Reviewer #1 (Public Review):

Summary:

Building upon their famous tool for the deconvolution of human transcriptomics data (EPIC), Gabriel et al. implemented a new methodology for the quantification of the cellular composition of samples profiled with Assay for Transposase-Accessible Chromatin sequencing (ATAC-seq). To build a signature for ATAC-seq deconvolution, they first created a compendium of ATAC-seq data and derived chromatin accessibility marker peaks and reference profiles for 12 cell types, encompassing immune cells, endothelial cells, and fibroblasts. Then, they coupled this novel signature with the EPIC deconvolution framework based on constrained least-square regression to derive a dedicated tool called EPIC-ATAC. The method was then assessed using real and pseudo-bulk ATAC-seq data from human peripheral blood mononuclear cells (PBMC) and, finally, applied to ATAC-seq data from breast cancer tumors to show it accurately quantifies their immune contexture.

Strengths:

Overall, the work is of very high quality. The proposed tool is timely; its implementation, characterization, and validation are based on rigorous methodologies and results in robust estimates. The newly-generated, validation data and the code are publicly available and well-documented. Therefore, I believe this work and the associated resources will greatly benefit the scientific community.

Weaknesses:

In the benchmarking analysis, EPIC-ATAC was compared also to deconvolution methods that were originally developed for transcriptomics and not for ATAC-seq data. However, the authors described in detail the specific settings used to analyze this different data modality as robustly as possible, and they discussed possible limitations and ideas for future improvement.

Reviewer #2 (Public Review):

Summary:

The manuscript expands the current bulk sequencing data deconvolution toolkit to include ATAC-seq. The EPIC-ATAC tool successfully predicts accurate proportions of immune cells in bulk tumour samples and EPIC-ATAC seems to perform well in benchmarking analyses. The authors achieve their aim of developing a new bulk ATAC-seq deconvolution tool.

Strengths:

The manuscript describes simple and understandable experiments to demonstrate the accuracy of EPIC-ATAC. They have also been incredibly thorough with their reference dataset collections and have been robust in their benchmarking endeavours and measured EPIC-ATAC against multiple datasets and tools. This tool will be valuable to the community it serves.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Building upon their famous tool for the deconvolution of human transcriptomics data (EPIC), Gabriel et al. implemented a new methodology for the quantification of the cellular composition of samples profiled with Assay for Transposase-Accessible Chromatin sequencing (ATAC-Seq). To build a signature for ATAC-seq deconvolution, they first created a compendium of ATAC-seq data and derived chromatin accessibility marker peaks and reference profiles for 21 cell types, encompassing immune cells, endothelial cells, and fibroblasts. They then coupled this novel signature with the EPIC deconvolution framework based on constrained least-square regression to derive a dedicated tool called EPIC-ATAC. The method was then assessed using real and pseudo-bulk RNA-seq data from human peripheral blood mononuclear cells (PBMC) and, finally, applied to ATAC-seq data from breast cancer tumors to show it accurately quantifies their immune contexture.

Strengths:

Overall, the work is of very high quality. The proposed tool is timely; its implementation, characterization, and validation are based on rigorous methodologies and resulted in robust results. The newly-generated, validation data and the code are publicly available and well-documented. Therefore, I believe this work and the associated resources will greatly benefit the scientific community.

Weaknesses:

CA few aspects can be improved to clarify the value and applicability of the EPIC-ATAC and the transparency of the benchmarking analysis.

(1) Most of the validation results in the main text assess the methods on all cell types together, by showing the correlation, RMSE, and scatterplots of the estimated vs. true cell fractions. This approach is valuable for showing the overall method performance and for detecting systematic biases and noisy estimates. However, it provides very limited insights regarding the capability of the methods to estimate the individual cell types, which is the ultimate aim of deconvolution analysis. This limitation is exacerbated for rare cell types, which could even have a negative correlation with the ground truth fractions, but not weigh much on the overall RMSE and correlation. I would suggest integrating into the main text and figures an in-depth assessment of the individual cell types. In particular, it should be shown and discussed which cell types can be accurately quantified and which ones are less reliable.

We thank the reviewer for raising this important point. Discussing the accuracy of EPIC-ATAC in predicting individual cell-type proportions would indeed be valuable in the main text. We have updated the text as follows.

In the first version of our manuscript, we had a section called “T cell subtypes quantification reveals the ATAC-Seq deconvolution limits for closely related cell types” which highlighted that EPIC-ATAC shows low performances when predicting the proportions of cell types that are closely related, e.g., CD4+ T cell or CD8+ T cell subtypes. The section is now named “Accuracy of ATAC-Seq deconvolution is determined by the abundance and specificity of each cell type” and has been expanded to discuss the accuracy of EPIC-ATAC predictions within each major cell type.

To do so, we represented in Figure 5A the performances of EPIC-ATAC in each cell type present in the benchmarking datasets from Figures 3 and 4. Additionally, we have kept in the supplementary figures the details of the correlation values and RMSE values within each cell type and for each tool (Supplementary Figures 9 and 10). The following text has been added in the main text to describe these analyses:

“Accuracy of ATAC-Seq deconvolution is determined by the abundance and specificity of each cell type

To investigate the impact of cell type abundance on the accuracy of ATAC-Seq deconvolution, we evaluated EPIC-ATAC predictions in each major cell type separately in the different benchmarking datasets (Figure 5A). NK cells, endothelial cells, neutrophils or dendritic cells showed lower correlation values. These values can be explained by the fact that these cell types are low-abundant in our benchmarking datasets (Figure 5A). For the endothelial cells and dendritic cells, the RMSE values associated to these cell types remain low. This suggests that while the predictions of EPIC-ATAC might not be precise enough to compare these cell-type proportions between different samples, the cell-type quantification within each sample is reliable. For the NK cells and the neutrophils, we observed more variability with higher RMSE values in some datasets which suggests that the markers and profiles for these cell types might be improved. Supplementary Figures 9 and 10 detail the performances of each tool when considering each cell type separately in the PBMC and the cancer datasets. As for EPIC-ATAC, the predictions from the other deconvolution tools are more reliable for the frequent cell types.”

(2) In the benchmarking analysis, EPIC-ATAC is compared to several deconvolution methods, most of which were originally developed for transcriptomics data. This comparison is not completely fair unless their peculiarities and the limitations of tweaking them to work with ATAC-seq data are discussed. For instance, some methods (including the original EPIC) correct for cell-type-specific mRNA bias, which is not present in ATAC-seq data and might, thus, result in systematic errors.

We thank the reviewer for this comment and have updated the results and methods sections as follows:

We provide in the Materials and methods section, the paragraph “Benchmarking of the EPIC-ATAC framework against other existing deconvolution tools” which describes how each tool included in the benchmark was used in the ATAC-Seq context. We have added a reference to this section in the main text when introducing the first benchmarking analysis.

For each tool, the main changes consisted in: (i) replacing the initial RNA-Seq profiles and markers by the EPIC-ATAC reference profiles and markers and (ii) providing as input a bulk ATAC-Seq dataset with matched ATAC-Seq features (the same approach as the one used in EPIC-ATAC was considered, see answer to the next comment). Having reference profiles/markers and an ATAC-Seq bulk query with matched features was the only requirement of the different deconvolution models to be able to run on ATAC-Seq data with the default methods parameters, except for quanTIseq. Indeed, this method, like EPIC, corrects its estimations for cell-type-specific mRNA content bias. We have disabled this option for the bulk ATAC-Seq deconvolution.

We can however not exclude that a hyper parametrization of each tool could have helped to improve their current performances. Also, for RNA-Seq data deconvolution, some of the methods followed specific features filtering, e.g., the quanTIseq framework removes a manually curated list of noisy genes as well as aberrant immune genes identified in the TCGA data and ABIS uses immune-specific housekeeping genes. We can hypothesize that additional filtering could be explored for the ATAC-Seq deconvolution to improve the performance of the tools.

We have clarified these points in the results section when introducing the benchmarking, in the methods and in the discussion section.

(3) On a similar note, it could be made more explicit which adaptations were introduced in EPIC, besides the ad-hoc ATAC-seq signature, to make it applicable to this type of data.

In the first version of the manuscript, we described the changes brought to EPIC to perform bulk ATAC-Seq deconvolution in the Material and methods section in the paragraph “Running EPIC-ATAC on bulk ATAC-Seq data”.  We have moved and completed this paragraph in the results section before the description of the evaluation of EPIC-ATAC in different datasets. The paragraph is the following:

“EPIC-ATAC integrates the marker peaks and profiles into EPIC to perform bulk ATAC-Seq data deconvolution

The cell-type specific marker peaks and profiles derived from the reference samples were integrated into the EPIC deconvolution tool (Racle et al., 2017; Racle and Gfeller, 2020). We will refer to this ATAC-Seq deconvolution framework as EPIC-ATAC. To ensure the compatibility of any input bulk ATAC-Seq dataset with the EPIC-ATAC marker peaks and reference profiles, we provide an option to lift over hg19 datasets to hg38 (using the liftOver R package) as the reference profiles are based on the hg38 reference genome. Subsequently, the features of the input bulk matrix are matched to our reference profiles’ features. To match both sets of features, we determine for each peak of the input bulk matrix the distance to the nearest peak in the reference profiles peaks. Overlapping regions are retained and the feature IDs are matched to their associated nearest peaks. If multiple features are matched to the same reference peak, the counts are summed. Before the estimation of the cell-type proportions, we transform the data following an approach similar to the transcripts per million (TPM) transformation which has been shown to be appropriate to estimate cell fractions from bulk mixtures in RNA-Seq data (Racle et al., 2017; Sturm et al., 2019). We normalize the ATAC-Seq counts by dividing counts by the peak lengths as well as samples depth and rescaling counts so that the counts of each sample sum to 106. In RNA-Seq based deconvolution, EPIC uses an estimation of the amount of mRNA in each reference cell type to derive cell proportions while correcting for cell-type-specific mRNA bias. For the ATAC-Seq based deconvolution these values were set to 1 to give similar weights to all cell-types quantifications. Indeed ATAC-Seq measures signal at the DNA level, hence the quantity of DNA within each reference cell type is similar.”

(4) Given that the final applicability of EPIC-ATAC is on real bulk RNA-seq data, whose characteristics might not be completely recapitulated by pseudo-bulk samples, it would be interesting to see EPIC and EPIC-ATAC compared on a dataset with matched, real bulk RNA-seq and ATAC-seq, respectively. It would nicely complement the analysis of Figure 7 and could be used to dissect the commonalities and peculiarities of these two approaches.

We thank the reviewer for raising this important point. EPIC-ATAC will be applied to real bulk ATAC-Seq data and pseudobulk data cannot indeed fully recapitulate the bulk signals.  Recently, a dataset composed of more than 100 samples with matched bulk RNA-Seq, bulk ATAC-Seq as well as matched flow cytometry data has been published by Morandini and colleagues in GeroScience in November 2023. We thus retrieved these data to compare the predictions obtained by EPIC-ATAC on the bulk ATAC-Seq data and the predictions of the original version of EPIC on the bulk RNA-Seq data to the cell-type quantification obtained by flow cytometry. We also assessed whether both modalities could be complementary using a simple approach averaging the predictions obtained from both modalities. The results of these analyzes have been summarized in the Figure 7C and are described in the main text in the last paragraph of the paper:

“We compared the predictions obtained using each modality to the flow cytometry cell-type quantifications. EPIC-ATAC predictions were better correlated with the flow cytometry measures for some cell types (e.g., CD8+, CD4+ T cells, NK cells) while this trend was observed with the EPIC-RNA predictions in other cell types (B cells, neutrophils, monocytes) (Figure 7C). We then tested whether the predictions obtained from both modalities could be combined to improve the accuracy of each cell-type quantification. Averaging the predictions obtained from both modalities shows a moderate improvement (Figure 7C), suggesting that the two modalities can complement each other.”

Reviewer #2 (Public Review):

Summary:

The manuscript expands the current bulk sequencing data deconvolution toolkit to include ATAC-seq. The EPIC-ATAC tool successfully predicts accurate proportions of immune cells in bulk tumour samples and EPIC-ATAC seems to perform well in benchmarking analyses. The authors achieve their aim of developing a new bulk ATAC-seq deconvolution tool.

Strengths:

The manuscript describes simple and understandable experiments to demonstrate the accuracy of EPIC-ATAC. They have also been incredibly thorough with their reference dataset collections. The authors have been robust in their benchmarking endeavours and measured EPIC-ATAC against multiple datasets and tools.

Weaknesses:

Currently, the tool has a narrow applicability in that it estimates the percentage of immune cells in a bulk ATAC-seq experiment.

Comments:

(1) Has any benchmarking been done on the runtime of the tool? Although EPIC-ATAC seems to "win" in benchmarking metrics, sometimes the differences are quite small. If EPIC-ATAC takes forever to run, compared to another tool that is a lot quicker, might some people prefer to sacrifice 0.01 in correlation for a quicker running tool?

We thank the reviewer for raising this point that was not addressed in the manuscript. We have added a supplementary figure (Supplementary Figure 8) which represents the CPU time used by each tool. The figure shows that all the tools could be run in less than 20 seconds in average. This figure has been mentioned at the end of the benchmarking paragraphs.

(2) In Figure 3B the data points look a bit squashed in the bottom-left corner. Could the plot be replotted with the data point spread out? There also seems to be some inter-patient variability. Could the authors comment on that?

We have updated Figure 3B to increase the visibility of the dots in the bottom-left corner. To do so, we have limited the x and y axes to the maximum of the predicted proportions for the y axis and true proportions for the x axis.

We also acknowledge that the accuracy of the predictions varies across samples. In particular, one sample (Sample4, star shape on Figure 3B) exhibits larger discrepancies between EPIC-ATAC predictions and the ground truth. To understand the lower performance, we have visualized our marker peaks in the five PBMC samples (Figure below). Based on this visualization, we can see that Sample4 might be an outlier sample considering that its cellular composition is similar to that of Sample2 and Sample5, however this sample shows particularly high ATAC-Seq accessibility at the monocytes and dendritic markers. This can explain why EPIC-ATAC overestimates the proportions of the two populations in this case. We have added the previously mentioned figures as a Supplementary Figure (Supplementary Figure 2) and have described it in the results section in the paragraph “EPIC-ATAC accurately estimates immune cell fractions in PBMC ATAC-Seq samples”.

(3) Could the authors comment on the possibility of expanding EPIC-ATAC into more than a percentage prediction tool? Perhaps EPIC-ATAC could remove the immune cell signal from the bulk ATAC-seq data to "purify" the uncharacterised cells in silico, or generate pseudo-ATAC-seq tracks of the identified cell types.

We thank the reviewer for this interesting question. As suggested by the reviewer, one approach to purify bulk genomics data using the cell-type proportions estimated by a cell-type deconvolution tool is to subtract the weighted sum of the signal observed in the reference data, weights corresponding to the predicted proportions. We used this approach on the EPIC-ATAC predictions obtained from pseudobulks built from scATAC-Seq data from diverse cancer types coming from the Human Tumor Atlas Network (HTAN) (See also the answer of the first recommendation of Reviewer 1). This dataset allows us to compare for a relatively large number of samples (a maximum of 25  samples in a cancer type cohort) the purified signal to the true signal derived from the single-cell data. The results are presented in the figure below which shows that the correlations between the predicted and true signals are relatively good in most of the cancer types (blue boxplots). However, these correlation levels are lower than the ones obtained when comparing the signal obtained from the entire pseudobulk (red boxplots) with the true signal. This suggests that this purification approach leads to a signal that is less precise and accurate than the signal resulting from all cells mixtures.

Author response image 1.

Boxplots of the correlation values obtained from the comparison of the bulk signal and the ground truth signal from the uncharacterized cells in each sample (red) and from the comparison of the predicted signal and the ground truth signal from the uncharacterized cells in each sample (blue).

Also, note that in our simple approach, negative values can be obtained. The predicted signal will thus be difficult to interpret and to use in downstream analyses. Methods claiming to perform purification of bulk samples use more complex and dedicated algorithms. For example, Symphony (Burdziak et al., 2019) (cited in our introduction) uses single-cell RNA-Seq data in addition to the bulk chromatin accessibility data to infer cluster-specific accessibility profiles. Considering that EPIC was not designed for purification purposes, we decided not to include this analysis in the updated version of the manuscript.

Recommendations For The Authors:

Reviewer #1 (Recommendations For The Authors):

(1) The original EPIC had two different signatures for application to blood or tumor RNA-seq. It is not clear instead if EPIC-ATAC applies with the same signature and framework to any tissue and disease context. This aspect should be clarified in the text.

We thank the reviewer for raising this point which was not clear in the previous version of the manuscript. As in the original version of EPIC, in EPIC-ATAC two reference profiles and sets of markers are available, the PBMC reference and the TME reference. We used the PBMC reference profiles and markers to deconvolve the PBMC samples and the TME reference profiles and markers to deconvolve the cancer samples. We have clarified this point in the result section of the main text in the paragraph “ATAC-Seq data from sorted cell populations reveal cell-type specific marker peaks and reference profiles” as follows (added text underlined):

“The resulting marker peaks specific only to the immune cell types were considered for the deconvolution of PBMC samples (PBMC markers). For the deconvolution of tumor bulk samples, the lists of marker peaks specific to fibroblasts and endothelial cells were added to the PBMC markers. This extended set of markers was further refined based on the correlation patterns of the markers in tumor bulk samples from the diverse solid cancer types from The Cancer Genome Atlas (TCGA) (Corces et al., 2018), i.e., markers exhibiting the highest correlation patterns in the tumor bulk samples were selected using the findCorrelation function from the caret R package (Kuhn, 2008) (Figure 1, box 4, see the Material and methods, section 2). The latter filtering ensures the relevance of the markers in the TME context since cell-type specific TME markers are expected to be correlated in tumor bulk ATAC-Seq measurements (Qiu et al., 2021). 716 markers of immune, fibroblasts and endothelial cell types remained after the last filtering (defined as TME markers). Considering the difference in cell types and the different filtering steps applied on the PBMC and TME markers, we recommend to use the TME markers and profiles to deconvolve bulk samples from tumor samples and the PBMC markers and profiles to deconvolve PBMC samples.”

We also note that when running EPIC-ATAC using the PBMC markers and the TME markers independently to perform the deconvolution of the cancer datasets, we see that overall the use of the TME markers leads to a better performance (Figure below).

Figure legend: Correlation and RMSE values obtained when running EPIC-ATAC on each cancer dataset (points) using the PBMC (red) and the TME (blue) markers.

To demonstrate that the TME markers can be applied to different cancer types, we have completed the evaluation of EPIC-ATAC on tumor samples by considering an additional dataset: the Human Tumor Atlas Network (HTAN) single-cell multiomic (scRNA-Seq and scATAC-Seq) dataset. We have processed this dataset and built scATAC-Seq pseudobulks for 7 cancer types on which EPIC-ATAC was applied to. This analysis has been summarized in Figure 4 and Supplementary Figure 4 and shows that EPIC-ATAC is applicable in a diverse set of tissues.

(2) EPIC and EPIC-ATAC have a valuable feature, which is absent from most deconvolution methods: the estimation of unknown content. It would be informative for the users to understand from the benchmarking analysis whether this feature gives an advantage to EPIC-ATAC with respect to the other approaches.

Indeed, among the tools that we included in our benchmarking analysis, only EPIC-ATAC and quanTIseq enable users to predict the proportions of cells that are not present in the reference profiles, i.e., the uncharacterized cells. For the other tools we thus fixed the estimated proportions of uncharacterized cells to 0. This approach provides a clear and significant advantage to EPIC-ATAC and to quanTIseq. For this reason, we also provide a version of the benchmarking in which we exclude the uncharacterized cells and rescale the true and estimated cell-type proportions to sum to 1. In this second benchmarking approach, EPIC-ATAC still outperforms some of the other deconvolution tools.

We have clarified this point in the results section, in the paragraph “EPIC-ATAC accurately predicts fractions of cancer and non-malignant cells in tumor samples”.

(3) The selection of the most discriminative markers is very well described in the text and beautifully illustrated in Figure 2. However, it is unclear why UMAP plots are used to represent cell-type similarities and dissimilarities. Would a linear dimensionality reduction approach like PCA be already sufficient to show these groups, especially considering the not-so-extreme dimensionality of the underlying data? In addition, a statistic that could be also considered to compare clusters to the cell type labels in the two scenarios is the Adjusted Rand Index (ARI).

We thank the reviewer for this relevant comment. We initially used UMAP to facilitate the visualization of the different cell-type groups. However, it is true that the three first axes of the principal component analyses performed based on each set of marker peaks already capture most of the structure in the data and that the use of UMAP can lead to an artificial enhancement of separation between the different groups of cells. We have updated Figure 2B by replacing the UMAP scatter plots by 3D representations of the first three principal components of the PCA and have added in Supplementary Figure 1B the pairwise scatter plots of these first 3 principal components. On the main figures, we have also added the ARI metric comparing the cell-type annotation and the clustering obtained using the first 10 axes of the PCA and model based clustering.

(4) In the introduction, it is stated that "the reasonable cost and technical advantages of these protocols foreshadow an increased usage of ATAC-Seq in cancer studies". I would suggest adding a reference to justify this trend. Also, it should be discussed how ATAC-seq deconvolution compares to other types of deconvolution approaches applied to cheaper epigenetic data like methylation one (e.g. epidish, methylcc, tca, minfi).

We have complemented this sentence with two references to justify the assertion: (i) a review published by Luo, Gribskov and Wang in 2022 showing the increasing number of ATAC-Seq studies in the field of cancer research, and (ii) a protocol paper from Grandi et al. published in 2022 on the state-of-the-art Omni-ATAC protocol for ATAC-sequencing which discusses the broad applicability and the technical advantages of ATAC-sequencing. Also in the preceding sentence, a recent ATAC-Seq protocol that can be applied to FFPE samples has been mentioned, FFPE samples being the most common samples in clinical cancer research.

We agree with the reviewer on the fact that other epigenetic assays such as methylation assays are cost effective. However, ATAC-sequencing provides additional information on the epigenetic landscape of a sample’s genome and some questions regarding regulatory regions and transcription factor activity cannot be answered with methylation data. Methods that can be applied on ATAC-Seq data specifically are thus needed. Most of the cell-type deconvolution algorithms existing so far are applicable on RNA-Seq or methylation data. These algorithms often use similar methodological concepts, e.g., linear combination of the reference profiles for reference-based methods, which could be used in different modalities. However, methylation-based deconvolution tools often take as input a data format that is specific to methylation data, e.g., two color micro array data (RGChannelSet R object) for the minfi deconvolution function (estimatesCellCounts) or leverage methylation-specific information to perform the deconvolution. For example, methylCC uses a model based on latent variables representing a binarized measures of the methylation status of cell-type specific regions (1 or 0 for clearly methylated or unmethylated regions). Such methods are more difficult to adapt than tools  based on RNA-Seq data where the signal is quantified using read counts similarly to ATAC-Seq data.

Nevertheless, some methods such as EPIdish or MethylCIBERSORT have proposed new methylation reference profiles and have used existing models that are not specific to methylation data to deconvolve the bulk data. In our work, we followed a similar approach where we propose new reference profiles specific to chromatin accessibility data, integrate them to an existing method EPIC as well as test them in other existing tools. Note that methylation reference profiles cannot be directly used for ATAC-Seq data deconvolution considering that methylation measures methylation status at CpG sites (dinucleotides) and ATAC-Seq measures the accessibility of regions of hundreds base pairs.

An analysis comparing the performance of methylation-based deconvolution and ATAC-Seq based deconvolution would be informative. However, such analysis is beyond the scope of our paper considering that none of the datasets used for our benchmarking provide these two modalities for the same samples.

In the manuscript, we have completed the references associated to the methylation-based deconvolution tools with the ones mentioned in the previous paragraphs and by the reviewer and have completed the discussion as follows:

“The comparison of EPIC-ATAC applied on ATAC-Seq data with EPIC applied on RNA-Seq data has shown that both modalities led to similar performances and that they could complement each other. Another modality that has been frequently used in the context of bulk sample deconvolution is methylation. Methylation profiling techniques such as methylation arrays are cost effective (Kaur et al., 2023) and DNA methylation signal is highly cell-type specific (Kaur et al., 2023; Loyfer et al., 2023). Considering that methylation and chromatin accessibility measure different features of the epigenome, additional analyses comparing and/or complementing ATAC-seq based deconvolution with methylation-based deconvolution could be of interest as future datasets profiling both modalities in the same samples become available.”

(5) In the Results section, some methodological steps could be phrased in a bit more extensive way to let the reader understand the rationale and the actual approach. I recognize there is also a reference to the Methods section, where all methodologies are reported in detail, but some of the sentences are hard to understand due to their synthetic format, e.g.: "markers with potential residual accessibility in human tissues were then filtered out".

We thank the reviewer for this comment and we have followed his recommendation to expand sentences with a synthetic format. Text changes and additions are underlined below:

“To limit batch effects, the collected samples were homogeneously processed from read alignment to peak calling. For each cell type, we derived a set of stable peaks observed across samples and studies, i.e. for each study, peaks detected in at least half of the samples were considered, and for each cell type, only peaks detected jointly in all studies were kept (see Materials and Methods, section 1).”

“To filter out markers that could be accessible in other human cell-types than those included in our reference profiles, we used the human atlas study (K. Zhang et al., 2021), which identified modules of open chromatin regions accessible in a comprehensive set of human tissues, and we excluded from our marker list the markers overlapping these modules (Figure 1, box 3, see Materials and Methods section 2).”

“For the deconvolution of tumor bulk samples, the lists of marker peaks specific to fibroblasts and endothelial cells were added to the PBMC markers. This extended set of markers was further refined based on the correlation patterns of the markers in tumor bulk samples from the diverse solid cancer types from The Cancer Genome Atlas (TCGA) (Corces et al., 2018), i.e., markers exhibiting the highest correlation patterns in the tumor bulk samples were selected using the findCorrelation function from the caret R package (Kuhn, 2008)  (Figure 1, box 4, see the Material and methods, section 2).”

Also, following the comments and recommendations of the Reviewer 1, we have: (i) moved the method section describing the adaptation of EPIC to ATACseq data to provide more details in the results section (see answer to the third comment of Reviewer 1), (ii) clarified how the existing tools used in the benchmarking analyses were adapted for ATAC-Seq deconvolution (see answer to the second comment of Reviewer 1), and (iii) detailed how the comparison between our estimations of the infiltration levels in the samples from Kumegawa et al. and the estimations from the original study was performed (see answer to the seventh recommendation of Reviewer 1).

(6) In the main text, it is stated that "the list of markers was further refined based on the correlation patterns of the markers in tumor bulk samples from diverse cancer types from The Cancer Genome Atlas". It should be clarified if these are only solid cancers, or if blood cancers were also used.

We have considered only the solid cancers and have clarified this point in the results section: “This extended set of markers was further refined based on the correlation patterns of the markers in tumor bulk samples from the diverse solid cancer types from The Cancer Genome Atlas”.

(7) When reporting that "these predictions are consistent with the infiltration level estimations reported in the original publication", it should be mentioned how the infiltration levels were quantified in this publication and how this agreement was quantified. This would be important also to claim in the abstract that "EPIC-ATAC accurately infers the immune contexture of the main breast cancer subtypes".

We thank the reviewer for this comment, we acknowledge that the agreement between the EPIC-ATAC predictions and the infiltration levels quantified in the original publication should be further described in the paper. We have expanded the text in the results section in the paragraph “EPIC-ATAC accurately infers the immune contexture in a bulk ATAC-Seq breast cancer cohort” to clarify this point. Additionally, we have added a panel in Figure 6 (panel A) which shows a good agreement between EPIC-ATAC predictions and the metric used in the original paper to evaluate the infiltration levels of different cell types.

The added text is underlined below:

“We applied EPIC-ATAC to a breast cancer cohort of 42 breast ATAC-Seq samples including samples from two breast cancer subtypes, i.e., 35 oestrogen receptor (ER)-positive human epidermal growth factor receptor 2 (HER2)-negative (ER+/HER2-) samples and 7 triple negative (TNBC) tumors (Kumegawa et al., 2023). No cell sorting was performed in parallel to the chromatin accessibility sequencing. For this reason, the authors used a set of cell-type-specific cis-regulatory elements (CREs) identified in scATAC-Seq data from similar breast cancer samples (Kumegawa et al., 2022) and estimated the amount of infiltration of each cell type by averaging the ATAC-Seq signal of each set of cell-type-specific CREs in their samples. We used EPIC-ATAC to estimate the proportions of different cell types of the TME. These predictions were then compared to the metric used by Kumegawa and colleagues in their study to infer levels of infiltration. A high correlation between the two metrics was observed for each cell type (Pearson’s correlation coefficient from 0.5 for myeloid cells to 0.94 for T cells, Figure 6A).”  

(8) It should be made explicit if EPIC-ATAC quantifies mDC, pDC, or their sum.

In our collection of reference ATAC-Seq samples from which the markers and profiles have been derived, mDCs and pDCs were both included in the dendritic cells.  EPIC-ATAC thus quantifies the total amount of dendritic cells, i.e., mDCs and pDCs included. We have added a sentence in the main text to clarify this point:

To identify robust chromatin accessibility marker peaks of cancer relevant cell types, we collected 564 samples of sorted cell populations from twelve studies including eight immune cell types (B cells […] dendritic cells (DCs) (mDCs and pDCs are grouped in this cell-type category) […] and  endothelial (Liu et al., 2020; Xin et al., 2020) cells (Figure 1 box 1, Figure 2A, Supplementary Table 1).

Reviewer #2 (Recommendations For The Authors):

The authors should double-check the naming of tools is done correctly e.g. ChIPSeeker has been spelled incorrectly in some instances throughout the manuscript.

We thank the reviewer for pointing out this mistake and have corrected the mistake in the main text.

eLife assessment

This is a useful paper regarding the roles of brown adipose tissue and skeletal muscle in thermogenesis in mice, with potential significance for the field. The overall approach is innovative but on balance the evidence for the claim is incomplete, as cast immobilization, while innovative, is likely stressful, may impact muscle and BAT directly, and imposes an energetic cost of motion on the animal that is not accounted for. Further experiments are also needed to directly assess the role of adipose-derived BCAAs in thermogenesis.

Reviewer #1 (Public Review):

Summary:

Heat production mechanisms are flexible, depending on a wide variety of genetic, dietary, and environmental factors. The physiology associated with each mechanism is important to understand since loss of flexibility is associated with metabolic decline and disease.

The phenomenon of compensatory heat production has been described in some detail in publications and reviews, notably by modifying BAT-dependent thermogenesis (for example by deleting UCP1 or impairing lipolysis, cited in this paper).

These authors chose to eliminate exercise as an alternative means of maintaining body temperature. To do this, they cast either one or both mouse hindlimbs.

This paper is set up as an evaluation of a loss of function of muscle on the functionality of BAT.

Strengths:

The study is supported by a variety of modern techniques and procedures.

Weaknesses:

The authors show that cast immobilization (CI) does not work as a (passive) loss of function, instead, this procedure produces a dramatic gain of function, putting the animal under considerable stress, inducing b-adrenergic effectors, increased oxygen consumption, and IL6 expression in a variety of tissues, together with commensurate cachectic effects on muscle and fat. The BAT is put under considerable stress, super-induced but relatively poor functioning.

Thus within hours and days of CI, there is massive muscle loss (leading to high circulating BCAAs), and loss of lipid reserves in adipose and liver. The lipid cycle that maintains BAT thermogenesis is depleted and the mouse is unable to maintain body temperature.

I cannot agree with these statements in the Discussion:

"We have here shown that cast immobilization suppressed skeletal muscle thermogenesis, resulting in failure to maintain core body temperature in a cold environment."<br /> • This result could also be attributed to high stress and decreased calorie reserves. Note also: CI suppresses 50% of locomotor activity, but the actual work done by the mouse carrying bilateral casts is not taken into account.

"Thermoregulatory system in endotherms cannot be explained by thermogenesis based on muscle contraction alone, with nonshivering thermogenesis being required as a component of the ability to tolerate cold temperatures in the long term."<br /> • This statement is correct, and it clearly showcases how difficult it is to interpret results using this CI strategy. The question to the author is- which components of muscle thermogenesis are actually inhibited by CI, and what is the relative heat contribution?

This conclusion is overinterpreted:

"In conclusion, we have shown that cast immobilization induced thermogenesis in BAT that was dependent on the utilization of free amino acids derived from skeletal muscle, and that muscle-derived IL-6 stimulated BCAA metabolism in skeletal muscle. Our findings may provide new insights into the significance of skeletal muscle as a large reservoir of amino acids in the regulation of body temperature".

In terms of the production of the article - the data shown in the heat maps has oddly obscure log10 dimensions. The changes are minimal, approx. 1.5x increase/decrease and therefore significance would be key to reporting these data. Fig.3C heatmap is not suitable. What are the 6 lanes to each condition? Overall, this has little/no information.

Rather than cherry-picking for a few genes, the results could be made more rigorous using RNA-seq profiling of BAT and muscle tissues.

Reviewer #2 (Public Review):

Summary:

In this study, the authors identified a previously unrecognized organ interaction where limb immobilization induces thermogenesis in BAT. They showed that limb immobilization by cast fixation enhances the expression of UCP1 as well as amino acid transporters in BAT, and amino acids are supplied from skeletal muscle to BAT during this process, likely contributing to increased thermogenesis in BAT. Furthermore, the experiments with IL-6 knockout mice and IL-6 administration to these mice suggest that this cytokine is likely involved in the supply of amino acids from skeletal muscle to BAT during limb immobilization.

Strengths:

The function of BAT plays a crucial role in the regulation of an individual's energy and body weight. Therefore, identifying new interventions that can control BAT function is not only scientifically significant but also holds substantial promise for medical applications. The authors have thoroughly and comprehensively examined the changes in skeletal muscle and BAT under these conditions, convincingly demonstrating the significance of this organ interaction.

Weaknesses:

Through considerable effort, the authors have demonstrated that limb-immobilized mice exhibit changes in thermogenesis and energy metabolism dynamics at their steady state. However, The impact of immobilization on the function of skeletal muscle and BAT during cold exposure has not been thoroughly analyzed.

eLife assessment

This important study combines experiment and theory to examine how the intrinsic physiological properties of neurons involved in orchestrating birdsong are related to the temporal structure of song. Intrinsic properties determine how neurons respond to inputs, and in this manuscript, the authors describe rules that connect these intrinsic properties to a learned behaviour, the learned song of an adult songbird. The experimental data are convincing and the computational model builds on a robust and well-validated biophysical framework. Although some key points of the model could be established more strongly, the evidence supporting the idea that song temporal structure is related to intrinsic physiology is solid and this research will be of general interest to researchers in the field and neurophysiologists.

Reviewer #1 (Public Review):

Summary:

Previous research from the Margoliash laboratory has demonstrated that the intrinsic electrophysiological properties of one class of projection neurons in the song nucleus HVC, HVCX neurons, are similar within birds and differ between birds in a manner that relates to the bird's song. The current study builds on this research by addressing how intrinsic properties may relate to the temporal structure of the bird's song and by developing a computational model for how this can influence sequence propagation of activity within HVC during singing.

First, the authors identify that the duration of the song motif is correlated with the duration of song syllables and particularly the length of harmonic stacks within the song. They next found positive correlations between some of the intrinsic properties, including firing frequency, sag ratio, and rebound excitation area with the duration of the birds' longest harmonic syllable and some other measure of motif duration. These results were extended by examining measures of firing frequency and sag ratio between two groups of birds that were experimentally raised to learn songs that only differed by the addition of a long terminal harmonic stack in one of the groups. Lastly, the authors present an HH-based model elucidating how the timing and magnitude of rebound excitation of HVCX neurons can function to support previously reported physiological network properties of these neurons during singing.

Strengths:

By trying to describe how intrinsic properties (IPs) may relate to the structure of learned behavior and providing a potentially plausible model (see below for more on this) for how differences in IPs can relate to sequence propagation in this neural network, this research is addressing an important and challenging issue. An understanding of how cell types develop IPs and how those IPs relate to the function and output of a network is a fundamental issue. Tackling this in the zebra finch HVC is an elegant approach because it provides a quantifiable and reliable behavior that is explicitly tied to the neurons that the authors are studying. Nonetheless, this is a difficult problem, and kudos to the authors for trying to unravel this.

Correlations between harmonic stack durations and song durations are well-supported and interesting. This provides a new insight that can and will likely be used by other research groups in correlating neuronal activity patterns to song behavior and motif duration. Additionally, correlations between IPs associated with rebound excitation are also well supported in this study.

The HH-model presented is important because it meaningfully relates how high or low rebound excitation can set the integration time window for HVCX neurons. Further, the synaptic connectivity of this model provides at least one plausible way in how this functions to permit the bursting activity of HVCX neurons during singing (and potentially during song playback experiments in sleeping birds). Thus, this model will be useful to the field for understanding how this network activity intersects with 'learned' IPs in an important class of neurons in this circuit.

Weaknesses:

The main weakness of the study is that there is somewhat of a disconnect between the physiological measurements described and the key components of the circuit model presented at the end of the paper. Thus, better support could be provided to link the magnitude of rebound excitation with song temporal structure. The rebound excitation area is shown to be positively correlated with the longest harmonic stack. Does this correlation hold when the four birds with unusually long stacks (>150ms) are excluded? Is rebound excitation area positively correlated with motif duration? Additionally, rebound excitation was not considered when examining experimentally tutored birds. Further analysis of these correlations can better link this research to the model presented.

The HH model is of general interest, but I am concerned about the plausibility of some of this circuitry, particularly because synaptic connectivity underlying information flow is a central component of the model. At several steps in the model, excitatory drive onto HVCX neurons is coming from another HVCX neuron. Although disynaptic inhibition between HVCX neurons and between HVCRA and HVCX neurons is well established, I am not aware of any data indicating direct synaptic connections between HVCX neurons.

Thus, how does the model change if all excitatory drive onto HVCX neurons are coming from HVCRA neurons? Currently, the model is realized through neurons being active at syllable or gesture transitions. What does the model predict about the distribution of HVCRA neurons activity across songs if they are the exclusive excitatory input to HVCX neurons? A better consideration of these issues can improve the suitability of the model in the context of known connectivity.

If I understand the model and ideas correctly, birds with longer motifs should exhibit longer pauses in the activity of tonically active HVC interneurons during singing and they should exhibit longer post-rebound integration windows. Experimental evidence supporting either of these ideas is not provided and would strengthen this research.

Reviewer #2 (Public Review):

Intrinsic properties of a neuron refer to the ion channels that a neuron expresses. These ion channels determine how a neuron responds to its inputs. How intrinsic properties link to behavior remains poorly understood. Medina and Margoliash address this question using the zebra finch, a well-studied songbird. Previous studies from their lab and other labs have shown that the intrinsic properties of adult songbird basal-ganglia projecting premotor neurons, are more similar within a bird than across birds. Across birds, this similarity is related to the extent of similarity in the songs; the more similar the song between two birds, the more similar the intrinsic properties between the neurons of these two birds. Finally, the intrinsic properties of these neurons change over the course of development and are sensitive to intact auditory feedback. However, the song features that relate to these intrinsic properties and the function of the within-bird homogeneity of intrinsic properties are unclear.

In this manuscript, the authors address these two questions by examining the intrinsic properties of basal-ganglia projecting premotor neurons in zebra finch brain slices. Specifically, they focus on the Ih current (as this is related to rhythmic activity in many pattern-generating circuits) and correlate the properties of the Ih current with song features. They find that the sag ratio (a measure of the driving force of the Ih current) and the rebound area (a measure of the post-inhibitory depolarisation) are both correlated with the temporal features of the song. First, they show the presence of correlations between the length of the song motif and the length of the longest syllable (most often a harmonic stack syllable). Based on this, they conclude that longer song motifs are composed of longer syllables. Second, they show that HVCX neurons within a bird have more similar sag ratios and rebound areas than across birds. Third, the mean sag ratio and mean rebound areas across birds were correlated with the duration of the longest harmonic stack within the song. These two results suggest that IPs are correlated with the temporal structure of the song. To further test this, the authors used natural and experimental tutoring procedures to have birds that learned two different types of songs that only differed in length; the longer song had an extra harmonic stack at the end. Using these two sets of birds, the authors find larger sag ratios and higher firing frequencies in birds with longer songs. Fifth, they show that the post-inhibitory rebound area allows neurons to respond to excitatory inputs and produce spikes. Neurons with a larger rebound area have a larger time window for responding to excitatory inputs. Based on this, they speculate that HVCX neurons with larger rebound areas integrate over larger time windows. Finally, they make a network model of HVC and show that one specific model could explain sequence-specific bursting of HVCX neurons.

Strengths

The question being addressed is an interesting question and the authors use appropriate techniques. The authors find a new temporal structure within the song, specifically, they find that longer songs typically have more syllables and longer syllables. As far as I know, this has not been shown earlier. The authors build on existing literature to suggest that IPs of HVCX neurons are correlated with the temporal structure of songs.

Weaknesses

I have a number of concerns with the statistics and interpretation of the results, insufficient controls for one experiment, and the specifics of the model that affect the implications of these results. These concerns are listed in the recommendations for the authors.

Reviewer #3 (Public Review):

It is rare to find systems in neuroscience where a detailed mechanistic link can be made between the biophysical properties of individual neurons and observable behaviors. In this study, Medina and Margoliash examined how the intrinsic physiological properties of a subclass of neurons in HVC, the main nucleus orchestrating the production of birdsong, might have an effect on the temporal structure of a song. This builds on prior work from this lab demonstrating that intrinsic properties of these neurons are highly consistent within individual animals and more similar between animals with similar songs, by identifying specific acoustic features of the song that covary with intrinsic properties and by setting forth a detailed biophysical network model to explain the relationship.

The main experimental finding is that excitability, hyperpolarization-evoked sag, and rebound depolarization are correlated with song duration and the duration of long harmonic elements. This motivates the hypothesis that rebound depolarization acts as a coincidence detector for the offset of inhibition associated with the previous song element and excitation associated with the start of the next element, with the delay and other characteristics of the window determined primarily by Ih. The idea is then that the temporal sensitivity of coincidence detection, which is common to all HVCx neurons, sets a global tempo that relates to the temporal characteristics of a song. This model is supported by some experimental data showing variation in the temporal integration of rebound spiking and by a Hodgkin-Huxley-based computational model that demonstrates proof of principle, including the emergence of a narrow (~50 ms) post-inhibitory window when excitatory input from other principal neurons can effectively evoke spiking.

Overall, the data are convincing and the model is compelling. The manuscript plays to the strengths of zebra finch song learning and the well-characterized microcircuitry and network dynamics of HVC. Of particular note, the design for the electrophysiology experiments employed both a correlational approach exploiting the natural variation in zebra finch song and a more controlled approach comparing birds that were tutored to produce songs that differed primarily along a single acoustical dimension. The modeling is based on Hodgkin-Huxley ionic conductances that have been pharmacologically validated, and the connections and functional properties of the network are consistent with prior work. This makes for a level of mechanistic detail that will likely be fruitful for future work.

There are some minor to moderate weaknesses. A minor weakness in the analysis of the experimental data relates to the handling of multiple correlations. There are several physiological variables that covary and several acoustical variables that covary, which makes it difficult to interpret standard Pearson correlation coefficients between any two individual variables. This is a minor concern because the results of the correlational analysis were confirmed in separate experiments with controlled tutoring, but a partial correlation analysis or latent factor analysis would be a more rigorous way of analyzing the natural live tutoring data.

eLife assessment

This preprint explores the involvement of cyclic di-GMP in genome stability and antibiotic persistence regulation in bacterial biofilms. The authors proposed a novel mechanism that, due to bacterial adhesion, increases c-di-GMP levels and influences persister formation through interaction with HipH. While the work may provide useful insights that could attract researchers in biofilm studies and persistence mechanisms, the main findings are inadequately supported and require further validation and refinement in experimental design.

Reviewer #1 (Public Review):

The authors propose a UPEC TA system in which a metabolite, c-di-GMP, acts as the AT with the toxin HipH. The idea is novel, but several key ideas are missing in regard to the relevant literature, and the experimental design is flawed. Moreover, they are absolutely not studying persister cells as Figure 1b clearly shows they are merely studying dying cells since no plateau in killing (or anything close to a plateau) was reached. So in no way has persistence been linked to c-di-GMP. Moreover, I do not think the authors have shown how the c-di-GMP sensor works. Also, there is no evidence that c-di-GMP is an antitoxin as no binding to HipH has been shown. So at best, this is an indirect effect, not a new toxin/antitoxin system as for all 7 TAs, a direct link to the toxin has been demonstrated for antitoxins.

Weaknesses:

(1) L 53: biofilm persisters are no different than any other persisters (there is no credible evidence of any different persister cells) so this reviewer suggests changing 'biofilm persisters' to 'persisters' throughout the text.

(2) L 51: persister cells do not mutate and, once resuscitated, mutate like any other growing cell so this sentence should be deleted as it promotes an unnecessary myth about persistence.

(3) L 69: please include the only metabolic model for persister cell formation and resuscitation here based on single cells (e.g., doi.org/10.1016/j.bbrc.2020.01.102 , https://doi.org/10.1016/j.isci.2019.100792 ); otherwise, you write as if there are no molecular mechanisms for persistence/resuscitation.

(4) The authors should cite in the Intro or Discussion that others have proposed similar novel TAs including a ppGpp metabolic toxin paired with an enzymatic antitoxin SpoT that hydrolyzes the toxin (http://dx.doi.org/10.1016/j.molcel.2013.04.002).

(5) Figure 1b: there are no results in this paper related to persister cells. Figure 1b simply shows dying cells were enumerated. Hence, the population of stressed cells increased, not 'persister cells' (Figure 1f), in the course of these experiments.

(6) Figure S1: I see no evidence that the authors have shown this c-di-GMP detects different c-di-GMP levels since there appears to be no data related to varying c-di-GMP concentrations with a consistent decrease. Instead, there is a maximum. What are the concentration of c-di-GMP on the X-axis for panels C, D, and E? How were c-di-GMP levels varied such that you know the c-di-GMP concentration?

(7) The viable portion of the VBNC population are persister cells so there is no reason to use VBNC as a separate term. Please see the reported errors often made with nucleic acid staining dyes in regard to VBNCs.

Reviewer #2 (Public Review):

Summary:

Hebin et al reported a fascinating story about antibiotic persistence in the biofilms. First, they set up a model to identify the increased persisters in the biofilm status. They found that the adhesion of bacteria to the surface leads to increased c-di-GMP levels, which might lead to the formation of persisters. To figure out the molecular mechanism, they screened the E.coli Keio Knockout Collection and identified the HipH. Finally, the authors used a lot of data to prove that c-di-GMP not only controls HipH over-expression but also inhibits HipH activity, though the inhibition might be weak.

Strengths:

They used a lot of state-of-the-art technologies, such as single-cell technologies as well as classical genetic and biochemistry approaches to prove the concept, which makes the conclusions very solid. Overall, it is a very interesting and solid story that might attract diverse readers working with c-di-GMP, persisters, and biofilm.

Weaknesses:

(1) Is HipH the only target identified by screening the E.coli Keio Knockout Collection?

(2) Since the story is complicated, a diagrammatic picture might be needed to illustrate the whole story. And the title does not accurately summarize the novelty of this study.

(3) The ratio of mVenus NB to mScarlet-I (R) negatively correlates with the concentration of c-di-GMP. Therefore, R -1 demonstrates a positive correlation with the concentration of c-di-GMP. Is this method validated with other methods to quantify c-di-GMP， or used in other studies？

(4) References are missing throughout the manuscript. Please add enough references for every conclusion.

(5) The novelty of this study should be clearly written and compared with previous references. For example, is it the first study to report the mechanism that the adhesion of bacteria to the surface leads to increased persister formation?

(6) in vitro DNA cleavage assay. Why not use bacterial genomic DNA to test the cleaving of HipH on the bacterial genome?

(7) C-di-gmp -HipH is not a TA, it does not fit in the definition of the TA systems. You can say C-di-gmp is an antitoxin based on your study, but C-di-gmp -HipH is not a TA pair.

Reviewer #1 (Public Review):

The authors introduce DIPx, a deep learning framework for predicting synergistic drug combinations for cancer treatment using the AstraZeneca-Sanger (AZS) DREAM Challenge dataset. While the approach is innovative, I have the following concerns and comments which hopefully will improve the study's rigor and applicability, making it a more powerful tool in the real clinical world.

(1) Test Set 1 comprises combinations already present in the training set, likely leading overfitting issue. The model might show inflated performance metrics on this test set due to prior exposure to these combinations, not accurately reflecting its true predictive power on unknown data, which is crucial for discovering new drug synergies. The testing approach reduces the generalizability of the model's findings to new, untested scenarios.

(2) The model struggles with predicting synergies for drug combinations not included in its training data (showing only a Spearman correlation of 0.26 in Test Set 2). This limits its potential for discovering new therapeutic strategies. Utilizing techniques such as transfer learning or expanding the training dataset to encompass a wider range of drug pairs could help to address this issue.

(3) The use of pan-cancer datasets, while offering broad applicability, may not be optimal for specific cancer subtypes with distinct biological mechanisms. Developing subtype-specific models or adjusting the current model to account for these differences could improve prediction accuracy for individual cancer types.

(4) Line 127, "Since DIPx uses only molecular data, to make a fair comparison, we trained TAJI using only molecular features and referred to it as TAJI-M.". TAJI was designed to use both monotherapy drug-response and molecular data, and likely won't be able to reach maximum potential if removing monotherapy drug-response from the training model. It would be critical to use the same training datasets and then compare the performances. From Figure 6 of TAJI's paper (Li et al., 2018, PMID: 30054332) , i.e., the mean Pearson correlation for breast cancer and lung cancer is around 0.5 - 0.6.

The following 2 concerns have been included in the Discussion section which is great:

(1) Training and validating the model using cell lines may not fully capture the heterogeneity and complexity of in vivo tumors. To increase clinical relevance, it would be beneficial to validate the model using primary tumor samples or patient-derived xenografts.

(2) The Pathway Activation Score (PAS) is derived exclusively from primary target genes, potentially overlooking critical interactions involving non-primary targets. Including these secondary effects could enhance the model's predictive accuracy and comprehensiveness.

Reviewer #2 (Public Review):

Trac, Huang, et al used the AZ Drug Combination Prediction DREAM challenge data to make a new random forest-based model for drug synergy. They make comparisons to the winning method and also show that their model has some predictive capacity for a completely different dataset. They highlight the ability of the model to be interpretable in terms of pathway and target interactions for synergistic effects. While the authors address an important question, more rigor is required to understand the full behavior of the model.

Major Points

(1) The authors compare DIPx to the winning method of the DREAm challenge, TAJI to show that from molecular features alone they retrain TAJI to create TAJI-M without the monotherapy data inputs. They mention that "of course, we could also use such data in DIPx...", but they never show the behaviour of DIPx with these data. The authors need to demonstrate that this statement holds true or else compare it to the full TAJI.

(2) It would be neat to see how the DIPx feature importance changes with monotherapy input. For most realistic scenarios in which these models are used robust monotherapy data do exist.

(3) In Figure 2, the authors compare DIPx and TAJI-M on various test sets. If I understood correctly, they also bootstrapped the training set with n=100 and reported all the model variants in many of the comparisons. While this is a nice way of showing model robustness, calculating p-values with bootstrapped data does not make sense in my opinion as by increasing the value of n, one can make the p-value arbitrarily small. The p-value should only be reported for the original models.

(4) From Figures 2 and 3, it appears DIPx is overfit on the training set with large gaps in Spearman correlations between Test Set 2/ONeil set and Test Set 1. It also features much better in cases where it has seen both compounds. Could the authors also compare TAJI on the ONeil dataset to show if it is as much overfit?

Reviewer #3 (Public Review):

Summary:

Predicting how two different drugs act together by looking at their specific gene targets and pathways is crucial for understanding the biological significance of drug combinations. Such combinations of drugs can lead to synergistic effects that enhance drug efficacy and decrease resistance. This study incorporates drug-specific pathway activation scores (PASs) to estimate synergy scores as one of the key advancements for synergy prediction. The new algorithm, Drug synergy Interaction Prediction (DIPx), developed in this study, uses gene expression, mutation profiles, and drug synergy data to train the model and predict synergy between two drugs and suggests the best combinations based on their functional relevance on the mechanism of action. Comprehensive validations using two different datasets and comparing them with another best-performing algorithm highlight the potential of its capabilities and broader applications. However, the study would benefit from including experimental validation of some predicted drug combinations to enhance its reliability.

Strengths:

The DIPx algorithm demonstrates the strengths listed below in its approach for personalized drug synergy prediction. One of its strengths lies in its utilization of biologically motivated cancer-specific (driver genes-based) and drug-specific (target genes-based) pathway activation scores (PASs) to predict drug synergy. This approach integrates gene expression, mutation profiles, and drug synergy data to capture information about the functional interactions between drug targets, thereby providing a potential biological explanation for the synergistic effects of combined drugs. Additionally, DIPx's performance was tested using the AstraZeneca-Sanger (AZS) DREAM Challenge dataset, especially in Test Set 1, where the Spearman correlation coefficient between predicted and observed drug synergy was 0.50 (95% CI: 0.47-0.53). This demonstrates the algorithm's effectiveness in handling combinations already in the training set. Furthermore, DIPx's ability to handle novel combinations, as evidenced by its performance in Test Set 2, indicates its potential for extrapolating predictions to new and untested drug combinations. This suggests that the algorithm can adapt to and make accurate predictions for previously unencountered combinations, which is crucial for its practical application in personalized medicine. Overall, DIPx's integration of pathway activation scores and its performance in predicting drug synergy for known and novel combinations underscore its potential as a valuable tool for personalized prediction of drug synergy and exploration of activated pathways related to the effects of combined drugs.

Weaknesses:

While the DIPx algorithm shows promise in predicting drug synergy based on pathway activation scores, it's essential to consider its limitations. One limitation is that the algorithm's performance was less accurate when predicting drug synergy for combinations absent from the training set. This suggests that its predictive capability may be influenced by the availability of training data for specific drug combinations. Additionally, further testing and validation across different datasets (more than the current two datasets) would be necessary to assess the algorithm's generalizability and robustness fully. It's also important to consider potential biases in the training data and ensure that DIPx predictions are validated through empirical studies including experimental testing of predicted combinations. Despite these limitations, DIPx represents a valuable step towards personalized prediction of drug synergy and warrants continued investigation and improvement. It would benefit if the algorithm's limitations are described with some examples and suggest future advancement steps.

eLife assessment

This study retrospectively analyzed clinical data to develop a risk prediction model for pulmonary hypertension in high-altitude populations. The evidence is solid and the findings are useful and hold clinical significance as the model can be used for intuitive and individualized prediction of pulmonary hypertension risk in these populations.

eLife assessment

This manuscript provides valuable evidence comparing the performance of mathematical models and opinions from experts engaged in outbreak response in forecasting the spatial spread of an Ebola epidemic. The evidence supporting the conclusions is convincing though the work might have benefited from the use of more than two models in the ensemble predictions. It will be of interest to disease modellers, infectious disease epidemiologists, policy-makers, and those who need to inform policy-makers during an outbreak.

Reviewer #1 (Public Review):

Munday, Rosello, and colleagues compared predictions from a group of experts in epidemiology with predictions from two mathematical models on the question of how many Ebola cases would be reported in different geographical zones over the next month. Their study ran from November 2019 to March 2020 during the Ebola virus outbreak in the Democratic Republic of the Congo. Their key result concerned predicted numbers of cases in a defined set of zones. They found that neither the ensemble of models nor the group of experts produced consistently better predictions. Similarly, neither model performed consistently better than the other, and no expert's predictions were consistently better than the others. Experts were also able to specify other zones in which they expected to see cases in the next month. For this part of the analysis, experts consistently outperformed the models. In March, the final month of the analysis, the models' accuracy was lower than in other months and consistently poorer than the experts' predictions.

A strength of the analysis is the use of consistent methodology to elicit predictions from experts during an outbreak that can be compared to observations, and that are comparable to predictions from the models. Results were elicited for a specified group of zones, and experts were also able to suggest other zones that were expected to have diagnosed cases. This likely replicates the type of advice being sought by policymakers during an outbreak.

A potential weakness is that the authors included only two models in their ensemble. Ensembles of greater numbers of models might tend to produce better predictions. The authors do not address whether a greater number of models could outperform the experts.

The elicitation was performed in four months near the end of the outbreak. The authors address some of the implications of this. A potential challenge to the transferability of this result is that the experts' understanding of local idiosyncrasies in transmission may have improved over the course of the outbreak. The model did not have this improvement over time. The comparison of models to experts may therefore not be applicable to the early stages of an outbreak when expert opinions may be less well-tuned.

This research has important implications for both researchers and policy-makers. Mathematical models produce clearly-described predictions that will later be compared to observed outcomes. When model predictions differ greatly from observations, this harms trust in the models, but alternative forms of prediction are seldom so clearly articulated or accurately assessed. If models are discredited without proper assessment of alternatives then we risk losing a valuable source of information that can help guide public health responses. From an academic perspective, this research can help to guide methods for combining expert opinion with model outputs, such as considering how experts can inform models' prior distributions and how model outputs can inform experts' opinions.

Reviewer #2 (Public Review):

Summary:

The manuscript by Munday et al. presents real-time predictions of geographic spread during an Ebola epidemic in north-eastern DRC. Predictions were elicited from individual experts engaged in outbreak response and from two mathematical models. The authors found comparable performance between experts and models overall, although the models outperformed experts in a few dimensions.

Strengths:

Both individual experts and mathematical models are commonly used to support outbreak response but rarely used together. The manuscript presents an in-depth analysis of the accuracy and decision-relevance of the information provided by each source individually and in combination.

Weaknesses:

A few minor methodological details are currently missing.

Reviewer #3 (Public Review):

Summary

The authors show that ELS induces a number of brain and behavioral changes in the adult lateral amygdala. These changes include enduring astrocytic dysfunction, and inducing astrocytic dysfunction via genetic interventions is sufficient to phenocopy the behavioral and neural phenotypes. This suggests that astrocyte dysfunction may play a causal role in ELS-associated pathologies.

Strengths:

A strength is the shift in focus to astrocytes to understand how ELS alters adult behavior.

Weaknesses:

The mechanistic links between some of the correlates - altered astrocytic function, changes in neural excitability, and synaptic plasticity in the lateral amygdala and behaviour - are underdeveloped.

eLife assessment

Early-life adversity or stress can enhance stress susceptibility by causing changes in emotion, cognition, and reward-seeking behaviors. This important manuscript highlights the involvement of lateral amygdala astrocytes in fear generalization and the associated synaptic plasticity, which are parallel to the effects of early life stress. With an elegant combination of behavioral models, morphological and functional assessments using immunostaining, electrophysiology, and viral-mediated loss-of-function approaches, the authors provide solid correlational and causal evidence that is consistent with the hypothesis that early life stress produces neural and behavioral dysfunction via perturbing lateral amygdala astrocytic function.

Reviewer #1 (Public Review):

Summary:

The manuscript asks the question of whether astrocytes contribute to behavioral deficits triggered by early life stress. This question is tested by experiments that monitor the effects of early life stress on anxiety-like behaviors, long-term potentiation in the lateral amygdala, and immunohistochemistry of astrocyte-specific (GFAP, Cx43, GLT-1) and general activity (c-Fos ) markers. Secondarily, astrocyte activity in the lateral amygdala is impaired by viruses that suppress gap-junction coupling or reduce astrocyte Ca2+ followed by behavioral, synaptic plasticity, and c-Fos staining. Early life stress is found to reduce the expression of GFAP and Cx43 and to induce translocation of the glucocorticoid receptor to astrocytic nuclei. Both early life stress and astrocyte manipulations are found to result in the generalization of fear to neutral auditory cues. All of the experiments are done well with appropriate statistics and control groups. The manuscript is very well-written and the data are presented clearly. The authors' conclusion that lateral amygdala astrocytes regulate amygdala-dependent behaviors is strongly supported by the data. However, the extent to which astrocytes contribute to behavioral and neuronal consequences of early life stress remains open to debate.

Strengths:

A strong combination of behavioral, electrophysiology, and immunostaining approaches is utilized and possible sex differences in behavioral data are considered. The experiments clearly demonstrate that disruption of astrocyte networks or reduction of astrocyte Ca2+ provokes generalization of fear and impairs long-term potentiation in the lateral amygdala. The provocative finding that astrocyte dysfunction accounts for a subset of behavioral effects of early life stress (e.g. not elevated plus or distance traveled observations) is also perceived as a strength.

