Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #2 (Public review):

In this valuable manuscript, Lin et al attempt to examine the role of long non coding RNAs (lncRNAs) in human evolution, through a set of population genetics and functional genomics analyses that leverage existing datasets and tools. Although the methods are incomplete and at times inadequate, the results nonetheless point towards a possible contribution of long non coding RNAs to shaping humans, and suggest clear directions for future, more rigorous study.

Comments on revisions:

I thank the authors for their revision and changes in response to previous rounds of comments. As it had been nearly two years since I last saw the manuscript, I reread the full text to familiarise myself again with the findings presented. While I appreciate the changes made and think they have strengthened the manuscript, I still find parts of it a bit too speculative or hyperbolic. In particular, I think claims of evolutionary acceleration and adaptation require more careful integration with existing human/chimpanzee genetics and functional genomics literature.

We thank the reviewer heartfully for the great patience and valuable comments, which have helped us further improve the manuscript. Before responding to comments point by point, we provide a summary here.

(1) On parameters and cutoffs.

Parameters and cutoffs influence data analysis. The large number of Supplementary Notes, Supplementary Figures, and Supplementary Tables indicates that we paid great attention to the influence of parameters and robustness of analyses. Specifically, here we explain the DBS sequence distance cutoff of 0.034, which determines the top 20% genes that most differentiate humans from chimpanzees and influences the gene set enrichment analysis (Figure 2). As described in the revised manuscript, we estimated this cutoff based on Song et al., verified its rationality based on Prufer et al. (Song et al. 2021; Prufer et al. 2017), and measured its influence by examining slightly different cutoff values (e.g., 0.035).

(2) Analyses of HS TFs and HS TF DBSs.

It is desirable to compare the contribution of HS lncRNAs and HS TFs to human evolution. Identifying HS TFs faces the challenges that different institutions (e.g., NCBI and Ensembl) annotate orthologous genes using different criteria, and that multiple human TF lists have been published by different research groups. Recently, Kirilenko et al. identified orthologous genes in hundreds of placental mammals and birds and organized different types of genes into datasets of parewise comparison (e.g., hg38-panTro6) using humans and mice as references (Kirilenko et al. Integrating gene annotation with orthology inference at scale. Science 2023). Based on (a) the many2zero and one2zero gene lists in the “hg38-panTro6” dataset, (b) three human TF lists reported by two studies (Bahram et al. 2015; Lambert et al. 2018) and used in the SCENIC package, we identified HS TFs. The number of HS TFs and HS lncRNAs (5 vs 66) alone lends strong evidence suggesting that HS lncRNAs have contributed more significantly to human evolution than HS TFs (note that 5 is the union of three intersections between <many2zero + one2zero> and the three <human TF list>).

TF DBS (i.e., TFBS) prediction has also been challenging because they are very short (mostly about 10 bp) and TF-DNA binding involves many cofactors (Bianchi et al. Zincore, an atypical coregulator, binds zinc finger transcription factors to control gene expression. Science 2025). We used two TF DBS prediction programs to predict HS TF DBSs, including the well-established FIMO program (whose results have been incorporated into the JASPAR database) (Rauluseviciute et al. JASPAR 2024: 20th anniversary of the open-access database of transcription factor binding profiles Open Access. NAR 2023) and the recently reported CellOracle program (Kamimoto et al. Dissecting cell identity via network inference and in silico gene perturbation. Nature 2023). Then, we performed downstream analyses and obtained two major results. One is that on average (per base), fewer selection signals are detected in HS TF DBSs (anyway, caution is needed because TF DBSs are very short); the other is that HS TFs and HS lncRNAs contribute to human evolution in quite different ways (Supplementary Figs. 25 and 26).

(3) On genes with more transcripts may appear as spurious targets of HS lncRNAs.

Now, the results of HS TF DBSs allow us to address the question of whether genes with more transcripts may appear as spurious targets of HS lncRNAs. We note that (a) we predicted HS lncRNA DBSs and HS TF DBSs in the same promoter regions before the same 179128 Ensembl-annotated transcripts (release 79), (b) we used the same GTEx transcript expression matrices in the analyses of HS TF DBSs and HS lncRNA DBSs (the GTEx database includes gene expression matrices and transcript expression matrices, the latter includes multiple transcripts of a gene). Thus, the analyses of HS TF DBSs provide an effective control for examining the question of whether genes with more transcripts may appear as spurious targets of HS lncRNAs, and consequently, cause the high percentages of HS lncRNA-target transcript pairs that show correlated expression in the brain (Figure 3). We find that the percentages of HS TF-target transcript pairs that show correlated expression are also high in the brain, but the whole profile in GTEx tissues is significantly different from that of HS lncRNA DBSs (Figure 3A; Supplementary Figure 25). On the other hand, on the distribution of significantly changed DBSs in GTEx tissues, the difference between HS lncRNA DBSs and HS TF DBSs is more apparent (Figure 3B; Supplementary Figure 26). Together, these suggest that the brain-enriched distribution of co-expressed HS lncRNA-target transcript pairs must arise from HS lncRNA-mediated transcriptional regulation rather than from the transcript number difference.

(4) Additional notes on HS TFs and HS TF DBSs.

First, the “many2zero” and “one2zero” gene lists in the “hg38-panTro6” dataset of Kirilenko et al. provide the most update, but not most complete, data on human-specific genes because “hg38-panTro6” is a pairwise comparison. On the other hand, the Ensembl database also annotates orthologous genes, but lacks such pairwise comparisons as “hg38-panTro6”. Therefore, not all HS genes based on “hg38-panTro6” agree with orthologous genes in the Ensembl database. Second, if HS genes are identified based on both Ensembl and Kirilenko et al., HS TFs will be fewer.

(5) On speculative or hyperbolic claims.

First, the title “Human-specific lncRNAs contributed critically to human evolution by distinctly regulating gene expression” is now further supported by HS TF DBSs analyses. Second, we have carefully revised the entire manuscript, trying to make it more readable, accurate, logically reasonable, and biologically acceptable. Third, specifically, in the revision, we avoid speculative or hyperbolic claims in results, interpretations, and discussions as possible as we can. This includes the tone-down of statements and claims, for example, using “reshape” to replace “rewire” and using “suggest” to replace “indicate”. Since the revisions are pervasive, we do not mark all of them, except those that are directly relevant to the reviewer’s comments.

(1) Line 155: "About 5% of genes have significant sequence differences in humans and chimpanzees," This statement needs a citation, and a definition of what is meant by 'significant', especially as multiple lines below instead mention how it's not clear how many differences matter, or which of them, etc.

Different studies give different estimates, from 1.24% (Ebersberger et al. Genomewide Comparison of DNA Sequences between Humans and Chimpanzees. Am J Hum Genet. 2002) to 5% (Britten RJ. Divergence between samples of chimpanzee and human DNA sequences is 5%, counting indels. PNAS 2002). The 5% for significant gene sequence differences arises when considering a broader range of genetic variations, particularly insertions and deletions of genetic material (indels). To provide more accurate information, we have replaced this simple statement with a more comprehensive one and cited the above two papers.

(2) line 187: "Notably, 97.81% of the 105141 strong DBSs have counterparts in chimpanzees, suggesting that these DBSs are similar to HARs in evolution and have undergone human-specific evolution." I do not see any support for the inference here. Identifying HARs and acceleration relies on a far more thorough methodology than what's being presented here. Even generously, pairwise comparison between two taxa only cannot polarise the direction of differences; inferring human-specific change requires outgroups beyond chimpanzee.

Here, we actually made an analogy but not an inference; therefore, we used such words as “suggesting” and “similar” instead of using more confirmatory words. We have revised the latter half sentence, saying “raising the possibility that these sequences have evolved considerably during human evolution”.

(3) line 210: "Based on a recent study that identified 5,984 genes differentially expressed between human-only and chimpanzee-only iPSC lines (Song et al., 2021), we estimated that the top 20% (4248) genes in chimpanzees may well characterize the human-chimpanzee differences". I do not agree with the rationale for this claim, and do not agree that it supports the cutoff of 0.034 used below. I also find that my previous concerns with the very disparate numbers of results across the three archaics have not been suitably addressed.

(1) Indeed, “we estimated that the top 20% (4248) genes in chimpanzees may well characterize the human-chimpanzee differences” is an improper claim; we made this mistake due to the flawed use of English.

(2) What we need is a gene number, which (a) indicates genes that effectively differentiate humans from chimpanzees, (b) can be used to set a DBS sequence distance cutoff. Since this study is the first to systematically examine DBSs in humans and chimpanzees, we must estimate this gene number based on studies that identify differentially expressed genes in humans and chimpanzees. We choose Song et al. 2021 (Song et al. Genetic studies of human–chimpanzee divergence using stem cell fusions. PNAS 2021), which identified 5984 differentially expressed genes, including 4377 genes whose differential expression is due to trans-acting differences between humans and chimpanzeees. To the best of our knowledge, this is the only published data on trans-acting differences between humans and chimpanzeees, and most HS lncRNAs and their DBSs/targets have trans-acting relationships (see Supplementary Table 2). Based on these numbers, we chose a DBS sequence distance cutoff of 0.034, which corresponds to 4248 genes (the top 20%), slightly fewer than 4377.

(3) If we chose DBS sequence distance cutoff=0.033 or 0.035, slightly more or fewer genes would be determined, raising the question of whether they would significantly influence the downstream gene set enrichment analysis (Figure 2). We found that 91 genes have a DBS sequence distance of 0.034. Thus, if cutoff=0.035, 4248-91=4157 genes were determined, and the influence on gene set enrichment analysis was very limited.

(4) On the disparate numbers of results across the three archaics. Figure 1A is based on Figure 2 in Prufer et al. 2017. At first glance, our Figure 1A indicates that Altai Neanderthal is older than Denisovan (upon kya), making our result “identified 1256, 2514, and 134 genes in Altai Neanderthals, Denisovans, and Vindija Neanderthals” unreasonable. However, Prufer et al. (2017) reported that “It has been suggested that Denisovans received gene flow from a hominin lineage that diverged prior to the common ancestor of modern humans, Neandertals, and Denisovans……In agreement with these studies, we find that the Denisovan genome carries fewer derived alleles that are fixed in Africans, and thus tend to be older, than the Altai Neandertal genome”. This note by Prufer et al. provides an explanation for our result, which is that more genes with large DBS sequence distances were identified in Denisovans than in Altai Neanderthals. Of course, the 1256, 2514, and 134 depend on the cutoff of 0.034. If cutoff=0.035, these numbers change slightly, but their relationships remain (i.e., more genes in Denisovans). We examined multiple cutoff values and found that more genes in Denisovans have large DBS sequence distances than in Altai Neanderthals.

(4) I also think that there is still too much of a tendency to assume that adaptive evolutionary change is the only driving force behind the observed results in the results. As I've stated before, I do not doubt that lncRNAs contribute in some way to evolutionary divergence between these species, as do other gene regulatory mechanisms; the manuscript leans down on it being the sole, or primary force, however, and that requires much stronger supporting evidence. Examples include, but are not limited to:

(1) Indeed, the observed results are also caused by other genomic elements and mechanisms (but it is hardly feasible to identify and differentiate them in a single study), and we do not assume that adaptive evolutionary change is the only driving force. Careful revisions have been made to avoid leaving readers the impression that we have this tendency or hold the simple assumption.

(2) Comparing HS lncRNAs to HS TFs is critical, and we have done this.

(5) line 230: "These results reveal when and how HS lncRNA-mediated epigenetic regulation influences human evolution." This statement is too speculative.

We have toned down the statement, just saying “These results provide valuable insights into when and how HS lncRNA-mediated epigenetic regulation impacts human evolution”.

Line 268: "yet the overall results agree well with features of human evolution." What does this mean? This section is too short and unclear.

(1) First, the sentence “Selection signals in YRI may be underestimated due to fewer samples and smaller sample sizes (than CEU and CHB), yet the overall results agree well with features of human evolution” has been deleted, because CEU, CHB, and YRI samples are comparable (100, 99, and 97, respectively).

(2) Now the sentence has been changed to “These results agree well with findings reported in previous studies, including that fewer selection signals are detected in YRI (Sabeti et al., 2007; Voight et al., 2006)”.

(3) On “This section is too short and unclear” - To make the manuscript more readable, we adopt short sections instead of long ones. This section expresses that (a) our finding that more selection signals were detected in CEU and CHB than in YRI agrees with well-established findings (Voight et al. A Map of Recent Positive Selection in the Human Genome. PLoS Biology 2006; Sabeti et al. Genome-wide detection and characterization of positive selection in human populations. Nature 2007), (b) in considerable DBSs, selection signals were detected by multiple tests.

Line 325: "and form 198876 HS lncRNA-DBS pairs with target transcripts in all tissues." This has not been shown in this paper - sequence based analyses simply identify the “potential” to form pairs.

This section describes transcriptomic analysis using the GTEx data. Indeed, target transcripts of HS lncRNAs are results of sequence-based analysis, and a predicted target is not necessarily regulated by the HS lncRNA in a tissue. Here, “pair” means a pair of HS lncRNA-target transcript whose expression shows significant Pearson correlation in a GTEx tissue (by the way, we do not mean correlation equals regulation; actually, we identified HS lncRNA-mediated transcriptional regulation upon both DBS-targeting relationship and correlation relationship).

Line 423: "Our analyses of these lncRNAs, DBSs, and target genes, including their evolution and interaction, indicate that HS lncRNAs have greatly promoted human evolution by distinctly rewiring gene expression." I do not agree that this conclusion is supported by the findings presented - this would require significant additional evidence in the form of orthogonal datasets.

(1) As mentioned above, we have used “reshape” to replace “rewire” and used “suggest” to replace “indicate”. In addition, we have substantially revised the Discussion, in which this sentence is replaced by “our results suggest that HS lncRNAs have greatly reshaped (or even rewired) gene expression in humans”.

(2) Multiple citations have been added, including Voight et al. 2006 (Voight et al. A Map of Recent Positive Selection in the Human Genome. PLoS Biology 2006) and Sabeti et al. 2007 (Sabeti et al. Genome-wide detection and characterization of positive selection in human populations. Nature 2007).

(3) We have analyzed HS TF DBSs, and the obtained results also support the critical contribution of HS lncRNAs.

I also return briefly to some of my comments before, in particular on the confounding effects of gene length and transcript/isoform number. In their rebuttal the authors argued that there was no need to control for this, but this does in fact matter. A gene with 10 transcripts that differ in the 5' end has 10 times as many chances of having a DBS than a gene with only 1 transcript, or a gene with 10 transcripts but a single annotated TSS. When the analyses are then performed at the gene level, without taking into account the number of transcripts, this could introduce a bias towards genes with more annotated isoforms. Similarly, line 246 focuses on genes with "SNP numbers in CEU, CHB, YRI are 5 times larger than the average." Is this controlled for length of the DBS? All else being equal a longer DBS will have more SNPs than a shorter one. It is therefore not surprising that the same genes that were highlighted above as having 'strong' DBS, where strength is impacted by length, show up here too.

(1) In gene set enrichment analysis (Figure 2, which is a gene-level analysis), when determining genes differentiating humans from chimpanzees based on DBS sequence distance, if a gene has multiple transcripts/DBSs, we choose the DBS with the largest distance. That is, the input to g:Profiler is a non-redundant gene list.

(2) In GTEx data analysis (Figure 3, which is a transcriptome-level analysis), the analyses of HS TF DBSs using the GTEx data provide evidence suggesting that different DBS/transcript numbers of genes are unlikely to cause confounding effects. As explained above, we predicted HS TF DBSs in the same promoter regions of 179128 Ensembl-annotated transcripts (release 79), but Supplementary Figures 25 and 26 are distinctly different from Figure 3AB.

(3) In evolutionary analysis, a gene with 10 DBSs has a higher chance of having selection signals than a gene with 1 DBS. This is biologically plausible, because many conserved genes have novel transcripts whose expression is species-, tissue-, or developmental period-specific, and DBSs before these novel transcripts may differ from DBSs before conserved transcripts.

(4) “line 246 focuses on genes with "SNP numbers in CEU, CHB, YRI are 5 times larger than the average." Is this controlled for the length of the DBS?” - This is a defect. We have now computed SNP numbers per base and used the new table to replace the old Supplementary Table 8. After examining the new table, we find that the major results of SNP analysis remain.

(5) On “Is this controlled for length of the DBS? All else being equal a longer DBS will have more SNPs than a shorter one” - We do not think there are reasons to control for the length of DBSs; also, what “All else being equal” means matters. First, DBS sequences have specific features; thus, the feature of a long DBS is stronger than the feature of a short one, making a long DBS less likely to be generated by chance in the genome and less likely to be predicted wrongly than a short one. This means that longer DBSs are less likely to be false ones (note our explanation that the chance of a DBS of 147 bp, the mean length of DBSs, to be wrongly predicted is extremely low, p<8.2e-19 to 1.5e-48). Second, the difference in length suggests a difference in binding affinity, which in turn influences the regulation of the specific transcripts and influences the analysis of GTEx data. Third, it cannot be excluded that some SNPs may be selection signals (detecting selection signal is challenging, and many selection signals cannot be detected by statistical tests, see Grossman et al. A composite of multiple signals distinguishes causal variants in regions of positive selection. Science 2010).

(6) On “It is therefore not surprising that the same genes that were highlighted above as having 'strong' DBS, where strength is impacted by length” - Indeed, strength is influenced by length, see the above response.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

Finally, figure 1 panels D and F are not legible - the font is tiny! There's also a typo in panel A, where "Homo Sapien" should be "Homo sapiens".

(1) “Homo sapien” is changed to “Homo sapiens”.

(2) Even if we double the font size, they are still too small. Inserting a very large panel D into Figure 1 will make Figure 1 ugly, and converting Figure 1D into an independent figure is unnecessary. Actually, panels 1D and F are illustrative figures; the full Fig.1D is Supplementary Figure 6, and the full Fig.1F is Figure 3. We have revised Fig.1’s legend to explain these.

Author response:

We would like to express our gratitude to all three reviewers for their time and valuable feedback on the manuscript. Below, we provide our point-by-point responses to their comments. Additionally, we summarize here the experiments we plan to conduct in accordance with the reviewers' suggestions:

Revision plan 1. To further explore the mechanisms of Notch signaling in the decision of regional EE pattern.

Our observation of EE subtype changes in Notch mutant clones revealed that Notch is required for the specification of Type II EEs, but whether it promotes the generation of Type III EEs is not quite clear. In this revision, we will complete the quantification of Type I and Type III EEs in Notch mutant clones to demonstrate whether Notch signaling participate the determination of these two EE subtypes. Further, we will attempt to combine Notch mutant with different manipulation of WNT and BMP gradients to investigate their interplays.

Revision plan 2. To supplement the global pattern of WNT and BMP gradient along the whole gut.

The levels of WNT and BMP gradients are variable in different gut regions both under normal condition and genetic manipulation, leading to different outcomes of EE subtype composition. To further support our model, we will supply the changes of WNT and BMP gradients along the whole gut after genetic manipulation, and perform semi-quantification of their levels to correlate with EE subtype compositions. Additionally, we will also test the gradient levels at different time point during pupal stage to interpret the establishment of regional identity during the development.

Revision plan 3. To investigate the involvement of apical-basal polarity in the determination of regional EE diversity.

Although we have demonstrated WNT and BMP gradients orchestrate the regional EE identity, but some observations cannot be fully explained by their roles, such as asymmetric expression of CCHa2 in EE pairs from R4b. A potential mechanism is apical-basal polarity, which has been reported to determine cell fate of ISC progenies at pupal stage. We will specifically knockdown or overexpress key genes related to apical-basal polarity in ISCs or EEs to test whether they are involved preliminarily.

Please find our detailed point-by-point responses below.

Public Reviews:

Reviewer #1 (Public review):

This valuable study explores the regulatory mechanisms underlying the regional distribution of enteroendocrine cell subtypes in the Drosophila midgut. The regional distribution of EE cell subtypes is carefully documented, and the data convincingly show that each EE cell subtype has a unique spatial pattern. The study aims at determining how the spatial distribution of EE cell subtypes is established and maintained, and explores the roles of three pathways: Notch, WNT, and BMP. The data show evidence that Notch signaling regulates the subtype specificity, being necessary for the specification of Type II, but not Type I and III EE cell subtype specification. The immunofluorescence data in Figure 3 are convincing, but the analysis is incomplete due to a lack of quantification. How Notch signaling activity relates to the emergence of the regional EE cell patterns remains unclear.

Indeed, the role of Notch signaling in regional EE determination was not fully characterized in this work. As the requirement of Notch activation for the differentiation of enterocytes, introduction of Notch or Delta mutant led to rapid accumulation of ISCs and EEs, making it being a challenge to dive into the details of how EE subtypes were generated. We will try to complete the quantification of Type I and Type III EEs in the Notch mutant clones from different gut regions to figure out whether Notch could influence the specification of these two EE subtypes. Additionally, different from WNT and BMP gradients, Notch signaling can only function locally and is not significantly changed along the whole gut, including Type II EE-enriched R1a and Type I EE-enriched R4b, which implies that function of Notch signaling may can be overridden by the impact of specific combination of WNT and BMP gradients. To test this hypothesis, we will attempt to combine Notch mutant with the activation or inhibition of WNT and BMP signaling since pupal stage, and further examine whether the regional EE identity could be altered, especially in R1a and R4b regions.

As WNT and BMP are known as morphogens, the study explores their expression patterns and their roles in establishing and maintaining the subtype identities. The observed patterns of WNT and BMP are consistent with earlier studies. Manipulation of WNT and BMP pathway activities in intestinal stem cells is shown to have some region-specific effects on specific EE cell subtypes. The overall conclusion that both WNT and BMP have local effects on EE cell subtypes is based on solid evidence. However, the study falls short in achieving its main objective, i.e., to explain the regional subtype patterns by the action of WNT and BMP gradients. Despite displaying a large volume of phenotypic data in Figures 4-7, the study remains incomplete in providing sufficient evidence to support the models shown in Figures 7 M and N. The main challenge is that the reader is provided with a large volume of individual data fragments of selected regions (e.g., Figures 4 and 5) or images of whole midgut without proper quantification (Figure 7). There is not sufficient effort made to display the data in a way that allows observing changes in the global patterns of EE cell subtypes throughout the midgut and compare these patterns with the observed WNT and BMP gradients.

As the variation of WNT and BMP gradients along the whole gut, manipulating these two pathways is not able to align their activation levels in different gut regions. This forced us to analyze the change of each region separately, making it to be a challenge to provide a comprehensive global overview. We will supplement the comprehensive profile of WNT and BMP activity under the manipulation of these two signaling pathways to correlated with the change of EE identity, and also try to perform a semi-quantitative interpretation to further support the model in Figure 7M and 7N.

Reviewer #2 (Public review):

Summary:

By labeling the three major enteroendocrine cell markers - AstC, Tk, and CCHa2-the authors systematically investigated the distribution of distinct EE subtypes along the Drosophila midgut, as well as their emergence via symmetric and asymmetric divisions of enteroendocrine progenitor cells. Moreover, they dissected the molecular mechanisms underlying regional patterning by modulating Wnt and BMP signaling pathways, revealing that these compartment boundary signals play key roles in regulating EE subtype diversity.

Strengths:

This work establishes a solid methodological and conceptual foundation for future studies on how stem cells acquire positional identity and modulate region-specific behaviors.

Weaknesses:

Given that the transcriptional profiles of intestinal stem cells across different regions are highly similar, it is reasonable to hypothesize that the behavior of ISCs and enteroendocrine precursor cells may be regulated non-autonomously, potentially through interactions with enterocytes, which exhibit more distinct region-specific characteristics.

This is a quite complicated point to discuss. Drosophila adult midgut is established by pISCs (pupal ISCs), which arise from AMPs (adult midgut progenitors) in larval midgut. AMPs are encased by PCs (peripheral cells) to be islands, scattered throughout the entire larval midgut by mid L3 stage (Mathur D. et al. Science. 2010). After pupariation, larval midgut is delaminated to become the yellow body and finally meconium in the pupal midgut. Simultaneously, PCs break down and die, allowing AMPs to give rise to the presumptive adult epithelium (generating enterocyte precursors) and the specification of ISCs in the adult midgut (Jiang H, Edgar BA. Development. 2009; Micchelli CA. et al. Gene Expr Patterns. 2011). During the pupal stage, pISCs only proliferate to generate new ISCs and EE lineages, while adult enterocytes start to appear after eclosion (Takashima S. et al. Dev Biol. 2011). This rules out the possibility that the interaction with enterocytes regulates regional ISC identity during pupal stage.

However, whether AMPs already acquire the regional identity during larval stage, and whether pISCs interact with enterocyte precursors at pupal stage, are not quite clear. Our study revealed that pISCs can be influenced by WNT and BMP gradients to acquire regional identity, and further establish regional EE diversity. The change of WNT and BMP gradients during the metamorphosis will be supplemented in revision. While WNT and BMP signaling ligands are provided by muscles and adult enterocytes, and even other surrounding tissues, to regulate regional ISC identity, which indicates that non-autonomous mechanisms indeed exist.

Reviewer #3 (Public review):

Summary:

The authors aimed to elucidate the mechanisms underlying the regional patterning of enteroendocrine cell (EE) subtypes along the Drosophila midgut. Through detailed immunohistochemical mapping and genetic perturbation of Notch, WNT, and BMP signaling pathways, they sought to determine how extrinsic morphogen gradients and intrinsic stem cell identity contribute to EE diversity.

Strengths:

A major strength of this work is the meticulous regional analysis of EE pairs and the use of multiple genetic tools to manipulate signaling pathways in a spatiotemporally controlled manner. The data robustly demonstrate that WNT and BMP signaling gradients play key roles in specifying EE subtypes and division modes across different gut regions.

Weaknesses:

However, the study does not fully explore the mechanistic basis for the region-specific dependence on Notch signaling. Additionally, while the authors propose that symmetric divisions occur in R1a and R4b, the observed heterogeneity in CCHa2 expression within AstC+ pairs in R4b suggests that asymmetric mechanisms may still be at play, possibly involving apical-basal polarity as previously reported.

As previously mentioned, we acknowledge that the role of Notch signaling in regional EE determination remains further exploration. We will supplement the quantification of Type I and Type III EEs in Figure 3 and Figure S4, and further combine Notch mutant with activation or inhibition of WNT and BMP signaling to test whether they have any interplays, especially in R1a and R4b.

Apical-basal polarity has been reported to determine the precise segregation of Pros to control ISC number and cell fate at the pupal stage (Wu S. et al. Cell Rep. 2023). During this time, generation of regional EEs are completed and may also be affected except for the influence of Notch, WNT and BMP pathways. Therefore, the apical-basal polarity is quite a potential mechanism to induce asymmetric cell division in R4b, which we will perform experiments to test.

Appraisal of achievements:

The authors successfully achieve their aims by providing a compelling model in which intercalated WNT and BMP gradients regulate EE subtype specification and EEP division modes. The genetic data strongly support the conclusion that these pathways are central to establishing regional EE diversity during pupal development.

Author response:

eLife Assessment

This valuable study addresses the effects of selection on aggression on fitness and life-history trade-offs in Drosophila melanogaster. However, the evidence presented is incomplete and does not support the claims proposed in the study of increased survival of highly aggressive males at the expense of reproductive success and shorter mating duration. The main limitation of the study is the choice to use males from only one aggressive Drosophila line in combination with CantonS females, that do not allow disambiguation between nonaggression-related factors, such as hybrid vigor and aggression-related factors influencing mating and lifespan.

We would like to clarify the points raised in the eLife assessment.

The report states that we relied on a single line of hyper-aggressive males tested with CantonS females, and implies that Bully and Cs have not co-evolved. This is a misunderstanding: Bully flies were derived from Cs population. Thus, Bully and Cs have co-evolved. In addition to the Bully A line presented in the main figures of the manuscript, we replicated several of our findings with a second independent selected line, Bully B. Results from courtship assays involving both Bully A and Bully B couples males and females were presented in Figure Supp1. We apologies for not having made this more explicit in the original manuscript, which we will correct. These experiments should alleviate the concerns from the reviewers; they demonstrate that our conclusions are supported by two independent hyper-aggressive lines, and these include assays with selected male and female flies.

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study asks how selection for male aggressiveness affects life-history and reproductive fitness traits in Drosophila melanogaster males.

Strengths:

Multiple comprehensive assays are used to address the question.

We thank the reviewer for recognizing these strengths.

Weaknesses:

(1) The flies used for comparisons are inadequate. Behavioral assays compare Bully males mated to non-coevolved Cs females with Cs males mated to coevolved Cs females.

We thank the reviewer for this comment, which made us realize that we had not sufficiently highlighted some of our experiments. The Bully lines used in our work were derived from Canton-S flies and thus did co-evolve with Cs. As originally described by Penn et al. (2010), highly aggressive “Bully” lines were generated through selective breeding from Canton-S males that consistently won aggressive encounters. After 34–37 generations, stable Bully lines were established. Thus, Bully and Cs flies have co-evolved and 2) the selection applied was male-specific. Independent selection replicates produced distinct lines, including Bully A and Bully B. Previous studies only characterized Bully A (Penn et al., 2010; Chowdhury et al., 2017), but our work includes both Bully A and Bully B (Fig. S1).

The rationale for pairing Bully or Cs males with Cs females (with which both male types co-evolved) follows the approach used by Dierick et al. (2006), who investigated how the male-specific selection for aggression affected courtship and mating behaviors by testing them with standard Canton-S females. This design allows to isolate the effects of male genotype and behavior on courtship and mating outcomes, avoiding confounding effects from female behavioral changes.

We initially compared selected Bully pairs (Bully males × Bully females) (Fig. S1) with Cs pairs and observed similarly shortened mating durations in both Bully × Bully and Bully × Cs matings (Fig. S1, Fig. 1F and G). Thus, the reduction in mating duration arises specifically from Bully males. We therefore chose to use Cs females as a standard background to assess the consequences of male-specific selection for aggression on reproductive behaviors.

(2) Lifespan analysis is done on male progeny of Cs females mated to either genetically more distant Bully or co-evolved Cs males; the longer lifespan and performance on the former is interpreted as a trade-off with aggressiveness, rather than a simple explanation of hybrid vigor.

We appreciate this comment, which again stems from a poor explanation from our part about the origin of the Bully line in the original manuscript. The Bully flies were derived from the same original population as the Cs line. Hybrid vigor typically arises when crossing individuals from distinct populations, which is not the case here as both Bully and CS come from the same population.

To further support our conclusions, we conducted additional experiments using progeny from within-line crosses (Bully males × Bully females) and results revealed the same phenotype: the progeny of these flies also exhibited significantly longer lifespans than Cs males x Cs females progeny. This finding argues against hybrid vigor as the main explanation for the observed phenotype, since both the Bully and Cs crosses result in inbreeding, yet give longer lifespan in Bully. We will include these additional longevity data (currently not included in the manuscript) to strengthen our results and reinforce our interpretation.

(3) Differences in CHCs between Bully and Cs males and Cs females mated to those males are not shown to cause differences in measured behavioral outcomes.

We thank the reviewer for raising this important point regarding causality. One way to establish a causal link between differences in CHCs observed in Bully and Cs flies and the corresponding behavioral outcomes would be to experimentally manipulate CHC profiles. For instance, one could perfume oenocyte-less males with the compounds found in higher abundance in Bully flies, then perform behavioral assays to assess causality. We agree that such experiments would be highly informative in determining the functional roles of specific CHCs elevated in Bully males. However, this approach is technically challenging, as the perfuming technique must be optimized to transfer precise amounts of each compound. For example, this method can be used to gradually perfume flies to assess dose–response behavioral effects, whereas matching exactly the natural concentrations found in individuals, especially given inter-individual variability, remains difficult.

We considered conducting such experiments during our study but did not pursue them for these technical reasons. Nevertheless, we can include a statement in the Discussion acknowledging this as an important future direction to test the causal relationship between CHC variation and behavior.

Reviewer #2 (Public review):

Summary:

The authors compare "Bully" lines, selected for male aggression, to Canton-S controls and find that Bully males have lower mating success, shorter mating durations, and remate sooner. Chemical analyses show Bully males have distinct cuticular hydrocarbons (CHC) signatures and transfer markedly less cVA to females, offering a plausible mechanistic link to weaker mate-guarding.

Paradoxically, Bully males live longer and remain fertile at older ages when CS males no longer mate, indicating a shift in the reproduction-survival trade-off in aggression-selected populations.

Importantly, the work sheds light on proximate mechanisms, demonstrating that shifts in CHCs and pheromone transfer co-occur with changes in fitness traits, thus offering new entry points for understanding life-history evolution.

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

Strengths:

The manuscript's strengths lie in its comprehensive and integrative approach framed within an evolutionary context. By combining behavioral assays, chemical profiling, and lifespan measurements, the authors reveal a coherent pattern linking aggression selection to life-history trade-offs. The direct quantification of cVA in female reproductive tracts after mating provides a particularly compelling mechanistic correlate, strengthening the link between behavior and chemical signaling. Findings on altered 5-T and 5-P levels further highlight how chemical communication shapes mating and mate-guarding strategies. Analytical approaches are largely rigorous, and the results provide valuable insights into the pleiotropic effects of selection on socially relevant traits. The study will be of interest to Drosophila biologists working on sexual selection, behavioral evolution, and aging.

We thank the reviewer for recognizing the integrative design and mechanistic contributions of our study.

Weaknesses:

The weaknesses are primarily conceptual rather than procedural. The generality of the findings is uncertain, as selection appears to be represented by only one (and a second closely related) Bully line, limiting conclusions about selection responses versus line-specific drift or founder effects. The causal link between aggression selection and increased longevity is not established: the data show a correlated shift but do not identify mechanisms underlying lifespan extension. In several places, the manuscript uses causal language (e.g., that selection 'influences' longevity or mating strategy) where association would be more accurate; this should be toned down to avoid overstatement. Ecological relevance is also not addressed, since laboratory conditions may bias the balance between costs and benefits of aggression compared with variable natural environments. Addressing these points would strengthen both the impact and clarity of the study.

(1) Generality of findings and potential line effects

We agree that our results presented in the main figures of the manuscript relied mainly on one Bully line (Bully A). To address potential line-specific effects, we replicated key courtship experiments with another independent line, Bully B, selected in parallel from the same Canton-S stock but through distinct selection replicates. The results obtained from Bully B closely matched those from Bully A, suggesting that the observed phenotypes are consistent consequences of aggression selection rather than random drift or founder effects.

(2) Causality versus correlation

We concur that some sentences in the manuscript could overstate causal interpretations. We will revise the text to clearly distinguish correlation from causation and to avoid implying direct causal relationships where data only support association.

(3) Ecological relevance

We appreciate this point. Our experiments were performed under controlled laboratory conditions, which may not fully capture the ecological contexts shaping the costs and benefits of aggression. We will acknowledge this limitation and expand the Discussion to consider how environmental variability could modulate the fitness trade-offs associated with aggression in natural populations.

We thank both reviewers for their constructive feedback, which will help us strengthen the rigor and clarity of the manuscript. We believe that the additional results and revisions will satisfactorily address their concerns.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

This unique study reports original and extensive behavioral data collected by the authors on 21 living mammal taxa in zoo conditions (primates, tree shrew, rodents, carnivorans, and marsupials) on how descent along a vertical substrate can be done effectively and securely using gait variables. Ten morphological variables reflecting head size and limb proportions are examined in relationship to vertical descent strategies and then applied to reconstruct modes of vertical descent in fossil mammals.

Strengths:

This is a broad and data-rich comparative study, which requires a good understanding of the mammal groups being compared and how they are interrelated, the kinematic variables that underlie the locomotion used by the animals during vertical descent, and the morphological variables that are associated with vertical descent styles. Thankfully, the study presents data in a cogent way with clear hypotheses at the beginning, followed by results and a discussion that addresses each of those hypotheses using the relevant behavioral and morphological variables, always keeping in mind the relationships of the mammal groups under investigation. As pointed out in the study, there is a clear phylogenetic signal associated with vertical descent style. Strepsirrhine primates much prefer descending tail first, platyrrhine primates descend sideways when given a choice, whereas all other mammals (with the exception of the raccoon) descend head first. Not surprisingly, all mammals descending a vertical substrate do so in a more deliberate way, by reducing speed, and by keeping the limbs in contact for a longer period (i.e., higher duty factors).

Weaknesses:

The different gait patterns used by mammals during vertical descent are a bit more difficult to interpret. It is somewhat paradoxical that asymmetrical gaits such as bounds, half bounds, and gallops are more common during descent since they are associated with higher speeds and lower duty factors. Also, the arguments about the limb support polygons provided by DSDC vs. LSDC gaits apply for horizontal substrates, but perhaps not as much for vertical substrates.

We analyzed gait patterns using methods commonly found in the literature and discussed our results accordingly. However, the study of limbs support polygons was indeed developed specifically for studying locomotion on horizontal supports, and may not be applicable for studying vertical locomotion, which is in fact a type of locomotion shared by all arboreal species. In the future, it would be interesting to consider new methods for analyzing vertical gaits.

The importance of body mass cannot be overemphasized as it affects all aspects of an animal's biology. In this case, larger mammals with larger heads avoid descending head-first. Variation in trunk/tail and limb proportions also covaries with different vertical descent strategies. For example, a lower intermembral index is associated with tail-first descent. That said, the authors are quick to acknowledge that the five lemur species of their sample are driving this correlation. There is a wide range of intermembral indices among primates, and this simple measure of forelimb over hindlimb has vital functional implications for locomotion: primates with relatively long hindlimbs tend to emphasize leaping, primates with more even limb proportions are typically pronograde quadrupeds, and primates with relatively long forelimbs tend to emphasize suspensory locomotion and brachiation. Equally important is the fact that the intermembral index has been shown to increase with body mass in many primate families as a way to keep functional equivalence for (ascending) climbing behavior (see Jungers, 1985). Therefore, the manner in which a primate descends a vertical substrate may just be a by-product of limb proportions that evolved for different locomotor purposes. Clearly, more vertical descent data within a wider array of primate intermembral indices would clarify these relationships. Similarly, vertical descent data for other primate groups with longer tails, such as arboreal cercopithecoids, and particularly atelines with very long and prehensile tails, should provide more insights into the relationship between longer tail length and tail-first descent observed in the five lemurs. The relatively longer hallux of lemurs correlates with tail-first descent, whereas the more evenly grasping autopods of platyrrhines allow for all four limbs to be used for sideways descent. In that context, the pygmy loris offers a striking contrast. Here is a small primate equipped with four pincer-like, highly grasping autopods and a tail reduced to a short stub. Interestingly, this primate is unique within the sample in showing the strongest preference for head-first descent, just like other non-primate mammals. Again, a wider sample of primates should go a long way in clarifying the morphological and behavioral relationships reported in this study.

We agree with this statement. In the future, we plan to study other species, particularly large-bodied ones with varied intermembral indexes.

Reconstruction of the ancient lifestyles, including preferred locomotor behaviors, is a formidable task that requires careful documentation of strong form-function relationships from extant species that can be used as analogs to infer behavior in extinct species. The fossil record offers challenges of its own, as complete and undistorted skulls and postcranial skeletons are rare occurrences. When more complete remains are available, the entire evidence should be considered to reconstruct the adaptive profile of a fossil species rather than a single ("magic") trait.

We completely agree with this, and we would like to emphasize that our intention here was simply to conduct a modest inference test, the purpose of which is to provide food for thought for future studies, and whose results should be considered in light of a comprehensive evolutionary model.

Reviewer #2 (Public review):

Summary:

This paper contains kinematic analyses of a large comparative sample of small to medium-sized arboreal mammals (n = 21 species) traveling on near-vertical arboreal supports of varying diameter. This data is paired with morphological measures from the extant sample to reconstruct potential behaviors in a selection of fossil euarchontaglires. This research is valuable to anyone working in mammal locomotion and primate evolution.

Strengths:

The experimental data collection methods align with best research practices in this field and are presented with enough detail to allow for reproducibility of the study as well as comparison with similar datasets. The four predictions in the introduction are well aligned with the design of the study to allow for hypothesis testing. Behaviors are well described and documented, and Figure 1 does an excellent job in conveying the variety of locomotor behaviors observed in this sample. I think the authors took an interesting and unique angle by considering the influence of encephalization quotient on descent and the experience of forward pitch in animals with very large heads.

Weaknesses:

The authors acknowledge the challenges that are inherent with working with captive animals in enclosures and how that might influence observed behaviors compared to these species' wild counterparts. The number of individuals per species in this sample is low; however, this is consistent with the majority of experimental papers in this area of research because of the difficulties in attaining larger sample sizes.

Yes, that is indeed the main cost/benefit trade-off with this type of study. Working with captive animals allows for large comparative studies, but there is a risk of variations in locomotor behavior among individuals in the natural environment, as well as few individuals per species in the dataset. That is why we plan and encourage colleagues to conduct studies in the natural environment to compare with these results. However, this type of study is very time-consuming and requires focusing on a single species at a time, which limits the comparative aspect.

Figure 2 is difficult to interpret because of the large amount of information it is trying to convey.

We agree that this figure is dense. One possible solution would be to combine species by phylogenetic groups to reduce the amount of information, as we did with Fig. 3 on the dataset relating to gaits. However, we believe that this would be unfortunate in the case of speed and duty factor because we would have to provide the complete figure in SI anyway, as the species-level information is valuable. We therefore prefer to keep this comprehensive figure here and we will enlarge the data points to improve their visibility, and provide the figure with a sufficiently high resolution to allow zooming in on the details.

Author response:

We thank the two anonymous reviewers who took the time and effort to read and evaluate our work. We look forward to submitting a revised version of the manuscript that addresses their comments.

A major concern shared between both reviewers is our use of Bayes factors instead pvalues to measure the strength of association. In revision, we will add a section in Supplementary to compare and constrast Bayes factor and p-values. Very briefly here, p-value is the tail probability under the null. Formally, it is defined as P(T > t|H₀), for a test statistic T with obvserved value t computed from data D. But our interest is P(H₀|D) and P(H₁|D), posterior probabilities of the null and alternative models, about which p-value says nothing. With FDR approach, a q-value, the minium FDR at which a null is rejected, which can be estimated from a collection of p-values, has a Bayesian interpretation as the probability that H₀ is true conditioning on rejecting that H₀. This is not quite P(H₀|D) but nevertheless a useful probabilistic statement. For FDR approach to work, however, the collection of tests need to be reasonably independent, and their effect sizes need to be mixed. Both implicit assumptions can fail for cis eQTL analysis.

On the other hand, with Bayes factors we can compute posterior probability P(H₀|D) and P(H₁|D) after specifying prior odds P(H₁)/P(H₀) (or equivalently P(H₁) since P(H₀)+ P(H₁) = 1). In our manuscript, the prior odds used to determined Bayes factor threshold is 1/1000, or about 1 cis eQTL per gene. Bayes factor also allows us to directly compare two non-nested alternative models P(paternal effect|D) and P(maternal effect|D), which is difficult to do using p-values.

It was suggested (by reviewer 2) that POE eQTL should be defined by testing H₀ : θ₀ = θ₁ against H₁ : θ₀ ̸= θ₁ where θ₀ and θ₁ are maternal and paternal effects respectively. This indeed was our initial approach, as evidenced in Table 1 (last column) and Section 4.5 in Methods. Our final approach is more stringent: H₀ : β₀ = β₁ = 0 against H₁ : β₀ = 0,β₁/= 0, to use test for paternal effect as an example (the test for maternal effect can be obtained in a similar fashion). That is, we not only require that paternal and maternal effects be the same, as suggested by reviewer, but also require that they are both 0 under the null. This is partially motivated by an example in Table 1 (Gene ZNF890P) where both β₀ > 0 and β₁ > 0, and β₀/= β₁. In other words, examples like this where both paternal and maternal effects are significant and their differences are also significant were not included in our downstream classification and further analysis.

Author response:

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

Reviewer #2 had several remaining suggestions:

In some instances, the authors face well-known limitations. For example, bath application of drugs. Blockers of Gly and Gaba receptors are likely problematic when studying a network that includes a diverse set of inhibitory interneurons. Likewise, the results derived from application of AMPAR and KAR blockers should impact HC cell fxn, and presumably inner retina interneuron networks. In the Discussion the authors are encouraged to address more of these concerns (e.g., Discussion line 709).

Rather than concluding that the bath application of drugs is without complications, they can conclude that under the experimental conditions, glutamate release from these On-bipolars continues to exhibit Transient and Sustained release. This is really the key point of their study.

This is a good suggestion.  We have added a discussion of the complications of the pharmacology starting on line 754.  

If indeed sustained release is a reflection of higher release rates, ribbon size is what point to but, there are many other possibilities, such as SV recycling, or recruitment of reserve pools of SVs, fusion machinery, Cav channel behavior. The authors could cite more literature in the Discussion.

We added a sentence to this effect in the discussion, starting on line 866.

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

Reviewer #1 (Public Review): 

Summary: 

In the retina, parallel processing of cone photoreceptor output under bright light conditions dissects critical features of our visual environment and is fundamental to visual function. Cone photoreceptor signals are sampled by several types of bipolar cells and passed onto the ganglion cells. At the output of retinal processing, retinal ganglion cells send about 40 different codes of the visual scene to the brain for further processing. In this study, the authors focus on whether subtype-specific differences in the size of synaptic ribbon-associated vesicle pools of bipolar cells contribute to different retinal ganglion cell (RGC) responses. Specifically, inputs to ON alpha RGCs producing transient versus sustained kinetics (ON-S vs. ON-T, respectively) are compared. The authors first demonstrate that ON-S vs. ON-T RGCs are readily identifiable in a whole mount preparation and respond differently to both static and to a spatially uniform, randomly fluctuating (Gaussian noise) light stimulus. Liner-nonlinear (LN) models were used to estimate the transformation between visual input and excitatory synaptic input for each RGCs; these models suggested the presence of transient versus sustained kinetics already in the excitatory inputs to ON-T and ON-S RGCs. Indeed, the authors show that (glutamatergic) excitatory inputs to ON-S vs. ON-T RGCs are of distinct kinetics. The subtypes of bipolar cells providing input to ON-S are known (i.e., type 6 and 7), but the source of excitatory bipolar inputs to ON-T RGCs needed to be determined. In a tedious process, it is elegantly shown here that ON-T RGCs receive most of their excitatory inputs from type 5 and 6 bipolars. Interestingly, the temporal properties of light-evoked responses of type 5, 6, and 7 bipolars recorded from the somas were indistinguishable and rather sustained, suggesting that the origin of transient kinetics of excitatory inputs to ON-T RGCs suggested by the LN model might be found in the processing of visual signals at the bipolar cell axon terminal. Blocking GABA- or glycinergic inhibitory inputs did not alter the light-evoked excitatory input kinetics to ON-T and ON-S RGCs. Twophoton glutamate sensor imaging revealed significantly faster kinetics of light-evoked glutamate signals at ON-T versus ON-S RGCs. Detailed EM analysis of bipolar cell ribbon synapses onto ON-T and ON-S RGCs revealed fewer ribbon-associated vesicles at ON-T synapses, which is consistent with stronger paired-flash depression of lightevoked excitatory currents in ON-T RGCS versus ON-S RGCs. This study suggests that bipolar subtype-specific differences in the size of synaptic ribbon-associated vesicle pools contribute to transient versus sustained kinetics in RGCs. 

Strengths: 

The use of multiple, state-of-the-art tools and approaches to address the kinetics of bipolar to ganglion cell synapse in an identified circuit. 

Weaknesses: 

For the most part, the data in the paper support the conclusions, and the authors were careful to try to address questions in multiple ways. Two-photon glutamate sensor imaging experiment showing that blocking GABA- and glycinergic inhibition does not change the kinetics of light-evoked glutamate signals at ON-T RGCs would strengthen the conclusion that bipolar subtype-specific differences in the size of synaptic ribbon-associated vesicle pools contribute to transient versus sustained kinetics in RGCs. 

Thank you for this suggestion. We have revised the text throughout to be careful not to imply that amacrine cells have no role in shaping EPSCs and spike output, but instead that the transience of the On-T responses persists without amacrine cells (see for example lines 91, 450-453, 514-518, 696-714). We have also added additional iGluSnFR experiments to the paper to further test this conclusion (new Figure 7). The new data shows that the transience of glutamate release from the On-T cells is retained when 1) spiking amacrine cell activity is suppressed by blocking voltage-gated Na⁺ channels with TTX or 2) all amacrine cell activity is suppressed by blocking AMPA receptors with NBQX. This does provide nice additional evidence that amacrine cells are not necessary for the sustained/transient distinction.

Reviewer #2 (Public Review): 

Summary: 

Goal of the study. The authors tried to pinpoint the origins of transient and sustained responses measured at retinal ganglion cells (rgcs), which is the output layer of the retina. Response characteristics of rgcs are used to group them into different types. The diversity of rgc types represents the ability of the retina to transform visual inputs into distinct output channels. They find that the physical dimensions of bipolar cell's synaptic ribbons (specialized release sites/active zones) vary across the different types of cone on-bpcs, in ways that they argue could facilitate transient or sustained release. This diversity of release output is what they argue underlies the differences in on-rgcs response characteristics, and ultimately represents a mechanism for creating parallel cone-driven channels. 

Strengths: 

The major strengths of the study are the anatomical approaches employed and the use of the "glutamate sniffer" to assay synaptic glutamate levels. The outline of the study is elegant and reflects the strengths of the authors. 

Weaknesses: 

The major weakness is that the ambitious outline is not matched with a complete set of results, and the set of physiological protocols is disjointed, not sufficient to bridge the systems-level question with the presynaptic release question. 

Thank you for this comment as it provides an opportunity (here and in the paper) for us to clarify our main goal. We wanted to link the well-established distinction between transient and sustained retinal responses to anatomy. This required locating where this difference arises within the circuitry – which we show to be at least largely the bipolar output synapse – and then examining the structure of this synapse in detail. While we would certainly be interested in connecting our results to a biophysical description of the synapse, that was not the primary focus of our study and was not something we could add without substantial additional work.  

Major comments on the results and suggestions. 

The ribbon model of release has been explored for decades and needs to be further adapted to systems-level work. The study under consideration by Kuo et al. takes on this task. Unfortunately, the experimental design does not permit a level of control over presynaptic/bpc behavior that is comparable to earlier studies, nor do they manipulate release in ways that test the ribbon model (i.e., paired recordings or Ribeye-ko). Furthermore, the data needs additional evaluation, and the presentation and interpretations should draw on published biophysical and molecular studies. 

As described above, our goal was to test several possible explanations for the difference between transient and sustained responses in OnT and OnS ganglion cells: (1) differences in the light responses of the bipolar cells that convey photoreceptor signals to the relevant ganglion cells; (2) shaping of bipolar transmitter release by presynaptic inhibition; (3) shaping of ganglion cell responses by postsynaptic inhibition or spike generation; (4) differences in feedforward bipolar synapses. We were surprised to find that the feedforward bipolar synapses play a central role in this difference, and your comment nicely prompts us to relate this to the large literature on biophysical studies of release from ribbon synapses. We have made substantial revisions in the text to do this. This includes anticipating the importance of feedforward synaptic properties in the abstract and introduction (lines 36-37 and 61-64), pointers in the results (lines 539-548), and several new paragraphs in the discussion (starting on lines 751, 773 and 787). By showing that the transient/sustained differences originates largely at feedforward bipolar synapses, we set the stage for future work that shows how biophysical properties of the synapse shape physiological signals that traverse it.

To build a ribbon-centric context, consider recent literature that supports the assertion that ribbons play a role in forming AZ release sites and facilitating exocytosis. Reference Ribeye-ko studies. For example, ribbonless bpcs show an 80% reduction in release (Maxeiner et al EMBO J 2016), the ribbonless retina exhibits signaling deficits at the output layer (Okawa et al ...Rieke, ..Wong Nat Comm 2019), and ribbonless rods show an 80% reduction the readily releasable pool (RRP) of SVs (Grabner Moser, elife 2021). In addition, the authors could refer to whole-cell membrane capacitance studies on mammalian rods, cones, and bpcs, because the size of the RRP of SVs scales with the dimensions and numbers of ribbons (total ribbon footprint). For comparison, bipolars see the review by Wan and Heidelberger 2011. For a comparison of mammalian rods and cones, see, rods: Grabner and Moser (2021 eLife), Mueller.. Regus Leidig et al. (2019; J Neurosci) and cones Grabner ...DeVries (Nat Comm 2023). A comparison of cell types shows that the extent of release is (1) proportional to the total size of the ribbon footprint, and (2) less release is witnessed when ribbons are deleted (also see photo ablation studies by Snellman.... And Mehta..Zenisek, Nat Neurosci and Neuron).

Thank you for these pointers into the literature.  We have included much of this work in the revised Discussion (see three paragraphs starting on line 751). The revised text focuses on the evidence that larger and more numerous ribbons lead to increased release. The direct evidence from previous work for this relationship supports our (indirect) conclusions in the current paper about the role of ribbon size and associated vesicle pools in transient vs sustained responses.  

Ribbon morphology may change in an activity-dependent manner. The rod ribbon AZ has been reported to lengthen in the dark (Dembla et al 2020), and deletion of the ribbon shortens the length of the AZ (defined by Cav1,4 or RIM2); in addition, the Ribeye-ko AZs fail to change in size with light and dark conditioning. Furthermore, EM studies on rod and cone AZs in light and dark argue that the number of SVs at the base of the ribbon increases in the dark, when PRs are depolarized (see Figure 10, Babai et al 2016 JNeurosci). Lastly, using goldfish Mb1 on-bipolars, Hull et al (2006, J Neurophysio) correlated an increase in release efficiency with an increase in ribbon numbers, which accompanied daylight. >> When release activity is high, ribbon AZ length increases (Dembla, rods), the number of docked SVs increases (Babai, rods cones), and the number of ribbons increases (Hull, diurnal Mb1s). 

We have extensively revised the discussion section to include more discussion of ribbons, particularly emphasizing evidence supporting the general argument that larger ribbons support higher release rates. We focused on studies that provided direct links between release rates and ribbon size or number of ribbon-associated vesicles.  This includes studies that pair electrophysiology and anatomy and those that measure the consequences of ablating ribbons,

The results under review, Kuo et al., were attained with SBF-SEM, which has the benefit of addressing large-volume questions as required here, yet it achieves lower spatial resolution than what is attained with TEM tomography and FIB-EM. Ideally, the EM description would include SV size, and the density of ribbon-tethered SVs that are docked at the plasma membrane, because this is where the SVs fuse (additional non-ribbon release sites may also exist? Mehta ... Singer 2014 J Neurosci). Studies by Graydon et al 2011 and 2014 (both in J Neurosci), and Jean ... Moser et al 2018 (eLife) are good examples of quantitative estimates of SVs docking sites at ribbons. SBF-SEM does not allow for an assessment of SVs within 5 nm of the PM, but if the authors can identify the number of SVs that appear within the limit of resolution (10 to 15 nm) from the PM, then this data would be useful. Also, what dimension(s) of the large ribbons make them larger? Typically, ribbons are fixed in height (at least in the outer retina, 200 to 250 nm), but their length varies and the number ribbons per terminal varies. Is the larger ribbon size observed in type 6 bpcs do to longer ribbons, or taller ribbons? A longer ribbon likely has more docked SVs. An additional possibility is that more SVs are about the ribbon-PM footprint, either more densely packed and/or expanding laterally (see definitions in Jean....Moser, elife 2018). 

We have included an additional analysis of ribbon surface area from our 3D SBFSEM reconstructions. As with the volume measurements included in the original submission, ribbon surface areas are distinct between type 5i and type 6 bipolar cells (Fig. S10A), ON-T RGCs on average receive input from ribbons with smaller surface area than ON-S RGCs (Fig. S10B), and ribbon surface area predicts the number of adjacent vesicles across bipolar cell types (Fig. S10C).  We agree that a higher resolution view of presynaptic structures would be very helpful, but the resolution of our SBF-SEM data is limited (e.g. each pixel is 40 nm on a side).  This resolution does not allow us to distinguish between vesicles at vs near the membrane. 

In our observations, both length and height of the ribbons showed variability across individual bipolar cells. And ribbons in type 6 bipolar cells tended to be either longer and/or taller compared to those in type 5 cells. We agree that a longer ribbon may accommodate more docked SVs. A more definitive analysis would benefit from higher-resolution, isotropic 3D reconstructions of ribbons, which would allow more precise shape analysis and ,together with a detailed assessment of docked SVs at the ribbons.

The ribbon literature given above makes the argument that ribbons increase exocytotic output, and morphological studies suggest that release activity enhances 1) ribbon length (Dembla) and 2) the density of SVs near the PM (Babai). These findings could lead one to propose that type 6 bpcs (inputs to On-sustained) are more active than type 5i (feed into On-transient). Here Kuo et al. show that the bpcs have similar Vm (measured from the soma) in response to light stimulation. Does Vm predict release? Not entirely as the authors acknowledge, because: Cav channel properties, SV availability, and negative feedback are all downstream of bpc Vm. The only experiment performed to test downstream factors focused on negative feedback from amacrines. The data presented in Figures 5C-F led me to conclude the opposite of what the authors concluded. My impression is that the T-ON rgc exhibits strong disinhibition when GABA-blockers are applied (the initial phase is greatly increased in amplitude and broadened with the drug), which contrasts with the S-On rgc responses that show a change in the amplitude of the initial phase but not its width (taus would be nice). Here and in many places the authors refer to changes in release kinetics, without implementing a useful description of kinetics. For instance, take the cumulative current (charge) in Figure 5C and fit the control and drug traces to arrive at taus, and their respective amplitudes, and use these values to describe kinetic phases. One final point, the summary in Figure 5D has a p: 0.06, very close to the cutoff for significance, which begs for more than an n = 5. Given that previous studies have shown that bpc output is shaped by immediate msec GABA feedback, in ways that influence kinetic phases of release (..Mb1 bipolars, see Vigh et al 2005 Neuron), more attention to this matter is needed before the authors rule out feedback inhibition in favor of ribbon size. If by chance, type 5i bpcs are under uniquely strong feedback inhibition, then ribbon size may result from less activity, not less output resulting from smaller ribbons.

The text surrounding Figure 5 led to some confusion, and we have revised that text and the figure for clarity.  First, the data in that figure is entirely from On-T cells (the upper and lower panels show block of GABA and glycine receptors separately).  Second, the observation that we make there is that block of inhibitory receptors increases the transience of the On-T excitatory input, rather than decreasing it as would be expected if the transience is created by presynaptic inhibition. We have added additional data and that increase in transience is now significant. Inhibitory block does substantially increase the amplitude of the postsynaptic response, and a likely origin of this change in response is inhibitory feedback to the bipolar synaptic terminal. We now indicate this in the text on page 13, lines 438-453. 

The key result of this figure for our purposes here is that the transience of the excitatory input to the OffT cell remains with inhibitory input blocked. We have clarified throughout the text that our results indicate that inhibitory feedback is not necessary for the difference between transient release into On-T and sustained release onto On-S. This does not mean that inhibitory feedback does not shape the responses in other ways or contribute to the transient/sustained difference - just that for the specific stimuli we use that difference is retained without presynaptic inhibition. We have also added citations to past work showing that activity of amacrine cells can modulate bipolar transmitter release. 

Whether strong feedback inhibition limits activity and therefore limits ribbon size in an activity-dependent way is an intriguing possibility. Indeed, addressing why ribbons are larger in type 6 bipolar cells vs. other bipolar types will be an interesting avenue of further study. However, it would be surprising if ribbon sizes changed during the acute pharmacological block conditions (~10-15 minutes) we employed in our study. Our point here is that there is an interesting correlation between presynaptic ribbon size and the kinetics of glutamate release. We do not think that the two possibilities stated in the last sentence (“…ribbon size may result from less activity, not less output resulting from smaller ribbons”) are mutually exclusive.

We have not further quantified the response kinetics in the experiments of Figure 5 as the large changes induced by the pharmacology (especially GABA receptor block) make it unclear how to interpret quantitative differences.  In other places we have quantified kinetics through the STA or specified that our focus was more qualitative (i.e. transient vs sustained kinetics). 

As mentioned above, the behavior of Cav channels is important here. This is difficult to address with voltage clamps from the soma, especially in the Vm range relevant to this study. Given that it has previously been modeled that the rod bpc to AII pathway adapts to prolonged depolarization of rbcs through downregulating Cav channel-mediated Ca²⁺ influx (Grimes ....Rieke 2014 Neuron), it seems important for Kou et al to test if there is a difference in Cav regulation between type 6 and 5i bpcs. Ca²⁺  imaging with a GCaMP strategy (Baden....Lagnado Current Biology, 2011) or filling the presynapse with Ca dyes (see inner hair cells: Ozcete and Moser, EMBO J 2020) would allow for the correlation of [Ca]intra with GluSnf signals (both local readouts).

This is a good suggestion but is outside the scope of our current paper. Our focus was on the circuit origin of the difference in response of the OnT and OnS responses rather than the specific biophysical mechanism.  We are of course interested in the mechanism, but the additional experiments needed to pin that down would need to be a part of future experiments. The work here represents an important step in that direction as it greatly reduces the number of possible locations and mechanisms for the sustained/transient difference and hence serves to focus any future mechanistic investigations.

