure 3, we wanted to specifically test if π-EB1 photoinactivation has an immediate effect on growth cone leading edge actin polymerization (for example because of rapid changes in Rho GTPase activity) by measuring F-actin retrograde flow. Because of photobleaching, these experiments are limited to relatively short time-lapse data sets, and within 4-5 min of blue light exposure, we found no significant difference between the dark and light conditions. As requested by this and another reviewer, we added a few more data points as well as a wild-type control. Statistical analysis by ANOVA shows no difference in retrograde flow between any of the four groups.

We did not see a consistent difference in overall F-actin organization after a few minutes of blue light, and we now include control and π-EB1 growth cones in Fig. 3 that are more similar to one another with the dark image shown more immediately before blue light exposure. The growth cone that we had in the original figure (and that remains in Video 5 to illustrate retrograde flow and how dynamic these growth cones are) was a poor choice for this figure as it undergoes quite dramatic F-actin reorganization before the blue light is turned on, and the morphology immediately before blue light exposure is much more similar to the growth cone during blue light compared with the -5 min time point that we had originally shown.

Lastly, the apparent relocalization of F-actin to the growth cone center is seen in both control and experimental conditions and we believe that has to do with photobleaching of the F-actin probe at the relatively high frame rates required to observe retrograde flow. We agree with the reviewer that it is important to know this, and we included a note in the figure legend.

2) Analysis of the effect of EB1 inactivation on growth cone advance and growth cone turning. Figure 4C, showing the neurite unable to cross the blue light barrier, is potentially quite compelling data, but it would be even more convincing if there were also data showing that the blue light barrier has no effect on a control neurite. Given that a number of previous recent studies have shown a detrimental effect of blue light on neurons, it seems important to include these negative controls in this current study.

The experiment growing neurites on a micropatterned laminin surface in combination with photoinactivation in (now) Figure 4D is incredibly low throughput but serves to illustrate repeated retraction from blue light over many hours of imaging. To show that blue light barriers do not affect control cells we have instead included a quantification of the retraction response of control and π-EB1 neurites growing randomly on a laminin-coated surface (not micropatterned stripes) in new Fig. 4C. It is also worth noting that the dose of blue light used for π-EB1 photoinactivation is much lower than what is typically used for fluorescence imaging (we analyzed and discussed this in great detail in our original π-EB1 publication), and especially in experiments with a blue light barrier, cells are not exposed to any blue light before they hit the barrier.

3) This concern also holds true for the final experiment, in which the authors examine whether localized blue light would lead to growth cone turning. The authors report difficulty with performing this technically challenging experiment of accurately targeting the light to only a localized region of the growth cone. Thus, the majority of the growth cones (72%) were completely retracted, and so only a small subset of growth cones showed turning. However, this data would be more compelling if there were also a control condition of blue light with neurons that are not expressing the light-inactivated EB1. Another useful control would be to examine whether precise region-of-interest blue light leads to localized loss of EGFP-Zdk1-EB1C on MT plus-ends within the growth cone, or if the loss extends throughout the growth cone. Either outcome would be helpful to potential readers.

We modified Fig. 5 to include control i3Neurons in this experiment. We also included a supplement to Fig. 5 showing that π-EB1 photodissociation remains localized to the blue light-exposed region. However, because in our π-EB1 line the C-terminal π-EB1 half is EGFP-tagged, we cannot show before and after images of local π-EB1 photodissociation.

Reviewer #3 (Public Review):

The major strength of the study was the approach of using photosensitive protein variants to replace endogenous protein with the 1-step Crispr-based gene editing, which not only allowed acute manipulation of protein function but also mimicked the endogenous targeted protein. However, the same strategy has been used by the same first author previously in dividing cells, somewhat reducing the novelty of the current study. In addition, the results obtained from the study were the same as those from previous studies using different approaches. In other words, the current study only confirmed the known findings without any novel or unexpected results. As a result, the study did not provide strong evidence regarding the advantage of the new experimental approach in our understanding of the function of EB1. Some specific comments are listed below.

1) In Figure 1, to show that the photosensitive EB1 variant did not affect stem cell properties and their neuronal differentiation, Oct4 staining and western blot of KIF2C and EB3 were not strong evidence. Some new experiments more specifically related to stem cell properties or iPSC-derived neurons are necessary.

While we did not attempt to fully characterize stemness in our π-EB1 edited i3N lines, we believe, most importantly, we show that π-EB1 i3N hiPSCs differentiate normally into i3Neurons. We show this morphologically as well as by immunoblotting and RT-qPCR experiments looking at marker proteins also including DCX, a well-established neuronal differentiation marker. Although not directly related to stemness, we included one additional RT-qPCR experiment more carefully analyzing the expression level of π-EB1 in the edited lines compared with EB1 in control i3N hiPSCs (new Fig. 1E).

In addition, the effect of EB1 inactivation on microtubule growth was quantified in stem cells but not in differentiated neurons, which supposed to be the focus of the study.

Quantification of MT dynamics in the hiPSCs parallels our previous experiments in cancer cell lines to demonstrate that π-EB1 photoinactivation had a similar inhibitory effect on MT growth in interphase cells. This serves as an additional control that our new system works as expected. Because of our inability to efficiently transfect i3Neurons, we could not measure MT growth in i3Neurons with the same method (i.e. automated EB1N tracking). However, as further outlined below we have added a quantification of MT growth rates in i3Neuron growth cones by additional manual tracking of SPY555-tubulin-labelled growth cone MTs after at least one minute of blue light exposure.

In Figure S2D, quantification is needed to show the effect of blue light-induced EB1 inactivation in growth cones.

Fig. 1 – supplement 2D (together with Video 3, and Fig. 2A) is simply to illustrate that the C-terminal π-EB1 half dissociates in blue light as expected. We previously characterized the kinetics of π-EB1 photodissociation and do not think redoing this would add substantially to the current manuscript. The remainder of the manuscript, however, examines the functional consequences of π-EB1 photoinactivation in i3Neurons.

2) In Figure 2, the effect of blue light on microtubule retraction in the control cells was examined, showing little effect. However, it is still unclear if the blue light per se would have any effect on microtubule plus end dynamics, a more sensitive behavior than that of retraction. In Figure 2C, the length of individual microtubules in different growth cones was presented, showing microtubule retraction after blue light. Quantification and statistical analysis are necessary to draw a strong conclusion.

Figure 2 shows that growth cone MTs in π-EB1 lines shorten in response to blue light and we did this by analyzing MTs that were visible in a short time window before and after blue light exposure. In response to another reviewer’s comment, we have redesigned this figure to better illustrate this result. We have now included statistical analysis comparing relative MT length 20 s before and during blue light exposure. In control cells that was not statistically significantly different. We also report statistical difference between control and π-EB1 lines at the 20 s by ANOVA in the text. Lastly, we also measured MT growth rates after at least one minute of blue light exposure showing that MT growth is greatly attenuated in π-EB1 lines (new Fig. 2D).

The results showed that EB3 did not seem to contribute to stabilizing microtubules in growth cones. It was discussed that EB3 might have a different function from that of EB1 in the growth cone, although they are markedly up-regulated in neurons. In the differentiated neuronal growth cones examined in the study, does EB3 actually bind to the microtubule plus ends? In the EB3 knockout cells without the blue light, the microtubules were stable, indicating that EB3 had no microtubule stabilization function in these cells. Is such a result consistent with previous studies? If not, some explanation and discussion are needed.

Other papers have shown that EB3 localizes to growth cone MT ends; for example, in rat cortical neurons (Poobalasingam et al., 2022). We did not test if endogenous EB3 is present on MT ends in i3Neurons, but transfected EB3 certainly is. Interestingly, it was reported by multiple groups that EB1 and EB3 do not bind to the exact same place near MT ends. EB3 trails behind EB1, which would be consistent with functional differences especially in controlling MT growth. We have expanded the discussion of such differences in the text, and thank Phillip Gordon-Weeks, who reminded us of this in a comment on the bioRxiv preprint.

3) In Figure 3, for the potential roles of EB1 on actin organization and dynamics, only the rates of retrograde flow were measured for 5 min. and no change was observed. However, based on the images presented, it seemed that there was a reduced number of actin bundles after blue light and the actin structure was somewhat disrupted. Some additional examination and measurement of actin organization are necessary to get a clear result.

This point was also raised by reviewer #1, and we now include images and quantification of retrograde flow in control growth cones and we increased the number of data points. We still see no difference in retrograde flow between all these groups. The original π-EB1 growth cone in Fig. 3A was a poor example because it underwent large morphological changes before the blue light was even turned on and just before light exposure is a lot more like the end point image. We therefore replaced this image with a different growth cone that is more similar to the wild-type growth cone shown, and also show images more immediately before blue light exposure. The bottomline is that we do not see a consistent difference in overall F-actin organization after a few minutes of blue light.

4) In Figure 4, the effect of blue light and EB1 inactivation on neurite extension need to be quantified in some way, such as the neurite length changes in a fixed time period, and the % of growth cones passing the blue light barrier compared with growth cones of the control cells.

We have included a statistical comparison (by ANOVA) at the 15 min time point, and a quantification of neurite retraction of growth cones encountering a blue light barrier.

5) For the quantification of growth cone turning, a control condition is needed to show that blue light itself has no effect on turning.

We have also added a control experiment to Fig. 5.

Author Response

Reviewer #1 (Public Review):

1) The role of increased temperature on immunity and homeostasis in cold-blooded vertebrates is an understudied yet important field. This work not only examines how immunity is impacted by fever, but also incorporates an infection model and examines resolution of the response. This work can serve as a model for other groups interested in the study of hyperthermia and immunity.

Thank you very much.

2) Generally speaking, I agree with the authors' strategy and interpretations of the data.

• In the Introduction, the authors chose to begin with how fever in endotherms impact the immune system. Considering that this work exclusively examines the response of a teleost (goldfish), the authors might consider flipping the way they present this work. After all, cold-blooded vertebrates rely on this response because of their basic physiology.

We chose to begin with a description of fever in endotherms because we know less about those immune mechanisms impacted by fever in ectotherms. The goal was to provide points of comparison based on published datasets. Indeed, we also expect differences between cold- and warm-blooded vertebrates based on their basic physiologies. However, it is interesting that despite different physiologies and thermoregulatory strategies, common biochemical pathways appear to regulate fever across cold- and warm-blooded vertebrates. This is now captured more clearly in the Introduction section (lines 134-136). Added support also comes from the work that we present in this study, including fever inhibition experiments using ketorolac tromethamine (lines 244-253; Figure 3C).

3) I thought the set up of the work in figure 1 was innovative and could provide an example of how to study such a problem.

Thank you. Very much appreciated.

4) Figure 2 was (to me) unexpected. One would not expect such tight response to hyperthermia and infection. This experiment in and of itself was quite interesting, and worth following up in future experiments (by the authors and other groups).

The level of homogeneity in the behavioural responses shown in Figure 2 was a big part of why we pursued this work. It was striking that fish would display such consistency in behaviour during the febrile window, regardless of whether they were evaluated in groups or individually. To us, this suggested that the temperature chosen and the kinetics of this thermal preference are central for modulation of downstream biological processes. Added support for the importance of precise thermal selection comes from "failed" experiments during this study where incoming aquatic facility water temperatures fluctuated due to factors outside of our control. This caused temporary disruption to the temperatures available to these fish in the annular thermal preference tank. In these cases, we noted disruption of both classical behaviours shown in Figure 2 as well as downstream benefits.

• The other work, on the response to infection and the resolution of infection were unique to this paper, and (sorry to be repetitive) can be an example of how to devise such studies.

Thank you.

• On the other hand, I am not sure this is a study of "fever." That implies how increased temperature impacts immunity and resolution in endotherms. Perhaps the authors could temper the comparisons between cold- and warm-blooded vertebrates regarding the response to hyperthermia.

We believe that for those mechanisms that are evolutionarily conserved, the teleost system will offer an opportunity for novel insights into the effects of fever induction and disruption. Indeed, this animal model offers multiple advantages. But we agree that much work remains to establish the extent of this conservation and now highlight this issue more clearly (lines 454-455).

An additional note on hyperthermia versus fever: although both terms are sometimes used interchangeably in the literature, we make a distinction between them. Hyperthermia captures an increase in core body temperature. However, this alone is not sufficient to engage the CNS (representative results shown in Figure 3-figure supplement 1). Consistent with prior descriptions of fever (e.g. Nat Rev Immunol (2015)15:335-49; Arch Intern Med (1998)158:1870-81), we also show that our model results in CNS engagement (Figure 3A), induces systemic pyrogen release (Figure 3B), triggers classical sickness behaviours (Figure 2), and promotes immune function (Figures 4-7).

Author Response

Reviewer 1 (Public Review):

The authors in this manuscript investigate the effect of co-substrate cycling on the metabolic flow. The main finding is that this cycling can limit the flux through a pathway. The authors examine implications of this effect in different simple configurations to highlight the potential impact on metabolic pathways. Overall, the manuscript follows logical steps and is accessible. Once the main point-reduction in flux of a pathway with limited pool of a cycled co-substrate-is established, some of the following steps become expected (e.g. the fraction of the flux in a branched pathway). Nevertheless, it is understandable that the authors have picked a few simple examples of the metabolic network motifs to highlight the implications. The results presented in the manuscript overall support the conclusions. One weakness is that some of the details of the assumptions (e.g. the choices of rates) are not explicitly spelt out in the manuscript. This work is impactful because it brings into light how cycling of some of the intermediates in a pathway can influence metabolic fluxes and dynamics. This is a factor in addition to (and separate from) reaction rates which are often considered as the main driver of metabolic fluxes.

We thank the reviewer for this accurate summary. Regarding the effect of parameters on the presented results, we note that the first part of the results are based on analytical solutions provided in the Appendix (formerly the SI). These results are given as inequalities comprising parameters, allowing direct evaluation of parameter effects. We have now made this point explicit in the presentation of the results.

In the second part of the results, we utilise numerical simulations and in this case, the observed results can possibly depend on parameters. We have explored effects of key parameters, that is kin and total substrate concentration through presented 'phase diagram' style figures - see Figure 2 and 4. For additional parameters, we have now included additional simulations exploring their effects - e.g. see Appendix - Figure 11 and Appendix – Figure 13.

Reviewer 2 (Public Review):

The cycling of "co-substrates" in metabolic reactions is possibly a very important but often overlooked determinant of metabolic fluxes. To better understand how the turnover dynamics of co-substrates affect metabolic fluxes the authors dissect a few metabolic reaction motifs. While these motifs are necessarily much simpler than real metabolic networks with dozens or hundreds of reactions, they still include important characteristics of the full network but allow for a deeper mathematical analysis. I found this mathematical approach of the manuscript convincing and an important contribution to the field as it provides more intuitive insights how co-substrate cycling could affect metabolic fluxes. In the manuscript, the authors stress particularly how the pool sizes of co-substrates and the enzymes involved in the cycling of those can constrain metabolic fluxes but the presented results also go substantially beyond this statement as the authors further illustrate how turnover characteristics of substrates in branches/coupled reactions can affect the ratio of produced substrates.

The authors further present an analysis of previously published experimental data (around Figure 3). This is a very nice idea as it can in principle add more direct proof that the cycling of co-substrates is indeed an important constraint shaping fluxes in real metabolic networks and (instead of being merely a theoretical phenomena which occurs only in unphysiological parameter regimes). However, the way currently presented, it remained unclear to which extent the data analysis is adding convincing support that co-cycling substantially constrains metabolic fluxes. Particularly, it remains unclear for which organisms and conditions the used experimental dataset holds, how it has been generated, and with what uncertainty different measured values come. For example, the comparison requires an estimation of v_max. How can these values determined in-vivo? Are (expected) uncertainties sufficiently low to allow for the statement that fluxes are higher than what enzyme kinetics predict? Furthermore, I am wondering to which extent the correlations between co-substrate pool levels and flux is supporting the idea that co-substrate cyling is important. The positive relation between ATP/AMP/ADP levels for example, is a nice observation. However, it remains a correlation which might occur due to many other factors beyond the limitations of cosubstrate cycling and which might change with provided conditions.

We thank the reviewer for this accurate summary. Although, we would like to clarify that we do not observe nor analyse any relation between ATP/AMP/ADP levels. Rather, in the analysis presented in Fig. 3B-D, we are looking at the relation between fluxes in co-substrate utilising reactions and the pool size of that co-substrate (e.g. total ATP, AMP, and ADP level for reactions utilising any one of these three co-substrates).

In their summary, the reviewer raises several valid points about the data analysis and its possible limitations. We address them here point by point:

How are Vmax values gathered/estimated? We have now added more information regarding how the Vmax values were gathered and from which organisms and conditions. Specifically, we used previously published values of Vmax from (Davidi et al. 2016) where it was estimated by multiplying the in vitro determined kcat by the concentration of the enzyme from proteomic measurement under different conditions - all for model organism Escherichia coli. See also below, reply to recommendation 2.

Are (expected) uncertainties sufficiently low? It is difficult to have an estimate for the uncertainty since much of the error in the previous analysis probably comes from the fact that the kinetic parameters determined in vitro are used to estimate fluxes under in vivo conditions - the main source of error is expected to be this discrepancy, which is hard to estimate. However, since the plot is in log-scale, we highlight only gaps that are more than 1 order of magnitude (dashed diagonal lines) and hopefully the uncertainty is lower than that. Furthermore, high uncertainty would probably contribute equally to over- and under-estimating the maximal flux, while we can clearly see that the flux rarely exceeds the Vmax. We have now included a statement in the revised text capturing this point.

Correlations offer weak evidence. Unfortunately, as we do not have measurements on co-substrate pool sizes and cycling kinetics under all conditions, our analyses of experimental data from cycling-involving reactions are admittedly limited. However, they do show that (1) measured fluxes are lower than those predicted by kinetics of the primary enzyme (i.e. enzyme involved in co-substrate and substrate conversion) alone, and (2) there is - for some cycling-involving reactions - a correlation between flux and co-substrate pool size. Both observations could indicate co-substrate pool sizes and/or co-substrate cycling dynamics being limiting. As the reviewer points out, we cannot state this as a certainty.

Other possible limitations include thermodynamic effects, i.e. limitation by the concentration of both substrate or product, or substrate saturation. We already explored the latter possibility and found that there is still a lower flux when taking into account the primary substrate saturation (see Fig. S6). The former effect is very difficult to analyse without more data, as calculating reaction thermodynamics requires knowledge of concentrations for all substrates and products, as well as enzyme Michaelis-Menten constants in both forward and backward directions. This information is currently not available except for few of the reactions among the ones we analysed. Nevertheless, to give as much insight as possible on the thermodynamic effect, we added a new figure (Appendix – Figure 8) where we plot the physiological Gibbs free energy (is calculated assuming that all reactants are at 1 mM and pH=7) against the normalized flux. The plot shows that although in few cases, such as malate dehydrogenase (MDH), the normalised flux seems to be greatly reduced by the thermodynamic barrier, the general picture is that there is little correlation between physiological Gibbs free energy and normalised flux. We have now included the resulting figure and associated discussion in the revised manuscript.

In relation to all these points on data-based support of the theory, we would also like to point out the comments from reviewer 3 and the fact that our theoretical work provides motivation for further future experimental studies of co-substrate cycling dynamics. Our main analysis about co-substrate dynamics becoming limiting is based on analytical solutions. These solutions provide an inequality of system parameters relating pathway influx, co-substrate pool size, and co-substrate related enzymatic parameters. When this inequality is satisfied, there will be flux limitation due to cosubstrate cycling. Future experimental studies can now be devised to explore this inequality under different conditions by measuring the key parameters more explicitly. This key point and aspects of the above replies are incorporated at the relevant points in the main text. In addition, we have included a new paragraph in the Discussion section (see reply to second recommendation of reviewer 3) and the following paragraph at the end of the Results section:

In summary, these results show that for reactions involving co-substrate cycling (1) measured fluxes are lower than those predicted by kinetics of the primary enzyme (i.e. enzyme involved in substrate conversion) alone, and (2) there is - for some reactions - a correlation between flux and co-substrate pool size. Both observations could indicate co-substrate pool sizes and/or co-substrate cycling dynamics being a main limiting factor for flux. We can not state this as a certainty, however, as there are possibly other factors acting as the extra limitation, including thermodynamic effects. These points call for further experimental analysis of co-substrate cycling within the study of metabolic system dynamics.

Reviewer 3 (Public Review):

In the study, the authors present a mathematical framework and data analysis approach that revisits an "old" idea in cell physiology: The role of co-substrate cycling as potential key determinant of reaction flux limits in enzyme-catalyzed reaction systems. The aim of the study is to identify metabolic network properties that indicate potential global flux regulatory capacities of co-substrate cycling.

The authors approached this aim in two steps. First, a mathematical framework, which is based on ODEs was developed and which reflects small abstract metabolic pathways including kinetic parameters of the involved reactions. While the modeled pathways are abstract, the considered pathway motifs are motivated by structures of known existing pathways such as glycolysis (as example of a linear pathway) and certain amino acid biosynthesis pathways (as example of branched pathways). The developed ODE-based models were used for steady state analysis and symbolic and numerical simulations of flux dynamics. As a main result of the first step, the authors highlight that co-substrate cycling can act as mechanism which limits specific metabolic fluxes across the metabolic network and that co-substrate cycling can facilitate flux regulation at branching points of the network. Second, the authors re-analyzed data on flux rates (experimental measurements and flux-balance-analysis predictions) from previous publications in order to assess whether the predicted role of co-substrate cycling could explain the observed flux distributions. In this data analysis, the author provide evidence that the fluxes of specific reactions in central metabolism could be constrained by co-substrate cycling, because their observed fluxes are often lower than expected by the kinetics of the corresponding enzymes.

A particular strength of the study is that the authors highlight that co-substrates are not limited to ATP and NAD(P)H, but could include a range of other metabolites and which could also be organism-specific. Building on this broad definition of cosubstrates, the authors developed an abstract mathematical framework that can be used to study the general potential 'design principle' of co-substrate cycling in cellular metabolism and to adapt the framework to study different co-substrates in specific organisms in future works.

Experimental data (i.e. measured fluxes using mass-spectrometry data and labeled substrates) that is available to date is limited and therefore also limits the broad evaluation of the developed mathematical framework across various different organisms and environmental conditions. However, with advances in metabolomics and derived metabolic flux measurements, the mathematical framework will serve as a valuable resource to understand the potential role of co-substrate cycling in more biological systems. The framework might also guide new experiments that generate data for a systematic evaluation of when and to what extent co-substrate cycling governs flux distributions, e.g. depending on growth rates or response to environmental stress.

We thank the reviewer for this accurate summary. We agree with the reviewer's final comments on limitations of current testing of our theory, due to limitations in existing data, and that this analysis will now motivate further experimental study of co-substrate dynamics. We have already included revisions of the manuscripts to further highlight and discuss limitations of the data-based analysis.

Author Response

Reviewer #1 (Public Review):

This study investigates the psychological and neurochemical mechanisms of pain relief. To this end, 30 healthy human volunteers participated in an experiment in which tonic heat pain was applied. Three different trial types were applied. In test trials, the volunteers played a wheel of fortune game in which wins and losses resulted in decreases and increases of the stimulation temperature, respectively. In control trials, the same stimuli were applied but the volunteers did not play the game so that stimulation decreases and increases were passively perceived. In neutral trials, no changes of stimulation temperature occurred. The experiment was performed in three conditions in which either a placebo, or a dopamineagonist or an opioid-antagonist was applied before stimulations. The results show that controllability, surprise, and novelty-seeking modulate the perception of pain relief. Moreover, these modulations are influenced by the dopaminergic but not the opioidergic manipulation.

Strengths

• The mechanisms of pain relief is a timely and relevant basic science topic with potential clinical implications.

• The experimental paradigm is innovative and well-designed.

• The analysis includes advanced assessments of reinforcement learning.

Weaknesses

• There is no direct evidence that the opioidergic manipulation has been effective. This weakens the negative findings in the opioid condition and should be directly demonstrated or at least critically discussed.

We agree that we cannot provide direct evidence on the effectiveness of the opioidergic manipulation in our study. However, previous literature strongly suggests that a dose of 50 mg naltrexone (p.o.) is effective in blocking 𝜇-opioid receptors in humans. Using positron emission tomography, Weerts et al. (2013) found a blockage of 𝜇-opioid receptors of more than 90% with 50 mg naltrexone (p.o.) although given repeatedly 4 days in a row. In addition, convincing effects on behavioral functions have been reported with comparable doses that support the efficacy of the opioidergic manipulation. For example, Chelnokova et al. (2014) found attenuating effects of 50 mg naltrexone (p.o.) on wanting as well as liking of social rewards, implicating the involvement of endogenous opioids in the processing of rewarding stimuli. The same dose was also found to attenuate reward directed effort exerted in a value-based decision-making task (Eikemo et al., 2017). Moreover, 50mg of naltrexone (p.o.) have been shown to reduce endogenous pain inhibition induced by conditioned pain modulation (King et al., 2013) and to reduce the perceived pleasantness of pain relief (Sirucek et al., 2021). Thus, based on the available literature we assume the effectiveness of our opioidergic manipulation. A corresponding reasoning including a note of caution on the of the lack of a direct manipulation check of the opioidergic manipulation can be found in the manuscript in the Discussion:

“The doses and methods used here are comparable to those used in other contexts which have identified opioidergic effects. Using positron emission tomography, Weerts et al. (2013) found a blockage of opioid receptors of more than 90% by 50 mg of naltrexone (p.o.) in humans given repeatedly over 4 days. In addition, effects on behavioral functions have been reported with comparable doses that support the efficacy of the opioidergic manipulation. Chelnokova et al. (2014) found attenuating effects of 50 mg naltrexone (p.o.) on wanting as well as liking of social rewards, implicating the involvement of endogenous opioids in the processing of rewarding stimuli. The same dose was also found to attenuate reward directed effort exerted in a value-based decision-making task (Eikemo et al., 2017). Moreover, 50 mg of naltrexone (p.o.) have been shown to reduce endogenous pain inhibition induced by conditioned pain modulation (King et al., 2013). Thus, based on the literature we assume that the opioidergic manipulation was effective in this study, although we do not have a direct manipulation check of this pharmacological manipulation. Despite its effectiveness in blocking endogenous opioid receptors, the effect of naltrexone on reward responses was found to be small (Rabiner et al., 2011). Hence, a lack of power may have limited our chances to find such effects in the present study.”

• The negative findings are exclusively based on the absence of positive findings using frequentist statistics. Bayesian statistics could strengthen the negative findings which are essential for the key message of the paper.

We agree with the reviewers that the power may not have been sufficient to detect potentially small effects of the pharmacological manipulations. The power calculation was based on the design and the medium effect size found in a previous study using a comparable experimental procedure for assessing pain-reward interactions (Becker et al., 2015). To acknowledge this weakness, we clarified in the manuscript the description of the a priori sample size calculation as follows:

“The power estimation was based on the design and the finding of a medium effect size in a previous study using a comparable version of the wheel of fortune game without pharmacological interventions (Becker et al., 2015). The a priori sample size calculation for an 80% chance to detect such an effect at a significance level of 𝛼=0.05 yielded a sample size of 28 participants (estimation performed using GPower (Faul et al., 2007 version 3.1) for a repeated-measures ANOVA with a three-level within-subject factor)."

Further, we did not aim to claim that endogenous opioids do not affect the perception of pain relief. Our phrasing in describing the results was in several instances too bold. The aim of the pharmacological manipulations was to investigate effects of dopamine and endogenous opioids on endogenous modulation of perceived intensity of pain relief. Here, we expected dopamine to enhance such endogenous modulation and naltrexone to reduce this modulation. The higher average pain modulation under naltrexone compared to placebo found in VAS ratings (naltrexone: -10.09, placebo: -7.31, see Table 1) suggests an increase in pain modulation by naltrexone compared to placebo, although this did not reach statistical significance, which is the opposite of what we had expected (see comment #11). Therefore, we concluded that we have no evidence to support our hypothesis of reduced endogenous modulation of pain relief by naltrexone. We do not want to claim that there are no effects of endogenous opioids on pain modulation. Although Bayesian statistics might be used to support such an interpretation, we think this might be misleading in our context here due to the considerations on the lack of power (which also affects null-hypothesis testing in Bayesian statistics) and the lack of a direct manipulation check mentioned above. Since we expected opposite effects of levodopa and naltrexone on pain modulation, we did not intend to compare these effects directly to avoid a distortion of the results. According to our hypotheses, we expected to see increased modulation of pain relief with enhanced dopamine availability and decreased modulation of pain relief with blocking of opioid receptors (see also comment #11). However, we had no a priori assumptions on potential differences in the absolute changes induced by the drug manipulations. Based on these considerations, we did now not include further direct comparisons of the effects of both drugs. Rather, we carefully went through the manuscript to tone down the descriptions and interpretations of our null findings and adjusted the respective section of the discussion to better reflect this interpretation.

• The effects were found in one (pain intensity ratings) but not the other (behaviorally assessed pain perception) outcome measure. This weakens the findings and should at least be critically discussed.

We thank the reviewers for highlighting this important aspect. We have considered the two outcome measures as indicative of two different aspects or dimensions of the pain experience, based also on previous results in the literature. Within our procedure, the ratings indicate the momentary perception of the stimulus intensity after phasic changes in nociceptive input (outcomes), while the behavioral measure indicates perceptual within-trial sensitization or habituation in response to the tonic stimulation within each trial. Supporting the assumption of such two different aspects, it has been shown before that pain intensity ratings and behavioral discrimination measures can dissociate (Hölzl et al., 2005). In line with the assumption that both outcome measures assess different aspects of the pain experience, a differential effect of controllability on these two outcome measures is conceivable. Similarly, Becker et al. (2015), using a very similar experimental paradigm, did only find endogenous pain facilitation in the losing condition of the wheel of fortune game in pain ratings but not in the behavioral outcome measure, while they found endogenous inhibition in both measures. Compared to Becker et al. (2015), we implemented here smaller changes in stimulation intensity as outcomes in the wheel of fortune game (-3°C vs -7°C for win trials, +1°C vs +5°C for lose trials), potentially resulting in the differential effects here. Nevertheless, we agree that this reasoning needs a more explicit discussion in the manuscript and we included the following sentences to the Discussion section:

“Although we did not assess the affective component of the relief experience, we implemented two outcome measures that are assumed to capture independent aspects of the pain experience: VAS ratings indicate perception of phasic changes (outcomes), while the behavioral measure indicates perceptual within-trial sensitization or habituation in response to the tonic stimulation within each trial. We found enhanced endogenous modulation by controllability and unpredictability in the VAS ratings, in line with the view that endogenous modulation enhances behaviorally relevant information. In contrast, the within-trial sensitization did not differ between the active and passive conditions under placebo. In contrast, in a previous study using a similar experimental paradigm Becker et al. (2015) found a reduction of within-trial sensitization after pain relief outcomes by controllability. Compared to this study, we implemented here smaller changes in stimulation intensity as outcomes in the wheel of fortune (-3 °C vs -7 °C for pain relief), potentially explaining the differential results.“

• The instructions given to the participants should be specified. Moreover, it is essential to demonstrate that the instructions do not yield differences in other factors than controllability (e.g., arousal, distraction) between test and control trials. Otherwise, the main interpretation of a controllability effect is substantially weakened.

Thanks for pointing out that specific information on instructions given to the participants was missing. We agree that factors other than controllability would confound the interpretation of differences between test and control trials. We aimed minimizing nonspecific effects of arousal and/or distraction while still giving all needed information with our instructions (see below). In addition, control and test trials were kept as similar as possible. In order to check for unspecific effects of arousal and/or distraction, we also included lose trials in the game as an additional control condition. For clarifying participants’ instructions, we added the following paragraph to the Materials and methods section: “The participants were instructed that there were two types of trials: trials in which they could choose a color to bet on the outcome of the wheel of fortune and trials in which they had no choice. Specifically, they were told that in the first type of trials they could use the left and right mouse button, respectively, to choose between the pink and blue section of the wheel of fortune. Participants were further instructed that if the wheel lands on the color they had chosen they will win, i.e. that the stimulation temperature will decrease, while if the wheel lands on the other color, they will lose, i.e. that the stimulation temperature will increase. For the second type of trials, participants were instructed that they could not choose a color, but were to press a black button, and that after the wheel stopped spinning the temperature would by chance either increase, decrease, or remain constant.”

In general, both arousal and distraction can be assumed to affect pain perception. If the active condition in the wheel of fortune resulted in higher arousal and/or distraction this should result in comparable effects on intensity ratings in both the win and lose outcomes compared to the passive condition. In contrast, controllability is expected to have opposite effects on pain perception in win and lose trials (decreased pain perception after winning and increased pain perception after losing in the active compared to the passive condition). These opposite effects of controllability are tested by the interaction ‘outcome × trial type’ when fitting separate models for each drug condition, which should be zero if unspecific effects of arousal and/or distraction predominated. Instead, we found a significant interaction in these models, confirming opposing effects of controllability in win and lose outcomes and contradicting such unspecific effects. We added this reasoning, marked in red here, to the Results section to better highlight this line of reasoning, as follows:

“To test whether playing the wheel of fortune induced endogenous pain inhibition by gaining pain relief during active (controllable) decision-making, a test condition in which participants actively engaged in the game and ‘won’ relief of a tonic thermal pain stimulus in the game was compared to a control condition with passive receipt of the same outcomes (Figure 1). As a further comparator the game included an opposite (‘lose’) condition in which participants received increases of the thermal stimulation as punishment. This active loss condition was also matched by a passive condition involving receipt of the same course of nociceptive input. Comparing the effects of active versus passive trials between the pain relief and the pain increase condition (interaction ‘outcome × trial type’) allowed us to test for unspecific effects such as arousal and/or distraction. If effects seen in the active compared to the passive condition were due to such unspecific effects, then actively engaging in the game should affect comparably pain in both win and lose trials. In contrast, if the effects were due to increased controllability, pain inhibition should occur in win trials and pain facilitation in lose trials.”

• The blinding assessment does not rule out that the volunteers perceived the difference between placebo on the one hand and levodopa/naltrexone on the other hand. It is essential to directly show that the participants were not aware of this difference.

We based our assessment of blinding on the fact that for none of the drug conditions the frequency of guessing correctly which drug was ingested was above chance (see Results section, page 8, lines 201ff). In addition, the frequency of side effects reported by the participants did not differ between the three drug conditions, supporting this notion indirectly. However, we agree with the reviewer that this does not rule out completely that participants may have perceived a difference between the placebo and the levodopa/naltrexone conditions. We ran additional analyses to test whether participants were more likely to answer correctly that they had ingested an active drug and whether they were more likely to report side effects in the active drug conditions compared to the placebo condition. In 7 out of 28 placebo sessions (25%) the participants assumed incorrectly to have ingested one of the active drugs. In 12 out of 43 drug sessions (21.8%) the participants assumed correctly that they had ingested one of the active drugs. These frequencies did not differ between placebo sessions on the one hand and the levodopa and naltrexone active drug sessions on the other hand (𝜒)(1) = 0.11, p = 0.737). In 9 out of 28 placebo sessions (32.1%) and in 23 out of 55 drug sessions (41.8%) participants reported to be tired at the end of the session. The frequency of reporting tiredness did not significantly differ between placebo sessions on the one hand and drug sessions on the other hand (𝜒)(1) = 1.06, p = 0.304). No other side effects were reported. We added the following information, marked in red here, to the Results section:

“In 32 out of 83 experimental sessions subjects reported tiredness at the end of the session. However, the frequency did not significantly differ between the three drug conditions (𝜒)(2) = 2.17, p = 0.337) or between the placebo condition compared to the levodopa and naltrexone condition (𝜒)(1) = 1.06, p = 0.304). No other side effects were reported. To ensure that participants were kept blinded throughout the testing, they were asked to report at the end of each testing session whether they thought they received levodopa, naltrexone, placebo, or did not know. In 43 out of 83 sessions that were included in the analysis (52%), participants reported that they did not know which drug they received. In 12 out of 28 sessions (43%), participants were correct in assuming that they had ingested the placebo, in 6 out of 27 sessions (22%) levodopa, and in 2 out of 28 sessions (7%) naltrexone. The amount of correct assumptions differed between the drug conditions (𝜒)(2) = 7.70, p = 0.021). However, posthoc tests revealed that neither in the levodopa nor in the naltrexone condition participants guessed the correct pharmacological manipulation significantly above chance level (p’s > 0.997) and the amount of correct assumptions did not differ significantly between placebo compared to levodopa and naltrexone sessions (𝜒)(1) = 0.11, p = 0.737), suggesting that the blinding was successful.”

• The effects of novelty seeking have been assessed in the placebo and the levodopa but not in the naltrexone conditions. This should be explained. Assessing novelty seeking effects also in the naltrexone condition might represent a helpful control condition supporting the specificity of the effects in the naltrexone condition.

We thank the reviewer for this interesting suggestion. Indeed, we did not report the association of pain modulation with novelty seeking in the naltrexone condition, because we did not have an a-priori hypothesis for this relationship. We now included correlations for all three drug conditions, testing if higher novelty seeking was associated with greater perceptual modulation in the active vs. passive condition. In line with comment 3, we applied a correction for multiple comparisons here (Bonferroni-Holm correction). This correction caused the correlation in the placebo condition to be no longer significant with an adjusted p-value of 0.073 (r = -0.412), while the correlation stays significant in the levodopa condition (r = -0.551, p = 0.013). Because of a reasonable effect size of the correlation under placebo (i.e. r = -0.412), we still report this correlation to highlight the increase under levodopa, while emphasizing that this correlation not significant We carefully toned down the interpretation of this correlation to reflected the change in significance with the correction for multiple testing.

We added the following information, marked in red here, in the Results section:

“Previous data suggest that endogenous pain inhibition induced by actively winning pain relief is associated with a novelty seeking personality trait: greater individual novelty seeking is associated with greater relief perception (pain inhibition) induced by winning pain relief (Becker et al., 2015). Similar to these results, we found here that endogenous pain modulation, assessed using self-reported pain intensity, induced by winning was associated with participants’ scores on novelty seeking in the NISS questionnaire (Need Inventory of Sensation Seeking; Roth & Hammelstein, 2012; subscale ‘need for stimulation’ (NS)), although this correlation failed to reach statistical significance after correction for multiple comparisons using Bonferroni-Holm method (r = -0.412, p = 0.073). A significant association between novelty seeking and endogenous pain modulation was found in the levodopa condition (r = 0.551, p = 0.013). More importantly, the higher a participants’ novelty seeking score in the NISS questionnaire, the greater the levodopa-related endogenous pain modulation when winning compared to placebo (NISS NS: r = -0.483, p = 0.034 Figure 7). In contrast, higher novelty seeking scores were not correlated with stronger pain modulation induced by winning in the naltrexone condition (r = 0.153, p = 0.381) and the naltrexone induced change in pain modulation showed no significant association with novelty seeking (r = 0.239, p = 0.499). Pain modulation after losing was not associated with novelty seeking in placebo (r = 0.083, p = 0.866), levodopa (r = -0.164, p = 0.783), or naltrexone (r = 0.405, p = 0.133).

No significant correlations with NISS novelty seeking score were found for behaviorally assessed pain modulation in the placebo, levodopa and naltrexone conditions during pain relief or pain increase (|r|’s < 0.35, p’s > 0.238). Similarly, the difference in pain modulation during pain relief or pain increase between the levodopa and the placebo condition and between the naltrexone and the placebo condition did also not correlate with novelty seeking (|r|’s < 0.22, p’s > 0.576).” <br /> We also edited the interpretation of the correlation in the Discussion:

“Overall, all three predictions were largely borne out by the data: relief perception as measured by VAS ratings was enhanced by controllability, unpredictability and showed a medium sized - although not significant - association with the individual novelty-seeking tendency,”

• The writing of the manuscript is sometimes difficult to follow and should be simplified for a general readership. Sections on the information-processing account of endogenous modulation in the introduction (lines 78-93), unpredictability and endogenous pain modulation in the results (lines 278-331) are quite extensive and add comparatively little to the main findings. These sections might be shortened and simplified substantially. Moreover, providing a clearer structure for the discussion by adding subheadings might be helpful.

We have reworked the manuscript to make it easier to follow. Specifically, we reworked the Introduction section to simplify it and to make it more concise. Further, we also shortened the extensive descriptions of modeling procedures that are not central for understanding the main findings. We think that these additions make it easier to follow the manuscript and our line of arguments, and to understand the applied analysis strategies.

• Effect sizes are generally small. This should be acknowledged and critically discussed. Moreover, effect sizes are given in the figures but not in the text. They should be included to the text or at least explicitly referred to in the text.

We agree that the effect sizes we report appear generally small. Importantly, the effect sizes were calculated by dividing differences in marginal means by the pooled standard deviation of the residuals and the random effects to obtain an estimate of the effect size of the underlying population rather than only for our sample. This procedure was used for the purpose of achieving more generalizable estimates. Due to considerable variance between subjects in our sample, this procedure resulted in comparatively small effect sizes. Nevertheless, we think this calculation of effects sizes results in more informative values because they can be viewed as estimates of population effects. We added specific information on the calculation of the effect sizes and a brief explanation that this procedure results in comparatively small effect sizes estimates to the Materials and methods and to the Results section (see below). In addition, we included standardized effect sizes whenever we report the respective post-hoc comparisons in the Results section.

“Effects sizes were calculated by dividing the difference in marginal means by the pooled standard deviation of the random effects and the residuals providing an estimate for the underlying population (Hedges, 2007).” (Materials and methods section)

“We used post-hoc comparisons to test direction and significance of differences in either outcome condition and report standardized effect sizes for these differences. Note that all reported effect sizes account for random variation within the sample, providing an estimate for the underlying population; due to considerable variance between participants in the present study, this results in comparatively small effect sizes.” (Results section)

• The directions of dopamine and opioid effects on pain relief should be discussed.

We amended our explanation of the hypothesis on the expected drug effects. As outlined there, we indeed expected opposite effects of levodopa and naltrexone on endogenous pain modulation in the active vs. the passive condition of the wheel of fortune.

Reviewer #2 (Public Review):

This study used the tonic heat stimulation combined with the probabilistic relief-seeking paradigm (which is a wheel of fortune gambling task) to manipulate the level of controllability and predictability of pain on 30 healthy participants. The authors focused on the influence of controllability and unpredictability on pain relief using pain reports and computational models and examined the involvement of dopamine and opioids in those effects. For that, the authors conducted the three-day experiments, which involved placebo, levodopa (dopamine precursor), and naltrexone (opioid receptor antagonist) administration on separate days. Lastly, the authors examined the relationship between dopamine-induced pain relief and novelty-seeking traits.

This is a strong and well-performed study on an important topic. The paper is well-written. I really enjoyed reading the introduction and discussion and learned a lot. Below, I have a few minor comments.

First, given that the Results section comes before the Methods section, it would be helpful to include some method and experimental design-related information crucial for the understanding of the results in the Results section. For example, how long was the thermal stimulus? What was the baseline temperature? etc. Maybe this information can be included in the caption of Figure 1.

We thank the reviewer for this helpful suggestion. We agree that due to the order of the manuscript sections, more information on experimental design and the statistical analysis strategies should be included in the results section. Accordingly, we included more detailed information on the analysis strategies in the Results section (please see responses to comments #5 & #9). In addition, we added more detailed information on the experimental design and information such as the duration of the stimuli and the baseline temperature, marked in red below, to the caption of Figure 1 (Results section).

“Figure 1: Time line of one trial with active decision-making (test trials) of the wheel of fortune game. Experimental pain was implemented using contact heat stimulation on capsaicin sensitized skin on the forearm. In each trial, the temperature increased from a baseline of 30 °C to a predetermined stimulation intensity perceived as moderately painful. In each testing session, one of the two colors (pink and blue) of the wheel was associated with a higher chance to win pain relief (counterbalanced across subjects and drug conditions). Pain relief (win) as outcome of the wheel of fortune game (depicted in green) and pain increase (loss; depicted in red) were implemented as phasic changes in stimulation intensity offsetting from the tonic painful stimulation. Based on a probabilistic reward schedule for theses outcomes, participants could learn which color was associated with a better chance to win pain relief. In passive control trials and neutral trials participants did not play the game, but had to press a black button after which the wheel started spinning and landed on a random position with no pointer on the wheel. Trials with active decision-making were matched by passive control trials without decision making but the same nociceptive input (control trials), resulting in the same number of pain increase and pain decrease trials as in the active condition. In neutral trials the temperature did not change during the outcome interval of the wheel. Two outcome measures were implemented in all trial types: i) after the phasic changes during the outcome phase participants rated the perceived momentary intensity of the stimulation on a visual analogue scale (‘VAS intensity’); ii) after this rating, participants had to adjust the temperature to match the sensation they had memorized at the beginning of the trial, i.e. the initial perception of the tonic stimulation intensity (‘self-adjustment of temperature’). This perceptual discrimination task served as a behavioral assessment of pain sensitization and habituation across the course of one trial. One trial lasted approximately 30 s, phasic offsets occurred after approximately 10 s of tonic pain stimulation. Adapted from Becker et al. (2015).”

Second, it would be helpful if the authors could provide their prior hypotheses on the drug effects. It could be a little bit confusing that the goal of using these drugs given that levodopa is a precursor of dopamine, whereas naltrexone is the opioid antagonist, i.e., the effects on the target neurotransmitters seem the opposite. Then, I wondered if the authors expected to see the opposite effects, e.g., levodopa enhances pain relief, while naltrexone inhibits pain relief, or to see similar effects, e.g., both enhance pain relief. Clarifying which direction of expected effects would be helpful for novice readers.

We thank the reviewer for pointing out that information on the expected drug effects should be explained in more detail. Indeed, we expected opposite effects of levodopa and naltrexone with respect to the effect of controllability on pain relief. Levodopa, as a precursor of dopamine, enhances dopamine availability and thus, phasic release of dopamine in response to events, for example, the reception of reward. Accordingly, we hypothesized that endogenous modulation by relief outcomes are increased in the active (reward) compared to the passive condition. In contrast, naltrexone blocks opioid receptors and as such it has been reported that naltrexone blocks placebo analgesia as a type of endogenous pain inhibition. Correspondingly, we hypothesized that naltrexone decreases endogenous pain modulation induced by actively winning pain relief compared to the passive condition. We expanded the explanation of these hypotheses in the Introduction section as follows:

“We expected increased dopamine availability to enhance phasic release of dopamine in response to rewards, and hence, to increase the effect of active compared to passive reception of pain relief. In contrast, we expected the inhibition of endogenous opioid signaling to decrease the effect of active controllability on pain relief. The latter is based on the observation that blocking of opioid receptors attenuates other types of endogenous pain inhibition such as placebo analgesia (Benedetti, 1996; Eippert et al., 2009) or conditioned pain modulation (King et al., 2013). “

Third, on the "Behaviorally assessed pain perception" results in Figs. 2D-F, I wonder why the results for the "pain increase" were still positive. Were the y values on the plots the temperature that participants adjusted (i.e., against the temperature right before the temperature adjustment)? or are the values showing the differences from the baseline (i.e., against the baseline temperature)?

The behavioral measure was calculated as the difference in temperatures between the memorization interval at the beginning of the trial (i.e. the predetermined temperature perceived as moderately painful) minus the self-adjusted temperature at the end of the trial so that positive values indicate sensitization (i.e. an increase in sensitivity) and negative values indicate habituation (i.e. a decrease in sensitivity) across the stimulation within on trial (i.e. approx. 30 seconds of stimulation). In general, for a stimulation of approximately 30 seconds with intensities perceived as painful, perceptual sensitization is expected to occur (Kleinböhl et al., 1999).

The outcome of the wheel of fortune game, i.e. the phasic decrease (winning) or increase (losing) in stimulation intensity, should indeed have opposite effects on this sensitization. A decrease in nociceptive input negatively reinforces pain perception, as seen in stronger sensitization in win trials, while an increase in nociceptive input punishes pain perception, as seen in reduced perceptual sensitization in lose trials. Using the a very similar task, Becker et al. (2015) found values indicating habituation within trials with temperature increases in lose outcomes. However, in this previous study, increases of +5°C were used for lose outcomes (as compared to +1 °C in the present study). Thus, in the present study the comparatively small increase in absolute stimulation temperature may not have been sufficient to induce within trial habituation to the tonic heat pain stimulation.

Nevertheless, independent of the effect of the outcome (increase or decrease of the stimulation intensity) our focus was on the additional effect that controllability (active vs. passive condition) had on the perception of the underlying tonic stimulation within each outcome condition (i.e. on the same nociceptive input). Here we expected to see endogenous inhibition after winning and endogenous facilitation after losing in the active compared to the passive condition.

We added more detailed information on the calculation of the behavioral measure and the expected perceptual modulation within each trial due to the stimulus duration in the Methods section as well as in the Results section.

Methods section:

“After this rating, participants had to adjust the stimulation temperature themselves to match the temperature they had memorized at the beginning of the trial. This self-adjustment operationalizes a behavioral assessment of perceptual sensitization and habituation within one trial (Becker et al., 2011, 2015; Kleinböhl et al., 1999). Participants adjusted the temperature using the left and right button of the mouse to increase and decrease the stimulation temperature. The behavioral measure was calculated as the difference in temperatures in the memorization interval at the beginning of each trial minus this selfadjusted temperature at the end of each trial. Positive values, i.e. self-adjusted temperatures lower than the stimulation intensity at the beginning of the trial, indicate perceptual sensitization, while negative values indicate habituation.” Results section:

“Positive values (i.e. lower self-adjusted temperatures compared to the stimulation intensity at the beginning of the trial) indicate perceptual sensitization across the course of one trial of the game, negative values indicate habituation. For tonic stimulation at intensities that are perceived as painful, perceptual sensitization is expected to occur (Kleinböhl et al., 1999). Differences between the outcome conditions (win, lose) reflect the effect of the phasic changes on the perception of the underlying tonic stimulus. Differences between active and passive trials reflect the effect of controllability on this perceptual sensitization within each outcome condition.”

Lastly, I wonder if it is feasible or not, but examining the effects of dopamine antagonists will be helpful for obtaining a more definitive answer to the role of dopamine in information-related pain relief. This could be a good suggestion for future studies.

We thank the reviewer for this suggestion. We agree that antagonistic manipulation of the dopaminergic system could provide further insights and confirm the role of dopamine in shaping pain related perception and behavior. Moreover, we think that bidirectional manipulations of opioidergic signaling could also provide valuable insights and should be used for future research. We added the following sentences to the Discussion section:

“Because the mechanisms underlying learning from pain and pain relief and their recursive influence on pain perception may contribute to the development and maintenance of chronic pain, it is crucial to better understand the roles of dopamine and endogenous opioids in these mechanisms. Accordingly, bidirectional manipulations of both transmitter systems should be used in future studies to better characterize their respective roles in shaping behavior and perception.“

Author Response

Joint Public review:

1) Line 215: The authors state that pairing TCRseq with RNAseq reflects the magnitude of TCR signaling. This is absolutely not the case. TCR sequencing does not reflect TCR signaling strength.

Thanks for the comments and we apologize for the usage of this misleading description. Actually in this part, we were trying to quantitatively assess the activation states of CD8 T cells based on the average expression of previously described activation-related gene signatures1 (also shown in Supplementary file 3). Therefore, TCRseq data was not involved in this analysis and the magnitude of TCR signaling could neither be reflected. We apologize again for this mistake and have corrected the corresponding texts and figures as follows (line 210-217): "Meanwhile, the activation states of CD8 T cell subpopulations were quantitatively assessed based on the average expression of previously described activation-related gene signatures1 (also shown in Supplementary file 3). Our results showed that the T-Tex cluster was the most activated, followed by the two P-Tex clusters (Fig. 2b left). In addition, CD8 T cells in tumor tissues were more activated than those in adjacent normal tissues (Fig. 2b, right top). And no significant difference in T cell activation states was observed between HPV-positive and HPV-negative samples (Fig. 2b right bottom)."

2) A lot of discussion around "activation" is presented, but there is no evidence to support which genes or gene programs are associated with "activation".

Thanks for the comments. The activation states of CD8 T cell subpopulations were quantitatively assessed based on the average expression of previously described activation-related gene signatures1 (also shown in Supplementary file 3). More specifically, activation-related gene signatures are as follows: "CD69, CCR7, CD27, BTLA, CD40LG, IL2RA, CD3E, CD47, EOMES, GNLY, GZMA, GZMB, PRF1, IFNG, CD8A, CD8B, CD95L, LAMP1, LAG3, CTLA4, HLA-DRA, TNFRSF4, ICOS, TNFRSF9, TNFRSF18".

3) Line 249: It is unclear why the authors are indicating that TCRseq was used in pseudotime analysis. This type of analysis does not take TCRs into account but rather looks at the proportion of spliced mRNA of individual genes from the DGE data.

Thanks for the comments and we apologize for the usage of this misleading description. As acknowledged by the reviewer, pseudotime analysis has nothing to do with TCRseq data. Actually in this part, we separately performed clonality analysis of CD8 T cells based on TCRseq data and pseudotime analysis based on RNAseq data. Shared TCRs were identified among certain cell subclusters, which could partially validate the potential lineage relationships simulated by pseudotime analysis. Therefore, we have corrected the texts as follows to avoid the misunderstanding that TCRseq was used in pseudotime analysis: "Given the clonal accumulation of CD8 T cells was a result of local T cell proliferation and activation in the tumor environment2, we further conducted clonality analysis of CD8 T cells based on TCRseq data. " (line 246-248) and "To further investigate their lineage relationships, we performed pseudotime analysis for CD3+ T cells on the basis of transcriptional similarities (Fig. 3j-l, Figure 3-figure supplementary 2d)." (line 277-279).

Author Response

Reviewer #1 (Public Review):

The authors develop and freely disseminate the THINGS-data collection, a large-scale dataset incorporating MRI, MEG, eye-tracking, and 4.7 million similarity ratings for 1,854 object concepts. Demonstrating the reliability of their data, the authors replicate nearly a dozen previous neuroimaging papers. This "big data" approach significantly advances our ability to link behavioral measures with neuroimaging at scale, with the potential to spark future insights into how the mind represents objects.

I thought that the article was well-written, with a sound methodological approach, high-quality results, and well-supported conclusions. I am overall enthusiastic about this work, and I think THINGS will provide an important benchmark for future big data approaches in cognitive and computational neuroscience.

However, I thought it was also important to articulate more directly the potential insights this dataset can offer to the field. Although the authors mentioned that they "provided five examples for potential research directions", it was not clear to me what these new research directions were, given that the authors entirely describe replications in the results.

We thank Reviewer 1 for their positive evaluation and the enthusiasm for our work! We have revised the manuscript to articulate more clearly and directly some potential research directions for the dataset. There are two aspects to consider: What sets these datasets apart from traditional small-scale research? And what sets them apart from other large-scale research? We elaborate on these two aspects in response to specific comments below.

Reviewer #2 (Public Review):

Hebart et al., present a large-scale multi-model dataset consisting of fMRI, EEG, and behavioral similarity measures towards the study of object representation in the mind and brain. The effort is immense, the methods are rigorous, and the data are of reasonable quality, the demonstrative analyses are extensive and provocative. (One small note regarding one leg of this multi-modal dataset is that the fMRI design consisted of a single image presentation for 0.5s without repetitions for most of the images; this design choice has particular analysis implications, e.g. the dataset will have more power when leveraging a priori grouping of images. However, unlike other datasets of this kind, here the number of images and how they were selected does support this analysis mode, e.g. multiple exemplars per object concept, and rich accompanying meta-data and behavioral data.)

The manuscript is well-written, and the THINGs website that lets you explore the datasets is easy to navigate, delivering on the promise of making this an integrated, expanding worldwide initiative. Further, the datasets have clear complementary strengths to recent other large-scale datasets, in terms of the ways that the images were sampled (not to mention being multi-modal)-thus I suspect that the THINGs dataset will be heavily used by the cognitive/computational/neuroscience research community going forward.

We would like to thank the reviewer for their positive evaluation of our work! We agree that the dataset has more power when leveraging a priori grouping of images, which is specifically the design choice we made here. We also agree that we can better highlight the strength of our dataset with respect to existing datasets regarding multiple exemplars per object concept and the semantic breadth of the included object categories.

Reviewer #3 (Public Review):

This manuscript presents a highly valuable dataset with multimodal functional human brain imaging data (fMRI and MEG) as well as behavioural annotations of the stimuli used (thousands of images from the THINGS collection, systematically covering multiple types of concrete nameable objects).