Weaknesses:

The main weakness is the absence of more direct evidence that behavioral and neuronal plasticity after early life stress can be attributed to astrocytes. It remains unknown what would happen if astrocyte activity were disrupted concurrently with early life stress or if the facilitation of astrocyte Ca2+ would attenuate early life stress outcomes. As is, the only evidence that early life stress involves astrocytes is nuclear translocation of GR and downregulation of GFAP and Cx43 in Figure 3 which may or may not provoke astrocyte Ca2+ or astrocyte network activity changes.

Reviewer #2 (Public Review):

Summary:

In this manuscript, Guayasamin et al. show that early-life stress (ELS) can induce a shift in fear generalisation in mice. They took advantage of a fear conditioning paradigm followed by a discrimination test and complemented learning and memory findings with measurements for anxiety-like behaviors. Next, astrocytic dysfunction in the lateral amygdala was investigated at the cellular level by combining staining for c-Fos with astrocyte-related proteins. Changes in excitatory neurotransmission were observed in acute brains slices after ELS suggesting impaired communication between neurons and astrocytes. To confirm the causality of astrocytic-neuronal dysfunction in behavioral changes, viral manipulations were performed in unstressed mice. Occlusion of functional coupling with a dominant negative construct for gap junction connexin 43 or reduction in astrocytic calcium with CalEx mimicked the behavioral changes observed after ELS suggesting that dysfunction of the astrocytic network underlies ELS-induced memory impairments.

Strengths:

Overall, this well-written manuscript highlights a key role for astrocytes in regulating stress-induced behavioral and synaptic deficits in the lateral amygdala in the context of ELS. Results are innovative, and methodological approaches relevant to decipher the role of astrocytes in behaviors. As mentioned by the authors, non-neuronal cells are receiving increasing attention in the neuroscience, stress, and psychiatry fields.

Weaknesses:

I do have several suggestions and comments to address that I believe will improve the clarity and impact of the work. For example, there is currently a lack of information on the timeline for behavioral experiments, tissue collection, etc.

eLife assessment

The authors report that chemogenetic methods targeting the ventral cervical spinal cord can be used to increase phrenic inspiratory motor output and subsequent diaphragm EMG activity and ventilation in rodents. These findings are important because they are a necessary first step towards using chemogenetic methods to drive inspiratory activity in disorders in which motor neurons are compromised, such as spinal injury and degenerative disease. The data are convincing, with rigorous assessments of phrenic inspiratory activity and its ability to drive the diaphragm and subsequent ventilation, as well as assessments of DREADD expression.

Reviewer #1 (Public Review):

Summary:<br /> The authors demonstrate that, while the loss of Ezrin increases lysosomal biogenesis and function, its presence is required for the specific endocytosis of EGFR. Upon further investigation, the authors reveal that Ezrin is a crucial intermediary protein that links EGFR to AKT, leading to the phosphorylation and inhibition of TSC. TSC is a critical negative regulator of the mTORC1 complex, which is dysregulated in various diseases, making their findings a valuable addition to multiple fields of study. Their cell signaling findings are translatable to an in vivo Medaka fish model and suggest that Ezrin may play a crucial role in retinal degeneration.

Strengths:<br /> Giamundo, Intartaglia, et al. utilized unbiased proteomic and transcriptomic screens in Ezrin KO cells to investigate the mechanistic function of Ezrin in lysosome and cell signaling pathways. The authors' findings are consistent with past literature demonstrating Ezrin's role in the EGFR and mTORC1 signaling pathways. They used several cell lines, small molecule inhibitors, and cellular and in vivo knockout models to validate signaling changes through biochemical and microscopy assays. Their use of multiple advanced microscopy techniques is also impressive.

Weaknesses:<br /> While the authors demonstrated activation of TSC1 (lysosomal accumulation) and inactivation of Akt (decreased phosphorylation in TSC1), as well as decreased mTORC1 signaling in Ezrin knockout cells, direct experiments showing the rescue of mTORC1 activity by AKT and TSC1 mutants are required to confirm the linear signaling pathway and establish Ezrin as a mediator of EGFR-AKT-TSC1-mTORC1 signaling. Although the authors presented representative images from advanced microscopy techniques to support their claims, there is insufficient quantification of these experiments. Additionally, several immunoblots in the manuscript lack vital loading controls, such as input lanes for immunoprecipitations and loading controls for western blots.

Reviewer #2 (Public Review):

Summary:<br /> The authors begin with the stated goal of gaining insight into the known repression of autophagy by Ezrin, a major membrane-actin linker that assembles signaling complexes on membranes. RNA and protein expression analysis is consistent with upregulation of lysosomal proteins in Ezrin-deficient MEFs, which the authors confirm by immunostaining and western blotting for lysosomal markers. Expression analysis also implicates EGF signaling as being altered downstream of Ezrin loss, and the authors demonstrate that Ezrin promotes relocalization of EGFR from the plasma membrane to endosomes. Ezrin loss impacts downstream MAPK/Akt/mTORC1 signaling, although the mechanistic links remain unclear. An Ezrin mutant Medaka fish line wa then generated to test Ezrin's role in retinal cells, which are known to be sensitive to changes in autophagy regulation. Phenotypes in this model appear generally consistent with observations made in cultured cells, though mild overall.

Strengths:<br /> Data on the impact of Ezrin-loss on relocalization of EGFR from the plasma membrane are extensive, and thoroughly demonstrate that Ezrin is required for EGFR internalization in response to EGF.

A new Ezrin-deficient in vivo model (Medaka fish) is generated.

Strong data demonstrates that Ezrin loss suppresses Akt signaling. Ezrin loss also clearly suppresses mTORC1 signaling in cell culture, although examination of mTORC1 activity is notably missing in Ezrin-deficient fish.

Weaknesses:<br /> LC3 is used as a readout of autophagy, however the lipidated/unlipidated LC3 ratio generally does not appear to change, thus there does not appear to be evidence that Ezrin loss is affecting autophagy in this study.

The conclusion is drawn that Ezrin loss suppresses EGF signaling, however this is complicated by a strong increase in phosphorylation of the p38 MAPK substrate MK2. Without additional characterization of MAPK and Erk signaling, the effect of Ezrin loss remains unclear.

Causative conclusions between effects on MAPK, Akt, and mTORC1 signaling are frequently drawn, but the data only demonstrate correlations. For example, many signaling pathways can activate mTORC1 including MAPK/Erk, thus reduced mTORC1 activity upon Ezrin-loss cannot currently be attributed to reduced Akt signaling. Similarly, other kinases can phosphorylate TSC2 at the sites examined here, so the conclusion cannot be drawn that Ezrin-loss causes a reduction in Akt-mediated TSC2 phosphorylation. In Figure 7, the conclusion cannot be drawn that retinal degeneration results from aberrant EGFR signaling.

It is unclear why TSC1 is highlighted in the title, as there does not appear to be any specific regulation of TSC1 here.

In Figure 1 the conclusion is drawn that there is an increase in lysosome number with Ezrin KO, however it does not appear that the current analysis can distinguish an increased number from increased lysosome size or activity. Similarly, conclusions about increased lysosome "biogenesis" could instead reflect decreased turnover.

Immunoprecipitation data for a role for Ezrin as a signaling scaffold appear minimal and seem to lack important controls.

In Figure 3A it seems difficult to conclude that EGFR dimerization is reduced since the whole blot, including the background between lanes, is lighter on that side.

In Figure 6C specificity controls for the TSC1 and TSC2 antibodies are not included, but seem necessary since their localization patterns appear very different from each other in WT cells.

In Figure 7 the signaling effects in Ezrin-deficient fish are mild compared to cultured cells, and effects on mTORC1 are not examined. Further data on the retinal cell phenotypes would strengthen the conclusions.

In Figure 7F there appears to be more EGFR throughout the cell, so it is difficult to conclude that more EGFR at the PM in Ezrin-/- fish means reduced internalization.

Reviewer #3 (Public Review):

Summary:<br /> In this study, the authors have attempted to demonstrate a critical role for the cytoskeletal scaffold protein Ezrin, in the upstream regulation of EGFR/AKT/MTOR signaling. They show that in the absence of Ezrin, ligand-induced EGFR trafficking and activation at the endosomes is perturbed, with decreased endosomal recruitment of the TSC complex, and a corresponding decrease in AKT/MTOR signaling.

Strengths:<br /> The authors have used a combination of novel imaging techniques, as well as conventional proteomic and biochemical assays to substantiate their findings. The findings expand our understanding of the upstream regulators of the EGFR/AKT MTOR signaling and lysosomal biogenesis, appear to be conserved in multiple species, and may have important implications for the pathogenesis and treatment of diseases involving endo-lysosomal function, such as diabetes and cancer, as well as neuro-degenerative diseases like macular degeneration. Furthermore, pharmacological targeting of Ezrin could potentially be utilized in diseases with defective TFEB/TFE3 functions like LSDs. While a majority of the findings appear to support the hypotheses, there are substantial gaps in the findings that could be better addressed. Since Ezrin appears to directly regulate MTOR activity, the effects of Ezrin KO on MTOR-regulated, TFEB/TFE3 -driven lysosomal function should be explored more thoroughly. Similarly, a more convincing analysis of autophagic flux should be carried out. Additionally, many immunoblots lack key controls (Control IgG in co-IPs) and many others merit repetition to either improve upon the quality of the existing data, validate the findings using orthogonal approaches, or provide a more rigorous quantitative assessment of the findings, as highlighted in the recommendation for authors.

eLife assessment

This valuable study by Cui et al. investigates mechanisms generating sighs, which are crucial for respiratory function and linked to emotional states. Utilizing advanced methods in mice, they provide solid evidence that increased excitability in specific preBötzinger complex neuronal subpopulations expressing Neuromedin B receptors, gastrin-releasing peptide receptors, or somatostatin, can induce sigh-like large-amplitude inspirations. With additional technical clarifications and further supporting evidence for the implied capability of the neuron subpopulations studied to intrinsically generate the normal slow sigh rhythm, the study will interest neuroscientists studying respiratory neurobiology and rhythmic motor systems.

Reviewer #1 (Public Review):

This manuscript validates and extends upon the sigh-generating circuit between the NMB/GRP+ RTN/parafacial neurons and the NMBR/GRPR+ preBötC neurons established in Li et al., 2016. The authors generate multiple transgenic lines that enable selective targeting of these various sub-populations of cells and demonstrate the sufficiency of each type in generating a sigh breath. Additionally, they show that NMBR and GPRP preBötC neurons are glutamatergic, have overlapping and distinct expressions, and do not express SST. Beyond this validation, the authors show that ectopic stimulation of SST neurons is sufficient to evoke sighs and that they are necessary for NMB/GRP-induced sighing. This data is the first time that preBötC neurons downstream of NMBR/GRPR neurons have been identified.

The five conclusions stated at the end of the introduction are supported by the data, but a strong emphasis throughout the manuscript is the identification of an unsubstantiated slow sigh rhythm that is produced by NMBR/GRPR neurons. To make such a novel (and quite surprising) claim requires many more studies and the conclusion is dependent on how the authors have defined a sigh. Moreover, some data within the paper conflicts with this idea.

In summary, the optogenetic and chemogenetic characterization of the neuropeptide pathway transgenic lines nicely aligns with and provides important validation of the previous study by Li et. al., 2016 and the SST neuron studies provide a new mechanism for the transformation of NMBR/GRPR neuropeptide activation into a sigh. These are important findings and they should be the points emphasized. The proposal of a slow sigh rhythm should be more rigorously established with new experiments and analysis or should be more carefully described and discussed.

Reviewer #2 (Public Review):

Summary:

This study investigates in mice neural mechanisms generating sighs, which are periodic large-amplitude breaths occurring during normal breathing that subserve physiological pulmonary functions and are associated with emotional states such as relief, stress, and anxiety. Sighs are generated by a structure called the preBötzinger complex (preBötC) in the medulla oblongata that generates various forms of inspiratory activity including sighs. The authors have previously described a circuit involving neurons producing bombesin-related peptides Neuromedin B (NMB) and gastrin-releasing peptide (GRP) that project to preBötC neurons expressing receptors for NMB (NMBRs) and GRP (GRPRs) and that activation of these preBötC neurons via these peptide receptors generates sighs. In this study, the authors further investigated mechanisms of sigh generation by applying optogenetic and chemogenetic strategies to selectively activate the subpopulations of preBötC neurons expressing NMBRs and/or GRPRs, and a separate subpopulation of neurons expressing somatostatin (SST) but not NMBRs and GRPRs. The authors present convincing evidence that sigh-like inspirations can be evoked by photostimulation of the preBötC neurons expressing NMBRs or GRPRs. Photostimulation of SST neurons can independently evoke sighs, and chemogenetic inhibition of these neurons can abolish sighs. The results presented support the authors' conclusion that the preBötC neurons expressing NMBRs or GRPRs produce sighs via pathways to downstream SST neurons. Thus, these studies have identified some of the preBötC cellular elements likely involved in generating sighs.

Strengths:

(1) This study employs an effective combination of electrophysiological, transgenic, optogenetic, chemogenetic, pharmacological, and neuron activity imaging techniques to investigate sigh generation by distinct subpopulations of preBötC neurons in mice.

(2) The authors extend previous studies indicating that there is a peptidergic circuit consisting of NMB and GRP expressing neurons that project from the parafacial (pF) nucleus region to the preBötC and provides sufficient input to generate sighs, since photoactivation of either pF NMB or GRP neurons evoke ectopic sighs in this study.

(3) Convincing evidence is presented that sighs can be evoked by direct photostimulation of preBötC neurons expressing NMBRs and/or GRPRs, and also a separate subpopulation of neurons expressing somatostatin (SST) but not NMBRs and GRPRs.

(4) The mRNA-expression data presented from in situ hybridization indicates that most preBötC neurons expressing NMBR, GRPR (or both) are glutamatergic and excitatory.

(5) Measurements in slices in vitro indicate that only the NMBR-expressing neurons are normally rhythmically active during normal inspiratory activity and endogenous sigh activity.

(6) Evidence is presented that activation of preBötC NMBRs and/or GRPRs is not necessary for sigh production, suggesting that sighs are not the unique product of the preBötC bombesin-peptide signaling pathway.

(7) The novel conclusion is presented that the preBötC neurons expressing NMBRs and/or GRPRs produce sighs via the separate downstream population of preBötC SST neurons, which the authors demonstrate can independently generate sighs, whereas chemogenetic inhibition of preBötC SST neurons selectively abolishes sighs generated by activating NMBRs and GRPRs.

Weaknesses:

(1) While these studies have identified subpopulations of preBötC neurons capable of episodically evoking sigh-like inspiratory activity, mechanisms producing the normal slow sigh rhythm were not investigated and remain unknown.

(2) Several key technical aspects of the study require further clarification to aid in interpreting the experimental results, including issues relating to the validation of the transgenic mouse lines and virally transduced expressions of proteins utilized for optogenetic and chemogenetic experiments, as well as justifying the optogenetic photostimulation paradigms used to evoke sighs.

Reviewer #3 (Public Review):

Summary:

This manuscript by Cui et al., studies the mechanisms for the generation of sighing, an essential breathing pattern. This is an important and interesting topic, as sighing maintains normal pulmonary function and is associated with various emotional conditions. However, the mechanisms of its generation remain not fully understood. The authors employed different approaches, including optogenetics, chemogenetics, intersectional genetic approach, slice electrophysiology, and calcium imaging, to address the question, and found several neuronal populations are sufficient to induce sighing when activated. Furthermore, ectopic sighs can be triggered without the involvement of neuromedin B (NMB) or gastrin-releasing peptide (GRP) or their receptors in the preBötzinger Complex (preBötC) region of the brainstem. Additionally, activating SST neurons in the preBötC region induces sighing, even when other receptors are blocked. Based on these results, the authors concluded that increased excitability in certain neurons (NMBR or GRPR neurons) activates pathways leading to sigh generation, with SST neurons serving as a downstream component in converting regular breaths into sighs

Strengths:

The authors employed a combination of various sophisticated approaches, including optogenetics, chemogenetics, intersectional genetic approach, slice electrophysiology and calcium imaging, to precisely pinpoint the mechanism responsible for sigh generation. They utilized multiple genetically modified mouse lines, enabling them to selectively manipulate and observe specific neuronal populations involved in sighing.

Using genetics and calcium imaging, the authors record the neuronal activity of NMBR and GRPR neurons, respectively, and identify their differences in activity patterns. Furthermore, by applying the intersectional approach, the authors were able to genetically target and manipulate several distinct neuronal populations, such as NMBR+, GRPR- neurons, and GRPR+, NMBR- neurons, and conducted a detailed characterization of their functions in influencing sighing.

Weaknesses:

The authors combined multiple approaches in this manuscript; however, the rationale and experimental details require further explanation, and their impacts on the conclusion require clarification. For instance, how and why the variability in optogenetic activation conditions could impact the experimental outcomes. Additionally, a more detailed characterization of the viral labeling efficiency and specificity is necessary to validate the claims made in these experiments. Without this, the results could be compromised by potential discrepancies in the number of labeled neurons or unintended labeling of other populations.

Moreover, the conclusion that preBötC NMBR and GRPR activations are unnecessary for sighing is not fully supported by the current experimental design. While the study shows that sighing can still be induced despite pharmacological inhibition of NMBR and GRPR, this does not conclusively prove that these receptors are not required under natural conditions. The artificial activation of downstream pathways through optogenetic or chemogenetic methods does not negate the potential physiological role of these receptors in sigh production. Therefore, the interpretation of these findings should be approached with caution, and further investigation is warranted to definitively determine the necessity of NMBR and GRPR activations in the natural sighing process.

eLife assessment

This study reports a fundamental observation concerning cell death regulation by the anti-apoptotic BCL2 family NOXA. The authors convincingly demonstrate that NOXA is destabilized through the interaction with WSB2, a substrate receptor in CRL5 ubiquitin ligase complex, sensitizing the cells to treatments. These are key findings for cell biologists and cancer researchers as they identified a new target impacting drug responsiveness in cancer therapies.

Reviewer #2 (Public Review):

Summary:

Exploring the DEP-MAP database and two drug-screen databases, the authors identify WSB2 as an interactor of several BCL2 proteins. In follow-up experiments, they show that CRL5/WSB2 controls NOXA protein levels via K48 ubiquitination following direct protein-protein interaction, and cell death sensitivity in the context of BH3 mimetic treatment, where WSB2 depletion synergizes with drug treatment.

Strengths:

The authors use a set of orthogonal methods across different model cell lines and a new WSB2 KO mouse model to confirm their findings. They also manage to correlate WSB2 expression with poor prognosis in prostate and liver cancer, supporting the idea that targeting WSB2 may sensitize cancers for treatment with BH3 mimetics.

Weaknesses:

The conclusions drawn based on the findings in cancer patients are very speculative, as regulation of NOXA cannot be the sole function of CRL5/WSB2 and it is hence unclear what causes correlation with patient survival. Moreover, the authors do not provide a clear mechanistic explanation of how exactly higher levels of NOXA promote apoptosis in the absence of WSB2. This would be important knowledge, as usually high NOXA levels correlate with high MCL1, as they are turned over together, but in situations like this, or loss of other E3 ligases, such as MARCH, the buffering capacity of MCL1 is outrun, allowing excess NOXA to kill (likely by neutralizing other BCL2 proteins it usually does not bind to, such as BCLX). Moreover, a necroptosis-inducing role of NOXA has been postulated. Neither of these options is interrogated here.

Author response:

We thank the editor and reviewers for the time they spent reviewing our manuscript entitled ‘Overnight fasting facilitates safety learning by changing the neurophysiological response to relief from threat omission’ which was sent as an original paper for a potential publication in eLife.

Since we take the reviewer comments at heart and recognize the very complex scenario of our previous and current results we will take more time to re-think the paper. This time will serve us to look back to the interpretation of the results of our previous behavioral study, to the preregistration plan as well as findings of our current fMRI (replication) study.

We aim to address the fundamental issues indicated by the reviewers as soon and as clearly as possible.

eLife assessment

This study reports a valuable finding for the treatment of colorectal cancer (CRC), as the authors demonstrated that the enzyme CPT1A plays a significant role in the response to radiotherapy in CRC patients. The methodology and results presented by the authors are solid, supporting the role of CPT1A in CRC radiosensitivity, as the authors determined the expression of CPT1A in CRC tumors and non-tumor tissue, and they validated these findings with in vitro experiments.

Reviewer #1 (Public Review):

Summary:

Fats and lipids serve many important roles in cancers, including serving as important fuels for energy metabolism in cancer cells by being oxidized in the mitochondria. The process of fatty acid oxidation is initiated by the enzyme carnitine palmitoyltransferase 1A (CPT1A), and the function and targetability of CPT1A in cancer metabolism and biology have been heavily investigated. This includes studies that have found important roles for CPT1A in colorectal cancer growth and metastasis.

In this study, Chen and colleagues use analysis of patient samples and functional interrogation in animal models to examine the role CPT1A plays in colorectal cancer (CRC). The authors find that CPT1A expression is decreased in CRC compared to paired healthy tissue and that lower expression correlates with decreased patient survival over time, suggesting that CPT1A may suppress tumor progression. To functionally interrogate this hypothesis, the authors both use CRISPR to knockout CPT1A in a CRC cell line that expresses CPT1A and overexpress CPT1A in a CRC cell line with low expression. In both systems, increased CPT1A expression decreased cell survival and DNA repair in response to radiation in culture. Further, in xenograft models, CPT1A decreased tumor growth basally and radiotherapy could further decrease tumor growth in CPT1A-expressing tumors. As CRC is often treated with radiotherapy, the authors argue this radiosensitization driven by CPT1A could explain why CPT1A expression correlates with increased patient survival.

Lastly, Chen and colleagues sought to understand why CPT1A suppresses CRC tumor growth and sensitizes the tumors to radiotherapy in culture. The antioxidant capacity of cells can increase cell survival, so the authors examine antioxidant gene expression and levels in CPT1A-expressing and non-expressing cells. CPT1A expression suppresses the expression of antioxidant metabolism genes and lowers levels of antioxidants. Antioxidant metabolism genes can be regulated by the FOXM1 transcription factor, and the authors find that CPT1A expression regulates FOXM1 levels and that antioxidant gene expression can be partially rescued in CPT1A-expressing CRC cells. This leads the authors to propose the following model: CPT1A expression downregulates FOXM1 (via some yet undescribed mechanism) which then leads to decreased antioxidant capacity in CRC cells, thus suppressing tumor progression and increasing radiosensitivity. This is an interesting model that could explain the suppression of CPT1A expression in CRC, but key tenets of the model are untested and speculative.

Strengths:

• Analysis of CPT1A in paired CRC tumors and non-tumor tissue using multiple modalities combined with analysis of independent datasets rigorously show that CPT1A is downregulated in CRC tumors at the RNA and protein level.

• The authors use paired cell line model systems where CPT1A is both knocked out and overexpressed in cell lines that endogenously express or repress CPT1A respectively. These complementary model systems increase the rigor of the study.

• The finding that a metabolic enzyme generally thought to support tumor energetics actually is a tumor suppressor in some settings is theoretically quite interesting.

Weaknesses:

• The authors propose that CPT1A expression modulates antioxidant capacity in cells by suppressing FOXM1 and that this pathway alters CRC growth and radiotherapy response. However, key aspects of this model are not tested. The authors do not show that FOXM1 contributes to the regulation of antioxidant levels in CRC cells and tumors or if FOXM1 suppression is key to the inhibition of CRC tumor growth and radiosensitization by CPT1A. Thus, the model the authors propose is speculative and not supported by the existing data.

• The authors propose two mechanisms by which CPT1A expression triggers radiosensitization: decreasing DNA repair capacity (Figure 3) and decreasing antioxidant capacity (Figure 5). However, while CPT1A expression does alter these capacities in CRC cells, neither is functionally tested to determine if altered DNA repair or antioxidant capacity (or both) are the reason why CRC cells are more sensitive to radiotherapy or are delayed in causing tumors in vivo. Thus, this aspect of the proposed model is also speculative.

• The authors find that CPT1A affects radiosensitization in cell culture and assess this in vivo. In vivo, CPT1A expression slows tumor growth even in the absence of radiotherapy, and radiotherapy only proportionally decreases tumor growth to the same extent as it does in CPT1A non-expressing CRC tumors. The authors propose from this data that CPT1A expression also sensitizes tumors to radiotherapy in vivo. However, it is unclear whether CPT1A expression causes radiosensitization in vivo or if CPT1A expression acts as an independent tumor suppressor to which radiotherapy has an additive effect. Additional experiments would be necessary to differentiate between these possibilities.

• The authors propose in Figure 3 that DNA repair capacity is inhibited in CRC cells by CPT1A expression. However, the gH2AX immunoblots performed in Figure 3H-I that measure DNA repair kinetics are not convincing that CPT1A expression impairs DNA repair kinetics. Separate blots are shown for CPT1A expressing and non-expressing cell lines, not allowing for rigorous comparison of gH2AX levels and resolution as CPT1A expression is modulated.

• There are conflicting studies (PMID: 37977042, 29995871) that suggest that CPT1A is overexpressed in CRC and contributes to tumor progression rather than acting as a tumor suppressor as the authors propose. It would be helpful for readers for the authors to discuss these studies and why there is a discrepancy between them.

Reviewer #2 (Public Review):

The manuscript by Chen et al. describes how low levels of CPT1A in colorectal cancer (CRC) confer radioresistance by expediting radiation-induced ROS clearance. The authors propose that this mechanism of ROS homeostasis is regulated through FOXM1. CPT1A is known for its role in fatty acid metabolism via beta-oxidation of long-chain fatty acids, making it important in many metabolic disorders and cancers.

Previous studies have suggested that the upregulation of CPT1A is essential for the tumor-promoting effect in colorectal cancers (CRC) (PMID: 32913185). For example, CPT1A-mediated fatty acid oxidation promotes colorectal cancer cell metastasis (PMID: 2999587), and repression of CPT1A activity renders cancer cells more susceptible to killing by cytotoxic T lymphocytes (PMID: 37722058). Additionally, inhibition of CPT1A-mediated fatty-acid oxidation (FAO) sensitizes nasopharyngeal carcinomas to radiation therapy (PMID: 29721083). While this suggests a tumor-promoting effect for CPT1A, the work by Chen et al. suggests instead a tumor-suppressive function for CPT1A in CRC, specifically that loss or low expression of CPT1A confers radioresistance in CRC. This makes the findings important given that they oppose the previously proposed tumorigenic function of CPT1A. However, the data presented in the manuscript is limited in scope and analysis.

Major Limitations:

(1) Analysis of Patient Samples

- Figure 1D shows that CPT1A levels are significantly lower in COAD and READ compared to normal tissues. It would be beneficial to show whether CPT1A levels are also significantly lower in CRC compared to other tumor types using TCGA data.<br /> - The analysis should include a comparison of closely related CPT1 isoforms (CPT1B and CPT1C) to emphasize the specific importance of CPT1A silencing in CRC.<br /> - Figure 2 lacks a clear description of how IHC scores were determined and the criteria used to categorize patients into CPT1A-high and CPT1A-low groups. This should be detailed in the text and figure legend.<br /> - None of Figure 2B or 2C show how many patients were assigned to the CPT1A-low and CPT1A-high groups.

(2) Model Selection and Experimental Approaches

- The authors primarily use CPT1A knockout (KO) HCT116 cells and CPT1A overexpression (OE) SW480 cells for their experiments, which poses major limitations.<br /> - The genetic backgrounds of the cell lines (e.g., HCT116 being microsatellite instable (MSI) and SW480 not) should be considered as they might influence treatment outcomes. This should be acknowledged as a major limitation.<br /> - Regardless of their CPT1A expression levels, for the experiments with HCT116 and SW480 cells in Figure 3C-F, it would be useful to see whether HCT116 cells can be further sensitized to radiotherapy in overexpression and whether SW480 cells can be desensitized through CPT1A KO.<br /> - The use of only two CRC cell lines is insufficient to draw broad conclusions. Additional CRC cell lines should be used to validate the findings and account for genetic heterogeneity. The authors should repeat key experiments with additional CRC cell lines to strengthen their conclusions.

(3) Pharmacological Inhibition

Several studies have reported beneficial outcomes of using CPT1 pharmacological inhibition to limit cancer progression (e.g., PMID: 33528867, PMID: 32198139), including its application in sensitization to radiation therapy (PMID: 30175155). Since the authors argue for the opposite case in CRC, they should show this through pharmacological means such as etomoxir and whether CPT1A inhibition phenocopies their observed genetic KO effect, which would have important implications for using this inhibitor in CRC patients.

(4) Data Representation and Statistical Analysis

- The relative mRNA expression levels across the seven cell lines (Supplementary Figure 1C) differ greatly from those reported in the DepMap (https://depmap.org/portal/). This discrepancy should be addressed.<br /> - The statistical significance of differences in mRNA and protein levels between RT-sensitive and RT-resistant cells should be shown (Supplementary Figure 1C, D).

Conclusion

The study offers significant insights into the role of CPT1A in CRC radioresistance, proposing a tumor-suppressive function. However, the scope and depth of the analysis need to be expanded to fully validate these claims. Additional CRC cell lines, pharmacological inhibition studies, and a more detailed analysis of patient samples are essential to strengthen the conclusions.

Reviewer #3 (Public Review):

Summary:

The study aims to elucidate the role of CPT1A in developing resistance to radiotherapy in colorectal cancer (CRC). The manuscript is a collection of assays and analyses to identify the mechanism by which CPT1A leads to treatment resistance through increased expression of ROS-scavenging genes facilitated by FOXM1 and provides an argument to counter this role, leading to a reversal of treatment resistance.

Strengths:

The article is well written with sound scientific methodology and results. The assays performed are well within the scope of the hypothesis of the study and provide ample evidence for the role of CPT1A in the development of treatment resistance in colorectal cancer. While providing compelling evidence for their argument, the authors have also rightfully provided limitations of their work.

Weaknesses:

The primary weakness of the study is acknowledged by the authors at the end of the Discussion section of the manuscript. The work heavily relies on bioinformatics and in vitro work with little backing of in vivo and patient data. In terms of animal studies, it is to be noted that the model they have used is nude mice with non-orthotopic, subcutaneous xenograft, which may not be the best recreation of the patient tumor.

eLife assessment

The authors provide useful data to support the existence of a regulatory pathway starting with SPI1-driven ZFP36L1 expression, that goes on to downregulate HDAC3 expression at the transcript level, leading to PD-L1 upregulation due to implied enhanced acetylation of its promoter region. This is therefore an interesting pathway that adds to our understanding of how PD-L1 expression is controlled in gastric cancer. However, this is likely one of many possible pathways that impact PD-L1 expression, and the data are currently incomplete to support the claims made.

Reviewer #1 (Public Review):

In this paper, the authors provide data to support the existence of a regulatory pathway starting with SPI1-driven ZFP36L1 expression, that goes on to downregulate HDAC3 expression at the transcript level, leading to PD-L1 upregulation due to implied enhanced acetylation of its promoter region. This is therefore an interesting pathway that adds to our understanding of how PD-L1 expression is controlled in gastric cancer. However, this is likely one of many possible pathways that impact PD-L1 expression, which is likely equally important. Thus, while potentially interesting, this is more additive information to the literature rather than a fundamentally new concept/finding.

Overall, there are many experiments presented, which appear to be of good quality, however, there are a number of issues with this that need attention. Moreover, the text is often difficult to follow, partly due to the standard of English, but mainly due to the sparsity of detail in the results section and figure legends. Thus providing an overall assessment of data conclusiveness is not possible at this time. This is exacerbated by frequently extrapolating conclusions beyond what is actually shown in an individual experiment.

Major issues:

(1) All the figure legends need to expand significantly, so it is clear what is being presented. All experiments showing data quantification need the numbers of independent biological replicates to be added, plus an indication of what the P-values are associated with the asterisks (and the tests used).

(2) Related to point 1, the description of the data in the text needs to expand significantly, so the figure panels are interpretable. Examples are given below but this is not an exhaustive list.

(3) The addition of "super-enhancer-driven" to the title is a distraction. This is the starting point but the finding is portrayed by the last part of the title. Moreover, it is not clear why this is a super enhancer rather than just a typical enhancer as only one seems to be relevant and functional. I suggest avoiding this term after initial characterisations.

(4) The descriptions of Figures 1B, C, and D are very poor. How for example do you go from nearly 2000 SE peaks to a couple of hundred target genes? What are the other 90% doing? What is the definition of a target gene? This whole start section needs a complete overhaul to make it understandable and this is important as is what leads us to ZFP36L1 in the first place.

(5) It is impossible to work out what Figures 1F, H, and I are from the accompanying text. The same applies to supplementary Figure S1D. Figure 1G is not described in the results.

(6) What is Figure 2A? There is no axis label or description.

(7) Why is CD274 discussed in the text from Figure 2E but none of the other genes? The rationale needs expanding.

(8) Figure 2G needs zooming in more over the putative SE region and the two enhancers labelling. This looks very strange at the moment and does not show typical peak shapes for histone acetylation at enhancers.

(9) The use of JQ1 does not prove something is a super enhancer, just that it is BRD4 regulated and might be a typical enhancer.

(10) An explanation of how the motifs were identified in E1 is needed. Enrichment over what? Were they purposefully looking for multiple motifs per enhancer? Otherwise what it all comes down to later in the figure is a single motif, and how can that be "enriched"?

(11) A major missing experiment is to deplete rather than over-express SPI1 for the various assays in Figure 4.

(12) The authors start jumping around cell lines, sometimes with little justification. Why is MGC803 used in Figure 4I rather than MKN45? This might be due to more endogenous SPI1. However, this does not make sense in Figure 5M, where ZFP36L is overexpressed in this line rather than MKN45. If SPI1 is already high in MGC803, then the prediction is that ZFP36L1 should already be high. Is this the case?

(13) In Figure 5, HDAC3 should also be depleted to show opposite effects to over-expression (as the latter could be artefactual). Also, direct involvement should be proven by ChIP.

(14) Figure 5G and H are not discussed in the text.

(15) Figure 6C needs explaining. Why are three patients selected here? Are these supposed to be illustrative of the whole cohort? What sub-type of GC are these?

(16) In Figure 6E onwards, they switch to MFC cell line. They provide a rationale but the key regulatory axis should be sown to also be operational in these cells to use this as a model system.

Reviewer #2 (Public Review):

Summary:

This manuscript by Wei et al studies the role of ZFP36L1, an RNA-binding protein, in promoting PD-L1 expression in gastric cancer (GC). They used human gastric cancer tissues from six patients and performed H3K27ac CUT&Tag to unbiasedly identify SE specific for the infiltrative type. They identified an SE driving the expression of ZFP36L1 and immune evasion through upregulation of PD-L1. Mechanistically, they show that SPI1 binds to ZFP36L1-SE and ZFP36L1 in turn regulates PD-L1 expression through modulation of the 3'UTR of HDAC3. This mechanism of PD-L1 regulation in gastric cancer is novel, and ZFP36L1 has not been previously implicated in GC progression. However, the data presented are largely correlations and no direct proof is presented that the identified SE regulates ZFP36L1 expression. Furthermore, the effect of ZFP36L1 manipulation elicited a modest effect on PDL1 expression. In fact, several cell lines (XGC1, MNK45) express abundant ZFP36L1 but no PD-L1, suggesting the ZFP36L1 per se is not a key stimulant of PD-L1 expression as IFNg is. Thus, the central conclusions are not supported by the data.

Strengths:

Use of human GC specimens to identify SE regulating PD-L1 expression and immune evasion.

Weaknesses:

Major comments:

(1) The difference in H3K27ac over the ZFP36L1 locus and SE between the expanding and infiltrative GC is marginal (Figure 2G). Although the authors establish that ZFP36L1 is upregulated in GC, particularly in the infiltrative subtype, no direct proof is provided that the identified SE is the source of this observation. CRISPR-Cas9 should be employed to delete the identified SE to prove that it is causatively linked to the expression of ZFP36L1.

(2) In Figure 3C the impact of shZFP36L1 on PD-L1 expression is marginal and it is observed in the context of IFNg stimulation. Moreover, in XGC-1 cell line the shZFP36L1 failed to knock down protein expression thus the small decrease in PD-L1 level is likely independent of ZFP36L1. The same is the case in Figure 3D where forced expression of ZFP36L1 does not upregulate the expression of PDL1 and even in the context of IFNg stimulation the effect is marginal.

(3) In Figure 4, it is unclear why ELF1 and E2F1 that bind ZFP36L1-SE do not upregulate its expression and only SPI1 does. In Figure 4D the impact of SPI overexpression on ZFP36L1 in MKN45 cells is marginal. Likewise, the forced expression of SPI did not upregulate PD-L1 which contradicts the model. Only in the context of IFNg PD-L1 is expressed suggesting that whatever role, if any, ZFP36L1-SPI1 axis plays is secondary.

(4) The data presented in Figure 6 are not convincing. First, there is no difference in the tumor growth (Figure 6E). IHC in Figure 6I for CD8a is misleading. Can the authors provide insets to point CD8a cells? This figure also needs quantification and review from a pathologist.

eLife assessment

This manuscript provides convincing evidence derived from diverse state-of-the-art approaches to suggest that non-dopaminergic projection neurons in the ventral tegmental area (VTA) make local synapses. These important findings challenge the prevailing wisdom that VTA interneurons exclusively form local synaptic contacts and instead reveal that VTA neurons expressing interneuron markers also form long-range projections to forebrain targets such as the cortex, ventral pallidum, and nucleus accumbens. Given the importance of VTA interneurons to many models of VTA-linked behavioral functions, these findings have significant implications for our understanding of the neural circuits underlying reward, motivation, and addiction.

Reviewer #1 (Public Review):<br /> The manuscript by Lucie Oriol et al. revisits the understanding of interneurons in the ventral tegmental area (VTA). The study challenges the traditional notion that VTA interneurons exclusively form local synapses within the VTA. Key findings of the study indicate that VTA GABA and glutamate projection neurons also make local synapses within the VTA. This evidence suggests that functions previously attributed to VTA interneurons could be mediated by these projection neurons.

The study tested four genetic markers-Parvalbumin (PV), Somatostatin (SST), Mu-opioid receptor (MOR), and Neurotensin (NTS)-to determine if they selectively label VTA interneurons. The findings indicate that these markers label VTA projection neurons rather than selectively identifying interneurons. Using a combination of anatomical tracing and brain slice physiological recordings, the study demonstrates that VTA projection neurons make functional inhibitory or excitatory synapses locally within the VTA. These data challenge the conventional view that VTA GABA neurons are purely interneurons and suggest that inhibitory projection neurons can serve functions previously attributed to VTA interneurons. Thus, some functions traditionally ascribed to interneurons may be carried out by projection neurons with local synapses. This has significant implications for understanding the neural circuits underlying reward, motivation, and addiction.

Reviewer #2 (Public Review):

Summary:

In this manuscript, the authors use a combination of transgenic animals, intersectional viruses, retrograde tracing, and ex-vivo slice electrophysiology to show that VTA projection neurons synapse locally. First, the authors injected a cre-dependent channelrhodopsin into the VTA of PV, SST, MOR, and NTS-Cre mice. Importantly, PV, SST, MOR, and NTS are molecular markers previously used to describe VTA interneurons. Imaging of known VTA target regions identified that these neurons are not localized to the VTA and instead project to the PFC, NAc, VP, and LHb. Next, the authors used an intersectional viral strategy to label projection neurons with both GFP (membrane localized) and Syn:Ruby (release sites). These experiments identified that VTA projection neurons also make intra-VTA synapses. Finally, the authors use a combination of optogenetics and ex-vivo slice electrophysiology to show that neurons projecting from the VTA to the NAc/VP/PFC also synapse locally. Overall, most of the conclusions seem to be well supported by the data.

Strengths:

Previous literature has described Pvalb, Sst, Oprm1, and Nts as selective markers of VTA interneurons. Here, the authors make use of cre driver lines to show that neurons defined by these genes are not interneurons and project to known VTA target regions. Additionally, the authors convincingly use intersectional viral approaches and slice electrophysiology to show that projection neurons synapse onto neighboring cells within the VTA

Weaknesses:

While the authors use several cre driver lines to identify GABAergic projection neurons, they then use wild-type mice to show that projection neurons synapse onto neighboring cells within the VTA. This does not seem to lend evidence to the idea that previously described "interneurons" are projection neurons that collateralize within the VTA.

Reviewer #3 (Public Review):

Summary:

This study from Oriol et al. first uses transgenic animals to examine projection targets of specific subtypes of VTA GABA neurons (expressing PV, SST, MOR, or NTS). They follow this with a set of optogenetic experiments showing that VTA projection neurons (regardless of genetic subtype) make local functional connections within the VTA itself. Both of these findings are important advances in the field. Notably, both GABAergic and glutamatergic neurons in the VTA likely exhibit these combined long/short-range projections.

Strengths:

The main strength of this study is the series of optogenetic/electrophysiological experiments that provide detailed circuit connectivity of VTA neurons. The long-range projections to the VP (but not other targets) are also verified to have functional excitatory and inhibitory components. Overall, the experiments are well executed and the results are very relevant in light of the rapidly growing knowledge about the complexity and heterogeneity of VTA circuitry.

Another strength of this study is the well-written and thoughtful discussion regarding the current findings in the context of the long-standing question of whether the VTA does or does not have true interneurons.

Weaknesses:

This study has a few modest shortcomings, of which the first is likely addressable with the authors' existing data, while the latter items will likely need to be deferred to future studies:

(1) Some key anatomical details are difficult to discern from the images shown. In Figure 1, the low-magnification images of the VTA in the first column, while essential for seeing what overall section is being shown, are not of sufficient resolution to distinguish soma from processes. A supplemental figure with higher-resolution images could be helpful. Also, where are the insets shown in the second column obtained from? There is not a corresponding marked region on the low-magnification images. Is this an oversight, or are these insets obtained from other sections that are not shown? Lastly, there is a supplemental figure showing the NAc injection sites corresponding to Figure 5, but not one showing VP or PFC injection sites in Figure 6. Why not?

(2) Because multiple ChR2 neurons are activated in the optogenetic experiments, it is not clear how common is it for any specific projection neuron to make local connections. Are the observed synaptic effects driven by just a few neurons making extensive local collateralizations (while other projection neurons do not), or do most VTA projection neurons have local collaterals? I realize this is a complex question, that may not have an easy answer.

(3) There is something of a conceptual disconnect between the early and later portions of this paper. Whereas Figures 1-4 examine forebrain projections of genetic subtypes of VTA neurons, the optogenetic studies do not address genetic subtypes at all. I do realize that is outside of the scope of the author's intent, but it does give the impression of somewhat different (but related) studies being stitched together. For example, the MOR-expressing neurons seem to project strongly to the VP, but it is not addressed whether these are also the ones making local projections. Also, after showing that PV neurons project to the LHb, the opto experiments do not examine the LHb projection target at all.

Reviewer #1 (Public Review):

In this study, Franke et al. explore and characterize color response properties across primary visual cortex, revealing specific color opponent encoding strategies across the visual field. The authors use awake 2P imaging to define the spectral response properties of visual interneurons in layer 2/3. They find that opponent responses are more pronounced at photopic light levels, and that diversity in color opponent responses exists across the visual field, with green ON/ UV OFF responses more strongly represented in the upper visual field. This is argued to be relevant for the detection of certain features that are more salient when using chromatic space, possibly due to noise reduction. In the revised version, Franke et al. have addressed the potential pitfalls in the discussion, which is an important point for the non-expert reader. Thus, this study provides a solid characterization of the color properties of V1 and is a valuable addition to visual neuroscience research.

Reviewer #2 (Public Review):

Summary:

Franke et al. characterize the representation of color in the primary visual cortex of mice, highlighting how this changes across the visual field. Using calcium imaging in awake, head-fixed mice, they characterize the properties of V1 neurons (layer 2/3) using a large center-surround stimulation where green and ultra-violet colors were presented in random combinations. Clustering of responses revealed a set of functional cell-types based on their preference to different combinations of green and UV in their center and surround. These functional types were demonstrated to have different spatial distributions across V1, including one neuronal type (Green-ON/UV-OFF) that was much more prominent in the posterior V1 (i.e. upper visual field). Modelling work suggests that these neurons likely support the detection of predator-like objects in the sky.

Strengths:

The large-scale single-cell resolution imaging used in this work allows the authors to map the responses of individual neurons across large regions of the visual cortex. Combining this large dataset with clustering analysis enabled the authors to group V1 neurons into distinct functional cell types and demonstrate their relative distribution in the upper and lower visual fields. Modelling work demonstrated the different capacity of each functional type to detect objects in the sky, providing insight into the ethological relevance of color opponent neurons in V1.

Weaknesses:

It is unfortunate the authors were unable to provide stronger mechanistic insights into how color opponent neurons in V1 are formed.

Overall, this study will be a valuable resource for researchers studying color vision, cortical processing, and the processing of ethologically relevant information. It provides a useful basis for future work on the origin of color opponency in V1 and its ethological relevance.

Reviewer #3 (Public Review):

This paper improves our understanding of the coding of chromatic signals in mouse visual cortex. Calcium responses of a large collection of cells are measured in response to a simple spot stimulus. These responses are used to estimate chromatic tuning properties - specifically sensitivity to UV and green stimuli presented in a large central spot or a larger still surrounding region. Cells are divided based on their responses to these stimuli into luminance or chromatic sensitive groups.

The paper has improved substantially in revisions and makes an important contribution to how color is coded in mouse V1. The revisions have nicely clarified a few limitations of the current study, and that serves to emphasize the strengths of the data and clear conclusions that can be drawn from it.

eLife assessment

Franke et al. explore and characterize color response properties of neurons in mouse primary visual cortex (V1), revealing specific color opponent encoding strategies across the visual field. The paper provides evidence for the existence of color opponency in a subset of neurons within V1 and shows that these color opponent neurons are more numerous in the upper visual field. Support for the main conclusions is convincing and the dataset that forms the basis of the paper is impressive. The paper will make an important contribution to understanding how color is coded in mouse V1.

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public Review):

Summary:

The manuscript considers a mechanistic extension of MacArthur's consumer-resource model to include chasing down food and potential encounters between the chasers (consumers) that lead to less efficient feeding in the form of negative feedback. After developing the model, a deterministic solution and two forms of stochastic solutions are presented, in agreement with each other. Finally, the model is applied to explain observed coexistence and rank-abundance data.

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

Strengths:

The application of the theory to natural rank-abundance curves is impressive. The comparison with the experiments that reject the competitive exclusion principle is promising. It would be fascinating to see if in, e.g. insects, the specific interference dynamics could be observed and quantified and whether they would agree with the model.

The results are clearly presented; the methods adequately described; the supplement is rich with details.

There is much scope to build upon this expansion of the theory of consumer-resource models. This work can open up new avenues of research.

We appreciate the reviewer for the very positive comments. We have followed many of the suggestions raised by the reviewer, and the manuscript is much improved as a result.

Following the reviewer’s suggestions, we have now used Shannon entropies to quantify the model comparison with experiments that reject the Competitive Exclusion Principle (CEP). Specifically, for each time point of each experimental or model-simulated community, we calculated the Shannon entropies using the formula:

, where is the probability that a consumer individual belongs to species C_i at the time stamp of t. The comparison of Shannon entropies in the time series between those of the experimental data and SSA results shown in Fig. 2D-E is presented in Appendix-fig. 7C-D. The time averages and standard deviations (δH) of the Shannon entropies for these experimental or SSA model-simulated communities are as follows:

, ; ,

, , .

Meanwhile, we have calculated the time averages and standard deviations (δC_i) of the species’ relative/absolute abundances for the experimental or SSA model-simulated communities shown in Fig. 2D-E, which are as follows:

, ; , ; , , , , where the superscript “(R)” represents relative abundances.

From the results of Shannon entropies shown in Author response image 1 (which are identical to those of Appendix-fig. 7C-D) and the quantitative comparison of the time average and standard deviation between the model and experiments presented above, it is evident that the model results in Fig. 2D-E exhibit good consistency with the experimental data. They share roughly identical time averages and standard deviations in both Shannon entropies and the species' relative/absolute abundances for most of the comparisons. All these analyses are included in the appendices and mentioned in the main text.

Author response image 1.

Shannon Entropies of the experimental data and SSA results in Fig. 2D-E, redrawn from Appendix-fig. 7C-D.

Weaknesses:

I am questioning the use of carrying capacity (Eq. 4) instead of using nutrient limitation directly through Monod consumption (e.g. Posfai et al. who the authors cite). I am curious to see how these results hold or are changed when Monod consumption is used.

We thank the reviewer for raising this question. To explain it more clearly, the equation combining the third equation in Eq. 1 and Eq. 4 of our manuscript is presented below as Eq. R1:

where x_il represents the population abundance of the chasing pair C_i^(P) ∨ R_l^(P), κ_l stands for the steady-state population abundance of species R_l (the carrying capacity) in the absence of consumer species. In the case with no consumer species, then x_il \= 0 since C_i \= 0 (i\=1,…,S_C), thus R_l = κ_l when R_l = 0.

Eq. R1 for the case of abiotic resources is comparable to Eq. (1) in Posfai et al., which we present below as Eq. R2:

where c_i represents the concentration of nutrient i, and thus corresponds to our R_l ; n_σ(t) is the population of species σ, which corresponds to our C_i ; s_i stands for the nutrient supply rate, which corresponds to our ζl ; µi denotes the nutrient loss rate, corresponding to our is the coefficient of the rate of species σ for consuming nutrient i, which corresponds to our in Posfai et al. is the consumption rate of nutrient i by the population of species σ, which corresponds to our x_il.

In Posfai et al., is the Monod function: and thus

In our model, however, since predator interference is not involved in Posfai et al., we need to analyze the form of x_il presented in the functional form of x_il ({R_l},{C_i}) in the case involving only chasing pairs. Specifically, for the case of abiotic resources, the population dynamics can be described by Eq. 1 combined with Eq. R1:

where and . For convenience, we consider the case of S_R \=1 where the Monod form was derived (Monod, J. (1949). Annu. Rev. Microbiol., 3, 371-394.). From , we have

where , and l =1. If the population abundance of the resource species is much larger than that of all consumer species (i.e., ), then,

and R_l^(F) ≈ R_l. Combined with R5, and noting that C_i \= C_i(F) + xil we can solve for x_il :

with l =1 since S_R \=1. Comparing Eq. R6 with Eq. R3, and considering the symbol correspondence explained in the text above, it is now clear that our model can be reduced to the Monod consumption form in the case of S_R \=1 where the Monod form was derived from.

Following on the previous comment, I am confused by the fact that the nutrient consumption term in Eq. 1 and how growth is modeled (Eq. 4) are not obviously compatible and would be hard to match directly to experimentally accessible quantities such as yield (nutrient to biomass conversion ratio). Ultimately, there is a conservation of mass ("flux balance"), and therefore the dynamics must obey it. I don't quite see how conservation of mass is imposed in this work.

We thank the reviewer for raising this question. Indeed, the population dynamics of our model must adhere to flux balance, with the most pertinent equation restated here as Eq. R7:

Below is the explanation of how Eq. R7, and thus Eqs. 1 and 4 of our manuscript, adhere to the constraint of flux balance. The interactions and fluxes between consumer and resource species occur solely through chasing pairs. At the population level, the scenario of chasing pairs among consumer species C_i and resource species R_l is presented in the follow expression:

where the superscripts "(F)" and "(P)" represent the freely wandering individuals and those involved in chasing pairs, respectively, "(+)" stands for the gaining biomass of consumer C_i from resource R_l. In our manuscript, we use x_l to represent the population abundance (or equivalently, the concentration, for a well-mixed system with a given size) of the chasing pair C_i^(P) ∨ R_l^(P), and thus, the net flow from resource species R_l to consumer species C_i per unit time is k_ilx_il. Noting that there is only one R_l individual within the chasing pair C_i^(P) ∨ R_l^(P), then the net effect on the population dynamics of species is −k_ilx_il. However, since a consumer individual from species C_i could be much heavier than a species R_l individual, and energy dissipation would be involved from nutrient conversion into biomass, we introduce a mass conversion ratio w_l in our manuscript. For example, if a species C_i individual is ten times the weight of a species R_l individual, without energy dissipation, the mass conversion ratio wil should be 1/10 (i.e., wil \= 0.1 ), however, if half of the chemical energy is dissipated into heat from nutrient conversion into biomass, then w_l \= 0.1 0.5× = 0.05. Consequently, the net effect of the flux from resource species _R_l to consumer species C_i per unit time on the population dynamics is , and flux balance is clearly satisfied.

For the population dynamics of a consumer species C_i, we need to consider all the biomass influx from different resource species, and thus there is a summation over all species of resources, which leads to the term of in Eq. R7. Similarly, for the population dynamics of a resource species R_l, we need to lump sum all the biomass outflow into different consumer species, resulting in the term of in Eq. R7.

Consequently, Eq. R7 and our model satisfy the constraint of flux balance.

These models could be better constrained by more data, in principle, thereby potential exists for a more compelling case of the relevance of this interference mechanism to natural systems.

We thank the reviewer for raising this question. Indeed, our model could benefit from the inclusion of more experimental data. In our manuscript, we primarily set the parameters by estimating their reasonable range. Following the reviewer's suggestions, we have now specified the data we used to set the parameters. For example, in Fig. 2D, we set 𝐷₂\=0.01 with τ=0.4 days, resulting in an expected lifespan of Drosophila serrata in our model setting of 𝜏⁄𝐷₂\= 40 days, which roughly agrees with experimental data showing that the average lifespan of D. serrata is 34 days for males and 54 days for females (lines 321-325 in the appendices; reference: Narayan et al. J Evol Biol. 35: 657–663 (2022)). To explain biodiversity and quantitatively illustrate the rank-abundance curves across diverse communities, the competitive differences across consumer species, exemplified by the coefficient of variation of the mortality rates - a key parameter influencing the rank-abundance curve, were estimated from experimental data in the reference article (Patricia Menon et al., Water Research (2003) 37, 4151) using the two-sigma rule (lines 344-347 in the appendices).

Still, we admit that many factors other than intraspecific interference, such as temporal variation, spatial heterogeneity, etc., are involved in breaking the limits of CEP in natural systems, and it is still challenging to differentiate each contribution in wild systems. However, for the two classical experiments that break CEP (Francisco Ayala, 1969; Thomas Park, 1954), intraspecific interference could probably be the most relevant mechanism, since factors such as temporal variation, spatial heterogeneity, cross-feeding, and metabolic tradeoffs are not involved in those two experimental systems.

The underlying frameworks, B-D and MacArthur are not properly exposed in the introduction, and as a result, it is not obvious what is the specific contribution in this work as opposed to existing literature. One needs to dig into the literature a bit for that.

The specific contribution exists, but it might be more clearly separated and better explained. In the process, the introduction could be expanded a bit to make the paper more accessible, by reviewing key features from the literature that are used in this manuscript.

We thank the reviewer for these very insightful suggestions. Following these suggestions, we have now added a new paragraph and revised the introduction part of our manuscript (lines 51-67 in the main text) to address the relevant issues. Our paper is much improved as a result.

Reviewer #2 (Public Review):

Summary:

The manuscript by Kang et al investigates how the consideration of pairwise encounters (consumer-resource chasing, intraspecific consumer pair, and interspecific consumer pair) influences the community assembly results. To explore this, they presented a new model that considers pairwise encounters and intraspecific interference among consumer individuals, which is an extension of the classical Beddington-DeAngelis (BD) phenomenological model, incorporating detailed considerations of pairwise encounters and intraspecific interference among consumer individuals. Later, they connected with several experimental datasets.

Strengths:

They found that the negative feedback loop created by the intraspecific interference allows a diverse range of consumer species to coexist with only one or a few types of resources. Additionally, they showed that some patterns of their model agree with experimental data, including time-series trajectories of two small in-lab community experiments and the rank-abundance curves from several natural communities. The presented results here are interesting and present another way to explain how the community overcomes the competitive exclusion principle.

We appreciate the reviewer for the positive comments and the accurate summary of our manuscript.

Weaknesses:

The authors only explore the case with interspecific interference or intraspecific interference exists. I believe they need to systematically investigate the case when both interspecific and intraspecific interference exists. In addition, the text description, figures, and mathematical notations have to be improved to enhance the article's readability. I believe this manuscript can be improved by addressing my comments, which I describe in more detail below.

We thank the reviewer for these valuable suggestions. We have followed many of the suggestions raised by the reviewer, and the manuscript is much improved as a result.

(1) In nature, it is really hard for me to believe that only interspecific interference or intraspecific interference exists. I think a hybrid between interspecific interference and intraspecific interference is very likely. What would happen if both the interspecific and intraspecific interference existed at the same time but with different encounter rates? Maybe the authors can systematically explore the hybrid between the two mechanisms by changing their encounter rates. I would appreciate it if the authors could explore this route.

We thank the reviewer for raising this question. Indeed, interspecific interference and intraspecific interference simultaneously exist in real cases. To differentiate the separate contributions of inter- and intra-specific interference on biodiversity, we considered different scenarios involving inter- or intra-specific interference. In fact, we have also considered the scenario involving both inter- and intra-specific interference in our old version for the case of S_C = 2 and S_R = 1, where two consumer species compete for one resource species (Appendix-fig. 5, and lines 147-148, 162-163 in the main text of the old version, or lines 160-161, 175-177 in the new version).

Following the reviewer’s suggestions, we have now systematically investigated the cases of S_C = 6, S_R = 1, and S_C = 20, S_R = 1, where six or twenty consumer species compete for one resource species in scenarios involving chasing pairs and both inter- and intra-specific interference using both ordinary differential equations (ODEs) and stochastic simulation algorithm (SSA). These newly added ODE and SSA results are shown in Appendix-fig. 5 F-H, and we have added a new paragraph to describe these results in our manuscript (lines 212-215 in the main text). Consistent with our findings in the case of S_C = 2 and S_R = 1, the species coexistence behavior in the cases of both S_C = 6, S_R = 1, and S_C = 20, S_R = 1 is very similar to those without interspecific interference: all consumer species coexist with one type of resources at constant population densities in the ODE studies, and the SSA results fluctuate around the population dynamics of the ODEs.

As for the encounter rates of interspecific and intraspecific interference, in fact, in a well-mixed system, these encounter rates can be derived from the mobility rates of the consumer species using the mean field method. For a system with a size of L2, the interspecific encounter rate between consumer species C_i and C_j (i ≠ j) is please refer to lines 100-102, 293-317 in the main text, and see also Appendix-fig. 1), where r^(I) is the upper distance for interference, while v_{C_i} and v_{C_j} represent the mobility rates of species C_i and C_j, respectively. Meanwhile, the intraspecific encounter rates within species C_i and species C_j are and , respectively.

Thus, once the intraspecific encounter rates a’_ii are a’_jj given, the interspecific encounter rate between species C_i and C_j is determined. Consequently, we could not tune the encounter rates of interspecific and intraspecific interference at will in our study, especially noting that for clarity reasons, we have used the mortality rate as the only parameter that varies among the consumer species throughout this study. Alternatively, we have made a systematic study on analyzing the influence of varying the separate rate and escape rate on species coexistence in the case of two consumers competing for a single type of resources (see Appendix-fig. 5A).

(2) In the first two paragraphs of the introduction, the authors describe the competitive exclusion principle (CEP) and past attempts to overcome the CEP. Moving on from the first two paragraphs to the third paragraph, I think there is a gap that needs to be filled to make the transition smoother and help readers understand the motivations. More specifically, I think the authors need to add one more paragraph dedicated to explaining why predator interference is important, how considering the mechanism of predator interference may help overcome the CEP, and whether predator interference has been investigated or under-investigated in the past. Then building upon the more detailed introduction and movement of predator interference, the authors may briefly introduce the classical B-D phenomenological model and what are the conventional results derived from the classical B-D model as well as how they intend to extend the B-D model to consider the pairwise encounters.

We thank the reviewer for these very insightful suggestions. Following these suggestions, we have added a new paragraph and revised the introduction part of our paper (lines 51-67 in the main text). Our manuscript is significantly improved as a result.

(3) The notations for the species abundances are not very informative. I believe some improvements can be made to make them more meaningful. For example, I think using Greek letters for consumers and English letters for resources might improve readability. Some sub-scripts are not necessary. For instance, R^(l)_0 can be simplified to g_l to denote the intrinsic growth rate of resource l. Similarly, K^(l)_0 can be simplified to K_l. Another example is R^(l)_a, which can be simplified to s_l to denote the supply rate. In addition, right now, it is hard to find all definitions across the text. I would suggest adding a separate illustrative box with all mathematical equations and explanations of symbols.