Stimulation protocol and presentation of Glutamate Sniffer data in Figure 6. In all of your figures where you state steady st as a % of pk amplitude, please indicate in the figure where you estimate steady state. Alternatively, if you take the cumulative dF/F signal, then you can fit the different kinetic phases. From the appearance of the data, the Sustained Glu signals look like square waves (Figure 6B ROI1-4), without a transient at onset, which is not predicted in your ribbon model that assumes different kinetic phases (1. depletion of docked SVs, and 2. refilling and repriming). The Transient responses (Figure 6B ROI5-8) are transient and more compatible with a depressing ribbon scheme. If you take the cumulative, for all of the On-S and compare it to all of the On-T responses, my guess is the cumulative dF/F will be 10 to 20 larger for the S-On. Would you conclude that bpc inputs to On-S (type 6) release 20fold more SVs per 4 seconds on a per ribbon basis, and does the surface area of the type 6 bpcs account for this difference? From Figures 8B and D, the volume of the ribbon is ~2 fold greater for type 6 vs 5i, but the Surface Area (both faces of ribbon) is more relevant to your model that claims ribbon size is the pivotal factor. If making cumulative traces, and comparisons on an absolute scale is unfounded, then we need to know how to compare different observations. The classic ribbon models always have a conversion factor such as the capacitance of an SV or q size that is used to derive SV numbers from total dCm or Qcontent. See Kim ....et al von Gersdorff, 2023, Cell Reports. Why not use the Gaussian noise stimulus in Fig 6 as in Figure 1 and 2? 

For iGluSnFR recordings, steady-state responses were measured from the mean fluorescence over the last 1 sec of the light step (2 sec duration) response. We have included this information in the figure caption and in the Methods. 

There is a good deal of variability in the iGluSnR responses from one ROI to another, and the ROIs shown in the original submission had a less prominent transient component than many other ROIs. We have replaced this figure with another that is more representative of the average behavior across ROIs. The full range of behavior is captured in Figure 6C; it is clear across ROIs that glutamate release near ON-S dendrites shows both sustained and transient components. The new experiments in which we block amacrine cell activity also include a few more example ROIs from ON-S cells, and those also show both transient and sustained components.

Your suggestion to integrate the iGluSnFR signals to compare to our structural analysis of ribbons is interesting. However, we are hesitant to make a quantitative comparison between the two without further experiments to validate how the iGluSnFR signals we measure relate to release of single vesicles. For example, a quantitative measure of release based on the iGluSnR experiments would require accounting for possible differences in the expression of the indicator - which could differ both in overall level and/or location relative to release sites. 

This comment and one above highlight the importance of measures of ribbon surface area, which we now provide (Figure S10).

Figure 7. What is the recovery time for mammalian cones derived from ribbon-based models? There are estimates from membrane capacitance studies. Ground squirrel cones take 0.7 to 1 sec to recover the ultrafast, primed pool of SVs when probed with a paired-pulse protocol (Grabner ...DeVries 2016, Neuron). Their off-bpcs take anywhere from under 0.2 sec to a second to recover, which is a combination of many synaptic factors (Grabner ...DeVries Nat Comm 2023). Rod On bpcs take over a second (Singer Diamond 2006, reviewed Wan and Heidelberger 2011). In Figure 7B, the recovery time is ~150 ms for the responses measured at rgcs. This brief recovery time is incompatible with existing ribbon models of release. Whole-cell membrane capacitance measurements would be helpful here.

Thanks for drawing our attention to this issue. Indeed, we see a relatively rapid recovery in the paired-flash experiments. We now discuss this recovery time in the context of past measurements of recovery of responses in cones and bipolar cells (paragraph starting on line 773). There are many factors that could contribute to the relatively rapid recovery we observe - including synaptic factors such as those highlighted by Grabner et al., (2016) either at the cone-to-bipolar synapses or the bipolar-to-RGC synapses. We are certainly interested in a more detailed understanding of this issue, but the additional experiments are outside the scope of this paper.  

Experimental Suggestion: Add GABA blockers and see if type 5i bpc responds with more release (GluSniff) and prolonged [Ca2+] intra (GCaMP). Compare this to type 6 bpc behavior with GABA/gly blockers. This will rule in or out whether feedback inhibition is involved. 

Figure 7 in the revised manuscript includes two new experiments examining glutamate release (without the simultaneous measurement of bipolar cell intracellular calcium) while blocking (1) all/most amacrine cell-mediated inhibition via inclusion of NBQX in the bath solution, and (2) blocking spiking amacrine cells via inclusion of TTX in the bath solution. The transient vs sustained difference in light-evoked glutamate release around ON-T and ON-S RGC dendrites remained with amacrine activity suppressed. These new results are consistent with the anatomical and pharmacological data that were included in the initial submission of the manuscript (Fig. 5) that indicate presynaptic inhibition does not have a major role in shaping release kinetics at these synapses. 

Reviewer #3 (Public Review): 

Summary: 

Different types of retinal ganglion cell (RGC) have different temporal properties - most prominently a distinction between sustained vs. transient responses to contrast. This has been well established in multiple species, including mice. In general, RGCs with dendrites that stratify close to the ganglion cell layer (GCL) are sustained; whereas those that stratify near the middle of the inner plexiform layer (IPL) are transient. This difference in RGC spiking responses aligns with similar differences in excitatory synaptic currents as well as with differences in glutamate release in the respective layers - shown previously and here, with a glutamate sensor (iGluSnFR) expressed in the RGCs of interest. Differences in glutamate release were not explained by differences in the distinct presynaptic bipolar cells' voltage responses, which were quite similar to one another. Rather, the difference in transient vs. sustained responses seems to emerge at the bipolar cell axon terminals in the form of glutamate release. This difference in the temporal pattern of glutamate release was correlated with differences in the size of synaptic ribbons (larger in the bipolar cells with more sustained responses), which also correlated with a greater number of vesicles in the vicinity of the larger ribbons. 

The main conclusion of the study relates to a correlation (because it is difficult to manipulate ribbon size or vesicle density experimentally): the bipolar cells with increased ribbon size/vesicle number would have a greater possibility of sustained release, which would be reflected in the postsynaptic RGC synaptic currents and RGC firing rates. This model proposes a mechanism for temporal channels that is independent of synaptic inhibition. Indeed, some experiments in the paper suggest that inhibition cannot explain the transient nature of glutamate release onto one of the RGC types. Still, it is surprising that such a diverse set of inhibitory interneurons in the retina would not play some role in diversifying the temporal properties of RGC responses. 

Strengths: 

(1) The study uses a systematic approach to evaluating temporal properties of retinal ganglion cell (RGC) spiking outputs, excitatory synaptic inputs, presynaptic voltage responses, and presynaptic glutamate release. The combination of these experiments demonstrates an important step in the conversion from voltage to glutamate release in shaping response dynamics in RGCs. 

(2) The study uses a combination of electrophysiology, two-photon imaging, and scanning block-face EM to build a quantitative and coherent story about specific retinal circuits and their functional properties. 

Weaknesses: 

(1) There were some interesting aspects of the study that were not completely resolved, and resolving some of these issues may go beyond the current study. For example, it was interesting that different extracellular media (Ames medium vs. ACSF) generated different degrees of transient vs. sustained responses in RGCs, but it was unclear how these media might have impacted ion channels at different levels of the circuit that could explain the effects on temporal tuning.

We do not have an explanation for the quantitative differences in response kinetics we observed in Ames’ medium vs. ACSF. There are modest differences in calcium and magnesium concentration and a larger difference in potassium (2.5 mM in ACSF vs 3.6 mM in Ames). It would be interesting to test which of these (or other) differences accounts for the difference in response kinetics.

(2) It was surprising that inhibition played such a small role in generating temporal tuning. At the same time, there were some gaps in the investigation of inhibition (e.g., IPSCs were not measured in either of the RGC types; pharmacology was used to investigate responses only in the transient RGCs).

We were also surprised at this result. We have included additional data on inhibition in the revised manuscript. Figure S3 shows light-evoked IPSC data from both RGC types (Fig. S3) and Fig. 7 shows additional iGluSnFR measurements around both ON-T and ON-S RGC dendrites with inhibition blocked via bath application of NBQX (Fig. 7) and separately with inhibition from spiking amacrine cells blocked with TTX. These experiments provide additional evidence for the small role of inhibition. We attempted to measure the kinetics of excitatory input to ON-S cells with inhibition blocked, but we found that the excitatory input showed strong spontaneous oscillations under these conditions and the light responses were changed so drastically that we did not feel we could make a clear comparison with control conditions.

(3) There could be additional discussion and references to the literature describing several topics, including: temporal dynamics of glutamate release at different levels of the IPL; previous evidence that release sites from a single presynaptic neuron can differ in their temporal properties depending on the postsynaptic target; previous investigations of the role of inhibition in temporal tuning within retinal circuitry. 

Thanks, we have included more discussion and references to the relevant literature as you have suggested in the recommendations to authors.

Reviewer #1 (Recommendations For The Authors): 

The presented raw data of the pharmacological experiments show that SR95531 and TPMPA robustly increased both the amplitude and duration of the transient component of the light step-evoked excitatory currents, with slight, if any enhancement of the sustained component in ON-T RGCs Figure 5C. Statistical analysis of the population data (n=5) with Wilcoxon signed rank test yielded no significant difference (ln 363). However, reanalyzing the data extracted from the graph (Figure 5D) revealed that the difference between the paired observations is normally distributed (Shapiro-Wilk normality test, P=0.48) allowing parametric statistics to be used, which provides higher statistical power. Accordingly, reanalyzing the presented data with paired Student's t-test data revealed significant differences (P=0.01) in the steady-state amplitude normalized to that of the peak, recorded in the presence of SR95531 and TPMPA. In other words, based on the (rough) analysis of the presented pharmacology data GABAergic feedback inhibition significantly contributes to shaping the transient portion of the light-evoked excitatory currents in ON-T RGCs, by making it more transient. I believe a similar analysis based on the actual data is necessary, and the results should be communicated either way. However, if warranted, two-photon glutamate sensor imaging experiments showing that blocking GABA- and glycinergic inhibition does not change the kinetics of light-evoked glutamate signals at ON-T RGCs should also be performed, as these would be critical in drawing a conclusion regarding the effect of feedback inhibition on glutamate release from bipolar cells.

Thanks for this feedback. We have added another cell to the data set in Fig. 5D. With this addition, SR95531/TPMPA application significantly increases the response transience of excitatory currents measured in ON-T RGCs compared to control. This enhanced transience in GABA_A/C receptor blockers is due to an increase in the amplitude of the initial peak component of the response (control peak amplitude: -833.7±103.3 pA; SR95531+TPMPA peak amplitude: 2023±372.7pA; p=0.03, Wilcoxon signed rank test), with no change to the later sustained component (control plateau amplitude: -200.7±14.71pA; SR95531+TPMPA plateau amplitude: -290.9±43.69pA; p=0.15, Wilcoxon signed rank test).

We should clarify that this result indicates that GABAergic inhibition makes the excitatory inputs to ON-T RGCs less transient. Block of GABA receptors increased transience, thus intact GABAergic transmission appears to limit the initial peak of the response and therefore make excitatory currents more sustained. We unfortunately were not able to examine whether sustained excitatory currents in ON-S RGCs would become more transient using the same approach. In our hands, bath application of SR95531+TPMPA led to the generation of large-amplitude (>1nA) oscillatory bursts of excitatory input that developed within 5 minutes and persisted for the duration of the incubation (up to ~30 min) in drugs. Further, presentation of light steps tended to induce variable amplitude responses, likely dependent on the presence of spontaneous bursts; when large amplitude responses were evoked, these typically oscillated for several seconds after the step.

To examine a potential role for presynaptic inhibition in transient vs. sustained bipolar cell output, we therefore chose to eliminate amacrine cell-mediated inhibition by bath application of the AMPA/kainate receptor antagonist NBQX in additional iGluSnFR measurements. This manipulation should leave ON bipolar cell responses intact while eliminating most amacrine cell-mediated responses (and OFF bipolar cell driven responses). In separate experiments, we also eliminated inhibition from spiking amacrine cells by bath application of TTX. As shown in new Fig. 7, sustained and transient responses persisted in distal versus proximal RGC dendrites, respectively. Compared to SR95531/TPMPA, bath application of NBQX was not associated with spontaneous bursts of glutamate release around ON-S dendrites. These results show that amacrine cell-mediated inhibition is not required for either sustained or transient glutamate release from bipolar cells that provide input to ON-S and ON-T RGCs.

Small points: 

(1) The legend of Figure 1 (D) refers to shaded areas to show {plus minus} SEM, but no shade is visible (at least in my printout).

The SEM shading is there in Fig. 1D but is mostly obscured by the mean lines for the respective RGC types. We have added this to the figure caption.

(2) I found the reported Vrest for the ON bipolar cells somewhat depolarized. Perhaps due to the uncompensated junction potentials? 

These measurements are indeed not corrected for the liquid junction potential (which is approximately -10.8 mV between K-gluconate internal and Ames’ solution). We did not apply this correction since the appropriate value is not clear in perforated patch recordings as the intracellular chloride concentration is unknown (and can differ from that in the pipette solution). We have clarified this in the results text where we describe the Vrest values (lines 335-338).

(3) It is Wilcoxon signed rank test, not Wilcoxan. 

Thanks for catching this. This has been corrected in the revised manuscript.

Reviewer #2 (Recommendations For The Authors): 

Some amacrines express vesicular Glut-3 transporter and are reported to release glutamate (Marshak, Vis Neurosci 2016). Are Amacrine vGlut3 signals postsynaptic (within ~0.5 um) to cone bpc ribbons?

We did not characterize VgluT3-expressing amacrine cells in our SEM datasets. A recent study by Friedrichson et al. (Nat. Comm. 2024; PMID 38580652) using 3D SEM reconstructions found that Vglut3-amacrines are postsynaptic to both type 5i and type 6 bipolar cells, as well as other type 5/xbc bipolar cells (and receive >50% of their input from type 3a OFF bipolar cells).

How far apart are the postsynaptic targets from the ribbon release sites? The ribbons at type 5i bpc/On-T input appear separated from the dendrites of On-T rgcs (Figure 8C). At least further away than the type 6 bpc ribbons are from On-S rgc dendrites (Figure 8C). Distance may create a thresholding phenomenon, whereby only multivesicular bouts at the onset of depolarization are able to elevate synaptic Glu to levels needed to activate On-T GluRs. See Grabner et al Nat Comm 2023 for such scenarios in the outer retina.

This is an intriguing possibility, but we should point out that the presynaptic ribbons in Fig. 9C (former Fig. 8C) are similar distances (within the resolution of our reconstructions) from the ON-T and ON-S dendrites. We have increased the brightness of the dendrite segments for both RGC types in the resubmission figure; note that ON-T RGCs have spine-like protrusions that may not have been as apparent in the previously submitted version of our manuscript.

In Figures 1 and 2, Sustained responses look like the derivative of Transient responses, minus the negative going inflection. In addition, the sustained responses appear to have a lower threshold of activation than the transient On rgcs, because there are more bouts of action potentials (and membrane depol in V-clamp) with earlier onset in sustained than transients traces. It would be great if the GLuSniff data captured these differences. Take cumulative dF/F and see what the onset time is, or an initial tau if possible.

This is a good suggestion. However, we are reluctant to make detailed quantitative comparisons such as this without further validation of how the kinetics of the iGluSnFR signals relate to kinetics of glutamate release.  A specific concern is that differences in the location and amount of iGluSnFR expression could impact any such comparisons.

A recent study by Kim et al von Gersdorff (Cell Reports, 2023) presents interesting phases of release in response to light flashes, measured from AIIs, and complementary results from pairs of rbcs-AIIs. The findings highlight the complexity of SV pools under well-controlled experiments. Could their results be explained as variations in rbc ribbon size through development, and possibly between rbcs or within an rbc? 

This certainly seems possible and would be consistent with the dependence of release on ribbon size that our results support.  It would be interesting to see if there are clear anatomical correlates of that change in release properties.  

Figure 5 is a pivotal point in the study, but my review has identified numerous weaknesses. The feedback inhibition onto bipolar cell terminals is likely to sculpt glutamate release, and the results do not convincingly rule out this possibility. The suggestions for improvements range from the data needing to be reanalyzed with regard to statistical tests, and/or adding a few more data points (n = 5) before concluding a p: 0.06 is insignificant. 

We have added an additional recording to this data set. With n= 6 cells, there is now a statistically significant difference between ON-T RGC excitatory currents measured in control conditions versus during GABA_A/C receptor blockade. Please note that all the recordings shown in Figure 5C-F are from ON-T RGCs (the two panels show separately block of GABergic and glycinergic receptors). We did not make it sufficiently clear that the original trend (now statistically significant) is opposite of that expected if presynaptic GABAergic inhibition contributes to response transience in ON-T RGCs.  What we see is that excitatory synaptic inputs to ON-T RGCs become more transient (rather than mpre sustained) during GABA_A/C receptor blockade. We have revised the text in that section to make this point more clearly.

We have also included new data from iGluSnFR measurements showing that bath application of NBQX does not affect light step-evoked glutamate release kinetics at proximal (sustained) or distal (transient) RGC dendrites (control: steady-state amp. as % of peak amp. 13 ± 10; mean ± S.D.; n = 189 ROIs/4 FOVs for ON-T dendrites vs 40 ± 12; mean ± S.D.; n = 287 ROIs/8 FOVs for ON-S dendrites; NBQX: 6 ± 3; mean ± S.D.; n = 112 ROIs/1 FOV for ON-T dendrites vs 23 ± 9; mean ± S.D.; n = 97 ROIs/2 FOVs for ON-S dendrites; *p<0.001). By blocking glutamate receptors on amacrine cells, NBQX (AMPA/KAR antagonist) eliminates all/most amacrine cell-mediated signaling in the retina and should therefore abolish presynaptic inhibitory input to bipolar cell terminals across the IPL. Taken together, our results indicate that presynaptic inhibition does not play a critical role in establishing transient versus sustained kinetics for the stimulus conditions we employed in our study.

There is a need to cite more recent literature on bipolar cell ribbons (e.g. see Wakeham et al., Front. Cell. Neurosci., 2023), in order to support experimental design and interpretation of the results. The authors should discuss their Ribeye-KO data from Okawa et al 2019 Nat Comm, Figure 7, in the context of their new iGluSnFR results. 

Thank you for prompting us on this issue. We have expanded the discussion regarding ribbons and included more citations to the ribbon literature. That is largely in the three paragraphs starting on line 727.

One point deserves emphasis because it is central to the authors' ribbon model but not consistent with their data. The ribbon model as they put it, and as commonly stated, holds that a transient phase of release at the onset of depolarization indicates the depletion of the primed SVs, and the subsequent slower rate of release (steady state release in the authors' terms) reflects recruiting, priming, and release of new SVs. The On-transient dendrite GluSnf responses agree with this multiphasic process, but the sustained responses show only an elevation in glutamate without a pronounced initial peak, creating a square-wave-shaped response (Figure 6B). This does not agree with the simple ribbon-based release model. I would expect the signals from the T- and S-on dendrites to have a comparable initial phase, while the sustained phase should be greater in amplitude for the S-on dendrites. More discussion may clarify possible mechanisms.

Thanks for pointing this out. The example iGluSnFR traces we originally included in the manuscript were not entirely representative in that they did not show much initial transient phase. Note there is a distribution of steady-state amplitudes for proximal dendrites in Fig. 6C; the examples are from ROIs from the upper end of the distribution. In the new Figure 7, we have included some additional examples that show both a clear transient and sustained component. The summary data in Figure 6C shows the distribution of sustained/transient ratios across ROIs.  

Reviewer #3 (Recommendations For The Authors): 

(1) It would be interesting to understand the differences in IPSCs in the two RGC types. Perhaps they are small in both types, which would explain their apparent lack of impact on temporal tuning. The authors may already have these data.

We did make measurements of noise-evoked IPSCs (as well as EPSCs) in a subset of ON-T and ON-S recordings. We have now included this data as Figure S3. There are slight differences in the kinetics of inhibition between RGC types (Fig. S3C) and there is a trend towards stronger inhibition (relative to excitation) in ON-T RGCs compared to ON-S RGCs (Fig. S3E), although there is not a statistically significant difference. In both cases excitatory synaptic currents are as large or larger than inhibitory currents, and this does not include the difference in driving force near spike threshold which will favor excitatory input by a factor of 2-3.  Hence our data suggests that postsynaptic inhibition does not play a major role in generating the differential temporal spiking responses of ON-T and ON-S RGCs. However, additional experiments examining the relative contribution of excitation and inhibition to spiking output in these RGCs would be needed to reach a firm conclusion.

The pharmacological experiments in which we blocked inhibition (Fig. 5C-F, new Fig. 7) were designed to test the effect of presynaptic inhibition on bipolar cell output (voltage-clamp isolation of excitatory currents in Fig. 5; iGluSnFR measurements of glutamate release in Fig. 7). We do not mean to suggest that postsynaptic inhibition does not have any role in shaping the spiking behavior of these RGC types, but that transient vs. sustained kinetics are already present in the bipolar cell output and that presynaptic inhibition of bipolar cell terminals does not appear to account for this difference.  We have revised the text throughout to be clearer on this point.

(2) It could be convincing to show transient/sustained differences between RGC types in dim light, where the response would depend on the rod bipolar/AII circuit. In this case, any difference in temporal properties would presumably be explained by differences that localize to the cone bipolar cell axon terminals. Indeed, is that the result in Figure 1B? This seems to be a dim stimulus presented on darkness, which may be driven through the rod bipolar pathway. The authors could then discuss the interpretation of this data in terms of the rod bipolar circuit. 

Yes, Figure 1B is a dim light step (~30R*/rod/s) presented from darkness and the distinction between cells is clear down at still lower light levels that more effectively isolate signaling through the rod bipolar pathway. Thanks for making this point that observation of distinct temporal responses under scotopic conditions where signals suggests these differences must arise at and/or downstream of cone bipolar cell output. We have included additional text (lines 361-365) in the results describing bipolar cell responses that raise this point.

(3) Glutamate release was already measured across the full IPL depth by Borghuis et al. (2013) and Franke et al. (2017). It would be appropriate to better motivate the current study based on these existing measurements.

We have clarified that these important studies provided important motivation for measuring excitatory synaptic input to ON-T vs. ON-S RGCs (lines 165-169).   

(4) Line 212/213. It would be appropriate to add to the list of papers showing the different stratification of transient vs. sustained responses: Borghuis et al. (2013) and Beaudoin et al. (2019).

Thank you - these references have been added.  

(5) Line 635-638. It would be useful to discuss papers by Pottackal et al. (2020, 2021), which suggested that a single presynaptic cell (starburst) can signal with different temporal properties depending on the postsynaptic target (other starburst vs. DSGCs). The mechanism was not completely resolved (i.e., it was not explained by differences in presynaptic Ca channels at the two synapse types), but it at least shows that neurotransmitter release can show different filtering depending on the postsynaptic target from the same presynaptic neuron. (This could also be at play for the type 6 bipolar cell inputs to ON-S vs. ON-T RGCs in the present study.)

We have added a reference to Pottackal et al 2021 in this section.

(6) Line 714. Should describe the procedure for embedding the tissue in agarose. 

We have added more detail regarding agarose embedding for preparation of retinal slices in the methods.

(7) Line 775. Need a better description of the virus (not the construct), what serotype? Provide the Addgene number if available. 

This has been added to the methods.

(8) Line 808. Was the SD for the gaussian really 50%? That would cut off a lot of the distribution, i.e., it would get clipped at 0. 

Yes, the SD for Gaussian noise was 50%. This high contrast stimulus was used in part to achieve measurable signals from bipolar cells. You are correct that some of the distribution was clipped at 0 (it was also clipped at twice the mean to make sure that the distribution remained symmetrical). The clipping was accounted for during our LN analyses.

(9) The paper should discuss Swygart et al. (2024) results showing different spatial surround properties of neighboring synapses from a type 6 bipolar cell. Based on this result, it would seem very likely that amacrine cells could play a role in shaping the temporal processing of bipolar cell glutamate release as well. Indeed, spatial and temporal processing will not be completely independent in a typical experiment. For example, with the spot stimulus used in the present study, bipolar cells within the center versus the edge of the spot will have different balances of center/surround activation, which could potentially influence their temporal processing.

We have included discussion of results from Swygart et al 2024 in the section of the Discussion in which we point out differences in surround inhibition between ON-S and ON-T RGCs (lines 710-714). We agree that spatial and temporal processing are not completely independent. Our results with SR95531/TPMPA indicate ON-T RGCs receive stronger GABAergic surround inhibition than ON-S RGCs (Fig. S8). However, our results in Fig. 5C-D show GABAergic surround inhibition makes ON-T excitation more sustained rather than more transient. So even though bipolar cells presynaptic to ON-T RGCs receive stronger surround inhibition (Fig. S8), this inhibition does not establish the transient kinetics of glutamate release from these bipolar cells (in fact, it works to make release more sustained). Additional iGluSnFR experiments where we used NBQX to block all/most amacrine cell-mediated responses also suggest presynaptic inhibition does not have an important role in establishing differential glutamate release kinetics onto ON-S vs. ON-T RGC dendrites (Fig. 7).

(10) Cui et al. 2016 described ON-S Alpha as having a divisive suppression mechanism that explained the temporal properties of white-noise response better than a standard LN model. Do the authors think the divisive suppression reflects a property of the excitatory synapses independent of inhibition?

This is an interesting question, but one for which we don’t have a good answer for now. As mentioned in some of the above responses and as we have tried to clarify in the manuscript, we do not mean to imply that there is no role for presynaptic inhibition in modulating bipolar cell output, including for the divisive suppression described by Cui et al. Rather, our point is that the distinction between transient and sustained excitatory input to ON-T and ON-S RGCs does not require presynaptic inhibition and is more likely an intrinsic property of the bipolar cell synapses. 

(11) Do the authors mean to imply that the pool size at bipolar cell ribbon synapses could depend on the use of Ames vs. ACSF? 

For now, we do not have a good answer as to why there are quantitative differences in response kinetics between Ames and ACSF. We have not done any experiments to investigate whether ribbon sizes or ribbon pools are different in the different solutions.

(12) More generally, different mean luminance levels could drive different levels of baseline glutamate release, which could alter the available pool of vesicles at bipolar cell ribbon synapses. Can we explain varying degrees of transient/sustained in the same cell at different levels of mean luminance based on this mechanism (e.g., Grimes et al., 2014)?

Yes, the emergence of a transient component of excitatory input to ON-S RGCs at ~100 R*/rod/s versus at scotopic levels (0.5 R*/rod/s) in Grimes et al. (2014) could be due to differences in the number of releasable vesicles (due to different type 6 bipolar cell axon terminal membrane potentials and hence differences in spontaneous release rates) at the different light levels.

We should note that although ON-T and ON-S RGCs exhibit some changes in transient/sustained kinetics across different light levels, the relative differences between these RGC types are preserved across light levels. We have included a statement about this in the text (lines 361-367).

(13) Figure 1. Have the authors considered performing the LN analysis of the firing responses, to compare the degree of rectification between the two RGC types?

This is a good suggestions. From an LN analysis of spiking responses, we do not observe a clear difference between the static nonlinearity component of the model for ON-T and ON-S RGCs. Both RGC types are strongly rectified under our experimental conditions.  

(14) Figure 5. Do the authors have the pharmacology data for the ON-S cells? There are examples of sustained EPSCs in amacrine cells that become more transient after blocking inhibition, which at least suggests that inhibition can play some role in the transient/sustained nature of glutamate release (Park et al., 2015, Figure 3). Perhaps ON-S cells likewise become more transient with inhibition blocked. 

(The colored symbols in A were not visible in a printout. It would be useful to indicate the cell type (ON-T) in C and E). 

As described above in the response to reviewer 1’s recommendation for authors, we were not able to use SR95531/TPMPA for recordings from ON-S RGCs. Bath application of these drugs led to oscillatory bursts of excitatory input to ON-S RGCs. However, the lack of effect of bath-applied NBQX on the kinetics of glutamate release around either ON-T or ON-S RGC dendrites (new Fig. 7) suggests that presynaptic inhibition does not contribute to generating sustained excitation to ON-S RGCs (or transient excitation to ON-T RGCs).  

We have corrected Fig. 5A to include the referenced colored symbols and have also edited Fig 5C and E to clarify that measurements in Fig. 5C-F are from ON-T RGCs.

(15) Figure 6 legend. Should be Kcng4-Cre, not KCNG-Cre. Also, it should make clear that this is cre-dependent expression of iGluSnFR. For C, were the statistics based on the number of FOVs? 

Thanks for catching this, we have corrected Figure 6 legend. The methods section includes a description of how we achieved iGluSnFR expression on alpha RGC dendrites via a cre-dependent viral strategy in Kcng4-Cre mice.  We have also clarified that the statistics are based on ROIs in Figure 6C.

(16) Figure 7, Flashes were apparently 400% contrast on a dim background. What was the background? Is there a rod component to the response in this case? 

In Figure 7 (now Figure 8), the same background (~3300 R*/rod/s; 2000 P*/Scone/s) was used as in the Gaussian noise and step response experiments. At this light level, the response should be primarily be mediated by cones.

(17) Figure S1. The colors here differ from those in previous figures (Here, ON-T, magenta; ON-S, cyan). Is something mislabeled? 

Thanks for catching this. We mistakenly swapped the labels in the legend for Fig. S1. The figure colors were correct, but we have corrected the legend in the revised manuscript.

(18) Figure S2. For the LN model for RGC synaptic currents, the ON-S are more rectified than some previous recordings (Cui et al., 2016). Is this perhaps explained by different light levels?

We aren’t sure why ON-S excitatory currents are more strongly rectified in our recordings compared to Cui et al., 2016. Cui et al. used an ~20-fold higher background light intensity (~40,000 P*/cone/s vs. ~2000 P*/cone/s in our study), so different light levels may be a factor (although we should point out that rectification increases in these RGCs between scotopic to low photopic light levels (see Grimes et al., 2014 and Kuo et al., 2016).

(19) The study is apparently comparing PV1 and PV2 described in Farrow et al. (2013; see Supplementary information for stratification analysis), which should be cited.

Thanks, we have corrected this oversight in the revised manuscript. We now cite Farrow et al and mention the connection to PV1 and PV2 in the first paragraph of Results (lines 104-108).

Author response:

Reviewer #1 (Public review):

Summary:

The manuscript "Adapting Clinical Chemistry Plasma as a Source for Liquid Biopsies" addresses a timely and practical question: whether residual plasma from heparin separator tubes can serve as a source of cfDNA for molecular profiling. This idea is attractive, since such samples are routinely generated in clinical chemistry labs and would represent a vast and accessible resource for liquid biopsy applications. The preliminary results are encouraging, but in its current form, the study feels incomplete and requires additional work.

We thank the reviewer for the encouragement and for recognizing the potential of clinical chemistry plasma as an accessible source for cfDNA-based analyses. We look forward to addressing the gaps described below.

My major concerns/suggestions are as follows:

(1) Context and literature

The introduction provides only limited background on prior attempts to use heparinized plasma for cfDNA work. It is well known that heparin can inhibit PCR and sequencing library preparation, which has historically discouraged its use. The authors should summarize the relevant literature more comprehensively and explain clearly why this approach has not been widely adopted until now, and how their work differs from or overcomes these earlier challenges.

We thank the reviewer for their valuable comments and agree that the review of prior work needs to be more thorough, with the gaps clearly identified. In the revised manuscript, we will expand the introduction to include a more comprehensive summary of prior studies. Some of the material was in the Discussion, but we will move it to the introduction in the revision. In general, we will comment briefly here about the novelty of this work and the previous gap in the literature:

(1) Previous pre-analytical studies use DNA fluorometry and qPCR, which cannot distinguish between genomic DNA contamination (from cells) and cfDNA. In contrast, our study uses adapter-based NGS with DNA spike-ins, which can exclude genomic DNA contamination and enable precise quantification of cfDNA input and measurement of their lengths. In Figure 5b-c, we demonstrate that we were able to match our paired sample results only under the measurements of our NGS study, not in previous attempts. Note the current Fig. 5 captions b&c should be swapped and will be corrected in the revision.

(2) As the reviewer has astutely mentioned, heparin is a well-recognized inhibitor of PCR, and heparinized specimens are historically contraindicated for molecular testing. However, most modern cfDNA assays now use NGS, which includes multiple purification steps before PCR amplification, minimizing the impact of heparin interference.

(3) Previous clinical chemistry tests used serum tubes, which are known to generate background gDNA during clotting and are therefore unsuitable for cfDNA-based analyses. In recent years, modern hospital chemistry laboratories, especially those supporting emergency departments, have gradually transitioned to heparin separator tubes for faster turnaround. Hence, residual plasma from heparin separator tubes is a more recent option, one that was not widely available when key pre-analytical studies on cfDNA were performed.

(2) Genome-wide coverage

The analyses focus on correlations in methylation patterns and fragmentation metrics, but there is no evaluation of sequencing coverage across the genome. For both WGS and WMS, it would be important to demonstrate whether cfDNA from heparin plasma provides unbiased coverage, or whether certain genomic regions are systematically under-represented. A comparison against coverage profiles from cell-derived DNA (e.g., PBMC genomic DNA) would help to put the results in context and assess whether the material is suitable for whole-genome applications.

Thank you for the insightful comment. We agree that evaluating sequencing coverage across the genome is important for assessing the suitability of cfDNA from heparin separators. In response, we are performing additional, in-depth runs to compare genome-wide coverage profiles in the Hospital Cohort. The results of these analyses will be included in the revised version of the manuscript.

(3) Viral detection sensitivity

The study shows strong concordance in viral detection between EDTA and heparin samples, but the sensitivity analysis is lacking. For clinical relevance, it is critical to demonstrate how well heparin-derived plasma performs in low viral load cases. A quantitative comparison of viral read counts and genome coverage across tube types would strengthen the conclusions.

We agree that evaluating analytical sensitivity in cases with low viral loads is important for understanding clinical performance. To address this point, we plan to include additional paired cases with viral loads below 1,000 IU/mL and examine the correlation of viral read counts between EDTA and heparin separators in this subset.

Reviewer #2 (Public review):

Summary:

The authors propose that leftover heparin plasma can serve as a source for cfDNA extraction, which could then be used for downstream genomic analyses such as methylation profiling, CNV detection, metagenomics, and fragmentomics. While the study is potentially of interest, several major limitations reduce its impact; for example, the study does not adequately address key methodological concerns, particularly cfDNA degradation, sequencing depth limitations, statistical rigor, and the breadth of relevant applications.

We thank the reviewer for the insightful comments and will work to clarify and address the mentioned issues. We do not find the residual plasma from the heparin separator to be a replacement for gold standard methods. Instead, we take it as a practical and complementary resource that may help broaden the accessibility of samples. Comparable cfDNA metrics highlight its potential to serve as an additional source for biobanking and research applications.

Strengths:

The paper provides a cheap method to extract cfDNA, which has broad application if the method is solid.

We thank the reviewer for the encouraging comment. While cost-effectiveness is a practical advantage, we believe the greater strength of this approach lies in the accessibility of sampling. Residual plasma from routine clinical tests offers an opportunity to include patients or time points that would otherwise be difficult to capture, such as those with severe illness or those sampled before treatment.

Weaknesses:

(1) The introduction lacks a sufficient review of prior work. The authors do not adequately summarize existing studies on cfDNA extraction, particularly those comparing heparin plasma and EDTA plasma. This omission weakens the rationale for their study and overlooks important context.

We thank both reviewers for this comment. See above under Reviewer 1’s responses for our provisional perspective on the background literature and gap. We will expand the Introduction to provide a more comprehensive summary of prior studies.

(2) The evaluation of cfDNA degradation from heparin plasma is incomplete. The authors did not compare cfDNA integrity with that extracted from EDTA plasma under realistic sample handling conditions. Their analysis (lines 90-93) focuses only on immediate extraction, which is not representative of clinical workflows where delays are common. This is in direct conflict with findings from Barra et al. (2025, LabMed), who showed that cfDNA from heparin plasma is substantially more degraded than that from EDTA plasma. A systematic comparison of cfDNA yields and fragment sizes under delayed extraction conditions would be necessary to validate the feasibility of their proposed approach.

We appreciate this thoughtful comment, which highlights reasonable concerns about cfDNA degradation in heparin. We would like to clarify that the Hospital Cohort, which only used leftover plasma in the clinical lab, was designed to reflect real-world clinical workflows, where unavoidable delays before plasma processing are already incorporated. In the Healthy Cohort, a subset of samples is also processed after controlled delays, as shown in Supplementary Figure 2.

Regarding the differing results in Barra et al. (2025, LabMed), where heparin tubes showed 85% cfDNA degradation, it is important to note that samples were incubated at 37 °C for 24 hours. We anticipate that endogenous nuclease would be active under 37 °C and would cause cfDNA degradation. However, this condition differs markedly from the relevant clinical workflows we describe here. In the routine hospital settings, blood samples are typically kept at room temperature for up to 60 minutes during transport and waiting. The outpatient setting can be more variable, but samples here are supposed to be refrigerated during transportation. They are then processed in high-throughput, fully automated systems that comply with nationally standardized quality regulations in the United States (CLIA). The resultant plasma will be physically separated from cellular components because of the gel in the heparin separators. The processed tubes are subsequently transferred to refrigerated storage at 4 °C. Under these conditions, samples do not experience prolonged exposure to elevated temperatures such as 37 °C, and refrigeration usually occurs within two hours of collection. We will incorporate these details in the revised manuscript.

Also, as we mentioned in our reply to Reviewer 1, Barra et al. used qPCR like most cfDNA pre-analytical studies, but qPCR is not a perfect DNA quantification method for NGS-based downstream analyses because it measures both cfDNA and contaminating genomic DNA. The latter can be excluded by most NGS assays. By using constant spike-in internal controls, our approach directly quantifies the amount of sequenceable cfDNA, providing a more accurate estimate of input DNA (Figure 5c). In one possible future experiment, the same sample in the Healthy Cohort can be delayed by 1-2 hours prior to processing (centrifugation and refrigeration) and kept at room temperature rather than 4 °C to mimic real-world delays. Outputs would be cfDNA yields and fragment sizes, and we would use constant spike-ins to quantify the amount of sequenceable DNA.

(3) The comparison of methylation profiles suffers from the same limitation. The authors do not account for cfDNA degradation and the resulting reduced input material, which in turn affects sequencing depth and data quality. As shown by Barra et al., quantifying cfDNA yield and displaying these data in a figure would strengthen the analysis. Moreover, the statistical method applied is inappropriate: the authors use Pearson correlation when Spearman correlation would be more robust to outliers and thus more suitable for methylation and other genomic comparisons.

We appreciate the reasonable concerns regarding cfDNA degradation and agree that the methylation profile is not an adequate metric for degradation. To evaluate for degradation, we will focus on NGS-derived length profiles (WGS data) and constant spike-in DNA. We appreciate the reviewer’s suggestion to use the Spearman correlation, and this will be incorporated.

(4) The CNV analysis also raises concerns. With low-coverage WGS (~5X) from heparin-derived cfDNA, only large CNVs (>100 kb) are reliably detectable. The authors used a 500 kb bin size for CNV calling, but they did not acknowledge this as a limitation. Evaluating CNV detection at multiple bin sizes (e.g., 1 kb, 10 kb, 50 kb, 100 kb, 250 kb) would provide a more complete picture. In addition, Figure 3 presents CNV results from only one sample, which risks bias. Similar bias would exist for illustrations of CNVs from other samples in the supplementary figures provided by the authors. Again, Spearman correlation should be applied in Figure 3c, where clear outliers are visible.

We appreciate the reviewer’s constructive comments regarding the CNV analysis. We agree that the use of low-coverage WGS (~5×) limits the reliable detection of small CNVs, and we will acknowledge this as a limitation in the revised manuscript. To address this point, we will perform additional analyses using 50kb as bin sizes. To reduce potential bias from single-sample representation, we will show the aggregated CNV plots for all CNA-positive cases along with their log₂ copy ratio correlations, and Spearman’s correlation will be applied as suggested.

(5) It is important to point out that depth-based CNV calling is just one of the CNV calling methods. Other CNV calling software using SNVs, pair-reads, split-reads, and coverage depth for calling CNV, such as the software Conserting, would be severely affected by the low-quality WGS data. The authors need to evaluate at least two different software with specific algorithms for CNV calling based on current WGS data.

Thank you for this suggestion. We will evaluate CNV profiles using alternative informatics methods.

(6) The authors omit an important application of cfDNA: somatic mutation detection. Degraded cfDNA and reduced sequencing depth could substantially impact SNV calling accuracy in terms of both recall and precision. Assessing this aspect with their current dataset would provide a more comprehensive evaluation of heparin plasma-derived cfDNA for genomic analyses.

We thank the reviewer for emphasizing SNVs as an important application of cfDNA. We agree that the limited volume of residual plasma is a constraint. Routine chemistry tests leave ~1–2 mL of plasma, and this limited volume places an upper limit on performing SNV analysis. We will expand the discussion of this limitation in the paper. Our approach is not intended to replace specialized tubes for large-volume cfDNA collection but rather to complement them by enabling the use of residual material.

Author response:

Reviewer #1:

We agree with the reviewer that a limitation of our study is its focus on cell-based assays rather than in vivo experiments. We did consider evaluating the effects of statins on B cell responses in vivo; however, this approach is complicated by findings that statins can influence antigen presentation by dendritic cells, thereby impacting antibody responses (Xia et al, 2018). One possible solution would be to use B cell-specific conditional knockout models to study the roles of the identified proteins in an in vivo context. However, we currently do not have access to these models and were therefore unable to include such experiments within a feasible timeframe. We will revise the discussion section to acknowledge these points.

The reviewer also noted that our study assessed the roles of HMGCR, SQLE, and prenylation in B cell activation using pharmacological inhibitors and genetic knockdown/out approaches. Loss-of-function techniques such as RNAi, siRNA, and CRISPR can be challenging to apply to primary B cells, but we are exploring their feasibility for future revisions. While we acknowledge the limitations of using pharmacological inhibitors, we have taken several steps to mitigate these, including targeting multiple steps in the cholesterol biosynthetic pathway using structurally distinct inhibitors and conducting rescue experiments by supplementing downstream metabolites. To further investigate potential off-target effects of statins, we have recently performed proteomic analysis of B cells treated with and without fluvastatin. The data suggest that fluvastatin primarily affects cholesterol metabolism and does not cause widespread off-target effects. We will include this new data in the revised manuscript.

Reviewer #2:

The reviewer suggested that the study would be strengthened by determining whether the observed changes are specific to LPS + IL-4 stimulation or represent a more general B cell response to mitogenic signals.

A complementary study by James et al. (James et al, 2024) investigated murine B cells stimulated via the B cell receptor (BCR) and CD40, using anti-IgM and anti-CD40 antibodies alongside IL-4. Their proteomic analysis showed that such co-stimulation induces a fivefold increase in total cellular protein mass within 24 hours, mirroring our findings with LPS + IL-4. They also reported upregulation of proteins associated with cell cycle progression, ribosome biogenesis, and amino acid transport. Furthermore, by using SLC7A5 knockout mice, they demonstrated that this transporter is required for B cell activation. We will expand our discussion to include and these findings.  We will also expand on the final figure in our paper showing that the effects of statins are not limited to LPS.

References:

James O, Sinclair LV, Lefter N, Salerno F, Brenes A & Howden AJM (2024) A proteomic map of B cell activation and its shaping by mTORC1, MYC and iron. bioRxiv 2024.12.19.629506 doi:10.1101/2024.12.19.629506 [PREPRINT]

Xia Y, Xie Y, Yu Z, Xiao H, Jiang G, Zhou X, Yang Y, Li X, Zhao M, Li L, et al (2018) The Mevalonate Pathway Is a Druggable Target for Vaccine Adjuvant Discovery. Cell 175: 1059-1073.e21

Author response:

The following is the authors’ response to the original reviews

Reviewer 1 (Public review):

This research investigates how the cellular protein quality control machinery influences the effectiveness of cystic fibrosis (CF) treatments across different genetic variants. CF is caused by mutations in the CFTR gene, with over 1,700 known disease-causing variants that primarily work through protein misfolding mechanisms. While corrector drugs like those in Trikafta therapy can stabilize some misfolded CFTR proteins, the reasons why certain variants respond to treatment while others don't remain unclear. The authors hypothesized that the cellular proteostasis network-the machinery that manages protein folding and quality control-plays a crucial role in determining drug responsiveness across different CFTR variants. The researchers focused on calnexin (CANX), a key chaperone protein that recognizes misfolded glycosylated proteins. Using CRISPR-Cas9 gene editing combined with deep mutational scanning, they systematically analyzed how CANX affects the expression and corrector drug response of 234 clinically relevant CF variants in HEK293 cells. 

In terms of findings, this study revealed that CANX is generally required for robust plasma membrane expression of CFTR proteins, and CANX disproportionately affects variants with mutations in the C-terminal domains of CFTR and modulates later stages of protein assembly. Without CANX, many variants that would normally respond to corrector drugs lose their therapeutic responsiveness. Furthermore, loss of CANX caused broad changes in how CF variants interact with other cellular proteins, though these effects were largely separate from changes in CFTR channel activity. 

This study has some limitations: the research was conducted in HEK293 cells rather than lung epithelial cells, which may not fully reflect the physiological context of CF. Additionally, the study only examined known diseasecausing variants and used methodological approaches that could potentially introduce bias in the data analysis. 

We agree that the approaches employed here are not fully physiological, though we would remind the reviewer that we previously benchmarked the results generated by this experimental platform against a variety of other published datasets (PMID: 37253358). Regarding the issue of bias, we outline several pieces of evidence suggesting we retain robust and near-uniform sampling of these variants across these experimental conditions. We hope our comments below address all of these concerns. Overall, we believe deep mutational scanning is actually remarkably unbiased relative to other approaches due to the fact that all measurements are taken from a single dish of cells that is processed in parallel. Moreover, we show the trends are highly reproducible across replicates and users (see Figure S1). 

How cellular quality control mechanisms influence the therapeutic landscape of genetic diseases is an emerging field. Overall, this work provides important cellular context for understanding CF mutation severity and suggests that the proteostasis network significantly shapes how different CFTR variants respond to corrector therapies. The findings could pave the way for more personalized CF treatments tailored to patients' specific genetic variants and cellular contexts. 

Strengths: 

(1) This work makes an important contribution to the field of variant effect prediction by advancing our understanding of how genetic variants impact protein function. 

(2) The study provides valuable cellular context for CFTR mutation severity, which may pave the way for improved CFTR therapies that are customized to patient-specific cellular contexts. 

(3) The research provides further insight into the biological mechanisms underlying approved CFTR therapies, enhancing our understanding of how these treatments work. 

(4) The authors conducted a comprehensive and quantitative analysis, and they made their raw and processed data as well as analysis scripts publicly available, enabling closer examination and validation by the broader scientific community. 

We are grateful for this broad perspective on the general relevance of this work.

Weaknesses: 

(1) The study only considers known disease-causing variants, which limits the scope of findings and may miss important insights from variants of uncertain significance. 

We agree with this caveat. A more comprehensive library of CFTR variants will undoubtedly be useful for assigning variants of uncertain significance, though we note that such a large library would involve trade-offs in depth/ coverage that will compromise the sensitivity/ precision of the measurements. This will, in turn, make it challenging to compare the effects of CFTR modulators across the spectrum of clinical variants. For this reason, we believe the current library will remain a useful tool for CF variant theratyping.

(2) The cellular context of HEK293 cells is quite removed from lung epithelia, the primary tissue affected in cystic fibrosis, potentially limiting the clinical relevance of the findings. 

We concede this limitation, but note that we did carry out functional measurements in FRT monolayers, which are a prevailing model that closely mimics pharmacological outcomes in the clinic (see Fig. 6). 

(3) Methodological choices, such as the expansion of sorted cell populations before genetic analysis, may introduce possible skew or bias in the data that could affect interpretation. 

We respectfully disagree with this point. The recombination system we employ in these studies generates millions of recombinant cells per transfection, which corresponds to tens of thousands of clones per variant. Moreover, our sequencing data contain exhaustive coverage of every variant characterized herein within each of the final data sets. Generally, we do not see any evidence to suggest certain variants are lost from the population. We note that, while HEK293T cells are not the most physiological relevant system, they are robust to uniformly express these variants in a manner that provides a precise comparison of their effects and/ or response to CFTR modulators. To address this concern, we added Document S1 to the revised draft, which shows the total number of reads for each variant within each fraction and each experiment.

(4) While the impact on surface trafficking is convincingly demonstrated, how cellular proteostasis affects CFTR function requires further study, likely within a lung-specific cellular context to be more clinically relevant.

We agree with this caveat.

Reviewer 1 (Recommendations for the authors):

Major Issues

Cell Growth Bias? After sorting cell populations into quartiles, cells were expanded before genetic analysis - if CFTR variants affect cell doubling time (e.g., severely misfolded variants causing cellular stress), this could skew variant abundance within sorted quartiles and bias results.

Based on several observations, we do not believe this to be a significant issue. First, we note that we previously benchmarked the quantitative outputs of these experiments against a variety of other investigations and found very good agreement with previous variant classifications and expression levels (PMID: 37253358). If there were significant bias, we believe this would have come up in our efforts to benchmark the assay. Second, we note that we typically create recombinant cell lines that express WT or ΔF508 CFTR only alongside each recombinant cellular library. Importantly, we have never observed any difference in the growth rate of cultures expressing different CFTR variants. Third, even if cells expressing certain variants grow slower, it seems likely this slow growth would consistently occur in the context of each sorted subpopulation. Given that scores are derived from the relative amount of identifications across each subpopulation, we do not suspect this should impact the scoring. Overall, we believe the robustness of this cell line is a key feature that allows us to avoid any such issues related to proteostatic toxicity.

(1) Please add methodological detail. The data analysis pipeline lacks adequate description beyond referencing prior studies - essential details about what the Plasma Membrane Expression (PME) values represent (fold enrichment vs input library) and calculation methods must be provided.

We thank the reviewer for this helpful comment. We have added the text below to the revised manuscript in order to provide more detail to the reader:

“Briefly, low quality reads that likely contain more than one error were first removed from the demultiplexed sequencing data. Unique molecular identifier sequences within the remaining reads were then counted within each sample to track the relative abundance of each variant. To compare read counts across fractions, the collection of reads within each population were then randomly down-sampled to ensure a consistent total read count across each sub-population. The surface immunostaining of each variant was then estimated by calculating the the weighted-average immunostaining intensity for each variant using the following equation:

where ⟨I⟩_variant is the weighted-average fluorescence intensity of a given variant, ⟨F⟩_i is the mean fluorescence intensity associated with cells from the ith FACS quartile, and Ni is the number of variant reads in the i^th FACS quartile. Variant intensities from each replicate were normalized relative to one another using the mean surface immunostaining intensity of the entire recombinant cell population for each experiment to account for small variations in laser power and/ or detector voltage. Finally, to filter out any noisy scores arising from insufficient sampling, we repeated the down-sampling and scoring process then rejected any variant measurements that exhibit more than X% variation in their intensity scores across the two replicate analyses. The reported intensity values represent the average normalized intensity values from two independent down-sampling iterations across three biologicals replicates.”

(3) Add detail on library composition. The distribution of CFTR variants within the parental HEK293T library after landing pad insertion needs documentation, including any variant dropout or overrepresentation issues.

As noted in our previous work (PMID: 37253358), our CF variant library is quite uniform, with each mutant contributing on average, 0.43% of the library with a standard deviation of +/- 0.16%. This corresponds to an average read depth of over 40K reads per variant, per experimental condition in the final analyses. Indeed, the most abundant variant in the pool was ΔF508 (1.67% of total reads). In contrast, the least sampled variant was S549R (1647T>G) was still sampled an average of 3,688 times per replicate, which corresponds to 0.09% of the total reads. See Doc S1.

(4) Documentation of CFTR variant overlap between parental and CANX KO HEK293T libraries is needed, including whether every variant was present at equivalent input abundance in both libraries.

We thank the reviewer for this suggestion. Though there are small deviations in the composition of recombinant parental and knockout cell lines, the relative abundances of individual variants within the recombinant populations only differs by an average of 18.5% between the parental and knockout lines. There are no cases in which we observe a single variant increasing by more than 50% in the knockout line relative to the parent. However, there is a single variant, Y563N, that exhibits a 96% decrease in its abundance in the context of the knockout cell line. Nevertheless, even this variant was sampled over 1,000 times, and it’s final score passed all quality control metrics. In the revised draft, we have provided a complete table containing the total number of reads and percent of total reads for each variant for each cell line and condition (see Doc. S1).

(5) The section reporting CANX impact on functional rescue of CF variants requires clearer logic flow - the conclusion about higher specific activity of CFTR assembled without CANX appears misleading, given later discussion about CANX allowing suboptimally folded CFTR to traffic to the surface.

We apologize for any confusion. We invoked the term “specific activity” in the enzymological sense, which is to say the proportion of active enzyme (i.e. channel) at the plasma membrane differs in the knockout line. The logic is quite simple- if protein levels are lower while ion conductance remains the same in the knockout cells, then a higher proportion of the mature channels must be inactive in the parental cell line. Thus, we suspect fewer of the channels at the plasma membrane are active in the context of the parental cell line containing CANX. We considered modifications to the text in the discussion, but ultimately feel the current text strikes a reasonable balance between nuance and simplicity.

(6) In your discussion, consider that HEK293T cellular context differs significantly from lung epithelia, and the hYFP quenching assay may have insufficient dynamic range or high noise for detecting relevant functional differences.

We modified the following sentence in the discussion to introduce this possibility:

“While these discrepancies could stem from differences in the dynamic range of the functional assays, they may also suggest the stringency of QC is more finely tuned to ion channel biosynthesis in epithelial monolayers.”

Minor Issues

(1) Include immunostaining quartiles as a supplementary figure overlaid on Figure 1A, and clarify whether quartiles were consistent across experiments or adjusted for each sort.

We added a new figure to demonstrate the gating approach in the revised manuscript (see Fig. S10). We have also added the following text to the Methods section:

“Sorting gates for surface immunostaining were independently set for each biological replicate and in each condition to ensure that the population was evenly divided into four equal subpopulations.”

(2) Figure 2C improvements. Flip the figure 180 degrees to position MSD1 and NBD1 on the left, replace the blue-to-red color scale with yellow-to-blue or monochromatic scaling for better intermediate value differentiation.

Respectfully, we prefer not to do this so that our figures can be easily compared across our previous and forthcoming publications. We chose this rendering because this view depicts certain trends in variant response more clearly. 

(3) Indicate the location of ECL4 on the protein structure shown in Figure 2C for better reference.

We appreciate the suggestion. However, most of ECL4 is missing from the experimental cryo-EM models of CFTR due to a lack of density. For this reason, we did not modify the figure. 

Reviewer 2 (Public review):

In this work, the authors use deep mutational scanning (DMS) to examine the effect of the endogenous chaperone calnexin (CANX) on the plasma membrane expression (PME) and potential pharmacological stabilization cystic fibrosis disease variants. This is important because there are over 1,700 loss-of-function mutations that can lead to the disease Cystic Fibrosis (CF), and some of these variants can be pharmacologically rescued by small-molecule "correctors," which stabilize the CFTR protein and prevent its degradation. This study expands on previous work to specifically identify which mutations affect sensitivity to CFTR modulators, and further develops the work by examining the effect of a known CFTR interactor-CANX-on PME and corrector response. 

Overall, this approach provides a useful atlas of CF variants and their downstream effects, both at a basal level as well as in the context of a perturbed proteostasis. Knockout of CANX leads to an overall reduced plasma membrane expression of CFTR with CF variants located at the C-terminal domains of CFTR, which seem to be more affected than the others. This study then repeats their DMS approach, using PME as a readout, to probe the effect of either VX-445 or VX-455 + VX-661-which are two clinically relevant CFTR pharmacological modulators. I found this section particularly interesting for the community because the exact molecular features that confer drug resistance/sensitivity are not clear. When CANX is knocked out, cells that normally respond to VX-445 are no longer able to be rescued, and the DMS data show that these non-responders are CF variants that lie in the VX-445 binding site. Based on computational data, the authors speculate that NBD2 assembly is compromised, but that remains to be experimentally examined. Cells lacking CANX were also resistant to combinatorial treatment of VX-445 + VX-661, showing that these two correctors were unable to compensate for the lack of this critical chaperone. 

One major strength of this manuscript is the mass spectrometry data, in which 4 CF variants were profiled in parental and CANX KO cells. This analysis provides some explanatory power to the observation that the delF508 variant is resistant to correctors in CANX KO cells, which is because correctors were found not to affect protein degradation interactions in this context. Findings such as this provide potential insights into intriguing new hypothesis, such as whether addition of an additional proteostasis regulators, such as a proteosome inhibitor, would facilitate a successful rescue. Taken together, the data provided can be generative to researchers in the field and may be useful in rationalizing some of the observed phenotypes conferred by the various CF variants, as well as the impact of CANX on those effects. 

To complete their analysis of CF variants in CANX KO cells, the research also attempted to relate their data, primarily based on PME, to functional relevance. They observed that, although CANX KO results in a large reduction in PME (~30% reduction), changes in the actual activation of CFTR (and resultant quenching of their hYFP sensor) were "quite modest." This is an important experiment and caveat to the PME data presented above since changes in CFTR activity does not strictly require changes in PME. In addition, small molecule correctors also do not drastically alter CFTR function in the context of CANX KO. The authors reason that this difference is due to a sort of compensatory mechanism in which the functionally active CFTR molecules that are successfully assembled in an unbalanced proteostasis system (CANX KO) are more active than those that are assembled with the assistance of CANX. While I generally agree with this statement, it is not directly tested and would be challenging to actually test. 

The selected model for all the above experiments was HEK293T cells. The authors then demonstrate some of their major findings in Fischer rat thyroid cell monolayers. Specifically, cells lacking CANX are less sensitive to rescue by CFTR modulators than the WT. This highlights the importance of CANX in supporting the maturation of CFTR and the dependence of chemical correctors on the chaperone. Although this is demonstrated specifically for CANX in this manuscript, I imagine a more general claim can be made that chemical correctors depend on a functional/balanced proteostasis system, which is supported by the manuscript data. I am surprised by the discordance between HEK293T PME levels compared to the CTFR activity. The authors offer a reasonable explanation about the increase in specific activity of the mature CFTR protein following CANX loss. 

For the conclusions and claims relevant to CANX and CF variant surveying of PME/function, I find the manuscript to provide solid evidence to achieve this aim. The manuscript generates a rich portrait of the influence of CF mutations both in WT and CANX KO cells. While the focus of this study is a specific chaperone, CANX, this manuscript has the potential to impact many researchers in the broad field of proteostasis.

We thank the reviewer for their thoughtful and comprehensive perspectives on the scope and relevance of this work.

Reviewer 2 (Recommendations for the authors):

While I did not identify any major weaknesses in this manuscript, I offer some suggestions below, as well as some conclusions to consider:

(1) Missing period at the end of line 51.

We thank the reviewer for catching this grammatical error and have added proper punctuation.

(2)Figure S1 "repre-sent"??

We have corrected this punctuation error.

(3) Figure S2 missing parentheses A)

We have corrected the punctuation error.

(4) Figure S5, "B) The total ΔRMSD of the active conformation of NBD2 is shown for variants bound to VX-445. Red bars show increasing deviations from the native NBD2 conformation in the mutant models, and blue bars show how much VX-445 suppresses these conformational defects in NBD2."

VX-445 should not bind/stabilize the G85E from the calculations in Figure S5A. As a confirmation, it would be nice to see the calculated hypothetical effect of VX-445 in the G85E variant as performed for L1077P and N1303K. I also want to point out that G58E is referred to as being non-responsive in S5A, but then in S5D, N103K is referred to as non-responsive, but this variant falls pretty far below the stabilized region calculated in S5A, right?

We agree that it would be insightful to examine the RMSD changes in a non-responsive variant such as G85E. We added the G85E NBD2 ∆RMSD to Supplemental Figure S5B and a G85E ∆RMSD structure map as an additional subpanel at Supplemental Figure S5C. As the reviewer expected, VX-445 fails to confer any stability to G85E as shown by a lack of significant change in NBD2 ∆RMSD or any visible ∆RMSD throughout the structure.  Finally, we acknowledge that N1303K falls below the stabilized region as calculated in S5A. However, we note that the binding energy only suggests it is likely to interact with the protein- this does not to necessarily mean that binding will allosterically suppress conformational defects in NBD2. Moreover, this is simply an in silico calculation, that does not necessarily capture all of the nuanced interactions in the cell (or lack thereof). We have corrected this in the Figure S5 caption, which reads as follows:

“Maps of the change in RMSD between N1303K modeled with and without VX-445 shows that few structural regions are stabilized by VX-445 for N1303K, which responds poorly to VX-445 in vitro.”

(5) "stan-dard" standard?

We have corrected this punctuation error.

(6) Line 270, "these variants" is written twice

We have corrected this typographical error.

(7) Figure 6 B. What is being compared? The text writes "there are prominent differences in the activity of these variants [those with CANX] (two-way ANOVA, p = 3.8 x 10-27." Does this mean WT vs. delF508, P5L, V232D, T1036N, and I1366N combined? I have not seen a set of 5 variables compared to a single variable. Usually, it would be WT vs. DelF508, WT vs. P5L, WT vs. V232D...right? Maybe this is normal in this specific field. The same goes for the CANX knockout comparison "(two-way ANOVA, p = 0.06).".