The manuscript presents details about the dataset, quality control measures, and a careful description of preprocessing choices. The tools and approaches that were used follow the state of the art of the field in human functional brain imaging and I praise the authors for being transparent in their methodological approaches by also sharing their code along with the data. The manuscript also presents a few analyses with the data: 1) multi-dimensional embedding of perceived similarity judgments 2) decoding of neural representations of objects both with fMRI and MEG 3) A replication of findings related to visual size and animacy of objects 4) representation similarity analysis between functional brain data and behavioural ratings 5) MEG-fMRI fusion.

We thank the reviewer for their overall positive assessment of our work!

Author Response

Reviewer #2 (Public Review):

In this manuscript, Polyák et al. report detailed and systematic functional, electrocardiographic, electrophysiologic (both in vivo and in vitro experiments) and histological analysis in a large animal (canine) model of exercise to assess risk of ventricular arrhythmia susceptibility. They find that exercise-trained dogs have a slower heart rate (not accounted by heightened vagal tone alone and consistent with recent work from Denmark), an increased ventricular mass and fibrosis, APD lengthening due to repolarisation abnormality, enhanced HCN4 expression and decreased outward potassium channel density together with increased ventricular ectopic beats and ventricular fibrillation susceptibility (open-chest burst pacing). The authors suggest these changes as underlying the risk of VA in athletes, and appropriately caution against consigning the beneficial effects of exercise. In general, this study is well done, reasonably well-written, with reasonable conclusions, supported by the data presented and is much needed. There are some methodological, however, given the paucity of experimental data in this area, I think it would still be additive to the literature.

Strengths:

1. This is an area with very limited experimental data- this is an area of need.

2. The study, in general seems to be well-conducted with two clear groups

3. The use of a large animal model is appropriate

4. The study findings, in general, support the authors conclusions

5. The authors have shown some restraint in their conclusions and the limitations section is detailed and well written.

Weaknesses:

1. There are some methodological issues:

a. Authors should explain what the conditioning protocol was and why it was necessary.

In order to cause as little discomfort as possible to the animals, we selected animals that were naturally cooperative with the researchers and not afraid of the noise of the treadmill. This selection period lasted about three weeks, during which the animals were not exercised in a formal setting, but familiarized with the experimental setting and walked on the treadmills for a few minutes. During the conditioning period, both control and trained animals were equally handled.

Following your remarks the corresponding part of the text was extended properly explaining the training protocol in more detail.  

b. The rationale for the exercise parameters chosen needs to be presented.

Experimental data on large animal models are very limited. Sled dogs are considered the highest elite of dog exercise. The distances they run are taken as a reference, although this protocol is not exactly the same due to the conditions of training, sledding, and weather. The most widely known races are the Norwegian Finnmarksløp and the Alaskan Iditarod, take place on snow and cover distances ranging from 500–1569 km in a continuous competition lasting for up to 14 days to be completed. (Calogiuri & Weydahl, 2017)

Based on these data, preliminary experiments were conducted to determine the maximum running time and intensity that dogs can sustain without distress, injuries, or severe fatigue. We increased the intensity of exercise in line with the animals' performance. The detailed training protocol and the daily running distances applied are presented in Table 1. Now, a new figure, Figure 1, and a new table, Table 1, illustrate a detailed experimental timeline in the revised manuscript.

Reference:

Calogiuri, G., & Weydahl, A. (2017). Health challenges in long-distance dog sled racing: A systematic review of literature. Int J Circumpolar Health, 76(1), 1396147. https://doi.org/10.1080/22423982.2017.1396147

c. Open chest VF induction was a limitation, and it was unnecessary.

d. A more refined VT/VF induction protocol was required. This is a major limitation to this work.

C, D: Thank you for the reviewer’s comment. For a detailed explanation of the VF induction procedures, please see our responses to question 11 of Reviewer #2.

e. The concept of RV dysfunction has not been considered in the study and its analysis.

Thank you for the suggestion. The complexity of our study and the capacity of our laboratory limited the work that could be carried out, but we are planning to perform additional studies involving the RV.

f. The lack of a quantitative measure for fibrosis is a limitation.

At the Department of Pathology, there was no opportunity to analyze myocardial fibrosis quantitatively. As described by Mustroph et al., quantitative analysis of fibrosis can be based on appropriate software measuring the amount of fibrotic area per total area on digitized slides. Such software was not available during the evaluation. This is a limitation of the study; however, the semi-quantitative assessment in histology reports is widely accepted in human pathology (Mustroph et al., 2021).

Reference:

Mustroph, J., Hupf, J., Baier, M. J., Evert, K., Brochhausen, C., Broeker, K., Meindl, C., Seither, B., Jungbauer, C., Evert, M., Maier, L. S., & Wagner, S. (2021). Cardiac Fibrosis Is a Risk Factor for Severe COVID-19. Front Immunol, 12, 740260. https://doi.org/10.3389/fimmu.2021.740260

1. Statistical analysis requires further detail (checking of normality of the data/appropriate statistical test).

Thank you for this comment. This question has been answered in response to question 12 of Reviewer #2 and the statistical part of the methodology in the manuscript has been updated.

1. The use of Volders et al. study as a corollary in the discussion does not seem justified given that this study used AV block induced changes as an acquired TdP model.

We agree with the reviewer that the two models involve completely different mechanisms. Therefore, in order to avoid misunderstandings, we have deleted the part of the discussion that made the comparison with the study by Volders et al.(Volders et al., 1998; Volders et al., 1999) Nevertheless, the exercise-induced compensatory adaptive mechanisms of the athlete's heart have been considered as a phenomenon completely distinct from pathological conditions, yet the electrical remodeling observed in our model indicates important similarities with the experimental model of long-term complete AV block. For example, both resulted in profound bradycardia, compensated cardiac hypertrophy, prolonged QTc interval, APD prolongation, and increased spatial and temporal dispersion of repolarization. These changes were attributed to the downregulation of potassium currents and were associated with increased ventricular arrhythmia susceptibility. Therefore, we hypothesized that the mechanisms of increased propensity for ventricular fibrillation in this model may have a similar electrophysiological background to the compensated hypertrophy studies of Volders et al. However, the autonomic changes, the potential impairment of the conduction system of the athlete’s heart, and the electrophysiological background require further, more detailed investigations.

References:

Volders, P. G., Sipido, K. R., Vos, M. A., Kulcsar, A., Verduyn, S. C., & Wellens, H. J. (1998). Cellular basis of biventricular hypertrophy and arrhythmogenesis in dogs with chronic complete atrioventricular block and acquired torsade de pointes. Circulation, 98(11), 1136-1147. https://doi.org/10.1161/01.cir.98.11.1136

Volders, P. G., Sipido, K. R., Vos, M. A., Spatjens, R. L., Leunissen, J. D., Carmeliet, E., & Wellens, H. J. (1999). Downregulation of delayed rectifier K(+) currents in dogs with chronic complete atrioventricular block and acquired torsades de pointes. Circulation, 100(24), 2455-2461. https://doi.org/10.1161/01.cir.100.24.2455

Author Response

Reviewer #1 (Public Review):

This article is aimed at constructing a recurrent network model of the population dynamics observed in the monkey primary motor cortex before and during reaching. The authors approach the problem from a representational viewpoint, by (i) focusing on a simple center-out reaching task where each reach is predominantly characterised by its direction, and (ii) using the machinery of continuous attractor models to construct network dynamics capable of holding stable representations of that angle. Importantly, M1 activity in this task exhibits a number of peculiarities that have pushed the authors to develop important methodological innovations which, to me, give the paper most of its appeal. In particular, M1 neurons have dramatically different tuning to reach direction in the movement preparation and execution epochs, and that fact motivated the introduction of a continuous attractor model incorporating (i) two distinct maps of direction selectivity and (ii) distinct degrees of participation of each neuron in each map. I anticipate that such models will become highly relevant as neuroscientists increasingly appreciate the highly heterogeneous, and stable-yet-non-stationary nature of neural representations in the sensory and cognitive domains.

As far as modelling M1 is concerned, however, the paper could be considerably strengthened by a more thorough comparison between the proposed attractor model and the (few) other existing models of M1 (even if these comparisons are not favourable they will be informative nonetheless). For example, the model of Kao et al (2021) seems to capture all that the present model captures (orthogonality between preparatory and movement-related subspaces, rotational dynamics, tuned thalamic inputs mostly during preparation) but also does well at matching the temporal structure of single-neuron and population responses (shown e.g. through canonical correlation analysis). In particular, it is not clear to me how the symmetric structure of connectivity within each map would enable the production of temporally rich responses as observed in M1. If it doesn't, the model remains interesting, as feedforward connectivity between more than two maps (reflecting the encoding of many more kinematic variables) or other mechanisms (such as proprioceptive feedback) could well explain away the observed temporal complexity of neural responses. Investigating such alternative explanations would of course be beyond the scope of this paper, but it is arguably important for the readers to know where the model stands in the current literature.

Below is a summary of my view on the main strengths and weaknesses of the paper:

1) From a theoretical perspective, this is a great paper that makes an interesting use of the multi-map attractor model of Romani & Tsodyks (2010), motivated by the change in angular tuning configuration from the preparatory epoch to the movement execution epoch. Continuous attractor models of angular tuning are often criticised for being implausibly homogeneous/symmetrical; here, the authors address this limitation by incorporating an extra dimension to each map, namely the degree of participation of each neuron (the distribution of which is directly extracted from data). This extension of the classical ring model seems long overdue! Another nice thing is the direct use of data for constraining the model's coupling parameters; specifically, the authors adjust the model's parameters in such a way as to match the temporal evolution of a number of "order parameters" that are explicitly manifested (i.e. observable) in the population recordings.

I believe the main weakness of this continuous attractor approach is that it - perhaps unduly binarises the configuration of angular tuning. Specifically, it assumes that while angular tuning switches at movement onset, it is otherwise constant within each epoch (preparation and execution). I commend the authors for carefully motivating this in Figure 2 (2e in particular), by showing that the circular variance of the distribution of preferred directions is higher across prep & move than within either prep or move. While this justifies a binary "two-map model" to first order, the analysis nevertheless shows that preferred directions do change, especially within the preparatory epoch. Perhaps the authors could do some bootstrapping to assess whether the observed dispersion of PDs within sub-periods of the delay epoch is within the noise floor imposed by the finite number of trials used to estimate tuning curves. If it is, then this considerably strengthens the model; otherwise, the authors should say that the binarisation reflects an approximation made for analytical tractability, and discuss any important implications.

We thank the reviewer for the suggested analysis. We have included this new analysis in Fig. S1.

First of all, in Fig 2e of the previous version of the manuscript, we were considering three time windows during preparation and two time windows during movement execution. We are now using a shorter time window of 160ms, so that we can fit three time windows within either epoch. The results do not change qualitatively, and the results of the bootstrap analysis below do not change based on the definition of this time window.

The bootstrap analysis is described in detail in the second paragraph of the Methods sections (“Preparatory and movement-related epochs of motion”). The bootstrap distribution is generated by resampling trials with repetitions (and keeping the number of trials per condition the same as in the data), while shuffling the temporal windows in time, within epochs. For example: for condition 1, we have 43 trials in the data. In one trial of the bootstrap distribution for condition 1, each one of the 3 time windows of the delay period is chosen at random (with repetitions) between the possible 43*3 windows from the data. The analysis shows that the median variance of preferred directions from the data is significantly larger than the one from the bootstrap samples.

This suggests that neurons do change their preferred direction within epochs, but these changes are smaller in magnitude than changes that occur between the epochs. We explicitly comment on this in the methods, and in the main text we point out that considering only two epochs is a simplifying assumption, and as such it can be thought as a first step towards building a more complete model that shows dynamics of tuning within both preparatory and execution epochs. Note, however, that this simple framework is enough for the model to recapitulate to a large extent neuronal activity, both at the level of single-units and at the population level.

2) While it is great to constrain the model parameters using the data, there is a glaring "issue" here which I believe is both a weakness and a strength of the approach. The model has a lot of freedom in the external inputs, which leads to relatively severe parameter degeneracies. The authors are entirely forthright about this: they even dedicate a whole section to explaining that depending on the way the cost function is set up, the fit can land the model in very different regimes, yielding very different conclusions. The problem is that I eventually could not decide what to make of the paper's main results about the inferred external inputs, and indeed what to make of the main claim of the abstract. It would be great if the authors could discuss these issues more thoroughly than they currently do, and in particular, argue more strongly about the reasons that might lead one to favour the solutions of Fig 6d/g over that of Fig 6a. On the other hand, I see the proposed model as an interesting playground that will probably enable a more thorough investigation of input degeneracies in RNN models. Several research groups are currently grappling with this; in particular, the authors of LFADS (Pandarinath et al, 2018) and other follow-up approaches (e.g. Schimel et al, 2022) make a big deal of being able to use data to simultaneously learn the dynamics of a neural circuit and infer any external inputs that drive those dynamics, but everyone knows that this is a generally ill-posed problem (see also discussion in Malonis et al 2021, which the authors cite). As far as I know, it is not yet clear what form of regularisation/prior might best improve identifiability. While Bachschmid-Romano et al. do not go very far in dissecting this problem, the model they propose is low-dimensional and more amenable to analytical calculations, such that it provided a valuable playground for future work on this topic.

We agree with the reviewer that the problem of disambiguating between feedforward and recurrent connections from observation of the state of the recurrent units alone is a degenerate problem in general.

By explicitly looking for solutions that minimize the role of external inputs in driving the dynamics, we argued that the solutions of Fig 4d/g are favorable over the one of Fig 4a because they are based on local computations implemented through shorter range connections compared to incoming connections from upstream areas; as such, they likely require less metabolic energy.

In the new version of the paper, we discuss this issue more explicitly:

Degeneracy of solutions. We considered the case where parameters are inferred by minimizing a cost function that equals the reconstruction error only (this corresponds to the case of very large values of the parameter α in the cost function). Figure 4—figure supplement 2 shows that after minimizing the reconstruction error, the cost function is flat in a large region of the order parameters. We also added Figure 5—figure supplement 5, to show that the dynamics of the feedforward network looks almost indistinguishable from the one of the recurrent network (Fig.5) - although the average canonical correlation coefficient is a bit lower for the purely feedforward case.

Breaking the degeneracy of solutions. We added Figure 4—figure supplement 1 to show that for a wide range of the parameter α, all solutions cluster in a small region of parameter space. Solutions are found both above and below the bifurcation line. Note that all solutions are such that parameters jA and jB are close to the bifurcation line that separate the region where tuned network activity requires tuned external input, and the region where tuned network activity can be sustained autonomously. Furthermore, the weight of recurrent-connections within map B (j_B) is much stronger than the corresponding weight for map A (j_A). Hence, we observe that external inputs play a stronger role in shaping the dynamics during motor preparation than during execution, while recurrent inputs dominate the total inputs during movement execution, for a broad range of values of alpha. This prediction needs to be tested experimentally, although it is in line with the results of ref. 39, as we explain in the Discussion, section “Interplay between external and recurrent currents”, last paragraph.

3) As an addition to the motor control literature, this paper's main strengths lie in the modelcapturing orthogonality between preparatory and movement-related activity subspaces (Elsayed et al 2016), which few models do. However, one might argue that the model is in fact half hand-crafted for this purpose, and half-tuned to neural data, in such a way that it is almost bound to exhibit the phenomenon. Thus, some form of broader model cross-validation would be nice: what else does the model capture about the data that did not explicitly inspire/determine its construction? As a starting point, I would suggest that the authors apply the type of CCA-based analysis originally performed by Sussillo et al (2015), and compare qualitatively to both Sussillo et al. (2015) and Kao et al (2021). Also, as every recorded monkey M1 neuron can be characterized by its coordinates in the 4-dimensional space of angular tuning, it should be straightforward to identify the closest model neuron; it would be very compelling to show side-by-side comparisons of single-neuron response timecourses in model and monkey (i.e., extend the comparison of Fig S6 to the temporal domain).

We thank the reviewer for these suggestions. We have added the following comparisons:

● A CCA-based analysis (Fig 5.a) shows that the performance of our model is qualitatively comparable to the Sussillo et al. (2015) and Kao et al (2021) at generating realistic motor cortical activity (average canonical correlation ρ = 0.77 during movement preparation and 0.82 during movement execution).

● For each of the 141 neurons in the data, we selected the corresponding one in the model that is closest in the eta- and theta- parameters space:

a) A side-by-side comparison of the time course of responses shows a good qualitative agreement (Fig 5.c).

b) We successfully trained a linear decoder to read the responses of these 141 neurons from simulations and output trial-averaged EMG activity recorded from a monkey performing the same task Fig 5.b.

c) Figure 5—figure supplement 4 shows that simulated data presents sequential activity, as does the recorded data.

In our simulations, the temporal variability in single-neuron responses is due to the temporal evolution of the inferred external inputs, and to noise, implemented by an Ornstein-Uhlenbeck (OU) process that is added to the total inputs. Another source of variability could be introduced in the synaptic connectivity: one could add a gaussian random variable to each synaptic efficacy, for example. We checked that this simple extension of our model is able to reproduce the dynamics of the order parameters seen in the data. A full characterization of this extended model is beyond the scope of our paper.

4) The paper's clarity could be improved.

We thank the reviewer for his feedback. We have significantly rewritten most sections of the paper to improve clarity.

Reviewer #2 (Public Review):

The authors study M1 cortical recordings in two non-human primates performing straight delayed center-out reaches to one of 8 peripheral targets. They build a model for the data with the goal of investigating the interplay of inferred external inputs and recurrent synaptic connectivity and their contributions to the encoding of preferred movement direction during movement preparation and execution epochs. The model assumes neurons encode movement direction via a cosine tuning that can be different during preparation and execution epochs. As a result, each type of neuron in the model is described with four main properties: their preferred direction in the cosine tuning during preparation (denoted by θ_A) and execution (denoted by θ_B) epochs, and the strength of their encoding of the movement direction during the preparation (denoted by η_A) and execution (denoted by η_B) epochs. The authors assume that a recurrent network that can have different inputs during the preparation and execution epochs has generated the activity in the neurons. In the model, these inputs can both be internal to the network or external. The authors fit the model to real data by optimizing a loss that combines, via a hyperparameter α, the reconstruction of the cosine tunings with a cost to discourage/encourage the use of external inputs to explain the data. They study the solutions that would be obtained for various values of α. The authors conclude that during the preparatory epoch, external inputs seem to be more important for reproducing the neuron's cosine tunings to movement directions, whereas during movement execution external inputs seem to be untuned to movement direction, with the movement direction rather being encoded in the direction-specific recurrent connections in the network.

Major:

1) Fundamentally, without actually simultaneously recording the activity of upstream regions, it should not be possible to rule out that the seemingly recurrent connections in the M1 activity are actually due to external inputs to M1. I think it should be acknowledged in the discussion that inferred external inputs here are dependent on assumptions of the model and provide hypotheses to be validated in future experiments that actually record from upstream regions. To convey with an example why I think it is critical to simultaneously record from upstream regions to confirm these conclusions, consider two alternative scenarios: I) The recorded neurons in M1 have some recurrent connections that generate a pattern of activity that is based on the modeling seems to be recurrent. II) The exact same activity has been recorded from the same M1 neurons, but these neurons have absolutely no recurrent connections themselves, and are rather activated via purely feed-forward connections from some upstream region; that upstream region has recurrent connections and is generating the recurrent-like activity that is later echoed in M1. These two scenarios can produce the exact same M1 data, so they should not be distinguishable purely based on the M1 data. To distinguish them, one would need to simultaneously record from upstream regions to see if the same recurrent-like patterns that are seen in M1 were already generated in an upstream region or not. I think acknowledging this major limitation and discussing the need to eventually confirm the conclusions of this modeling study with actual simultaneous recordings from upstream regions is critical.

We agree with the reviewer that it is not possible to rule out the hypothesis that motor cortical activity is purely generated by feedforward connectivity.

In the new version of the paper, we discuss more explicitly the fact that neural activity can be fully explained by feedforward inputs, and we added Figure 5—figure supplement 5 to show that the dynamics of the feedforward network looks almost indistinguishable from the one of the recurrent network (Fig.5), provided their parameters are appropriately tuned. Notice, however, that a canonical correlation analysis comparing the activity from recording with the one from simulations shows that the average canonical correlation coefficient is slightly lower for the case of a purely feedforward network (Fig.5.a vs Fig.S12.a).

A summary of our approach is:

• We observe that both a purely feedforward and a recurrent network can reproduce the temporal course of the recordings equally well (see also our answer to question 5 below);

• We point out that a solution that would save metabolic energy consumption is one where the activity is generated by recurrent currents (with shorter range local connections) rather than by feedforward inputs from upstream regions (long-range connections).

• We study the solution that best reproduces the recorded activity and minimizes inputs from upstream regions.

In the Discussion, we included the Reviewer’s observation that our hypothesis needs to be tested by simultaneous recordings of M1 and upstream regions, as well as measures of synaptic strength between motor cortical neurons. See the second paragraph of page 14: “ Our prediction (…) will be necessary to rule out alternative explanations”. Yet, we think that the results of reference [51] are consistent with our results.

One last point we would like to stress is that external inputs drive the network's dynamics at all times, even in the solution that we argue would save metabolic energy consumption: untuned inputs are present throughout the whole course of the motor action, also during movement execution, and they determine the precise temporal pattern of neurons firing rates.

2) The ring network model used in this work implicitly relies on the assumption that cosinetuning models are good representations of the recorded M1 neuronal activity. However, this assumption is not quantitatively validated in the data. Given that all conclusions depend on this, it would be important to provide some goodness of fit measure for the cosine tuning models to quantify how well the neurons' directional preferences are explained by cosine tunings. For example, reporting a histogram of the cosine tuning fit error over all neurons in Fig 2 would be helpful (currently example fits are shown only for a few neurons in Fig. 2 (a), (b), and Figure S6(b)). This would help quantitatively justify the modeling choice.

We thank the reviewer for this observation. Fig.S2.e-f shows the R^2 coefficient of the cosine fit; in particular, we show that the R^2 of the cosine fit strongly correlates with the variables \eta, which represent the degree of participation of single units to the recurrent currents. Units with higher \eta (the ones that contribute more to the recurrent currents) are the ones whose tuning curves better resemble a cosine. However, the plot also shows that the R^2 coefficient of the cosine fit is pretty low for many cells. To show that a model with cosine tuning can yield this result, we repeated the same analysis on the units in our simulated network. In our simulations, all neurons receive a stochastic input mimicking large fluctuations around mean inputs that are expected to occur in vivo. We selected the 141 units whose activity more strongly resembled the activity of the 141 recorded neurons (see figure caption for details). We then looked at the tuning curves of these 141 units from simulations, and calculated the R^2 coefficient of the cosine fit. Figure 5—figure supplement 2.c shows that the result agrees well with the data: the R^2 coefficient is pretty low for many neurons, and correlates with the variable \eta. To summarize, a model that assumes cosine tuning, but also incorporates noise in the dynamics, reproduces well the R^2 coefficient of the cosine fit of tuning curves from data. We added the paragraph “Cosine tuning “ in the Discussion to comment on this point.

3) The authors explain that the two-cylinder model that they use has "distinct but correlated"maps A and B during the preparation and movement. This is hard to see in the formulation. It would be helpful if the authors could expand in the Results on what they mean by "correlation" between the maps and which part of the model enforces the correlation.

We thank the reviewer for this comment. By correlation, we meant the correlation between neural activity during the preparatory and movement-related temporal intervals. In the model, the correlation between the vectors θA and θB induces correlation in the preparatory and movement-related activity patterns. To make the paper easier to read, we are not mentioning this concept in the Results; in the Discussion, we explicitly refer to it in the following two paragraphs:

“A strong correlation between the selectivity properties of the preparatory and movement-related epochs will produce strongly correlated patterns of activity in these two intervals and a strong overlap between the respective PCA subspaces.” (Discussion, section Orthogonal spaces dedicated to movement preparation and execution)

“The correlation between the vectors θAand θB (Discussion, section Interplay between external and recurrent currents)”

4) The authors note that a key innovation in the model formulation here is the addition ofparticipation strengths parameters (η_A, η_B) to prior two-cylinder models to represent the degree of neuron's participation in the encoding of the circular variable in either map. The authors state that this is critical for explaining the cosine tunings well: "We have discussed how the presence of this dimension is key to having tuning curves whose shape resembles the one computed from data, and decreases the level of orthogonality between the subspaces dedicated to the preparatory and movement-related activity". However, I am not sure where this is discussed. To me, it seems like to show that an additional parameter is necessary to explain the data well, one would need to compare fit to data between the model with that parameter and a model without that parameter. I don't think such a comparison was provided in the paper. It is important to show such a comparison to quantitatively show the benefit of the novel element of the model.

We thank the reviewer for this comment.

● The key observation is that without the parameters eta_A, eta_B, the temporal evolution of all neurons in the network is the same (only the noise term added to the dynamics is different). To show this, we have performed a comparison of the temporal evolution of the firing rates of single neurons of the model with data. Fig 5.c shows a comparison between the time-course of single neurons firing rates from data and simulations (good agreement), while Figure 6—figure supplement 2.a shows the same comparison for a model in which all neurons have the same value of the eta_A, eta_B parameters (worse agreement: the range of firing rates is the same for all neurons). In summary, the parameters eta_A, eta_B introduce the variability in the coupling strengths that is necessary to generate heterogeneity in neuronal responses.

● At the end of section “PCA subspaces dedicated to movement preparation and execution”, we refer to (Figure 6—figure supplement 2).c, showing that a model with eta_A=1=eta_B for all neurons yields less orthogonal subspaces.

5) The model parameters are fitted by minimizing a total cost that is a weighted average of twocosts as E_tot = α E_rec + E_ext, with the hyperparameter α determining how the two costs are combined. The selection of α is key in determining how much the model relies on external inputs to explain the cosine tunings in the data. As such, the conclusions of the paper rely on a clear justification of the selection of α and a clear discussion of its effect. Otherwise, all conclusions can be arbitrary confounds of this selection and thus unreliable. Most importantly, I think there should be a quantitative fit to data measure that is reported for different scenarios to allow comparison between them (also see comment 2). For example, when arguing that α should be "chosen so that the two terms have equal magnitude after minimization", this would be convincing if somehow that selection results in a better fit to the neural data compared with other values of α. If all such selections of α have a similar fit to neural data, then how can the authors argue that some are more appropriate than others? This is critical since small changes in alpha can lead to completely different conclusions (Fig. 6, see my next two comments).

All the points raised in questions 5 to 8 are interrelated, and we address them below, after Major issue 8.

6) The authors seem to select alpha based on the following: "The hyperparameter α was chosen so that the two terms have equal magnitude after minimization (see Fig. S4 for details)". Why is this the appropriate choice? The authors explain that this will lead to the behavior of the model being close to the "bifurcation surface". But why is that the appropriate choice? Does it result in a better fit to neural data compared with other choices of α? It is critical to clarify and justify as again all conclusions hinge on this choice.

7) Fig 6 shows example solutions for 2 close values of α, and how even slight changes in the selection of α can change the conclusions. In Fig. 6 (d-e-f), α is chosen as the default approach such that the two terms E_rec and E_ext have equal magnitude. Here, as the authors note, during movement execution tuned external inputs are zero. In contrast, in Fig. 6 (g-h-i), α is chosen so that the E_rec term has a "slightly larger weight" than the E_ext term so that there is less penalty for using large external inputs. This leads to a different conclusion whereby "a small input tuned to θ_B is present during movement execution". Is one value of α a better fit to neural data? Otherwise, how do the authors justify key conclusions such as the following, which seems to be based on the first choice of α shown in Fig. 6 (d-e-f): "...observed patterns of covariance are shaped by external inputs that are tuned to neurons' preferred directions during movement preparation, and they are dominated by strong direction-specific recurrent connectivity during movement execution".

8) It would be informative to see the extreme case of very large and very small α. For example, if α is very large such that external inputs are practically not penalized, would the model rely purely on external inputs (rather than recurrent inputs) to explain the tuning curves? This would be an example of the hypothetical scenario mentioned in my first comment. Would this result in a worse fit to neural data?

We agree with the reviewer that it is crucial to discuss how the choice of the parameter alpha affects the results, and we have strived to improve this discussion in the revised manuscript.

I. When we looked for the coupling parameters that best explain the data, without introducing a metabolic cost, we found multiple solutions that were equally good (see Figure 4—figure supplement 2 and our answer to question (1) above). These included the solution with all couplings set to zero ( j_s^B = j_s^A = j_a = 0), as well as many solutions with different values of synaptic couplings parameters. The solution with the strongest couplings is close to the bifurcation line, in the area where j_s^B > j_s^A.

II. We then introduced a metabolic cost to break the degeneracy between these different solutions. The cost function we minimized contains two terms; their relative strength is modulated by alpha. The case of very small alpha (i.e., only minimizing external input) yields a very poor reconstruction of neural dynamics and is not interesting. The case of very large alpha reduces to the case (I) above. We added Figure 4—figure supplement 1 to show the results for intermediate values of alpha - alpha is large enough to yield a good reconstruction of neural dynamics, yet small enough to ensure that we find a unique solution. For these intermediate values of alpha, the two terms of the cost function have comparable magnitudes. Although slight changes in the selection of alpha do change whether the solutions are above or below the bifurcation surface, Figure 4—figure supplement 1 shows that all solutions are close to the bifurcation surface. In particular, the value of j_s^B is close to its critical value, while we never find solutions where j_s^A is close to its critical value - we never find solutions in the lower-right region of the plot in Figure 4—figure supplement 1. The critical value for j_s^B is the one above which no tuned external inputs are necessary to sustain the observed activity during movement execution. For values of j_s^B close to the bifurcation line but below it (for example, Fig.4g) inferred tuned inputs are still much weaker than the untuned ones, during movement execution. Also, the inferred direction-specific couplings are strong and amplify the weak external inputs tuned to map B, therefore still playing a major role in shaping the observed dynamics during movement execution.

We have rewritten accordingly the abstract, introduction and conclusions of the paper. Instead of focusing on only one solution for a particular value of alpha, we now discuss all solutions and their implications.

9) The authors argue in the discussion that "the addition of an external input strengthminimization constraint breaks the degeneracy of the space of solutions, leading to a solution where synaptic couplings depend on the tuning properties of the pre- and post-synaptic neurons, in such a way that in the absence of a tuned input, neural activity is localized in map B". In other words, the use of the E_ext term, apparently reduces "degeneracy" of the solution. This was not clear to me and I'm not sure where it is explained. This is also related to α because if alpha goes toward very large values, it would be like the E_ext term is removed, so it seems like the authors are saying that the solution becomes degenerate if alpha grows very large. This should be clarified.

We thank the reviewer for pointing this out. By degeneracy of solution, we mean that the model can explain the data equally well for different choices of the recurrent couplings parameters (j_s^A, j_s^B, j_a). In other words, if we look for the coupling parameters that best explain the data, there are many equivalent solutions. When we introduce the E_ext term in the cost function, we then find one unique solution for each choice of alpha. So by “breaking the degeneracy”, we mean going from a scenario where there are many solutions that are equally valid, to one single solution. We added this explanation in the paper, along with the explanation on how our conclusion depends on the ‘choice of alpha’.

10) How do the authors justify setting Φ_A = Φ_B in equation (5)? In other words, how is the last assumption in the following sentence justified: "To model the data, we assumed that the neurons are responding both to recurrent inputs and to fluctuating external inputs that can be either homogeneous or tuned to θ_A; θ_B, with a peak at constant location Φ_A = Φ_B ≡ Φ". Does this mean that the preferred direction for a given neuron is the same during preparation and movement epochs? If so, how is this consistent with the not-so-high correlation between the preferred directions of the two epochs shown in Fig. 2 c, which is reported to have a circular correlation coefficient of 0.4?

We would like to stress the important distinction between the parameters \theta and the parameters Φ. While the parameters \theta_A and \theta_B represent the preferred direction of single neurons during preparatory and execution epochs, respectively, the parameters Φ_A, Φ_B represent the direction of motion that is encoded at the population level during these two epochs. The mean-field analysis shows that Φ_A = Φ_B, even though single neurons change their preferred direction from one epoch to the next. We added a more extensive explanation of the order parameters in the Results section.

Reviewer #3 (Public Review):

In this work, Bachschmid-Romano et al. propose a novel model of the motor cortex, in which the evolution of neural activity throughout movement preparation and execution is determined by the kinematic tuning of individual neurons. Using analytic methods and numerical simulations, the authors find that their networks share some of the features found in empirical neural data (e.g., orthogonal preparatory and execution-related activity). While the possibility of a simple connectivity rule that explains large features of empirical data is intriguing and would be highly relevant to the motor control field, I found it difficult to assess this work because of the modeling choices made by the authors and how the results were presented in the context of prior studies.

Overall, it was not clear to me why Bachschmid-Romano et al. couched their models within a cosine-tuning framework and whether their results could apply more generally to more realistic models of the motor cortex. Under cosine-tuning models (or kinematic encoding models, more generally), the role of the motor cortex is to represent movement parameters so that they can presumably be read out by downstream structures. Within such a framework, the question of how the motor cortex maintains a stable representation of movement direction throughout movement preparation and execution when the tuning properties of individual neurons change dramatically between epochs is highly relevant. However, prior work has demonstrated that kinematic encoding models provide a poor fit for empirical data. Specifically, simple encoding models (and the more elaborate extensions [e.g., Inoue, et al., 2018]) cannot explain the complexity of single-neuron responses (Churchland and Shenoy, 2007), and do not readily produce the population-level signals observed in the motor cortex (Michaels, Dann, and Scherberger, 2016) and cannot be extended to more complex movements (Russo, et al., 2018).

In both the Introduction and Discussion, the authors heavily cite an alternative to kinematic encoding models, the dynamical systems framework. Here, the correlations between kinematics and neural activity in the motor cortex are largely epiphenomenal. The motor cortex does not 'represent' anything; its role is to generate patterns of muscle activity. While the authors explicitly acknowledge the shortcomings of encoding models ('Extension to modeling richer movements', Discussion) and claim that their proposed model can be extended to 'more realistic scenarios', they neither demonstrate that their models can produce patterns of muscle activity nor that their model generates realistic patterns of neural activity. The authors should either fully characterize the activity in their networks and make the argument that their models better provide a better fit to empirical data than alternative models or demonstrate that more realistic computations can be explained by the proposed framework.

Major Comments

1) In the present manuscript, it is unclear whether the authors are arguing that representing movement direction is a critical computation that the motor cortex performs, and the proposed models are accurate models of the motor cortex, or if directional coding is being used as a 'proof of concept' that demonstrates how specific, population-level computations can be explained by the tuning of individual neurons.

If the authors are arguing the former, then they need to demonstrate that their models generate activity similar to what is observed in the motor cortex (e.g., realistic PSTHs and population-level signals). Presently, the manuscript only shows tuning curves for six example neurons (Fig. S6) and a single jPC plane (Fig. S8). Regarding the latter, the authors should note that Michaels et al. (2016) demonstrated that representational models can produce rotations that are superficially similar to empirical data, yet are not dependent on maintaining an underlying condition structure (unlike the rotations observed in the motor cortex).

If the authors are arguing the latter - and they seem to be, based on the final section of the Discussion - then they need to demonstrate that their proposed framework can be extended to what they call 'more realistic scenarios'. For example, could this framework be extended to a network that produces patterns of muscle activity?

We thank the reviewer for raising these issues.

Is our model a kinematic encoding model or a dynamical system?

Our model is a dynamical system, as can be seen by inspecting equations (1,2). The main difference between our model and recently proposed dynamical system models of motor cortex is that the synaptic connectivity matrix in our model is built from the tuning properties of neurons, instead of being trained using supervised learning techniques (we come back to this important difference below). Since the network’s connectivity and external input depend on the neurons’ tuning to the direction of motion (eq 5-6), kinematic parameters emerge from the dynamic interaction between recurrent and feedforward currents, as specified by equations (1-6). Thus, kinematic parameters can be decoded from population activity.

While in kinematic encoding models neurons’ firing rates are a function of parameters of the movement, we constrained the parameters of our model by requiring the model to reproduce the dynamics of a few order parameters, which are low-dimensional measures of the activity of recorded neurons. Our model is fitted to neural data, not to the parameters of the movement.

Although we observed that a linear decoder of the network’s activity can reproduce patterns of muscle activity without decoding any kinematic parameter (see below), discussing whether tuning in M1 plays a computational role in controlling muscle activity is outside of the scope of our work. Rather, the scope of our paper is to discuss how a specific connectivity structure can generate the observed patterns of neural activity, and which connectivity structure requires minimum external inputs to sustain the dynamics. In our approach, the correlations between kinematics and neural activity in the motor cortex are not merely epiphenomenal, but emerge from a specific structure of the connectivity that has likely been shaped by hebbian-like learning mechanisms.

Can the model generate realistic PSTHs and patterns of muscle activity? Yes, it can. As suggested, we have added the following comparisons:

● A CCA-based analysis (Fig 5.a) shows that the performance of our model is qualitatively comparable to the Sussillo et al. (2015) and Kao et al (2021) at generating realistic motor cortical activity (average canonical correlation ρ = 0.77 for motor preparation, 0.82 for motor execution).

● For each of the 141 neurons in the data, we selected the corresponding most similar unit in the model (the closest neurons in the eta- and theta- parameters space, i.e. the one with smallest euclidean distance in the space defined by (\theta_A, \theta_B, \eta_A, \eta_B)). A side-by-side comparison of the time course of responses (Fig 5.c) shows a good qualitative agreement.

● We successfully trained a linear decoder to read the responses of these 141 units from simulations and output trial-averaged EMG activity recorded from a monkey performing the same task (Fig 5.b).

● The model displays sequential activity and rotational dynamics (Fig. S10) without the need to introduce neuron-specific latencies (Michaels, Dann, and Scherberger, 2016).

Can our model explain the complexity of single-neuron tuning?

We have shown that our model captures the heterogeneity of neural responses. Yet, it has been shown that neurons’ tuning properties depend on many features of movement. For example, the current version of the model does not describe the dependence of tuning on speed (Churchland and Shenoy, 2007). However, our model could be extended to incorporate it. Preliminary results suggest that in a network model in which neurons differ by the degree of symmetry of their synaptic connectivity the speed of neural trajectories can be modulated by external inputs targeting preferentially neurons that are asymmetrically connected. In our model, all connections are a sum of a symmetric and an asymmetric term. We could extend our model to incorporate variability in the degree of symmetry in the connections, and speculate that in such a model tuning would depend on the speed of movement, for appropriate forms of external inputs. We leave this study to future work.

Can our model explain neural activity underlying more complex trajectories? When limb trajectories are more complex than simple reaches (Russo, et al., 2018), a single neuron’s activity displays intricate response patterns. Our work could be extended to model more complex movement in several ways. A simplifying assumption we made is that the task can be clearly separated into a preparatory phase and one movement-related phase. A possible extension is one where the motor action is composed of a sequence of epochs, corresponding to a sequence of maps in our model. It will be interesting to study the role of asymmetric connections for storing a sequence of maps. Such a network model could be used to study the storing of motor motifs in the motor cortex (Logiaco et al, 2021); external inputs could then combine these building blocks to compose complex actions.

In summary, we proposed a simple model that can explain recordings during a straight-reaching task. It provides a scaffold upon which we can build more sophisticated models to explain the activity underlying more complex tasks. We point out that a similar limitation is present in modeling approaches where a network is trained to perform specific neural or muscle activity. The question of whether/how trained recurrent networks can generalize is not yet solved, although currently under investigation (e.g., Dubreuil et al 2022; Driscoll et al 2022).

What is the advantage of the present model, compared to an RNN trained to output specific neural/muscle activity?

Its simplicity. Our model is a low-rank recurrent neural network: the structure of the connectivity matrix is simple enough to allow for analytical tractability of the dynamics. The model can be used to test specific hypotheses on the relationship between network connectivity, external inputs and neural dynamics, and to test hypotheses on the learning mechanisms that may lead to the emergence of a given connectivity structure. The model is also helpful to illustrate the problem of degeneracy of network models. An interesting future direction would be to compare the connectivity matrices of trained RNNs and our model.

We addressed these points in the Discussion, in sections: “Representational vs dynamical system approaches” and “Extension to modeling activity underlying more complex tasks.”

2) Related to the above point, the authors claim in the Abstract that their models 'recapitulatethe temporal evolution of single-unit activity', yet the only evidence they present is the tuning curves of six example units. Similarly, the authors should more fully characterize the population-level signals in their networks. The inferred inputs (Fig. 6) indeed seem reasonable, yet I'm not sure how surprising this result is. Weren't the authors guaranteed to infer a large, condition-invariant input during movement and condition-specific input during preparation simply because of the shape of the order parameters estimated from the data (Fig. 6c, thin traces)?

We thank the reviewer for this comment. Regarding the first part of the question: we added new plots with more comparisons between the activity of our model and neural recordings (see the answer above referring to Fig 5).

Regarding the second part: It is true that the shape of the latent variables that we measure from data constrains the solution that we find. However, a “condition-invariant input during movement and condition-specific input during preparation” is not the only scenario compatible with the data. Let’s take a step back and focus on the parameters that we are inferring from data. We are inferring both the strength of external inputs and the couplings parameters. This is done in a two-step inference procedure: we start from a random guess of the couplings parameters, then we infer the strength of the external inputs, and finally we compute the cost function, which depends on all parameters. This is done iteratively, by moving in the space of the coupling parameters; for each point in the space of the coupling parameters, there is one possible configuration of external inputs. The space of the coupling parameters is shown in Fig 4.a, for example (see also Fig. S4). The solutions that we find do not trivially follow from the shape of the latent variables. For example, one possible solution could be: large parameter j_s^A, small parameter j_s^B, which correspond to a point in the lower-right region of the parameter space in Fig 4.a (Fig. S4). The resulting external input would be a strong condition-specific external input during movement execution, but a condition-invariant input during movement preparation: the model is such that, for example, exciting for a short time-interval a few neurons whose preferred direction corresponds to the direction of motion would be enough to “set the direction of motion” for the network; the pattern of tuned activity could be sustained during the whole delay period thanks to the strong recurrent connections j_s^A. We could not rule out this solution by simply looking at the shape of the latent variables. However, it is a solution we have never observed. We only found solutions in the region where j_s^B is large and close to its critical value. This implies the presence of condition-specific inputs during the whole delay period, and condition-invariant external inputs that dominate over condition-specific ones during movement execution.

3) In the Abstract and Discussion (first paragraph), the authors highlight that the preparatory andexecution-related spaces in the empirical data and their models are not completely orthogonal, suggesting that this near-orthogonality serves an important mechanistic purpose. However, networks have no problem transferring activity between completely orthogonal subspaces. For example, the generator model in Fig. 8 of Elsayed, et al. (2016) is constrained to use completely orthogonal preparatory and execution-related subspaces. As the authors point out in the Discussion, such a strategy only works because the motor cortex received a large input just before movement (Kaufman et al., 2016).

We thank the reviewer for this observation. We would like to stress the fact that we are not claiming that having an overlap between subspaces is necessary to transfer activity. Instead, our model shows that a small overlap between the maps can be exploited by the network to transfer activity between subspaces without requiring direction-specific external inputs right before movement execution. A solution where activity is transferred through feedforward inputs is also possible. Indeed, one of the observations of our work (which we highlight more in the new version of the paper) is that by looking at motor cortical activity only, we are not able to distinguish between the activity generated by a feedforward network, and one generated by a recurrent one. However, we argue that a solution where external inputs are minimized can be favorable from a metabolic point of view, as it requires fewer signals to be transmitted through long-range connections. This informs our cost function, and yields a solution where activity is transferred through recurrent connections, by exploiting the small correlation between subspaces.

Author Response

Reviewer #1 (Public Review):

DeRisi and colleagues used a new phage-display peptide platform, with 238,068 tiled 62-amino acid peptides covering all known P falciparum coding regions (and numerous other entities), to survey seroreactivity in 198 Ugandan children and adults from two cohorts. They find that the breadth of responses to repeat-containing peptides was twofold higher in children living in the high versus moderate exposure setting, while no such differences were observed for peptides without repeats. Additionally, short motifs associated with seroreactivity were extensively shared among hundreds of antigens, with much of this driven by motifs shared with PfEMP1 antigens.

Malaria immunity is complex, and this new platform is a potentially valuable addition to the toolkit for understanding humoral responses. The two cohorts differed in fundamental ways: 1) high versus moderate exposure to infective bites; 2) samples drawn at the time of malaria for most donors in the high zone versus ~100 days after the last malaria episode in the moderate zone. The effect of acute malaria to boost short-term cross-reactive antibodies can confound the ability to draw inferences when comparing the two cohorts, and this should be further explored to understand its role in the patterns of seroreactivity observed.

We thank the reviewer for this very insightful comment. In endemic areas, this potential confounder is a natural occurrence – in areas of higher transmission, people will on average be more likely to have an active or recent infection. The question is whether the differences seen in repeat-containing peptides are due to cumulative exposure or recency/active exposure. To address this point, we have added new analyses, as suggested, taking into account infection status in both exposure settings. In the moderate exposure setting, we find that the breadth of response in children to repeat containing peptides significantly narrows between the most recently exposed subjects, and those that have been infection free for >240 days, indicative of a short-lived response. This difference was not observed for peptides without repeats. (New figure: Figure 5, Supplement 4). We also observe an increase in breadth for repeat-containing peptides in high vs. moderate exposure settings, regardless of infection status (New figure: Figure 5, Supplement 3), a difference that was absent in non-repeat containing peptides. Overall, these data suggest that responses to repeats are not only more exposure-dependent, but also short-lived relative to non-repeats in children. We have included this new analysis (lines 409-435.)

Reviewer #2 (Public Review):

This work profiles naturally acquired antibodies against Plasmodium falciparum proteins in two Ugandan cohorts, at incredibly high resolution, using a comprehensive library of overlapping peptides. These findings highlight the ubiquity and importance of intra- and inter-protein repeat elements in the humoral immune response to malaria. The authors discuss evidence that repeat elements reside in more seroreactive proteins, and that the breadth of immunity to repeat-containing antigens is associated with transmission intensity in children.

A key strength and value added to publicly available data are the breadth of proteome coverage and unprecedented resolution from using tiling peptides. The authors point out that a known limitation of PhIP-seq is that conformational and discontinuous-linear epitopes cannot be detected with short linear peptides. In addition, disulfide linkages and post-translational modifications would be absent in the T7 representations.

Several significant conclusions drawn from the results in this study are based on the humoral response to repeat elements that are present in multiple locations, including different genes. If antibodies to these regions are cross-reactive as described, it is not clear how the assay can differentiate antibodies that were developed against one or many of these loci. This potential confounding could change the conclusions about inter-protein motifs.

• We thank the reviewer for their comments on the study. We have added a note about post-translational modifications to the text (Line 675-676) as recommended.

• With regards to interprotein motifs (Figure 6), we only suggest a potential for antibody cross-reactivity across these motifs based on sequence similarity alone. We do not claim direct evidence that they are indeed cross-reactive, especially given the complex polyclonal nature of the response we are measuring. We present this sequence analysis only as a landscape of potential cross-reactivity among linear epitopes in the proteome, derived from the pool of seroreactive peptides enriched in this cohort.

• Regardless, we have included a new analysis following the suggestion of Reviewer #1 to determine whether reactivity to these shared motifs indeed correlates between peptides from different proteins sharing a motif within the same individual. While this analysis shows apparent cross reactivity within individuals, we point out that the data is derived from complex polyclonal repertoires inherent to each individual, and thus these observations must be taken in that context and do not definitively establish cross reactivity. Along with the new analysis (Line 495-503), we have sought to be clear on these limitations (Line 632-635).

Reviewer #3 (Public Review):

This work provides a new tool, a comprehensive PhIP-seq library, containing 238,068 individual 62-amino acids peptides tiled every 25-amino acid peptide covering all known 8,980 proteins of the deadliest malaria parasite, Plasmodium falciparum, to systematically profile antibody targets in high resolution. This phage display library has been screened by plasma samples obtained from 198 Ugandan children and adults in high and moderate malaria transmission settings and 86 US controls. This work identified that repeat elements were commonly targeted by antibodies. Furthermore, extensive sharing of motifs associated with seroreactivity indicated the potential for extensive cross-reactivity among antigens in P. falciparum. This paper provides a new proteome-wide high-throughput methodology to identify antibody targets that have been investigated by protein arrays and alpha screens to date. Importantly, only this methodology (PhIP-seq library) is able to investigate repeat-containing antigens and cross-reactive epitopes in high resolution (25-amino acid resolution).

Strengths:

1) Novel technology

Firstly, the uniqueness of this study is the use of novel technology, the PhIP-seq library. This PhIP-seq library in this study contains >99.5% of the parasite proteome and is the highest coverage among existing proteome-wide tools for P. falciparum. Moreover, this library can identify antibody responses in high resolution (25 amino acids).

Secondly, the PhIP-seq converts a proteomic assay (ie. protein array and alpha screen) into a genomic assay, leveraging the massive scale and low-cost nature of next-generation short-read sequencing.

Thirdly, the phage display system is the ability to sequentially enrich and amplify the signal to noise. Finally, a high-quality strategic bioinformatic analysis of PhIP-seq data was applied.

2) Novel findings

The major findings of this study were obtained only by using this novel technology because of its full-proteome coverage and high resolution. Repeat elements were the common target of naturally acquired antibodies. Furthermore, extensive sharing of motifs associated with seroreactivity was observed among hundreds of parasite proteins, indicating the potential for extensive cross-reactivity among antigens in P. falciparum.

3) Usefulness for the future research

Importantly, plasma samples from longitudinal cohort studies will give the scientific community important insights into protective humoral immunity which will be important for the identification of vaccine and exposure-marker candidates in the near future.

Weaknesses:

Although the paper does have strengths in principle, the weaknesses of the paper are the insufficient description of the selected parasite proteins and seroreactivity ranking of the selected proteins such as TOP100 proteins.

We thank the reviewer for their comments, corrections, and suggestions. We have made a number of changes and added new analyses, all of which have improved the work. These changes include the following:

• Analysis of breadth of seroreactivity to repeat and non-repeat regions taking into account infection status in both exposure settings.

• Analysis to test whether reactivity to peptides with interprotein motifs correlates within the same individual

• A table listing top 100 proteins in terms of their seropositivity % in response to the reviewer’s comment (Supplementary table 2b).

Author Response

Reviewer #1 (Public Review):

This well-done platform trial identifies that ivermectin has no impact on SARS-CoV-2 viral clearance rate relative to no study drug while casirivimab lead to more rapid clearance at 5 days. The figures are simple and appealing. The study design is appropriate and the analysis is sound. The conclusions are generally well supported by the analysis. Study novelty is somewhat limited by the fact that ivermectin has already been definitively assessed and is known to lack efficacy against SARS-CoV-2. Several issues warrant addressing:

1) Use of viral load clearance is not unique to this study and was part of multiple key trials studying paxlovid, remdesivir, molnupiravir, and monoclonal antibodies. The authors neglect to describe a substantial literature on viral load surrogate endpoints of therapeutic efficacy which exist for HIV, hepatitis B and C, Ebola, HSV-2, and CMV. For SARS-CoV-2, the story is more complicated as several drugs with proven efficacy were associated with a decrease in nasal viral loads whereas a trial of early remdesivir showed no reduction in viral load despite a 90% reduction in hospitalization. In addition, viral load kinetics have not been formally identified as a true surrogate endpoint. For maximal value, a reduction in viral load would be linked with a reduction in a hard clinical endpoint in the study (reduction in hospitalization and/or death, decreased symptom duration, etc...). This literature should be discussed and data on the secondary outcome, and reduction in hospitalization should be included to see if there is any relationship between viral load reduction and clinical outcomes.

This is an important point and we thank the reviewer for raising it. We agree that there is a rich literature on the use of viral load kinetics in optimizing treatment of viral infectious diseases, and we are clearly not the first to think of it! We have added the following sentence in the discussion.

“The method of assessing antiviral activity in early COVID-19 reported here builds on extensive experience of antiviral pharmacodynamic assessments in other viral infections.”

We agree that more information is needed to link viral clearance measures to clinical outcomes. We have addressed this in the discussion as follows:

“Using less frequent nasopharyngeal sampling in larger numbers of patients, clinical trials of monoclonal antibodies, molnupiravir and ritonavir-boosted nirmatrelvir, have each shown that accelerated viral clearance is associated with improved clinical outcomes [1,4,5]. These data suggest reduction in viral load could be used as a surrogate of clinical outcome in COVID-19. In contrast the PINETREE study, which showed that remdesivir significantly reduced disease progression in COVID-19, did not find an association between viral clearance and therapeutic benefit. This seemed to refute the usefulness of viral clearance rates as a surrogate for rates of clinical recovery [16]. However, the infrequent sampling in all these studies substantially reduced the precision of the viral clearance estimates (and thus increased the risk of type 2 errors). Using the frequent sampling employed in the PLATCOV study, we have shown recently that remdesivir does accelerate SARS-CoV-2 viral clearance [17], as would be expected from an efficacious antiviral drug. This is consistent with therapeutic responses in other viral infections [18, 19]. Taken together the weight of evidence suggests that accelerated viral clearance does reflect therapeutic efficacy in early COVID-19, although more information will be required to characterize this relationship adequately.”

2) The statement that oropharyngeal swabs are much better tolerated than nasal swabs is subjective. More detail needs to be paid to the relative yield of these approaches.

The statement is empirical. We know of other studies in progress where there are high rates of discontinuation because of patient intolerance of repeated nasopharyngeal sampling. Not one of 750 patients enrolled to date in PLATCOV has refused sampling, which we believe is useful information for research involving multiple sampling. This is clearly a critical point for pharmacodynamic studies.

We agree that the optimal site of swabbing for SARS-CoV-2 and relative yields for the given test requirements (sensitivity vs quantification) need to be considered, although the literature on this is large and sometimes contradictory.

We have added the following line:

Oropharyngeal viral loads have been shown to be both more and less sensitive for the detection of SARS-CoV-2 infection. Although rates of clearance are very likely to be similar from the two body sites, this should be established for comparison with other studies.

3) The stopping rules as they relate to previously modeled serial viral loads are not described in sufficient detail.

The initial stopping rules were chosen based on previously modelled data (reference 11). We have added details to the text (lines 199-219):

“Under the linear model, for each intervention, the treatment effect β is encoded as a multiplicative term on the time since randomisation: eβT, where T=1 if the patient was assigned the intervention, and zero otherwise. Under this specification β=0 implies no effect (no change in slope), and β>0 implies increase in slope relative to the population mean slope. Stopping rules are then defined with respect to the posterior distribution of β, with futility defined as Prob[β<λ]>0.9; and success defined as Prob[β>λ]>0.9, where λ≥0. Larger values of λ imply a smaller sample size to stop for futility but a larger sample size to stop for efficacy. λ was chosen so that it would result in reasonable sample size requirements, as was determined using a simulation approach based on previously modelled serial viral load data [11]. This modelling work suggested that a value of λ=log(1.05) [i.e. 5% increase] would requireapproximately 50 patients to demonstrate increases in the rate of viral clearance of ~50%, with control of both type 1 and type 2 errors at 10%. The first interim analysis (n=50) was prespecified as unblinded in order to review the methodology and the stopping rules (notably the value of λ). Following this, the stopping threshold was increased from 5% to 12.5% [λ=log(1.125)] because the treatment effect of casirivimab/imdevimab against the SARS-CoV-2 Delta variant was larger than expected and the estimated residual error was greater than previously estimated. Thereafter trial investigators were blinded to the virus clearance results. Interim analyses were planned every batch of additional 25 patients’ PCR data however, because of delays in setting up the PCR analysis pipeline, the second interim analysis was delayed until April 2022. By that time data from 145 patients were available (29 patients randomised to ivermectin and 26 patients randomized to no study drug).”

4) The lack of blinding limits any analysis of symptomatic outcomes.

We added this line to the discussion:

“Finally, although not primarily a safety study, the lack of blinding compromises safety or tolerability assessments.”

5) It is unclear whether all 4 swabs from 2 tonsils are aggregated. Are the swabs placed in a single tube and analyzed?

The data are not aggregated but treated as independent and identically distributed under the linear model. 4 swabs were taken at randomization, followed by two at each follow-up visit. We have added line 183:

“[..] (18 measurements per patient, each swab is treated as as independent and identically distributed conditional on the model).”

Swabs were stored separately and not aggregated.

6) In supplementary Figure 7, both models do well in most circumstances but fail in the relatively common event of non-monotonic viral kinetics (multiple peaks, rebound events). Given the importance of viral rebound during paxlovid use, an exploratory secondary analysis of this outcome would be welcome.

Thank you for the suggestion. We agree, although the primary goal is to estimate the mean change in slope. Rebound is a relatively rare event and tends to occur after the first seven days of illness in which we are assessing rate of clearance.