We thank the reviewer for these very useful suggestions. We have now followed many of the suggestions to improve the readability of our manuscript. Given that we have used many English letters for consumers and there are already many symbols of English and Greek letters for different variables and parameters in the appendices, we have opted to use Greek letters for parameters specific to resource species and English letters for those specific to consumer species. Additionally, we have now added Appendix-tables 1-2 in the appendices (pages 16-17 in the appendices) to illustrate the symbols used throughout our manuscript.

(4) What is the f_i(R^(F)) on line 131? Does it refer to the growth rate of C_i? I noticed that f_i(R^(F)) is defined in the supplementary information. But please ensure that readers can understand it even without reading the supplementary information. Otherwise, please directly refer to the supplementary information when f_i(R^(F)) occurs for the first time. Similarly, I don't think the readers can understand \Omega^\prime_i and G^\prime_i on lines 135-136.

We thank the reviewer for raising these questions. We apologize for not illustrating those symbols and functions clearly enough in our previous version of the manuscript. f_i⟮R^(F)⟯ is a function of the variable R^(F) with the index i, which is defined as and for i=2. Following the reviewer’s suggestions, we have now added clear definitions for symbols and functions and resolved these issues. The definitions of \Omega_i, \Omega^\prime_i, G, and G^\prime are overly complex, and hence we directly refer to the Appendices when they occur for the first time in the main text.

Reviewer #3 (Public Review):

Summary:

A central question in ecology is: Why are there so many species? This question gained heightened interest after the development of influential models in theoretical ecology in the 1960s, demonstrating that under certain conditions, two consumer species cannot coexist on the same resource. Since then, several mechanisms have been shown to be capable of breaking the competitive exclusion principle (although, we still lack a general understanding of the relative importance of the various mechanisms in promoting biodiversity).

One mechanism that allows for breaking the competitive exclusion principle is predator interference. The Beddington-DeAngelis is a simple model that accounts for predator interference in the functional response of a predator. The B-D model is based on the idea that when two predators encounter one another, they waste some time engaging with one another which could otherwise be used to search for resources. While the model has been influential in theoretical ecology, it has also been criticized at times for several unusual assumptions, most critically, that predators interfere with each other regardless of whether they are already engaged in another interaction. However, there has been considerable work since then which has sought either to find sets of assumptions that lead to the B-D equation or to derive alternative equations from a more realistic set of assumptions (Ruxton et al. 1992; Cosner et al. 1999; Broom et al. 2010; Geritz and Gyllenberg 2012). This paper represents another attempt to more rigorously derive a model of predator interference by borrowing concepts from chemical reaction kinetics (the approach is similar to previous work: Ruxton et al. 1992). The main point of difference is that the model in the current manuscript allows for 'chasing pairs', where a predator and prey engage with one another to the exclusion of other interactions, a situation Ruxton et al. (1992) do not consider. While the resulting functional response is quite complex, the authors show that under certain conditions, one can get an analytical expression for the functional response of a predator as a function of predator and resource densities. They then go on to show that including intraspecific interference allows for the coexistence of multiple species on one or a few resources, and demonstrate that this result is robust to demographic stochasticity.

We thank the reviewer for carefully reading our manuscript and for the positive comments on the rigorously derived model of predator interference presented in our paper. We also appreciate the reviewer for providing a thorough introduction to the research background of our study, especially the studies related to the BeddingtonDeAngelis model. We apologize for our oversight in not fully appreciating the related study by Ruxton et al. (1992) at the time of our first submission. Indeed, as suggested by the reviewer, Ruxton et al. (1992) is relevant to our study in that we both borrowed concepts from chemical reaction kinetics. Now, we have reworked the introduction and discussion sections of our manuscript, cited, and acknowledged the contributions of related works, including Ruxton et al. (1992).

Strengths:

I appreciate the effort to rigorously derive interaction rates from models of individual behaviors. As currently applied, functional responses (FRs) are estimated by fitting equations to feeding rate data across a range of prey or predator densities. In practice, such experiments are only possible for a limited set of species. This is problematic because whether a particular FR allows stability or coexistence depends on not just its functional form, but also its parameter values. The promise of the approach taken here is that one might be able to derive the functional response parameters of a particular predator species from species traits or more readily measurable behavioral data.

We appreciate the reviewer's positive comments regarding the rigorous derivation of our model. Indeed, all parameters of our model can be derived from measurable behavioral data for a specific set of predator species.

Weaknesses:

The main weakness of this paper is that it devotes the vast majority of its length to demonstrating results that are already widely known in ecology. We have known for some time that predator interference can relax the CEP (e.g., Cantrell, R. S., Cosner, C., & Ruan, S. 2004).

While the model presented in this paper differs from the functional form of the B-D in some cases, it would be difficult to formulate a model that includes intraspecific interference (that increases with predator density) that does not allow for coexistence under some parameter range. Thus, I find it strange that most of the main text of the paper deals with demonstrating that predator interference allows for coexistence, given that this result is already well known. A more useful contribution would focus on the extent to which the dynamics of this model differ from those of the B-D model.

We appreciate the reviewer for raising this question and apologize for not sufficiently clarifying the contribution of our manuscript in the context of existing knowledge upon our initial submission. We have now significantly revised the introduction part of our manuscript (lines 51-67 in the main text) to make this clearer. Indeed, with the application of the Beddington-DeAngelis (B-D) model, several studies (e.g., Cantrell, R. S., Cosner, C., & Ruan, S. 2004) have already shown that intraspecific interference promotes species coexistence, and it is certain that the mechanism of intraspecific interference could lead to species coexistence if modeled correctly. However, while we acknowledge that the B-D model is a brilliant phenomenological model of intraspecific interference, for the specific research topic of our manuscript on breaking the CEP and explaining the paradox of the plankton, it is highly questionable regarding the validity of applying the B-D model to obtain compelling results.

Specifically, the functional response in the B-D model of intraspecific interference can be formally derived from the scenario involving only chasing pairs without consideration of pairwise encounters between consumer individuals (Eq. S8 in Appendices; related references: Gert Huisman, Rob J De Boer, J. Theor. Biol. 185, 389 (1997) and Xin Wang and Yang-Yu Liu, iScience 23, 101009 (2020)). Since we have demonstrated that the scenario involving only chasing pairs is under the constraint of CEP (see lines 139-144 in the main text and Appendix-fig. 3A-C; related references: Xin Wang and Yang-Yu Liu, iScience 23, 101009 (2020)), and given the identical functional response mentioned above, it is thus highly questionable regarding the validity of the studies relying on the B-D model to break CEP or explain the paradox of the plankton.

Consequently, one of the major objectives of our manuscript is to resolve whether the mechanism of intraspecific interference can truly break CEP and explain the paradox of the plankton in a rigorous manner. By modeling intraspecific predator interference from a mechanistic perspective and applying rigorous mathematical analysis and numerical simulations, our work resolves these issues and demonstrates that intraspecific interference enables a wide range of consumer species to coexist with only one or a handful of resource species. This naturally breaks CEP, explains the paradox of plankton, and quantitatively illustrates a broad spectrum of experimental results.

For intuitive understanding, we introduced a functional response in our model (presented as Eq. 5 in the main text), which indeed involves approximations. However, to rigorously break the CEP or explain the paradox of plankton, all simulation results in our study were directly derived from equations 1 to 4 (main text), without relying on the approximate functional response presented in Eq. 5.

The formulation of chasing-pair engagements assumes that prey being chased by a predator are unavailable to other predators. For one, this seems inconsistent with the ecology of most predator-prey systems. In the system in which I work (coral reef fishes), prey under attack by one predator are much more likely to be attacked by other predators (whether it be a predator of the same species or otherwise). I find it challenging to think of a mechanism that would give rise to chased prey being unavailable to other predators. The authors also critique the B-D model: "However, the functional response of the B-D model involving intraspecific interference can be formally derived from the scenario involving only chasing pairs without predator interference (Wang and Liu, 2020; Huisman and De Boer, 1997) (see Eqs. S8 and S24). Therefore, the validity of applying the B-D model to break the CEP is questionable.".

We appreciate the reviewer for raising this question. We fully agree with the reviewer that in many predator-prey systems (e.g., coral reef fishes as mentioned by the reviewer, wolves, and even microbial species such as Myxococcus xanthus; related references: Berleman et al., FEMS Microbiol. Rev. 33, 942-957 (2009)), prey under attack by one predator can be targeted by another predator (which we term as a chasing triplet) or even by additional predator individuals (which we define as higher-order terms). However, since we have already demonstrated in a previous study (Xin Wang, Yang-Yu Liu, iScience 23, 101009 (2020)) from a mechanistic perspective that a scenario involving chasing triplets or higher-order terms can naturally break the CEP, while our manuscript focuses on whether pairwise encounters between individuals can break the CEP and explain the paradox of plankton, we deliberately excluded confounding factors that are already known to promote biodiversity, just as we excluded prevalent factors such as cross-feeding and temporal variations in our model.

However, the way "chasing pairs" are formulated does result in predator interference because a predator attacking prey interferes with the ability of other predators to encounter the prey. I don't follow the author's logic that B-D isn't a valid explanation for coexistence because a model incorporating chasing pairs engagements results in the same functional form as B-D.

We thank the reviewer for raising this question, and we apologize for not making this point clear enough at the time of our initial submission. We have now revised the related part of our manuscript (lines 56-62 in the main text) to make this clearer.

In our definition, predator interference means the pairwise encounter between consumer individuals, while a chasing pair is formed by a pairwise encounter between a consumer individual and a resource individual. Thus, in these definitions, a scenario involving only chasing pairs does not involve pairwise encounters between consumer individuals (which is our definition of predator interference).

We acknowledge that there can be different definitions of predator interference, and the reviewer's interpretation is based on a definition of predator interference that incorporates indirect interference without pairwise encounters between consumer individuals. We do not wish to argue about the appropriateness of definitions. However, since we have proven that scenarios involving only chasing pairs are under the constraint of CEP (see lines 139-144 in the main text and Appendix-fig. 3A-C; related references: Xin Wang and Yang-Yu Liu, iScience 23, 101009 (2020)), while the functional response of the B-D model can be derived from the scenario involving only chasing pairs without consideration of pairwise encounters between consumer individuals (Eq. S8 in Appendices; related references: Gert Huisman, Rob J De Boer, J. Theor. Biol. 185, 389 (1997) and Xin Wang and Yang-Yu Liu, iScience 23, 101009 (2020)), it is thus highly questionable regarding the validity of applying the B-D model to break CEP.

More broadly, the specific functional form used to model predator interference is of secondary importance to the general insight that intraspecific interference (however it is modeled) can allow for coexistence. Mechanisms of predator interference are complex and vary substantially across species. Thus it is unlikely that any one specific functional form is generally applicable.

We thank the reviewer for raising this issue. We agree that the general insight that intraspecific predator interference can facilitate species coexistence is of great importance. We also acknowledge that any functional form of a functional response is unlikely to be universally applicable, as explicit functional responses inevitably involve approximations. However, we must reemphasize the importance of verifying whether intraspecific predator interference can truly break CEP and explain the paradox of plankton, which is one of the primary objectives of our study. As mentioned above, since the B-D model can be derived from the scenario involving only chasing pairs (Eq. S8 in Appendices; related references: Gert Huisman, Rob J De Boer, J. Theor. Biol. 185, 389 (1997) and Xin Wang and Yang-Yu Liu, iScience 23, 101009 (2020)), while we have demonstrated that scenarios involving only chasing pairs are subject to the constraint of CEP (see lines 139-144 in the main text and Appendix-fig. 3A-C; related references: Xin Wang and Yang-Yu Liu, iScience 23, 101009 (2020)), it is highly questionable regarding the validity of applying the B-D model to break CEP.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

I do not see any code or data sharing. They should exist in a prominent place. The authors should make their simulations and the analysis scripts freely available to download, e.g. by GitHub. This is always true but especially so in a journal like eLife.

We appreciate the reviewer for these recommendations. We apologize for our oversight regarding the unsuccessful upload of the data in our initial submission, as the data size was considerable and we neglected to double-check for this issue. Following the reviewer’s recommendation, we have now uploaded the code and dataset to GitHub (accessible at https://github.com/SchordK/Intraspecific-predator-interference-promotesbiodiversity-in-ecosystems), where they are freely available for download.

The introduction section should include more background, including about BD but also about consumer-resource models. Part of the results section could be moved/edited to the introduction. You should try that the results section should contain only "new" stuff whereas the "old" stuff should go in the introduction.

We thank the reviewer for these recommendations. Following these suggestions, we have now reorganized our manuscript by adding a new paragraph to the introduction section (lines 51-62 in the main text) and revising related content in both the introduction and results sections (lines 63-67, 81-83 in the main text).

I found myself getting a little bogged down in the general/formal description of the model before you go to specific cases. I found the most interesting part of the paper to be its second half. This is a dangerous strategy, a casual reader may miss out on the most interesting part of the paper. It's your paper and do what you think is best, but my opinion is that you could improve the presentation of the model and background to get to the specific contribution and specific use case quickly and easily, then immediately to the data. You can leave the more general formulation and the details to later in the paper or even the appendix. Ultimately, you have a simple idea and a beautiful application on interesting data-that is your strength I think, and so, I would focus on that.

We appreciate the reviewer for the positive comments and valuable suggestions. Following these recommendations, we have revised the presentation of the background information to clarify the contribution of our manuscript, and we have refined our model presentation to enhance clarity. Meanwhile, as we need to address the concerns raised by other reviewers, we continue to maintain systematic investigations for scenarios involving different forms of pairwise encounters in the case of S_C = 2 and S_R = 1 before applying our model to the experimental data.

Reviewer #2 (Recommendations For The Authors):

(1) I believe the surfaces in Figs. 1F-H corresponds to the zero-growth isoclines. The authors should directly point it out in the figure captions and text descriptions.

We thank the reviewer for this suggestion, and we have followed it to address the issue.

(2) After showing equations 1 or 2, I believe it will help readers understand the mechanism of equations by adding text such as "(see Fig. 1B)" to the sentences following the equations.

We appreciate the reviewer's suggestion, and we have implemented it to address the issue.

(3) Lines 12, 129 143 & 188: "at steady state" -> "at a steady state"

(4) Line 138: "is doom to extinct" -> "is doomed to extinct"

(5) Line 170: "intraspecific interference promotes species coexistence along with stochasticity" -> "intraspecific interference still robustly promotes species coexistence when stochasticity is considered"

(6) Line 190: "The long-term coexistence behavior are exemplified" -> "The long-term coexistence behavior is exemplified"

(7) Line 227: "the coefficient of variation was taken round 0.3" -> "the coefficient of variation was taken around 0.3"?

(8) Line 235: "tend to extinct" -> "tend to be extinct"

We thank the reviewer for all these suggestions, and we have implemented each of them to revise our manuscript.

Reviewer #3 (Recommendations For The Authors):

I think this would be a much more useful paper if the authors focused on how the behavior of this model differs from existing models rather than showing that the new formation also generates the same dynamics as the existing theory.

We thank the reviewers for this suggestion, and we apologize for not explaining the limitations of the B-D model and the related studies on the topic of CEP clearly enough at the time of our initial submission. As we have explained in the responses above, we have now revised the introduction part of our manuscript (lines 5167 in the main text) to make it clear that since the functional response in the B-D model can be derived from the scenario involving only chasing pairs without consideration of pairwise encounters between consumer individuals, while we have demonstrated that a scenario involving only chasing pairs is under the constraint of CEP, it is thus highly questionable regarding the validity of the studies relying on the B-D model to break CEP or explain the paradox of the plankton. Consequently, one of the major objectives of our manuscript is to resolve whether the mechanism of intraspecific interference can truly break CEP and explain the paradox of the plankton in a rigorous manner. By modeling from a mechanistic perspective, we resolve the above issues and quantitatively illustrate a broad spectrum of experimental results, including two classical experiments that violate CEP and the rank-abundance curves across diverse ecological communities.

Things that would be of interest:

What are the conditions for coexistence in this model? Presumably, it depends heavily on the equilibrium abundances of the consumers and resources as well as the engagement times/rates.

We thank the reviewer for raising this question. We have shown that there is a wide range of parameter space for species coexistence in our model. Specifically, for the case involving two consumer species and one resource species (S_C = 2 and S_R \= 1), we have conducted a systematic study on the parameter region for promoting species coexistence. For clarity, we set the mortality rate 𝐷_i (i = 1, 2) as the only parameter that varies with the consumer species, and the order of magnitude of all model parameters was estimated from behavioral data. The results for scenarios involving intraspecific predator interference are shown in Appendix-figs. 4B-D, 5A, 6C-D and we redraw some of them here as Fig. R2, including both ODEs and SSA results, wherein Δ = (𝐷₁-𝐷₂)/ 𝐷₂ represents the competitive difference between the two consumer species. For example, Δ =1 means that species C2 is twice the competitiveness of species C₁. In Fig. R2 (see also Appendix-figs. 4B-D, 5A, 6C-D), we see that the two consumer species can coexist with a large competitive difference in either ODEs and SSA simulation studies.

Author response image 2.

The parameter region for two consumer species coexisting with one type of abiotic resource species (S_C =2 and S_R \=1). (A) The region below the blue surface and above the red surface represents stable coexistence of the three species at constant population densities. (B) The blue region represents stable coexistence at a steady state for the three species. (C) The color indicates (refer to the color bar) the coexisting fraction for long-term coexistence of the three species. Figure redrawn from Appendixfigs. 4B, 6C-D.

For systems shown in Fig. 3A-D, where the number of consumer species is much larger than that of the resource species, we set each consumer species with unique competitiveness through a distinctive 𝐷_i (i =1,…, S_C). In Fig. 3A-D (see also Appendix fig. 10), we see that hundreds of consumer species may coexist with one or three types of resources when the coefficient of variation (CV) of the consumer species’ competitiveness was taken around 0.3, which indicates a large parameter region for promoting species coexistence.

Is there existing data to estimate the parameters in the model directly from behavioral data? Do these parameter ranges support the hypothesis that predator interference is significant enough to allow for the coexistence of natural predator populations?

We appreciate the reviewer for raising this question. Indeed, the parameters in our model were primarily determined by estimating their reasonable range from behavioral data. Following the reviewer's suggestions, we have now specified the data we used to set the parameters. For instance, in Fig. 2D, we set 𝐷₂\=0.01 with τ=0.4 Day, resulting in an expected lifespan of Drosophila serrata in our model setting of 𝜏⁄𝐷₂\= 40 days, which roughly agrees with experimental behavioral data showing that the average lifespan of D. serrata is 34 days for males and 54 days for females (lines 321325 in the appendices; reference: Narayan et al. J Evol Biol. 35: 657–663 (2022)). To account for competitive differences, we set the mortality rate as the only parameter that varies among the consumer species. As specified in the Appendices, the CV of the mortality rate is the only parameter that was used to fit the experiments within the range of 0.15-0.43. This parameter range (i.e., 0.15-0.43) was directly estimated from experimental data in the reference article (Patricia Menon et al., Water Research 37, 4151(2003)) using the two-sigma rule (lines 344-347 in the appendices).

Given the high consistency between the model results and experiments shown in Figs. 2D-E and 3C-D, where all the key model parameters were estimated from experimental data in references, and considering that the rank-abundance curves shown in Fig. 3C-D include a wide range of ecological communities, there is no doubt that predator interference is significant enough to allow for the coexistence of natural predator populations within the parameter ranges estimated from experimental references.

Bifurcation analyses for the novel parameters of this model. Does the fact that prey can escape lead to qualitatively different model behaviors?

Author response image 3.

Bifurcation analyses for the separate rate d’_i and escape rate d_i (i =1, 2) of our model in the case of two consumer species competing for one abiotic resource species (S_C =2 and S_R \=1). (A) A 3D representation: the region above the blue surface signifies competitive exclusion where C₁ species extinct, while the region below the blue surface and above the red surface represents stable coexistence of the three species at constant population densities. (B) a 2D representation: the blue region represents stable coexistence at a steady state for the three species. Figure redrawn from Appendix-fig. 4C-D.

We appreciate the reviewer for this suggestion. Following this suggestion, we have conducted bifurcation analyses for the separate rate d’_i and escape rate d_i of our model in the case where two consumer species compete for one resource species (S_C =2 and S_R \=1). Both 2D and 3D representations of these results have been included in Appendix-fig. 4, and we redraw them here as Fig. R3. In Fig. R3, we set the mortality rate 𝐷_i (i =1, 2) as the only parameter that varies between the consumer species, and thus Δ = _(D1-𝐷₂)/𝐷₂ represents the competitive difference between the two species.

As shown in Fig. R3A-B, the smaller the escape rate d_i, the larger the competitive difference Δ tolerated for species coexistence at steady state. A similar trend is observed for the separate rate d’_i. However, there is an abrupt change for both 2D and 3D representations at the area where d’_i =0, since if d’_i =0, all consumer individuals would be trapped in interference pairs, and then no consumer species could exist. On the contrary, there is no abrupt change for both 2D and 3D representations at the area where d_i\=0, since even if d_i\=0, the consumer individuals could still leave the chasing pair through the capture process.

Figures: I found the 3D plots especially Appendix Figure 2 very difficult to interpret. I think 2D plots with multiple lines to represent predator densities would be more clear.

We thank the reviewer for this suggestion. Following this suggestion, we have added a 2D diagram to Appendix-fig. 2.

eLife assessment

This valuable work uses unbiased approaches to discover critical molecules in C. elegans and its bacterial food for nutrition sensing and food choice, providing a framework for other studies. The data convincingly support their model that C. elegans uses UPRER and immune response pathways to evaluate sugar contents in the bacteria to change their behaviors.

Reviewer #3 (Public Review):

Summary:<br /> Animals can evaluate food quality in many ways. In contrast to the rapid sensory evaluation with smell and taste, the mechanism of slow nutrient sensation and its impact on food choice is unexplored. The authors utilize C. elegans larvae and their bacterial food as an elegant model to tackle this question and reveal the detailed molecular mechanism to avoid nutrient-poor foods.

Strength:<br /> The strength of this study is that they identified the molecular identities of the critical players in bacterial food and C. elegans using unbiased approaches, namely metabolome analysis, E. coli mutant screening, and RNA sequencing. Furthermore, they strengthened their findings by thorough experiments combining multiple methods such as genetics, fluorescent reporter analysis, and Western blot.

Weakness:<br /> The major caveat of this study is the reporter genes; specifically, transcriptional reporters used to monitor the UPRER and immune responses in the intestine of C. elegans. However, their tissue-specific rescue experiments suggest that the genes in the UPRER and immune response function in the neurons. Thus, we should carefully interpret the results of the reporter genes. Another point to be aware of is that although they show that lack of carbohydrates elicits the response to "low-quality" food, carbohydrate supplementation with heat-killed E. coli was insufficient to support animal growth.

Overall, this work provides convincing data to support their model. In the C. elegans field, the behaviors of larvae are not well studied compared to adults. This work will pose an interesting question about the difference between larvae and adults in nutrition sensing in C. elegans and provide a framework and candidate molecules to be studied in other organisms.

Reviewer #2 (Public Review):

Summary:<br /> In this work, the authors aim to better understand how C. elegans detects and responds to heat-killed (HK) E. coli, a low-quality food. They find that HK food activates two canonical stress pathways, ER-UPR and innate immunity, in the nervous system to promote food aversion. Through the creative use of E. coli genetics and metabolomics, the authors provide evidence that the altered carbohydrate content of HK food is the trigger for the activation of these stress responses and that supplementation of HK food with sugars (or their biosynthetic product, vitamin C), reduces stress pathway induction and food avoidance. This work makes a valuable addition to the literature on metabolite detection as a mechanism for evaluation of nutritional value; it also provides some new insight into physiologically relevant roles of well-known stress pathways in modulating behavior.

Strengths:<br /> -The work addresses an important question by focusing on understanding how the nervous system evaluates food quality and couples this to behavioral change.<br /> -The work takes full advantage of the tools available in this powerful system and builds on extensive previous studies on feeding behavior and stress responses in C. elegans.<br /> -Creative use of E. coli genetics and metabolite profiling enabled identification of carbohydrate metabolism as a candidate source of food-quality signals.<br /> -For the most part, the studies are rigorous and logically designed, providing good support for the authors' model.

Weaknesses:<br /> -The authors' claim that they can detect induction of hsp-4 and irg-5 expression in neurons (Fig 1-S2A) requires further support. The two tail cells shown are quite a bit larger than would by typically expected for neurons. The rescue they observe by neuronal expression is largely convincing, so it's quite possible that these pathways do indeed function in neurons, but that their level of induction in the nervous system is below reporter detection limits (or is 'swamped out' by much higher levels of expression in the intestine).<br /> -The authors conclude that "the induction of Pirg-5::GFP was abolished in pmk-1 knockdown animals fed with HK-E. coli" (Fig 2D). Because a negative control for induction (e.g., animals fed with control E. coli) is not shown, this conclusion must be regarded as tentative.<br /> -The effect sizes in the food-preference assay shown in Figure 5 are extremely small and do not provide strong support for the strong conclusions about the role of stress response pathways in food preference behavior.

eLife assessment

The findings presented by the authors are useful within the focused scope of endometriosis treatment, providing a potential new therapeutic approach. The strength of the evidence is, however, incomplete, as the main claims are only partially supported by the authors' data. The research nevertheless offers promising initial evidence for KMO inhibition as a novel non-hormonal therapy for endometriosis, but further studies are needed to confirm efficacy and address any potential side effects.

Reviewer #1 (Public Review):

Summary:

This study explores the therapeutic potential of KMO inhibition in endometriosis, a condition with limited treatment options.

Strengths:

KNS898 is a novel specific KMO inhibitor and is orally bioavailable, providing a convenient and non-hormonal treatment option for endometriosis. The promising efficacy of KNS898 was demonstrated in a relevant preclinical mouse model of endometriosis with pathological and behavioural assessments performed.

Weaknesses:

(1) The expression of KMO in human normal endometrium and endometrial lesions was not quantified. Western blot or quantification of IHC images will provide valuable insight. If KMO is not overexpressed in diseased tissues ie it may have homeostatic roles, and inhibition of KMO may have consequences on general human health and wellbeing. In addition, KMO expression in control mice was not shown or quantified. Images of KMO expression in endometriosis mice with treatments should be shown in Figure 4. The images showing quantification analysis (Figure 4A-F) can be moved to supplementary material.

(2) Figure 1 only showed representative images from a few patients. A description of whether KMO expression varies between patients and whether it correlates with AFS stages/disease severity will be helpful. Images from additional patients can be provided in supplementary material.

(3) For Home Cage Analysis, different measurements were performed as stated in methods including total moving distance, total moving time, moving speed, isolation/separation distance, isolated time, peripheral time, peripheral distance, in centre zones time, in centre zones distance, climbing time, and body temperature. However, only the finding for peripheral distance was reported in the manuscript.

(4) The rationale for choosing the different dose levels of KNS898 - 0.01-25mg/kg was not provided. What is the IC50 of a drug?

(5) Statistical significance:<br /> (a) Were stats performed for Fig 3B-E?<br /> (b) Line 141 - 'P = 0.004 for DEGLS per group'<br /> However, statistics were not shown in the figure.<br /> (c) Line 166 - 'the mechanical allodynia threshold in the hind paw was statistically significantly lower compared to baseline for the group'<br /> However, statistics were not shown in the figure.<br /> (d) Line 170 - 'Two-way ANOVA, Group effect P = 0.003, time effect P < 0.0001' The stats need to be annotated appropriately in Figure 5A as two separate symbols.<br /> (e) Figure 5B - multiple comparisons of two-way ANOVA are needed. G4 does not look different to G3 at D42.<br /> (f) Line 565 - 'non-significant improvement in KNS898 treated groups'. However, ** was annotated in Figure 5A.

(6) Discussion is very light. No reference to previous publications was made in the discussion. Discussion on potential mechanistic pathways of KYR/KMO in the pathogenesis of endometriosis will be helpful, as the expression and function of KMO and/or other metabolites in endometrial-related conditions.

The findings in this study generally support the conclusion although some key data which strengthen the conclusion eg quantification of KMO in normal and diseased tissue is lacking. Before KMO inhibitors can be used for endometriosis, the function of KMO in the context of endometriosis should be explored eg KMO knockout mice should be studied.

Reviewer #2 (Public Review):

Summary:

The authors aim to address the clinical challenge of treating endometriosis, a debilitating condition with limited and often ineffective treatment options. They propose that inhibiting KMO could be a novel non-hormonal therapeutic approach. Their study focuses on:<br /> • Characterising KMO expression in human and mouse endometriosis tissues.<br /> • Investigating the effects of KMO inhibitor KNS898 on inflammation, lesion volume, and pain in a mouse model of endometriosis.<br /> • Demonstrating the efficacy of KMO blockade in improving histological and symptomatic features of endometriosis.

Strengths:

• Novelty and Relevance: The study addresses a significant clinical need for better endometriosis treatments and explores a novel therapeutic target.<br /> • Comprehensive Approach: The authors use both human biobanked tissues and a mouse model to study KMO expression and the effects of its inhibition.<br /> • Clear Biochemical Outcomes: The administration of KNS898 reliably induced KMO blockade, leading to measurable biochemical changes (increased kynurenine, increased kynurenic acid, reduced 3-hydroxykynurenine).

Weaknesses:

• Limited Mechanistic Insight: The study does not thoroughly investigate the mechanistic pathways through which KNS898 affects endometriosis. Specifically, the local vs. systemic effects of KMO inhibition are not well differentiated.<br /> • Statistical Analysis Issues: The choice of statistical tests (e.g., two-way ANOVA instead of repeated measures ANOVA for behavioral data) may not be the most appropriate, potentially impacting the validity of the results.<br /> • Quantification and Comparisons: There is insufficient quantitative comparison of KMO expression levels between normal endometrium and endometriosis lesions, and the systemic effects of KNS898 are not fully explored or quantified in various tissues.<br /> • Potential Side Effects: The systemic accumulation of kynurenine pathway metabolites raises concerns about potential side effects, which are not addressed in the study.

Achievement of Aims:

• The authors successfully demonstrated that KMO is expressed in endometriosis lesions and that KNS898 can induce KMO blockade, leading to biochemical changes and improvements in endometriosis symptoms in a mouse model.

Support of Conclusions:

• While the data supports the potential of KMO inhibition as a therapeutic strategy, the conclusions are somewhat overextended given the limitations in mechanistic insights and statistical analysis. The study provides promising initial evidence but requires further exploration to firmly establish the efficacy and safety of KNS898 for endometriosis treatment.

Impact on the Field:

• The study introduces a novel therapeutic target for endometriosis, potentially leading to non-hormonal treatment options. If validated, KMO inhibition could significantly impact the management of endometriosis.

Utility of Methods and Data:

• The methods used provide a foundation for further research, although they require refinement. The data, while promising, need more rigorous statistical analysis and deeper mechanistic exploration to be fully convincing and useful to the community.

eLife assessment

This study presents a useful computational data preprocessing methodology for de-biasing/denoising high-throughput genomic signals using optimal transport techniques. The evidence supporting the claims of the authors is, however, in parts incomplete, with a partially insufficient experimental setup for validation. The method needs to be be compared with other algorithms, using datasets that demonstrate broad applicability of the algorithm presented. The work could be of interest to scientists in the field of computational genomics.

Reviewer #1 (Public Review):

Summary:

The authors applied a domain adaptation method using the principal of optimal transport (OT) to superimpose read count data onto each other. While the title suggests that the presented method is independent from and performs better than other methods of bias correction, the presented work uses a self-implemented version of GC bias correction apart of the OT domain adaptation. Performance comparisons were done both on normalized read counts as well as on copy number profiles which is already the complete set of presented use cases. Results involving copy number profiles from iChorCNA were also subjected to the bias correction measures implemented there. It is not clear at many points which correction method actually causes the observed performance.

Strengths:

The quality of superimposing distributions of normalized read counts (and copy number profiles) was sufficiently shown using uniformly distributed p-values in the interval of 0 to 1 for healthy controls D7 and D8 which differed in the choice of library preparation kit.

The ability to select a sample from the source domain for samples in the target domain was demonstrated.

Weaknesses:

Experiment Design:

The chosen bias correction methods are not explicitly designed for nor aimed at domain adaptation. The benchmark against GC bias correction while doing GC bias correction during the OT procedure is probably the most striking flaw of the entire work. GC bias correction has the purpose of correction GC biases, wherever present, NOT correcting categorical pre-analytical variables of undefined character. A more thorough examination of the presented results should address why plain iChor CNA is the best performing "domain adaptation" in some cases. Also, the extent to which the implemented GC bias correction is contributing to the performance increase independent of the OT procedure should be assessed separately in each case.<br /> Moreover, the center-and-scale standardization is probably not the most relevant contestant in domain adaptation that is out there.

Comparison of cohorts (domains) - especially healthy from D7 and D8 - it is not described which type of ChIP analysis was done for the healthy controls of the D7 domain. The utilized library preparation kit implies that D7 represents a subset of available cfDNA in a plasma sample by precipitating only certain cfDNA fragments to which undisclosed type of protein was bound. Even if the type of protein turns out to be histones, the extracted subset of cfDNA should not be regarded as coming from the same distribution of cfNDAs. For example, fragments with sub-mononucleosomal length would be depleted in the ChIP-seq data set while these could be extracted in an untargeted cfDNA sequencing data set. It needs to be clarified why the authors deem D7 and D8 healthy controls to be identical with regards to SCNA analysis. Best start with the protein targets of D7 ChIP-seq samples.

From the Illumina TruSeq ChIP product description page:<br /> "TruSeq ChIP Libary Preparation Kits provide a simple, cost-effective solution for generating chromatin immunoprecipitation sequencing (ChIP-Seq) libraries from ChIP-derived DNA. ChIP-seq leverages next-generation sequencing (NGS) to quickly and efficiently determine the distribution and abundance of DNA-bound protein targets of interest across the genome."

Redundancy:

Some parts throughout the results and discussion part reappear in the methods. The description of the methodology should be concentrated in the method section and only reiterated in a summarizing fashion where absolutely necessary.<br /> Unnecessary repetition inflate the presented work which is not appealing to the reader. Rather include more details of the utilized materials and methods in the corresponding section.

Transparency:

At the time point of review, the code was not available under the provided link.<br /> A part of the healthy controls from D8 is not contained under the provided accession (367 healthy samples are available in the data base vs. sum of D7 and D8 healthy controls is 499)

Neither in the paper nor in reference 4 is an explanation of what was targeted with the ChIP-seq approach.

Consistency:

It is not evident why a ChIP-seq library prep kit was used (sample cohorts designated as D7). The DNA isolation procedure was not presented as having an immunoprecipitation step. Furthermore, it is not clear which DNA bound proteins were targeted during ChIP seq, if such an immunoprecipitation was actually carried out.The authors self-implemented a GC bias correction procedure although they already mentioned other procedures earlier like LIQUORICE. Also, there already exist tools that can be used to correct GC bias, like deepTools (github.com/deeptools/deepTools). Other GC bias correction algorithms designed specifically for cfDNA would be Griffin (github.com/adoebley/Griffin) and GCparagon (github.com/BGSpiegl/GCparagon). When benchmarking against state-of-the-art cfDNA GC bias correction, these algorithms should appear in a relevant scientific work, somewhere other than the introduction, preferably in the results section. It should be shown that the chosen GC bias correction method is performing best under the given circumstances.

Accuracy:

Use clear labels for each group of samples. The domain number is not sufficient to effectively distinguish sample groups. Already the source name plus a simple enumeration would improve the clarity at some points.

The healthy controls of D7 and D8 are described but the numbers do not add up (257 healthy controls in line 227 vs. 260 healthy controls in line 389). Please double check this and use representative sample cohort labels in the materials description for improved clarity!

Avoid statements like "the rest" when talking about a mixed set of samples. It is not clear how many samples from which domain are addressed.

For optimal transport, knowledge about the destination is required ("where do I want to transport to?") and, thus, the proposed method can never be unsupervised. It is always necessary to know the label of both the source and target domains. In practice, this is not often the case and users might fall prey to the error of superimposing data that is actually separated by valid differences in some experimental variables.

Seemingly arbitrary cutoff values are mentioned. For example, it is not clear if choosing "the cutoff that produced the highest MCCs" is meant across methods or for each method separately (are the results for each method reported that also resulted in the highest MCC for that method?).

The Euclidean metric for assessing the similarity of (normalized) read counts is questionable for a high dimensional space: read counts are assessed for 1 Mb genomic intervals which yields around 3000 intervals (dimensions), depending on the number of excluded intervals (which was not described in more detail). There might be more appropriate measures in this high dimensional space.

It is sometimes not clear what data actually is presented. An example would be the caption of Figure 2, (C): it is suggested that all (320) ovarian cancer cases are shown in one copy number profile.

Furthermore, the authors do not make a distinction between male and female samples. A clarification is needed why the authors think SCNAs of ovarian cancer samples should be called against a reference set that contains male controls.<br /> The procedure would likely benefit from a strict separation of male and female cases which would also allow for chrX (and chrY) being included in downstream analysis.

The GC bias and mappability correction implicitly done by iChorCNA for the SCNA profile comparison is presented as "no correction" which is highly misleading. (for clarification, this is also deemed inappropriate, not just inaccurate))

The majority of interpretations presented procedure does not give any significant improvement regarding the similarity of copy number profiles are off and in many instances favor the OT procedure in an unscientific and highly inappropriate manner.

Apart of duplicate marking (which is not specified any further - provide the command(s)!), there is no information on which read (pairs) were used (primary, secondary, supplementary, mapped in a proper pair, fragment length restrictions, clipping restrictions, etc.). The authors should explain why base quality score re-calibration was done as this might be an unnecessary step if the base quality values are not used later on.

The adaptation method presented as "center-and-scale standardization" is inappropriate for unbalanced cancer profiles since it assumes the presence of identical SCNAs in all samples belonging to the same cancer entity.<br /> Please explain why normalizing 1 Mb genomic intervals to the average copy number across different cancer samples should be valid or use another domain adaptation method for performance comparison.

Statements like in line 83 (unsupervised DA) are plain wrong because transport from one domain to another requires the selection of a target domain based on a label, e.g., based on health status, cancer entity, or similar.

Relevance and Appropriateness:

Many of the presented results are not relevant or details of the procedure were incomprehensible or incomplete: the results presented in table 2 - sample assignment. The Euclidean metric seems to be inappropriate for high dimensional data. Also the selection of the cutoff based on Euclidean distance seems to enable the optimization in favor of the OT procedure. It is hypothesized that there might exist other cutoff values for which the selection of samples form the source domain would also work for other correction methods but this is not further described. It could simply be the case that OT can assign a relationship between domains

The statement that there are no continuous pre-analytical variables is wrong (304). The effect of target depth-of-coverage (DoC) was not analyzed although this represents one of the most common (continuous) and difficult to control variables in NGS data analysis. The inclusion of multiple samples from a single patient in a cohort likely represents introduction of a confounding factor ["contamination"] to the model training procedure: the temporal difference that lies between the taken samples of that patient represents leakage of information. As far as can be told from the presented data, this potential bias has not been ruled out (e.g., exclusion of all samples beyond the first from each patient or alternatively: picking all samples of a patient either for the training set or the test set).

Conscientiousness:

Statements like "good"/"best" on their own should be avoided. A clear description of why a certain procedure/methodology/algorithm performs better should be preferred in scientific writing (e.g., "highest MCC values" instead of "best MCC values").<br /> Otherwise, such statements represent mere opinions of the author rather than an unbiased evaluation of the results.<br /> The domain D8 of healthy controls seems to contain samples from multiple sources (some published other in-house). Contrary to the data availability statement (533), not all healthy control samples of the HEMA data set are available from ArrayExpress

Other Major Concerns:

Potential Irrelevance:

The manuscript represents a mere performance assessment of the proposed sWGS per-bin-read-count fitting procedure and, thus, a verification in its character, not a validation (although the model training itself was "validated" - but this is to be viewed separately from the validity of the achieved correction in a biological context). A proper (biological) validation is missing.

It is of utmost importance that parameters of the adapted (transported) samples -that lie outside of what has been optimized to be highly similar- are checked to actually validate the procedure. Especially biological signals and genome-wide parameters (GC content distribution before/after transport) need to be addressed also in hindsight of the rampant criticism towards GC bias correction by the authors. At no point in the manuscript was GC bias addressed properly, i.e., how much of an improvement is expected from GC bias correction if there is no significant GC bias?

The (potential - not clear so far) ability of making ChIP-seq data look like cfDNA data (even if only the copy number profiles SCNAs appear highly similar) raises the concern of potential future users of the tool to superimpose domains that should not be superimposed form a biological point of view because the true domain the superimposed cohorts belong to are different. The ability to superimpose anything onto anything s troubling. There is no control mechanism that allows for failure in cases where the superposition is invalid.

Chromosome X was excluded which could be avoided if data sets were split according to biological sex.

The difference between the distributions was never attributed to GC bias, hence, the benchmark against GC bias correction tools might not be relevant in the first place.

Stability of OT data transformation:

The authors state that the straight forward choice of lambda resulted in many occasions where disruptions (of unspecified nature and amplitude) are introduced in the copy number profiles of transformed data. It is not evident from the proposed work to which extent this behavior was removed from the procedure and if it can occur and how the user could resolve such a problem on their own.

In summary, the presented work needs considerable adaptation and additions before it can actually be considered a valuable contribution to the liquid biopsy field.

Reviewer #2 (Public Review):

The authors present a computational methodology for de-biasing/denoising high-throughput genomic signals using optimal transport techniques, thus allowing disparate datasets to be merged and jointly analysed. They apply this methodology on liquid biopsy data and they demonstrate improved performance (compared to simpler bias-correcting approaches) for cancer detection using common machine learning algorithms. This is a theoretically interesting and potentially useful approach for addressing a very common practical problem in computational genomics.

I have the following recommendations:

(1) When comparing performance metrics between different approaches (e.g., tables 3 and 4), 95% confidence intervals should also be provided and a pairwise statistical test should be applied to establish whether the observed difference in each performance metric between the proposed method and the alternatives is statistically significant, thus justifying the claim that the proposed method offers an improvement over existing methodologies.

(2) The commonly used center-and-scale and GC debias approaches presented by the authors are fairly simple. How does their methodology compare to more elaborate approaches, such as tangent normalisation (https://academic.oup.com/bioinformatics/article/38/20/4677/6678978) and robust PCA (https://github.com/mskilab-org/dryclean)?

(3) What is the computational cost of the proposed methodology and how does it compare to the alternatives?

(4) The proposed approach relies on a reference dataset, against which a given dataset is adapted. What are the implications for cross-validation experiments (which are essential for assessing the out-of-sample error of every methodology), particularly with regards to the requirement to avoid information leakage between training and validation/test data sets?

In conclusion, this is an interesting and potentially useful paper and I would like to encourage the authors to address the above points, which hopefully will strengthen their case.

Author response:

The following is the authors’ response to the current reviews.

Reviewer #1 (Public Review):

Summary:

This manuscript explores the impact of serotonin on olfactory coding in the antennal lobe of locusts and odor-evoked behavior. The authors use serotonin injections paired with an odorevoked palp-opening response assay and bath application of serotonin with intracellular recordings of odor-evoked responses from projection neurons (PNs).

Strengths:

The authors make several interesting observations, including that serotonin enhances behavioral responses to appetitive odors in starved and fed animals, induces spontaneous bursting in PNs, directly impacts PN excitability, and uniformly enhances PN responses to odors.

Weaknesses:

The one remaining issue to be resolved is the theoretical discrepancy between the physiology and the behavior. The authors provide a computational model that could explain this discrepancy and provide the caveat that while the physiological data was collected from the antennal lobe, but there could be other olfactory processing stages involved. Indeed other processing stages could be the sites for the computational functions proposed by the model. There is an additional caveat which is that the physiological data were collected 5-10 minutes after serotonin application whereas the behavioral data were collected 3 hours after serotonin application. It is difficult to link physiological processes induced 5 minutes into serotonin application to behavioral consequences 3 hours subsequent to serotonin application. The discrepancy between physiology and behavior could easily reflect the timing of action of serotonin (i.e. differences between immediate and longer-term impact).

For our behavioral experiments, we waited 3 hours after serotonin injection to allow serotonin to penetrate through the layers of air sacks and the sheath, and for the locusts to calm down and recover their baseline POR activity levels. For the physiology experiments, we noticed that the quality of the patch decreased over time after serotonin introduction. Hence, it was difficult to hold cells for that long. However, the point raised by the reviewer is well-taken. We have performed additional experiments to show that the changes in POR levels to different odorants are rapid and can be observed within 15 minutes of injecting serotonin (Author response image 2) and that the physiological changes in PNs (bursting spontaneous activity, maintenance of temporal firing patterns, and increase odor-evoked responses) persists when the cells are held for longer duration (i.e. 3 hours akin to our behavioral experiments). It is worth noting that 3-hour in-vivo intracellular recordings are not easily achievable and come with many experimental constraints. So far, we have managed to record from two PNs that were held for this long and add them to this rebuttal to support our conclusions. (Author response image 1).

Author response image 1.

Spontaneous and odor-evoked responses in individual PNs remain consistent for three hours after serotonin introduction into the recording chamber/bath.<br /> (A) Representative intracellular recording showing membrane potential fluctuations in a projection neuron (PN) in the antennal lobe. Spontaneous and odor-evoked responses to four odorants (pink color bars, 4 s duration) are shown before (control) and after serotonin application (5HT). Voltage traces 30 minutes (30min), 1 hour (1h), 2 hours (2h), and 3 hours (3h) after 5HT application are shown to illustrate the persisting effect of serotonin during spontaneous and odor-evoked activity periods.<br /> (B) Rasterized spiking activities in two recorded PNs are shown. Spontaneous and odor-evoked responses are shown in all 5 consecutive trials. Note that the odor-evoked response patterns are maintained, but the spontaneous activity patterns are altered after serotonin introduction.

Author response image 2.

Palp-opening response (POR) patterns to different odorants remain consistent following serotonin introduction. The probability of PORs is shown as a bar plot for four different odorants; hexanol (green), benzaldehyde (blue), linalool (red), and ammonium (purple). PORs before serotonin injection (solid bars) are compared against response levels after serotonin injection (striped bars). As can be noted, PORs to the four odorants remain consistent when tested 15 minutes and 3 hours after (5HT) serotonin injection.

Overall, the study demonstrates the impact of serotonin on odor-evoked responses of PNs and odor-guided behavior in locusts. Serotonin appears to have non-linear effects including changing the firing patterns of PNs from monotonic to bursting and altering behavioral responses in an odor-specific manner, rather than uniformly across all stimuli presented.

We thank the reviewer for again providing very useful feedback for improving our manuscript.

Reviewer #2 (Public Review):

Summary:

The authors investigate the influence of serotonin on feeding behavior and electrophysiological responses in the antennal lobe of locusts. They find that serotonin injection changes behavior in an odor-specific way. In physiology experiments, they can show that projection neurons in the antennal lobe generally increase their baseline firing and odor responses upon serotonin injection. Using a modeling approach the authors propose a framework on how a general increase in antennal lobe output can lead to odor-specific changes in behavior.

Strengths:

This study shows that serotonin affects feeding behavior and odor processing in the antennal lobe of locusts, as serotonin injection increases activity levels of projection neurons. This study provides another piece of evidence that serotonin is a general neuromodulator within the early olfactory processing system across insects and even phyla.

Weaknesses:

I still have several concerns regarding the generalizability of the model and interpretation of results. The authors cannot provide evidence that serotonin modulation of projection neurons impacts behavior.

This is true and likely to be true for any study linking neural responses to behavior. There are multiple circuits and pathways that would get impacted by a neuromodulator like serotonin. What we showed with our physiology is how spontaneous and odor-evoked responses in the very first neural network that receives olfactory sensory neuron input are altered by serotonin. Given the specificity of the changes in behavioral outcomes (i.e. odor-specific increase and decrease in an appetitive behavior) and non-specificity in the changes at the level of individual PNs (general increase in odor-evoked spiking activity), we presented a relatively simple computational model to address the apparent mismatch between neural and behavioral responses. (Author response image 4).

The authors show that odor identity is maintained after 5-HT injection, however, the authors do not show if PN responses to different odors were differently affected after serotonin exposure.

The PN responses to different odorants changed in a qualitatively similar fashion. (Author response image 3)

Author response image 3.

PN activity before and after 5HT application are compared for different cellodor combinations. As can be noted, the changes are qualitatively similar in all cases. After 5HT application, the baseline activity became more bursty, but the odor-evoked response patterns were robustly maintained for all odorants.

Regarding the model, the authors show that the model works for odors with non-overlapping PN activation. However, only one appetitive, one neutral, and one aversive odor has been tested and modeled here. Can the fixed-weight model also hold for other appetitive and aversive odors that might share more overlap between active PNs? How could the model generate BZA attraction in 5-HT exposed animals (as seen in behavior data in Figure 1) if the same PNs just get activated more?

Author response image 4.

Testing the generality of the proposed computational model. To test the generality of the model proposed we used a published dataset [Chandak and Raman, 2023]: Neural dataset – 89 PN responses to a panel of twenty-two odorants; Behavioral dataset – probability of POR responses to the same twenty-two odorants. We built the model using just the three odorants overlapping between the two datasets: hexanol, benzaldehyde and linalool. The true probability of POR values of the twenty odorants and the POR probability predicted by the model are shown for all twenty-two odorants as a scatter plot. As can be noted, there is a high correlation (0.79) between the true and the predicted values.

The authors should still not exclude the possibility that serotonin injections could affect behavior via modulation of other cell types than projection neurons. This should still be discussed, serotonin might rather shut down baseline activation of local inhibitory neurons - and thus lead to the interesting bursting phenotypes, which can also be seen in the baseline response, due to local PN-to-LN feedback.

As we agreed, there could be other cells that are impacted by serotonin release. Our goal in this study was to characterize how spontaneous and odor-evoked responses in the very first neural network that receives olfactory sensory neuron input are altered by serotonin. Within this circuit, there are local inhibitory neurons (LNs), as correctly indicated by this reviewer. Surprisingly, our preliminary data indicates that LNs are not shut down but also have an enhanced odor-evoked neural response. (Author response image 5.) Further data would be needed to verify this observation and determine the mechanism that mediate the changes in PN excitability. Irrespective, since PN activity should incorporate the effects of changes in the local neuron responses and is the sole output from the antennal lobe that drives all downstream odor-evoked activity, we focused on them in this study.

Author response image 5.

Representative traces showing intracellular recording from a local neuron in the antennal lobe. Five consecutive trials are shown. Note that LNs in the locust antennal lobe are non-spiking. The LN activity before, during, and after the presentation of benzaldehyde and hexanol (colored bar; 4s) are shown. The Left and Right panels show LN activity before and after the application of 5HT. As can be noted, 5HT did not shut down odor-evoked activity in this local neuron.

The authors did not fully tone down their claims regarding causality between serotonin and starved state behavioral responses. There is no proof that serotonin injection mimics starved behavioral responses.

Specific minor issues:<br /> It is still unclear how naturalistic the chosen odor concentrations are. This is especially important as behavioral responses to different concentrations of odors are differently modulated after serotonin injection (Figure 2: Linalool and Ammonium). The new method part does not indicate the concentrations of odors used for electrophysiology.

All odorants were diluted to 0.01-10% concentration by volume in either mineral oil or distilled water. This information is included in the Methods section. For most odorants used in the study, the lower concentrations only evoked a very weak neural response, and the higher concentrations evoked more robust responses. The POR responses for these odorants at various concentrations chosen are included in Figure 2. Note, that the responses to linalool and ammonium remained weak throughout the concentration changes, compared to hexanol and benzaldehyde.

Did all tested PNs respond to all odorants?

No, only a subset of them responses to each odorant. These responses have been well characterized in earlier publications [included refs].

The authors do not show if PN responses to different odors were differently affected after serotonin exposure. They describe that ON responses were robust, but OFF responses were less consistent after 5-HT injection. Was this true across all odors tested? Example traces are shown, but the odor is not indicated in Figure 4A. Figure 4D shows that many odor-PN combinations did not change their peak spiking activity - was this true across odorants? In Figure 5 - are PNs ordered by odor-type exposure?

Also, Figure 6A only shows example trajectories for odorants - how does the average look? Regarding the data used for the model - can the new dataset from the 82 odor-PN pairs reproduce the activation pattern of the previously collected dataset of 89 pairs?

What is shown in Figure 6A is the trial-averaged response trajectory combining activities of all 82 odor-PN pairs. 82 odor-PN pair was collected intracellularly examining the responses to four odorants before and after 5HT application. The second dataset involving 89 PN responses to 22 odorants was collected extracellularly. They have qualitative similarities in each odorant activate a unique subset of those neurons.

The authors toned down their claims that serotonin injection can mimic the starved state behavioral response. However, some sentences still indicate this finding and should also be toned down:

last sentence of introduction - "In sum, our results provide a more systems-level view of how a specific neuromodulator (serotonin) alters neural circuits to produce flexible behavioral outcomes."

We believe we showed this with our computational model, how uniform changes in the neural responses could lead to variable and odor-specific changes in behavioral PORs.

discussion: "Finally, fed locusts injected with serotonin generated similar appetitive responses to food-related odorants as starved locusts indicating the role of serotonin in hunger statedependent modulation of odor-evoked responses." This claim is not supported.

Figure 7 shows that the fed locusts had lower POR to hex and bza. The POR responses significantly increased after the 5HT application. However, we have rephrased this sentence to limit our claims to this result. "Finally, fed locusts injected with serotonin generated similar appetitive palp-opening responses to food-related odorants as observed in starved locusts”

last results: "However, consistent with results from the hungry locusts, the introduction of serotonin increased the appetitive POR responses to HEX and BZA. Intriguingly, the appetitive responses of fed locusts treated with 5HT were comparable or slightly higher than the responses of hungry locusts to the same set of odorants."

Again this sentence simply describes the result shown in Figure 7.

In Figure 7 - BZA response seems unchanged in hungry and fed animals and only 5-HT injection enhances the response. There is only one example where 5-HT application and starvation induce the same change in behavior - N=1 is not enough to conclude that serotonin influences food-driven behaviors.

The reviewer is ignoring the lack of changes to PORs to linalool and ammonium. Taken together, serotonin increased PORs to only two of the four odorants in starved locusts. The responses after 5HT modulation to these four odorants were similar in fed locusts treated with 5HT and starved locusts.

Also, this seems to be wrongly interpreted in Figure 7: "It is worth noting that responses to LOOL and AMN, non-food related odorants with weaker PORs, remained unchanged in fed locusts treated with 5HT." The authors indicate a significant reduction in POR after 5-HT injection on LOOL response in Figure 7.

Revised.<br /> It is worth noting that responses to LOOL and AMN, non-food related odorants with weaker PORs, and reduced in fed locusts treated with 5HT."

Also, the newly added sentence at the end of the discussion does not make sense: "However, since 5HT increased behavioral responses in both fed and hungry locusts, the precise role of 5HT modulation and whether it underlies hunger-state dependent modulation of appetitive behavior still remains to be determined."<br /> The authors did not test 5-HT injection in starved animals

The results shown in Figure 1 compare the POR responses of starved locusts before and after 5HT introduction.

We again thank the reviewer for useful feedback to further improve our manuscript.

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The following is the authors’ response to the original reviews.

Reviewer #1 (Public Review):

Summary:

This manuscript explores the impact of serotonin on olfactory coding in the antennal lobe of locusts and odor-evoked behavior. The authors use serotonin injections paired with an odor-evoked palp-opening response assay and bath application of serotonin with intracellular recordings of odor-evoked responses from projection neurons (PNs).

Strengths:

The authors make several interesting observations, including that serotonin enhances behavioral responses to appetitive odors in starved and fed animals, induces spontaneous bursting in PNs, and uniformly enhances PN responses to odors. Overall, I had no technical concerns. Weaknesses:

While there are several interesting observations, the conclusions that serotonin enhanced sensitivity specifically and that serotonin had feeding-state-specific effects, were not supported by the evidence provided. Furthermore, there were other instances in which much more clarification was needed for me to follow the assumptions being made and inadequate statistical testing was reported.

Major concerns.

• To enhance olfactory sensitivity, the expected results would be that serotonin causes locusts to perceive each odor as being at a relatively higher concentration. The authors recapitulate a classic olfactory behavioral phenomenon where higher odor concentrations evoke weaker responses which is indicative of the odors becoming aversive. If serotonin enhanced the sensitivity to odors, then the dose-response curve should have shifted to the left, resulting in a more pronounced aversion to high odor concentrations. However, the authors show an increase in response magnitude across all odor concentrations. I don't think the authors can claim that serotonin enhances the behavioral sensitivity to odors because the locusts no longer show concentration-dependent aversion. Instead, I think the authors can claim that serotonin induces increased olfactory arousal.

The reviewer makes a valid point. Bath application of serotonin increased POR behavioral responses across all odor concentrations, and concentration-dependent aversion was also not observed. Furthermore, the monotonic relationship between projection neuron responses and the intensity of current injection is altered when serotonin is exogenously introduced (see Author response image 1; see below for more explanation). Hence, our data suggests that serotonin alters the dose-response relationship between neural/behavioral responses and odor intensity. As recommended, we have followed what the reviewer has suggested and revised our claim to serotonin inducing increase in olfactory arousal. The new physiology data has been added as Supplementary Figure 3 to the revised manuscript.

• The authors report that 5-HT causes PNs to change from tonic to bursting and conclude that this stems from a change in excitability. However, excitability tests (such as I/V plots) were not included, so it's difficult to disambiguate excitability changes from changes in synaptic input from other network components.

To confirm that the PN excitability did indeed change after serotonin application, we performed a new set of current-clamp recordings. In these experiments, we monitored the spiking activities in individual PNs as we injected different levels of current injections (200 – 1000 pico Amperes). Note that locust LNs that provide recurrent inhibition arborize and integrate inputs from a large number of sensory neurons and projection neurons. Therefore, activating a single PN should not activate the local neurons and therefore the antennal lobe network.

We found that the total spiking activity monotonically increased with the magnitude of the current injection in all four PNs recorded (Author response image 1). However, after serotonin injection, we found that the spiking activity remained relatively stable and did not systematically vary with the magnitude of the current injection. While the changes in odor-evoked responses may incorporate both excitability changes in individual PNs and recurrent feedback inhibition through GABAergic LNs, these results from our current injection experiments unambiguously indicate that there are changes in excitability at the level of individual PNs. We have added this result to the revised manuscript.

Author response image 1.

Current-injection induced spiking activity in individual PNs is altered after serotonin application. (A) Representative intracellular recordings showing membrane potential fluctuations as a function of time for one projection neuron (PNs) in the locust antennal lobe. A two-second window when a positive 200-1000pA current was applied is shown. Firing patterns before (left) and after (right) serotonin application are shown for comparison. Note, the spiking activity changes after the 5HT application. The black bar represents the 20mV scale. (B) Dose-response curves showing the average number of action potentials (across 5 trials) during the 2second current pulse before (green) and after (purple) serotonin for each recorded PN. Note that the current intensity was systematically increased from 200 pA to 1000 pA. The (C) The mean number of spikes across the four recorded cells during current injection is shown. The color progression represents the intensity of applied current ranging 200pA (leftmost bar) to 1000pA (rightmost bar). The dose-response trends before (green) and after (purple) 5HT application are shown for comparison.. The error bars represent SEM across the four cells.

• There is another explanation for the theoretical discrepancy between physiology and behavior, which is that odor coding is further processing in higher brain regions (ie. Other than the antennal lobe) not studied in the physiological component of this study. This should at least be discussed.

This is a valid argument. For our model of neural mapping onto behavior to work, we only need the odorant that evokes or suppresses PORs to activate a distinct set of neurons. Having said that, our extracellular recording results (Fig. 6E) indicate that hexanol (high POR) and linalool (low POR) do activate highly non-overlapping sets of PNs in the antennal lobe. Hence, our results suggest that the segregation of neural activity based on behavioral relevance already begins in the antennal lobe. We have added this clarification to the discussion section.

• The authors cannot claim that serotonin underlies a hunger state-dependent modulation, only that serotonin impacts responses to appetitive odors. Serotonin enhanced PORs for starved and fed locusts, so the conclusion would be that serotonin enhances responses regardless of the hunger state. If the authors had antagonized 5-HT receptors and shown that feeding no longer impacts POR, then they could make the claim that serotonin underlies this effect. As it stands, these appear to be two independent phenomena.

This is also a valid point. We have clarified this in the revised manuscript.