In this instance, the two-way ANOVA test is evaluating whether there are differences in the half-lives of individual variants and/ or systematic differences across the variant measurements in the knockout line relative to the parental cells. The test gives independent p-values for these two variables (variant and cell line). We chose this test because it makes it clear that, when you consider the trends together, one variable has a significant effect while the other does not.

(8) Why don't the CFTR modulators rescue CFTR activity in the WT FRT monolayers?

We thank the reviewer for this inquiry. Please note that compared to DMSO, VX-661 does significantly enhance the forskolin-mediated response of WT-CFTR (red asterisk). Treatments with VX-445 alone, VX-661+VX-445, or VX-661+VX-445+VX-770 showed no significant forskolin stimulation of WT-CFTR. These observations could be attributable to the brief period in which WT-CFTR cDNA is transiently transfected. However, it is not necessarily anticipated that modulators would enhance WT-CFTR function. Correctors and potentiators are designed to rescue processing and gating abnormalities, respectively. WT-CFTR channels do not exhibit such defects.

In both constitutive overexpression systems and primary human airway epithelia, published literature demonstrates that prolonged exposure to CFTR modulators has resulted in variable consequences on WT-CFTR activity. For example, forskolin-mediated responsiveness of WT-CFTR is not altered by chronic application of VX-445 (PMID: 34615919) nor VX-770 (PMID: 28575328, 27402691, 37014818). In contrast, short-circuit current measurements show that forskolin stimulation of WT-CFTR is augmented by chronic treatment with VX-809 (PMID: 28575328), an analog of VX-661. Thus, our findings are congruent with observations reported by other groups.

(9) General comment: As someone not familiar with the field, it would be nice to see the structures of VX-445 and VX-661 somewhere in the figures or at least in the SI.

We appreciate this suggestion, but do not feel that we include enough structural analyses to justify a stand-alone figure for these purposes. The structures of these compounds are easily referenced on a variety of internetbased resources.

(10) Weakness: As an ensemble, the data points CANX as required for plasma membrane expression, particularly those that lie in the C-terminal domain, but when considering individual CF variants, there is no clear trend. Similarly, when looking at the effect of the pharmacological correctors on PME, no variant strays from the linear trend.

We generally agree that the predominant trend is a uniform decrease in CFTR PME across all variants and that individual variant effects are hard to generalize. Indeed, this latter point has been widely appreciated in the CF community for several decades. Our approach exposes this variability in detail, but we concede that we cannot yet fully interpret the full complexity of the trends.

(11) Something to consider: Knockout of calnexin, a central ER chaperone, is going to set off the UPR, which in turn will activate the ISR and attenuate translation. From what I can tell, in general, all CF variant PME is decreased. Is this simply because less CF protein is being synthesized?

The reviewer raises an excellent point. However, to investigate this possibility further, we compared whole-cell proteomic data for the parental and knockout cell lines. Our analysis suggests there is no significant upregulation of proteins associated with UPR activation, as is shown in the graphic to the right. In fact, only proteins associated with the PERK branch of the UPR exhibit any statistically significant changes between these two cell lines across three biological replicates. Based on this consideration, we suspect any wider changes in ER proteostasis must be relatively subtle. 

Author response image 1.

Author response:

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

Reviewer #1 (Public review):

However, some methodological choices, such as the use of a 5-year sliding window to compute trend values, are insufficiently justified and under-explained. The paper also does not fully address disparities in data coverage across disciplines and time, which may affect the reliability of historical comparisons. Finally, minor issues in grammar and clarity reduce the overall polish of the manuscript.

We thank the reviewer for pointing out the weakness of the manuscript. We addressed these comments in our response to Recommendations A and B. Minor grammar and clarity issues have also been addressed.

Reviewer #2 (Public review):

The first thing that comes to mind is the epistemic mechanism of the study. Why should there be a joint discussion combining internationalism and interdisciplinarity? While internationalism is the tendency to form multinational research teams to work on research projects, interdisciplinarity refers to the scope and focus of papers that draw inspiration from multiple fields. These concepts may both fall into the realm of diversity, but it remains unclear if there is any conceptual interplay that underlies the dynamics of their increase in research journals.

We thank the reviewer for pointing out the lack of clarity in our decision to conduct a joint discussion of interdisciplinarity and internationalization.

It is a well-known fact that team science has increased in importance over time. An important question then is whether teams have only grown in size and frequency or whether they have changed in other aspects. Interdisciplinarity and internationalization are two aspects in which teams could have changed.

We revised the Introduction (Lines 68–70 of the revised manuscript) to address this matter.

It is also unclear why internationalization is increasing. Although the authors have provided a few prominent examples in physics, such as CERN and LAGO, which are complex and expensive experimental facilities that demand collective efforts and investments from the global scientific community, whether some similar concerns or factors drive the growth of internationalism in other fields remains unknown. I can imagine that these concerns do not always apply in many fields, and the authors need to come up with some case studies in diverse fields with some sociological theory to support their empirical findings.

We thank the reviewer for requesting further evidence concerning why our findings may be correct. Physics is an area where the need for extraordinary resources has naturally led to large international collaborative efforts. As we discuss in line 255 of the revised manuscript, this is actually also the case for biology. The Human Genome Project and subsequent projects have also required massive investments, leading to further internationalization.

We believe that the drive toward internationalization for medicine has to do with the need for establishment of robust results that are not specific to a single country or medical system. Additionally, the impact of global epidemics — Acquired immunodeficiency Syndrome (AIDS), Severe Acute Respiratory Syndrome (SARS) — has also increased the needs to involve researchers from around the world.

The case for increased internationalization in the social sciences is, we believe, related to the desire to identify phenomena that extend beyond the Western, educated, industrialized, rich and democratic (WEIRD) societies.

We have expanded the discussion around these points in lines 274–283 of the revised manuscript.

The authors use Shannon entropy as a measure of diversity for both internationalism and interdisciplinarity. However, entropy may fail to account for the uneven correlations between fields, and the range of value chances when the number of categories changes. The science of science and scientometrics community has proposed a range of diversity indicators, such as the RaoStirling index and its derivatives. One obvious advantage of the RS index is that it explicitly accounts for the heterogeneous connections between fields, and the value ranges from 0 to 1. Using more state-of-the-art metrics to quantify interdisciplinarity may help strengthen the data analytics.

We thank the reviewer for pointing the need to provide a deeper discussion of the impact of different metrics on how disciplinary diversity is calculated. We chose Shannon’s entropy because it accounts for both richness (the number of distinct fields) and evenness (the balance of representation across fields). While measures such as the Rao-Stirling index can be very useful when considering disciplines at different levels of aggregation, since to consider only level 0 Field-of-Study (FoS) tags, that problem is not as much a concern for our analysis.

We have added a further clarification in lines 145–151 of the revised manuscript.

Reviewer #1 (Recommendations for the authors)

Ambiguity in the Trend Calculation Methodology in Figure 4 and 5

The manuscript uses a 5-year sliding window to calculate recent trends in interdisciplinarity (I_d) and internationalization (I_n), but the method is not clearly described. Could the authors clarify whether the trend is calculated by (1) performing linear regression on the index values over the past 5 years, (2) using the regression slope as the trend value, and (3) interpreting the sign and magnitude of the slope to indicate increasing, decreasing, or stable trends? Additionally, the rationale for choosing a 5-year window over other durations (e.g., 10 or 15 years) is not discussed. Given that different time windows could yield different insights, a brief justification or sensitivity check would strengthen the methodological transparency.

Thank you for pointing the lack of clarity in our description. In an attempt to increase clarity, we added a specific case study to illustrate the use of 5-year trend in the Supplementary Information: Estimation of tendency of the revised manuscript (Lines 691–704 of the revised manuscript).

Specifically, imagine we want to calculate the trend of the Interdisciplinarity Index for 2010 for Annalen der Physik. We would perform an ordinary least squares linear fit to the 6 data points for the Index in years 2005–2010.

The reason to focus on a 5-year window is two-fold. First, a longer time period would — as suggested by the data on Figure S10 — likely aggregate over multiple trends. Second, a shorter time period would result in too great an uncertainty in the estimation of the trend.

This is the reason why we did not implement a sensitivity analysis. Reasonable time windows that consider the two reasons expressed above would be too narrow to provide a worthwhile analysis.

Lack of Discussion on Temporal Coverage Disparities Across Disciplines

The study spans publications from 1900 to 2021, but the completeness and representativeness of the data-especially in earlier decades-may differ significantly across disciplines. For instance, OpenAlex has limited coverage for publications before the mid-20th century, and disciplines such as Medicine and Political Science may have adopted journal-based publishing at different historical periods compared to Physics or Chemistry. These temporal disparities could bias cross-disciplinary comparisons of long-term trends in interdisciplinarity and internationalization. I recommend that the authors briefly discuss this limitation and, if possible, report when coverage becomes reliable for each discipline. A sensitivity analysis starting from a common baseline year (e.g., 1950 or 1970) could also help assess whether the observed disciplinary differences are driven in part by unequal temporal data availability.

We thank the reviewer for the requesting further clarification on this matter. We completely agree that “completeness and representativeness of the data – especially in earlier decades-may differ significantly across disciplines”. That is exactly the reason why we made the analyses choices described in the manuscript.

Indeed, we consider only three journals for the analysis of the entire 1900–2021 period. Those 3 journals, Nature, PNAS and Science are ones that we know to be well recorded.

When conducting the disciplinary analysis, we focus on the period 1960–2021. While we know that the coverage for the social sciences is less robust until the 1990s, we address this concern by implementing several safeguards:

Manual selection of representative journals in each discipline to ensured that their publications are well represented in OpenAlex.

Decade by decade analysis of interdisciplinarity and internationalization so that changes over time can be identified and potential issues with data coverage are restricted to only some aspects of the analysis.

We also acknowledge the potential coverage disparities in earlier years of the data source (Lines 319-326 of the revised manuscript).

The authors use both interdisciplinarity and multidisciplinarity. While these concepts offer similar definitions of diversity, it may help the reader if there is some explanation to clarify their subtle differences. (Reviewer #2)

It is a well-known fact that team science has increased in importance over time. An important question then is whether teams have only grown in size and frequency or whether they have changed in other aspects. Interdisciplinarity and internationalization are two aspects in which teams could have changed.

We revised the Introduction (Lines 68–70 of the revised manuscript) to address this matter.

Minor Comments

Several sentences

(1) Line 11: The phrase “authors form multiple countries” contains a typographical error. The word “form” should be corrected to “from” so that the sentence reads: “authors from multiple countries.”

tences and phrases throughout the manuscript could be improved for grammatical accuracy, clarity, and stylistic appropriateness:

(2) Line 63: The clause “these expansion is well described by a logistic model” contains a subject-verb agreement error. “These” should be replaced by the singular demonstrative pronoun “this”, resulting in: “This expansion is well described by a logistic model.”

(3) Line 89: The phrase “were quickly overcame” misuses the verb form. “Overcame” is a past tense form and should be replaced with the past participle “overcome” to match the passive construction. Suggested revision: “were quickly overcome.”

(4) Line 106: The verb “refered” is misspelled. It should be corrected to “referred” for proper past tense. The corrected phrase should read: “we referred to...”

(5) Line 127: The phrase “sing discipline papers” contains a typographical error. “Sing” should be “single”, yielding: “single discipline papers.”

(6) Lines 238–239: The sentence “An exception to this pattern are the two mega open-access journals: PLOS One and Scientific Reports, which have internationalization indices as high the the most internationalized Physics journals.” contains multiple grammatical issues.

First, the subject “An exception” is singular, but the verb “are” is plural; this results in a subject-verb agreement error.

Second, the phrase “the the” includes a typographical repetition.

Third, the comparative construction is incomplete; “as high the the...” is ungrammatical and should use “as high as.”

Suggested revision: “An exception to this pattern is the pair of mega open-access journals— PLOS One and Scientific Reports—which have internationalization indices as high as those of the most internationalized Physics journals.”

(7) Line 254: The sentence “biological research been revolutionized...” lacks an auxiliary verb. To be grammatically correct, it should read: “biological research has been revolutionized...”

(8) Line 258: The phrase “need global spread of...” is syntactically awkward. Depending on the intended meaning, it could be revised to either “the global spread of...” or “the global need for the spread of...” for clarity.

(9) Figure S2 Caption: The term “Microsofe Academic Graph” is a typographical error and should be corrected to “Microsoft Academic Graph.”

(10) Reference [40]: The link “ttps://doi.org/10.1038/nature02168” is missing the “h” in “https.” The corrected version is: “https://doi.org/10.1038/nature02168.”

We appreciate your comments on the grammar and clarity of the manuscript. We have thoroughly reviewed and corrected these issues to improve the overall clarity of the text.

Line 11: We changed the typo “form” to “from”.

Line 63: We changed the sentence to “There has been a significant expansion in the number of countries where scientists are publishing in selective journals”.

Line 89 (Line 93 of the revised manuscript): We revised the sentence as suggested, and the revised sentence becomes “Even the significant impacts on publication rates of the two World Wars were quickly overcome, and exponential growth resumed. ”

Line 106 (Line 110 of the revised manuscript): We changed the typo “refered” to “referred”.

Line 127 (Line 131 of the revised manuscript): We changed the typo “Sing” to “single”.

Lines 238-239 (Lines 245-247 of the revised manuscript): We thank the issues pointed out by the reviewer, and we took the reviewer’s suggested version and changed the original sentence to “An exception to this pattern is the pair of mega open-access journals — PLOS One and Scientific Reports — which have internationalization indices as high as those of the most internationalized Physics journals”.

Line 254 (Line 262 of the revised manuscript): We added the auxiliary verb to the sentence, and the sentence now becomes “biological research has been revolutionized”

Line 258 (Line 266 of the revised manuscript): We changed the phrase to “the global need for the spread of”.

Figure S2 Caption: We corrected the typo of “Microsoft Academic Graph”.

Reference [40]: We corrected the URL of the reference.

Reviewer #2 (Recommendations for author):

Some typos:

(1) Page 2: On page 2, “contributions from a multiple disciplines” and ”these expansion is well described”.

(2) Page 4: “World Wars were quickly overcame”.

(3) Page 5: “to quantify the the internationalization of a journal”.

(4) Page 10: “indices as high the the most internationalized Physics journals”

(5) Page 10: The sentence “indices as high the the most internationalized Physics journals” contains multiple issues. The phrase “the the” is a typographical error, and the comparative construction is incomplete. It should be revised to: “indices as high as those of the most internationalized Physics journals.”

We revised those typographical errors on page 2, 4, 5, and 10 pointed out by the reviewer. We truly thank the reviewer’s critical examination on the syntax of the manuscript.

Page 2: We removed “a” so now the sentence reads: “contributions from multiple disciplines.”

Page 2: We changed the sentence to “There has been a significant expansion in the number of countries where scientists are publishing in selective journals”.

Page 4: We replaced “overcame” with the past participle “overcome” , resulting in: “World Wars were quickly overcome.”

Page 5: The phrase “to quantify the the internationalization of a journal” contains a typographical repetition. We changed it to: “to quantify the internationalization of a journal.”

Page 10: For the sentence “indices as high the the most internationalized Physics journals”, we removed duplicated “the” as a typographical error. We revised the sentence into: “indices as high as those of the most internationalized Physics journals.”

Author response:

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

Reviewer #1 (Public review):

(1) The authors should provide a detailed description of the pathogenesis of Haemorrhagic Fever with Renal Syndrome (HFRS) and elaborate on the crucial role of IgG proteins in the disease's progression (line 65).

As suggested, we have now provided a detailed description of the pathogenesis of HFRS and elaborated on the crucial role of IgG proteins in the disease's progression:

"Hantaviruses are tri-segmented, single-stranded, negative-sense RNA viruses, whose genomes consist of three regions: large (L), medium (M), and small (S). The glycoproteins Gn and Gc, encoded by the M segment, can infect target cells - primarily vascular endothelial cells - via β3 integrin receptors (Pizarro et al., 2019). Simultaneously, they could also infect other cell types, such as mononuclear macrophages and dendritic cells, leading to systemic viral infection. Although hantavirus replication is thought to occur primarily in the vascular endothelium without direct cytopathic effects, a plethora of innate immune cells mediate host antiviral defenses. These include natural killer cells, neutrophils, monocytes, and macrophages, together with pattern recognition receptors (PRRs), interferons (IFNs), antiviral proteins, and complement activation, e.g., via the pentraxin 3 (PTX3) pathway, which can exacerbate HFRS disease progression leading to immunopathological damage through cytokine/chemokine production, cytoskeletal rearrangements in endothelial cells, ultimately amplifying vascular dysfunction (Tariq & Kim, 2022). Rapid and effective humoral immune responses, however, such as neutralizing antibody responses targeting the glycoproteins Gn/Gc, contribute to rapid recovery from HFRS and are critical for protection from severe disease (Engdahl & Crowe, 2020; Li et al., 2020)." Please see the Introduction (Page 4, lines 65-81).

(2) An additional discussion on the significance of glycosylation, particularly IgG N-glycosylation, in viral infections should be included in the Introduction section.

Thank you for the suggestion and we have added an additional discussion on the significance of glycosylation in viral infections in the revised Introduction section.

"Immunoglobulin G (IgG) N-linked glycosylation mediates critical functions modulating antiviral immunity during viral infection. Changes in the conserved N-linked glycan Asn297 in the Fc region of IgG typically by fucosylation, galactosylation, or sialylation can alter antibody effector function. A reduction in core fucosylation decreases IgG binding to NK cell FcγRIIIa promotes antibody-dependent cellular cytotoxicity (ADCC) necessary for clearance of viruses, including SARS-CoV-2, dengue and HIV-1 whereas sialylation can attenuate immune responses resulting in immune evasion (Ash et al., 2022; Haslund-Gourley et al., 2024; Hou et al., 2021; Wang et al., 2017). Changes in IgG and other protein N-linked glycosylation profiles therefore shape virus-host interactions and disease progression." (Page 4, lines 82-91).

(3) In the abstract section, the authors state that HTNV-specific IgG antibody titers were detected and IgG N-glycosylation was analyzed. However, the analysis of plasma IgG N-glycans is described in the Methods section. Therefore, the authors should clarify the glycome analysis process. Was the specific IgG glycome profile similar to the total IgG N-glycome? Given the biological relevance of specific IgG in immunological diseases, characterizing the specific IgG N-glycome profile would be more significant than analyzing the total plasma IgG.

We are grateful to the reviewer for the comments. Previous studies on viral infections have revealed that the pattern of virus-specific IgG N-glycans may be similar to that of total IgG N-glycome, and we therefore analyzed the total plasma IgG glycosylation profiling in the HFRS patients. However, we have discussed this in the Discussion section.

"Despite establishing a well-characterized patient cohort and performing systematic IgG glycosylation profiling based on HTNV NP antibody status, this study has several noteworthy limitations. Most notably, while preliminary comparisons suggested similar patterns between virus-specific and total IgG N-glycome, our total plasma IgG analysis may have introduced confounding factors in the observed associations. This methodological constraint could potentially affect the interpretation of certain disease-specific glycosylation signatures." Please see the Discussion (Page 12, lines 274-280). 

References

(1) Mads Delbo Larsen, Erik L de Graaf, Myrthe E Sonneveld, et al. Afucosylated IgG characterizes enveloped viral responses and correlates with COVID-19 severity. Science . 2021 Feb 26;371(6532):eabc8378.

(2) Chakraborty S, Gonzalez J, Edwards K, et al. Proinflammatory IgG Fc structures in patients with severe COVID-19. Nat Immunol. 2021 Jan;22(1):67-73.

(3) Tea Petrović, Amrita Vijay, Frano Vučković, et al. IgG N-glycome changes during the course of severe COVID-19: An observational study. EBioMedicine. 2022 Jul ;81: 104101. 

(4) Hou H, Yang H, Liu P, et al. Profile of Immunoglobulin G N-Glycome in COVID-19 Patients: A Case-Control Study. Front Immunol. 2021 Sep 23;12:748566.

(4) Further details regarding the N-glycome analysis should be provided, including the quantity of IgG protein used and the methodology employed for analyzing IgG N-glycans (lines 286-287).

As suggested, we have provided further details regarding the N-glycome analysis in the Method section.

"Briefly, the diluted plasma samples were transferred onto a 96-well protein G monolithic plate (BIA Separations, Slovenia) for the isolation of IgG. The isolated IgG was eluted with 1 mL of 0.1 M formic acid and was immediately neutralized with 170 µL of 1M ammonium bicarbonate.

The released N-glycans were labelled with 2-aminobenzamide (2-AB) and were then purified from a mixture of 100% acetonitrile and ultrapure water in a 1:1 ratio (v/v). This was then analyzed by hydrophilic interaction liquid chromatography using ultra-performance liquid chromatography (HILIC-UPLC; Walters Corporation, Milford, MA) (Hou et al., 2019). As previously reported, the chromatograms were separated into 24 IgG glycan peaks (GPs) (Menni et al., 2018)." Please see the Method section (Page 15, lines 346-355).

(5) Additional statistical analyses should be performed, including multiple comparisons with p-value adjustment, false discovery rate (FDR) control, and Pearson correlation (line 291).

As suggested, we have performed additional statistical analyses and mentioned the results in the revised manuscript.

"Positive correlations were observed between the ASM subsets and both galactosylation (p=0.017, r_s=0.418) and sialylation (p=0.008, r_s=0.458) in the antibody Fc region, as well as between the PB subsets and sialylation (p=0.036, r_s=0.372) (Figure 4A-C). (Page 8, lines 180-183)"

"The Benjamini - Hochberg (BH) method was used to adjust the raw p-values from DEG analysis, controlling the false discovery rate (FDR)." Please see the Materials and Methods (Page 16, lines 369-371).

(6) Quality control should be conducted prior to the IgG N-glycome analysis. Additionally, both biological and technical replicates are essential to assess the reproducibility and robustness of the methods.

Thank you for the suggestion. We have added descriptions on the biological and technical replicates in the Method section.

"Our study incorporated both biological and technical replicates to ensure a robust glycomic profiling analysis. Specifically, we analyzed paired acute/convalescent-phase samples from 65 confirmed HFRS patients to assess inter-individual biological variability, while technical reproducibility was validated through comparison with standard chromatographic peak plots (Vučković et al., 2016). This dual-replicate strategy enabled a comprehensive evaluation of both biological heterogeneity and assay precision." (Page 15, lines 356-362).

(7) Multiple regression analysis should be conducted to evaluate the influence of genetic and environmental factors on the IgG N-glycome.

As suggested, we have conducted multiple regression analysis to evaluate the influence of genetic and environmental factors on the IgG N-glycome. These results have been provided in the revised Result section.

"Multivariate linear regression was employed to mitigate potential confounding by genetic and environmental factors in the glycomics analysis. While no significant associations were observed for most glycan models (fucosylation, p=0.526; bisecting GlcNAc, p=0.069; and sialylation, p=0.058), we discovered sex showed a potentially influential effect on galactosylation (p=0.001) (Supplementary files 5-8). These results suggest that while most glycan features appear unaffected by the examined covariates, galactosylation may be subject to sex-specific biological regulation." (Page 7, lines 153-160).

(8) Line 196. Additional discussions should be included, focusing on the underlying correlation between the differential expression of B-cell glycogenes and the dysregulated IgG N-glycome profile, as well as the potential molecular mechanisms of IgG N-glycosylation in the development of HFRS.

Thank you for your suggestions. We have added these contents in the Discussion section.

"Antibody-related glycogenes are significantly activated following Hantaan virus infection. We noted that ribophorin I and II (RPN1 and RPN2) were significantly upregulated in the ASM/IM/PB/RM subsets after Hantaan virus infection, which linked the high mannose oligosaccharides with asparagine residues found in the Asn-X-Ser/Thr consensus motif (Hwang et al., 2025). We speculate that they continuously attach the synthesized glycan chains to the constant region of antibodies during antibody synthesis. Similarly, fucosyltransferase 8 (FUT8) in the ASM subset, catalyzing the alpha1-2, alpha1-3, and alpha1-4 fucose addition (Wang & Ravetch, 2019; Yang et al., 2015), was downregulated in the mRNA translation, and the levels of fucosylated antibodies were naturally lower in the acute HFRS patients. Meanwhile, the beta-1,4-galactosyltransferase (beta4GalT) gene expression was significantly elevated in the ASM subpopulation during the acute phase, which also correlated with increased levels of galactosylated antibodies in serum (Wang & Ravetch, 2019). However, we did not observe significant upward changes in sialyltransferase mRNA expression in the acute HFRS patients, similar with the finding from severe COVID-19 cohorts (Haslund-Gourley et al., 2024). The neuraminidase 1 (NEU1) gene is strikingly upregulated and may potentially explain the decreased sialylation on the secreted HTNV-specific IgG antibodies during convalescence. Overall, the glycosylation of immunoglobulin G is regulated by a large network of B-cell glycogenes during HTNV infection." Please see the Discussion (Page 11, lines 254-273).

Reviewer #2 (Public review):

(1) While it is great to reference prior publications in the Materials and Methods section, the current level of detail is insufficient to clearly understand the study design and experimental procedures performed. Readers should not be expected to consult multiple previous papers to grasp the core methodological aspects of the present paper. For instance, the categorization of HFRS patients into different clinical subtypes/ courses, and the methods for measuring Fc glycosylation should be explicitly described in the Materials and Methods section of this manuscript. 

Many thanks for your comments. We have added more details regarding the study design and experimental procedures in the Materials and Methods section. "Clinical specimens were collected from HFRS patients who were hospitalized in Baoji Central Hospital between October 2019 and January 2022. Patients were categorized into four clinical subtypes (mild, moderate, severe, and critical) based on the diagnostic criteria for HFRS issued by the Ministry of Health (Ma et al., 2015). This study was approved by the ethics committee of the Shandong First Medical University & Shandong Academy of Medical Sciences (R201937). Written informed consent was obtained from each participant or their guardians.

The clinical course of HFRS is grouped into acute (febrile, hypotensive, and oliguric stages) and convalescent (diuretic and convalescent stages) phases. The acute phase was defined as within 12 days of illness onset, and the convalescent phase was defined as a period of illness lasting 13 days or longer (Tang et al., 2019; Zhang et al., 2022). The earliest sample was selected if there were multiple blood samples available in the acute phase and the last available sample before discharge was selected if there were multiple blood samples in the convalescent phase.

Briefly, the diluted plasma samples were transferred onto a 96-well protein G monolithic plate (BIA Separations, Slovenia) for the isolation of IgG. The isolated IgG was eluted with 1 mL of 0.1 M formic acid and was immediately neutralized with 170 µL of 1M ammonium bicarbonate.

The released N-glycans were labelled with 2-aminobenzamide (2-AB) and were then purified from a mixture of 100% acetonitrile and ultrapure water in a 1:1 ratio (v/v). This was then analyzed by hydrophilic interaction liquid chromatography using ultra-performance liquid chromatography (HILIC-UPLC; Walters Corporation, Milford, MA) (Hou et al., 2019). As previously reported, the chromatograms were separated into 24 IgG glycan peaks (GPs) (Menni et al., 2018)." Please see the Materials and Methods (Page 13, lines 290-303, and Page 15, lines 346-355).

(2) The authors should explain the nature of their cohort in a bit more detail. While it appears that HFRS cases were identified based on IgM ELISA and/or PCR, these are indicators of the Haantan virus infection. My understanding is that not all Haantan virus infections progress to HFRS. Thus, it is unclear whether all patients in the HFRS group actually had hemorrhagic fever. This distinction is critical for interpreting how the results observed relate to disease severity.

We are sincerely grateful for this valuable suggestion. We have carefully revised Figure 1 and the texts (Page 5, lines 104-107) in the revised manuscript.

"To characterize the humoral immune profiles in HFRS patients, we enrolled 166 suspected HTNV-infected patients who were admitted to Baoji Central Hospital in Shaanxi Province, China, between October 2019 and January 2022. Among them, 65 met the inclusion criteria and were included in the study (Figure 1)."

(3) The authors state that: "A 4-fold or greater increase in HTNV-NP-specific antibody titers usually indicates a protective humoral immune response during the acute phase", but they do not cite any references or provide any context that supports this claim. Given that in their own words, one of the most significant findings in the study is changes in glycosylation coinciding with this 4-fold increase, it is important to ground this claim in evidence. Without this, the use of a 4-fold threshold appears arbitrary and weakens the rationale for using this immune state as a proxy for protective immunity.

Thank you for the suggestion and we have provided relevant references in the Results section (Page 8, lines 171-173).

According to the Expert Consensus on Prevention and Treatment of Hemorrhagic  Fever with Renal Syndrome (HFRS) (https://ts-cms.jundaodsj.com/file/163823638693909.pdf), a confirmed diagnosis requires, based on a suspected or clinical diagnosis, one of the following: positive serum-specific IgM antibodies, detection of Hantavirus RNA in patient specimens, a four-fold or greater rise in titer of serum-specific IgG antibodies in the convalescent phase compared to the acute phase, or isolation of Hantavirus from patient specimens. A four-fold or greater rise in titer of convalescent serum-specific IgG antibodies compared to the acute phase not only suggests a recent Hantaan virus infection, but also the production of antibodies helping to combat the viral infection. In addition, the antibody glycosylation modifications may thus play a significant role in the antiviral immune response.

(4) The authors also claim that changes in Fc glycosylation influence recovery from HFRS - a point even emphasized in the manuscript title. However, this conclusion is not well supported by the data for two main reasons. First, the authors appear to measure bulk IgG Fc glycans, not Fc glycans of Hantaan virus-specific antibodies. While reasonable, this is something that should be communicated in the manuscript. Hantaan virus-specific antibodies are likely a very small fraction of total circulating IgG antibodies (perhaps ~1%), even during acute infection. As a result, changes in bulk Fc glycosylation may (or may not) accurately reflect the glycosylation state of Hantaan virus-specific antibodies. Second, even if the bulk Fc glycan shifts do mirror those of Hantaan virus-specific antibodies, it remains unclear whether these changes causally drive recovery or are merely a consequence of the infection being resolved. Thus, while the differences in Fc glycosylation observed are interesting - and it is tempting to speculate on their functional significance - the manuscript treats the observed correlations as causal mechanistic insight without sufficient data or justification.

Thank you for your valuable comments. This study measured bulk IgG Fc glycans, not Fc glycans of Hantaan virus-specific antibodies. We have described this limitation in the Discussion section (Page 12, lines 274-280). As reported in previous studies (references provided below), the changed pattern of virus-specific IgG N-glycans may reflect the total IgG N-glycome. Nevertheless, more studies are clearly needed to directly measure virus-specific IgGs and to clarify the causal mechanistic insights.

References

(1) Mads Delbo Larsen, Erik L de Graaf, Myrthe E Sonneveld, et al. Afucosylated IgG characterizes enveloped viral responses and correlates with COVID-19 severity. Science. 2021 Feb 26;371(6532): eabc8378.

(2) Chakraborty S, Gonzalez J, Edwards K, et al. Proinflammatory IgG Fc structures in patients with severe COVID-19. Nat Immunol. 2021 Jan;22(1):67-73.

(3) Tea Petrović, Amrita Vijay, Frano Vučković, et al. IgG N-glycome changes during the course of severe COVID-19: An observational study. EBioMedicine. 2022 Jul ;81: 104101. 

(4) Hou H, Yang H, Liu P, et al. Profile of Immunoglobulin G N-Glycome in COVID-19 Patients: A Case-Control Study. Front Immunol. 2021 Sep 23;12: 748566.

(5) Fc glycosylation is known to be influenced by covariates such as age and sex. While it is helpful that the authors stratified the patients by age group and looked for significant differences in glycosylation across them, a more robust approach would be to directly control for these covariates in the statistical analysis - such as by using a linear mixed effects model, in which disease state (e.g., acute vs. convalescent), age, and sex are treated as fixed effects, and subject ID is included as a random effect to account for repeated measures. This would allow the authors to assess whether observed differences in Fc glycosylation remain significant after accounting for potential confounders. This could be important given that some of the reported differences are quite small, for example, 94.29% vs. 94.89% fucosylation.

Thank you for your valuable suggestion. As suggested, we have conducted multiple regression analysis to evaluate the influence of genetic and environmental factors on the IgG N-glycome, and have provided these results in the revised Result section.

"Multivariate linear regression was employed to mitigate potential confounding by genetic and environmental factors in the glycomics analysis. While no significant associations were observed for most glycan models (fucosylation, p=0.526; bisecting GlcNAc, p=0.069; and sialylation, p=0.058), we discovered sex showed a potentially influential effect on galactosylation (p=0.001) (Supplementary files 5-8). These results suggest that while most glycan features appear unaffected by the examined covariates, galactosylation may be subject to sex-specific biological regulation." (Page 7, lines 153-160).

(6) The manuscript states that there are limited studies on antibody glycosylation in the context of HFRS, but does not cite any relevant literature. If prior work exists, it should be cited to contextualize the current study. If no prior studies have been conducted/reported, to the author's knowledge, that should be stated explicitly to show the novelty of the work.

Thank you for your suggestion. To our knowledge, there has been no prior reports regarding the regulation of IgG glycosylation in HFRS, particularly in relation to seroconversion. We have reworded this sentence in the revised manuscript. "Importantly, there have not been prior studies specifically examining plasma IgG N-glycome profiles derived from chromatographic peak data in HFRS patients, particularly in relation to seroconversion status. This gap in our knowledge motivated our systematic investigation of both total and virus-specific IgG glycosylation dynamics during acute infection." Please see the Introduction (Page 5, lines 92-96).

Reviewer #2 (Recommendations for the authors):

Minor points:

(1) Line 47, 78: The use of the word 'However' appears to be an incorrect expression.

We have made this correction.

(2) Line 127: The term 'glycome' should be replaced with 'N-glycome,' and all relevant expressions should be corrected accordingly, such as 'N-glycosylation.

We have made this correction.

(3) Line 84-87: The sentence 'A total of 166 HFRS patients...' contains a grammatical error.

We have made tis correction (Page 5, lines 99-101).

Author response:

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

Reviewer #1 (Public review): 

Summary: 

In this manuscript, the authors describe a good-quality ancient maize genome from 15th-century Bolivia and try to link the genome characteristics to Inca influence. Overall, the manuscript is below the standard in the field. In particular, the geographic origin of the sample and its archaeological context is not well evidenced. While dating of the sample and the authentication of ancient DNA have been evidenced robustly, the downstream genetic analyses do not support the conclusion that genomic changes can be attributed to Inca influence. Furthermore, sections of the manuscript are written incoherently and with logical mistakes. In its current form, this paper is not robust and possibly of very narrow interest. 

Strengths: 

Technical data related to the maize sample are robust. Radiocarbon dating strongly evidenced the sample age, estimated to be around 1474 AD. Authentication of ancient DNA has been done robustly. Spontaneous C-to-T substitutions, which are present in all ancient DNA, are visible in the reported sample with the expected pattern. Despite a low fraction of C-to-T at the 1st base, this number could be consistent with the cool and dry climate in which the sample was preserved. The distribution of DNA fragment sizes is consistent with expectations for a sample of this age. 

Weaknesses: 

Thank you for all your thoughtful comments. See below for comments on each.

(1) Archaeological context for the maize sample is weakly supported by speculation about the origin and has unreasonable claims weighing on it. Perhaps those findings would be more convincing if the authors were to present evidence that supports their conclusions: i) a map of all known tombs near La Paz, ii) evidence supporting the stone tomb origins of this assemblage, and iii) evidence supporting non-Inca provenance of the tomb. 

We believe we are clear about what information we have about context.  First, the intake records from the MSU Museum from 1890 are not as detailed as we would like, but we cannot enhance them. The mummified girl and her accoutrements, including the maize, came from a stone tower or chullpa south of La Paz, in what is now Bolivia. We do not know which stone chullpa, so a map would be of limited use.  The mortuary group is identified as Inca, but as we note the accoutrements do not appear of high status, so it is possible that she is not an elite.  Mud tombs are normally attributed to the local population, and stone towers to Inca or elites. We have clarified at multiple places in the text that the maize is from the period of Inca incursion in this part of Bolivia and have modified text to reflect greater uncertainty of Inca or local origin, but that selection for environmentally favorable characteristics had taken place.  Regardless, there are three 15th c CE or AD AMS ages on the maize, a cucurbita rind, and a camelid fiber.  The maize is almost certainly mid to late 15th century CE.

(2) Dismissal of the admixture in the reported samples is not evidenced correctly. Population f3 statistic with an outgroup is indeed one of the most robust metrics for sample relatedness; however, it should not be used as a test of admixture. For an admixture test, the population f3 statistic should be used in the form: i) target population, ii) one possible parental population, iii) another possible parental population. This is typically done iteratively with all combinations of possible parental populations. Even in such a form, the population f3 statistic is not very sensitive to admixture in cases of strong genetic drift, and instead population f4 statistic (with an outgroup) is a recommended test for admixture. 

We have removed “Our admixture f3-statistics test results suggest aBM is not admixed” in our revised manuscript. Since our goal here is to identify which group(s) has(have) the highest relatedness with aBM, so population f3 statistic with an outgroup is the most robust metric to do the test and to support our conclusion here.

(3) The geographic placement of the sample based on genetic data is not robust. To make use of the method correctly, it would be necessary to validate that genetic samples in this region follow the assumption of the 'isolation-by-distance' with dense sampling, which has not been done. Additionally, the authors posit that "This suggests that aBM might not only be genetically related to the archaeological maize from ancient Peru, but also in the possible geographic location." The method used to infer the location is based on pure genetic estimation. The above conclusion is not supported by this method, and it directly contradicts the authors' suggestion that the sample comes from Bolivia.  

We understood that it is necessary to validate the assumption of the 'isolation-by-distance' with dense sampling. But we did not do it because: 1) the ancient maize age ranges from ~5000BP to ~100BP and they were found in very different countries at different times. 2) isolation-by-distance is a population genetic concept and it's often used to test whether populations that are geographically farther apart are also more genetically different. Considering we only have 17 ancient samples in total our sample size is not sufficient for a big population test.

For "It directly contradicts the authors' suggestion that the sample comes from Bolivia.”, as we described in our manuscript that “Given the provenience of the aBM and its age, it is possible the samples were local or alternatively were introduced into western highland Bolivia from the Inca core area – modern Peru.” The sample recording file did show the aBM sample was found in Bolivia, but we do not know where aBM originally came from before it was found in Bolivia. To answer this question, we used locator.py to predict the potential geographic location that aBM may have originally come from, and our results showed that the predicted location is inside of modern Peru and is also very close to archaeological Peruvian maize.  

Therefore, our conclusion that "This suggests that aBM might not only be genetically related to the archaeological maize from ancient Peru, but also in the possible geographic location” does not contradict that the sample was found Bolivia.

(4) The conclusion that Ancient Andean maize is genetically similar to European varieties and hence shares a similar evolutionary history is not well supported. The PCA plot in Figure 4 merely represents sample similarity based on two components (jointly responsible for about 20% of the variation explained), and European samples could be very distant based on other components. Indeed, the direct test using the outgroup f3 statistic does not support that European varieties are particularly closely related to ancient Andean maize. Perhaps these are more closely related to Brazil? We do not know, as this has not been measured. 

Our conclusion is “We also found that a few types of maize from Europe have a much closer distance to the archaeological maize cluster compared to other modern maize, which indicates maize from Europe might expectedly share certain traits or evolutionary characteristics with ancient maize. It is also consistent with the historical fact that maize spread to Europe after Christopher Columbus's late 15th century voyages to the Americas. But as shown, maize also has diversity inside the European maize cluster. It is possible that European farmers and merchants may have favored different phenotypic traits, and the subsequent spread of specific varieties followed the new global geopolitical maps of the Colonial era”.

We understood your concerns that two components only explain about 20% of the variation. But as you can see from the Figure 2b in Grzybowski, M.W. et al., 2023 publication, it described that “the first principal component (PC1) of variation for genetic marker data roughly corresponded to the division between domesticated maize and maize wild relatives is only 1.3%”. It shows this is quite common in maize, especially when the datasets include landraces, hybrids, and wild relatives. For our maize dataset, we have archaeological maize data ranging from ~5,000BP to ~100BP, and we also have modern maize, which makes the genetic structure of our data more complicated. Therefore, we think our two components are currently the best explanation currently possible. We also included PCA plot based on component 1 and 3 in Fig4_PCA13.pdf. It does not show that the European samples are very distant.

For “Perhaps these are more closely related to Brazil?”, thank you for this very good question, but we apologize that we cannot answer this question from our current study because our study focuses on identifying the location where aBM originally came from, establishing and explaining patterns of genetic variability of maize, with a specific focus on maize strains that are related to our current aBM. Thus, we will not explore the story between maize from Brazil and European maize in our current study.

(5) The conclusion that long branches in the phylogenetic tree are due to selection under local adaptation has no evidence. Long branches could be the result of missing data, nucleotide misincorporations, genetic drift, or simply due to the inability of phylogenetic trees to model complex population-level relationships such as admixture or incomplete lineage sorting. Additionally, captions to Figure S3, do not explain colour-coding.  

We have removed “aBM tends to have long branches compare to tropicalis maize, which can be explained by adaption for specific local environment by time.” in our revised manuscript.

We have added the color-coding information under Fig. S3 in our revised manuscript.

(6) The conclusion that selection detected in aBM sample is due to Inca influence has no support. Firstly, selection signature can be due to environmental or other factors. To disentangle those, the authors would need to generate the data for a large number of samples from similar cultural contexts and from a wide-ranging environmental context, followed by a formal statistical test. Secondly, allele frequency increase can be attributed to selection or demographic processes, and alone is not sufficient evidence for selection. The presented XP-EHH method seems more suitable. Overall, methods used in this paper raise some concerns: i) how accurate are allele-frequency tests of selection when only single individual is used as a proxy for a whole population, ii) the significance threshold has been arbitrary fixed to an absolute number based on other studies, but the standard is to use, for example, top fifth percentile. Finally, linking selection to particular GO terms is not strong evidence, as correlation does not imply causation, and links are unclear anyway. 

In sum, this manuscript presents new data that seems to be of high quality, but the analyses are frequently inappropriate and/or over-interpreted. 

Regarding your suggestion that “from similar cultural contexts and from a wide-ranging environmental context, followed by a formal statistical test”, we apologize that this cannot be done in our current study because we could not find other archaeological maize samples/datasets that are from similar cultural contexts.

For “Secondly, allele frequency increase can be attributed to selection or demographic processes, and alone is not sufficient evidence for selection.” Yes, we agree, and that’s why we said it “inferred” the conclusion instead of “indicated”. Furthermore, we revised the whole manuscript following all reviewers’ comments and reorganized and reduced the part on selection on aBM.

For “The presented XP-EHH method seems more suitable”, we do not think XP-EHH is the best method that could be used here because we only have one aBM sample, but XP-EHH is more suitable for a population analysis.

For “Finally, linking selection to particular GO terms is not strong evidence, as correlation does not imply causation, and links are unclear anyway.”, as we described in our manuscript, our results “inferred” instead of “indicated” the conclusion.

Reviewer #2 (Public review): 

Summary: 

The manuscript presents valuable new datasets from two ancient maize seeds that contribute to our growing understanding of the maize evolution and biodiversity landscape in pre-colonial South America. Some of the analyses are robust, but the selection elements are not supported. 

Strengths: 

The data collection is robust, and the data appear to be of sufficiently high quality to carry out some interesting analytical procedures. The central finding that aBM maize is closely related to maize from the core Inca region is well supported, although the directionality of dispersal is not supported. 

Weaknesses: 

Thank you for your comments and suggestions. See below for responses and explanations.

The selection results are not justified, see examples in the detailed comments below. 

(1) The manuscript mentions cultural and natural selection (line 76), but then only gives a couple of examples of selecting for culinary/use traits. There are many examples of selection to tolerate diverse environments that could be relevant for this discussion, if desired. 

We have added related examples with references supported in our revised manuscript.  

(2) I would be extremely cautious about interpreting the observations of a Spanish colonizer (lines 95-99) without very significant caveats. Indigenous agriculture and food ways would have been far more nuanced than what could be captured in this context, and the genocidal activities of the Europeans would have impacted food production activities to a degree, and any contemporaneous accounts need to be understood through that lens.  

We agree with the first part of this comment and have softened our use of this particular textual material such that it is far less central to interpretation.While of interest, we cannot evaluate the impact of colonial European activities or observational bias for purposes of this analysis.

(3) The f3 stats presented in Figure 2 are not set up to test any specific admixture scenarios, so it is unsupported to conclude that the aBM maize is not admixed on this basis (lines 201-202). The original f3 publication (Patterson et al, 2012) describes some scenarios where f3 characteristics associate with admixture, but in general, there are many caveats to this approach, and it's not the ideal tool for admixture testing, compared with e.g., f4 and D (abba-baba) statistics.  

You make an important point that f3 stats is not the ideal tool for admixture testing. Since our study goal here is to identify which group(s) has(have) the highest relatedness with aBM, the population f3 statistic with an outgroup is the most robust metrics with which to do the test and to support our conclusion here. We have removed the “Our admixture f3-statistics test results suggest aBM is not admixed” in our revised manuscript.

(4) I'm a little bit skeptical that the Locator method adds value here, given the small training sample size and the wide geographic spread and genetic diversity of the ancient samples that include Central America. The paper describing that method (Battey et al 2020 eLife) uses much larger datasets, and while the authors do not specifically advise on sample sizes, they caution about small sample size issues. We have already seen that the ancient Peruvian maize has the most shared drift with aBM maize on the basis of the f3 stats, and the Locator analysis seems to just be reiterating that. I would advise against putting any additional weight on the Locator results as far as geographic origins, and personally I would skip this analysis in this case.  

As we described in our manuscript, we have 17 archaeological samples in total. Please find more detailed information from the “geographical location prediction” section.

We cannot add more ancient samples because they are all that we could find from all previous publications. We may still want to keep this analysis because f3 stats indicates the genome similarity, but the purpose of locator.py analysis is indicating the predicted location of origin of a genetic sample by comparing it to a set of samples of known geographic origin. 

(5) The overlap in PCA should not be used to confirm that aBM is authentically ancient, because with proper data handling, PCA placement should be agnostic to modern/ancient status (see lines 224-226). It is somewhat unexpected that the ancient Tehuacan maize (with a major teosinte genomic component) falls near the ancient South American maize, but this could be an artifact of sampling throughout the PCA and the lack of teosinte samples that might attract that individual.  

We have removed “which supports the authenticity of aBM as archaeological maize” in our revised manuscript. The PCA was only applied for all maize samples, so we did not include any teosinte samples in the analysis.

(6) What has been established (lines 250-251) is genetic similarity to the Inca core area, not necessarily the directionality. Might aBM have been part of a cultural region supplying maize to the Inca core region, for example? Without a specific test of dispersal directionality, which I don't think is possible with the data at hand, this is somewhat speculative. 

We added this and re-wrote this part in our revised manuscript.

(7) Singleton SNPs are not a typical criterion for identifying selection; this method needs some citations supporting the exact approach and validation against neutral expectations (line 278). Without Datasets S2 and S3, which are not included with this submission, it is difficult to assess this result further. However, it is very unexpected that ~18,000 out of ~49,000 SNPs would be unique to the aBM lineage. This most likely reflects some data artifact (unaccounted damage, paralogs not treated for high coverage, which are extremely prevalent in maize, etc). I'm confused about unique SNPs in this context. How can they be unique to the aBM lineage if the SNPs used overlap the Grzybowski set? The GO results do not include any details of the exact method used or a statistical assessment of the results. It is not clear if the GO terms noted are statistically enriched.  

We have added references 53 and 54 in our revised manuscript, and we also uploaded the Datasets S2 and S3.

For “I'm confused about unique SNPs in this context. How can they be unique to the aBM lineage if the SNPs used overlap the Grzybowski set?”, as we described in our materials and method part that “To achieve potential unique selection on aBM, we calculated the allele frequency for each SNPs between aBM and other archaeological maize, resulting in allele frequency data for 49,896 SNPs. Of these,18,668 SNPs were unique to aBM.”  Thus, the unique SNPs for aBM came from the comparison between aBM with other archaeological maize, and we did not use any modern maize data from the Grzybowski set.

For “The GO results do not include any details of the exact method used or a statistical assessment of the results. It is not clear if the GO terms noted are statistically enriched.” We did not do GO Term enrichment, so there are no statistical assessments for the results. What we have done was we retained the GO Terms information for each gene by checking their biological process from MaizeGDB, after that, we summarized the results in Dataset S4.

(8) The use of XP-EHH with pseudo haplotype variant calls is not viable (line 293). It is not clear what exact implementation of XP-EHH was used, but this method generally relies on phased or sometimes unphased diploid genotype calls to observe shared haplotypes, and some minimum population size to derive statistical power. No implementation of XP-EHH to my knowledge is appropriate for application to this kind of dataset. 

We used the same XP-EHH as this publication “Sabeti, P.C. et al. Genome-wide detection and characterization of positive selection in human populations. Nature 449, 913-918 (2007).” Specifically in our analysis, the SNP information of modern maize was compared with ancient maize. The code is available in https://doi.org/10.5061/dryad.w6m905qtd.

XP-EHH is a statistical method used in population genetics to detect recent positive selection in one population compared to another, and it often applied in modern large maize populations in previous research. In our study, we wanted to detect recent positive selection in modern maize compared to ancient maize, thus, we applied XP-EHH here. Although the population size of ancient maize is not big, it is the best method that we can apply for our dataset here to detect recent selection on modern maize.

Reviewer #3 (Public review): 

Summary: 

The authors seek to place archaeological maize samples (2 kernels) from Bolivia into genetic and geographical context and to assess signatures of selection. The kernels were dated to the end of the Incan empire, just prior to European colonization. Genetic data and analyses were used to characterize the distance from other ancient and modern maize samples and to predict the origin of the sample, which was discovered in a tomb near La Paz, Bolivia. Given the conquest of this region by the Incan empire, it is possible that the sample could be genetically similar to populations of maize in Peru, the center of the Incan empire. Signatures of selection in the sample could help reveal various environmental variables and cultural preferences that shaped maize genetic diversity in this region at that time. 

Strengths: 

The authors have generated substantial genetic data from these archaeological samples and have assembled a data set of published archaeological and modern maize samples that should help to place these samples in context. The samples are dated to an interesting time in the history of South America during a period of expansion of the Incan empire and just prior to European colonization. Much could be learned from even this small set of samples. 

Weaknesses: 

Many thanks for your comments and suggestions.  We have addressed these below and provided further explanation.

(1) Sample preparation and sequencing: 

Details of the quality of the samples, including the percentage of endogenous DNA are missing from the methods. The low percentage of mapped reads suggests endogenous DNA was low, and this would be useful to characterize more fully. Morphological assessment of the samples and comparison to morphological data from other maize varieties is also missing. It appears that the two kernels were ground separately and that DNA was isolated separately, but data were ultimately pooled across these genetically distinct individuals for analysis. Pooling would violate assumptions of downstream analysis, which included genetic comparison to single archaeological and modern individuals. 

We did not do the morphological assessment of the samples and comparison to morphological data from other maize varieties because we only have 2 aBM kernels, and we do not have other archaeological samples that could be used to do comparison.

For “It appears that the two kernels were ground separately and that DNA was isolated separately, but data were ultimately pooled across these genetically distinct individuals for analysis”, as you can see from our Materials and Methods section that “Whole kernels were crushed in a mortar and pestle”, these two kernels were ground together before sequenced. 

While morphological assessment of the sample would be interesting, most morphological data reported for maize are from microremains (starch, phytoliths, pollen) and this is beyond the scope of our study. Most studies of macrobotanical remains do not appear to focus solely on individual kernels, but instead on (or in combination with) cob and ear shape, which were not available in the assemblage.

(2) Genetic comparison to other samples: 

The authors did not meaningfully address the varying ages of the other archaeological samples and modern maize when comparing the genetic distance of their samples. The archaeological samples were as old as >5000 BP to as young as 70 BP and therefore have experienced varying extents of genetic drift from ancestral allele frequencies. For this reason, age should explicitly be included in their analysis of genetic relatedness. 

We have changed related part in our revised manuscript.

(3) Assessment of selection in their ancient Bolivian sample: 

This analysis relied on the identification of alleles that were unique to the ancient sample and inferred selection based on a large number of unique SNPs in two genes related to internode length. This could be a technical artifact due to poor alignment of sequence data, evidence supporting pseudogenization, or within an expected range of genetic differentiation based on population structure and the age of the samples. More rigor is needed to indicate that these genetic patterns are consistent with selection. This analysis may also be affected by the pooling of the Bolivian archaeological samples.  

We do not think it is because of poor alignment of sequence data since we used BWA v0.7.17 with disabled seed (-l 1024) and 0 mismatch alignment. Therefore, there are no SNPs that could come from poor alignment. Please see our detailed methods description here “For the archaeological maize samples, adapters were removed and paired reads were merged using AdapterRemoval60 with parameters --minquality 20 --minlength 30. All 5՛ thymine and 3՛ adenine residues within 5nt of the two ends were hard-masked, where deamination was most concentrated. Reads were then mapped to soft-masked B73 v5 reference genome using BWA v0.7.17 with disabled seed (-l 1024 -o 0 -E 3) and a quality control threshold (-q 20) based on the recommended parameter61 to improve ancient DNA mapping”.

For “More rigor is needed to indicate that these genetic patterns are consistent with selection”, Could you please be more specific about which method or approach we should use here? For example, methods from specific publications that could be referenced? Or which specific tool could be used?

“This analysis may also be affected by the pooling of the Bolivian archaeological samples.” As we could not prove these two seeds came from two different individual plants, we do not think this analysis was affected by the pooling of the Bolivian archaeological samples.

(4) Evidence of selection in modern vs. ancient maize: In this analysis, samples were pooled into modern and ancient samples and compared using the XP-EHH statistic. One gene related to ovule development was identified as being targeted by selection, likely during modern improvement. Once again, ancient samples span many millennia and both South, Central, and North America. These, and the modern samples included, do not represent meaningfully cohesive populations, likely explaining the extremely small number of loci differentiating the groups. This analysis is also complicated by the pooling of the Bolivian archaeological samples. 

Yes, it is possible that ovule development might be a modern improvement. We re-wrote this part in our revised manuscript.

Reviewer #1 (Recommendations for the authors): 

My suggestion is to address the comments that outline why the methods used or results obtained are not sufficient to support your conclusions. Overall, I suggest limiting the narrative of Inca influence and framing it as speculation in the discussion section. Presenting conclusions of Inca influence in the title and abstract is not appropriate, given the very questionable evidence. 

We agree and have changed the title to “Fifteenth century CE Bolivian maize reveals genetic affinities with ancient Peruvian maize”.

Reviewer #2 (Recommendations for the authors): 

(1) Line 74: Mexicana is another subspecies of teosinte; the distinction is between ssp. mexicana and ssp. parviglumis (Balsas teosinte), not mexicana and teosinte. 

We have corrected this in our revised manuscript.

(2) Line 100-102: This is a bit confusing, it cannot have been a symbol of empire "since its first introduction", since its introduction long predates the formation of imperial politics in the region. Reference 17 only treats the late precolonial Inca context, while ref 22 (which cites maize cultivation at 2450 BC, not 3000 BC) makes no reference to ritual/feasting contexts; it simply documents early phytolith evidence for maize cultivation. As such, this statement is not supported by the references offered.

lines 100-102. This point is well taken and was poor prose on our part.  We have modified this discussion to reflect both the confusing statement and we have corrected our mistake in age for reference 22. associated prose has been modified accordingly.

We have corrected them as “Indeed, in the Andes, previous research showed that under the Inca empire, maize was fulfilled multiple contextual roles. In some cases, it operated as a sacred crop” and “…since its first introduction to the region around 2500 BC”.

(3) Line 161: IntCal is likely not the appropriate calibration curve for this region; dates should probably be calibrated using SHCal.  

We greatly appreciate this important (and correct) observation. We have completely recalibrated the maize AMS result based on the southern hemisphere calibration curve, discussed the new calibrations, and have also invoked two other AMS dates also subjected to the southern hemisphere calibration on associated material for comparison.We are confident in a 15th century AD/CE age for the maize, most likely mid- to late 15th century.  

(4) Lines 167-169: The increase of G and A residues shown in Supplementary Figure S1a is just before the 5' end of the read within the reference genome context, and is related to fragmentation bias - a different process from postmortem deamination. Deamination leads to 5' C->T and 3' G->A, resulting in increased T at 5' ends and increased A at 3' ends, and the diagnostic damage curve. The reduction of C/T just before reads begin is not a result of deamination. 

We have removed the “Both features are indicative of postmortem deamination patterns” in our revised manuscript.

(5) Lines 187-196 This section presents a lot of important external information establishing hypotheses, and needs some references.  

We have added the related references here.

(6) Line 421: This makes it sound like damage masking was done BEFORE read mapping. However, this conflicts with the previous paragraph about map Damage, and Supplementary Figure 1 still shows a slight but perceptible damage curve, which is impossible if all terminal Ts and As are hard-masked. This should be reconciled.  

The Supplementary Figure 1 shows the raw ancient maize DNA sample before damage masking. Specifically, Step1: We used map Damage to check/estimate if the damage exists, and we made the Supplementary Figure 1. Step 2: Then we used our own code hard-masked the damage bases and did read mapping.

The purpose of Supplementary Figure 1 is to show the authenticity of aBM as archaeological maize. Therefore, it should show a slight but perceptible damage curve.

(7) Line 460: PCA method is not given (just the LD pruning and the plotting).  

The merged dataset of SNPs for archaeological and modern maize was used for PCA analysis by using “plink –pca”.

(8) "tropicalis" maize is not common usage, it is not clear to me what this refers to. 

We have changed all “tropicalis maize” as “tropical maize” in our revised manuscript.

(9) The Figure 4 color palette is not accessible for colorblind/color-deficient vision.  

We have changed the color of Figure 4. Please find the new colors in our upload Figure 4.

(10) Datasets S2 and S3 are not included with this submission. 

Thank you for letting us know and your suggestion. We have included Datasets S2 and S3 here.

Author response:

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

Reviewer #1 (Public review):

Summary:

In Causal associations between plasma proteins and prostate cancer: a Proteome-Wide Mendelian Randomization, the authors present a manuscript which seeks to identify novel markers for prostate cancer through analysis of large biobank-based datasets and to extend this analysis to potential therapeutic targets for drugs. This is an area that is already extensively researched, but remains important, due to the high burden and mortality of prostate cancer globally.

Strengths:

The main strengths of the manuscript are the identification and use of large biobank data assets, which provide large numbers of cases and controls, essential for achieving statistical power. The databases used (deCODE, FinnGen, and the UK Biobank) allow for robust numbers of cases and controls. The analytical method chosen, Mendelian Randomization, is appropriate to the problem. Another strength is the integration of multi-omic datasets, here using protein data as well as GWAS sources to integrate genomic and proteomic data.

Thank you for your positive feedback regarding the overall quality of our work and we greatly appreciate you taking time and making effort in reviewing our manuscript.

Weaknesses:

The main weaknesses of the manuscript relate to the following areas:

(1) The failure of the study to analyse the data in the context of other closely related conditions such as benign prostatic hyperplasia (BPH) or lower urinary tract symptoms (LUTS), which have some pathways and biomarkers in common, such as inflammatory pathways (including complement) and specific markers such as KLK3. As a consequence, it is not possible for readers to know whether the findings are specific to prostate cancer or whether they are generic to prostate dysfunction. Given the prevalence of prostate dysfunction (half of men reaching their sixth decade), the potential for false positives and overtreatment from non-specific biomarkers is a major problem, resulting in the evidence presented in this manuscript being weak. Other researchers have addressed this issue using the same data sources as presented here, for example, in this paper, looking at BPH in the UK Biobank population. https://www.nature.com/articles/s41467-018-06920-9

Thank you for your valuable comment. We fully agree that biomarker development must prioritize specificity to avoid overtreatment. While our study is a foundational step toward identifying potential therapeutic targets or complementary biomarkers for prostate cancer—not as a direct endorsement of these proteins for standalone clinical diagnosis. Mendelian randomization analysis strengthens causal inference by design, and we further ensured robustness through sensitivity analyses (e.g., MR-Egger regression for pleiotropy, Bonferroni correction for multiple testing). These methods distinguish true causal effects from nonspecific associations. Importantly, while PSA’s lack of specificity is widely recognized, its role in reducing PCa mortality underscores the value of biomarker-driven screening. Our findings align with the need to integrate multiple markers (e.g. combining a novel protein with PSA) to improve diagnostic precision. Translating these causal insights into clinical tools remains challenging but represents a necessary next step, and we emphasize that this work provides a rigorous starting point for future validation studies.

(2) There is no discussion of Gleason scores with regard to either biomarkers or therapies, and a general lack of discussion around indolent disease as compared with more aggressive variants. These are crucial issues with regard to the triage and identification of genomically aggressive localized prostate cancers. See, for example, the work set out in: https://doi.org/10.1038/nature20788

Thank you for pointing this out. We acknowledge that our original analysis did not directly address this critical issue due to a key data limitation: the publicly available GWAS summary statistics for PCa (from openGWAS and FinnGen) do not provide genetic associations stratified by phenotypic severity or molecular subtypes. This limitation precluded MR analysis of proteins specifically linked to aggressive disease. To partially bridge this gap, we integrate evidence from recent studies in the revised Discussion section to explore the relevance of potential biomarkers to aggressive PCa.