Nevertheless, we agree that this is an important point. It remains unclear how to model viral rebound. In over 700 profiles now available from the study, only a few have strong evidence of viral rebound.

Reviewer #2 (Public Review):

This manuscript details the analytic methods and results of one arm of the PLATCOV study, an adaptive platform designed to evaluate low-cost COVID-19 therapeutics through enrollment of a comparatively smaller number of persons with acute COVID-19, with the goal of evaluating the rate of decrease in SARS-CoV-2 clearance compared to no treatment through frequent swabbing of the oropharynx and a Bayesian linear regression model, rather than clinical outcomes or the more routinely evaluated blunt virologic outcomes employed in larger trials. Presented here, is the in vivo virologic analysis of ivermectin, with a very small sample of participants who received the casirivimab/imdevimab, a drug shown to be highly effective at preventing COVID-19 progression and improving viral clearance (during circulation of variants to which it had activity) included for comparison for model evaluation.

The manuscript is well-written and clear. It could benefit however from adding a few clarifications on methods and results to further strengthen the discussion of the model and accurately report the results, as detailed below.

Strengths of this study design and its report include:

1) Selection of participants with presumptive high viral loads or viral burden by antigen test, as prior studies have shown difficulty in detecting effect in those with a lower viral burden.

2) Adaptive sample size based on modeling- something that fell short in other studies based on changing actuals compared to assumptions, depending on circulating variant and "risk" of patients (comorbidities, vaccine state, etc) over time. There have been many other negative studies because the a priori outcomes assumptions were different from the study design to the time of enrollment (or during the enrollment period). This highlight of the trial should be emphasized more fully in the discussion.

3) Higher dose and longer course of ivermectin than TOGETHER trial and many other global trials: 600ug/kg/day vs 400mcg/kg/day.

4) Admission of trial participants for frequent oropharyngeal swabbing vs infrequent sampling and blunter analysis methods used in most reported clinical trials

5) Linear mixed modeling allows for heterogeneity in participants and study sites, especially taking the number of vaccine doses, variant, age, and serostatus into account- all important variables that are not considered in more basic analyses.

6) The novel outcome being the change in the rate of viral clearance, rather than time to the undetectable or unquantifiable virus, which is sensitive, despite a smaller sample size

7) Discussion highlights the importance of frequent oral sampling and use of this modeled outcome for the design of both future COVID-19 studies and other respiratory viral studies, acknowledging that there are no accepted standards for measuring virologic or symptom outcomes, and many studies have failed to demonstrate such effects despite succeeding at preventing progression to severe clinical outcomes such as hospitalization or death. This study design and analyses are highly important for the design of future studies of respiratory viral infections or possibly early-phase hepatitis virus infections.

Weaknesses or room for improvement:

1) The methods do not clearly describe allocation to either ivermectin or casirivimab/imdevimab or both or neither. Yes, the full protocol is included, but the platform randomization could be briefly described more clearly in the methods section.

We have added additional text to the Methods:

“The no study drug arm comprised a minimum proportion of 20% and uniform randomization ratios were then applied across the treatment arms. For example, for 5 intervention arms and the no study drug arm, 20% of patients would be randomized to no study drug and 16% to each of the 5 interventions. Additional details on the randomization are provided in the Supplementary Materials. All patients received standard symptomatic treatment.”

2) The handling of unquantifiable or undetectable viruses in the models is not clear in either the manuscript or supplemental statistical analysis information. Are these values imputed, or is data censored once below the limits of quantification or detection? How does the model handle censored data, if applicable?

We have added lines 185-186:

“Viral loads below the lower limit of quantification (CT values ≥40) were treated as left-censored under the model with a known censoring value.”

3) Did the study need to be unblinded prior to the first interim analysis? Could the adaptive design with the first analysis have been done with only one or a subset of statisticians unblinded prior to the decision to stop enrolling in the ivermectin arm?

The unblinded interim analysis was done on the first 50 patients enrolled in the study. The study at that time was enrolling into five arms including ivermectin, casirivimab-imdevimab, remdesivir, favipiravir, and a no study drug arm (there were exactly 10 per arm as a result of the block randomization).

The main rationale for making this interim analysis unblinded was to determine the most reasonable value of λ (this defines stopping for futility/success), which is a trade-off between information gain, reasonable sample size expectations, and the balance between quickly identifying interventions which have antiviral activity versus the certainty of stopping for futility.

Once the value of 12.5% was decided, the trial investigators remained blinded to the results until the stopping rules were met and the unblinded statistician discussed with the independent Data Safety and Management Board who agreed to unblind the ivermectin arm.

4) Can the authors comment on why the interim analysis occurred prior to the enrollment of 50 persons in each of the ivermectin and comparison arms? Even though the sample sizes were close (41 and 45 persons), the trigger for interim analysis was pre-specified.

After the first interim analysis at 50 patients enrolled into the study, they were planned every additional 25 patients (i.e. very frequently). The trigger for the interim analysis was not 50 patients into a specific arm, but 50 patients in total, and thereafter were planned to occur with every 25 new patients enrolled into the study. In practice there were backlogs in the data pipeline (which we explain), and interim analyses occurred less frequently than planned- the second one being in April 2022.

5) The reporting of percent change for the intervention arms is overstated. All credible intervals cross zero: the clearance for ivermectin is stated to be 9% slower, but the CI includes + and - %, so it should be reported as "not different." Similarly, and more importantly for casirivimab/imdevimab, it was reported to be 52% faster, although the CI is -7.0 to +115%. This is likely a real difference, but with ten participants underpowered- and this is good to discuss. Instead, please report that the estimate was faster, but that it was not statistically significant. Similarly, the clearance half-life for ivermectin is not different, rather than "slower" as reported (CI was -2 to +6.6 hours). This result was however statistically significant for casirivimab/imdevimab.

Thank you for your comments. The confidence interval for casirivimab/imdevimab did not cross zero and was +7.0 to +115.1%, and we thank the reviewer for picking up the error in the results section (it was correct in the abstract) where it was written -7.0 to +115.1%. We have made this correction. Elsewhere, we have provided more precise language to discriminate clinical significance from statistical significance, as per the essential revisions.

6) While the use of oropharyngeal swabs is relatively novel for a clinical trial, and they have been validated for diagnostic purposes, the results of this study should discuss external validity, especially with respect to results from other studies that mainly use nasopharyngeal or nasal swab results. For example, oropharyngeal viral loads have been variably shown to be more sensitive for the detection of infection, or conversely to have 1-log lower viral loads compared to NP swabs. Because these models look for longitudinal change within a single sampling technique, they do not impact internal validity but may impact comparisons to other studies or future study designs.

We have added the following sentence to the discussion:

“Oropharyngeal viral loads have been shown to be both more and less sensitive for the detection of SARS-CoV-2 infection. Although rates of viral clearance are very likely to be similar from the two sites, this should be established for comparison with other studies.”

7) Caution should be used around the term "clinically significant" for viral clearance. There is not an agreed-upon rate of clinically significant clearance, nor is there a log10 threshold that is agreed to be non-transmissible despite moderately strong correlations with the ability to culture virus or with antigen results at particular thresholds.

We agree. We have addressed this partly in our response to Reviewer 1.

8) Additional discussion could also clarify that certain drugs, such as remdesivir, have shown in vivo activity in the lungs of animal models and improvement in clinical outcomes in people, but without change in viral endpoints in nasopharyngeal samples (PINETREE study, Gottlieb, NEJM 2022). Therefore, this model must be interpreted as no evidence of antiviral activity in the pharyngeal compartment, rather than a complete lack of in vivo activity of agents given the limitations of accessible and feasible sampling. That said, strongly agree with the authors about the conclusion that ivermectin is also likely to lack activity in humans based on the results of this study and many other clinical studies combined.

As above this has been addressed in our response to Reviewer 1.

Reviewer #3 (Public Review):

This is a well-conducted phase 2 randomized trial testing outpatient therapeutics for Covid-19. In this report of the platform trial, they test ivermectin, demonstrating no virologic effect in humans with Covid-19.

Overall, the authors' conclusions are supported by the data.

The major contribution is their implementation of a new model for Phase 2 trial design. Such designs would have been ideal earlier in the pandemic.

We thank the reviewer for their encouraging comments.

Author Response

Reviewer #2 (Public Review):

1) The manuscript assumes an understanding of both economic terminology and statistical approaches that will not be familiar to most of the audience, if I am a representative example. This begins in the abstract, much of which I found incomprehensible. I still am not sure about the definition of "nominal costs ", and I certainly have no idea what they mean by a "wholly non-parametric machine learning regression". This continues throughout-presenting much of the data as Log10-transformed costs means that many of the graphs become impossible for a normal mortal like me to interpret.

We agree with the reviewer. We provide definitions of terms in the Introduction (lines 29-41) and explain the regression methods in greater detail in the text (lines 173-182) and appendix (Tables 1 and 2).

2) The version presented is written like some early outline draft. Rather than using narrative to guide the reader through the data, it reads like a series of Figure legends. For example, I literally thought the text on page 4 were the Figure legends, but they are not. "Figure 2 shows...." "Table 1 shows...". The Discussion is similarly difficult to follow. Given the complexity and importance of the data they present, this is a major missed opportunity/

We agree with the reviewer. We have extensively rewritten the text as recommended by the reviewer.

3) What will most interest my own part of the NIH-community is the assertion that "real dollar adjusted" grant funding has not decreased, but has instead remained flat. Few people I know will believe this. The authors address in a less-than-clear fashion some of the reasons for this-solicited versus non-solicited awards, clinical trials, etc, but do not dig into their own data to identify what are likely to be other issues. I doubt any one of the 20+ NIH-funded researchers in my Department (predominantly NIGMS funded) has a grant that reaches the "median level"-I do not after 32 years of continuous NIH-funding. Most new NIGMS-funded researchers, including many in my Department, are coming in funded by MIRA grants, which at $250K are half the median grant size. They do spend a few moments on disparities in Figure 7, but much more could be pulled out of this data set. Digging into issues like this-distributions in different NIH Institutes, at different career levels, etc, would make this work much more impactful.

We agree with the reviewer. We provide additional data on R01-equivalent awards (as previously noted) and on the $250K and $500 nominal values. See new Tables 2 and 4. We acknowledge that our analysis is based on NIH as an agency, not on individual Institutes and Centers (lines 259-260).

Author Response

Reviewer #1 (Public Review):

The authors devised a new mRNA imaging approach, MASS, and showed that it can be applied to investigate the activation of gene expression and the dynamics of endogenous mRNAs in the epidermis of live C. elegans. The approach is potentially useful, but this manuscript will benefit by addressing the following questions:

We thank the reviewer for spending time reviewing our manuscript and for the insightful comments.

Major comments:

1) In Figure 1-figure supplement 1, the authors claimed that MASS could verify the lamellipodia-localization of beta-actin mRNAs. However, the image showed the opposite of the authors' claim as the concentration of beta-actin mRNA was lower in lamellipodia than the rest of the cytosol. This result disagreed with ref. 17 (Katz, Z.B. et al., Genes and Development, 2012). Hence, the authors cannot make the statement that "MASS can be readily used to image RNA molecules in live cells without affecting RNA subcellular localization". To thoroughly test this notion, the authors should image beta-actin mRNA using MASS and the conventional MS2 system side by side and calculate the polarization index in the same way as shown in Katz, Z.B. et al., Genes and Development, 2012.

We noticed that b-ACTIN mRNAs were less polarized in our image compared to that shown in Katz, Z.B. et al. (Genes and Development, 2012). It is likely due to different cell lines being used. In the previous study, mouse embryonic fibroblasts (MEFs) were used. In our initial experiment, HeLa cells were used. Our data showed b-that ACTIN mRNAs labeled with MASS could be localized to the lamellipodia.

As suggested by the reviewer, we performed new experiments to image b-ACTIN mRNAs using MASS and the conventional MS2 system side by side in NIH3T3 cells, a mouse fibroblast cell line (MEF cells are not available in our lab). We did not find cells with extensively polarized b-ACTIN mRNAs localization, potentially due to different cell lines. We, therefore, did not calculate the polarization index. However, we found that b-ACTIN mRNAs detected by both methods showed a similar localization pattern. These new data suggest that MASS does not affect RNA subcellular localization. We added the new results and updated Figure 1-figure supplement 3.

2) The experiments that validate this new RNA imaging method are not sufficient. The authors need to systematically compare MASS and the MS2 system, including their RNA signal intensity, signal-to-background ratio.

We have systematically compared MASS and the conventional MS2 system, including signal intensity and signal-to-noise ratio, and measured the velocities of mRNA movement. We found that MASS showed a similar signal-to-noise ratio and higher signal intensity to the conventional MS2 system. We have now revised the information in the text on pages 4 and 5, and in Figure 1-figure supplement 4, 5, and 6.

3) In line with this, does beta-actin mRNA display the same behavior as in (Figure 1C-F) when the mRNA was imaged with the MS2 system? The movies do not indicate the type of motility expected of mRNA. For instance, it seems that almost all of the GFP dots, which are presumably single beta-actin mRNAs, stayed stationary over a time course of tens of seconds (Movie 1). This seems to be very different from what has been observed before. It's not clear that the dots are real mRNAs molecules. This further stresses the importance for them to compare their new imaging system with the conventional MS2 application.

We noticed that the mobility of b-ACTIN mRNAs vary in different cells. It is possible that the mobility of mRNAs was regulated in a cell context-dependent manner.

To confirm that the GFP foci detected are real mRNA molecules, we performed MASS combined with single-molecule RNA FISH. We found that MASS detected a similar number of GFP foci compared to the spots detected by smFISH. In addition, the majority (72%) of GFP foci colocalized with the smFISH spots of b-ACTIN-8xMS2 mRNAs. It is reported that not all MS2 stem-loop will be bound by the MCP (Wu et al., Biophysical journal 2012). As only 8xMS2 was used in MASS, it is likely that some mRNAs were not entirely bound by MCP and were not detected. On the other hand, only sixteen probes were used in the smFISH experiment, and it is possible that some mRNAs were miss labeled by smFISH. Therefore, 100% colocalization of MASS foci with the smFISH spots was hard to achieve. Thus these results suggest that GFP dots are real mRNA molecules. We have added the new data in Figure 1, Figure 1-figure supplement 1, and the text on page 3.

We measured the velocity of (b-ACTIN mRNA movement tracked by MASS and the conventional MS2 system. We added this information in Figure 1-figure supplement 5 and to the text on pages 4 and 5. With the conventional MS2 system, we observed similar behavior to those observed by MASS.

4) The authors claimed that a major advantage of MASS is that it has only 8xMS2 stemloops (350 nt) and overcomes "the previous obstacle of the requirement of inserting a long 1,300 nt 24xMS2". This statement lacks experimental support in this manuscript. The authors need to quantitatively compare the genomic tagging efficiency of 8xMS2 and 24xMS2.

It has been reported by several decent studies that the knock-in efficiency decreases dramatically with increasing insert size. For example:

~10-fold decrease of knockin frequency with a 1085 bp compared to a 767 bp insertion of DNA fragment (Extended Data Fig.8. Wang, J. et al. Nature methods, 2022).

~30-fold decrease of knockin frequency with an 1122 bp compared to a 714 bp insertion of DNA fragment (Figure 3 and Table S1. Paix, A. et al. PNAS, 2017).

In this study, we did not directly examine the knock-in efficiency of 8xMS2 and 24xMS2. Based on published data from other laboratories, we assumed that the efficiency of the knock-in of 8xMS2 (350 nt) would be higher than that of 24xMS2 (~1300 nt).

5) MASS has the same strategy as SunRISER (Guo, Y. & Lee, R.E.C., Cell Reports Methods, 2022). Both methods use Suntag to amplify signals of MS2- or PP7-tagged RNA. The authors need to elaborate the discussions and describe the similarities and differences of the two studies. In particular, the Guo paper needs to be properly referenced.

We have cited the paper and discussed the similarities and differences between our method and the SunRISER (page 7). Taking both studies together, Guo and we demonstrated that it is an efficient strategy to combine the MS2 system and the Suntag system as a signal amplifier for long-term and endogenous mRNA imaging in live cells.

6) In Guo, Y. & Lee, R.E.C., Cell Reports Methods, 2022, they showed that 8XPP7 with 24XSunTag configuration led to fewer mRNA per cell (Figure 5B of the Cell Reports Methods paper). Does MASS, which has 8xMS2 with 24xSunTag, similarly lead to few mRNAs? The authors should compare the number of mRNAs detected by MASS and the conventional MS2, or by FISH.

We compared the number of mRNAs detected by MASS and by smFISH. We performed MASS combined with single-molecule RNA FISH and found that MASS detected a similar number of GFP foci compared to the spots detected by smFISH.

In addition, the majority (72%) of GFP foci colocalized with the smFISH spots of b-ACTIN8xMS2 mRNAs. It is reported that not all MS2 stem-loop will be bound by the MCP. As only 8xMS2 was used in MASS, it is likely that some mRNAs were not entirely bound by MCP and were not detected. On the other hand, only sixteen probes were used in the smFISH experiment, and it is possible that some mRNAs were miss labeled by smFISH. Therefore, 100% colocalization of MASS foci with the smFISH spots was hard to achieve. These data indicated that MASS could label the majority of mRNAs from a specific gene in live cells.

We have added the new data in Figure 1, Figure 1-figure supplement 1, and the text on page 3.

Reviewer #2 (Public Review):

Hu et al. developed a new reagent to enhance single mRNA imaging in live cells and animal tissues. They combined an MS2-based RNA imaging technique and a Suntag system to further amplify the signal of single mRNA molecules. They used 8xMS2 stem-loops instead of the widely-used 24xMS2 stem-loops and then amplified the signal by fusing a 24xSuntag array to an MS2 coat protein (MCP). While a typical 24xMS2 approach can label a single mRNA with 48 GFPs, this technique can label a single mRNA with 384 GFPs, providing an 8-fold higher signal. Such high amplification allowed the authors to image endogenous mRNA in the epidermis of live C. elegans. While a similar approach combining PP7 and Suntag or Moontag has been published, this paper demonstrated imaging endogenous mRNA in live animals. Data mostly support the main conclusions of this paper, but some aspects of data analysis and interpretation need to be clarified and extended.

Strengths:

Because the authors further amplified the signal of single mRNA, this technique can be beneficial for mRNA imaging in live animal tissues where light scattering and absorption significantly reduce the signal. In addition, the size of an MS2 repeat cassette can be reduced to 8, which will make it easier to insert into an endogenous gene. Also, the MCP24xSuntag and scFv-sfGFP constructs can be expressed in previously developed 24xMS2 knock-in animal models to image single mRNAs in live tissues more easily.

The authors performed control experiments by omitting each one of the four elements of the system: MS2, MCP, 24xSuntag, and scFV. These control data confirm that the observed GFP foci are the labeled mRNAs rather than any artifacts or GFP aggregates. And the constructs were tested in two model systems: HeLa cells and the epidermis of C. elegans. These data demonstrate that the technique may be used across different species.

We thank the reviewer for spending time reviewing our manuscript and for the insightful comments.

Weaknesses:

Although the paper has strength in providing potentially useful reagents, there are some weaknesses in their approach.

Each MCP-24xSunTag is labeled with 24 GFPs, providing enough signal to be visualized as a single spot. Although the authors showed an image of a control experiment without MS2 in Figure 1B, the authors should at least mention this potential problem and discuss how to distinguish mRNA from MCP tagged with many GFPs. MCP-24xSunTag labeled with 24 GFPs may diffuse more rapidly than the labeled mRNA. Depending on the exposure time, they may appear as single particles or smeared background, but it will certainly increase the background noise. Such trade-offs should be discussed along with the advantage of this method.

With MCP-24xSuntag, in theory, there will be up to 24 GFP molecules tethered to one MCP molecule, which may lead to the formation of GFP puncta. However, under our imaging conditions (100 ms to 500 ms) with a spinning disk confocal microscopy, puncta of MCP24xSuntag were not detected. As the reviewer suggested, it might be because MCP24xSuntag is diffusing too fast to be detected as a spot.

For the signal-to-noise ratio, we did more experiments and analyses. We imaged overexpressed b-ACTIN mRNAs using the conventional 24xMS2 system or MASS with different repeats of Suntag arrays (MCP-24xSuntag, MCP-12xSuntag, MCP-6xSuntag). For the conventional 24xMS2 system, we followed the previous protocol that added a nuclear localization signal (NLS) to MCP, and b-ACTIN mRNAs were nicely detected with a signal-to-noise ratio of 1.21.

We found that MASS showed a comparable or better signal-to-noise ratio than the conventional 24xMS2 system. (MASS with MCP-24xSuntag: 1.79, MASS with MCP12xSuntag: 1.48, MASS with MCP-6xSuntag: 1.42). These data indicate that using Suntag as a signal amplifier did not increase background noise.

Also, more quantitative image analysis would be helpful to improve the manuscript. For instance, the authors can measure the intensity of each GFP foci, show an intensity histogram, and provide some criteria to determine whether it is an MCP-24xSuntag, a single mRNA, or a transcription site. For example, it is unclear if the GFP spots in Figure 2D are transcription sites or mRNA granules.

Under our imaging conditions, MCP-24xSuntag was not detected as GFP foci.

We performed MASS combined with single-molecule RNA FISH and found that MASS detected a similar number of GFP foci compared to the spots detected by smFISH.

In addition, the majority (72%) of GFP foci colocalized with the smFISH spots of b-ACTIN8xMS2 mRNAs. It is reported that not all MS2 stem-loop will be bound by the MCP. As only 8xMS2 was used in MASS, it is likely that some mRNAs were not entirely bound by MCP and were not detected. On the other hand, only sixteen probes were used in the smFISH experiment, and it is possible that some mRNAs were miss labeled by smFISH. Therefore, 100% colocalization of MASS foci with the smFISH spots was hard to achieve. These data indicated that MASS could label the majority of mRNAs from a specific gene in live cells.

We have added the new data in Figure 1, Figure 1-figure supplement 1, and the text on page 3.

The GFP spots in Figure 2D are not transcription sites, as they were localized in the cytoplasm, not in the nucleus. We imaged exogenous BFP-8xMS2 mRNAs in the epidermis of C. elegans and found that the size of the GFP foci of endogenous C42D4.38xMS2 mRNAs is larger than that of BFP-8xMS2 mRNAs. Those data suggest that the GFP spots in Figure 2D (C42D4.3-8xMS2 mRNA) are mRNA granules. We added those new data in Figure 2-figure supplement 5 and the text on page 7.

Another concern is that the heavier labeling with 24xSuntag may alter the dynamics of single mRNA. Therefore, it would be desirable to perform a control experiment to compare the diffusion coefficient of mRNAs when they are labeled with MCP-GFP vs MCP- 24xSuntag+scFv-sfGFP.

We thank the reviewer for raising this critical issue. We have performed live imaging of bACTIN mRNA using the conventional 24xMS2 system or MASS with different lengths of Suntag arrays (MCP-24xSuntag, MCP-12xSuntag, MCP-6xSuntag). We then measured the velocity of mRNA movement in each imaging condition. We found that compared to the conventional 24xMS2 system, mRNA labeled with MCP-24xSuntag or by MCP-12xSuntag showed a smaller velocity, indicating that heavier labeling affected mRNA movement speed.<br /> In contrast, we found that mRNAs labeled with MCP-6xSuntag showed a similar velocity to that tagged with the conventional 24xMS2 system. Those data pointed out that when MASS is used to measure the speed of mRNA movement, a short Suntag array (MCP6xSuntag) should be used. We added those new data in Figure 1-figure supplement 5 and to the text on pages 4, 5.

The authors could briefly explain about the genes c42d4.3 and mai-1. Why were these specific genes chosen to study gene expression upon wound healing? Did the authors find any difference in the dynamics of gene expression between these two genes?

The function of C42D4.3 and mai-1 is currently not known. Through mRNA deep sequencing, It has been shown that the expression level of C42D4.3 and mai-1 was quickly increased after wounding of the epidermis of C. elegans. We, therefore, choose those two mRNAs for imaging. We added more information about C42D4.3 and mai-1 to the text on page 6.

We observed similar dynamics of gene expression between C42D4.3 and mai-1 (Video 7 ,8, 9).

Reviewer #3 (Public Review):

It is a brilliant idea to combine the MS2-MCP system with Suntag. As the authors stated, it reduces the copies of the MS2 stem loops, which can create challenges during cloning process. The Suntag system can easily amplify the signal by several to tens of folds to boost the signal for live RNA tagging. One of the best ways to claim that MASS works better than the MS2 system by itself is to compare their signal-to-noise ratios (SNRs) within the same model system, such as HeLa cells or the C. elegans epidermis. Because the authors' main argument is that they made an improvement in live RNA tagging method, it is necessary to compare it with other methods side-by-side. The authors claim that MASS can significantly improves the efficiency of CRISPR by reducing the size of the insert, it still requires knocking in several transgenes, which can be even more challenging in some model systems where there are not many selection markers are available. Another possible issue is that the bulky, heavy tagging (384 scFv-sfGFP along with 24xSuntag) can affect the mobility or stability of the target mRNAs. If it also tags preprocessed RNA in the nucleus, it may affect the RNA processing and nuclear export. A few experiments to address these possibilities will strengthen the authors' arguments. I am proposing some experiments below in detailed comments.

We thank the reviewer for spending time reviewing our manuscript and for the insightful comments.

1) For the experiments with HeLa cells, it is not clear whether the authors used one focal plane or the whole z-stack for their assessment of mRNA kinetics, such as fusion, fission, and anchoring. If it was from one z-plane, it was possible that many mRNAs move along the z-axis of the images to assume kinetics. If the kinetics is true, is it expected by the authors? Are beta-actin mRNAs bound to some RNA-binding proteins or clustered in RNP complexes?

One focal plane was used in the experiments showing mRNAs' fusion, fission, and anchoring behavior. We have now added this information in the figure legend of figure 1. Yes, b-ACTIN mRNA are bound to specific RNA-binding proteins, for example, ZBP1, and it has been reported that ZBP1 forms granules with b-ACTIN mRNAs (Farina, K.L., et al., Journal of cell biology, 2003).

2) Some quantifications on beta-actin mRNA kinetics, such as a plot of their movement speed or fusion rate, etc., would help readers better understand the behaviors of the mRNAs and assess whether the MASS tagging did not affect them.

We thank the reviewer for raising this critical issue. We have performed live imaging of bACTIN mRNA using the conventional 24xMS2 system or MASS with different lengths of Suntag arrays (MCP-24xSuntag, MCP-12xSuntag, MCP-6xSuntag). We then measured the velocity of mRNA movement in each imaging condition. We found that compared to the conventional 24xMS2 system, mRNA labeled with MCP-24xSuntag or by MCP-12xSuntag showed a smaller velocity, indicating that heavier labeling affected mRNA movement speed.<br /> In contrast, we found that mRNAs labeled with MCP-6xSuntag showed a similar velocity to that tagged with the conventional 24xMS2 system. Those data pointed out that when MASS is used to measure the speed of mRNA movement, a short Suntag array (MCP6xSuntag) should be used. We added those new data in Figure 1-figure supplement 5 and the text on pages 4 and 5.

3) Using another target gene for MASS tagging would further confirm the efficacy of the system. Assuming the authors generated a parental strain of HeLa cell, where MCP24xSuntag and scFv-sfGFP are already stably expressed (shown in Fig. 1B), CRISPR-ing in another gene should be relatively easy and fast.

For exogenous genes, in addition to b-ACTIN, we imaged mRNAs from three more genes, C-MYC, HSPA1A, and KIF18B, with MASS in HeLa cells. For endogenous genes, we imaged C42D4.3 and mai-1 in the epidermis of C. elegans. These data indicated that MASS is able to image both exogenous and endogenous mRNAs in live cells. We have now added those new data in Figure 1-figure supplement 2, Figure 2-figure supplement 2, and to the text on pages 3, 4, and 6.

4) Adding a complementary approach to the data presented in Fig. 1, such as qRT-PCR for beta-actin, with or without the MASS system would ensure the intense tagging did not affect the mRNA expression or stability.

To address this question, we performed more experiments to test whether MASS affected the mRNA expression and stability. Because b-ACTIN mRNA is very stable; thus it is not suitable for measuring mRNA stability. We, therefore, tested three genes, including C-MYC, HSPA1A, and KIF18B, which were reported as medium-stable mRNAs. We found that MASS did not affect the stability of those three mRNAs in HeLa cells. We also tested the expression level and the stability of endogenous C42D4.3 mRNA in the epidermis of C. elegans and found that both the expression and the stability were not affected by MASS. We have now added those new data in Figure 1-figure supplement 2, Figure 2-figure supplement 2, and to the text on pages 3, 4, and 6.

5) For experiments with the C. elegans epidermis, including at least one more MASS movie clip for C42D4.3 and a movie for mai-1 would be helpful for readers to appreciate the RNA labeling and its dynamics.

We showed two movies (video 7 and video 8) and the snapshots for C42D4.3 mRNA (Figure 2D and Figure 2-figure supplement 3). We also added a movie (Video 9) for mai-1.

6) The difference between Fig. 2D and Fig. 2-fig supp. 3 is unclear. The authors should address the different patterns of RNA signal propagation. Is it due to the laser power used too much, resulting in photobleach in Fig. 2D?

We have noticed the difference between Figure 2D and Figure 2-figure supplement 3. In Figure 2D, GFP foci did not appear within the injury area after wounding. In Figure 2-figure supplement 3, GFP foci quickly appeared within the injury area. Although we kept the laser power setting constant when performing the laser wounding experiment, there are indeed variations in the actual laser power used. As the reviewer suggested, the difference may be due to photobleaching in Figure 2D. Alternatively, it is possible that the location of the injury site or the degree of injury could affect the dynamics of gene expression.

However, we would like to point out that the dynamics of gene expression pattern in Figure 2D (Video 7) and Figure 2-figure supplement 3 (Video 8) is similar. GFP foci of C42D4.3 mRNAs were first detected around the injury sites. Then GFP foci gradually appeared from the area around the injury site to distal regions.

7) Movie 7 is the key data the authors are presenting, but there are a few discrepancies between their arguments and what is seen from the movie. The authors say the RNAs are "gradually spread" (the line 120 in the manuscript). However, it seems that the green foci just appear here and there in the epidermis and the majority of them stay where they were throughout the timelapse. This pattern seems to be different from the montage in Fig. 2-fig supp. 3, which indeed looks like the mRNA spots are formed around the lesion and spread overtime. Additional explanation on this will strengthen the arguments. Given the dramatic increase of c42d4.3 mRNA abundance 1 min. after the laser wounding, there must be a tremendous boost of transcription at the active transcription sites, which should be captured as much bigger and fewer green foci that are located inside the nucleus. Is this simply because those nuclear sites are out of focus or in a similar size as mRNA foci? Regardless, this should be addressed in the discussion.

We apologize for the confusing description of our original data. We wrote "gradually spread", but we did not mean that mRNAs were transcribed at the wounding site and moved to the distal regions. We actually mean that GFP foci first appeared close to the wounding site and more GFP foci gradually appeared at the distal regions. We have changed our writing to "the appearance of GFP foci gradually spreads from the area around the injury site to distal regions".

For the difference between Figure 2D and Figure 2-figure supplement 3, please see our discussion for comment 6.

For transcription, we also expected a boost of transcription after wounding. However, we failed to detect the appearance of bigger GFP foci in the nucleus. We agree with the reviewer that this is because the active nuclear sites are out of focus. The epidermis of C. elegans is a syncytium with 139 nuclei located in different regions and focal planes. With our microscopy, we were able to image only one focal plane, in which there are usually only four to ten nuclei. Therefore, it is likely that the nuclei with active transcription were out of focus. We have now discussed this point in the revised manuscript (page 6).

8) One clear way to confirm that MASS labels mRNAs and does not affect their stability/localization is to compare the imaging data with single-molecule RNA fluorescence in situ hybridization (smFISH) that the Singer lab developed decades ago. The authors can target the endogenous c42d4.3 or mai-1 RNAs using smFISH and compare their abundance and subcellular localization patterns with their data.

To confirm that the GFP foci detected are real mRNA molecules, we performed MASS combined with single-molecule RNA FISH and found that MASS detected a similar number of GFP foci compared to the spots detected by smFISH. In addition, the majority (72%) of GFP foci colocalized with the smFISH spots of b-ACTIN-8xMS2 mRNAs. It is reported that not all MS2 stem-loop will be bound by the MCP. As only 8xMS2 was used in MASS, it is likely that some mRNAs were not fully bound by MCP and were not detected. On the other hand, only sixteen probes were used in the smFISH experiment, and it is possible that some mRNAs were miss labeled by smFISH. Therefore, 100% colocalization of MASS foci with the smFISH spots was hard to achieve. These data indicated that MASS could detect single mRNA molecules and label the majority of mRNAs from a specific gene in live cells. We have now added the new data in Figure 1, Figure 1-figure supplement 1, and to the text on page 3.

We performed more experiments to test whether MASS affected the mRNA expression and stability. Because b-ACTIN mRNA is very stable; thus it is not suitable for measuring mRNA stability. We, therefore, tested three genes, including C-MYC, HSPA1A, and KIF18B, which were reported as medium-stable mRNAs. We found that MASS did not affect the stability of those three mRNAs in HeLa cells. We also tested the expression level and the stability of endogenous C42D4.3 mRNA in the epidermis of C. elegans and found that both the expression and the stability were not affected by MASS. We have now added those new data in Figure 1-figure supplement 2, Figure 2-figure supplement 2, and to the text on pages 3, 4, and 6.

To test whether MASS affected the mRNA localization, we performed new experiments to image b-ACTIN mRNAs using MASS and the conventional MS2 system side by side in NIH3T3 cells, which is a mouse fibroblast cell line. We found that b-ACTIN mRNAs showed similar localization in both methods. These new data suggest that MASS does not affect RNA subcellular localization. We have now added the new results in Figure 1-figure supplement 2.

9) One of the main purposes to live image RNAs is to assess their dynamics. Adding some more analyses, such as the movement speed of the foci, would be helpful to show how effective this system is to assess those dynamics features.

We thank the reviewer for raising this critical issue. We have performed live imaging of bACTIN mRNA using the conventional 24xMS2 system or MASS with different lengths of Suntag arrays (MCP-24xSuntag, MCP-12xSuntag, MCP-6xSuntag). We then measured the velocity of mRNA movement in each imaging condition. We found that compared to the conventional 24xMS2 system, mRNA labeled with MCP-24xSuntag or by MCP-12xSuntag showed a smaller velocity, indicating that heavier labeling affected mRNA movement speed.

In contrast, we found that mRNAs labeled with MCP-6xSuntag showed a similar velocity to that tagged with the conventional 24xMS2 system. Those data pointed out that when MASS is used to measure the speed of mRNA movement, a short Suntag array (MCP6xSuntag) should be used. We added those new data in Figure 1-figure supplement 5 and to the text on pages 4 and 5.

Reviewer #4 (Public Review):

Hu et al introduced the MS2-Suntag system into C. elegans to tag and image the dynamics of individual mRNAs in a live animal. The system involves CRISPR-based integration of 8x MS2 motifs into the target gene, and two transgene constructs (MCP-Suntag; scFv-sfGFP) that can potentially recruit up to 384 GFP molecule to an mRNA to amplify the fluorescent signal. The images show very high signal to background ratio, indicating a large range of optimization to control phototoxicity for live imaging and/or artifacts caused by excessive labeling. The use of epidermal wound repair as a case study provides a simplified temporal context to interpret the results, such as the initiation of transcription upon wounding. The preliminary results also reveal potentially novel biology such as localization of mRNAs and dynamic RNP complexes in wound response and repair. On the other hand, the system recruits a large protein complex to an mRNA molecule, an immediate question is to what extent it may interfere with in vivo regulation. Phenotypic assays, e.g., in development and wound repair, would have been a powerful argument but are not explored. In all, C. elegans is powerful system for live imaging, and the genome is rich in RNA binding proteins as well as miRNAs and other small RNAs for rich posttranscriptional regulation. The manuscript provides an important technical progress and valuable resource for the field to study posttranscriptional regulation in vivo.

We thank the reviewer for spending time reviewing our manuscript and for the insightful comments.

Author Response

Reviewer #1 (Public Review):

Auxin-induced degradation is a strong tool to deplete CHK-2 and PLK-2 in the C. elegans germ line. The authors strengthen their conclusions through multiple approaches, including rescuing mutant phenotypes and biochemical analyses of CHK-2 and PLK-2.

The authors overcame a technical limitation that would hinder in vitro analysis (low quantity of CHK-2) through the clever approach of preventing its degradation via the proteasome. In vitro phosphorylation assays and mass spectrometry analysis that establishes that CHK-2 is a substrate of PLK-2 nicely complement the genetic data.

The authors argue that the inactivation of CHK-2 by PLK-2 promotes crossover designation; however, the data only indicate that PLK-2 promotes proper timing of crossover designation.

We thank the reviewer for this point of clarification. While we believe that PLK activity is essential to inactivate CHK-2 and trigger CO designation, we agree that this has not been firmly established with the tools available to us, as elaborated below. We have revised the text to avoid overstating the conclusions.

It is not clear whether the loss of CHK-2 function with the S116A and T120A mutations is the direct result of the inability to phosphorylate these residues or whether it is caused by the apparent instability of these proteins, as their abundance was reduced in IPs compared to wild-type. Agreed. The instability of the mutant proteins was a source of significant frustration during the course of this work, and limits the strength of our conclusions.

The mechanism of CHK-2 inactivation in the absence of PLK-2 remains unclear, though the authors were able to rule out multiple candidates that could have played this role.

Reviewer #2 (Public Review):

In this manuscript, Zhang et al., address the role of Polo-like kinase signaling in restricting the activity of Chk2 kinase and coordinating synapsis among homologous chromosomes with the progression of meiotic prophase in C. elegans. While individual activities of PLK-2 and CHK-2 have been demonstrated to promote chromosome pairing, and double-strand break formation necessary for homologous recombination, in this manuscript the authors attempt to link the function of these two essential kinases to assess the requirement of CHK-2 activity in controlling crossover assurance and thus chromosome segregation. The study reveals that CHK-2 acts at distinct regions of the C. elegans germline in a Polo-like kinase-dependent and independent manner.

Strengths:

The study reveals distinct mechanisms through which CHK-2 functions in different spatial regions of meiosis. For example, it appears that CHK-2 activity is not inhibited by PLK's (1 and 2) in the leptotene/zygotene meiotic nuclei where pairing occurs. This suggests that either CHK-2 is not phosphorylated by PLK-2 in the distal nuclei or that it has a kinase-independent function in this spatial region of the germline. These are interesting observations that further our understanding of how the processes of meiosis are orchestrated spatially for coordinated regulation of the temporal process.

Weaknesses:

While the possibilities stated above are interesting, they lack direct support from the data. A key missing element in the study is the actual role of PLK-2 signaling in controlling CHK-2 activity and thus function. I expand on this below.

Throughout the manuscript, the authors test the role of each of the kinases (CHK-2 or PLK-1, or 2) using auxin-induced degradation, which would eliminate both phosphorylated and unphosphorylated pools of proteins. This experiment thus does not test the role of PLK-2 signaling in controlling CHK-2 function or the role of CHK-2 activation. To test the role of signaling from PLK-2 or CHK-2, the authors need to generate appropriate alleles such as phospho-mutants or kinase-dead mutants. The authors do generate unphosphorylatable and phosphomimetic versions of CHK-2, however, they find that the protein level for both these alleles is lower than wild-type CHK-2 (which the authors state is already low). The authors conclude that the lower level of protein in the CHK-2 phospho-mutants is because the mutations cause destabilization of the protein. I am sympathetic with the authors since clearly these results make interpretations of actual signaling activity more challenging. But there needs to be some evidence of this activity, for example through the generation of a phosphor-specific antibody to phosphorylated CHK-2. While not functional, at least the phosphorylation status of CHK-2 would provide more information on its spatial pattern of activation and inactivation. In addition, it would still be of interest to the readership to present the data on these phosphor-mutant alleles with crossover designation and COSA-1::GFP. Is the phenotype of the WT knockin, and each of the phosphomutant knock-ins similar to auxin-induced degradation of CHK-2?

We thank the reviewer for these comments. We have made several attempts over the past decade that have failed to elicit a CHK-2 antibody that works for either immunofluorescence or western blots, likely due to the very low abundance of CHK-2. This has discouraged us from investing yet more resources to try to develop a phospho-specific antibody. Moreover, our evidence suggests that phosphorylation may promote CHK-2 degradation. Since the phosphomutants of CHK-2 are not stable, we do not think knock-in of these phosphomutants will provide new insights.

Given that the CHK-2 phosphomutants did not pan out for assessing the signaling regulation of PLK-2 on CHK-2, to directly assess whether PLK-2 activity restricts CHK-2 function in mid-pachytene but not leptotene/zygotene, the authors should generate PLK-2 kinase dead alleles. These alleles will help decouple the signaling function of PLK-2 from a structural function.

Similarly, to assess the potentially distinct roles of CHK-2 in leptotene/zygotene and mid-pachytene it would be important to assess CHK-2 kinase-dead mutant alleles. At this time, all of the analysis is based on removing both active CHK-2 and inactive CHK-2 (i.e. phosphorylated and unphosphorylated pool) using auxin-induced degradation. The kinase-dead alleles will help infer the role of the kinase more directly. The authors can then superimpose the auxin-induced degradation and assess the impact of complete removal of the protein vs only loss of its kinase function. These experiments may help clarify the role of signaling outcomes of these proteins, vs their complete loss. For example, what does kinase dead PLK-2 recruitment to the synapsed chromosomes appear like? Are their distinct activities for active and inactive PLK-2 that are spatially regulated? The same can be tested for CHK-2.

A kinase-dead allele of plk-2 has been generated in previous work and we have used it for other purposes. However, the fact that CHK-2 and PLK-2 are required for homolog pairing and synapsis, which are prerequisites for crossover designation, precludes their use here.

Author Response

Reviewer #2 (Public Review):

This is an interesting manuscript establishing a role for Ecdysone signaling in the control of sleep. The authors show that the Ecdysone receptor EcR is required primarily in cortex glia for the control of sleep and that its target E75 is also involved in sleep regulation. This is a novel function for both cortex glia and steroid signaling in Drosophila. The authors also present evidence that Ecdysone signaling would be important for response to starvation, and that lipid droplet mobilization would mediate the effect of ecdysone on sleep. This work is certainly innovative. However, the main conclusions need to be strengthened. In particular: variability in sleep amounts in certain strains could complicate interpretation, the idea that ecdysone modulates sleep response to starvation is not sufficiently well supported, and genetic evidence for mobilization of lipid droplets being the mechanism linking steroid signaling to sleep is currently quite weak.

Major concerns:

1) I have concerns with the variability observed with the GS drivers (whether nSyb or repo). This is particularly striking in figure S3 when comparing experiments conducted with EcR-c and the Ecl RNAi. Daytime is most affected, but even nighttime looks significantly different. Definitely, nighttime quantification should be shown in addition to total sleep in figure S3. However, I feel that confirming the key results of this study with an additional driver would be reassuring. Could repo-GAL4 combined with GAL80ts be used to drive EcR RNAi, instead of repo-GS? The same combination could help determine whether glia is responsible for the 20E-mediated increase in sleep after starvation (figure S4A).

We have updated the old Figure S3 source data (now Figure 2 - source data 5) with both daytime and nighttime sleep and the conclusion is similar, please also see our response to essential revision question 1. Regarding the GAL80ts experiment, as noted in our detailed response to essential revision question 1, we conducted this experiment and confirmed that adult-specific knockdown of EcR in glia affects sleep. We also tried to do this experiment under starvation conditions (Figure 3 – figure supplement 1A), but this is more challenging to conduct and interpret as it requires temperature shifts, ecdysone treatment and starvation. In particular, high temperature coupled with starvation turned to be an extreme stressor for Repo-Gal4; TublinGal80ts>EcR RNAi #1 flies, as 8 of 12 flies died after 1 day in our first run; thus, we did not proceed with this experiment.

2) The idea that ecdysone might suppress the response to starvation is interesting, but the results are not convincing. First, there is an important control missing. It is important to test the effect of Ecdysone on fed flies, to ensure that Ecdysone does not simply make flies sleepy. Second, it is not clear that EcR RNAi has a specific effect on starved flies. Starvation reduces sleep, but is this reduction really exaggerated in flies expressing EcR RNAi than in control flies? It seems to me that starvation reduces sleep by the same amount when comparing results in panels 3D and E. The effect of EcRNAi and starvation might be simply additive, which would suggest that 20E impacts sleep independently of starvation.

We now show effects of exogenous ecdysone on fed flies. As expected, and previously, shown, ecdysone promotes sleep in fed and starved flies (Figures 3 and 6). We agree with the reviewer that 20E impacts sleep independently of starvation. The major point we made with this experiment was that robust effects of starvation on sleep are maintained in RepoGS-EcR RNAi flies. The fact that these two manipulations together virtually eliminate sleep suggests that glial ecdysone signaling is required for the sleep that remains during starvation.

3) The material and method section needs to be improved. In particular, it is not clear to me how the starvation/ecdysone feeding assay was done. There are some additional explanations in the figure legend, but the approach is still not clear to me. Indicate clearly when the flies were starved, and when they were exposed to Ecdysone.

We rewrote the ecdysone treatment and starvation assay section with more details in Methods. We hope it is now clear.

4) I am not convinced that the Lsd2 results necessarily support the idea that this gene is required for the effect of 20E on sleep. Sleep is dramatically reduced during the day in the Lsd2 mutant. This is actually an interesting observation, but this strong effect on baseline sleep might be masking the ability of 20E to modulate sleep.

Thanks so much for this great comment. As noted in our response to essential revision question 4, we now demonstrate that lsd2 mutants respond effectively to GABA, showing that their sleep can be modulated.

Author Response:

Reviewer #2 (Public Review):

The manuscript reports on the complex variability of expression, trafficking, assembly/stability, and peptide loading among different MHC I haplotypes. In particular by analyzing two distinct MHC I molecules as representative members of groups of allotypes, that favor canonical or non-canonical assembly modes, the PI reports on preferential cytosolic or endo-lysosomal MHC I loading. Overall, the data shed light on the intersection between MHC I conformation and subcellular sites of peptide loading and help explain MHC I immunosurveillance at a different subcellular location.

In the first series of experiments the authors report an uneven surface expression of HLA-B vs HLA-A, and C on circulating monocytes, with HLA-B being expressed 4 times higher, also they report that as compared to the TAP-dependent allotype B*08:01 the TAP-independent allotype B*35:01 has a lower surface half-life and if often present as an empty molecule. These data set the basis for the author's hypothesis that B*35:01 could traffic in Rab11+ compartment and be involved in cross-presentation, which indeed is demonstrated in a series of pulse-chase peptide experiments and using cathepsin inhibitors.

Overall, the experiments could be improved by performing subcellular fractionation and organelle purification to conclusively demonstrate the differential trafficking of B*08:01 vs B*35:01, as well as quantitative mass spectrometry to determine cytosolic vs endosomal processing for one selected epitope presented by the different haplotypes.

We thank the reviewer for this suggestion, and agree that this would be a powerful method for further validating differential HLA-B trafficking and antigen processing. Unfortunately, we were unable to perform subcellular fractionation experiments for mass spec, as protocols for fractionation require upwards of 10 million cells to obtain endosomal fractions. For our donor samples, we typically obtain 1- 2 million moDCs after isolation and differentiation, greatly limiting the types of experiments we can perform with primary cells from specific donors. We considered performing these experiments in a cell line but were concerned that ER as well as endosomal trafficking and processing pathways might differ between cell lines and primary cells, which would necessitate a number of additional studies to validate use of the cell lines.

Author Response

Reviewer #1 (Public Review):

The authors set out to answer the standing mystery of an origin of a unique and complex system that is hagfish slime. They formulated a cogent scenario for the co-option of epidermal thread cells and mucous cells into slime and slime glands. Both histology and EM images back this up. It is a delight to see detailed and careful morphological analysis of both the cells and the secretion. The weakness of the manuscript lies in: a) the absence of an alternative hypothesis (therefore the lacking sense of hypothesis testing); and b) oversimplification and insufficient description of results in transcriptomic and phylogenetic comparison.

These are both key elements of the narrative. Because all the data "support" the only scenario considered in this paper, it could risk giving the impression of a just-so story. My reading of the results of their transcriptomic and phylogenetic analyses is more nuanced than explained in the paper. For example, the authors didn't explain in sufficient detail how the data summary in Fig. 5 "demonstrate" that the epidermal thread cells are "ancestral", and that the diversity of alpha and gamma thread biopolymer genes is a prerequisite to slime (without a functional analysis), or that the gene duplication events facilitated the origin of hagfish slime.

Thank you for these thoughtful comments.

We have made extensive changes to address the two issues raised by the reviewer. For the first one, we added discussion of an alternative hypothesis, namely a cloacal origin of hagfish slime glands (see Line 369). For the second, we added new transcriptomic data from a second species (E. stoutii), and provided more detailed phylogenetic analyses and explanations. Details are provided below and can be seen in the revised manuscript.

Reviewer #2 (Public Review):

The study is a careful investigation of the physical properties of hagfish slime and the underlying cellular framework that enables this extraordinary evolutionary innovation. I appreciate the careful and detailed measurements and images that the authors provide. The results presented here will surely be extremely important for researchers working on this particular organism and those interested in understanding the evolution, biomedical relevance, and biochemistry of mucus. However, I had difficulty contextualizing the findings in broader biological questions (e.g., the evolution of functional novelty, the adaptive processes, and the links between genetic and phenotypic evolution). I also think that the conclusions on the evolutionary origins and underlying genetics of hagfish slime based on comparative transcriptomic data may be premature.

Thank you for the thoughtful comments. In this revision, we have rewritten several sections and reorganized the Introduction for clearer readability. Also, we added discussion of an alternative hypothesis that the slime glands might be derived from cloacal glands (see Discussion, Line 369). Further, we provided more detailed transcriptomic data and phylogenetic analyses, along with enriched interpretations, to address the evolution of thread genes.

Author Response

Reviewer #1 (Public Review):

The manuscript aims to provide a comprehensive insight into the development of the tuberal hypothalamus of the chick by carefully analyzing the expression patterns of a plethora of proteins involved and perturbation of BMP signaling.

Strengths:

This manuscript presents the results of an in-depth analysis aimed to unravel the expression of a variety of transcription factors, and the role of signaling molecules, in particular BMP, SHH and Notch, and, and the role of BMP for the development of the tubular hypothalamus. For this, the authors applied a variety of methods, including in-situ RNA hybridizations to chick embryos, fate mapping, explant cultures, and loss and gain-functions studies in embryos, complemented by carefully mining previously performed scRNA-Seq data. From the data they derive a model, which explains the dynamic changes of expression of signaling molecules and transcription factors from anterior to posterior during chick development. In addition, they show that fate specification and growth occur concomitantly. Overall, the data provide a plethora of information on expression patterns and consequences of BMP signaling perturbation, which will be valuable for scientists interested in the events taking place during the development of the chick tubular hypothalamus.

We thank the reviewer for recognising the value of this study for development of the chick tuberal hypothalamus.

Weaknesses:

The plethora of data presented makes it very difficult for a reader, who is not familiar with this system, to follow the major conclusions from each of the panels. This difficulty is enhanced by the lack of a concise, simple and focused summary at the end of most chapters, which, from my point of view, still contains too many details. Similarly, the discussion too often refers to details presented in the figures of the Results section, rather than giving a broader and focused summary and pointing out to novel conclusions.

We have extensively revised the manuscript, to ensure that it is easier to follow and is less detailed. We have tightened and shortened the Introduction, without losing content or context. We have revised the narrative in the Results section, to reflect revisions to figures (detailed below and in response to Reviewer 2 comments), cut back on detail, and summarised each section. We have streamlined the Discussion, so that the broader points and novel conclusions are more prominent.

Revisions to figures are as follows:

1. Several main Figures and associated Supplementary Figures have been rearranged so that the text and figures are easier to follow. The rearrangements mean that the reader can follow critical conceptual points without having to jump from main to supplementary figures. Key rearrangements have been made between Figure 1 and Figure 1-figure supplement 1; Figure 2 and Figure 2-figure supplement 1; Figure 2 and Figure 2-figure supplement 2; Figure 6 and Figure 6 supplement 1.

2. Throughout the manuscript, we have added new images/replaced previous images in cases where key points were not coming across clearly (see Reviewer 2 comments). New data is shown in Figures 1F, G, T-T”; Figures 2G-P’; Figure 2-figure supplement 1 (panels A and E); Figure 2-figure supplement 2 (panels B, E-G; Q-T).

3. Throughout the manuscript we have improved the schematics, making it easier to follow key domains and, separately, gene expression patterns

4. Finally, in light of the comment on the plethora of data, detail and the overall difficulty in following the manuscript, we have removed in situ data that was not needed for our central arguments (previous panels 1F-J and 1R-T).

I also suggest that the authors check the Materials and Methods section, which does not always contain the information required. For example, in the chapter on "Chicken HCR": I guess they used the HCR IHC kit from Molecular Instruments? What kind of "modification" of the Molecular Instruments protocol did they introduce?

We have revised the Material and Methods section as required. We followed the Molecular Instrument Protocol HCRv3-Chicken, but included a methanol dehydration step, which we have now added.

Author Response

Reviewer #1 (Public Review):

This is an interesting article that uses the power of drosophila to explore how organisms work with their symbionts to adapt to a changing environment. The authors show that reducing some nonessential amino acids that cannot be produced by the "symbiont" Lactobacillus can nevertheless be rescued by the presence of this bacteria. They suggest it is not through provisioning from the bacteria using genetic screens in the bacteria, they find four bacterial strains that have a reduced ability to restore the delay. They then show that the mutants have transposon insertions in r/tRNA loci and reduced rRNA levels. These mutants and a newly generated deletion allele shows similar phenotypes (although very modest (~1day change). due to imabalance. Experiments next demonstrate that colonization with Lp leads to induction of an ATF4 reporter independent of diet. But that colonization of the mutant Lp, has reduced activation during a balanced diet but not in an imbalanced diet. This was also the case for a mutant identified in the screen. Next the authors explore the role of enterocyte GCN2. They show that there are selective requirements for GNC2 depending on the diet and aa imbalance. This is very complicated. As the depletion of GCN2 by one allele does not impact GF pupation on an imbalanced diet, it does for other alleles. And they find that this activity is independent of ATF4 and 4EBP, two known members of the pathway.

Major strengths include the screen for bacterial mutants and demonstration that depletion of specific amino acids have specific dependencies (both bacterial and host). However, there is a disconnect between the bacterial mutants and the host physiology. How do the mutants impact host biology? Is it through an RNA signal? If so how does this get sensed? Is GCN2 involved, and if so by what mechanism?

We thank the reviewer for his/her evaluation. The connection between the L. plantarum (Lp) mutants and host physiology is mostly established by the following observations:

1) bacterial mutants for r/tRNAs failed to activate GCN2 to the same extent as WT bacteria. Although the difference on imbalanced diet is not significant (p-value=0.069, new Fig. 5A-B), there is a trend towards a decreased activation with the r/tRNA deletion mutant. We also observed this trend with the r/tRNA insertion mutant (new Fig. S4A-B). This decrease reached statistical significance when we performed short-term association (new Fig. S4E-F) or on balanced diet (new Fig. 5C-D and new Fig. S4C-D).

2) providing tRNAs to larvae supports activation of GCN2 in enterocytes (new Fig. 5E-F).

3) knocked-down of GCN2 in enterocytes using RNAi triggers a growth delay in larvae (new Fig. 6A, new Fig. S5A-B).

4) when we knocked-down GCN2 using RNAi, we did not observe any difference between the growth of larvae associated with Lp WT and the r/tRNA mutant (new Fig. 6H-I).

We believe these results strongly indicate that the phenotype of delayed growth upon association with r/tRNA mutant relies at least partly on a decreased GCN2 activation in enterocytes. Given the mechanism of activation of GCN2 (GCN2 is activated by structured RNA such as tRNAs or rRNAs) we propose that GCN2 is a sensor of bacterial r/tRNAs. This is supported by our new finding that Lp produces extracellular vesicles containing r/tRNAs (new Fig. 3). However, we agree that this point remains speculative. We amended our Abstract and Discussion accordingly (L30, L924-929) to clarify that direct activation of GCN2 by Lp’s r/tRNAs remains speculative.