Reviewer #2 (Public Review):

Summary:

The authors investigate the influence of serotonin on feeding behavior and electrophysiological responses in the antennal lobe of locusts. They find that serotonin injection changes behavior in an odorspecific way. In physiology experiments, they can show that antennal lobe neurons generally increase their baseline firing and odor responses upon serotonin injection. Using a modeling approach the authors propose a framework on how a general increase in antennal lobe output can lead to odorspecific changes in behavior. The authors finally suggest that serotonin injection can mimic a change in a hunger state.

Strengths:

This study shows that serotonin affects feeding behavior and odor processing in the antennal lobe of locusts, as serotonin injection increases activity levels of antennal lobe neurons. This study provides another piece of evidence that serotonin is a general neuromodulator within the early olfactory processing system across insects and even phyla. Weaknesses:

I have several concerns regarding missing control experiments, unclear data analysis, and interpretation of results.

A detailed description of the behavioral experiments is lacking. Did the authors also provide a mineral oil control and did they analyze the baseline POR response? Is there an increase in baseline response after serotonin exposure already at the behavioral output level? It is generally unclear how naturalistic the chosen odor concentrations are. This is especially important as behavioral responses to different concentrations of odors are differently modulated after serotonin injection (Figure 2: Linalool and Ammonium).

POR protocol: Sixth instar locusts (Schistocera americana) of either sex were starved for 24-48 hours before the experiment or taken straight from the colony and fed blades of grass for the satiated condition. Locusts were immobilized by placing them in the plastic tube and securing their body with black electric tape (see Author response image 2). Locusts were given 20 - 30 minutes to acclimatize after placement in the immobilization tube. As can be noted, the head of the locusts along with the antenna and maxillary palps protruded out of this immobilization tube so they can be freely moved by the locusts. Note that the maxillary palps are sensory organs close to the mouth parts that are used to grab food and help with the feeding process.

It is worth noting that our earlier studies had shown that the presentation of ‘appetitive odorants’ triggers the locust to open their maxillary palps even when no food is presented (Saha et al., 2017; Nizampatnam et al., 2018; Nizampatnam et al., 2022; Chandak and Raman, 2023.) Furthermore, our earlies results indicate that the probability of palp opening varies across different odorants (Chandak and Raman, 2023). We chose four odorants that had a diverse range of palp-opening: supra-median (hexanol), median (benzaldehyde), and sub-median (linaool). Therefore, each locust in our experiments was presented with one concentration of four odorants (hexanol, benzaldehyde, linalool, and ammonium) in a pseudorandomized order. The odorants were chosen based on our physiology results such that they evoked different levels of spiking activities.

The odor pulse was 4 s in duration and the inter-pulse interval was set to 60 s. The experiments were recorded using a web camera (Microsoft) placed right in front of the locusts. The camera was fully automated with the custom MATLAB script to start recording 2 seconds before the odor pulse and end recording at odor termination. An LED was used to track the stimulus onset/offset. The POR responses were manually scored offline. Responses to each odorant were scored a 0 or 1 depending on if the palps remained closed or opened. A positive POR was defined as a movement of the maxillary palps during the odor presentation time window as shown on the locust schematic (Main Paper Figure 1).

Author response image 2.

Pictures showing the behavior experiment setup and representative palp-opening responses in a locust.

As the reviewer inquired, we performed a new series of POR experiments, where we explored POR responses to mineral oil and hexanol, before and after serotonin injection. For this study, we used 10 locusts that were starved 24-48 hours before the experiment. Note that hexanol was diluted at 1% (v/v) concentration in mineral oil. Our results reveal that locusts PORs to hexanol (~ 50% PORs) were significantly higher than those triggered by mineral oil (~10% PORs). Injection of serotonin increased the POR response rate to hexanol but did not alter the PORs evoked by mineral oil (Author response image 3).

Author response image 3.

Serotonin does not alter the palp-opening responses evoked by paraffin oil. The PORs before and after (5HT) serotonin injection are summarized and shown as a bar plot for hexanol and paraffin oil. Striped bars signify the data collected after 5HT injection. Significant differences are identified in the plot (one-tailed paired-sample t-test; (*p<0.05).

Regarding recordings of potential PNs - the authors do not provide evidence that they did record from projection neurons and not other types of antennal lobe neurons. Thus, these claims should be phrased more carefully.

In the locust antennal lobe, only the cholinergic projection neurons fire full-blown sodium spikes. The GABAergic local neurons only fire calcium ‘spikelets’ (Laurent, TINS, 1996; Stopfer et al., 2003; see Author response image 4 for an example). Hence, we are pretty confident that we are only recording from PNs. Furthermore, due to the physiological properties of the LNs, their signals being too small, they are also not detected in the extracellular recordings from the locust antennal lobe. Hence, we are confident with our claims and conclusion.

Author response image 4.

PN vs LN physiological differences: Left: A representative raw voltage traces recorded from a local neuron before, during, and after a 4-second odor pulse are shown. Note that the local neurons in the locust antennal lobe do not fire full-blown sodium spikes but only fire small calcium spikelets. On the right: A representative raw voltage trace recorded from a representative projection neuron is shown for comparison. Clear sodium spikes are clearly visible during spontaneous and odor-evoked periods. The gray bar represents 4 seconds of odor pulse. The vertical black bar represents the 40mV.

The presented model suggests labeled lines in the antennal lobe output of locusts. Could the presented model also explain a shift in behavior from aversion to attraction - such as seen in locusts when they switch from a solitarious to a gregarious state? The authors might want to discuss other possible scenarios, such as that odor evaluation and decision-making take place in higher brain regions, or that other neuromodulators might affect behavioral output. Serotonin injections could affect behavior via modulation of other cell types than antennal lobe neurons. This should also be discussed - the same is true for potential PNs - serotonin might not directly affect this cell type, but might rather shut down local inhibitory neurons.

There are multiple questions here. First, regarding solitary vs. gregarious states, we are currently repeating these experiments on solitary locusts. Our preliminary results (not included in the manuscript) indicate that the solitary animals have increased olfactory arousal and respond with a higher POR but are less selective and respond similarly to multiple odorants. We are examining the physiology to determine whether the model for mapping neural responses onto behavior could also explain observations in solitary animals.

Second, this reviewer makes the point raised by Reviewer 1. We agree that odor evaluation and decisionmaking might take place in higher brain regions. All we could conclude based on our data is that a segregation of neural activity based on behavioral relevance might provide the simplest approach to map non-specific increase in stimulus-evoked neural responses onto odor-specific changes in behavioral outcome. Furthermore, our results indicate that hexanol and linalool, two odorants that had an increase and decrease in PORs after serotonin injection, had only minimal neural response overlap in the antennal lobe. These results suggest that the formatting of neural activity to support varying behavioral outcomes might already begin in the antennal lobe. We have added this to our discussion.

Third, regarding serotonin impacting PNs, we performed a new set of current-clamp experiments to examine this issue (Author response image 1). Our results clearly show that projection neuron activity in response to current injections (that should not incorporate feedback inhibition through local neurons) was altered after serotonin injection. Therefore, the observed changes in the odor-evoked neural ensemble activity should incorporate modulation at both individual PN level and at the network level. We have added this to our discussion as well.

Finally, the authors claim that serotonin injection can mimic the starved state behavioral response. However, this is only shown for one of the four odors that are tested for behavior (HEX), thus the data does not support this claim.

We note that Hex is the only appetitive odorant in the panel. But, as reviewer 1 has also brought up a similar point, we have toned down our claims and will investigate this carefully in a future study.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

• Was the POR of the locusts towards linalool and ammonium higher than towards a blank odor cartridge? I ask because the locusts appear to be less likely to respond to these odors and so I am concerned that this assay is not relevant to the ecological context of these odors. In other words, perhaps serotonin did not enhance the responses to these odors in this assay, because this is not a context in which locusts would normally respond to these odors.

The POR response to linalool and ammonium is lower and comparable to that of paraffin oil. Serotonin does not increase POR responses to paraffin oil but does increase response to hexanol (an appetitive odorant). We have clarified this using new data (Author response image 5).

• It seems to me that Figure 5C is the crux for understanding the potential impact of 5-HT on odor coding, but it is somewhat confusing and underutilized. Is the implication that 5-HT decorrelates spontaneous activity such that when an odor stimulus arrives, the odor-evoked activity deviates to a greater degree? The authors make claims about this figure that require the reader to guess as to the aspect of the figure to which they are referring.

The reviewer makes an astute observation. Yes, the spontaneous activity in the antennal lobe network before serotonin introduction is not correlated with the ensemble spontaneous activity after serotonin bath application. Remarkably, the odor-evoked responses were highly similar, both in the reduced PCA space and when assayed using high-dimensional ensemble neural activity vectors. Whether the changes in network spontaneous activity have a function in odor detection and recognition is not fully understood and cannot be convincingly answered using our data. But this is something that we had pondered.

• The modeling component summarized in Figure 6 needs clarification and more detail. Perhaps example traces associated with positive weighting within neural ensemble 1 relative to neural ensemble 2? I struggled to understand conceptually how the model resolved the theoretical discrepancy between physiology and behavior.

As recommended, here is a plot showing the responses of four PNs that had positive weights to hexanol and linalool. As can be expected, each PN in this group had higher responses to hexanol and no response to linalool. Further, the four PNs that received negative weights had response only to linalool.

Author response image 5.

Odor-evoked responses of four PNs that received positive weights in the model (top panel), and four PNs that were assigned negative weights in the model (bottom).

• Was there a significant difference between the PORs of hungry vs. fed locusts? The authors state that they differ and provide statistics for the comparisons to locusts injected with 5-HT, but then don't provide any statistical analyses of hungry vs. fed animals.

The POR responses to HEX (an appetitive odorant) were significantly different between the hungry and starved locusts.

Author response image 6.

A bar plot summarizing PORs to all four odors for satiated locust (highlighted with stripes), before (dark shade), and after 5HT injection (lighter shade). To allow comparison before 5HT injection for starved locust plotted as well (without stripes). The significance was determined using a one-tailed paired-sample ttest(*p<0.05).

• Were any of the effects of 5-HT on odor-evoked PN responses significant? No statistics are provided.

We examined the distribution of odor-evoked responses in PNs before and after 5HT introduction. We found that the overall distribution was not significantly different between the two (one-tailed pairedsample t-test; p = 0.93).

Author response image 7.

Comparison of the distribution of odor-evoked PN responses before (green) and after (purple) 5HT introduction. One-tailed paired sample t-test was used to compare the two distributions.

• The authors interchangeably use "serotonin", "5HT" and "5-HT" throughout the manuscript, but this should be consistent.

This has been fixed in the revised manuscript.

• On page 2 the authors provide an ecological relevance for linalool as being an additive in pesticides, however, linalool is a common floral volatile chemical. Is the implication that locusts have learned to associate linalool with pesticides?

Linalool is a terpenoid alcohol that has a floral odor but has also been used as a pesticide and insect repellent [Beier et al., 2014]. As shown in Author response image 2, it evoked the least POR responses amongst a diverse panel of 22 odorants that were tested. We have clarified how we chose odorants based on the prior dataset in the Methods section.

• In Figure 1, there should be a legend in the figure itself indicating that the black box indicates the absence of POR and the white box indicates presence, rather than just having it in the legend text.

Done.

• In Figure 2, the raw data from each animal can be moved to the supplements. The way it is presented is overwhelming and the order of comparisons is difficult to follow.

Done.

• For the induction of bursting in PNs by the application of 5-HT, were there any other metrics observed such as period, duration of bursts, or peak burst frequency? The authors rely on ISI, but there are other bursting metrics that could also be included to understand the nature of this observation. In particular, whether the bursts are likely due to changes in intrinsic biophysical properties of the PNs or polysynaptic effects.

We could use other metrics as the reviewer suggests. Our main point is that the spontaneous activity of individual PNs changed. We have added a new current-injection experiments to show that the PNs output to square pulses of current becomes different after serotonin application (Author response image 1)

• Were 4-vinyl anisole, 1-nonanol, and octanoic acid selected as additional odors because they had particular ecological relevance, or was it for the diversity of chemical structure?

These odorants were selected based on both, chemical structure and ecological relevance. The logic behind this was to have a very diverse odor panel that consisted of food odorant – Hexanol, aggregation pheromone – 4-vinyl anisole, sex pheromone – benzaldehyde, acid – octanoic acid, base – ammonium, and alcohol – 1-nonanol. Additionally, we selected these odors based on previous neural and behavioral data on these odorants (Chandak and Raman, 2023, Traner and Raman, 2023, Nizampatnam et al, 2022 & 2018; Saha et al., 2017 & 2013).

Reviewer #2 (Recommendations For The Authors):

The electrophysiology dataset combines all performed experiments across all tested different PN-odor pairs. How many odors have been tested in a single PN and how many PNs have been tested for a single odor? This information is not present in the current manuscript. Can the authors exclude that there are odor-specific modulations?

In total, our dataset includes recordings from 19 PNs. Seven PNs were tested on a panel of seven odorants (4-vinyl anisole, 1-nonanol, octanoic acid, Hex, Bza, Lool, and Amn), and the remaining twelve were tested with the four main odorants used in the study (Hex, Bza, Lool, and Amn). This information has been added to the Methods section

How did the authors choose the concentrations of serotonin injections and bath applications - is this a naturalistic amount?

The serotonin concentration for ephys experiments was chosen based on trial-error experiments:

0.01mM was the highest concentration that did not cause cell death. For the behavioral experiments, we increased the concentration (0.1 M) due to the presence of anatomical structures in the locust's head such as air sacks, sheath as well as hemolymph which causes some degree of dilution that we cannot control.

Behavior experiments were performed 3 hours after injection - ephys experiments 5-10 minutes following bath application. Can the authors exclude that serotonin affects neural processing differently on these different timescales?

We cannot exclude this possibility. We did ePhys experiments 5-10 minutes after bath application as it would be extremely hard to hold cells for that long.

A longer delay was required for our behavioral experiments as the locusts tended to be a bit more agitated with larger spontaneous movements of palps as well as exhibited unprompted vomiting. A 3hour period allowed the locust to regain its baseline level movements after 5HT introduction. [This information has been added to the methods section of the revised manuscript]

Concerning the analysis of electrophysiological data. The authors should correct for changes in the baseline before performing PCA analysis. And how much of the variance is explained by PC1 and PC2?

We did not correct for baseline changes or subtract baseline as we wanted to show that the odor-evoked neural responses still robustly encoded information about the identity of the odorant.

The authors should perform dye injections after recordings to visualize the cell type they recorded from. Serotonin might affect also other cell types in the antennal lobe.

As mentioned above, in the locust antennal lobe only PNs fire full-blown sodium spikes, and LNs only fire calcium spikelets (Author response image 4). Since these signals are small, they will be buried under the noise floor when using extracellular recording electrodes for monitoring responses in the AL antennal lobe.

Hence we are pretty certain what type of cells we are recording from.

There were several typos in the manuscript, please check again.

We have fixed many of the grammatical errors and typos in the revised version.

eLife assessment

This important study presents genome-wide high-resolution chromatin-based 3D genomic interaction maps for over 50 diverse human cell types and integrates these data with pediatric obesity GWAS. The work provides convincing evidence that multiple pancreatic islet cell types are key effector cell types. The authors also perform variant-to-gene mapping to nominate genes underlying several GWAS hits. Overall, the results will be of interest to bth the fields of 3D genome architecture and pediatric obesity.

Summary:

This paper studies the genetic factors contributing to childhood obesity. Through a comprehensive analysis integrating genome-wide association study (GWAS) data with 3D genomic datasets across 57 human cell types, consisting of Capture-C/Hi-C, ATAC-seq, and RNA-seq, the study identifies significant genetic contributions to obesity using stratified LD score regression, emphasizing the enrichment of genetic signals in pancreatic alpha cells and identification of significant effector genes at obesity-associated loci such as BDNF, ADCY3, TMEM18, and FTO. Additionally, the study implicated ALKAL2, a gene responsive to inflammation in nerve nociceptors, as a novel effector gene at the TMEM18 locus. This suggests a role for inflammatory and neurological pathways in obesity's pathogenesis which was supported through colocalization analysis using eQTL derived from the GTEx dataset. This comprehensive genomic analysis sheds light on the complex genetic architecture of childhood obesity, highlighting the importance of cellular context for future research and the development of more effective strategies.

Strengths:

Overall, the paper has several strengths, including leveraging large-scale, multi-modal datasets, using computational reasonable tools, and having an in-depth discussion of the significant results.

Weaknesses:

(1) The results are somewhat inconclusive or not validated.

The overall results are carefully designed, but most of the results are descriptive. While the authors are able to find additional evidence either from the literature or explain the results with their existing knowledge, none of the results have been biologically validated. Especially, the last three result sections (signaling pathways, eQTLs, and TF binding) further extended their findings, but the authors did not put the major results into any of the figures in the main text.

(2) Some technical details are missing.

While the authors described all of their analysis steps, a lot of the time, they did not mention the motivation. Sometimes, the details were also omitted. For example:

- The manuscript would benefit from a detailed explanation of the methods used to define cREs, particularly the process of intersecting OCRs with chromatin conformation data. The current description does not fully clarify how the cREs are defined.

- How did the authors define a contact region?

- The manuscript would benefit from an explanation regarding the rationale behind the selection of the 57 human cell types analyzed. it is essential to clarify whether these cell types have unique functions or relevance to childhood development and obesity.

- I wonder whether the used epigenome datasets are all from children. Although the authors use literature to support that body weight and obesity remain stable from infancy to adulthood, it remains uncertain whether epigenomic data from other life stages might overlook significant genetic variants that uniquely contribute to childhood obesity.

- Given that the GTEx tissue samples are derived from adult donors, there appears to be a mismatch with the study's focus on childhood obesity. If possible, identifying alternative validation strategies or datasets more closely related to the pediatric population could strengthen the study's findings.

(3) The writing needs to improve.

Author response:

“Overall, the paper has several strengths, including leveraging large-scale, multi-modal datasets, using computational reasonable tools, and having an in-depth discussion of the significant results.”

We thank the reviewer for the very supportive comments.

Based on the comments and questions, we have grouped the concerns and corresponding responses into three categories.

(1) The scope and data selection

“The results are somewhat inconclusive or not validated.

The overall results are carefully designed, but most of the results are descriptive. While the authors are able to find additional evidence either from the literature or explain the results with their existing knowledge, none of the results have been biologically validated. Especially, the last three result sections (signaling pathways, eQTLs, and TF binding) further extended their findings, but the authors did not put the major results into any of the figures in the main text.”

The goal of this manuscript is to provide a list of putative childhood obesity target genes to yield new insights and help drive further experimentation. Moreover, the outputs from signaling pathways, eQTLs, and TF binding, although noteworthy and supportive of our method, were not particularly novel. In our manuscript we placed our focus on the novel findings from the analyses. We did, however, report the part of the eQTLs analysis concerning ADCY3, which brought new insight to the pathology of obesity, in Figure 4C.

“The manuscript would benefit from an explanation regarding the rationale behind the selection of the 57 human cell types analyzed. it is essential to clarify whether these cell types have unique functions or relevance to childhood development and obesity.”

We elected to comprehensively investigate the GWAS-informed cellular underpinnings of childhood development and obesity. By including a diverse range of cell types from different tissues and organs, we sought to capture the multifaceted nature of cellular contributions to obesity-related mechanisms, and open new avenues for targeted therapeutic interventions.

There are clearly cell types that are already established as being key to the pathogenesis of obesity when dysregulated: adipocytes for energy storage, immune cell types regulating inflammation and metabolic homeostasis, hepatocytes regulating lipid metabolism, pancreatic cell types intricately involved in glucose and lipid metabolism, skeletal muscle for glucose uptake and metabolism, and brain cell types in the regulation of appetite, energy expenditure, and metabolic homeostasis.

While it is practical to focus on cell types already proven to be associated with or relevant to obesity, this approach has its limitations. It confines our understanding to established knowledge and rules out the potential for discovering novel insights from new cellular mechanisms or pathways that could play significant roles in the pathogenesis if obesity. Therefore, it is was essential to reflect known biology against the unexplored cell types to expand our overall understanding and potentially identify innovative targets for treatment or prevention.

“I wonder whether the used epigenome datasets are all from children. Although the authors use literature to support that body weight and obesity remain stable from infancy to adulthood, it remains uncertain whether epigenomic data from other life stages might overlook significant genetic variants that uniquely contribute to childhood obesity.”

The datasets utilized in our study were derived from a combination of sources, both pediatric and adult. We recognize that epigenetic profiles can vary across different life stages but our principal effort was to characterize susceptibility BEFORE disease onset.

“Given that the GTEx tissue samples are derived from adult donors, there appears to be a mismatch with the study's focus on childhood obesity. If possible, identifying alternative validation strategies or datasets more closely related to the pediatric population could strengthen the study's findings.” 

We thank the reviewer for raising this important point. We acknowledge that the GTEx tissue samples are derived from adult donors, which might not perfectly align with the study's focus on childhood obesity. The ideal strategy would be a longitudinal design that follows individuals from childhood into adulthood to bridge the gap between pediatric and adult data, offering systematic insights into how early-life epigenetic markers influencing obesity later in life. In future work, we aim to carry out such efforts, which will represent substantial time and financial commitment.

Along the same lines, the Developmental Genotype-Tissue Expression (dGTEx) Project is a new effort to study development-specific genetic effects on gene expression at 4 developmental windows spanning from infant to post-puberty (0-18 years). Donor recruitment began in August 2023 and remains ongoing. Tissue characterization and data production are underway. We hope that with the establishment of this resource, our future research in the field of pediatric health will be further enhanced.

“Figure 1B: in subplots c and d, the results are either from Hi-C or capture-C. Although the authors use different colors to denote them, I cannot help wondering how much difference between Hi-C and capture-C brings in. Did the authors explore the difference between the Hi-C and capture-C?”.

Thank you for your comment. It is not within the scope of our paper to explore the differences between the Hi-C and Capture-C methods. In the context of our study, both methods serve the same purpose of detecting chromatin loops that bring putative enhancers to sometimes genomically distant gene promoters. Consequently, our focus was on utilizing these methods to identify relevant chromatin interactions rather than comparing their technical differences.

(2) Details on defining different categories of the regions of interest

“Some technical details are missing.

While the authors described all of their analysis steps, a lot of the time, they did not mention the motivation. Sometimes, the details were also omitted.”

We will add a section to the revision to address the rationale behind different OCRs categories.

“Line 129: should "-1,500/+500bp" be "-500/+500bp"? 

A gene promoter was defined as a region 1,500 bases upstream to 500 bases downstream of the TSS. Most transcription factor binding sites are distributes upstream (5’) from TSS, and the assembly of transcription machinery occurs up to 1000 bases 5’ from TSS. Given our interest in SNPs that can potentially disrupt transcription factor binding, this defined promoter length allowed us to capture such SNPs in our analyses.

“How did the authors define a contact region?”

Chromatin contact regions identified by Hi-C or Capture-C assays are always reported as pairs of chromatin regions. The Supplementary eMethods provide details on the method of processing and interaction calling from the Hi-C and Capture-C data.

“The manuscript would benefit from a detailed explanation of the methods used to define cREs, particularly the process of intersecting OCRs with chromatin conformation data. The current description does not fully clarify how the cREs are defined.”

“In the result section titled "Consistency and diversity of childhood obesity proxy variants mapped to cREs", the authors introduced the different types of cREs in the context of open chromatin regions and chromatin contact regions, and TSS. Figure 2A is helpful in some way, but more explanation is definitely needed. For example, it seems that the authors introduced three chromatin contacts on purpose, but I did not quite get the overall motivation.”

We apologize for the confusion. Our definition of cREs is consistent throughout the study. Figure 2A will be the first Figure 1A in the revision in order to aid the reader.

The 3 representative chromatin loops illustrate different ways the chromatin contact regions (pairs of blue regions under blue arcs) can overlap with OCRs (yellow regions under yellow triangles – ATAC peaks) and gene promoters.

[1] The first chromatin loop has one contact region that overlaps with OCRs at one end and with the gene promoter at the other. This satisfies the formation of cREs; thus, the area under the yellow ATAC-peak triangle is green.

[2] The second loop only overlapped with OCR at one end, and there was no gene promoter nearby, so it is unqualified as cREs formation.

[3] The third chromatin loop has OCR and promoter overlapping at one end. We defined this as a special cRE formation; thus, the area under the yellow ATAC-peak triangle is green.

To avoid further confusion for the reader, we will eliminate this variation in the new illustration for the revised manuscript.

“Figure 2A: The authors used triangles filled differently to denote different types of cREs but I wonder what the height of the triangles implies. Please specify.”

The triangles are illustrations for ATAC-seq peaks, and the yellow chromatin regions under them are OCRs. The different heights of ATAC-seq peaks are usually quantified as intensity values for OCRs. However, in our study, when an ATAC-seq peak passed the significance threshold from the data pipeline, we only considered their locations, regardless of their intensities. To avoid further confusion for the reader, we will eliminate this variation in the new illustration for the revised manuscript.

“Figure 1B-c. the title should be "OCRs at putative cREs". Similarly in Figure 1B-d.”

cREs are a subset of OCRs.

- In the section "Cell type specific partitioned heritability", the authors used "4 defined sets of input genomic regions". Are you corresponding to the four types of regions in Figure 2A? 

Figure 2A will be the first Figure 1A in the revision and will be modified to showcase how we define OCRs and cREs.

“It seems that the authors described the 771 proxies in "Genetic loci included in variant-to-genes mapping" (ln 154), and then somehow narrowed down from 771 to 94 (according to ln 199) because they are cREs. It would be great if the authors could describe the selection procedure together, rather than isolated, which made it quite difficult to understand.”

In the Methods section entitled “Genetic loci included in variant-to-genes mapping," we described the process of LD expansion to include 771 proxies from 19 sentinel obesity-significantly associated signals. Not all of these proxies are located within our defined cREs. Figure 2B, now Figure 2A in the revision, illustrates different proportions of these proxies located within different types of regions, reducing the proxy list to 94 located within our defined cREs.

“Figure 2. What's the difference between the 771 and 758 proxies? “

13 out of 771 proxies did not fall within any defined regions. The remaining 758 were located within contact regions of at least one cell type regardless of chromatin state.

(3) Typos

“In the paragraph "Childhood obesity GWAS summary statistics", the authors may want to describe the case/control numbers in two stages differently. "in stage 1" and "921 cases" together made me think "1,921" is one number.”

This will be amended in the revision.

“Hi-C technology should be spelled as Hi-C. There are many places, it is miss-spelled as "hi-C". In Figure 1, the author used "hiC" in the legend. Similarly, Capture-C sometime was spelled as "capture-C" in the manuscript.”

“At the end of the fifth row in the second paragraph of the Introduction section: "exisit" should be "exist".

“In Figure 2A: "Within open chromatin contract region" should be "Within open chromatin contact region". 

These typos and terminology inconsistencies will be amended in the revision.

eLife assessment

This study presents valuable insights into the involvement of miR-26b in the progression of metabolic dysfunction-associated steatohepatitis (MASH). The delivery of microRNA-containing nanoparticles to reduce MASH severity has practical implications as a therapeutic strategy. Whereas convincing evidence is provided on the phenotypic changes produced by miR-26, the analyses of its precise role and function are incomplete and need more comprehensive evaluation including mechanistic studies.

Reviewer #1 (Public Review):

Based on previous publications suggesting a potential role for miR-26b in the pathogenesis of metabolic dysfunction-associated steatohepatitis (MASH), the researchers aim to clarify its function in hepatic health and explore the therapeutical potential of lipid nanoparticles (LNPs) to treat this condition. First, they employed both whole-body and myeloid cell-specific miR-26b KO mice and observed elevated hepatic steatosis features in these mice compared to WT controls when subjected to WTD. Moreover, livers from whole-body miR-26b KO mice also displayed increased levels of inflammation and fibrosis markers. Kinase activity profiling analyses revealed distinct alterations, particularly in kinases associated with inflammatory pathways, in these samples. Treatment with LNPs containing miR-26b mimics restored lipid metabolism and kinase activity in these animals. Finally, similar anti-inflammatory effects were observed in the livers of individuals with cirrhosis, whereas elevated miR-26b levels were found in the plasma of these patients in comparison with healthy control. Overall, the authors conclude that miR-26b plays a protective role in MASH and that its delivery via LNPs efficiently mitigates MASH development.

The study has some strengths, most notably, its employ of a combination of animal models, analyses of potential underlying mechanisms, as well as innovative treatment delivery methods with significant promise. However, it also presents numerous weaknesses that leave the research work somewhat incomplete. The precise role of miR-26b in a human context remains elusive, hindering direct translation to clinical practice. Additionally, the evaluation of the kinase activity, although innovative, does not provide a clear molecular mechanisms-based explanation behind the protective role of this miRNA.

Therefore, to fortify the solidity of their conclusions, these concerns require careful attention and resolution. Once these issues are comprehensively addressed, the study stands to make a significant impact on the field.

Reviewer #2 (Public Review):

Summary:

This manuscript by Peters, Rakateli, et al. aims to characterize the contribution of miR-26b in a mouse model of metabolic dysfunction-associated steatohepatitis (MASH) generated by a Western-type diet on the background of Apoe knock-out. In addition, the authors provide a rescue of the miR-26b using lipid nanoparticles (LNPs), with potential therapeutic implications. In addition, the authors provide useful insights into the role of macrophages and some validation of the effect of miR-26b LNPs on human liver samples.

Strengths:

The authors provide a well-designed mouse model, that aims to characterize the role of miR-26b in a mouse model of metabolic dysfunction-associated steatohepatitis (MASH) generated by a Western-type diet on the background of Apoe knock-out. The rescue of the phenotypes associated with the model used using miR-26b using lipid nanoparticles (LNPs) provides an interesting avenue to novel potential therapeutic avenues.

Weaknesses:

Although the authors provide a new and interesting avenue to understand the role of miR-26b in MASH, the study needs some additional validations and mechanistic insights in order to strengthen the author's conclusions.

(1) Analysis of the expression of miRNAs based on miRNA-seq of human samples (see https://ccb-compute.cs.uni-saarland.de/isomirdb/mirnas) suggests that miR-26b-5p is highly abundant both on liver and blood. It seems hard to reconcile that despite miRNA abundance being similar in both tissues, the physiological effects claimed by the authors in Figure 2 come exclusively from the myeloid (macrophages).

(2) Similarly, the miRNA-seq expression from isomirdb suggests also that expression of miR-26a-5p is indeed 4-fold higher than miR-26b-5p both in the liver and blood. Since both miRNAs share the same seed sequence, and most of the supplemental regions (only 2 nt difference), their endogenous targets must be highly overlapped. It would be interesting to know whether deletion of miR-26b is somehow compensated by increased expression of miR-26a-5p loci. That would suggest that the model is rather a depletion of miR-26.

UUCAAGUAAUUCAGGAUAGGU mmu-miR-26b-5p mature miRNA<br /> UUCAAGUAAUCCAGGAUAGGCU mmu-miR-26a-5p mature miRNA

(3) Similarly, the miRNA-seq expression from isomirdb suggests also that expression of miR-26b-5p is indeed 50-fold higher than miR-26b-3p in the liver and blood. This difference in abundance of the two strands is usually regarded as one of them being the guide strand (in this case the 5p) and the other being the passenger (in this case the 3p). In some cases, passenger strands can be a byproduct of miRNA biogenesis, thus the rescue experiments using LNPs with both strands in equimolar amounts would not reflect the physiological abundance miR-26b-3p. The non-physiological overabundance of miR-26b-3p would constitute a source of undesired off-targets.

(4) It would also be valuable to check the miRNA levels on the liver upon LNP treatment, or at least the signatures of miR-26b-3p and miR-26b-5p activity using RNA-seq on the RNA samples already collected.

(5) Some of the phenotypes described, such as the increase in cholesterol, overlap with the previous publication by van der Vorst et al. BMC Genom Data (2021), despite in this case the authors are doing their model in Apoe knock-out and Western-type diet. I would encourage the authors to investigate more or discuss why the initial phenotypes don't become more obvious despite the stressors added in the current manuscript.

(6) The authors have focused part of their analysis on a few gene makers that show relatively modest changes. Deeper characterization using RNA-seq might reveal other genes that are more profoundly impacted by miR-26 depletion. It would strengthen the conclusions proposed if the authors validated that changes in mRNA abundance (Sra, Cd36) do impact the protein abundance. These relatively small changes or trends in mRNA expression, might not translate into changes in protein abundance.

(7) In Figures 5 and 7, the authors run a phosphorylation array (STK) to analyze the changes in the activity of the kinome. It seems that a relatively large number of signaling pathways are being altered, I think that should be strengthened by further validations by Western blot on the collected tissue samples. For quite a few of the kinases, there might be antibodies that recognise phosphorylation. The two figures lack a mechanistic connection to the rest of the manuscript.

Author response:

Provisional author response to Reviewer #1<br /> We would like the reviewer for his/her careful evaluation of our manuscript and appreciate his/her appraisal for the strengths of our study. Regarding the weaknesses, we plan to address these as good as possible during the revision of our manuscript.<br /> We can already state that miR-26b has clear anti-inflammatory effects on human liver slices, which is in line with our results demonstrating that miR-26b plays a protective role in MASH development in mice. The notion that patients with liver cirrhosis have increasing plasma levels of miR-26b, seems contradictory at first glance. However, we believe that this increased miR-26b expression is a compensatory mechanism to counteract the MASH/cirrhotic effects. However, the exact source of this miR-26b remains to be elucidated in future studies.<br /> The performed kinase activity analysis revealed that miR-26b affects kinases that particularly play an important role in inflammation and angiogenesis. Strikingly and supporting these data, these effects could be inverted again by LNP treatment. Combined, these results already provide strong mechanistic insights on molecular and intracellular signalling level. Although the exact target of miR-26b remains elusive and its identification is probably beyond the scope of the current manuscript due to its complexity, we believe that the kinase activity results already provide a solid mechanistic basis.

Provisional author response to Reviewer #2<br /> We would like the reviewer for his/her careful evaluation of our manuscript and appreciate his/her appraisal for the strengths of our study. Regarding the weaknesses, we plan to address these as good as possible during the revision of our manuscript. Particularly the validation suggestions are very valuable and we plan to address these in the revision by performing additional experiments.

eLife assessment

This study presents an important finding on the metabolism-independent role of IDH1 in regulating nuclear chromatin during terminal erythropoiesis. The evidence supporting IDH1's role on chromatin regulation is solid, but the analysis of its proposed non-metabolic activity is incomplete. The mechanistic perspective of this work, along with other intriguing observations, such as the connection between NAD+-dependent deacetylase SIRT1 and IDH1, should be of great interest to researchers working on erythropoiesis and erythroid disorders.

Reviewer #1 (Public Review):

The manuscript by Li et al. investigates the metabolism-independent role of nuclear IDH1 in chromatin state reprogramming during erythropoiesis. The authors describe accumulation and redistribution of histone H3K79me3, and downregulation of SIRT1, as a cause for dyserythropoiesis observed due to IDH1 deficiency. The authors studied the consequences of IDH1 knockdown, and targeted knockout of nuclear IDH1, in normal human erythroid cells derived from hematopoietic stem and progenitor cells and HUDEP2 cells respectively. They further correlate some of the observations such as nuclear localization of IDH1 and aberrant localization of histone modifications in MDS and AML patient samples harboring IDH1 mutations. These observations are intriguing from a mechanistic perspective and they hold therapeutic significance, however there are major concerns that make the inferences presented in the manuscript less convincing.

(1) The authors show the presence of nuclear IDH1 both by cell fractionation and IF, and employ an efficient strategy to knock out nuclear IDH1 (knockout IDH1/ Sg-IDH1 and rescue with the NES tagged IDH1/ Sg-NES-IDH1 that does not enter the nucleus) in HUDEP2 cells. However, some important controls are missing.<br /> A) In Figure 3C, for IDH1 staining, Sg-IDH1 knockout control is missing.<br /> B) Wild-type IDH1 rescue control (ie., IDH1 without NES tag) is missing to gauge the maximum rescue that is possible with this system.

(2) Considering the nuclear knockout of IDH1 (Sg-NES-IDH1 referenced in the previous point) is a key experimental system that the authors have employed to delineate non-metabolic functions of IDH1 in human erythropoiesis, some critical experiments are lacking to make convincing inferences.<br /> A) The authors rely on IF to show the nuclear deletion of Sg-NES-IDH1 HUDEP2 cells. As mentioned earlier since a knockout control is missing in IF experiments, a cellular fractionation experiment (similar to what is shown in Figure 2F) is required to convincingly show the nuclear deletion in these cells.<br /> B) Since the authors attribute nuclear localization to a lack of metabolic/enzymatic functions, it is important to show the status of ROS and alpha-KG in the Sg-NES-IDH1 in comparison to control, wild type rescue, and knockout HUDEP2 cells. The authors observe an increase of ROS and a decrease of alpha-KG upon IDH1 knockdown. If nuclear IDH1 is not involved in metabolic functions, is there only a minimal or no impact of the nuclear knockout of IDH1 on ROS and alpha-KG, in comparison to complete knockout? These studies are lacking.<br /> C) Authors show that later stages of terminal differentiation are impacted in IDH1 knockdown human erythroid cells. They also report abnormal nuclear morphology, an increase in euchromatin, and enucleation defects. However, the authors only report abnormal nuclear morphology in Sg-NES-IDH1 cells, as evaluated by cytospins. It is important to show the status of the other phenotypes (progression through terminal differentiation, euchromatin %, and enucleation) similar to the quantitations in the IDH1 knockdown cells.

(3) The authors report abnormal nuclear phenotype in IDH1 deficient erythroid cells. It is not clear what parameters are used here to define and quantify abnormal nuclei. Based on the cytospins (eg., Figure 1A, 3D) many multinucleated cells are seen in both shIDH1 and Sg-NES-IDH1 erythroid cells, compared to control cells. Importantly, this phenotype and enucleation defects are not rescued by the administration of alpha-KG (Figures 1E, F). The authors study these nuclei with electron microscopy and report increased euchromatin in Figure 4B. However, there is no discussion or quantification of polyploidy/multinucleation in the IDH1 deficient cells, despite their increased presence in the cytospins.

A) PI staining followed by cell cycle FACS will be helpful in gauging the extent of polyploidy in IDH1 deficient cells and could add to the discussions of the defects related to abnormal nuclei.<br /> B) For electron microscopy quantification in Figures 4B and C, how the quantification was done and the labelling of the y-axis (% of euchromatin and heterochromatin) in Figure 4 C is not clear and is confusingly presented. The details on how the quantification was done and a clear label (y-axis in Figure 4C) for the quantification are needed.<br /> C) As mentioned earlier, what parameters were used to define and quantify abnormal nuclei (e.g. Figure 1A) needs to be discussed clearly. The red arrows in Figure 1A all point to bi/multinucleated cells. If this is the case, this needs to be made clear.

(4) The authors mention that their previous study (reference #22) showed that ROS scavengers did not rescue dyseythropoiesis in shIDH1 cells. However, in this referenced study they did report that vitamin C, a ROS scavenger, partially rescued enucleation in IDH1 deficient cells and completely suppressed abnormal nuclei in both control and IDH1 deficient cells, in addition to restoring redox homeostasis by scavenging reactive oxygen species in shIDH1 erythroid cells. In the current study, the authors used ROS scavengers GSH and NAC in shIDH1 erythroid cells and showed that they do not rescue abnormal nuclei phenotype and enucleation defects. The differences between the results in their previous study with vitamin C vs GSH and NAC in the context of IDH1 deficiency need to be discussed.

(5) The authors describe an increase in euchromatin as the consequential abnormal nuclei phenotype in shIDH1 erythroid cells. However, in their RNA-seq, they observe an almost equal number of genes that are up and down-regulated in shIDH1 cells compared to control cells. If possible, an RNA-Seq in nuclear knockout Sg-NES-IDH1 erythroid cells in comparison with knockout and wild-type cells will be helpful to tease out whether a specific absence of IDH1 in the nucleus (ie., lack of metabolic functions of IDH) impacts gene expression differently.

(6) In Figure 8, the authors show data related to SIRT1's role in mediating non-metabolic, chromatin-associated functions of IDH1.<br /> A) The authors show that SIRT1 inhibition leads to a rescue of enucleation and abnormal nuclei. However, whether this rescues the progression through the late stages of terminal differentiation and the euchromatin/heterochromatin ratio is not clear.<br /> B) In addition, since the authors attribute a role of SIRT1 in mediating non-metabolic chromatin-associated functions of IDH1, documenting ROS levels and alpha-KG is important, to compare with what they showed for shIDH1 cells.

(7) In Figure 4 and Supplemental Figure 8, the authors show the accumulation and altered cellular localization of H3K79me3, H3K9me3, and H3K27me2, and the lack of accumulation of other three histone modifications they tested (H3K4me3, H3K35me4, and H3K36me2) in shIDH1 cells. They also show the accumulation and altered localization of the specific histone marks in Sg-NES-IDH1 HUDEP2 cells.<br /> A) To aid better comparison of these histone modifications, it will be helpful to show the cell fractionation data of the three histone modifications that did not accumulate (H3K4me3, H3K35me4, and H3K36me2), similar to what was shown in Figure 4E for H3K79me3, H3K9me3, and H3K27me2).<br /> B) Further, the cell fractionation and staining for histone marks is done in human primary erythroid cells on day15 of terminal differentiation, and these studies revealed that H3K79me3, H3K9me3, and H3K27me2 were retained in the nucleus in shIDH1 cells unlike a cellular localization observed in control cells. The authors cite reference #40 in relation to the cellular localization of histones - in this study, it was shown that the cellular export of histone to cytosol happens during later stages of terminal differentiation. In the current manuscript, the authors observe nuclear IDH1 throughout erythropoiesis and have shown this at both early and late time points of differentiation (between day7 to day15 of differentiation in primary erythroid cells, between day0 to day8 in HUDEP2 cells) in Figure 2. To help correlate the dynamics of localization and to discuss the mechanism for the retention of histone marks in the nucleus in IDH1 deficient cells, it will be helpful to show the cellular location of histone marks using cell fractionations for both early and late time points in terminal erythroid differentiation, similar to what they showed for IDH1 localization studies.<br /> C) Among the three histone marks that are dysregulated in IDH1 deficient cells (H3K79me3, H3K9me3, and H3K27me2), the authors show via ChIP-seq (Fig5) that H3K79me3 is the critical factor. However, the ChIP-seq data shown here lacks many details and this makes it hard to interpret the data. For example, in Figure 5A, they do not mention which samples the data shown correspond to (are these differential peaks in shIDH1 compared to shLuc cells?). There is also no mention of how many replicates were used for the ChIP seq studies.

Reviewer #2 (Public Review):

Li and colleagues investigate the enzymatic activity-independent function of IDH1 in regulating erythropoiesis. This manuscript reveals that IDH1 deficiency in the nucleus leads to the redistribution of histone marks (especially H3K79me3) and chromatin state reprogramming. Their findings suggest a non-typical localization and function of the metabolic enzyme, providing new insights for further studies into the non-metabolic roles of metabolic enzymes. However, there are still some issues that need addressing:

(1) Could the authors show the RNA and protein expression levels (without fractionation) of IDH1 on different days throughout the human CD34+ erythroid differentiation?

(2) Even though the human CD34+ erythroid differentiation protocol was published and cited in the manuscript, it would be helpful to specify which erythroid stages correspond to cells on days 7, 9, 11, 13, and 15.

(3) It is important to mention on which day the lentiviral knockdown of IDH1 was performed. Will the phenotype differ if the knockdown is performed in early vs. late erythropoiesis? In Figures 1C and 1D, on which day do the authors begin the knockdown of IDH1 and administer NAC and GSH treatments?

(4) The authors validate that IDH1 regulates erythropoiesis in an enzymatic activity-independent manner by adding ROS scavengers or α-KG. Given the complexity of metabolic pathways, these two strategies may not suffice. Mutating the enzymatic active site could provide a clearer explanation for this point.

(5) While the cell phenotype of IDH1 deficiency is quite dramatic, yielding cells with larger nuclei and multi-nuclei, the authors only attribute this phenotype to defects in chromatin condensation. Is it possible that IDH1-knockdown cells also exhibit primary defects in mitosis/cytokinesis (not just secondary to the nuclear condensation defect)?), given the function of H3K79Me in cell cycle regulation?

(6) Why are there two bands of Histone H3 in Figure 4A?

(7) Are the density and localization of histone modifications (not just H3K79me3) in Sg-NEG-IDH1 HuDEP2 cells similar to those in IDH1-shRNA erythroid cells compared to control cells?

(8) Displaying a heatmap and profile plots in Figure 5A between control and IDH1-deficient cells will help illustrate changes in H3K79me3 density in the nucleus after IDH1 knockdown.

(9) Are the distribution and intensity of H3K79me3 in primary healthy erythroid cells in the bone marrow similar to or distinct from those in AML and MDS cells? The authors could present at least one sample of healthy donor cells for comparison.

(10) In Figure 7E, why are the bands of Luciferase-shRNA in the input and probe both light, while the bands of IDH1-shRNA are both dark? This suggests that the expression of KLF1 is much higher in IDH1-shRNA cells than in control cells. Therefore, this result may not strongly support the increased binding of KLF1 at the SIRT1 promoter after IDH1 knockdown.

Reviewer #3 (Public Review):

Li, Zhang, Wu, and colleagues describe a new role for nuclear IDH1 in erythroid differentiation independent from its enzymatic function. IDH1 depletion results in a terminal erythroid differentiation defect with polychromatic and orthochromatic erythroblasts showing abnormal nuclei, nuclear condensation defects, and an increased proportion of euchromatin, as well as enucleation defects. Using ChIP-seq for the histone modifications H3K79me3, H3K27me2, and H3K9me3, as well as ATAC-seq and RNA-seq in primary CD34-derived erythroblasts, the authors elucidate SIRT1 as a key dysregulated gene that is upregulated upon IDH1 knockdown. They furthermore show that chemical inhibition of SIRT1 partially rescues the abnormal nuclear morphology and enucleation defect during IDH1-deficient erythroid differentiation. The phenotype of delayed erythroid maturation and enucleation upon IDH1 shRNA-mediated knockdown was described in the group's previous co-authored study (PMID: 33535038). The authors' new hypothesis of an enzyme- and metabolism-independent role of IDH1 in this process is currently not supported by conclusive experimental evidence as discussed in more detail further below. On the other hand, while the dependency of IDH1 mutant cells on NAD+, as well as cell survival benefit upon SIRT1 inhibition, has already been shown (see, e.g, PMID: 26678339, PMID: 32710757), previous studies focused on cancer cell lines and did not look at a developmental differentiation process, which makes this study interesting.

(1) The central hypothesis that IDH1 has a role independent of its enzymatic function is interesting but not supported by the experiments. One of the author's supporting arguments for their claim is that alpha-ketoglutarate (aKG) does not rescue the IDH1 phenotype of reduced enucleation. However, in the group's previous co-authored study (PMID: 33535038), they show that when IDH1 is knocked down, the addition of aKG even exacerbates the reduced enucleation phenotype, which could indicate that aKG catalysis by cytoplasmic IDH1 enzyme is important during terminal erythroid differentiation. A definitive experiment to test the requirement of IDH1's enzymatic function in erythropoiesis would be to knock down/out IDH1 and re-express an IDH1 catalytic site mutant. The authors perform an interesting genetic manipulation in HUDEP-2 cells to address a nucleus-specific role of IDH1 through CRISPR/Cas-mediated IDH1 knockout followed by overexpression of an IDH1 construct containing a nuclear export signal. However, this system is only used to show nuclear abnormalities and (not quantified) accumulation of H3K79me3 upon nuclear exclusion of IDH1. Otherwise, a global IDH1 shRNA knockdown approach is employed, which will affect both forms of IDH1, cytoplasmic and nuclear. In this system and even the NES-IDH1 system, an enzymatic role of IDH1 cannot be excluded because (1) shRNA selection takes several days, prohibiting the assessment of direct effects of IDH1 loss of function (only a degron approach could address this if IDH1's half-life is short), and (2) metabolic activity of this part of the TCA cycle in the nucleus has recently been demonstrated (PMID: 36044572), and thus even a nuclear role of IDH1 could be linked to its enzymatic function, which makes it a challenging task to separate two functions if they exist.

(2) It is not clear how the enrichment of H3K9me3, a prominent marker of heterochromatin, upon IDH1 knockdown in the primary erythroid culture (Figure 4), goes along with a 2-3-fold increase in euchromatin. Furthermore, in the immunofluorescence (IF) experiments presented in Figure 4Db, it seems that H3K9me3 levels decrease in intensity (the signal seems more diffuse), which seems to contrast the ChIP-seq data. It would be interesting to test for localization of other heterochromatin marks such as HP1gamma. As a related point, it is not clear at what stage of erythroid differentiation the ATAC-seq was performed upon luciferase- and IDH1-shRNA-mediated knockdown shown in Figure 6. If it was done at a similar stage (Day 15) as the electron microscopy in Figure 4B, then the authors should explain the discrepancy between the vast increase in euchromatin and the rather small increase in ATAC-seq signal upon IDH1 knockdown.

(3) The subcellular localization of IDH1, in particular its presence on chromatin, is not convincing in light of histone H3 being enriched in the cytoplasm on the same Western blot. H3 would be expected to be mostly localized to the chromatin fraction (see, e.g., PMID: 31408165 that the authors cite). The same issue is seen in Figure 4A.

(4) This manuscript will highly benefit from more precise and complete explanations of the experiments performed, the material and methods used, and the results presented. At times, the wording is confusing. As an example, one of the "Key points" is described as "Dyserythropoiesis is caused by downregulation of SIRT1 induced by H3K79me3 accumulation." It should probably read "upregulation of SIRT1".

eLife assessment

This study provides a valuable examination of the social recognition abilities of a jumping spider, Phippidus regius. Behavioral essays yielded solid evidence that these spiders discriminate between familiar and unfamiliar individuals on the basis of visual cues, but the experimental support for individual recognition and long-term memory is incomplete.

Reviewer #1 (Public Review):

Summary:

The paper sets out to examine the social recognition abilities of a 'solitary' jumping spider species. It demonstrates that based on vision alone spiders can habituate and dishabituate to the presence of conspecifics. The data support the interpretation that these spiders can distinguish between conspecifics on the basis of their appearance.

Strengths:

The study presents two experiments. The second set of data recapitulates the findings of the first experiment with an independent set of spiders, highlighting the strength of the results. The study also uses a highly quantitative approach to measuring relative interest between pairs of spiders based on their distance.

Weaknesses:

The study design is overly complicated, missing key controls, and the data presented in the figures are not clearly connected to the study. The discussion is challenging to understand and appears to make unsupported conclusions.

(1) Study design: The study design is rather complicated and as a result, it is difficult to interpret the results. The spiders are presented with the same individual twice in a row, called a habituation trial. Then a new individual is presented twice in a row. The first of these is a dishabituation trial and the second is another habituation trial (but now habituating to a second individual). This is done with three pairings and then this entire structure is repeated over three sessions. The data appear to show the strong effects of differences between habituation and dishabituation trials in the first session. The decrease in differential behavior between the so-called habituation and dishabituation trials in sessions 2 and 3 is explained as a consequence of the spiders beginning to habituate in general to all of the individuals. The claim that the spiders remember specific individuals is somewhat undercut because all of the 'dishabituation' trials in session 2 are toward spiders they already met for 14 minutes previously but seemingly do not remember in session 2. In session 3 it is ambiguous what is happening because the spiders no longer differentiate between the trial types. This could be due to fatigue or familiarity. A second experiment is done to show that introducing a totally novel individual, recovers a large dishabituation response, suggesting that the lack of differences between 'habituation' and 'dishabituation' trials in session 3 is the result of general habituation to all of the spiders in the session rather than fatigue. As mentioned before, these data do support the claim that spiders differentiate among individuals.

The data from session 1 are easy to interpret. The data from sessions 2 and 3 are harder to understand, but these are the trials in which they meet an individual again after a substantial period of separation. Other studies looking at recognition in ants and wasps (cited by the authors) have done a 4 trial design in which focal animal A meets B in the first trial, then meets C in the second trial, meets B again in the third trial, and then meets D in the last trial. In that scenario trials 1, 2, and 4 are between unfamiliar individuals and trial 3 is between potentially familiar individuals. In both the ants and wasps, high aggression is seen in species with and without recognition on trial 1, with low aggression specifically for trials with familiar individuals in species with recognition. Across different tests, species or populations that lack recognition have shown a general reduction in aggression towards all individuals that become progressively less aggressive over time (reminiscent of the session 2 and 3 data) while others have maintained modest levels of aggression across all individuals. The 4 session design used in those other studies provides an unambiguous interpretation of the data while controlling for 'fatigue'. That all trials in sessions 2 and 3 are always with familiar individuals makes it challenging to understand how much the spiders are habituating to each other versus having some kind of associative learning of individual identity and behavior.

The data presentation is also very complicated. How is it the case that a negative proportion of time is spent? The methods reveal that this metric is derived by comparing the time individuals spent in each region relative to the previous time they saw that individual. At the very least, data showing the distribution of distances from the wall would be much easier to interpret for the reader.

(2) "Long-term social memory": It is not entirely clear what is meant by the authors when they say 'long-term social memory', though typically long-term memory refers to a form of a memory that requires protein synthesis. While the precise timing of memory formation varies across species and contexts, a general rule is that long-term memory should last for > 24 hours (e.g., Dreier et al 2007 Biol Letters). The longest time that spiders are apart in this trial setup is something like an hour. There is no basis to claim that spiders have long-term social memory as they are never asked to remember anyone after a long time apart. The odd phrasing of the 'long-term dishabutation' trial makes it seem that it is testing a long-term memory, but it is not. The spiders have never met. The fact that they are very habituated to one set of stimuli and then respond to a new stimulus is not evidence of long-term memory. To clearly test memory (which is the part really lacking from the design), the authors would need to show that spiders - upon the first instance of re-encountering a previously encountered individual are already 'habituated' to them but not to some other individuals. The current data suggest this may be the case, but it is just very hard to interpret given the design does not directly test the memory of individuals in a clear and unambiguous manner.

(3) Lack of a functional explanation and the emphasis on 'asociality': It is entirely plausible that recognition is a pleitropic byproduct of the overall visual cognition abilities in the spiders. However, the discussion that discounts territoriality as a potential explanation is not well laid out. First, many species that are 'asocial' nevertheless defend territories. It is perhaps best to say such species are not group living, but they have social lives because they encounter conspecifics and need to interact with them. Indeed, there are many examples of solitary living species that show the dear enemy effect, a form of individual recognition, towards familiar territorial neighbors. The authors in this case note that territorial competition is mediated by the size or color of the chelicerae (seemingly a trait that could be used to distinguish among individuals). Apparently, because previous work has suggested that territorial disputes can be mediated by a trait in the absence of familiarity has led them to discount the possibility that keeping track of the local neighbors in a potentially cannibalistic species could be a sufficient functional reason. In any event, the current evidence presented certainly does not warrant discounting that hypothesis.

Reviewer #2 (Public Review):

Summary:

In this manuscript, the authors investigated whether a salticid spider, Phidippus regius, recognizes other individuals of the same species. The authors placed each spider inside a container from which it could see another spider for 7 minutes, before having its view of the other spider occluded by an opaque barrier for 3 minutes. The spider was then either presented with the same individual again (habituation trial) or a different individual (dishabituation trial). The authors recorded the distance between the two spiders during each trial. In habituation trials, the spiders were predicted to spend more time further away from each other and, in dishabituation trials, the spiders were predicted to spend more time closer to each other. The results followed these predictions, and the authors then considered whether the spiders in habituation trials were generally fatigued instead of being habituated to the appearance of the other spider, which may have explained why they spent less time near the other individual. The authors presented the spiders with a different (novel) individual after a longer period of time (which they considered to be a long-term dishabituation trial), and found that the spiders switched to spending more time closer to the other individual again during this trial. This suggested that the spiders had recognized and had habituated to the individual that they had seen before and that they became dishabituated when they encountered a different individual.

Strengths:

It is interesting to consider individual recognition by Phidippus regius. Other work on individual recognition by an invertebrate has been, for instance, known for a species of social wasp, but Phidippus regius is a different animal. Importantly and more specifically, P. regius is a salticid spider, and these spiders are known to have exceptional eyesight for animals of their size, potentially making them especially suitable for studies on individual recognition. In the current study, the results from experiments were consistent with the authors' predictions, suggesting that the spiders were recognizing each other by being habituated to individuals they had encountered before and by being dishabituated to individuals they had not encountered before. This is a good start in considering individual recognition by this species.

Weaknesses:

The experiments in this manuscript (habituation/dishabituation trials) are a good start for considering whether individuals of a salticid species recognize each other. I am left wondering, however, what features the spiders were specifically paying attention to when recognizing each other. The authors cited Sheehan and Tibbetts (2010) who stated that "Individual recognition requires individuals to uniquely identify their social partners based on phenotypic variation." Also, recognition was considered in a paper on another salticid by Tedore and Johnsen (2013).

Tedore, C., & Johnsen, S. (2013). Pheromones exert top-down effects on visual recognition in the jumping spider Lyssomanes viridis. The Journal of Experimental Biology, 216, 1744-1756. doi: 10.1242/jeb.071118

In this elegant study, the authors presented spiders with manipulated images to find out what features matter to these spiders when recognizing individuals.

Part of the problem with using two living individuals in experiments is that the behavior of one individual can influence the behavior of the other, and this can bias the results. However, this issue can be readily avoided because salticids are well known, for example, to be highly responsive to lures (e.g. dead prey glued in lifelike posture onto cork disks) and to computer animation. These methods have already been successful and helpful for standardizing the different stimuli presented during many different experiments for many different salticid spiders, and they would be helpful for better understanding how Phidippus regius might recognize another individual on the basis of phenotypic variation. There are all sorts of ways in which a salticid might recognize another individual. Differences in face or body structure, or body size, or all of these, might have an important role in recognition, but we won't know what these are using the current methods alone. Also, I didn't see any details about whether body size was standardized in the current manuscript.

For another perspective, my thoughts turn to a paper by Cross et al.

Cross, F. R., Jackson, R. R., & Taylor, L. A. (2020). Influence of seeing a red face during the male-male encounters of mosquito-specialist spiders. Learning & Behavior, 48, 104-112. doi: 10.3758/s13420-020-00411-y

These authors found that males of Evarcha culicivora, another salticid species that is known to have a red face, become less responsive to their own mirror images after having their faces painted with black eyeliner than if their faces remained red. In all instances, the spiders only saw their own mirror images and never another spider, and these results cannot be interpreted on the basis of habituation/dishabituation because the spiders were not responding differently when they simply saw their mirror image again. Instead, it was specifically the change to the spider's face which resulted in a change of behavior. The findings from this paper and from Tedore and Johnsen can help give us additional perspectives that the authors might like to consider. On the whole, I would like the authors to further consider the features that P. regius might use to discern and recognize another individual.

Reviewer #3 (Public Review):

Summary:

Jumping spiders (family Salticidae) have extraordinarily good eyesight, but little is known about how sensitive these small animals might be to the identity of other individuals that they see. Here, experiments were carried out using Phidippus regius, a salticid spider from North America. There were three steps in the experiments; first, a spider could see another spider; then its view of the other spider was blocked; and then either the same or a different individual spider came into view. Whether it was the same or a different individual that came into view in the third step had a significant effect on how close together or far apart the spiders positioned themselves. It has been demonstrated before that salticids can discriminate between familiar and unfamiliar individuals while relying on chemical cues, but this new research on P. regius provides the first experimental evidence that a spider can discriminate by sight between familiar and unfamiliar individuals.

Clark RJ, Jackson RR (1995) Araneophagic jumping spiders discriminate between the draglines of familiar and unfamiliar conspecifics. Ethology, Ecology and Evolution 7:185-190

Strengths:

This work is a useful step toward a fuller understanding of the perceptual and cognitive capacities of spiders and other animals with small nervous systems. By providing experimental evidence for a conclusion that a spider can, by sight, discriminate between familiar and unfamiliar individuals, this research will be an important milestone. We can anticipate a substantial influence on future research.

Weaknesses:

(1) The conclusions should be stated more carefully.

(2) It is not clearly the case that the experimental methods are based on 'habituation (learning to ignore; learning not to respond). Saying 'habituation' seems to imply that certain distances are instances of responding and other distances are instances of not responding but, as a reasonable alternative, we might call distance in all instances a response. However, whether all distances are responses or not is a distracting issue because being based on habituation is not a necessity.

(3) Besides data related to distances, other data might have been useful. For example, salticids are especially well known for the way they communicate using distinctive visual displays and, unlike distance, displaying is a discrete, unambiguous response.