(3) An additional issue is that the field of PCa research is fast-moving. The manuscript cites ~80 references, but too few of these are from recent studies, and many important and relevant papers are not included. The manuscript would be much stronger if it compared and contrasted its findings with more recent studies of PCa biomarkers and targets, especially those concerned with multi-omics and those including BPH.

Thank you for your professional comments. We have rigorously updated the manuscript to include more recent publications and we systematically compare and contrast our findings with these recent studies in the revised Discussion section.

(4) The Methods section provides no information on how the Controls were selected. There is no Table providing cohort data to allow the reader to know whether there were differences in age, BMI, ethnic grouping, social status or deprivation, or smoking status, between the Cases and Controls. These types of data are generally recorded in Biobank data, so this sort of analysis should be possible, or if not, the authors' inability to construct an appropriately matched set of Controls should be discussed as a Limitation.

We thank the reviewer for raising this important methodological concern. We have expanded the Limitations section to state it.

“Lastly, our analysis relied exclusively on publicly available GWAS summary statistics from openGWAS and FinnGen, which did not provide individual-level data on covariates, resulting in no direct assessment of demographic or clinical differences between cases and controls.”

Reviewer #2 (Public review):

This is potentially interesting work, but the analyses are attempted in a rather scattergun way, with little evident critical thought. The structure of the work (Results before Methods) can work in some manuscripts, but it is not ideal here. The authors discuss results before we know anything about the underlying data that the results come from. It gives the impression that the authors regard data as a resource to be exploited, without really caring where the data comes from. The methods can provide meaningful insights if correctly used, but while I don't have reasons to doubt that the analyses were conducted correctly, findings are presented with little discussion or interpretation. No follow-up analyses are performed.

In summary, there are likely some gems here, but the whole manuscript is essentially the output from an analytic pipeline.

We thank the reviewer for the thoughtful evaluation of our work. In response to the concerns regarding manuscript structure and interpretative depth, we have restructured the manuscript to present the Methods section before Results, ensuring transparency in data sources and analytical workflows. Additionally, the Discussion section has been substantially revised to provide mechanistic explanations for key findings (e.g., associated phenotype, causal proteins, druggable targets), contextualize results within recent multi-omics studies and highlight clinical implications.  These revisions aim to transform the work from a pipeline-driven analysis to a biologically grounded investigation, offering actionable insights into prostate cancer pathogenesis and therapeutic development.

Taking the researchers aims in turn:

(1) Meta-GWAS - while combining two datasets together can provide additional insights, the contribution of this analysis above existing GWAS is not clear. The PRACTICAL consortium has already reported the GWAS of 70% of these data. What additional value does this analysis provide? (Likely some, but it's not clear from the text.) Also, the presentation of results is unclear - authors state that only 5 gene regions contained variants at p<5x10-8, but Figure 1 shows dozens of hits above 5x10-8. Also, the red line in Figure 1 (supposedly at 5x10-8) is misplaced.

Thank you very much for your feedback. Although the PRACTICAL consortium constituted the majority of PCa GWAS data, our meta-analysis integrating FinnGen data enhanced statistical power enabling robust detection of low-frequency variants with minor allele frequencies. Moreover, FinnGen's Finnish ancestry (genetic isolate) helps distinguish population-specific effects. The presentation of results showed the top 5 gene regions contained variants at p < 5×10⁻⁸. We apologize for not noticing that the red line was not displayed correctly in the original figures included in the manuscript. We have updated it in the revised manuscript.

(2) Cross-phenotype analysis. It is not really clear what this analysis is, or why it is done. What is the iCPAGdb? A database? A statistical method? Why would we want to know cross-phenotype associations? What even are these? It seems that the authors have taken data from an online resource and have written a paragraph based on this existing data with little added value.

We appreciate the opportunity to clarify this analysis. The cross-phenotype analysis was designed to systematically identify phenotypic traits that share genetic or molecular pathways with prostate cancer, thereby uncovering pleiotropic mechanisms or shared risk factors. Here, iCPAGdb (integrated Cross-Phenotype Association Genetics Database) is a curated repository that aggregates GWAS summary statistics and evaluates genetic pleiotropy using LD-proxy associations from the NHGRI-EBI GWAS Catalog. Prostate carcinogenesis involves multisystem interactions, including spanning endocrine dysregulation, immune microenvironment remodeling and metabolic reprogramming, rather than isolated molecular pathway disruptions. Therefore, it is indispensable for discriminating primary pathogenic drivers from secondary compensatory responses, ultimately informing the development of precision therapeutic strategies. 

In response to your concerns, we have revised the Results section to explicitly define the rationale and methodology of cross-phenotype analysis and restructured the Discussion to interpret phenotype-PCa associations within unified biological frameworks (e.g., metabolic dysregulation, androgen signaling), rather than presenting them as isolated findings.

(3) PW-MR. I can see the value of this work, but many details are unclear. Was this a two-sample MR using PRACTICAL + FinnGen data for the outcome? How many variants were used in key analyses? Again, the description of results is sparse and gives little added value.

We thank you for raising this issue. Two-sample MR refers to an analytical design where genetic instruments for the exposure (plasma proteins) and genetic associations with the outcome (PCa) are derived from non-overlapping populations. This ensures complete sample independence between exposure and outcome datasets to avoid confounding biases, regardless of whether the outcome data originate from single or multiple cohorts. The meta-analysis of PRACTICAL and FinnGen GWAS generates 27,210 quality-controlled variants (p < 5×10⁻⁸, MAF ≥ 1%, LD-clumped r² < 0.1) used in key analyses. Regarding the concern about sparse interpretation, we have substantially expanded the Discussion by comparing significant protein findings (e.g., MSMB, SERPINA3) with results from existing functional studies and multi-omics datasets and unravelling new insights.

(4) Colocalization - seems clear to me.

(5) Additional post-GWAS analyses (pathway + druggability) - again, the analyses seem to be performed appropriately, although little additional insight other than the reporting of output from the methods.

The post-MR druggability and pathway analyses serve two primary scientific purposes: (1) therapeutic prioritization - systematically evaluating which MR-identified proteins represent tractable drug targets (either through existing FDA-approved agents or compounds in clinical development) with direct relevance to cancer or PCa management, and (2) mechanistic hypothesis generation - mapping these candidate proteins to coherent biological pathways to guide future functional validation studies investigating their causal roles in prostate carcinogenesis. In response to your feedback, we have restructured the Discussion section under the subheading “Biological Mechanisms and Druggable Targets” to synthesize these findings, explicitly linking biological pathway to therapeutic targets.

Minor points:

(6) The stated motivation for this work is "early detection". But causality isn't necessary for early detection. If the authors are interested in early detection, other analysis approaches are more appropriate.

We appreciate your insightful feedback. Early detection is one motivation for this work, meanwhile, our goal is also to identify causally implicated proteins that may serve as intervention targets for PCa prevention or therapy.  Establishing causality is critical for distinguishing biomarkers that drive disease pathogenesis from those that are secondary to disease progression, as the former holds greater specificity for early detection and prioritization of therapeutic targets. While we acknowledge that validation for early detection may require additional methodologies, MR analysis provides a foundational step by prioritizing candidate proteins with causal links to disease. This approach ensures that downstream efforts focus on biomarkers and targets with the greatest potential to alter disease trajectories, rather than merely correlative markers.

(7) The authors state "193 proteins were associated with PCa risk", but they are looking at MR results - these analyses test for disease associations of genetically-predicted levels of proteins, not proteins themselves.

True, in MR, the exposure of interest is the lifelong effect of genetically predicted protein levels. This approach is designed to infer causality while avoiding confounding and reverse causation, as genetic variants are fixed at conception and unaffected by disease processes. When we state “193 proteins were associated with PCa risk,” we specifically refer to proteins whose genetically predicted levels (based on instrument SNPs from protein QTLs) show causal links to PCa. Importantly, MR does not measure the direct association between observed protein concentrations and disease. Instead, it estimates the lifelong causal effect of protein levels predicted by genetics. This distinction is critical for disentangling cause from consequence. For example, a protein elevated due to tumor progression would not be identified as causal in MR if its genetic predictors are unrelated to PCa risk.

We acknowledge that clinical translation requires further validation of these proteins in observational studies measuring actual protein levels. However, MR provides a robust first step by prioritizing candidates with causal roles, thereby reducing the risk of investing in biomarkers confounded by disease processes.

Reviewer #1 (Recommendations for the authors):

As outlined above, the major weakness of the manuscript is its failure to consider BPH / LUTS, and whether the markers and targets are specific to PCa or not. Specific improvements that the authors could consider might include a literature review of the features identified for their 20 high-risk proteins, and ideally also analyze whether these proteins are upregulated or downregulated in the databases they have analysed (for example it will be easy to analyze whether these proteins are dysregulated in BPH patients as these are specifically identified in the UK Biobank).

The authors may be able to gain context for this approach by looking at papers analyzing BPH and the complement cascade and other proteins from the authors' top 10 or top 20, for example: https://doi.org/10.1002/pros.24639IF: 2.6 Q2

Other sources can be identified by examining the literature for recent omics papers analysing BPH, especially those that analyse and compare BPH / PCa specifically.

Thank you for highlighting the critical need to distinguish PCa-specific biomarkers from those shared with BPH. In response, we conducted a literature review of multi-omics datasets and prospective cohort studies, systematically evaluating the specificity of prioritized proteins by comparing their expression trends in PCa and BPH or benign prostate tissues. These findings are now integrated into the revised Discussion section under the subheading " Plasma Proteins Causal Links to Prostate Cancer".

In the Discussion, the paragraph (line 288) on PSA is extremely weak. The authors state that further research is needed, and yet only reference four articles (from 2008, 2010, 2012, 2014), none of which are from the last decade. Considerable amounts of research from the last ten years have been published on PSA, for example, see this article from 2018, which specifically analyses PSA in the context of the UK Biobank. This section should be made more up-to-date with the latest literature findings. https://doi.org/10.1038/s41467-018-06920-9

Thank you very much for your feedback. We acknowledge the need to strengthen the discussion on PSA by incorporating recent literature. In the revised manuscript, we have expanded the PSA discussion to integrate contemporary research on the prognostic role of PSA in the progression of PCa and its limitations in cancer screening, ensuring that our discussion reflected the current consensus and controversies. 

Also in the Discussion, the analysis of phenotypic indicators is insufficiently comprehensive and should reference other recent research. For example, this recent UK Biobank study dealt with a wide range of conditions, including prostate cancer, and identified similar factors to those identified in this paper. The authors should compare and contrast their phenotypic findings with the existing literature. https://doi.org/10.1038/s41588-024-01898-1

Thank you for addressing the comprehensiveness of phenotypic analysis. We have learned recent large-scale phenome-wide analyses (linked in your feedback) that explore multi-omics biomarkers and their associations with a range of different diseases. We have compared and contrasted our phenotypic findings with the existing literature and revised the Discussion section to interpret phenotype-PCa associations, emphasizing both shared pathways and disease-specific signals.

Under Methods, there is too little information on how Controls were selected, whether any matching process was conducted, or whether there are fundamental differences between the cases and controls (such as smoking status, BMI, comorbidities). The authors use R, and a library such as MatchIt could be used to ensure that the Controls cohort is appropriately matched to the Cases.

As outlined above, we acknowledge that our original analysis did not directly address this critical issue due to a key data limitation. The publicly available GWAS summary statistics for PCa (from openGWAS and FinnGen) do not provide individual-level data on covariates, resulting in no direct assessment of demographic or clinical differences between cases and controls.

An important final point is that, as far as I can tell, no UK Biobank Application Number has been specified in the manuscript. This is vital to establish that there was an original hypothesis being investigated (as opposed to data dredging of open access resources), especially in light of the largely mechanistic flow of the manuscript and lack of PCa and relevant confounder-specific discussion. The authors may be aware of the work of Stender et al (2024) regarding formulaic papers using Mendelian randomization, especially that "[All] combinations of exposure and outcome results based on data available in IEU openGWAS (https://gwas.mrcieu.ac.uk/) can be browsed online on epigraphDB.org. In other words, these results are, in effect, already published. Reporting them again in a scientific paper adds nothing to what can be looked up online in minutes." The authors may wish to address this issue directly.

Stender, S., Gellert-Kristensen, H. & Smith, G.D. Reclaiming Mendelian randomization from the deluge of papers and misleading findings. Lipids Health Dis 23, 286 (2024). https://doi.org/10.1186/s12944-024-02284-w

We confirm that all data used in this study were obtained from publicly available GWAS summary statistics (e.g., PRACTICAL consortium, FinnGen) and proteomic datasets (deCODE, UKB-PPP). Our research was guided by a predefined hypothesis to investigate causal plasma protein biomarkers for prostate cancer, rather than exploratory data mining. The analytical pipelines and integrative approaches (e.g., colocalization, druggability assessment) were specifically designed to address this hypothesis, aligning with the ethical use of open-access resources.

Reviewer #2 (Recommendations for the authors):

There are several specific recommendations in the public review (e.g., clarify the contribution of the GWAS). Otherwise, there is nothing clearly incorrect, but translational insight is missing - the analyses are not clearly connected to the scientific literature. This is a limitation rather than a flaw - the manuscript will likely still be useful to readers.

We thank you for highlighting the need to strengthen translational insights and contextualize our findings within existing literature. In the revised manuscript, we have expanded the Discussion section to systematically compare our results with prior mechanistic and clinical studies, including the shared pathways of associated phenotypes, the potential of significant proteins in biomarkers and therapeutic targeting. These revisions ensure our analyses are firmly rooted in the scientific literature.

Author response:

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

Public Reviews:

We thank the Reviewers for their thorough attention to our paper and the interesting discussion about the findings. Before responding to more specific comments, here some general points we would like to clarify:

(1) Ecological niche models are indeed correlative models, and we used them to highlight environmental factors associated with HPAI outbreaks within two host groups. We will further revise the terminology that could still unintentionally suggest causal inference. The few remaining ambiguities were mainly in the Discussion section, where our intent was to interpret the results in light of the broader scientific literature. Particularly, we will change the following expressions:

-  “Which factors can explain…” to  “Which factors are associated with…” (line 75);

-  “the environmental and anthropogenic factors influencing” to “the environmental and anthropogenic factors that are correlated with” (line 273);

-  “underscoring the influence” to “underscoring the strong association” (line 282).

(2) We respectfully disagree with the suggestion that an ecological niche modelling (ENM) approach is not appropriate for this work and the research question addressed therein. Ecological niche models are specifically designed to estimate the spatial distribution of the environmental suitability of species and pathogens, making them well suited to our research questions. In our study, we have also explicitly detailed the known limitations of ecological niche models in the Discussion section, in line with prior literature, to ensure their appropriate interpretation in the context of HPAI.

(3) The environmental layers used in our models were restricted to those available at a global scale, as listed in Supplementary Information Resources S1 (https://github.com/sdellicour/h5nx_risk_mapping/blob/master/Scripts_%26_data/SI_Resource_S1.xlsx). Naturally, not all potentially relevant environmental factors could be included, but the selected layers are explicitly documented and only these were assessed for their importance. Despite this limitation, the performance metrics indicate that the models performed well, suggesting that the chosen covariates capture meaningful associations with HPAI occurrence at a global scale.

Reviewer #1 (Public review):

The authors aim to predict ecological suitability for transmission of highly pathogenic avian influenza (HPAI) using ecological niche models. This class of models identify correlations between the locations of species or disease detections and the environment. These correlations are then used to predict habitat suitability (in this work, ecological suitability for disease transmission) in locations where surveillance of the species or disease has not been conducted. The authors fit separate models for HPAI detections in wild birds and farmed birds, for two strains of HPAI (H5N1 and H5Nx) and for two time periods, pre- and post-2020. The authors also validate models fitted to disease occurrence data from pre-2020 using post-2020 occurrence data. I thank the authors for taking the time to respond to my initial review and I provide some follow-up below.

Detailed comments:

In my review, I asked the authors to clarify the meaning of "spillover" within the HPAI transmission cycle. This term is still not entirely clear: at lines 409-410, the authors use the term with reference to transmission between wild birds and farmed birds, as distinct to transmission between farmed birds. It is implied but not explicitly stated that "spillover" is relevant to the transmission cycle in farmed birds only. The sentence, "we developed separate ecological niche models for wild and domestic bird HPAI occurrences ..." could have been supported by a clear sentence describing the transmission cycle, to prime the reader for why two separate models were necessary.

We respectfully disagree that the term “spillover” is unclear in the manuscript. In both the Methods and Discussion sections (lines 387-391 and 409-414), we explicitly define “spillover” as the introduction of HPAI viruses from wild birds into domestic poultry, and we distinguish this from secondary farm-to-farm transmission. Our use of separate ecological niche models for wild and domestic outbreaks reflects not only the distinction between primary spillover and secondary transmission, but also the fundamentally different ecological processes, surveillance systems, and management implications that shape outbreaks in these two groups. We will clarify this choice in the revised manuscript when introducing the separate models. Furthermore, on line 83, we will add “as these two groups are influenced by different ecological processes, surveillance biases, and management contexts”.

I also queried the importance of (dead-end) mammalian infections to a model of the HPAI transmission risk, to which the authors responded: "While spillover events of HPAI into mammals have been documented, these detections are generally considered dead-end infections and do not currently represent sustained transmission chains. As such, they fall outside the scope of our study, which focuses on avian hosts and models ecological suitability for outbreaks in wild and domestic birds." I would argue that any infections, whether they are in dead-end or competent hosts, represent the presence of environmental conditions to support transmission so are certainly relevant to a niche model and therefore within scope. It is certainly understandable if the authors have not been able to access data of mammalian infections, but it is an oversight to dismiss these infections as irrelevant.

We understand the Reviewer’s point, but our study was designed to model HPAI occurrence in avian hosts only. We therefore restricted our analysis to wild birds and domestic poultry, which represent the primary hosts for HPAI circulation and the focus of surveillance and control measures. While mammalian detections have been reported, they are outside the scope of this work.

Correlative ecological niche models, including BRTs, learn relationships between occurrence data and covariate data to make predictions, irrespective of correlations between covariates. I am not convinced that the authors can make any "interpretation" (line 298) that the covariates that are most informative to their models have any "influence" (line 282) on their response variable. Indeed, the observation that "land-use and climatic predictors do not play an important role in the niche ecological models" (line 286), while "intensive chicken population density emerges as a significant predictor" (line 282) begs the question: from an operational perspective, is the best (e.g., most interpretable and quickest to generate) model of HPAI risk a map of poultry farming intensity?

We agree that poultry density may partly reflect reporting bias, but we also assumed it a meaningful predictor of HPAI risk. Its importance in our models is therefore expected. Importantly, our BRT framework does more than reproduce poultry distribution: it captures non-linear relationships and interactions with other covariates, allowing a more nuanced characterisation of risk than a simple poultry density map. Note also that we distinguished in our models intensive and extensive chicken poultry density and duck density. Therefore, it is not a “map of poultry farming intensity”. 

At line 282, we used the word “influence” while fully recognising that correlative models cannot establish causality. Indeed, in our analyses, “relative influence” refers to the importance metric produced by the BRT algorithm (Ridgeway, 2020), which measures correlative associations between environmental factors and outbreak occurrences. These scores are interpreted in light of the broader scientific literature, therefore our interpretations build on both our results and existing evidence, rather than on our models alone. However, in the next version of the paper, we will revise the sentence as: “underscoring the strong association of poultry farming practices with HPAI spread (Dhingra et al., 2016)”. 

I have more significant concerns about the authors' treatment of sampling bias: "We agree with the Reviewer's comment that poultry density could have potentially been considered to guide the sampling effort of the pseudo-absences to consider when training domestic bird models. We however prefer to keep using a human population density layer as a proxy for surveillance bias to define the relative probability to sample pseudo-absence points in the different pixels of the background area considered when training our ecological niche models. Indeed, given that poultry density is precisely one of the predictors that we aim to test, considering this environmental layer for defining the relative probability to sample pseudo-absences would introduce a certain level of circularity in our analytical procedure, e.g. by artificially increasing to influence of that particular variable in our models." The authors have elected to ignore a fundamental feature of distribution modelling with occurrence-only data: if we include a source of sampling bias as a covariate and do not include it when we sample background data, then that covariate would appear to be correlated with presence. They acknowledge this later in their response to my review: "...assuming a sampling bias correlated with poultry density would result in reducing its effect as a risk factor." In other words, the apparent predictive capacity of poultry density is a function of how the authors have constructed the sampling bias for their models. A reader of the manuscript can reasonably ask the question: to what degree are is the model a model of HPAI transmission risk, and to what degree is the model a model of the observation process? The sentence at lines 474-477 is a helpful addition, however the preceding sentence, "Another approach to sampling pseudo-absences would have been to distribute them according to the density of domestic poultry," (line 474) is included without acknowledgement of the flow-on consequence to one of the key findings of the manuscript, that "...intensive chicken population density emerges as a significant predictor..." (line 282). The additional context on the EMPRES-i dataset at line 475-476 ("the locations of outbreaks ... are often georeferenced using place name nomenclatures") is in conflict with the description of the dataset at line 407 ("precise location coordinates"). Ultimately, the choices that the authors have made are entirely defensible through a clear, concise description of model features and assumptions, and precise language to guide the reader through interpretation of results. I am not satisfied that this is provided in the revised manuscript.

We thank the Reviewer for this important point. To address it, we compared model predictive performance and covariate relative influences obtained when pseudo-absences were weighted by poultry density versus human population density (Author response table 1). The results show that differences between the two approaches are marginal, both in predictive performance (ΔAUC ranging from -0.013 to +0.002) and in the ranking of key predictors (see below Author response images 1 and 2). For instance, intensive chicken density consistently emerged as an important predictor regardless of the bias layer used.

Note: the comparison was conducted using a simplified BRT configuration for computational efficiency (fewer trees, fixed 5-fold random cross-validation, and standardised parameters). Therefore, absolute values of AUC and variable importance may differ slightly from those in the manuscript, but the relative ranking of predictors and the overall conclusions remain consistent.

Given these small differences, we retained the approach using human population density. We agree that poultry density partly reflects surveillance bias as well as true epidemiological risk, and we will clarify this in the revised manuscript by noting that the predictive role of poultry density reflects both biological processes and surveillance systems. Furthermore, on line 289, we will add “We note, however, that intensive poultry density may reflect both surveillance intensity and epidemiological risk, and its predictive role in our models should be interpreted in light of both processes”.

Author response table 1.

Comparison of model predictive performances (AUC) between pseudo-absence sampling were weighted by poultry density and by human population density across host groups, virus types, and time periods. Differences in AUC values are shown as the value for poultry-weighted minus human-weighted pseudo-absences.

Author response image 1.

Comparison of variable relative influence (%) between models trained with pseudo-absences weighted by poultry density (red) and human population density (blue) for domestic bird outbreaks. Results are shown for four datasets: H5N1 (<2020), H5N1 (>2020), H5Nx (<2020), and H5Nx (>2020).

Author response image 2.

Comparison of variable relative influence (%) between models trained with pseudo-absences weighted by poultry density (red) and human population density (blue) for wild bird outbreaks. Results are shown for three datasets: H5N1 (>2020), H5Nx (<2020), and H5Nx (>2020).

The authors have slightly misunderstood my comment on "extrapolation": I referred to "environmental extrapolation" in my review without being particularly explicit about my meaning. By "environmental extrapolation", I meant to ask whether the models were predicting to environments that are outside the extent of environments included in the occurrence data used in the manuscript. The authors appear to have understood this to be a comment on geographic extrapolation, or predicting to areas outside the geographic extent included in occurrence data, e.g.: "For H5Nx post-2020, areas of high predicted ecological suitability, such as Brazil, Bolivia, the Caribbean islands, and Jilin province in China, likely result from extrapolations, as these regions reported few or no outbreaks in the training data" (lines 195-197). Is the model extrapolating in environmental space in these regions? This is unclear. I do not suggest that the authors should carry out further analysis, but the multivariate environmental similarly surface (MESS; see Elith et al., 2010) is a useful tool to visualise environmental extrapolation and aid model interpretation.

On the subject of "extrapolation", I am also concerned by the additions at lines 362-370: "...our models extrapolate environmental suitability for H5Nx in wild birds in areas where few or no outbreaks have been reported. This discrepancy may be explained by limited surveillance or underreporting in those regions." The "discrepancy" cited here is a feature of the input dataset, a function of the observation distribution that should be captured in pseudo-absence data. The authors state that Kazakhstan and Central Asia are areas of interest, and that the environments in this region are outside the extent of environments captured in the occurrence dataset, although it is unclear whether "extrapolation" is informed by a quantitative tool like a MESS or judged by some other qualitative test. The authors then cite Australia as an example of a region with some predicted suitability but no HPAI outbreaks to date, however this discussion point is not linked to the idea that the presence of environmental conditions to support transmission need not imply the occurrence of transmission (as in the addition, "...spatial isolation may imply a lower risk of actual occurrences..." at line 214). Ultimately, the authors have not added any clear comment on model uncertainty (e.g., variation between replicated BRTs) as I suggested might be helpful to support their description of model predictions.

Many thanks for the clarification. Indeed, we interpreted your previous comments in terms of geographic extrapolations. We thank the Reviewer for these observations. We will adjust the wording to further clarify that predictions of ecological suitability in areas with few or no reported outbreaks (e.g., Central Asia, Australia) are not model errors but expected extrapolations, since ecological suitability does not imply confirmed transmission (for instance, on Line 362: “our models extrapolate environmental suitability” will be changed to “Interestingly, our models extrapolate geographical”). These predictions indicate potential environments favorable to circulation if the virus were introduced.

In our study, model uncertainty is formally assessed when comparing the predictive performances of our models (Fig. S3, Table S1), the relative influence (Table S3) and response curves (Fig. 2) associated with each environmental factor (Table S2). All the results confirming a good converge between these replicates. Finally, we indeed did not use a quantitative tool such as a MESS to assess extrapolation but did rely on qualitative interpretation of model outputs.

All of my criticisms are, of course, applied with the understanding that niche modelling is imperfect for a disease like HPAI, and that data may be biased/incomplete, etc.: these caveats are common across the niche modelling literature. However, if language around the transmission cycle, the niche, and the interpretation of any of the models is imprecise, which I find it to be in the revised manuscript, it undermines all of the science that is presented in this work.

We respectfully disagree with this comment. The scope of our study and the methods employed are clearly defined in the manuscript, and the limitations of ecological niche modelling in this context are explicitly acknowledged in the Discussion section. While we appreciate the Reviewer’s concern, the comment does not provide specific examples of unclear or imprecise language regarding the transmission cycle, niche, or interpretation of the models. Without such examples, it is difficult to identify further revisions that would improve clarity.

Reviewer #2 (Public review):

The geographic range of highly pathogenic avian influenza cases changed substantially around the period 2020, and there is much interest in understanding why. Since 2020 the pathogen irrupted in the Americas and the distribution in Asia changed dramatically. This study aimed to determine which spatial factors (environmental, agronomic and socio-economic) explain the change in numbers and locations of cases reported since 2020 (2020--2023). That's a causal question which they address by applying correlative environmental niche modelling (ENM) approach to the avian influenza case data before (2015--2020) and after 2020 (2020--2023) and separately for confirmed cases in wild and domestic birds. To address their questions they compare the outputs of the respective models, and those of the first global model of the HPAI niche published by Dhingra et al 2016.

We do not agree with this comment. In the manuscript, it is well established that we are quantitatively assessing factors that are associated with occurrences data before and after 2020. We do not claim to determine the causality. One sentence of the Introduction section (lines 75-76) could be confusing, so we intend to modify it in the final revision of our manuscript. 

ENM is a correlative approach useful for extrapolating understandings based on sparse geographically referenced observational data over un- or under-sampled areas with similar environmental characteristics in the form of a continuous map. In this case, because the selected covariates about land cover, use, population and environment are broadly available over the entire world, modelled associations between the response and those covariates can be projected (predicted) back to space in the form of a continuous map of the HPAI niche for the entire world.

We fully agree with this assessment of ENM approaches.

Strengths:

The authors are clear about expected bias in the detection of cases, such geographic variation in surveillance effort (testing of symptomatic or dead wildlife, testing domestic flocks) and in general more detections near areas of higher human population density (because if a tree falls in a forest and there is no-one there, etc), and take steps to ameliorate those. The authors use boosted regression trees to implement the ENM, which typically feature among the best performing models for this application (also known as habitat suitability models). They ran replicate sets of the analysis for each of their model targets (wild/domestic x pathogen variant), which can help produce stable predictions. Their code and data is provided, though I did not verify that the work was reproducible.

The paper can be read as a partial update to the first global model of H5Nx transmission by Dhingra and others published in 2016 and explicitly follows many methodological elements. Because they use the same covariate sets as used by Dhingra et al 2016 (including the comparisons of the performance of the sets in spatial cross-validation) and for both time periods of interest in the current work, comparison of model outputs is possible. The authors further facilitate those comparisons with clear graphics and supplementary analyses and presentation. The models can also be explored interactively at a weblink provided in text, though it would be good to see the model training data there too.

The authors' comparison of ENM model outputs generated from the distinct HPAI case datasets is interesting and worthwhile, though for me, only as a response to differently framed research questions.

Weaknesses:

This well-presented and technically well-executed paper has one major weakness to my mind. I don't believe that ENM models were an appropriate tool to address their stated goal, which was to identify the factors that "explain" changing HPAI epidemiology.

Here is how I understand and unpack that weakness:

(1) Because of their fundamentally correlative nature, ENMs are not a strong candidate for exploring or inferring causal relationships.

(2) Generating ENMs for a species whose distribution is undergoing broad scale range change is complicated and requires particular caution and nuance in interpretation (e.g., Elith et al, 2010, an important general assumption of environmental niche models is that the target species is at some kind of distributional equilibrium (at time scales relevant to the model application). In practice that means the species has had an opportunity to reach all suitable habitats and therefore its absence from some can be interpreted as either unfavourable environment or interactions with other species). Here data sets for the response (N5H1 or N5Hx case data in domestic or wild birds ) were divided into two periods; 2015--2020, and 2020--2023 based on the rationale that the geographic locations and host-species profile of cases detected in the latter period was suggestive of changed epidemiology. In comparing outputs from multiple ENMs for the same target from distinct time periods the authors are expertly working in, or even dancing around, what is a known grey area, and they need to make the necessary assumptions and caveats obvious to readers.

We thank the Reviewer for this observation. First, we constrained pseudo-absence sampling to countries and regions where outbreaks had been reported, reducing the risk of interpreting non-affected areas as environmentally unsuitable. Second, we deliberately split the outbreak data into two periods (2015-2020 and 2020-2023) because we do not assume a single stable equilibrium across the full study timeframe. This division reflects known epidemiological changes around 2020 and allows each period to be modeled independently. Within each period, ENM outputs are interpreted as associations between outbreaks and covariates, not as equilibrium distributions. Finally, by testing prediction across periods, we assessed both niche stability and potential niche shifts. These clarifications will be added to the manuscript to make our assumptions and limitations explicit.

Line 66, we will add: “Ecological niche model outputs for range-shifting pathogens must therefore be interpreted with caution (Elith et al., 2010). Despite this limitation, correlative ecological niche models  remain useful for identifying broad-scale associations and potential shifts in distribution. To account for this, we analysed two distinct time periods (2015-2020 and 2020-2023).”

Line 123, we will revise “These findings underscore the ability of pre-2020 models in forecasting the recent geographic distribution of ecological suitability for H5Nx and H5N1 occurrences” to “These results suggest that pre-2020 models captured broad patterns of suitability for H5Nx and H5N1 outbreaks, while post-2020 models provided a closer fit to the more recent epidemiological situation”.

(3) To generate global prediction maps via ENM, only variables that exist at appropriate resolution over the desired area can be supplied as covariates. What processes could influence changing epidemiology of a pathogen and are their covariates that represent them? Introduction to a new geographic area (continent) with naive population, immunity in previously exposed populations, control measures to limit spread such as vaccination or destruction of vulnerable populations or flocks? Might those control measures be more or less likely depending on the country as a function of its resources and governance? There aren't globally available datasets that speak to those factors, so the question is not why were they omitted but rather was the authors decision to choose ENMs given their question justified? How valuable are insights based on patterns of correlation change when considering different temporal sets of HPAI cases in relation to a common and somewhat anachronistic set of covariates?

We agree that the ecological niche models trained in our study are limited to environmental and host factors, as described in the Methods section with the selection of predictors. While such models cannot capture causality or represent processes such as immunity, control measures, or governance, they remain a useful tool for identifying broad associations between outbreak occurrence and environmental context. Our study cannot infer the full mechanisms driving changes in HPAI epidemiology, but it does provide a globally consistent framework to examine how associations with available covariates vary across time periods.

(4) In general the study is somewhat incoherent with respect to time. Though the case data come from different time periods, each response dataset was modelled separately using exactly the same covariate dataset that predated both sets. That decision should be understood as a strong assumption on the part of the authors that conditions the interpretation: the world (as represented by the covariate set) is immutable, so the model has to return different correlative associations between the case data and the covariates to explain the new data. While the world represented by the selected covariates *may* be relatively stable (could be statistically confirmed), what about the world not represented by the covariates (see point 3)?

We used the same covariate layers for both periods, which indeed assumes that these environmental and host factors are relatively stable at the global scale over the short timeframe considered. We believe this assumption is reasonable, as poultry density, land cover, and climate baselines do not change drastically between 2015 and 2023 at the resolution of our analysis. We agree, however, that unmeasured processes such as control measures, immunity, or governance may have changed during this time and are not captured by our covariates.

Recommendations for the Authors:

Reviewer #1 (Recommendations for the authors):

- Line 400-401: "over the 2003-2016 periods" has an extra "s"; "two host species" (with reference to wild and domestic birds) would be more precise as "two host groups".

- Remove comma line 404

Many thanks for these comments, we have modified the text accordingly.

Reviewer #2 (Recommendations for the authors):

Most of my work this round is encapsulated in the public part of the review.

The authors responded positively to the review efforts from the previous round, but I was underwhelmed with the changes to the text that resulted. Particularly in regard to limiting assumptions - the way that they augmented the text to refer to limitations raised in review downplayed the importance of the assumptions they've made. So they acknowledge the significance of the limitation in their rejoinder, but in the amended text merely note the limitation without giving any sense of what it means for their interpretation of the findings of this study.

The abstract and findings are essentially unchanged from the previous draft.

I still feel the near causal statements of interpretation about the covariates are concerning. These models really are not a good candidate for supporting the inference that they are making and there seem to be very strong arguments in favour of adding covariates that are not globally available.

We never claimed causal interpretation, and we have consistently framed our analyses in terms of associations rather than mechanisms. We acknowledge that one phrasing in the research questions (“Which factors can explain…”) could be misinterpreted, and we are correcting this in the revised version to read “Which factors are associated with…”. Our approach follows standard ecological niche modelling practice, which identifies statistical associations between occurrence data and covariates. As noted in the Discussion section, these associations should not be interpreted as direct causal mechanisms. Finally, all interpretive points in the manuscript are supported by published literature, and we consider this framing both appropriate and consistent with best practice in ecological niche modelling (ENM) studies.

We assessed predictor contributions using the “relative influence” metric, the terminology reported by the R package “gbm” (Ridgeway, 2020). This metric quantifies the contribution of each variable to model fit across all trees, rescaled to sum to 100%, and should be interpreted as an association rather than a causal effect.

L65-66 The general difficulty of interpreting ENM output with range-shifting species should be cited here to alert readers that they should not blithely attempt what follows at home.

I believe that their analysis is interesting and technically very well executed, so it has been a disappointment and hard work to write this assessment. My rough-cut last paragraph of a reframed intro would go something like - there are many reasons in the literature not to do what we are about to do, but here's why we think it can be instructive and informative, within certain guardrails.

To acknowledge this comment and the previous one, we revised lines 65-66 to: “However, recent outbreaks raise questions about whether earlier ecological niche models still accurately predict the current distribution of areas ecologically suitable for the local circulation of HPAI H5 viruses. Ecological niche model outputs for range-shifting pathogens must therefore be interpreted with caution (Elith et al., 2010). Despite this limitation, correlative ecological niche models  remain useful for identifying broad-scale associations and potential shifts in distribution.”

We respectfully disagree with the Reviewer’s statement that “_there are many reasons in the literature not to do what we are about to do”._ All modeling approaches, including mechanistic ones, have limitations, and the literature is clear on both the strengths and constraints of ecological niche models. Our manuscript openly acknowledges these limits and frames our findings accordingly. We therefore believe that our use of an ENM approach is justified and contributes valuable insights within these well-defined boundaries.

Reference: Ridgeway, G. (2007). Generalized Boosted Models: A guide to the gbm package. Update, 1(1), 2007.

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

Reviewer #1(Public review):

I am concerned by the authors' conceptualisation of "niche" within the manuscript. Is the "niche" we are modelling the niche of the pathogen itself? The niche of the (wild) bird host species as a group? The niche of HPAI transmission within (wild) bird host species (i.e., an intersection of pathogen and bird niches)? Or the niche of HPAI transmission in poultry? The precise niche being modelled should be clarified in the Introduction or early in the Methods of the manuscript. The first two definitions of niche listed above are relevant, but separate from the niche modelled in the manuscript - this should be acknowledged.

We acknowledge that these concepts were probably not enough clearly defined in the previous version of our manuscript, and we have now included an explicit definition in the fourth paragraph of the Introduction section: “We developed separate ecological niche models for wild and domestic bird HPAI occurrences, these models thus predicting the ecological suitability for the risk of local viral circulation leading to the detection of HPAI occurrences within each host group (rather than the niche of the virus or the host species alone).”

The authors should consider the precise transmission cycle involved in each HPAI case: "index cases" in farmed poultry, caused by "spillover" from wild birds, are relevant to the wildlife transmission cycle, while the ecological conditions coinciding with subsequent transmission in farmed poultry are likely to be fundamentally different. (For example, subsequent transmission is not conditional on the presence of wild birds.) Modelling these two separate, but linked, transmission cycles together may omit important nuances from the modelling framework.

We thank the Reviewer for highlighting the distinction between primary (wild-todomestic) and secondary (farm-to-farm) transmission cycles. Our modelling framework was designed to assess the ecological suitability of HPAI occurrences in wild and domestic birds separately. In the domestic poultry models, the response variables are the confirmed outbreaks data and do not distinguish between index cases resulting from primary or secondary infections.

One of the aims of the study is to evaluate the spatial distribution of areas ecologically suitable for local H5N1/x circulation either leading to domestic or wild bird cases, i.e. to identify environmental conditions where the virus may have persisted or spread, whether as a result of introduction by wild birds or farm-to-farm transmission. Introducing mechanistic distinctions in the response variable would not necessarily improve or affect the ecological suitability maps, since each type of transmission is likely to be associated with different covariates that are included in the models.

Also, the EMPRES-i database does not indicate whether each record corresponds to an index case or a secondary transmission event, so in practice it would not be possible to produce two different models. However, we agree that distinguishing between types of transmission is an interesting perspective for future research. This could be explored, for example, by mapping interfaces between wild and domestic bird populations or by inferring outbreak transmission trees using genomic data when available.

To avoid confusion, we now explicitly clarify this aspect in the Materials and Methods section: “It is important to note that the EMPRES-i database does not distinguish between index cases (e.g., primary spillover from wild birds) and secondary farm-to-farm transmissions. As such, our ecological niche models are trained on confirmed HPAI outbreaks in poultry that may result from different transmission dynamics — including both initial introduction events influenced by environmental factors and subsequent spread within poultry systems.”

We now also address this limitation in the Discussion section: “Finally, our models for domestic poultry do not distinguish between primary introduction events (e.g., spillover from wild birds) and secondary transmission between farms due to limitations in the available surveillance data. While environmental factors likely influence the risk of initial spillover events, secondary spread is more often driven by anthropogenic factors such as biosecurity practices and poultry trade, which are not included in our current modelling framework.”

The authors should clarify the meaning of "spillover" within the HPAI transmission cycle: if spillover transmission is from wild birds to farmed poultry, then subsequent transmission in poultry is separate from the wildlife transmission cycle. This is particularly relevant to the Discussion paragraph beginning at line 244: does "farm to farm transmission" have a distinct ecological niche to transmission between wild birds, and transmission between wild birds and farmed birds? And while there has been a spillover of HPAI to mammals, could the authors clarify that these detections are dead-end? And not represented in the dataset? Dhingra et al., 2016 comment on the contrast between models of "directly transmitted" pathogens, such as HPAI, and vector-borne diseases: for vector-borne diseases, "clear eco-climatic boundaries of vectors can be mapped", whereas "HPAI is probably not as strongly environmentally constrained". This is an important piece of nuance in their Discussion and a comment to a similar effect may be of use in this manuscript.

Following the Reviewer’s previous comment, we have now added clarifications in the Methods and Discussion sections defining spillover as the transmission of HPAI viruses from wild birds to domestic poultry (index cases), and secondary transmission as onward spread between farms. As mentioned in our answer above, we now emphasise that our models do not distinguish these dynamics, which are likely to be influenced by different drivers — ecological in the case of spillover, and often anthropogenic (e.g., poultry trade movement, biosecurity) in the case of farm-to-farm transmission.

The discussion regarding farm-to-farm transmission and spillovers is indeed an interpretation derived from the covariates analysis (see the second paragraph in the Discussion section). Specifically, we observed a stronger association between HPAI occurrences and domestic bird density after 2020, which may suggest that secondary infections (e.g., farm-to-farm transmission) became more prominent or more frequently reported. We however acknowledge that our data do not allow us to distinguish primary introductions from secondary transmission events, and we have added a sentence to explicitly clarify this: “However, this remains an interpretation, as the available data do not allow us to distinguish between index cases and secondary transmission events.”

We thank the Reviewer for raising the point of mammalian infections. While spillover events of HPAI into mammals have been documented, these detections are generally considered dead-end infections and do not currently represent sustained transmission chains. As such, they fall outside the scope of our study, which focuses on avian hosts and models ecological suitability for outbreaks in wild and domestic birds. However, we agree that future work could explore the spatial overlap between mammalian outbreak detections and ecological suitability maps for wild birds to assess whether such spillovers may be linked to localised avian transmission dynamics.

Finally, we have added a comment about the differences between pathogens strongly constrained by the environments and HPAI: “This suggests that HPAI H5Nx is not as strongly environmentally constrained as vector-borne pathogens, for which clear eco-climatic boundaries (e.g., vector borne diseases) can be mapped (Dhingra et al., 2016).” This aligns with the interpretation provided by Dhingra and colleagues (2016) and helps contextualise the predictive limitations of ecological niche models for directly transmitted pathogens like HPAI.

There are several places where some simple clarification of language could answer my questions related to ecological niches. For example, on line 74, "the ecological niche" should be followed by "of the pathogen", or "of HPAI transmission in wild birds", or some other qualifier that is most appropriate to the Authors' conceptualisation of the niche modelled in the manuscript. Similarly, in the following sentence, "areas at risk" could be followed by "of transmission in wild birds", to make the transmission cycle that is the subject of modelling clear to the reader. On line 83, it is not clear who or what is the owner of "their ecological niches": is this "poultry and wild birds", or the pathogen?

We agree with that suggestion and have now modified the related part of the text  accordingly (e.g., “areas at risk for local HPAI circulation” and “of HPAI in wild or domestic birds”).

I am concerned by the authors' treatment of sampling bias in their BRT modelling framework. If we are modelling the niche of HPAI transmission, we would expect places that are more likely to be subject to disease surveillance to be represented in the set of locations where the disease has been detected. I do not agree that pseudo-absence points are sampled "to account for the lack of virus detection in some areas" - this description is misleading and does not match the following sentence ("pseudo-absence points sampled ... to reflect the greater surveillance efforts ..."). The distribution of pseudo-absences should aim to capture the distribution of probable disease surveillance, as these data act as a stand-in for missing negative surveillance records. It is sensible that pseudo-absences for disease detection in wild birds are sampled proportionately to human population density, as the disease is detected in dead wild birds, which are more likely to be identified close to areas of human occupation (as stated on line 163). However, I do not agree that the same applies to poultry - the density of farmed poultry is likely to be a better proxy for surveillance intensity in farmed birds. Human population density and farmed poultry density may be somewhat correlated (i.e., both are low in remote areas), but poultry density is likely to be higher in rural areas, which are assumed to have relatively lower surveillance intensity under the current approach. The authors allude to this in the Discussion: "monitoring areas with high intensive chicken densities ... remains crucial for the early detection and management of HPAI outbreaks".

We agree with the Reviewer's comment that poultry density could have potentially been considered to guide the sampling effort of the pseudo-absences to consider when training domestic bird models. We however prefer to keep using a human population density layer as a proxy for surveillance bias to define the relative probability to sample pseudoabsence points in the different pixels of the background area considered when training our ecological niche models. Indeed, given that poultry density is precisely one of the predictors that we aim to test, considering this environmental layer for defining the relative probability to sample pseudo-absences would introduce a certain level of circularity in our analytical procedure, e.g. by artificially increasing to influence of that particular variable in our models.

Furthermore, it is also worth noting that, to better account for variations in surveillance intensity, we also adjusted the sampling effort by allocating pseudo-absences in proportion to the number of confirmed outbreaks per administrative unit (country or sub-national regions for Russia and China). This approach aimed to reduce bias caused by uneven reporting and surveillance efforts between regions. Additionally, we restricted model training to countries or regions with a minimum surveillance threshold (at least five confirmed outbreaks per administrative unit). Therefore, both presence and pseudo-absence points originated from areas with more consistent surveillance data.

We acknowledge in the Materials and Methods section that the approach proposed by the Reviewer could have been used: “Another approach to sampling pseudo-absences would have been to distribute them according to the density of domestic poultry.” Finally, our approach is also justified in our response to the next comment of the Reviewer.

Having written my review, including the paragraph above, I briefly scanned Dhingra et al., and found that they provide justification for the use of human population density to sample pseudoabsences in farmed birds: "the Empres-i database compiles outbreak locations data from very heterogeneous sources and in the absence of explicit GPS location data, the geo-referencing of individual cases is often through the use of place name gazetteers that will tend to force the outbreak location populated place, rather in the exact location of the farm where the disease was found, which would introduce a bias correlated with human population density." This context is entirely missing from the manuscript under review, however, I maintain the comment in the paragraph above - have the Authors trialled sampling pseudo-absences from poultry density layers?

We agree with the Reviewer’s comment and have now added this precision in the Materials and Methods section (in the third paragraph dedicated to ecological niche modelling): “However, as pointed out by Dhingra and colleagues (2016), the locations of outbreaks in the EMPRES-i database are often georeferenced using place name nomenclatures due to a lack of accurate GPS data, which could introduce a spatial bias towards populated areas.”

The authors indirectly acknowledge the role of sampling bias in model predictions at line 163, however, this point could be clearer: there is sampling bias in the set of locations where HPAI has been observed and failure to adequately replicate this sampling bias in pseudo-absence data could lead covariates that are correlated with the observation distribution to appear to be correlated with the target distribution. This point is alluded to but should be clearly acknowledged to allow the reader to appropriately interpret your results. I understand the point being made on line 163 is that surveillance of HPAI in wild birds has become more structured and less opportunistic over time - if this is the case, a statement to this effect could replace "which could influence earlier data sets", which is a little ambiguous. The Authors acknowledge the role of sampling bias in lines 241-242 - this may be a good place to remind the reader that they have attempted to incorporate sampling bias through the selection of their pseudoabsence dataset, particularly for wild bird models.

We thank the Reviewer for this comment. We have now clarified in the text that observed data on HPAI occurrence are inherently influenced by heterogeneous surveillance efforts and that failure to replicate this bias in pseudo-absence sampling could effectively lead to misleading correlations with covariates associated with surveillance effort rather than true ecological suitability. We have now rephrased the related sentence as follows: “This decline may indicate a reduced bias in observation data: typically, dead wild birds are more frequently found near human-populated areas due to opportunistic detections, whereas more recent surveillance efforts have become increasingly proactive (Giacinti et al., 2024).”

Dhingra et al. aimed to account for the effect of mass vaccination of birds in China. This does not appear to be included in the updated models - is this a relevant covariate to consider in updated models? Are the models trained on pre-2020 data predicting to post-2020 given the same presence dataset as previous models? It may be helpful to provide a comment on this if we consider the pre-2020 models in this work to be representative of pre-2020 models as a cohort. Given the framing of the manuscript as an update to Dhingra et al., it may be useful for the authors to briefly summarise any differences between the existing models and updated models. Dhingra et al., also examine spatial extrapolation, which is not addressed here. Environmental extrapolation may be a useful metric to consider: are there areas where models are extrapolating that are predicted to be at high risk of HPAI transmission? Finally, they also provide some inset panels on global maps of model predictions - something similar here may also be useful.

We thank the Reviewer for these comments. Vaccination coverage is indeed a relevant covariate for HPAI suitability in domestic birds. However, we did not include this variable in our updated models for two reasons. First, comprehensive vaccination data were only available for China, so it is not possible to include this variable in a global model. Second, available data were outdated and vaccination strategies can vary substantially over time.

We however agree with the Reviewer that the Materials and Methods section did not clarify clearly the differences with Dhingra et al. (2016), and we now detail these differences at the beginning of the Materials and Methods section: “Our approach is similar to the one implemented by Dhingra and colleagues (2016). While Dhingra et al. (2016) developed their models only for domestic birds over the 2003-2016 periods, our models were developed for two host species separately (wild and domestic birds) and for two time periods (2016-2020 and 2020-2023).”

We also detail the main difference concerning the pseudo-absences sampling:  Dhingra and colleagues (2016) used human population density to sample pseudo-absences to reflect potential surveillance bias and also account for spatial filtering (min/max distances from presence). We adopted a similar strategy but also incorporated outbreak count per country or province (in the case of China and Russia) into the pseudo-absence sampling process to further account for within-country surveillance heterogeneity. We have now added these specifications in the Materials and Methods section: “To account for heterogeneity in AIV surveillance and minimise the risk of sampling pseudo-absences in poorly monitored regions, we restricted our analysis to countries (or administrative level 1 units in China and Russia) with at least five confirmed outbreaks. Unlike Dhingra et al. (2016), who sampled pseudoabsences across a broader global extent, our sampling was limited to regions with demonstrated surveillance activity. In addition, we adjusted the density of pseudo-absence points according to the number of reported outbreaks in each country or admin-1 unit, as a proxy for surveillance effort — an approach not implemented in this previous study.”

We have now also provided a comparison between the different outputs, particularly in the Results section: “Our findings were overall consistent with those previously reported by Dhingra and colleagues (Dhingra et al., 2016), who used data from January 2004 to March 2015 for domestic poultry. However, some differences were noted: their maps identified higher ecological suitability for H5 occurrences before 2016 in North America, West Africa, eastern Europe, and Bangladesh, while our maps mainly highlight ecologically suitable regions in China, South-East Asia, and Europe (Fig. S5). In India, analyses consistently identified high ecologically suitable areas for the risk of local H5Nx and H5N1 circulation for the three time periods (pre-2016, 2016-2020, and post-2020). Similar to the results reported by Dhingra and colleagues, we observed an increase in the ecological suitability estimated for H5N1 occurrence in South America's domestic bird populations post-2020. Finally, Dhingra and colleagues identified high suitability areas for H5Nx occurrence in North America, which are predicted to be associated with a low ecological suitability in the 2016-2020 models.”

We acknowledge that some regions predicted as highly suitable correspond to areas where extrapolation likely occurs due to limited or no recorded outbreaks. We have now added these specifications when discussing the resulting suitability maps obtained for domestic birds: “For H5Nx post-2020, areas of high predicted ecological suitability, such as Brazil, Bolivia, the Caribbean islands, and Jilin province in China, likely result from extrapolations, as these regions reported few or no outbreaks in the training data”, and, for wild birds: “Some of the areas with high predicted ecological suitability reflect the result of extrapolations. This is particularly the case in coastal regions of West and North Africa, the Nile Basin, Central Asia (Kyrgyzstan, Tajikistan, Uzbekistan), Brazil (including the Amazon and coastal areas), southern Australia, and the Caribbean, where ecological conditions are similar to those in areas where outbreaks are known to occur but where records of outbreaks are still rare.”

For wild birds (H5Nx, post-2020), high ecological suitability was predicted along the West and North African coasts, the Nile basin, Central Asia (e.g., Kyrgyzstan, Tajikistan, Uzbekistan), the Brazilian coast and Amazon region, Caribbean islands, southern Australia, and parts of Southeast Asia. Ecological suitability estimated in these regions may directly result from extrapolations and should therefore be interpreted cautiously.

We also added a discussion of the extrapolation for wild birds (in the Discussion section): “Interestingly, our models extrapolate environmental suitability for H5Nx in wild birds in areas where few or no outbreaks have been reported. This discrepancy may be explained by limited surveillance or underreporting in those regions. For instance, there is significant evidence that Kazakhstan and Central Asia play a role as a centre for the transmission of avian influenza viruses through migratory birds (Amirgazin et al., 2022; FAO, 2005; Sultankulova et al., 2024). However, very few wild bird cases are reported in EMPRES-i. In contrast, Australia appears environmentally suitable in our models, yet no incursion of HPAI H5N1 2.3.4.4b has occurred despite the arrival of millions of migratory shorebirds and seabirds from Asia and North America. Extensive surveillance in 2022 and 2023 found no active infections nor evidence of prior exposure to the 2.3.4.4b lineage (Wille et al., 2024; Wille and Klaassen, 2023).”

We agree that inset panels can be helpful for visualising global patterns. However, all resulting maps are available on the MOOD platform (https://app.mood-h2020.eu/core), which provides an interactive interface allowing users to zoom in and out, identify specific locations using a background map, and explore the results in greater detail. This resource is referenced in the manuscript to guide readers to the platform.

Related to my review of the manuscript's conceptualisation above, there are several inconsistencies in terminology in the manuscript - clearing these up may help to make the methods and their justification clearer to the reader. The "signal" that the models are estimating is variously described as "susceptibility" and "risk" (lines 179-180), "HPAI H5 ecological suitability" (line 78), "likelihood of HPAI occurrences" (line 139), "risk of HPAI circulation" (line 187), "distribution of occurrence data" (line 428). Each of these quantities has slightly different meanings and it is confusing to the reader that all of these descriptors are used for model output. "Likelihood of HPAI occurrences" is particularly misleading: ecological niche models predict high suitability for a species in areas that are similar to environments where it has previously been identified, without imposing constraints on species movement. It is intuitively far more likely that there will be HPAI occurrences in areas where the disease is already established than in areas where an introduction event is required, however, the niche models in this work do not include spatial relationships in their predictions.

We agree with the Reviewer’s comments. We have now modified the text so that in the Results section we refer to ecological suitability when referring to the outputs of the models. In the context of our Discussion section, we then interpret this ecological suitability in terms of risk, as areas with high ecological suitability being more likely to support local HPAI outbreaks.

I also caution the authors in their interpretation of the results of BRTs, which are correlative models, so therefore do not tell us what causes a response variable, but rather what is correlated with it. On Line 31, "correlated with" may be more appropriate than "influenced by". On Line 82, "correlated with" is more appropriate than "driving". This is particularly true given the authors' treatment of sampling bias.

We agree with the Reviewer’s comment and have now rephrased these sentences as follows: “The spatial distribution of HPAI H5 occurrences in wild birds appears to be primarily correlated with urban areas and open water regions” and “Our results provide a better understanding of HPAI dynamics by identifying key environmental factors correlated with the increase in H5Nx and H5N1 cases in poultry and wild birds, investigating potential shifts in their ecological niches, and improving the prediction of at-risk areas.”

The following sentences in line 201 are ambiguous: "For both H5Nx and H5N1, however, isolated areas on the risk map should be interpreted with caution. These isolated areas may result from sparse data, model limitations, or local environmental conditions that may not accurately reflect true ecological suitability." By "isolated", do the authors mean remote? Or ecologically dissimilar from the set of locations where HPAI has been detected? Or ecologically dissimilar from the set of locations in the joint set of HPAI detection locations and pseudo-absences? Or ecologically similar to the set of locations where HPAI has been detected but spatially isolated? These four descriptors are each slightly different and change the meaning of the sentences. "Model limitations" are also ambiguous - could the authors clarify which specific model limitations they are referring to here? Ultimately, the point being made is probably that a model may predict high ecological suitability for HPAI transmission in areas where the disease has not yet been identified, or where a model is extrapolating in environmental space, however, uncertainty in these predictions may be greater than uncertainty in predictions in areas that are represented in surveillance data. A clear comment on model uncertainty and how it is related to the surveillance dataset and the covariate dataset is currently missing from the manuscript and would be appropriate in this paragraph.

We understand the Reviewer’s concerns regarding these potential ambiguities, and have now rephrased these sentences as follows: “For both H5Nx and H5N1, certain areas of predicted high ecological suitability appear spatially isolated, i.e. surrounded by regions of low predicted ecological suitability. These areas likely meet the environmental conditions associated with past HPAI occurrences, but their spatial isolation may imply a lower risk of actual occurrences, particularly in the absence of nearby outbreaks or relevant wild bird movements.”

I am concerned by the wording of the following sentence: "The risk maps reveal that high-risk areas have expanded after 2020" (line 203). This statement could be supported by an acknowledgement of the assumptions the models make of the HPAI niche: are we saying that the niche is unchanged in environmental space and that there are now more geographic areas accessible to the pathogen, or that the niche has shifted or expanded, and that there are now more geographic areas accessible to the pathogen? The authors should review the sentence beginning on line 117: if models trained on data from the old timepoint predicting to the new timepoint are almost as good as models trained on data from the new timepoint predicting to the new timepoint, doesn't this indicate that the niche, as the models are able to capture it, has not changed too much?

We thank the Reviewer for this comment. The statement that "high-risk areas have expanded after 2020" indeed refers to an increase in the geographic extent of areas predicted to have high ecological suitability in models trained on post-2020 data. This expansion likely reflects new outbreak data from regions that had not previously reported cases, which in turn influenced model training.

However, models trained on pre-2020 data retain reasonable predictive performance when applied to post-2020 data (see the AUC results reported in Table S1), suggesting that the models suggest an expansion in the ecological suitability, but do not provide definitive evidence of a shift in the ecological niche. We have now added a statement at the end of this paragraph to clarify this point: “However, models trained on pre-2020 data maintained reasonable predictive performance when tested on post-2020 data, suggesting that the overall ecological niche of HPAI did not drastically shift over time.”

The final two paragraphs of the Results might be more helpful to include at the beginning of the Results, as the data discussed there are inputs to the models. Is it possible that the "rise in Shannon index for sea birds" that "suggests a broadening of species diversity within this category from 2020 onwards" is caused by the increasingly structured surveillance of HPAI in wild birds alluded to earlier in the Results? Is the "prevalence" discussed in line 226 the frequency of the families Laridae and Sulidae being represented in HPAI detection data? Or the abundance of the bird species themselves? The language here is a little ambiguous. Discussion of particular values of Shannon/Simpson indices is slightly out of context as the meanings of the indices are in the Methods - perhaps a brief explanation of the uses of Shannon/Simpson indices may be helpful to the reader here. It may also be helpful to readers who are not acquainted with avian taxonomy to provide common names next to formal names (for example, in brackets) in the body of the text, as this manuscript is published in an interdisciplinary journal.

We thank the Reviewer for these comments. First, we acknowledge that the paragraphs on species diversity and Shannon/Simpson indices describe important data, but we have chosen to present them after the main modelling results in order to maintain a logical narrative flow. Our manuscript first presents the ecological niche models and their predictive performance, followed by interpretations of the observed patterns, including changes in avian host diversity. Diversity indices were used primarily to support and contextualise the patterns observed in the modelling results.

For clarity, we have revised the relevant paragraphs in the Results (i) to briefly remind readers of the interpretation of the Shannon and Simpson indices (“Note that these indices reflect the diversity of bird species detected in outbreak records, not necessarily their abundance in the wild”) and (ii) to clarify that “prevalence” refers to the frequency of HPAI detection in wild bird species of the Laridae (gulls) and Sulidae (boobies and gannets) families, and not their total abundance. Family of birds includes several species, so the “common name” of a family can sometimes refer to species from other families. We have now added the common names for each family in the manuscript (even if we indeed acknowledge that “penguins” can be ambiguous).

In the Methods, it is stated: "To address the heterogeneity of AIV surveillance efforts and to avoid misclassifying low-surveillance areas as unsuitable for virus circulation, we trained the ecological niche models only considering countries in which five or more cases have been confirmed." However, it is not clear how this processing step prevents low-surveillance areas from being misclassified. If pseudo-absences are appropriately sampled, low-surveillance areas should be less represented in the pseudo-absence dataset, which should lead the models to be uncertain in their predictions of these areas. Perhaps "To address the heterogeneity of AIV surveillance efforts and to avoid sampling pseudo-absence data in realistically low-surveillance areas" is a more accurate introduction to the paragraph. I am not entirely convinced that it is appropriate to remove detection data where the national number of cases is low. This may introduce further sampling bias into the dataset.

We take the opportunity of the Reviewer’s comment to further clarify this important step aiming to mitigate bias associated with countries with substantial uncertainty in reporting and/or potentially insufficient HPAI surveillance data. While we indeed acknowledge that this procedure may exclude countries that had effective surveillance but low virus detection, we argue that it constitutes a relevant conservative approach to minimising the risk of sampling a significant number of pseudo-absence points in areas associated with relatively high yet undetected local HPAI circulation due to insufficient surveillance. Furthermore, given that five cases over two decades is a relatively low threshold — particularly for a highly transmissible virus such as AIV — non-detection or non-reporting remains a more plausible explanation than true absence.