Reviewer #2 (Public Review):

This manuscript investigates an intriguing observation, the data are strong, and the manuscript is clearly written. The authors very convincingly demonstrate that regions of the chromosome that encode L. plantarum tRNAs are also necessary for activation of D. melanogaster GCN2 and accelerated development in the setting of AA imbalance and that this effect on development is dependent on GCN2. They further provide transcriptomic data that broaden our understanding of the host intestinal response to L. plantarum in the setting of AA imbalance. In other host-microbe interactions such as the squid-Vibrio fischeri symbiosis, the bacterial RNA has been visualized in host cells, suggesting transport. Here, experimental data demonstrating bacterial RNA in host cells is lacking and then direct interaction of GCN2 with prokaryotic tRNAs is hypothesized but not proven. As a result, the basis of the observed effect of bacterial tRNAS remains vague. Open questions such how/if the bacterial tRNA enters the host enterocytes, whether these interact with GCN2, and whether other bacterial products are required for the response remain to be answered.

We thank the reviewer for his/her interest in our work. Association with LpΔopr/tRNA leads to reduced activation of GCN2 in enterocytes, and tRNAs feeding activate GCN2. Given the mechanism of activation of GCN2, we speculate that tRNAs produced by Lp directly interacts with GCN2 in enterocytes. We add new data showing that Lp produces extracellular vesicles, and these vesicles contain r/tRNAs (new Fig. 8). Since extracellular vesicles can transport molecules from bacteria to hosts (Brown et al. 2015) this observation supports our model: enterocytes may acquire Lp’s r/tRNAs from extracellular vesicles.

Reviewer #3 (Public Review):

The strength of this study relies on the use of a chemically well-defined diet of the host and of the identification of Lp mutants that fail to rescue the noxious effects of an imbalanced amino-acid regimen. Thus, the genetic approach in both host and symbiont is a major asset of this study. The results are surprising as an imbalance of one essential amino-acid in the diet, valine, can nevertheless be compensated by Lp, even though it is itself unable to synthesize this amino-acid. The experiments are well-conducted and conclusions are appropriate.

We thank the reviewer for his/her kind words and for his/her interest in our work.

This study however does not identify how GCN2 promotes growth in this context. There is just a descriptive transcriptomics approach that is however not validated at the functional level (and also not by RTqPCR experiments) as it does not provide obvious leads beyond a Gene Ontology exploitation of the data.

To answer the reviewer’s questions, we have further characterized one hit from our RNAseq analysis: Lp association causes down-regulation of the growth repressor fezzik. We show that fezzik knock-down in enterocytes improves larval growth, which suggests that Lp improves growth partly through GCN2-dependant r/tRNA-dependent repression of fezzik expression (new Fig. 8 and new Fig. S8).

The authors propose that Lp promotes a more thorough absorption of valine, a possibility that makes sense but is not backed up by any data.

We now provide new data showing that association with Lp increases the amounts of Valine in larva’s hemolymph (new Fig. 1E). Since Lp cannot produce Valine, this supports our model of increased nutrient absorption by the gut of Lp-associated larvae.

Also, how Lp releases r/tRNAs is not addressed experimentally.

We now provide new data showing that Lp produces extracellular vesicles that contain r/tRNAs (new Fig. 3).

A minor logical flaw is the use of GCN2 pathway activation read-outs that are actually not required to mediate Lp's beneficial action.

Our hypothesis is that GCN2 activation leads to both activation of ATF4, which is not required to mediate Lp’s beneficial action, and induction of other targets (e.g. fezzik repression, EGFR activation) that are required to mediate Lp’s beneficial action. We showed that ATF4 activation is a good readout of GCN2 activation (GCN2 knock-down completely suppresses the reporter’s expression in the anterior midgut, new Fig. 4C-F).

The authors claim that GCN2 action is not mediated through ATF4 or Thor based on RNA interference experiments. However, in contrast to the GCN2 case, they have not validated the RNAi lines and tested also only one for each.

To address the reviewer’s concerns, we have used two lines of 4E-BP loss-of-function alleles. These lines do not show a growth delay on imbalanced diet (new Fig. S5I). Regarding ATF4, we used the RNAseq to validate the ATF4-RNAi: the Mex>ATF4RNAi-Lp condition shows a statistically significant ~8 fold reduction in ATF4 expression compared to the control-Lp condition (N.B. ATF4 is annotated as crc in our dataset).

Author Response

Reviewer #1 (Public Review):

The data presented throughout are solid, however, some of the structures drawn of the oxysterols in Figure 1 are not chemically correct. 24(S)HC is drawn as 24(R)HC and visa versa, also the oxysterol sulfate should have a bond between C-3 and the O of OSO3H. It would also help the reader if the vehicle for oxysterol additions was clarified.

We thank the reviewer for pointing out these embarrassing errors! All structures have been corrected. The vehicle for oxysterol (ethanol) is indicated in the Methods.

The data presented in Figures 2 and 3 show that inhibition of SREBP processing by 25HC is important for the long-term maintenance of depletion of plasma membrane accessible cholesterol, but I wonder if activation of LXR may also be important here. I appreciate that the data in Figure 2 points against LXR being involved in the rapid depletion of accessible cholesterol in HEK293 cells, but perhaps it is important for the long-term depletion of accessible cholesterol. Could there be some cell type specificity here?

We agree with the reviewer that 25HC’s effects on multiple signaling pathways complicates mechanistic interpretations. Our studies suggest that ACAT activity is absolutely required for the rapid depletion of accessible PM cholesterol and LXRs play a minor role at this stage. The long-term contributions could very well arise from any of the other 25HC targets, including LXRs, and the relative contributions of ACAT, SREBPs, and LXRs could vary between cell types.

Something that always concerns me when the antimicrobial activity of 25HC is discussed is the fact that 25HC is usually a minor side-chain oxysterol compared to 24(S)HC and 27HC (and 22(R)HC in steroidogenic tissue), except for a short time after infection. Perhaps any long-term antimicrobial activity, and diminishment of accessible cholesterol, results from these other side-chain oxysterols. This may be worthy of some additional discussion.

We agree with the reviewer that we cannot rule out the contribution of other oxysterols to long-term antimicrobial activity. While we have kept our focus on 25HC in this study, we point out in the Discussion that other ACAT-activating oxysterols such as 20(R)HC, 24(R)HC, 24(S)HC, and 27HC, all of which diminish accessible cholesterol, could also have long-term immunological effects.

Reviewer #2 (Public Review):

The paper describes a fairly complete set of experiments describing a mechanism by which 4-hour treatment with 25HC can provide reductions in plasma membrane cholesterol for up to 22 hours. The basic finding is that 25HC depletes the ER of cholesterol by stimulating esterification and that SREBP activation is also inhibited. This effect is associated with the slow loss of 25HC from the cells.

The paper describes detailed studies of the long-lasting effects of a 4-hour exposure to 25HC on the loss of plasma membrane cholesterol. The paper characterizes the effects on SREBP processing to account for this. The possible long-lasting effects of ACAT stimulation were not investigated but may play an equal role.

The paper presents data that the effects on plasma membrane cholesterol can account for the inhibitory effects on some bacterial toxins and viruses.

We thank the reviewer for their positive comments.

Reviewer #3 (Public Review):

The paper uses multiple approaches in cultured cells to show that the rapid depletion of accessible plasma membrane cholesterol by 25-hydroxycholesterol is mediated by the activation of the cholesterol-esterifying enzyme acylCoA:cholesterol acyltransferase (ACAT). They carefully consider and exclude other potential mechanisms that could explain the effects of 25-OH cholesterol on the plasma membrane cholesterol pool, such as decreased cholesterol biosynthesis or activation of LXR transcription factors. Cell lines with mutations in ACAT and in cholesterol homeostatic factors are used in an ingenious fashion to support the role of ACAT and exclude these other mechanisms. The in vivo relevance of accessible membrane cholesterol and ACAT is then demonstrated for toxic cytolysin binding to cells, Listeria infection in vivo, and Zika and Coronavirus infections of cultured liver cells. Overall, the evidence is exceptional that ACAT modulates the plasma membrane accessible cholesterol pool as a strategy of the host to protect against various infectious agents. The discussion of the paper could be broadened to include other mechanisms that are known concerning the role of 25-OH cholesterol in infectious processes and the body's responses.

We thank the reviewer for their positive assessment.

Author Response

Reviewer #1 (Public Review):

The authors Rem et al., examine the mechanism of action of APP, a protein implicated in Alzheimer's disease pathology, on GABAB receptor function. It has been reported earlier that soluble APP (sAPP) binds to the Sushi domain 1 of the GABAB1a subunit. In the current manuscript, authors examine this issue in detail and report that sAPP or APP17 interacts with GABABR with nano Molar affinity. However, binding of APP to GABAB receptor does not influence any of the canonical effects such as receptor function, K+ channel currents, spontaneous release of glutamate, or EPSC in vivo. The experimental evidence provided to support the conclusions is thorough and statistically sound. The range of techniques used to address each of the aims has been carefully curated to draw meaningful conclusions.

The authors use HEK293T heterologous cell line to confirm the affinity of APP17 for the receptor, ligand displacement, and receptor activation. They also use this method to study PKA activation downstream of the GPCR. They use slice electrophysiology to measure changes in glutamatergic transmission EPSC and then in vivo 2-photon microscopy to measure functional changes in vivo.

The work is significant for the field of Alzheimer's and also GABAB receptor biology, as it has been assumed for sAPP acts via GABAB receptors to influence neurotransmission in the brain. The results presented here open up the question yet again, what is the physiological function of sAPP in the brain?

The manuscript is clearly written and easy to follow. The main criticism would be that the manuscript fails to identify the mechanism downstream of APP17 interaction with GB1a SD1.

Our results show that APP17 does not influence GABAB receptor signaling in heterologous expression systems, neuronal cultures and anesthetized mice. Thus, our data do not support the existence of a “mechanism downstream of APP17 interaction with GB1a SD1”. As discussed in our manuscript, full-length APP controls GABAB receptor trafficking and surface stability in axons (Dinamarca et al., 2019), thus already providing a biological function for binding of APP to GB1a.

Author Response

Reviewer #1 (Public Review):

The authors studied Eurasian perch in an experimental setup facilitated by a nuclear cooling plant to provide a natural laboratory. The heated area of the ecosystem raised in temperature by 8 degrees centigrade, while a reference area remained unheated. The authors provide a thorough and convincing description that the two areas are segregated such that individuals could not escape from one area to another prior to 2004, and such use data only until 2003 to test their hypotheses. The authors used both length-at-catch and age-increment data in a series of Bayesian mixed effects models to estimate the growth rate and length-at-age. They find that in the warmed area, both younger, smaller fish and older adults grew faster, contrary to the prediction of the temperature-size rule as well as many predictions and observations from other systems that fish reach smaller terminal body sizes in warmer environments due to increased metabolic demands. The authors furthermore combine the estimated body sizes with a mortality rate to determine the size-spectrum slope for both areas and determine the increased growth and increased mortality combine to essentially leave the size-spectrum slope observed in the ecosystem unchanged.

This is a thorough and interesting paper presented clearly and succinctly. These authors present a strong and thorough analysis of how temperature affects growth when all other ecosystem factors remain unchanged in a population. The dataset is a powerful one to support this type of analysis, and the statistical analysis methods the authors used appear to be robust and thorough. The diagnostics and visualizations are complete and inspire confidence in the convergence and accuracy of the modeling approach. The use of the size spectrum exponent to roll up individual-level changes across the population into a single metric was useful and interesting.

The estimates of the von Bertalanffy growth parameters in the results and discussion are less convincing than the growth increment and length-at-age estimates which seem much more robust. The presentation of estimates of the von Bertalanffy growth parameters in Figure S6 exhibit the high negative correlation between the k and L infinity parameters that are typical whenever multiple VBGF models are fit to subsets of data. It is difficult to determine which changes in parameters correspond to actual differences in early vs late life stage growth when, in any given year, if k is estimated low, L infinity will skew high simply due to the model structure. An example of this can be seen in 1995-1997 where L infinity is quite high but k is estimated quite low concurrently - in this case, it seems more reasonable to conclude the likelihood surface is quite flat between different parameter values than that fish suddenly reached a larger asymptotic size in these three years than all of the rest. The data in this case so strongly show larger growth in the heated area even without the VBGF results, and it would be more credible to base the discussion and results of this paper on the growth rate or observed length-at-age (e.g. Figure S4) estimates which are so clear.

We agree with the limitations of the von Bertalanffy growth equation (VBGE), and we agree with you and with Reviewer #2, that the estimated parameters for cohorts 1995–1997 are different, in particular for the L_infinity parameter in the heated area (see also reply to Reviewer#2 for a longer reply to that issue). The main reason for the size-at-age analysis in addition to growth-at-size is because the growth rates in theory could become similar between the areas for a given size, but if the initial growth rates were higher, there would still be a difference in the size-at-age, and size-at-age is an important trait in the context of the temperature-size rule (TSR). We could overcome the issues with the 3-parameter VBGE model by fitting multiple linear models to size-at-age for one age at the time. However, such models would not account for that cohorts may share similar growth trajectories. Therefore, we suggest instead to still use the VBGE growth equation, but put less emphasis on the specific parameter estimates, and instead present the results of the predictions of length-at-age only in that figure. We also wish to clarify that the size-at-age figure referred to here (Figure 2-figure supplement 4) is the predicted size-at-age from the VBGE model, rather than just the data or predictions from some other model.

In summary, we have downplayed the role of the specific parameter estimates and instead focused on the predicted size-at-age. Part of Figure 2 has been made a supporting figure (Figure 2-figure supplement 8). We have also conducted sensitivity analysis with respect to cohorts 1995–1997. This extra analysis shows that omitting these cohorts still results in a clear difference in size-at-age between the areas but reduces the predicted difference in size-at-age by a few percentage points. See first paragraph of the results, and lines 373–378. a

Author Response

Reviewer #1 (Public Review):

Caetano and colleagues describe the changes caused by periodontal inflammation in terms of tissue structure and provide additional evidence to understand the involvement of fibroblasts in altering the immune microenvironment.

While interesting and a concise study, the authors should improve their work on two major points:

1) To improve the resolution, the authors introduced a method that addresses improving the resolution by combining more information from the neighbour structure and the existing database. This raises the question of whether the lack of previous gingival tissue spatial transcriptome sequencing results weakens the reliability of this method. Does it miss the identification of some gingival tissue-specific cells? Is the failure to match two populations of fibroblasts between single-cell sequencing and spatial transcriptome sequencing of gingival tissue fibroblasts related to this?

Thank you for raising these concerns. We don’t think that the lack of previous spatial transcriptome data of oral mucosa tissue affects the reliability of this method; however, as the technology matures our limitations will be overcome particularly regarding resolution. Understanding the exact cellular and molecular mechanisms of oral mucosa cellular remodelling processes in disease in their spatial context will be key to improve our current understanding of oral mucosa physiology. In contrast to single-cell RNA sequencing methods, we are not treating or digesting the tissue with enzymes or extracting cells from their local environment, therefore the impact on gene expression is substantially inferior compared to single-cell RNA sequencing. Because of this key difference, we expect differences between single-cell RNA sequencing and spatial data, which can preclude successful data integration. We were not successful in mapping all fibroblasts using one strategy (anchor-based integration) because this integration is performed on low resolution Visium datasets which is unable to uncover fine cell subtypes, such as fibroblasts. When we performed integration using a higher spatial resolution method, we could map these cells. In our initial single-cell RNA sequencing datasets, some gingiva cells were indeed missing due to technical limitations; for example, neutrophils were not captured given their fragile nature and low RNA content. With the spatial data, we could detect these and other immune cell types that were originally undetected. In conclusion, for a robust and unbiased molecular characterisation of human oral mucosa, spatial transcriptome data is essential.

2) Although the authors did the identification of the captured tissues, the results seem to require more analysis. Take Figure 5A as an example, there is a clear overlap between endothelial cells and basal cells. In addition, it is suggested that the authors indicate the specific location of the 10 clusters of cells in Figures 1D and 2C.

Thank you for your comment. Endothelial cells in Figure 5A have a predominantly subepithelial location as shown; however, these also localise in interpapillary regions which can be confounded with basal areas given the current resolution. We highlight that these analyses are not single-cell resolution. We applied a deconvolution method to increase the original spatial data resolution (55 µm), but it is still not true single-cell resolution.

In Figure 1D and 2C we are not showing clusters of cells, but spatial/anatomical cluster regions; for example, epithelial and stromal regions. These regions contain, especially stromal areas, information of multiple cell types. We can map epithelial regions as these are generally well defined (Figure 2F), but validating stromal regions becomes more difficult. To address this, we mapped individual cell types (Figures 5 and 6) and focused on locating and validating our cell type of interest (Fibroblast 5).

Author Response

Reviewer #3 (Public Review):

In this manuscript, Kim et al. use a deep generative model (a Variational Auto Encoder previously applied to adult data) to characterize neonatal-fetal functional brain development. The authors suggest that this approach is suitable given the rapid non-linear development taking place in the human brain across this period. Using two large neonatal and one fetal datasets, they describe that the resultant latent variables can lead to improved characterization of prenatal-neonatal development patterns, stable age prediction and that the decoder can reveal resting state networks. The study uses already accessible public datasets and the methods have been also made available.

The manuscript is clearly written, the figures excellent and the application in this group novel. The methods are generally appropriate although there are some methodological concerns which I think would be important to address. Although the authors demonstrate that the methods are broadly generalisable across study populations - however, I am unsure about the general interest of the work beyond application of their previously described VAE approach to a new population and what new insight this offers to understanding how the human brain develops. This is a particular consideration given that the major results are age prediction (which is easily done with various imaging measures including something as simple as whole brain volume) and recapitulation of known patterns of functional activity in neonates. As such, the work will be of interest to researchers working in fMRI analysis methods and deep learning, but perhaps less so to a wider neuroscience/clinical readership.

Specific comments:

1) (M1) If I understand correctly, the method takes the functional data after volume registration into template space and then projects this data onto the surface. Given the complexities of changing morphology of the development brain. would it not be preferable to have the data in surface space for standard space alignment (rather than this being done later?). This would certainly help with one of the concerns expressed by the authors of "smoothing" in the youngest fetuses leading to a negative relationship between age and performance.

While projecting onto the cortical surface has its advantages, as suggested here18, several studies have also shown that with careful registration, such as in the current study, volumetric registration can yield comparable performance19. Regardless, we did attempt to directly generate cortical surfaces for our fetuses. We refer the reviewer to our response to the RE-M2 [page 9].

Regarding the “smoothing” effect in the youngest fetuses, we want to clarify that the smoothing effect in the scans of young fetuses is not unique to the choice of registration method. In other words, the same smoothing effect must be seen with cortical registration as well. Regarding this perspective, we kindly refer the reviewer to our response to RE-M1 [page 7]. Regarding the specific change made in the revised manuscript, we kindly refer to our response to R1-m5 [p21] or [page 9 line 191-213] in the main manuscript.

2) (M2) A key limitation which I feel is important to consider if the method is aiming to be used for fetuses is the effects of the analysis being limited only to the cortical surface - and therefore the role of subcortical tissue (such as developmental layers in the immature white matter and key structures like the thalami) cannot be included. This is important, as in the fetal (and preterm neonatal) brain, the cortex is still developing and so not only might there be not the same kind of organisation to the activity, but also there is likely an evolving relationship with activity in the transient developmental layers (like the subplate) and inputs from the thalamus.

The reviewer raises an important point. We agree with the reviewer that the subcortical region plays a critical role in fetal and newborn neurodevelopment. Unfortunately, our current VAE model cannot utilize such information without a major change in the model structure. We added this as a limitation of our study and discussed why our VAE model, in its current form, did not include subcortical areas. Please see our detailed response to RE-M1 [page 4] or [page 25 line 558-570] in the main manuscript.

3) (M3) As the authors correctly describe, brain development and specifically functional relationships are likely evolving across the study time window. Beyond predicting age and a different way of estimating resting state networks using the decoding step, it is not clear to me what new insight the work is adding to the existing literature - or how the method has been specifically adapted for working with this kind of data. Whilst I agree that these developmental processes are indeed likely non-linear, to put the work in context, I think the manuscript would benefit from explaining how (or if) the method has been adapted and explicitly mentioning what additional neuroscientific/biological gains there are from this method.

We appreciate the reviewer’s critical insights. In the revised paper, we included additional results that, we hope, can address the reviewer’s concerns. We believe that the strength of the VAE model is that, relative to linear models, it can be more generalizable across different datasets and ages (adult vs. full-term babies vs. preterm babies vs. fetuses). In the original manuscript, this was supported by the superior age prediction performance of the VAE over linear models when applied to different datasets covering the fetal to neonatal periods. Age prediction could also be done using other imaging modalities, as the reviewer pointed out. However, we do not think this undermines the potential impact of having the ability to accurately estimate age based on functional connectivity patterns. Brain function-structure relationships may not exactly be one-to-one20. It is entirely possible that for one disease, brain functional connectivity alterations precede structural changes such that delayed growth trajectories will first manifest in the functional space. There are also certain aspects of brain function that cannot be mapped directly to its structural characteristics (i.e., structural connectivity patterns). For example, brain changes its functional connectivity patterns dynamically over different brain states (resting vs. task-engaging)21, mental disorders (depression22, anxiety23, Schizophrenia24), cognitive traits25, 26, and individual uniqueness25, etc. Therefore, we believe that estimating the functional age of fetuses and neonates given their functional connectivity profiles may provide a biomarker for tracking neurodevelopment trajectories, allowing clinicians to identify deviations early and intervene in a timely manner if necessary. For these reasons, we believe that superior age prediction performance of the VAE model compared to linear models is scientifically significant.

The value of the VAE lies in its ability to capture FC features that are otherwise not modeled by linear strategies. For example, here, we showed that only the VAE model can extract latent variables representing brain networks that are similar across different datasets. In contrast, linear models, showed higher network pattern similarity between full-term and preterm infants within the dHCP dataset. This suggests that the VAE model can be a very useful tool for capturing common brain networks in datasets acquired using different recording parameters and preprocessing steps. Moreover, the VAE representations predicted age with higher accuracy compared to linear representations. Together, these findings show that the methodology is effective in extracting functionally relevant features of the brain. Please see RE-M1 [page 3] and R1-m13 regarding the specific changes made in the revised manuscript.

4) (M4) The unavoidable smoothing effect of VAE is very noticeable in the figures - does this suggest that the method will be relatively insensitive to the fine granularity which is important to understand brain development and the establishment of networks (such as the evolving boundaries between functional regions with age) - reducing inference to only the large primary sensory and associative networks? This will also be important to consider for the individual "reconstruction degree" - (which it would likely then overstate - and would need careful intersubject comparison also) if it was to be used as a biomarker or predictor of cognition as suggested by the authors.

Regarding the first concern, yes. Greater smoothing will tend to yield less granular network patterns; this is true for all representational models (not only VAE, but also models like ICA or PCA). This effect becomes ever more pronounced when representations consist of fewer components (e.g., IC50); the smoothing effect becomes stronger, leading to coarser brain patterns (see Fig. 3 in the revised manuscript). In this regard, higher number of components is desired, but on the flipside, IC maps with higher components are generally less interpretable. In short, there will always be trade-offs between interpretability and spatial resolution. Also, higher components tend to cause over-fitting issue, as shown in our age prediction performance across different datasets (worse performance in the IC300 vs. IC50). In this sense, what matters for the representations is how informative each latent variable (or component) is. In the revised Fig. 2, we showed that latent variables from the VAE model were more informative in representing rsfMRI than linear representations. It is also noteworthy that the smoothing effect of the VAE is comparable to IC300 (similar effect to manual smoothing at the level of FWHM=5mm; revised Fig. 3). Given above results, we believe the VAE model may be more suitable for investigating finer scale of brain networks, than linear models. The above perspective was updated in the revised manuscript as [page 23 line 506-511]:

"Another interesting observation was that the smoothing effect of the VAE is comparable to IC300 (similar effect to manual smoothing at the level of FWHM=5mm; Fig. 3). Given the above, we believe the VAE model may be more suitable for investigating finer scale of brain networks, than linear models. Perhaps, the VAE model with a greater number of latent variables (e.g., 512 or 1024 instead of 256 in the current VAE) can be utilized to find brain networks at finer scale."

On top of the points raised above, network mapping with linear models is limited when it comes to mapping the spatial evolution of brain networks over aging due to their linear nature. This limitation can be observed in the ICA study with dHCP dataset (Fig. 4 in 7). On the other hand, thanks to its nonlinearity nature, the VAE model may have a potential to observe the spatial gradient of brain network over aging, while this expectation needs confirmation. To that end, we revised our discussion to reflect our perspective. We refer the full change made in the revised manuscript to our response to R1-m13.

Author Response

Reviewer #1 (Public Review):

The manuscript by Shaikh and Sunagar addresses the question of the origin of spider venom proteins. It has been known for many years that an important component of spider venoms is a diverse group of small proteins known as disulfide-rich peptides (DRPs). However, it has not been clear whether this group of proteins has a common origin or evolved convergently in different lineages. The authors collected sequences of the genes encoding these proteins from publicly available genomes of spiders from a range of families. They aligned the sequences using the structural cysteines as guides and carried out a phylogenetic analysis of the different sequences, ultimately classifying the different proteins into over 50 super-families. One thing that is not clear from the text or from the references cited (I am not an expert on spider venom) is how many of these superfamilies were known before and how many are novel. There is also no clear indication of what criteria were used to define a subset of sequences as a superfamily. Nonetheless, the authors show that all these superfamilies have a single common ancestor, predating the divergence of araneomorphs and mygalomorphs and that the DRPs underwent independent diversification in each of these two lineages.

We have identified 78 novel superfamilies in this study and 33 were previously identified (Pineda et al. 2020 PNAS). We had previously described information in lines 90, 101 and 106 regarding the description of novel superfamilies from previous studies and the ones described in this study.

Line 90 “Recently, using a similar approach, 33 novel spider toxin superfamilies have been identified from the venom of the Australian funnel-web spider, Hadronyche infensa (9).”

Line 101 “This approach enabled the identification of 33 novel toxin superfamilies along the breadth of Mygalomorphae (Figures S1 and S2).”

Line 106 “Moreover, analyses of Araneomorphae toxin sequences using the strategy above resulted in the identification of 45 novel toxin superfamilies from Araneomorphae, all of which but one (SF109) belonged to the DRP class of toxins (Figures S3 and S4).”

Spider toxin superfamilies have been named after gods/deities of death, destruction and the underworld based on nomenclature introduced by Pineda et al. (2014 BMC genomics). We have now included this explanation in the manuscript under the methods and results sections. We have also provided additional details pertaining to this nomenclature in Table S1.

The authors also looked at selective forces acting on the sequences using dN/dS analyses. They reach the conclusion that there are different modes of selection acting on different sequences based on their role - defensive or predatory venoms - building on previous work by the lead author on venom sequence evolution in diverse animals.

All in all, this is an admirable piece of molecular evolution work, providing new data on the evolution of spider venom proteins. There are some confusions in terminology that need to be cleared up, and somewhat more context needs to be given for non-specialists as detailed in the points below:

We thank the reviewer for their constructive and critical suggestions, as well as the kind words of encouragement. Their suggestions have helped us in significantly improving the quality of our work.

Suggestion 1) Common names of the main spider infraorders should be given.

We thank the reviewer for their helpful input. We have now introduced spider infraorders with well-known spiders and their common names under the introduction section. Furthermore, we have also included a schematic representation of the spider phylogeny, and highlighted lineages under investigation as Figure 1.

Suggestion 2) Opisthothelae is not the common ancestor of Mygalomorphae and Araneamorphae, but the clade that encompasses those two clades. This incorrect statement appears in several places. Further on, it is stated that Opisthothelae is the common ancestor of all extant spiders. This is wrong both from a terminological point of view (a clade cannot be ancestral to another clade) and from a factual point of view, since there are extant spiders not included in Opisthothelae.

We thank the reviewer for pointing out this oversight. We have now corrected it to suborder Opisthothelae as the clade encompassing Mygalomorphae and Araneomorphae spiders.

Suggestion 3) Several proteins and proteins families are mentioned without being introduced, e.g. knottin. Please provide short descriptions.

We have now provided a short introduction to terms such as Knottin.

Reviewer #2 (Public Review):

This interesting study looks into the evolution of putative spider venom toxins, specifically disulfide-rich peptides (DRPs). The authors use published sequence data to gain new insights into the evolution of DRPs, which are the major component of most spider venoms. Through a series of sequence comparisons and phylogenetic analyses they identify a substantial number of new spider toxin superfamilies with distinct cysteine scaffolds, and they trace these back to a primitive scaffold that must have been present in the last common ancestor of mygalomorph and araneomorph spiders. Looking at the taxonomic distribution of these putative venom DRPs, they conclude that mygalomorph and araneomorph DRPs have evolved in different ways, with the former being recruited into venom at the level of genera, and the latter at the level of families. In addition, they perform selection analyses on the DRP superfamilies to uncover the surprising result that mygalomorph and araneomorph DRPs have evolved under different selective regimes, with the evolution of the former being characterised by positive selection, and the latter by purifying (negative) selection.

However, I don't think that in the current state of the manuscript these conclusions are robustly supported for several reasons. First, it seems that not all previously published data were included in the phylogenetic analyses that were used to identify new superfamilies of DRPs.

We have, indeed, analysed all spider toxin sequences available to date. We have relied on the signal and propeptide regions for identifying novel superfamilies, which is an accepted convention: Pineda et al. (2014 BMC Genomics); Pineda et al. (2020 PNAS).

Although many additional superfamilies can be identified, we have only retained those sequences for which there were at least 5 representatives for the identification of toxin superfamilies, and 15 representatives for selection analyses to ensure robustness. This filtering step ensured that the generated alignments, phylogenetic trees, and evolutionary assessments were robust and devoid of noise that stems from single-representative groups. Adding in those sequences would have enabled us to identify many more superfamilies, solely based on the signal and propeptide examination, but it wouldn’t have been possible to support them with other lines of evidence that were provided for all other superfamilies in this study, jeopardising the overall quality of the manuscript. Nonetheless, there is strong evidence that the left-out sequences are also related to the ones analysed in this study (Figure S10). In future, when more transcriptomes are sequenced, it would be possible to designate these newer toxin superfamilies with much stronger support.

Second, much of the data were obtained from whole-body transcriptome data, which leaves a degree of uncertainty that these data indeed derive from the venom glands that produce the toxins.

We respectfully disagree with the reviewer that ‘much of the data’ are from the whole-body transcriptomes. Nearly all sequences in our study are sourced from Pineda et al. (2014 BMC Genomics and 2020 PNAS), Sunagar et al (2013 Toxins), Cole and Brewer (2020 bioRxiv) and transcriptome sequence assembly data from established online repositories NCBI (NR and TSA) and ENA. All the above-mentioned studies (KS is a part of many of these) under their methods section clearly state that the transcriptomes were generated using mRNA isolated from venom gland tissue (BioProject accessions: PRJEB14734; PRJEB6062; PRJNA189679, PRJNA587301 and PRJNA189679, where source tissue type is designated as venom gland).

We would like to direct the reviewer’s attention to the following excerpts from reference papers from which data for this study has been sourced:

1. Pineda S et al. (2020 PNAS): “Three days later, they were anesthetized, and their venom glands were dissected and placed in TRIzol reagent (Life Technologies). Total RNA from pooled venom glands was extracted following the standard TRIzol protocol.”
2. Sunagar et al (2013 Toxins): “Paired venom glands were dissected out and pooled from nine mature females on the fourth day after venom depletion by electrostimulation. Total RNA was extracted using the standard TRIzol Plus method ...”
3. Cole and Brewer (2020 bioRxiv): “... the venom glands of each ctenid were dissected out, whole RNA was isolated from the venom glands …”

We would also like to point out that hexatoxins are widely studied and are some of the most well-understood spider venom toxins. Many representatives have been functionally characterised and shown to be potent in affecting prey and predatory species [Sunagar et al (2013 Toxins); Pineda et al. (2014 BMC Genomics and 2020 PNAS); Volker, et al. (2020 PNAS) - KS is a part of most of these studies as well]. However, the current technologies do not permit the high-throughput screening of the enormous diversity of toxins in spiders, which is why not every toxin sequence identified from the venom gland is functionally characterised. Nonetheless, venom researchers will not contest the role of these highly expressed venom gland proteins in envenoming, especially given that they share significant sequence identities with toxins that are functionally well-characterised.

The only exception to the above is non-ctenid araneomorph toxin superfamily sequences, which are retrieved from whole-body transcriptomes (Cole and Brewer; 2020 bioRxiv). The authors of the paper indicated these as putative toxins. As explained above, homologs of these peptides are well-characterised to be venom toxins. Additionally, in our phylogenetic trees (Figures 3, 4, S6 and S9), they are nested within the toxin clades, reaffirming their identity.

Third, the taxonomic representation of mygalomorph and araneomorph diversity in this study is so sparse that it becomes impossible to distinguish whether toxin recruitments have happened at the level of genera, families, or even higher-level taxa.

We respectfully disagree with this suggestion. The taxonomic breadth investigated in this study isn’t sparse. Analysed sequences belong to groups across the breadth of the spider phylogeny. To address this criticism, we are now including a schematic representation of spider phylogeny, where lineages under investigation are highlighted (Figure 1A). Given this broader taxonomic breadth, all of our interpretations are parsimoniously extendable to their common ancestors. For instance, we establish the common origin of all DRPs in the members of these widespread spider families. Therefore, not including sequences from other sister groups will not invalidate this hypothesis, and the most parsimonious explanation will be that the missing members too are likely to have DRPs in their venom (which is also a common understanding of the spider venom research). Whether DRPs dominate the venoms of these missing groups will only come to light upon investigation, but their presence in the venom is highly likely. Moreover, please do note that we have analysed nearly all sequences available in the literature to date.

As for the recruitment of the toxin superfamily at the taxon level, we would like to point out the phylogenies in Figures 2 and 3 that clearly show the differential recruitment events. We would also like to point out lines 120 and 136 state that this may not only be a result of recruitment and could arise from differential rates of diversification (also evident in other analyses presented in Figures 5 and Tables S2 and S3).

Line 120 “Interestingly, the plesiotypic DRP scaffold seems to have undergone lineage-specific diversification in Mygalomorphae, where the selective diversification of the scaffold has led to the origination of novel toxin superfamilies corresponding to each genus (Figure 2).”

Line 136 “However, we also documented a large number of DRP toxins (n=32) that were found to have diversified in a family-specific manner, wherein, a toxin scaffold seems to be recruited at the level of the spider family, rather than the genus. As a result, and in contrast to mygalomorph DRPs, araneomorph toxin superfamilies were found to be scattered across spider lineages (Figure 3; Figure S6; node support: ML: >90/100; BI: >0.95).”

Adding any number of missing lineages will neither change the fact that araneomorphs ‘appear’ to have recruited these superfamilies at the genera level, nor the family-level recruitment of toxin superfamilies in a large number of examined mygalomorphs.

We have now introduced a new figure (Figure 7) that highlights the different scenarios that explain the observed differences in the evolution of mygalomorph and araneomorph spider toxins. We have also included additional text in the manuscript to explain this better.

Fourth, only a selection of DRP superfamilies was used for natural selection analyses, without the authors explaining how this selection was made. Yet, they attempted to draw general conclusions about toxin evolution in mygalomorphs and araneomorphs, even though most of the striking differences they found were restricted to just two mygalomorph genera, and one family of araneomorphs.

From our experience and previous reports [Sunagar and Moran (2015, PLoS genetics); Sunagar, et al. (2012, MBE); Yang, Z. (2007, MBE)], the unavailability of enough sequences from datasets results in inaccurate estimation of omega values. For instance, if there are only a couple of sequences in a superfamily, both of which are slightly different from one another, then even these minor differences in them would be exaggerated. Hence, we have resorted to performing selection analysis on datasets for which there are at least 15 sequences. No doubt that this conservative approach reduces the number of datasets analysed, but it also ensures that our findings are well-supported. We have now clarified this in our manuscript under the methods section.

However, we did previously include sequences from all toxin superfamilies described to date in our alignment figure (Fig S10) and analysed their signal and propeptide regions. They were only excluded from selection analyses. It can be seen that they too are DRPs, but they belong to distinct superfamilies from the ones being described here.

If these concerns are addressed this study can shed important new light on venom toxin evolution in one of the most diverse venomous taxa on Earth.

We thank the reviewer for their constructive inputs and suggestions which have enabled us to make this manuscript more accessible to a wider audience.

Reviewer #3 (Public Review):

This work aims to elucidate the evolutionary origins of disulfide-rich spider toxin superfamilies and to determine the modes of natural selection and associated ecological pressures acting upon them. The authors provide a compelling line of evidence for a single evolutionary origin and differing factors (e.g., prey capture strategies and methods of anti-predator defense) that have shaped the evolution of these toxins. Additionally, the two major spider infraorders are claimed to have experienced differing selective pressures regarding these toxins.

The results presented here are novel and generally well-presented. The evidence for a single origin of DRP toxins in spiders is exciting and changes the paradigm of spider venom evolution.

The data are well analyzed, but the methods lack enough detail to reproduce the results. More information regarding the parameters passed to each software package, version numbers of all software employed, and models of molecular evolution employed in phylogenetic analyses are among the necessary missing information.

We thank the reviewer for their kind words and constructive and critical suggestions. Their suggestions have contributed towards improving the quality of our work. Upon their suggestion, we have now expanded the methods section to include more details.

The differences in the evolutionary pressures between mygalomorphs and RTA-clade spider DRP toxins are clear, but expanding RTA results to all araneomorphs may be overreaching. Additional araneomorph sequence data is available, despite the claims within this manuscript (e.g., see Jiang et al.. 2013 Toxins; He et al.. 2013 PLoS ONE; and Zobel-Thropp et al.. 2017 PEERJ). These papers include cDNA sequences of spider venom glands and contain representatives of inhibitory cysteine knot toxins, which are DRP toxins. These data would greatly enhance the strengths of the results presented herein.

In response to the expansion of RTA results to araneomorphs, we would like to point out that RTA comprises about 50% of the diversity recorded in Araneomorphae. The araneomorph data analysed in our study covers a range of araneomorph family divergence time Agelenidae (<70 MYA), Pisauridae (<50 MYA) and Theridiidae (~200 MYA, Magalhaes 2020, Biological Reviews 95.1). We report a strong signature of purifying selection influencing the evolution of araneomorph toxin SFs, despite the long evolutionary time separating them (50 - 200 MYA). We firmly believe that further addition of toxin sequence data from other groups will not deviate from the general trend of molecular evolution observed in both these lineages across such large period of time; barring certain certain exceptions (such as SF13 a defensive toxin identified from Hadronyche experiencing purifying selection; Volker, et al. 2020 PNAS).

We had initially excluded non-ctenid datasets from our analyses on account of poor sequence annotation and lack of representative sequence data. However, we have now incorporated Dolomedes mizhoanus (DRP) (Jiang et al. 2013 Toxins) and Latrodectus tredecimguttatus (non-DRP) (He et al. 2013 PLoS ONE) toxin dataset into our analyses, following reviewer’s suggestion. This has led to identification of 5 novel superfamilies, providing additional support to our spider venom evolution hypothesis.

Author Response

Reviewer #1 (Public Review):

Lin et al. characterise cellular pathologies in PLA2G6 mutant patient-derived neuronal cells (neuronal progenitor cells, NPCs, and IPSc-derived dopaminergic neurones) and a novel compound heterozygous PLA2G6 mutant mouse model. They build on their previous findings in an INAD fly model (lacking PLA2G6) to show that lysosomal and mitochondrial defects are evolutionary conserved in PLA2G6 deficiency. The authors proceed to use their INAD fly model and to screen a number of compounds that are predicted to modulate endo-lysosomal function using a bang sensitivity assay. They then show that the drugs that can rescue this fly behavioural phenotype also reduce LAMP2 expression in patientderived NPCs on Western blot analysis. Lastly, the manuscript reports the creation of new genetic constructs that express human PLA2G6 and study expression levels in a human kidney cell line as well as in patent-derived NPCs. In the latter neuronal model, they show that expression of human PLA2G6 can rescue mitochondrial fragmentation associated with PLA2G6 loss-of-function. Lin et al then show that ICV (intracerebroventricular) and IV (intravenous) injection of a human PLA2G6-containing construct is able to partially rescue the rotarod phenotype in PLA2G6 transheterozygous PLA2G6 mutant mice between ~110 and 150 days. There is also an associated improvement in lifespan and body weight.

The strengths of this work are that the authors use a number of different model organism systems, including patient-derived neuronal cells, Drosophila models (INAD flies) and mouse models to study PLA2G6-associated neurodegeneration (PLAN) at the cellular level. They also screen drug compounds that are predicted to target endo-lysosomal trafficking and sphingolipid metabolic pathways to ameliorate PLAN, thus identifying potential new therapeutic strategies. The work in mice, showing that gene therapy with human PLA2G6 can rescue a behavioural phenotype and lifespan is the first proof-ofconcept of such an advancement. This work will hopefully lead to further studies for optimisation toward clinical advancement.

We thank the reviewer and editor for the positive comments about our manuscript.

The major weaknesses are that the pathogenic mechanisms shown in the patient-derived neuronal cells and mice do not extend as far as those previously shown in the fly model published by the authors. Of note, ceramide levels and retromer function are not studied, both key pathologies described in the previous fly models. In addition, the drug screening is limited by its testing in one fly behavioural assay and LAMP2 Western blot analysis on patient derived NPCs.

The results, in general, support the conclusions of the authors and represent well-performed work. However, the significance of elevated glucosylceramide levels is not clear in the present study. Although this was previously found to be elevated in INAD flies, it was ceramide levels that were thought to be the main toxic insult, with drugs aimed at reducing ceramide levels being shown to rescue INAD flies.

We addressed these concerns. Please refer to our response to each of the specific point listed below.

This work will no doubt be of significant interest to the field, confirming several previous findings in the Drosophila model of PLA2G6 (iPLA2-VIA) knockout. It also extends upon the fly work by identifying compounds that can be further studied for potential drug-re-purposing for the treatment of PLA2G6associated disease. The gene therapy studies are also very interesting and a first proof-of-principle in PLAN using ICV and IV delivery in a mouse model.

We thank the reviewers and editor as addressing all these concerns really improved the manuscript.

Reviewer #2 (Public Review):

This article aims to extend human disease-related studies of PLA2G6 from fly models to iPS-neurons, mouse models, to look for drugs that suppress phenotypes and test them, and to attempt AAV whole body rescue. Generally, each of these questions/aims/experiments is excellent, but as presented, it's a bit of an underdeveloped hodgepodge of results, with each experiment somewhat underdeveloped or analyzed for the respective phenotype, in my opinion. I think the general thrust of the experiments is excellent. But the data are relatively cursory in many instances. Further development and characterization of the phenotypes would require quite a bit of work but vastly improve the paper.

We thank the reviewer for the positive comments about our manuscript. We have addressed most of the concerns.

Author Response

Reviewer #1 (Public Review):

Like other sensory organs, the inner ear has a rich population of pericytes, essential for sensory hair cell heath and normal hearing. In this study, using an inducible and conditional pericyte depletion mouse (PdgfrbCreERT2/iDTR) model, the authors demonstrate that the pericytes play critical roles in maintaining vascular volume and integrity of spiral ganglion neurons (SGNs) in the cochlea. Moreover, using the coculture models, they show vigorous vascular and neuronal growth in neonatal SGN explants in the presence of exogenous pericytes. Mechanistically, this study demonstrates that these roles are achieved mainly through the interactions between pericyte-released exosomes containing VEGF-A and VEGFR2-expressing the vessels and SGNs.

Overall, the data are analyzed thoroughly, and the conclusions are novel and convincing. It is mechanistically solid. The study is somewhat translationally limited. Nevertheless, understanding the roles of organ-specific pericytes is paramount, making this study timely and significant.

We thank Reviewer #1 for the positive comment. We agree the pericyte depletion model is not a translational disease model. However, pericyte pathologies, including the decline in pericyte number, pericyte migration, and pericyte trans-differentiation, are frequently seen in aging and noise-induced hearing loss animal models. Moreover, hearing dysfunction due to pericyte pathology has been demonstrated in recent studies (Hou et al., 2020; Hou et al., 2018; Neng et al., 2015).

Reviewer #2 (Public Review):

The present study from Xiaorui Shi's lab investigated the effect of pericyte depletion on spiral ganglion neurons and auditory function. Results in vitro culture system proposed that pericyte-derived exosomes contain VEGF, and promote not just vascular stability but neuronal survival through Flk1. This study is an extension of their previous study showing pericyte depletion causes auditory dysfunction, which is ameliorated by VEGF gene therapy (Zhang et al., JCI insight 2021). Overall, the data are clear and sophisticated and promote our understanding of the biological roles of pericytes in neuronal function. Several points should be thoroughly discussed or supported by definitive experiments like analysis of neuron-specific Flk1 KO mice.

We thank Reviewer #2 for the encouraging positive comments on our study. We especially appreciated the reviewer’s view that there would be value in using neuron-specific Flk1 KO mice to consolidate the results. However, since our in vitro adult SGN neuron cell culture model cearly demonstrates the direct role of exosome-VEGF-A signaling on adult SGN health, as shown in Figs. 5D & E and Figs. 9C & E, we are confident our conclusion is valid. A recent study used neuron-specific Flk1 conditional KO mice to demonstrate neuronal atrophy and dysfunction in memory impairment (Deyama et al., 2020). We do presume disruption of neuronal VEGF/FLK1 signaling in a specific neuronal Flk-1 deletion animal model would cause similar spiral ganglion death and subsequent hearing loss. To test this possibility, we are seeking a Cre-SGN driver animal model from the auditory community and Flk1 floxed mice from the larger research community. Of course, obtaining these models and setting up for a future study will require some time. Nevertheless, reviewer #2’s suggestion is excellent, we have added discussion of the suggestion to the Discussion section.

Reviewer #3 (Public Review):

Zhang et al focus on investigating the role of pericytes in the vasculature of the inner ear. They propose that pericyte-derived VEGF is required for vessels and SGN survival. Functionally, they show that pericyte ablation leads to hearing loss.

This work is interesting to the scientific community. It describes a very specific organ vasculature and its potential crosstalk with the neuronal compartment in the peripheral nervous system.

Major strengths and weaknesses:

• The study is well explained, written, and discussed;

• The design of the experiments is adequate;

• The study is performed in vivo, in vitro, and with functional readouts;

• Results are convincing.

We thank the reviewer for the positive comments on our study. We especially appreciate the reviewer’s suggestions for improving the soundness and quality of the study. We address Review#3’s specific concerns below.

The main conclusion of the study is that pericyte-derived VEGF acts on inner ear vessels and SGNs to maintain their functionality and survival. While all presented data supports this model, there could be other potential interpretations that should be tested and validated with further evidence:

The in vitro experiments are performed with SGN explants. Using this system the authors see that pericyte-derived conditioned medium or exosomes lead to increase vessel branching and SGN neurite outgrowth. As explants contain vessels and neurons, there is the possibility that VEGF is primarily acting on endothelial cells, which then in turn signal to neurons (independent of VEGF, even when neurons express VEGFR2). This should be tested. Perhaps by targeting VEGFR2 specifically in neurons, or by culturing isolated SGN neurons and testing the effect of pericyte-derived exosomes.

This is a great point. To confirm the effect of exosome VEGF-A on SGN neurite outgrowth, we treated isolated adult SGNs with exosomes. As shown in Figs.9C & E, we found much greater SGN dendrite and branch growth in the treated than in the untreated groups.

• Pericyte ablation via DTA might result in the activation of the immune system, which could also influence vessel and neuronal survival. It should be checked whether there is immune activation upon pericyte ablation.

Excellent point. We checked on macrophage activation at two weeks after pericyte depletion. We didn’t see any obvious signs of macrophage activation, but we did notice a decrease in macrophage number. We presume the reduction in macrophage number results from insufficiency blood flow and nutrient availability.

Author Response

Reviewer #1 (Public Review):

The authors examined the impact of pre-gravid obesity in human mothers on the monocytes of newborns by collecting umbilical cord blood. Additionally, the authors also used a non-human primate (NHP) model of diet-induced obesity to isolate fetal macrophage and assess the impact of maternal obesity on fetal macrophage function. The comprehensive analysis of the human umbilical cord blood monocytes by studying cytokine release, bulk RNA-seq and bulk ATAC-seq, single cell RNA-seq and single cell ATAC-seq, responses to pathogen stimulation as well as metabolic studies such as glucose uptake are major strength of the work. They present convincing evidence that the monocytes of offspring with obese mothers have epigenetic and transcriptomic profiles consistent with impaired immune responses, both during baseline conditions and upon stimulation.

We thank the reviewer for these positive remarks

However, it is not clear from the data how the epigenetic data and the transcriptomic data are related to each other. The implication that the epigenetic changes drive the downstream transcriptional differences is not clearly demonstrated. Furthermore, it is not clear which of the observed attenuations of monocyte transcriptional responses overlap with chromatin accessibility differences. Such an overlap would make a stronger case for the mechanistic link.

We thank the reviewer for this suggestion. We have included an integration section - with overlap of baseline ATAC-Seq (data from this study) with gene expression responses (from a previous study; https://doi.org/10.4049/jimmunol.1700434) following LPS stimulation in lean and obese groups - Figure 4E. Additionally, we report overlap of LPS induced chromatin changes with gene expression changes following LPS, E.coli and RSV stimulation in Figure 5I. Collectively, these changes provide the reader with a better link between chromatin accessibility and gene expression differences and their discordance with maternal obesity.

The increased phagocytosis of E.coli in umbilical cord monocytes of newborns with obese mothers appear counter-intuitive because it implies greater host defense capacity.

E.coli uptake assay is a standard way of measuring cellular phagocytosis by flow cytometry. We would like to clarify that despite impaired ex vivo cytokine responses and poor migration, UCB monocytes demonstrate higher ability to phagocytize pathogens. This is counterintuitive but not surprising, given that enhanced phagocytosis is a hallmark of regulatory monocytes/macrophages.

One of the most remarkable aspects of the manuscript is the analysis of the fetal macrophages in a non-human primate (NHP) model of diet induced obesity because of the challenge of studying fetal macrophages in humans. The cytokine assays nicely show that the fetal macrophages in the obesity model show impaired cytokine production, consistent with what was seen in the umbilical cord blood monocytes of human newborns. This is especially important because circulating monocytes or monocyte progenitors seed the fetal tissues and give rise to fetal macrophages, thus elegantly linking the human work on circulating umbilical cord blood monocytes to the tissue macrophages in the NHP model. However, the NHP studies do not show any additional macrophage characterization beyond the cytokine assays. Flow cytometry analysis of the macrophage phenotype and functional assays would strengthen the conclusions regarding macrophage dysregulation.

We have now included phenotyping data for ileal and splenic macrophages in Figure 6C-6E, which were collected during cell sorting. We unfortunately are not able to carry out additional functional assays since we don’t have any additional cells from these animals.

Reviewer #2 (Public Review):

This paper will be of interest to scientists studying the molecular effects of maternal obesity on offspring health. The paper represents an extension to earlier findings that have linked epigenomic alterations of monocyte population to aberrant immune responses in offsprings of obese mothers. Bulk and single cell technologies have been implemented to characterize monocytic responses to bacterial and viral pathogens at the transcriptional and epigenetic level. A macaque model of western-style diet induced obesity is also described to provide in vivo evidence in support of monocyte/immune cell reprogramming by western diet/obesity. However, enthusiasm for the paper is significantly dampened by a lack of clarity in data presentation and robustness of the analysis

We thank the reviewer for this comprehensive summary and thoughtful assessment

Reviewer #3 (Public Review):

The manuscript by Sureshchandra et al is a very extensive analysis of monocyte function and their molecular landscape in cord bloods from lean and obese mothers. They aimed to analyze the effects of pre-pregnancy BMI on the functioning of the innate immune system in newborns in a very extensive way. The combination of functional and molecular analyses strengthens their observations and shows many different sides of monocyte activation. I think this approach needs to be praised and should be an inspiration to many others who study monocyte function. This allows for a broad view on the matter and also shows where potential targeting will be necessary in the future. Overall, the manuscript and particularly the methods section is very well written and extensive, making it easy to study how robust the data are.

We thank the reviewer for their comprehensive and positive assessment of our work

Author Response

Reviewer #2 (Public Review):

This is an interesting study investigating the effects of sensory conflict on rhythmic behaviour and gene expression in the sea anemone Nematostella vectensis. Sensory conflict can arise when two environmental inputs (Zeitgeber) that usually act cooperatively to synchronize circadian clocks and behaviour, are presented out of phase. The clock system then needs to somehow cope with this challenge, for example by prioritising one cue and ignoring the other. While the daily light dark cycle is usually considered the more reliable and potent Zeitgeber, under some conditions, daily temperature cycles appear to be more prominent, and a certain offset between light and temperature cycles can even lead to a breakdown of the circadian clock and normal daily behavioural rhythms. Understanding the weighting and integration of different environmental cues is important for proper synchronization to daily environmental cycles, because organisms need to distinguish between 'environmental noise' (e.g., cloudy weather and/or sudden, within day/night temperature changes) and regular daily changes of light and temperature. In this study, a systematic analysis of different offsets between light and temperature cycles on behavioural activity was conducted. The results indicated that several degrees of chronic offset results in the disruption of rhythmic behaviour. In the 2nd part of the study the authors determine the effect of sensory conflict (12 hr offset that leads to robust disruption of rhythmic behaviour) on overall gene expression rhythms. They observe substantial differences between aligned and offset conditions and conclude a major role for temperature cycles in setting transcriptional phase. While the study is thoroughly conducted and represents and impressive amount of experimental and analytical work, there are several issues, which I think question the main conclusions. The main issue being that temperature cycles by themselves do not seem to fulfil the criteria for being considered a true Zeitgeber for the circadian clock of Nematostella.

Major points:

Line 53: 'However, many of these studies did not compare more than two possible phase relationships.....'. Harper et al. (2016) did perform a comprehensive comparison of different phase relationships between light and temperature Zeitgebers (1 hr steps between 2 and 10 hr offsets), similar to the one conducted here. I think this previous study is highly relevant for the current manuscript and -- although cited -- should be discussed in more detail. For example, Harper et al. show that during smaller offsets temperature is the dominant Zeitgeber, and during larger sensory conflict light becomes the dominant Zeitgeber for behavioural synchronization. Only during a small offset window (5-7 hr) behavioural synchronization becomes highly aberrant, presumably because of a near breakdown of the molecular clock, caused by sensory conflict. Do the authors see something similar in Nematostella? Figure 3 suggests otherwise, at least under entrainment conditions, where behaviour becomes desynchronized only at 10 and 12 hr offset conditions. But in free-run conditions behaviour appears largely AR already at 6 hr offset, but not so much at 4 and 8 hr offsets (Table 2). So there seems to be at least some similarity to the situation in Drosophila during sensory conflict, which I think is worth mentioning and discussing.

We have added a more detailed discussion of our results in the context of Harper et al. 2016 (L468-476).

Line 111: The authors state that 14-26C temperature cycle is 'well within the daily temperature range experienced by the source population'. Too me this is surprising, as I was not expecting that water temperature changes that much on a daily basis. Is this because Nematostella live near the water surface, and/or do they show vertical daily migration? Also, I do not understand what is meant by '...range of in situ diel variation (of temperature)'. I think a few explanatory words would be helpful here for the reader not familiar with this organism.

In fact, one of our motivations for studying temperature is that Nematostella naturally experience extreme temperature variation. The data we cite (Tarrant et al. 2019) are from in-situ water measurements. Nematostella live in extremely shallow water (in salt marshes), and the local population in Massachusetts experience wide swings in temperature due to the temperate latitude.

We have added this information to the Introduction (L88-90), and we also added a discussion of Nematostella’s ecology in the Discussion section (L591-654).

Lines 114-117: I was surprised that clock genes can basically not be synchronized by temperature cycles alone. Only cry2 cycled during temperature cycles but not in free-run, so the cry2 cycling during temperature cycles could just be masking (response to temperature). Later the authors show robust molecular cycling during combined LD and temperature cycles (both aligned and out of phase), indicating that LD cycles are required to synchronize the molecular clock. Moreover, a previous study has demonstrated that LD cycles alone (i.e., at constant temperature) are able to induce rhythmic molecular clock gene expression (Oren et al. 2015). Similarly, the free running behaviour after temperature cycles does not look rhythmic to me. In Figure 2A, 14-26C there is at best one peak visible on the first day of DD, and even that shows a ~6 phase delay compared to the entrained condition. After the larger amplitude temperature cycle (8:32C) behaviour looks completely AR and peak activity phases in free-run appear desynchronized as well (Fig. 2B). Overall, I think the authors present data demonstrating that temperature cycles alone are not sufficient to synchronize the circadian clock of Nematostella. One way to proof if the clock can be entrained is to perform T-cycle experiments, so changing the thermoperiod away from 24 hr (e.g., 10 h warm : 10 h cold). If in a series of different T-cycles the peak activity always matches the transition from warm to cold (as in 12:12 T-cycles shown in Fig. 1A) this would speak against entrainment and vice versa.