(4) Methods more aligned with salticids having extraordinarily good eyesight would be useful. For example, with salticids, standardising and manipulating stimuli in experiments can be achieved by using mounts, video playback, and computer-generated animation.

(5) An asocial-versus-social distinction is too imprecise, and it may have been emphasised too much. With P. regius, irrespective of whether we use the label asocial or social, the important question pertains to the frequency of encounters between the same individuals and the consequences of these encounters.

(6) Hypotheses related to not-so-strictly adaptive factors are discussed and these hypotheses are interesting, but these considerations are not necessarily incompatible with more strictly adaptive influences being relevant as well.

eLife assessment

This study is potentially valuable, however currently its findings are incomplete, in that the paper's promise to deliver multiscale models that further our understanding of striatal function remains largely unfulfilled. A major weakness is that the findings are not integrated well within the rich landscape of existing striatal network modeling literature. Another major weakness is that the model is explored only in overly simplified scenarios and with limited comparison to data.

Reviewer #1 (Public Review):

Summary:

The authors aimed to develop a mean-field model that captures the key aspects of activity in the striatal microcircuit of the basal ganglia. They start from a spiking network of individual neuron models tuned to fit striatal data. They show that an existing mean-field framework matches the output firing rates generated by the spiking network both in static conditions and when the network is subject to perfectly periodic drive. They introduce a very simplified representation of dopaminergic cortico-striatal plasticity and show that simulated dopamine exposure makes model firing rates go up or down, in a way that matches the design of the model. Finally, they aim to test the performance of the model in a reinforcement learning scenario, with two very simplified channels corresponding to the selection between two actions. Overall, I do not find that this work will be useful for the field or provide novel insights.

Strengths:

The mean-field model dynamics match well with the spiking network dynamics in all scenarios shown. The authors also introduce a dopamine-dependent synaptic plasticity rule in the context of their reinforcement learning task, which can nicely capture the appropriate potentiation or depression of corticostriatal synapses when dopamine levels change.

Weaknesses:

From the title onwards, the authors refer to a "multiscale" model. They do not, in fact, work with a multiscale model; rather, they fit a spiking model to baseline data and then fit a mean-field model to the spiking model. The idea is then to use the mean-field model for subsequent simulations.

The mean-field modeling framework that is used was already introduced previously by the authors, so that is not a novel aspect of this work in itself. The model includes an adaptation variable for each population in the network. Mean-field models with adaptation already exist, and there is no discussion of why this new framework would be preferable to those. Moreover, as presented, the mean-field model is not a closed system. It includes a variable w (in equation 7) that is never defined.

Overall, the paper shows that a mean-field model behaves similarly to a spiking model in several scenarios. A much stronger result would be to show that the mean-field model captures the activity of neurons recorded experimentally. The spiking model is supposedly fit to data from recordings in some sort of baseline conditions initially, but the quality of this fit is not adequately demonstrated; the authors just show a cursory comparison of data from a single dSPN neuron with the activity of a single model dSPN, for one set of parameters.

The authors purport to test their model via its response to "the main brain rhythms observed experimentally". In reality, this test consists of driving the model with periodic input signals. This is far too simplistic to achieve the authors' goals in this part of the work.

The work also presents model responses to simple simulations of dopamine currents, treated as negative or positive inputs to different model striatal populations. These are implemented as changes in glutamate conductance and possibly in an additional depolarizing/hyperpolarizing current, so the results that are shown are guaranteed to occur by the direct design of the simulation experiment; nothing new is learned from this. The consideration of dopamine also points out that the model is apparently designed and fit in a way that does not explicitly include dopamine, even though the fitting is done to control (i.e., with-dopamine) data, so it's not clear how this modeling framework should be adapted for dopamine-depleted scenarios.

For the reinforcement learning scenario, the model network considered is extremely simplified. Moreover, the behavior generated is unrealistic, with action two selected several times in succession independent of reward outcomes and then an instant change to a pattern of perfectly alternating selection of action 1 and action 2.

Finally, various aspects of the paper are sloppily written. The Discussion section is especially disappointing, because it is almost entirely a summary of the results of the paper, without an actual discussion of their deeper implications, connections to the existing literature, predictions that emerge, caveats or limitations of the current work, and natural directions for future study, as one would expect from a usual discussion section.

Reviewer #2 (Public Review):

Summary:

The present article by Tesler et al proposes a 3-population model of the striatum input-output function including the direct pathway (D1) striatal projection neurons (dSPNs), the indirect pathway (D2) striatal projection neurons (iSPNs), and the fast-spiking striatal interneurons. The authors derive a mean-field version of the model where the firing rate of each population follows the transfer function obtained from a spiking (AdEx) neuron model for each cell population. They report the response of the mean-field circuit to oscillatory inputs from the cortex, the effect of dopamine on dSPNs and iSPNs, and how a simple reinforcement learning rule at cortico-striatal synapses would adapt the model's output in the face of 2 distinct inputs.

Strengths:

The model is simple and easy to understand.

Weaknesses:

Feedforward inhibition from FSI and interconnections between dSPNs and iSPNs does not seem to have any significant impact on the input-output response of dSPNs and iSPNs to cortical inputs. Therefore, all of the results shown can be derived relatively easily from the basic knowledge we have about mean-field neuronal models and their responses to external inputs: all populations have an output that linearly follows the input. Concerning the reinforcement learning paradigm, showing that 2 distinct inputs can be associated with opposite outputs based on a tri-partite synaptic learning rule does not appear new either. As it is, it's unclear to me how this model contributes to new knowledge concerning striatal neuronal activity. Moreover, the assumptions made concerning the effect of dopamine and the synaptic plasticity rules appear rather simplistic and relatively outdated.

Many of the goals set in the introduction do not appear met:

"understanding and modelling the complex dynamics and functions of the striatum constitutes a very relevant and challenging task".<br /> I'm not sure if the authors aim to understand and model the complex dynamics of the striatum here: there are no complex dynamics that are revealed or explained in the model, as the dSPNs and iSPNs mainly appear to have a linear relationship to their inputs (with added noise) in 3 for example. I did not find any non-trivial dynamics highlighted in the presentation of the results either.

"modelling and studying the functions of the striatum and its associated neuronal dynamics requires to investigate these cellular/microcircuits mechanisms, and how the small-scale mechanisms affect large-scale behavior"<br /> I also did not find a statement about the effect of cellular/microcircuit mechanisms on behavior or large-scale activity in the results or discussion. The effects of micro-circuits are rather transparent as dSPNs and iSPNs do not seem to differ from feedforward responses to cortical inputs.

"existing mean-fields are based on generic models (sometimes inspired by cortical circuits) [7, 8], which do not consider the rich and specific cellular and synaptic variability observed along brain regions."<br /> The authors argue here that specific input-output relationships of striatal neurons may contribute to the circuit dynamics. However, the input-output they derive from a spiking neuron model (AdEx) in Figure 2, are very typical IF curves used in most mean-field models. Apart from a slight saturation effect at large rates (which is incorporated in many mean-field models and may not even be relevant here given the max firing of these cells), the I-F curve looks exactly like what is expected from the most basic rate model neuron with a rectifying transfer function in the presence of synaptic noise. What cellular or synaptic properties would the authors like to highlight here? Linking to molecular and cellular parameters, as advertised in the intro, seems much beyond the current achievements of the present model.

"This approach permits an efficient transition between scales and, furthermore, it allows to explore the effects of cellular parameters at the network level, as we will show for the case of dopaminergic effects in the striatum."<br /> If the authors mean the excitation of D1 SPNS and the inhibition of D2 SPNs by dopamine, this statement seems slightly oversold. It's very well known that dopaminergic effects cannot simply be resumed by a change in excitability as it acts on non-linear currents and complex synaptic parameters. They model it as follows: "To model these effects of dopamine in dSPN cells we will assume the increase of excitability due to D1 activation in dPSNs can be described as an increase in the glutamatergic conductance (Qe in our model) together with the action of a depolarizing current" Which basically means an additional excitatory input and a depolarizing current. The expected effect on the firing rate of these 2 effects is rather simple and does not require circuit modelling I believe.

This effect of dopamine is referred to in the discussion as: "This analysis allowed us to show how modifications at the cellular level can be incorporated within the mean-field model which can in turn predict and capture the emergent changes at the network level generated by them, and in addition has provided further validation to our model."<br /> Again, I don't see any emergent property or model validation here. Maybe the authors can be a bit more precise about what emergent property they refer to.

"In addition it illustrates how changes at the cellular level can lead to emerging effects at the network level, which can be captured by the mean-field model"<br /> I did not find any description of 'emerging effects at the network level" in the results or discussion. Maybe the authors could elaborate on what they mean here.

"shows the capabilities of the model to reproduce specific brain functions"<br /> The capacity of a network to associate stim A to a positive output and stim B to a negative one through reward-driven synaptic plasticity is rather well described and is a bit far from 'specific brain functions'. Concerning the discussion, it highlights how the model 'could be useful' rather than highlighting any strength of the model or relation to existing work. In particular, the (large) literature on circuit modelling in the striatum and BG circuits is not cited at all beyond self-citations, except in one book chapter (Houk et al, 1995) and one paper (Bogacz, 2020).

"The RL model proposed can very easily be improved to capture more biologically complex scenarios"<br /> Why did the authors not implement such an 'easy' improvement?

Reviewer #2 (Public Review):

Summary:

In the manuscript "Metabolic heterogeneity of colorectal cancer as a prognostic factor: insights gained from fluorescence lifetime imaging" by Komarova et al., the authors used fluorescence lifetime imaging and quantitative analysis to assess the metabolic heterogeneity of colorectal cancer. Generally, this work is logically well-designed, including in vitro and in vivo animal models and ex vivo patient samples. Although the key parameter (BI index) used in this study was already published by this group, it was shown that heterogeneity of patients' samples had associations with clinical characteristics of tumors. Additional samples from 8 patients were added to the data pool during the revision process, which is helpful and important for the conclusions that the authors are trying to draw. Overall, the revisions that the authors have made greatly strengthen this study.

Strengths:

(1) Solid experiments are performed and well-organized, including in vitro and in vivo animal models and ex vivo patient samples;

(2) Attempt and efforts to build the association between the metabolic heterogeneity and prognosis for colorectal cancer.

Weaknesses:

(1) Although additional data acquired from 8 patients were collected, maybe more patients should be involved in the future for reliable diagnosis and prognosis.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this study, Komarova et al. investigate the clinical prognostic ability of cell-level metabolic heterogeneity quantified via the fluorescence lifetime characteristics of NAD(P)H. Fluorescence lifetime imaging microscopy (FLIM) has been studied as a minimally invasive approach to measure cellular metabolism in live cell cultures, organoids, and animal models. Its clinical translation is spearheaded through macroscopic implementation approaches that are capable of large sampling areas and enable access to otherwise constrained spaces but lack cellular resolution for a one-to-one transition with traditional microscopy approaches, making the interpretation of the results a complicated task. The merit of this study primarily lies in its design by analyzing with the same instrumentation and approach colorectal samples in different research scenarios, namely in vitro cells, in vivo animal xenografts, and tumor tissue from human patients. These conform to a valuable dataset to explore the translational interpretation hurdles with samples of increasing levels of complexity. For human samples, the study specifically investigates the prediction ability of NAD(P)H fluorescence metrics for the binary classification of tumors of low and advanced stage, with and without metastasis, and low and high grade. They find that NAD(P)H fluorescence properties have a strong potential to distinguish between high- and low-grade tumors and a moderate ability to distinguish advanced-stage tumors from low-stage tumors. This study provides valuable results contributing to the deployment of minimally invasive optical imaging techniques to quantify tumor properties and potentially migrate into tools for human tumor characterization and clinical diagnosis.

Strengths:

The investigation of colorectal samples under multiple imaging scenarios with the same instrument and approach conforms to a valuable dataset that can facilitate the interpretation of results across the spectrum of sample complexity.

The manuscript provides a strong discussion reviewing studies that investigated cellular metabolism with FLIM and the metabolic heterogeneity of colorectal cancer in general.

The authors do a thorough acknowledgement of the experimental limitations of investigating human samples ex vivo, and the analytical limitation of manual segmentation, for which they provide a path forward for higher throughput analysis.

Weaknesses:

To substantiate the changes in fluorescence properties at the examined wavelength range (associated with NAD(P)H fluorescence) in relationship to metabolism, the study would strongly benefit from additional quantification of metabolic-associated metrics using currently established standard methods. This is especially interesting when discussing heterogeneity, which is presumably high within and between patients with colorectal cancer, and could help explain the particularities of each sample leading to a more in-depth analysis of the acquired valuable dataset.

In order to address this issue, we have performed immunohistochemical staining of the available tumor samples for the two standard metabolic markers GLUT3 and LDHA.

The results are included in Supplementary (Fig.S4). Discussion has been extended.

Additionally, NAD(P)H fluorescence does not provide a complete picture of the cell/tissue metabolic characteristics. Including, or discussing the implications of including fluorescence from flavins would comprise a more compelling dataset. These additional data would also enable the quantification of redox metrics, as briefly mentioned, which could positively contribute to the prognosis potential of metabolic heterogeneity.

We agree with the Reviewer that fluorescence from flavins could be helpful to obtain more complete data on cellular metabolic states. However, we lack to detect sufficiently intensive emission from flavins in colorectal cancer cells and tissues. The paragraph about flavins was added in Discussion and representative images - in Supplementary Material (Figure S5).

In the current form of the manuscript, there is a diluted interpretation and discussion of the results obtained from the random forest and SHAP analysis regarding the ability of the FLIM parameters to predict clinicopathological outcomes. This is, not only the main point the authors are trying to convey given the title and the stated goals, but also a novel result given the scarce availability of these type of data, which could have a remarkable impact on colorectal cancer in situ diagnosis and therapy monitoring. These data merit a more in-depth analysis of the different factors involved. In this context, the authors should clarify how is the "trend of association" quantified (lines 194 and 199).

We thank the Reviewer for this suggestion. The section has been updated with SHAP analysis using different parameters (dispersion D of t2, a1, tm and bimodality index BI of t2, a1, tm). It is now more clear that D-a1 is more strongly associated with clinicopathological outcomes compared with other variables. We have also added some biological interpretation of these results in the Discussion.

Reviewer #2 (Public Review):

Summary:

In the manuscript "Metabolic heterogeneity of colorectal cancer as a prognostic factor: insights gained from fluorescence lifetime imaging" by Komarova et al., the authors used fluorescence lifetime imaging and quantitative analysis to assess the metabolic heterogeneity of colorectal cancer. Generally, this work is logically well-designed, including in vitro and in vivo animal models and ex vivo patient samples. However, since the key parameter presented in this study, the BI index, is already published in a previous paper by this group (Shirshin et al., 2022), and the quantification method of metabolic heterogeneity has already been well (and even better) described in previous studies (such as the one by Heaster et al., 2019), the novelty of this study is doubted. Moreover, I am afraid that the way of data analysis and presentation in this study is not well done, which will be mentioned in detail in the following sections.

Strengths:

(1) Solid experiments are performed and well-organized, including in vitro and in vivo animal models and ex vivo patient samples.

(2) Attempt and efforts to build the association between the metabolic heterogeneity and prognosis for colorectal cancer.

Weaknesses:

(1) The human sample number (from 21 patients) is very limited. I wonder how the limited patient number could lead to reliable diagnosis and prognosis;.

Additional 8 samples of patients’ tumors collected while the manuscript was under review were added to the present data. We agree that the number is still limited to conclude about the prognostic value of cell-level metabolic heterogeneity. But at this point we can expect that this parameter will become a metric for prognosis. We will continue this study to collect more samples of colorectal tumors and expand the approach to different cancer types.

(2) The BI index or similar optical metrics have been well established by this and other groups; therefore, the novelty of this study is doubted.

The purpose of this research was to quantify and compare the cellular metabolic heterogeneity across the systems of different complexity - commercial cell lines, tumor xenografts and patients’ tumors - using previously established FLIM-based metrics. For the first time, using FLIM, it was shown that heterogeneity of patients’ samples is much higher than of laboratory models and that it has associations with clinical characteristics of the tumors - the stage and the grade. In addition, this study provides evidence that bimodality (BI) in the distribution of metabolic features in the cell population is less important than the width of the spread (the dispersion value D).

Some corrections have been made in the text on this point.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

The following comments should be addressed to strengthen the rigor and clarity of the manuscript.

(1) The ethical committee that approved the human studies should also be mentioned in the methods section, as was done with the animal studies.

Information about the ethics committee has been added in the Manuscript.

The study with the use of patients’ material was approved by the ethics committee of the Privolzhsky Research Medical University (approval № 09 from 30.06.2023).

(2) The captions in Figures 2 and 3 must be revised. In Figure 2, it seems the last 2 sentences for the description of (C) do not belong there, and instead, the last sentence in the description of (D) may need to be included in (C) instead. Figure 3 is similar.

The captions were revised.

(3) From supplement Figure S2 it seems that EpCam and vimentin staining were only done in two of the mouse tumor types. No further mention is made in the results or methods section. Is there any reason this was not performed in the other tumor types? Were the histology and IHC protocols the same for the mouse and human tumors?

The data on other tumor types and patients’ tumors have been added in Figure S3. Discussion was extended with the following paragraph.

One of the possible reasons for metabolic heterogeneity could be the presence of stromal cells or diversity of epithelial and mesenchymal phenotypes of cancer cells within a tumor. Immunohistochemical staining of tumors for EpCam (epithelial marker) and vimentin (mesenchymal marker) showed that the fraction of epithelial, EpCam-positive, cells was more than 90% in tumor xenografts and on average 76±10 % in patients’ tumors (Figure S3). However, the ratio of EpCam- to vimentin-positive cells in patients’ samples neither correlated with D-a1 nor with BI-a1, which means that the presence of cells with mesenchymal phenotype did not contribute to metabolic heterogeneity of tumors identified by NAD(P)H FLIM.

(4) Clarify the design of the experiments: The results come from 50 - 200 cells in each sample (except 30 in the CaCo2 cell culture) that were counted from 5 - 10 images acquired from each sample. There were 21 independent human samples. How many independent samples were included in the cell culture experiments and the mouse tumor models? Why is there an order of magnitude fewer cells included in the CaCo2 group compared to the other groups (Figure 1)? From the image (Figure 1A - CaCo2), it seems to be a highly populated type of sample, yet only 30 cells were quantified. What prevents the inclusion of the same number of cells to be quantified in each group for a more systematic evaluation?

We thank the Reviewer for this comment.

Cell culture experiments included two independent replicates for each cell line, the data from which were then combined. In animal experiments measurements were made in three mice (numbered 1-3 in Figure 2C) for each tumor type. We have made calculations for additional >100 cells of CaCo2 cell line. In the revised version the number of Caco2 cells is 146.

The text of the Manuscript was revised accordingly.

(5) Regarding references: Some claims throughout the text would benefit from an additional reference. For example: line 70 "Metabolic heterogeneity [...] is believed to have prognostic value"; line 121 " [...] the uniformity of cell metabolism in a culture, which is consistent with the general view on standard cell lines [...]". The clinical translational aspect (i.e., paragraph in line 255) warrants the inclusion of the efforts already done with FLIM imaging in the clinical setting both in vivo and ex vivo with point-spectroscopy and macroscopy imaging (e.g., Jo Lab, Marcu Lab, French Lab, and earlier work by Mycek and Richards-Kortum in colorectal cancer to name a few).

Additional references were added.

Reviewer #2 (Recommendations For The Authors):

(1) In the Introduction, line 85, the authors mention that "Specifically, the unbound state of NAD(P)H has a short lifetime (~0.4 ns) and is associated with glycolysis, while the protein-bound state has a long lifetime (~1.7-3.0 ns) and is associated with OXPHOS". I do not think this claim is appropriate. One cannot simply say that the unbound state is associated with glycolysis, nor that the bound state is associated with OXPHOS; both unbound and bound state are associated with almost all the metabolic pathways. Instead, the expression of "glycolytic/ OXPHOS shift", as authors used in other sections of this manuscript, is a more appropriate one in this case.

The text of the Introduction was revised.

(2) What are the biological implications of the bimodality index (BI)? Please provide specific insights.

Bimodal distribution indicates there are two separate and independent peaks in the population data. In the metabolic FLIM data, this indicates that there are two sub-populations of cells with different metabolic phenotypes. Previously, we have observed bimodal distribution in the population of chemotherapy treated cancer cells, where one sub-population was responsive (shifted metabolism) and the second - non-responsive (unchanged metabolism) [Shirshin et al., PNAS, 2022]. In the naive tumor, a number of factors have an impact on cellular metabolism, including genetics features and microenvironment, so it is difficult to determine which ones resulted in bimodality. Our data on correlation of bimodality (BI) with clinical characteristics of the tumors show that there are no associations between them. What really matters is the width of the parameter spread in the population. The early-stage tumors (T1, T2) were metabolically more heterogeneous than the late-stage ones (T3, T4). A degree of heterogeneity was also associated with differentiation state, a stage-independent prognostic factor in colorectal cancer where the lower grade correlates with better the prognosis. The early-stage tumors (T1, T2) and high-grade (G3) tumors had significantly higher dispersion of NAD(P)H-a1, compared with the late-stage (T3, T4) and low-grade ones (G1, G2). From the point of view of biological significance of heterogeneity, this means that in stressful and unfavorable conditions, to which the tumor cells are exposed, the spread of the parameter distribution in the population rather than the presence of several distinct clusters (modes) matters for adaptation and survival. The high diversity of cellular metabolic phenotypes provided the survival advantage, and so was observed in more aggressive (undifferentiated or poorly differentiated) and the least advanced tumors.

The discussion has been expanded on this account.

(3) Have you run statistics in Figure 1B? If yes, do you find any significance? The same question also applies to Figures 2C and 3C.

We performed statistical analysis to compare different cell lines in in vitro and in vivo models, the results obtained are presented in Table S4.

(4) Line 119, why is the BI threshold set at 1.1?

When setting the BI threshold at 1.1, we relied on the work by Wang et al, Cancer Informatics, 2009. The authors recommended the 1.1 cutoff as more reliable to select bimodally expressed genes. Further, we validated this BI threshold to identify chemotherapy responsive and non-responsive sub-populations of cancer cells (Shirshin et al. PNAS, 2022)

(5) Line 123, what does the high BI of mean lifetime stand for? Please provide biological implications and insights.

The sentence was removed because inclusion of additional CaCo2 cells (n=146) for quantification NAD(P)H FLIM data showed no bimodality in this cell culture.

(6) In the legend for Figure 2C, the authors mention that "the bimodality index (BI-a1) is shown above each box"; however, I do not see such values. It is also true for Figure 3C.

The legends for Fig. 2 and 3 were corrected.

(7) In Figure 2, t1-t3 were not explained and mentioned in the main text. What do they mean? Do they mean different time points or different tumors?

t1-t3 means different tumors in a group. Changes have been made to the figure - individual tumors are indicated by numbers.

(8) In Figure 3, what do p13, p15 and p16 mean? It is not clearly explained. If they just represent patients numbered 13, 15, and 16, then why are these patients chosen as representatives? Do they represent different stages or are they just chosen randomly?

Figure 3 was revised. Representative images were changed and a short description for each representative sample was included. In the revised version, representatives have been selected to show different stages and grades.

(9) In Figure 3, instead of showing the results for each patient, I would suggest that authors show representative results from tumors at different stages; or, at least, clearly indicate the specific information for each patient. I do not think that providing the patient number only without any patient-specific information is helpful.

Figure 3 was revised.

(10) The sample number (21 patients) is very limited. I wonder how the limited patient number could lead to reliable diagnosis and prognosis.

Additional eight samples were added. The text, figures and tables were revised accordingly.

(11) In Discussion, it would be helpful to compare the BI index used in this study with the previously developed OMI-index (Line 275).

We believe that BI index and OMI index describe different things and, therefore, it is hard to compare them. While BI index is used to describe the degree of the metabolic heterogeneity, OMI index is an integral parameter that includes redox ratio, mean fluorescence lifetimes of NAD(P)H and FAD, and rather indicates the metabolic state of a cell. In this sense it is more relevant to compare it with conventional redox ratio or Fluorescence Lifetime Redox Ratio (FLIRR) (H. Wallrabe et al., Segmented cell analyses to measure redox states of autofluorescent NAD(P)H, FAD & Trp in cancer cells by FLIM, Sci. Rep. 2018; 8: 79). The assessment of the heterogeneity of the FLIM parameters has been previously reported using the weighted heterogeneity (wH) index (Amy T. Shah et al, In Vivo Autofluorescence Imaging of Tumor Heterogeneity in Response to Treatment, Neoplasia 17, pp. 862–870 (2015). To the best of our knowledge, this is the only metric to quantify metabolic heterogeneity on the basis of FLIM data for today. A comparison of BI with the wH-index showed that the value of wH-index provides results similar to BI in the heterogeneity evaluation as demonstrated in our earlier paper (E.A. Shirshin et al, Label-free sensing of cells with fluorescence lifetime imaging: The quest for metabolic heterogeneity, PNAS 119 (9) e2118241119 (2022).  Yet, the BI provides dimensionless estimation on the inherent heterogeneity of a sample, and therefore it can be used to compare heterogeneity assessed by different decay parameters and FLIM data analysis methods. The limitation of using the OMI index for FLIM data analysis is the low intensity of the FAD signal, which was the case in our experiments.

eLife assessment

This study presents a valuable finding on the heterogeneity of tumour metabolism using fluorescence lifetime imaging, measured across 4 cell lines, 4 tumour types of in vivo mouse models, and 29 patient samples. The indication is that the level of heterogeneity of cellular metabolism increases with model complexity and demonstrates high heterogeneity at a clinical level. The evidence supporting the claims of the authors is solid, and at the revision stage, the authors have included additional samples from 8 patients in the data pool, which is helpful for the conclusions that the authors are trying to draw. The work will be of interest to medical biologists developing methods for quantifying metabolic heterogeneity.

Reviewer #1 (Public Review):

Summary:

In this study, Komarova et al. investigate the clinical prognostic ability of cell-level metabolic heterogeneity quantified via the fluorescence lifetime characteristics of NAD(P)H. Fluorescence lifetime imaging microscopy (FLIM) has been studied as a minimally invasive approach to measure cellular metabolism in live cell cultures, organoids, and animal models. Its clinical translation is spearheaded though macroscopic implementation approaches that are capable of large sampling areas and enable access to otherwise constrained spaces but lack cellular resolution for a one-to-one transition with traditional microscopy approaches, making the interpretation of the results a complicated task. The merit of this study primarily lies in its design by analyzing with the same instrumentation and approach colorectal samples in different research scenarios, namely in vitro cells, in vivo animal xenografts, and ex vivo tumor tissue from human patients. These conform to a valuable dataset to explore the translational interpretation hurdles with samples of increasing levels of complexity. For human samples, which exhibited the highest degree of heterogeneity from the experiments presented, the study specifically investigates the prediction ability of NAD(P)H fluorescence metrics for the binary classification of tumors of low and advanced stage, with and without metastasis, and low and high grade. They find that NAD(P)H fluorescence properties have a strong potential to distinguish between high- and low-grade tumors and a moderate ability to distinguish advanced stage tumors from low stage tumors. This study provides valuable results contributing to the deployment of minimally invasive optical imaging techniques to quantify tumor properties and potentially migrating into tools for human tumor characterization and clinical diagnosis.

Strengths:

The investigation of colorectal samples under multiple imaging scenarios with the same instrument and approach conforms to a valuable dataset that can facilitate interpretation of results across the spectrum of sample complexity.

The manuscript provides a strong discussion reviewing studies that investigated cellular metabolism with FLIM and the metabolic heterogeneity of colorectal cancer in general.

The authors do a thorough acknowledgement of the experimental limitations of investigating human samples ex vivo, and the analytical limitation of manual segmentation, for which they provide a path forward for higher throughput analysis.

Weaknesses:

NAD(P)H fluorescence provides a partial picture of the cell/tissue metabolic characteristics. Including fluorescence from flavins would comprise a more compelling dataset. These additional data should enable the quantification of redox metrics, which could positively contribute to the prognosis potential of metabolic heterogeneity. The authors did attempt to incorporate flavin fluorescence, unfortunately they could not find strong enough signal to proceed with the analysis.

Reviewer #1 (Public Review):

Summary:

Starting from an unbiased search for somatic mutations (from COSMIC) likely disrupting binding of clinically approved antibodies the authors focus on mutations known to disrupt binding between two ERBB2 mutations and Pertuzamab. They use a combined computational and experimental strategy to nominate position which when mutated could result in restoring the therapeutic activity of the antibody. Using in vitro assays the authors confirm that the engineered antibody binds to the mutant ERBB2 and prevents ERBB3 phosphorylation

Strengths:

(1) In my assessment, the data sufficiently demonstrates that a modified version of Pertuzamab can bind both the wild-type and S310 mutant forms of ERBB2.

(2) The engineering strategy employed is rational and effectively combines computational and experimental techniques.

(3) Given the clinical activity of HER2-targeting ADCs, antibodies unaffected by ERBB2 mutations would be desired

Weaknesses:

(1) There is no data showing that the engineered antibody is equally specific as Pertuzamab i.e. that it does not bind to other (non-ERBB2) proteins.

(2) There is no data showing that the engineered antibody has the desired pharmacokinetics/pharmacodynamics properties or efficacy in vivo.

(3) Computational approaches are only used to design a phage-screen library, but not used to prioritize mutations that are likely to improve binding (e.g. based on predicted impact on the stability of the interaction). A demonstration how computational pre-screening or lead optimization can improve the time-intensive process would be a welcome advance.

Comments on revised version:

I have nothing to add beyond my first review, because no substantial changes, additional experiments and/or data, have been made to the manuscript.

eLife assessment

In this important manuscript, the authors used unbiased approaches to identify somatic mutations in publicly available databases that would disrupt clinically approved antibodies targeting HER2. Using a solid combination of both computational and experimental approaches they identify mutations that could restore therapeutic antibody sensitivity in a series of disease-relevant model systems. Additional cell-based and in vivo assays would strengthen the work and increase the translational and potential clinical relevance of the proposed work.

Reviewer #2 (Public Review):

Summary:

Peled et al identified HER2 mutations in connection with resistance to the anti-HER2 antibody Pertuzumab-mediated therapy. After constructing a yeast display library of Pertuzumab variants with 3.86×10^11 sequences for targeted screening of variant combinations in chosen 6 out of 14 residues, the authors performed experimental screening to obtain the clones that bind to HER2 WT and/or mutants (S310Y and S310F), and then combined new variations to obtain antibodies with a broad spectrum binding to both WT and two HER2 mutants. These are interesting studies of clinical impact and translational potential.

Strengths:

(1) Deep computational analyses of large datasets of clinical data provide useful information about HER2 mutations and their potential relevance to antibody therapy resistance.

(2) There is valuable information analyzing the residues within or near the interface between the antigen HER2 and the Pertuzumab antibody (heavy chain).<br /> The experimental antibody library screening obtained 90+ clones from 3.86×10^11 sequences for further functional validation.

Weaknesses:

(1) There is lack of assessment for antibody variant functions in cancer cell phenotypes in vitro (proliferation, cell death, motility) or in vivo (tumor growth and animal survival). The only assay was the western blotting of phosphopho-HER3 in Figure 4. However, HER2 levels and phosphor-HER2 were not analyzed.

(2) There is misleading impression from the title of computational engineering of a therapeutic antibody and the statement in the abstract "we designed a multi-specific version of Pertuzumab that retains original function while also bindings these HER2 variants" for a few reasons:

a. The primary method used for variant antibody identification for HER2 mutant binding is rather traditional experimental screening based on yeast display instead of computational design of a multi-specific version of Pertuzumab.

b. There is insufficient or lack of computational power in the antibody design or prioritization in choosing variant residues for the library construction of 3.86×1011 sequences. It seems random combinations from 6 residues out of 4 groups with 20 amino acid options.<br /> c. The final version of tri-binding variant is a combination of screened antibody clones instead of computation design from scratch.<br /> d. There is incomplete experimental evidence about the therapeutic values of newly obtained antibody clones.

Comments on revised version:

Two major comments remain and have not been well addressed. Comment 1 is expecting any cellular phenotypic analysis if not in vivo. Comment 2 requires some modifications to avoid overstating.

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public Review):

Strengths:

(1) In my assessment, the data sufficiently demonstrates that a modified version of Pertuzamab can bind both the wild-type and S310 mutant forms of ERBB2.

(2) The engineering strategy employed is rational and effectively combines computational and experimental techniques.

(3) Given the clinical activity of HER2-targeting ADCs, antibodies unaffected by ERBB2 mutations would be desired.

Weaknesses:

(1) There is no data showing that the engineered antibody is equally specific as Pertuzamab i.e. that it does not bind to other (non-ERBB2) proteins.

Showing the specificity of the engineered antibodies is indeed important. We did not address it in the current ms, but it can be tested in the future.

(2) There is no data showing that the engineered antibody has the desired pharmacokinetics/pharmacodynamics properties or efficacy in vivo.

In this ms we did not conduct in-vivo experiments. When moving forward, pharmacokinetics/pharmacodynamics properties and efficacy will be tested as well.

(3) Computational approaches are only used to design a phage-screen library, but not used to prioritize mutations that are likely to improve binding (e.g. based on predicted impact on the stability of the interaction). A demonstration of how computational pre-screening or lead optimization can improve the time-intensive process would be a welcome advance.

Thank you for this important comment. In the present ms we indeed used a computational approach for prioritizing residues to be mutated, but we did not prioritize the mutations that are likely to improve binding. In the initial library design, we did prioritize the mutations. However, due to experimental approach limitations with codon’s selection for the library, we had decided to allow all possible residues in each position, knowing that the selection will remove non-binding variants.

Context:

The conflict of interest statement is inadequate. Most authors of the study (but not the first author) are employees of Biolojic, a company developing multi-specific antibodies, but the statements do not clarify whether the presented antibodies represent Biolojic IP, whether the company sponsored the research, and whether the company is further developing the specific antibodies presented.

The Conflict-of-Interest statement will be revised as such: The Biolojic Design authors are employees of Biolojic Design and have stock options in Biolojic Design. The company did not sponsor the research, does not hold IP for the presented antibodies, and is not further developing the presented antibodies.

Reviewer #2 (Public Review):

Strengths:

(1) Deep computational analyses of large datasets of clinical data provide useful information about HER2 mutations and their potential relevance to antibody therapy resistance.

(2) There is valuable information analyzing the residues within or near the interface between the antigen HER2 and the Pertuzumab antibody (heavy chain). The experimental antibody library screening obtained 90+ clones from 3.86×1011 sequences for further functional validation.

Weaknesses:

(1) There is a lack of assessment for antibody variant functions in cancer cell phenotypes in vitro (proliferation, cell death, motility) or in vivo (tumor growth and animal survival). The only assay was the western blotting of phosphopho-HER3 in Figure 4. However, HER2 levels and phosphor-HER2 were not analyzed.

We indeed did not assess the engineered antibodies function in cancer cells. While a complete signaling assessment obviously requires functional assessment as well, due to the complexity of this assay, papers in this field (for example [1-3]) measure the signaling activation following HER2-HER3 dimerization by measuring pHER3, and we relied on them in this ms.

(2) There is a misleading impression from the title of computational engineering of a therapeutic antibody and the statement in the abstract "we designed a multi-specific version of Pertuzumab that retains original function while also bindings these HER2 variants" for a few reasons:

a. The primary method used for variant antibody identification for HER2 mutant binding is rather traditional experimental screening based on yeast display instead of the computational design of a multi-specific version of Pertuzumab.

b. There is insufficient or lack of computational power in the antibody design or prioritization in choosing variant residues for the library construction of 3.86×1011 sequences. It seems random combinations from 6 residues out of 4 groups with 20 amino acid options.

c. The final version of the tri-binding variant is a combination of screened antibody clones instead of computation design from scratch.

d. There is incomplete experimental evidence about the therapeutic values of newly obtained antibody clones.

Thank you for this relevant comment. When addressing relevant residues to be mutated, the number of potential variants is enormous. The computational approach was aimed at identifying the most preferable residues, in which variation can improve binding and is not likely to harm important interactions. Although an initial smaller number of residues could be chosen, we decided to broaden our view and create a larger library, in the aim of combining the computational selection with an experimental selection. This indeed is not a computational design from scratch, but rather an intercourse between the computer and the lab, that yielded the presented results.

(3) Figures can be improved with better labeling and organization. Some essential pieces of data such as Supplementary Figure 1B on HER2 mutations in S310 that abrogated its binding to Pertuzumab should be placed in the main figures.

Thank you for this comment, the relevant figures were moved to the main text, and the labels were revised.

(4) It is recommended to provide a clear rationale or flowchart overview into the main Figure 1. Figure 2A can be combined with Figure 1 to the list of targeted residues.

Figures 1 and 2 were divided differently, and the rationale was moved to the main text.

(5) The quality of Figures such as Figure 2B-C flow data needs to be improved.

High-quality figures were submitted with the revised ms.

Reviewer #1 (Recommendations for The Authors):

Major:

(1) It should be clarified whether the S310 somatic mutations represent resistance mutations to Pertuzamab (i.e. emerge post-therapy) or are general mutations that activate HER2. This is important because mutations that specifically "evade" the binding of an antibody may be substantially more difficult to overcome than mutations that only by chance occur in the antibody binding site. This concern should be addressed in the introduction and discussion as it changes the interpretation of the data.

This is a very important note. To the best of our knowledge, these mutations were not identified as resistance mutations that emerged post-therapy. However, as mentioned in the introduction, these mutations form hydrophobic interactions that stabilize HER2 dimerization. Moreover, cells expressing these mutations show hyperphosphorylation of HER2 and an increase in the subsequent activation of signaling pathways. Thus, these mutations do not necessarily evade Pertuzumab binding, but benefit cancer growth. This point was clarified in the introduction of the revised text.

(2) While the authors claim that S310 germline pathogenic variants exist, I could not find evidence that this is the case. The dbGAP ID does not provide any evidence (either in the form of a citation or prevalence). The variants do not exist in GnomAD. A recent article discussing pathogenic ERBB2 germline variants only mentions S310 as a somatic variant https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8268839/ and I could not find evidence for S310 being a germline variant in the references provided by the author (https://www.nature.com/articles/nbt.3391) - where it is only mentioned as a somatic mutation. I could not find evidence of a cancer predisposition syndrome associated with this variant.

Thank you for highlighting this matter. We had assumed that the presence of the variant in dbSNP means it is also a germline mutations, what may not be correct. However, we did find some evidence of this mutation as germline in ClinVar, and this was edited in the revised ms. https://www.ncbi.nlm.nih.gov/clinvar/RCV001311879.7.

(3) The authors should consider experiments that show that the modified Pertuzamab has the same mechanism of action as the original Pertuzamab in preventing dimerization of the ERBB2 homodimer and/or interactions with ERBB3. I cannot recommend a specific approach, but at present it is not clear whether the mechanism or just the effect (phosphorylation of ERBB3) is the same.

As mentioned above, for the assessment of HER-HER3 binding and HER3 signaling, in this ms we relied on a previous works [1-3] that also measured the signaling activation following HER2-HER3 dimerization by measuring pHER3.

(4) The authors should perform in vitro experiments to demonstrate that the engineered antibody has similar on-target specificity not only sensitivity. I don't know what the ideal experiments would be, but should probably probe native epitopes. Western blots, immunoprecipitation of cell lysates?

As mentioned above, showing the specificity of the engineered antibodies is indeed important. We did not address it in the current ms, but it can be tested in future work.

Minor:

(1) The introduction should review better the literature on the computational/rational design of antibodies, especially multi-specific - and likely de-emphasize small molecules (and mutations associated with the resistance thereof) as the presented research does not inform the design of mutation-agnostic small molecules.

Thank you for these comments, the introduction was revised accordingly.

(2) The authors should better present the fact that the lack of binding of Pertuzamab to HER2 S310 was previously known, thus the whole strategy of searching COSMIC, and computationally predicting their binding impact was unnecessary. Rather it would be helpful to learn how many other COSMIC hotspots could have a similar effect on other clinical antibodies.

The lack of binding was indeed previously known, as mentioned in the introduction. However, we did not start our analysis targeting HER2 specifically, but we rather found these mutations because they were located in the binding pocket, which enabled our strategy to compensate for these mutations with alteration of the original Pertuzumab. Regarding other potential hotspots, the numbers appeared in Supplementary Table 1, and were moved to the main text.

Stylistic:

(1) Avoid using the term "drug" for an antibody.

The term was changed to “antibody therapeutics” in the revised text.

(2) Avoid repetition in the introduction.

Thank you, we revised the introduction with this comment in mind.

Reviewer #2 (Recommendations For The Authors):

The quality of Figure 2B-C flow data needs to be improved:

a. The diagonal populations suggest inappropriate color compensation or indicate cells are derived from unhealthy populations.

We believe there may be some confusion here. The figures you are referring to are figures of very diverse library. The selected clones show nice diagonals, as shown in Supplementary Figure 5.

b. Additional round 3 and round 4 did not seem to improve the enrichment of targeted clones but rather had similar binding profiles to each of the three proteins over and over.

Two sets of the fourth round of selection were done, each originated from a different sub-population in round 3: 1. Clones that bind the S310Y mutation 2. Clones that bind the S310F mutation. The aim of the R4 was to examine this binders against the second mutation and canonical HER2 in the search for multi-specificity. Additional clarification of this point will be added to the main text.

c. Figure legends are vague with non-specific descriptions of cells and conditions, and unclear statements of "FACS results...".

The legends were edited in the revised version.

d. Text fonts are in low resolution.

High-quality figures were submitted with the revised ms.

(1) Diwanji, D., et al., Structures of the HER2-HER3-NRG1β complex reveal a dynamic dimer interface. Nature, 2021. 600(7888): p. 339-343.

(2) Yamashita-Kashima, Y., et al., Mode of action of pertuzumab in combination with trastuzumab plus docetaxel therapy in a HER2-positive breast cancer xenograft model. Oncol Lett, 2017. 14(4): p. 4197-4205.

(3) Kang, J.C., et al., Engineering multivalent antibodies to target heregulin-induced HER3 signaling in breast cancer cells. MAbs, 2014. 6(2): p. 340-53.

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Recommendations For The Authors):

We would like to see the major conclusions constrained to better fit the data presented in the manuscript. Speed is only a single performance metric of a very complicated, very diverse system of locomotion.

If the authors would like to maintain the broader conclusions, the study should be repeated with a number of different performance metrics to shore up the manuscript's results. Particularly with efficiency, speed is not a reliable measure of efficiency to begin with, so this needs to be explored in a more targeted and appropriate manner.

We agree with Reviewer 1 that we should be more precise about the fitness metrics used and more constrained about the conclusions. Considering the points raised in each paragraph, we’ve modified the text as follows:

- [line 17] “... to test the necessity of both traits for sustained and effective displacement on the ground.”

- [starting on line 105] “We generate the robot’s sample using an artificial evolutionary process that selects for better locomotion ability - defined as higher average speed as it is a proxy for organisms with sustained and effective displacement.”

- [starting on line 287] “We also found that different gravitational environments require different shape structures to optimize locomotion average speed.”

- [starting on line 311] “This consistency is evidence that a small number of sparsely connected modules is a morphological computation principle for an organism’s optimized average speed.”

- [starting on line 348] “Beyond that, extending the tests for other important aspects of locomotion behavior - as noise on the ground, energetic costs, and maneuverability - by using other locomotion metrics - as energy efficiency, stability margin, and dissipated power (Paez and Melo, 2014; Aoi et al., 2016 ) - would also be relevant to evaluate the principle’s robustness.”

- [starting on line 524] “As the robots with the highest average speed are the ones that succeed in maximizing displacement and having robust dynamics (they will not tumble with time), we defined $\bar s$ as the fitness value using it as a proxy of successful directed locomotion. Selecting for bodies that maximize speed is a common locomotion bias in natural selection, as both predators and prey and thus fecundity and mortality depend on it (Alexander, 2006). Other measures - such as energy efficiency - can capture distinct important aspects of the locomotion complexity (Paez and Melo, 2014) and would be worthy of investigating in future work.”

Paper Premise/Mission Statement: As defined in the abstract and also called out in the text starting on line 59 is "investigate whether symmetry and modularity are features of an organism's shape need [authors italics] to have for better-directed locomotion..."

If we understood correctly the reviewer is asking for more precision in the statement. We modified the respective sentence in the following way:

- [line 62] “... need to have for optimizing average speed on the ground,”

Reviewer #2 (Recommendations For The Authors):

i) a lot of details that are in the captions should be moved in the main text;

Thank you for this comment. We reviewed all the captions and text making modifications to ensure that all the information in the captions is also present in the main text. Below, we highlighted some of the changes:

- [line 57] “Thus, locomotion on the ground is present in phylogenetically distant species (such as the maned wolf and frogfish in Figure 1A) and depends upon … “

- [starting on line 64] “Figure 1B shows a schematic representation of symmetry and modularity on the maned wolf and frogfish bodies.”

- [starting on line 277] “There is a negative correlation between the proportion of feet voxels and the robot’s locomotion transference capability when the robots go to an environment with higher gravity, i.e., water to mars (dark blue in Figure 5C), water to earth (light blue), and mars to earth (red) - with a Spearman correlation coefficients of r = -0.39, r = -0.43, and r = -0.32, respectively, all with p < 1e-08.”

ii) hypotheses should be spelled out more clearly;

We verified the experiments and certified that every experiment had a clear hypothesis statement in the original manuscript. Before each section defining the hypothesis and describing the experiment, we added the following statement:

- [starting on line 119] “ With this sample, we tested the hypotheses about the relationships between locomotion performance and body modularity and symmetry (Figure 1I).”

iii) performance metrics and other features should be better defined using mathematical terms if possible (for example, instability);

Thank you for the comment. We added a definition for instability in the text:

- [starting on line 218] “Nonetheless, locomotion requires a minimum instability - the dynamic possibility of translating the center of mass - in the direction axis to generate the necessary forward displacement (Bruijn et al., 2013; Nagarkar et al., 2021).”

Despite the different definitions of instability in literature (Bruijn et al., 2013, Paez and Melo, 2014; Aoi et al., 2016, Nagarkar et al., 2021), we didn’t find one mathematical definition that fits perfectly in our context.

Following the reviewer's comment, when necessary we expanded the definition for other features:

- [starting on line 199] “... the distribution of body weight. As the robots do not have sensory feedback abilities, the weight balance is defined as the body’s movement due to gravity forces (consequences of the weight distribution and surface contact points) (Benda et al., 1994). We hypothesized that the robots with the best directed locomotion ability would tend to have a symmetric body shape. A robot with a low XY shape symmetry (XY shape symmetry < 0.5) has a higher chance of having a poor weight balance, increasing the chance of the body tipping over, thus leading it to a lousy locomotion performance (blue dotted line in Figure 3C). “

iv)  more details regarding the simulations should be included;

We thank the reviewer for this comment. If we understood correctly the Reviewer 2 is asking for more details regarding: “a) the adequacy of the spatial resolution, whereby I failed to see a compelling argument regarding the completeness of 64 voxels; b) the realism of the oscillatory patterns, whereby all the voxels are set to oscillate at the same, constant, frequency of 2Hz; and c) the accuracy of simulations in water where added mass effects seem to be neglected.”. We modified the text to better satisfy these concern:

a) [starting on line 96] “We choose to first explore exhaustively the $4^3$ space dimension, as it is the minimal possible space that allows meaningful body plans. We also did control experiments within 6^3 and 8^3 to check for dimension size effects.”

- [starting on line 432] “We did control experiments with robots within 6³ and 8³ dimensions to check for dimension size effects - and we found that the results found in 4³ remained valid. We choose to focus our analysis in the 4³ design space because we consider it the minimum coarse-grain to approach the biological question about the contingency of shape outcomes pressured for locomotion. Smaller spaces do not allow sufficient complexity in the body structures, and increasing spatial resolution reduces the extensiveness of the investigated search space.”

b) [starting on line 451] “… we used a fixed oscillation frequency of 𝑓 = 2 Hz (Kriegman et al.,2020). A fixed frequency value reduces the number of degrees of freedom in the search for solutions, but in return, it narrows the direct connection between the simulated organisms and animals. Exploring different frequency values in future work would be important to investigate the impact of varied oscillatory frequencies in the shape solutions for directed locomotion.”

c) The environment we call “water” is not an accurate modeling of aquatic habitats as we didn’t simulate essential forces such as draff effects. This choice is explained in text starting on line 110: “In the water-like environment the bodies have nullifying body weight but do not have drag effects. We did not add drag in our simulations because our aim is to study just the body weight influences in locomotion independently of other forces.”

v) a full paragraph about limitations should be included in the discussions, focusing on both simulation aspects (for example, the use of simple spring elements in the voxels) and theoretical assumptions (for example, addressing the potential role of non-locomotion-related aspects).

We thank the reviewer for the comment. We edited some paragraphs of the discussion section to make more explicit some limitations of our work:

[starting on line 398] “We expect that including other important aspects of an animal's body as a developmental process and sensory functions could influence the shape's outcomes with other layers of principles. Although we based our simulations on an already successful transference of \textit{in silico} behavior to organisms made of biological tissue

\citep{kriegman_scalable_2020}, there is an intrinsic gap between spring-mass robots modeling and animal’s bodies that is worthy of exploring to ensure the generality of our results. Other methods, such as the inclusion of rigid body elements in the simulation (possible in Voxelyze), the use of finite element modeling (FEM) (Coevoet et al., 2019), and the construction of physical robots (Aguilar et al., 2016), are important complements to this work. Beyond that, principles on other scales as in the genotypes (Johnston et al., 2022) and in other behavioral phenotypes (Gomez-Marin et al., 2016) could also be investigated.”

To address the potential role of non-locomotion-related aspects, we revised the section

“Discussion - Contingency of evolutionary outcomes” where we discussed other functional and biological roles:

[starting on line 354 ] “Here we investigate how a specific functional cause - optimization of average speed during directed locomotion on the ground - externally defines the phenotypic space of shape possibilities.”

[starting on line 359] “For simplification purposes, we choose to not explicitly control other important factors of locomotion (i.e., energy consumption, maneuverability) that nonlinearly interact during locomotion. In future studies, it would be important to conduct similar studies on a wider range of factors to study the shape and dynamic principles in different conditions.“

eLife assessment

This study provides an important, original framework to study locomotion on the ground with physics-based simulations. Through numerical simulations, the authors propose that intermediate numbers of body modules and high body symmetry enhance speed. The current way discussions and conclusions are written is overly broad: evidence that evolution may favour bilateral symmetry and modularity for efficient directed locomotion is still incomplete as further performance metrics and a more accurate description of the dynamics in water are needed.

Reviewer #1 (Public Review):

The manuscript presents a framework for studying biomechanical principles and their links to morphology and provides interesting insights into a particular question regarding terrestrial locomotion and speed. The goal of the paper is to derive general principals of directed terrestrial locomotion, speed, and symmetry.

Major strengths:

The manuscript is a unique and creative work that explores performance spaces of a complicated question through computational modeling. Overall, the paper is well written and well crafted and was a pleasure to read.

The methods presented here (variable agents used to represent ultra-simplified body configurations that are not inherently constrained) are interesting and there's significant potential in them for a properly constrained question. For the data that is present here their hypotheses (while they can be anticipated from first principles) are very well validated and serve as a robust validation of these expectations and can help.

Of particular interest was the discussion of the transferability of morphologies designed under one system and moving to another. From a deep-time perspective, of particular interest is the transition from subaqueous to terrestrial locomotion which we know was a major earth life transition. The results of this study show that the best suited morphologies for subaqueous movement are ill-suited (from a locomotor speed standpoint at least) to fully terrestrial locomotion which begs the questions on if there are a suite of forms that have balanced performance in both and how that would differ from aquatic morphologies.

Major weaknesses:

(1) There is a major disagreement between target and parameters.

From a biomechanics perspective the target of this study, Directed Locomotion, is a fairly broad behavioral mode. However, what the authors are ultimately evaluating their model organisms on is a single performance parameter (speed, or distance traveled after 30s). Statements such as "bilateral symmetry showed to be a law-like pattern in animal evolution for efficient directed locomotion purposes" (p 12 line 365-366) are problematic for this reason.

Attaining the highest possible speed is a relevant but limited subset of ways one might interpret performance for directed locomotion. Efficiency, power generation, and limb loading/strain are equally relevant components.

The focus on speed coupled with selection for only the highest performing morphologies, rather than setting a minimum performance threshold fundamentally restricts the dynamics of the system in a way that is not representative of their specified target and pulls the simulations toward a specific, anticipatable, result.

Locomotor efficiency is alluded to later in the manuscript as one of the observed outcomes, but speed is not equivalent to locomotor efficiency (in much the same way that it is not the sole metric for describing performance with respect to directed locomotion). Energy/work/power have not been accounted for in the manuscript so this is not a parameter this study weighs in on.

The data and analyses the others present do show an interesting validation of these methods in assessing first order questions relating the shape of a single performance surface to a theoretical morphology, which has significant potential value.

(2) There is significant population and/or sample size and biasing.

Thirty simulations of a population of 101 morphologies seems small for a study of this kind, particularly looking to investigate such a broad question at an abstract level. Particularly when the top 50% of morphologies are chosen to mutate. It would be very easy for artificial biases to rapidly propagate through this system depending on the parameters bounding the formation of the initial generation.

This strong selection choosing the best 50 morphologies and mutating them enforces an aggressive effect that simulates and even more potent phylogenetic inertia than one might anticipate for an actual evolutionary history (it's no surprise then that all of the simulations were able to successfully retrieve a suite of morphotypes that recovered the performance peak for this system within 1500 generations)

Similarly, why is it that a 4^3 voxel limit was chosen? One can imagine that an increase in this voxel limit would allow for the development of more extreme geometries, which might be successful. It is likely that there might be computational resource constraints involved in this, it would be useful for the authors to add additional context here.

Review of resubmission:

I appreciate the clarification of points dealing with the details of computational modeling and methods and clarifications throughout the text.

However, the authors have failed to address the major weaknesses that were previously identified, specifically regarding the broader conclusions of the work, that either 1) the authors need to use an additional metric besides average speed, or 2) the conclusions need to be significantly reigned in to reflect the very narrow nature of the work.

Reviewer #2 (Public Review):

Summary:<br /> I believe the authors have done a wonderful job at dissecting a very complex topic, starting with basic building blocks of locomotion and introducing a powerful simulation approach to the exploring the landscape of growth and form in intelligent behavior.

Strengths:<br /> This is a very original, timely, and robust piece of work that I believe can inspire further computational studies in evo-devo-etho.

Weaknesses:<br /> More detail on the simulations and also greater clarity regarding the generalizability of their claims would improve the message and further studies.

Reviewer #2 (Public Review):

This work makes substantial progress towards understanding physical aspects of formation locomotion, notably the hydrodynamic stability of groups of flappers and the modifications to energy costs associated with flow interactions.

Major strengths pertain to the fact that this topic is timely, interesting and complex, and the authors have advanced the understanding through their characterizations.

The weaknesses may relate to the many idealizations employed in the simulations and models, which may raise questions about how to interpret their results and whether the outcomes hold generally. But given the complexity of the problem, simplifications are necessary. The authors have certainly provided a clear presentation with appropriate details and caveats that will help the reader extract the main messages and form their own conclusions.

Overall, the work is a positive addition to the growing set of studies into schooling, flocking and related problems where unsteady flow interactions lead to interesting collective effects.

eLife assessment

This fundamental study provides a modeling regime that provides new insight into the energy-preservation parameters among schooling fish. The strength of the evidence supporting observations such as distilled dynamics between leading and lagging schooling fish which are derived from emergent properties is compelling. Overall, the study provides exciting insights into energetic coupling with respect to group swimming dynamics.

Reviewer #1 (Public Review):

Summary:<br /> The study seeks to establish accurate computational models to explore the role of hydrodynamic interactions on energy savings and spatial patterns in fish schools. Specifically, the authors consider a system of (one degree-of-freedom) flapping airfoils that passively position themselves with respect to the streamwise direction, while oscillating at the same frequency and amplitude, with a given phase lag and at a constant cross-stream distance. By parametrically varying the phase lag and the cross-stream distance, they systematically explore the stability and energy costs of emergent configurations. Computational findings are leveraged to distill insights into universal relationships and clarify the role of the wake of the leading foil.

Strengths:<br /> (1) The use of multiple computational models (computational fluid dynamics, CFD, for full Navier-Stokes equations and computationally-efficient inviscid vortex sheet, VS, model) offers an extra degree of reliability of the observed findings and backing to the use of simplified models for future research in more complex settings.

(2) The systematic assessment of the stability and energy savings in multiple configurations of pairs and larger ensembles of flapping foils is an important addition to the literature.

(3) The discovery of a linear phase-distance relationship in the formation attained by pairs of flapping foils is a significant contribution, which helps compare different experimental observations in the literature.

(4) The observation of a critical size effect for in-line formations, above which cohesion and energetic benefits are lost at once, is a new discovery to the field.

Weaknesses:<br /> (1) The extent to which observations on one-degree-of-freedom flapping foils could translate to real fish schools is presently unclear, so that some of the conclusions on live fish schools are likely to be overstated and would benefit from some more biological framing.

(2) The analysis of non-reciprocal coupling is not as novel as the rest of the study and potentially not as convincing due to the chosen linear metric of interaction (that is, the flow agreement).

Overall, this is a rigorous effort on a critical topic: findings of the research can offer important insight into the hydrodynamics of fish schooling, stimulating interdisciplinary research at the interface of computational fluid mechanics and biology.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The study seeks to establish accurate computational models to explore the role of hydrodynamic interactions on energy savings and spatial patterns in fish schools. Specifically, the authors consider a system of (one degree-of-freedom) flapping airfoils that passively position themselves with respect to the streamwise direction, while oscillating at the same frequency and amplitude, with a given phase lag and at a constant cross-stream distance. By parametrically varying the phase lag and the cross-stream distance, they systematically explore the stability and energy costs of emergent configurations. Computational findings are leveraged to distill insights into universal relationships and clarify the role of the wake of the leading foil.

We would like to thank the referee for their careful read of the manuscript and for their constructive feedback. We appreciate it.

Strengths:

(1) The use of multiple computational models (computational fluid dynamics, CFD, for full Navier-Stokes equations and computationally efficient inviscid vortex sheet, VS, model) offers an extra degree of reliability of the observed findings and backing to the use of simplified models for future research in more complex settings.

(2) The systematic assessment of the stability and energy savings in multiple configurations of pairs and larger ensembles of flapping foils is an important addition to the literature.

(3) The discovery of a linear phase-distance relationship in the formation attained by pairs of flapping foils is a significant contribution, which helps compare different experimental observations in the literature.

(4) The observation of a critical size effect for in-line formations of larger, above which cohesion and energetic benefits are lost at once, is a new discovery in the field.

Thank you for this list of strength – we are delighted that these ideas were clearly communicated in our manuscript.

Note that Newbolt et al. PNAS, 2019 reported distance as a function of phase for pairs of flapping hydrofoils, and Li et al, Nat. Comm., 2020 also reported phase-distance relationship in robotic and biological fish (calling it Vortex Phase Matching). We compiled their results, together with our and other numerical and experimental results, showing that the linear distance-phase relationship is universal.

Weaknesses:

(1) The extent to which observations on one-degree-of-freedom flapping foils could translate to real fish schools is presently unclear so some of the conclusions on live fish schools are likely to be overstated and would benefit from some more biological framing.

Thank you for bringing up this point. Indeed, flapping foils that are free to translate in both the x- and y-directions and rotate in the x-y plane could drift apart in the y-direction. However, this drift occurs at a longer time scale than the forward swimming motion; it is much slower. For this reason, we feel justified to ignore it for the purpose of this study, especially that the pairwise equilibria in the swimming x-direction are reached at a faster time scale.

Below, we include two snapshots taken from published work from the group of Petros Koumoutsakos (Gazzola et al, SIAM 2014). The figures show, respectively, a pair and a group of five undulating swimmers, free to move and rotate in the x-y plane. The evolution of the two and five swimmers is computed in the absence of any control. The lateral drift is clearly sub-dominant to the forward motion. Similar results were reported in Verma et al, PNAS 2018.

These results are independent on the details of the flow interactions model. For example, similar lateral drift is observed using the dipole model dipole model (Kanso & Tsang, FDR 2014, Tsang & Kanso, JNLS 2023).