To improve clarity, we have now revised the related sentence as follows: “To account for heterogeneity in AIV surveillance and minimise the risk of sampling pseudo-absences in poorly monitored regions, we restricted our analysis to countries (or administrative level 1 units in China and Russia) with at least five confirmed outbreaks.”

The reporting of spatial and temporal resolution of data in the manuscript could be significantly clearer. Is there a reason why human population density is downscaled to 5 arcminutes (~10km at the equator) while environmental covariate data has a resolution of 1km? The projection used is not reported. The authors should clarify the time period/resolution of the covariate data assigned to the occurrence dataset, for example, does "day LST annual mean" represent a particular year pre- or post-2020? Or an average over a number of years? Given that disease detections are associated with observation and reporting dates, and that there may be seasonal patterns in HPAI occurrence, it would be helpful to the reader to include this information when the eco-climatic indices are described. It would also be helpful to the reader to summarise the source, spatial and temporal resolution of all covariates in a table, as in Dhingra et al. Could the Authors clarify whether the duck density layer is farmed ducks or wild ducks?

The projection is WGS 84 (EPSG:4326) and the resolution of the output maps is around 0.0833 x 0.0833 decimal degrees (i.e. 5 arcmin, or approximately 10 km at the equator). We have now added these specifications in the text: “All maps are in a WGS84 projection with a spatial resolution of 0.0833 decimal degrees (i.e. 5 arcmin, or approximately 10 km at the equator).” In addition, we have now specified in the text that duck refers to domestic duck for clarity. 

Environmental variables retrieved for our analyses were here available as values averaged over distinct periods of time (for further detail see Supplementary Information Resources S1 — description and source of each environmental variable included in the original sets of variables — available at https://github.com/sdellicour/h5nx_risk_mapping). In future works, this would indeed be interesting to associate the occurrences to a specific season with the variables accordingly, specially for viruses such as HPAI which have been found correlated with seasons. However, we did not conduct this type of analysis in the present study, occurrences being here associated with averaged values of environmental data only.

In line 407, the authors state a number of pseudo-absence points used in modelling, relative to the number of presence points, without clear justification. Note that relative weights can be assigned to occurrence data in most ECN software (e.g., R package gbm), to allow many pseudo-absence points to be sampled to represent the full extent of probable surveillance effort and subsequently down-weighted.

We thank the Reviewer for this suggestion. We acknowledge that alternative approaches such as down-weighting pseudo-absence points could offer a certain degree of flexibility in representing surveillance effort. However, we opted for a fixed 1:3 ratio of pseudoabsences to presence points within each administrative unit to ensure a consistent and conservative sampling distribution. This approach aimed to limit overrepresentation of pseudoabsences in areas with sparse presence data, while still reflecting areas of likely surveillance.

There are a number of typographical errors and phrasing issues in the manuscript. A nonexhaustive list is provided below.

- Line 21: "its" should be "their" - Line 25: "HPAI cases"

Modifications have been done.

- Line 63: sentence beginning "However" is somewhat out of context - what is it (briefly) about recent outbreaks that challenge existing models?

We have now edited that sentence as follows: “However, recent outbreaks raise questions about whether earlier ecological niche models still accurately predict the current distribution of areas ecologically suitable for the local circulation of HPAI H5 viruses.”

- Lines 71 and 390: "AIV" is not defined in the text - Line 73: "do" ("are" and "what" are not capitalised)

Modifications have been done.

- Line 115: "predictability" should be "predictive capacity"

We have now replaced “predictability” by “predictive performance”.

- Line 180: omit "pinpointing"

- Line 192 sentence beginning "In India," should be re-worded: is the point that there are detections of HPAI here and the model predicts high ecological suitability?

- Line 195 sentence beginning "Finally," phrasing could be clearer: Dhingra et al. find high suitability areas for H5Nx in North America which are predicted to be low suitability in the new model.

- Line 237: omit "the" in "with the those"

- Line 374: missing "."

- Line 375: "and" should be "to" (the same goes for line 421)

- Line 448: Rephrase "Simpson index goes" to "The Simpson index ranges"

Modifications have been done.

Reviewer #2 (Public Review):

What is the justification for separating the dataset at 2020? Is it just the gap in-between the avian influenza outbreaks?

We chose 2020 as a cut-off based on a well-documented shift in HPAI epidemiology, notably the emergence and global spread of clade 2.3.4.4b, which may affect host dynamics and geographic patterns. We have now added this precision in the Materials and Methods section: “We selected 2020 as a cut-off point to reflect a well-documented shift in HPAI epidemiology, notably the emergence and global spread of clade 2.3.4.4b. This event marked a turning point in viral dynamics, influencing both the range of susceptible hosts and the geographical distribution of outbreaks.”

If the analysis aims to look at changing case numbers and distribution over time, surely the covariate datasets should be contemporaneous with the response?

Thank you for raising this important point. While we acknowledge that, ideally, covariates should match the response temporally, such high-resolution spatiotemporal environmental data were not available for most environmental factors considered in our ecological niche modelling analyses. While we used predictors (e.g., land-use variables, poultry density) that reflect long-term ecological suitability, we acknowledge that rather considering short-term seasonal variation could be an interesting perspective in future works, which is now explicitly stated in the Discussion section: “In addition, aligning outbreak occurrences with seasonally matched environmental variables could further refine predictions of HPAI risk linked to migratory dynamics.”

I would expect quite different immunity dynamics between domestic and wild birds as a function of lifespan and birth rates - though no obvious sign of that in the raw data. A statement on assumptions in that respect would be good.

Thank you for the comment. We agree that domestic and wild birds likely exhibit different immunity dynamics due to differences in lifespan, turnover rates, and exposure. However, our analyses did not explicitly model immunity processes, and the data did not show a clear signal of these differences.

Decisions and analytical tactics from Dhingra et al are adopted here in a way that doesn't quite convey the rationale, or justify its use here.

We thank the Reviewer for this observation. However, we do not agree with the notion that the rationale for using Dhingra et al.’s analytical framework is insufficiently conveyed. We adapted key components of their ecological niche modelling approach — such as the use of a boosted regression tree methodology and pseudo-absences sampling procedure — to ensure comparability with their previous findings, while also extending the analysis to additional time periods and host categories (wild vs. domestic birds). This framework aligns with the main objective of our study, which is to assess shifts in ecological suitability for HPAI over time and across host species, in light of changing viral dynamics.  

Please go over the manuscript and harmonise the language about the model target - it is usually referred to as cases, but sometimes the pathogen, and others the wild and domestic birds where the cases were discovered.

We agree and we have now modified the text to only use the “cases” or “occurrences” terminology when referring to the model inputs.

Is the reporting of your BRT implementation correct? The text suggests that only 10 trees were run per replicate (of which there were 10 per response (domestic/wild x H5N1 / H5Nx) x distinct covariate set), but this would suggest that the authors were scarcely benefiting from the 'boosting' part of the BRTs that allow them to accurately estimate curvilinear functions. As additional trees are added, they should still be improving the loss function, and dramatically so in the early stages. The authors seem heavily guided by Elith et al's excellent paper[1] explaining BRTs and the companion tutorial piece, but in that work, the recommended approach is to run an initial model with a relatively quick learning rate that achieves the best fit to the held-out data at somewhere over 1000 trees, and then to refine the model to that number of trees with a slower learning rate. If the authors did indeed run only 10 trees I think that should be explained.

For each model, we used the “gbm.step” function to fit boosted regression trees, initiating the process with 10 trees and allowing up to 10,000 trees in steps of 5. The optimal number of trees was automatically determined by minimising the cross-validated deviance, following the recommended approach of Elith and colleagues (2008, J. Anim. Ecol.). This setup allows the boosting algorithm to iteratively improve model performance while avoiding overfitting. These aspects are now further clarified in the Materials and Methods section: “All BRT analyses were run and averaged over 10 cross-validated replicates, with a tree complexity of 4, a learning rate of 0.01, a tolerance parameter of 0.001, and while considering 5 spatial folds. Each model was initiated with 10 trees, and additional trees were incrementally added (in steps of 5) up to a maximum of 10,000, with the optimal number selected based on cross-validation tests.”

I'm uncomfortable with the strong interpretation of changes in indices such as those for diversity in the case of bird species with detected cases of avian influenza, and the relative influence of covariates in the environmental niche models. In the former case, if surveillance effort is increasing it might be expected that more species will be found to be infected. In the latter, I'm just not convinced that these fundamentally correlative models can support the interpretation of changing epidemiology as asserted by authors. This strikes me as particularly problematic in light of static and in some cases anachronistic predictor sets.

We thank the Reviewer for drawing attention to how changes in surveillance intensity might influence our diversity estimates. We have now integrated a new analysis to evaluate the increase in the number of wild birds tested and discussed the potential impact of this increase on the comparison of the bird species diversity metrics presented in our study, which is now interpreted with more caution: “To evaluate whether the post-2020 increase in species diversity estimated for infected wild birds could result from an increase in the number of tests performed on wild birds, we compared European annual surveillance test counts (EFSA et al., 2025, 2019) before and after 2020 using a Wilcoxon rank-sum test. We relied on European data because it was readily accessible and offered standardised and systematically collected metrics across multiple years, making it suitable for a comparative analysis. Although borderline significant (p-value = 0.063), the Wilcoxon rank-sum test indeed highlighted a recent increase in the number of wild bird tests (on average >11,000/year pre-2020 and >22,000 post-2020), which indicates that the comparison of bird species diversity metrics should be interpreted with caution. However, such an increase in the number of tests conducted in the context of a passive surveillance framework would thus also be in line with an increase in the number of wild birds found dead and thus tested. Therefore, while the increase in the number of tests could indeed impact species diversity metrics such as the Shannon index, it can also reflect an absolute higher wild bird mortality in line with a broadened range of infected bird species.”

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public Review):

Summary:

In this study, the authors identified and described the transcriptional trajectories leading to CMs during early mouse development, and characterized the epigenetic landscapes that underlie early mesodermal lineage specification.

The authors identified two transcriptomic trajectories from a mesodermal population to cardiomyocytes, the MJH and PSH trajectories. These trajectories are relevant to the current model for the First Heart Field (FHF) and the Second Heart Field (SHF) differentiation. Then, the authors characterized both gene expression and enhancer activity of the MJH and PSH trajectories, using a multiomics analysis. They highlighted the role of Gata4, Hand1, Foxf1, and Tead4 in the specification of the MJH trajectory. Finally, they performed a focused analysis of the role of Hand1 and Foxf1 in the MJH trajectory, showing their mutual regulation and their requirement for cardiac lineage specification.

Strengths:

The authors performed an extensive transcriptional and epigenetic analysis of early cardiac lineage specification and differentiation which will be of interest to investigators in the field of cardiac development and congenital heart disease. The authors considered the impact of the loss of Hand1 and Foxf1 in-vitro and Hand1 in-vivo.

Weaknesses:

The authors used previously published scRNA-seq data to generate two described transcriptomic trajectories.

We agree that a two-route cardiac development model has been described, which is consistent with our analyses. However, the developmental origins and key events by early lineage specification is unclear. Our study provided new insights from the following aspects:

a) Computational analyses inferred the earliest cardiac fate segregation by E6.75-7.0.

b) Provided the new-generated E7.0 multi-omics data which revealed the transcriptomic and chromatin accessibility landscape.

c) Utilized multi-omics and ChIP-seq data to construct a core regulatory network underlying the JCF lineage specification.

d) Applied in vitro and in vivo analyses, which elucidated the synergistic and different roles of key transcription factors, HAND1 and FOXF1.

Q1R1: Details of the re-analysis step should be added, including a careful characterization of the different clusters and maker genes, more details on the WOT analysis, and details on the time stamp distribution along the different pseudotimes. These details would be important to allow readers to gain confidence that the two major trajectories identified are realistic interpretations of the input data.

R1R1: Thank you for the valuable suggestion. In the last version, we characterized the two major trajectories by identifying their common or specific gene sets, and by profiling the expression dynamics along pseudotime (Figure 1F). But we realized a careful description was not provided. In the revised manuscript, we have made the following improvements:

a) Provided marker gene analyses based on cell types as well as developmental lineages to support the E7.0 progenitor clusters (Figure S1F).

b) For Figure 1F: revised the text and introduced characteristic genes for the two trajectories.

c) For WOT analysis: provided more details in the first paragraph of the ‘Results’ section.

R2R1: The authors have also renamed the cardiac trajectories/lineages, departing from the convention applied in hundreds of papers, making the interpretation of their results challenging.

R2R1: Agreed. We have changed the MJH as JCF lineage and PSH as SHF lineage.

Q3R1: The concept of "reverse reasoning" applied to the Waddington-OT package for directional mass transfer is not adequately explained. While the authors correctly acknowledged Waddington-OT's ability to model cell transitions from ancestors to descendants (using optimal transport theory), the justification for using a "reverse reasoning" approach is missing. Clarifying the rationale behind this strategy would be beneficial.

R3R1: Thank you for pointing out the unclear explanation. As mentioned in R1R1, we have clarified the rationale in the revised manuscript. 

We would like to provide some additional details: WOT is designed for time-series scRNA-seq data where the time/stage each single cell is given. At any adjacent time points t_i and t_i+1, WOT estimates the transition probability of all cells at t_i to all cells at t_i+1. One can select a cell set of interest at any time point t_i to infer their ancestors at t_i-1 or their descendants at t_i+1 by sums of the transition probabilities. As introduced in the original paper, WOT allows for both ‘forward’ and ‘reverse’ inference (DOI: 10.1016/j.cell.2019.01.006).

Q3R1: As the authors used the EEM cell cluster as a starting point to build the MJH trajectory, it's unclear whether this trajectory truly represents the cardiac differentiation trajectory of the FHF progenitors:

- This strategy infers that the FHF progenitors are mixed in the same cluster as the extra-embryonic mesoderm, but no specific characterization of potential different cell populations included in this cluster was performed to confirm this.

To build the MJH trajectory, we performed a two-step analysis:

(1) Firstly, we used E8.5 CM cells as a starting point to perform WOT computational reverse lineage tracing and identify CM progenitors at each time point.

(2) Secondly, we selected EEM cells from the E7.5 CM progenitor pool, as a starting point to perform WOT analysis. Cells along this trajectory consist of the JCF lineage (Figure 1B).

The reason why we chose to use this subset of E7.5 EEM cells was due to its purity. It is distinct from the SHF lineage as suggested by their separation in the UMAP. It is also different from FHF cells as no FHF/CM markers were detected by E7.5. 

It is admitted that it is infeasible to achieve 100% purity in this single cell omics analysis, but we believe the current strategy of defining the JCF lineage is reasonable. The distinct gene expression dynamics (Figure 1F) and spatial mapping results (Figure 1C), between JCF and SHF lineages, also supported our conclusion.

- The authors identified the EEM cluster as a Juxta-cardiac field, without showing the expression of the principal marker Mab21l2 per cluster and/or on UMAPs.

Thank you for your suggestion. We have added Mab21l2 expression plots in the ICA layout (new Figure S1D), showing its transient expression dynamics, consistent with Tyser et al (DOI: 10.1126/science.abb2986).

- As the FHF progenitors arise earlier than the Juxta-cardiac field cells, it must be possible to identify an early FHF progenitor population (Nkx2-5+; Mab21l2-) using the time stamp. It would be more accurate to use this FHF cluster as a starting point than the EEM cluster to infer the FHF cardiac differentiation trajectory.

We appreciate your insights. We used the early FHF progenitor population (E7.75 Nkx2-5+; Mab21l2- CM cells) as the starting point and identified its progenitor cells by E7.0 (Figure S2A). Results suggest both JCF and SHF lineages contribute to the early FHF progenitor population, consistent with live imaging-based single cell tracing by Dominguez et al (DOI: 10.1016/j.cell.2023.01.001).

These concerns call into question the overall veracity of the trajectory analysis, and in fact, the discrepancies with prior published heart field trajectories are noted but the authors fail to validate their new interpretation. Because their trajectories are followed for the remainder of the paper, many of the interpretations and claims in the paper may be misleading. For example, these trajectories are used subsequently for annotation of the multiomic data, but any errors in the initial trajectories could result in errors in multiomic annotation, etc, etc.

Thank you for your valuable comments. In the revised manuscript, we have added details about the trajectory analysis including the procedure of WOT lineage inference, marker gene expression and early FHF lineage tracing. We also renamed the two trajectories to avoid confusion with prior published heart field trajectories. Generally, our trajectories are consistent with the published evidence about two major lineages contributing to the linear heart tube:

a) Clonal analysis: two trajectories exist which demonstrate differential contribution to the E8.5 cardiac tube (Meilhac et al, DOI: 10.1016/s1534-5807(04)00133-9).

b) Live imaging: JCF cells contribute to the forming heart (Tyser et al, DOI: 10.1126/science.abb2986; Dominguez et al, DOI: 10.1016/j.cell.2023.01.001).

c) Genetic labelling based lineage tracing: early Hand1+ mesodermal cells differentiate and contribute to the cardiac crescent (Zhang et al, DOI: 10.1161/CIRCRESAHA.121.318943).

Molecular events by the initial segregation of the two lineages were not characterized before, which are the main focus of our paper. Our analyses suggest that the JCF lineage segregates earlier from the nascent/mixed mesoderm status, also consistent with the clonal analysis (Meilhac et al, DOI: 10.1016/s1534-5807(04)00133-9).

Q4R1: As mentioned in the discussion, the authors identified the MJH and PSH trajectories as nonoverlapping. But, the authors did not discuss major previously published data showing that both FHF and SHF arise from a common transcriptomic progenitor state in the primitive streak (DOI: 10.1126/science.aao4174; DOI: 10.1007/s11886-022-01681-w). The authors should consider and discuss the specifics of why they obtained two completely separate trajectories from the beginning, how these observations conflict with prior published work, and what efforts they have made at validation.

R4R1: Thank you for the important question. For trajectory analysis, we assigned cells to the trajectory with higher fate probability, resulting in ‘non-overlapping’ cell sets. However, the statement of ‘two non-overlapping trajectories’ is inaccurate. We performed analysis of fate divergence between two trajectories (which was not shown in the first version), which suggests, before E7.0, mesodermal cells have similar probabilities to choose either trajectory (Figure S1E). We agree with you and previously published data that the JCF and SHF arise from a common progenitor pool. Correction has been made in the revised manuscript.

Q5R1: Figures 1D and E are confusing, as it's unclear why the authors selected only cells at E7.0. Also, panels 1D 'Trajectory' and 'Pseudotime' suggest that the CM trajectory moves from the PSH cells to the MJH. This result is confusing, and the authors should explain this observation.

R5R1: Thank you for pointing out the confusion. As mentioned in R4R1, trajectory analysis indicates JCFSHF fate segregation by E7.0 and we used Figures 1D and E to characterize the cellular status. By E7.0, JCF progenitors are at EEM or MM status, while SHF progenitors are still at the earlier differentiation stage (NM). This result is consistent with previous clonal analysis (Meilhac et al, DOI: 10.1016/s1534-5807(04)00133-9) which demonstrates an apparent earlier segregation of the first lineage. Our interpretation of the pseudotime analysis is that it represents different levels of differentiation, instead of developmental direction.

Q6R1: Regarding the PSH trajectory, it's unclear how the authors can obtain a full cardiac differentiation trajectory from the SHF progenitors as the SHF-derived cardiomyocytes are just starting to invade the heart tube at E8.5 (DOI: 10.7554/eLife.30668).

R6R1.1: We agree with your opinion. Our trajectory analysis covers E8.5 SHF-derived CM cells and progenitors. Cells that differentiate as CM cells after E8.5 were missed.

The above notes some of the discrepancies between the author's trajectory analysis and the historical cardiac development literature. Overall, the discrepancies between the author's trajectory analysis and the historical cardiac development literature are glossed over and not adequately validated.

R6R1.2: Historical cardiac development related literature provided evidence, using multiple techniques, which support the existence of two cardiac lineages with common progenitors at the beginning and overlapping contribution of the four-chamber heart. Our trajectory analysis is in agreement with this model and provides more detailed molecular insights about lineage segregation by E7.0. Thank you for pointing out our mistakes describing the observations. We have corrected the text and provided additional data (Figure S1D-F and S2), aiming to resolved the confusions.

Q7R1: The authors mention analyzing "activated/inhibited genes" from Peng et al. 2019 but didn't specify when Peng's data was collected. Is it temporally relevant to the current study? How can "later stage" pathway enrichment be interpreted in the context of early-stage gene expression?

R7R1: The gene sets of "activated/inhibited genes" were collected from several published perturbation datasets (Gene Expression Omnibus accession numbers GSE48092, GSE41260, GSE17879, GSE69669, GSE15268 and GSE31544) using mouse ES cells or embryos. For a specific pathway, the gene set is fixed but the gene expression levels, which change over time, reflect the pathway enrichment. This explains the differential pathway enrichment between early and late stages.

Q8R1: Motif enrichment: cluster-specific DAEs were analyzed for motifs, but the authors list specific TFs rather than TF families, which is all that motif enrichment can provide. The authors should either list TF families or state clearly that the specific TFs they list were not validated beyond motifs.

R8R1: Thank you for your comment. For the DAE motif analysis, we firstly inferred the motif and TF families, then tested which specific TFs are expressed in the corresponding cell cluster. We have added this information in the legend of Figure 2D.

Q9R1: The core regulatory network is purely predictive. The authors again should refrain from language implying that the TFs in the CRN have any validated role.

R9R1: Thank you for your kind suggestion. We have revised the manuscript to avoid any misleading implications, as follows:

“Through single-cell multi-omics analysis, a predicted core regulatory network (CRN) in JCF is identified, consisting of transcription factors (TFs) GATA4, TEAD4, HAND1 and FOXF1.”

Q10R1: Regarding the in vivo analysis of Hand1 CKO embryos, Figures 6 and 7:

How can the authors explain the presence of a heart tube in the E9.5 Hand1 CKO embryos (Figure 6B) if, following the authors' model, the FHF/Juxta-cardiac field trajectory is disrupted by Hand1 CKO? A more detailed analysis of the cardiac phenotype of Hand1 CKO embryos would help to assess this question.

R10R1: Thank you for your valuable suggestion. In the revised manuscript, we have added detailed analysis of the cardiac phenotype of Hand1 CKO embryo (Figure S8C). Data suggest that by E8.5 when heart looping initiate in control group (14/17), the hearts of Hand1 CKO embryos (3/3) still demonstrate a linear tube morphology. By E9.5 when atrium and ventricle become distinct in WT embryos, heart looping of Hand1 CKO embryos is abnormal. The cardiac defects of our MESP1CRE driven Hand1 conditional KO are consistent with those of Hand1-null mutant mice (Doi: 10.1038/ng0398-266; D oi: 10.1038/ng0398-271).

Author response image 1.

The bright field images of E8.5-E9.5 Ctrl and Hand1 CKO mouse embryos. The arrows indicating the embryonic heart (h) and head folds (hf). Scale bars (E8.5): 200 μm; scale bars (E9.5): 500 μm.

Q11R1: The cell proportion differences observed between Ctrl and Hand1 CKO in Figure 6D need to be replicated and an appropriate statistical analysis must be performed to definitely conclude the impact of Hand1 CKO on cell proportions.

R11R1: We appreciate your valuable suggestion. As Figure 6D is based on scRNA-seq experiment, where replicates were merged as one single sequencing library, statistical analysis is infeasible. To address potential concerns about cell proportions, we added IF staining experiments of EEM marker gene, Vim, in serial embryo sections (Figure S8D). Statistical analysis indicates a significant decrease of VIM+ EEM cell proportion of Hand1 CKO embryos.

Q12R1: The in-vitro cell differentiations are unlikely to recapitulate the complexity of the heart fields invivo, but they are analyzed and interpreted as if they do.

R12R1: We agree with your opinion. In the revised manuscript, we tuned down the interpretation of the invitro cell differentiation data. 

Previous version:

I.  “The analysis indicated that HAND1 and FOXF1 could dually regulate MJH specification through directly activating the MJH specific genes and inhibiting the PSH specific genes.”

II. “Together, our data indicated that mutual regulation between HAND1 and FOXF1 could play a key role in MJH cardiac progenitor specification.”

III. “Thus, our data further supported the specific and synergistic roles of HAND1 and FOXF1 in MJH cardiac progenitor specification.”

Revised version:

I.  “The analysis indicated that HAND1 and FOXF1 were able to directly activate the JCF specific genes.”

II. “Together, our in vitro experimental data indicated that mutual regulation between HAND1 and FOXF1 could play a key role in activation of JCF specific genes.”

III. “These results suggest that HAND1 and FOXF1 may cooperatively regulate early cardiac lineage specification by promoting JCF-associated gene expression and suppressing alternative mesodermal programs.”

Q13R1: The schematic summary of Figure 7F is confusing and should be adjusted based on the following considerations:

(a) the 'Wild-type' side presents 3 main trajectories (SHF, Early HT and JCF), but uses a 2-color code and the authors described only two trajectories everywhere else in the article (aka MJH and PSH). It's unclear how the SHF trajectory (blue line) can contribute to the Early HT, when the Early HT is supposed to be FHF-associated only (DOI: 10.7554/eLife.30668). As mentioned previously in Major comment 3., this model suggests a distinction between FHF and JCF trajectories, which is not investigated in the article.

R13R1(a): Thank you for your great insights. The paper you mentioned used Nkx2.5_cre/+; Rosa26tdtomato+/- and _Nkx2.5_eGFP embryos to reconstruct the cardiac morphologies between E7.5 and E8.2. Their 3D models clearly demonstrate the transition from yolk sac to FHF and then SHF (Figure 2A’ and A’’). The location of yolk sac is defined as JCF in later literature (DOI: 10.1126/science.abb2986). However, as _Nkx2.5 mainly marks cells after the entry of the heart tube, it is unable to reflect the lineage contribution by JCF or SHF. As in R3R1, more and more evidence support the contribution of both lineages to the Early HT, which is discussed in a recent review paper (DOI: 0.1016/j.devcel.2023.01.010).

(b) the color code suggests that the MJH (FHF-related) trajectory will give rise to the right ventricle and outflow tract (green line), which is contrary to current knowledge.

R13R1(b): Thank you for pointing out the confusion. The coloring of outflow tract is not an indication of JCF lineage contribution. We have changed the color of JCF/SHF trajectory in the revised model.

Minor comments:

Q14R1: How genes were selected to generate Figure 1F? Is this a list of top differentially expressed genes over each pseudotime and/or between pseudotimes?

R14R1: For each trajectory, we ranked genes by the correlation between expression levels and pseudotime.

Top 1000 genes for each group were selected.

Q15R1: Regarding Figure 1G, it's unclear how inhibited signaling can have an increased expression of underlying genes over pseudotimes. Can the authors give more details about this analysis and results?

R15R1: The increased expression of ‘inhibited genes’ could be explained as an indication of decreasing signaling levels or compensation effect by other signaling pathways. We appreciate your kind suggestion. Details about this analysis have been added in the Method section.

Q16R1: How do the authors explain the visible Hand1 expression in Hand1 CKO in Figure S7C 'EEM markers'? Is this an expected expression in terms of RNA which is not converted into proteins?

R16R1: Our opinion is that the visible Hand1 expression caused by the imperfect knock-out efficiency by Mesp1-Cre driven system.

Q17R1: The authors do not address the potential presence of doublets (merged cells) within their newly generated dataset. While they mention using "SCTransform" for normalization and artifact removal, it's unclear if doublet removal was explicitly performed.

R17R1: We appreciate your kind reminder. Doublet removal was performed using R package ‘DoubletFinder’ (DOI: 10.1016/j.cels.2019.03.003). We have added this information in the revised manuscript.

Reviewer #2 (Public review):

Summary of goals:

The aims of the study were to identify new lineage trajectories for the cardiac lineages of the heart, and to use computational and cell and animal studies to identify and validate new gene regulatory mechanisms involved in these trajectories.

Strengths:

The study addresses the long-standing yet still not fully answered questions of what drives the earliest specification mechanisms of the heart lineages. The introduction demonstrates a good understanding of the relevant lineage trajectories that have been previously established, and the significance of the work is well described. The study takes advantage of several recently published data sets and attempts to use these in combination to uncover any new mechanisms underlying early mesoderm/cardiac specification mechanisms. A strength of the study is the use of an in vitro model system (mESCs) to assess the functional relevance of the key players identified in the computational analysis, including innovative technology such as CRISPR-guided enhancer modulations. Lastly, the study generates mesoderm-specific Hand1 LOF embryos and assesses the differentiation trajectories in these animals, which represents a strong complementary approach to the in vitro and computational analysis earlier in the paper. The manuscript is clearly written and the methods section is detailed and comprehensive.

Comments and Weaknesses:

Overall: The computational analysis presented here integrates a large number of published data sets with one new data point (E7.0 single cell ATAC and RNA sequencing). This represents an elegant approach to identifying new information using available data. However, the data presentation at times becomes rather confusing, and relatively strong statements and conclusions are made based on trajectory analysis or other inferred mechanisms while jumping from one data set to another. The cell and in vivo work on Hand1 and Foxf1 is an important part of the study. Some additional experiments in both of these model systems could strongly support the novel aspects that were identified by the computational studies leading into the work.

We appreciate your positive comments and insightful suggestions. In the revised manuscript, we have incorporated additional analyses and experimental validations to address the concerns raised. Specifically, we added RNA velocity analysis to independently support the identification of the MJH and PSH trajectories, performed immunofluorescence staining of mesodermal and cardiac markers in Hand1 and Foxf1 knockout models, and included Vim staining-based quantification in Hand1 CKO embryos to assess developmental outcomes in vivo. Furthermore, we revised potentially overinterpreted conclusions, clarified methodological details of WOT analysis. These revisions have strengthened both the rigor and clarity of the manuscript.

Q1R2: Definition of MJH and PSH trajectory:

The study uses previously published data sets to identify two main new differentiation trajectories: the MJH and the PSH trajectory (Figure 1). A large majority of subsequent conclusions are based on in-depth analysis of these two trajectories. For this reason, the method used to identify these trajectories (WTO, which seems a highly biased analysis with many manually chosen set points) should be supported by other commonly used methods such as for example RNA velocity analysis. This would inspire some additional confidence that the MJH and PSH trajectories were chosen as unbiased and rigorous as possible and that any follow-up analysis is biologically relevant.

R1R2: We appreciate your valuable comments. It is totally agreed that other commonly used methods help strengthen our conclusion about the two main trajectories. To this end, we performed RNA velocity analysis for the cardiac specification. Results support the contribution to CM along the MJH and PSH routes.

Author response image 2.

UMAP layout is colored by cell types. Developmental directions, shown as arrows, are inferred by RNA-velocity analysis.

Actually, several recent studies indicated a convergence cardiac developing model where progenitors reach a myocardial state along two trajectories (DOI: 10.1016/j.devcel.2023.01.010). However, when and how specification between the two routes were unclear. Our data and analysis revealed a clear fate separation by E7.0 from transcriptomic and epigenetic perspectives, where unbiased RNA velocity analysis was performed (Figure 2C).

We would like to clarify how we performed WOT (DOI: 10.1016/j.cell.2019.01.006) analysis: the only manually chosen cell set was the starting set, which was all cardiomyocyte cells by E8.5, of computational reverse lineage tracing. The ancestor cells were predicted in an unbiased manner among all mesodermal cells.

Q2R2.1: Identification of MJH and PSH trajectory progenitors:

The study defines various mesoderm populations from the published data set (Figure 1A-E), including nascent mesoderm, mixed mesoderm, and extraembryonic mesoderm. It further assigns these mesoderm populations to the newly identified MJH/PSH trajectories. Based on the trajectory definition in Figure 1A it appears that both trajectories include all 3 mesoderm populations, albeit at different proportions and it seems thus challenging to assign these as unique progenitor populations for a distinct trajectory, as is done in the epigenetic study by comparing clusters 8 (MJH) and 2 (PSH)(Figure 2). 

R2R2.1: According to our model, the most significant difference between the two trajectories is their enrichment of EEM and PM cell types (Figure 1B), which represent the middle stages of cardiac development. Both trajectories begin as Mesp1+ Nascent mesoderm cells (Figure 1F), which is supported by Mesp1 lineage tracing (DOI: 10.1161/CIRCRESAHA.121.318943), and ends as cardiomyocytes. Our epigenetic analysis focused on the E7.0 stage when the two trajectories could be clearly separated and when JCF and SHF lineages were at mixed mesoderm and nascent mesoderm states, respectively. However, SHF lineage was predicted to bypass mixed mesoderm state later on.

Q2R2.2: Along similar lines, the epigenetic analysis of clusters 2 and 8 did not reveal any distinct differences in H3K4m1, H3K27ac, or H3K4me3 at any of the time points analyzed (Figure 2F). While conceptually very interesting, the data presented do not seem to identify any distinct temporal patterns or differences in clones 2 and 8 (Figure 2H), and thus don't support the conclusion as stated: "the combined transcriptome and chromatin accessibility analysis further supported the early lineage segregation of MJH and the epigenetic priming at gastrulation stage for early cardiac genes".

R2R2.2: In the epigenetic analysis, we delineated the temporal dynamics of E7.0 cluster-specific DAEs by selecting earlier (E6.5) and later (E7.5) time points. DAEs of C8 and C2 represent regulatory elements for the JCF and SHF lineages, respectively. We also included C1 DAEs as a reference to demonstrate the relative activity of C8 and C2. The overall temporal pattern suggests activation of C8 & C2, as their H3K4me1 and H3K27ac levels surpass C1 over time. Between C8 and C2, the following distinctions could be observed:

a) H3K4me1 levels of C8 are higher by E6.5 and E7.0, with low H3K27ac levels, indicating early priming of C8 DAEs.

b) By E7.5, H3K4me1 levels of C8 are caught up by C2 in E7.5 anterior mesoderm (E7.5_AM, Figure 2F column 3), where cardiac mesoderm is located.

c) H3K4me1 and H3K27ac levels of C8 are similar as C1 in the posterior mesoderm (E7.5_P, Figure 2F column 4) and much higher than C2.

d) From the perspective of chromatin accessibility, hundreds of characteristic DAEs were identified for C2 and C8 (Figure 2D), exemplified by the primed and active enhancers which were predicted to interact with cluster-specific genes (Figure 2H).

Together with the transcriptomic analyses (Figure 2C), these data are consistent with our conclusion about early lineage segregation and epigenetic priming.

Q3R2: Function of Hand1 and Foxf1 during early cardiac differentiation:

The study incorporated some functional studies by generating Hand1 and Foxf1 KO mESCs and differentiated them into mesoderm cells for RNA sequencing. These lines would present relevant tools to assess the role of Hand1 and Foxf1 in mesoderm formation, and a number of experiments would further support the conclusions, which are made for the most part on transcriptional analysis. For example, the study would benefit from quantification of mesoderm cells and subsequent cardiomyocytes during differentiation (via IF, or more quantitatively, via flow cytometry analysis). These data would help interpret any of the findings in the bulk RNAseq data, and help to assess the function of Hand1 and Foxf1 in generating the cardiac lineages. Conclusions such as "the analysis indicated that HAND1 and FOXF1 could dually regulate MJH specification through directly activating the MJH specific genes and inhibiting PSH specific genes" seem rather strong given the data currently provided.

R3R2: Thank you for your kind suggestions. We added IF staining of mesodermal (Zic3), JCF (Hand1) and cardiac markers (Tnnt2), followed by cell quantification. Results indicate that Hand1 and Foxf1 knockout leads to reduced commitment to the JCF lineage, evidenced by the loss of Hand1 expression, accumulation of undifferentiated Zic3+ mesoderm, and impaired cardiomyocyte formation (Tnnt2+), consistent with the up-regulation of JCF lineage specific genes and the downregulation of SHF lineage specific genes.

We also revised the conclusion as “These results suggest that HAND1 and FOXF1 may cooperatively regulate early cardiac lineage specification by promoting JCF-associated gene expression and suppressing alternative mesodermal programs.”.

(4) Analysis of Hand1 cKO embryos:

Adding a mouse model to support the computational analysis is a strong way to conclude the study. Given the availability of these early embryos, some of the findings could be strengthened by performing a similar analysis to Figure 7B&C and by including some of the specific EEM markers found to be differentially regulated to complement the structural analysis of the embryos.

R4R2: hank you for your positive comments and help. In the revised manuscript, we performed IF staining of EEM marker Vim in a similar fashion as Figure 7B&C (Figure S8D). In comparison with control embryos, the Hand1 CKO embryos demonstrated significant less number of Vim+ cells, further strengthening the conclusion that Hand1 CKO blocked the developmental progression toward JCF direction.

Q5R2: Current findings in the context of previous findings:

The introduction carefully introduces the concept of lineage specification and different progenitor pools. Given the enormous amount of knowledge already available on Hand1 and Foxf1, and their role in specific lineages of the early heart, some of this information should be added, ideally to the discussion where it can be put into context of what the present findings add to the existing understanding of these transcription factors and their role in early cardiac specification.

R5R2: We appreciate your positive comments and kind reminder. We have added discussion about how our study could be put into the body of findings on Hand1 and Foxf1. Although these two genes have been validated to be functionally important for heart development, it is unclear when and how they affect this process. Using in-vivo and in-vitro models and single cell multi-omics analyses, we provided evidence to fill the gaps from multiple aspects, including cell state temporal dynamics, regulatory network, and epigenetic regulation underlying the very early cardiac lineage specification.

Reviewer #3 (Public review):

Q1R3: In Figure 1A, could the authors justify using E8.5 CMs as the endpoint for the second lineage and better clarify the chamber identities of the E8.5 CMs analysed? Why are the atrial genes in Figure 1C of the PSH trajectory not present in Table S1.1, which lists pseudotime-dependent genes for the MJH/PSH trajectories from Figure 1F?

R1R3: Thank you for your comments. We used E8.5 CMs as the endpoint of the second (SHF) lineage because this stage represents a critical point where SHF-derived cardiomyocytes have begun distinct differentiation, allowing us to capture terminal lineage states reliably. The chamber identities of E8.5 CMs were determined based on known marker genes (DOI: 10.1186/s13059-025-03633-3). The atrial genes shown in Figure 1C reflect cluster-specific markers that may not meet the strict pseudotime-dependency criteria used to generate Table S1.1, which lists genes dynamically changing along the MJH/PSH trajectories.

Q2R3: Could the authors increase the resolution of their trajectory and genomic analyses to distinguish between the FHF (Tbx5+ HCN4+) and the JCF (Mab21l2+/ Hand1+) within the MJH lineage? Also, clarify if the early extraembryonic mesoderm contributes to the FHF.

R2R3: Thank you for your great suggestions. To distinguish between the FHF and JCF trajectories, we used early FHF progenitor population (E7.75 Nkx2-5+; Mab21l2- CM cells) as the starting point and performed WOT lineage inference (Figure S2A). Results suggest that both JCF and SHF progenitors contribute to the FHF, consistent with live imaging-based single cell tracing by Dominguez et al (DOI: 10.1016/j.cell.2023.01.001) and lineage tracing results by Zhang et al (DOI: 10.1161/CIRCRESAHA.121.318943). We also analyzed the expression levels of FHF marker genes (Tbx5, Hcn4) and observed their activation along both trajectories (Figure S2B).

Q3R3: The authors strongly assume that the juxta-cardiac field (JCF), defined by Mab21l2 expression at E7.5 in the extraembryonic mesoderm, contributes to CMs. Could the authors explain the evidence for this? Could the authors identify Mab21l2 expression in the left ventricle (LV) myocardium and septum transversum at E8.5 (see Saito et al., 2013, Biol Open, 2(8): 779-788)? If such a JCF contribution to CMs exists, the extent to which it influences heart development should be clarified or discussed.

R3R3: Thank you for the important question. For the JCF contribution to the heart tube, several lines of evidence have been published in recent years using micro-dissection of mouse embryonic heart (DOI: 10.1126/science.abb2986), live imaging (DOI: 10.1016/j.cell.2023.01.001) and lineage tracing approaches (DOI: 10.1161/CIRCRESAHA.121.318943). According to Tyser et al (DOI: 10.1126/science.abb2986), Mab21l2 expression is detected in septum transversum at E8.5 and the Mab21l2+ lineage contribute to LV, basically consistent with the literature you mentioned (Saito et al., 2013, Biol Open, 2(8): 779-788). Our lineage inference analyses further support the model and suggest earlier specification by JCF. However, the focus of our work is the transcriptional and epigenetic regulation of underlying the JCF developmental trajectory.

Q4R3: Could the authors distinguish the Hand1+ pericardium from JCF progenitors in their single-cell data and explain why they excluded other cell types, such as the endocardium/endothelium and pericardium, or even the endoderm, as endpoints of their trajectory analysis? At the NM and MM mesoderm stages, how did the authors distinguish the earliest cardiac cells from the surrounding developing mesoderm?

R4R3: We appreciate your insightful question. In our other study (DOI: 10.1186/s13059-025-03633-3), we tried to further divide the CM cells as subclusters and it seems that their difference is mainly driven by the segmentation of the heart tube (e.g. LV, RV, OFT etc.). By the E8.5 stage, we are unable to identify the Hand1+ pericardium cluster. 

Also, it seems infeasible to distinguish endocardium from other endothelium cells only using singlecell data. High resolution spatial transcriptome data is required. Alternatively, we analyzed the E7.0 mesodermal lineages and determined C5/6 as hematoendothelial progenitors. Marker gene analysis indicate that their lineage segregation has started by this stage (Figure S4C and Author response image 3).

Author response image 3.

UMAP layout, using scRNA-seq (Reference data) and snRNA-seq (Multiome data), is colored by cell types (left). Expression of hematoendothelial progenitor marker genes is shown (right).

We did observe the difference between the earliest cardiac cells from the surrounding developing mesoderm. As in Figure 1D, cells belonging to the JCF lineage (Hand1 high/Lefty2 low) were clustered at the EEM/MM end, in contrast to the NM cells.

Q5R3: Could the authors contrast their trajectory analysis with those of Lescroart et al. (2018), Zhang et al., Tyser et al., and Krup et al.?

R5R3: Thank you for the valuable suggestion. We compared our model with the suggested ones and summarized as follows:

(1) Lescroart et al: The JCF and SHF progenitor cells match their DCT2 (Bmp4+) and DCT3 (Foxc2+) clusters, respectively.

(2) Zhang et al: The JCF lineage matches their EEM-DC (developing CM)-CM trajectory. The SHF lineage is consistent with their NM-LPM (lateral plate mesoderm)-DC (developing CM)-CM trajectory. Notably, their EEM-DC-CM also expressed FHF marker (Tbx5) at later stages.

(3) Tyser et al: we performed data integration analysis and found the correspondence between JCF progenitors (EEM cells from the cardiac trajectory) and their Me5, as well as SHF progenitors (PM cells from the cardiac trajectory) with Me7. In their model, both Me5 and Me7 contribute to Me4 (representing the FHF), consistent with our results (see Tyser et al., 2021 and Pijuan-Sala et al., 2019).

(4) Krup et al also performed URD lineage inference, providing a model with CM (12) and Cardiac mesoderm (29) as cardiac end points. Their model did not seem to suggest distinct trajectories between JCF and SHF lineages, as both JCF (Hand1) and SHF (Isl1) markers co-expressed in CM.

Q6R3: Previous studies suggest that Mesp2 expression starts at E8 in the presomitic mesoderm (Saga et al., 1997). Could the authors provide in situ hybridization or HCR staining to confirm the early E7 Mesp2 expression suggested by the pseudo-time analysis of the second lineage.

R6R3: We validated the expression of E7 Mesp2 using Geo-seq spatial transcriptome data (Author response image 4, upper). Results suggest the high spatial enrichment of Mesp2 expression in primitive streak (T+) and/or nascent mesoderm (Mesp1+) cells, which correspond to the progenitors of the second lineage.

In situ hybridization data (PMID: 17360776) also supports the early expression of Mesp2 by E7 (Author response image 4, lower).

Author response image 4.

(Upper) E7 Geo-seq data for selected genes: T, Mesp1, and Mesp2. (Lower) Mesp2 expression during early development; image acquired from Morimoto et al. (PMID: 17360776).

Q7R3: Could the authors also confirm the complementary Hand1 and Lefty2 expression patterns at E7 using HCR or in situ hybridization? Hand1 expression in the first lineage is plausible, considering lineage tracing results from Zhang et al.

R7R3: Thank you for your great suggestion. We observed spatially complementary expression patterns of Hand1 and Lefty2 in the Geo-seq spatial transcriptomic data. In the mesoderm layer, Hand1 is highly expressed in the proximal end. While Lefty2+ cells exhibit preference toward the distal direction.

Author response image 5.

E7 Geo-seq data for selected genes: Hand1 and Lefty2.

Q8R3: Could the authors explain why Hand1 and Lefty2+ cells are more likely to be multipotent progenitors, as mentioned in the text?

R8R3: Thank you for your question. Here, we observed E7.0 Mesp1+ and Lefty2+ nascent mesodermal cells assigned to both the JCF and SHF lineages (Figure 1D), indicating their multipotency. On the other hand, we also found low expressions of JCF markers, Hand1 and Msx2, by the early stage of the SHF trajectory (Figure 1F). Thus, we concluded that both Hand1+ and Lefty2+ E7.0 mesodermal cells are likely to be multipotent.

Q9R3: Could the authors comment on the low Mesp1 expression in the mesodermal cells (MM) of the MJH trajectory at E7 (Figure 1D)? Is Mesp1 transiently expressed early in MJH progenitors and then turned off by E7? Have all FHF/JCF/SHF cells expressed Mesp1?

R9R3: Thank you for the insightful questions. Zhang et al. (PMID: 34162224) performed scRNA-seq analysis of Mesp1 lineage-traced cells, which indicate the contribution of Mesp1+ cells to FHF, JCF, and SHF. This is also supported by Dominguez et al. utilizing live imaging approaches (PMID: 36736300). Our temporal dynamics analysis suggests that along the JCF trajectory, Mesp1 is turned off as JCF characteristic genes were up regulated (Figure 1F and S1D).

Q10R3: Could the authors clarify if their analysis at E7 comprises a mixture of embryonic stages or a precisely defined embryonic stage for both the trajectory and epigenetic analyses? How do the authors know that cells of the second lineage are readily present in the E7 mesoderm they analysed (clusters 0, 1, and 2 for the multiomic analysis)?

R10R3: Thank you for your questions. Although embryos were collected at E7.0, the developmental stages could be variable. As exemplified by Karl Theiler’s book, “The House Mouse: Atlas of Embryonic Development”, mesoderm was visible for some E7.0 egg cylinders but not in others. To test whether cells of the second lineage are present in the E7.0 mesoderm, we analyzed the WOT lineage tracing results and the cell type composition by E7.0 (Author response image 6, left panel). Most cells belong to the nascent mesoderm (NM) or mixed mesoderm (MM), while almost no cells were assigned to the primitive streak (PS). To avoid the possibility that the E7.0 embryos represented later stages, we also analyzed the E6.75 cells of the second lineage (Author response image 6, middle panel). Results suggest that NM cells were still the dominant contributors to the second lineage, although ~22.6% cells were assigned to the PS. The abovementioned analyses were performed using the scRNA-seq data. The embryos of the E7.0 single-cell multi-omics represent similar developmental stages as the scRNAseq data, as suggested by the well-aligned UMAPs (Figure S1D, right panel). Thus, we conclude that for the multi-omics data, the cells of the second lineage are also readily present in the mesoderm.

Author response image 6.

(Left and middle) Lineage inference and cell type composition at E7.0 and E6.75. (Right) UMAPs of E7.0 multi-omics and scRNA-seq data.

Q11R3: Could the authors further comment on the active Notch signaling observed in the first and second lineages, considering that Notch's role in the early steps of endocardial lineage commitment, but not of CMs, during gastrulation has been previously described by Lescroart et al. (2018)?

R11R3: We appreciate your kind suggestion. As reported by Lescroart et al. (2018), using Notch1CreERT2/Rosa-tdTomato mice and tamoxifen administration at E6.5, early expression of Notch1 mostly marked endocardial cells (ECs, 76.9-83.9%), with minor contribution to the cardiomyocytes (6.0-16.6%) and to the epicardial cells (EPs, 6.0-6.5%). The lineage specificity of Notch1 is consistent with our E7.0 multi-omics data, where its expression was mainly observed in the NM and hematoendothelial progenitors (Author response image 7). Interestingly, expression of other NOTCH receptor genes (Notch2 and Notch3) and ligand genes (Dll1 and Dll3) in the CM lineages. Notch3 demonstrate higher expression in the first lineage, while Dll1 and Dll3 were highly expressed in the second lineage. The study by Lescroart et al. (2018) emphasized the role of Notch1 as an EC lineage marker, while our analyses aimed at the activity of the NOTCH pathway.

Author response image 7.

Expression of representative NOTCH genes at E7.0 (multi-omics data).

Q12R3: In cluster 8, Figure 2D, it seems that levels of accessibility in cluster 8 are relatively high for genes associated with endothelium/endocardium development in addition to MJH genes. Could the authors comment and/or provide further analysis?

R12R3: Thanks for you for raising this interesting point. To confirm the association of these genes with endothelium (EC) and/or MJH, we analyzed their expression levels by E7.0 (progenitor stage) and E8.0 (differentiated stage) (Author response image 8). Among target genes of MJH-specific DAEs (cluster 3/7/8 in Figure 2D), Pmp22, Mest, Npr1, Pkp2, and Pdgfb were expressed in the hematoendothelial progenitors. The Nrp1 gene and PDGF pathway play critical roles in endothelial development by modulating cell migration (PMID: 15920019 and 28167492), which is also important for MJH cells. In addition, we observed common ATAC-seq peaks in both hematoendothelial and MJH clusters (Author response image 9), indicating shared regulatory elements. Interestingly, Pdgfb is not expressed by CM in vivo, it is actively expressed in the CM of the in vitro system (Author response image 9). These results indicate regulatory and functional closeness between hematoendothelial and MJH cell groups, at early stages of lineage establishment.

Author response image 8.

Regulatory connection between MJH and endothelial cells (ECs).

Author response image 9.

Representative genome browser snapshots of scATAC-seq (aggregated gene expression and chromatin accessibility for each cluster) and RNA-seq at the Pdgfb locus.

Q13R3: Can the authors clarify why they state that cluster 8 DAEs are primed before the full activation of their target genes, considering that Bmp4 and Hand1 peak activities seem to coincide with their gene expression in Figure 2G?

R13R3: Thanks for your great question. The overall analyses indicate low to medium levels of H3K4me1 and H3K27ac by E6.5-7.0 at cluster 8 DAEs, which were fully activated by E7.5 (Figure 2F). Further inspections suggest different epigenetic status of individual DAEs (Figure 3H), which could be active (K4me1+/K27ac+), primed (K4me1+/K27ac-), or inactive (K4me1-/K27ac-). Thus, we concluded that many DAEs could be primed before full activation. The coincidence of enhancer peak activities and gene expression was observed by aggregating single cell clusters at a single stage E7.0, which does not rule out the possibility that these enhancers are epigenetically primed at earlier stages.

Q14R3: Did the authors extend the multiomic analysis to Nanog+ epiblast cells at E7 and investigate if cardiac/mesodermal priming exists before mesodermal induction (defined by T/Mesp1 onset of expression)?

R14R3: We appreciate your kind suggestion. We observed low levels of T/Mesp1 expression in the E7.0 Nanog+ epiblast cells (Author response image 10). Interestingly, the T+/Mesp1+ cells were not clustered toward any specific differentiation directions in the UMAP. We also analyzed DAE activities in each single cell by averaging over the C1/C2/C8 DAE sets. The C2 and C8 DAEs were clearly less active than the C1 DAEs. But C2/C8-DAE active cells were observed among the E7.0 Nanog+ epiblast cells. These data indicate the early priming exists in epiblast cells before the commitment to cardiac/mesodermal differentiation.

Author response image 10.

Gene expression and DAE activity levels of E7.0 Nanog+ epiblast cells shown in UMAP layout.

Q15R3: In the absence of duplicates, it is impossible to statistically compare the proportions of mesodermal cell populations in Hand1 wild-type and knockout (KO) embryos or to assess for abnormal accumulation of PS, NM, and MM cells. Could the authors analyse the proportions of cells by careful imaging of Hand1 wild-type and KO embryos instead?

R15R3: Thank you for your important question. To assess the proportions of mesodermal cell populations in E7.25 wild-type and Hand1-CKO embryos, we analyzed the serial coronal sections of the extraembryonic portions and performed staining of the Vim gene, which marks the extra-embryonic mesodermal (EEM) cells (Figure S8D). We then counted the numbers of mesodermal/Vim+ EEM cells and calculated the relative proportion of Vim+ EEM cells in each section. The proportion of Vim+ EEM cells was statistically lower in the Hand1-CKO embryo, consistent with our model that Hand1 deletion led to blocked MJH specification.

Q16R3: Could the authors provide high-resolution images for Figure 7 B-C-D as they are currently hard to interpret?

R16R3: Thank you for your suggestion. We have replaced Figure 7B-C-D with high-resolution images.

Recommendations for the authors:  

Reviewing Editor Comments:

Discussions among reviewers emphasize the importance of better addressing and validating the trajectory analysis by using more common and alternative bioinformatics and spatial approaches. Further discussion on whether there is a common transcriptional progenitor between the two trajectories is also required to enhance the significance of the study. For functional analysis, further validations are needed as the current data only partially support the claims. Please see public reviews for details.

Reviewer #2 (Recommendations For The Authors):

Beyond the suggestions made in the public review, below are some minor aspects for consideration:

The manuscript is well written overall but may benefit from a thorough read-through and editing of some minor grammatical errors.

We have carefully read through the manuscript and corrected minor grammatical errors to improve clarity and readability.

Figure 2C: RNA velocity information gets largely lost due to the color choice of EEM and MM (black) on which the direction of arrows can't be appreciated.

We have updated the color scheme in Figure 2C.

Figure 6D: sample information is partially cut off in the graph.

Sample information is completely shown now.

The last paragraph of the discussion has some formatting issues with the references.

We have corrected the formatting issues with the references.

The methods and results section does not comment on if, or how many embryos were pooled for the sequencing analysis performed for this study.

We have added the numbers of embryos for sequencing analyses in the methods section.

Reviewer #3 (Recommendations For The Authors):

Minor:

In the discussion, authors could reconsider the sentence: "The process of cardiac lineage segregation is a complex one that may involve TF regulatory networks and signaling pathways," as it is not informative.

We have re-written the sentence as: “Thus, additional regulation must exist and instructs the process of JCF-SHF lineage segregation.”

Author response:

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

Reviewer #1 (Public review):

This manuscript reports a descriptive study of changes in gene expression after knockdown of the nuclear envelope proteins lamin A/C and Nesprin2/SYNE2 in human U2OS cells. The readout is RNA-seq, which is analyzed at the level of gene ontology and focused investigation of isoform variants and non-coding RNAs. In addition, the mobility of telomeres is studied after these knockdowns, although the rationale in relation to the RNA-seq analyses is rather unclear.

We sincerely thank the reviewer for the thoughtful summary and valuable feedback. Regarding the telomere mobility analyses, our intention was to provide additional evidence supporting the hypothesis that knockdown of lamins and nesprins disrupts nuclear architecture. Although the connection to the RNA-seq data was not explicitly detailed, we believe that the increased telomere mobility may reflect broader changes in chromatin organization, which could contribute to the observed differential gene expression. We have revised the manuscript to clarify this rationale and improve the integration between the two analyses.

RNA-seq after knockdown of lamin proteins has been reported many times, and the current study does not provide significant new insights that help us to understand how lamins control gene expression. This is particularly because the vast majority of the observed effects on gene expression appear to occur in regions that are not bound by lamin A. It seems likely that these effects are indirect. There is also virtually no overlap between genes affected by laminA/C and by SYNE2, which remains unexplained; for example, it would be good to know whether laminA/C and SYNE2 bind to different genomic regions. The claim in the Title and Abstract that LMNA governs gene expression / acts through chromatin organization appears to be based only on an enrichment of gene ontology terms "DNA conformation change" and "covalent chromatin conformation" in the RNA-seq data. This is a gross over-interpretation, as no experimental data on chromatin conformation are shown in this study. The analyses of transcript isoform switching and ncRNA expression are potentially interesting but lack a mechanistic rationale: why and how would these nuclear envelope proteins regulate these aspects of RNA expression? The effects of lamin A on telomere movements have been reported before; the effects of SYNE2 on telomere mobility are novel (to my knowledge), but should be discussed in the light of previously documented effects of SUN1/2 on the dynamics of dysfunctional telomeres (Lottersberger et al, Cell 2015).

We sincerely thank the reviewer for this thoughtful and detailed critique. We agree that RNA-seq following knockdown of lamin proteins has been previously reported and appreciate the concern regarding the novelty and mechanistic interpretation of our findings. However, For our study, we revealed novel findings that there is distinct isoform switching and lncRNA affected by lamins and nesprins, which have not been reported yet by previous studies. Furthermore, we also revealed not only lamin A, but also nesprin-2 could also affect chromatin mobility.

For the analysis of LMNA ChIP-seq data from  human fibroblast (Kohta Ikegami, 2021). Their data revealed that Lamin A/C modulates gene expression through interactions with enhancers. The pathogenesis of disorders associated with LMNA mutations may stem primarily from disruptions in this gene regulatory function, rather than from impaired tethering of chromatin to LADs.

We acknowledge the reviewer’s concern that gene ontology enrichment related to chromatin conformation alone is insufficient to support claims about chromatin structural changes. We have therefore revised the “Title” and “Abstract” to avoid overstating conclusions and to more accurately reflect the scope of our data.

Regarding telomere dynamics, while Lamin A's role has indeed been previously documented, our study provides evidence that SYNE2/Nesprin-2 also regulates telomere mobility. We have now expanded the discussion to include prior work, particularly the findings of Lottersberger et al. (Cell, 2015), to better contextualize our results and distinguish the contributions of SYNE2.

Finally, we appreciate the reviewer’s suggestion about transcript isoform and noncoding RNA expression. While our study primarily provides descriptive data, we agree that further mechanistic investigation is warranted. We have clarified this point in the “Discussion” and framed our findings as a foundation for future studies exploring the broader regulatory roles of nuclear envelope proteins.

We are grateful for the reviewer’s comments, which have helped us improve the clarity and rigor of our manuscript. Please see the revised highlights in our revised manuscript.

As indicated below, I have substantial concerns about the experimental design of the knockdown experiments.

Altogether, the results presented here are primarily descriptive and do not offer a significant advance in our understanding of the roles of LaminA and SYNE2 in gene regulation or chromatin biology, because the results remain unexplained mechanistically and functionally. Furthermore, the RNAseq datasets should be interpreted with caution until off-target effects of the shRNAs can be ruled out.

We fully acknowledge that the original version of our manuscript lacked sufficient mechanistic insight. In response, we have revised the manuscript to include additional analyses and explanations that clarify the potential functional relevance of our findings. For example, we added following text “These findings further underscore the functional relevance of lamin A in coordinating transcriptional programs through modulation of nuclear architecture. In contrast, LMNA knockdown led to differential expression of genes enriched in pathways related to chromatin organization, suggesting potential disruptions in chromatin regulatory networks. Although direct measurements of chromatin conformation were not performed, these transcriptional changes indicate that LMNA may contribute to maintaining nuclear architecture and genomic stability, which aligns with its established involvement in laminopathies and genome integrity disorders.“ More analyses could be found in the main text.

Regarding the concern about off-target effects of the shRNA-based knockdowns, we agree that this is an important consideration. While shRNA approaches inherently carry the risk of off-target effects, we have now performed additional analyses that help address this issue. These analyses support the specificity of our observations and suggest that the majority of gene expression changes are likely to be directly related to the targeted knockdown. Nonetheless, we have clearly stated the limitations of the approach in the revised discussion and emphasized the need for future validation using complementary methods.

We hope that these revisions strengthen the overall impact and interpretability of our study.

Specific comments:

(1) Knockdowns were only monitored by qPCR. Efficiency at the protein level (e.g., Western blots) needs to be determined.

We agree that complementary protein-level validation (e.g., by Western blot) would strengthen the findings, and we are in the process of obtaining suitable reagents to address this point in future experiments. We have now clarified this limitation in the revised manuscript  

(2) For each knockdown, only a single shRNA was used. shRNAs are infamous for offtarget effects; therefore, multiple shRNAs for each protein, or an alternative method such as CRISPR deletion or degron technology, must be tested to rule out such offtarget effects.

We fully acknowledge the concern regarding the use of only a single shRNA per knockdown and agree that shRNAs are prone to off-target effects. We recognize the importance of validating our findings using multiple independent shRNAs or alternative knockdown strategies, such as CRISPR deletion or degron-based approaches, to ensure specificity. To address this concern, we have conducted qPCR confirmation the knockdown of target proteins from RNA-seq findings, further supporting the validity of our data. In line with this, we are currently optimizing an auxin-inducible degron system (AtAFB2) for targeted and controlled depletion of lamin C. Our preliminary results indicate approximately a 40% knockdown efficiency after 16 hours of auxin induction, highlighting the necessity for further system optimization (Author response image 1). Future experiments will integrate this improved degron technology alongside multiple independent approaches to rigorously address and mitigate concerns about off-target effects, thereby enhancing the robustness and reproducibility of our data.

Author response image 1.