Thank you for these thoughtful comments and constructive suggestions. We have conducted an additional experiment, which provides further evidence that temperature cycles can, in fact, synchronize the circadian clock. To do this, we measured the behavior of animals entrained in cycles with a short (12h) period, half the length of a circadian period. This takes advantage of a phenomenon called “frequency demultiplication”, in which organisms in 12h environmental cycles display both 12h and 24h components--essentially, the clock perceives every other cycle as a “day” (Bruce, 1960; Merrow et al., 1999). The important thing is that the 24h behavioral component can only occur if the signal is entraining a circadian clock—otherwise, we would only observe a directly-driven 12h behavior pattern.

We first show that this phenomenon occurs with 6:6 LD cycles—which we expected, because we know light is a zeitgeber. We then show that animals entrained to a temperature cycle with a 12h period also display 24h behavioral rhythms—and in fact the 24h component is stronger than the 12h component. We believe this is strong evidence that temperature is a bona fide zeitgeber in this system. This experiment is now explained in the Results (L127-154) and in Figure 2–Figure supplement 1.

In terms of our original data, the reviewer is correct that the statistically-detectable free-running rhythms were weak and not visually obvious). Our confidence in thermal entrainment came from the fact that some individual animals had 24h rhythmicity in free-run, even if the signal was weak in the mean time series—this suggested that temperature must be at least capable of synchronizing internal clocks. It is also important to note that even light-entrained rhythms are “noisy” in cnidarians, which is why we were not surprised that the signal was weak. We have added a discussion of this observation in L601-612.

Lines 210-226: As mentioned above, I think it is not clear that temperature alone can synchronize the Nematostella clock and it is therefore problematic to call it a Zeitgeber. Nevertheless, Figure 3A, B, D show that certain offsets of the temperature cycle relative to the LD cycle do influence rhythmicity and phase in constant conditions. This is most likely due to a direct effect of temperature cycles on the endogenous circadian clock, which only becomes visible (measureable) when the animals are also exposed to certain offset LD cycles. My interpretation of the combined results would be that temperature cycles play only are very minor role in synchronizing the Nematostella clock (after all, LD and temperature cycles are not offset in nature), perhaps mainly supporting entrainment by the prominent LD cycles.

With our new data (see previous point), we believe we can safely say that temperature is a zeitgeber. We are not totally clear on what is meant by “a direct effect of temperature cycles on the endogenous circadian clock.” We argue that, because we see changes in free-running behavior during certain offsets, the timing of temperature cycles must affect the internal clock in a way that persists during constant conditions—it can’t just be a direct (clock-independent) effect of temperature.

Gene expression part: The authors performed an extensive temporal transcriptomic analysis and comparison of gene expression between animals kept in aligned LD and temperature cycles and those maintained in a 12 hr offset. While this was a tremendous amount of experimental work that was followed by sophisticated mathematical analysis, I think that the conclusions that can be drawn from the data are rather limited. First of all, it is known from other organisms that temperature cycles alone have drastic effects on overall gene expression and importantly in a clock independent manner (e.g., Boothroyd et al. 2007). Temperature therefore seems to have a substantially larger effect on gene expression levels compared to light (Boothroyd et al. 2007). In the current study, except for a few clock gene candidates (Figure 2C), the effects of temperature cycles alone on overall gene expression have not been determined. Instead the authors analysed gene expression during aligned and 12 h offset conditions making it difficult to judge which of the observed differences are due to clock independent and clock dependent temperature effects on gene expression. This is further complicated by the lack of expression data in constant conditions. I think the authors need to address these limitations of their study and tone down their interpretations of 'temperature being the most important driver of rhythmic gene expression' (e.g., line 401). At least they need to acknowledge that they cannot distinguish between clock independent, driven gene expression and potential influences of temperature on clock-dependent gene expression rhythms. Moreover, in their comparison between their own data and LD data obtained at constant temperature (taken from Oren et al. 2015), they show that temperature has only a very limited effect (if any) on core clock gene expression, further questioning the role of temperature cycles in synchronising the Nematostella clock. Nevertheless, I noted in Table 3 that there is a 1.5 to 3 hr delay when comparing the phase of eight potential key clock genes between the current study (temperature and LD cycles aligned) and LD constant temperature (determined by Oren et al.). To me, this is the strongest argument that temperature cycles at least affect the phase of clock gene expression, but the authors do not comment on this phase difference.

We agree with these points about the limitations of our study, and have revised the manuscript to phrase our conclusions more carefully. We still think it is reasonable to observe that temperature was a stronger drive of gene expression than light in our study, but this may not be true in other contexts.

In terms of the comparison with Oren et al. 2015, we didn’t want to over-interpret these results because there are other differences between the studies (L1181-1185), including the use of a different source population. In addition, we would prefer denser sampling (2h time points rather than 4h) and larger sample sizes to make claims about phase differences.

Network analysis: This last section of the results was very difficult to read and follow (at least for me). For example, do the colours in Figure 6A correspond to those in Figure 6B, C? A legend for each colour, i.e., which GO terms are included in each colour would perhaps be helpful. As mentioned above, I also do not think we can learn a lot from this analysis, since we do not know the effects of temperature cycles alone and we have no free-run data to judge potential influence on clock controlled gene expression. Under aligned conditions genes are expressed at a certain phase during the daily cycle (either morning to midday, or evening to midnight), which interestingly, is very similar to temperature cycle-only driven genes in Drosophila (Boothroyd et al. 2007). Inverting the temperature cycle has drastic effects on the peak phases of gene expression, but not so much on overall rhythmicity. But since no free-run data are available, we do not know to what extend these (expected) phase changes reflect temperature-driven responses, or are a result of alterations in the endogenous circadian clock.

We have revised and streamlined this section and Fig. 6, including removing panel 6C. The colors do correspond across panels in the figure. For space, GO terms of select modules are included in Fig. 6, and GO results for all modules are included in the Supplemental Data and discussed in the Results.

It is true that we can’t distinguish temperature-driven versus clock effects here, and it does seem like many modules simply follow the temperature cycle (which we say in this section). The most interesting finding from this section is probably that the co-expression structure (correlations between rhythmic genes) are substantially weakened during SC, and we do discuss certain modules of genes that lose or gain rhythmicity. We have revised this section to focus on the main points and have cut several of the less pertinent results.

Reviewer #3 (Public Review):

This article reflects a significant effort by the authors and the results are interesting.

For the third set of experiments, are temperature and light really out of synch? While peak in temperature no longer occurs along with lights on, we do still have two 24 hour cycles where changes in the environmental cues still occur simultaneously (lights on with peak in temperature, lights off with min in temperature). I wonder what would happen if light remained at a 24 hour cycle and temperature became either sporadic (randomly changing cycles) or was placed on a longer cycle altogether (temperature taking 20 hours to increase from min to max, and then another 20 hours to go from max to min).

Thank you for your interesting suggestions for future experiments. This point is addressed in our revisions responding to Reviewer #1, who requested a discussion of the phrase “sensory conflict.” We agree that the binary “in-sync vs. out-of-sync” may be too simplistic. Our original conception of sensory conflict was a situation in which light and temperature provide different phase information, as informed by experiments with only light (prior literature) or only temperature (this work).

In our revised manuscript, we discuss the idea that “sensory conflict” is not always a useful framework because there are many possible relationships between light and temperature. Although our 12h offset is certainly less “natural” than our aligned time series, it may be useful to think of them simply as 2 different possible light and temperature regimes in which the two signals interact, rather than abstract ideals of “aligned” or “misaligned.”

An area that could significantly benefit a broader readership would be to improve overall clarity of figures and rethink if all the results are necessary to convert the key findings of the paper. As written, the results sections is somewhat confusing.

We have revised Figs. 1 and 6 for clarity, and we have also shortened the network analysis portion of the Results.

Author Response

Reviewer #1 (Public Review):

Here the authors sought to understand how BPGM/2,3-BPG levels are involved in adaptive responses to hypoxia and whether they are involved in fetal growth restriction. In the current state, I find the data to be confusing and lacking in mechanistic data to justify that increased BPGM is an adaptive response to hypoxia. While the authors find increased staining for the enzyme BPGM in SpA-TGCs after hypoxia, they did not assess 2,3-BPG in cord blood. This would show that increased enzymatic levels have a downstream impact. MRI experiments assessing placental and fetal haemoglobin-oxygenation, showed no differences. Human FGR samples, however, showed reduced 2,3-BPG in cord blood. Further evidence is required to show hypoxia increases BPGM as a compensatory mechanism to permit adequate 2,3-BPG and placental-fetal oxygenation levels as the authors claim.

Additional experiments that demonstrate that BPGM is advantageous in the context of hypoxia would strengthen the authors arguments, and would provide a novel mechanism for adaptive responses to hypoxia in the placenta which is highly interesting.

Obtaining cord-blood from mouse embryos and analyzing its 2,3 BPG content is technically not feasible thus we concentrated on the human data only. However note that the dominant physiological effect would be on maternal blood in the placenta, where local elevation of 23BPG can aid in oxygen release.

Reviewer #2 (Public Review):

Summary:

This manuscript will be of interest for investigators in the field of development and the biology of pregnancy. The major strengths of the data are the detailed description of a hypoxia-induced mouse model of fetal growth restriction, where phenotypes, tissue histology, MRI images and metabolic analysis combine to characterize the experimental system. The data seem descriptive and preliminary, and the comparison to human pregnancy is neither supportive nor rigorous.

Strengths

• The mouse pregnancy has been used by the authors and by others as a model for placental insufficiency. The manuscript provides incremental data to characterize hypoxia- induced fetal growth restriction

• The 15.2T MR imaging technology is high quality and informative, even if the results did not reveal marked changes.

• The detailed characterization of BPGM expression in the apical mouse placental surfaces is valuable.

• The provided model may be useful for future studies by the authors.

Weaknesses

• The metabolic analysis was restricted to one enzyme and metabolite. Placental analysis of 2,3-BPG and BPGM were already published (ref 29-30). At best, if the 2,3 BPG is related to the phenotype, it night be interpreted as a part of the injury in human cases, and adaptive response in the mouse models (as the authors suggested lines 286-288 and 332-336.). However, these assumptions are not tested.

In the paper of Pritlove et al. (ref. 29) the authors demonstrated the expression of BPGM in normal human cohort. However, they did not test BPGM expression or 2,3 BPG levels in FGR placentae. In the paper of Gu et al. (ref. 30) the authors analyze murine placental BPGM expression secondary to igf2 deletion. Our study is the first to demonstrate the impact of maternal hypoxia on placental BPGM levels in murine gestational hypoxia models .

• The human cases are not very informative. The causes of FGR were not known, but clearly (Table 1) not analogous to that of the mouse model. Systemic hypoxia in humans might have been more informative. In its absence, the value of cross-species comparison is low. -

• While the provided experiments are of good quality, the approach is very descriptive and not advancing mechanistic understanding of FGR-related placental insufficiency.

The human placenta were specifically selected to exclude known causes of FGR such as heavy smoking or iron deficiency. We will work to expand the diversity of cases to test the potential role of BPGM in those cases as well.

Author Response

Reviewer #1 (Public Review)

This manuscript describes a new method to perform online movement correction and extraction of calcium signals from a miniscope. The efficiency of the algorithm is tested by quantifying the accuracy of animal location decoding from hippocampal place cells. The online decoding happens with virtually no delay which is promising for closed-loop methods. It seems to be superior to online decoding without motion correction, which was the state of the art.

The strength of this technique is therefore that it achieves real-time processing.

The weakness of the study is the lack of comparison of the decoding accuracy with what can be obtained with electrophysiological state of the art, which prevents really estimating how precise the technique is.

In revision, we present data showing that when our system is used to decode contour-based calcium traces from N≈50 neurons, the decoder achieves a mean distance error of ~30 cm which is worse than the mean error of ~20 cm achieved using maximum likelihood decoding of single unit spike trains from electrophysiological recordings (Fig. 7E). However, when decoding of N=900 contour-free calcium traces from the same image frames in the same rats, the mean decoding error goes down to ~15 cm, which is better than the mean for electrophysiological recordings. From this we conclude that real-time decoding of position from calcium traces achieves accuracies similar to those achievable with electrophysiology.

Although less critical, there is no demonstration of a closed-loop application.

It is true that we have not yet demonstrated a real-time closed loop application, but by demonstrating short latency generation of TTL outputs triggered by the decoder, we demonstrate the capability for closed-loop applications.

Real-time position decoding is technically nice, but the position can be obtained from tracking the animal so it is practically useless.

We offer two points in reply to this comment. First, decoding position from neural activity could offer useful (though not yet demonstrated) capabilities that would not be achievable with simple position tracking; for example, the position decoder could be trained on CA1 signals obtained during waking and then used to read out position trajectories generating during REM sleep.

Second, and more importantly, position decoding was selected as a benchmark for performance testing mainly because it allows highly precise comparisons between decoder predictions and ground truth, which is important for establishing that the fidelity of calcium signals imaged in real time is adequate for accurate decoding of behavior at short latencies.

It is also clear that decoding position on a linear track is easier than on a 2D arena, therefore it is difficult to estimate how much the efficiency of the method can be challenged in harder settings.

It is true that decoding in a 2D arena would be a greater challenge than a 1D linear track, but in pursuit of our goal to rapidly disseminate a system with capabilities for short latency decoding of behavior from calcium signals, optimizing system performance for one specific application (e.g,, position decoding) is not our main priority. A higher priority is to offer versatility for a wide range of experimental applications. To better demonstrate such versatility, the revised manuscript includes a new section in the Results that demonstrates categorical classification of behaviors during an instrumental touchscreen task.

Reviewer #2 (Public Review):

In this paper, the authors developed a new device for online decoding of position based on calcium imaging in freely moving rodents. This device could be used in the brain-computer interface to investigate neurofeedback-based therapies for neurological disorders. The technical part is properly done and gives convincing results that can be truly helpful for the scientific community using the miniscope. Nevertheless, as a methodological article, there should be more details regarding the accuracy of the decoding and of the different steps to follow if someone wants to use their methodology. Moreover, a true online real-time experiment should be performed to validate the device.

Please find below my comments:

• From what I read the authors did not perform a true real-time experiment. I think this step iscrucial to ensure the quality of their device.

It is unclear from this comment where to draw the bar for a “true real-time experiment.” Some previous publications of real-time approaches (such as refs #6,#11,#26) have proposed causal algorithms without performance tests in hardware at all, whereas others (such as ref #14) have performance tested their system in hardware by carrying full experiments using closed-loop feedback (albeit with much smaller numbers of calcium trace predictors than we demonstrate here) without comparing different algorithmic approaches. Here we use an intermediate strategy of feeding raw offline video from a virtual sensor through the hardware processing pipeline (verifying that calcium trace outputs were identical for the real and virtual sensors). We adopted this intermediate approach to achieve the dual objectives of testing a true hardware implementation on real-time performance measures (e.g., microsecond processing latencies) while also benchmarking different algorithms (such as CB versus CF trace extraction as in Fig. 3, or raw calcium traces versus deconvolved spikes as in panel A of the Supplement to Fig. 3) against one another on the same datasets.

• There should be a validation against a classical offline Bayesian decoding.

We have presented an accuracy comparison for decoding linear track position from calcium traces with DeCalciOn versus decoding from single-unit spikes with electrophysiological recording data (Fig. 7E); decoding from single-unit spikes utilized a classical Bayesian maximum likelihood approach (see Methods), so Fig. 7E not only offers a comparison between calcium imaging versus electrophysiology, but between online linear classifier versus classical offline Bayesian approaches as well. In addition, we compared the performance of the linear classifier to a naïve Bayes decoder in panel B of the Supplement to Fig 3, showing that performance is better for the linear classifier than naïve Bayes.

• "To mimic these steps using the virtual sensor in our performance tests, one session of imagedata was collected and stored from each of the 13 rats, yielding ~7 min (8K-9K frames) of sensor and position tracking data per rat. The linear classifier was then trained on data from the first half of each session and tested on data from the second half." This sentence is not clear enough. The authors should clearly describe the exact time needed for each experimental step. What is the time needed for instance for the experimental step 2, during which the linear classifier is trained to decode behavior from the initial dataset? This is crucial information if someone wants to use this device.

In response to this comment, the Results section of the revised manuscript includes an extensive subsection (‘Steps of a real-time imaging session’) that describes each experimental step in detail (pages 4-6), including the time required for each step. In addition, this information is now more thoroughly summarized in the diagram of Fig. 1B.

How the accuracy varies with the duration (or the quality) of the initial dataset? It is important that the authors provide an investigation of this to validate their device.

This issue is now discussed in the Results near the bottom of page 5. In addition, Fig. 3G now plots how position decoding improves as a function of the size of the training dataset.

• For instance, what is the decrease in decoding accuracy 1) with fewer place cells?

The scatterplots in the right panels of Fig. 3D show that decoding accuracy improves as a function of the number of neurons imaged in given rat.

What is the approximative number of place cells to obtain reliable decoding?

This question is addressed by showing how decoding accuracy improves with the number of imaged neurons (Fig. 3D scatterplots). We also address this issue on our performance comparison of CB versus CF and CF+ traces since differing numbers of calcium trace predictors appear to be an important factor in accounting for the observed performance differences, as discussed in the main text (page 16, last paragraph).

2) With the duration of the initial recording session. Here it seems to be of the order of 3-4 min.What if the recording session is shorter? Is there some constraint about this recording session (in terms of speed, stops, etc...) to obtain good decoding?

The revised Fig. 3G plots how position decoding improves as a function of the size of the training dataset.

3) Is there a link between the decoding accuracy and the number of place cells nearby?

We did not select calcium traces that met a spatial criterion (i.e, “place cells”) to be include in the decoding analysis, Instead, all detected CA1 calcium traces provided input to the decoder, regardless of their spatial tuning properties (Fig. 3D and panels D,E of the Supplement to Fig. 3 show that many cells were indeed spatially tuned). Also note that when contour-free (CF) trace extraction methods were used, each calcium trace could detect fluorescence from multiple neurons. Under this methodology it is not straightforward to analyze how decoding accuracy at a given position varies with the “number of place cells nearby” and we are not convinced that presenting such an analysis would advance our main goal of demonstrating DeCalciOn’s capabilities to researchers.

• The authors specified the time delay of 2.5ms for their device. Yet, it is pointless regarding thepurpose of the decoding. The important information is the precise position of the animal when the device is used to trigger a stimulation at a given location. Again, a true online experiment should be done to validate that a TTL can be triggered by the device at a precise location (with a quantification of the error made).

We agree that this is an important issue, and it has been thoroughly addressed in the revised manuscript.

• There is no information on the accuracy of the decoding with respect to the location in thelinear track. It is likely that the extremities of the linear track will be better identified. Figure 4C does not provide a clear description of the error made. The choice of D=2 (which seems to represent the spatial bin) is not justified. Two spatial bins seem to represent +/-40 cm which is quite large.

Polar plots in Fig. 3F of the revised manuscript show mean accuracy in each position bin for decoders trained on offline, CB, CF,. and CB+ calcium traces.

• The movement artefacts are not equally observed in the maze. The way they are correctedmight be captured by the linear decoder. These artefacts might have a strong influence on the decoding. Please provide a quantification of the correction made during steps 1 and 2 in relation to the position of the animal on the linear track. The authors should provide a correlation between the presence of these corrections with the decoding accuracy.

Regardless of whether analysis is done offline or online, any calcium imaging and decoding experiment is vulnerable to two potential problems arising from motion artifact:

PROBLEM #1. Image motion can generate noise in calcium signals that disrupts the accuracy of decoding.

PROBLEM #2. Image motion that is correlated with behavior can convey uncontrolled information that allows the decoder to learn predictions from image motion rather than calcium signals. Very few published in-vivo calcium imaging experiments provide adequate controls for these two possible sources of artifact (again, such controls are just as necessary for offline as for online experiments). In response to the referee comments, we have provided controls for these confounds in our performance tests of DeCalciOn’s online decoding capabilities.

Fig. 4B of the revised paper shows that without online motion correction, several rats in the linear track experiment show a significant correlation between position error and motion artifact (indicated by positive values on the y-axis); hence, motion artifact impairs decoding of position on the linear track in these rats (problem #1 above). This correlation between motion artifact and decoding error is reduced or eliminated by online motion correction (as indicated by values near zero on the x-axis), demonstrating that online motion correction helps to prevent motion artifact from impairing the accuracy of decoding.

Fig. 6 of the revised paper shows that during an operant touchscreen experiment, motion artifact occurs preferentially during specific behaviors such as visiting the food magazine (reward retrieval, Fig. 6A) or touching the screen to make a response (correct choice, Fig. 6B). When motion correction is not used (top graphs in Figs. 6C-F), the average motion artifact is higher during frames when the decoder accurately predicts behavior than during frames when the decoder fails to predict behavior; hence, motion artifact appears to improve the accuracy of predicting these behaviors (problem #2 above). When motion correction is used, the average motion artifact no longer differs for correctly versus incorrectly decoded frames (except in one case, bottom right graph of Fig. 6E), indicating that motion correction helps to prevent the decoder from learning to predict behavior from motion artifact.

• Besides the methodological part, I have some physiological questions. It is quite common inlinear tracks to have bi-directional and unidirectional place cells. Is it the case here? How many? It is difficult to see this in figure C. Is there an error due to the online decoding of the position in the two directions of the linear track?

Again, since we did not select calcium traces that met a spatial criterion (i.e, “place cells”) to be include in the decoding analysis, and since CF traces could detect fluorescence from multiple neurons, we are not convinced that presenting a detailed analysis of this issue would advance our primary goal of demonstrating DeCalciOn’s capabilities to reseachers.

Reviewer #3 (Public Review):

DeCalciOn is an innovative contribution to the toolbox of real-time processing of calcium imaging data. It provides calcium traces from hippocampal CA1 neurons with a roughly two-millisecond latency and uses them to decode the position of rats running along a linear track - setting the stage for closed-loop experiments requiring fast interpretation of neural activity. The manuscript would be strengthened by a more systematic, empirical comparison to other, currently available alternative approaches. In addition, the decoding analysis does not fully account for the possibility of artifactual motion in the imaging video being informative of position.

We suggest strengthening this manuscript by addressing the following four points:

1) In the discussion of other platforms, the authors state that "Any system that lacks motionstabilization would also be vulnerable to artifactually decoding behavior from brain motion (which can be correlated with behavior) rather than neural activity." It follows that the same problem might also occur with incomplete motion correction. While the motion-corrected video shown in Supplementary Video 1 has reduced motion compared to the raw video, motion is still visible, including outside of the marked jitter. It remains possible that the linear decoders for the position in the linear track are utilizing brain motion-induced, as opposed to calcium fluorescence-induced, signal changes. A critical first step to assess this issue is to ask whether the motion in the video is related to the rat's behavior. One could test whether the 2D motion displacement traces can be used to predict rat position using linear classifiers.

Briefly, we show that motion correction helps to prevent the decoder from learning to predict behavior from motion artifact.

2) The manuscript would benefit from repeating the experiment in a more complex environment,such as a 2D arena. This would increase the generalizability of the findings. In addition, increasing the complexity of the environment would reduce the possibility that particular types of brain motion are closely linked with positions in the environment.

We have diversified our performance testing by presenting results for decoding calcium activity from a different brain region (OFC rather than CA1) during a different kind of behavior (an instrumental touchscreen task rather than a linear track).

3) The authors present an interesting comparison between "contour-free" and traditionalcontour-based source extraction. A more comprehensive discussion on the history or novelty of "contour-free" calcium imaging processing would contextualize this result.

The revised Discussion section contains a new subsection titled “Source identification” to contextualize this issue.

4) In the discussion, the authors compare DeCalciOn to two previous online calcium imagingalgorithms. The technical innovations of this work would be better highlighted by directly testing all three of these algorithms, ideally on similar datasets.

Briefly, one of the two cited systems is designed for compatibility with benchtop 2P microscopes and does not interface with miniscopes; public resources are not available for the other cited online algorithm.

Author Response

Reviewer #3 (Public Review):

This is an interesting study to examine how alveolar bone responds to oral infection using unbiased scRNA-seq. The manuscript is well-written and the results are convincing.

1) The authors should revise the abstract. The study did nothing with the understanding of healing. The whole conditions were performed under infection and inflammation which actually induce bone loss, but not healing.

Thank you for raising this point. We have revised the manuscript accordingly.

2) Since periapical inflammation causes progressive bone loss, how MSC with increasing osteogenic potentials contributes to bone loss? The authors should discuss it.

We would like to thank the reviewer for this important comment. Although AP is an inflammatory disease with periapical bone loss, the progression of AP is usually self-limiting in which a new equilibrium has been established between root canal pathogens and anti-infective defense mechanisms (Wang, Zhang, Xiong, & Peng, 2011). Animal experiments revealed that the bone lesion size reached to stable 21 days after establishing AP, which was resulted from a balance of bone remodeling (Márton & Kiss, 2014; Wang et al., 2011). Previous studies have shown that human apical granulation tissues contain osteogenic cells (Maeda, Wada, Nakamuta, & Akamine, 2004). A population of MSCs were isolated from human periapical cysts, which tended to be directed to differentiate toward the osteogenesis lineage (Marrelli, Paduano, & Tatullo, 2013, 2015; Tatullo et al., 2015). Activated by inflammatory bone destruction, these MSCs with increased osteogenic potentials may rescue the bone resorption process, which reach the equilibrium between bone formation and resorption then drive the progression of AP into stable states (Márton & Kiss, 2014). Since the pathologic stimuli exists constantly, the protective actions can alleviate the bone loss to some extent. In clinical practice, root canal therapy (RCT) aims to disinfect and remove the pathogenic factors, which makes the protective activities overweigh the destructive ones (L. M. Lin, Ricucci, Lin, & Rosenberg, 2009). The bone lesions of AP patients receiving RCT usually fully recovered with resolution of radiolucency after the inflammation is controlled in apical area (Soares, Santos, Silveira, & Nunes, 2006). The healing of AP lesion is highly correlated with the osteogenic potential of inflamed MSCs (L. M. Lin et al., 2009).

We added the related contents in the discussion section.

3) Did the authors detect osteoclasts by scRNA-seq? If not, are there any precursors of osteoclasts identified in inflammatory alveolar bones? 1) I suggest that the authors provide a more detailed analysis of inflammation since this is a unique model to study oral bone inflammation.

Thank you for this valuable point. Bone destruction is a major pathological factor in chronic inflammatory diseases such as AP. Various cytokines including TNF-α, IL-1α, IL-6 were released by immunocytes to recruit the osteoclast precursors and induce the maturation of osteoclasts. We detected osteoclast markers including Ctsk, Acp5, Mmp9 and Nfatc1 by scRNA-seq. Moreover, Csfr1, Cx3cr1, Itgam, and Tnfrs11a were used to identify osteoclast precursors. The expression pattern of these osteoclast-related markers in all clusters were presented in Figure 3A. Markers of osteoclast and osteoclast precursors were highly expressed in the clusters of monocyte and macrophage. The expression levels of these markers were analyzed in all clusters (Figure 3B). The GO analysis showed that inflammation related immune reactions and bone resorption activity were significantly enriched in macrophage cluster (Figure 3C). Moreover, pseudotime analysis was performed for the clusters of macrophage and monocyte. Two independent branch points were determined and five monocyte/macrophage subclusters scattered at different branches in the developmental tree (Figure 3D, G). The results showed that the monocyte cluster differentiated into the macrophage cluster (Figure 3E). During this trajectory, the gene expression pattern across pseudotime showed that osteoclastic genes, such as Ctsk, Acp5, Mmp9, Atp6v0d2, and Dcstamp were progressively elevated (Figure 3F). Of note, we have observed a branch which was highly positive for Ctsk and Acp5 (Figure 3H), indicating the mature osteoclasts were differentiated from monocyte/macrophage lineage and contributed to inflammatory bone resorption during AP. We have also analyzed the expression of osteoclast related genes using the bulk RNA-seq library built on mandibular samples extracted from mice with AP. Markers of osteoclast and osteoclast precursors were significantly upregulated, confirming the osteoclasts activity in the inflammatory-related bone lesion (Figure 3I). Please see page 9 and figure 3.

4) It is known that macrophages can be classified into M1 and M2. Based on scRNA-seq, did the authors observe these two types?

We appreciate this point raised by the reviewer. We used CD86, CD80, IL1β, and TNF as markers of M1-like macrophages. CD163, CD206, MSR1 and IL-10 were used as markers to detect M2 subset in the macrophage cluster. The analysis of macrophage cluster showed the M1-like macrophage accounted for the vast majority in AP lesions. The expression pattern of M2 markers were also presented in macrophage cluster (Figure 3-figure supplement 1A, B).

Author Response

Reviewer #1 (Public Review):

This study intended to identify the metabolic at-risk profile within PLWH on ART, by integrating and analyzing the multiomics data from multi-omics including untargeted plasma metabolomic, lipidomic, and fecal 16s microbiome. The overall strength of the study is the long-term treatment (~15 years) of the study subjects with well-recovered CD4 cell count and viral suppression. The integration and analysis of multi-omics data using similarity network fusion and factor analysis, etc. to group or differentiate HIV patients are informative and useful. The weakness of the study is the lack of presentation of comparability between patients and healthy controls and the use of multiple regression analysis for controlling potential confounders.

We are thankful to the reviewer for the critical reading of our manuscript. The primary aim of our study was to identify the molecular data-driven phenotypic patient stratification in a cohort of PLWHART with prolonged suppressive therapy to identify the at-risk metabolic profile following long-term successful therapy. We and others have reported in several studies (e.g., Ref#9 and 10) that there were distinct systemic patterns in multi-omics data. However, as suggested, we have now provided Table 1-source data 1. We have kept HC in the analysis to define which group is presenting an HC-like profile among HIV, but we are not using them to perform statistics and draw conclusions.

Reviewer #2 (Public Review):

This study systematically integrates multi-omics (plasma lipidomic and metabolomic, and fecal 16s microbiome) data to identify the metabolic at-risk profiles within people living with HIV on antiretroviral therapy (PLWHART). As a result, three groups of PLWHART (SNF-1 to 3) were identified, which showed distinct phenotypes. Such insights cannot be obtained by a single type of omics data or clinical data, and have implications in personalized medicine and lifestyle intervention. Connecting the findings in this study with specific medical/clinical insights is the next challenge.

We are thankful to the reviewer for the suggestion. System biology's application in identifying a disease state's biological mechanism in HIV-infected individuals is a relatively new field. We agree with the reviewer that connecting the findings in this study with specific medical/clinical insights is the next challenge. However, the first proof-of-concept study on 108 patients showed that multi-omics studies could generate a correlation network of communities of related analytes associated with physiology and disease. More importantly, the behavioral coaching informed by personal data helped participants to improve clinical biomarkers [PMID: 28714965]. The applications of multi-omics data are more and more valuable in non-communicable diseases [PMID: 35528975, PMID: 36503356 etc.]. As suggested by the reviewer, we have now elaborated on the medical/clinical value in identifying metabolic at-risk profiles, in particular the potential to improve individual risk stratification and to personalize lifestyle interventions. Still, as our study is an association study, data should be regarded as exploratory, and not sufficient to suggest any changes in clinical practice.

We have concluded the manuscript as follows:

“However, alterations in the metabolomics profile and higher CD4 T-cell count at the time of sample collection indicate a complex systemic interplay between host immunity and metabolic health. It can lead to an aggravated higher inflammation profile leading to a cardiometabolic risk profile among the MSM that might affect healthy aging in this population. Integrative analytical approaches that reflect the overall systemic health profile of PLWH may improve patient stratification and individual therapeutic and preventive strategies. Given the complex interplay between the clinical and molecular metabolic profile, the application of the multi-omics data for much larger cohorts of PLWH might facilitate a better identification of network perturbations and molecular network connections to detect early disease transition toward metabolic complications at an earlier stage. Developing a more personalized model or targeting the interaction networks rather than individual clinical or omics features may provide novel treatment strategies in countering dysregulated metabolic traits, aiming to achieve healthier aging.”

Author Response

Reviewer #2 (Public Review):

This is a highly interesting paper that provides important insights into the understanding of how HC-derived osteoblasts contribute to trabecular bone formation. Using single-cell transcriptomics, the authors found that HC descendent cells activate MMP14 and the PTH pathway as they transition to osteoblasts in neonatal and adult mice. They further demonstrate that HC lineage-specific Mmp14 null mutants (Mmp14ΔHC) produce more bone. By performing a panel of elegant in vitro studies, the authors show that MMP14 cleaves the extracellular domain of PTH1R, dampening PTH signaling. The authors provide more in vivo evidence showing that HC-derived osteogenic cells respond to PTH which is enhanced in Mmp14ΔHC. Generally, this is a very well-performed study that may contribute important novel aspects to the field.

I have the following issues for the authors to address:

1) The novel mechanism identified in this study (i.e. MMP14-induced PTH1R cleavage) is intriguing. It is unclear how specific this pathway is in the transition of HCs to osteoblasts. Are other MMPs besides MMP14 involved in the PTH1R cleavage? Is PTH1R the only substrate of MMP14?

Thank you for your interest in our findings. ADAMs are known to cleave various transmembrane proteins such as RANKL. As described in supplementary fFgure 4A we tested A Disintegrin And Metalloproteinase (ADAMs) for their potential ability to cleave PTH1R. We did not find that ADAM10, 15, 17 could cleave PTH1R. The lack of the cleaved PTH1R peptide in extracts isolated from osteoblasts isolated from MMP 14 null bones (New Fig. 3E) suggest that there is not another major MMP that cleaves PTH1R. In regard to other substrates that are cleaved by MMP14 – we do review these in the manuscript and the possibility that the phenotype is contributed by deficiency in other substrates.

2) Would it be possible for the authors to detect the truncated PTH1R fragment(s) from the conditioned medium prepared from either 293T or osteoblast culture?

We tried to detect whether there could be PTH1R cleaved fragment in cultured medium by western blot of PCA precipitates of cultured medium. We could not detect any free peptide using anti-Flag or anti-HA antibody. It has been reported the ligand binding domain are linked by disulphide bond in vivo, therefore cleavage of PTH1R at the unstructured loop domain does not necessarily imply a release of cleaved fragment.

3) The finding that HC-descendants persist and contribute to the anabolic response to PTH in aged mice is interesting. Have the authors examined the changes in MMP14 expression in bone with age and in response to PTH treatment?

Thank you for your question, we added additional data showing induction of MMP14 expression upon PTH treatment in Figure 7—figure supplement 1. It has also been published that PTH stimulation increased MMP14 expression in osteocytes (1).

Author Response

Reviewer #2 (Public Review):

Susswein et al. analyze a fine-scale, novel data stream of human mobility, openly available from Safegraph, based on the usage of mobile apps with GPS and sampled from over 45 million smartphone devices. They define a metric $\sigma_{it}$, properly normalized, that quantifies the propensity for visits to indoor locations relative to outdoor locations in a given county $i$ at week $t$. For each pair of counties $i$ and $j$, they compute the Pearson correlation coefficient $\rho_{ij}$ between the corresponding $\sigma$ metrics. This generates a correlation matrix that can be interpreted as the adjacency matrix of a network. They then perform community detection on this network/matrix, effectively clustering together time series that are correlated. This identifies three main clusters of counties, characterized geographically as either in the north of the country, in the south of the country, and possibly in tourism active areas. They then show, via a simple model, how including over-simplified models of seasonality may affect infectious disease models.

This work is very interesting for the infectious disease modeling community, as it addresses a complex problem introducing a new data stream.

This work builds on several strengths, among which:

It is the first analysis of the Safegraph dataset to capture seasonality in indoor behavior.

It provides a simple metric to quantify indoor activity, that thanks to the dataset can be computed with a high level of spatial detail.

It aims at characterizing clusters of counties with a similar pattern of indoor activity.

It aims at quantifying the impact of neglecting finer-scale patterns of seasonality, for example considering seasonality to be homogeneous at the US level.

We thank the reviewer for the positive review of our work.

At the same time, it presents several weaknesses that should be addressed to improve the methodology, its results, and the implication:

There is no quantitative comparison of the newly introduced metric for indoor activity with other proxies of seasonality (e.g. temperature or relative humidity). The (dis)similarity with other proxies may help in assessing the importance of this metric, showing why it can not be exchanged with other data sources (like temperature data) that are widely available and are not affected by sampling issues (more on that later).

We have now added supplementary figures (Figure S3) to illustrate how indoor activity seasonality compares with temperature and humidity. We have also added text to the Results and the Discussion to discuss this point.

A major flow of the analysis is to perform community detection on a network defined by the correlation between time series with an algorithm that is based on modularity optimization. As explained in Macmahon et al.[1], all modularity optimization methods rely on null assumptions that in the case of correlation between time series are violated. Therefore, there is a very strong potential bias in their results that is not accounted for. Possible solutions could be to proceed via the methodology presented in [1] or via a different type of algorithm (e.g. Infomap [2]). In both cases, as the network is thresholded (considering only a correlation larger than 0.9), a more quantitative assessment of the impact of the threshold value should be included.

References

[1] Mel MacMahon and Diego Garlaschelli Phys. Rev. X 5, 021006 (2015).

[2] Martin Rosvall and Carl T. Bergstrom PNAS 105, 1118 (2008).

We thank the reviewer for making this excellent point. We have now added Supplementary Figures S13 and S14. In Figure S13, we demonstrate the robustness of our clustering results with different correlation thresholds. (We have also corrected a typo in our original Methods section which mistakenly stated our correlation threshold as 0.9 rather than the 90th percentile which is what we used.) In Figure S14, we show the clustering results using a different clustering algorithm. In an effort to test a non-network-based clustering approach, we use a hierarchical clustering approach and find a consistent partition of the US to our main results.

It is not clear what is the added value of the data on indoor activity, as no fitting to real data is performed. Although this may be considered beyond the scope of this paper, I think it would be crucial to quantify how much a data-informed model would better describe real epidemic data (for example in the case of COVID-19). For now, only the impact of neglecting heterogeneity in indoor activity is shown, comparing a model with region-average parameters vs a model with county-level average parameters. Given that the dataset comes with potential bias in sampling (more on this later) it would be good to assess its goodness in predicting real epidemic spread. When showing results from different models, no visible errors are shown on the plot. How have the errors been estimated?

We appreciate this point by the reviewer, and agree that future work will have to consider how indoor activity seasonality affects our ability to capture observed transmission trends. However, such work would additionally need careful characterization of other seasonal factors hypothesized to drive transmission (including environmental and other behavioral factors), and is beyond the scope of our work. Instead, in Figure 4 we aim to (a) provide the infectious disease modeling community with empirically-inferred parameters for a simple sinusoidal model which is commonly used in infectious disease models to capture transmission seasonality; and (b) demonstrate the implications of ignoring geographic heterogeneity in transmission seasonality in theoretical models of disease dynamics, which are commonly used for scenario analysis and model-based intervention design. As we demonstrate, transmission seasonality described by such sinusoidal models, even when they are empirically characterized as in our case, can lead to meaningfully different epidemic dynamics when transmission seasonality varies from the assumptions.

Additionally, there is no uncertainty included in Figure 4B because transmission seasonality is either based on empirical data point per time step, or on the fitted sinusoidal model (where the estimated parameters have negligible standard errors).

The dataset is presented as representative of the US population. However, this has not been assessed over time. As adherence to social distancing is influenced by several socio-economic determinants the lack of representativity in certain strata of the population at a given time may introduce an important bias in the dataset. Although this is an inherent limitation of the dataset, it should be discussed in the paper more thoroughly.

We agree with the reviewer that this is a limitation. However, we do not have any way of assessing demographic representation in the dataset over time. We have instead included an additional sentence into the Discussion section acknowledging this point.

In conclusion, I think that the methodology should be revised to account for the fact that the analysis is performed on a correlation matrix. Capturing seasonal patterns of indoor activity can help in tackling the crucial problem of seasonality in human behavior. This could help in identifying effective strategies of disease containment able to curb disease spread at a lower societal cost than fully-fledged lockdowns.

We thank the reviewer again for their helpful suggestions.

Author Response

Reviewer #1 (Public Review):

The authors characterized the expression of DDR2 in the developing craniofacial skeleton. The authors showed that Ddr2-deficient mice exhibited defects in craniofacial bones including impaired calvarial growth and frontal suture formation, cranial base hypoplasia due to aberrant chondrogenesis, and delayed ossification at growth plate synchondroses. The histological studies are well done. However, the studies as shown in this manuscript do not provide cellular and molecular mechanisms beyond what is already known, particularly beyond what the authors have already published in a similar study in Bone Research (Mohamed et al., 2022 Feb 9;10(1):11). With the same Cre lines and analytic approaches, the authors already showed in the Bone Research paper that Ddr2 in the Gli1+ cells is required for chondrocyte proliferation and polarity in growth plate development and osteoblast differentiation. Cartilage development and bone formation occur in both long bones and craniofacial skeleton, the authors showed similar functions of Ddr2 in similar skeletal tissues, although the location is different. One new point in this manuscript might be: the authors indicated that loss of Ddr2 led to ectopic chondrocyte hypertrophic (Fig. 7I). But what the data actually showed was delayed chondrocyte hypertrophy and abnormal location of the delayed hypertrophic chondrocytes, which could be well caused by abnormal chondrocyte polarity. This interesting defect was superficially described with no mechanistic investigation at cellular or molecular level.

New data is now provided showing that Ddr2 deficiency is associated with abnormal collagen organization and orientation as measured by second harmonic generation (SHG) (Fig 3-figure supplement 1). Specifically, collagen orientation as reflected by SHG anisotropy measurements was disrupted in Ddr2-deficient synchondroses. This result complements data showing that the distribution of type II collagen as measured by immunofluorescence changes with Ddr2 deficiency such that no collagen is seen in the interterritorial matrix between chondrocyte bundles (Fig 3a). This loss of collagen organization provides a potential mechanism to explain the disruption of chondrocyte polarity and altered localization of hypertrophic cells in synchondroses. In further support of this concept, other recently published studies described in the Discussion have shown that Ddr2 deficiency is associated with disruption of collagen fibril orientation in other experimental systems such as in CAF cells surrounding breast tumors as well as at sites of heterotopic ossification and that these abnormalities are associated with defective integrin signaling. Additional studies beyond the scope of the present communication will be required to determine if these matrix changes can explain the observed phenotypes. However, we believe this proposed mechanism is the most likely explanation for DDR2 effects based on current data.

Reviewer #2 (Public Review):

DDR2 is a collagen-binding receptor that is required for proper skull development. Ddr2 loss-of-function in humans is associated with the developmental disease spondylo-meta-epiphyseal dysplasia (SMED). Here, the authors aim to elucidate the role of DDR2 in skull development. In this work, the role of DDR2 in skull and face development is studied in mice, which exhibit SMED-like symptoms in the absence of Ddr2. Histological studies showed that Ddr2 knockout disrupts organization and proper differentiation within progenitor-rich regions of the skull from which bone growth occurs. Histology and lineage tracing studies revealed that DDR-expressing cells in/around these zones 1) generally also express the proliferation regulator Gli1, and 2) eventually contribute to osteogenic and chondrogenic lineages. Cell-type specific knockout studies were used to show that DDR2 has a development-specific role: knockout of Ddr2 in Gli+ cells re-capitulated the developmental abnormalities observed in global Ddr2 knockout mice; knockout in chondrocytes partially recapitulated developmental abnormalities, and osteoblast-specific knockout mice were indistinguishable from their wild-type littermates. This work also catalogues the locations of Ddr2 positive cells and their lineages at various stages of development. Additionally, the anatomical effects of loss of DDR2 function on skull and face development are thoroughly described in global and cell-type specific knockouts.

This work is a vital and stimulating contribution to the scientific literature. The authors' claims and conclusions are well supported by the evidence they present.

The scientific approach is sound and the conclusions important. However, a limitation of the work's discussion is a lack of attention paid to the specific biophysical mechanism that DDR2 is playing during development. The discussion of the positioning of the golgi is nice, but a lack of golgi polarity is likely a downstream effect of processes occurring within the cell adhesion and mechanotransduction machinery. Perhaps, like integrins, DDR2 is a mechanosensor that the cell needs to properly sense local collagen orientation, polarize, and secrete properly-organized COL2. It would be beneficial to put up some guideposts that will facilitate engagement from the molecular biophysics/mechanobiology community.

Thank you for this suggestion. In response, we added new studies showing that DDR2 is necessary for ECM organization (please see reviewer 1 comments and additions to the Discussion section). In addition, the Discussion has been revised to include speculation on the relationship between DDR2-dependent ECM organization, mechanical properties of the matrix and cell differentiation. Because very little is known about DDR2 from a mechanistic perspective, much of what we propose is currently conjecture, but hopefully can guide future study.

Reviewer #3 (Public Review):

From this work, the authors investigated a number of parameters in order to profoundly understand and demonstrate the vital role of ongoing interaction between components of extracellular matrix and particular stem cells to induce normal Craniofacial development. Thus, there was a focus on the genetic manipulation (knockout) impact of molecules behind the above-mentioned interaction, and on determining how such modification would be reflected on skull bone morphogenesis.

Strengths and Weaknesses

• Using different animals' backgrounds in the same experiment might impact work outcomes.

• Better to have (ethical approval) at the beginning of the material and methods in separate paragraphs.

• It is great that the authors precisely explain all the measurements.

• Supplementary file to have details of used antibodies might be required.

• All methods have been written in academic and clear ways.

• It is nice that there is a conclusion sentence by end of the results paragraph, which made it easy for readers to fully remember and understand.

• It is possible to see a reduction in proliferative chondrocyte, with no change in apoptosis rate?

Reductions in proliferation are certainly seen in many systems. Proliferation and apoptosis are not necessarily coupled.

• Results are supposed to be compatible.

• Very nice and representative images from the immunofluorescence protocol.

• Using different techniques to confirm observations is clearly manifested in methods and results.

It is clear that the author has used different methods and techniques in order to meet his work's objectives. Importantly, there was more than one procedure to confirm observations that are related to one or more than one aim.

Although determining to what extent the outcomes of this work could be applied to community need might require a subspecialist physician's opinion, it seems that observations of the present study are likely to require a series of further investigations in order to take it to the level of human users. Notably, identification of molecules and pathways behind skull development abnormalities would open a door to early diagnosis reasons for such deformities, thus mitigating future abnormalities either by developing new prevention methods or discovering unique medications.

Thank you for these comments. Additional commentary has been added to the Discussion to provide a more mechanistic interpretation of our results, however speculative they may be at this time. Ln 555-605

Author Response

Reviewer #1 (Public Review):

King et al. provide an interesting reanalysis of existing fMRI data with a novel functional connectivity modeling approach. Three connectivity models accounting for the relationship between cortical and cerebellar regions are compared, each representing a hypothesis. Evidence is presented that - contrary to a prominent theoretical account in the literature - cortical connectivity converges on cerebellar regions, such that the cerebellum likely integrates information from the cortex (rather than forming parallel loops with the cortex). If true, this would have large implications for understanding the likely computational role of the cerebellum in influencing cortical functions. Further, this paper provides a unique and potentially groundbreaking set of methods for testing alternate connectivity hypotheses in the human brain. However, it appears that insufficient details were provided to properly evaluate these methods and their implications, as described below.

Strengths:

• Use of a large task battery performed by every participant, increasing confidence in the generality ofthe results across a variety of cognitive functions.

• Multiple regression was used to reduce the chance of confounding (false connections driven by a thirdregion) in the functional connectivity estimates.

• A focus on the function and connectivity of the cerebellum is important, given that it is clearly essentialfor a wide variety of cognitive processes but is studied much less often than the cortex.

• The focus on clear connectivity-based hypotheses and clear descriptions of what would be expectedin the results if different hypotheses were true.

• Generalization of models to a completely held-out dataset further increases confidence in thegeneralizability of the models.

Concerns:

1) The main conclusion of the paper (including in the title) involves a directional inference, and yet it is notoriously difficult to make directional inferences with fMRI. The term "input" into the cerebellum is repeatedly used to describe the prediction of cerebellar activity based on cortical activity, and yet the cerebellum is known to form loops with the cortex. With the slow temporal resolution of fMRI it is typically unclear what is the "input" versus the "output" in the kinds of predictions used in the present study. Critically, this may mean that a cerebellar region could receive input from a single cortical region (i.e., the alternate hypothesis supposedly ruled out by the present study), then output to multiple cortical regions, likely resulting (using the fMRI-based approach used here) in a faulty inference that convergent signals from cortex drove the results. On pg. 4 it is stated: "We chose this direction of prediction, as the cerebellar BOLD signal overwhelmingly reflects mossy-fiber input, with minimal contribution from cerebellar output neurons, the Purkinje cells (Mathiesen et al., 2000; Thomsen et al., 2004)." First, it would be good to know how certain this is in 2022, given the older references and ongoing progress in understanding the relationship between neuronal activity and the BOLD signal (e.g., Drew 2019). Second, given that it's likely that activity in the mossy-fiber inputs has an impact on Purkinje cell outputs, and that some cortical activity supposedly reflects cerebellar output, it is possible that FC could also reflect the opposite direction (cerebellumcortex). It would seem important to consider these possibilities in the interpretation of the results.

We agree that making directional inferences with fMRI BOLD signals is difficult. We also note that because of the low temporal resolution of fMRI BOLD signals, we have not tried to extract directional information based on temporal lags. Rather, we emphasize that the relationship between neural activity and BOLD differs between the neocortex and cerebellum. In the cerebellum, mossy fiber activity releases glutamate which activates granule cells and the release of Nitric oxide (NO). NO is mostly released by granule cells and stellate cells. The release of NO increases the diameter of capillaries which in turn causes changes in blood flow and blood volume, two major contributors to BOLD signal changes (Alahmadi et al. 2016; Alahmadi et al. 2015; Drew 2019; Mapelli et al. 2017; Gagliano et al. 2022). Importantly, there is a negligible contribution of NO from the Purkinje cells. Taken together, these data make a strong case that the BOLD signal in the cerebellar cortex reflects activity at the input stage. We acknowledge that the references cited in our initial submission were somewhat dated. We have now provided additional references (which are in agreement with the findings from the earlier papers).. Based on this evidence, we chose to predict cerebellar activity from cortical activity.

References: Alahmadi, A. A., Samson, R. S., Gasston, D., Pardini, M., Friston, K. J., D’Angelo, E., ... & Wheeler-Kingshott, C. A. (2016). Complex motor task associated with non-linear BOLD responses in cerebro-cortical areas and cerebellum. Brain Structure and Function, 221(5), 2443-2458.

Alahmadi, A. A., Pardini, M., Samson, R. S., D'Angelo, E., Friston, K. J., Toosy, A. T., & Gandini Wheeler‐Kingshott, C. A. (2015). Differential involvement of cortical and cerebellar areas using dominant and nondominant hands: an FMRI study. Human brain mapping, 36(12), 5079-5100.

Mapelli, L., Gagliano, G., Soda, T., Laforenza, U., Moccia, F., & D'Angelo, E. U. (2017). Granular layer neurons control cerebellar neurovascular coupling through an NMDA receptor/NO-dependent system. Journal of Neuroscience, 37(5), 1340-1351.

Gagliano, G., Monteverdi, A., Casali, S., Laforenza, U., Gandini Wheeler-Kingshott, C. A., D’Angelo, E., & Mapelli, L. (2022). Non-Linear Frequency Dependence of Neurovascular Coupling in the Cerebellar Cortex Implies Vasodilation–Vasoconstriction Competition. Cells, 11(6), 1047.

Drew, P. J. (2019). Vascular and neural basis of the BOLD signal. Current Opinion in Neurobiology, 58, 61–69.

2) It would be helpful to have more details included in the "Connectivity Models" sub-section of the Methods section. The GLM-based connectivity approach is highly non-standard, such that more details on the logic behind it and any validation of the approach would be helpful. More specifically, it would be helpful to have clarity on how this form of functional connectivity relates to more standard forms, such as Pearson correlation and perhaps less standard multiple regression (or partial correlation) approaches. If I understand this approach correctly, each cortical parcel's time series is modulated (up or down) using that parcel's task-evoked beta weights, then "normalized" by the standard deviation of that parcel's time series, with the resulting time series then used in a multiple regression model to explain variance in a given cerebellar voxel's time series. It would be helpful if each of these steps were better explained and justified. For example, it is unclear what modulation of the cortical parcel time series by task-related beta weights does to the functional connectivity estimates, and thus how they should be interpreted.

All of the models are multiple regression models. The independent variables (X) are the fitted (task-evoked) time series of the cortical parcels and the dependent variables (Y) are the fitted time series of each cerebellar voxel. Coefficients from multiple regression are identical to partial correlation coefficients if the cortical and cerebellar time series are z-standardized (SD=1). Here we only standardized the cortical time series. This only retains the weighting of the different cerebellar voxels (a cerebellar voxel that has a strong task-related signal should contribute more to the overall evaluation than a voxel where the task-related signal is weak); beyond this, the conclusions will be the same as that obtained with a partial correlation analysis.

Because the number of predictors (#cortical parcels) approaches or outstrips the number of available observations (#task-related regressors), the ordinary-least-squares (OLS) solution to the multiple regression problem is not unique. We thus compared 3 common ways of regularizing a multiple regression problem: a) Picking only the most important regressor (a form of feature selection or optimal subspace selection), Ridge regression (L2 regularization) or Lasso regression (L1 regularization). Each method biases the solution in a particular way: The winner-take-all solution is obviously very sparse, the Lasso solution somewhat less sparse, and the Ridge solution quite dispersed. Here we exploited these differences in inductive bias, reasoning that the method with the bias that best matches the structure of the data-generating process will lead to better prediction performance on independent data.

The results clearly favored a distributed input to each cerebellar voxel from the cortical parcels. We have rewritten the method section on connectivity models to better communicate the main idea.

3) It appears that task-related functional connectivity is used in the present study, and yet the potential for task-evoked activations to distort such connectivity estimates does not appear to be accounted for (Norman-Haignere et al. 2012; Cole et al. 2019). For example, voxel A may respond to just the left hemifield of visual space while voxel B may respond to just the right hemifield of visual space, yet their correlation will be inflated due to task-evoked activity for any centrally presented visual stimuli. There are multiple methods for accounting for the confounding effect of task-evoked activations, none of which appear to be applied here. For example, the following publications include some options for reducing this confounding bias: (Cole et al. 2019; Norman-Haignere et al. 2012; Ito et al. 2020; Rissman, Gazzaley, and D'Esposito 2004; Al-Aidroos, Said, and Turk-Browne 2012). If this concern does not apply in the current context it would be important to explain/show why.

The papers cited by the reviewer focus on the problem of how to remove task-evoked activity to estimate the correlation of spontaneous (task-independent) fluctuations. Here we are doing the opposite. We removed almost all spontaneous fluctuations and noise by averaging across trials and runs in order to fit the task-evoked activity. Additionally, we used a crossed approach as a way to control for the influence of task-independent fluctuations on the regression models: Within each task set, cerebellar activity from one half of the runs was predicted from cortical activity from the other half of the runs. Returning to the papers cited by the reviewer, these are designed to look at connectivity not related to task-evoked activity. We briefly summarize each below:

● Cole et al. (2019): Demonstrates that the removal of mean task-evoked activations while preserving task-evoked response shape is an important preprocessing step for validating task-based FC.

● Ito et al. (2020): Addressed the issue of shared variability between brain regions during task-evoked activity by estimating time series variance. They removed task-evoked activity from the time series in order to get a direct measure of neural-to-neural correlations (e.g., “background connectivity”) rather than task-to-neural associations.

● Al-Aidroos et al. (2012): Confronted with a similar problem of interpreting intrinsic correlations related to a goal (e.g., attending to scenes) from correlations related to synchronized stimulus-evoked responses. To mitigate this confound, they removed stimulus-evoked responses from the data resulting in “background connectivity” which was then used to assess inter-region coupling.

● Rissman et al. (2004): Introduced a new approach to characterize inter-region correlations during event-related activity by allowing inter-regional interactions to be assessed independent of activity at individual stages of a task.

● Norman-Haignere et al. (2012): To assess inter-region interactions (between fusiform gyrus and parahippocampal cortex), the authors removed the mean stimulus-evoked response and examined the correlations that occurred in the background of stimulus-locked changes (e.g., background connectivity).