Another reason why we feel justified to ignore these additional degrees of freedom is the following: we assume a live fish or robotic vehicle would have feedback control mechanisms that correct for such drift. Given that it is a slowly-growing drift, we hypothesize that the organism or robot would have sufficient time to respond and correct its course.

Indeed, in Zhu et al. 2022, an RL controller, which drives an individual fish-like swimmer to swim at a given speed and direction, when applied to pairs of swimmers, resulted in the pair "passively" forming a stable school without any additional information about each other.

We edited the main manuscript in page 4 of the manuscript to include reference to the work cited here and to explain the reasons for ignoring the lateral drift.

Citations:  

Gazzola, M., Hejazialhosseini, B., & Koumoutsakos, P. (2014). Reinforcement learning and wavelet adapted vortex methods for simulations of self-propelled swimmers. SIAM Journal on Scientific Computing, 36(3), B622-B639. DOI: https://doi.org/10.1137/130943078

Verma, S., Novati, G., & Koumoutsakos, P. (2018). Efficient collective swimming by harnessing vortices through deep reinforcement learning. Proceedings of the National Academy of Sciences, 115(23), 5849-5854. DOI: https://doi.org/10.1073/pnas.1800923115

Tsang, A. C. H. & Kanso, E., (2013). Dipole Interactions in Doubly Periodic Domains. Journal of Nonlinear Science 23 (2013): 971-991. DOI: https://doi.org/10.1007/s00332-013-9174-5

Kanso, E., & Tsang, A. C. H. (2014). Dipole models of self-propelled bodies. Fluid Dynamics Research, 46(6), 061407. DOI: https://doi.org/10.1088/0169-5983/46/6/061407

Zhu, Y., Pang, J. H., & Tian, F. B. (2022). Stable schooling formations emerge from the combined effect of the active control and passive self-organization. Fluids, 7(1), 41. DOI: https://doi.org/10.3390/fluids7010041

Author response image 1.

Antiphase self-propelled anguilliform swimmers. (a) – (d) Wavelet adapted vorticity fields at, respectively, t = T, t = 4T, t = 10T. (e) Absolute normalized velocities |U|/L. (f) Swimmers’ centre of mass trajectories.

Author response image 2.

Parallel schooling formation. (a) – (d) wavelet adapted vorticity fields at, respectively, t = T, t = 4T, t = 7T, t = 10T. (e) Absolute normalized velocities |U|/L. (f) Swimmers’ center of mass trajectories.

(2) The analysis of non-reciprocal coupling is not as novel as the rest of the study and potentially not as convincing due to the chosen linear metric of interaction (that is, the flow agreement).

We thank the referee for this candid and constructive feedback. In fact, we view this aspect of the study as most “revolutionary” because it provides a novel approach to pre-computing the locations of stable equilibria even without doing expensive all-to-all coupled simulations or experiments.

Basically, the idea is the following: you give me a flow field, it doesn’t matter how you obtained it, whether from simulations or experimentally, and I can tell you at what locations in this flow field a virtual flapping swimmer would be stable and save hydrodynamic energy!

In the revised version, we changed page 3 and 7 in main text, and added a new section “Diagnostic tools” in SI to better illustrate this.

Overall, this is a rigorous effort on a critical topic: findings of the research can offer important insight into the hydrodynamics of fish schooling, stimulating interdisciplinary research at the interface of computational fluid mechanics and biology.

We thank the referee again for their careful read of the manuscript and their constructive feedback.

Reviewer #2 (Public Review):

The document "Mapping spatial patterns to energetic benefits in groups of flow-coupled swimmers" by Heydari et al. uses several types of simulations and models to address aspects of stability of position and power consumption in few-body groups of pitching foils. I think the work has the potential to be a valuable and timely contribution to an important subject area. The supporting evidence is largely quite convincing, though some details could raise questions, and there is room for improvement in the presentation. My recommendations are focused on clarifying the presentation and perhaps spurring the authors to assess additional aspects:

We would like to thank the referee for their careful read of the manuscript and for their constructive feedback. We appreciate it.

(1) Why do the authors choose to set the swimmers free only in the propulsion direction? I can understand constraining all the positions/orientations for investigating the resulting forces and power, and I can also understand the value of allowing the bodies to be fully free in x, y, and their orientation angle to see if possible configurations spontaneously emerge from the flow interactions. But why constrain some degrees of freedom and not others? What's the motivation, and what's the relevance to animals, which are fully free?

We would like to thank the referee for raising this point. It is similar to the point raised above by the first referee. As explained above the reason is the following: in freely-swimming, hydrodynamically-interacting “fish,” the lateral drift is sub-dominant to the forward swimming motion. Therefore, we ignore it in the model. Please see our detailed response above for further clarification, and see changes in page 4 in the main manuscript.

(2) The model description in Eq. (1) and the surrounding text is confusing. Aren't the authors computing forces via CFD or the VS method and then simply driving the propulsive dynamics according to the net horizontal force? It seems then irrelevant to decompose things into thrust and drag, and it seems irrelevant to claim that the thrust comes from pressure and the drag from viscous effects. The latter claim may in fact be incorrect since the body has a shape and the normal and tangential components of the surface stress along the body may be complex.

Thank you for pointing this out! It is indeed confusing.

In the CFD simulations, we are computing the net force in the swimming x-direction direction by integrating using the definition of force density in relation to the stress tensor. There is no ambiguity here.

In the VS simulations, however, we are computing the net force in the swimming x-direction by integrating the pressure jump across a plate of zero thickness. There is no viscous drag. Viscous drag is added by hand, so-to-speak. This method for adding viscous drag in the context of the VS model is not new, it has been used before in the literature as explained in the SI section “Vortex sheet (VS) model” (pages 30 and 31).

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(3) The parameter taudiss in the VS simulations takes on unusual values such as 2.45T, making it seem like this value is somehow very special, and perhaps 2.44 or 2.46 would lead to significantly different results. If the value is special, the authors should discuss and assess it. Otherwise, I recommend picking a round value, like 2 or 3, which would avoid distraction.

Response: The choice of dissipation time is both to model viscous effect and reduce computational complexity. Introducing it is indeed introduces forcing to the simulation. Round value, like 2 or 3, is equal to an integer multiple of the flapping period, which is normalized to T=1, Therefore, an integer value of  would cause forcing at the resonant frequency and lead to computational blow up. To avoid this effect, a parameter choice of  = 2.45, 2.44 or 2.46 would be fine and would lead to small perturbation to the overall simulation, compared to no dissipation at all. This effect is studied in detail in the following published work from our group:

Huang, Y., Ristroph, L., Luhar, M., & Kanso, E. (2018). Bistability in the rotational motion of rigid and flexible flyers. Journal of Fluid Mechanics, 849, 1043-1067. DOI: https://doi.org/10.1017/jfm.2018.446

(4) Some of the COT plots/information were difficult to interpret because the correspondence of beneficial with the mathematical sign was changing. For example, DeltaCOT as introduced on p. 5 is such that negative indicates bad energetics as compared to a solo swimmer. But elsewhere, lower or more negative COT is good in terms of savings. Given the many plots, large amounts of data, and many quantities being assessed, the paper needs a highly uniform presentation to aid the reader.

Thank you for pointing this out! We updated Figures 3,6 as suggested.

(5) I didn't understand the value of the "flow agreement parameter," and I didn't understand the authors' interpretation of its significance. Firstly, it would help if this and all other quantities were given explicit definitions as complete equations (including normalization). As I understand it, the quantity indicates the match of the flow velocity at some location with the flapping velocity of a "ghost swimmer" at that location. This does not seem to be exactly relevant to the equilibrium locations. In particular, if the match were perfect, then the swimmer would generate no relative flow and thus no thrust, meaning such a location could not be an equilibrium. So, some degree of mismatch seems necessary. I believe such a mismatch is indeed present, but the plots such as those in Figure 4 may disguise the effect. The color bar is saturated to the point of essentially being three tones (blue, white, red), so we cannot see that the observed equilibria are likely between the max and min values of this parameter.

Thank you for pointing this out! You are correct in your understanding of the flow agreement parameter, but not in your interpretation.

Basically, “if the match were perfect, then the swimmer would generate no relative flow and thus no thrust,” means that “such a location could not be is an equilibrium.” Let me elaborate. An equilibrium is one at which the net thrust force is zero. The equilibrium is stable if the slope of the thrust force is negative. Ideally, this is what maximizing the flow agreement parameter would produce.

For example, consider an ideal fluid where the flow velocity is form  in vertical direction. Consider a “ghost swimmer” heaving at a velocity  . Under this scenario, flow agreement and thrust parameters are

Let’s now consider a balance of forces on the “ghost swimmer.” The ghost swimmer is in relative equilibrium if and only if:

It gives us

We then consider stability at this equilibrium by calculating the derivative of thrust parameter over phase

The corresponding values at equilibria are

Thus, when taking the positive which means the equilibria is a stable fixed point. We included this analysis in a new section in the SI page 32.

(6) More generally, and related to the above, I am favorable towards the authors' attempts to find approximate flow metrics that could be used to predict the equilibrium positions and their stability, but I think the reasoning needs to be more solid. It seems the authors are seeking a parameter that can indicate equilibrium and another that can indicate stability. Can they clearly lay out the motivation behind any proposed metrics, and clearly present complete equations for their definitions? Further, is there a related power metric that can be appropriately defined and which proves to be useful?

Thank you – these are excellent suggestions. Indeed, we needed to better explain the motivation and equations. Perhaps the main idea for these metrics can be best understood when explained in the context of the simpler particle model, which we now do in the SI and explain the main text.

(7) Why do the authors not carry out CFD simulations on the larger groups? Some explanations should be given, or some corresponding CFD simulations should be carried out. It would be interesting if CFD simulations were done and included, especially for the in-line case of many swimmers. This is because the results seem to be quite nuanced and dependent on many-body effects beyond nearest-neighbor interactions. It would certainly be comforting to see something similar happen in CFD.

We are using a open-source version of the Immersed Boundary Method that is not specifically optimized for many interacting swimmers. Therefore, the computational cost of performing CFD simulations for more swimmers is high. Therefore, we used the CFD simulations sporadically with fewer simmers (2 or 3) and we performed systematic simulations in the context of the VS model.

For the same Reynolds number in Figure 1, we simulated three and four swimmers in CFD: three swimmers forms a stable formation, four swimmers don’t, consistent with the VS model, with the forth swimmer colliding with the third one. Results are included in the SI figure 8 of the main text.

(8) Related to the above, the authors should discuss seemingly significant differences in their results for long in-line formations as compared to the CFD work of Peng et al. [48]. That work showed apparently stable groups for numbers of swimmers quite larger than that studied here. Why such a qualitatively different result, and how should we interpret these differences regarding the more general issue of the stability of tandem groups?

Thank you for bringing up this important comparison. Peng et al. [48] (Hydrodynamic schooling of multiple self-propelled flapping plates) studied inline configuration of flapping airfoils at Reynolds number =200. There are several differences between their work and ours. The most important one is that they used a flexible plate, which makes the swimmer more adaptive to changes in the flow field, e.g. changes in tailbeat amplitude and changes in phase along its body and diverts some of the hydrodynamic energy to elastic energy. We edited the main text page 10 at the end of section “Critical size of inline formations beyond which cohesion is lost” to explain this distinction.

(9) The authors seem to have all the tools needed to address the general question about how dynamically stable configurations relate to those that are energetically optimal. Are stable solutions optimal, or not? This would seem to have very important implications for animal groups, and the work addresses closely related topics but seems to miss the opportunity to give a definitive answer to this big question.

Indeed, that is exactly the point – in pairwise formations, stable configurations are also energetically optimal! In larger groups, there is no unique stable configuration – each stable configuration is associated with a different degree of energy savings. Interestingly, when exploring various equilibrium configurations in a school of four, we found the diamond formation of D. Weihs, Nature, 1972 to be both stable and most optimal among the configurations we tested. However, claiming this as a global optimum may be misleading – our standpoint is that fish schools are always dynamic and that there are opportunities for energy savings in more than one stable configuration.

We added a section in new text “Mapping emergent spatial patterns to energetic benefits”, and added a new figure in the maintext (Fig. 10) and a new figure in the SI (Fig. S. 8)

(10) Time-delay particle model: This model seems to construct a simplified wake flow. But does the constructed flow satisfy basic properties that we demand of any flow, such as being divergence-free? If not, then the formulation may be troublesome.

The simplified wake flow captures the hydrodynamic trail left by the swimmer in a very simplified manner. In the limit of small amplitude, it should be consistent with the inviscid vortex sheet shed of T. Wu’s waving swimmer model (Wu TY. 1961).

The model was compared to experiments and used in several recent publications from the Courant Institute (Newbolt et al. 2019, 2022, 2024).

Citations:  

Wu, T. Y. T. (1961). Swimming of a waving plate. Journal of Fluid Mechanics, 10(3), 321-344. DOI: https://doi.org/10.1017/S0022112061000949

Newbolt, J. W., Lewis, N., Bleu, M., Wu, J., Mavroyiakoumou, C., Ramananarivo, S., & Ristroph, L. (2024). Flow interactions lead to self-organized flight formations disrupted by self-amplifying waves. Nature Communications, 15(1), 3462. DOI: https://doi.org/10.1038/s41467-024-47525-9

Newbolt, J. W., Zhang, J., & Ristroph, L. (2022). Lateral flow interactions enhance speed and stabilize formations of flapping swimmers. Physical Review Fluids, 7(6), L061101. DOI: https://doi.org/10.1103/PhysRevFluids.7.L061101

Newbolt, J. W., Zhang, J., & Ristroph, L. (2019). Flow interactions between uncoordinated flapping swimmers give rise to group cohesion. Proceedings of the National Academy of Sciences, 116(7), 2419-2424.  DOI: https://doi.org/10.1073/pnas.1816098116

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Congratulations on such a comprehensive and well-thought-out study; I truly enjoyed reading it and have only a couple of suggestions that I believe will help further strengthen the paper. I am including a bunch of references here that are very familiar to me without the expectation of you to include them all, just to point at areas that I feel you might consider useful.

We thank the referee again for their careful read of the manuscript and for their constructive feedback. We appreciate it.

First, I believe that some more rationale is needed to justify the chosen modeling framework. I am fully aware of how difficult is to run these simulations, but I see some critical assumptions that need to be at least spelled out for the reader to appreciate the limitations of the study: (1) Constraining the cross-stream coordinate (a stability analysis should include perturbations on the cross-stream coordinate as well, see, for example, https://doi.org/10.1017/flo.2023.25 -- I know this is much simpler as it discards any vortex shedding) and (2) Assuming equal frequency and amplitude (there are studies showing variation of tail beat frequency in animals depending on their position in the school, see, for example, https://doi.org/10.1007/s00265-014-1834-4).

Thank you for these suggestions. These are indeed important and interesting points to discuss in the manuscript. See response above regarding point 1. Regarding point 2, this is of course important and will be pursued in future extensions of this work. We edited the intro and discussion of the main text to explain this.

In the paper “Stability of schooling patterns of a fish pair swimming against a flow”, The authors considered a pair of swimmers swimming in a channel. They analyzed stability of the system and find multiple equilibria of the system, including inline and staggered formation, and a special formation of perpendicular to the wall. Studying fish school in confined domain and analyzing their stability is very interesting. We added citation to this paper in the discussion section at the end of page 10.

In the paper “Fish swimming in schools save energy regardless of their spatial position”, the authors measured the reduction in power of fish by measuring tail beat frequency and oxygen consumption and compared them to measurements in solitary fish. They found that in a school of fish, individuals always save power comparing to swimming alone.  However, there is one important caveat in this study: they considered a larger school of fish and expressed the results in terms of pairwise configurations (see schematics we draw below). This is misleading because it may suggest that formations with only two fish provide benefits each other, while in fact, the data is obtained from a larger school with many neighbors. They only consider a fish’s relationship to its nearest neighbor. But in a large school, other neighbors will also have influence on their energy consumption.  In the schematics below, we emphasized on several focal fishes, marking them as red, green, and blue. We also marked their nearest neighbors using the same color, but lighter. The nearest neighbors are what the authors are considering to show its neighbor relationship. For example, a problematic one is the red fish, for which its nearest neighbor is behind it, but indeed, its power saving may come from the other neighbors, which are around or ahead it.

Author response image 3.

Second, I would like to see more biology context with respect to limitations that are inherent to a purely mechanical model, including, neglecting vision that we know plays a synergistic role in determining schooling patterns. For example, a recent study https://doi.org/10.1016/j.beproc.2022.104767 has presented experiments on fish swimming in the dark and in bright conditions, showing that it is unlikely that hydrodynamics alone could explain typically observed swimming patterns in the literature.

Thank you for this suggestion and for sharing us with the paper “Collective response of fish to combined manipulations of illumination and flow”. This is a great study, and we are sorry to have missed it.

In this paper, the authors found that when having illumination, fish swim more cohesively, which is in consistent with another paper we already cited “The sensory basis of schooling by intermittent swimming in the rummy-nose tetra (Hemigrammus rhodostomus)”. Another important conclusion in this paper is that when having brighter illumination and with flow, fish school spend more time side by side. This connects well to the conclusion in another paper we cited “Simple phalanx pattern leads to energy saving in cohesive fish schooling,” where at lower flow speed in a water channel, fish tended to form a dynamic school while at higher flow speed, they organized in a side-by-side/ phalanx configuration. This conclusion is consistent with our study that in side-by-side formation, fish share power saving.

Importantly, it is well known that both vision and flow sensing play important roles in fish schooling. This study aimed to merely explore what is possible through passive hydrodynamic interactions, without visual and flow sensing and response. We clarify this in the revised version of the manuscript.

Third, I am not too convinced about the flow agreement metric, which only accounts for linear interactions between the foils. More sophisticated approaches could be utilized as the one proposed here https://doi.org/10.1017/jfm.2018.369, based on a truly model-agnostic view of the interaction - therein, the authors show non-reciprocal (in strength and time-scale) coupling between two in-line flapping foils using information theory. I also would like to mention this older paper https://doi.org/10.1098/rsif.2012.0084, where an equivalent argument about the positioning of a trailing fish with respect to a leading robotic fish is made from experimental observations.

Thank you for these remarks and for sharing these two interesting papers.

The flow agreement metric is not specific to two fish, as we show in Fig. 6 of the manuscript. We edited the manuscript and SI to better explain the motivation and implementation of the flow agreement parameter. We edited the main text, see revisions on page 7, and added a new section call “diagnostic tools.”.

In the paper “An information-theoretic approach to study fluid–structure interactions”, the authors calculate the transfer entropy between two oscillating airfoils when they are hydrodynamically coupled.  This is an interesting study! We will apply this approach to analyzing larger schools in the future. We cited this paper in the introduction.

In the paper “Fish and robots swimming together: attraction towards the robot demands biomimetic locomotion”, the authors found that fish will swim behind an artificial fish robot, especially when the fish robot is beating its tail instead of static. At specific conditions, the fish hold station behind the robot, which may be due to the hydrodynamic advantage obtained by swimming in the robot’s wake. DPIV resolved the wake behind a static/ beating fish robot, but did not visualize the flow field when the fish is there. This study is similar to a paper we already cited “In-line swimming dynamics revealed by fish interacting with a robotic mechanism”, in which, they considered fish-foil interaction. In the revised manuscript, we cite both papers.

For the reviewer’s comments about flow agreement only accounts for linear interactions between the foils, we want to explain more to clarify this. The flow agreement parameter is a nonlinear metric, which considered the interaction between a virtual swimmer and an arbitrary unsteady flow field. Although the metric is a linear function of swimmer’s speed, it is indeed a nonlinear function of spacing and phase, which are the quantities we care about. Moreover, the flow field can by generated by either experiment or CFD simulation, and behind one or more swimmers. It is true that it is a one way coupled system since the virtual swimmer does not perturb the flow field.

Again, this is great work and I hope these suggestions are of help.

Thank you again! We are delighted to receive such a positive and constructive feedback.

Reviewer #2 (Recommendations For The Authors):

(1) About Figure 1: Panel C should be made to match between CFD and VS with regard to the swimmer positions. Also, if the general goal of the figure is to compare CFD and VS, then how about showing a difference map of the velocity fields as a third column of panels across A-D?

Thank you for pointing this out. Figure 1 C is updated accordingly.

The general goal is to show the CFD and VS simulations produce qualitatively similar results. Some quantities are not the same across models, e.g. the swimming speed of swimmers are different, but the scaled distance is the same.

(2) Figure 3: In A, it would be nice to keep the y-axis the same across all plots, which would aid quick visual comparison. In B, the legend labels for CFD and VS should be filled in with color so that the reader can more easily connect to the markers in the plot.

Thank you for pointing this out, we’ve updated figure 3 and 6.

(3) Figures 4, 9, and Supplementary Figures too: As mentioned previously, the agreement parameter plots are saturated in the color map, possibly obscuring more detailed information.

Thank you for pointing this out. The goal is to show that there is a large region with positive flow agreement parameter.

We picked up the flow agreement behind a single swimmer in VS simulation (Fig.4B) and added the counter lines to it (represents 0.25 and 0.5).  Not many details are hidden by the saturated colormap.

Author response image 4.

We also updated Fig 4 and Fig 9 accordingly.

(4) Figure 6: Is this CFD or VS? Why show one or the other and not both? In B, it seems that there are only savings available and no energetically costly positions. This seems odd. In C, it seems the absolute value on dF/dd is suppressing some important information about stability - the sign of this seems important. In E, the color bar seems to be reflected from what is standard, i.e. 0 on the left and 100 on the right, as in F.

Thank you for asking. Fig. 6 is based only on VS simulations. There are hundreds of simulations in this figure, we are not running CFD simulations to save computational effort. Representative CFD simulations are shown in Figure 1,2,3, for comparison. We added a sentence in the figure caption for clarification.

In C, since  is always negative for emergent formations (only stable equilibria can appear during forward time simulation), we are showing its absolute value for comparison.

In E, we are flipping this because larger flow agreement parameter corresponds to more power saving, in the other word, negative changes in COT.

(5) Fig. 8: For cases such as in D that have >100% power savings, does this mean that the swimmer has work done by the flow? How to interpret this physically for a flapping foil and biologically for a fish?

Yes, it means the hydrofoil/fish gets a free ride, and even able to harvest energy from the incoming flow. Actually, similar phenomenon has been reported in the biology and engineering literature. For example, Liao et al. 2003, Beal et al. 2006 found that live or dead fish can harvest energy from incoming vortical flow by modulating their body curvature.

In engineering, Chen et al. 2018, Ribeiro et al. 2021 have found that the following airfoil in a tandem/ inline formation can harvest energy from the wake of leading swimmer in both simulation and experiemnts.

Citations:  

Liao, J. C., Beal, D. N., Lauder, G. V., & Triantafyllou, M. S. (2003). Fish exploiting vortices decrease muscle activity. Science, 302(5650), 1566-1569. DOI: https://doi.org/10.1126/science.1088295

Beal, D. N., Hover, F. S., Triantafyllou, M. S., Liao, J. C., & Lauder, G. V. (2006). Passive propulsion in vortex wakes. Journal of fluid mechanics, 549, 385-402. DOI: https://doi.org/10.1017/S0022112005007925

Chen, Y., Nan, J., & Wu, J. (2018). Wake effect on a semi-active flapping foil based energy harvester by a rotating foil. Computers & Fluids, 160, 51-63. DOI: https://doi.org/10.1016/j.compfluid.2017.10.024

Ribeiro, B. L. R., Su, Y., Guillaumin, Q., Breuer, K. S., & Franck, J. A. (2021). Wake-foil interactions and energy harvesting efficiency in tandem oscillating foils. Physical Review Fluids, 6(7), 074703. DOI: https://doi.org/10.1103/PhysRevFluids.6.074703

Reviewer #2 (Public Review):

Summary:

This was a well-executed and well-written paper. The authors have provided important new datasets that expand on previous investigations substantially. The discovery that changes in diet are not so closely correlated with the presence of alkaloids (based on the expanded sampling of non-defended species) is important, in my opinion.

Strengths:

Provision of several new expanded datasets using cutting edge technology and sampling a wide range of species that had not been sampled previously. A conceptually important paper that provides evidence for the importance of intermediate stages in the evolution of chemical defense and aposematism.

Weaknesses:

There were some aspects of the paper that I thought could be revised. One thing I was struck by is the lack of discussion of the potentially negative effects of toxin accumulation, and how this might play out in terms of different levels of toxicity in different species. Further, are there aspects of ecology or evolutionary history that might make some species less vulnerable to the accumulation of toxins than others? This could be another factor that strongly influences the ultimate trajectory of a species in terms of being well-defended. I think the authors did a good job in terms of describing mechanistic factors that could affect toxicity (e.g. potential molecular mechanisms) but did not make much of an attempt to describe potential ecological factors that could impact trajectories of the evolution of toxicity. This may have been done on purpose (to avoid being too speculative), but I think it would be worth some consideration.

In the discussion, the authors make the claim that poison frogs don't (seem to) suffer from eating alkaloids. I don't think this claim has been properly tested (the cited references don't adequately address it). To do so would require an experimental approach, ideally obtained data on both lifespan and lifetime reproductive success.

eLife assessment

This important study sheds light on how poison frogs gain their toxins, with surprising new data on low levels of toxins in previously non-toxic frogs. The authors propose a new theory for evolution of toxicity based on convincing evidence, but the manuscript needs restructuring to be clearer. While the manuscript will benefit from improved presentation, this research has the potential to greatly impact our understanding of animal defense mechanisms.

Reviewer #1 (Public Review):

This is a very relevant study, clearly with the potential of having a high impact on future research on the evolution of chemical defense mechanisms in animals. The authors present a substantial number of new and surprising experimental results, i.e., the presence in low quantities of alkaloids in amphibians previously deemed to lack these toxins. These data are then combined with literature data to weave the importance of passive accumulation mechanisms into a 4-phases scenario of the evolution of chemical defense in alkaloid-containing poison frogs.

In general, the new data presented in the manuscript are of high quality and high scientific interest, the suggested scenario compelling, and the discussion thorough. Also, the manuscript has been carefully prepared with a high quality of illustrations and very few typos in the text. Understanding that the majority of dendrobatid frogs, including species considered undefended, can contain low quantities of alkaloids in their skin provides an entirely new perspective to our understanding of how the amazing specializations of poison frogs evolved. Although only a few non-dendrobatids were included in the GCMS alkaloid screening, some of these also included minor quantities of alkaloids, and the capacity of passive alkaloid accumulation may therefore characterize numerous other frog clades, or even amphibians in general.

While the overall quality of the work is exceptional, major changes in the structure of the submitted manuscript are necessary to make it easier for readers to disentangle scope, hypotheses, evidence and newly developed theories.

eLife assessment

This valuable study examines whether the BMP signaling pathway has a role in H3.3K27M DMG tumors, regardless of the presence of ACRVR1 activating mutations. The authors provide solid evidence that BMP2/7 synergizes with H3.3K27M to induce a transcriptomic rewiring associated with a quiescent but invasive cell state. Although this work could be further enhanced by the inclusion of additional models, the study overall points to BMP2/7 as a potential target for future therapies in this deadly cancer.

Reviewer #1 (Public Review):

Summary:

Mutational analysis of diffuse midline glioma (DMG) found that ACVR1 mutations, which up-regulate BMP signaling pathway are found in most H3.1K27M, but not H3.3K27M DMG cases. In this manuscript, Huchede et al attempted to determine whether the BMP signaling pathway has any role in H3.3K27M DMG tumors. They found that the BMP signaling is activated to a similar level in H3.3K27M DMG cells with wild type ACVR1 compared to ACVR1 DMG cells, likely due to the expression of BMP7 or BMP2. They went on to test whether cells treated with BMP7 or BMP2 treatments affected the gene expression and cell fitness of tumor cells with H3.3K27M mutation. They concluded that BMP2/7 synergizes with H3.3K27M to induce a transcriptomic rewiring associated with a quiescent but invasive cell state. The major issue for this conclusion is that the authors did not use the right models/controls to obtain results to support this conclusion as detailed below. Therefore, in order to strengthen the conclusion, the authors need to address the major concerns below.

Strength:<br /> Address an important question in DMG field.

Major concerns/weakness:<br /> (1) All the results in Fig. 2 utilized two glioma lines SF188 and Res259. The authors should repeat all these experiments in a couple of H3.3K27M DMG lines by deleting H3.3K27M mutation first.<br /> (2) Fig. 3. The experiments of BMP2 treatment should be repeated in another H3.3K27M DMG line using H3.1K27M ACVR1 mutant tumor lines as controls.

Minor concerns<br /> Fig.2A. BMP2 expression increased in H3.3K27M SF188 cells. Therefore, the statement "whereas BMP2 and BMP4 expressions are not significantly modified (Figure 2A and Figure 2-figure supplement A-B)"is not accurate

Comments on revised version:

I had three issues listed above on the initial version. The authors did not address my major concerns of #1 and #2, which are re-listed above.

eLife assessment

This important study extends existing sequentially Markovian coalescent approaches to include the combined use of SNPs and hypervariable loci such as epimutations. This is an intriguing addition to infer population size history in the recent past, and the authors provide solid validation of their methods via simulation and analysis of empirical data in Arabidopsis thaliana. Given the increasing availability of such data, this work is a timely contribution and represents a foundation for further developments to explore when and where these methods will be best used.

Reviewer #1 (Public Review):

The authors developed an extension to the pairwise sequentially Markov coalescent model that allows to simultaneously analyze multiple types of polymorphism data. In this paper, they focus on SNPs and DNA methylation data. Since methylation markers mutate at a much faster rate than SNPs, this potentially gives the method better power to infer size history in the recent past. Additionally, they explored a model where there are both local and regional epimutational processes.

Integrating additional types of heritable markers into SMC is a nice idea which I like in principle. However, a major caveat to this approach seems to be a strong dependence on knowing the epimutation rate. In Fig. 6 it is seen that, when the epimutation rate is known, inferences do indeed look better; but this is not necessarily true when the rate is not known. (See also major comment #1 below about the interpretation of these plots.) A roughly similar pattern emerges in Supp. Figs. 4-7; in general, results when the rates have to be estimated don't seem that much better than when focusing on SNPs alone. This carries over to the real data analysis too: the interpretation in Fig. 7 appears to hinge on whether the rates are known or estimated, and the estimated rates differ by a large amount from earlier published ones.

Overall, this is an interesting research direction, and I think the method may hold more promise as we get more and better epigenetic data, and in particular better knowledge of the epigenetic mutational process.

Reviewer #2 (Public Review):

A limitation in using SNPs to understand recent histories of genomes is their low mutation frequency. Tellier et al. explore the possibility of adding hypermutable markers to SNP based methods for better resolution over short time frames. In particular, they hypothesize that epimutations (CG methylation and demethylation) could provide a useful marker for this purpose. Individual CGs in Arabidopsis tends to be either close to 100% methylated or close to 0%, and are inherited stably enough across generations that they can be treated as genetic markers. Small regions containing multiple CGs can also be treated as genetic markers based on their cumulative methylation level. In this manuscript, Tellier et al develop computational methods to use CG methylation as a hypermutable genetic marker and test them on theoretical and real data sets. They do this both for individual CGs and small regions. My review is limited to the simple question of whether using CG methylation for this purpose makes sense at a conceptual level, not at the level of evaluating specific details of the methods. I have a small concern in that it is not clear that CG methylation measurements are nearly as binary in other plants and other eukaryotes as they are in Arabidopsis. However, I see no reason why the concept of this work is not conceptually sound. Especially in the future as new sequencing technologies provide both base calling and methylating calling capabilities, using CG methylation in addition to SNPs could become a useful and feasible tool for population genetics in situations where SNPs are insufficient.

Author response:

The following is the authors’ response to the previous reviews.

Public Reviews:

Reviewer #1 (Public Review):

The authors developed an extension to the pairwise sequentially Markov coalescent model that allows to simultaneously analyze multiple types of polymorphism data. In this paper, they focus on SNPs and DNA methylation data. Since methylation markers mutate at a much faster rate than SNPs, this potentially gives the method better power to infer size history in the recent past. Additionally, they explored a model where there are both local and regional epimutational processes. Integrating additional types of heritable markers into SMC is a nice idea which I like in principle. However, a major caveat to this approach seems to be a strong dependence on knowing the epimutation rate. In Fig. 6 it is seen that, when the epimutation rate is known, inferences do indeed look better; but this is not necessarily true when the rate is not known. (See also major comment #1 below about the interpretation of these plots.) A roughly similar pattern emerges in Supp. Figs. 4-7; in general, results when the rates have to be estimated don't seem that much better than when focusing on SNPs alone. This carries over to the real data analysis too: the interpretation in Fig. 7 appears to hinge on whether the rates are known or estimated, and the estimated rates differ by a large amount from earlier published ones.

Overall, this is an interesting research direction, and I think the method may hold more promise as we get more and better epigenetic data, and in particular better knowledge of the epigenetic mutational process. At the same time, I would be careful about placing too much emphasis on new findings that emerge solely by switching to SNP+SMP analysis.

Major comments:

- For all of the simulated demographic inference results, only plots are presented. This allows for qualitative but not quantitative comparisons to be made across different methods. It is not easy to tell which result is actually better. For example, in Supp. Fig. 5, eSMC2 seems slightly better in the ancient past, and times the trough more effectively, while SMCm seems a bit better in the very recent past. For a more rigorous approach, it would be useful to have accompanying tables that measure e.g. mean-squared error (along with confidence intervals) for each of the different scenarios, similar to what is already done in Tables 1 and 2 for estimating $r$.

We believe this comment was addressed in the previous revision (Sup Table 6-10) by adding Root Mean Square Errors for the demographic estimates (and RMSE for recent versus past portions of the demography). 

- 434: The discussion downplays the really odd result that inputting the true value of the mutation rate, in some cases, produces much worse estimates than when they are learned from data (SFig. 6)! I can't think of any reason why this should happen other than some sort of mathematical error or software bug. I strongly encourage the authors to pin down the cause of this puzzling behaviour. (Comment addressed in revision. Still, I find the explanation added at 449ff to be somewhat puzzling -- shouldn't the results of the regional HMM scan only improve if the true mutation rate is given?)

We do understand that our results and explanation can appear counter-intuitive. As acknowledged by the reviewer, in the previous round of revision we have at length clarified this puzzling behaviour by the discrepancy in assessing methylation regions using the HMM method which then differs from the HMM for the SMC inference. We are happy to clarify further in response to the new question of reviewer 1:

If the Reviewer #1 means the SNP mutations (e.g. A → T), knowing the true mutation rate does not help the HMM to recover the region level methylation status. 

If the Reviewer #1 means the epimutations (whether it is the region, site or both), knowing the true epimutations rates could theoretically help the HMM to recover the region level methylation status. However, at present, our method does not leverage information from epimutation rates to infer the region level methylation status. As inferring the epimutations rates is one of the goals of this study in the SMC inference, and that region level methylation status is required to infer those rates, we suspect that using epimutations rates to infer the region level methylation status could be statistically inappropriate (generating some kind of circular estimations). Instead, our HMM uses only the proportion of methylated and unmethylated sites (estimated from the genome) to determine whether or not a region status is most-likely to be methylated or unmethylated. We now explicit this fact in the HMM for methylation region in the method section.

We acknowledge that our HMM to infer region level methylation status could be improved, but this would be a complete project and study on its own (due to the underlying complexity of the finite site and the lack of a consensus model for epimutations at evolutionary time scale). We believe our HMM to have been the best compromise with what was known from methylation and our goals when the study was conducted, and future work is definitely worth conducting on the estimation of the methylation regions.

- As noted at 580, all of the added power from integrating SMPs/DMRs should come from improved estimation of recent TMRCAs. So, another way to study how much improvement there is would be to look at the true vs. estimated/posterior TMRCAs. Although I agree that demographic inference is ultimately the most relevant task, comparing TMRCA inference would eliminate other sources of differences between the methods (different optimization schemes, algorithmic/numerical quirks, and so forth). This could be a useful addition, and may also give you more insight into why the augmented SMC methods do worse in some cases. (Comment addressed in revision via Supp. Table 7.).

- A general remark on the derivations in Section 2 of the supplement: I checked these formulas as best I could. But a cleaner, less tedious way of calculating these probabilities would be to express the mutation processes as continuous time Markov chains. Then all that is needed is to specify the rate matrices; computing the emission probabilities needed for the SMC methods reduces to manipulating the results of some matrix exponentials. In fact, because the processes are noninteracting, the rate matrix decomposes into a Kronecker sum of the individual rate matrices for each process, which is very easy to code up. And this structure can be exploited when computing the matrix exponential, if speed is an issue.

We believe this comment was acknowledged in the previous revision (line 649), and we thank the reviewer for this interesting insight.

- Most (all?) of the SNP-only SMC methods allow for binning together consecutive observations to cut down on computation time. I did not see binning mentioned anywhere, did you consider it? If the method really processes every site, how long does it take to run?

We believe this comment was addressed in the previous revision and was added to the manuscript in the methods Section (subsection :  SMC optimization function).

- 486: The assumed site and region (de)methylation rates listed here are several OOM different from what your method estimated (Supp. Tables 5-6). Yet, on simulated data your method is usually correct to within an order of magnitude (Supp. Table 4). How are we to interpret this much larger difference between the published estimates and yours? If the published estimates are not reliable, doesn't that call into question your interpretation of the blue line in Fig. 7 at 533? (Comment addressed in revision.)

Reviewer #2 (Public Review):

A limitation in using SNPs to understand recent histories of genomes is their low mutation frequency. Tellier et al. explore the possibility of adding hypermutable markers to SNP based methods for better resolution over short time frames. In particular, they hypothesize that epimutations (CG methylation and demethylation) could provide a useful marker for this purpose. Individual CGs in Arabidopsis tends to be either close to 100% methylated or close to 0%, and are inherited stably enough across generations that they can be treated as genetic markers. Small regions containing multiple CGs can also be treated as genetic markers based on their cumulative methylation level. In this manuscript, Tellier et al develop computational methods to use CG methylation as a hypermutable genetic marker and test them on theoretical and real data sets. They do this both for individual CGs and small regions. My review is limited to the simple question of whether using CG methylation for this purpose makes sense at a conceptual level, not at the level of evaluating specific details of the methods. I have a small concern in that it is not clear that CG methylation measurements are nearly as binary in other plants and other eukaryotes as they are in Arabidopsis. However, I see no reason why the concept of this work is not conceptually sound. Especially in the future as new sequencing technologies provide both base calling and methylating calling capabilities, using CG methylation in addition to SNPs could become a useful and feasible tool for population genetics in situations where SNPs are insufficient.

We thank again the reviewer #2 for his positive comments.  

Reviewer #3 (Public Review):

I very much like this approach and the idea of incorporating hypervariable markers. The method is intriguing, and the ability to e.g. estimate recombination rates, the size of DMRs, etc. is a really nice plus. I am not able to comment on the details of the statistical inference, but from what I can evaluate it seems reasonable and in principle the inclusion of highly mutable sties is a nice advance. This is an exciting new avenue for thinking about inference from genomic data. I remain a bit concerned about how well this will work in systems where much less is understood about methylation,

The authors include some good caveats about applying this approach to other systems, but I think it would be helpful to empiricists outside of thaliana or perhaps mammalian systems to be given some indication of what to watch out for. In maize, for example, there is a nonbimodal distribution of CG methlyation (35% of sites are greater than 10% and less than 90%) but this may well be due to mapping issues. The authors solve many of the issues I had concerns with by using gene body methylation, but this is only briefly mentioned on line 659. I'm assuming the authors' hope is that this method will be widely used, and I think it worth providing some guidance to workers who might do so but who are not as familiar with these kind of data.

We thank the reviewer #3 for his positive comments. And we agree with Reviewer #3 concerning the application to data and that our approach needs to be carefully thought before applied. Our results clearly show that methylation processes are not well enough understood to apply our approach as we initially (maybe naively) designed it. Further investigations need to be conducted and appropriate theoretical models need to be developed before reliable results can be obtained. And we hope that our discussion points this out. However, our approach, the theoretical models and the additional tools contained in this study can be used to help researchers in their investigations to whether or not use different genomic markers to build a common (potentially more reliable) ancestral history. We enhanced the discussion in this second revision by clarifying also the use of the methylation from genic regions to avoid  confusion (lines 700-731).

Recommendations for the authors:  

Reviewer #1 (Recommendations For The Authors):

In added Supp. Table 7, I don't think these are in log10 units as stated in the caption.

Well Spotted! Indeed, the RMSE is not in log10 scale, we corrected the caption. We also added that the TMRCA used for MRSE calculations is in generations units to avoid potential confusion.  

Reviewer #3 (Recommendations for The Authors):

I very much appreciate the authors' attention to previous questions. I would ask that a bit more is spent in the discussion on concerns/approaches empiricists should keep in mind -- I am wary of this being uncritically applied to data from non-model species. It was not clear to me, for example (only mentioned on line 659 in the discussion) that the thaliana data is only using gene-body methylation. This poses potential issues with background selection that the authors acknowledge appropriately, but also assuages many of my concerns about using genome-wide data. I think text with recommendations for data/filtering/etc or at least cautions of assumptions empiricists should be aware of would help.

We apologize for the confusion at line 659. As written in the other section of the manuscript we meant CG sites in genic regions (and not only gene body methylated regions).

Due to the manuscript’s structure, the data from Arabidopsis thaliana is only described at the very end of the manuscript (line 900+). However, a brief description could also be found line 291-296. We however added a sentence in the introduction (line 128) for clarity. 

We however agree with the comment made by reviewer #3 concerning the application to data. We pointed in the discussion the risk of applying our approach on ill-understood (or illprepared) data and stressed the current need of studies on the epimutations processes at evolutionary time scale ( i.e. at Ne time scale) (line 700-703).

eLife assessment

This work presents valuable information on the structure of the spirosome's native extended conformation as the active form of the aldehyde-alcohol dehydrogenase (AdhE) enzyme. The evidence is solid, although the work does not provide a mechanistic understanding of the function and dynamics of AdhE.

Reviewer #1 (Public Review):

Clostridium thermocellum serves as a model for consolidated bioprocessing (CBP) in lignocellulosic ethanol production. The primary ethanol production pathway involves the enzyme aldehyde-alcohol dehydrogenase (AdhE), which exhibits complex regulation, forming long oligomeric structures known as spirosomes.

The present study describes the cryo-EM structure of C. thermocellum AdhE, resolved at 3.28 Å resolution. By integrating cryo-EM data with molecular dynamics simulations, this study showed that the aldehyde intermediate resides longer in the channel of the extended form, supporting the mechanistic model in which the extended spirosome conformation represents the active form of AdhE.

These findings advance the understanding of the function and regulation of AdhE, a key enzyme involved in the ethanol biosynthesis pathway in Clostridium thermocellum, a model organism for ethanol production in consolidated bioprocessing.

Reviewer #2 (Public Review):

Summary:

The manuscript by Ziegler et al, entitled 'Structural characterization and dynamics of AdhE ultrastructure from Clostridium thermocellum: A containment strategy for toxic intermediates?" presents the atomic resolution cryo-EM structure of C. thermocellum AdhE showing that it show dominantly an extended form while E.coli AdhE shows dominantly a compact form. With comparative analysis of their C. thermocellum structure and the previous E.coli AdhE structure, they tried to reveal the mechanism by which C.thermocellum and E.coli show different dominant conformations. In addition, they also analyzed the substrate channel by comparative and computational approaches. Lastly, their computational analysis using CryoDRGN reveals conformational heterogeneity in the sample. Despite this the manuscript is very descriptive and does not provide a mechanistic understanding by which AdhE works, this work will provide structural frame works to further investigate the function and mechanism of AdhE dynamics.

Strengths:

This manuscript provides the first C. thermocellum (Ct) AdhE structure and comparatively analyzed this structure with E.coli AdhE.

Weaknesses:

This work is very descriptive and does not provide mechanistic understanding of the function and dynamics of AdhE.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews: 

Reviewer #1 (Public Review): 

Summary:

Clostridium thermocellum serves as a model for consolidated bioprocess (CBP) in lignocellulosic ethanol production, but yet faces limitations in solid contents and ethanol titers achieved by engineered strains thus far. The primary ethanol production pathway involves the enzyme aldehydealcohol dehydrogenase (AdhE), which forms long oligomeric structures known as spirosomes, previously characterized via the 3.5 Å resolution E. coli AdhE structure using single-particle cryoEM. The present study describes the cryo-EM structure of the C. thermocellum ortholog, sharing 62% sequence identity with E. coli AdhE, resolved at 3.28 Å resolution. Detailed comparative structural analysis, including the Vibrio cholerae AdhE structure, was conducted. Integrating cryoEM data with molecular dynamics simulations indicated that the aldehyde intermediate resides longer in the channel of the extended form, supporting the hypothesis that the extended spirosome represents the active form of AdhE. 

Strengths: 

The study conducts a comprehensive structural comparative analysis of oligomerization interfaces and the acetaldehyde channel across compact and extended conformations. Structural and computational results suggest the extended spirosome as the most likely active state of AdhE. 

Weaknesses: 

The overall resolution of the C. thermocellum structure is similar to the E. coli ortholog, which shares 62% sequence identity, and the oligomerization interfaces and the acetaldehyde channel were previously described. 

Reviewer #2 (Public Review): 

Summary: 

The manuscript by Ziegler et al, entitled 'Structural characterization and dynamics of AdhE ultrastructure from Clostridium thermocellum: A containment strategy for toxic intermediates?" presents the atomic resolution cryo-EM structure of C. thermocellum AdhE showing that it show dominantly an extended form while E. coli AdhE shows dominantly a compact form. With comparative analysis of their C. thermocellum structure and the previous E. coli AdhE structure, they tried to reveal the mechanism by which C. thermocellum and E. coli show diXerent dominant conformations. In addition, they also analyzed the substrate channel by comparative and computational approaches. Lastly, their computational analysis using CryoDRGN reveals conformational heterogeneity in the sample. Although this manuscript suggests a potential mechanism of the diXerent features of AdhEs, this manuscript is very descriptive and does not provide suXicient data to support the authors' conclusions, which may be due to the lack of experimental data to support their findings from the computational analysis. 

Strengths: 

This manuscript provides the first C. thermocellum (Ct) AdhE structure and comparatively analyzed this structure with E. coli AdhE. 

Weaknesses: 

Their main conclusions obtained mostly by computational and comparative analysis are not supported by experimental data. 

Reviewer #3 (Public Review): 

This study describes the first structure of Gram-positive bacterial AdhE spirosomes that are in a native extended conformation. All the previous structures of AdhE spirosomes obtained come from Gram-negative bacterial species with native compact spirosomes (E. coli, V. cholerae). In E. coli, AdhE spirosomes can be found in two diXerent conformational states, compact and extended, depending on the substrates and cofactors they are bound to. 

The high-resolution cryoEM structure of the extended C. thermocellum AdhE spirosomes produced in E. coli in an apo state (without any substrate or cofactors) is compared to the E. coli extended and compact AdhE spirosomes structures previously published. The authors have modeled (in Swiss-Model) the structure of compact C. thermocellum AdhE spirosomes, using E. coli compact AdhE spirosome conformation as a template, and performed molecular dynamics simulations. They have identified a channel in which the toxic reaction intermediate aldehyde could transit from the aldehyde dehydrogenase active site to the alcohol dehydrogenase active site, in an analogous manner to E. coli spirosomes. These findings are in line with the hypothesis that the extended spirosomes could correspond to the active form of the enzyme. 

In this work, the authors speculate that the C. thermocellum AdhE spirosomes could switch from the native extended conformation to a compact conformation, in a way that is inverse of E. coli spirosomes. Although attractive, this hypothesis is not supported by the literature. Amazingly, in some Gram-positive bacterial species (S. pneumoniae, S. sanguinis or C. di8icile...), AdhE spirosomes are natively extended and have never been observed in a compact conformation. On the opposite, E. coli (and other Gram-negative bacteria) native AdhE spirosomes are compact and are able to switch to an extended conformation in the presence of the cofactors (NAD+, coA, and iron). The data presented as they are now are not convincing to confirm the existence of C. thermocellum AdhE spirosomes in a compact conformation. 

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

Major points: 

(1) The claim of achieving the highest resolution AdhE structure lacks strong support since the E. coli structure was solved at 3.5A, whereas the C. thermocellum was solved at 3.28A. Conducting a local resolution analysis could provide insights into distinct structural interpretations, enhancing the strength of the claim. 

We have modified the sentence claiming this as the highest resolution AdhE structure to say, “In this study, we presented and analyzed a high-resolution structure of the AdhE spirosome from C. thermocellum.” We have included the local resolution map in Figure 2C – all structural analysis was performed in regions from the center of the molecule, where the highest resolution information was determined.

(2) The comparative structural analysis of the oligomerization interface is thorough, yet it could benefit from greater conciseness. Focusing on highlighting major findings would streamline the presentation and enhance clarity. 

We altered a few places in the comparative structural analysis in response to other reviewers. We also divided the main structure section into two subsections (spirosome interfaces and AdhE active sites) to enhance clarity.

Reviewer #2 (Recommendations For The Authors): 

(1) The authors should change the tile containing "?". Does it mean that the conclusions that the authors made are still in question? 

We have removed the question mark to indicate that our results point to a channeling mechanism.

(2) Figure 1B: Clarify Ct Fwd. Is this adding NADH, and Ct Rev adding NAD+? 

This information is described in the text in lines 98-100. It is also at the bottom of figure 1B.

(3) Line 131: Please revise accordingly for clarity: "The extended dimer interfaces" è "The extended E.coli dimer interface". 

This has been edited for clarity. We have added the following sentence resulting to indicate which interfaces that are being discussed: “Both the E. coli and C. thermocellum extended dimer interfaces bury ~5000 Å2. While the compact C. thermocellum compact dimer interface buries a similar surface area of ~4800 Å2, the E. coli dimer interface buries ~3800 Å2.”

(4) Line 133-136: Why that does not seem to be the case? These sentences are not clear what the authors exactly mean. 

We altered the text to say, “One would expect the compact structure in E. coli to have a larger buried surface area due to it being the predominant form when it is examined without additives, but that is not the case; further corroborating that factors other than buried surface area must impact the apo state of the spirosome.” We hope this clarifies our intent.

(5) Line 138-145: The authors should provide a logic for how the diXerent distribution of the charged residues would change the form of AdhE. It may just be a diXerent distribution nothing to do with the conformational change. 

After further analysis of the interface amino acid distribution, we agree that the distribution may have nothing to do with the conformational change. We have changed this section to end with the sentence “Analysis of the residues buried in these interfaces reveals that while many of the residues are identical in the C. thermocellum and E. coli extended structures, there are some diXerences in amino acid type distribution, although nothing that directly indicates control of conformer state (Supplemental Figure 3).” 

(6) Line 169: Kim et al. è Cho et al.

We have corrected this error.

(7) Line 122-235: The whole section is just describing the diXerence between Ct and Ec AdhE suggesting that this diXerence may contribute to the conformational diXerence without any evidence. The author cannot say that the diXerences in the interface, active sites cofactor pockets, etc explain why two AdhE (Ct, Ec) have diXerent domain conformers unless they provide experimental data. 

We did not conclude that any diXerences we observed structurally were responsible for the conformation change. The purpose of this section was solely to compare the structures to determine if we could find a structural basis for the diXerence between E. coli and C. thermocellum conformation – we stated a few times throughout the section and in the discussion that there were no immediate structural reasons for this diXerence in shape. We have added a few sentences in the discussion to address whether Gram-positive vs. Gram-negative is influencing the shape, addressed in reviewer #3 comment #4. 

(8) Line 237: The whole section "Identification..." analyzed the substrate channel by computational analysis. The author should provide experimental evidence that these residues identified are critical for channeling by generating mutants and measuring their activity. 

We agree that mutagenesis is the next logical step for these results, however it is outside the scope of work of this paper as this study will not be that straightforward. We have included a sentence in the discussion to indicate our plans for further investigation to the channel that says, “Future mutagenesis studies will be needed to confirm whether the spirosome exists to control the reaction flux in high-reactant conditions.”

Reviewer #3 (Recommendations For The Authors): 

(1) The capacity of C. thermocellum AdhE spirosomes to switch from a natively extended conformation to a compact conformation is not demonstrated in this manuscript, as it is now. Because this would be the first time that Gram-positive bacterial AdhE spirosomes are observed in a compact conformation, the authors should provide a clear demonstration of their existence by presenting reliable and good images of C. thermocellum compact spirosomes. 

We have modified Figure 1A to zoom in on one compact and extended spirosome that we have identified from each C. thermocellum sample. We have included triangles of the same size and shape to indicate the proximity of a turn of a helix, showing that the identified compact spirosomes have a tighter conformation than extended spirosomes.

(2) The authors should show at least an image of the compact C. thermocellum spirosomes, that they claim to observe in the presence of NADH or in the forward reaction conditions mentioned in Figure 1. The authors have added diXerent reactants to the extended C. thermocellum spirosomes and visualized their conformation by negative stain. An image of each condition tested would be valuable and would nicely complete the distribution of compact versus extended spirosomes presented in Figure 1. 

We have created a new supplemental figure with spirosomes circled for all of the experimental conditions for C. thermocellum (Supplemental figure 1). We have added a reference to supplemental figure 1 in the text to direct the reader to these images.

(3) The cryoEM classes presented in Figure 8 are not convincing and could correspond to dimers or rosettes of AdhE or to E. coli endogenous AdhE. CryoEM classes showing longer compact C. thermocellum spirosomes should be shown. The percentage of these compact spirosomes visualized in the micrographs should be added and discussed in the text as it would increase confidence in these findings and confirm that C. thermocellum compact spirosomes exist. Heterologous production of C. thermocellum AdhE in E. coli depleted for its endogenous AdhE would be required to definitively prove that these are compact C. thermocellum AdhE spirosomes in the cryoEM. 

We included the pictures of the theoretical compact spirosomes, as generated from the 8-mer of E. coli AdhE (6AHC) to address the possibility of rosettes. We have now indicated in the text that there were 6.7% of the particles in the compact conformation, which is less than seen by negative stain. We further mentioned that the compact spirosome is less compact than that seen in E. coli. We added a sentence to the discussion about the possibility of contaminating E. coli spirosomes (though this is very unlikely ) in our compact spirosome analysis: “While these compact spirosomes could result from expression in E. coli, though this is very unlikely, we also identified compact spirosomes in a native C. thermocellum lysate, which would not have similar contamination issues.”

(4) The authors should include and discuss in the text previous findings (among which Laurenceau et al., 2015...) describing the diXerences between Gram-positive and Gram-negative spirosomes. AdhE spirosomes are natively extended in most Gram-positive bacterial species (S. pneumoniae, S. sanguinis or C. diXicile...), and have never been observed in a compact conformation. On the opposite, E. coli (and other Gram-negative bacteria) native AdhE spirosomes are compact and are able to switch to an extended conformation in the presence of the cofactors (NAD+, coA, and iron). 

We have added the following sentences to the discussion to address this comment: “This could potentially be due to the diXerences between Gram-positive and Gram-negative bacteria. In previous studies, compact spirosomes have only been isolated from Gram-negatives while solely extended spirosomes have been isolated from Gram-positives. Furthermore, while the compact spirosomes can transition to extended in the presence of cofactors, the reverse has not been previously observed with an extended spirosome.”

(5) The authors have spotted some diXerences between the E. coli and C. thermocellum structures, that they believe could explain the intrinsic capacity of these spirosomes to be natively extended or compact. It would be interesting to confirm this hypothesis by measuring C. thermocellum extended AdhE spirosome activity and comparing it to E. coli extended spirosomes. The impact of mutations in the regions proposed by the authors to be important in the capacity of C. thermocellum AdhE to be extended (especially the GxGxxG motif and the D494 position) would be appreciated to confirm this hypothesis. 

We agree that this would be an interesting avenue of research although it is currently outside the scope of this paper. We are looking into experiments that we can perform where we can track both activity and conformation but have not found an ideal experiment at this time.

(6) Many statements and result interpretations are overstated in several parts of the manuscript and would need to be rewritten to balance the absence of clear evidence of C. thermocellum compact spirosomes. 

We have shown that we have identified compact spirosomes, addressed in multiple comments above. We have adjusted the language of the paper to indicate more uncertainty that will be followed up in future mutagenesis experiments. However, these mutations are not that simple to identify and this research would require a fairly large study that is better suited for a follow up manuscript.

(7) The Figure 7 legend would need to be corrected.

We are unsure as to what needs to be corrected in the figure 7 legend based on this comment.

eLife assessment

This important study demonstrates that combining AlphaFold2 with the author's sampling method AF2-RAVE improves protein-ligand docking for three protein kinases and their inhibitors. The evidence is compelling and the results will be of interest to researchers who work on computer-aided drug design.

Reviewer #1 (Public Review):

The development of effective computational methods for protein-ligand binding remains an outstanding challenge to the field of drug design. This impressive computational study combines a variety of structure prediction (AlphaFold2) and sampling (RAVE) tools to generate holo-like protein structures of three kinases (DDR1, Abl1, and Src kinases) for binding to type I and type II inhibitors. Of central importance to the work is the conformational state of the Asp-Phy-Gly "DFG motif" where the Asp points inward (DFG-in) in the active state and outward (DFG-out) in the inactive state. The kinases bind to type I or type II inhibitors when in the DFG-in or DFG-out states, respectively.

It is noted that while AlphaFold2 can be effective in generating ligand-free apo protein structures, it is ineffective at generating holo structures appropriate for ligand binding. Starting from the native apo structure, structural fluctuations are necessary to access holo-like structures appropriate for ligand-binding. A variety of methods, including reduced multiple sequence alignment (rMSA), AF2-cluster, and AlphaFlow may be used to create decoy structures. However, those methods can be limited in the diversity of structures generated and lack a physics-based analysis of Boltzmann weight critical to their relative evaluation.

To address this need, the authors combine AlphaFold2 with the Reweighted Autoencoded Variational Bayes for Enhanced Sampling (RAVE) method, to explore metastable states and create a Boltzmann ranking. With that variety of structures in hand, grid-based docking methods Glide and Induced-Fit Docking (IFD) were used to generate protein-ligand (kinase-inhibitor) complexes.

The authors demonstrate that using AlphaFold2 alone, there is a failure to generate DFG-out structures needed for binding to type II inhibitors. By applying the AlphaFold2 with rMSA followed by RAVE (using short MD trajectories, SPIB-based collective variable analysis, and enhanced sampling using umbrella sampling), metastable DFG-out structures with Boltzmann weighting are generated enabling protein-ligand binding. Moreover, the authors found that the successful sampling of DFG-out states for one kinase (DDR1) could be used to model similar states for other proteins (Abl1 and Src kinase). The AF2RAVE approach is shown to result in a set of holo-like protein structures with a 50% rate of docking type II inhibitors.

Overall, this is excellent work and a valuable contribution to the field that demonstrates the strengths and weaknesses of state-of-the-art computational methods for protein-ligand binding. The authors also suggest promising directions for future study, noting that potential enhancements in the workflow may result from the use of binding site prediction models and free energy perturbation calculations.

Reviewer #2 (Public Review):

This manuscript explores the utility of AlphaFold2 (AF2) and the author's own AF2-RAVE method for drug discovery. As has been observed elsewhere, the predictive power of docking against AF2 structures is quite limited, particularly for proteins like kinases that have non-trivial conformational dynamics. However, using enhanced sampling methods like RAVE to explore beyond AF2 starting structures leads to a significant improvement.

Comments on revised version:

I'm happy with the changes made.

Reviewer #3 (Public Review):

In this manuscript, the authors aim to enhance AlphaFold2 for protein conformation-selective drug discovery through the integration of AlphaFold2 and physics-based methods, focusing on improving the accuracy of predicting protein structures ensemble and small molecule binding of metastable protein conformations to facilitate targeted drug design.

The major strength of the paper lies in the methodology, which includes the innovative integration of AlphaFold2 with all-atom enhanced sampling molecular dynamics and induced fit docking to produce protein ensembles with structural diversity. Moreover, the generated structures can be used as reliable crystal-like decoys to enrich metastable conformations of holo-like structures. The authors demonstrate the effectiveness of the proposed approach in producing metastable structures of three different protein kinases and perform docking with their type I and II inhibitors. The paper provides strong evidence supporting the potential impact of this technology in drug discovery. However, limitations may exist in the generalizability of the approach across other structures, especially complex structures such as protein-protein or DNA-protein complexes.

The authors largely achieved their aims by demonstrating that the AF2RAVE-Glide workflow can generate holo-like structure candidates with a 50% successful docking rate for known type II inhibitors. This work is likely to have a significant impact on the field by offering a more precise and efficient method for predicting protein structure ensemble, which is essential for designing targeted drugs. The utility of the integrated AF2RAVE-Glide approach may streamline the drug discovery process, potentially leading to the development of more effective and specific medications for various diseases.