FACS analysis of the lamin C degron system at 0, 1, 3, and 16 hours postinduction with 500 μM indole-3-acetic acid (IAA) (Sigma).

(3) It is not clear whether the replicate experiments are true biological replicates (i.e., done on different days) or simply parallel dishes of cells done in a single experiment (= technical replicates). The extremely small standard deviations in the RT-qPCR data suggest the latter, which would not be adequate.

We appreciate the reviewer’s insightful comment regarding the nature of our replicates. The RT-qPCR experiments were indeed performed as true biological replicates, with samples collected on different days and from independently cultured cell batches. We have added this to the manuscript Methods. While we observed some variability in the Scramble control group, the low standard deviations in the shRNAtreated samples likely reflect the consistent and efficient knockdown of target genes.

For the RNA-seq experiments, samples were collected as two batches during RNA extraction and library preparation. The samples still represent biological replicates, as they were derived from independently prepared cultures in separate experimental setups. This approach was chosen to strike a balance between biological variation and technical consistency, thereby improving the reliability of the RNA-seq results.

Reviewer #2 (Public review):

Summary:

This study focused on the roles of the nuclear envelope proteins lamin A and C, as well as nesprin-2, encoded by the LMNA and SYNE2 genes, respectively, on gene expression and chromatin mobility. It is motivated by the established role of lamins in tethering heterochromatin to the nuclear periphery in lamina-associated domains (LADs) and modulating chromatin organization. The authors show that depletion of lamin A, lamin A and C, or nesprin-2 results in differential effects of mRNA and lncRNA expression, primarily affecting genes outside established LADs. In addition, the authors used fluorescent dCas9 labeling of telomeric genomic regions combined with live-cell imaging to demonstrate that depletion of either lamin A, lamin A/C, or nesprin-2 increased the mobility of chromatin, suggesting an important role of lamins and nesprin2 in chromatin dynamics.

We sincerely appreciate the reviewer’s thoughtful summary of our study and the key findings. Our work is indeed motivated by the well-established roles of lamin A/C in chromatin tethering at the nuclear periphery and the emerging understanding of their broader influence on chromatin organization and gene regulation. In our study, we aimed to further explore these roles by examining the consequences of depleting lamin A, lamin A/C, and nesprin-2 (SYNE2) on both gene expression and chromatin mobility.

As the reviewer accurately notes, we observed differential effects on mRNA and lncRNA expression, with many changes occurring outside of previously defined LADs. This finding suggests that lamins and nesprin-2 may also influence transcriptional regulation through mechanisms beyond direct LAD association. Furthermore, using live-cell imaging of fluorescently labeled telomeric regions, we demonstrated that loss of these nuclear envelope components leads to increased chromatin mobility, supporting their role in maintaining chromatin stability and nuclear architecture.

We thank the reviewer for highlighting these aspects, which we believe contribute to a more nuanced understanding of how nuclear envelope proteins modulate chromatin behavior and gene regulation.

Strengths:

The major strength of this study is the detailed characterization of changes in transcript levels and isoforms resulting from depletion of either lamin A, lamin A/C, or nesprin-2 in human osteosarcoma (U2OS) cells. The authors use a variety of advanced tools to demonstrate the effect of protein depletion on specific gene isoforms and to compare the effects on mRNA and lncRNA levels.

The TIRF imaging of dCas9-labeled telomeres allows for high-resolution tracking of multiple telomeres per cell, thus enabling the authors to obtain detailed measurements of the mobility of telomeres within living cells and the effect of lamin A/C or nesprin-2 depletion.

We are grateful that the reviewer recognized the comprehensive analysis of transcript and isoform changes upon depletion of lamin A, lamin A/C, or nesprin-2 in U2OS cells. We also thank the reviewer for acknowledging our use of advanced tools to investigate isoform-specific effects and to distinguish between changes in mRNA and lncRNA expression.

Furthermore, we are pleased that the reviewer highlighted the strength of our TIRF imaging approach using dCas9-labeled telomeres. This technique enabled us to capture high-resolution, multi-locus dynamics within single living cells, and we agree that it is instrumental in revealing the impact of lamin A/C and nesprin-2 depletion on telomere mobility.

Weaknesses:

Although the findings presented by the authors overall confirm existing knowledge about the ability of lamins A/C and nesprin to broadly affect gene expression, chromatin organization, and chromatin dynamics, the specific interpretation and the conclusions drawn from the data presented in this manuscript are limited by several technical and conceptual challenges.

One major limitation is that the authors only assess the knockdown of their target genes on the mRNA level, where they observe reductions of around 70%. Given that lamins A and C have long half-lives, the effect at the protein level might be even lower. This incomplete and poorly characterized depletion on the protein level makes interpretation of the results difficult. The description for the shRNA targeting the LMNA gene encoding lamins A and C given by the authors is at times difficult to follow and might confuse some readers, as the authors do not clearly indicate which regions of the gene are targeted by the shRNA, and they do not make it obvious that lamin A and C result from alternative splicing of the same LMNA gene. Based on the shRNA sequences provided in the manuscript, one can conclude that the shLaminA shRNA targets the 3' UTR region of the LMNA gene specific to prelamin A (which undergoes posttranslational processing in the cell to yield lamin A). In contrast, the shRNA described by the authors as 'shLMNA' targets a region within the coding sequence of the LMNA gene that is common to both lamin A and C, i.e., the region corresponding to amino acids 122-129 (KKEGDLIA) of lamin A and C. The authors confirm the isoform-specific effect of the shLaminA isoform, although they seem somewhat surprised by it, but do not confirm the effect of the shLMNA construct. Assessing the effect of the knockdown on the protein level would provide more detailed information both on the extent of the actual protein depletion and the effect on specific lamin isoforms. Similarly, given that nesprin-2 has numerous isoforms resulting from alternative splicing and transcription initiation. In the current form of the manuscript, it remains unclear which specific nesprin-2 isoforms were depleted, and to what extent (on the protein level).

We have revised the Methods section to include a clearer and more detailed description of the shRNA design, including the specific regions of the LMNA gene targeted by each construct, as well as the relationship between lamin A and C isoforms resulting from alternative splicing. We agree that this clarification will help prevent confusion for readers.

Regarding the shLMNA construct, we acknowledge the importance of confirming the knockdown at the protein level, especially given the long half-lives of lamin proteins. In our revised manuscript, we now refer to Supplementary Figure S2, which demonstrates that the shLMNA construct effectively reduces both lamin A and lamin C transcript levels. While we initially focused on mRNA quantification, we recognize that additional proteinlevel validation is valuable and have accordingly emphasized this point in the revised discussion.

We also appreciate the comment on nesprin-2 isoforms. Given the complexity of nesprin-2 splicing, we are currently working to further characterize the specific isoforms affected and will aim to include protein-level data in a future study. 

Another substantial limitation of the manuscript is that the current analysis, with the exception of the chromatin mobility measurements, is exclusively based on transcriptomic measurements by RNA-seq and qRT-PCR, without any experimental validation of the predicted protein levels or proposed functional consequences. As such, conclusions about the importance of lamin A/C on RNA synthesis and other functions are derived entirely from gene ontology terms and are not sufficiently supported by experimental data. Thus, the true functional consequences of lamin A/C or nesprin depletion remain unclear. Statements included in the manuscript such as "our findings reveal that lamin A is essential for RNA synthesis, ..." (Lines 79-80) are thus either inaccurate or misleading, as the current data do not show that lamin A is ESSENTIAL for RNA synthesis, and lamin A/C and lamin A deficient cells and mice are viable, suggesting that they are capable of RNA synthesis.

We agree that our current data do not support the claim that lamin A is essential for RNA synthesis, and we acknowledge the importance of distinguishing between correlation and causal relations in our conclusions. In light of this, we have revised the statement in the manuscript to more accurately reflect our findings:

“Our findings suggest that lamin A contributes to RNA synthesis, supports chromatin spatial organization through LMNA, and that SYNE2 influences chromatin modifications as reflected in transcript levels.”

We hope this revision better aligns with the limitations of our dataset and addresses the reviewer’s concerns regarding the interpretation of functional consequences based solely on transcriptomic data.

Another substantial weakness is that the data and analysis presented in the manuscript raise some concerns about the robustness of the findings. Given that the 'shLMNA' construct is expected to deplete both lamin A and C, i.e., its effect encompasses the depletion of lamin A, which is achieved by the 'shLaminA' construct, one would expect a substantial overlap between the DEGs in the shLMNA and shLaminA conditions, with the shLMNA depletion producing a broader effect as it targets both lamin A and C. However, the Venn Diagram in Figure 4a, the genomic loci distribution in Figure 4b, and the correlation analysis in Supplementary Figure S2 show little overlap between the shLMNA and shLaminA conditions, which is quite surprising. In the mapping of the DEGs shown in Figure 4b, it is also surprising not to see the gene targeted by the shRNA, LMNA, found on chromosome 1,  in the results for the shLMNA and shLamin A depletion.

We have added the discussion into the revised edition: “Interestingly, although both shLMNA and shLaminA constructs target lamin A, with shLMNA additionally depleting lamin C, the DEGs identified under these two conditions show limited overlap. This unexpected finding suggests that depletion of lamin C in the shLMNA condition may trigger distinct or compensatory transcriptional responses that are not elicited by lamin A knockdown alone. Furthermore, variation in shRNA efficiency or off-target effects may contribute to these differences. Notably, despite directly targeting LMNA, the overlap in DEGs between the two conditions remained limited under our stringent threshold criteria. Together, these observations highlight the complex and non-linear regulatory roles of lamin isoforms in gene expression and underscore the need for further mechanistic studies to dissect their individual and combined contributions [28,29].”

The correlation analysis in Supplementary Figure S2 raises further questions. The authors use doc-inducible shRNA constructs to target lamin A (shLaminA), lamin A/C (shLMNA), or nesprin-2 (shSYNE2). Thus, the no-dox control (Ctr) for each of these constructs would be expected to be very similar to the non-target scrambled controls (Ctrl.shScramble and Dox.shScramble). However, in the correlation matrix, each of the no-dox controls clusters more closely with the corresponding dox-induced shRNA condition than with the Ctrl.shScramble or Dox.shScramble conditions, suggesting either a very leaky dox-inducible system, strong effects from clonal selection, or substantial batch effects in the processing. Either of these scenarios could substantially affect the interpretation of the findings. For example, differences between different clonal cell lines used for the studies, independent of the targeted gene, could explain the limited overlap between the different shRNA constructs and result in apparent differences when comparing these clones to the scrambled controls, which were derived from different clones.

We thank the reviewer for this thoughtful observation. We would like to clarify that the samples shown in Supplementary Figure S2 were processed and sequenced in two separate batches, and the data presented in the correlation matrix are unnormalized. As such, batch effects are indeed present and likely contribute to the clustering pattern observed, particularly the closer similarity between the dox-induced and no-dox samples for each individual shRNA construct.

Importantly, our analyses focus on within-construct comparisons (i.e., doxycyclinetreated vs untreated samples for the same shRNA), rather than direct comparisons across different constructs or scrambled controls. Each experimental pair (dox vs nodox) was processed in parallel within its respective batch to ensure internal consistency. Thus, while the global clustering pattern may reflect batch-related differences or baseline variations between independently derived cell lines, these factors do not affect the main conclusions drawn from the within-construct differential expression analysis.

The manuscript also contains several factually inaccurate or incorrect statements or depictions. For example, the depiction of the nuclear envelope in Figure 1 shows a single bilipid layer, instead of the actual double bi-lipid layer of the inner and outer nuclear membranes that span the nuclear lumen. The depiction further lacks SUN domain proteins, which, together with nesprins, form the LINC complex essential to transmit forces across the nuclear envelope. The statement in line 214 that "Linker of nucleoskeleton and cytoskeleton (LINC) complex component nesprin-2 locates in the nuclear envelope to link the actin cytoskeleton and the nuclear lamina" is not quite accurate, as nesprin-2 also links to microtubules via dynein and kinesin.

We sincerely thank the reviewer for pointing out these important inaccuracies. In response, we have revised Figure 1 to accurately depict the nuclear envelope as a double bi-lipid membrane and included SUN domain proteins to better reflect the structural components of the LINC complex. Additionally, we have updated the statement and citations 

This is the revised part that is incorporated in the manuscript “The linker of nucleoskeleton and cytoskeleton (LINC) complex component nesprin-2 is a nuclear envelope protein that connects the nucleus to the cytoskeleton by interacting not only with actin filaments but also with microtubules through motor proteins such as dynein and kinesin. This structural linkage contributes to cellular architecture and facilitates mechanotransduction between the nuclear interior and the extracellular matrix (ECM) [8,21]

”We appreciate the reviewer’s insights, which have helped improve the accuracy and clarity of our manuscript.

The statement that "Our data show that Lamin A knockdown specifically reduced the usage of its primary isoform, suggesting a potential role in chromatin architecture regulation, while other LMNA isoforms remained unaffected, highlighting a selective effect" (lines 407-409) is confusing, as the 'shLaminA' shRNA specifically targets the 3' UTR of lamin A that is not present in the other isoforms. Thus, the observed effect is entirely consistent with the shRNA-mediated depletion, independent of any effects on chromatin architecture.

We have rephrased the statement “Our data show that knockdown with shLaminA, which specifically targets the 3' UTR unique to the lamin A isoform, selectively reduced lamin A expression without affecting other LMNA isoforms.”

The premise of the authors that lamins would only affect peripheral chromatin and genes at LADs neglects the fact that lamins A and C are also found in the nuclear interior, where they form stable structure and influence chromatin organization, and the fact that lamins A and C and nesprins additionally interact with numerous transcriptional regulators such as Rb, c-Fos, and beta-catenins, which could further modulate gene expression when lamins or nesprins are depleted.

Based on the reviewer’s comment we have added the statement into Discussion part “Beyond their well-established role in tethering heterochromatin at the nuclear periphery through lamina-associated domains (LADs), A-type lamins (lamins A and C) also localize to the nuclear interior, where they contribute to chromatin organization and gene regulation independently of LADs [27,28]. Nuclear lamins can form intranuclear foci that associate with active chromatin and are implicated in supporting transcriptional activity. Additionally, both lamins and nesprins participate in diverse protein-protein interactions that may influence transcriptional regulation. For example, lamin A/C interacts with the retinoblastoma protein (Rb) to modulate E2F-dependent transcription [29], and with c-Fos to regulate its nuclear retention and activity [30]. While βcatenin acts as a co-activator in Wnt signaling relies on nuclear translocation and interaction with transcriptional complexes, and evidence suggests that nuclear architecture and envelope components, including nesprins, can influence this process [31]. Therefore, the observed gene expression changes following depletion of lamins or nesprins are likely not restricted to genes located within lamina-associated domains (LADs), but may also result from broader perturbations in nuclear architecture and transcriptional regulatory networks. This is consistent with our findings that lamins and nesprins influence gene expression in distal, non-LAD regions.”

The comparison of the identified DEGs to genes contained in LADs might be confounded by the fact that the authors relied on the identification of LADs from a previous study (ref #28), which used a different human cell type (human skin fibroblasts) instead of the U2OS osteosarcoma cells used in the present study. As LADs are often highly cell-type specific, the use of the fibroblast data set could lead to substantial differences in LADs.

DamID in various mammalian cell types has shown that some LADs are cell-type invariant (constitutive LADs [cLADs]), while others interact with the NL in only certain cell types (facultative LAD [fLADs]) (Bas van Steensel, 2017). We agree that facultative LADs (fLADs), which comprise approximately half of all LADs, are often highly cell-type specific. We acknowledge that this specificity may influence the interpretation of our findings. At present, publicly available LAD datasets for U2OS cells are limited to those associated with LMNB. We concur that generating LMNA-specific LAD maps in U2OS cells would enhance the accuracy and relevance of our analyses, and we view this as an important direction for future research.

Another limitation of the current manuscript is that, in the current form, some of the figures and results depicted in the figures are difficult to interpret for a reader not deeply familiar with the techniques, based in part on the insufficient labeling and figure legends. This applies, for example, to the isoform use analysis shown in Figure 3d or the GenometriCorr analysis quantifying spatial distance between LADs and DEGs shown in Figure 4c.

For Figure 3, we added text in the caption to make the figure more readable “Isoform switching analysis reveals differential expression of alternative transcript variants between conditions, highlighting a shift in predominant isoform usage.” For Figure 4c, we added text in the caption “GenometriCorr analysis was used to quantify the spatial relationship between LADs and DEGs, evaluating whether the observed genomic proximity deviates from random expectation through empirical distributionbased statistical testing of pairwise distances between genomic intervals.” And also in the ‘Methods”.

Overall appraisal and context:

Despite its limitations, the present study further illustrates the important roles the nuclear envelope proteins lamin A, lamin C, and nesprin-2 have in chromatin organization, dynamics, and gene expression. It thus confirms results from previous studies (not always fully acknowledged in the current manuscript) previously reported for lamin A/C depletion. For example, the effect of lamin A/C depletion on increasing mobility of chromatin had already been demonstrated by several other groups, such as Bronshtein et al. Nature Comm 2015 (PMID: 26299252) and Ranade et al. BMC Mol Cel Biol 2019 (PMID: 31117946). Additionally, the effect of lamin A/C depletion on gene and protein expression has already been extensively studied in a variety of other cell lines and model systems, including detailed proteomic studies (PMIDs 23990565 and 35896617).

We add more discussions as below “Our findings reinforce the pivotal roles of nuclear envelope proteins lamin A, LMNA and nesprin 2 in regulating chromatin organization, chromatin mobility, and gene expression. These results are consistent with and extend prior studies investigating the consequences of lamin depletion. For instance, increased chromatin mobility following the loss of lamin A/C has been previously demonstrated using live-cell imaging approaches [26,35], supporting our observations of nuclear structural relaxation and chromatin redistribution. Additionally, proteomic profiling following lamin A depletion has been extensively documented across both cellular and mouse models, providing valuable insights into the molecular consequences of nuclear envelope disruption [36,37]. While these earlier studies provide a strong foundation, our work contributes novel insights by integrating isoform-specific perturbations with spatial chromatin measurements. This approach emphasizes contextdependent regulatory mechanisms that involve not only lamina-associated regions but also nesprin-associated domains and distal genomic loci, thereby expanding the current understanding of nuclear envelope protein function in gene regulation.”

The finding that that lamin A/C or nesprin depletion not only affects genes at the nuclear periphery but also the nuclear interior is not particularly surprising giving the previous studies and the fact that lamins A and C are also founding within the nuclear interior, where they affect chromatin organization and dynamics, and that lamins A/C and nesprins directly interact with numerous transcriptional regulators that could further affect gene expression independent from their role in chromatin organization.

We have added the following statement into the Discussion part “Beyond their well-established role in tethering heterochromatin at the nuclear periphery through lamina-associated domains (LADs), A-type lamins (lamins A and C) also localize to the nuclear interior, where they contribute to chromatin organization and gene regulation independently of LADs [27,28]. Nuclear lamins can form intranuclear foci that associate with active chromatin and are implicated in supporting transcriptional activity. Additionally, both lamins and nesprins participate in diverse protein-protein interactions that may influence transcriptional regulation. For example, lamin A/C interacts with the retinoblastoma protein (Rb) to modulate E2F-dependent transcription [29], and with c-Fos to regulate its nuclear retention and activity [30]. While β-catenin acts as a co-activator in Wnt signaling relies on nuclear translocation and interaction with transcriptional complexes, and evidence suggests that nuclear architecture and envelope components, including nesprins, can influence this process [31]. Therefore, the observed gene expression changes following depletion of lamins or nesprins are likely not restricted to genes located within lamina-associated domains (LADs), but may also result from broader perturbations in nuclear architecture and transcriptional regulatory networks. This is consistent with our findings that lamins and nesprins influence gene expression in distal, non-LAD regions.”

The authors provide a detailed analysis of isoform switching in response to lamin A/C or nesprin depletion, but the underlying mechanism remains unclear. Similarly, their analysis of the genomic location of the observed DEGs shows the wide-ranging effects of lamin A/C or nesprin depletion, but lets the reader wonder how these effects are mediated. A more in-depth analysis of predicted regulator factors and their potential interaction with lamins A/C or nesprin would be beneficial in gaining more mechanistic insights.

We agree that the current findings, while highlighting the broad impact of lamin A/C or nesprin depletion on isoform usage and gene expression, do not fully elucidate the underlying regulatory mechanisms. We acknowledge the importance of identifying upstream regulators and understanding their potential interactions with lamins and nesprins. Future investigations integrating epigenetic approaches, such as ChIP-seq for transcription factors and chromatin-associated proteins, will be essential to clarify how lamins and nesprins contribute to isoform switching and to uncover the mechanistic basis of these regulatory effects.

Reviewer #3 (Public review):

Summary:

This manuscript describes DOX inducible RNAi KD of Lamin A, LMNA coded isoforms as a group, and the LINC component SYNE2. The authors report on differentially expressed genes, on differentially expressed isoforms, on the large numbers of differentially expressed genes that are in iLADs rather than LADs, and on telomere mobility changes induced by 2 of the 3 knockdowns.

Strengths:

Overall, the manuscript might be useful as a description for reference data sets that could be of value to the community.

We acknowledge that the initial version of our manuscript lacked comprehensive comparisons with previous studies. In our revised manuscript, we have included more detailed discussions highlighting how our findings complement and extend existing knowledge. Specifically, our study presents novel insights into the role of lamins and nesprins in regulating non-coding RNAs and isoform switching, areas that have not been extensively explored in prior literatures. We hope these additions will clarify the contribution of our work and demonstrate the potential value to the field.

Weaknesses:

The results are presented as a type of data description without formulation of models or explanations of the questions being asked and without follow-up. Thus, conceptually, the manuscript doesn't appear to break new ground.

In our study, we proposed a conceptual model in which gene expression changes are linked to RNA synthesis, chromatin conformation alterations, and chromatin modifications, potentially mediated by lamin A, LMNA, and nesprin-2 at the transcriptional level. However, we acknowledge that this model remains preliminary and largely unexplored. We agree that additional mechanistic insights and identification of specific regulatory factors are needed to strengthen this framework. Future studies will aim to experimentally validate these hypotheses and clarify the pathways and regulators involved.

Not discussed is the previous extensive work by others on the nucleoplasmic forms of LMNA isoforms. Also not discussed are similar experiments- for instance, gene expression changes others have seen after lamin A knockdowns or knockouts, or the effect of lamina on chromatin mobility, including telomere mobility - see, for example, a review by Roland Foisner (doi.org/10.1242/jcs.203430) on nucleoplasmic lamina. The authors need to do a thorough search of the literature and compare their results as much as possible with previous work.

We sincerely thank the reviewer for pointing out the important body of previous work on the nucleoplasmic forms of LMNA isoforms and the impact of lamin A depletion on gene expression and chromatin mobility. In the revised version, we have now included relevant citations. Please see the highlights in the Discussion.

The authors don't seem to make any attempt to explore the correlation of their findings with any of the previous data or correlate their observed differential gene expression with other epigenetic and chromatin features. There is no attempt to explore the direction of changes in gene expression with changes in nuclear positioning or to ask whether the genes affected are those that interact with nucleoplasmic pools of LMNA isoforms. The authors speculate that the DEG might be related to changing mechanical properties of the cells, but do not develop that further.

We sincerely appreciate the reviewer’s insightful comments. In our revised manuscript, we have addressed this concern by comparing our telomere mobility results with previously published data (Bronshtein et al., 2015), and we observe consistent findings showing that lamin A depletion leads to increased telomere motility. Furthermore, our study provides novel evidence that nesprin-2 depletion similarly enhances telomere migration, suggesting a broader role for nuclear envelope components in chromatin dynamics.

We acknowledge the importance of integrating gene expression data with epigenetic and chromatin features. However, to our knowledge, such datasets are currently limited for U2OS cells, particularly in the context of lamin and nesprin perturbation. We agree that understanding the correlation between differentially expressed genes and nuclear positioning or interactions with nucleoplasmic pools of LMNA isoforms is a promising direction. We are actively planning future studies that include chromatin profiling and mechanical perturbation assays to further explore these mechanisms.

The technical concerns include: 1) Use of only one shRNA per target. Use of additional shRNAs would have reduced concern about possible off-target knockdown of other genes; 2) Use of only one cell clone per inducible shRNA construct. Here, the concern is that some of the observed changes with shRNA KDs might show clonal effects, particularly given that the cell line used is aneuploid. 3) Use of a single, "scrambled" control shRNA rather than a true scrambled shRNA for each target shRNA.

(1) Regarding the use of a single shRNA per target, we agree that utilizing multiple independent shRNAs would strengthen the conclusions. In our study, we selected validated shRNA sequences with minimal predicted off-targets and confirmed knockdown efficiency at mRNA level (by qPCR).

(2) As for the use of a single cell clones per inducible construct, we understand the concern that clonal variability, particularly in an aneuploid cell line, could influence the observed phenotypes. To clarify this, we have revised in the manuscript “Multiple independent clones per shRNA were screened for knockdown efficiency using reverse transcription quantitative real-time PCR (RT-qPCR). Three clones demonstrating robust and consistent knockdown were selected and expanded. These clones were subsequently pooled to minimize clonal variability and used for downstream analyses, including RNA-seq”. To mitigate this, we ensured consistent results across biological replicates and used inducible systems to reduce variability introduced by random integration. 

(3) We also acknowledge that the use of a single scrambled shRNA control, rather than matched scrambled controls for each construct, is a limitation. While we used a standard non-targeting scrambled shRNA commonly applied in similar studies, we understand that distinct scrambled sequences might better control for construct-specific effects. .

Reviewer #1 (Recommendations for the authors):

Please make the processed RNA-seq data available for each individual experiment, not only the raw reads and averaged data.

In response to your suggestion, we have now included the raw count data for each individual experiment in Supplementary Table S5 to enhance transparency and reproducibility.   

Reviewer #2 (Recommendations for the authors):

The current text contains numerous typos, and some of the text could benefit from additional editing for clarity and conciseness. In addition, several statements, particularly in the section encompassing lines 321-329, lack supporting references.

In our revised version, we have carefully edited the text for clarity and conciseness.

We have included related citations from lines 321-329: “The majority of genes located within LADs tend to be transcriptionally repressed or expressed at low levels. This is because LADs are associated with heterochromatin , a tightly packed form of DNA that is generally inaccessible to the cellular machinery required for gene expression 12,23. Lamin mutations and levels have shown to disrup LAD organization and gene expression that have been implicated in various diseases, including cancer and laminopathies 24,25.”

The figures would benefit from better labeling, including a clear schematic of which specific regions of the LMNA and SYNE2 genes are targeted by the different shRNA constructs, and by labeling the different isoforms in Figure S1 with the common names. Furthermore, note that lamin A arises from posttranslational processing of prelamin A, not from a different transcript. Likely, the "different LMNA genes" shown in Supplementary Figure S1 are just different annotations, with the exceptions of the splice isoforms lamin C and lamin delta10.

In the Method, we have clearly denoted the design of corresponding shRNAs as suggested “The shRNA designated as shLMNA targets a region within the coding sequence of LMNA that is shared by both lamin A and lamin C, corresponding to amino acids 122–129 (KKEGDLIA) of lamin A/C (RefSeq: NM_001406985.1). The shRNA against SYNE2 (shSYNE2) targets a sequence encoding amino acids 5133– 5140 (KRYERTEF) of the SYNE2 protein (RefSeq: NM_182914.3).”

For Figure S1, we have added common isoform names to figure and captions. “lamin A (ENST00000368300.9), LMNA 227 (ENST00000675431.1), pre-lamin A/C (ENST00000676385.2), and lamin C (ENST00000677389.1)."

Several statements about the novelty of the findings or approach are inaccurate. For example, the authors state in the introduction that "However, whether lamins and nesprins actively govern chromatin remodeling and isoform switching beyond their wellcharacterized functions in mechanotransduction remains an open question", as several previous studies have provided detailed characterization of lamin A/C depletion or mutations on chromatin organization, mobility, and gene expression. The authors should revise these statements and better acknowledge the previous work.

We have added the citations of previous works and revised the text “While significant progress has been made in understanding the role of lamins in genome organization, the precise mechanisms by which lamins and nesprins regulate gene expression through distal chromatin interactions remain incompletely understood [10,11]. Notably, recent evidence suggests a reciprocal interplay between transcription and chromatin conformation, where gene activity can influence chromatin folding and vice versa [12]. However, whether lamins and nesprins actively govern chromatin remodeling and isoform switching beyond their well-characterized functions in mechanotransduction remains an open question.”

Reviewer #3 (Recommendations for the authors):

Overall, the manuscript might be useful as a description for reference data sets that could be of value to the community. Otherwise, I did not derive meaningful biological insights from the manuscript. It was not clear to me also how much might be repeating previous work already reported in the literature (see below). For example, I cited a review on nucleoplasmic lamins by Roland Foisner at the end of the specific comments - scanning it very quickly shows that there are already papers on increased chromatin mobility after lamin perturbations, including telomeres. I know there have also been studies of changes in gene expression after lamin A and B KD. The authors need to do a thorough search of the literature and compare their results as much as possible with previous work.

We acknowledge that the roles of lamins in regulating chromatin dynamics and gene expression, including the effects of lamin perturbations on chromatin mobility and telomere behavior, have been previously reported. In response, we have revised the manuscript to incorporate relevant citations and to better contextualize our results within the existing literature. Importantly, to our knowledge, the finding that nesprin-2 influences telomere mobility has not been previously reported, and we have highlighted this novel observation in the revised text.

In response, we have now conducted a more comprehensive literature review and revised the manuscript accordingly to better contextualize our findings. Specifically, we have added comparisons to prior studies reporting chromatin mobility changes following lamin A/C depletion. We also now emphasize the novel aspects of our study, such as the isoform-specific perturbations and the integration of spatial chromatin organization with transcriptomic outcomes.

We hope these revisions strengthen the manuscript’s contribution as both a useful resource and a mechanistic investigation.

Not even acknowledged is the previous extensive work on the nucleoplasmic forms of LMNA isoforms - I know Robert Goldman published extensively on this, implicating lamin A, for example, on DNA replication in the nuclear interior as well as transcription. More recently, Roland Foisner worked on this, including with molecular approaches. For example, a 2017 review mentions previous ChIP-seq mapping of lamin A binding to iLAD genes and also describes previous work on chromatin mobility, including telomere mobility. Yet the entire writing in the manuscript seems to only discuss the role of LMNA isoforms in the nuclear lamina per se, explaining the surprise in seeing many iLAD genes differentially expressed after KD.

We have added related studies as suggested by the reviewer and  added the following statement: “Nucleoplasmic lamins bind to chromatin and have been indicated to regulate chromatin accessibility and spatial chromatin organization [24]. Lamins in the nuclear interior regulate gene expression by dynamically binding to heterochromatic and euchromatic regions, influencing epigenetic pathways and chromatin accessibility. They also contribute to chromatin organization and may mediate mechanosignaling [25]. However, the contribution of nesprins and lamins to isoform switch and chromatin dynamics has not been fully understood [7,10,26]. ”

Overall, I found a surprising lack of review and citation of previous work (see Specific comments below), including the lack of citations for various declarative statements about previous conclusions in the field about lamin A.

(1) Introduction:

"However, the contribution of nesprins and lamins to gene 220 expression has not been fully understood."

There is a literature about changes in gene expression- at least for lamin KD and KO- both in vitro and in vivo- that the authors could and should review and summarize here.

To address this, we have now revised the manuscript to include a more comprehensive discussion of the relevant literature and added appropriate citations in the corresponding section. We hope this addition provides better context for our current findings and clarifies the contribution of lamins and nesprins to gene regulation.

(2) Results:

"A fragment of shRNA that targeting 3' untranslated region (UTR) in LMNA genes was chosen to knockdown lamin A (shLaminA). A fragment of shRNA that targeting coding sequence (CDS) region in LMNA genes was chosen to knockdown LMNA (shLMNA)". The authors should explain more - does one KD both lamin A and C (shLMNA), versus the other being specific to lamin A but not lamin C? It appears so from later text, but the authors should explicitly explain their targeting strategy right at the beginning to make this clear.

To make the method clearer, we have clear added the text “The shRNA against lamin A (shLaminA) targets the 3′ untranslated region (UTR) of the LMNA gene, specific to prelamin A, which is post-translationally processed into mature lamin A. The shRNA designated as shLMNA targets a region within the coding sequence of LMNA that is shared by both lamin A and lamin C, corresponding to amino acids 122–129 (KKEGDLIA) of lamin A/C (RefSeq: NM_001406985.1). The shRNA against SYNE2 (shSYNE2) targets a sequence encoding amino acids 5133–5140 (KRYERTEF) of the SYNE2 protein (RefSeq: NM_182914.3).”

But more importantly, the convention with RNAi is to demonstrate consistent results with at least two different small RNAs. This is to rule out that a physiological result is due to the KD of a non-target gene(s) rather than the target gene. The scrambled shRNA controls are not sufficient for this as they test a general effect of the shRNA culture conditions, including tranfection and dox treatment, etc, rather than a specific KD of a different gene(s) than the target due to off-target RNAi.

We fully acknowledge the concern regarding the use of only a single shRNA per knockdown and agree that shRNAs are prone to off-target effects. However, we have conducted qPCR confirmation of key RNAseq findings, which strongly supports the specificity and validity of our observed results. Additionally, we recognize the importance of validating our findings using multiple independent shRNAs or alternative knockdown strategies, such as CRISPR deletion or degron-based approaches. To address this rigorously, we are currently optimizing an auxin-inducible degron system (AtAFB2) for targeted depletion of lamin C. Our preliminary data indicate approximately 40% knockdown efficiency after 16 hours of auxin induction, highlighting ongoing optimization efforts (Author response image 1). Future experiments will integrate this improved degron system and multiple independent shRNAs to further substantiate our results and definitively rule out potential off-target effects, thereby enhancing the robustness and reproducibility of our data.

(3) "Single-cell clones 114 were subsequently isolated and expanded in the presence of 2 μg ml-1 puromycin to 115 establish doxycycline-inducible shRNA-knockdown stable cell lines."

The authors need to describe explicitly in the Results how exactly they did these experiments. Did they do their analysis using a single clone from each lentivirus shRNA transduction? Did they do analysis - ie RNA-seq- on several clones from the same shRNA transduction and compare? Did they pool clones together?

In our study, single-cell clones and pooled the three independent clones were mixed following lentiviral transduction with doxycycline-inducible shRNA constructs and selected with 2 μg/ml puromycin. For each shRNA, we screened multiple clones for knockdown efficiency and selected a representative clone exhibiting robust knockdown for downstream experiments, including RNA-seq. We did pool three multiple clones; all functional analyses were performed on pooled clones. We have now revised the Method section to explicitly describe this experimental design: “Multiple independent clones per shRNA were screened for knockdown efficiency using reverse transcription quantitative real-time PCR (RT-qPCR). Three clones demonstrating robust and consistent knockdown were selected and expanded. These clones were subsequently pooled to minimize clonal variability and used for downstream analyses, including RNAseq.”

One confounding problem is that there are clonal differences among cells cloned from a single cell line. This is particularly true for aneuploid cell lines like U2OS. Ideally, they would use mixed clones, but if not, they should at least explain what they did.

We added the text to method “Three single-cell clones exhibiting robust knockdown efficiency were individually expanded and subsequently pooled. The pooled clones were maintained in medium containing 2 µg ml ¹ puromycin to establish stable cell lines with doxycycline-inducible shRNA expression. Multiple independent clones per shRNA were screened for knockdown efficiency using reverse transcription quantitative real-time PCR (RT-qPCR). Three clones demonstrating robust and consistent knockdown were selected and expanded. These clones were subsequently pooled to minimize clonal variability and used for downstream analyses, including RNA-seq.”

(4) I am confused by their shScramble control. This is typically done for each shRNA- ie, a separate scrambled control for each of the different target shRNAs. This is because there are nucleotide composition effects, so the scrambled idea is to keep the nucleotide composition the same.

However, looking at STable 1 and SFig. 2- shows they used a single scrambled control, thus not controlling for different nucleotide composition among the three shRNAs that they used.

In our study, we used a single non-targeting shRNA (shScramble) as a control to account for potential effects of the shRNA vector and delivery system. This approach is commonly accepted in the field when the scrambled sequence is validated as non-targeting and does not share significant homology with the genes of interest. While we acknowledge that using separate scrambled controls matched in nucleotide composition for each targeting shRNA can further minimize sequence-dependent effects, we believe that the use of a single validated scramble control is appropriate for the scope of this study.

(5) In Figure 2 - what is on the x-axis? Number of DEG? Please state this explicitly in the figure legend.

We have added “Counts” as figure legend, and added the caption “Gene counts are displayed on the x-axis.”

(6) More importantly, in Figure 2 they only show pathway analysis of DEG. They should show more: a) Fold-change of DEG displayed for all DEG; b) Same for genes in LADs vs iLADs. More explicitly, are the DEG primarily in LADs or iLADs, or a mix? Are the DEGs in LADs biased towards increased expression, as might be expected for LAD derepression? Conversely, what about iLADs - is there a bias towards increased or decreased expression?

We agree that a more detailed characterization of the differentially expressed genes (DEGs) will strengthen the conclusions. In response we have revised the manuscript as following: “Furthermore, differential expression analysis revealed that the majority of DEGs following depletion of lamins and nesprins were located outside lamina-associated domains (non-LADs). Specifically, for shLaminA knockdown, 8 DEGs within LADs were downregulated and 8 were upregulated, whereas 59 non-LAD DEGs were downregulated and 79 were upregulated. For shLMNA, 7 LAD-associated DEGs were downregulated and 15 were upregulated, with 88 downregulated and 140 upregulated DEGs in non-LAD regions. In the case of shSYNE2 knockdown, 161 LAD DEGs were downregulated and 108 were upregulated, while 2,009 non-LAD DEGs were downregulated and 1,851 were upregulated (Figure 2d). These results indicate that the transcriptional changes resulting from the loss of lamins or nesprins predominantly occur at non-LAD genomic regions.”

We appreciate the reviewer’s comments, which helped improve the clarity and depth of our analysis.

(7) Is there a scientific rationale for the authors' focus on DE of isoforms? Is this somehow biologically meaningful and different from the overall DE of all genes? The authors should explain in the Results section what their motivation was in deciding to do this analysis.

We have add the following statement in response to the reviewer “To uncover transcript-specific regulatory changes, we performed isoform-level differential expression analysis. Many genes produce functionally distinct isoforms, and shifts in their usage can occur without changes in total gene expression, making isoform-level analysis essential for detecting subtle but meaningful transcriptional regulation.  Our analysis demonstrated that depletion of lamins and nesprins induced significant alterations in specific transcript isoforms, indicating regulatory changes in alternative splicing or transcription initiation that are not captured by gene-level differential expression analysis.”

(8) "Expectedly, the DEGs from 327 depletion of lamin A, LMNA, and SYNE2 seldom intersected with genes in 328 LADs (Figure 4a)."

Why was this expected? The authors have only cited one review paper. Others have seen significant numbers of genes in LADs that are DE after KD of lamina proteins. What was the fold cutoff used for DE? Was there a cutoff for the level of expression prior to KD? The authors should cite relevant primary literature showing that there are active genes in LADs and that some perturbations of the lamina proteins do result in DE of genes in LADs.

We acknowledge the reviewer's concerns regarding our statement: "Expectedly, the DEGs from 327 depletion of lamin A, LMNA, and SYNE2 seldom intersected with genes in 328 LADs (Figure 4a)." To clarify, this expectation stems from previous observations that LAD-associated genes are typically transcriptionally silent or expressed at very low levels (Guelen et al., 2008). However, dynamic changes in LADs and gene expression status do occur during cellular differentiation (Peric-Hupkes et al., 2010), and some LAD-resident genes can become active and transcriptionally responsive under specific conditions, such as T cell activation. We applied specific foldchange and baseline expression level thresholds in our analysis, as detailed in the Methods section. We added the following text in the “Method”: “Differential gene expression analysis was performed using thresholds of baseMean > 50, absolute log fold change > 0.5, and p-value < 0.05.”  We agree that additional relevant primary literature demonstrating active gene expression changes within LADs upon perturbation of lamina proteins should be cited and we have added the following statement:

“LADs exhibit dynamic reorganization and changes in gene expression during cellular differentiation [30]. Although genes within LADs are generally transcriptionally silent or expressed at low levels [31], some LAD-resident genes remain active and can be transcriptionally modulated in response to specific stimuli, such as T cell activation [32].”

(9) "Expectedly, the DEGs from 327 depletion of lamin A, LMNA, and SYNE2 were seldomly intersected with genes in 328 LADs (Figure 4a)." I disagree with the wording of "seldom" which by definition means rarely. I don't see that this applies to the significant number of genes that are in LADs that are DE as shown in the Venn diagram, Fig. 4a. For example, this includes 57 genes for the shLamin A and ~400 genes for the shSYNE2.

Is there anything of note about which genes are DE within LADs?

We have rephrased the text to the following “The Venn diagram analysis revealed limited overlap between DEGs resulting from knockdown of lamin A (shLaminA), LMNA (shLMNA), or SYNE2 (shSYNE2) and genes located within laminaassociated domains (LADs). Specifically, only a small subset of DEGs intersected with LAD-associated genes across all three knockdowns, suggesting that the majority of transcriptional changes occur outside LAD regions”. The DEGs in LADs and non-LADs were shown in supplementary Table S4.

(10) "The relative distance from DE genes (query features) to LADs (reference feature) is plotted by GenometriCorr package (v 1.1.24). The color depicting deviation from the expected distribution and the line indicating the density of the data at relative distance are shown." The authors should explicitly describe what the reference "expected distribution" was based on. This is all very cryptic right now, so we can't assess the biological possible significance. Third, they should clearly explain what is plotted on the x and y axes of Figure 4C. I really don't have a clue. I assume the x-axis is some measure of "relative distance" but what on earth does that mean? I really don't understand this plot, which is crucial to the whole story. What is on the y-axis? Density of DEGs? What? And they need to explain not only what is plotted on the x and y axes but also provide units.

We have revised the text to clarify that the GenometriCorr analysis (v1.1.24) was used to assess the spatial association between differentially expressed genes (DEGs, query features) and lamina-associated domains (LADs, reference features). Specifically, this method evaluates whether the observed distances between query and reference genomic intervals significantly deviate from a null distribution generated by random permutation of query features across the genome, while preserving size and chromosomal context.

In the revised figure legend and main text, we now clarify that the x-axis represents the relative genomic distance between each differentially expressed gene (DEG) and the nearest LAD, scaled between –1 and 1, where values near 0 indicate close proximity, and values approaching –1 or 1 reflect greater distances on either side of the LADs. The y-axis denotes the density (or proportion) of query features (DEGs) at each relative distance bin. The color gradient overlays the plot to indicate deviation from the expected null distribution (based on randomized query positions): red indicates enrichment (closer than expected), while blue indicates depletion (further than expected).

“GenometriCorr analysis (v1.1.24) was used to assess the spatial relationship between DEGs (query) and LADs (reference) [48]. The x-axis shows the relative genomic distance between each DEG and the nearest LAD, scaled from –1 (far upstream) to 1 (far downstream), with 0 indicating closest proximity. The y-axis represents the density of DEGs at each distance bin. A color gradient indicates deviation from a randomized null distribution: red signifies enrichment (closer than expected), and blue signifies depletion. Statistical significance was determined using the Jaccard test (p < 0.05).”

Second, to correlate with other features and to give more meaning, the authors should show the chromosome location of the DEGs and scale this by the actual DNA sequence distances. This will be needed to correlate with other features from other studies.

The genomic positions of DEGs have now been displayed in Figure 4b, with distances shown in base pairs to facilitate cross-reference with other features in future studies.

Third, they should attempt some kind of analysis themselves to try to understand what might correlate with the DEGs. To begin with, they might try to correlate with lamin A ChiP-seq or other molecular proximity assays. Others in fact have shown that lamin A interacts with 5' regulatory regions of a subset of genes- presumably this is the diffuse nucleoplasmic pool of lamin A that has been studied by others in the past.

We agree that understanding potential regulatory mechanisms underlying DEG distribution is essential. In response, we have expanded our analysis (Figure 2d) to highlight that a substantial portion of DEGs are located outside of LADs, suggesting potential regulation by the nucleoplasmic pool of lamin A. This is consistent with previous studies showing lamin A interaction with regulatory elements such as 5′ UTRs and enhancers, independent of LAD localization. We have now cited relevant literature to support this hypothesis.

Fourth, in the table, they should go beyond just giving the fold change in expression. Particularly for genes that are expressed at very low levels, this is not particularly meaningful as it is very sensitive to noise. They should provide a metric related to levels of expression both before and after the KD.

We acknowledge the reviewer’s concern regarding fold-change interpretation for low-abundance transcripts. To improve clarity and interpretability, we have now included Supplementary Table S4, which provides the raw counts and baseMean values (average normalized expression across all samples) for all DEGs. Additionally, we note that in our differential expression analysis, genes with baseMean < 50 and absolute log₂fold change > 0.5 were filtered out to reduce potential noise from low-expression genes.

(11) The figure legend and description in the Results section were completely inadequate. I had little understanding of what was being plotted. It is not sufficient to simply state the name of some software package that they used to measure "XYZ" and to show the results. It has no meaning for the average reader.

Without some type of explanation of rationale, questions being asked, and conclusions made of biological relevance, this section made zero impact on me.

Yes- details can be provided in the Methods. But conceptually, the methods and the conceptual underpinnings of the approach and as the question being asked and the rationale for the approach, with the significance of the results, need to be developed in the Results section.

In response, we have revised the “Results” section to better articulate the rationale behind the analysis, the specific biological questions we aimed to address, and the conceptual relevance of the method used. We have also clarified the meaning of the plotted data and how it supports our conclusions.

While technical details remain in the “Methods” section, we now provide a more accessible narrative in the Results to guide the reader through the approach and highlight the biological significance of our findings. We hope these revisions make the section more informative and impactful.

(12) The telomere movement part of the manuscript seems to come out of nowhere. Why telomeres? Where are telomeres normally positioned, particularly relative to the nuclear lamina? Does this change with the KDs - particularly for those that increase motion? The MSD for SYNE2 appears unconstrained- they should explore longer delta time periods to see if it reaches a point of constrained movement.

If the telomeres are simply tethered at the nuclear lamina, then is that the explanation- that they become untethered? But if they are not typically at the periphery, then where are they relative to other nuclear compartments? And why is there mobility changing? Is it related to the loss of nuclear lamina positioning of adjacent LAD regions to the telomeres? Is it an indirect, secondary effect? What would they see after an acute KD? What about other chromosome regions? Again, there is little explanation for the rationale for these observations. It is one of many possible experiments they could have done. Why did they do this one?

We added the following explanation “Although telomeres are not uniformly tethered to the nuclear lamina, they can transiently associate with the nuclear periphery, particularly during post-mitotic nuclear reassembly, through interactions involving SUN1 and RAP1 36. Given that lamins and nesprins are key components of the nuclear envelope that regulate chromatin organization and mechanics 37,38, we examined telomere dynamics as a proxy for changes in nuclear architecture. Using EGFP-tagged dCas9 to label telomeric regions in live U2OS cells, we assessed whether knockdown of these proteins leads to increased telomere mobility, reflecting a loss of structural constraint or altered chromatin–nuclear envelope interactions 17.” And “To probe how nuclear envelope components regulate chromatin dynamics, we tracked telomeres as a representative genomic locus whose mobility reflects changes in nuclear mechanics and chromatin organization. Although telomeres are not stably tethered to the nuclear lamina, their motion can be influenced by nuclear architecture and transient peripheral associations [36]. Upon depletion of lamin A, LMNA, or SYNE2, we observed significantly increased telomere mobility and nuclear area explored, quantified by mean square displacement and net displacement (Figure 6b–c, Supplementary Movie S1). These changes likely reflect altered chromatin–lamina interactions or disrupted nuclear mechanical constraints, consistent with prior studies showing that lamins modulate chromatin dynamics and nuclear stiffness [37,38,39]. Thus, our findings support a role for lamins and nesprins in constraining chromatin motion through nuclear structural integrity.”

(13) "Notably, Lamin A depletion led to enrichment of 392 pathways associated with RNA biosynthesis, supporting its previously suggested role 393 in transcriptional activation and ribonucleotide metabolism."

There is a literature on this. Say more and cite the references.

Notably, lamin A depletion led to enrichment of pathways associated with RNA biosynthesis, supporting its previously suggested role in transcriptional activation and ribonucleotide metabolism 45.  

(14) "This aligns with prior studies indicating that Lamin A contributes to chromatin accessibility and RNA polymerase activity." Again, there is a literature on this. Say more and cite the references.

This aligns with prior studies indicating that lamin A contributes to chromatin accessibility and RNA polymerase activity 46. These findings further underscore the functional relevance of lamin A in coordinating transcriptional programs through modulation of nuclear architecture.

(15) "In contrast, LMNA knockdown was linked to alterations in chromatin conformation." No. The authors show gene ontology and implicate perturbed RNA levels for genes implicated in "chromatin conformation". That is not the same thing as measuring chromatin conformation, which is not done, and showing changes in conformation.

Based on the reviewer’s comment we have revised the text as the following: “In contrast, LMNA knockdown led to differential expression of genes enriched in pathways related to chromatin organization, suggesting potential disruptions in chromatin regulatory networks. Although direct measurements of chromatin conformation were not performed, these transcriptional changes indicate that LMNA may contribute to maintaining nuclear architecture and genomic stability, which aligns with its established involvement in laminopathies and genome integrity disorders.”

(16) "The findings that DEGs are predominantly located in non-LAD regions highlight a unique regulatory aspect of lamins and nesprins, emphasizing their spatial specificity in gene expression". Is this novel? Can the authors separate direct from indirect effects? Is the percentage of genes in LADs that are altered in expression different from the percentage of genes in iLADs that are altered in expression? There are many more active genes in iLADs, so one expects more DEGs in iLADs even if this is random. Also - how does this correlate with lamin A binding near 5' regulatory regions detected by ChIP-seq? See the following review for references to this question and also previous work on lamin A versus chromatin mobility, including telomeres. J Cell Sci (2017) 130 (13): 2087-2096. https://doi.org/10.1242/jcs.203430

We appreciate the reviewer’s valuable comments and feedback, we have revised the manuscript as the following to address the feedback. “Furthermore, differential expression analysis revealed that the majority of DEGs following depletion of lamins and nesprins were located outside lamina-associated domains (non-LADs). Specifically, for shLaminA knockdown, 8 DEGs within LADs were downregulated and 8 were upregulated, whereas 59 non-LAD DEGs were downregulated and 79 were upregulated. For shLMNA, 7 LAD-associated DEGs were downregulated and 15 were upregulated, with 88 downregulated and 140 upregulated DEGs in non-LAD regions. In the case of shSYNE2 knockdown, 161 LAD DEGs were downregulated and 108 were upregulated, while 2,009 non-LAD DEGs were downregulated and 1,851 were upregulated (Figure 2d, Supplementary Table S4). These results indicate that the transcriptional changes resulting from the loss of lamins or nesprins predominantly occur at non-LAD genomic regions.

The percentage of DEGs was consistently higher in non-LADs, which are gene rich and transcriptionally active, whereas LADs, known to be enriched for silent or lowly expressed genes, showed fewer expression changes. These findings are consistent with previous studies demonstrating that active genes are more prevalent in non-LADs and that LAD associated genes are generally repressed or less responsive to perturbation [27,28]. Together, these results support a model in which lamins and nesprins influence gene expression through both structural organization and promoter proximal interactions, particularly within euchromatic nuclear regions [10,26,29].”

Author response:

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

Reviewer #1 (Public Review):

Weaknesses:

While scRNA-seq data clearly revealed different subsets of microglia, macrophages, and DCs in the brain, it remains somewhat challenging to distinguish DC-like cells from P2ry12- macrophages by immunohistochemistry or flow cytometry.

Indeed, in flow cytometry analyses of adult brain samples, the p2ry12^-; mpeg1⁺ fraction could, in theory, encompass not only DC-like cells but also other macrophage subsets, as well as B cells, since B cells have been reported to express mpeg1 in zebrafish (Ferrero et al., 2020; Moyse et al., 2020). Nevertheless, our data strongly indicate that within the brain parenchyma, DC-like cells represent the predominant component of this population. This conclusion is supported by the pronounced reduction of p2ry12^-; mpeg1⁺ cells in brain sections from ba43 mutants, in which DC development is impaired. Currently, further phenotypic resolution is constrained by the limited availability of zebrafish-specific antibodies and the restricted palette of fluorescent reporter lines capable of distinguishing MNP subsets. We anticipate that future efforts, including the generation of novel transgenic lines informed by our dataset (initiatives already underway in our group), will enable more precise discrimination among these distinct subsets.

Reviewer #2 (Public Review):

A weakness of this study is that it is mainly based on FACS sorting, which might modify the proportion of different subtypes.

We agree that reliance solely on FACS could potentially introduce biases in the proportions of different subtypes. To minimize this concern, we complemented our flow cytometry data with quantification performed directly on brain sections using immunohistochemistry. This approach allowed us to validate cell population distributions in situ, thereby confirming that the trends observed by FACS accurately reflect the cellular composition of microglia and DC-like cells within the brain parenchyma.

Reviewer#3 (Public Review):

A weakness is the lack of specific reporters or labeling of this dendritic cell population using specific genes found in their single-cell dataset. Additionally, it is difficult to remove the meningeal layers from the brain samples and thus can lead to confounding conclusions. Overall, I believe this study should be accepted contingent on sufficient labeling of this population and addressing comments.

While the generation of DC-like specific transgenic lines is indeed a promising direction (and such efforts are currently underway in our group), creating and validating these lines is time-consuming. Importantly, although these additional tools will be valuable for future functional investigations, we believe they would not impact the main conclusions or core message of our current work, where we already provide detailed spatial information on DC-like cells, and we demonstrated their lineage identity through the use of our newly generated batf3 mutant line. 

Recommendations for the authors:

Major Comments: 

The authors should discuss another recent report demonstrating DCs in the zebrafish brain, which also developed independently of Csf1ra, and compare the two datasets (Zhou et al. Cell reports, 2023).

Thank you for highlighting the study by Zhou et al., which offers complimentary insight into the dendritic cell population in the zebrafish brain. We note that in this work, the authors reclassify ccl34b.1^- mpeg1⁺ brain-resident cells as conventional DCs, thus revising their earlier interpretation of these cells as microglia (Wu et al., 2020). This shift in interpretation is based on their transcriptional comparison between the previously characterized ccl34b.1^- mpeg1⁺ population and a new dataset of brain

mpeg1⁺ cells. This updated classification aligns closely with our findings. Given that our data already demonstrate the equivalence between the DC-like cells described in our study and the ccl34b.1^- mpeg1⁺ population, repeating a direct transcriptional comparison would be redundant. We have now included a discussion of this work in the revised manuscript. Specifically, we have added the following sentences in the discussion: “Importantly, since the submission of our manuscript, the Wen lab published an independent study in which they now reclassify the ccl34b.1^- mpeg1⁺ cells in the zebrafish brain as cDCs, revising their earlier interpretation of these cells as microglia (Zhou et al., 2023)”. 

Data reported in Figure 5 should be quantified (cell numbers, how many brains analyzed). 

Thank you for this comment. We would like to clarify that the primary purpose of Figure 5 (and Figure 5 supplement 1) is to provide an initial qualitative overview of the different MNP subsets present in the adult brain, using the currently available transgenic and immunohistochemical tools. These descriptive analyses were instrumental in identifying the most reliable combination, namely the Tg(p2ry12:p2ry12GFP; mpeg1.1:mCherry) double transgenic line in conjunction with L-plastin immunostaining, to distinguish microglia from other parenchymal MNPs. Quantitative analyses using this optimized strategy are presented in Figure 7 (Figure 7 supplement 1), where we systematically enumerate the different MNPs. We therefore believe that performing additional quantification in Figure 5 would be redundant with the more robust data already shown in Figure 7. As requested, we have now included in the Figure 5 legend that images are representative of brain tissue sections from 2-3 fish. 

The title mentions an "atlas", but there is no searchable database or website associated with the paper. Please provide one.

We agree and fully support the importance of data accessibility. To facilitate use of our dataset by the scientific community, we have developed a user-friendly, searchable web interface that allows users to explore gene expression pacerns within our dataset. This website is available at https://scrna-analysis zebrafish.shinyapps.io/scatlas/

This information has now been included in the “Data availability statement” section of the manuscript.  

Reviewer #1 (Recommendations For The Authors): 

Specific comments: 

The authors should discuss another recent report demonstrating DCs in the zebrafish brain, which also developed independently of Csf1ra, and compare the two datasets (Zhou et al. Cell reports, 2023). 

Thank you for this suggestion. Please refer to our response in the major comments section, where we address this point in detail.

Within macrophages, the authors identified 5 clusters including 4 microglia clusters and 1 MF cluster (Figure 4). Does the laUer relate to 'BAMs' and express markers previously described in murine BAMs, including Lyve1, CD206, etc.? Or to monocytes? By flow cytometry, monocytes were detected (Figure 1B), but not by scRNA-seq.  

You have raised an important point here. As described in lines 197-202 (“results” section), the cells in the MF cluster exhibit a macrophage identity, based on their expression of classical macrophage markers such as marco, mfap4 or csf1ra. However, we were unable to confidently annotate this cluster more specifically. We also considered whether this population might resemble mammalian BAMs or monocytes, cell types that, to our knowledge, have not yet been clearly identified in zebrafish. However, orthologous markers typically associated with murine BAMs were not detected (lyve1) or not specifically enriched (mrc1a/mrc1b) in the MF cluster (see below). Based on these findings, we can only cautiously propose that this cluster may represent blood-derived macrophages and / or monocytes.

To further address your suggestion, we performed a cell type enrichment analysis using the marker genes of the MF cluster, following the same strategy as for the microglia and DC-like clusters presented in Figure 4 supplement 2 C,D. This analysis revealed significant for “monocytes” and “macrophages”, further supporting a general monocytic/macrophage identity (see below). At present, further characterization of this cluster is limited by the lack of zebrafish-specific antibodies and the restricted palette of fluorescent reporter lines that distinguish among MNP subsets. We anticipate that future studies, including the development of new transgenic lines guided by our dataset, will allow for a more precise analysis of this distinct population. 

Author response image 1.

Do all 4 DC clusters identified by scRNA-seq represent cDC1s? or are there also cDC2s, and cDC3s present?  

In our analyses, the four dendritic cell clusters identified by scRNA-seq (DC1-DC4) exhibit transcriptional profiles consistent with a conventional type 1 dendritic cell (cDC1) identity. These clusters uniformly express hallmark cDC1-associated genes, while lacking expression of markers typically associated with mammalian cDC2 or plasmacytoid dendritic cells (pDCs). For instance, irf4, a key transcription factor required for cDC2 development, is not detected in our dataset. Similarly, we do not observe expression of genes characteristic of pDCs. 

That said, the absence of cDC2 or pDC-like signatures in our dataset does not rule out the presence of these populations in zebrafish.  

While they show that DC-like cells did not express Csf1rb (Figure 4D) or other macrophage/microglia genes, DC-like cells were affected in the Csf1rb mutants and in double mutants, demonstrating that their development depends on Csf1rb signaling, as known for macrophages but not DCs. Can the authors discuss this in more detail with regard to DC differentiation/precursors? 

Thank you for pointing this out. As previously demonstrated, CSF1R signaling in zebrafish is more complex than in mammals, due to the presence of two paralogs, csf1ra and csf1rb, which exhibit partially non-overlapping functions (Ferrero et al., 2021). We and others have shown that csf1rb signaling is implicated in the regulation of definitive hematopoiesis, particularly in the regulation of hematopoietic stem cell (HSC)-derived myelopoiesis. Although the developmental origin of zebrafish brain DC-like cells remains uncharacterized, their reduced numbers in the csf1rb mutant, despite their lack of csf1rb expression, supports the current model in which csf1rb acts at the progenitor level, promoting myeloid lineage commitment. According to this, csf1rb disruption affects the differentiation of multiple myeloid subsets, which likely include DC-like cells. We have developed this point in the discussion section (lines 502506).  

Do the DCs express Csf1ra? 

Csf1ra transcripts are not found in DCs in our dataset. As shown below, csf1ra expression is restricted to the microglia and macrophage clusters. These observations are in line with those made by Zhou et al., 2023.

Author response image 2.

Fig. 5, the number of brains analyzed should be added, and also quantifications of cell numbers included. It is mentioned (line 260) that P2ry12GFP+mpeg1mCherry+ microglia are abundant across brain regions while P2ry12GFP- mpeg1mCherry+ cells particularly localize in the ventral part of the posterior brain parenchyma. It would be nice if images of the different brain regions were provided. 

Regarding the quantification, we refer to our response in the major comments section, where we explain that detailed quantification of microglia and other MNP subsets is provided in Figure 7, using a more refined strategy for distinguishing cell types.

As requested, we have now included representative sections from the forebrain, midbrain and hindbrain of adult Tg(mhc2dab:GFP; cd45:DsRed) fish. These images illustrate the spatial distribution of DC-like cells across brain regions. Notably, DC-like cells are most abundant in the ventral areas of the midbrain and hindbrain, and are also present in the posterior telencephalon, particularly concentrated in the region of the commissura anterior. This regional annotation is based on the zebrafish brain atlas by Wullimann et al., 1996 (Neuroanatomy of the zebrafish brain, https://doi.org/10.1007/978-3-0348-8979-7).