4) It is stated (pg. 21): "To reduce the influence of these noise correlations, we used a "crossed" approach to train the models: The cerebellar time series for the first session was predicted by the cortical time series from the second session, and vice-versa (see Figure 1). This procedure effectively negates the influence of noise processes, given that noise processes are uncorrelated across sessions." However, this does not appear to be strictly true, given that the task design (parts of which repeat across sessions) could interact with sources of noise. For example, task instruction cues (regardless of the specific task) likely increase arousal, which likely increases breathing and heart rates known to impact global fMRI BOLD signals. The current approach likely reduces the impact of noise relative to other approaches, but such strong certainty that noise processes are uncorrelated across sessions appears to be unwarranted.

We completely agree. What we meant to say is that the procedure “negates the influence of any noise process that is uncorrelated with the tasks.” If we can predict the cerebellar activity patterns in session 2 by the cortical activity patterns measured in session 1, we can conclude that this prediction must be based on task-related signal changes given that the sequence of tasks is randomized. However, we do not know whether these task-related signals are caused directly by neural processes or indirectly by physiological processes (for example increased heart-rate in some conditions). The procedure only removes the influence of noise processes that are unrelated to the tasks. In our experience, these noise correlations can be quite strong and methods to remove them can introduce biases. For task-related noise processes we relied on high-pass filtering, a standard approach in task-based GLM approaches (see Methods).

5) It appears possible that the sparse cerebellar model does worse simply because there are fewer predictors than the alternate models. It would be helpful to verify that the methods used, such as cross-validation, rule out (or at least reduce the chance) that this result is a trivial consequence of just having a different number of predictors across the tested models. It appears that the "model recovery" simulations may rule this out, but it is unclear how these simulations were conducted. Additional details in the Methods section would be important for evaluating this portion of the study.

Our methods ensure full correction for model complexity (see response to major comment #2). Note that the sparse methods select regressors from all available cortical parcels; as such, “model complexity” is not well summarized by the number of non-zero regressors. We have now clarified these issues in the Methods section and have also revised the paper to better describe our model recovery simulations designed to address the issue of possible biases caused by different degrees of collinearity between cortical regressors.

Reviewer #2 (Public Review):

The human cerebellum likely has a significant but understudied contribution to cognition and behavior beyond the motor domain. Clarifying its functional relationship with the cerebral cortex is a critical detail necessary for understanding cerebellar functions. This paper addresses this challenge by testing three simple but intuitive models: winner-take-all, one-to-one model versus two converging input models. Results showed that the convergence model outperformed the one-to-one mapping model, indicating that cerebellar regions received multiple converging inputs from the different cortical regions. Overall the paper is well-written, and the results are clean and interesting. The methodological rigor of using cross-validation and generalization is also a strength of this paper.

1) The authors concluded that some cerebellar regions receive converging inputs from multiple cortical regions because the Ridge and Lasso models outperformed the WTA model. The WTA model has a fixed diagonal pattern, in contrast, Ridge/Lasso models included more weights in the connectivity matrix. Considering what's being estimated in this matrix, then perhaps the findings are not surprising because even after penalizing and regularization, the ridge regression models are still more complex than the WTA model (more elements are allowed to vary). In other words, Lasso/Ridge models allow more variables from the X side to explain variances in Y, similar to how throwing in more regressors can always improve the R square. I am unsure if cross-validation mitigates this issue. It would be more straightforward for the authors to compare model performance in a way that controls for the number of variables in the Ridge/Lasso models.

We now recognize that we could have done a better job in explaining our approach on this issue in the original submission. The models (including connectivity weights and regularization parameter) are trained solely on data from Task set A. They are tested on 2 independent datasets: 1) Data from the same participants performing novel tasks; 2) Data from new participants performing novel tasks. This allows us to compare models of different structure and complexity.

2) The authors did an excellent job reviewing the anatomical relationship between the cerebral cortex and the cerebellum. There are several issues that the authors should address in the introduction or discussion. First, if the anatomical relationship between the cerebellum and the cortex is closed-loop as suggested in the intro, then how convergence can arise from multiple cortical inputs given there is no physical cross-talk? Second, there are multiple synapses connecting a cerebellar region and the cortex, and therefore could integration occur at other sites but not the cerebellum? For example, the caudate, the thalamus, or even the cortex (integrating inputs before sending to the cerebellum)?

We agree that the correlation structure of BOLD signals in the neocortex and cerebellum is shaped by the closed-loop (bi-directional) interactions between the two structures. As such, some of the observed convergence could be caused by divergence of cerebellar output. We have added a new section to the discussion on the directionality of the model (Page 18).

That said, there are strong reasons to believe that our results are mainly determined by how the neocortex sends signals to the cerebellum, and not vice versa. An increasing body of physiological studies (and this includes newer papers, see response to reviewer #1, comment #1 for details) show that cerebellar blood flow is determined by signal transmission from mossy fibers to granule cells and parallel fibers, followed by Nitric oxide signaling from molecular layer interneurons. Importantly, it is clear that Purkinje cells, the only output cell of the cerebellar cortex, are not reflected in the BOLD signal from the cerebellar cortex. (We also note that increases in the firing rate of inhibitory Purkinje cells means less activation of the neocortex). Thus, while we acknowledge that cerebellar-cortical connectivity likely plays a role in the correlations we observed, we cannot use fMRI observations from the cerebellar cortex and neocortex to draw conclusions about cerebellar-cortical connectivity. To do so we would need to measure activity in the deep cerebellar nuclei (and likely thalamus).

The situation is different when considering the other direction (cortico-cerebellar connections). Here we have the advantage that the cerebellar BOLD signal is mostly determined by the mossy fiber input which, at least for the human cerebellum, comes overwhelmingly from cortical sources. On the neocortical side, the story is admittedly less clear: The cortical BOLD signal is likely determined by a mixture of incoming signals from the thalamus (which mixes inputs from the basal ganglia and cerebellum), subcortex, other cortical areas, and local cortical inputs (e.g., across layers). While the cortical BOLD signal (in contrast to the cerebellum) also reflects the firing rate of output cells, not all output cells will send collaterals to the pontine nuclei. These caveats are now clearly expressed in the discussion section2.

On balance, there is an asymmetry: Cerebellar BOLD signal is dominated by neocortical input without contribution from the output (Purkinje) cells. Neocortical BOLD signal reflects a mixture of many inputs (with the cerebellar input making a small contribution) and cortical output firing. This asymmetry means that the observed correlation structure between cortical and cerebellar BOLD activity (the determinant of the estimated connectivity weights) will be determined more directly by cortico-cerebellar connections than by cerebellar-cortical connections. Given this, we have left the title and abstract largely the same, but have tempered the strength of the claim by discussing the influence of connectivity in the opposite direction.

3) The dispersion metric quantifying the spread level in cortical inputs is interesting. Could the authors expand this finding and show anatomically what the physical spread is like in cortical space? The metric is novel but hard to interpret. A figure demonstrating the physical spread in the cortex should help readers interpret this result.

Figure 3 (previously Figure 4) was included to provide examples of differences in the spatial spread of cortical inputs. For example, regions 1 and 2 are explained by a more restricted and spatially contiguous set of cortical inputs (e.g., primary motor cortices) whereas regions 7 & 8 are explained by a set of spatially disparate regions (e.g., angular gyrus, superior and middle frontal cortices, and superior temporal gyrus). Prompted by this comment, we have opted to reverse the order of Figures 3 and 4 to give the reader a chance to visualize differences in physical spread of cortical regions before we walk through the quantitative analysis.

4) At the end of the discussion section, the authors discussed how results are more likely driven by cortical inputs to the cerebellum but not the other way around. This interpretation is likely overstated given the hemodynamic blurring and low temporal resolution of BOLD. Without a faster imaging sequence and accurate models that account for differences in hemodynamic properties, the more parsimonious interpretation is results are driven by bidirectional cortico-cerebellar interactions. The results are still very interesting without this added nuisance.

Our analyses do not rely on the exact time course or delays between neocortical and cerebellar activation, but only on the activity profiles across a wide range of tasks. In terms of bidirectionality, please see our response above. We have added a dedicated section in the revised Discussion on this issue.

Author Response

Reviewer #1 (Public Review):

The authors sought to define the molecular mechanism of activation of the thrombopoietin receptor (TpoR), a very important cytokine receptor that regulates megakaryocyte differentiation and platelet production. They conducted a thorough series of experiments combining mutagenesis experiments with sophistical biological assays and that also includes solid-state NMR structural measurements. This work builds on a body of previous studies of TpoR from this group and from others. They focused both on (1) the role and impact of W515 located in the juxtamembrane cytosolic domain and (2) the impact of introducing either Asn at sites in the transmembrane domain to induce various dimerization modes, or insertion of pairs of Ala residues to induce helical rotation to the TM domain. There is a lot of nice data in this paper, which is fairly intricate - a tough read, but that's because it's a complicated system. The writing is excellent.

This paper presents a model for receptor activation in which the inactive receptor is the monomeric form of the receptor in which the juxtamembrane domain, including W515, maintains a helical structure. Activation of the receptor triggers dimerization of the transmembrane domain and loss of helicity of the juxtamembrane segment, which facilitates optimal interactions of the kinase domains with their JACK2 domain phosphorylation substrates.

There is a lot to like in this careful work and the resulting manuscript. There is one major shortcoming in this manuscript, which concerns W515. It is known that mutation of W515 to any of 17 of the canonical amino acids, including Phe, is sufficient to trigger homodimerization and receptor activation. The authors present some evidence that the phenomenon behind this is that mutation of W515 to almost any other residues disrupts the helical secondary structure of the critical juxtamembrane segment, which promotes dimerization and receptor activation. What I find puzzling is why a Trp at site 515 promotes helix formation, but nearly all other amino acids at this site disrupt helix formation. This strongly suggests the side chain of W515 must be interacting with another domain of the protein in the inactive state, in a manner that is responsible for how Trp stabilizes the juxtamembrane helix, which is a central feature that helps define that state. I think that for this paper, this dangling missing piece of their mechanistic model should be resolved.

We agree with the reviewers that the mechanism by which Trp515 stabilizes the TM helix is central to the mechanism of activation. More broadly, our studies over the past decade have sought to address the importance of the entire RWQFP insert in the TM domain. Our working model for this sequence has been that cation-π interactions are central to the role of the Trp and the accompanying amino acids.

Arginine and tryptophan both are over-represented at the cytoplasmic TM-JM boundaries of membrane proteins. Arginine is positively charged and part of the “positive-inside” rule for membrane protein insertion. Arginine and lysine define the cytoplasmic ends of TM helices and prefer to be accessible to the water-exposed membrane surface. In contrast, tryptophan residues prefer hydrophobic head-group or membrane interior locations. A revealing aspect of the RWQFP motif is that the arginine and tryptophan are located at the membrane to cytosolic border. As a result, in order to accommodate arginine in a more water-inaccessible membrane environment, it interacts with the surface of the tryptophan indole ring. Partitioning of the RWQF sequence in a more water-inaccessible environment also drives the formation of helical secondary structure as an unpaired backbone C=O...NH in a hydrophobic environment is estimated to cost 3-6 kcal/mol of energy.

We have taken two approaches in respond to this essential criticism of the reviewers: one structural and one computational. Additional NMR data (structural approach) has been included in the supporting information (see response to point 2 below). Computational approaches provide a second way to address whether a cation– interaction between Trp515 and the positively charged Arg514 is responsible for stabilizing the C-terminal TM helix. We have included a new supporting figure using Alpha-Fold 2.0 that probes the structural changes upon mutation of Trp515. In the wild-type receptor, Arg514 is predicted to form a cation– interaction with Trp515. In the W515K mutant, the helical secondary structure in the RKQFP sequence is disrupted and Arg514 forms a new cation– interaction with Trp529. Similar changes occur in other Trp515 mutants (e.g. W515A) highlighting the ability of Alpha-Fold to predict such interactions and the consequences of mutation. Overall, 15 out of 19 W515X mutants are predicted to be unfolded. Experimentally, 17 out of 19 mutations lead to activation. Importantly, W515C and W515P are the only two amino acid substitutions that do not cause constitutive activity experimentally (Defour, Chachoua, Pecquet, & Constantinescu, 2016). Computationally, these two sites do not predict helix unraveling. In short, the overall the predictions of Alpha-Fold agree with the unique nature of tryptophan at position 515.

In addition, we have expanded the arguments supporting the potential role of cation–π interactions by adding a new section entitled “Unfolding of the RWQF -helical motif is a common mechanism of receptor activation”.

These modifications are now in the revised manuscript starting with line 213:

Our working model for the mechanism of activation in the wild-type or mutant receptors is that the RWQF motif is stabilized in the inactive state as an -helix as a result of a cation- interaction between R514 and W515. This interaction allows the RWQF sequence to partition into the more hydrophobic head-group region of the bilayer. Both Arg and Trp are over-represented at the cytoplasmic ends of TM helices (von Heijne, 1992), but whereas Arg prefers a water-accessible environment, Trp prefers to be buried in a more hydrophobic environment (Yau, Wimley, Gawrisch, & White, 1998). Since Arg and Trp are located at the border between membrane and cytosolic domains and Arg precedes Trp in the sequence, partitioning into the membrane head-group region results in a favorable interaction of the positive charge associated with the guanidinium group of the R514 side chain with the partial negative charge associated with the aromatic surface of the W515 side chain. Partitioning of the RWQF sequence into the more water-inaccessible environment drives the formation of helical secondary structure as an unpaired backbone C=O...NH in a hydrophobic environment is estimated to cost 6 kcal/mol of energy (Engelman, Steitz, & Goldman, 1986). In this model, activation of the receptor results in or is caused by disruption of the R514-W515 cation-π interaction. In the W515 mutants, R514 is no longer stabilized in a membrane environment and the helix containing the RWQFP sequence unravels to allow the positively charged side chain to reach outside of the membrane. In the case of the Asn mutants and in the wild-type receptor with bound Tpo, dimerization of hTpoR (or rotation of the TM helices in mTpoR dimer), places W515 in the center of the helix-helix interface. The data suggest that a steric clash of the W515 side chains results in unraveling of the cytoplasmic end of the TM helix.<br /> Computational and additional NMR data are provided in the supplementary figures to support the model of helix unraveling suggested by the solid-state NMR studies. Computationally, we used AlphaFold 2.0 (Jumper et al., 2021) calculations of hTpoR TM-JM peptides to predict the influence of all possible mutations at position 515 on the TM-JM helix structure. Remarkably, -helix unraveling was predicted for 15 out of 20 possible amino acids at 515 (supplement 2 to Figure 3). Importantly, two of the mutations that are not predicted to cause helix unraveling are W515C and W515P. Experimentally, these two amino acid substitutions are the only ones that do not induce constitutive activity among all possible amin oacid substitutions at W515 (Defour et al., 2016). Introducing a Trp at the preceding position 514 instead of R/K in W515K/R mutants reverses helix unfolding in AlphaFold simulations (supplement 3 to Figure 3). This result agrees with our previous data that the WRQFP mutant is inactive and is essentially monomeric (J. P. Defour et al., 2013). Structurally, we have undertaken solution-NMR studies of the wild-type hTpoR TM-JM peptide and its W515K mutant. Relaxation measurements of the backbone 15N resonances show that W515K mutation leads to association of the TM helices, and that it induces upfield chemical shift changes in the RWQF sequence consistent with helix unraveling (supplement 1 to Figure 3).

Reviewer #2 (Public Review):

The thrombopoietin receptor (TpoR) regulates stem cell proliferation, platelet production, and megakaryocyte differentiation. Past cell biology and biophysical studies have established that ligand-induced dimerization constitutes the mechanism of activation of TpoR. Specifically, ligands bind to the extracellular domain of TpoR and generate an allosteric response that is transmitted to the transmembrane domain, activating downstream signaling. However, up to now the molecular details of how the allosteric signals are transmitted to the intramembrane domains have been elusive. In this manuscript, Constantinescu and co-workers combined NMR, in vitro, and in vivo assays to investigate the activation and oncogenicity of TpoR. The authors concluded that the unwinding of the juxtamembrane domain is the main structural event that determines TpoR activation and regulates oncogenicity. The solid-state NMR studies were carried out in lipid membranes with polypeptides spanning the juxtamembrane and transmembrane residues. The authors show a series of spectra of 13CO resonances that encompass the juxtamembrane domain that is diagnostic of a structural transition from a helical conformation to a partially disordered state. The unwinding of the helical juxtamembrane domain was confirmed by site-specific mutations in this region. The chemical shift changes clearly indicate the transition from order to disorder (and vice versa) for selected sites. These conclusions are compounded by INEPT-type experiments that detect the most dynamic region of polypeptides. To rationalize the molecular mechanism for activation, the authors also used Ala-Ala insertions at strategic positions along the transmembrane domain. These experiments showed that the specific orientation of the transmembrane residues is central for TpoR activation, and a slight rotation of the helix is critical for activation of the receptor. Transcriptional activity assays confirm the importance of the proper orientation of the transmembrane domain for receptor activation.

Overall, I believe the data are solid, and both biophysical and cell biology studies support the conclusions of the authors. These new findings represent a significant advancement in understanding cytokine receptor activation.

We thank the reviewer for these comments.

Reviewer #3 (Public Review):

The authors sought to propose a mechanism by which cancer-causing mutations in the thrombopoietin receptor (TpoR) activate the receptor. To do so, they used a systematic approach of introducing non-native and naturally occurring mutations into the receptor and use a combination of in-vivo and cell-based assays and solid-state NMR spectroscopy. They propose that the proximity of the asparagine mutations to the cytosolic boundary influences the secondary structure of the receptor and suggests that this structural change induces receptor activation.

The strengths of this work are the importance of the system being studied and tackling a problem that is not yet fully resolved. The authors acquired a large and convincing set of biological data, including in vivo experiments that support the gain-of-function/activating role of the mutations studied. The solid-state NMR data are of high quality as well. In particular, the INEPT data in figure 6a display very clear differences within one region of the wild-type compared to the mutants.

One significant weakness is the validity of the conclusions given the limited atomistic measurements presented. Namely, the authors make rather specific conclusions about protein folding based on a single set of 13C alanine carbonyl chemical shifts in the wild-type and mutant TM peptides. Essentially, the authors observe chemical shift perturbations at this carbonyl carbon when mutations are introduced into a protein and use this information to make conclusions about secondary structure. I am not convinced that the authors have presented sufficient evidence to justify the conclusion that the helix unwinds and that this is responsible for the mechanism of activation. While the other cell-based experiments in mutations are interesting, deciphering such a specific folding mechanism with limited atomistic data is not justified.

We added both computational data and solution NMR to support our conclusion.

Author Response

Reviewer #1 (Public Review):

Proton pumps are necessary to set up gradients necessary for myriad biological processes. The malaria-causing parasite Plasmodium falciparum, uses two main pathways to achieve this, the vacuolar ATPase (V-type ATPase) and a more ancient vacuolar pyrophosphatase (PfPV1). The proton motive force set up across the parasite plasma membrane holds particular significance since it is necessary for transport of nutrients and waste products into and out of the cell. Motivated by the observation that the V-type ATPase is no expressed until several hours after the parasite has entered host cells, the present study examines the function of PfVP1. The authors demonstrate PfVP1 depletion blocks the early development of Plasmodium-specifically the transition from the ring to the trophozoite stage-and this is associated with changes to cellular pH and pyrophosphate levels, consistent with predicted functions. Complementation of the conditional knockdown suggests that pyrophosphatase activity alone is not sufficient to overcome the loss of PfVP1. Overall, data supporting a critical role for PfVP1 in parasite energetics is compelling. However, the lack of several key controls somewhat weakens the conclusions of the paper when it comes to complementation of the mutants and description of which activities are needed for parasite survival. Because the proximal activities of the enzyme ATP generation and the proton motive force are incompletely examined, some of the major conclusions from the study remain speculative.

We thank the reviewer for these constructive comments. We are grateful to the reviewer for his/her recognition of the significance of our study. The major discovery of this manuscript is to uncover PfVP1’s essential role in the early-stage development of the 48h asexual lifecycle in P. falciparum. Our data suggest PPi is an energy source when ATP level is likely low in the ring stage malaria parasite and its transition to the trophozoite stage. We have performed additional experiments and tried the best to address each comment from the reviewer.

Reviewer #2 (Public Review):

In this work, the authors characterize a proton pump from the parasite Plasmodium falciparum that uses pyrophosphate as an energy source (PfVP1).

They looked at the expression and localization of the pump in different stages of the parasite and determined that it localizes to the plasma membrane and it is highly expressed in the ring stage. They studied the biochemical function by expressing the gene in Saccharomyces followed by isolation of vesicles and measurements of proton transport and PPi hydrolysis. They also characterized the biological role of PfVP1 in the parasites by creating conditional mutants that express PfVP1 when cultured in the presence of anhydrotetracycline (ATC). Upon removal of ATC the expression of PfVP1 is downregulated, which impacted growth and transition to the trophozoite stage. Mutant parasites struggled to progress through the ring state and failed to become trophozoites in the second intraerythrocytic cycle. They complemented the mutants with the yeast inorganic pyrophosphatase gene and the Arabidopsis vacuolar pyrophosphatase.

We thank the reviewer for positive and constructive comments. We have seriously worked on every comment raised by the reviewer. We have tried the best to perform additional experiments.

Reviewer #3 (Public Review):

Solebo and coworkers investigated the energy requirements of blood-stage malaria parasites (the stage of infection that causes symptoms). Traditionally, parasites were thought to be somewhat quiescent during the first half of their life cycle in red blood cells and become metabolically active as they prepare for replication. Consequently, antimalarial drugs are more active against parasites during the second half of their life cycle. In this report, the authors show that the metabolic by-product pyrophosphate is an essential energy source for the development of early-stage malaria parasites and that it is consumed by a vacuolar pyrophosphatase (PfVP1). Knock down studies showed that PfVP1 is required for the development of early-stage parasites and localization studies established that it is located in the parasite plasma membrane. Characterization of PfVP1 heterologously expressed in yeast confirmed that it is a pyrophosphate hydrolyzing proton pump. Consequently, loss of PfVP1 in early-stage parasites results in reduced pyrophosphate consumption and a reduction in pH (accumulation of protons). The authors further show that a similar vacuolar pyrophosphatase from Arabidopsis thaliana can complement the loss of the parasite ortholog, but a general pyrophosphatase enzyme cannot. Consistent with this result, mutations designed to inactivate either the pyrophosphatase activity or the proton-pumping activity demonstrated that both activities are essential for the development and survival of early-stage parasites.

The conclusions of this paper are firmly supported by data, often from more than one type of experimental approach. The conclusions provide fundamental information about the stage of parasite development that has been hard to target with antimalarial drugs. The most energy-consuming process in a cell is the maintenance of membrane potential and in malaria parasites, it is known that proton pumps (rather than sodium pumps) are responsible for this process. Although PfVP1 was previously reported to be located internally in an organelle of the parasite, the data presented in this report clearly define its location on the plasma membrane and its essential role in maintaining the membrane potential. PfVP1 inhibitors could preferentially target early stage malaria parasites and the current results support efforts to find these inhibitors. Perhaps the most exciting aspect of this work is the potential to act synergistically and enhance the effect of current antimalarial drugs on early stage parasites. In this vein, the authors tested four antimalarial compounds in conjunction with knockdown of PfVP1 to determine whether there was enhanced activity. These experiments were not conducted in a systematic way and this is perhaps the only weakness of the paper.

We thank the reviewer for positive, constructive, and encouraging comments. We really appreciate that. We are also very excited about our discovery that a non-ATP driven proton pump plays essential roles in the early-stage development of the asexual lifecycle. Our data suggest PPi is an energy source in the malaria parasite P. falciparum.

Author Response

Reviewer #1 (Public Review):

Voltage-clamp fluorometry combines electrophysiology, reporting on channel opening, with a fluorescence signal reporting on local conformational changes. Classically, fluorescence changes are reported by an organic fluoropohore tethered to the receptor thanks to the cysteine chemistry. However, this classical approach does not allow fluorescent labeling of solvent-inaccessible regions or cytoplasmic regions. Incorporation of the fluorescent unnatural amino acid ANAP directly in the sequence of the protein allows counteracting these limitations. However, expression of ANAP-containing receptors is usually weak, leading to very small ANAP-related fluorescence changes (ΔFs).

In this paper, the authors developed an improved method for expression of full-length, ANAP-mutated proteins in Xenopus oocytes. In particular, they managed to increase the ratio of full-length over truncated proteins for C-terminal ANAP incorporation sites. Since C-terminally truncated P2X receptors are usually functional, it is important to maximize the full-length over truncated protein ratio to have a good correspondence between the observed current and fluorescence. Using their improved strategy, they screened for ANAP incorporation sites and ATP-mediated ANAP ΔFs along the whole structure of the P2X7 receptor: extracellular ligand binding domain (head domain), M2 transmembrane segment (gate), as well as a large extracellular domain specific for the P2X7 subtype, the "ballast" domain. The functional role of this domain and its motions following ATP application are indeed unknown. Monitoring ANAP fluorescence changes in this region following ATP binding provides a unique way to study those questions. By analyzing ATP-induced ΔFs from different parts of the receptors, the authors conclude that the ATP-binding domain mainly follows gating, while intracellular "ballast" motions are largely decoupled from ATP-binding

Strengths of the paper:

This paper provides an improved method for efficient unnatural amino acid incorporation in Xenopus oocytes. Thanks to this technique, they managed to enhance membrane expression of ANAP-mutated P2X7 receptors and observed strong fluorescent changes upon ATP application. The paper furthermore describes an impressive screen of ANAP-incorporation sites along the whole protein sequence, which allows them to monitor conformational changes of solvent-inaccessible regions (transmembrane domains) and cytoplasmic regions that were not accessible to cysteine-reactive fluorophores. This screen was performed in a very thorough manner, each ANAP mutant being characterized biochemically for membrane expression, as well as in term of fluorescence changes. The limitations of the approach -small ΔF upon ATP application on wt receptors, problem of baseline fluorescence variations in presence of calcium- are well explained. Overall, this study should thus not only serve as a guide to anyone willing to perform VCF on P2X7 receptors but it should be useful to the whole community of researchers using unnatural amino acids. Thanks to orthogonal labeling with TMRM and ANAP, the authors managed to simultaneously monitor the motions of the extracellular and intracellular domains of P2X7. Finally, they propose methods to simultaneously monitor intracellular domain motion and downstream signaling.

Weaknesses:

Although the fluorescence screen is impressive and well conducted, the biological conclusions remain superficial at this stage. The paper furthermore lacks quantitative analysis. Finally, the title only reflects a minor part of the paper and is therefore not representative of the paper content.

Quantitative analyses (DRCs and current rise times) were now added for the key mutations. In addition, we performed a variety of experiments to address the challenging question of mechanistic insight (mutants that track facilitation) and effects of intracellular factors (mutation of calmodulin binding site, FRET experiments with calmodulin). These data confirmed that deletion of a cysteine-rich intracellular region eliminates current facilitation (Roger et al., 2010) and that some of our mutants indeed track facilitation. However, mutation of the CaM binding site and FRET experiments did not support an effect of calmodulin or were inconclusive. As pointed out above, we think that VCF has limited capacity to identify novel biologically relevant consequences of receptor activation but is more suited to determine the sites and dynamics of already defined interactions.

The title was changed to: "Improved ANAP incorporation and VCF analysis reveals details of P2X7 current facilitation and a limited conformational interplay between ATP binding and the intracellular ballast domain"

Reviewer #2 (Public Review):

The authors aimed to elucidate the structural rearrangements and activation mechanisms of P2X7 upon ATP application by voltage clamp fluorometry (VCF) using fluorescent unnatural amino acid (fUAA) and other fluorophores. They improved the fUAA methodology and detected ATP binding evoked changes in the ATP binding region and other regions. They also observed facilitation of fluorescence (F) changes by repeated application of ATP associated with gating. The F change in the cytoplasmic ballast region was minor, and with their experimental data, they discussed this region is involved in activation by other cytoplasmic factors, such as Ca2+.

The strengths of the study are as follows.

(1) fUAA methodology was improved to enable experiments by one time injection to oocytes (Figs. 1 and Suppl).

(2) They performed intensive mutagenesis study of as many as 61 mutants (Figs. 3, 4, 5).

(3) A careful evaluation of the successful Anap incorporation and formation of full length proteins was performed by western blot analysis (Fig. 2).

(4) By three wave lengths F recording, they obtained better information, i.e. they classified the interpretation of F changes to, quenching, dequenching, increase in polarity and decrease in polarity (Fig. 3E).

(5) They detected F changes upon ATP application in various regions of P2X7, but not many in the ballast region, showing that the ballast region is not well involved in the ATP evoked gating.

(6) They analyzed the kinetics of F and current and their changes upon repeated ATP application to approach the known facilitation mechanisms. The data are very interesting. They concluded that it is intrinsic to the P2X7 molecule and that it is associated not with the ATP binding but with the gating process (Figs. 3F, 4D, 6A).

(7) They performed interesting analysis to clarify the mechanisms of activation by cytoplasmic factors, especially Ca2+ entered via P2X7 (Fig. 6).

The weaknesses of the study are as follows.

(1) As both structures of P2X in the open and closed states are already solved, and the ATP binding evoked structural rearrangements from the ATP binding site to the gate are already known in detail. The structural rearrangements detected in the extracellular region (Fig. 3) and TM region (Fig. 4) upon ATP application are just as expected. The impact and scientific merits of this part are rather limited.

We generally agree that the cryo-EM structures clarified basic principles of receptor function. However, considering the specific features of the P2X7 receptor and its likely regulation/modulation by membrane components and environment and the fact that the actual states of the receptor structures (e.g. facilitated or not?) is not known, we think that VCF analysis of its dynamics in a more native cellular environment is still required to confirm the predicted motions and also has the potential to identify details of "P2X7 fine tuning".

(2) The facilitation mechanism is of high interest. The authors showed it is intrinsic to P2X2 and associated with the gating rather than ATP binding. However, this reviewer cannot have better understanding about the actual mechanism. (a) What is the mechanistic trigger of facilitation? Possibilities are discussed, but it appears there is no clear answer with experimental evidences yet. (b) How is the memory of the 1st ATP application stored in the molecule, i.e. how does the P2X7 structure just before the 1st application differ from that just before the 2nd application of ATP?

These are indeed fundamental questions but based on the available information we do not see a rational approach to address this issue any further. Additional extensive "screening" for ideal fluorophore positions would probably be required and is beyond our possibilities in the present study.

(3) The structural rearrangement of the CaM-M13 region (Fig. 6B, C) attached at the C-terminus by Ca2+ influx through P2X7 upon ATP application is natural due course and not very surprising. Also, it is not accepted as an evidence proving that Ca2+ is the mediator of facilitation.

We apologize, this is a misunderstanding. We only provided protocols for parallel recordings of ANAP with other fluorophores for further analysis of downstream signaling pathways but we did not show or propose any functional consequences of the Ca2+ influx (see also point 7 above).

(4) As to the ballast region, data showed its limited involvement in the ATP-induced structural rearrangements. The function of the ballast region is not clear yet. A possible involvement in GDP binding and/ or metabolism is discussed, but there is no clear experimental evidence.

We are aware of these limitations. In the absence of a clear fluorescence change around the GTP/GDP-binding site or information about its role, it is difficult to investigate its molecular function by VCF. The fact, that (un-)binding of the guanosine nucleotide does not seem to be related to channel opening (McCarthy et al., 2019) further limits our options to study its function and currently it is not even known whether GDP/GTP has just a structural role. However, we identified A564* as a potential reporter for yet undefined processes that might affect GTP/GDP binding and/or metabolism.

Reviewer #3 (Public Review):

This research contributes to optimizing the amber stop-codon suppression protocol for voltage-clamp fluorometry (VCF) experiments using Xenopus oocyte heterologous expression system. By in vitro RNA synthesizing the tRNA and tRNA synthetases, combined with the dominant-negative release factor initially developed by Jason Chin's lab, L-Anap can be site-specifically labeled to proteins by a single microinjection of a mixture of molecular components into the cytoplasm of oocytes. Although it avoids nuclear microinjection to oocytes, it adds more RNA synthesis steps. This strategy of using eRF dominant negative variant (eRF1-E55D), was previously applied to the Anap incorporation system using mammalian cell lines and model proteins (Gordon et al, eLife, 2018). In this previous 2018 paper, with eRF1-E55D, the percentage of full-length protein expression increased substantially. Using oocytes in this paper, this percentage apparently did not increase significantly as shown in Fig. 1D, different from the previous paper. Nevertheless, the overall expression level increased successfully by this method, which could facilitate macroscopic fluorescence measurements, especially considering that L-Anap is relatively dim as a fluorophore.

Anap fluorescence change was measured mostly using its environmental sensitivity, which has limited information in interpreting structural changes. The structural mechanisms proposed could be potentially strengthened and the conclusions could be further validated by combining FRET or other distance ruler experiments with the VCF method. The engineered CaM-M13 FRET experiments mostly report the calcium entry, not measuring the rearrangements of P2X7 directly.

We tried FRET analyses with ANAP-labeled P2X7 and mNeonGreen-labeled CaM but unfortunately, results were inconclusive.

In addition, results of ATP dose-response relationship for channel activation correlated with ATP dose-dependent Anap fluorescence change, especially for sites showing a large percentage of ATP-induced change in fluorescence, would provide more insights regarding the allosteric mechanism of the channel.

We agree, but unfortunately, bleaching of ANAP and the variation of background fluorescence in individual oocytes prevented such analyses .

Author Response

Reviewer #1 (Public Review):

This study provides evidence for previously unknown relationship between oncogenic protein kinase A (PKA) signaling and MYC family members. Specifically, the authors have employed a combination of systems biology and biochemical assays to capture mediators of oncogenic PKA signaling in a fibrolamellar carcinoma and melanoma cell line. This lead to identification of Aurora A and PIM kinases as potential effectors of constitutively active PKA. Aurora A and PIM kinases have been previously shown to stabilize MYC proteins. Accordingly, evidence is provided that the effects of PKA/Aurora A and PKA/PIM axis are mediated via MYC. Collectively, these findings suggest a model whereby the effects of aberrant PKA signaling are mediated via Aurora A and PIM kinases and related feedback mechanisms that ultimately result in stabilization of MYC proteins. Importantly, PKA-driven cancer cell lines exhibited high sensitivity to Aurora A kinase inhibitors in cell culture-based assays. These findings not only provide pioneering insights into oncogenic PKA signaling, but may also have implications for developing therapeutic approaches for neoplasia that harbor constitutively active PKA.

Strengths:

This study addresses the role of aberrant PKA signaling in cancer, which represents a major gap in knowledge in cancer biology. Systems biology approaches and dissection of signaling networks downstream of constitutively active PKA are found to be exciting in the context of this study and likely to provide a wealth of information for future studies. Results from samples obtained from fibrolamellar carcinoma patients partially confirmed correlations observed in cell lines, which was seen as an advantage. Notwithstanding that, it was thought that orthogonal genetic validation may in some cases be warranted, pharmacological approaches using e.g. Aurora A inhibitors hold a promise for accelerated translation of observed findings into the clinic.

We appreciate this positive assessment of our work and are hopeful that we have solidified the significance and potential impact of our findings through additional analysis.

Weaknesses:

The major drawback of the study is the lack of in vivo models to validate observations garnered from the cell lines. This is particularly important considering that experiments carried out in samples from fibrolamellar carcinoma patients suggested additional Aurora A and PIM kinase-independent mechanisms of PKA-driven increase in MYC levels and likely in neoplastic growth may be implicated in vivo. In addition, it was thought that more mechanistic evidence is required for linking PKA to PIM kinase, especially because different PIM kinases were implicated in stabilization of MYC in fibrolamellar carcinoma vs. melanoma cell lines. Finally, although pharmacological approaches were appreciated, due to potential issues with the specificity of the inhibitors, it was thought that orthogonal genetic approaches are warranted to further corroborate the proposed model.

We acknowledge the lack of in vivo treatment modeling in this manuscript. The work presented here provides motivation for these important experiments, but they remain outside the scope of this manuscript. The expansion of the manuscript in revision with new investigations into protein translation and several additional data sets creates a more complete systems biology analysis of PKA signaling and PKA-induced signaling dependencies. This expanded scope makes in vivo validation of specific treatments and treatment combinations an even larger undertaking. The text has been modified to emphasize this point. We further acknowledge the accuracy of the reviewer’s assessment of our findings on PIM2. The limited reagents to study PIM kinases made this relatively difficult to expand. We shifted the focus of the work to include assessment of PKA effects on mRNA translation as a mechanism of c-MYC regulation. We have strengthened our assessments with loss- and gain-of-function genetic and pharmacological models, which we believe will more completely answer the reviewer’s concerns.

Reviewer #2 (Public Review):

Protein kinase A (PKA) is often stimulated and contributes to cancer growth, yet the downstream kinase signaling cascades remain unclear. Here the authors use a global phosphoproteomics and kinome activity profile to show that not only is the RAS/MAPK pathway activated, as expected, but the authors also suggest Aurora kinase A (AURKA) and PIM kinases are activated to stabilize the expression of MYC expression; a potent oncoprotein associated with poor prognosis and aggressive disease. The authors use a number of different cell lines in this study, but focus on fibrolamellar carcinoma as PKA is known to contribute to this disease.

Strengths: It has been notoriously difficult to map kinases and their substrates as these protein-protein interactions are not always amenable to traditional biochemical techniques due to their labile nature, and kinase substrate consensus sites are often overlapping and not highly specific. Thus, the authors' pipeline to delineate such kinase cascades is quite novel and useful. They apply it here to determine PKA signaling in cancer using sophisticated computational strategies and then validate with classic molecular techniques.

We appreciate this positive assessment of our analytical tools and the importance of understanding oncogenic PKA signaling.

Weaknesses: The lack of mechanistic evidence linking aberrant PKA activation with regulation of MYC family members was considered to be a major weakness of the study. As it stands, it is hard to delineate whether observed changes in the levels of MYC family members are indeed a consequence of aberrant PKA signaling. It also remains unclear which MYC phosphorylation sites are implicated in the context of neoplastic PKA function and whether MYC family members are regulated at the level of protein stability or mRNA translation. Moreover, some methodological issues (e.g. using single siRNAs) were also observed. Collectively it was thought that these weaknesses should be addressed to corroborate author's conclusions.

We acknowledge these concerns about our initially submitted manuscript and present extensive data that advances the manuscript in answering the key questions posed by the reviewer. We note that with the development of data showing PKA-induced phosphorylation of translation initiation components and sensitivity of c-MYC levels to eIF4A inhibition, some detailed evaluations of c-MYC phosphorylation were not undertaken, although key c-MYC mutants were tested in the course of our study and are included for reviewer interest.

Author Response

Reviewer #1 (Public Review):

In the current study, the authors reanalyze a prior dataset testing effects of D2 antagonism on choices in a delay discounting task. While the prior report using standard analysis, showed no effects, the current study used a DDM to examine more carefully possible effects on different subcomponents of the decision process. This approach revealed contrasting effects of D2 blockade on the effect of reward size differences and bias. Effects were uncorrelated, suggesting separate mechanisms perhaps. The authors speculate that these opposing effects explain the variability in effects across studies, since they mean that effects would depend on which of these factors is more important in a particular design. Overall the study is novel and well-executed, and the explanation offers interesting insight into neural processes.

We thank the reviewer for judging our study as interesting and well-executed.

Reviewer #2 (Public Review):

The authors aim to test the hypothesis that dopamine mediates the evaluation of temporal costs in intertemporal choice in humans, with a specific goal of synthesizing the competing accounts and previous results regarding whether dopamine increases or decreases evaluation of delays in comparing differently delayed future rewards. To do this, they computationally dissect the impact of the drug amisulpride, a D2R antagonist, using a variant of a sequential sampling model, the drift-diffusion model (DDM), that is well established in decision-making literature as a cognitive process model of choice. This model allows the dissociation of starting bias from the rate at which decision evidence is integrated ('drift'), which the authors map to different accounts of the role of dopamine: the temporal proximity of an outcome is proposed to impact bias, while the cost of a delay to impact the drift rate of evidence evaluation/accumulation. Consistent with previous results, and perhaps integrating conflicting findings, the authors find that d2R blockade impacts both bias and drift rate in a cohort of 50 participants, demonstrating dopaminergic action at this receptor is implicated in dissociable components of intertemporal choice, with D2R block reducing the bias towards sooner, more temporally proximate rewards as well as enhancing the contrast between reward magnitudes irrespective of delay, effectively diminishing the effect of delay in the drug condition. These effects are consistent across a small subset of alternative models, confirming the multiple cognitive mechanisms through which D2R block impacts intertemporal choice is a robust feature of decisions on this task.

Overall, this study is a detailed dissection of the specific effects of amisulpride on a type of future-oriented, hypothetical intertemporal choice, and provides consistent evidence integrating conflicting accounts that implicate dopaminergic signaling on evaluation of the cognitive costs, such as a delay, on choice. However the specificity of the empirical intervention and the task design limits the interpretation of the broader dopaminergic mechanisms at play in intertemporal choice, especially given the complexity of receptor specificity of this drug, dopamine precursor availability and individual differences and the specifics of the intertemporal choice in this task. As it stands, the results contribute an interesting, synthesized account of how D2R manipulation can impact evaluation of delays in multiple ways, that will likely be useful for motivating future studies and more detailed computational assessments of the cognitive process-level components of intertemporal choice more generally.

We thank the reviewer for the positive overall evaluation of our study. We revised the manuscript according to the reviewer’s comments, addressing also the receptor specificity of amisulpride and the specifics of the administered intertemporal choice task, which further improved the quality of the manuscript.

The focus of this study is important, and delineating the role of DA in intertemporal choice is of high relevance given DA disfunction is prevalent in many psychiatric disorders and a key target of pharmacological treatment. While the hypotheses of the current study are framed with respect to "costs", the task used by the authors reduces these to evaluation of a hypothetical delay, one which the participants do not necessarily experience in the context of the task. In some respects this is reasonable, given the prevalence of this task paradigm in testing temporal aspects of choice in humans in an economic sense. However, humans are also notoriously subject to framing effects and the impact of instructions in cognitive tasks like these, which can limit the generality of the conclusions, and in particular the specific ways in which a delay can be interpreted as costly (for eg cost as loss of potential earnings, cost as effortful waiting, cost as computational/simulation cost in future evaluation). Given the hypothesis recruits the idea of cost in assessing the role of dopamine, testing for generality in the effects of amisulpride in related but differently framed tasks seems critical for making this link in a general sense, and in connecting it to the previous studies in the literature the authors point to as demonstrating conflicting effects.

We agree that it is important to discuss whether our findings for delay costs can be generalized to other costs types as well, such as risk, social costs, effort, or opportunity costs. Based on a recent literature review (Soutschek, Jetter, & Tobler, 2022), we speculate that dopamine may moderate proximity effects also for risk and social costs but not for effortful rewards, though we emphasize that these hypotheses still require more direct empirical evidence. We also discuss the issue that delays can be perceived as costly in different ways. While in some tasks participants actually experience the waiting time until reward delivery, such that delayed rewards are associated with opportunity costs, in our current task paradigm delayed rewards were virtually free of opportunity costs as participants could engage in other reward-related behaviors during the waiting time. Previous studies suggest that lower tonic dopamine levels reduce the sensitivity to opportunity costs (Niv et al., 2007), which seems in line with our finding that amisulpride decreases the influence of delays on the starting bias parameter. Nevertheless, we emphasize that further evidence is needed to decide whether dopamine shows similar effects for experienced and non-experienced waiting costs. In the revised manuscript, we discuss the cost specificity of our findings on p.22:

“An important question refers to whether our findings for delay costs can be generalized to other types of costs as well, including risk, social costs (i.e., inequity), effort, and opportunity costs. In a recent review, we proposed that dopamine might also moderate proximity effects for reward options differing in risk and social costs, whereas the existing literature provides no evidence for a proximity advantage for effort-free over effortful rewards (Soutschek et al., 2022). However, these hypotheses need to be tested more explicitly by future investigations. Dopamine has also been ascribed a role for moderating opportunity costs, with lower tonic dopamine reducing the sensitivity to opportunity costs (Niv et al., 2007). While this appears consistent with our finding that amisulpride (under the assumption of postsynaptic effects) reduced the impact of delay on the starting bias, it is important to note that choosing delayed rewards did not involve any opportunity costs in our paradigm, given that participants could pursue other rewards during the waiting time. Thus, it needs to be clarified whether our findings for delayed rewards without experienced waiting time can be generalized to choice situations involving experienced opportunity costs.”

Further, while the study aims to test the actions of dopamine broadly, the empirical manipulation is limited to the action of amisulpride, a D2R anatgonist. There is little to no discussion of, or control for, the relationship between dopaminergic action at D2 receptors (the site of amisulpride effects) and wider mechanisms of dopaminergic action at other sites eg D1-like receptors, and the interplay between activation at these two receptor types alongside baseline levels of dopamine concentration. This is necessary for a comprehensive account of dopamine effects on intertemporal choice as the authors aim to test, as opposed to a specific test of the role of the D2 receptor, which is what the study achieves. On a related note, in some preparations at least, amisulpride also acts at some of the 5-HT receptors, raising the possibility of a non-dopaminergic mechanism by which this drug might impact intertemporal decisions. This possibility, while it would not be expected to act without dopaminergic effects as well, is consistent with established effects of serotonin on waiting behaviors and patience. Granted, the limits of pharmacology in humans does not necessarily mean this can be controlled for, it should be kept in mind with a systemic manipulation such as this.

We agree with the reviewer that it is important to distinguish between the contributions of D1 and D2 receptors to decision making, given that these receptor families are hypothesized to have dissociable functional roles. We therefore re-analyzed also data on the impact of a D1 agonist on intertemporal decision making (previous findings for this data set were published in Soutschek et al., 2020, Biological Psychiatry). This analysis provided no evidence for significant effects of D1R stimulation on parameters from a drift diffusion model. This suggests that D2R, rather than D1R, activation mediates the impact of proximity on intertemporal choices.

In the revised manuscript, we report the findings for the D1 agonist study on p.16:

“To assess the receptor specificity of our findings, we conducted the same analyses on the data from a study (published previously in Soutschek et al. (2020)) testing the impact of three doses of a D1 agonist (6 mg, 15 mg, 30 mg) relative to placebo on intertemporal choices (between-subject design). In the intertemporal choice task used in this experiment, the SS reward was always immediately available (delay = 0), contrary to the task in the D2 experiment where the delay of the SS reward varied from 0-30 days. Again, the data in the D1 experiment were best explained by DDM-1 (DICDDM-1 = 19,657) compared with all other DDMs (DICDDM-2 = 20,934; DICDDM-3 = 21,710; DICDDM-5 = 21,982; DICDDM-6 = 19,660; note that DDM-4 was identical with DDM-1 for the D1 agonist study because the delay of the SS reward was 0). Neither the best-fitting nor any other model yielded significant drug effects on any drift diffusion parameter (see Table 4 for the best-fitting model). Also model-free analyses conducted in the same way as for the D2 antagonist study revealed no significant drug effects (all HDI95% included zero). There was thus no evidence for any influence of D1R stimulation on intertemporal decisions.”

We discuss the specificity of D2 receptors for moderating the proximity bias on p.17: “This finding represents first evidence for the hypothesis that tonic dopamine moderates the impact of proximity (e.g., more concrete versus more abstract rewards) on cost-benefit decision making (Soutschek et al., 2022; Westbrook & Frank, 2018). Pharmacological manipulation of D1R activation, in contrast, showed no significant effects on the decision process. This provides evidence for the receptor specificity of dopamine’s role in intertemporal decision making (though as caveat it is worth keeping the differences between the tasks administered in the D1 and the D2 studies in mind).”

We also agree that amisulpride acts also on 5-HT7 receptors, such that it remains unclear whether also such effects contribute to the observed result pattern. We discuss this limitation in the revised manuscript on p.21:

“Lastly, while the actions of amisulpride on D2/D3 receptors are relatively selective, it also affects serotonergic 5-HT7 receptors (Abbas et al., 2009). Because serotonin was related to impulsive behavior (Mori, Tsutsui-Kimura, Mimura, & Tanaka, 2018), it is worth keeping in mind that amisulpride effects on serotonergic, in addition to dopaminergic, activity might contribute to the observed result pattern.”

Overall the modeling methods are robust and appropriate for the specific test of decision impacts of D2R blockade, and include several prima facie variable alternative models for comparison. Some caution is warranted, since there are not many trials per subject, and some trials are discarded as well as outliers, which raises the question of power. Given the models are fit hierarchically, which gives both group-level and individual-level parameter estimates, the elements are there to probe more deeply into individual differences, and to test how reliably this approach can dissociate the dual effects of bias and drift rate at the individual level, and perhaps correlate it with other informative subject measures of either dopamine activity/capacity or other dopamine-dependent behaviors. Alternative DDMs might also capture some of this individual variation, with meaningful differences potentially in model comparison at the individual level. It should be noted that the scope of these models do not exhaust the ways in which proximity (here, temporal) of rewards and contrast between choice options might be incorporated into a cognitive process model account of choice; all alternatives here rest on the same implicit 2-alternative forced choice assumption of the DDM, and the assumptions of this model are not here tested against other accounts of choice, for example the linear ballistic accumulator (LBA) and its derivatives. Further, the concept of proximity as a global feature of a trial (on average, how soon are these options overall?) is never tested on my read of the alternative models.

We thank the reviewer for these interesting suggestions. First, to explore whether measures of dopaminerigc activity correlate with individual differences in drug effects on DDM parameters, we now report correlations between DDM parameters and performance in the digit span backward task as proxy for dopamine synthesis capacity (Cools et al., 2008). None of these correlation analyses showed significant results. In the revised manuscript, we report these analyses on p.13:

“However, we observed no evidence that individual random coefficients for the drug effects on the drift rate or on the starting bias correlated with body weight, all r < 0.22, all p > 0.10. There were also no significant correlations between DDM parameters and performance in the digit span backward task as proxy for baseline dopamine synthesis capacity (Cools, Gibbs, Miyakawa, Jagust, & D'Esposito, 2008), all r < 0.17, all p > 0.22. There was thus no evidence that pharmacological effects on intertemporal choices depended on body weight as proxy of effective dose or working memory performance as proxy for baseline dopaminergic activity.”

Regarding model comparisons on the individual level, we note that the hierarchical Bayesian modelling approach allows (to the best of our knowledge) computing indices of model fit like DIC only on the group, not the individual level (while accounting for individual differences). However, we agree with the reviewer that theoretically different models might work best in different individuals (depending, for example, on the individual sensitivity to proximity). While such fine-grained model comparisons on the individual level are beyond the scope of the current study (and might not yield robust results given the limited number of trials for each participant), we now discuss this limitation in the revised manuscript (p.17-18):

“We note that the hierarchical modelling approach allowed us to compare models on the group level only, such that in some individuals behavior might better be explained by a different model than DDM-1. Such model comparisons on the individual level, however, were beyond the scope of the current study and might not yield robust results given the limited number of trials per individual.”

Likewise, linear ballistic accumulator (LBA) models represent a further class of process models with different assumptions on the mechanisms underlying the choice process than DDMs. In LBAs, evidence is accumulated separately for each choice alternative, whereas DDMs assume only one accumulation process which integrates attributes from two choice options, limiting the use of DDMs to two-alternative forced-choice scenarios. Nevertheless, proximity effects might be incorporated also in LBA models via modulating the starting point of the option-specific accumulators as a function of proximity. To the best of our knowledge, there is no built-in function in JAGS that allows estimating LBA models in a hierarchical Bayesian fashion (in contrast to, e.g., STAN), such that in the context of the current study it is difficult to directly compare our DDM-based approach with LBA models. It is importance to emphasize, however, that similar to other studies we do not make any claims about whether the choice process per se is best explained by DDMs or LBA models; instead, we focus on how rewards and delay costs affect different components of the decision process within a class of decision models. Nevertheless, we discuss such alternative modelling approaches in the revised manuscript on p.18:

“We also emphasize that alternative process models like the linear ballistic accumulator (LBA) model make different assumptions than DDMs, for example by positing the existence of separate option-specific accumulators rather than only one as assumed by DDMs. However, proximity effects as investigated in the current study might be incorporated in LBA models as well by varying the starting points of the accumulators as function of proximity.”

Lastly, we thank the reviewer for the interesting suggestion to assess whether the starting bias parameter is affected by the overall proximity of offers (sum of delays) instead of by the difference in proximity between the options. We ran a further DDM to test this hypothesis, but this model explained the data worse (DIC = 9,492) than the original DDM (DIC = 9,478). Nevertheless, also the overall proximity DDM yielded a significant amisulpride effect on the impact of reward magnitude on the drift rate, HDImean = 0.83, HDI95% = [0.04; 1.75], underlining the robustness of this effect. In the revised manuscript, we report this analysis on p.12:

“In a further model (DDM-4), we explored whether the starting bias is affected by the overall proximity of the options (sum of delays, Delaysum) rather than the difference in proximity (Delaydiff; see Table 3 for an overview over the parameters included in the various models). Importantly, our original DDM-1 (DIC = 9,478) explained the data better than DDM-2 (DIC = 9,481), DDM-3 (DIC = 10,224), or DDM-4 (DIC = 9,492). Nevertheless, amisulpride moderated the impact of Magnitudediff on the drift rate also in DDM-2, HDImean = 0.86, HDI95% = [0.18; 1.64], and DDM-4, HDImean = 0.83, HDI95% = [0.04; 1.75], and amisulpride also lowered the impact of Delaydiff on the starting bias in DDM-3, HDImean = -0.02, HDI95% = [-0.04; -0.001]. Thus, the dopaminergic effects on these subcomponents of the choice process are robust to the exact specification of the DDM.”

Reviewer #3 (Public Review):

Soutschek and Tobler provide an intriguing re-analysis of inter-temporal choice data on amisulpride versus placebo which provides evidence for an as-yet untested hypothesis that dopamine interacts with proximity to bias choices.

The modeling methods are sound with a robust and reasonably exhaustive set of models for comparison, with good posterior predictive checks at the single subject level, and decent evidence of parameter recoverability. Importantly, they show that while there is no main effect of drug on the proportion of larger, later (LL) versus smaller, sooner (SS) choices, this obscures conflicting-directional effects on drift rate versus starting point bias which are under-the-hood, yet anticipated by the hypothesis of interest.

We thank the reviewer for judging our findings as intriguing and the modelling approach as robust and convincing.

While I have no major concerns about methodology, I think the Authors should consider an alternative interpretation - albeit an interpretation which would actually support the hypothesis in question more directly than their current interpretation. Namely, the Authors should re-consider the possibility that amisulpride's effects are mediated primarily by acting at pre-synaptic receptors. If the D2R antagonist were to act pre-synaptically, it would drive more versus less post-synaptic dopamine signaling.

There are multiple reason for this inference. First, the Authors observe that the drug increases sensitivity to differences in the relative offer amounts (in terms of effects on the drift rate). With respect to the canonical model of dopamine signaling in the direct versus indirect pathway, greater post-synaptic signaling should amplify sensitivity to reward benefits - which is what the Authors observe.

Second, the Authors also observe an effect on the starting bias which may also be consistent with an increase in post-synaptic dopamine signaling. Note that according to the Westbrook & Frank hypothesis, a proximity bias in delay discounting should favor the SS over the LL reward, yet the Authors primarily observe a starting bias in the direction of the LL reward. This contradiction can be resolved with the ancillary assumption that, independent of any choice attribute, participants are on average predisposed to select the LL option. Indeed, the Authors observe a reliable non-zero intercept in their logistic regression model indicating that participants selected the LL more often, on average. As such, the estimated starting point may reflect a combination of a heightened predisposition to select the LL option, opposed by a proximity bias towards the sooner option. Perhaps the estimated DDM starting point is positive because the predisposition to select the LL option has a larger effect on choices than the proximity bias towards sooner rewards does in this data set. To the extent that amisulpride increases post-synaptic dopamine signaling (by antagonizing pre-synaptic D2Rs) it should amplify the proximity bias arising from the differences in delay, shifting the starting bias towards the SS option. Indeed, this is also what the Authors observe.

Note that it remains unclear why an increase in post-synaptic dopamine signaling would amplify one kind of proximity bias (towards sooner over later rewards) without amplifying the other (towards a predisposition to select the LL option). Perhaps the cognitive / psychological nature of the sooner bias is more amenable to interacting with dopamine signaling than the latter. Or maybe proximity bias effects are most sensitive to dopamine signaling when they are smaller, and the LL predisposition bias is already at ceiling in the context of this task. These assumptions would help explain why a potential increase in post-synaptic dopamine signaling both amplified the proximity effect of delay when it was smallest (when the differences in delay were smaller), and also failed to amplify the predisposition to select the LL option (which may already be maxed out). More importantly, the assumption that there are opposing proximity biases would also help explain why there is a negative effect of delay magnitude on the estimated starting point on placebo. Namely - as the delay gets larger, the psychological proximity of sooner over later rewards grows, counteracting the proximity bias arising from choice predisposition / repetition.