Comments on revised version:

The revised manuscript looks great to me. I have no further comments.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public Review):

The development of effective computational methods for protein-ligand binding remains an outstanding challenge to the field of drug design. This impressive computational study combines a variety of structure prediction (AlphaFold2) and sampling (RAVE) tools to generate holo-like protein structures of three kinases (DDR1, Abl1, and Src kinases) for binding to type I and type II inhibitors. Of central importance to the work is the conformational state of the Asp-Phy-Gly "DFG motif" where the Asp points inward (DFG-in) in the active state and outward (DFG-out) in the inactive state. The kinases bind to type I or type II inhibitors when in the DFG-in or DFG-out states, respectively.

It is noted that while AlphaFold2 can be effective in generating ligand-free apo protein structures, it is ineffective at generating holo-structures appropriate for ligand binding. Starting from the native apo structure, structural fluctuations are necessary to access holo-like structures appropriate for ligand binding. A variety of methods, including reduced multiple sequence alignment (rMSA), AF2-cluster, and AlphaFlow may be used to create decoy structures. However, those methods can be limited in the diversity of structures generated and lack a physics-based analysis of Boltzmann weight critical to their relative evaluation.

To address this need, the authors combine AlphaFold2 with the Reweighted Autoencoded Variational Bayes for Enhanced Sampling (RAVE) method, to explore metastable states and create a Boltzmann ranking. With that variety of structures in hand, grid-based docking methods Glide and Induced-Fit Docking (IFD) were used to generate protein-ligand (kinase-inhibitor) complexes.

The authors demonstrate that using AlphaFold2 alone, there is a failure to generate DFG-out structures needed for binding to type II inhibitors. By applying the AlphaFold2 with rMSA followed by RAVE (using short MD trajectories, SPIB-based collective variable analysis, and enhanced sampling using umbrella sampling), metastable DFG-out structures with Boltzmann weighting are generated enabling protein-ligand binding. Moreover, the authors found that the successful sampling of DFG-out states for one kinase (DDR1) could be used to model similar states for other proteins (Abl1 and Src kinase). The AF2RAVE approach is shown to result in a set of holo-like protein structures with a 50% rate of docking type II inhibitors.

Overall, this is excellent work and a valuable contribution to the field that demonstrates the strengths and weaknesses of state-of-the-art computational methods for protein-ligand binding. The authors also suggest promising directions for future study, noting that potential enhancements in the workflow may result from the use of binding site prediction models and free energy perturbation calculations.

Reviewer #2 (Public Review):

Summary:

This manuscript explores the utility of AlphaFold2 (AF2) and the author's own AF2-RAVE method for drug discovery. As has been observed elsewhere, the predictive power of docking against AF2 structures is quite limited, particularly for proteins like kinases that have non-trivial conformational dynamics. However, using enhanced sampling methods like RAVE to explore beyond AF2 starting structures leads to a significant improvement.

Strengths:

This is a nice demonstration of the utility of the authors' previously published RAVE method.

Weaknesses:

My only concern is the authors' discussion of induced fit. I'm quite confident the structures discussed are present in the absence of ligand binding, consistent with conformational selection. It seems the author's own data also argues for an important role in conformational selection. It would be nice to acknowledge this instead of going along with the common practice in drug discovery of attributing any conformational changes to induced fit without thoughtful consideration of conformational selection.

The reviewer is correct. We aim to highlight the significant role of conformational selection. To clarify this, we have expanded the discussion on conformational selection in the introduction.

Reviewer #3 (Public Review):

In this manuscript, the authors aim to enhance AlphaFold2 for protein conformation-selective drug discovery through the integration of AlphaFold2 and physics-based methods, focusing on improving the accuracy of predicting protein structures ensemble and small molecule binding of metastable protein conformations to facilitate targeted drug design.

The major strength of the paper lies in the methodology, which includes the innovative integration of AlphaFold2 with all-atom enhanced sampling molecular dynamics and induced fit docking to produce protein ensembles with structural diversity. Moreover, the generated structures can be used as reliable crystal-like decoys to enrich metastable conformations of holo-like structures. The authors demonstrate the effectiveness of the proposed approach in producing metastable structures of three different protein kinases and perform docking with their type I and II inhibitors. The paper provides strong evidence supporting the potential impact of this technology in drug discovery. However, limitations may exist in the generalizability of the approach across other structures, especially complex structures such as protein-protein or DNA-protein complexes.

Proteins undergo thermodynamic fluctuations and can occasionally reach metastable configurations. It can be assumed that other biomolecules, such as proteins and DNA, stabilize these metastable states when forming protein-protein or protein-DNA complexes. Since our method has the potential to identify these metastable states, it shows promise for designing drugs targeting proteins in allosteric configurations induced by other biomolecules.

The authors largely achieved their aims by demonstrating that the AF2RAVE-Glide workflow can generate holo-like structure candidates with a 50% successful docking rate for known type II inhibitors. This work is likely to have a significant impact on the field by offering a more precise and efficient method for predicting protein structure ensemble, which is essential for designing targeted drugs. The utility of the integrated AF2RAVE-Glide approach may streamline the drug discovery process, potentially leading to the development of more effective and specific medications for various diseases.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Suggestions

(1) The computational protocol is found to be insufficient to generate precise values of the relative free energies between structures generated. The authors note in the Conclusion that an enhancement in the workflow might result from the addition of free energy calculations. Can the authors comment on the prospects for generating more accurate estimates of the free energy that might be used to qualitatively evaluate poses and the free energy landscape surrounding putative metastable states? What are the principal challenges and what might help overcome them? What would the most effective computational protocol be?

More accurate estimates of the free energy can theoretically be achieved by increasing the number of umbrella sampling windows and extending the simulation length until the PMF converges. However, there is always a trade-off between PMF accuracy and computational costs, so we have chosen to stick with the current setup. Metadynamics is another method to obtain a more accurate free energy profile, which we have used in previous versions of AlphaFold2-RAVE, but for the specific systems we investigated, it had issues in achieving back and forth movement given the high entropic nature of the activation loop. Research in enhanced sampling methods and dimensionality reduction techniques for reaction coordinates is continually evolving and will play a critical role in alleviating this problem.

(2) I was surprised that there was not more correlation of a funnel-like shape in Figures S16 and S18, showing a stronger correlation between low RMSD and better docking score. This is true for both the ponatinib and imatinib applications in DDR1 and Abl1. That also seems true for the trimmed results for Src kinase in Figure S19. I was also surprised that there are structures with very large RMSD but docking scores comparable to the best structures of the lowest RMSD. Might something be done to make the docking score a more effective discriminator?

The docking algorithm and docking score are used to filter out highly improbable docking poses. False positives in predicted docking poses are a common issue across all docking methods as described for instance in:

Fan, Jiyu, Ailing Fu, and Le Zhang. "Progress in molecular docking." Quantitative Biology 7 (2019): 83-89.

Ferreira, R.S., Simeonov, A., Jadhav, A., Eidam, O., Mott, B.T., Keiser, M.J., McKerrow, J.H., Maloney, D.J., Irwin, J.J. and Shoichet, B.K., 2010. "Complementarity between a docking and a high-throughput screen in discovering new cruzain inhibitors." Journal of medicinal chemistry, 53(13), pp.4891-4905.

Moreover, there is always a trade-off between docking accuracy and computational cost. While employing more accurate docking methods may decrease false positives, it can also be resource-intensive. In such scenarios, our approach to enriching holo-structures can be impactful by reducing the number of pocket structures in the input ensembles and significantly enhancing docking efficiency.

(3) I think that it is fine to identify one structure as "IFD winner" but also feel that its significance is overstressed, especially given that it can be identified only in a retrospective analysis rather than through de novo prediction.

We agree with the reviewer. We did not intend to emphasize the specific structure "IFD winner". Rather, we aimed to demonstrate that our method can enrich promising candidates for holo-structures. We verified this by showing that our holo-structure candidates performed well in retrospective docking using IFD, which we previously referred to as "IFD winner". We have now revised this term to "holo-model".

Minor Points

p. 3 "DymanicBind" should be "DynamicBind"

p. 3 Change "We chosen" to "We have chosen" or "we chose."

p. 3 In identifying the Schrödinger software Glide and IFD, I recommend removing the subjective modifier "industry-leading."

Modifications done.

Reviewer #2 (Recommendations For The Authors):

In the view of this reviewer, the writing is 'choppy'.

We have tried to improve the writing.

Reviewer #3 (Recommendations For The Authors):

(1) In Figure 1, the workflow labels (i) to (iv) are not shown on the figures, making it difficult for readers to follow. Consider adding these labels to the figures.

Modifications done.

(2) Explain how Boltzmann ranks were calculated based on unbiased MD simulations to guide the enrichment of holo-like structures in metastable states.

The Methods section is now updated for clarification.

(3) The authors could clarify how the classical DFG-out decoys in the DDR1 rMSA AF2 ensemble are transferred to Abl1 kinase in the Methods section.

The Methods section is now updated for clarification.

(4) The authors can clarify the methodology section by providing more detailed explanations about how the unbiased MD simulations are performed, including which MD simulation software was used and whether energy minimization and equilibrium steps were needed as in conventional MD simulations, and other setup details.

The Methods section is now updated for clarification.

(5) The validation of the proposed approach in this work used three kinase proteins. The authors can enhance the discussion section by addressing other types of protein structure prediction that can use the proposed approach in drug discovery, beyond the three kinase proteins tested.

The proposed approach is theoretically applicable to other types of proteins, such as GPCRs, where both conformational selection and the induced-fit effect are crucial. We have expanded the discussion on the generalization of our protocol in the Conclusion section.

(6) The authors should add appropriate citations for the software and tools used in the manuscript. For example, a reference should be added for the Glide XP docking experiments that utilized the Maestro software. Double-check all related software citations.

We have now updated the citations for docking experiments based on the instruction of the Maestro Glide User manual and IFD User manual.

(7) The authors should consider offering a comprehensive list of software tools and databases utilized in the study to assist in replicating the experiments and further validating the results.

We have now added a summary of tools used in the Methods section.

eLife assessment

This valuable manuscript describes a novel role of Vangl2, a core planar cell polarity protein, in linking the NF-kB pathway to selective autophagic protein degradation in myeloid cells. The mechanistic studies provide convincing evidence that Vangl2 targets p65 for NDP52-mediated autophagic degradation, limiting inflammatory NF-kB response, with functional significance of the proposed mechanism in sepsis. Additional future studies dissecting autophagic Vangl2 functions in various myeloid subsets in the context of inflammation could be informative, and additional Vangl2 targets in the inflammatory pathway, including IKK2, could also be explored. Overall, this exciting study can advance our understanding of NF-kB control, particularly in the context of inflammatory diseases.

Reviewer #1 (Public Review):

The study shows a new mechanism of NFkB-p65 regulation mediated by Vangl2-dependent autophagic targeting. Autophagic regulation of p65 has been reported earlier; this study brings an additional set of molecular players involved in this important regulatory event, which may have implications for chronic and acute inflammatory conditions.

Reviewer #2 (Public Review):

Vangl2, a core planar cell polarity protein involved in Wnt/PCP signaling, cell proliferation, differentiation, homeostasis, and cell migration. Vangl2 malfunctioning has been linked to various human ailments, including autoimmune and neoplastic disorders. Interestingly, it was shown that Vangl2 interacts with the autophagy regulator p62, and autophagic degradation limits the activity of inflammatory mediators, such as p65/NF-κB. However, the possible role of Vangl2 in inflammation has not been investigated. In this manuscript, Lu et al. describe that Vangl2 expression is upregulated in human sepsis-associated PBMCs and that Vangl2 mitigates experimental sepsis in mice by negatively regulating p65/NF-κB signaling in myeloid cells. Their mechanistic studies further revealed that Vangl2 recruits the E3 ubiquitin ligase PDLIM2 to promote K63-linked poly-ubiquitination of p65. Vangl2 also facilitated the recognition of ubiquitinated p65 by the cargo receptor NDP52. These molecular processes caused selective autophagic degradation of p65. Indeed, abrogation of PDLIM2 or NDP52 functions rescued p65 from autophagic degradation, leading to extended p65/NF-κB activity in myeloid cells. Overall, the manuscript presents convincing evidence for novel Vangl2-mediated control of inflammatory p65/NF-kB activity. The proposed pathway may expand interventional opportunities restraining aberrant p65/NF-kB activity in human ailments.

IKK is known to mediate p65 phosphorylation, which instructs NF-kB transcriptional activity. In this manuscript, Vangl2 deficiency led to an increased accumulation of phosphorylated p65 and IKK also at 30 minutes post-LPS stimulation; however, autophagic degradation of p-p65 may not have been initiated at this early time point. Therefore, this set of data put forward the exciting possibility that Vangl2 could also be regulating the immediate early phase of inflammatory response involving the IKK-p65 axis - a proposition that may be tested in future studies.

Reviewer #3 (Public Review):

Lu et al. describe Vangl2 as a negative regulator of inflammation in myeloid cells. The primary mechanism appears to be through binding p65 and promoting its degradation, albeit in an unusual autolysosome/autophagy dependent manner. Overall, these findings are novel, valuable and the crosstalk of PCP pathway protein Vangl2 with NF-kappaB is of interest.

Comments on latest version:

Lu et al. now address all my comments. All data included for the reviewers should be included in the main manuscript or Supplement and should be available to the readers. Please ensure that this criteria is met. I have no further comments.

Author response:

The following is the authors’ response to the previous reviews.

Responses to Reviewer #1:

Reviewer #1: The study shows a new mechanism of NFkB-p65 regulation mediated by Vangl2-dependent autophagic targeting. Autophagic regulation of p65 has been reported earlier; this study brings an additional set of molecular players involved in this important regulatory event, which may have implications for chronic and acute inflammatory conditions.

Comments on the revised version:

The authors have addressed the earlier concerns and I am satisfied with the revised version. I have no additional comments to make.

We appreciate the reviewer’s comments on our revised manuscript.

Responses to Reviewer #2:

Reviewer #2: Vangl2, a core planar cell polarity protein involved in Wnt/PCP signaling, cell proliferation, differentiation, homeostasis, and cell migration. Vangl2 malfunctioning has been linked to various human ailments, including autoimmune and neoplastic disorders. Interestingly, it was shown that Vangl2 interacts with the autophagy regulator p62, and autophagic degradation limits the activity of inflammatory mediators, such as p65/NF-κB. However, the possible role of Vangl2 in inflammation has not been investigated. In this manuscript, Lu et al. describe that Vangl2 expression is upregulated in human sepsis-associated PBMCs and that Vangl2 mitigates experimental sepsis in mice by negatively regulating p65/NF-κB signaling in myeloid cells. Their mechanistic studies further revealed that Vangl2 recruits the E3 ubiquitin ligase PDLIM2 to promote K63-linked poly-ubiquitination of p65. Vangl2 also facilitated the recognition of ubiquitinated p65 by the cargo receptor NDP52. These molecular processes caused selective autophagic degradation of p65. Indeed, abrogation of PDLIM2 or NDP52 functions rescued p65 from autophagic degradation, leading to extended p65/NF-κB activity in myeloid cells. Overall, the manuscript presents convincing evidence for novel Vangl2-mediated control of inflammatory p65/NF-kB activity. The proposed pathway may expand interventional opportunities restraining aberrant p65/NF-kB activity in human ailments.

IKK is known to mediate p65 phosphorylation, which instructs NF-kB transcriptional activity. In this manuscript, Vangl2 deficiency led to an increased accumulation of phosphorylated p65 and IKK also at 30 minutes post-LPS stimulation; however, autophagic degradation of p-p65 may not have been initiated at this early time point. Therefore, this set of data put forward the exciting possibility that Vangl2 could also be regulating the immediate early phase of inflammatory response involving the IKK-p65 axis - a proposition that may be tested in future studies.

We appreciate the reviewer’s comments on our manuscript, and we have added the discussion about IKK-p65 axis in revised version. (Page 15, lines 467-474)

Responses to Reviewer #3:

Reviewer #3: Lu et al. describe Vangl2 as a negative regulator of inflammation in myeloid cells. The primary mechanism appears to be through binding p65 and promoting its degradation, albeit in an unusual autolysosome/autophagy dependent manner. Overall, these findings are novel, valuable and the crosstalk of PCP pathway protein Vangl2 with NF-kappaB is of interest. While generally solid, some concerns still remain about the rigor and conclusions drawn.

Comments on the revised version:

(1) Lu et al. address my comments through responses and new experimental data. However, some of the explanations provided are inadequate.

However, in response to my enquiry regarding directly exploring PCP effects, the authors simply assert "Our study revealed that Vangl2 recruits the E3 ubiquitin ligase PDLIM2 to facilitate K63-linked ubiquitination of p65, which is subsequently recognized by autophagy receptor NDP52 and then promotes the autophagic degradation of p65. Our findings by using autophagy inhibitors and autophagic-deficient cells indicate that Vangl2 regulates NFkB signaling through a selective autophagic pathway, rather than affecting the PCP pathway, WNT, HH/GLI, Fat-Dachsous or even mechanical tension."

I do not agree that the use of autophagy inhibitors and autophagy-deficient cells can rule out the contributions of PCP or any other pathways. Only experimentally inhibiting the pathway(s) with adequate demonstration of target inhibition/abolition of well-known effector function and documenting unaltered p65 regulation under these conditions can be considered proof. Autophagy inhibitors and autophagy-deficient cells only prove that this particular pathway is necessary. Nonetheless, I do not want to dwell on proving a negative and agree that Vangl2 is a novel regulator of p65 through its role in promoting p65 degradation. The inclusion of a statement discussing the limitations of their approach would have sufficed. The response from the authors could have been better.

We thank the reviewer for helping us improve the quality of the manuscript. We provided new data and revised the Discussion as suggested.

To ascertain whether Vangl2 degrades p65 through a selective autophagic pathway or the PCP pathway, 293T cells were transfected with p65, together with or without the Vangl2 plasmids, and treated with different pharmacological inhibitors. We found the degradation of p65 induced by Vangl2 was blocked by autolysosome inhibitor (CQ), but not by the JNK inhibitor (SP600125) or Wnt/β-catenin inhibitor (FH535) (New Figure. 1). These data suggest that Vangl2 primarily degrades p65 through a selective autophagic pathway, rather than through the JNK or Wnt signaling pathway. Nevertheless, additional pathway inhibitions, such as those of the HH/GLI and Fat-Dachsous pathways, should also be employed to further elucidate the function of Vangl2 in p65 degradation. As suggested, we have added a statement about the limitation of the approach in the discussion (Page 12, lines 378-385).

Author response image 1.

Vangl2 degrades p65 through a selective autophagic pathway, but not by the PCP pathway. HEK293T cells were transfected with Flag-p65 and HA-Vangl2 plasmids, and treated with DMSO, CQ (50 mM) for 6 h, SP600125 (20 mM) for 1 h or FH535 (30 mM) for 6 h. The cell lysates were analyzed by immunoblot.

(2) I am also not satisfied with the explanation that "immune cells represent a minor fraction of the lungs and liver". There are lots of resident immune cells in the lungs and liver (alveolar macrophages in the lung and Kuppfer cells in the liver). For example, it may be so that Vangl2 is important in monocytes and not in the resident population. This might be a potential explanation. But this is not explored. The restricted tissue-specificity of the interaction between two ubiquitously present proteins is still a challenge to understand. The response from the authors is not satisfactory. There is plenty of Vangl2 in the liver in their western blot.

We thank the reviewer for this question. We added this explanation in the Discussion. (Page 13, lines 398-404)

(3) I had also simply pointed out PMID: 34214490 with reference to the findings described in the manuscript. There were no suggestions of contradiction. In fact, I would refer to the publication in discussion to support the findings and stress the novelty. The response from the authors could have been better.

Thank you for the reviewer's insightful comments. We have modified this discussion as suggested. (Page 13, lines 410-415; Page 14, lines 419-421)

(4) The response to my enquiry regarding homo- or heterozygosity is unsupported by any reference or data.

As suggested, we provided the data that only Vangl2 deficient homozygous showed inhibition of the activation of NF-kB in New Figure. 2.

Author response image 2.

Vangl2 deficiency promotes NF-kB activation. (A) The survival rates of WT, Vangl2ΔM/ΔM and Vangl2ΔM/WT mice treated with high-dosage of LPS (30 mg/kg, i.p.) (n≥4). (B) IL-6 and TNF-a secretion by WT and Vangl2-deficient BMDMs treated with LPS for 6 h was measured by ELISA. IL-1β secretion by WT, Vangl2ΔM/ΔM and Vangl2ΔM/WT BMDMs treated with LPS for 6 h and ATP for 30 min was measured by ELISA.

(5) The listing of 8 patients and healthy controls are also appreciated. The body temperature of #6 doesn't fall in the <36 or >38 degree C SIRS criteria. The inclusion of CRP, PCT, heart rate and respiratory rate, and other lab values would have further improved the inclusion criteria. Moreover, it is difficult to understand why there are 16 value points for healthy and sepsis cohorts in Fig 1 when there are 8 patients.

We thank the reviewer for this valuable suggestion. We are sorry for our mistake that we entered data from two repeated experiments in Figure. 1 A and we have revised this data in the updated version (Figure. 1 A, Pages 12 Lines 146). As suggested, we have added CRP, WBC and heart rate in sepsis patients’ information. (Supplementary Materials and Methods)

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

The proposition that Vangl2 may target additional mediators of inflammation could be indicated in the text.

We thank the reviewer for this valuable suggestion. We had added discussion in modified version. (Page 15, lines 467-474)

Reviewer #3 (Recommendations For The Authors):

It is advised that some of the deficiencies pointed out by Reviewer #3 are textually addressed. Additionally, there could be some inconsistency in the number of healthy controls and patients (see Fig S1A and FIg 1A and Supplementary table, also see comments from Reviewer #3) - this should be carefully scrutinised and revised, if necessary.

We thank the reviewer for this valuable suggestion. We are sorry for our mistake that we entered data from two repeated experiments in Figure. 1 A and we have revised this data in the updated version (Figure. 1 A, Pages 12 Lines 146).

eLife assessment

This valuable study presents a new framework (ASBAR) that combines open-source toolboxes for pose estimation and behavior recognition to automate the process of categorizing behaviors in wild apes from video data. The authors present compelling evidence that this pipeline can categorize simple wild ape behaviors from out-of-context video at a similar level of accuracy as previous models, while simultaneously vastly reducing the size of the model. The study's results should be of particular interest to primatologists and other behavioral biologists working with natural populations.

Reviewer #1 (Public Review):

Summary:

Advances in machine vision and computer learning have meant that there are now state-of-the-art and open-source toolboxes that allow for animal pose estimation and action recognition. These technologies have the potential to revolutionize behavioral observations of wild primates but are often held back by labor-intensive model training and the need for some programming knowledge to effectively leverage such tools. The study presented here by Fuchs et al unveils a new framework (ASBAR) that aims to automate behavioral recognition in wild apes from video data. This framework combines robustly trained and well-tested pose estimate and behavioral action recognition models. The framework performs admirably at the task of automatically identifying simple behaviors of wild apes from camera trap videos of variable quality and contexts. These results indicate that skeletal-based action recognition offers a reliable and lightweight methodology for studying ape behavior in the wild and the presented framework and GUI offer an accessible route for other researchers to utilize such tools.

Given that automated behavior recognition in wild primates will likely be a major future direction within many subfields of primatology, open-source frameworks, like the one presented here, will present a significant impact on the field and will provide a strong foundation for others to build future research upon.

Strengths:

- Clearly articulated the argument as to why the framework was needed and what advantages it could convey to the wider field.

- For a very technical paper it was very well written. Every aspect of the framework the authors clearly explained why it was chosen and how it was trained and tested. This information was broken down in a clear and easily digestible way that will be appreciated by technical and non-technical audiences alike.

- The study demonstrates which pose estimation architectures produce the most robust models for both within-context and out-of-context pose estimates. This is invaluable knowledge for those wanting to produce their own robust models.

- The comparison of skeletal-based action recognition with other methodologies for action recognition helps contextualize the results.

Weaknesses

While I note that this is a paper most likely aimed at the more technical reader, it will also be of interest to a wider primatological readership, including those who work extensively in the field. When outlining the need for future work I felt the paper offered almost exclusively very technical directions. This may have been a missed opportunity to engage the wider readership and suggest some practical ways those in the field could collect more ASBAR-friendly video data to further improve accuracy.

Reviewer #2 (Public Review):

Fuchs et al. propose a framework for action recognition based on pose estimation. They integrate functions from DeepLabCut and MMAction2, two popular machine-learning frameworks for behavioral analysis, in a new package called ASBAR.

They test their framework by

- Running pose estimation experiments on the OpenMonkeyChallenge (OMC) dataset (the public train + val parts) with DeepLabCut.

- Annotating around 320 image pose data in the PanAf dataset (which contains behavioral annotations). They show that the ResNet-152 model generalizes best from the OMC data to this out-of-domain dataset.

- They then train a skeleton-based action recognition model on PanAf and show that the top-1/3 accuracy is slightly higher than video-based methods (and strong), but that the mean class accuracy is lower - 33% vs 42%. Likely due to the imbalanced class frequencies. This should be clarified. For Table 1, confidence intervals would also be good (just like for the pose estimation results, where this is done very well).

eLife assessment

This important study shows that in teleost fish, the RIG-I-like protein MDA5 can compensate for the absence of RIG-I by detecting 5'-triphosphorylated RNA. A fish virus containing such RNA can nevertheless evade MDA5 detection through a mechanism involving m6A methylation-induced silencing. The conclusions, which are supported by solid data, advance our understanding of antiviral immunity and virus-host conflicts in vertebrates.

Reviewer #1 (Public Review):

This study offers valuable insights into host-virus interactions, emphasizing the adaptability of the immune system. Readers should recognize the significance of MDA5 in potentially replacing RIG-I and the adversarial strategy employed by 5'ppp-RNA SCRV in degrading MDA5 mediated by m6A modification in different species, further indicating that m6A is a conservational process in the antiviral immune response.<br /> However, caution is warranted in extrapolating these findings universally, given the dynamic nature of host-virus dynamics. The study provides a snapshot into the complexity of these interactions, but further research is needed to validate and extend these insights, considering potential variations across viral species and environmental contexts.

Reviewer #2 (Public Review):

Panel 2N and 2O should have been done with and without SCRV treatment, so that the reader can assess whether SCRV induces additional IFN activation (on top of MDA5 and STING autoactivation). I would recommend the authors include a sentence in the text to explain that ectopic expression of MDA5 or STING (i.e. overexpression from a plasmid) induces autoactivation of these proteins. Therefore, the IFN induction that is seen in panel 2N is likely due to MDA5/STING overexpression. SCRV treatment may further boost IFN induction, but this cannot be assessed without the 'mock' conditions. This information will help the readers to interpret Fig. 2N and 2O correctly.

Author response:

The following is the authors’ response to the previous reviews.

eLife assessment

The authors present evidence suggesting that MDA5 can substitute as a sensor for triphosphate RNA in a species that naturally lacks RIG-I. The key findings are potentially important for our understanding of the evolution of innate immune responses. Compared to an earlier version of the paper, the strength of evidence has improved but it is still partially incomplete due to a few key missing experiments and controls.

We would like to thank the editorial team for their positive comments and constructive suggestions on improving our manuscript. We have made further improvements based on the valuable suggestions of the reviewers, and we are pleased to send you the revised manuscript now. After revising the manuscript and further supplementing with experiments, we think that our existing data can support our claims.

Public Reviews:

Reviewer #1 (Public Review):

This study offers valuable insights into host-virus interactions, emphasizing the adaptability of the immune system. Readers should recognize the significance of MDA5 in potentially replacing RIG-I and the adversarial strategy employed by 5'ppp-RNA SCRV in degrading MDA5 mediated by m6A modification in different species, further indicating that m6A is a conservational process in the antiviral immune response.

However, caution is warranted in extrapolating these findings universally, given the dynamic nature of host-virus dynamics. The study provides a snapshot into the complexity of these interactions, but further research is needed to validate and extend these insights, considering potential variations across viral species and environmental contexts. Additionally, it is noted that the main claims put forth in the manuscript are only partially supported by the data presented.

After meticulous revisions of the manuscript, including adjustments to the title, abstract, results, and discussion, the main claim of our study now is the arm race between the MDA5 receptor and SCRV virus in a lower vertebrate fish, M. miiuy. This mainly includes two parts: Firstly, the MDA5 of M. miiuy can recognize virus invasion and initiate host immune response by recognizing the triphosphate structure of SCRV. Secondly, as an adversarial strategy, 5’ppp-RNA SCRV virus can utilize the m6A mechanism to degrade MDA5 in M. miiuy. Based on the reviewer's suggestions, we have further supplemented the critical experiments (Figure 3F-3G, Figure 4D, Figure 5G) and provided a more detailed and accurate explanation of the experimental conclusions, we believe that our existing manuscript can support our main claims. In addition, because virus-host coevolution complicates the derivation of universal conclusions, we will further expand our insights in future research.

Reviewer #2 (Public Review):

This manuscript by Geng et al. aims to demonstrate that MDA5 compensates for the loss of RIG-I in certain species, such as teleost fish miiuy croaker. The authors use siniperca cheats rhabdovirus (SCRV) and poly(I:C) to demonstrate that these RNA ligands induce an IFN response in an MDA5-dependent manner in m.miiuy derived cells. Furthermore, they show that MDA5 requires its RD domain to directly bind to SCRV RNA and to induce an IFN response. They use in vitro synthesized RNA with a 5'triphosphate (or lacking a 5'triphosphate as a control) to demonstrate that MDA5 can directly bind to 5'-triphosphorylated RNA. The second part of the paper is devoted to m6A modification of MDA5 transcripts by SCRV as an immune evasion strategy. The authors demonstrate that the modification of MDA5 with m6A is increased upon infection and that this causes increased decay of MDA5 and consequently a decreased IFN response.

One critical caveat in this study is that it does not address whether ppp-SCRV RNA induces IRF3-dimerization and type I IFN induction in an MDA5 dependent manner. The data demonstrate that mmiMDA5 can bind to triphosphorylated RNA (Fig. 4D). In addition, triphosphorylated RNA can dimerize IRF3 (4C). However, a key experiment that ties these two observations together is missing.

Specifically, although Fig. 4C demonstrates that 5'ppp-SCRV RNA induces dimerization (unlike its dephosphorylated or capped derivatives), this does not proof that this happens in an MDA5-dependent manner. This experiment should have been done in WT and siMDA5 MKC cells side-by-side to demonstrate that the IRF3 dimerization that is observed here is mediated by MDA5 and not by another (unknown) protein. The same holds true for Fig. 4J.

Thank you for the referee's professional suggestions. In fact, we have transfected SCRV RNA into WT and si-MDA5 MKC cells, and subsequently assessed the dimerization of IRF3 and the IFN response (Figure 2P-2Q). The results indicated that knockdown of MDA5 prevents immune activation of SCRV RNA. However, considering the potential for SCRV RNA to activate immunity independent of the triphosphate structure, this experimental observation does not comprehensively establish the MDA5-dependent induction of IRF3 dimer by 5’ppp-RNA. Accordingly, in accordance with the referee's recommendation, we proceeded to investigate the inducible activity of 5'ppp-SCRV on IRF3 dimerization in WT and si-MDA5 MKC cells, revealing that 5'ppp-SCRV indeed elicits immunity in an MDA5-dependent manner (Figure 4D). Additionally, poly(I:C)-HMW, a known ligand for MDA5, demonstrated a residual, albeit attenuated, activation of IRF3 following MDA5 knockdown, potentially attributed to its capacity to stimulate immunity through alternative pathways such as TLR3.

- Fig 1C-D: these experiments are not sufficiently convincing, i.e. the difference in IRF3 dimerization between VSV-RNA and VSV-RNA+CIAP transfection is minimal.

We have reconstituted the necessary materials and repeated the pertinent experiments depicted in Fig 1C-1D. The results demonstrate that SCRV-RNA+CIAP and VSV-RNA+CIAP exhibit a mitigating effect on the induction activity of SCRV-RNA and VSV-RNA on IRF3 dimerization, albeit without complete elimination (Figure 1C and 1D). These findings suggest the presence of receptors within M. miiuy and G. gallus capable of recognizing the viral triphosphate structure; however, it is worth noting that RNA derived from SCRV and VSV viruses does not exclusively depend on the triphosphate structure to activate the host's antiviral response.

Fig. 2N and 2O: why did the authors decide to use overexpression of MDA5 to assess the impact of STING on MDA5-mediated IFN induction? This should have been done in cells transfected with SCRV or polyIC (as in 2D-G) or in infected cells (as in 2H-K). In addition, it is a pity that the authors did not include an siMAVS condition alongside siSTING, to investigate the relative contribution of MAVS versus STING to the MDA5-mediated IFN response. Panel O suggests that the IFN response is completely dependent on STING, which is hard to envision.

In our previous laboratory investigations, we have substantiated the induction effect of STING on IFN under SCRV infection or poly(I:C) stimulation, as documented in the relevant literature (10.1007/s11427-020-1789-5), which we have referenced in our manuscript (lines 177-178). While we did assess the impact of STING on MDA5-mediated IFN induction in SCRV-infected cells, as indicated in the figure legends, we have revised Figure 2N-2O for improved clarity, and similarly, Figure 1H-1I has also been updated. Furthermore, considering that RNA virus infection can activate the cGAS/STING axis (10.3389/fcimb.2023.1172739) and the significant role of MAVS in sensing RNA virus invasion in the NLR pathway (10.1038/ni.1782), it is challenging to ascertain the respective contributions of STING and MAVS to the immune signaling cascade mediated by MDA5 during RNA virus infection. We intend to explore this aspect further in future research endeavors.

Fig. 3F and 3G: where are the mock-transfected/infected conditions? Given that ectopic expression of hMDA5 is known to cause autoactivation of the IFN pathway, the baseline ISG levels should be shown (ie. In absence of a stimulus or infection). Normalization of the data does not reveal whether this is the case and is therefore misleading.

Based on the reviewer's suggestions, we have rerun the experiment. We examined the effects of MDA5 and MDA5-ΔRD on antiviral factors in both uninfected, SCRV-infected, and poly(I:C)-HMW-stimulated MKC cells. Results showed that overexpression of both MDA5 and MDA5-ΔRD stimulated the expression of antiviral genes. However, when cells were infected or stimulated with SCRV or poly(I:C)-HMW, only the overexpression of MDA5, not MDA5-ΔRD, significantly increased the expression of antiviral genes (Figure 3F-3I).

Fig. 4F and 4G: can the authors please indicate in the figure which area of the gel is relevant here? The band that runs halfway the gel? If so, the effects described in the text are not supported by the data (i.e. the 5'OH-SCRV and 5'pppGG-SCRV appear to compete with Bio-5'ppp-SCRV as well as 5'ppp-SCRV).

Apologies for any confusion. The relevant areas in the gel pertaining to the experimental findings were denoted with asterisks and elaborated upon in the figure legends (Figure 4G, 4H, and 4M). The findings indicated that 5'ppp-SCRV, in contrast to 5'OH-SCRV and 5'pppGG-SCRV, demonstrated the ability to compete with bio-5'ppp-SCRV.

My concerns about Fig. 5 remain unaltered. The fact that MDA5 is an ISG explains its increased expression and increased methylation pattern. The authors should at the very least mention in their text that MDA5 is an ISG and that their observations may be partially explained by this fact.

First, as our m6A change analysis pipeline controls for changes in gene expression, these data should represent true changes in m6A modification rather than changes in the expression of m6A-modified transcripts (10.1038/s41598-020-63355-3). Similar studies demonstrated that m6A modification in RIOK3 and CIRBP mRNAs are altered following Flaviviridae infection (10.1016/j.molcel.2019.11.007). The specific calculation method is as follows: relative m6A level for each transcript was calculated as the percent of input in each condition normalized to that of the respective positive control spike-in. Fold change of enrichment was calculated with mock samples normalized to 1. Therefore, changes in the expression level of MDA5 can partially explain the increase in m6A modification on all MDA5 mRNA in cells, but it cannot indicate changes in m6A modification on each mDA5 transcript. We have supplemented the calculation method process in the manuscript and cited relevant literature (Lines 606-608). In addition, we have elaborated on the fact that MDA5 is an ISG gene in the experimental results (lines 260-261), and emphasized its compatibility with enhanced m6A modification of MDA5 in the discussion section (lines 405-409).

Reviewer #3 (Public Review):

In this manuscript, the authors explored the interaction between the pattern recognition receptor MDA5 and 5'ppp-RNA in the Miiuy croaker. They found that MDA5 can serve as a substitute for RIG-I in detecting 5'ppp-RNA of Siniperca cheilinus rhabdovirus (SCRV) when RIG-I is absent in Miiuy croaker. Furthermore, they observed MDA5's recognition of 5'ppp-RNA in chickens (Gallus gallus), a species lacking RIG-I. Additionally, the authors documented that MDA5's functionality can be compromised by m6A-mediated methylation and degradation of MDA5 mRNA, orchestrated by the METTL3/14-YTHDF2/3 regulatory network in Miiuy croaker during SCRV infection. This impairment compromises the innate antiviral immunity of fish, facilitating SCRV's immune evasion. These findings offer valuable insights into the adaptation and functional diversity of innate antiviral mechanisms in vertebrates.

We extend our sincere appreciation for your professional comments and insightful suggestions on our manuscript, as they have significantly contributed to enhancing its quality.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) The interpretation of Figures 1H and I, along with the captions, seems unclear. Particularly, understanding the meaning of the X-axis in Figure I is challenging. Additionally, the designation of "H2O = 1" on the Y-axis in Figure 1E lacks clarity. It would be helpful if the author could revise and clarify these figures for better comprehension.

We appreciate your reminder and have corrected and clarified these figures and figure legends (lines 768-772). We have replaced the Y-axis of Figure 1I with "Relative mRNA expression" instead of " Relative IFN-1 expression" (Figure 1I). In addition, we have added an explanation of "H2O=1" in the legend of Figure 1E.

(2) The interpretation of Figure 5 in section 2.5 seems incomplete. The author mentioned that both m6A levels and MDA5 expression levels are increased (lines 256-257), prompting questions about the relationship between m6A and MDA5 expression. If higher m6A levels typically lead to MDA5 mRNA instability and lower MDA5 expression, observing both increasing simultaneously appears contradictory. Considering the dynamic changes shown in Figure 5, it would be more appropriate to propose an alteration in both m6A levels and MDA5 expression levels. Given the fluctuating nature of these changes, definitively labeling them as solely "increased" is challenging. Therefore, offering a nuanced interpretation of the results and clarifying this aspect would bolster the study's conclusions.

While changes in m6A modification and the expression of m6A-modified transcripts are biologically relevant, identifying bona fide m6A alterations during viral infection will allow us to understand how m6A modification of cellular mRNA is regulated. As our m6A change analysis pipeline controls for changes in gene expression, these data should represent true changes in m6A modification rather than changes in the expression of m6A-modified transcripts (10.1038/s41598-020-63355-3). Similar studies demonstrated that m6A modification in RIOK3 and CIRBP mRNAs are altered following Flaviviridae infection (10.1016/j.molcel.2019.11.007). The specific calculation method is as follows: relative m6A level for each transcript was calculated as the percent of input in each condition normalized to that of the respective positive control spike-in. Fold change of enrichment was calculated with mock samples normalized to 1. Therefore, the upregulation of MDA5 expression can partially explain the increase in m6A modification on all MDA5 mRNA in cells, but it cannot indicate changes in m6A modification on each mDA5 transcript. We have supplemented the calculation method process in the manuscript and cited relevant literature. I hope to receive your understanding.

In addition, although higher m6A levels often lead to unstable MDA5 mRNA and lower MDA5 expression, SCRV can affect MDA5 expression through multiple pathways. For example, since MDA5 is an interferon-stimulated gene, the infection of SCRV virus can cause strong expression of interferon and indirectly induce high-level expression of MDA5. Therefore, the expression of MDA5 is not contradictory to the simultaneous increase in MDA5 modification (24 h). In order to further enhance our experimental conclusions, we supplemented the dual fluorescence experiment. The results indicate that, the infection of SCRV can inhibit the fluorescence activity of MDA5-exon1 reporter plasmids containing m6A sites but not including the promoter sequence of the MDA5 gene, and this inhibitory effect can be counteracted by cycloleucine (CL, an amino acid analogue that can inhibit m6A modification) (Figure 5G). This further indicates that SCRV can reduce the expression of MDA5 through the m6A pathway.

Finally, in light of the fluctuations in MDA5 expression levels, we have changed the subheadings of Results 2.5 section and provided a more comprehensive and precise elucidation of the experimental outcomes. We are grateful for your valuable feedback.

(3) In the discussion section, it would indeed be advantageous for the author to explore the novelty of this work more comprehensively, moving beyond merely acknowledging the widespread loss of RIG-I and suggesting MDA5 as a compensatory mechanism. Considering the well-established roles of MDA5 and m6A in host-virus interactions, the findings of this study may seem familiar in light of previous research. To enhance the discussion, it would be valuable for the author to delve into the implications of this evolutionary model. For instance, does the compensation or loss of RIG-I impact a species' susceptibility to specific types of viruses? Exploring such questions would provide insight into the broader significance of this compensation model and its potential effects on host-virus interactions, thus adding depth to the study's contribution.

We appreciate the expert advice provided by the referee. In response, we have expanded our discussion in the relevant section, addressing the potential influence of RIG-I deficiency and MDA5 compensation on the antiviral immune system in vertebrates (lines 371-376). Furthermore, we underscore the significance of exploring the impact of SCRV infection on MDA5 m6A modification, considering its compatibility with MDA5 as an ISG gene, in elucidating the host response to viral infection (lines 405-409).

(4) To improve the manuscript, it would be beneficial if the editors could aid the author in refining the language. Many descriptions in the article are overly redundant, and there should be appropriate differentiation between experimental methods and results.

We appreciate the reviewer’s comment. We have carefully revised the manuscript and removed redundant descriptions in the experimental results and methods.

Reviewer #3 (Recommendations For The Authors):

The authors have addressed all of my concerns.

eLife assessment

This study presents valuable findings describing how the midbrain periaqueductal gray matter and basolateral amygdala communicate when a predator threat is detected. Though the periaqueductal gray is usually viewed as a downstream effector, this work contributes to a growing body of literature from this lab showing that the periaqueductal gray produces effects by acting on the basolateral amygdala, the experimental design, data collection and analysis methods provide solid evidence for the main claims. The anatomical and immediately early gene evidence that the paraventricular nucleus of the thalamus may serve as a mediator of dorsolateral periaqueductal gray to basolateral amygdala neurotransmission provides and impetus for future functional assessment of this possibility. This study will appeal to a broad audience, including basic scientists interested in neural circuits, basic and clinical researchers interested in fear, and behavioral ecologists interested in foraging.

Reviewer #1 (Public Review):

In the presence of predators, animals display attenuated foraging responses and increased defensive behaviors that serve to protect them from potential predatory attacks. Previous studies have shown that the basolateral nucleus of the amygdala (BLA) and the periaqueductal gray matter (PAG) are necessary for the acquisition and expression of conditioned fear responses. However, it remains unclear how BLA and PAG neurons respond to predatory threats when animals are foraging for food. To address this question, Kim and colleagues conducted in vivo electrophysiological recordings from BLA and PAG neurons and assessed approach-avoidance responses while rats searched for food in the presence of a robotic predator.

The authors observed that rats exhibited a significant increase in the latency to obtain the food pellets and a reduction in the pellet success rate when the predator robot was activated. A subpopulation of PAG neurons showing an increased firing rate in response to the robot activation didn't change their activity in response to food pellet retrieval during the pre- or post-robot sessions. Optogenetic stimulation of PAG neurons increased the latency to procure the food pellet in a frequency- and intensity-dependent manner, similar to what was observed during the robot test. Combining optogenetics with single-unit recordings, the authors demonstrated that photoactivation of PAG neurons increased the firing rate of 10% of BLA cells. A subsequent behavioral test in 3 of these same rats demonstrated that BLA neurons responsive to PAG stimulation displayed higher firing rates to the robot than BLA neurons nonresponsive to PAG stimulation. Next, because the PAG does not project monosynaptically to the BLA, the authors used a combination of retrograde and anterograde neural tracing to identify possible regions that could convey robot-related information from PAG to the BLA. They observed that neurons in specific areas of the paraventricular nucleus of the thalamus (PVT) that are innervated by PAG fibers contained neurons that were retrogradely labeled by the injection of CTB in the BLA. In addition, PVT neurons showed increased expression of the neural activity marker cFos after the robot test, suggesting that PVT may be a mediator of PAG signals to the BLA.

Overall, the idea that the PAG interacts with the BLA via the midline thalamus during a predator vs. foraging test is new and quite interesting. The authors have used appropriate tools to address their questions. However, there are some major concerns regarding the design of the experiments, the rigor of the histological analyses, the presentation of the results, the interpretation of the findings, and the general discussion that largely reduces the relevance of this study.

The authors have fully addressed all my concerns.

Reviewer #2 (Public Review):

The authors characterized the activity of the dorsal periaqueductal gray (dPAG) - basolateral amygdala (BLA) circuit. They show that BLA cells that are activated by dPAG stimulation are also more likely to be activated by a robot predator. These same cells are also more likely to display synchronous firing.

The authors also replicate prior results showing that dPAG stimulation evokes fear and the dPAG is activated by a predator.

Lastly, the report performs anatomical tracing to show that the dPAG may act on the BLA via the paraventricular thalamus (PVT). Indeed, the PVT receives dPAG projections and also projects to the BLA. However, the authors do not show if the PVT mediates dPAG to BLA communication with any functional behavioral assay.

The major impact in the field would be to add evidence to their prior work, strengthening the view that the BLA can be downstream of the dPAG.

Reviewer #3 (Public Review):

In the present study, the authors examined how dPAG neurons respond to predatory threats and how dPAG and BLA communicate threat signals. The authors employed single-unit recording and optogenetics tools to address these issues in an 'approach food-avoid predator' paradigm. They characterized dPAG and BLA neurons responsive to a looming robot predator and found that dPAG opto-stimulation elicited fleeing and increased BLA activity. Importantly, they found that dPAG stimulation produces activity changes in subpopulations of BLA neurons related to predator detection, thus supporting the idea that dPAG conveys innate fear signals to the amygdala. In addition, injections of anterograde and retrograde tracers into the dPAG and BLA, respectively, along with the examination of c-FOS activity in midline thalamic relay stations, suggest that the paraventricular nucleus of the thalamus (PVT) may serve as a mediator of dPAG to BLA neurotransmission. Of relevance, the study helps to validate an important concept that dPAG mediates primal fear emotion and may engage upstream amygdala targets to evoke defensive responses. The series of experiments provides a compelling case for supporting their conclusions. The study brings important concepts revealing dynamics of fear-related circuits particularly attractive to a broad audience, from basic scientists interested in neural circuits to psychiatrists.

Author response:

The following is the authors’ response to the previous reviews.

Public Reviews

Reviewer 1 summarized that: In this revised version of the manuscript, the authors have made important modifications in the text, inserted new data analyses, and incorporated additional references, as recommended by the reviewers. These modifications have significantly improved the quality of the manuscript.

We are grateful for the reviewer's positive recognition of our revisions.

Reviewer 2 noted that:

(1) The authors do not show if the PVT mediates dPAG to BLA communication with any functional behavioral assay.

We appreciate the reviewer’s suggestion to include a functional assay to investigate the role of the PVT in mediating communication between the dPAG and BLA. Our primary objective was to confirm the upstream role of the dPAG in processing and relaying naturalistic predatory threat information to the BLA, thereby broadening our current understanding of the dPAG-BLA relationship based on Pavlovian fear conditioning paradigms.

Given previous anatomical findings indicating the absence of direct monosynaptic projections from the dPAG to the BLA (Cameron et al. 1995, McNally, Johansen, and Blair 2011, Vianna and Brandao 2003), we employed both anterograde and retrograde tracers, supplemented by c-Fos expression analysis following predatory threats, to explore possible routes through which threat signals may be conveyed from the dPAG to the BLA. Our findings indicated significant activity within the midline thalamic regions, particularly the PVT as a mediator of dPAG-BLA interactions, corroborating the possibility of dPAGàBLA information flow.

Investigating the PVT's functional role appropriately would require single-unit recordings, correlation analysis of PVT neuronal responses with dPAG and BLA neuronal responses, and pathway-specific causal techniques, involving other midline thalamic regions for controls. This comprehensive study would represent an independent study.

In response to previous feedback, we have carefully revised our manuscript to moderate the emphasis on the PVT's role. Both the Abstract, Results, and Discussion refer more broadly to "midline thalamic regions" and “The midline thalamus” (subheading) rather than specifically to the PVT. In the Introduction, we mention that the PVT "may be part of a network that conveys predatory threat information from the dPAG to the BLA." Our conclusions about the functional interaction between the dPAG and BLA, which broaden the view of Pavlovian fear conditioning, are not contingent on confirming a specific intermediary role for the PVT.

(2) The author also do not thoroughly characterize the activity of BLA cells during the predatory assay.

Our previous studies have extensively detailed BLA cell firing characteristics, including their responsiveness to food and/or a robot predator during the predatory assay (Kim et al. 2018, Kong et al. 2021), and compared these findings to other predator studies (Amir et al. 2019, Amir et al. 2015). In the current study, out of 85 BLA cells, 3 were food-specific and 4 responded to both the pellet and the robot, with none of these 7 cells responding to dPAG stimulation.

Given our earlier findings of the immediate responses of BLA neurons to robot activation, we specifically examined whether robot-responsive BLA neurons receive signals from the dPAG. For this analysis, we excluded all food-related cells (pellet cells and BOTH cells) and focused on the time window immediately after robot activation (within 500 ms after robot onset). This approach enabled us to avoid potential confounds from residual effects of robot-induced immediate BLA responses during the animals’ flight and nest entry behaviors.

Furthermore, as previously described, the robot is programmed to move forward a fixed distance and then return, repeatedly triggering foraging behavior. This setup facilitates the analysis of neural changes during food approach and predator avoidance conflicts. However, animals quickly adapt to the robot, reducing freezing and stretch-attend behaviors, making time-stamped analysis of these behaviors unfeasible.

We would like to highlight that the present study explicitly focused on demonstrating whether BLA neurons that responded to intrinsic dPAG optogenetic stimulation also responded to extrinsic predatory robot activation, and compared their firing characteristics to those BLA neurons that did not respond to dPAG stimulation (Figure 3). This targeted analysis provides insights into the responsiveness of BLA neurons to both intrinsic and extrinsic stimuli, furthering our understanding of the dPAG-BLA interaction in the context of predatory threats.

Reviewer 3 also raised no concerns and stated that: The series of experiments provide a compelling case for supporting their conclusions. The study brings important concepts revealing dynamics of fear-related circuits particularly attractive to a broad audience, from basic scientists interested in neural circuits to psychiatrists.

We sincerely thank the reviewer for the positive feedback on our revisions.

Recommendations for the Authors

Reviewer 1: There are a few minor concerns that the authors may want to fix:

(1) Point 5) The sentence: "The complexity of targeting the dPAG, which includes its dorsomedial, dorsolateral, lateral, and ventrolateral subdivisions" is hard to follow because the ventrolateral subdivision is not part of the dPAG. The authors may want to say specific subregions of the PAG instead. It is also unclear why transgenic animals would be needed for this projection-defined manipulations. The combination of retrograde Cre-recombinase virus with inhibitory opsin or chemogenetic approach may be sufficient.

We appreciate the reviewer’s insightful feedback regarding our description of the dPAG and the use of transgenic mice in future studies. As suggested, we have corrected the manuscript to exclude the 'ventrolateral' subdivision from the dPAG description, now accurately aligning with pioneering studies (Bandler, Carrive, and Zhang 1991, Bandler and Keay 1996, Carrive 1993) that designated dPAG as including the dorsomedial (dmPAG), dorsolateral (dlPAG) and lateral (lPAG) regions, as cited in our revised manuscript.

We acknowledge the reviewer’s helpful suggestion regarding the use of retrograde Cre-recombinase virus with inhibitory opsins or chemogenetic approaches as viable alternatives. These methods have been incorporated into our discussion (pages 14-15): “While our findings demonstrate that opto-stimulation of the dPAG is sufficient to trigger both fleeing behavior and increased BLA activity, we have not established that the dPAG-PVT circuit is necessary for the BLA’s response to predatory threats. To establish causality and interregional relationships, future studies should employ methods such as pathway-specific optogenetic inhibition (using retrograde Cre-recombinase virus with inhibitory opsins; Lavoie and Liu 2020, Li et al. 2016, Senn et al. 2014) or chemogenetics (Boender et al. 2014, Roth 2016) in conjunction with single unit recordings to fully characterize the dPAG-PVT-BLA circuitry’s (as opposed to other midline thalamic regions for controls) role in processing predatory threat-induced escape behavior. If inactivating the dPAG-PVT circuits reduces the BLA's response to threats, this would highlight the central role of the dPAG-PVT pathway in this defense mechanism. Conversely, if the BLA's response remains unchanged despite dPAG-PVT inactivation, it could suggest the existence of multiple pathways for antipredatory defenses.”

This revision addresses the critique by clarifying the anatomical description of the dPAG and emphasizing the feasibility of using targeted viral approaches without the necessity for transgenic animals.

(2) Point 6e) The authors mentioned that "pellet retrieval" was indicated by the animal entering a designated zone 19 cm from the pellet, driven by hunger. Entering the area 19cm of distance should be labeled as food approaching rather then food retrieval because in many occasions the animals may be some seconds away of grabbing the pellet.

We agree and incorporate the change (pg. 22).

(3) Point 11) We would strongly recommend the authors to replace the terminology "looming" by "approaching" to avoid confusion with several previous studies looking at defensive behaviors in responses to looming induced by the shadow of an object moving closer to the eyes.

Done.

(4) Point 17) The authors mentioned that "A total of three rats were utilized for the robot testing experiments depicted in Fig. 2 G-J." However, the figure indicates a total of 9 ChR2 and 4 controls.

We apologize for the confusion in our previous author responses. To examine the optical stimulation effects on behavior in Fig. 2G-J, we used a total of 9 ChR2 and 4 EYFP rats. The experimental sequence is detailed in the previously revised manuscript (pg. 20): “For optical stimulation and behavioral experiments, the procedure included 3 baseline trials with the pellet placed 75 cm away, followed by 3 dPAG stimulation trials with the pellet locations sequentially set at 75 cm, 50 cm, and 25 cm. During each approach to the pellet, rats received 473-nm light stimulation (1-2 s, 20-Hz, 10-ms width, 1-3 mW) through a laser (Opto Engine LLC) and a pulse generator (Master-8; A.M.P.I.). Additional testing to examine the functional response curves was conducted over multiple days, with incremental adjustments to the stimulation parameters (intensity, frequency, duration) after confirming that normal baseline foraging behavior was maintained. For these tests, one parameter was adjusted incrementally while the others were held constant (intensity curve at 20 Hz, 2 s; frequency curve at 3 mW, 2 s; duration curve at 20 Hz, 3 mW). If the rat failed to procure the pellet within 3 min, the gate was closed, and the trial was concluded.”

This clarification ensures that the actual number of animals used is accurately reflected and aligns with the figure data, addressing the reviewer's concern.

Reviewer 2: The authors made important changes in the text to address study limitations, including citations requested by the Reviewers and additional discussions about how this work fits into the existing literature. These changes have strengthened the manuscript.

(1) However, the authors did not perform new experiments to address any of the issues raised in the previous round of reviews. For example, they did not make optogenetic manipulations of the pathway including the PVT, and did not add any loss of function experiments. The justification that these experiments are better suited for future reports using mice is not convincing, because hundreds of papers performing these types of circuit dissection assays have been performed in rats.

We appreciate the reviewer's comments regarding the experimental scope of our study. Our study’s primary objective was to explore the dPAG’s upstream functional role in processing and conveying naturalistic predatory threat information to the BLA, extending our current understanding of the dPAG-BLA relationship based on Pavlovian fear conditioning paradigms. We believe that our findings effectively address this goal.

Our use of anterograde and retrograde tracers, supplemented by c-Fos expression analysis in response to predatory threats, was primarily conducted to verify the possibility of the dPAGàBLA information flow during predator encounters. This involved exploring potential routes through which threat signals might be conveyed from the dPAG to the BLA, given the lack of direct monosynaptic projections from the dPAG to BLA neurons (Cameron et al. 1995, McNally, Johansen, and Blair 2011, Vianna and Brandao 2003). This methodology helped us identify a potential structure, PVT, for more in-depth future studies. A thorough examination of the PVT's role would require single-unit recordings and causal techniques, incorporating other midline thalamic regions as controls, representing a significant and separate study on its own.

In response to prior feedback, we have carefully revised our manuscript to generally address the role of "midline thalamic regions" rather than focusing specifically on the PVT. We wish to emphasize that our findings, which illustrate unique functional interactions between the dPAG and BLA in response to a predatory imminence, remain compelling and informative even without definitive evidence of the PVT’s involvement.

Reviewer 3: In the revised version of the manuscript, the authors addressed adequately all the concerns raised by the reviewers. 

We thank the reviewer for the thoughtful feedback on the earlier version of our manuscript and for reexamining the revisions we have made.

References

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eLife assessment

This study explores the role of calcyphosine-like (CAPSL) in Familial Exudative Vitreoretinopathy (FEVR) via the MYC pathway, offering valuable insights into disease mechanisms that are supported by a solid, multi-pronged approach. The manuscript, which presents the phenotype of an interesting new mouse model, provides convincing evidence that CAPSL variants cause disease.

Reviewer #1 (Public Review):

Summary:<br /> The author presents the discovery and characterization of CAPSL as a potential gene linked to Familial Exudative Vitreoretinopathy (FEVR), identifying one nonsense and one missense mutation within CAPSL in two distinct patient families afflicted by FEVR. Cell transfection assays suggest that the missense mutation adversely affects protein levels when overexpressed in cell cultures. Furthermore, conditionally knocking out CAPSL in vascular endothelial cells leads to compromised vascular development. The suppression of CAPSL in human retinal microvascular endothelial cells results in hindered tube formation, a decrease in cell proliferation, and disrupted cell polarity. Additionally, transcriptomic and proteomic profiling of these cells indicates alterations in the MYC pathway.

Strengths:<br /> The study is nicely designed with a combination of in vivo and in vitro approaches, and the experimental results are good quality.

Weaknesses:<br /> My reservations lie with the main assertion that CAPSL is associated with FEVR, as the genetic evidence from human studies appears relatively weak. Further careful examination of human genetics evidence in both patient cohorts and the general population will help to clarify. In light of human genetics, more caution needs to be exercised when interpreting results from mice and cell model and how is it related to the human patient phenotype. Future replication by finding more FEVR patients with a mutation in CAPSL will strengthen the findings.

Reviewer #2 (Public Review):

Summary:<br /> This work identifies two variants in CAPSL in two generation familial exudative vitreoretinopathy (FEVR) pedigrees, and using a knockout mouse model, they link CAPSL to retinal vascular development and endothelial proliferation through the MYC pathway. Together, these findings suggest that the identified variants may be causative and that CAPSL is a new FEVR-associated gene.

Strengths:<br /> The authors data provides compelling evidence that loss of the poorly understood protein CAPSL can lead to reduced endothelial proliferation in mouse retina and suppression of MYC signaling, consistent with the disease seen in FEVR patients. The paper is clearly written, and the data generally support the author's hypotheses.

Weaknesses:<br /> (1) Both pedigrees described suggest autosomal dominant inheritance in humans, but no phenotype was observed in Capsl heterozygous mice. Additional studies would be needed to determine the cause of this disparity.

(2) Additional discussion of the hypothesized functional mechanism of the p.L83F variant would have improved the manuscript. While the human genetic data is compelling, it remains unclear how this variant may effect CAPSL function. In vitro, p.L83F protein appears to be normally localized within the cell and it is unclear why less mutant protein was detected in transfected cells. Was the modified protein targeted for degradation?

(3) Authors did not describe how the new crispr-generated Capsl-loxp mouse model was screened for potential off-target gene editing, raising the possibility that unrelated confounding mutations may have been introduced.