These additional images have been included in Figure 5 Supplement 1 (A-E).

It is sometimes not evident whether the Pr2y12- cells included DC-like cells and macrophages, which should be discussed. 

Thank you for bringing this to our attention. Upon review, we agree this point required clearer explanation throughout the text, particularly beginning with the description of putative DC-like cells in Figure 5. We have now revised the manuscript to improve clarity and becer guide readers through the phenotypic identification of DC-like cells using the Tg(p2ry12:p2ry12-GFP;mpeg1:mCherry) line. Specifically, we have modified the titles in the results section from page 5 to page 9, so that readers can more easily follow the step-by-step approach we used to distinguish DC-like cells from microglia. 

To directly address your comment: the p2ry12^-; mpeg1⁺ fraction may, in theory, include not only DC-like cells but also other macrophage subsets and B cells, as B cells have been shown to express mpeg1 in zebrafish (Ferrero et al., 2020; Moyse et al., 2020). Nevertheless, our data strongly indicate that within the brain parenchyma, DC-like cells represent the predominant component of this population. This conclusion is supported by the pronounced reduction of p2ry12^-; mpeg1⁺ cells in brain sections from ba43 mutants, in which DC development is impaired. 

We have revised the text accordingly to clarify this point in the results section of the manuscript (line 355).

For example, the DC-like cell population in Figure 6C appears to include two populations of cells. Thus, it is unclear whether the sorted mhc2dab:GFP+;CD45:DsRedhi population for bulk-seq also contains the MF population identified in Fig. 2. 

Thank you for this thoughtful observation. During the course of this study, we indeed considered how best to isolate non-microglial macrophages in order to specifically recover the MF population identified in our scRNA-seq analysis. However, with the current repertoire of fluorescent transgenic zebrafish lines, it remains technically challenging to selectively isolate non-microglial macrophages from the adult brain. As a result, the mhc2dab:GFP₊; cd45:DsRedhi sorted population used for bulk RNA-seq may indeed include a mixture of DC-like and other mononuclear phagocytes, potentially the MF population. In contrast, our data demonstrate that the Tg(p2ry12:p2ry12-GFP) line provides a more selective tool for isolating microglia, minimizing contamination from other mononuclear phagocyte subsets.

In Figure 7, a reduction of GFP-mpeg+ cells can be seen in baf3 mutants. Could the remaining cells be the (non-microglia) macrophages? Or in Figure 8, could the remaining P2ry12GFP-Lcp1+ cells in Irf8 mutants be macrophages? 

Indeed, we believe it is likely that the remaining mpeg1⁺ cells observed in ba43 mutants include non-microglial macrophages and/or B cells, as we and others previously showed that zebrafish B cells express mpeg1.1 transcripts and are labeled in the mpeg1.1 reporters (Ferrero et al., 2020). This interpretation is further supported by the observation that the reduction in mepg1+ cells is more pronounced in brain sections than in flow cytometry samples, where non-parenchymal mpeg+ cells, such as peripheral macrophages or B cells, are likely enriched. To explore this possibility, we attempted to assess the expression of MF- and B cell-specific markers in the remaining mpeg1+ population isolated from ba43 mutants. However, due to the very low numbers of cells recovered per animal, we were limited to analyzing only a few markers. Despite multiple attempts, qPCR analyses proved unconclusive, likely due to low transcript abundance. We thank you for your understanding of the technical limitations that currently prevent a more definitive characterization of these remaining cells.  

Regarding the irf8 mutants (Figure 8), irf8 is a well-established master regulator of mononuclear phagocyte development. In mice, deficiency results in developmental defects and functional impairments across multiple myeloid lineages, including microglia, which exhibit reduced density (Kierdorf et al., 2013) and an immature phenotype (Vanhove and al., 2019). Similarly, in zebrafish, irf8 mutants show abnormal macrophage development, with an accumulation of immature and apoptotic cells during embryonic and larval stages (Shiau et al., 2014). Based on these findings, it is plausible that the residual p2ry12:GFP^- Lcp1⁺ cells observed in the irf8 mutant brains represent immature or arrested mononuclear phagocytes, possibly including both microglia and DC-like cells. This is supported by their distinct morphology and specific localization along the ventricle borders. However, as previously noted, our current tools do not permit to conclusively identify these cells.

Reviewer #2 (Recommendations For The Authors): 

A few sentences are not easy to understand for a "non zebrafish specialist". 

(1) Page 3 line 111 The sentence "Interestingly, analyses of brain cell suspensions from double transgenics showed p2ry12:GFP+ microglia accounted for half of cd45:DsRed+ cells (50.9 % {plus minus} 2.9; n=4) (Figure 1D,E). Considering that mpeg1:GFP+ cells comprised ~75% of all leukocytes, these results indicated that approximately 25% of brain mononuclear phagocytes do not express the microglial p2ry12:GFP+ transgene." is not clear. This point is significant and deserves a more detailed explanation. 

We apologize for the lack of clarity in this section. The quantification presented in Figure 1 refers specifically to cd45:Dsred⁺ leukocytes, meaning that the reported percentages of p2ry12:GFP⁺ and mpeg1:GFP⁺ cells are calculated relative to the total cd45+ population (defined as 100%). Specifically, we observed that approximately 51% of all cd45+ cells were p2r12:GFP⁺ microglia, while around ti5% were mpeg1:GFP⁺. From these values, we infer that about 25% of mpeg1:GFP⁺ leukocytes do not express the p2ry12:GFP transgene and therefore likely represent non-microglial mononuclear phagocytes. We agree that this distinction is important and have revised the text accordingly to clarify the interpretation for readers who may be less familiar with zebrafish transgenic lines or gating strategies. See page 3, lines 107 117.

(2) Line 522; Like human and mouse ILC2s, "these cells do not express the T cell receptor cd4-1" is confusing (T cell receptor should be reserved to the ag specific TCR). Also, was TCR isotypes expression analyzed (and how was genome annotation used in this case ?) 

Thank you for this insightful comment.  We agree that the term “T cell receptor” should be used specifically to refer to antigen-specific TCRs, and we have revised the discussion accordingly to avoid any confusion. Regarding your question on the analysis of TCR isotype expression and the use of genome annotation: due to technical limitations, we did not pursue TCR isotype-level analysis in this study. Instead, we relied on established markers such as cd4-1 and cd8a to distinguish T cell populations, acknowledging that cd4-1 is not expressed by ILC2-like cells in our dataset. We have clarified these points in the relevant sections of the manuscript (see lines 168 and 535)

The analysis of single-cell data might be more detailed, with more explanation about possible doublet identification and normalization procedures. 

Thank you for highlighting the need for additional clarity regarding our scRNA-seq analysis.

As noted in the Seurat tutorial, “cell doublets or multiplets often exhibit abnormally high gene count” (https://sa7jalab.org/seurat/archive/v3.0/pbmc3k_tutorial). To evaluate this, we performed a dedicated doublet detection analysis using the scDblFinder R package (https://rdrr.io/bioc/scDblFinder/f/vigneces/2_scDblFinder.Rmd). Our results indicated that the proportion of predicted doublets is low (see Figure below), and when present, these doublets are distributed among the different clusters. This contrasts with the typical clustering of doublets into discrete groups and indicates that our single-cell sequencing workflow was sufficiently robust to predominantly capture singlets.

Regarding normalization, we have clarified this in the manuscript. Briefly, single-cell data were normalized using Seurat’s SCTransform method with the following custom parameters: “variable.features.n=4000 and return.only.var.genes=F”. These settings are now clearly described to ensure reproducibility.

Author response image 3.

Reviewer #3 (Recommendations For The Authors):

Major issues

Though baf3 mutants were generated the manuscript will greatly benefit from in situ labeling by RNAscope or the generation of transgenic reporters to conclusively localize this dendritic cell population and address any potential contamination issues. 

We thank you for this constructive suggestion. We agree that in situ labeling approaches such as RNAscope would offer valuable complementary insights. In our current study, however, we already provide detailed spatial information on DC-like cells, and we demonstrated their lineage identity through the use of our newly generated batf3 mutant line. 

To address concerns regarding potential contamination, we have carefully analyzed more than two dozens adult brains to date and consistently observed abundant DC-like cells within the brain parenchyma, exhibiting a reproducible and specific spatial distribution, as described in the manuscript. This consistent localization across multiple samples strongly supports the genuine presence of these cells in the brain rather than artifactual contamination.

While the generation of DC-like specific transgenic lines is indeed a promising direction (and such efforts are currently underway in our group) we note that creating and validating these lines is time-consuming and falls beyond the scope of the present study. Importantly, although these additional tools will be valuable for future functional investigations, we believe they would not impact the main conclusions or core message of our current work. 

The morphological characterization of CD45:DsRed+ macrophages stained with May-Grunwald-Giemsa has been previously reported in the paper, "Characterization of the mononuclear phagocyte system in the zebrafish" Wittamer et al., 2011."Morphologic analyses revealed that the majority of cells exhibited the characteristics of monocytes/macrophages namely low nuclear to cytoplasm ratios and a high number of cytoplasmic vacuoles (Figure 3B). 

We thank you for pointing out the reference to Wittamer et al., 2011. In that study, we indeed provided the first morphological characterization of mononuclear phagocytes (MNPs) in various adult zebrafish organs using the cd45:DsRed line in combination with the mhc2dab:GFP reporter. The focus was primarily on MNPs across peripheral tissues. In the current study, our aim is broader: we investigate the full diversity of brain immune cells, using cd45 as a general marker for leukocytes. As part of this comprehensive characterization, we applied MGG staining, a widely accepted cytological technique, to gain morphological insight into the sorted CD45:DsRed+ population. This method remains a valuable and rapid approach to visually assess cell type heterogeneity, especially when evaluating samples where multiple immune cell lineages may be present. 

While there is some overlap with the methodology used in Wittamer et al., the context, scope, and tissue examined differ substantially. Thus, the inclusion of MGG staining in this study serves to complement our broader transcriptomic analyses by providing supporting morphological evidence specific to brain-resident immune cells.

We have now clarified this distinction in the revised manuscript to better differentiate the current work from our previous findings (see line 85).

Figure 5 data should be quantified.

Please refer to our response in the major comments section, where we address this question in detail.

Figure 7- Figure Supplement 1. J, K has no CD45:DsRed positive cells in baf3 mutants, which is counterintuitive because CD45:DsRed should capture all hematopoietic cells and is not specific to dendritic cells. 

It is correct that cd45 is a general leukocyte marker, labeling all immune cells, including dendritic cells. In this Figure, we used the Tg(cd45:DsRed) transgenic line to visualize the phenotype because it offers an alternative to IHC, with the advantage of strong endogenous fluorescence and easier screening of vibratome sections. However, this technique has limitations: due to fixation, only cells with high fluorescence (e.g. cd45^highdendritic cells) are captured, while those with medium/low expression (e.g. cd45^low microglia) are often not visible. This explains why fewer cells are observed in both wild-type and ba43 mutant brains (Figure 5 KN, Figure 7 – supplement 1 JK). While this approach is quicker and allows for thicker sections, IHC remains the preferred method for the rest of the analyses, including the use of additional markers to identify all relevant cell populations. 

Thank you for bringing this point of confusion to our attention. To improve clarity, we have amended the text in the relevant sections (see lines 704-706, and legend of Figure 7 Supplement 1)

Minor issues: 

The terms in the title, "A single-cell transcriptomic atlas..." are used. What is meant by "atlas"? A searchable database or website is not provided.

Please refer to our response in the major comments section, where we explain that we have made our dataset accessible through a searchable web interface (https://scrna-analysiszebrafish.shinyapps.io/scatlas/) which is now referenced in the Data Availability Statement.

This reviewer considers that it is offensive to use terminology such as "poorly characterized" in reference to others' work. 

Thank you for pointing this out. We understand the concern and have revised the wording to ensure it remains respectful and neutral when referring to previous work. The changes are reflected in lines 20 and 49.

The introduction of this manuscript should consider restructuring and editing. Example: Lines 51-57 introduce the importance of immune cells in zebrafish regeneration studies. However, this study does not investigate such processes. Additionally, the authors focus on the concept of immune heterogeneity in the brain throughout the text however, these studies have been conducted previously by others (Silva et al., 2021) at single-cell level.

The novelty of this manuscript is the identification of "dendritic-like cells" and yet the introduction and text are limited to 68-71 lines. The introduction would benefit by introducing this cell type "dendritic-like cells" and differences between vertebrates. 

Thank you for these valuable comments. In response, we have revised the introduction to better align with the focus of the study (see edited text in page 2). We now emphasize that, while macrophages have been extensively studied in zebrafish, dendritic cells remain much less well characterized in this model.  Also, while we acknowledge that Silva et al. addressed aspects of immune heterogeneity in the zebrafish brain, their study primarily focused on mononuclear phagocytes. In contrast, our work provides a broader and more detailed characterization of the brain immune landscape, integrating transcriptomic data with multiple fluorescent reporter lines and hematopoietic mutants to strengthen cell identity assignments. Importantly, we note that Silva et al. classified DC-like cells within the microglial compartment, whereas our findings support that these cells represent a distinct population. While our data challenge this specific aspect of their conclusions, we believe both studies offer complementary insights that collectively advance our understanding of zebrafish brain immunity. 

Though Figure 6 is a great conformation of scRNA sequencing, it seems redundant and should be supplemental data.

We respectfully disagree with the reviewer’s suggestion. We believe that presenting the data in Figure 6 as the main figure enhances its visibility and impact, particularly highlighting the distinction between microglia and DC-like cells, an aspect we consider highly valuable information for the zebrafish research community. This is especially important given that our conclusions challenge two previous independent reports, further underscoring the relevance of these findings to the field.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

The study by Li and coworkers addresses the important and fundamental question of replication initiation in Escherichia coli, which remains open, despite many classic and recent works. It leverages single-cell mRNA-FISH experiments in strains with titratable DnaA and novel DnaA activity reporters to monitor DNA activity peaks versus size. The authors find oscillations in DnaA activity and show that their peaks correlate well with the estimated population-average replication initiation volume across conditions and imposed dnaA transcription levels. The study also proposes a novel extrusion model where DNA-binding proteins regulate free DnaA availability in response to biomass-DNA imbalance. Experimental perturbations of H-NS support the model validity, addressing key gaps in current replication control frameworks.

Strengths:

I find the study interesting and well conducted, and I think its main strong points are:

(1) the novel reporters obtained with systematic synthetic biology methods, and combined with a titratable dnaA strain.

(2) the interesting perturbations (titration, production arrest, and H-NS).

(3) the use of single-cell mRNA FISH to monitor transcripts directly.

The proposed extrusion model is also interesting, though not fully validated, and I think it will contribute positively to the future debate.

We thank the reviewer for acknowledging the strengths of our study.

Weaknesses and Limitations:

(1) A relevant limitation in novelty is that DnaA activity and concentration oscillations have been reported by the cited Iuliani and coworkers previously by dynamic microscopy, and to a smaller extent by the other cited study by Pountain and coworkers using mRNA FISH.

(2) An important limitation is that the study is not dynamic. While monitoring mRNA is interesting and relevant, the current study is based on concentrations and not time variations (or nascent mRNA). Conversely, the study by Iuliani and coworkers, while having the drawback of monitoring proteins, can directly assess production rates. It would be interesting for future studies or revisions to monitor the strains and reporters dynamically, as well as using (as a control) the technique of this study on the chromosomal reporters used by Iuliani et al.

We acknowledge the value of dynamic measurements and clarify our methodological rationale.

While luliani et al. provided valuable temporal resolution through protein dynamics, our mRNA FISH approach achieves direct decoupling of transcriptional vs. post-translational regulation (Fig 4F-H), and condition flexibility across 7 growth rates (30-66 min doubling times). This trade-off sacrifices temporal resolution for enhanced population-scale resolution and perturbation flexibility. To directly address temporal coupling, future work will implement dual-color live imaging of DnaA activity concurrent with replication initiation events.

(3) Regarding the mathematical models, a lot of details are missing regarding the definitions and the use of such models, which are only presented briefly in the Methods section. The reader is not given any tools to understand the predictions of different models, and no analytical estimates are used. The falsification procedures are not clear. More transparency and depth in the analysis are needed, unless the models are just used as a heuristic tool for qualitative arguments (but this would weaken the claims). The Berger model, for example, has many parameters and many regimes and behaviors. When models are compared to data (e.g., in Figure 2G), it is not clear which parameters were used, how they were fixed, and whether and how the model prediction depends on parameters.

We agree that model transparency is essential for quantitative validation. To address this, all model parameters (DnaA synthesis rate, activation/deactivation rates etc.) are explicitly tabulated in Supplementary Information Table S6. For the titration (Hansen et al. 1991) and extrusion models, we derive analytical expressions for initiation mass (IM) sensitivity to DnaA expression in Supplementary Note 1. For Figure 2G/S6, we used published parameters (Berger & Wolde 2022 SI Table 2) with experiment growth conditions (μ = 1.54 h^-1).

The extrusion model's validation relies primarily on its ability to resolve paradoxical initiation events under dnaA shutdown (Fig 6C), a test where other models fail categorically. While the Berger titration-switch hybrid can fit steady-state IM trends (Fig S6A), it cannot reproduce post-shutdown dynamics without ad hoc modifications (Fig S6B). We acknowledge that comprehensive analysis of all model regimes exceeds this study's scope but provide full simulation code for independent verification: https://github.com/BaiYangBqdq/dynamics_of_biomass_DNA_coordination

(4) Importantly, the main statement about tight correlations of peak volumes and average estimated initiation volume does not establish coincidence, and some of the claims by the authors are unclear in these respects (e.g., when they say "we resolve a 1:1 coupling between DnaA activity thresholds and replication initiation", the statement could be correct but is ambiguous). Crucially, the data rely on average initiation volumes (on which there seems to be an eternally open debate, also involving the authors), and the estimate procedure relies on assumptions that could lead to biases and uncertainties added to the population variability (in any case, error bars are not provided).

We acknowledge the limitations of population-level inference and have refined our claims: "Replication initiation volume scales proportionally with peak DnaA activity volume with a slope of 1.0 (R₂=0.98, Fig 7G), indicating predictive correspondence rather than absolute coincidence. While population-level  𝑉_𝑖 estimation cannot resolve single-cell stochasticity, the consistent 𝑉*: 𝑉_𝑖 relationship across 20 conditions suggest DnaA activity thresholds predict initiation timing within physiological error margins”. Future work will implement simultaneously DnaA activity and replication forks by using microfluidic single-cell tracking.

(5) The delays observed by the authors (in both directions) between the peaks of DnaAactivity conditional averages with respect to volume and the average estimated initiation volumes are not incompatible with those observed dynamically by Iuliani and coworkers. The direct experiment to prove the authors' point would be to use a direct proxy of replication initiation, such as SeqA or DnaN, and monitor initiations and quantify DnaA activity peaks jointly, with dynamic measurements.

We acknowledge the observed temporal deviations between DnaA activity peaks (𝑉*) and population-derived volumes at initiation ( 𝑉_𝑖) in certain conditions, in line with the findings of Iuliani et al. This might be mechanistically consistent with the time required for orisome assembly or oriC sequestration. They do not contradict our core finding that initiation occurs at a defined DnaA activity threshold (slope=1.0, R₂=0.98 in 𝑉*: 𝑉_𝑖 correlation).

(6) While not being an expert, I had some doubt that the fact that the reporters are on plasmid (despite a normalization control that seems very sensible) might affect the measurements. Also, I did not understand how the authors validated the assumptions that the reporters are sensitive to DnaA-ATP specifically. It seems this assumption is validated by previous studies only.

We employed a plasmid-based reporter system to circumvent the significant confounding effects of chromosomal position on promoter activity, as extensively documented by Pountain et al., where local genomic context (e.g., nucleoid occlusion, supercoiling gradients, and neighboring operons) introduces uncontrolled variability. By housing the P_syn66 test promoter and P_con normalization control in identical low-copy pSC101 vectors (<8 copies/ cell, Peterson & Phillips, Plasmid 2008), we ensured they experience equivalent physical and biochemical environments. This ratiometric design, where DnaA activity is calculated, actively corrects for global fluctuations in RNA polymerase availability, nucleotide pools, and plasmid copy number. Critically, P_syn66’s architecture emulates natural DnaA-responsive elements: its strong DnaAboxes report free DnaA concentration, while its weak box is preferentially bound by DnaA-ATP (Speck et al., EMBO journal 1999), mirroring the nucleotide-state sensitivity of oriC and the native dnaA promoter. This system was indispensable for our central finding, as it uniquely enabled the decoupling of DnaA activity oscillations from transcriptional feedback (Fig. 4F-H), an experiment fundamentally impossible with chromosomally integrated reporters due to autoregulatory interference.

Overall Appraisal:

In summary, this appears as a very interesting study, providing valuable data and a novel hypothesis, the extrusion model, open to future explorations. However, given several limitations, some of the claims appear overstated. Finally, the text contains some selfevaluations, such as "our findings redefine the paradigm for replication control", etc., that appear exaggerated.

We thank the reviewer for highlighting the need for precise language in framing our conclusions. We have implemented the following substantive revisions throughout the manuscript to ensure claims align strictly with empirical evidence:

(1) Changed "redefine the paradigm for replication control" into "advance the paradigm for replication control" (Introduction)

(2) Changed "redefine bacterial cell cycle control" into "refine bacterial cell cycle control as a dynamic interplay..." (Discussion)

(3) Removed the term "spatial" from the Discussion's description of DnaA-chromosome interactions (Discussion, first paragraph).

(4) Changed "provides a blueprint" into "provides a valuable tool for dissecting spatial regulation..." (Discussion, final paragraph)

(5) Scrutinized all superlatives (e.g., "critical feat" into "important capability"; "fundamental principle of cellular organization" into "potential organizational strategy")

(6) Replaced the instances of "robust" with evidence-backed descriptors (e.g., "sensitive," "consistent")

(7) We agree that the extrusion model requires further validation and have emphasized this in Discussion: "While H-NS perturbation supports extrusion mechanism, future work should identify the full extruder interactome and elucidate how metabolic signals modulate their activity" (final paragraph)

This calibrated language more accurately represents our study as a conceptual advance with testable mechanisms, not a complete paradigm shift.

Reviewer #2 (Public review):

Summary:

The authors show that in E. coli, the initiator protein DnaA oscillates post-translationally: its activity rises and peaks exactly when DNA replication begins, even if dnaA transcription is held constant. To explain this, they propose an "extrusion" mechanism in which nucleoidassociated proteins such as H-NS, whose amount grows with cell volume, dislodge DnaA from chromosomal binding sites; modelling and H-NS perturbations reproduce the observed drop in initiation mass and extra initiations seen after dnaA shut-down. Together, the data and model link biomass growth to replication timing through chromosome-driven, posttranslational control of DnaA, filling gaps left by classic titration and ATP/ADP-switch models.

Strengths:

(1) Introduces an "extrusion" model that adds a new post-translational layer to replication control and explains data unexplained by classic titration or ATP/ADP-switch frameworks.

(2) A major asset of the study is that it bridges the longstanding gap between DnaA oscillations and DNA-replication initiation, providing direct single-cell evidence that pulses of DnaA activity peak exactly at the moment of initiation across multiple growth conditions and genetic perturbations.

(3) A tunable dnaA strain and targeted H-NS manipulations shift initiation mass exactly as the model predicts, giving model-driven validation across growth conditions.

(4) A purpose-built Psyn66 reporter combined with mRNA-FISH captures DnaA-activity pulses with cell-cycle resolution, providing direct, compelling data.

We thank the reviewer for acknowledging the strengths of our study.

Weaknesses:

(1) What happens to the (C+D) period and initiation time as the dnaA mRNA level changes? This is not discussed in the text or figure and should be addressed.

We thank the reviewer for this important observation. Our data demonstrate that increased dnaA mRNA levels induce two compensatory changes in cell cycle progression:

(1) Earlier replication initiation, manifested as a reduced initiation mass: the initiation mass decreased from 5.6 to 2.6 (OD₆₀₀·ml per 10¹⁰ cells) as the relative dnaA mRNA level increased from 0.2 to 7.2 (normalized to the wild-type level) (Fig. 2F, red).

(2) Prolonged C+D period: Increased by approximately 60% (from 1.05 to 1.66 hours, Fig. 2F blue).

The complete quantitative relationship is now explicitly described in the Results section: “Concurrently, the initiation mass was reduced by 50%, and the period from initiation to division (C+D) was increased by ~60% (Fig. 2F)”

(2) It is unclear what is meant by "relative dnaA mRNA level." Relative to what? Wild-type expression? Maximum expression? This should be explicitly defined.

The relative dnaA mRNA level was obtained by normalizing to that in wild-type MG1655 cells grown in the same medium. To clarify this point, we have now marked the wild-type level in Fig. 1B, and a clear description of this has also been included in the figure caption.

(3) It would be helpful to provide some intuition for why an increase in dnaA mRNA level leads to a decrease in initiation mass per ori and an increase in oriC copy number.

Thank you for your valuable suggestion. Increased dnaA mRNA accelerates DnaA accumulation, causing cells to reach the initiation threshold at a smaller cell size (reducing initiation mass, Fig. 2F red). This earlier initiation increases oriC copies per cell at populational level (Fig. 2E). This mechanistic interpretation now appears in the Results: “As the DnaA expression level increases, DnaA activity reaches the initiation threshold earlier. Given that cell mass remained nearly unchanged, this earlier initiation led to an increase in population-averaged cellular oriC numbers (Fig. 2E).”

(4) The titration and switch models do not explicitly include dnaA mRNA in the dynamics of DnaA protein. Yet, in Figure 2G, initiation mass is shown to decrease linearly with dnaA mRNA level in these models. How was dnaA mRNA level represented or approximated in these simulations?

All models presented in this article omit explicit modeling of dnaA mRNA dynamics for simplicity. However, at steady state, the relative level of dnaA mRNA can be approximated by the relative expression rate of DnaA protein, as both reflect the expression level of DnaA. This detail is now clarified in the caption of Figure 2G.

(5) Is Schaechter's law (i.e., exponential scaling of average cell size with growth rate) still valid under the different dnaA mRNA expression conditions tested?

Schaechter's law describes the exponential scaling of average cell size with growth rate in bacteria. In our prior work (Zheng et al., Nature Microbiology 2020), where we demonstrated that Schaechter's law fails in slow-growth regimes. However, in current study, growth rate remained constant across different dnaA expression levels (Fig. 2C), and cell mass showed no significant change (Fig. 2D). Since Schaechter's law specifically addresses how cell size scales with growth rate, it does not apply here, as growth rate was invariant in our perturbations, which selectively alter replication initiation dynamics, not growth rate or size scaling.

(6) The manuscript should explain more explicitly how the extrusion model implements posttranslational control of DnaA and, in particular, how this yields the nonlinear drop in relative initiation mass versus dnaA mRNA seen in Figure 6E. Please provide the governing equation that links total DnaA, the volume-dependent "extruder" pool, and the threshold of free DnaA at initiation, and show - briefly but quantitatively - how this equation produces the observed concave curve.

The governing equations linking initiation mass and DnaA expression level is now provided in Supplementary Note S1 for both the titration and the extrusion model. In general, the dependence of initiation mass (𝑉_𝐼) on dnaA expression level (𝛼_𝐴) dependency takes an inverse 1 proportionality form: . In the extrusion model, the incorporated extruder protein is assumed to have similar synthesis dynamics as DnaA and can release DnaA from DnaA-box. After denoting the synthesis rate of the extruder as 𝛼_𝐻, the combined effect of DnaA and the extruder on replication initiation can be briefly described as: . Then the additive contribution of 𝛼_𝐻 dampens the sensitivity of initiation mass to changes in 𝛼_𝐴, resulting in a significantly flattened curve. As a result, the predicted 𝑉_𝐼 − 𝛼_𝐴 relationship has a concave shape in the semi-log plots.

(7) Does this Extrusion model give well well-known adder per origin, i.e., initiation to initiation is an adder.

Yes, the extrusion model can provide the initiation-to-initiation adder phenomenon, this information was provided in fig. S3C.

(8) DnaA protein or activity is never measured; mRNA is treated as a linear proxy. Yet the authors' own narrative stresses post-translational (not transcriptional) control of DnaA. Without parallel immunoblots or activity readouts, it is impossible to know whether a sixfold mRNA increase truly yields a proportional rise in active DnaA.

We acknowledge the reviewer's valid concern regarding the indirect nature of our DnaA activity measurements. While mRNA levels alone cannot resolve active DnaA dynamics, our approach integrates functional replication outcomes with a validated synthetic reporter to infer activity. Crucially, elevated dnaA mRNA causes demonstrable biological effects: earlier replication initiation (Fig. 2F) and increased oriC copies (Fig. 2E), directly confirming enhanced functional DnaA activity at the oriC locus. The P_syn66 reporter, engineered with DnaA-boxes mirroring oriC's architecture, provides orthogonal validation, showing progressive repression to dnaA induction (Fig. 3C). Our operational metric , bases on P_syn66 responds sensitively to DnaA-chromosome interactions within its characterized 8-fold dynamic range (Fig. 3C). Immunoblots would be inadequate here, as they cannot distinguish functionally critical pools: free versus chromosome-bound DnaA, or DnaA-ATP versus DnaAADP, precisely the post-translational states our study implicates in regulation. We therefore prioritize functional readouts (initiation timing) and the P_syn66 reporter, which probes the biologically active fraction relevant to replication control.

(9) Figure 2 infers both initiation mass and oriC copy number from bulk measurements (OD₆₀₀ per cell and rifampicin-cephalexin run-out) instead of measuring them directly in single cells. Any DnaA-dependent changes in cell size, shape, or antibiotic permeability could skew these bulk proxies, so the plotted relationships may not accurately reflect true initiation events.

We acknowledge the reviewer's valid methodological concern and clarify that while bulk measurements carry inherent limitations, our approach is grounded in established techniques with demonstrated reliability. Cell mass was inferred from OD600/cell, which correlates strongly with direct dry weight measurements and microscopic cell volumes across diverse growth conditions, as validated in our prior work (Zheng et al., Nature Microbiology 2020). Crucially, cell mass remained invariant across dnaA expression levels (Fig. 2D).

Regarding oriC quantification, the rifampicin-cephalexin run-out assay is a wildly applied for replication initiation studies. Our data shows expected 2ⁿ oriC distributions without abnormal ploidy (as shown below). While single-cell methods offer superior resolution, our bulk approach provides accurate population-level trends.

Author response image 1.

Recommendations for the authors:

Reviewing Editor Comments:

The reviewers felt that the mathematical modeling was not adequately explained in the paper, and that this affected the readability of the manuscript. The authors are encouraged to elaborate on this aspect of the paper (in addition to strengthening other claims, if possible, per the reviewers' comments).

We thank the editor and reviewers for their constructive feedback. We have comprehensively strengthened the mathematical modeling framework to enhance clarity and rigor.

Reviewer #1 (Recommendations for the authors):

The only revision I would do is a recalibration of the claims and a major effort to clarify the modeling part (including a detailed SI appendix), without necessarily performing additional work.

To enhance mathematical modeling transparency, we have completed model description in the method section and a parameter table with literature-sourced values in Supplementary Information Table S6. Moreover, analytical derivations of initiation mass dependencies are performed and presented in the Supplementary Information Note S1.

Of course, there are extra experiments (mentioned in the public review) that would help support some of the big claims, but that can be considered a different project.

Thank you for your suggestion. This will be addressed in our future work.

Minor suggestion: please put signposts or plot jointly to compare the maxima/minima in Figures 4D, E, G, and H.

We added dashed lines in Figures 4D, and E, to synchronize visualization of DnaA activity peaks and transcriptional minima across panels, facilitating direct biological comparisons.

Reviewer #2 (Recommendations for the authors):

(1) Should define what DNA activity is.

We have explicitly defined DnaA activity in the Introduction as “the capacity to initiate replication…” and noted that it is “governed by free DnaA concentration, DnaA-ATP/-ADP ratio, and orisome assembly competence”.

(2) Word repetition - “...grown in in Luria-Bertani (LB) medium...”.

Corrected.

(3) Typographical error - “FISH ... was preformed" should be "performed”.

Corrected.

(4) The manuscript alternates between “ng ml^-1” and “ng·ml^-1”; choose one style and apply it uniformly.

Standardized the units to ng·ml^-1 throughout.

(5) Reference duplicates - Some citations appear twice in the bibliography (e.g., "Bintu et al., 2005a/b" and "Bintu et al., 2005b" listed again later).

The studies by Bintu et al. (2005a, 2005b) represent separate works: 2005a details applications, and 2005b develops models.

Author response:

Response to Reviewer #1:

We plan to extend the discussion section to discuss the clinical implications of this new function. We will note the algorithm's applicability to broader genetic counseling contexts beyond cancer risk assessment.

Response to Reviewer #2:

We will clarify the four points raised:

(1) "Close-to-optimal" definition: We will explain that in multiple-mating cases, finding the global optimum is NP-hard (equivalent to the Weighted Feedback Vertex Set problem). We will clarify that our greedy algorithm provides practically efficient solutions suitable for clinical use, though without theoretical optimality guarantees.

(2) Example clarity: We will improve Figure 1's caption to explain the cost calculations and note that with equal weights, both shown solutions are equivalent.

(3) Non-optimal examples: We will describe scenarios where the greedy algorithm may not achieve the global optimum, particularly in multiple-mating cases with heterogeneous weights.

(4) Warning message: The current version not provide a warning when the solution might be non-optimal. This may be added in the future to the function.

We appreciate your feedback and suggestions to help improve the manuscript.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review):

This work provides a new Python toolkit for combining generative modeling of neural dynamics and inversion methods to infer likely model parameters that explain empirical neuroimaging data. The authors provided tests to show the toolkit's broad applicability, accuracy, and robustness; hence, it will be very useful for people interested in using computational approaches to better understand the brain.

Strengths:

The work's primary strength is the tool's integrative nature, which seamlessly combines forward modelling with backward inference. This is important as available tools in the literature can only do one and not the other, which limits their accessibility to neuroscientists with limited computational expertise. Another strength of the paper is the demonstration of how the tool can be applied to a broad range of computational models popularly used in the field to interrogate diverse neuroimaging data, ensuring that the methodology is not optimal to only one model. Moreover, through extensive in-silico testing, the work provided evidence that the tool can accurately infer ground-truth parameters even in the presence of noise, which is important to ensure results from future hypothesis testing are meaningful.

We appreciate the positive feedback on our open-source tool that delivers rapid forward simulations and flexible Bayesian model inversion for a broad range of whole-brain models, with extensive in-silico validation, including scenarios with dynamical/additive noise.

Weaknesses

The paper still lacks appropriate quantitative benchmarking relative to non-Bayesian-based inference tools, especially with respect to performance accuracy and computational complexity and efficiency. Without this benchmarking, it is difficult to fully comprehend the power of the software or its ability to be extended to contexts beyond large-scale computational brain modelling.

Non-Bayesian inference methods were beyond the scope of this study, as we focused on full posterior estimation to enable uncertainty quantification and detection of degeneracy. Their advantages and disadvantages are briefly discussed in the Introduction and Discussion sections.

Reviewer #2 (Public review):

Whole-brain network modeling is a common type of dynamical systems-based method to create individualized models of brain activity incorporating subject-specific structural connectome inferred from diffusion imaging data. This type of model has often been used to infer biophysical parameters of the individual brain that cannot be directly measured using neuroimaging but may be relevant to specific cognitive functions or diseases. Here, Ziaeemehr et al introduce a new toolkit, named "Virtual Brain Inference" (VBI), offering a new computational approach for estimating these parameters using Bayesian inference powered by artificial neural networks. The basic idea is to use simulated data, given known parameters, to train artificial neural networks to solve the inverse problem, namely, to infer the posterior distribution over the parameter space given data-derived features. The authors have demonstrated the utility of the toolkit using simulated data from several commonly used whole-brain network models in case studies.

Strength:

Model inversion is an important problem in whole-brain network modeling. The toolkit presents a significant methodological step up from common practices, with the potential to broadly impact how the community infers model parameters.

Notably, the method allows the estimation of the posterior distribution of parameters instead of a point estimation, which provides information about the uncertainty of the estimation, which is generally lacking in existing methods.

The case studies were able to demonstrate the detection of degeneracy in the parameters, which is important. Degeneracy is quite common in this type of models. If not handled mindfully, they may lead to spurious or stable parameter estimation. Thus, the toolkit can potentially be used to improve feature selection or to simply indicate the uncertainty.

In principle, the posterior distribution can be directly computed given new data without doing any additional simulation, which could improve the efficiency of parameter inference on the artificial neural network is well-trained.

We thank the reviewer for the careful consideration of important aspects of the VBI tool, such as uncertainty quantification rather than point estimation, degeneracy detection, features selection, parallelization, and amortization strategy.

Weaknesses:

The z-scores used to measure prediction error are generally between 1-3, which seems quite large to me. It would give readers a better sense of the utility of the method if comparisons to simpler methods, such as k-nearest neighbor methods, are provided in terms of accuracy. - A lot of simulations are required to train the posterior estimator, which is computationally more expensive than existing approaches. Inferring from Figure S1, at the required order of magnitudes of the number of simulations, the simulation time could range from days to years, depending on the hardware. The payoff is that once the estimator is well-trained, the parameter inversion will be very fast given new data. However, it is not clear to me how often such use cases would be encountered. It would be very helpful if the authors could provide a few more concrete examples of using trained models for hypothesis testing, e.g., in various disease conditions.

We agree with the reviewer that for some parameters the z-score is large, which could be due to the limited number of simulations, the informativeness of the data features, or non-identifiability, and we do address these possible limitations in the Discussion. In line with our previous study, we stick to Bayesian metrics such as posterior z-scores and shrinkage. The application of an amortized strategy needs to be demonstrated in future work, for example in anonymized personalization of virtual brain twins (Baldy et al., 2025).

Ref: Baldy N, Woodman MM, Jirsa VK. Amortizing personalization in virtual brain twins. arXiv preprint arXiv:2506.21155.

Reviewer #1 (Recommendations for the authors):

(1) The authors want to keep the term "spatio-temporal" data features to make it consistent with the language they use in their code, even though they only refer to statistical and temporal features of the time series. I stand by my previous comment that this is misleading and should be avoided as much as possible because it doesn't take into account the actual spatial characteristics of the data. At the very least, the authors should recognize this in the text.

We have now recognized this point.

(2) There are still some things that need further clarification and/or explanation:

(a) It remains unclear why PCA needs to be applied to the FC/FCD matrices. It was also unclear how many PCs were kept as data features.

We aim to use as many features as possible as a battery of metrics to reduce the number of simulations. The role of each feature can be investigated in future studies.  For instance, PCA is used in the LEiDA approach (Cabral et al., 2017) to enhance robustness to high-frequency noise, thereby overcoming a limitation common to all quasi-instantaneous measures of FC. In this work, the default setting was two PCA components. 

Ref:  Cabral J, Vidaurre D, Marques P, Magalhães R, Silva Moreira P, Miguel Soares J, Deco G, Sousa N, Kringelbach ML. Cognitive performance in healthy older adults relates to spontaneous switching between states of functional connectivity during rest. Scientific reports. 2017 Jul 11;7(1):5135.

(b) It was also unclear which features were used for each model. This is important for reproducibility and to make the users of the software aware of which features are most likely to work best for each model.

We have done our best to indicate the class of features used in each case. This is illustrated more clearly in the notebook examples provided in the repository.

Reviewer #2 (Recommendations for the authors):

Thanks for responding to my suggestions. Here is only one remaining point:

Section 2.1: Please mention the atlas used to parcellate the brain; without this information, readers won't know what area 88 is in Figure 1, for example. 

We have now mentioned this point. In this study we used AAL Atlas.

Author response:

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

Reviewer #1 (Public review): 

In recent years, our understanding of the nuclear steps of the HIV-1 life cycle has made significant advances. It has emerged that HIV-1 completes reverse transcription in the nucleus and that the host factor CPSF6 forms condensates around the viral capsid. The precise function of these CPSF6 condensates is under investigation, but it is clear that the HIV-1 capsid protein is required for their formation. This study by Tomasini et al. investigates the genesis of the CPSF6 condensates induced by HIV-1 capsid, what other co-factors may be required, and their relationship with nuclear speckels (NS). The authors show that disruption of the condensates by the drug PF74, added post-nuclear entry, blocks HIV-1 infection, which supports their functional role. They generated CPSF6 KO THP-1 cell lines, in which they expressed exogenous CPSF6 constructs to map by microscopy and pull down assays of the regions critical for the formation of condensates. This approach revealed that the LCR region of CPSF6 is required for capsid binding but not for condensates whereas the FG region is essential for both. Using SON and SRRM2 as markers of NS, the authors show that CPSF6 condensates precede their merging with NS but that depletion of SRRM2, or SRRM2 lacking the IDR domain, delays the genesis of condensates, which are also smaller. 

The study is interesting and well conducted and defines some characteristics of the CPSF6-HIV-1 condensates. Their results on the NS are valuable. The data presented are convincing. 

I have two main concerns. Firstly, the functional outcome of the various protein mutants and KOs is not evaluated. Although Figure 1 shows that disruption of the CPSF6 puncta by PF74 impairs HIV-1 infection, it is not clear if HIV-1 infection is at all affected by expression of the mutant CPSF6 forms (and SRRM2 mutants) or KO/KD of the various host factors. The cell lines are available, so it should be possible to measure HIV-1 infection and reverse transcription. Secondly, the authors have not assessed if the effects observed on the NS impact HIV-1 gene expression, which would be interesting to know given that NS are sites of highly active gene transcription. With the reagents at hand, it should be possible to investigate this too. 

We thank the reviewer for her/his valuable feedback on our manuscript. We are pleased to see her/his appreciation of our results, and we did our utmost to address the highlighted points to further improve our work.

To correctly perform the infectivity assay, we generated stable cell clones—a process that required considerable time, particularly during the selection of clones expressing protein levels comparable to wild-type (WT) cells. To accurately measure infectivity, it was essential to use stable clones expressing the most important deletion mutant, ∆FG CPSF6, at levels similar to those of CPSF6 in WT cells (new Fig.5 A-B). Importantly, we assessed the reproducibility of our experiments by freezing and thawing these clones.

Regarding SRRM2, in THP-1 cells we were only able to achieve a knockdown, which still retains residual SRRM2 protein, albeit at much lower levels. Due to the essential role of SRRM2 in cell survival, obtaining a complete knockout in this cell line is not feasible, making it difficult to draw definitive conclusions from these experiments.

In contrast, 293T cells carrying the endogenous SRRM2 deletion mutant (ΔIDR) cannot be infected with replication-competent HIV-1, as they lack expression of CD4 and either CCR4 or CCR5. These cells were instead used to monitor the dynamics of CPSF6 puncta assembly within nuclear speckles. However, they are not a suitable model for studying the impact of the depletion of SRRM2 in viral infection.

Thus, we performed infectivity assays in a more relevant cell line for HIV-1 infection, THP-1 macrophage-like cells, using both a single-round virus and a replication-competent virus. The new results, shown in Figure 5 C-D, indicate that complete depletion of CPSF6 reduces infectivity, as measured by luciferase expression in a single-round infection (KO: ~65%; ΔFG: ~74%; compared to WT: 100% on average). Notably, a more pronounced defect in viral particle production was observed when WT virus was used for infection (KO: ~21%; ΔFG: ~16%; compared to WT: 100% on average). These findings support the referee’s insightful suggestion that the absence of CPSF6 could also impair HIV-1 gene expression. 

Reviewer #2 (Public review): 

Summary: 

HIV-1 infection induces CPSF6 aggregates in the nucleus that contain the viral protein CA. The study of the functions and composition of these nuclear aggregates have raised considerable interest in the field, and they have emerged as sites in which reverse transcription is completed and in the proximity of which viral DNA becomes integrated. In this work, the authors have mutated several regions of the CPSF6 protein to identify the domains important for nuclear aggregation, in addition to the alreadyknown FG region; they have characterized the kinetics of fusion between CPSF6 aggregates and SC35 nuclear speckles and have determined the role of two nuclear speckle components in this process (SRRM2, SUN2). 

Strengths: 

The work examines systematically the domains of CPSF6 of importance for nuclear aggregate formation in an elegant manner in which these mutants complement an otherwise CPSF6-KO cell line. In addition, this work evidences a novel role for the protein SRRM2 in HIV-induced aggregate formation, overall advancing our comprehension of the components required for their formation and regulation. 

Weaknesses: 

Some of the results presented in this manuscript, in particular the kinetics of fusion between CPSF6aggregates and SC35 speckles have been published before (PMID: 32665593; 32997983). 

The observations of the different effects of CPSF6 mutants, as well as SRRM2/SUN2 silencing experiments are not complemented by infection data which would have linked morphological changes in nuclear aggregates to function during viral infection. More importantly, these functional data could have helped stratify otherwise similar morphological appearances in CPSF6 aggregates. 

Overall, the results could be presented in a more concise and ordered manner to help focus the attention of the reader on the most important issues. Most of the figures extend to 3-4 different pages and some information could be clearly either aggregated or moved to supplementary data. 

First, we thank the reviewer for her/his appreciation of our study and to give to us the opportunity to better explain our results and to improve our manuscript. We appreciate the reviewer’s positive feedback on our study, and we will do our best to address her/his concerns. In the meantime, we would like to clarify the focus of our study. Our research does not aim to demonstrate an association between CPSF6 condensates (we use the term "condensates" rather than "aggregates," as aggregates are generally non-dynamic (Alberti & Hyman, 2021; Banani et al., 2017; Scoca et al., JMCB 2022), and our work specifically examines the dynamic behavior of CPSF6 puncta formed during infection and nuclear speckles. The association between CPSF6 puncta and NS has already been established in previous studies, as noted in the manuscript (PMID: 32665593; 32997983). The previous studies (PMID: 32665593; 32997983) showed that CPSF6 puncta colocalize with SC35 upon HIV infection and in the submitted study we study their kinetics.

About the point highlighted by the reviewer: "Kinetics of fusion between CPSF6-aggregates and SC35 speckles have been published before."  

Our study differs from prior work PMID 32665593 because we utilize a full-length HIV genome, and we did not follow the integrase (IN) fluorescence in trans and its association with CPSF6 but we specifically assess if CPSF6 clusters form in the nucleus independently of NS factors and next to fuse with them. In the current study we evaluated the dynamics of formation of CPSF6/NS puncta, which it has not been explored before. Given this focus, we believe that our work offers a novel perspective on the molecular interactions that facilitate HIV / CPSF6-NS fusion.

We calculated that 27% of CPSF6 clusters were independent from NS at 6 h post-infection, compared to only 9% at 30 h. This likely reflects a reduction in individual clusters as more become fused with nuclear speckles over time. At the same time, these data suggest that the fusion process can begin even earlier. Indeed, it has been reported that in macrophages, the peak of viral nuclear import occurs before 6 h post-infection (doi: 10.1038/s41564-020-0735-8).

In addition, we have incorporated new experiments assessing viral infectivity in the absence of CPSF6, or in CPSF6-knockout cells expressing either a CPSF6 mutant lacking the FG peptide or the WT protein. As shown in our new Figure 5, these results demonstrate that the FG peptide is critical for viral replication in THP-1 cells.

For better clarity, we would like to specify that our study focuses on the role of SON, a scaffold factor of nuclear speckles, rather than SUN2 (SUN domain-containing protein 2), which is a component of the LINC (Linker of Nucleoskeleton and Cytoskeleton) complex.

As suggested by the reviewer, we have revised the text and combined figures to improve clarity and facilitate reader comprehension. We appreciate the constructive comment of the reviewer.

Reviewer #3 (Public review): 

In this study, the authors investigate the requirements for the formation of CPSF6 puncta induced by HIV-1 under a high multiplicity of infection conditions. Not surprisingly, they observe that mutation of the Phe-Gly (FG) repeat responsible for CPSF6 binding to the incoming HIV-1 capsid abrogates CPSF6 punctum formation. Perhaps more interestingly, they show that the removal of other domains of CPSF6, including the mixed-charge domain (MCD), does not affect the formation of HIV-1-induced CPSF6 puncta. The authors also present data suggesting that CPSF6 puncta form individual before fusing with nuclear speckles (NSs) and that the fusion of CPSF6 puncta to NSs requires the intrinsically disordered region (IDR) of the NS component SRRM2. While the study presents some interesting findings, there are some technical issues that need to be addressed and the amount of new information is somewhat limited. Also, the authors' finding that deletion of the CPSF6 MCD does not affect the formation of HIV-1-induced CPSF6 puncta contradicts recent findings of Jang et al. (doi.org/10.1093/nar/gkae769). 

We thank the reviewer for her/his thoughtful feedback and the opportunity to elaborate on why our findings provide a distinct perspective compared to those of Jang et al. (doi.org/10.1093/nar/gkae769).

One potential reason for the differences between our findings and those of Jang et al. could be the choice of experimental systems. Jang et al. conducted their study in HEK293T cells with CPSF6 knockouts, as described in Sowd et al., 2016 (doi.org/10.1073/pnas.1524213113). In contrast, our work focused on macrophage-like THP-1 cells, which share closer characteristics with HIV-1’s natural target cells. 

Our approach utilized a complete CPSF6 knockout in THP-1 cells, enabling us to reintroduce untagged versions of CPSF6, such as wild-type and deletion mutants, to avoid potential artifacts from tagging. Jang et al. employed HA-tagged CPSF6 constructs, which may lead to subtle differences in experimental outcomes due to the presence of the tag.

Finally, our investigation into the IDR of SRRM2 relied on CRISPR-PAINT to generate targeted deletions directly in the endogenous gene (Lester et al., 2021, DOI: 10.1016/j.neuron.2021.03.026). This approach provided a native context for studying SRRM2’s role.

We will incorporate these clarifications into the discussion section of the revised manuscript.  

Reviewer #1 (Recommendations for the authors): 

(1) Figure 2E: The statistical analysis should be extended to the comparison between the "+HIV" samples. 

We showed the statistics between only HIV+ cells now new Fig. 2D.  

(2) Figure 4A top panel is out of focus. 

We modified the figure now figure 6A.

Reviewer #2 (Recommendations for the authors): 

(1) Some of the sentences could be rewritten for the sake of simplicity, also taking care to avoid overstatement. 

We modified the sentences as best as we could.

(2) For instance: There is no evidence that "viral genomes in nuclear niches may be contributing to the formation of viral reservoirs" (lines 33-35). 

We changed the sentence as follows: “Despite antiretroviral treatment, viral genomes can persist in these nuclear niches and reactivate upon treatment interruption, raising the possibility that they could play a role in the establishment of viral reservoirs.”

(3) Line 53: unclear sentence. "The initial stages of the viral life cycle have been understood....." The authors certainly mean reverse transcription, but as formulated this is not clear. The authors should also bear in mind that reverse transcription starts already in budding/just released virions. 

We clarified the concept as follows: “the initial stages of the viral life cycle, such as the reverse transcription (the conversion of the viral RNA in DNA) and the uncoating (loss of the capsid), have been understood to mainly occur within the host cytoplasm.”

(4) Line 124: the results in Figure 1 are not at all explained in the text. PF74 does not act on CPSF6, it acts on CA and this in turn leads to CPS6 puncta disappearance. 

PF74 binds the same hydrophobic pocket of the viral core as CPSF6. However, when viral cores are located within CPSF6 puncta, treatment with a high dose of PF74 leads to a rapid disassembly of these puncta, while viral cores remain detectable up to 2 hours post-treatment (Ay et al., EMBO J. 2024). Here, we simply describe what we observed by confocal microscopy. Said that HIV-Induced CPSF6 Puncta include both CPSF6 proteins and viral cores as we have now specified.

(5) Line 130; 'hinges into two key ...' should be 'hinges on'. 

Thanks we modified it.

(6) Supplementary Figures are not cited sequentially in the text. 

We have now modified the numbers of the supplementary figures according to their appearance in the text.

(7) Line 44: define FG. 

We defined it.

Reviewer #3 (Recommendations for the authors): 

Specific comments that the authors should address are outlined below. 

(1) As mentioned in the summary above, the authors' findings seem to be in direct contradiction with recent work published by Alan Engelman's lab in NAR. The authors should address the possible reason(s) for this discrepancy. 

We mention the potential reasons for the differences in the results between our study and Engelman’s lab study in the discussion.

(2) The major finding here that deletion of the CFSF6 FG repeat prevents the formation of CFSP6 puncta is unsurprising, as the FG repeat is responsible for capsid binding. This has been reported previously and such mutants have been used as controls in other studies. 

Our study demonstrates that the FG domain is the sole region responsible for the formation of CPSF6 puncta, rather than the LCR or MCD domains. The unique role of the FG domain in CPSF6 that promotes the formation of CPSF6 puncta without the help of the other IDRs during viral infection is a finding particularly novel, as it has not yet been reported in the literature.

(3) Line 339, the authors state: "incoming viral RNA has been observed to be sequestered in nuclear niches in cells treated with the reversible reverse transcriptase inhibitor, NEV. When macrophage-like cells are infected in the presence of NEV, the incoming viral RNA is held within the nucleus (Rensen et al., 2021; Scoca et al., 2023). This scenario is comparable to what is observed in patients undergoing antiretroviral therapy". In what way is this comparable to what is observed in individuals on ART? I see no basis for this statement. Sequestration of viral RNA in the nucleus is not the basis for maintaining the viral reservoir in individuals on therapy. 

Thanks, we rephrased the sentence.

(4) General comment: analyzing single-cell-derived KO clones is very risky because of random clonal variability between individual cells in the population. If single-cell-derived clones are used, phenotypes could be confirmed with multiple, independent clones. 

We used a clone completely KO for CPSF6 mainly to investigate the role of a specific domain in condensate formation and it will be difficult that clone selection could have introduced artifacts in this context. Other available clones retain residual endogenous protein, which prevents us from accurately assessing CPSF6 cluster formation in the various deletion mutants. A complete CPSF6 knockout is essential for studying puncta formation, as it eliminates potential artifacts arising from protein tags that could alter the phase separation properties of the protein under investigation.

(5) Line 214. "It is predicted to form two short α helices and a ß strand, arranged as: α helix - FG - ß strand - α helix". What is this based on? No citation is provided and no data are shown. 

In fact, the statement "It is predicted to form two short α helices and a ß strand, arranged as: α helix - FG - ß strand - α helix" is based on the data shown in Figure 4E presenting data generated by PSIPRED. 

(6) Figure 1B. "Luciferase values were normalized by total proteins revealed with the Bradford kit". What does this mean? I couldn't find anything explaining how the viral inputs were normalized. 

The amount of the virus used is the same for all samples, we used MOI 10 as described in the legend of Figure 1. It is important to normalize the RLU (luciferase assay) with the total amount of proteins to be sure that we are comparing similar number of cells. Obviously, the cells were plated on the same amount on each well, the normalization in our case it is just an additional important control.

(7) I can't interpret what is being shown in the movies. 

We updated the movie 1B and rephrased the movie legends and we added a new suppl. Fig.4B.

(8) Figure 5B. The differences seen are very small and of questionable significance. The data suggest that by 6 hpi, around 75% of HIV-induced CPSF6 puncta are already fused with NSs. 

We calculated that 27% of CPSF6 clusters were independent from NS at 6 h post-infection, compared to only 9% at 30 h. This likely reflects a reduction in individual clusters as more become fused with nuclear speckles over time. At the same time, these data suggest that the fusion process can begin even earlier. Indeed, it has been reported that in macrophages, the peak of viral nuclear import occurs before 6 h post-infection (doi: 10.1038/s41564-020-0735-8).

(9) Figure 6. Immunofluorescence is not a good method for quantifying KD efficiency. The authors should perform western blotting to measure KD efficiency. This is an important point, because the effect sizes are small, quite likely due to incomplete KD. 

We performed WB and quantified the results, which correlated with the IF data and their imaging analysis. These new findings have been incorporated into Figure 8A. Of note, deletion of the IDR of SRRM2 does not affect the number of SON puncta (Fig.8C), but significantly reduces the number of CPSF6 puncta in infected cells compared to those expressing full-length SRRM2 (Fig.8D).

(10) There are a variety of issues with the text that should be corrected. 

The authors use "RT" to mean both the enzyme (reverse transcriptase) and the process (reverse transcription). This is incorrect and will confuse the reader. RT refers to the enzyme (noun, not verb). 

The commonly used abbreviation for nevirapine is NVP, not NEV. 

In line 60, it is stated that the capsid contains 250 hexamers. This number is variable, depending on the size and shape of the capsid. By contrast, the capsid has exactly 12 pentamers. 

Line 75. Typo: "nuclear niches containing, such as like". 

Line 82. Typo: "the mechanism behinds". 

Line 102. Typo: "we aim to elucidate how these HIV-induced CPSF6 form". 

Line 107. Type: "CPSF6 is responsible for tracking the viral core" ("trafficking the viral core"?). 

Thanks, we corrected all of them.

Author response:

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

Reviewer #1 (Public review): 

Zhu and colleagues used high-density Neuropixel probes to perform laminar recordings in V1 while presenting either small stimuli that stimulated the classical receptive field (CRF) or large stimuli whose border straddled the RF to provide nonclassical RF (nCRF) stimulation. Their main question was to understand the relative contribution of feedforward (FF), feedback (FB), and horizontal circuits to border ownership (Bown), which they addressed by measuring crosscorrelation across layers. They found differences in cross-correlation between feedback/horizontal (FH) and input layers during CRF and nCRF stimulation. 

Although the data looks high quality and analyses look mostly fine, I had a lot of difficulty understanding the logic in many places. Examples of my concerns are written below. 

(1) What is the main question? The authors refer to nCRF stimulation emerging from either feedback from higher areas or horizontal connections from within the same area (e.g. lines 136 to 138 and again lines 223-232). I initially thought that the study would aim to distinguish between the two. However, the way the authors have clubbed the layers in 3D, the main question seems to be whether Bown is FF or FH (i.e., feedback and horizontal are clubbed). Is this correct? If so, I don't see the logic, since I can't imagine Bown to be purely FF. Thus, just showing differences between CRF stimulation (which is mainly expected to be FF) and nCRF stimulation is not surprising to me. 

We thank the reviewer for their thoughtful comments. As explained in the discussion, we grouped cortical layers to reduce uncertainty in precisely assigning laminar boundaries and to increase statistical power. Consequently, this limits our ability to distinguish the relative contributions of feedback inputs, primarily targeting layers 1 and 6, and horizontal connections, mainly within layers 2/3 and 5. Nevertheless, previous findings, especially regarding the rapid emergence of B_own signals, suggest that feedback is more biologically plausible than horizontal-based mechanisms.

Importantly, the emergence of B_own signals in the primate brain should not be taken for granted. Direct physiological evidence that distinguishes feedforward from feedback/horizontal mechanisms has been lacking. While we agree it is unlikely that B_own is mediated solely by feedforward processing, we felt it was necessary to test this empirically, particularly using highresolution laminar recordings.

As discussed, feedforward models of B_own have been proposed (e.g., Super, Romeo, and Keil, 2010; Saki and Nishimura, 2006). These could, in theory, be supported by more general nCRF modulations arising through early feedforward inhibitions, such as those observed in the retinogeniculate pathway (e.g., Webb, Tinsley, Vincent and Derrington, 2005; Blitz and Regehr, 2005; Alitto and Usrey, 2008). However, most B_own models rely heavily on response latency, yet very few studies have recorded across layers or areas simultaneously to address this directly. Notably, recent findings in area V4 show that B_own signals emerge earlier in deep layers than in granular (input) layers, suggesting a non-feedforward origin (Franken and Reynolds, 2021).

Furthermore, although previous studies have shown that the nCRF can modulate firing rates and the timing of neuronal firing across layers, our findings go beyond these effects. We provide clear evidence that nCRF modulation also alters precise spike timing relationships and interlaminar coordination, and that the magnitude of nCRF modulation depends on these interlaminar interactions. This supports the idea that B_own , or more general nCRF modulation, involves more than local rate changes, reflecting layer-specific network dynamics consistent with feedback or lateral integration.

(2) Choice of layers for cross-correlation analysis: In the Introduction, and also in Figure 3C, it is mentioned that FF inputs arrive in 4C and 6, while FB/Horizontal inputs arrive at "superficial" and "deep", which I take as layer 2/3 and 5. So it is not clear to me why (i) layer 4A/B is chosen for analysis for Figure 3D (I would have thought layer 6 should have been chosen instead) and (ii) why Layers 5 and 6 are clubbed. 

We thank the reviewer for raising this important point. The confusion likely stems from our use of the terms “superficial” and “deep” layers when describing the targets of feedback/horizontal inputs. To clarify, by “superficial” and “deep,” we specifically refer to layers 1–3 and layers 5–6, respectively, as illustrated in Figure 3C. Feedback and horizontal inputs relatively avoid entire layer 4, including both 4C and 4A/B.

We also emphasize that the classification of layers as feedforward or feedback/horizontal recipients is relative rather than absolute. For example, although layer 6 receives both feedforward and feedback/horizontal inputs, it contains a higher proportion of feedback/horizontal inputs compared to layers 4C and 4A/B. 

We had addressed this rationale in the Discussion, but recognize it may not have been sufficiently emphasized. We have revised the main text accordingly to clarify this point for readers in the final manuscript version.