We thank the reviewer for suggesting this alternative interpretation of our data. We agree that the administered dose of 400 mg amisulpride can show both postsynaptic (reducing D2R activation) and presynaptic effects (enhancing D2R activation), which in many studies makes it difficult to decide whether the observed behavioral effects are caused by presynaptic or postsynaptic mechanisms.

The reviewer suggests that the observed stronger influence of reward magnitudes on drift rates under amisulpride compared with placebo speaks in favor of presynaptic effects, because according to theoretical accounts higher dopamine levels should increase reward seeking (e.g., Frank & O’Reilly, 2006). On the other hand, Figure 2C suggests that amisulpride (compared with placebo) increased the preference only for relatively high, above-average rewards. If the difference between reward magnitudes was below average, amisulpride reduced rather than increased the preference for the larger reward. In our view, this is consistent with the hypothesis that D2R activation implements a cost control, with higher D2R activation increasing the attractiveness of costly rewards and lower D2R activation reducing it. In other words, under low dopamine levels individuals should decide for the costlier reward only if the magnitude of the costlier reward is sufficiently large compared with the lower, less costly reward. In fact, this is exactly what we find in our data according to Figure 2C. In our view, the amisulpride effect on drift rates is thus compatible with both presynaptic and postsynaptic mechanisms of action, depending on the underlying conceptual account of dopamine, as we now discuss in the revised manuscript.

According to the reviewer, also the observed influence of amisulpride on the starting bias speaks in favor of increased rather than reduced dopamine levels. We agree with the reviewer that the result pattern for the starting bias is somewhat complex and seems to combine the effects of two different biases: a general tendency to choose LL over SS rewards (intercept of starting bias where the difference in delays is close to zero), and a shift towards the SS option under placebo if one options has a strong (temporal) proximity advantage over the other. Amisulpride shows opposite effects on the two different biases, as it shifts the intercept of the starting bias further away from the LL option but also reduces the proximity advantage of the SS over the LL reward for larger differences in delay. The reviewer writes that “To the extent that amisulpride increases post-synaptic dopamine signaling (by antagonizing pre-synaptic D2Rs) it should amplify the proximity bias arising from the differences in delay, shifting the starting bias towards the SS option. Indeed, this is also what the Authors observe.” In contrast to that statement, in our study amisulpride reduced rather than increased the starting bias arising from delay (as in Figure 2K the regression line is flatter under amisulpride compared with placebo, despite the differences regarding the intercept). We believe that the amisulpride effects on both the intercept and the delay-dependent slope can be explained via postsynaptic effects: First, the shift of the intercept of the starting bias (small differences in proximity) from the LL towards the SS option under amisulpride is consistent with the assumption that lower dopamine reduces the preference for larger reward (e.g., Beeler & Mourra, 2018; Salamone & Correa, 2012). Second, the finding that amisulpride weakens the proximity advantage of SS over LL rewards (delay-dependent slope) is consistent with the proximity account by Westbrook & Frank (2018) according to which lower tonic dopamine should reduce proximity effects. Thus, if we assume that the result pattern for the starting bias parameter is driven by dopaminergic effects on two separate decision biases (as suggested by the reviewer), we believe that both effects can better be explained by pharmacologically reduced rather than increased dopamine levels.

In the revised manuscript, we extensively discuss the question as to whether the observed drug effects are caused by postsynaptic versus presynaptic effects. We clarify that the amisulpride effect on drift rates seems consistent with both presynaptic and postsynaptic effects (depending on the underlying conceptual account). We moreover discuss that the starting bias effects may reflect the interaction between two different bias types, and the drug effects on both bias types can more easily be reconciled with postsynaptic than presynaptic effects. On balance, we believe that the observed effects are more likely to reflect lower as compared to higher dopamine levels, but the extended discussion of this issue gives all readers the opportunity to weigh the arguments for and against these alternatives. If the reviewer should not agree with some aspects of our argumentation as outlined above, we would of course be happy to modify the discussion according to the reviewer’s advice.

In the revised manuscript, we modified the discussion of presynaptic versus postsynaptic effects as follows (p.20-21):

“While higher doses of amisulpride (as administered in the current study) antagonize post-synaptic D2Rs, lower doses (50-300 mg) were found to primarily block pre-synaptic dopamine receptors (Schoemaker et al., 1997), which may result in amplified phasic dopamine release and thus increased sensitivity to benefits (Frank & O'Reilly, 2006). At first glance, the stronger influence of differences in reward magnitude on drift rates under amisulpride compared with placebo might therefore speak in favor of presynaptic (higher dopamine levels) rather than postsynaptic mechanisms of action in the current study. On the other hand, one could argue that amisulpride reduced the preference for the LL reward if the gain from the costlier LL option compared with the SS option was small (as suggested by Figure 2C), which is consistent with the cost control hypothesis of dopamine (Beeler & Mourra, 2018). The impact of amisulpride on the drift rate thus appears ambiguous regarding the question of pre- versus postsynaptic effects. The result pattern for the starting bias parameter, in turn, suggests the presence of two distinct response biases, reflected by the intercept and the delay-dependent slope of the bias parameter (see Figure 2K), which are both under dopaminergic control but in opposite directions. First, participants seem to have a general bias towards the LL option in the current task (intercept), which is reduced under amisulpride compared with placebo, consistent with the assumption that dopamine strengthens the preference for larger rewards (Beeler & Mourra, 2018; Salamone & Correa, 2012; Schultz, 2015). Second, amisulpride reduced the proximity advantage of SS over LL rewards with increasing differences in delay, as predicted by the proximity account of tonic dopamine (Westbrook & Frank, 2018). On balance, the current results thus appear more likely under the assumption of postsynaptic rather than presynaptic effects. Unfortunately, the lack of a significant amisulpride effect on decision times (which should be reduced or increased as consequence of presynaptic or postsynaptic effects, respectively) sheds no additional light on the issue.”

Regardless of the final interpretation, showing that pharmacological intervention into striatal dopamine signaling can simultaneously modify a starting point bias and drift rate (in opposite directions - thus having systematic effects on choice biases without altering the average proportion of LL choices) provides crucial first evidence for the hypothesis that dopamine and proximity interact to influence decision-making. These results thereby enrich our understanding of the neuromodulatory mechanisms influencing inter-temporal choice, and take an important step towards resolving prior contradictions in this literature. They also have implications for how striatal dopamine might impact decision-making in diverse domains of impulsivity beyond inter-temporal choice, ranging from cognitive neuroscience (e.g. in numerous cognitive control tasks) to psychiatry (treating diverse disorders of impulse control).

We thank the reviewer for highlighting the importance of the current findings for understanding dopamine’s role in decision making.

Author Response

Reviewer #1 (Public Review):

Liau and colleagues have previously reported an approach that uses PAM-saturating CRISPR screens to identify mechanisms of resistance to active site enzyme inhibitors, allosteric inhibitors, and molecular glue degraders. Here, Ngan et al report a PAM-saturating CRISPR screen for resistance to the hypomethylating agent, decitabine, and focus on putatively allosteric regulatory sites. Integrating multiple computational approaches, they validate known - and discover new - mechanisms that increase DNMT1 activity. The work described is of the typical high quality expected from this outstanding group of scientists, but I find several claims to be slightly overreaching.

Major points:

The paper is presented as a new method - activity-based CRISPR scanning - to identify allosteric regulatory sites using DNMT1 as a proof-of-concept. Methodologically, the key differentiating feature from past work is that the inhibitor being used is an activity-based substrate analog inhibitor that forms a covalent adduct with the enzyme. I find the argument that this represents a new method for identifying allosteric sites to be relatively unconvincing and I would have preferred more follow-up of the compelling screening hits instead. The basic biology of DNMT1 and the translational relevance of decitabine resistance are undoubtedly of interest to researchers in diverse fields. In contrast, I am unconvinced that there is any qualitative or quantitative difference in the insights that can be derived from "activity-based CRISPR scanning" (using an activity-based inhibitor) compared to their standard "CRISPR suppressor scanning" (not using an activity-based inhibitor). Key to their argument, which is expanded upon at length in the manuscript, is that decitabine - being an activity-based inhibitor that only differs from the substrate by 2 atoms - will enrich for mutations in allosteric sites versus orthosteric sites because it will be more difficult to find mutations that selectively impact analog binding than it is for other active-site inhibitors. However, other work from this group clearly shows that non-activity-based allosteric and orthosteric inhibitors can just as easily identify resistance mutations in allosteric sites distal from the active site of an enzyme (https://www.biorxiv.org/content/10.1101/2022.04.04.486977v1). If the authors had compared their decitabine screen to a reversible DNMT1 inhibitor, such as GSK3685032, and found that decitabine was uniquely able to identify resistance mutations in allosteric sites, then I would be convinced. But with the data currently available, I see no reason to conclude that "activity-based CRISPR scanning" biases for different functional outcomes compared to the "CRISPR suppressor scanning" approach.

We appreciate the reviewer’s comments and thank them for their constructive feedback. We agree with the reviewer that our claims regarding the utility of activity-based CRISPR scanning would be more strongly supported with a head-to-head comparison against a non-covalent, reversible inhibitor. To address this point, we conducted a CRISPR scanning experiment on DNMT1 and UHRF1 using GSK3484862 (GSKi), which is shown in Fig. 1e–h. We observed that the top enriched sgRNA under GSKi treatment targets H1507, which directly interacts with the drug and contributes to compound binding. (Fig. 1e,h, Supplementary Fig. 1e). Our results are consistent with previous structural and biochemical studies of these inhibitors (reported in Pappalardi, M.B. et al., Nat. Cancer 2021), in which they demonstrate that the H1507Y mutation reduces GSK3685032 (a derivative of GSK3484862) inhibition of DNMT1 by >350-fold compared to wild-type DNMT1. By contrast, the top enriched sgRNA under decitabine (DAC) treatment targets D702 in the autoinhibitory linker region (Fig. 1c). Furthermore, comparison of sgRNA resistance scores across DAC and GSKi treatment conditions reveals highly distinct sgRNA enrichment profiles (Fig. 1g). Taken together, our data suggest that these two mechanistic classes of inhibitors may exert differential selective pressures that lead to unique enrichment profiles.

While we consider these data to strengthen our claim that activity-based CRISPR scanning can preferentially enrich for mutations in allosteric sites versus orthosteric sites, we also recognize that allosteric site mutations can be identified without the use of activity-based inhibitors, as the reviewer points out. To address this point, we have modified the text to suggest that the use of activity-based inhibitors may exert a greater bias for the enrichment of allosteric site mutations but clarifying that the enrichment of such mutations are not exclusive to the use of activity-based inhibitors.

How can LOF mutations from cluster 2 be leading to drug resistance? It is speculated in the paper that a change in gene dosage decreases the DNA crosslinks that cause toxicity. However, the immediate question then would be why do the resistance mutations cluster around the catalytic site? If it's just gene dosage from LOF editing outcomes, would you not expect the effect to occur more or less equally across the entire CDS?

This is an excellent point. As outlined previously above, we recognize that our gene dosage hypothesis regarding the mechanism of cluster 2 sgRNAs may lack sufficient explanation to convey our reasoning clearly, and we have added more text and data to clarify and support our claim.

Mutations that are highly likely to lead to a nonfunctional protein product (i.e., frameshift, nonsense, splice site disrupting) are annotated as “loss-of-function” (LOF) in the text, with all other protein coding mutations designated as “in-frame.” The key insight underlying our gene dosage hypothesis is that sgRNAs targeting essential protein regions and functional domains generate greater proportions of null (i.e., knockout) mutations and undergo stronger negative selection compared to sgRNAs targeting non-essential protein regions (see Shi, J. et al., Nat. Biotechnol. 2015). This is because in-frame coding mutations in protein regions that are functionally important (e.g., DNMT1 catalytic domain) are more likely to disrupt protein function than those in non-essential protein regions. As a result, sgRNAs targeting functional protein regions are more likely to generate in-frame mutations resulting in a null allele and are thus “effectively LOF.” Importantly, the observation that sgRNAs targeting specific protein regions are more likely to lead to null mutations also implies that 1. not all CDS-targeting sgRNAs are equivalent at inducing LOF effects and 2. sgRNAs that are more effective at generating null mutations may exhibit preferential clustering within functionally important protein regions.

In this context, we reasoned that cluster 2 sgRNAs, which target the essential catalytic domain, may be more effective at reducing DNMT1 gene dosage than other DNMT1-targeting sgRNAs because in-frame mutations generated by these sgRNAs are more likely to lead to nonfunctional DNMT1 protein. That is, cluster 2 sgRNAs may generate greater proportions of “effectively LOF” in-frame mutations that disrupt DNMT1’s essential function. Consequently, we posited that the observed clustering of these sgRNAs in the catalytic domain is likely a reflection of its functional importance. To test this idea, we transduced WT K562 cells with 6 individual sgRNAs targeting the N-terminus, RFTS domain, and catalytic domain of DNMT1, and performed genotyping on the cellular pools over 28 days (Fig. 4f). We observed that sgRNAs targeting outside of the catalytic domain exhibited increasing frequencies of in-frame mutations over time, consistent with the idea that these sgRNAs generate functional in-frame mutations that are not under strong negative selection. By contrast, catalytic-targeting sgRNAs exhibited significant depletion of inframe mutations over time, supporting the notion that in-frame mutations in essential regions are functional knockouts and thus negatively selected under normal growth conditions. Consequently, the ability of catalytic-targeting sgRNAs to generate greater proportions of null mutations would therefore make them more effective at conferring resistance through gene dosage reduction than other DNMT1-targeting sgRNAs.

Our hypothesis implies that a large proportion of in-frame mutations generated by cluster 2 sgRNAs are functionally equivalent to LOF mutations (i.e., frameshift, nonsense, splice site disruption), and therefore neither in-frame or LOF mutations should be preferentially selected for under DAC treatment, in contrast to the positive selection of gain-of-function (GOF) in-frame mutations in cluster 1 sgRNAs. Consistent with this idea, our data indicate that the relative proportions of in-frame and LOF mutations in cluster 2 sgRNAs remain comparable across vehicle and DAC treatments (Fig. 4b). Furthermore, since the selective pressure on in-frame and LOF mutations should be similar if they are functionally equivalent, the relative proportions of in-frame versus LOF mutations in cluster 2 sgRNAs should be primarily dictated by their frequencies as editing outcomes. Consistent with this idea, the observed proportions of in-frame versus LOF mutations in cluster 2 sgRNAs under DAC treatment do not deviate significantly from their expected proportions as predicted by inDelphi (Supplementary Fig. 4c). Conversely, cluster 1 sgRNAs exhibit greater ratios of in-frame versus LOF mutations under DAC treatment than their predicted ratios from inDelphi (Supplementary Fig. 4c,d). Altogether, these data are consistent with the notion that cluster 2 sgRNAs may operate through a gene dosage reduction effect.

In general, I found the screens, and integrative analyses, highly compelling. But the follow-up was rather narrow. For example, how much do these mutations shift the IC50 curves for DAC?

To address this point, we derived two clonal cell lines from the screen harboring endogenous DNMT1 mutations in either the autoinhibitory linker or the RFTS domain (Supplementary Fig. 3g). We treated these cell lines, in addition to WT K562 cells, with varying concentrations of DAC and observed a partial growth rescue in the mutant cell lines relative to WT K562 cells (Fig. 3i). We also show that these mutant cell lines exhibit DAC-mediated degradation of DNMT1, consistent with our fluorescent reporter results (Supplementary Fig. 3h). To further validate whether these endogenous DNMT1 mutations confer partial resistance to DAC, we transduced WT K562 cells with vectors encoding an shRNA targeting the 3' UTR of the endogenous DNMT1 transcript and a DNMT1 overexpression vector encoding WT and mutant DNMT1 constructs (Supplementary Fig. 3i). Upon treating these knockdown and overexpression cells with varying concentrations of DAC, we again observed a partial growth rescue in the presence of mutant versus WT DNMT1 (Fig. 3j).

What kinetic parameters have changed to increase catalytic activity?

We performed enzyme activity assays at various temperatures with recombinant DNMT1 protein for WT and mutant DNMT1 constructs, observing that mutant DNMT1 constructs exhibit varying degrees of overactivity relative to WT DNMT1 at different temperatures (Fig. 3h, Supplementary Fig. 4f). Whereas the autoinhibitory linker mutations display consistently higher levels of activity relative to WT DNMT1 at all temperatures tested, we observed that RFTS and CXXC mutants exhibited decreasing levels of overactivity with increasing temperature (Fig. 3h). Previous studies (see Berkyurek, A.C. et al., J. Biol. Chem. 2014) have observed similar behavior with RFTS mutations, suggesting that these mutations may disrupt critical hydrogen bonds at the autoinhibitory interface that reduce the activation energy required to release DNMT1 from an autoinhibited to active conformation. Our RFTS and CXXC mutations exhibit behavior that are consistent with this hypothesis, which may explain the decreasing levels of overactivity with increasing temperature.

Do the mutants with increased catalytic activity alter the abundance of methylated DNA (naively or in response to the drug)? It is speculated that several UHRF1 sgRNAs disrupt PPIs and not DNA binding, but this is never tested.

While we derived clonal cell lines containing DNMT1 mutations, as noted above, it proved too difficult to compare these drug-resistant cells to naïve cells because they were cultured in the presence of DAC for 2 months, leading to large changes in DNA methylation that may confound any conclusions about the effects of the mutations alone. Additionally, the reviewer also brings up valid limitations regarding our studies on UHRF1, which also proved very difficult to biochemically purify and beyond our expertise. After some initial studies, we chose not to pursue these additional experiments further but instead prioritized the GSKi CRISPR-suppressor scan and cluster 2 studies, as suggested by the reviewers. We acknowledge these limitations in the text.

Reviewer #2 (Public Review):

In this manuscript, Ngan and coworkers described a CRISPER-based screening approach to identify potential variants of DNMT1 and UHRF1 that can suppress the anti-proliferation role of decitabine. In theory, such an effect can be achieved by at least two types of gain-of-activity DNMT1/UHRF1 mutants by directly boosting the enzymatic activity or by indirectly abolishing the intrinsic inhibitory activity of the DNMT1-UHRF1 axis. Through systematically targeting the DNMT1-UHRF1 reading frames with a rationally designed sgRNA library, the authors identified and characterized a few potential hotspots within multiple autoinhibitory motifs. While the approach has its merits in regard to the unbiased screening of the target proteins in living cells, there are the following serious concerns in terms of how the data were interpreted and the limitation of the approach itself as detailed below.

(1) Although the authors identified multiple hotspots in the DNMT1-UHRF1 complex with their alterations associated with the resistance to decitabine, it is risky to argue these mutations increase DNMT1 activity simply because they are clustered within known auto-inhibitory regions. There are many alternative explanations for this observation. For instance, some mutants may allosterically alter how DNMT1 recognizes decitabine-containing vs native GpC motifs; others may recruit other proteins as modulators. The key gap here is to associate the decitabine-resistance phenotype to the loss of auto-inhibitory functions because multiple hotspots were in the auto-inhibitory regions.

In our original manuscript, we supported our claim that gain-of-function DNMT1 mutations enhance DNMT1 activity with experimental data using purified DNMT1 protein constructs in enzyme activity assays (Fig. 3g, Fig. 4g), so our conclusion was not solely inferred from sgRNA clustering at the autoinhibitory interface, but also experimentally validated. In our revised manuscript, we provide additional experimental biochemical characterization to further support the claim that autoinhibition is weakened in the DNMT1 mutants we identified (Fig. 3h, Supplementary Fig. 4f). Moreover, we provide cellular data using clonal cell lines harboring endogenous DNMT1 mutations in addition to knockdown/overexpression experiments, demonstrating that RFTS and autoinhibitory linker mutations confer partial growth rescue to DAC treatment (Fig. 3i,j). We agree that we cannot rule out the possibility that these mutations may exert other effects that independently contribute to the observed resistance phenotype (e.g., altered CpG recognition), and we have added a statement acknowledging this limitation.

(2) Lack of general biological relevance of the corresponding findings. Through this work, the author identified multiple DNMT1-UHRF1 variants that alter the anti-proliferation role of decitabine. However, the observation that the multiple mutants were clustered in a hotspot doesn't mean that these mutants have to act via the same mechanism. The authors seem to underestimate the complexity of how these mutants can render the same biological readouts and even haven't considered the possibility of transcriptional modulation of antagonists or agonists in the DNMT1-UHRF1. Therefore, the biological relevance of these findings remains unclear.

We agree that although the cluster 1 mutations share a common property of increased DNMT1 activity, it does not preclude alternative mechanisms. Indeed, it is likely that these mutations have complex and nuanced mechanistic differences in the biochemical alterations underlying their observed increases in DNMT1 activity. Indeed, we have included enzyme activity data suggesting that autoinhibitory linker mutations may exhibit a different biochemical basis for increased DNMT1 activity than RFTS and CXXC mutations. That said, we did not intend to make broader claims regarding biological relevance and were instead focused on conveying that this activity-based methodology can identify gain-of-function mutations, which we directly support with experimental data. To clarify these points, we have adapted the text to more precisely convey our intended claims and have acknowledged that other complex mechanisms may also be involved.

(3) Collectively for reasons (1) and (2), the mechanistic analysis seems only to associate the current findings with known regulatory pathways. Without detailed in vitro and in-cell characterization of the DNMT1-UHRF1 mutants, the novel regulatory mechanisms, which may exist, could be largely missed.

We have added some additional characterization of these mutations in the revised manuscript, which have been detailed above, and we would like to note that we identified new sites in DNMT1 and UHRF1 that may be functional based off our allele analysis. However, since this manuscript is intended more as a methodology, we believe that extensively exploring novel regulatory mechanisms and their mechanism is beyond the scope of this report.

(4) The current CRISPER-based screening approach has the technical limitation of mainly screen deletion with some exceptions for point mutations. As a result, the majority of loss/gain-of-function point mutations will be missed by the CRISPER-based screening method.

We acknowledge that a technical limitation of this Cas nuclease-based mutational scan is that it is biased toward insertion/deletion mutations versus point mutations. However, we disagree with the reviewer’s claim that this means that the majority of the loss-/gain-of-function mutations will be missed, since insertion/deletions are often larger perturbations than point mutations and thus have stronger effect sizes in many cases. In principle, the selection modalities (e.g., activity-based inhibitors) used here — which are the primary focus of the study — can also be combined with alternative genomic editing approaches to assess distinct mutational perturbations, such as base editing for point mutations (see Lue, N.Z. et al., Nat. Chem. Biol. 2022). To acknowledge the reviewer’s concern, however, we have added additional text explicitly stating that the screen is biased against point mutations and that future integration with base editing and other mutational modalities may be useful to complement our nuclease-based approach.

(5) The current CRISPER-based screening approach can work only in the context of living cells. As a result, robust cellular readouts are needed. The DNMT1-UHRF1 in combination with decitabine is among few suitable targets for such application.

While running CRISPR-based screens requires robust cellular assays, the main advantage of CRISPRbased mutational scanning is the ability to mutagenize the endogenous protein target in situ and assess the effect of the perturbation in the native cellular and genomic context. Resistance to activity-based probes — and small molecules more broadly — provides a robust phenotypic readout that has been extensively used by our group and many others. Alternatively, other types of phenotypic readouts that do not rely on cell viability can also be employed with these screens, including those used to assess DNA methylation (see Lue, N.Z. et al., Nat. Chem. Biol. 2022). Given the increasingly large body of literature applying CRISPR-based screens towards a multitude of biological pathways and diverse targets, we disagree with the reviewer’s claim that only a few targets can be evaluated in such a manner.

(6) Although the authors claim that their mutants are "gain-of-function" for DNMT1/UHRF1, they were indeed due to the loss of inhibitory regulation. It is a little disappointing because the screening outcomes still fall into the conventional expectation of the loss-of-function variants.

We agree that the mutations are not truly neomorphic, but instead likely hypermorphic due to loss of an autoinhibitory mechanism, resulting in gain-of-function increase in catalytic activity. While discovering neomorphic mutations would be extraordinary, we do not believe that our results are disappointing since the identification of autoinhibitory mechanisms is nevertheless impactful.

Collectively, the current status of the manuscript is short of merits in terms of the impacts of technology and biological findings.

We respectfully disagree with the reviewer’s comment as we believe that the experimental and computational methodology may be broadly useful for the field. Indeed, we have already implemented many of the tools developed here in our current ongoing work.

Author Response

Reviewer #2 (Public Review):

This manuscript presents a rather technical modelling analysis of the impact of local lockdowns on Covid-19 hospitalisations in the Netherlands. The major strength of the study is that the authors attempt to calibrate their model to a novel data source, a commercial database of mobility patterns between municipalities. The major weakness is that the model seems overly complicated, many parameters seem to have been 'guessed' without a formal uncertainty analysis, e.g. within a Bayesian framework, so that it is impossible to judge how robust the results and therefore the conclusions are.

Major points:

1) In some aspects the structure of the model presented seems overly complicated: It is not clear why the authors chose the 1:100 population scale and why they didn't go directly for modelling the full population. Artificially reducing the population size has important stochastic effects at the early phase of the epidemic. Also it is not clear what it means when 1:100 of one municipality mixes with 1:100 of another municipality? The authors should at least attempt to see what impact this has on output, i.e. conduct a sensitivity analysis.

The reason for choosing a 1:100 population scale instead of the full population is computational speed. Indeed, this (and its consequences) is not mentioned explicitly and will be added. Moreover, to identify the sensitivity of the results to population scale, we add runs on different population scales.

• Added reasoning and consequences associated with the 1:100 population scale in SI C.1.

• The sensitivity of the results to population scale is now discussed in SI C.1 using runs with other population resolutions.

2) On the other hand the model goes into (too) much detail regarding mixing behaviour and attempts to model processes during each hour of the day. This does not seem to be informed by actual data, but the data seems to be made up e.g. as in A.6. As an ex-student and a father of a teenager I can tell you that the susceptibility profile guessed in Table 3 does not seem to be very realistic. As it is stated in the appendix, the Mezuro data set only provides daily averages of travelling between communitities, so it is not clear why the hourly resolution is actually needed in the model.

Indeed, several aspects in the model are informed by “secondary statistics” which unfortunately add uncertainty. An example would be the normalization of the mobility matrices by means of data on how people spend their time (see SI A.3). Note that the example of the susceptibility profile that the reviewer mentions, however, does not involve such secondary statistics and happens to be exactly reported by the Dutch health agency (cited in SI A.5).

We agree that the model includes much detail, which potentially has weaknesses as the reviewer rightfully mentions. However, one of the main points of this paper is that in order to address the questions of local interventions, geographical spread and associated hospital admissions, we simply need this level of detail, or even higher. In other words, assessments of such mechanics would be even more uncertain if this level of detail is not included.

We agree that the motivation for hourly resolution is not well described in the manuscript – this will be added. The reasoning is that mixing of the population is highly heterogeneous throughout the day: clearly, seen in Fig. S5 (SI A.7), mixing at work is fully different from mixing at school or at home.

Moreover, people meet at work in different municipalities and then return to home to potentially spread the disease further. It is exactly such mechanics that we are after in our analysis.

• Added a more in-depth discussion of the mobility data in SI C.2.

• Added the motivation for hourly resolution in SI A.1-A.3.

3) It is not clear why the authors rely on only one short period of the Mezuro data set in March 2019 and not investigate the same data source during the actual lockdown in 2020, or even for the full year, as travelling is likely to be very season dependent. This would provide much better estimates of the effects of lockdown on travel patterns. The analysis presented and categorisation into frequent, regular and incidental also need further explanation. It is not clear how international travel is accounted for in the mobility data.

The reviewer is correct that using a longer mobility dataset or one that is exactly addressing the period of the actual lockdown would be beneficial. The reason we are not doing so is simply that this data is not available.

The model accounts for international travel by means of its initialization, but not further. In practise, international travel got severely reduced throughout this period. Hence, we deem the uncertainties associated with not accounting for international travel limited.

• Added a discussion on the effect of using this mobility dataset in SI C.2. • Added a further explanation of categorizing the movements (in SI C .2).

• Added a discussion on international travel in SI C.2.

4) Beyond the technical points on the modelling, the main hypothesis of whether local lockdowns may work has also not been sufficiently discussed outside of the Dutch context. The authors fail to mention that this was the approach chosen in Northern Italy at the start of the epidemic (https://en.wikipedia.org/wiki/COVID-19_lockdowns_in_Italy) where it didn't work, as we all know. On the other hand, more recent local lockdowns in China appeared to be successful, albeit at a great societal cost in terms of restrictions to freedom (https://en.wikipedia.org/wiki/COVID19_lockdown_in_China).

The reviewer is correct that we only show this in the Dutch context. We can reason about other situations, but clearly these situations differ vastly from country to country.

Reviewer #3 (Public Review):

This work uses an agent-based model of SARS-CoV-2 transmission (calibrated to the first wave in the Netherlands) to examine how the societal impact of interventions could have been reduced - while maintaining epidemiological impact - if they were implemented at a subnational (eg, municipality) rather than a national level. After more than two years of lockdowns and mobility restrictions, the societal cost of such measures is becoming better understood, and it is important to evaluate the effectiveness of such measures and reflect upon how they can be deployed in a minimally disruptive fashion. Mathematical and computational models are a natural choice for such investigations as they enable researchers to explore counter-factual scenarios ("what might have happened had we acted differently?")

The authors conclude that subnational interventions, triggered via prevalence in a particular municipality, could have controlled the first wave of SARS-CoV-2 in the Netherlands with minimal health cost but less societal disruption than national interventions. This claim is supported by reference to Figure 4 showing the impact on (a) hospital admissions and (b) municipalities without interventions through different phases of the outbreak. For more remote/rural municipalities, the use of interventions is delayed by ~1 week, although some (6%) of municipalities avoid interventions altogether.

Strengths:

As noted above, the general objective of this study is important and of potentially broad interest. The agent-based model is complex, but not unreasonably so, and makes good use of rich demographic, mobility, epidemiological/clinical, etc. data for calibration. The simulations conducted using the model support the specific conclusions of the manuscript.

Weaknesses:

While the motivation and approach are strong points of this work, the analysis and interpretation would benefit from further development. The robustness of model behaviour to the threshold used to trigger subnational interventions is explored; however, there are other aspects of the model that are not subjected to sensitivity analysis, including:

1) The impact of imperfect surveillance (eg, due to asymptomatic transmission, reporting delays, etc);

2) The impact of non-compliance, which could potentially differ for subnational versus national interventions;

3) The impact of pathogens/variants with transmission/severity characteristics different from the original SARS-CoV-2 strain.

In the absence of such analyses, it is difficult to generalise the findings beyond "this is how subnational interventions could have been used to control the first wave of SARS-CoV-2 in the Netherlands" to "this is how subnational interventions could be used effectively in the event of future outbreaks" (of a SARS-CoV-2 variant or other pathogen).

The discussion focuses on limitations associated with the model but does not consider other potential implications of subnational interventions. For example:

1) Subnational interventions may produce unintended consequences if populations respond by relocating from regions with interventions (high prevalence) to regions without interventions (low prevalence).

2) Subnational interventions would require extremely effective public health messaging to avoid confusing populations. Particularly in densely populated regions where municipalities may be small and tightly connected, the feasibility of communicating (and enforcing compliance with) interventions may be challenging.

3) A proposal to implement subnational interventions - following the results of this work - may raise ethical questions about cost-benefit trade-offs (eg, whether 355 additional hospital admissions is an acceptable price to pay for 36 million person-days without interventions; ie, two days per citizen, on average). The fact that such decisions would (in the even of a future outbreak) need to be made rapidly, in the face of potential uncertainty about pathogen characteristics, heightens the need for clear understanding of how situational factors may affect the likely effectiveness of interventions (at any scale).

Impact and broader utility:

As noted, the question addressed - how we can reduce the disruption caused by interventions for transmission control - is important. Thus, the work presented in this manuscript has the potential for broad utility. Currently, this is limited by the focus on specific outbreak instance.

In general terms, we agree with the reviewer. That said, the “possibility space” of policymaking is infinite dimensional, in the sense that the intervention measures can take an infinitely many forms, starting times and durations. The framework that we have built upon combining data sources such as demography, mobility, interactions and disease parameters now makes it possible to explore these possibilities. These will be explored in future work.

Author Response

Reviewer #1 (Public Review):

The data that is presented is quite clear, and expected given the prior in vitro work, as well as prior work in vivo with helminth infection and BCG vaccination. Overall, it is important to demonstrate that observations from in vitro experiments are relevant in vivo, however, there are concerns with the design of this study which limits its impact. In addition, the study confirms what is expected from prior work, but falls short of adding any new mechanistic insight.

We thank the Reviewer for evaluation of the manuscript and for the comments. Indeed, published studies have shown that helminth infection can impair the response to the BCG vaccine. However, this manuscript shows for the first time that IL-4 and helminth infection impair MINCLE expression in vivo. In addition, it is the first report demonstrating a negative effect of helminth infections on the antigen-specific Th1/Th17 response after vaccination with a MINCLE-dependent adjuvant.

Regarding mechanistic insight, we have employed mice deficient in IL-4/IL-13 to determine whether the thwarted Th1/Th17 response is caused by these Th2 cytokines in helminth-infected mice. New Figure 6 in the revised manuscript indeed demonstrates recovery of antigen-specific IFN and IL-17 production in the absence of IL-4/IL-13.

In terms of the in vivo experimental design, it is unclear why the authors chose to administer BCG IP, when the vaccine is given SC (and then based on more recent data, IV could be arguably interesting and relevant). The focus on the peritoneum limits the potential application of these findings to address the important question of the effects of helminth infection on BCG vaccine responses. The ultimate in vivo experiment to be able to demonstrate a physiological relevance of the mechanisms explored here would be to see what the effect was on Mtb infection in the lung.

BCG was injected i.p. to induce upregulation of MINCLE on peritoneal cells and to be able to ask whether IL-4 and/or helminth infection will lead to a down-regulation of MINCLE expression on myeloid cells in vivo. Thus, we were not interested in this context in the adaptive immune response to BCG. Instead, the peritoneal BCG injection provided access to myeloid cells exposed to Th2 immune condition in vivo for analysis of MINCLE protein levels on the surface. As stated in the Discussion section (lines 400-405 in the revised manuscript), detection of MINCLE by flow cytometry from tissue cells is complicated by the loss of cell surface protein during enzymatic organ digestion.

We agree that it would be interesting to study the impact of helminth infection on BCG-induced protection to Mtb challenge infection in the lung. As we have described here the impairment of Th1/Th17 immune responses after immunization with H1/CAF01 that induces protection (Werninghaus et al. 2009 J Exp Med), it would make most sense to perform such challenge infections first in this setting. However, Mtb infection requires a dedicated BSL3 animal facility, we therefore consider such challenge experiments beyond the scope of this manuscript

The authors do report different responses in the spleen and lymphnode, which is interesting, but lines 336-337 accurately point out that compartmentalized overexpression of IL-10 in the spleens but not the lymph nodes has been described in mice with chronic schistosomiasis. Mechanistic insight into this phenomenon was lacking, and the relevance to Mtb infection is still unknown.

We agree that the mechanism for the compartmentalized regulation of adaptive immune differentiation in helminth-infected mice is not clear.

Reviewer #2 (Public Review):

The manuscript entitled "IL-4 and helminth infection downregulate Mincle-dependent macrophage response to mycobacteria and Th17 adjuvanticity" by Schick et al. demonstrate the inhibitory activity of IL-4 and helminth infection on mycobacteria-mediated Th17 immunity. Overall, the authors reported interesting findings with solid data that advance our understanding of CLR function in fungal-bacterial co-infection.

We thank the Reviewer for the appreciation of our study.

Reviewer #3 (Public Review):

The authors first demonstrated in bone marrow-derived macrophages (BMMs) that IL-4 treatment of BMMs led to a significant reduction of BCG- and TDB-induced MINCLE expression (Fig. 1). While IL-4 treatment did not impact BCG phagocytosis by BMMs, it led to a reduced production of the cytokines G-CSF and TNF by BMMs (Fig. 2). In an elegant model using hydrodynamic injection of mini-circle DNA encoding IL-4, the authors show that IL-4 overexpression abrogated the increased MINCLE expression in monocytes upon BCG infection in vivo. Similar findings were observed in a co-infection model with the hookworm Nippostrongylus brasiliensis, where MINCLE expression on inflammatory monocytes from BCG-infected mice was reduced compared to control mice infected only with BCG (Fig. 3). The key findings of the manuscript include the two murine helminth infection models, S. mansoni as a chronic infection, and N. brasiliensis as a transient infection, in both of which the authors showed an organ-specific inhibition of the Th17 response in a vaccination setting with a MINCLE-dependent adjuvant (Fig. 4 and 5).

Data shown in the manuscript represents a major advance over previous studies because for the first time a relation between IL-4 and MINCLE expression and function is demonstrated in vivo in relevant co-infection models. All experiments have been done with care. Appropriate controls have been included and conclusions are largely supported by the data. Future studies in human patients will be needed to determine the clinical relevance of the findings observed in the murine helminth infection models.

We thank the Reviewer for the positive comments and agree that it will be interesting to study the impact of helminth infection on CLR expression and function in human infection and vaccination settings.

Author Response

Reviewer #1 (Public Review):

COVID-19 severity has been previously linked to a genetic region on chromosome 3 introgressed from Neandertals. The authors use several computational methods to, within this region, identify specific regions that putatively regulate gene expression, and to identify genes within these regions associated with COVID-19 severity. The use of several complementary computational approaches is a major strength of the paper as it bolsters confidence in the findings and narrows the search for significant genomic regions down to most likely candidates. They find 14 genes that exhibit expression regulated by the identified introgressed genomic regions. Among these are several chemokine receptors including two - CCR1 and CCR5 - whose upregulation is associated with severe COVID-19. The authors then use functional genomics to determine whether the identified regions do regulate gene expression.

We thank this Reviewer for highlighting these strengths.

In contrast to the robustness of the computational findings, the authors' MPRA results are less robust with respect to the significance of the paper to clinical severity of COVID-19. The MPRA shows that the computational methods were reasonably effective at identifying regulatory elements within the introgressed region (53%). The authors then focus on emVars where the H.n. allele differentially regulates expression and identify 4 putative emVars that may regulate expression of CCR1 and CCR5. However, the authors found in their MPRA that these emVars downregulate reporter gene expression, whereas the genes of interest CCR1 and CCR5 are upregulated during severe COVID.

This result highlights the principal weakness of using the MPRA in this context, as it assumes that reporter gene expression using a minimal promoter has identical regulatory determinants as expression of the gene of interest. Its strength is the high-throughput nature of the assay, but its weakness is the lack of specificity with respect to the question at hand. This lack of specificity mitigates the impact of the functional aspect of the work. The authors' computational findings certainly bolster previous work that H.n. introgressed alleles are associated with COVID-19 severity and that this association may be at least partly dependent on gene expression differences between the archaic and modern alleles. However, the specific question at hand, whether chemokine receptor expression is linked to the clinical phenotype, remains unaddressed.

Ultimately the authors results support the conclusions that the 4 emVars identified do regulate gene expression. However, the hypothesis that these specific regions are linked to COVID-19 severity is not supported. The authors' speculation as to why their results may differ from the observed upregulation during disease is intriguing, but lacks support.

We thank the Reviewer for providing these important points and we hope through our new experimental approach we helped to strengthen our findings. However, we also have modified the manuscript to also be more critical of our findings in the context of the issues Reviewer has brought up. This is shown in our updated Discussion, whose parts are provided above in the section addressed to the Editor, as well as in the newly revised manuscript.

Reviewer #2 (Public Review):

Previous research using GWAS and population genetics approach identified a genetic haplotype on chromosome 3 derived from Neanderthals as the major risk factor for severe COVID-19. However, the specific variants that are causative of the severe COVID-19 phenotype remain unknown. Here, Jagoda et al. aim to identify the causative variants for the severe COVID-19 by leveraging eQTL analysis followed by Massively parallel reporter assays (MPRA). Their datasets and results are unique and novel. Their research is well designed, and will serve as a model strategy for future studies of functional annotation of disease-associated variants.

We thanks Reviewer #2 for these compliments.

However, there are following critical weaknesses in this manuscript that reduce the impact of this work; (1) The quantitativity of the MPRA output is questionable because of their incomplete definition of MPRA activity, which is based on absolute barcode counts without comparing negative controls. (2) Molecular mechanisms (binding transcription factors, etc.) of causative variants that underlie the regulation of CCR1/5 expression and COVID19 severity are not analyzed and validated.

We hope that below we have addressed these comments through our analyses and new experiments.

Reviewer #3 (Public Review):

This manuscript by Jagoda et al. addresses the genetic mechanism of the haplotype at chromosome 3 where introgressed from Neanderthals shows the strong association with COVID-19 severity in Europeans. They re-evaluate the adoptively introgressed segment using Sprime and U and Q95 methods and analyze cis- and trans- eQTLs based on the whole blood dataset. All the 361 Sprime-identified introgressed variants act as eQTLs in the whole blood and alter the expression of 14 genes including seven chemokine receptor genes. Then they tested the 613 variants using a Massively Parallel Reporter Assay (MPRA) in K562 cells and narrow downed the 20 emVars. In the end, they selected the four variants based on four criteria regarding the association of COVID-19 severity, eQTL data, chromosomal interaction, and epigenetic marks in immune cells. They highlighted variant rs35454877 (CCR5 regulation), rs71327024, rs71327057, and rs34041956 (CCR1 regulation).

Narrowing down the four critical variants from the around 800 kb introgressed region is impressive work. However, MPRA and eQTL data are not consistent, and these data don't support clinical gene expression data (increased expression of CCR1 in severe COVID-19 patients).

We thank this Reviewer for noting our impressive work, we have now addressed these concerns.

Author Response

Reviewer #1 (Public Review):

This is an interesting and timely paper investigating the impact on participation in cancer screening programs across Italy during the COVID-19 pandemic where there was massive disruptions to health services. What is of particular interest in this analysis was the investigation of social, educational and cultural factors that might have impacted access and participation to screening.

• In the present study, the authors analyzed data collected by PASSI between 2017 and 2021, from interviews of more than 106,000 people, a representative sample of the Italian population aged 25-69 was selected but its not clear what was the representativeness by region, gender and age educational attainment? Also what is the total population (so I don't have to look it up). I am wondering if participation differed by characteristics and what approach to achieving the representative sample was made (e.g. replacement of individuals or oversampling certain strata where participation was lower).

PASSI is one of the two routinely collected Italian National Health Interviews. It has been described in several papers and there is a website reporting in detail methods, percentage of refusals, and numbers of interviews. Nevertheless, we agree with the reviewer that a brief summary of the methods is needed, and we added some details on data collection. Furthermore, details on the number of interviews according to the selected period, age, and sex strata cannot be found in the general description of the survey. Therefore, we gave more details also on the sample used for this study in supplementary table 1.

• For figures 5-8 what is the N for the different groups not just the %?

We agree with the reviewer that giving the actual numbers on which the percentages are computed is necessary. Nevertheless, as with any stratified sample, estimates from PASSI are computed using weights, therefore percentages cannot be computed directly from the observed numbers. We decided to add supplementary table 1, which reports the number of valid interviews on which percentages are estimated.

• Table 2 to me is a key piece of information and very interesting can the authors formally test if there are significant differences between the time periods?

Thank the reviewer for this suggestion. Firstly, we added a table in which we analyzed all the data together and we included the calendar period, categorized as before and after the pandemic, among covariates. Secondly, we checked if any of the differences between the prevalence ratios observed in the two periods were significantly different at a 0.05 alpha error threshold and we added a comment in the text: “Nevertheless, the differences could be due to random fluctuations”. We did not add p-values for the interaction of all the variables in each cancer screening because the table is already very complex, and three more columns would make it difficult to read.

Reviewer #3 (Public Review):

This study is primarily a descriptive analysis that provides a clear and accessible account of how screening activity varied across Italy and between groups. While primarily a simple descriptive account such work is important to document what were the impacts of the pandemic on preventative health services and to understand how they differed across groups. The combination of survey responses from regional screening programmes and individuals is a useful use of two data sources. The study is very clearly written and does not over-interpret the presented data.

The methods description states that the analysis presents the "standard months" required for the programmes to recover from the service delays. The subsequent reporting of these delays in the results section did not use the same terminology and I see scope for clarification by using common language regarding this assessment throughout the paper. I see scope for further disaggregation of the regional results within the study but equally I understand why the authors might not wish to report outcomes for specific regions. I see scope for improvement in the figures within the manuscript but this is a relatively presentational matter. I would like to see some further description of the Poisson regression analysis as what is included within the manuscript appears rather brief. There is also one section of the methods that seems as if it would better belong in the introduction, but overall the manuscript was very clearly structured.

We thank the reviewer for his encouraging comments. We checked all the manuscript and we tried to use always the same name for each concept.<br /> We expanded the method section giving more details on models and statistical analysis. We decided not to report data at the regional level but the variability within macro areas.

The analysis presented achieves the authors' stated aims in my view. I see a useful contribution in documenting the impact of the COVID-19 pandemic on screening in Italy. This may inform further work on assessing the eventual health impact of delays as well as work considering how best to make screening programmes more resilient to such shocks. Ultimately it will take time to observe just how significant the impacts of service interruptions were on cancer prevention. Readers should remember that many screening services may still provide good protection against cancer as long as the interruptions are limited to simply to delays in coverage rather than the longer-term loss of participation, especially for those with incomplete screening histories or of otherwise elevated risk of disease.

Further work may wish to consider how programmes prioritised capacity or what efforts have been made to restart screening. Similarly, there is scope for more detailed disaggregation assessment of who received screening as programs restarted. Both these issues are beyond the scope of the present study however. The present submission provides a good basis for any further such exploration.

We thank the reviewer for these comments. We tried to capture some of the concepts in our discussion.

Author Response

eLife assessment

The purpose of this study was to determine whether heme oxygenase -2 deficiency translates to deficiencies in motor neuron function. This paper plays a plausible mechanism by which heme oxygenase-2 deficiency can lead to obstructive apneas. Indeed, this is among the first papers to comprehensively describe a signaling pathway in motor neurons and the consequences of its deficiency. Furthermore, the work completed here may be relevant to other diseases in which motor neuron signal transmission is a key contributor.

We thank for their assessment and constructive comments. Based on their input below we performed additional analyses focused on the impact of HO-2 dysregulation on the rhythmogenesis from the preBötC.

Author Response

Reviewer #1 (Public Review):

The manuscript discussed the combination use of pyrotinib, tamoxifen, and dalpiciclib against HER2+/HR+ breast cancer cells. Through a series of in vitro drug sensitivity studies and in vivo drug susceptibility studies, the authors revealed that pyrotinib combined with dalpiciclib exhibits better therapeutic efficacy than the combination use of pyrotinib with tamoxifen. Moreover, the authors found that CALML5 may serve as a biomarker in the treatment of HER2+/HR+ breast cancer.

The authors provide solid evidence for the following:

1) The combination use of pyrotinib with dalpiciclib exhibits better therapeutic efficacy than the combination use of pyrotinib with tamoxifen.

2) Nuclear ER distribution is increased upon anti-HER2 therapy and could be partially abrogated by the treatment of dalpiciclib.

3) CALML5 may serve as a putative risk biomarker in the treatment of HER2+/HR+ breast cancer.

The manuscript has significant strengths and several weaknesses. The strengths include the identification of the novel role of dalpiciclib in the treatment of HER2+/HR+ breast cancer. Moreover, the authors provide solid evidence that the combined use of dalpiciclib with pyrotinib significantly decreased the total and nuclear expression of ER. The main weakness of the manuscript is that the manuscript is difficult to read due to language inconsistency. In addition, some figure captions and figure legends should be carefully amended.

Thanks for your comments on our manuscript. We feel sincerely sorry for the inconsistency of the manuscript due to poor language. We have improved our manuscript as well as the figures according to your valuable suggestions.

Reviewer #2 (Public Review):

The authors performed preclinical studies to investigate the underlying mechanism of how the combination of pyrotinib, letrozole and dalpiciclib achieved satisfactory clinical outcomes in the MUKDEN 01 clinical trial (NCT04486911). Mechanistically, using anti-HER2 drugs such as pyrotinib and trastuzumab could degrade HER2 and facilitate the nuclear transportation of ER in HER2+HR+ breast cancer, which enhanced the function of ER signaling pathway. The introduction of dalpiciclib partially abrogated the nuclear transportation of ER and exerted its canonical function as cell cycle blockers, which led to the optimal cytotoxicity effect in treating HER2+HR+ breast cancer. Furthermore, using mRNA-seq analysis and in vivo drug susceptibility test, the authors succeeded in identifying CALML5 as a novel risk factor in the treatment of HER2+HR+ breast cancer.

Thanks for your comments and valuable suggestions, we’ve improved our manuscript according to your suggestions.

Reviewer #3 (Public Review):

In this research, the authors explore a novel mechanism of CDK4/6 inhibitor dalpiciclib in HER2+HR+ breast cancers, in which dalpiciclib could reverse the process of ER intra-nuclear transportation upon HER2 degradation. The conclusions are significant to gain insight into the biological behavior of TPBC and provided a conceptual basis for the ideal efficacy in the published clinical trial. The findings are supported by supplemented in vivo assay and transcriptomic analysis.

Thanks for your comments and valuable suggestions to us so that we could improve this manuscript.

Author Response

Reviewer #2 (Public Review):

The majority of genetic effects discovered in genome-wide association studies (GWAS) of common human diseases point to non-coding variants with putative gene regulatory effects. In principle, studying genetic effects on gene expression phenotypes, as mediators between genotype and disease, can help understand the underlying function of GWAS variants.

Lafferty et al., set to study the regulation of microRNA (miRNA) levels in mid-gestation human neocortical tissues as a potential contributor to brain-related phenotypes. To this end they performed miRNA expression profiling via small-RNA sequencing, followed by assaying expression quantitative trait loci (eQTLs) that locally regulate miRNA genes.

In addition to reporting some properties of miRNA-eQTLs, e.g., their tissue-specificity, the authors searched for potential overlap or "colocalization" between these eQTL loci and GWAS loci for several putatively brain-related phenotypes. They reported colocalization at the locus containing the SNP rs4981455 which is an eQTL for miR-4707-3p and is also associated with global cortical surface area (GSA) and educational attainment phenotypes in GWAS. They further showed that exogenously increased expression of miR-4707-3p in primary human neural progenitor cells (as a model to study neurogenesis) derives an increased rate of proliferation.

The reported results are interesting and important, particularly for the understanding of miRNA biology. That said, as I detail below, the claim that miR-4707-3p expression modulates brain size and thus cognitive ability, although potentially consistent with the data, is not unequivocally supported by the analyses. As such, considering the potential social impact of the misinterpretations of these results, I believe the authors should explicitly discuss caveats, alternative explanations consistent with the data, and broader implications:

We thank the reviewer for their positive evaluation of our work and detailed comments. We agree that misinterpretation of our results could have negative social impacts, and now have added caveats and alternative explanations to our discussion section.

1) The colocalization analysis used effectively tests whether miRNA-eQTL and GWAS variants are in linkage disequilibrium (LD), and does not formally test whether the miRNA-eQTL and GWAS signals are explained by the same genetic variant which is necessary for establishing causality. In this study, a formal test of colocalization is challenging given that the LD patterns in the eQTL data (from mixed ancestries) are dissimilar to the GWAS data (from European-descent samples). Furthermore, even if GWAS and miRNA-eQTL signals are explained by the same variant, this could be due to confounding (a confounder affecting both), or pleiotropy (genotype independently affecting both), and not necessarily that the miRNA-eQTL signal mediates the GWAS signal. These are also true for colocalization analyses of miRNA-eQTLs with mRNA-eQTLs or splicing-QTLs. One practical suggestion is whether authors can perform the colocalization analysis better, e.g., with methods such as SMR (https://yanglab.westlake.edu.cn/software/smr/#Overview).

As the reviewer mentioned, testing colocalized genetic signals using the eQTL dataset presented in this study remains challenging given the mixed-ancestry of the samples. We believe our primary test for colocalization, conditioning the miRNA-eQTL association using a secondary signal index variant, is sufficient evidence for a shared genetic signal (Nica et al., 2010). This is particularly true when looking for colocalizations between the miRNA-eQTLs and mRNA-e/sQTLs because both datasets used largely the same samples for expression quantification. However, the colocalization between the miRNA-eQTL for miR-4707-3p expression and the GWAS signal for educational attainment warrants greater scrutiny because the GWAS signal was discovered in European-descent samples.

To address this concern, we have conducted an additional colocalization test using the SMR and HEIDI methods as suggested by the reviewer (Zhu et al., 2016). We have updated the results section, “Colocalization of miR-4707-3p miRNA-eQTL with brain size and cognitive ability GWAS”:

"In addition to the HAUS4 mRNA-eQTL colocalization, the miRNA-eQTL for miR-4707-3p expression is also co-localized with a locus associated with educational attainment (Figure 5A)(2). Conditioning the miR-4707-3p associations with the educational attainment index SNP at this locus (rs1043209) shows a decrease in association significance, which is a hallmark of colocalized genetic signals (Figure 5-figure supplement 2A)(58,59). Additionally, the significance of the variants at this locus associated with miR-4707-3p expression are correlated to the significance for their association with educational attainment (Pearson correlation=0.898, p=5.1x10-7, Figure 5-figure supplement 2B). To further test this colocalization, we ran Summary-data-based Mendelian Randomization (SMR) at this locus which found a single causal variant to be associated with both miR-4707-3p expression and educational attainment (p=7.26x10-7)(60). Finally, the heterogeneity in dependent instruments test (HEIDI), as implemented in the SMR package to test for two causal variants linked by LD, failed to reject the null hypothesis that there is a single causal variant affecting both gene expression and educational attainment when using the mixed-ancestry samples in this study as the reference population (p=0.159). The HEIDI test yielded similar results when estimating LD with 1000 Genomes European samples (p=0.120). All this evidence points to a robust colocalization between variants associated with both miR-4707-3p expression and educational attainment despite the different populations from which each study discovered the genetic associations."

To strengthen the argument for colocalization, we added Figure 5-figure supplement 2.

Given the unique problem of colocalizing genetic signals from datasets with different LD patterns, we also attempted to colocalize the miRNA-eQTL for miR-4707-3p and educational attainment GWAS using eCAVIAR and coloc (Hormozdiari et al., 2016; Wang et al., 2020). Neither of these methods produced a significant colocalization between these two genetic signals at this locus. However, neither of these methods were designed or tested using mix-ancestry reference populations, and therefore we are still confident in declaring a shared genetic signal at this locus.

2) Although possible, there is no direct evidence that the GWAS signals at rs4981455 for educational attainment and GSA are driven by variation in miRNA levels in the studied tissue. As the authors noted, rs4981455 is also an eQTL for the gene HAUS4. Furthermore, rs4981455 is a significant e/sQTL across almost all adult tissues in GTEx, and so likely has regulatory activity across wide ranges of cell or tissue types. Therefore, pinpointing the causal contexts mediating the effect in GWAS is impossible with the current data.

We agree that fully understanding the causal relationship, or mechanism, between rs4981455 and educational attainment is impossible with the current data. However, we believe the miRNA-eQTL at rs4981455, discovered in developing brain tissue, provides clues as to the causal context of this locus on educational attainment. We have updated the language throughout the manuscript to temper the notion that expression differences in miR-4707-3p is causal for changes in educational attainment (discussed below), yet we maintain that the evidence provided is consistent with miR-4707-3p playing a role in brain development ultimately leading to changes in adult educational attainment. The updated hypothesized causal relationship is shown in Figure 6H and expanded discussion on the caveats of this study, addressed in the next section, also serve to mitigate this concern.

3) Orthogonal to the issues above, the genotype-to-phenotype pathway as hypothesized, i.e., genotype → miRNA levels → brain structure → educational attainment, is oversimplistic and rests on an implicit prior belief that genetic associations with educational attainment can be trivially mapped to fundamental brain features that determine cognitive ability. To illustrate the problem with this prior I refer to an old example by Christopher Jencks: in a society that prevents red-hair kids to go to school, genetic effects on hair color would be associated with educational attainment, despite having no intrinsic biological relationship with cognition. I give two scenarios consistent with the specific case of rs4981455 that are fundamentally different from what is implied in the paper: (i) The case of indirect genetic effects (see Kong et al., Science 2018). In this case, rs4981455 affects the nurturing behavior of an individual's parents, which in turn influences the individual's educational achievements and consequently brain structure features. (ii) The case of confounding. In this case, the genetic effects on brain structure are shared with another feature, such as facial shape (see Naqvi et al., Nature Genetics 2021). Variation in facial shape in a discriminatory educational environment can covary with educational attainment.