Reviewer #3 (Public Review):

Summary:<br /> This manuscript by Liu et al. presents a case that CAPSL mutations are a cause of familial exudative vitreoretinopathy (FEVR). Attention was initially focused on the CAPSL gene from whole exome sequence analysis of two small families. The follow-up analyses included studies in which Capsl was manipulated in endothelial cells of mice and multiple iterations of molecular and cellular analyses. Together, the data show that CAPSL influences endothelial cell proliferation and migration. Molecularly, transcriptomic and proteomic analyses suggest that CAPSL influences many genes/proteins that are also downstream targets of MYC and may be important to the mechanisms.

Strengths:<br /> This multi-pronged approach found a previously unknown function for CAPSL in endothelial cells and pointed at MYC pathways as high-quality candidates in the mechanism. Through the review process, some statements and interpretations were initially challenged. However, the issues were addressed with new experimentation and modifications to the text - leaving a strengthened presentation that makes a compelling case.

Weaknesses:<br /> Two issues shape the overall impact for me. First, it remains unclear how common CAPSL variants may be in the human population. From the current study, it is possible that they are rare - perhaps limiting an immediate clinical impact. However, sharing the data may help identify additional variants in FEVR or other vascular diseases. The findings also make advances in basic biology which could ultimately contribute to therapies of broad relevance. Thus, this weakness is considered modest. Second, the links to the MYC axis are largely based on association, which will require additional experimentation to help understand.

One interesting technical point raised in the study, which might be missed without care by the readership, is that the variants appear to act dominantly in human families, but only act recessively in the mouse model. The authors cite other work from the field in which this same mismatch occurs, likely pointing to limits in how closely a mouse model might be expected to recapitulate a human disease. This technical point is likely relevant to ongoing studies of FEVR and many other multigenic diseases as well.

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public Review): 

Summary: 

The author presents the discovery and characterization of CAPSL as a potential gene linked to Familial Exudative Vitreoretinopathy (FEVR), identifying one nonsense and one missense mutation within CAPSL in two distinct patient families afflicted by FEVR. Cell transfection assays suggest that the missense mutation adversely affects protein levels when overexpressed in cell cultures. Furthermore, conditionally knocking out CAPSL in vascular endothelial cells leads to compromised vascular development. The suppression of CAPSL in human retinal microvascular endothelial cells results in hindered tube formation, a decrease in cell proliferation, and disrupted cell polarity. Additionally, transcriptomic and proteomic profiling of these cells indicates alterations in the MYC pathway. 

Strengths: 

The study is nicely designed with a combination of in vivo and in vitro approaches, and the experimental results are good quality. 

We thank the reviewer for the conclusion and positive comments.

Weaknesses: 

My reservations lie with the main assertion that CAPSL is associated with FEVR, as the genetic evidence from human studies appears relatively weak. Further careful examination of human genetics evidence in both patient cohorts and the general population will help to clarify. In light of human genetics, more caution needs to be exercised when interpreting results from mice and cell models and how is it related to the human patient phenotype. 

We thank the reviewer for careful reading and constructive suggestion. we added several experiments to address the concern of reviewer are as follows:

(1) The pLI score of LOF allele of CAPSL is based of general population, among which Europeans account for ~77% and East Asians make up less than 3%. Since the FEVR families in this article all come from China, the pLI score may not be accurate. Of course, we will continue to collect FEVR pedigrees.

(2) We evaluated the phenotype of Capsl heterozygous mice at P5, and the results showed no overt difference in vascular progression, vessel density and branchpoints with littermate wildtype controls (Fig.S4). The lack of pronounced phenotype in FEVR heterozygous mice may be due to different sensitivity between human and mice. A similar example is LRP5 mutations associated with FEVR. Heterozygous mutations in LRP5 were reported in FEVR patients in multiple populations (PMID: 16929062, 33302760, 27486893, 35918671, 36411543). However, heterozygous Lrp5 knockout mice exhibited no visible angiogenic phenotype (PMID: 18263894). Corresponding description was added in the manuscript at page 6.

(3) We further assessed the angiogenic phenotype when angiogenesis almost complete at P21, and the resulted revealed no difference observed between Ctrl and CapsliECKO/iECKO mice (Fig.S5). And corresponding description was added in the manuscript at page 7.

(4) We evaluated the expression of MYC downstream genes in vivo using lung tissue form P35 Ctrl and _Capsl_iECKO/iECKO mice (Fig.S8). Consistent with the results from in vitro HRECs, _Capsl_iECKO/iECKO mice showed downregulated expression of MYC targets. And corresponding description was added in the manuscript at page 11.

Reviewer #2 (Public Review): 

Summary: 

This work identifies two variants in CAPSL in two-generation familial exudative vitreoretinopathy (FEVR) pedigrees, and using a knockout mouse model, they link CAPSL to retinal vascular development and endothelial proliferation. Together, these findings suggest that the identified variants may be causative and that CAPSL is a new FEVR-associated gene. 

Strengths: 

The authors' data provides compelling evidence that loss of the poorly understood protein CAPSL can lead to reduced endothelial proliferation in mouse retina and suppression of MYC signaling in vitro, consistent with the disease seen in FEVR patients. The study is important, providing new potential targets and mechanisms for this poorly understood disease. The paper is clearly written, and the data generally support the author's hypotheses. 

We thank the reviewer for the conclusion and positive comments.

Weaknesses: 

(1) Both pedigrees described appear to suggest that heterozygosity is sufficient to cause disease, but authors have not explored the phenotype of Capsl heterozygous mice. Do these animals have reduced angiogenesis similar to KOs? Furthermore, while the p.R30X variant protein does not appear to be expressed in vitro, a substantial amount of p.L83F was detectable by western blot and appeared to be at the normal molecular weight. Given that the full knockout mouse phenotype is comparatively mild, it is unclear whether this modest reduction in protein expression would be sufficient to cause FEVR - especially as the affected individuals still have one healthy copy of the gene. Additional studies are needed to determine if these variants alter protein trafficking or localization in addition to expression, and if they can act in a dominant negative fashion. 

We thank the reviewer for the suggestion. We evaluated the phenotype of Capsl heterozygous mice at P5 (Fig.S4), and the results showed no overt difference in angiogenesis compared with littermate control mice.

We transfected CAPSL wild-type plasmid, p.R30X mutant plasmid and p.L83F mutant plasmid into 293T cells to assess the intracellular localization change of CAPSL mutant proteins (Fig.S1). The result showed that the point mutation did not affect the localization of the mutated protein, and corresponding description was added in the manuscript at page 5.

(2) The manuscript nicely shows that loss of CAPSL leads to suppressed MYC signaling in vitro. However, given that endothelial MYC is regulated by numerous pathways and proteins, including FOXO1, VEGFR2, ERK, and Notch, and reduced MYC signaling is generally associated with reduced endothelial proliferation, this finding provides little insight into the mechanism of CAPSL in regulating endothelial proliferation. It would be helpful to explore the status of these other pathways in knockdown cells but as the authors provide only GSEA results and not the underlying data behind their RNA seq results, it is difficult for the reader to understand the full phenotype. Volcano plots or similar representations of the underlying expression data in Figures 6 and 7 as well as supplemental datasets showing the differentially regulated genes should be included. In addition, while the paper beautifully characterizes the delayed retinal angiogenesis phenotype in CAPSL knockout mice, the authors do not return to that model to confirm their in vitro findings. 

We thank the reviewer for the suggestion. Although endothelial MYC can be regulated by FOXO1, VEGFR2, ERK, and Notch signaling pathway, these pathways are not enriched in the RNA seq data of CAPSL-depleted HRECs. This suggests that the down regulated MYC targets may not be influenced by the signaling pathway mentioned above. RNA-seq raw data have been uploaded to the Genome Sequence Archive (https://ngdc.cncb.ac.cn/gsa/browse/HRA010305) and proteomic profiling raw data have been uploaded to the Genome Sequence Archive (https://www.ebi.ac.uk/pride/archive), and the assigned accession number was PXD051696. Corresponding description was added in the manuscript at page 20-21. The datasets represent the differentially regulated genes in Figure 6 and 7 were listed at Dataset S1 and S2.

(3) In Figure S2D, the result of this vascular leak experiment is unconvincing as no dye can be seen in the vessels. What are the kinetics for biocytin tracers to enter the bloodstream after IP injection? Why did the authors choose the IP instead of the IV route for this experiment? Differences in the uptake of the eye after IP injection could confound the results, especially in the context of a model with vascular dysfunction as here. 

We thank the reviewer for suggestion. In Figure S2D (now Fig.S6D), we used a non-representative image to show vascular leakage. We replaced the images with more representative ones. We are sorry that we are not clear about the kinetics for biocytin tracers to enter the bloodstream after IP injection. Since the experiment was carried out on mice at P5, it is not feasible to do IV injection in P5 neonatal mice. We followed the methods described in the previous study involving mice of same age (PMID:35361685).

(4) In Figure 5, it is unclear how filipodia and tip cells were identified and selected for quantification. The panels do not include nuclear or tip cell-specific markers that would allow quantification of individual tip cells, and in Figure 5C it appears that some filipodia are not highlighted in the mutant panel. 

We thank the reviewer for the comments. In Figure 5, we used HRECs to examine the cell proliferation, migration and polarity in vitro, and therefore there is no distinction between tip cells and stalk cells. The quantification of filopodia/lamellipodia was performed as previous studies (PMID: 30783090, PMID: 28805663). In briefly, wound scratch was performed on confluent layers of transfected HRECs, and 9 hours after initiating cell migration by scratch, cells were fixed and stained with phalloidin. Cells at the edge of wound were considered as leader cells and quantified for number of filopodia/lamellipodia.

Reviewer #3 (Public Review): 

Summary: 

This manuscript by Liu et al. presents a case that CAPSL mutations are a cause of familial exudative vitreoretinopathy (FEVR). Attention was initially focused on the CAPSL gene from whole exome sequence analysis of two small families. The follow-up analyses included studies in which CAPSL was manipulated in endothelial cells of mice and multiple iterations of molecular and cellular analyses. Together, the data show that CAPSL influences endothelial cell proliferation and migration. Molecularly, transcriptomic and proteomic analyses suggest that CAPSL influences many genes/proteins that are also downstream targets of MYC and may be important to the mechanisms. 

Strengths: 

This multi-pronged approach found a previously unknown function for CAPSLs in endothelial cells and pointed at MYC pathways as high-quality candidates in the mechanism. 

Weaknesses: 

Two issues shape the overall impact for me. First, the unreported population frequency of the variants in the manuscript makes it unclear if CAPSL should be considered an interesting candidate possibly contributing to FEVR, or possibly a cause. Second, it is unclear if the identified variants act dominantly, as indicated in the pedigrees. The studies in mice utilized homozygotes for an endothelial cell-specific knockout, leaving uncertainty about what phenotypes might be observed if mice heterozygous for a ubiquitous knockout had instead been studied. 

In my opinion, the following scientific issues are specific weaknesses that should be addressed: 

(1) Please state in the manuscript the number of FEVR families that were studied by WES. Please also describe if the families had been selected for the absence of known mutations, and/or what percentage lack known pathogenic variants. 

We thank the reviewer for thoughtful comments. 120 FEVR families were studied by WES and we added corresponding description in the manuscript at page 4.

(2) A better clinical description of family 3104 would enhance the manuscript, especially the father. It is unclear what "manifested with FEVR symptoms, according to the medical records" means. Was the father diagnosed with FEVR? If the father has some iteration of a mild case, please describe it in more detail. If the lack of clinical images in the figure is indicative of a lack of medical documentation, please note this in the manuscript. 

We thank the reviewer for thoughtful comments. The father of family 3104 has also been identified as a carrier of this heterozygous variant, manifested with FEVR symptoms, according to the medical records. Nevertheless, clinical examination images are presently unavailable. We added corresponding description in the manuscript at page 5.

(3) The TGA stop codon can in some instances also influence splicing (PMID: 38012313). Please add a bioinformatic assessment of splicing prediction to the assays and report its output in the manuscript. 

We thank the reviewer for thoughtful comments. We predicted the splicing of c.88C>T variant of CAPSL using MaxEntScan (http://hollywood.mit.edu/burgelab/maxent/Xmaxentscan_scoreseq.html) and SpliceTool (https://rddc.tsinghua-gd.org/ai) (Fig.S2). MaxEntScan and SpliceTool were used to predict the impact of TGA stop codon of c.88C>T variant on the formation of a cryptic donor splice site.

(4) More details regarding utilizing a "loxp-flanked allele of CAPSL" are needed. Is this an existing allele, if so, what is the allele and citation? If new (as suggested by S1), the newly generated CAPSL mutant mouse strain needs to be entered into the MGI database and assigned an official allele name - which should then be utilized in the manuscript and who generated the strain (presumably a core or company?) must be described. 

We added detailed description of Capsl flxoed allele to Method section on page 14-15: “Capslloxp/+ model was generated using the CRISPR/Cas9 nickase technique by Viewsolid Biotechology (Beijing, China) in C57BL/6J background and named Capslem1zxj. The genomic RNA (gRNA) sequence was as follows: Capsl-L gRNA: 5’-CTATCCCAA TTGTGCTCCTGG-3’; Capsl-R gRNA: 5’-TGGGACTCATGGTTCTAGAGG-3’. ”

(5) The statement in the methods "All mice used in the study were on a C57BL/6J genetic background," should be better defined. Was the new allele generated on a pure C57BL/6J genetic background, or bred to be some level of congenic? If congenic, to what generation? If unknown, please either test and report the homogeneity of the background, or consult with nomenclature experts (such as available through MGI) to adopt the appropriate F?+NX type designation. This also pertains to the Pdgfb-iCreER mice, which reference 43 describes as having been generated in an F2 population of C57BL/6 X CBA and did not designate the sub-strain of C57BL/6 mice. It is important because one of the explanations for missing heritability in FEVR may be a high level of dependence on genetic background. From the information in the current description, it is also not inherently obvious that the mice studied did not harbor confounding mutations such as rd1 or rd8. 

We thank the reviewer for suggestion. We added the following description to “Mouse model and genotyping” method section on page 14. “Capslloxp/+ model was generated using the CRISPR/Cas9 nickase technique by Viewsolid Biotechology (Beijing, China) in C57BL/6J background and named Capslem1zxj. The genomic RNA (gRNA) sequence was as follows: Capsl-L gRNA: 5’-CTATCCCAA TTGTGCTCCTGG-3’; Capsl-R gRNA: 5’-TGGGACTCATGGTTCTAGAGG-3’. Pdgfb-iCreER[43] transgenic mice on a mixed background of C57BL/6 and CBA was obtainted from Dr. Marcus Fruttiger and backcrossed to background for 6 generations. Capslloxp/+ mice were bred with Pdgfb-iCreER[43] transgenic mice to generate Capslloxp/loxp, Pdgfb-iCreER mice.” Sanger sequencing was performed on experimental mice to identify whether they harbor confounding mutations such as Pde6b or Crb1. The results showed the mice did not harbor confounding mutations (Fig.S9) and corresponding description was added in the manuscript at page 15.

(6) In my opinion, more experimental detail is needed regarding Figures 2 and 3. How many fields, of how many retinas and mice were analyzed in Figure 2? How many mice were assessed in Figure 3? 

We thank the reviewer for thoughtful comments. We have already presented the detailed information in the manuscript, please refer to the “Methods-Quantification of retinal parameters” section for experimental details.

(7) I suggest adding into the methods whether P-values were corrected for multiple tests. 

We thank the reviewer for suggestion. Actually, the statistical analysis was performed using unpaired Student’s t-test for comparison between two groups or one-way ANOVA followed by Dunnett multiple comparison test for comparison of multiple groups. The above description was added to “Methods-Image acquisition and statistical analysis” section to make it clear.

Recommendations for the authors:

Reviewing Editor (Recommendations For The Authors): 

In summary, the following concerns should addressing reviewers' concerns as outlined below could bolster the evidence from "solid" to "convincing" and further strengthen the study's impact. 

(1) Analysis of the phenotype in CAPSLheterozygous mice, as highlighted by all 3 reviews. 

We thank the editor for thoughtful comments. The phenotype analysis of Capsl heterozygous mice was added to Fig.S4, with the corresponding description provided at page 6.

(2) Analysis of Capsl KO mice to determine if the pathways identified in vitro are modified (as suggested by reviewers 1 & 2). 

We thank the editor for suggestion. In Fig.S7, RT-qPCR was performed on lung tissues from Capsl Ctrl and KO mice to validate the expression of MYC targets in vivo. And the result indicated that the downstream targets of MYC signaling were also downregulated in vivo, consistent with the in vitro findings.

(3) Additional description of the genetic pedigrees and variants to address the points raised by reviewer #3. 

We thank the editor for suggestion. The father of family 3104 has also been identified as a carrier of this heterozygous variant, manifested with FEVR symptoms, according to the medical records. Nevertheless, clinical examination data are presently unavailable. We added corresponding description in the manuscript page 5.

(4) Validation of the identified protein variants, especially L83F which appears to be expressed at a near normal level. Are these proteins mislocalized, do the variants to interfere with sites of known or predicted protein-protein interactions, could they act in a dominant-negative fashion by aggregation with co-expressed WT protein etc. Given the comparatively weak genetic data, additional validation is required to establish plausibility of CAPSL as a FEVR gene. 

We thank the editor for suggestion. As substantial amount of p.L83F was detectable at normal molecular weight, we further investigated whether this variant affects protein localization. Fig.S1, immunocytochemistry results indicated that this variant does not affect the subcellular localization of the protein.

(5) Improved description of experimental details and statistical analyses as outlined by reviewer #3. 

We thank the editor for suggestion. The more detailed information about Capsl mice was added in the manuscript at page 14-15. The experimental details regarding Figure 2 and Figure 3 have already presented in the “Methods-Quantification of retina parameters” section in the manuscript at page 19-20. And the statistical analysis was performed using unpaired Student’s t-test for comparison between two groups or one-way ANOVA followed by Dunnett multiple comparison test for comparison of multiple groups. The above description was added to “Methods-Image acquisition and statistical analysis” section at page 21 to make it clear.

Reviewer #1 (Recommendations For The Authors): 

My reservations lie with the main assertion that CAPSL is associated with FEVR, as the genetic evidence from human studies appears relatively weak. My concerns are as follows: 

(1) The molecular characterization of the identified mutations suggests a loss of function (LOF). Notably, in one family, both the father and son exhibit the FEVR phenotype and share the LOF mutation, suggesting a dominant mode of inheritance. However, the prevalence of the LOF allele of CAPSL in the general population is high, and its pLI score is 0, according to the GNOMAD database. This raises doubts about the LOF variant of CAPSL being causative for FEVR. 

We thank the reviewer for recommendation. The pLI score of LOF allele of CAPSL is based of general population, among which Europeans account for ~77% and East Asians make up less than 3%. Since the FEVR families in this article all come from China, the pLI score may not be accurate. Of course, we will continue to collect FEVR pedigrees and screen for CAPSL mutations.

(2) In the conditional knockout study, a delay in vascular development is observed in the retina up to P14. What the phenotype looks like in adult mice and whether it replicates the human FEVR phenotype? 

We thank the reviewer for recommendation. We further assessed the phenotype when angiogenesis almost complete at P21, the resulted showed no difference in Ctrl and CapsliECKO/iECKO mice (Fig.S5). And corresponding description was added in the manuscript at page 7.

(3) The conditional knockout mice lack both alleles of CAPSL. The phenotype resulting from the knockout of a single allele needs investigation to align with observed human phenotypes and genetic data. 

We thank the reviewer for recommendation. The phenotype of Capsl heterozygous mice at P5 showed no overt difference in vascular progression, vessel density and branchpoints with littermate wildtype controls (Fig.S4). The lack of pronounced phenotype in FEVR heterozygous mice may be due to different sensitivity between human and mice. A similar example is LRP5 mutations associated with FEVR. Heterozygous mutations in LRP5 were reported in FEVR patients in multiple populations. However, heterozygous Lrp5 mice exhibited no visible angiogenic phenotype (PMID: 18263894).

(4) The MYC pathway has been identified as influenced by CAPSL. Whether MYC downregulation is observed in the mouse model in vivo? 

We thank the reviewer for recommendation. MYC expression was identified at both mRNA and protein level in Figure S8, and corresponding description was added in the manuscript at page 11.

Reviewer #2 (Recommendations For The Authors): 

Minor comments: 

(1) While authors note that little is known about CAPSL protein function, more introductory detail about the protein (structure, domains intracellular localization etc) and additional discussion on potential mechanisms would aid the reader in interpreting the findings and model.

We thank the reviewer for recommendation. The subcellular localization of the CAPSL protein is distributed in both the nucleus and cytoplasm (https://www.proteinatlas.org/). The immunochemistry analysis confirmed that CAPSL protein is expressed in both the cell nucleus and cytoplasm (Fig.S1). And corresponding description was added in the manuscript at page 5.

(2) Pg 7 states that Capsl knockout mainly leads to "...defects in retinal vascular ECs rather than other vascular cells.". Consider rephrasing to describe "other vasculature-associated cells", as no vascular cells outside the retina were examined in the manuscript. 

We thank the reviewer for recommendation. We rephrased the "...defects in retinal vascular ECs rather than other vascular cells." into "...defects in retinal vascular ECs rather than other vasculature-associated cells" at page 8.

(3) The manuscript is well written but contains numerous typos. E.g. "" (Pg 14), "MCY signaling axis" (figure 6 legend), "shCAPAL" (figure 5 K). Please correct these, and search carefully for others. 

We are sorry for the careless mistakes we made, and we have checked the manuscript and correct these mistakes.

Reviewer #3 (Recommendations For The Authors): 

The following are somewhat grammatical, but significant issues, that I feel should be addressed before making the pre-print final: 

(1) Perhaps the largest issue with the manuscript to me is whether CAPSL is an interesting candidate (as stated repeatedly) or causative of FEVR. Within the scope of what is feasible, this is a challenging problem. Since the publication of the pre-print, it would be great if another group independently reported the detection of mutations specifically in FEVR patients. That lacking, meaningful additions to the manuscript that I'd recommend are the inclusion of a paragraph on caveats of the study and reporting the allele frequencies based on public databases. As the authors know the data better than anyone and will have invested thought into the implications, they are the ones best positioned to alert the field to the study's limitations - amongst them- the factors that might practically distinguish whether CAPSL is a candidate or cause.

We thank the reviewer for recommendation. We will collect more samples from FEVR families and screen for other mutation sites within the CAPSL gene in further studies.

(2) It is unclear why the modeling with mice did not attempt to recapitulate the observations in humans, i.e., why were heterozygotes for a ubiquitous knockout not studied? Any data with heterozygotes, or ubiquitous alleles (which would be easier to generate than the strain studied) should be shared in the manuscript. If no such data exists, this reviewer would find it a worthwhile new experiment to add, but it is appreciated that new experiments are sometimes beyond the scope of what is possible. At the least, this would be worthwhile to discuss in the requested caveats paragraph of the discussion. 

We thank the reviewer for recommendation. We evaluated the phenotype of Capsl heterozygous mice at P5, and the results showed no overt difference in vascular progression, vessel density and branchpoints with littermate wildtype controls (Fig.S4). The lack of pronounced phenotype in FEVR heterozygous mice may be due to different sensitivity between human and mice. For example, heterozygous Lrp5 mice exhibited no visible angiogenic phenotype (PMID: 18263894). Corresponding description was added in the manuscript at page 6.

(3) The statement in the Abstract "which provides invaluable information for genetic counseling and prenatal diagnosis of FEVR" should be toned down, better supported, or rephrased. This appears to be the 18th disease-associated gene for FEVR, with variants identified in 4 patients of the same ethnicity. In my opinion, the word "invaluable" is currently overstated. 

We thank the reviewer for recommendation. We have changed "which provides invaluable information for genetic counseling and prenatal diagnosis of FEVR" into "which provides valuable information for genetic counseling and prenatal diagnosis of FEVR" in the abstract.

(4) The transcriptomic and proteomic data should be deposited into a public repository and accession numbers added to the manuscript. 

We thank the reviewer for recommendation. We have uploaded the raw data of transcriptomic and proteomic to the Genome Sequence Archive (https://ngdc.cncb.ac.cn/gsa/browse/HRA010305) and the Genome Sequence Archive (https://www.ebi.ac.uk/pride/archive), respectively.

(5) The links to MYC are over-stated in the title "through the MYC axis", the abstract "CAPSL function causes FEVR through MYC axis", and the discussion "we demonstrated that the defects in CAPSL affect EC function by down-regulating the MYC signaling cascade". The links to MYC are entirely by association, there were no experiments testing that the transcriptomic and proteomic changes observed were determinative of the CAPSL-mediated phenotype. It seems appropriate to conjecture that these changes are important, but the above statements all need to be altered and conjectures need to be clearly identified as such. 

We are sorry to overstate the link between CAPSL-mediated phenotype and MYC axis in the abstract and discussion sections, and we have altered the statements in these sections to make it more logical. For example, we changed “This study also reveals that compromised CAPSL function causes FEVR through MYC axis, shedding light on the potential involvement of MYC signaling in the pathogenesis of FEVR.” into “This study also reveals that compromised CAPSL function causes FEVR may through MYC axis, shedding light on the potential involvement of MYC signaling in the pathogenesis of FEVR.” in the abstract. And in the discussion we changed “…cause FEVR through inactivating MYC signaling, expanding FEVR-involved signaling pathway and providing a potential therapeutic target for the intervention of FEVR” to “…cause FEVR may through inactivating MYC signaling, expanding FEVR-involved signaling pathway and providing a potential therapeutic target for the intervention of FEVR”.

(6) Finally, I suggest that the following grammatical issues in the pre-print be corrected before making the pre-print final: 

We have checked the manuscript and correct these mistakes.

(a) p2. Suggest rewriting the sentence "Nevertheless, the molecular mechanisms by which CAPSL regulates cell processes and signaling cascades have yet to be elucidated." The preceding sentences only state that CASPL is a candidate in another disease - the word "nevertheless" seems to reflect a logic that isn't described. 

We have checked the manuscript and correct these mistakes.

(b) p5. Please correct the grammar "We, generated an inducible" 

We corrected this mistake.

(c) p5. Suggest rephrasing "impairing CAPSL expression." The word "expression" is often used in reference to transcription. To avoid confusion, something such as "eliminating or reducing protein abundance" might be better. 

We corrected this mistake.

(d) p6. Please correct the grammar "As expected, the radial vascular growth, as well as vessel density and vascular branching, are dramatically reduced in..." - note subject-verb agreement issue 

We corrected this mistake.

(e) Figure 3 legend - correct "(A) Hyloaid vessels"

We corrected this mistake.

Reviewer #1 (Public Review):

Summary:<br /> The authors analyzed how biotic and abiotic factors impact antagonistic host-parasitoid interaction systems in a large BEF experiment. They found the linkage between the tree community and host-parasitoid community from the perspective of the multi-dimensionality of biodiversity. Their results revealed that the structure of the tree community (habitat) and canopy cover influence host-parasitoid compositions and their interaction pattern. This interaction pattern is also determined by phylogenetic associations among species. This paper provides a nice framework for detecting the determinants of network topological structures.

Strengths:<br /> This study was conducted using a five-year sampling in a well-designed BEF experiment. The effects of the multi-dimensional diversity of tree communities have been well explained in a forest ecosystem with an antagonistic host-parasitoid interaction.

The network analysis has been well conducted. The combination of phylogenetic analysis and network analysis is uncommon among similar studies, especially for studies of trophic cascades. Still, this study has discussed the effect of phylogenetic features on interacting networks in depth.

Weaknesses:<br /> (1) The authors should examine species and interaction completeness in this study to confirm that their sampling efforts are sufficient.<br /> (2) The authors only used Rao's Q to assess the functional diversity of tree communities. However, multiple metrics of functional diversity exist (e.g., functional evenness, functional dispersion, and functional divergence). It is better to check the results from other metrics and confirm whether these results further support the authors' results.<br /> (3) The authors did not elaborate on which extinction sequence was used in robustness analysis. The authors should consider interaction abundance in calculating robustness. In this case, the author may use another null model for binary networks to get random distributions.<br /> (4) The causal relationship between host and parasitoid communities is unclear. Normally, it is easy to understand that host community composition (low trophic level) could influence parasitoid community composition (high trophic level). I suggest using the 'correlation' between host and parasitoid communities unless there is strong evidence of causation.

Reviewer #2 (Public Review):

Summary:<br /> In their manuscript, Multi-dimensionality of tree communities structure host-parasitoid networks and their phylogenetic composition, Wang et al. examine the effects of tree diversity and environmental variables on communities of reed-nesting insects and their parasitoids. Additionally, they look for the correlations in community composition and network properties of the two interacting insect guilds. They use a data set collected in a subtropical tree biodiversity experiment over five years of sampling. The authors find that the tree species, functional, and phylogenetic diversity as well as some of the environmental factors have varying impacts on both host and parasitoid communities. Additionally, the communities of the host and parasitoid showed correlations in their structures. Also, the network metrices of the host-parasitoid network showed patterns against environmental variables.

Strengths:<br /> The main strength of the manuscript lies in the massive long-term data set collected on host-parasitoid interactions. The data provides interesting opportunities to advance our knowledge on the effects of environmental diversity (tree diversity) on the network and community structure of insect hosts and their parasitoids in a relatively poorly known system.

Weaknesses:<br /> To me, there are no major issues regarding the manuscript, though sometimes I disagree with the interpretation of the results and some of the conclusions might be too far-fetched given the analyses and the results (namely the top-down control in the system). Additionally, the methods section (especially statistics) was lacking some details, but I would not consider it too concerning. Sometimes, the logic of the text could be improved to better support the studied hypotheses throughout the text. Also, the results section cannot be understood as a stand-alone without reading the methods first. The study design and the rationale of the analyses should be described somewhere in the intro or presented with the results.

eLife assessment

This study provides important new insights into the contribution of local DNA features to the molecular mechanisms and dynamics of copy number variation (CNV) formation during adaptive evolution. While limited to a single CNV, the experiments are carefully controlled and present convincing evidence that supports the conclusions. This work will be of general interest to those studying genome architecture and evolution from yeast biologists to cancer researchers.

Reviewer #1 (Public Review):

Summary:

The work by Chuong et al. provides important new insights into the contribution of different molecular mechanisms in the dynamics of CNV formation. It will be of interest to anyone curious about genome architecture and evolution from yeast biologists to cancer researchers studying genome rearrangements.

Strengths:

Their results are especially striking in that the "simplest" mechanism of GAP1 amplification-non-allelic homologous recombination between the flanking Ty-LTR elements is not the most common route taken by the cells, emphasizing the importance of experimentally testing what might seem on the surface to be obvious answers. One of the important developments of their work is the use of their neural network simulation-based inference (nnSBI) model to derive rates of amplicon formation and their fitness effects.

Weaknesses:

The manuscript reads as though two different people wrote two different sections of the manuscript - an experimental evolutionist and a computational scientist. If the goal is to reach both groups of readers, there needs to be more explanation of both types of work. I found the computational sections to be particularly dense but even the experimental sections need clearer explanations and more specific examples of the rearrangements found. I will point out these areas in the detailed remarks to the authors. While I have no reason to question their conclusions, I couldn't independently verify the results that ODIRA was the majority mechanism since the sequence of amplified clones was not made available during the review. I've encouraged the authors to include specific, detailed sequence information for both ODIRA events as well as the specific clones where GAP1 was amplified but the flanking gene GFP was not.

Reviewer #2 (Public Review):

Summary:

This study examines how local DNA features around the amino acid permease gene GAP1 influence adaptation to glutamine-limited conditions through changes in GAP1 Copy Number Variation (CNV). The study is well motivated by the observation of numerous CNVs documented in many organisms, but difficulty in distinguishing the mechanisms by which they are formed, and whether or how local genomic elements influence their formation. The main finding is convincing and is that a nearby Autonomous Replicating Sequence (ARS) influences the formation of GAP1 CNVs and this is consistent with a predominate mechanism of Origin Dependent Inverted Repeat Amplification (ODIRA). These results along with finding and characterizing other mechanisms of GAP1 CNV formation will be of general interest to those studying CNVs in natural systems, experimental evolution, and in tumor evolution. While the results are limited to a single CNV of interest (GAP1), the carefully controlled experimental design and quantification of CNV formation will provide a useful guide to studying other CNVs and CNVs in other organisms.

Strengths:

The study was designed to examine the effects of two flanking genomic features next to GAP1 on CNV formation and adaptation during experimental evolution. This was accomplished by removing two Long Terminal Repeats (LTRs), removing a downstream ARS, and removing both LTRs and the ARS. Although there was some heterogeneity among replicates, later shown to include the size and breakpoints of the CNV and the presence of an unmarked CNV, both marker-assisted tracking of CNV formation and modeling of CNV rate and fitness effects showed that deletion of the ARS caused a clear difference compared to the control and the LTR deletion.

The consequence of deletion of local features (LTR and ARS) was quantified by genome sequencing of adaptive clones to identify the CNV size, copy number and infer the mechanism of CNV formation. This greatly added value to the study as it showed that i) ODIRA was the most common mechanism but ODIRA is enhanced by a local ARS, ii) non-allelic homologous recombination (NAHR) is also used but depends on LTRs, and iii) de novo insertion of transposable elements mediate NAHR in strains with both ARS and LTR deletions. Together, these results show how local features influence the mechanism of CNV formation, but also how alternative mechanisms can substitute when primary ones are unavailable.

Weaknesses:

The CNV mutation rate and its effect on fitness are hard to disentangle. The frequency of the amplified GFP provides information about mutation rate differences as well as fitness differences. The data and analysis show that each evolved population has multiple GAP1 CNV lineages within it, with some being unmarked by GFP. Thus, estimates of CNV fitness are more of a composite view of all CNV amplifications increasing in frequency during adaptation. Another unknown but potential complication is whether the local (ARS, LTR) deletions influence GAP1 expression and thus the fitness gain of GAP1 CNVs. The neural network simulation-based inference does a good job at estimating both mutation rates and fitness effects, while also accounting for unmarked CNVs. However, the model does not account for the population heterogeneity of CNVs and their fitness effects. Despite these limitations of distinguishing mutation rate and fitness differences, the authors' conclusions are well supported in that the LTR and ARS deletions have a clear impact on the CNV-mediated evolutionary outcome and the mechanism of CNV formation.

Reviewer #3 (Public Review):

Summary:

The authors represent an elegant and detailed investigation into the role of cis-elements, and therefore the underlying mechanisms, in gene dosage increase. Their most significant finding is that in their system copy number increase frequently occurs by what they call replication errors that result from the origin of replication firing.

The authors somewhat quantitatively determine the effect of the presence of a proximal origin of replication or LTR on the different CNV scenarios.

Strengths:

(1) A clever and elegant experimental design.

(2) A quantitative determination of the effect of a proximal origin of replication or LTR on the different CNV scenarios. Measuring directly the contribution of two competing elements.

(3) ODIRA can occur by firing of a distal ARS element.

(4) Re-insertion of Ty elements is interesting.

Weaknesses:

(1) Overall, the research does not considerably advance the current knowledge. The research does not investigate what the maximum distance between ARS for ODIRA is to occur. This is an important point since ODIRA was previously described. A considerable contribution to the field would be to understand under what conditions ODIRA wins NAHR.

(2) The title and some sentences in the abstract give a wrong impression of the generality and the novelty of the observations presented. Below are some examples of much earlier work that dealt with mechanisms of CNV and got different conclusions. The Lobachev lab (Cell 2006) published a different scenario years ago, with a very different mechanism (hair-pin capped breaks). The Argueso lab found something different (NAHR) (Genetics 2013).

In fact, the CUP1 system presents a good example of this point. The Houseley group showed a complex replication transcription-based mechanism (NAR 2022, cited), the Argueso group showed Ty-based amplification and the Resnick group showed aneuploidy-based amplification. While aneuploidy is a minor factor here the numerous works in Candida albicans, Cryptococcus neoformans, and Yeast suggest otherwise (Selmecki et al Science 2006, Yona et al PNAS 2013, Yang et al Microbiology Spectrum 2021).

(3) The authors added a mathematical model to their experimental data. For me, it was very difficult to understand the contribution of the model to the research. I anticipated, for example, that the model would make predictions that would be tested experimentally. For example, " ARSΔ and ALLΔ are predicted to be almost eliminated by generation 116, as the average predicted WT proportion is 0.998 and 0.999" But to my understanding without testing the model.

eLife assessment

This solid and innovative study explores the uptake of fixed nitrogen in maize chloroplasts facilitated by symbiotic Gluconacetobacter diazotrophicus bacteria. The findings provide valuable insights into plant-microbe interactions, particularly highlighting a symbiotic mechanism of nitrogen delivery independent nodule formation. Additional controls would help to substantiate the findings and enhance the overall strength of the evidence.

Reviewer #1 (Public Review):

The study uses nanoscale secondary ion mass spectrometry to show that maize plants inoculated with a bacteria, Gd, incorporated fixed nitrogen into the chloroplast. The authors then state that since "chloroplasts are the chief engines that drive plant growth," that it is this incorporation that explains the maize's enhanced growth with the bacteria.

But the authors don't present the total special distribution of nitrogen in plants. That is, if the majority of nitrogen is in the chloroplast (which, because of Rubisco, it likely is) then the majority of fixed nitrogen should go into the chloroplast.

Also, what are the actual controls? In the methods, the authors detail that the plants inoculated with Gd are grown without nitrogen. But how did the authors document the "enhanced growth rates of the plants containing this nitrogen fixing bacteria." Were there other plants grown without nitrogen and the Gd? If so, of course, they didn't grow as well. Nitrogen is essential for plant growth. If Gd isn't there to provide it in n-free media, then the plants won't grow. Do we need to go into the mechanism for this, really? And it's not just because nitrogen is needed in the chloroplast, even if that might be where the majority ends up.

Furthermore, it is not novel to say that nitrogen from a nitrogen fixing bacteria makes its way into the chloroplast. For any plant ever successfully grown on N free media with a nitrogen fixing bacteria, this must be the case. We don't need a fancy tool to know this.

The experimental setup does not suit the argument the authors are trying to make (and I'm not sure if the argument the authors are trying to make has any legitimacy). The authors contend that their study provides the basis of a "detailed agronomic analysis of the extent of fixed nitrogen fertilizer needs and growth responses in autonomous nitrogen-fixing maize plants." But what is a "fixed nitrogen fertilizer need"? The phrase makes no sense. A plant has nitrogen needs. This nitrogen can be provided via nitrogen fixing bacteria or fertilizer. But are there fixed nitrogen fertilizer needs? It sounds like the authors are suggesting that a plant can distinguish between nitrogen fixed by bacteria nearby and that provided by fertilizer. If that is the contention, then a new set of experiments is needed - with other controls grown on different levels of fertilizer.

What is interesting, and potentially novel, in this study is figure 1D (and lines 90-99). In that image, is the bacteria actually in the plant cell? Or is it colonizing the region between the cells? Either way, it looks to have made its way into the plant leaf, correct? I believe that would be a novel and fascinating finding. If the authors were to go into more detail into how Gd is entering into the symbiotic relationship with maize (e.g. fixing atmospheric nitrogen in the leaf tissue rather than in root nodules like legumes) I believe that would be very significant. But be sure to add to the field in relation to reference 9, and any new references since then.

Also, it would be helpful to have an idea of how fast these plants, grown in n free media but inoculated with the bacteria, grow compared to plants grown on various levels of fertilizer.

Reviewer #2 (Public Review):

Summary:<br /> In agriculture, nitrogen fertilizers are used to allow for optimum growth and yield of crops. The use of these fertilizers has a large negative impact on the environment and climate. In this report McMahon et al. have inoculated maize seeds with a nitrogen fixing bacterium: Gluconacetobacter diazotrophicus. It has been demonstrated before that nitrogen fixed by this bacterium can be incorporated in a plant. In this study the spatial distribution of the incorporated nitrogen was revealed using NanoSIMS. The nitrogen was strongly enriched in the chloroplasts and especially the stromal region where the Calvin-Benson cycle enzymes are located.

Strengths:<br /> The topic is very interesting as nitrogen supply is of great importance for agriculture. The study is well designed, and the data convincingly show enrichment of 15N (fixed by the bacterium) in the chloroplasts.

Weaknesses:<br /> Some of the data that is discussed is not presented in the (supplement) of the paper. First, in the abstract it is mentioned "help explain the observation of enhanced growth rates in plants containing this nitrogen fixing bacterium". It is unclear if this refers to literature or to this study. Either, it should be mentioned in the introduction, or the data should be shown in the paper. Second, it is mentioned that the chloroplast had a significantly higher nitrogen isotope ratio value compared to the nuclei and the xylem cell walls. Please provide the numbers of the ratios (preferably also an image of the xylem cell wall) and the type of statistical analysis that has been performed.

The paper could benefit from a more in-depth analysis of why the nitrogen isotope ratio is higher in the chloroplast. It seems to be correlated with the local nitrogen abundance, did the authors plot the two against each other? What would it mean if it is correlated? What minimal nitrogen concentration/signal should there be to make a reliable estimate of the ratio? Does the higher ratio mean that the turnover rate of the Calvin-Benson cycle enzymes is higher than for other proteins?

For the small structures that could be the nitrogen fixing bacteria the 15N enrichment is up to 270x the natural ratio. Does this mean that 100% (270*0.0036=1) of their nitrogen is fixed from the provided atmosphere?

Could one also provide the absolute ratio in the chloroplasts? It would be nice if the authors discuss, based on their data, the potential of using nitrogen fixing bacteria to provide nitrogen to crops.

eLife assessment

This important study offers insights into the function and connectivity patterns of a relatively unknown afferent input from the endopiriform to the CA1 subfield of the ventral hippocampus, suggesting a neural mechanism that suppresses the processing of familiar stimuli in favor of detecting novelty. The strength of evidence is solid, with careful anatomical and electrophysiological circuit characterization, although the functional role of this pathway in behavior is not firmly established. The work will be of broad interest to researchers studying the neural circuitry of behavior.

Reviewer #1 (Public Review):

Summary:

The anatomical connectivity of the claustrum and the role of its output projections has, thus far, not been studied in detail. The aim of this study was to map the outputs of the endopiriform (EN) region of the claustrum complex, and understand their functional role. Here the authors have combined sophisticated intersectional viral tracing techniques, and ex vivo electrophysiology to map the neural circuitry of EN outputs to vCA1, and shown that optogenetic inhibition of the EN→vCA1 projection impairs both social and object recognition memory. Interestingly the authors find that the EN neurons target inhibitory interneurons providing a mechanism for feedforward inhibition of vCA1.

Strengths:

The strength of this study was the application of a multilevel analysis approach combining a number of state-of-the-art techniques to dissect the contribution of the EN→vCA1 to memory function.

Weaknesses:

Some authors would disagree that the vCA1 represents a 'node for recognition of familiarity' especially for object recognition although that is not to say that it might play some role in discrimination, as shown by the authors. I note however that the references provided in the Introduction, concerning the role of vCA1in memory refer to anxiety, social memory, temporal order memory, and not novel object recognition memory. Given the additional projections to the piriform cortex shown in the results, I wonder to what extent the observations may be explained by odour recognition effects. In addition, I wondered whether the impairments in discrimination following Chemo-genetic inhibition of the EN→vCA1 were due to the subject treating the novel and familiar stimuli as either both novel- which might be observed as an increase in exploration, or both stimuli as familiar, with a decrease in overall exploration.

Reviewer #2 (Public Review):

Summary:

Yamawaki et al., conducted a series of neuroanatomical tracing and whole-cell recording experiments to elucidate and characterise a relatively unknown pathway between the endopiriform (EN) and CA1 of the ventral hippocampus (vCA1) and to assess its functional role in social and object recognition using fibre photometry and dual vector chemogenetics. The main findings were that the EN sends robust projections to the vCA1 that colateralise to the prefrontal cortex, lateral entorhinal cortex, and piriform cortex, and these EN projection neurons terminate in the stratum lacunosum-moleculare (SLM) layer of distal vCA1, synapsing onto GABAergic neurons that span across the Pyramidal-Stratum Radiatum (SR) and SR-SML borders. It was also demonstrated that EN input disynaptically inhibits vCA1 pyramidal neurons. vCA1 projecting EN neurons receive afferent input from the piriform cortex, and from within EN. Finally, fibre photometry experiments revealed that vCA1 projecting EN neurons are most active when mice explore novel objects or conspecifics, and pathway-specific chemogenetic inhibition led to an impairment in the ability to discriminate between novel vs. familiar objects and conspecifics.

This is an interesting mechanistic study that provides valuable insights into the function and connectivity patterns of afferent input from the endopiriform to the CA1 subfield of the ventral hippocampus. The authors propose that the EN input to the vCA1 interneurons provides a feedforward inhibition mechanism by which novelty detection could be promoted. The experiments appear to be carefully conducted, and the methodological approaches used are sound. The conclusions of the paper are supported by the data presented on the whole.

However, some aspects of methodology and data interpretation will need to be clarified and further evidence provided to enhance the utility of the data to the rest of the field.

The authors used dual retrograde tracing and observed that the highest percentage (~30%) of vCA1 projecting EN cells also projected to the PFC. They then employed an intersectional approach to show the presence of collaterals in other cortical areas such as the entorhinal cortex and piriform cortex in addition to the PFC. However, they state that 'Projection to prefrontal cortex was sparse relative to other areas, as expected based on the retrograde labeling data' (referring to Figure 2K) and subsequently appear to dismiss the initial data set indicating strong axonal projections to the PFC.

Since this is a relatively unknown connection, it would be helpful if some evidence/discussion is provided for whether the EN projects to other subfields (CA3, DG) of the ventral hippocampus. This is important, as the retrograde tracer injections depicted in Figure 1B clearly show a spread of the tracer to vCA3 and potentially vDG and it is not possible to ascertain the regional specificity of the pathway.

The vCA1 projecting EN cells appear to originate from an extensive range along the AP axis. Is there a topographical organization of these neurons within the vCA1? A detailed mapping of this kind would be valuable.

Given this extensive range in the location of vCA1 EN originating cells, how were the targets (along the AP axis) in EP selected for the calcium imaging?

The vCA1 has extensive reciprocal connections with the piriform cortex as well, which is in close proximity to the EN. How certain are the authors that the chemogenetic targeting was specific to the EN-vCA1 connection?

Raw data for the sociability and discrimination indices should be provided so that the readers can gain further insight into the nature of the impairment.

Line 222: It is unclear how locomotor activity informs anxiety in the behavioral tests.

Figure 7 title; It is stated that activity of EN neurons 'predict' social/object discrimination performance. However, caution must be exercised with this interpretation as the correlational data are underpowered (n=5-8). Furthermore, the results show a significant correlation between calcium event ratios and the discrimination index in the social discrimination test but not the object discrimination test.

While both male and female mice were included in the anatomical tracing and recording experiments, only male mice were used for behavioral tests.

eLife assessment

The study by Asabuki et al. is a valuable contribution to understanding how cortical neural networks encode internal models into spontaneous activity. It uses a recurrent network of spiking neurons subject to predictive learning principles and provides a novel mechanism to learn the spontaneous replay of probabilistic sensory experiences. While promising in its ability to explain spontaneous network dynamics, the manuscript is incomplete in terms of the strength of support for its main findings. The difference of the proposed sampling dynamics from Markovian types of sampling is unclear and the use of non-negative synaptic strengths is applied in a non-biological manner.

Reviewer #1 (Public Review):

In their manuscript, the authors propose a learning scheme to enable spiking neurons to learn the appearance probability of inputs to the network. To this end, the neurons rely on error-based plasticity rules for feedforward and recurrent connections. The authors show that this enables the networks to spontaneously sample assembly activations according to the occurrence probability of the input patterns they respond to. They also show that the learning scheme could explain biases in decision-making, as observed in monkey experiments. While the task of neural sampling has been solved before in other models, the novelty here is the proposal that the main drivers of sampling are within-assembly connections, and not between-assembly (Markov chains) connections as in previous models. This could provide a new understanding of how spontaneous activity in the cortex is shaped by synaptic plasticity.

The manuscript is well written and the results are presented in a clear and understandable way. The main results are convincing, concerning the spontaneous firing rate dependence of assemblies on input probability, as well as the replication of biases in the decision-making experiment. Nevertheless, the manuscript and model leave open several important questions. The main problem is the unclarity, both in theory and intuitively, of how the sampling exactly works. This also makes it difficult to assess the claims of novelty the authors make, as it is not clear how their work relates to previous models of neural sampling.

Regarding the unclarity of the sampling mechanism, the authors state that within-assembly excitatory connections are responsible for activating the neurons according to stimulus probability. However, the intuition for this process is not made clear anywhere in the manuscript. How do the recurrent connections lead to the observed effect of sampling? How exactly do assemblies form from feedforward plasticity? This intuitive unclarity is accompanied by a lack of formal justification for the plasticity rules. The authors refer to a previous publication from the same lab, but it is difficult to connect these previous results and derivations to the current manuscript. The manuscript should include a clear derivation of the learning rules, as well as an (ideally formal) intuition of how this leads to the sampling dynamics in the simulation.

Some of the model details should furthermore be cleared up. First, recurrent connections transmit signals instantaneously, which is implausible. Is this required, would the network dynamics change significantly if, e.g., excitation arrives slightly delayed? Second, why is the homeostasis on h required for replay? The authors show that without it the probabilities of sampling are not matched, but it is not clear why, nor how homeostasis prevents this. Third, G and M have the same plasticity rule except for G being confined to positive values, but there is no formal justification given for this quite unusual rule. The authors should clearly justify (ideally formally) the introduction of these inhibitory weights G, which is also where the manuscript deviates from their previous 2020 work. My feeling is that inhibitory weights have to be constrained in the current model because they have a different goal (decorrelation, not prediction) and thus should operate with a completely different plasticity mechanism. The current manuscript doesn't address this, as there is no overall formal justification for the learning algorithm.

Finally, the authors should make the relation to previous models of sampling and error-based plasticity more clear. Since there is no formal derivation of the sampling dynamics, it is difficult to assess how they differ exactly from previous (Markov-based) approaches, which should be made more precise. Especially, it would be important to have concrete (ideally experimentally testable) predictions on how these two ideas differ. As a side note, especially in the introduction (line 90), this unclarity about the sampling made it difficult to understand the contrast to Markovian transition models.

There are also several related models that have not been mentioned and should be discussed. In 663 ff. the authors discuss the contributions of their model which they claim are novel, but in Kappel et al (STDP Installs in Winner-Take-All Circuits an Online Approximation to Hidden Markov Model Learning) similar elements seem to exist as well, and the difference should be clarified. There is also a range of other models with lateral inhibition that make use of error-based plasticity (most recently reviewed in Mikulasch et al, Where is the error? Hierarchical predictive coding through dendritic error computation), and it should be discussed how the proposed model differs from these.

Reviewer #2 (Public Review):

Summary:

The paper considers a recurrent network with neurons driven by external input. During the external stimulation predictive synaptic plasticity adapts the forward and recurrent weights. It is shown that after the presentation of constant stimuli, the network spontaneously samples the states imposed by these stimuli. The probability of sampling stimulus x^(i) is proportional to the relative frequency of presenting stimulus x^(i) among all stimuli i=1,..., 5.

Methods:

Neuronal dynamics:

For the main simulation (Figure 3), the network had 500 neurons, and 5 non-overlapping stimuli with each activating 100 different neurons where presented. The voltage u of the neurons is driven by the forward weights W via input rates x, the inhibitory recurrent weights G, are restricted to have non-negative weights (Dale's law), and the other recurrent weights M had no sign-restrictions. Neurons were spiking with an instantaneous Poisson firing rate, and each spike-triggered an exponentially decaying postsynaptic voltage deflection. Neglecting time constants of the postsynaptic responses, the expected postsynaptic voltage reads (in vectorial form) as

u = W x + (M - G) f (Eq. 5)

where f =; phi(u) represents the instantaneous Poisson rate, and phi a sigmoidal nonlinearity. The rate f is only an approximation (symbolized by =;) of phi(u) since an additional regularization variable h enters (taken up in Point 4 below). The initialisation of W and M is Gaussian with mean 0 and variance 1/sqrt(N), N the number of neurons in the network. The initial entries of G are all set to 1/sqrt(N).

Predictive synaptic plasticity:

The 3 types of synapses were each adapted so that they individually predict the postsynaptic firing rate f, in matrix form

ΔW ≈ (f - phi( W x ) ) x^T<br /> ΔM ≈ (f - phi( M f ) ) f^T<br /> ΔG ≈ (f - phi( M f ) ) f^T but confined to non-negative values of G (Dale's law).

The ^T tells us to take the transpose, and the ≈ again refers to the fact that the ϕ entering in the learning rule is not exactly the ϕ determining the rate, only up to the regularization (see Point 4).

Main formal result:

As the authors explain, the forward weight W and the unconstrained weight M develop such that, in expectations,

f =; phi( W x ) =; phi( M f ) =; phi( G f ) ,

consistent with the above plasticity rules. Some elements of M remain negative. In this final state, the network displays the behaviour as explained in the summary.

Major issues:

Point 1: Conceptual inconsistency

The main results seem to arise from unilaterally applying Dale's law only to the inhibitory recurrent synapses G, but not to the excitatory recurrent synapses M.

In fact, if the same non-negativity restriction were also imposed on M (as it is on G), then their learning rules would become identical, likely leading to M=G. But in this case, the network becomes purely forward, u = W x, and no spontaneous recall would arise. Of course, this should be checked in simulations.

Because Dale's law was only applied to G, however, M and G cannot become equal, and the remaining differences seem to cause the effect.

Predictive learning rules are certainly powerful, and it is reasonable to consider the same type of error-correcting predictive learning rule, for instance for different dendritic branches that both should predict the somatic activity. Or one may postulate the same type of error-correcting predictive plasticity for inhibitory and excitatory synapses, but then the presynaptic neurons should not be identical, as it is assumed here. Both these types of error-correcting and error-forming learning rules for same-branches and inhibitory/excitatory inputs have been considered already (but with inhibitory input being itself restricted to local input, for instance).

Point 2: Main result as an artefact of an inconsistently applied Dale's law?

The main result shows that the probability of a spontaneous recall for the 5 non-overlapping stimuli is proportional to the relative time the stimulus was presented. This is roughly explained as follows: each stimulus pushes the activity from 0 up towards f =; phi( W x ) by the learning rule (roughly). Because the mean weights W are initialized to 0, a stimulus that is presented longer will have more time to push W up so that positive firing rates are reached (assuming x is non-negative). The recurrent weights M learn to reproduce these firing rates too, while the plasticity in G tries to prevent that (by its negative sign, but with the restriction to non-negative values). Stimuli that are presented more often, on average, will have more time to reach the positive target and hence will form a stronger and wider attractor. In spontaneous recall, the size of the attractor reflects the time of the stimulus presentation. This mechanism so far is fine, but the only problem is that it is based on restricting G, but not M, to non-negative values.

Point 3: Comparison of rates between stimulation and recall.

The firing rates with external stimulations will be considerably larger than during replay (unless the rates are saturated).

This is a prediction that should be tested in simulations. In fact, since the voltage roughly reads as<br /> u = W x + (M - G) f,<br /> and the learning rules are such that eventually M =; G, the recurrences roughly cancel and the voltage is mainly driven by the external input x. In the state of spontaneous activity without external drive, one has<br /> u = (M - G) f ,<br /> and this should generate considerably smaller instantaneous rates f =; phi(u) than in the case of the feedforward drive (unless f is in both cases at the upper or lower ceiling of phi). This is a prediction that can also be tested.

Because the figures mostly show activity ratios or normalized activities, it was not possible for me to check this hypothesis with the current figures. So please show non-normalized activities for comparing stimulation and recall for the same patterns.

Point 4: Unclear definition of the variable h.<br /> The formal definition of h = hi is given by (suppressing here the neuron index i and the h-index of tau)

tau dh/dt = -h if h>u, (Eq. 10)<br /> h = u otherwise.

But if it is only Equation 10 (nothing else is said), h will always become equal to u, or will vanish, i.e. either h=u or h=0 after some initial transient. In fact, as soon as h>u, h is decaying to 0 according to the first line. If u is >0, then it stops at u=h according to the second line. No reason to change h=u further. If u<=0 while h>u, then h is converging to 0 according to the first line and will stay there. I guess the authors had issues with the recurrent spiking simulations and tried to fix this with some regularization. However as presented, it does not become clear how their regulation works.

BTW: In Eq. 11 the authors set the gain beta to beta = beta0/h which could become infinite and, putatively more problematic, negative, depending on the value of h. Maybe some remark would convince a reader that no issues emerge from this.

Added from discussions with the editor and the other reviewers:

Thanks for alerting me to this Supplementary Figure 8. Yes, it looks like the authors did apply there Dale's law for both the excitatory and inhibitory synapses. Yet, they also introduced two types of inhibitory pathways converging both to the excitatory and inhibitory neurons. For me, this is a confirmation that applying Dale's law to both excitatory and inhibitory synapses, with identical learning rules as explained in the main part of the paper, does not work.

Adding such two pathways is a strong change from the original model as introduced before, and based on which all the Figures in the main text are based. Supplementary Figure 8 should come with an analysis of why a single inhibitory pathway does not work. I guess I gave the reason in my Points 1-3. Some form of symmetry breaking between the recurrent excitation and recurrent inhibition is required so that, eventually, the recurrent excitatory connection will dominate.

Making the inhibitory plasticity less expressive by applying Dale's law to only those inhibitory synapses seems to be the answer chosen in the Figures of the main text (but then the criticism of unilaterally applying Dale's law).

Applying Dale's law to both types of synapses, but dividing the labor of inhibition into two strictly separate and asymmetric pathways, and hence asymmetric development of excitatory and inhibitory weights, seems to be another option. However, introducing such two separate inhibitory pathways, just to rescue the fact that Dale's law is applied to both types of synapses, is a bold assumption. Is there some biological evidence of such two pathways in the inhibitory, but not the excitatory connections? And what is the computational reasoning to have such a separation, apart from some form of symmetry breaking between excitation and inhibition? I guess, simpler solutions could be found, for instance by breaking the symmetry between the plasticity rules for the excitatory and inhibitory neurons. All these questions, in my view, need to be addressed to give some insights into why the simulations do work.

Overall, Supplementary Figure 8 seems to me too important to be deferred to the Supplement. The reasoning behind the two inhibitory pathways should appear more prominently in the main text. Without this, important questions remain. For instance, when thinking in a rate-based framework, the two inhibitory pathways twice try to explain the somatic firing rate away. Doesn't this lead to a too strong inhibition? Can some steady state with a positive firing rate caused by the recurrence, in the absence of an external drive, be proven? The argument must include the separation into Path 1 and Path 2. So far, this reasoning has not been entered.

In fact, it might be that, in a spiking implementation, some sparse spikes will survive. I wonder whether at least some of these spikes survive because of the other rescuing construction with the dynamic variable h (Equation 10, which is not transparent, and that is not taken up in the reasoning either, see my Point 4).

Perhaps it is helpful for the authors to add this text in the reply to them.

Reviewer #3 (Public Review):

Summary:

The work shows how learned assembly structure and its influence on replay during spontaneous activity can reflect the statistics of stimulus input. In particular, stimuli that are more frequent during training elicit stronger wiring and more frequent activation during replay. Past works (Litwin-Kumar and Doiron, 2014; Zenke et al., 2015) have not addressed this specific question, as classic homeostatic mechanisms forced activity to be similar across all assemblies. Here, the authors use a dynamic gain and threshold mechanism to circumnavigate this issue and link this mechanism to cellular monitoring of membrane potential history.

Strengths:

(1) This is an interesting advance, and the authors link this to experimental work in sensory learning in environments with non-uniform stimulus probabilities.

(2) The authors consider their mechanism in a variety of models of increasing complexity (simple stimuli, complex stimuli; ignoring Dale's law, incorporating Dale's law).

(3) Links a cellular mechanism of internal gain control (their variable h) to assembly formation and the non-uniformity of spontaneous replay activity. Offers a promise of relating cellular and synaptic plasticity mechanisms under a common goal of assembly formation.

Weaknesses:

(1) However, while the manuscript does show that assembly wiring does follow stimulus likelihood, it is not clear how the assembly-specific statistics of h reflect these likelihoods. I find this to be a key issue.

(2) The authors' model does take advantage of the sigmoidal transfer function, and after learning an assembly is either fully active or nearly fully silent (Figure 2a). This somewhat artificial saturation may be the reason that classic homeostasis is not required since runaway activity is not as damaging to network activity.

(3) Classic mechanisms of homeostatic regulation (synaptic scaling, inhibitory plasticity) try to ensure that firing rates match a target rate (on average). If the target rate is the same for all neurons then having elevated firing rates for one assembly compared to others during spontaneous activity would be difficult. If these homeostatic mechanisms were incorporated, how would they permit the elevated firing rates for assemblies that represent more likely stimuli?