The causal pathway presented in the original version of this manuscript was indeed too simplistic and inferred a causal pathway between rs4981455 and educational attainment that was not fully backed by our data nor could be fully proved experimentally. The point we had hoped to make, and which is better represented by the updated version of Figure 6H, is that if there is a causal relationship between rs4981455 and educational attainment mediated by miR-4707-3p expression, we may be able to detect the influence of miR-4707-3p on a cellular phenotype that would explain the association of rs4981455 with cortical surface area, intracranial volume, and head size.

An updated discussion summarizes how we were not able to find evidence for a molecular mechanism consistent with the radial unit hypothesis, but that a biological link between the miRNA-eQTL and GWAS phenotypes may yet be uncovered:

"We did find one colocalization between a miRNA-eQTL for miR-4707-3p expression and GWAS signals for brain size phenotypes and educational attainment. This revealed a possible molecular mechanism by which genetic variation causing expression differences in this miRNA during fetal cortical development may influence adult brain size and cognition (Figure 6H). Experimental overexpression of miR-4707-3p in proliferating phNPCs showed an increase in both proliferative and neurogenic gene markers with an overall increase in proliferation rate. At two weeks in differentiating phNPCs, we observed an overall increase in the number of cells upon miR-4707-3p overexpression, but we did not detect a difference in the number of neurons at this time point. Based on the radial unit hypothesis (26,73), we expected to find an overall decrease in proliferation or increase in neurogenesis upon miR-4707-3p overexpression which would explain decreased cortical surface area. However, our in vitro observations with phNPCs do not point to a mechanism consistent with the radial unit hypothesis by which increased miR-4707-3p expression during cortical development leads to decreased brain size. This has also been seen in similar studies using stem cells to model brain size differences linked with genetic variation (74). Nevertheless, the transcriptomic differences associated with overexpression of miR-4707-3p in differentiating phNPCs suggest this miRNA may influence synaptogenesis or neuronal maturation, but these phenotypes may be better interrogated at later differentiation time points, by jointly expressing HAUS4 and mir-4707, or with assays to directly measure neuronal migration, maturation, or synaptic activity."

We believe the two cases addressed by the reviewer of indirect genetic effects and confounding which may actually explain the association between rs4981455 and educational attainment are less likely to be influencing the miRNA expression of miR-4707-3p measured in developing cortical tissue. This is combined with a discussion on the caveats of our findings and is addressed in the next section.

4) The paper lacks a discussion on caveats to protect against potential misinterpretation of findings, especially considering the troubled history of linking facial shape and head morphology to human behavior and intelligence. I refer to the last paragraph of Naqvi et al., Nature Genetics 2021, as an example of such discussion. This is particularly crucial given that the frequency of rs4981455 varies across human populations. For example, it is important to state that the GSA and education attainment GWAS findings are in individuals of European descent, and may not necessarily point to an effect in other ancestries or even in European-descent individuals that differ from the GWAS samples in various ways, e.g., socioeconomic status (see Mostafavi et al., eLife 2020). In other words, these findings pertain to variation within the studied samples. On this note, it is important to state the amount of variation in multiple phenotypes explained by rs4981455 (which is likely tiny), and that it by no means determines the phenotype.

We have added a paragraph to the discussion highlighting the caveats of our analysis and protecting from overinterpretation of our findings:

"Here we have proposed a biological mechanism linking genetic variation to inter-individual differences in educational attainment. Given the important societal implications education plays on health, mortality, and social stratification, a proposed causal mechanism between genes and education warrants greater scrutiny (75,76). Any given locus associated with educational attainment may be driven by a direct effect on brain development, structure, and function, an indirect genetic effect such as parental nurturing behavior, or confounding caused by discriminatory practices or societal biases (77,78). Given that expression was measured in prenatal cortical tissue, where confounding societal biases are less likely to drive genetic associations and that experimental overexpression of miR-4707 affected molecular and cellular processes in human neural progenitors, the evidence at this locus is consistent with a direct effect of genetic variation on brain development, structure, and function rather than being driven by confounding or indirect effects. However, there are some important caveats to this statement. First, our study only provides evidence for the direct effect on the brain at this one educational attainment locus. Our study does not provide evidence for the direct brain effects of any other locus identified in the educational attainment GWAS. Second, common variation at this locus explains a mere 0.00802% of the variation in educational attainment in a population, so this locus is clearly not predictive or the sole determinant of this phenotype. Third, the GWAS for educational attainment and brain structure were conducted in populations of European ancestry, and allele frequency differences at these loci cannot be used to predict differences in educational attainment or brain size across populations. Finally, though both experimental and association evidence suggests a causal link between this locus and educational attainment mediated through brain development, we are unable to directly test the influence of miR-4707-3p expression during fetal cortical development on adult brain structure and function phenotypes. Therefore, we cannot rule out the possibility that the causal mechanism between rs4981455 and adult cognition may be a result of genetic pleiotropy rather than mediation at this locus. Despite these caveats, identifying the mechanisms leading from genetic variation to inter-individual differences in educational attainment will likely be useful for understanding the basis of psychiatric disorders because educational attainment is genetically correlated with many psychiatric disorders and brain-related traits (2,79)."

We hope that this paragraph contextualizes our results sufficiently to emphasize the high bar that must be surpassed to propose a biological link between a miRNA-eQTL and a risk loci for brain related traits while maintaining that we can not completely rule out the possibility of genetic pleiotropy.

5) The main colocalization signal is tentatively shown for GSA. However, the authors casually refer to links with "brain size" or "head size" throughout the paper.

In addition to the locus showing a sub-genome wide significant association to global cortical surface area (GSA) presented in Figure 5, a GWAS for head size (Knol et al., 2020) and a GWAS for intracranial volume (Nawaz et al., 2022) (recently published since submitting the original manuscript) both show genomic associations at this locus for miR-4707-3p expression. The index variants for both traits colocalize with the miRNA-eQTL for miR-4707-3p and their effect directions match: alleles increasing expression of miR-4707-3p show association to decreased head size and decreased intracranial volume. For both of these studies, the summary data is not yet publicly available, preventing us from constructing plots at this locus (similar to those shown in Figure 5) or conducting additional colocalization analyses. To be more consistent throughout the paper, we have replaced many “head size” references with “brain size” when talking about this locus.

Author Response

Reviewer #2 (Public Review):

Weaknesses

The author's approach, as with traditional approaches to molecular identification of vector species, relies on expert entomologists capable of identifying mosquitoes in the field which is rare in most places. The authors do not provide citations for the taxonomic keys used for morphological identification, which in many places are outdated or unavailable for specific locations.

We have added references for taxonomic identification keys in lines 677–679.

The authors give no explanation as to why they chose rRNA-seq as their method of next-generation sequencing, which is most commonly used for transcriptomics, instead of traditional DNA-based metagenomics which is more commonly used to define community relationships as would be more appropriate for this study.

We have added a sentence in the Introduction (lines 65–66) to explain why RNA-seq is a frequent choice for surveillance and virus discovery in mosquitoes.

Author Response

Reviewer #1 (Public Review):

This paper shows that nuclear pore complex components are required for Kras/p53 driven liver tumors in zebrafish. The authors previously found that nonsense mutation in ahctf1 disrupted nuclear pore formation and caused cell death in highly proliferative cells in vivo. In the absence of this gene, there are multiple mitotic functions involving the nuclear pore that are defective, leading to p53 dependent cell death. Heterozygous fish are viable but have reduced kras/p53 liver tumor growth, and this is associated with multiple nuclear and mitotic defects that lead to cancer cell death/lack of growth. This therapeutic window suggests targetability of this pathway in cancer. I think the data are robust, rigorous, and clearly presented. I believe this in vivo work will encourage therapeutic targeting of NPCs in cancer.

We are pleased that this reviewer believes that our data are robust, rigorous, and clearly presented and that our in vivo work will encourage therapeutic targeting of NPCs in cancer.

Reviewer #2 (Public Review):

Overall this is a very interesting and important paper that demonstrates a novel synthetic interaction between nucleoporin inhibition and oncogene-driven hyperproliferation. This work is especially significant because of the paucity of effective treatments for hepatocellular carcinoma (HCC). The authors' demonstration that the Nup inhibitor Selinexor decreases larval liver size in KRAS-overexpressing zebrafish but does not cause toxicity in wild-type animals lays the groundwork for exploiting this class of drugs in HCC treatment. This paper represents an elegant demonstration of the utility of zebrafish models in cancer studies. The relevance of this work to human cancer is supported by the authors' studies using TCGA data, wherein they demonstrate that decreased NUP expression is associated with increased survival in HCC.

Other major strengths of the paper include beautiful pictures demonstrating that ahctf1+/- decreases the density and volume of nuclear pores in TO(kras) larvae and increases the rate of multipolar spindle formation, misaligned chromosomes, and anaphase bridges. The experiments are very well-controlled, including detailed analysis of the effects of ahctf1 heterozygosity and Selinexor on wild-type animals. The inclusion of distinct methods for disruption nucleoporins (ranbp2 heterozygosity and drug treatment) bolsters the authors' conclusion that this represents a viable drug target in HCC.

My major concerns are as follows:

1) The authors state that "the beneficial effect of ahctf1 heterozygosity to reduce tumour burden persists in the absence of functional Tp53, due to compensatory increases in the levels of tp63 and tp73". However, tp63 and tp73 appear similarly upregulated in ahctf1 heterozygotes regardless of tp53 status. The authors do not provide enough evidence that tp63 and tp73 are compensating for tp53 loss. An alternative possibility based on the data presented is that the effects of ahctf1+/- are independent of tp53 family members, and the effects on apoptosis go through a different pathway.

We agree with this reviewer that we did not provide enough evidence that tp63 and tp73 are compensating for tp53 loss. Accordingly, we have addressed this issue comprehensively.

2) The authors state in multiple locations that nucleoporin inhibition decreases tumor burden. In my opinion, this is not strictly correct. The TO(kras) model clearly results in HCC in adults, but it's a little unclear whether the larval liver overgrowth is truly HCC or not based on the original paper by Nguyen et al. (2012 Dis Model Mech).

We agree with these comments and accordingly, we performed several new experiments in adult fish.

Reviewer #3 (Public Review):

The nuclear transport machinery is aberrantly regulated in many cancers in a context-dependent fashion, and mounting evidence with cultured cell and animal models indicates that reducing the activity or expression of certain nuclear transport proteins can selectively kill cancer cells while sparing nontransformed cells. Here the authors further explore this concept using a zebrafish model for hepatocellular carcinoma (HCC) induced by liver-specific transgenic expression of oncogenic krasG12V. The transgene causes greatly increased liver size by day 7 in larvae, associated with a gene expression profile that resembles early-stage human HCC. This study focuses on Ahctf1, a nuclear pore complex (NPC) protein known to be essential for postmitotic NPC assembly. Using the krasG12V background, the authors analyze animals that are heterozygous for a recessive mutation in the ahctf1 gene that leads to ~50% reduction in ahctf1 mRNA (and likely the encoded protein). The authors show that the ~4-fold increase in liver volume of krasG12V animals is reduced by ~1/3 in the ahctf1 heterozygous mutants. This is associated with increased apoptosis, decreased DNA replication, up-regulation of pro-apoptotic and cdk-inhibitor genes, and down-regulation of anti-apoptotic gene. These effects found to be substantially Tp53-dependent. Consistent with previous Ahctf1 depletion studies, hepatocytes of ahctf1 heterozygotes show decreased NPC density at the nuclear surface, elevated levels of aberrant mitoses and increased DNA damage/double stranded breaks. Finally, the authors show that combining the achtf1 heterozygous mutant with a heterozygous mutation in another NPC protein- RanBP2- or treating animals with a chemical inhibitor of exportin-1 (Selinexor) can further reduce liver volume. Overall they suggest that combinatorial targeting of the nuclear transport machinery can provide a therapeutic approach for targeting HCC.

This is an interesting study that bolsters the notion that reduction in the levels of discrete nucleoporins (and/or inhibiting specific nuclear transport pathways) can result in cancer cell-selective killing. Moreover, the work extends previous studies involving cultured cell and mouse xenografts to a new cancer model (HCC) and nucleoporin (Ahctf1). Whereas the authors describe multiple aberrant cellular phenotypes associated with the dosage reduction in ahctf1, the exact causes for reduction in liver size in the krasG12V model remain unclear. Although it would be desirable to parse effects of Ahctf1 related to NPC number, aberrant mitoses, licensing of DNA replication and chromatin regulation, this is a tall order at present, given the limited understanding of Ahctf1. However, useful insight on these and related questions could be gained with further analysis of the system as outlined below.

We are pleased this reviewer thinks this is an interesting study that bolsters the notion that reduction in the levels of discrete nucleoporins (and/or inhibiting specific nuclear transport pathways) can result in cancer cell-selective killing. This reviewer also suggests that useful insight on these and related questions could be gained with further analysis of the system as outlined below:

1) In the krasG12V model, it would be helpful to distinguish the contribution of increased cell death vs decreased cell proliferation to the change in liver size seen with heterozygous ahctf1. Is this predominantly due to decreased proliferation?

We think this question is difficult to address, because the relative contributions of the two processes may vary with time. Our data show definitively that by 7 dpf, the impact of ahctf1 heterozygous mutation has disrupted multiple cellular processes, leading to a 40% increase in the number of hepatocytes expressing Annexin 5 (dying cells), and a 40% decrease in the number of hepatocytes incorporating EdU over a 2 h incubation (fewer cells in S-phase). Both responses are likely to contribute to the reduction in liver volume observed in response to ahctf1 heterozygosity. It is worth stating that in our experiments, we captured snapshots of apoptosis and DNA replication in the livers of larvae at 7 days post-fertilisation after 5d of dox treatment/KrasG12V expression. To answer the Reviewer’s question properly, we would need to monitor the behaviour of individual cells over time. If such experiments were technically possible, we think that some cells that undergo growth arrest in response to dox treatment might ultimately succumb to apoptosis (unless dox treatment is withdrawn) while other cells might enter into a state of prolonged senescence. However, given the technical challenges, we did not attempt to test this in the current manuscript.

2) It would be good to know whether the heterozygous ahctf1 state blunts the overall level of Ras activity in krasG12V animals.

We have addressed this interesting question thoroughly in new Fig. 1g, h. To do this, we used a commercial RAS-RBD pulldown kit followed by western blot analysis to determine the levels of activated GTP-bound Kras protein. Our results demonstrate that the levels of GTP-bound Kras protein, expressed as a proportion of total Kras protein, do not change in response to ahctf1 heterozygosity. We conclude from these data that the potentially therapeutic value of reduced ahctf1 expression in a cancer setting is not caused by inhibiting Kras activity.

3) Notwithstanding the analysis of Tp53 target genes presented in this study, it would be helpful to see detailed transcriptional profiling of hepatocytes in the krasG12V model with the heterozygous ahctf1 mutation, and to assess the effects of Selinexor. GSEA type analysis offers a way to start untangling the effects of these pathways. Moreover this analysis could provide insight on the relevance of this model to human HCC.

We used RNAseq to address the relevance of our larval model to human HCC. Specifically, we performed differential gene expression analysis to identify up- and downregulated genes in cohorts of ahctf1+/+ (WT) larvae versus dox-treated ahctf1+/+(WT);krasG12V larvae. We used gene set enrichment analysis to compare these differentially regulated transcripts with the gene expression signature of 369 patient samples in the Liver hepatocellular carcinoma (LIHC) dataset versus healthy liver samples in the TCGA. These analyses revealed a significant association between the patterns of gene expression in our larval model of zebrafish HCC and those of human HCC (Fig. 1-figure supplement 1c, d).

The genetic experiments we report in Figures 4, 5, 6 show that WT Tp53 is required for the reductions in liver enlargement (Fig. 4), apoptosis (Fig. 5) and DNA replication (Fig. 6) that occurs in response to ahctf1 heterozygosity in dox-treated krasG12V larvae. We also used RT-qPCR to show that a Tp53-mediated transcriptional program was activated in these ahctf1 heterozygous livers (Fig. 5). Similarly, in adult livers, ahctf1 heterozygosity triggered the upregulation of Tp53 target genes, including pro-apoptotic genes (pmaip1, bbc3, bim and bax) and cell cycle arrest genes (cdkn1a and ccng1) (new Fig. 6-figure supplement 1). These results show that to obtain the full potential of ahctf1 heterozygosity in reducing growth and survival of KrasG12V-expressing hyperplastic hepatocytes requires activation of WT Tp53. This is an important conclusion from our paper that is likely to be relevant in a clinical setting, for instance in patient selection, if ELYS inhibitors are developed for the treatment of HCC in which the KRAS/MAPK pathway is activated.

Also, one reviewer mentions performing genome-wide transcriptional profiling of hepatocytes in the krasG12V model in response to ahctf1 heterozygosity and the presence and absence of Selinexor treatment. While these are potentially interesting experiments, they are substantial in nature and not crucial for the main messages of our paper. Therefore, we respectively contend that they are beyond the scope of the current manuscript.

4) Functions of Achtf1 in regard to chromatin regulation could be compromised in this model. Scholz et al (Nat Gen 2019) report that Ahctf1 is involved in increasing Myc expression via gene gating mechanism. It would be good to know what the effects are in this system.

The Scholz, 2019 and Gondor, 2022 papers from the same group, are very interesting in that they demonstrate a novel role for the ELYS protein in addition to the ones we pursued in our paper. The authors showed that in HCT116 cells, a human colorectal cancer cell line in which proliferation is driven by aberrant WNT/CTNNB1 signalling, the longevity of nascent MYC mRNA was increased by accelerating its movement from the nucleus to the cytoplasm, thereby preventing its degradation by nuclear surveillance mechanisms. The authors showed that siRNA knockdown of AHCTF1 in HCT-116 cells reduced the rate of nuclear export of MYC transcripts without changing the transcriptional rate of the MYC gene. They proposed a mechanism that depended on the formation of a complex chromatin architecture comprising transcriptionally active MYC and CCAT1 alleles plus proteins including β-Catenin, CTCF and ELYS. Together these interacting components guided nascent MYC mRNA molecules to nuclear pores, enhanced their export to the cytoplasm to be translated, resulting in activation of a MYC transcriptional program that induced expression of pro-proliferation genes.

In theory, this role of ELYS in protecting MYC from nuclear degradation might extrapolate to other cancer settings where MYC expression is elevated. While interplay between MYC and mutant KRAS to enhance cancer growth has been previously reported, to date, most emphasis on this interaction has focused on the role of mutant KRAS in increasing the stability of the MYC protein, for example via RAS effector protein kinases (ERK1/2 and ERK5) that stabilise MYC by phosphorylation at S62 (Farrell and Sears, 2014: https://doi.org/10.1101/cshperspect.a014365) (Vaseva and Blake 2018: DOI:https://doi.org/10.1016/j.ccell.2018.10.001). While we appreciate the novelty of the recent papers, the current findings are limited to -Catenin activated HCT-116 cells and may not be relevant to our zebrafish model of mutant Kras-driven HCC. Accordingly, we have not allocated a high priority to following this up in our current manuscript.

6) The synthetic lethality argument pressed in this manuscript seems exaggerated. Standard anti-cancer treatments typically target several cellular pathways, and nucleoporins directly affect a multiplicity of pathways besides nuclear transport.

While we do not disagree that standard anti-cancer treatments may target several cellular pathways, we believe our data are consistent with the accepted definition of a synthetic lethal interaction whereby single mutations in two separate genes (kras and ahctf1) cooperate to cause cell death, whereas cells harbouring just one of these mutations are spared.

Author Response

Reviewer #1 (Public Review):

1) Context and definitions for stochasticity and heritability: The authors provide well-referenced introductions and explanations throughout the manuscript. However, key understanding of concepts for their central hypothesis on transient heritability are not shared until well into the results sections (Lines 215-227), leaving the introduction somewhat unclear on the authors thinking and motivation. The manuscript would benefit by including clear definitions of "stochastic", "transiently heritable", and "heritable" and their relationships to "intrinsic" and "deterministic" in the introduction.

Regarding the first point, we agree it is important to include clear definitions timely. Therefore, we added much more detail to the introduction (see tracked changes), and added the following definitions and additional explanations:

Multilayered stochasticity: “stochasticity originating from different levels over the course of an infection.“

“Importantly, although the terms stochasticity and determinism seem highly dichotomous, deterministic features (e.g., epigenetic regulation) are often, if not always, stochastically regulated (Zernicka-Goetz and Huang, 2010). However, in cellular decision-making, the major difference between a stochastic process and a deterministic process boils down to the effects of (varying) inputs on dictating (varying) outputs. In fact, a stochastic process in characterized by the exact same stimulus leading to varying response outcomes, often as a result of varying host-intrinsic factors (Symmons and Raj, 2016). In contrast, a deterministic process is characterized by an outcome (e.g., IFN-I production) that is fixed, or at least to a large degree, while the input can be variable. How cells are epigenetically predispositioned, in turn, can again be a stochastic process, similar to the fundamentals of developmental biology in which cells are randomly pushed towards deterministic outcomes (Zernicka-Goetz and Huang, 2010).”

“Transient heritability refers to heritable epigenetic profiles [e.g., profiles encoding cellular fates for the production IFN-Is] that only transfer over a couple of generations, as observed across cell types and systems including cancer drug resistance (Shaffer et al., 2020), cancer fitness (Fennell et al., 2022; Oren et al., 2021), NK cell memory (Rückert et al., 2022), HIV reactivation in T cells (Lu et al., 2021), epithelial immunity (Clark et al., 2021), and trained immunity (Katzmarski et al., 2021).”

“Besides a growing body of evidence on the role of transient heritable fates dictating cellular behaviors, the effects of population density, often referred to as quorum sensing, are getting more established for immune (signaling) systems (Antonioli et al., 2019; Polonsky et al., 2018; Van Eyndhoven and Tel, 2022). On top of the intrinsic features characterized by stochasticity and determinism, individual immune cells can communicate in various ways to elicit appropriate systemic immune responses. Typically, cytokine-mediated communication is categorized into two types: autocrine and paracrine signaling. Autocrine signaling is defined by cells secreting signaling molecules while simultaneously expressing the cognate receptor. Paracrine signaling is defined by cells either secreting signaling molecules without expressing the cognate receptor, or cells expressing the receptor without secreting the molecule. In essence, quorum sensing can be considered a phenomenon in which autocrine cells determine their population density based on cells engaging in neighbor communication, but without self-communication (Doğaner et al., 2016; Van Eyndhoven and Tel, 2022). Especially in the presence of other competitive decision makers [i.e., cytokine consumers and producers], it is critical for individual cells to assess cellular density, and act accordingly (Oyler-Yaniv et al., 2017).”

2) Generalizability of findings to other cell types, systems, and triggers: The cell line and Poly(I:C) delivery method used by the authors lacks sufficient characterization to extend the conclusions derived from its use. Notably, the NIH3T3-IRF7-CFP cell line expresses IRF7 constitutively and thus may only be a good model for cells with similar expression levels; many primary cells only express IRF7 at low levels or not at all until stimulated (PMID: 2140621). The conclusions would be greatly strengthened by demonstrating similar first responder dynamics/heritability in other cell types. The experiments measuring the efficiency of Poly(I:C) delivery by transfection lack sufficient resolution to determine if the Poly(I:C) is intracellular or membrane bound. IFN-I response kinetics, and potentially quality, would likely be distinct between cytosolic and endosomal sensing and may impact the likelihood of becoming a first responder.

Regarding the generalizability of findings to other cell types, systems, and triggers, we thank reviewer 1 for binging up this crucial point. About the IRF7 expression, IRF7 is expressed at a low amount in most cells and is strongly induced by type I IFN-mediated signaling (Marie et al., 1998; Sato et al., 1998b; Honda et al., 2006). How we used the word “constitutively” refers to the IRF7 molecules always being fluorescent, not that IRF7 is always highly expressed in these cells. Therefore, NIH3T3 is similar to all other cells, except for plasmacytoid dendritic cells, which are known for their high background levels of IRF7. We changed the revised manuscript accordingly:

“Accordingly, we used a NIH3T3:IRF7-CFP reporter cell line, expressing low, physiological background levels of IRF7-CFP fusion proteins, to monitor signaling dynamics during early phase IFN-I response dynamics (Figure 1b).”

Regarding the comparison with other cell types, we emphasized the similar responders numbers observed in plasmacytoid dendritic cells (an argument that the intrinsic factor of IRF7 background differences is not determining responders). We changed the revised manuscript accordingly:

“In short, IFN-I responses are elicited by fractions of so-called first responding cells, also referred to as ‘precocious cells’ or ‘early responding cells’, which start the initial IFN-I production upon viral detection, both validated in vitro, in vivo, and across cell types (Bauer et al., 2016; Hjorton et al., 2020; Patil et al., 2015; Shalek et al., 2014; Van Eyndhoven et al., 2021a; Wimmers et al., 2018).”

“This percentage is in line with what has been found across literature, species [i.e., human and mice] and cell types [i.e., fibroblasts, monocyte derived dendritic cells, plasmacytoid dendritic cells], which ranges from 0.8 to 10% of early responders, emphasizing the elegant yet robust feature of only a fraction of first responding cells driving the population-wide IFN-I system (Bauer et al., 2016; Drayman et al., 2019; Patil et al., 2015; Shalek et al., 2014; Van Eyndhoven et al., 2021a; Wimmers et al., 2018).”

Regarding the hypothesis brought up by the reviewer on the role of cytosolic versus endosomal sensing impacting IFN-I response kinetics, and potentially quality, we hypothesize otherwise. Shalek and colleagues tested LPS (TLR4 ligand), PIC (TLR3 ligand, endosomal), and PAM (TLR2 ligand), all eliciting similar early responding cells, which they called precocious cells. This argues that the phenomenon of first responders is independent of the type of stimulation. Besides, for plasmacytoid dendritic cells, both R848 (TLR7/8 ligand), and CpG-C (TLR9 ligand) elicit very similar early IFN-I responses. In contrast, R848 and CpG-C elicit very different late IFN-I response dynamics, reflected by the fraction and activation dynamic of second responders (yet unpublished). We clarified accordingly:

“Moreover, various stimuli (live and synthetic) targeted membrane, cytosolic, and endosomal receptors, arguing that the mode of activation is not driving the discrepancies in responder fates.”

3) Epigenetic regulation of transient heritability: To test the contribution of epigenetic regulation on first responder fate, the authors treat their cells with DNMTi. While treatment with this drug does increase the proportion of first responder cells, the authors don't provide evidence that the mechanism of action is mediated by inhibiting DNA methylation. This is further confounded by the reduced responder frequencies in DNMTi treated cells transduced with Poly(I:C) (Fig 4g). The authors offer an explanation for this observation, but their reported data (Fig 4h) doesn't measure whether DNMTi, leads to latent retrovirus activation, broader demethylation, or a combination of the two.

We are well aware that the hypothesis on retrovirus activation are inconclusive. Unfortunately, we currently do not have the ability to utilize the tools to properly assess this hypothesis. Instead, we can only speculate. However, we were able to assess the effects of a different epigenetic drug [i.e., HDACi], as suggested later by the reviewer. Therefore, to strengthen our data interpretation, we added the following additional information and experimental data to the revised manuscript:

“Also the treatment with varying dosages and durations of Trichostatin A, an histone deacetylase inhibitor (HDACi), increased the number of responding cells (Supplementary Figure 5).”

“The rather long timescales of switching from responders to non-responders, and the other way around, imply epigenetic mechanisms at play, and indeed, prior work has indicated an important role for epigenetics dictating IFN-I response dynamics (reviewed in (Barrat et al., 2019)).”

“Both methylation and histone acetylation have been suggested in dictating transient heritable cellular fates (Clark et al., 2021; Lu et al., 2021; Shaffer et al., 2020).”

4) Temporal experimental data to validate and extend transient heritability and quorum sensing: Developing a model for cellular-decision making during early IFN-I responses, the authors formalize and test the hypothesis of transient heritability. While the data largely fit the model proposed (Fig 6D-F), the reported data points lack sufficient temporal resolution to validate the model during the earlier and more variable generations. Given that by generation 9 variability in first responder frequency has almost stabilized, there is only one data point (generation 6) to evaluate the fit of the ODE described. More densely sampled data points below generation 10 are necessary to validate the model. Moreover, a discussion of Kon calculation/observation, meaning, and validation is missing. To partially test their claim that Kon is a function of density (i.e., quorum sensing), the authors plate cells at different densities and measure the responder frequency at generation 6. This analysis lacks contextualization of other autocrine and paracrine signals potentially impacting IFN-I response. Moreover, these signals will be diverse in different cell types and could impact Kon and/or the overall model.

We agree that our first model validation was suboptimal, indeed because of lacking sufficient temporal resolution. Therefore, we performed additional experiments on clones of generation 1, 2, 3, 4, 5, of which the results turned out to be remarkably robust. We changed the revised manuscript accordingly:

“Surprisingly, the data obtained from clones of generation one through nine resulted in a mean higher than 2.134% (Figure 6d; Supplementary Figure 9), and a fluctuating CV (Figure 6e). From generation 13 onwards, both the mean and the CV start to meet the data obtained from the regular cultures again, which are similar to the theoretical outcomes of a stochastic process. Accordingly, we modeled first responders as a binary switch, where individual cells are either responding (ON) or nonresponding (OFF), similar to the transient heritable fates characterized and modeled before (Shaffer et al., 2020). Details on the ODE model are provided in the Materials and Methods section. We could fit the transient heritability model to the data when starting from 100% responders at generation zero [i.e., a single cell isolated from the regular culture]. Cells switch OFF after 5 generation on average, with a constant kon rate throughout. Interestingly, in generation zero we observed (nearly) only IFN-I responders, which we believe might be caused by single cells being deprived from any paracrine cues, which could include inhibitory factors that normally limited responsiveness. However, single IFN-I-producing cells [i.e., plasmacytoid dendritic cells and monocyte derived dendritic cells] encapsulated in picoliter droplets or captured in small microfluidic chambers did not display this behavior (Shalek et al., 2014; Wimmers et al., 2018). Instead, one could argue that single cells establish a different microenvironment, compared to a situation in which cells are close to neighboring cells, which elicits behavioral changes accordingly. The dimensions of microfluidic droplets and chambers are in the same range of cell-to-cell contacts in vitro, while single cells seeded for cloning are surrounded by rather massive areas and volumes without other cells present. Therefore, we hypothesize that these single cells lack biochemical, and perhaps biomechanical cues provided by dense cell populations, which result in behavioral changes in these cells, in our case, making them more responsive. Similarly, in quorum sensing, cells secrete soluble signaling molecules (called autoinducers), which enables cells to get a sense of their cell density (Postat and Bousso, 2019; Waters and Bassler, 2005). Without signaling of these molecules, cells perceive being isolated from the rest. In microfluidic droplets and chambers, these molecules accumulate, given the relatively small volumes.”

Regarding the contextualization of autocrine and paracrine signaling impacting IFN-I response dynamics in these studies, we added the following additional information:

“On top of the intrinsic features characterized by stochasticity and determinism, individual immune cells can communicate in various ways to elicit appropriate systemic immune responses. Typically, cytokine-mediated communication is categorized into two types: autocrine and paracrine signaling. Autocrine signaling is defined by cells secreting signaling molecules while simultaneously expressing the cognate receptor. Paracrine signaling is defined by cells either secreting signaling molecules without expressing the cognate receptor, or cells expressing the receptor without secreting the molecule. In essence, quorum sensing can be considered a phenomenon in which autocrine cells determine their population density based on cells engaging in neighbor communication, but without self-communication (Doğaner et al., 2016; Van Eyndhoven and Tel, 2022).”

Regarding the point that signals will be diverse in different cell types and could impact Kon and/or the overall model, yes, but we expect this to be only minor. Besides, the model can be easily adjusted to the different parameters per cell type (see Saint-Antoine et al., 2022).

Reviewer #3 (Public Review):

1) For the small fraction of cells that respond in the absence of Poly(I:C), are these cells just showing IRF7 translocation or are they fully responding with IFNB production? Has this been observed in other experimental systems or contexts? Do you also observe secondary responders in the unstimulated samples (as shown in the stimulated in Fig. 2G-I)?

Regarding the first point on the unstimulated translocated cells, excellent point. Although we have not experimentally validated it, we hypothesize that cells are able to produce constitutive levels of IFN-Is, as thoroughly described in literature, so we assume that these translocated cells produce IFN-Is. We provided additional speculation in the revised manuscript:

“Besides, the background numbers of translocated cells possibly reflect the intrinsic feature of the IFN-I system to ensure basal IFN-I expression and IFNAR signaling to equip immune cells to rapidly mobilize effective antiviral immune responses, and homeostatic balance through tonic signaling (Gough et al., 2012; Ivashkiv and Donlin, 2014).”

2) Do the second responders only arise through direct IFN-I production by first responders? Is it possible that this response has any relationship with the initial transfection with Poly(I:C)?

From the droplet-based experiments with plasmacytoid dendritic cells performed before (Wimmers et al., 2018; Van Eyndhoven et al., 2021), we could conclude that the second responders indeed required the activation and subsequent early IFN-I production of first responders. Whereas droplet-based microfluidics is a very stable, and controlled method, producing thousands of homogeneous droplets, we concluded that the difference between first and second responders is not elicited upon variations in activation (e.g., transfection discrepancies).

Author Response

Reviewer #1 (Public Review):

The authors use their expertise in live-cell imaging and mathematical modeling to further explore the relationship between chromatin structure, gene positioning and transcriptional coregulation. One of the strengths of the manuscript arises from the authors analysis of two publicly available datasets encompassing chromatin tracing and transcriptional activity. Using spatial analysis and modeling, the authors have impressively extended the findings of Su et. al, Cell 2020, who generated the analyzed dataset. A number of important concepts were explored including 1.) do genes re-position upon activation and 2.) can spatial proximity be correlated with transcriptional co-regulation. In general the authors conclusions are supported by their findings and should provide a blueprint for analysis of additional related big imaging datasets in the future.

However there are a number of weaknesses including lack of statistical analysis or incomplete description (e.g. bootstrapping parameters, statistical methods, number of genes/cells/measurements, etc.) on some figures that make it difficult to interpret the significance of the trends. In addition, the modeling using live-cell studies is generalized based on a behavior (e.g. diffusion) of a single gene. The manuscript is densely written in a way that may be inaccessible for non-specialists. A final schematic model that summarizes biological findings would help alleviate this weakness.

We are glad that the reviewer considers the observed phenomenon important and that our overall findings are consistent with our results. We implemented changes in response to each of the above requests:

1) we added additional explanation of test statistics;

2) we analyzed diffusion of additional genes;

3) we tried to simplify the text;

4) we added a final schematic.

Reviewer #2 (Public Review):

In their manuscript, Bohrer and Larson reanalyse previously published imaging datasets in order to tackle a long-standing question in modern genome biology: does the physical proximity of transcribed genes correlate with their co-expression?

The authors start off by reanalysing fixed-cell data, in which they find that active genes (i.e., any gene with RNA FISH signal) often repositions towards the centroid of the imaged chromatin environment one transcriptionally active. The analysis is straightforward, but the notion of "closer to the centroid" remains a bit vague to me, and is not well defined as regards its functional significance. There is no doubt of the clear trend in the analysed data -- but the interpretation could be strengthened.

We tried to clarify this part of the text and also added a schematic illustration to the discussion to help clarify this important point (Fig. 5).

Then, using the same dataset, the question on physical gene proximity is addressed. This is not only an important and timely question, but also one which the authors address very nicely. They deduce that when a pair of loci are brought within sufficiently low physical 3D proximity (unrelated to their genomic distance) they are more likely than expected to be co-expressed. In cis, this distance can be defined to approx. <2.5 Mb of genomic separation. They also looked in trans, via a complex transfer of knowledge from live-cell imaging to the fixed-cell dataset, to show that genes brought within approx. 400 nm from one another display quite a high coexpression correlation. Despite the parsimonious nature of the model and assumptions that the authors use for this (testing more complex parameters might prove beneficial here), their postulations can quite adequately explain observations published by others that were previously left largely without interpretation.

In my opinion, the main strength of this manuscript lies with the initial analysis of the fixed-cell data and the clear trends therein. The latter part, which nicely identifies caveats in available data and analyses and which makes a solid effort to combine live-cell with fixed-cell data, leaves more scenarios to be tested. Nevertheless, based on the outcome of this analysis (mostly found in Fig. 4), the value of ~400 nm as a physical proximity cutoff for co-expression is reasonable (based on previous knowledge) and does provide a solid first step in the direction of deciphering the rules that allow coordinated gene expression in mammalian cells.

We agree that the modelling section is more of a first step and that future work will need to be done to investigate further. In the revision, we make this point explicit within the main text (See below).

Overall, this is a conceptual advance of merit that can re-shape ways of approaching the stillopen issue of gene co-bursting in light of novel (mostly imaging) technologies.

We appreciate the comment.

Author Response

Reviewer #1 (Public Review):

The authors set out to develop an in vitro model of multiple species representing diversity in the CF airway as a platform for a range of studies on why polymicrobial communities resist therapy. The rationale for their design is sound and the methods appear justifiable and reproducible. The major strength of this work is in producing a method for a range of future work, ideally for multiple groups in the field. The primary findings are interesting but not groundbreaking. One weakness in the method of reporting interspecies interactions and another in evaluating alternative causes of lasR advantages present opportunities for a stronger research contribution beyond this terrific method.

We thank the reviewer for this accurate summary of the data presented in our manuscript. We have addressed the raised concerned in the revised document. The modifications and comments can be seen in the “Essential Revisions” section above.

Reviewer #2 (Public Review):

Differences between the infection environment and in vitro model systems likely contribute to disconnects between the antimicrobial susceptibility profile of bacterial isolates and the clinical response of patients. The authors of this paper focus on a specific aspect of the infection environment, the polymicrobial nature of some chronic infections like those in people with Cystic Fibrosis (CF), as a factor that could impact antibiotic tolerance. They first use published genomic datasets and computational techniques to identify a clinically relevant, four-member polymicrobial community composed of Pseudomonas aeruginosa, Staphylococcus aureus, Streptococcus spp., and Prevotella spp. They then develop a high throughput methodology in which this community grows and persists in a CF-like environment and in which antibiotic susceptibility can be tested. The authors determine that living as a member of this community decreases the antibiotic tolerance of some strains of biofilm-associated P. aeruginosa and increases the tolerance of most strains of planktonic and biofilm-associated S. aureus and planktonic and biofilm-associated Streptococcus. They focus on the decreased tolerance of P. aeruginosa and determine that a ΔlasR mutant of P. aeruginosa does not display increased tobramycin susceptibility in the mixed community. One of the phenotypes associated with a ΔlasR mutant is an overproduction of phenazines. The authors find that by deleting the phenazine biosynthesis genes from ΔlasR, they can restore community-acquired susceptibility. They further investigate this phenomenon by showing that a specific type of phenazine, PCA, is significantly increased in mixed communities with the ΔlasR mutant compared to WT. Finally, they demonstrate that adding a specific phenazine, pyocyanin, to mixed communities can restore the tolerance of WT P. aeruginosa.

Strengths:

With this study the authors address a very important problem in infectious disease microbiology - our in vitro drug susceptibility assays do a poor job of mimicking the infection environment and therefore do a poor job of predicting how effective particular drugs will be for a particular patient. By demonstrating how an infection-relevant community modifies tolerance to a clinically relevant drug, tobramycin, the authors identify specific interactions that could be targeted with therapeutics to improve our ability to treat the chronic infections associated with CF. In addition, this study provides a framework for how to effectively model polymicrobial infections in vitro.

The experiments in the paper are very rigorous and well-controlled. Statistical analysis is appropriate. The paper is very well-written and clear.

The authors do an admirable job of using in silico analysis to inform their in vitro studies. Specifically, they provide a comprehensive rationale for why they chose and studied the specific community they did.

The authors provide a very robust dataset which includes determining how strain differences of each of their four community members affect community dynamics and antibiotic tolerance. These types of analyses are laborious but very important for understanding how broadly applicable any given result is.

We appreciate the reviewer’s thorough summary of our work and their positive comments.

Weaknesses:

The authors very clearly and convincingly demonstrate that WT P. aeruginosa becomes more susceptible to tobramycin in their mixed community. Our ability to turn these types of observations into therapeutic development depends on mechanistic insight. That said, it is unclear if the authors can make any solid conclusions about what specific aspects of the polymicrobial environment cause WT P. aeruginosa to become more susceptible. The authors make a compelling case that increased phenazine production by the ΔlasR mutant restores tolerance in the mixed community and that exogenous phenazine addition increases the survival of WT P. aeruginosa in the mixed community. However, it remains a plausible explanation that the effects of phenazines on tobramycin susceptibility are independent of the initial observation that WT. P. aeruginosa becomes susceptible to tobramycin in the mixed community.

We agree with the reviewer’s comment here as it pertains to the initial observation of P. aeruginosa becoming more susceptible to tobramycin in the mixed community. However, as mentioned by the reviewer, we provide several lines of evidence that phenazines play a key role in the tolerance of the lasR mutant tobramycin, including genetic studies and feeding studies wherein exogenous addition of this molecule to WT P. aeruginosa phenocopies the lasR mutant exposed to tobramycin. Why the community impacts phenazine production of the WT strain is an open question, and the subject of future work. We have modified the abstract of the manuscript as follows at Lines 41–43:

“Our data suggest that the molecular basis of this community-specific recalcitrance to tobramycin for the P. aeruginosa LasR mutant is increased production of phenazines.”

Some aspects of the methodology are unclear. Specifically, the authors note that they use a specific sealed container system to grow their strains in anoxic conditions, which mimic portions of CF sputum. However, it is unclear how the authors change medium over the course of their experiments, or how they test susceptibility to tobramycin, without exposing the cells to oxygen. It is well understood that oxygen exposure impacts the susceptibility of P. aeruginosa to tobramycin, so it is very important that the methodology involving oxygen deprivation and exposure is described in detail.

We have made the necessary modifications to the manuscript as indicated in the “Essential Revisions” section to address these concerns (see Comment #3). Furthermore, new validation experiments were performed in a controlled anoxic environmental chamber that yielded observations similar to the data presented in the original manuscript, thereby confirming that we were using anoxic conditions with the GasPak anaerobic jar system (see Figure 1 - figure supplement 2 and Figure 2 - figure supplement 7).

Lines 198–204: “The impact of residual oxygen negatively influencing the growth of P. melaninogenica in monoculture was ruled out by performing these experiments using an anoxic environmental chamber (Figure 1 – figure supplement 2). That is, we did not detect CFU counts for either planktonic or biofilm populations of P. melaninogenica when grown in ASM in the anaerobic chamber, but as a positive control, significant growth was detected when using a medium shown previously to support growth of this microbe (10) (Prevotella Growth Medium, or PGM) (Figure 1 – figure supplement 2).”

Lines 406–414: “Also, we ruled out the possibility of remaining oxygen in ASM negatively impacting the viability of P. melaninogenica by reproducing our results using an anoxic chamber (Figure 1 – figure supplement 2). That is, we observed that P. melaninogenica can robustly grow as a planktonic or biofilm monospecies community in a medium capable of sustaining its growth (PGM) while this microbe fails to grow in ASM (Figure 1 – figure supplement 2). Thus, we argue that the mixed-community-specific growth of Prevotella spp. we observed across several conditions (Figure 1C, Figure 1 – figure supplement 5, Figure 2 – figure supplement 6) is not due to residual oxygen.”

Lines 290–293: “Growing and replenishing the preformed biofilm communities with fresh ASM supplemented or not with tobramycin using an anoxic environmental chamber resulted in similar phenotypes for all tested microorganisms (Figure 2 – figure supplement 7), indicating that the use of the GasPak system provides a robust anoxic environment.”

Lines 533–540: “Plates were incubated using an AnaeroPak-Anaerobic container with a GasPak sachet (ThermoFisher) at 37 °C for 24 hours. Then, unattached cells were aspirated with a multichannel pipette and the pre-formed biofilms replenished with 100 µl of fresh ASM on the bench and incubated for an additional 24 hours at 37 °C using an AnaeroPak-Anaerobic container with a GasPak sachet (ThermoFisher). Similar experiments were performed using an anoxic environmental chamber (Whitley A55 - Don Whitley Scientific, Victoria Works, UK) with 10% CO2, 10% H2, 80% N2 mixed gas at 37 °C, yielding results identical to those observed for the GasPak system.”

Reviewer #3 (Public Review) :

This manuscript by Jean-Pierre et al. describes the creation and experimentation with a model CF lung community in an artificial sputum medium. The group uses data from 16S rRNA sequencing studies to select organisms for creating the model and then performs experiments to determine outcomes of growth competition and antibiotic tolerance in a community context. The main finding of the manuscript is that P. aeruginosa, notorious for its antimicrobial resistance phenotypes, is more susceptible to tobramycin in the community context than when grown alone. The manuscript is well prepared and follow-up experiments with mutant strains and phenazines greatly strengthen the project overall. The initial results paragraph where the authors go through the rationale for selecting the different organisms is perhaps a bit overkill, the organisms selected make sense based on their prevalence in CF airways, which in and of itself is a strong enough rationale. This aspect of the manuscript could be minimized to focus more on the exciting culture experiments in the latter parts of the results. Overall, this is a strong and well-crafted manuscript that will have a broad interest in the CF and microbial ecology fields.

We thank the reviewer for this thoughtful review of our manuscript. We have not minimized the “front-end” of the paper because we believe the rationale for selecting the community and its members, and the validation of the model system are key for placing the resulting observations in a robust context, and for providing the underlying rationale to support the relevance of the findings.

Major Critiques. I have two major critiques of this study.

(1) Prevotella growth in monoculture. After reading the methods section it appears that the cultures were extensively washed and prepped prior to the inoculation into ASM. Prevotella did not grow alone, is this due to oxygen penetration of the cells during preparation? Perhaps oxygen is present in ASM prior to placement in an anaerobic bag? It is interesting, and perhaps worth exploring, whether the mixed community draws down oxygen from the media explaining the ability of Prevotella to grow. I suspect this is the case, but more detail is needed in the methods and this experiment would help us understand this interesting result.

As presented in the “Essential Revisions” section (Comment #3), we have repeated the experiment using fully anoxic conditions (i.e., using an anoxic environmental chamber where the cultures were grown, washed and mixed before incubation) and still observed absence of growth of Prevotella cultivated in ASM in both biofilm and planktonic populations. Moreover, including a positive control, Prevotella Growth Medium, resulted in robust growth of this microbe. Taken together, our data suggest that residual oxygen in ASM is not the driver of the community-specific growth of P. melaninogenica.

(2) Dilution of the community reproducing toby tolerance of P. aeruginosa. In supplemental figures, the replication of the 1:1000 dilution of the mixed community with P. aeruginosa shows poor replication and very large error bars. This experiment should be repeated to ensure it is reproducible.

The diluted mixed community experiment was repeated a fourth time, yielding the same statistical conclusions. An updated “Figure 2 – figure supplement 1” was added to the paper. The highest (1:1000) dilution still yielded high variation which could perhaps be explained by low (i.e., ~103 CFU/mL) inoculum for S. aureus, S. sanguinis and P. melaninogenica used in these experiments; see updated “Microbial assays” paragraph of the “Materials and Methods” section). Thus, the variation at low inoculum is robust and reproducible. The Materials and Methods section was also updated to clarify the CFU counts used for those experiments. We have added modifications to the text as follows to address this critique:

Lines 526–532: “The optical density (OD600) was then measured for each bacterial suspension and diluted to an OD600 of 0.2 in ASM. Monocultures and co-culture conditions were prepared from the OD600 = 0.2 suspension and diluted to a final OD600 of 0.01 for each microbial species in ASM corresponding to final bacterial concentrations of 1x107 CFU/mL, 3.5x106 CFU/mL, 1.2x106 CFU/mL and 4.6x106 CFU/mL of P. aeruginosa, S. aureus, Streptococcus spp. and Prevotella spp. respectively. A volume of 100 µl of bacterial suspension all at a final OD600 of 0.01 each in the mix was added to three wells.”

Lines 558–570: “For experiments with varying concentrations of S. aureus, S. sanguinis and P. melaninogenica in monocultures and co-cultures, the organisms were grown from bacterial suspensions adjusted to an OD600 = 0.8 in ASM. Suspensions were further diluted in ASM to an OD600 of either 0.1, 0.001, 0.0001 or 0.00001 while maintaining P. aeruginosa at OD600 = 0.01 (approximating 1x107 CFU/mL) in all conditions. The OD600 = 0.1 dilution factor resulted in CFU/mL count average of 3.8x108 CFU/mL for S. aureus, 1.6x108 CFU/mL for S. sanguinis and 1.0x108 CFU/mL for P. melaninogenica. The OD600 = 0.001 dilution factor resulted in a CFU/mL count average of 6.7x105 CFU/mL for S. aureus, 1.1x105 CFU/mL for S. sanguinis and 1.4x105 CFU/mL for P. melaninogenica. The OD600 = 0.0001 dilution factor resulted in a CFU/mL count average of 4.2x104 CFU/mL for S. aureus, 3.3x104 CFU/mL for S. sanguinis and 4.6x104 CFU/mL for P. melaninogenica. The OD600 = 0.00001 dilution factor resulted in a CFU/mL count average of 5.6x103 CFU/mL for S. aureus, 4.4x103 CFU/mL for S. sanguinis and 6.2x103 CFU/mL for P. melaninogenica.”

Author Response

Reviewer #4 (Public Review):

The study employs a number of methods, including TEM morphometric analysis, immunochemistry, western blotting, genomics, genetically modified models, whole heart measurements.

However, the manuscript seems to be a collection of two unfinished works: one on the transition p20-p60 in post-natal development of the heart, second about the role of ephrinB1 in the maturation of the crests of the sarcolemma. Otherwise, it is not clear why in the first figure there is no staining for ephB1, and why there is staining for claudin 5 instead.

The reason is clearly explained in the text on page 6. The first figure explores the postnatal maturation of the CM crests and their molecular determinants and our previous paper described Claudin-5 as the first molecular determinant of the crests (Guilbeau-Frugier et al, Cardiovasc Research 2019). Based on our previous demonstration of ephrin-B1 as a direct claudin-5 partner and regulator (Genet et al, Circulation Research 2012), we thus intuitively proposed ephrin-B1 as another potential molecular determinant of the crests that we explored for the first time in our current paper in revision. Moreover, ephrin-B1 is part of a large family of direct physical cell-cell communication proteins (Eph-Ephrin system), its role in the lateral crest-crest interaction was also obvious.

This is why at the beginning of the paper we explored claudin-5 and thereafter ephrin-B1 to explore more the functional role of the crests using Efnb1 KO mouse model we had already established in the lab.

The authors are trying to defend the idea that development of the heart in rats doesn't finish on postnatal day 20 and goes on for up to day 60. However, it is not convincing.

It is no surprise transcription profile is different between day 20 and day 60, I am sure as life goes on development continues into aging and any comparison of samples collected with sufficient time lapse will give transcriptional differences. Whether these differences represent a truly separate development stage is not a clear-cut story.

Most of the argument is based on morphometric study of TEM images.

But also on confocal microscopy studies and more importantly on transcriptomic data.

Whether it was evident that transcription profile is different between day 20 and day 60, then most of the studies in this postnatal field would have extended their study window over P20 which is not the case. As we mentioned it in the manuscript, most people in the field were assuming terminal maturity of the CM based essentially on its typical rod-shape which is already acquired at P20. Then growth of the heart between P20 and P60 was assumed to rely only on an increase in tissue quantitative content and not on transcriptomic changes, i.e. in qualitative content.

However, the method is not described at all. There is reference to another paper by the authors, but this paper doesn't provide a concise description of the morphometry either. It is unclear how randomisation of images and fields of view has been achieved and what statistical methods has been implemented. In TEM it is often possible to find all sorts of oddities depending on how you choose the images.

We agree with the author that TEM is often associated with “all sorts of oddities” and that‘s the reason our recent paper (Guilbeau-Frugier et al, Cardiovasc Research 2019) was dedicated to the analysis of technical pitfalls and analysis. All this paper relies on that: How to proceed the cardiac tissue to avoid artifacts on the crests/SSM visualization and how to quantify them?.

Now, instead of only citing our previous paper, we have implemented the “Material and methods” / “Transmission electron microscopy (TEM) and quantitative analysis” section (Main manuscript, page 20-21) by highly detailing all the TEM observation/quantification.

The question of randomization of images of the number fields of view is a general question in all imaging techniques and not specific at all with our TEM study. In imaging, there is no randomization.

All statistical analysis of TEM data quantifications are accurately described in all figure legends. For instance, in the figure 1: (B) Quantification of crest heights / sarcomere length (left panel), SSM number / crest (middle panel) and SSM area (right panel) from TEM micrographs obtained from P20- or P60 rat hearts (P20 n=6, P60 n=6; 4 to 8 CMs/rat, ~ 70 crests/rat). However, to better clarify the “P20 n=6, P60 n=6”, we have now specified “P20 or P60 n=6 rats”. This have been now specified in the figure legends for all statistical analysis (highlighted in yellow in the revised manuscript).

Why didn't the authors use microscopy of live isolated cells, which may be more relevant to study crest height?

We clearly explained it at the very beginning of the results section of our paper (first paragraph, second sentence (i, ii). The use of living CMs is a non-sense based on our two previous papers on this topic (Dague et al JMCC 2014 and Guilbeau-Frugier et al, Cardiovasc Research 2019). Our first paper was essentially based on AFM studies using isolated CMs and we found that rapidly after isolation, CM surface crests/SSM have a high tendency to shrink and disappear in control mice. This is why the second paper was based on an extensive characterization of the crests within the tissue using TEM experiments and the comparison of CM crests between tissue and living cells is also highlighted in this paper. More importantly, in this recent paper, we have described for the first time using high resolution imaging techniques (TEM and STEAD), the existence of intermittent physical interactions between neighboring CMs on their lateral side through crest-crest interaction via the extracellular domain of claudin-5. This crest-crest physical interaction can only be observed within the tissue since isolated adult CMs remain isolated and do not reproduce CM-CM physical interactions (through lateral or physical interactions at the longitudinal level, i.e. the intercalated disk level).

Both claudin5 and EphrinB1 seem to be expressed highly after p5, which doesn't correlate with the proposed maturation of crests at days 20 to 60.

Many processes do not rely only on gene/protein expression but on post-translational processes and localization/trafficking of proteins within the cell. This is exactly what we show with ephrin-B1 and claudin-5 proteins that traffic from the cytoplasm to the lateral membrane at the surface of the CMs between P20 and P60, as shown by our confocal images of the cardiac tissue while the global expression level of these two proteins doesn’t change (western blot results).

There is no causative relationship between the lack of ephrinb1 and crest maturing, at least to my mind.

Comparing the cardiac tissue between P20 an P60 and showing both ephrin-B1 trafficking at the CM lateral surface and crest maturation is obviously not a criterion of any relationship between these two events. However, when you delete a specific protein, i.e ephrin-B1, from a specific cell, i.e. the CM, and the phenotype of the KO mice is again a lack of crest maturation, you can at least deduce that ephrin-B1 is involved, directly or indirectly we don’t know, in the maturation process of the crests in the CM.

Now, because of the constitutive deletion of Efnb1, we couldn’t completely exclude that the phenotype of the constitutive Efnb1 CM-KO mice we described at the adult stage was directly related to specific alteration of CM surface crest/diastolic function at the adult stage or more likely related to other earlier developmental defects (secondary mechanisms). Also, to discriminate between these two possibilities, we have now used in the revision process a tamoxifen-inducible conditional-knockout (Mer-Cre-Mer) of Efnb1 in the CM (MHC promotor). This mouse model has never been reported before but its characterization (new Supplementary Figure 16) indicated that tamoxifen injection can lead to up to 50 % of Efnb1 deletion in CMs. In these conditions, deletion of Efnb1 (tamoxifen injection) was initiated at the young adult stage (2-month old) and the systolic and diastolic function (echo Doppler and LV-catheterism) but also CM crest phenotype (TEM) were examined one month later. As shown in the new Figure 7, deletion of efnb1 at the adult stage led to partial loss of CM surface crests (New Fig 7B), agreeing with the partial deletion of Efnb1, associated with a significant increase in the IVRT (echo-doppler), LVEDP (LV catheterism) with no modification of the ejection fraction (echo) compared to the control mouse littermates (tamoxifen injected) (New Fig. 7C, D). Thus, these data clearly demonstrate that ephrin-B1 is a specific determinant of the crest architecture at the CM surface and of the diastolic function at the adult stage.