(3) Addressing the main question using cross-correlation analysis: I think the nice peaks observed in Figure 3B for some pairs show how spiking in one neuron affects the spiking in another one, with the delay in cross-correlation function arising from the conduction delay. This is shown nicely during CRF stimulation in Figure 3D between 4C -> 2/3, for example. However, the delay (positive or negative) is constrained by anatomical connectivity. For example, unless there are projections from 2/3 back to 4C which causes firing in a 2/3 layer neuron to cause a spike in a layer 4 neuron, we cannot expect to get a negative delay no matter what kind of stimulation (CRF versus nCRF) is used. 

We thank the reviewer for the insightful comment. The observation that neurons within FH_i laminar compartments (layers 2/3, 5/6) can lead those in layer 4 (4C, 4A/B) during nCRF stimulation may indeed seem unexpected. However, several anatomical pathways could mediate the propagation of B_own signals from FH_i compartments to layer 4. We have revised the Discussion section in the final version of the manuscript to address this point explicitly.

In Macaque V1, projections from layers 2/3 to 4A/B have been documented (Blasdel et al., 1985; Callaway and Wiser, 1996), and neurons in 4A/B often extend apical dendrites into layers 2/3 (Lund, 1988; Yoshioka et al., 1994). Although direct projections from layers 2/3 to 4C are generally sparse (Callaway, 1998), a subset of neurons in the lower part of layer 3 can give off collateral axons to 4C (Lund and Yoshioka, 1991). Additionally, some 4C neurons extend dendrites into 4B, enabling potential dendritic integration of inputs from more superficial layers (Somogyi and Cowey, 1981; Mates and Lund, 1983; Yabuta and Callaway, 1998). Sparse connections from 2/3 to layer 4 have also been reported in cat V1 (Binzegger, Douglas and Martin, 2004). Moreover, layers 2/3 may influence 4C neurons disynaptically, without requiring dense monosynaptic connections. 

Importantly, while CCGs can suggest possible circuit arrangements, functional connectivity may arise through mechanisms not fully captured by traditional anatomical tracing. Indeed, the apparent discrepancy between anatomical and functional data is not uncommon. For example, although 4B is known to receive anatomical input primarily from 4Cα, but not 4Cβ, photostimulation experiments have shown that 4B neurons can also be functionally driven by 4Cβ (Sawatari and Callaway, 1996). Our observation of functional inputs from layers 2/3 to layer 4 is also consistent with prior findings in rodent V1, where CCG analysis (e.g., Figure 7 in Senzai, Fernandez-Ruiz and Buzsaki, 2019) or photostimulation (Xu et al., 2016) revealed similar pathways. 

Layers 5/6 provide dense projections to layers 4A/B (Lund, 1988; Callaway, 1998). In particular, layer 6 pyramidal neurons, especially the subset classified as Type 1 cells, project substantially to layer 4C (Wiser and Callaway, 1996; Fitzpatrick et al., 1985). 

Reviewer #2 (Public review): 

Summary: 

The authors present a study of how modulatory activity from outside the classical receptive field (cRF) differs from cRF stimulation. They study neural activity across the different layers of V1 in two anesthetized monkeys using Neuropixels probes. The monkeys are presented with drifting gratings and border-ownership tuning stimuli. They find that border-ownership tuning is organized into columns within V1, which is unexpected and exciting, and that the flow of activity from cellto-cell (as judged by cross-correlograms between single units) is influenced by the type of visual stimulus: border-ownership tuning stimuli vs. drifting-grating stimuli. 

Strengths: 

The questions addressed by the study are of high interest, and the use of Neuropixels probes yields extremely high numbers of single-units and cross-correlation histograms (CCHs) which makes the results robust. The study is well-described. 

Weaknesses: 

The weaknesses of the study are (a) the use of anesthetized animals, which raises questions about the nature of the modulatory signal being measured and the underlying logic of why a change in visual stimulus would produce a reversal in information flow through the cortical microcircuit and (b) the choice of visual stimuli, which do not uniquely isolate feedforward from feedback influences. 

(1) The modulation latency seems quite short in Figure 2C. Have the authors measured the latency of the effect in the manuscript and how it compares to the onset of the visually driven response? It would be surprising if the latency was much shorter than 70ms given previous measurements of BO and figure-ground modulation latency in V2 and V1. On the same note, it might be revealing to make laminar profiles of the modulation (i.e. preferred - non-preferred border orientation) as it develops over time. Does the modulation start in feedback recipient layers? 

(2) Can the authors show the average time course of the response elicited by preferred and nonpreferred border ownership stimuli across all significant neurons? 

We thank the reviewer for the insightful comment—this is indeed an important and often overlooked point. As noted in the Discussion, B_own modulation differs from other forms of figure-ground modulation (e.g., Lamme et al., 1998) in that it can emerge very rapidly in early visual cortex—within ~10–35 ms after response onset (Zhou et al., 2000; Sugihara et al., 2011). This rapid emergence has been interpreted as evidence for the involvement of fast feedback inputs, which can propagate up to ten times faster than horizontal connections (Girard et al., 2001). Moreover, interlaminar interactions via monosynaptic or disynaptic connections can occur on very short timescales (a few milliseconds), further complicating efforts to disentangle feedback influences based solely on latency.

Thus, while the early onset of modulation in our data may appear surprising, it is consistent with prior B_own findings, and likely reflects a combination of fast feedback and rapid interlaminar processing. This makes it challenging to use conventional latency measurements to resolve laminar differences in B_own modulation. Latency comparisons are well known to be susceptible to confounds such as variability in response onset, luminance, contrast, stimulus size, and other sensory parameters. 

Although we did not explicitly quantify the latency of B_own modulation in this manuscript, our cross-correlation analysis provides a more sensitive and temporally resolved measure of interlaminar information flow. We therefore focused on this approach rather than laminar modulation profiles, as it more directly addresses our primary research question.

(3) The logic of assuming that cRF stimulation should produce the opposite signal flow to borderownership tuning stimuli is worth discussing. I suspect the key difference between stimuli is that they used drifting gratings as the cRF stimulus, the movement of the stimulus continually refreshes the retinal image, leading to continuous feedforward dominance of the signals in V1. Had they used a static grating, the spiking during the sustained portion of the response might also show more influence of feedback/horizontal connections. Do the initial spikes fired in response to the borderownership tuning stimuli show the feedforward pattern of responses? The authors state that they did not look at cross-correlations during the initial response, but if they do, do they see the feedforward-dominated pattern? The jitter CCH analysis might suffice in correcting for the response transient. 

We thank the reviewer for the insightful comment. As noted in the final Results section, our CRF and nCRF stimulation paradigms differ in respects beyond the presence or absence of nonclassical modulation, including stimulus properties within the CRF.

We agree with the reviewer’s speculation that drifting gratings may continually refresh the retinal image, promoting sustained feedforward dominance in V1, whereas static gratings might allow greater influence from feedback/horizontal inputs during the sustained response. Likewise, the initial response to the B_own stimulus could be dominated by feedforward activity before feedback/horizontal influences arrive. 

This contrast was a central motivation for our experimental design: we deliberately used two stimulus conditions — drifting gratings to emphasize feedforward processing, and B_own stimuli, which are known to engage feedback modulation — to test whether these two conditions yield different patterns of interlaminar information flow. Our results confirm that they do. While we did not separately analyze the very initial spike period, our focus is on interlaminar information flow during the sustained response, which serves as the primary measure of feedback/horizontal engagement in this study.

Finally, beyond this direct comparison, we show in Figure 5 that under nCRF stimulation alone, the direction and strength of interlaminar information flow correlate with the magnitude of B_own modulation, further supporting the idea that our cross-correlation approach reveals functionally meaningful differences in cortical processing.

(4) The term "nCRF stimulation" is not appropriate because the CRF is stimulated by the light/dark edge. 

We thank the reviewer for the comment. As noted in the Introduction, nCRF effects described in the literature invariably involve stimulation both inside and outside the CRF. Our use of the term “nCRF stimulation” refers to this experimental paradigm, rather than suggesting that the CRF itself is unstimulated. We hope this clarifies our use of the term.

Reviewer #3 (Public review): 

Summary: 

The paper by Zhu et al is on an important topic in visual neuroscience, the emergence in the visual cortex of signals about figures and ground. This topic also goes by the name border ownership. The paper utilizes modern recording techniques very skillfully to extend what is known about border ownership. It offers new evidence about the prevalence of border ownership signals across different cortical layers in V1 cortex. Also, it uses pairwise cross-correlation to study signal flow under different conditions of visual stimulation that include the border ownership paradigm. 

Strengths: 

The paper's strengths are its use of multi-electrode probes to study border ownership in many neurons simultaneously across the cortical layers in V1, and its innovation of using crosscorrelation between cortical neurons -- when they are viewing border-ownership patterns or instead are viewing grating patterns restricted to the classical receptive field (CRF). 

Weaknesses: 

The paper's weaknesses are its largely incremental approach to the study of border ownership and the lack of a critical analysis of the cross-correlation data. The paper as it is now does not advance our understanding of border ownership; it mainly confirms prior work, and it does not challenge or revise consensus beliefs about mechanisms. However, it is possible that, in the rich dataset the authors have obtained, they do possess data that could be added to the paper to make it much stronger. 

Critique: 

The border ownership data on V1 offered in the paper replicates experimental results obtained by Zhou and von der Heydt (2000) and confirms the earlier results using the same analysis methods as Zhou. The incremental addition is that the authors found border ownership in all cortical layers extending Zhou's results that were only about layer 2/3. 

The cross-correlation results show that the pattern of the cross-correlogram (CCG) is influenced by the visual pattern being presented. However, the results are not analyzed mechanistically, and the interpretation is unclear. For instance, the authors show in Figure 3 (and in Figure S2) that the peak of the CCG can indicate layer 2/3 excites layer 4C when the visual stimulus is the border ownership test pattern, a large square 8 deg on a side. But how can layer 2/3 excite layer 4C? The authors do not raise or offer an answer to this question. Similar questions arise when considering the CCG of layer 4A/B with layer 2/3. What is the proposed pathway for layer 2/3 to excite 4A/B? Other similar questions arise for all the interlaminar CCG data that are presented. What known functional connections would account for the measured CCGs? 

We thank the reviewer for raising this important point. As noted in our response to a previous comment, several anatomical pathways could mediate apparent functional inputs from layers 2/3 to 4C and 4A/B. In macaque V1, projections from layers 2/3 to 4A/B have been documented (Blasdel et al., 1985; Callaway and Wiser, 1996), and neurons in 4A/B often extend apical dendrites into layers 2/3 (Lund, 1988; Yoshioka et al., 1994). Although direct projections from layers 2/3 to 4C are generally sparse (Callaway, 1998), a subset of lower layer 3 neurons can give off collateral axons to 4C (Lund and Yoshioka, 1991). Some 4C neurons also extend dendrites into 4B, potentially allowing dendritic integration of inputs from more superficial layers (Somogyi and Cowey, 1981; Mates and Lund, 1983; Yabuta and Callaway, 1998). Sparse connections from 2/3 to layer 4 have also been reported in cat V1 (Binzegger et al., 2004).

Moreover, layers 2/3 may influence 4C neurons disynaptically, without requiring dense monosynaptic connections. While CCGs suggest possible circuit arrangements, functional connectivity may arise through mechanisms not fully captured by anatomical tracing, and apparent discrepancies between anatomical and functional data are not uncommon. For example, although 4B is known to receive anatomical input primarily from 4Cα, 4B neurons can also be functionally driven by 4Cβ using photostimulation (Sawatari and Callaway, 1996). Our observation of functional inputs from layers 2/3 to layer 4 is also consistent with prior findings in rodent V1, where CCG analysis (e.g., Figure 7 in Senzai, Fernandez-Ruiz and Buzsaki, 2019) or photostimulation (Xu et al., 2016) revealed similar pathways. 

Layers 5/6 also provide dense projections to layers 4A/B (Lund, 1988; Callaway, 1998). In particular, layer 6 pyramidal neurons, especially the subset classified as Type 1 cells, project substantially to layer 4C (Wiser and Callaway, 1996; Fitzpatrick et al., 1985). 

We have revised the Discussion section to explicitly address these points and clarify the potential anatomical and functional pathways underlying the measured interlaminar CCGs, highlighting how inputs from layers 2/3 and 5/6 to layer 4 can be mediated via both direct and indirect connections.

The problems in understanding the CCG data are indirectly caused by the lack of a critical analysis of what is happening in the responses that reveal the border ownership signals, as in Figure 2. Let's put it bluntly - are border ownership signals excitatory or inhibitory? The reason I raise this question is that the present authors insightfully place border ownership as examples of the action of the non-classical receptive field (nCRF) of cortical cells. Most previous work on the nCRF (many papers cited by the authors) reveal the nCRF to be inhibitory or suppressive. In order to know whether nCRF signals are excitatory or inhibitory, one needs a baseline response from the CRF, so that when you introduce nCRF signals you can tell whether the change with respect to the CRF is up or down. As far as I know, prior work on border ownership has not addressed this question, and the present paper doesn't either. This is where the rich dataset that the present authors possess might be used to establish a fundamental property of border ownership. 

Then we must go back to consider what the consequences of knowing the sign of the border ownership signal would mean for interpreting the CCG data. If the border ownership signals from extrastriate feedback or, alternatively, from horizontal intrinsic connections, are excitatory, they might provide a shared excitatory input to pairs of cells that would show up in the CCG as a peak at 0 delay. However, if the border ownership manuscript signals are inhibitory, they might work by exciting only inhibitory neurons in V1. This could have complicated consequences for the CCG.The interpretation of the CCG data in the present version of the m is unclear (see above). Perhaps a clearer interpretation could be developed once the authors know better what the border ownership signals are. 

We thank the reviewer for raising this fundamental and thought-provoking question. As noted, B_own signals arise from nCRF, which has often been associated with suppressive effects. However, Zhang and von der Heydt (2010) provided important insight into this issue by systematically varying the placement of figure fragments outside the CRF while keeping an edge centered within the CRF. They found that contextual fragments on the preferred side of B_own produce facilitation, while those on the non-preferred side produce suppression. Thus, the nCRF contribution to B_own reflects both excitatory and inhibitory modulation, depending on the spatial configuration of the figure.

These effects were well explained by their model in which feedback from grouping cells in higher areas selectively enhances or suppresses V1/V2 neuron responses, depending on their B_own preference. In this framework, the B_own signal itself is not inherently excitatory or inhibitory; rather, it results from the net effect of feedback, which can be either facilitative or suppressive. Importantly, it is the input that is modulated — not that the receiving neurons are necessarily inhibitory themselves.

In the current study, our analysis focused on CCGs showing excessive coincident spiking, i.e., positive peaks, which are typically interpreted as evidence for shared excitatory input or excitatory connections. Due to the limited number of connections, we did not analyze inhibitory interactions, such as anti-correlations or delayed suppression in the CCGs, which would be expected if the reference neuron were inhibitory. Therefore, the CCGs we report here likely reflect the excitatory component of the B_own signal, and possibly its upstream drive via feedback. While a full separation of excitatory and inhibitory components remains an important goal for future work, our data suggest that B_own modulation is at least partially mediated through excitatory feedback input.

My critique of the CCG analysis applies to Figure 5 also. I cannot comprehend the point of showing a very weak correlation of CCG asymmetry with Border Ownership Index, especially when what CCG asymmetry means is unclear mechanistically. Figure 5 does not make the paper stronger in my opinion. 

We thank the reviewer for this comment. As described in the Results section for Figure 5, the observation that interlaminar information flow correlates with B_own modulation is important because it demonstrates that these flow patterns are specifically related to the magnitude of B_own signals, independent of the comparisons between CRF and nCRF stimulation. 

In Figure 3, the authors show two CCGs that involve 4C--4C pairs. It would be nice to know more about such pairs. If there are any 6--6 pairs, what they look like also would be interesting. The authors also in Figure 3 show CCG's of two 4C--4A/B pairs and it would be quite interesting to know how such CCGs behave when CRF and nCRF stimuli are compared. In other words, the authors have shown us they have many data but have chosen not to analyze them further or to explain why they chose not to analyze them. It might help the paper if the authors would present all the CCG types they have. This suggestion would be helpful when the authors know more about the sign of border ownership signals, as discussed at length above. 

We thank the reviewer for the insightful comment. The rationale for selecting specific laminar pairs is described in the Results section after Figure 3C and further discussed in the Discussion. In brief, we focused on CCGs computed from pairs in which one neuron resided in laminar compartments receiving feedback/horizontal inputs (layers 2/3 and 5/6) and the other within compartments relatively devoid of these inputs (layers 4C and 4A/B).

To mitigate uncertainty in defining exact laminar boundaries and to maximize statistical power, we combined some anatomical layers into distinct laminar compartments. This approach allowed us to compare the relative spike timing between neuronal pairs during CRF and nCRF stimulation. If feedback/horizontal inputs contribute more during nCRF than CRF stimulation, we expect this to be reflected in the lead-lag relationships of the CCGs. While other pairs (e.g., 5/6–5/6 or 4C– 4A/B) could in principle be analyzed, the hypothesized patterns for these pairs are less clear, and thus they were not the focus of our study. Nonetheless, these additional pairs represent interesting directions for future work.

Author response:

The following is the authors’ response to the original reviews

We thank all the reviewers for their constructive comments. We have carefully considered your feedback and revised the manuscript accordingly. The major concern raised was the applicability of SegPore to the RNA004 dataset. To address this, we compared SegPore with f5c and Uncalled4 on RNA004, and found that SegPore demonstrated improved performance, as shown in Table 2 of the revised manuscript.

Following the reviewers’ recommendations, we updated Figures 3 and 4. Additionally, we added one table and three supplementary figures to the revised manuscript:

· Table 2: Segmentation benchmark on RNA004 data

· Supplementary Figure S4: RNA translocation hypothesis illustrated on RNA004 data

· Supplementary Figure S5: Illustration of Nanopolish raw signal segmentation with eventalign results

· Supplementary Figure S6: Running time of SegPore on datasets of varying sizes

Below, we provide a point-by-point response to your comments.

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors describe a new computational method (SegPore), which segments the raw signal from nanopore-direct RNA-Seq data to improve the identification of RNA modifications. In addition to signal segmentation, SegPore includes a Gaussian Mixture Model approach to differentiate modified and unmodified bases. SegPore uses Nanopolish to define a first segmentation, which is then refined into base and transition blocks. SegPore also includes a modification prediction model that is included in the output. The authors evaluate the segmentation in comparison to Nanopolish and Tombo, and they evaluate the impact on m6A RNA modification detection using data with known m6A sites. In comparison to existing methods, SegPore appears to improve the ability to detect m6A, suggesting that this approach could be used to improve the analysis of direct RNA-Seq data.

Strengths:

SegPore addresses an important problem (signal data segmentation). By refining the signal into transition and base blocks, noise appears to be reduced, leading to improved m6A identification at the site level as well as for single-read predictions. The authors provide a fully documented implementation, including a GPU version that reduces run time. The authors provide a detailed methods description, and the approach to refine segments appears to be new.

Weaknesses:

In addition to Nanopolish and Tombo, f5c and Uncalled4 can also be used for segmentation, however, the comparison to these methods is not shown.

The method was only applied to data from the RNA002 direct RNA-Sequencing version, which is not available anymore, currently, it remains unclear if the methods still work on RNA004.

Thank you for your comments.

To clarify the background, there are two kits for Nanopore direct RNA sequencing: RNA002 (the older version) and RNA004 (the newer version). Oxford Nanopore Technologies (ONT) introduced the RNA004 kit in early 2024 and has since discontinued RNA002. Consequently, most public datasets are based on RNA002, with relatively few available for RNA004 (as of 30 June 2025).

Nanopolish and Tombo were developed for raw signal segmentation and alignment using RNA002 data, whereas f5c and Uncalled4are the only two software supporting RNA004 data.  Since the development of SegPore began in January 2022, we initially focused on RNA002 due to its data availability. Accordingly, our original comparisons were made against Nanopolish and Tombo using RNA002 data.

We have now updated SegPore to support RNA004 and compared its performance against f5c and Uncalled4 on three public RNA004 datasets.

As shown in Table 2 of the revised manuscript, SegPore outperforms both f5c and Uncalled4 in raw signal segmentation. Moreover, the jiggling translocation hypothesis underlying SegPore is further supported, as shown in Supplementary Figure S4.

The overall improvement in accuracy appears to be relatively small.

Thank you for the comment.

We understand that the improvements shown in Tables 1 and 2 may appear modest at first glance due to the small differences in the reported standard deviation (std) values. However, even small absolute changes in std can correspond to substantial relative reductions in noise, especially when the total variance is low.

To better quantify the improvement, we assume that approximately 20% of the std for Nanopolish, Tombo, f5c, and Uncalled4 arises from noise. Using this assumption, we calculate the relative noise reduction rate of SegPore as follows:

Noise reduction rate = (baseline std − SegPore std) / (0.2 × baseline std) ​​

Based on this formula, the average noise reduction rates across all datasets are:

- SegPore vs Nanopolish: 49.52%

- SegPore vs Tombo: 167.80%

- SegPore vs f5c: 9.44%

- SegPore vs Uncalled4: 136.70%

These results demonstrate that SegPore can reduce the noise level by at least 9% given a noise level of 20%, which we consider a meaningful improvement for downstream tasks, such as base modification detection and signal interpretation. The high noise reduction rates observed in Tombo and Uncalled4 (over 100%) suggest that their actual noise proportion may be higher than our 20% assumption.

We acknowledge that this 20% noise level assumption is an approximation. Our intention is to illustrate that SegPore provides measurable improvements in relative terms, even when absolute differences appear small.

The run time and resources that are required to run SegPore are not shown, however, it appears that the GPU version is essential, which could limit the application of this method in practice.

Thank you for your comment.

Detailed instructions for running SegPore are provided in github (https://github.com/guangzhaocs/SegPore). Regarding computational resources, SegPore currently requires one CPU core and one Nvidia GPU to perform the segmentation task efficiently.

We present SegPore’s runtime for typical datasets in Supplementary Figure S6 in the revised manuscript.  For a typical 1 GB fast5 file, the segmentation takes approximately 9.4 hours using a single NVIDIA DGX‑1 V100 GPU and one CPU core.

Currently, GPU acceleration is essential to achieve practical runtimes with SegPore. We acknowledge that this requirement may limit accessibility in some environments. To address this, we are actively working on a full C++ implementation of SegPore that will support CPU-only execution. While development is ongoing, we aim to release this version in a future update.

Reviewer #2 (Public review):

Summary:

The work seeks to improve the detection of RNA m6A modifications using Nanopore sequencing through improvements in raw data analysis. These improvements are said to be in the segmentation of the raw data, although the work appears to position the alignment of raw data to the reference sequence and some further processing as part of the segmentation, and result statistics are mostly shown on the 'data-assigned-to-kmer' level.

As such, the title, abstract, and introduction stating the improvement of just the 'segmentation' does not seem to match the work the manuscript actually presents, as the wording seems a bit too limited for the work involved.

The work itself shows minor improvements in m6Anet when replacing Nanopolish eventalign with this new approach, but clear improvements in the distributions of data assigned per kmer. However, these assignments were improved well enough to enable m6A calling from them directly, both at site-level and at read-level.

Strengths:

A large part of the improvements shown appear to stem from the addition of extra, non-base/kmer specific, states in the segmentation/assignment of the raw data, removing a significant portion of what can be considered technical noise for further analysis. Previous methods enforced the assignment of all raw data, forcing a technically optimal alignment that may lead to suboptimal results in downstream processing as data points could be assigned to neighbouring kmers instead, while random noise that is assigned to the correct kmer may also lead to errors in modification detection.

For an optimal alignment between the raw signal and the reference sequence, this approach may yield improvements for downstream processing using other tools.<br /> Additionally, the GMM used for calling the m6A modifications provides a useful, simple, and understandable logic to explain the reason a modification was called, as opposed to the black models that are nowadays often employed for these types of tasks.

Weaknesses:

The work seems limited in applicability largely due to the focus on the R9's 5mer models. The R9 flow cells are phased out and not available to buy anymore. Instead, the R10 flow cells with larger kmer models are the new standard, and the applicability of this tool on such data is not shown. We may expect similar behaviour from the raw sequencing data where the noise and transition states are still helpful, but the increased kmer size introduces a large amount of extra computing required to process data and without knowledge of how SegPore scales, it is difficult to tell how useful it will really be. The discussion suggests possible accuracy improvements moving to 7mers or 9mers, but no reason why this was not attempted.

Thank you for pointing out this important limitation. Please refer to our response to Point 1 of Reviewer 1 for SegPore’s performance on RNA004 data. Notably, the jiggling behavior is also observed in RNA004 data, and SegPore achieves better performance than both f5c and Uncalled4.

The increased k-mer size in RNA004 affects only the training phase of SegPore (refer to Supplementary Note 1, Figure 5 for details on the training and testing phases). Once the baseline means and standard deviations for each k-mer are established, applying SegPore to RNA004 data proceeds similarly to RNA002. This is because each k-mer in the reference sequence has, at most, two states (modified and unmodified). While the larger k-mer size increases the size of the parameter table, it does not increase the computational complexity during segmentation. Although estimating the initial k-mer parameter table requires significant time and effort on our part, it does not affect the runtime for end users applying SegPore to RNA004 data.

Extending SegPore from 5-mers to 7-mers or 9-mers for RNA002 data would require substantial effort to retrain the model and generate sufficient training data. Additionally, such an extension would make SegPore’s output incompatible with widely used upstream and downstream tools such as Nanopolish and m6Anet, complicating integration and comparison. For these reasons, we leave this extension for future work.

The manuscript suggests the eventalign results are improved compared to Nanopolish. While this is believably shown to be true (Table 1), the effect on the use case presented, downstream differentiation between modified and unmodified status on a base/kmer, is likely limited as during actual modification calling the noisy distributions are usually 'good enough', and not skewed significantly in one direction to really affect the results too terribly.

Thank you for your comment. While current state-of-the-art (SOTA) methods perform well on benchmark datasets, there remains significant room for improvement. Most SOTA evaluations are based on limited datasets, primarily covering DRACH motifs in human and mouse transcriptomes. However, m6A modifications can also occur in non-DRACH motifs, where current models may underperform. Additionally, other RNA modifications—such as pseudouridine, inosine, and m5C—are less studied, and their detection may benefit from improved signal modeling.

We would also like to emphasize that raw signal segmentation and RNA modification detection are distinct tasks. SegPore focuses on the former, providing a cleaner, more interpretable signal that can serve as a foundation for downstream tasks. Improved segmentation may facilitate the development of more accurate RNA modification detection algorithms by the community.

Scientific progress often builds incrementally through targeted improvements to foundational components. We believe that enhancing signal segmentation, as SegPore does, contributes meaningfully to the broader field—the full impact will become clearer as the tool is adopted into more complex workflows.

Furthermore, looking at alternative approaches where this kind of segmentation could be applied, Nanopolish uses the main segmentation+alignment for a first alignment and follows up with a form of targeted local realignment/HMM test for modification calling (and for training too), decreasing the need for the near-perfect segmentation+alignment this work attempts to provide. Any tool applying a similar strategy probably largely negates the problems this manuscript aims to improve upon.

We thank the reviewer for this insightful comment.

To clarify, Nanopolish provides three independent commands: polya, eventalign, and call-methylation.

- The polya command identifies the adapter, poly(A) tail, and transcript region in the raw signal.

- The eventalign command aligns the raw signal to a reference sequence, assigning a signal segment to individual k-mers in the reference.

- The call-methylation command detects methylated bases from DNA sequencing data.

The eventalign command corresponds to “the main segmentation+alignment for a first alignment,” while call-methylation corresponds to “a form of targeted local realignment/HMM test for modification calling,” as mentioned in the reviewer’s comment. SegPore’s segmentation is similar in purpose to Nanopolish’s eventalign, while its RNA modification estimation component is similar in concept to Nanopolish’s call-methylation.

We agree the general idea may appear similar, but the implementations are entirely different. Importantly, Nanopolish’s call-methylation is designed for DNA sequencing data, and its models are not trained to recognize RNA modifications. This means they address distinct research questions and cannot be directly compared on the same RNA modification estimation task. However, it is valid to compare them on the segmentation task, where SegPore exhibits better performance (Table 1).

We infer the reviewer may suggest that because m6Anet is a deep neural network capable of learning from noisy input, the benefit of more accurate segmentation (such as that provided by SegPore) might be limited. This concern may arise from the limited improvement of SegPore+m6Anet over Nanopolish+m6Anet in bulk analysis (Figure 3). Several factors may contribute to this observation:

(i) For reads aligned to the same gene in the in vivo data, alignment may be inaccurate due to pseudogenes or transcript isoforms.

(ii) The in vivo benchmark data are inherently more complex than in vitro datasets and may contain additional modifications (e.g., m5C, m7G), which can confound m6A calling by altering the signal baselines of k-mers.

(iii) m6Anet is trained on events produced by Nanopolish and may not be optimal for SegPore-derived events.

(iv) The benchmark dataset lacks a modification-free (IVT) control sample, making it difficult to establish a true baseline for each k-mer.

In the IVT data (Figure 4), SegPore shows a clear improvement in single-molecule m6A identification, with a 3~4% gain in both ROC-AUC and PR-AUC. This demonstrates SegPore’s practical benefit for applications requiring higher sensitivity at the molecule level.

As noted earlier, SegPore’s contribution lies in denoising and improving the accuracy of raw signal segmentation, which is a foundational step in many downstream analyses. While it may not yet lead to a dramatic improvement in all applications, it already provides valuable insights into the sequencing process (e.g., cleaner signal profiles in Figure 4) and enables measurable gains in modification detection at the single-read level. We believe SegPore lays the groundwork for developing more accurate and generalizable RNA modification detection tools beyond m6A.

We have also added the following sentence in the discussion to highlight SegPore’s limited performance in bulk analysis:

“The limited improvement of SegPore combined with m6Anet over Nanopolish+m6Anet in bulk in vivo analysis (Figure 3) may be explained by several factors: potential alignment inaccuracies due to pseudogenes or transcript isoforms, the complexity of in vivo datasets containing additional RNA modifications (e.g., m5C, m7G) affecting signal baselines, and the fact that m6Anet is specifically trained on events produced by Nanopolish rather than SegPore. Additionally, the lack of a modification-free control (in vitro transcribed) sample in the benchmark dataset makes it difficult to establish true baselines for each k-mer. Despite these limitations, SegPore demonstrates clear improvement in single-molecule m6A identification in IVT data (Figure 4), suggesting it is particularly well suited for in vitro transcription data analysis.”

Finally, in the segmentation/alignment comparison to Nanopolish, the latter was not fitted(/trained) on the same data but appears to use the pre-trained model it comes with. For the sake of comparing segmentation/alignment quality directly, fitting Nanopolish on the same data used for SegPore could remove the influences of using different training datasets and focus on differences stemming from the algorithm itself.

In the segmentation benchmark (Table 1), SegPore uses the fixed 5-mer parameter table provided by ONT. The hyperparameters of the HHMM are also fixed and not estimated from the raw signal data being segmented. Only in the m6A modification task,  SegPore does perform re-estimation of the baselines for the modified and unmodified states of k-mers. Therefore, the comparison with Nanopolish is fair, as both tools rely on pre-defined models during segmentation.

Appraisal:

The authors have shown their method's ability to identify noise in the raw signal and remove their values from the segmentation and alignment, reducing its influences for further analyses. Figures directly comparing the values per kmer do show a visibly improved assignment of raw data per kmer. As a replacement for Nanopolish eventalign it seems to have a rather limited, but improved effect, on m6Anet results. At the single read level modification modification calling this work does appear to improve upon CHEUI.

Impact:

With the current developments for Nanopore-based modification largely focusing on Artificial Intelligence, Neural Networks, and the like, improvements made in interpretable approaches provide an important alternative that enables a deeper understanding of the data rather than providing a tool that plainly answers the question of whether a base is modified or not, without further explanation. The work presented is best viewed in the context of a workflow where one aims to get an optimal alignment between raw signal data and the reference base sequence for further processing. For example, as presented, as a possible replacement for Nanopolish eventalign. Here it might enable data exploration and downstream modification calling without the need for local realignments or other approaches that re-consider the distribution of raw data around the target motif, such as a 'local' Hidden Markov Model or Neural Networks. These possibilities are useful for a deeper understanding of the data and further tool development for modification detection works beyond m6A calling.

Reviewer #3 (Public review):

Summary:

Nucleotide modifications are important regulators of biological function, however, until recently, their study has been limited by the availability of appropriate analytical methods. Oxford Nanopore direct RNA sequencing preserves nucleotide modifications, permitting their study, however, many different nucleotide modifications lack an available base-caller to accurately identify them. Furthermore, existing tools are computationally intensive, and their results can be difficult to interpret.

Cheng et al. present SegPore, a method designed to improve the segmentation of direct RNA sequencing data and boost the accuracy of modified base detection.

Strengths:

This method is well-described and has been benchmarked against a range of publicly available base callers that have been designed to detect modified nucleotides.

Weaknesses:

However, the manuscript has a significant drawback in its current version. The most recent nanopore RNA base callers can distinguish between different ribonucleotide modifications, however, SegPore has not been benchmarked against these models.

I recommend that re-submission of the manuscript that includes benchmarking against the rna004_130bps_hac@v5.1.0 and rna004_130bps_sup@v5.1.0 dorado models, which are reported to detect m5C, m6A_DRACH, inosine_m6A and PseU.<br /> A clear demonstration that SegPore also outperforms the newer RNA base caller models will confirm the utility of this method.

Thank you for highlighting this important limitation. While Dorado, the new ONT basecaller, is publicly available and supports modification-aware basecalling, suitable public datasets for benchmarking m5C, inosine, m6A, and PseU detection on RNA004 are currently lacking. Dorado’s modification-aware models are trained on ONT’s internal data, which is not publicly released. Therefore, it is not currently feasible to evaluate or directly compare SegPore’s performance against Dorado for m5C, inosine, m6A, and PseU detection.

We would also like to emphasize that SegPore’s main contribution lies in raw signal segmentation, which is an upstream task in the RNA modification detection pipeline. To assess its performance in this context, we benchmarked SegPore against f5c and Uncalled4 on public RNA004 datasets for segmentation quality. Please refer to our response to Point 1 of Reviewer 1 for details.

Our results show that the characteristic “jiggling” behavior is also observed in RNA004 data (Supplementary Figure S4), and SegPore achieves better segmentation performance than both f5c and Uncalled4 (Table 2).

Recommendations for the authors:

Reviewing Editor:

Please note that we also received the following comments on the submission, which we encourage you to take into account:

took a look at the work and for what I saw it only mentions/uses RNA002 chemistry, which is deprecated, effectively making this software unusable by anyone any more, as RNA002 is not commercially available. While the results seem promising, the authors need to show that it would work for RNA004. Notably, there is an alternative software for resquiggling for RNA004 (not Tombo or Nanopolish, but the GPU-accelerated version of Nanopolish (f5C), which does support RNA004. Therefore, they need to show that SegPore works for RNA004, because otherwise it is pointless to see that this method works better than others if it does not support current sequencing chemistries and only works for deprecated chemistries, and people will keep using f5C because its the only one that currently works for RNA004. Alternatively, if there would be biological insights won from the method, one could justify not implementing it in RNA004, but in this case, RNA002 is deprecated since March 2024, and the paper is purely methodological.

Thank you for the comment. We agree that support for current sequencing chemistries is essential for practical utility. While SegPore was initially developed and benchmarked on RNA002 due to the availability of public data, we have now extended SegPore to support RNA004 chemistry.

To address this concern, we performed a benchmark comparison using public RNA004 datasets against tools specifically designed for RNA004, including f5c and Uncalled4. Please refer to our response to Point 1 of Reviewer 1 for details. The results show that SegPore consistently outperforms f5c and Uncalled4 in segmentation accuracy on RNA004 data.

Reviewer #2 (Recommendations for the authors):

Various statements are made throughout the text that require further explanation, which might actually be defined in more detail elsewhere sometimes but are simply hard to find in the current form.

(1) Page 2, “In this technique, five nucleotides (5mers) reside in the nanopore at a time, and each 5mer generates a characteristic current signal based on its unique sequence and chemical properties (16).”

5mer? Still on R9 or just ignoring longer range influences, relevant? It is indeed a R9.4 model from ONT.

Thank you for the observation. We apologize for the confusion and have clarified the relevant paragraph to indicate that the method is developed for RNA002 data by default. Specifically, we have added the following sentence:

“Two versions of the direct RNA sequencing (DRS) kits are available: RNA002 and RNA004. Unless otherwise specified, this study focuses on RNA002 data.”

(2) Page 3, “Employ models like Hidden Markov Models (HMM) to segment the signal, but they are prone to noise and inaccuracies.”

That's the alignment/calling part, not the segmentation?

Thank you for the comment. We apologize for the confusion. To clarify the distinction between segmentation and alignment, we added a new paragraph before the one in question to explain the general workflow of Nanopore DRS data analysis and to clearly define the task of segmentation. The added text reads:

“The general workflow of Nanopore direct RNA sequencing (DRS) data analysis is as follows. First, the raw electrical signal from a read is basecalled using tools such as Guppy or Dorado, which produce the nucleotide sequence of the RNA molecule. However, these basecalled sequences do not include the precise start and end positions of each ribonucleotide (or k-mer) in the signal. Because basecalling errors are common, the sequences are typically mapped to a reference genome or transcriptome using minimap2 to recover the correct reference sequence. Next, tools such as Nanopolish and Tombo align the raw signal to the reference sequence to determine which portion of the signal corresponds to each k-mer. We define this process as the segmentation task, referred to as "eventalign" in Nanopolish. Based on this alignment, Nanopolish extracts various features—such as the start and end positions, mean, and standard deviation of the signal segment corresponding to a k-mer. This signal segment or its derived features is referred to as an "event" in Nanopolish.”

We also revised the following paragraph describing SegPore to more clearly contrast its approach:

“In SegPore, we first segment the raw signal into small fragments using a Hierarchical Hidden Markov Model (HHMM), where each fragment corresponds to a sub-state of a k-mer. Unlike Nanopolish and Tombo, which directly align the raw signal to the reference sequence, SegPore aligns the mean values of these small fragments to the reference. After alignment, we concatenate all fragments that map to the same k-mer into a larger segment, analogous to the "eventalign" output in Nanopolish. For RNA modification estimation, we use only the mean signal value of each reconstructed event.”

We hope this revision clarifies the difference between segmentation and alignment in the context of our method and resolves the reviewer’s concern.

(3) Page 4, Figure 1, “These segments are then aligned with the 5mer list of the reference sequence fragment using a full/partial alignment algorithm, based on a 5mer parameter table. For example, 𝐴𝑗 denotes the base "A" at the j-th position on the reference.”

I think I do understand the meaning, but I do not understand the relevance of the Aj bit in the last sentence. What is it used for?

When aligning the segments (output from Step 2) to the reference sequence in Step 3, it is possible for multiple segments to align to the same k-mer. This can occur particularly when the reference contains consecutive identical bases, such as multiple adenines (A). For example, as shown in Fig. 1A, Step 3, the first two segments (μ₁ and μ₂) are aligned to the first 'A' in the reference sequence, while the third segment is aligned to the second 'A'. In this case, the reference sequence AACTGGTTTC...GTC, which contains exactly two consecutive 'A's at the start. This notation helps to disambiguate segment alignment in regions with repeated bases.

Additionally, this figure and its subscript include mapping with Guppy and Minimap2 but do not mention Nanopolish at all, while that seems an equally important step in the preprocessing (pg5). As such it is difficult to understand the role Nanopolish exactly plays. It's also not mentioned explicitly in the SegPore Workflow on pg15, perhaps it's part of step 1 there?

We thank the reviewer for pointing this out. We apologize for the confusion. As mentioned in the public response to point 3 of Reviewer 2, SegPore uses Nanopolish to identify the poly(A) tail and transcript regions from the raw signal. SegPore then performs segmentation and alignment on the transcript portion only. This step is indeed part of Step 1 in the preprocessing workflow, as described in Supplementary Note 1, Section 3.

To clarify this in the main text, we have updated the preprocessing paragraph on page 6 to explicitly describe the role of Nanopolish:

“We begin by performing basecalling on the input fast5 file using Guppy, which converts the raw signal data into ribonucleotide sequences. Next, we align the basecalled sequences to the reference genome using Minimap2, generating a mapping between the reads and the reference sequences. Nanopolish provides two independent commands: "polya" and "eventalign".
The "polya" command identifies the adapter, poly(A) tail, and transcript region in the raw signal, which we refer to as the poly(A) detection results. The raw signal segment corresponding to the poly(A) tail is used to standardize the raw signal for each read. The "eventalign" command aligns the raw signal to a reference sequence, assigning a signal segment to individual k-mers in the reference. It also computes summary statistics (e.g., mean, standard deviation) from the signal segment for each k-mer. Each k-mer together with its corresponding signal features is termed an event. These event features are then passed into downstream tools such as m6Anet and CHEUI for RNA modification detection. For full transcriptome analysis (Figure 3), we extract the aligned raw signal segment and reference sequence segment from Nanopolish's events for each read by using the first and last events as start and end points. For in vitro transcription (IVT) data with a known reference sequence (Figure 4), we extract the raw signal segment corresponding to the transcript region for each input read based on Nanopolish’s poly(A) detection results.”

Additionally, we revised the legend of Figure 1A to explicitly include Nanopolish in step 1 as follows:

“The raw current signal fragments are paired with the corresponding reference RNA sequence fragments using Nanopolish.”

(4) Page 5, “The output of Step 3 is the "eventalign," which is analogous to the output generated by the Nanopolish "eventalign" command.”

Naming the function of Nanopolish, the output file, and later on (pg9) the alignment of the newly introduced methods the exact same "eventalign" is very confusing.

Thank you for the helpful comment. We acknowledge the potential confusion caused by using the term “eventalign” in multiple contexts. To improve clarity, we now consistently use the term “events” to refer to the output of both Nanopolish and SegPore, rather than using "eventalign" as a noun. We also added the following sentence to Step 3 (page 6) to clearly define what an “event” refers to in our manuscript:

“An "event" refers to a segment of the raw signal that is aligned to a specific k-mer on a read, along with its associated features such as start and end positions, mean current, standard deviation, and other relevant statistics.”

We have revised the text throughout the manuscript accordingly to reduce ambiguity and ensure consistent terminology.

(5) Page 5, “Once aligned, we use Nanopolish's eventalign to obtain paired raw current signal segments and the corresponding fragments of the reference sequence, providing a precise association between the raw signals and the nucleotide sequence.”

I thought the new method's HHMM was supposed to output an 'eventalign' formatted file. As this is not clearly mentioned elsewhere, is this a mistake in writing? Is this workflow dependent on Nanopolish 'eventalign' function and output or not?

We apologize for the confusion. To clarify, SegPore is not dependent on Nanopolish’s eventalign function for generating the final segmentation results. As described in our response to your comment point 2 and elaborated in the revised text on page 4, SegPore uses its own HHMM-based segmentation model to divide the raw signal into small fragments, each corresponding to a sub-state of a k-mer. These fragments are then aligned to the reference sequence based on their mean current values.

As explained in the revised manuscript:

“In SegPore, we first segment the raw signal into small fragments using a Hierarchical Hidden Markov Model (HHMM), where each fragment corresponds to a sub-state of a k-mer. Unlike Nanopolish and Tombo, which directly align the raw signal to the reference sequence, SegPore aligns the mean values of these small fragments to the reference. After alignment, we concatenate all fragments that map to the same k-mer into a larger segment, analogous to the "eventalign" output in Nanopolish. For RNA modification estimation, we use only the mean signal value of each reconstructed event.”

To avoid ambiguity, we have also revised the sentence on page 5 to more clearly distinguish the roles of Nanopolish and SegPore in the workflow. The updated sentence now reads:

“Nanopolish provides two independent commands: "polya" and "eventalign".
The "polya" command identifies the adapter, poly(A) tail, and transcript region in the raw signal, which we refer to as the poly(A) detection results. The raw signal segment corresponding to the poly(A) tail is used to standardize the raw signal for each read. The "eventalign" command aligns the raw signal to a reference sequence, assigning a signal segment to individual k-mers in the reference. It also computes summary statistics (e.g., mean, standard deviation) from the signal segment for each k-mer. Each k-mer together with its corresponding signal features is termed an event. These event features are then passed into downstream tools such as m6Anet and CHEUI for RNA modification detection. For full transcriptome analysis (Figure 3), we extract the aligned raw signal segment and reference sequence segment from Nanopolish's events for each read by using the first and last events as start and end points. For in vitro transcription (IVT) data with a known reference sequence (Figure 4), we extract the raw signal segment corresponding to the transcript region for each input read based on Nanopolish’s poly(A) detection results.”

(6) Page 5, “Since the polyA tail provides a stable reference, we normalize the raw current signals across reads, ensuring that the mean and standard deviation of the polyA tail are consistent across all reads.”

Perhaps I misread this statement: I interpret it as using the PolyA tail to do the normalization, rather than using the rest of the signal to do the normalization, and that results in consistent PolyA tails across all reads.

If it's the latter, this should be clarified, and a little detail on how the normalization is done should be added, but if my first interpretation is correct:

I'm not sure if its standard deviation is consistent across reads. The (true) value spread in this section of a read should be fairly limited compared to the rest of the signal in the read, so the noise would influence the scale quite quickly, and such noise might be introduced to pores wearing down and other technical influences. Is this really better than using the non-PolyA tail part of the reads signal, using Median Absolute Deviation to scale for a first alignment round, then re-fitting the signal scaling using Theil Sen on the resulting alignments (assigned read signal vs reference expected signal), as Tombo/Nanopolish (can) do?

Additionally, this kind of normalization should have been part of the Nanopolish eventalign already, can this not be re-used? If it's done differently it may result in different distributions than the ONT kmer table obtained for the next step.

Thank you for this detailed and thoughtful comment. We apologize for the confusion. The poly(A) tail–based normalization is indeed explained in Supplementary Note 1, Section 3, but we agree that the motivation needed to be clarified in the main text.

We have now added the following sentence in the revised manuscript (before the original statement on page 5 to provide clearer context:

“Due to inherent variability between nanopores in the sequencing device, the baseline levels and standard deviations of k-mer signals can differ across reads, even for the same transcript. To standardize the signal for downstream analyses, we extract the raw current signal segments corresponding to the poly(A) tail of each read. Since the poly(A) tail provides a stable reference, we normalize the raw current signals across reads, ensuring that the mean and standard deviation of the poly(A) tail are consistent across all reads. This step is crucial for reducing…..”

We chose to use the poly(A) tail for normalization because it is sequence-invariant—i.e., all poly(A) tails consist of identical k-mers, unlike transcript sequences which vary in composition. In contrast, using the transcript region for normalization can introduce biases: for instance, reads with more diverse k-mers (having inherently broader signal distributions) would be forced to match the variance of reads with more uniform k-mers, potentially distorting the baseline across k-mers.

In our newly added RNA004 benchmark experiment, we used the default normalization provided by f5c, which does not include poly(A) tail normalization. Despite this, SegPore was still able to mask out noise and outperform both f5c and Uncalled4, demonstrating that our segmentation method is robust to different normalization strategies.

(7) Page 7, “The initialization of the 5mer parameter table is a critical step in SegPore's workflow. By leveraging ONT's established kmer models, we ensure that the initial estimates for unmodified 5mers are grounded in empirical data.”

It looks like the method uses Nanopolish for a first alignment, then improves the segmentation matching the reference sequence/expected 5mer values. I thought the Nanopolish model/tables are based on the same data, or similarly obtained. If they are different, then why the switch of kmer model? Now the original alignment may have been based on other values, and thus the alignment may seem off with the expected kmer values of this table.

Thank you for this insightful question. To clarify, SegPore uses Nanopolish only to identify the poly(A) tail and transcript regions from the raw signal. In the bulk in vivo data analysis, we use Nanopolish’s first event as the start and the last event as the end to extract the aligned raw signal chunk and its corresponding reference sequence. Since SegPore relies on Nanopolish solely to delineate the transcript region for each read, it independently aligns the raw signals to the reference sequence without refining or adjusting Nanopolish’s segmentation results.

While SegPore's 5-mer parameter table is initially seeded using ONT’s published unmodified k-mer models, we acknowledge that empirical signal values may deviate from these reference models due to run-specific technical variation and the presence of RNA modifications. For this reason, SegPore includes a parameter re-estimation step to refine the mean and standard deviation values of each k-mer based on the current dataset.

The re-estimation process consists of two layers. In the outer layer, we select a set of 5mers that exhibit both modified and unmodified states based on the GMM results (Section 6 of Supplementary Note 1), while the remaining 5mers are assumed to have only unmodified states. In the inner layer, we align the raw signals to the reference sequences using the 5mer parameter table estimated in the outer layer (Section 5 of Supplementary Note 1). Based on the alignment results, we update the 5mer parameter table in the outer layer. This two-layer process is generally repeated for 3~5 iterations until the 5mer parameter table converges.This re-estimation ensures that:

(1) The adjusted 5mer signal baselines remain close to the ONT reference (for consistency);

(2) The alignment score between the observed signal and the reference sequence is optimized (as detailed in Equation 11, Section 5 of Supplementary Note 1);

(3) Only 5mers that show a clear difference between the modified and unmodified components in the GMM are considered subject to modification.

By doing so, SegPore achieves more accurate signal alignment independent of Nanopolish’s models, and the alignment is directly tuned to the data under analysis.

(8) Page 9, “The output of the alignment algorithm is an eventalign, which pairs the base blocks with the 5mers from the reference sequence for each read (Fig. 1C).”

“Modification prediction

After obtaining the eventalign results, we estimate the modification state of each motif using the 5mer parameter table.”

This wording seems to have been introduced on page 5 but (also there) reads a bit confusingly as the name of the output format, file, and function are now named the exact same "eventalign". I assume the obtained eventalign results now refer to the output of your HHMM, and not the original Nanopolish eventalign results, based on context only, but I'd rather have a clear naming that enables more differentiation.

We apologize for the confusion. We have revised the sentence as follows for clarity:

“A detailed description of both alignment algorithms is provided in Supplementary Note 1. The output of the alignment algorithm is an alignment that pairs the base blocks with the 5mers from the reference sequence for each read (Fig. 1C). Base blocks aligned to the same 5-mer are concatenated into a single raw signal segment (referred to as an “event”), from which various features—such as start and end positions, mean current, and standard deviation—are extracted. Detailed derivation of the mean and standard deviation is provided in Section 5.3 in Supplementary Note 1. In the remainder of this paper, we refer to these resulting events as the output of eventalign analysis or the segmentation task. ”

(9) Page 9, “Since a single 5mer can be aligned with multiple base blocks, we merge all aligned base blocks by calculating a weighted mean. This weighted mean represents the single base block mean aligned with the given 5mer, allowing us to estimate the modification state for each site of a read.”

I assume the weights depend on the length of the segment but I don't think it is explicitly stated while it should be.

Thank you for the helpful observation. To improve clarity, we have moved this explanation to the last paragraph of the previous section (see response to point 8), where we describe the segmentation process in more detail.

Additionally, a complete explanation of how the weighted mean is computed is provided in Section 5.3 of Supplementary Note 1. It is derived from signal points that are assigned to a given 5mer.

(10) Page 10, “Afterward, we manually adjust the 5mer parameter table using heuristics to ensure that the modified 5mer distribution is significantly distinct from the unmodified distribution.”

Using what heuristics? If this is explained in the supplementary notes then please refer to the exact section.

Thank you for pointing this out. The heuristics used to manually adjust the 5mer parameter table are indeed explained in detail in Section 7 of Supplementary Note 1.

To clarify this in the manuscript, we have revised the sentence as follows:

“Afterward, we manually adjust the 5mer parameter table using heuristics to ensure that the modified 5mer distribution is significantly distinct from the unmodified distribution (see details in Section 7 of Supplementary Note 1).”

(11) Page 10, “Once the table is fixed, it is used for RNA modification estimation in the test data without further updates.”

By what tool/algorithm? Perhaps it is your own implementation, but with the next section going into segmentation benchmarking and using Nanopolish before this seems undefined.

Thank you for pointing this out. We use our own implementation. See Algorithm 3 in Section 6 of Supplementary Note 1.

We have revised the sentence for clarity:

“Once a stabilized 5mer parameter table is estimated from the training data, it is used for RNA modification estimation in the test data without further updates. A more detailed description of the GMM re-estimation process is provided in Section 6 of Supplementary Note 1.”

(12) Page 11, “A 5mer was considered significantly modified if its read coverage exceeded 1,500 and the distance between the means of the two Gaussian components in the GMM was greater than 5.”

Considering the scaling done before also not being very detailed in what range to expect, this cutoff doesn't provide any useful information. Is this a pA value?

Thank you for the observation. Yes, the value refers to the current difference measured in picoamperes (pA). To clarify this, we have revised the sentence in the manuscript to include the unit explicitly:

“A 5mer was considered significantly modified if its read coverage exceeded 1,500 and the distance between the means of the two Gaussian components in the GMM was greater than 5 picoamperes (pA).”

(13) Page 13, “The raw current signals, as shown in Figure 1B.”

Wrong figure? Figure 2B seems logical.

Thank you for catching this. You are correct—the reference should be to Figure 2B, not Figure 1B. We have corrected this in the revised manuscript.

(14) Page 14, Figure 2A, these figures supposedly support the jiggle hypothesis but the examples seem to match only half the explanation. Any of these jiggles seem to be followed shortly by another in the opposite direction, and the amplitude seems to match better within each such pair than the next or previous segments. Perhaps there is a better explanation still, and this behaviour can be modelled as such instead.

Thank you for your comment. We acknowledge that the observed signal patterns may appear ambiguous and could potentially suggest alternative explanations. However, as shown in Figure 2A, the red dots tend to align closely with the baseline of the previous state, while the blue dots align more closely with the baseline of the next state. We interpret this as evidence for the "jiggling" hypothesis, where k-mer temporarily oscillates between adjacent states during translocation.

That said, we agree that more sophisticated models could be explored to better capture this behavior, and we welcome suggestions or references to alternative models. We will consider this direction in future work.

(15) Page 15, “This occurs because subtle transitions within a base block may be mistaken for transitions between blocks, leading to inflated transition counts.”

Is it really a "subtle transition" if it happens within a base block? It seems this is not a transition and thus shouldn't be named as such.

Thank you for pointing this out. We agree that the term “subtle transition” may be misleading in this context. We revised the sentence to clarify the potential underlying cause of the inflated transition counts:

“This may be due to a base block actually corresponding to a sub-state of a single 5mer, rather than each base block corresponding to a full 5mer, leading to inflated transition counts. To address this issue, SegPore’s alignment algorithm was refined to merge multiple base blocks (which may represent sub-states of the same 5mer) into a single 5mer, thereby facilitating further analysis.”

(16) Page 15, “The SegPore "eventalign" output is similar to Nanopolish's "eventalign" command.”

To the output of that command, I presume, not to the command itself.

Thank you for pointing out the ambiguity. We have revised the sentence for clarity:

“The final outputs of SegPore are the events and modification state predictions. SegPore’s events are similar to the outputs of Nanopolish’s "eventalign" command, in that they pair raw current signal segments with the corresponding RNA reference 5-mers. Each 5-mer is associated with various features — such as start and end positions, mean current, and standard deviation — derived from the paired signal segment.”

(17) Page 15, “For selected 5mers, SegPore also provides the modification rate for each site and the modification state of that site on individual reads.”

What selection? Just all kmers with a possible modified base or a more specific subset?

We revised the sentence to clarify the selection criteria:

“For selected 5mers that exhibit both a clearly unmodified and a clearly modified signal component, SegPore reports the modification rate at each site, as well as the modification state of that site on individual reads.”

(18) Page 16, “A key component of SegPore is the 5mer parameter table, which specifies the mean and standard deviation for each 5mer in both modified and unmodified states (Figure 2A).”

Wrong figure?

Thank you for pointing this out. You are correct—it should be Figure 1A, not Figure 2A. We intended to visually illustrate the structure of the 5mer parameter table in Figure 1A, and we have corrected this reference in the revised manuscript.

(19) Page 16, Table 1, I can't quite tell but I assume this is based on all kmers in the table, not just a m6A modified subset. A short added statement to make this clearer would help.

Yes, you are right—it is averaged over all 5mers. We have revised the sentence for clarity as follows:

" As shown in Table 1, SegPore consistently achieved the best performance averaged on all 5mers across all datasets..…."

(20) Page 16, “Since the peaks (representing modified and unmodified states) are separable for only a subset of 5mers, SegPore can provide modification parameters for these specific 5mers. For other 5mers, modification state predictions are unavailable.”

Can this be improved using some heuristics rather than the 'distance of 5' cutoff as described before? How small or big is this subset, compared to how many there should be to cover all cases?

We agree that more sophisticated strategies could potentially improve performance. In this study, we adopted a relatively conservative approach to minimize false positives by using a heuristic cutoff of 5 picoamperes. This value was selected empirically and we did not explore alternative cutoffs. Future work could investigate more refined or data-driven thresholding strategies.

(21) Page 16, “Tombo used the "resquiggle" method to segment the raw signals, and we standardized the segments using the polyA tail to ensure a fair comparison.”

I don't know what or how something is "standardized" here.

Standardized’ refers to the poly(A) tail–based signal normalization described in our response to point 6. We applied this normalization to Tombo’s output to ensure a fair comparison across methods. Without this standardization, Tombo’s performance was notably worse. We revised the sentence as follows:

“Tombo used the "resquiggle" method to segment the raw signals, and we standardized the segments using the poly(A) tail to ensure a fair comparison (See preprocessing section in Materials and Methods).”

(22) Page 16, “To benchmark segmentation performance, we used two key metrics: (1) the log-likelihood of the segment mean, which measures how closely the segment matches ONT's 5mer parameter table (used as ground truth), and (2) the standard deviation (std) of the segment, where a lower std indicates reduced noise and better segmentation quality. If the raw signal segment aligns correctly with the corresponding 5mer, its mean should closely match ONT's reference, yielding a high log-likelihood. A lower std of the segment reflects less noise and better performance overall.”

Here the segmentation part becomes a bit odd:

A: Low std can be/is achieved by dropping any noisy bits, making segments really small (partly what happens here with the transition segments). This may be 'true' here, in the sense that the transition is not really part of the segment, but the comparison table is a bit meaningless as the other tools forcibly assign all data to kmers, instead of ignoring parts as transition states. In other words, it is a benchmark that is easy to cheat by assigning more data to noise/transition states.

B: The values shown are influenced by the alignment made between the read and expected reference signal. Especially Tombo tends to forcibly assign data to whatever looks the most similar nearby rather than providing the correct alignment. So the "benchmark of the segmentation performance" is more of an "overall benchmark of the raw signal alignment". Which is still a good, useful thing, but the text seems to suggest something else.

Thank you for raising these important concerns regarding the segmentation benchmarking.

Regarding point A, the base blocks aligned to the same 5mer are concatenated into a single segment, including the short transition blocks between them. These transition blocks are typically very short (4~10 signal points, average 6 points), while a typical 5mer segment contains around 20~60 signal points. To assess whether SegPore’s performance is inflated by excluding transition segments, we conducted an additional comparison: we removed 6 boundary signal points (3 from the start and 3 from the end) from each 5mer segment in Nanopolish and Tombo’s results to reduce potential noise. The new comparison table is shown in the following:

SegPore consistently demonstrates superior performance. Its key contribution lies in its ability to recognize structured noise in the raw signal and to derive more accurate mean and standard deviation values that more faithfully represent the true state of the k-mer in the pore. The improved mean estimates are evidenced by the clearly separated peaks of modified and unmodified 5mers in Figures 3A and 4B, while the improved standard deviation is reflected in the segmentation benchmark experiments.

Regarding point B, we apologize for the confusion. We have added a new paragraph to the introduction to clarify that the segmentation task indeed includes the alignment step.

“The general workflow of Nanopore direct RNA sequencing (DRS) data analysis is as follows. First, the raw electrical signal from a read is basecalled using tools such as Guppy or Dorado, which produce the nucleotide sequence of the RNA molecule. However, these basecalled sequences do not include the precise start and end positions of each ribonucleotide (or k-mer) in the signal. Because basecalling errors are common, the sequences are typically mapped to a reference genome or transcriptome using minimap2 to recover the correct reference sequence. Next, tools such as Nanopolish and Tombo align the raw signal to the reference sequence to determine which portion of the signal corresponds to each k-mer. We define this process as the segmentation task, referred to as "eventalign" in Nanopolish. Based on this alignment, Nanopolish extracts various features—such as the start and end positions, mean, and standard deviation of the signal segment corresponding to a k-mer. This signal segment or its derived features is referred to as an "event" in Nanopolish. The resulting events serve as input for downstream RNA modification detection tools such as m6Anet and CHEUI.”

(23) Page 17 “Given the comparable methods and input data requirements, we benchmarked SegPore against several baseline tools, including Tombo, MINES (26), Nanom6A (27), m6Anet, Epinano (28), and CHEUI (29).”

It seems m6Anet is actually Nanopolish+m6Anet in Figure 3C, this needs a minor clarification here.

m6Anet uses Nanopolish’s estimated events as input by default.

(24) Page 18, Figure 3, A and B are figures without any indication of what is on the axis and from the text I believe the position next to each other on the x-axis rather than overlapping is meaningless, while their spread is relevant, as we're looking at the distribution of raw values for this 5mer. The figure as is is rather confusing.

Thanks for pointing out the confusion. We have added concrete values to the axes in Figures 3A and 3B and revised the figure legend as follows in the manuscript:

“(A) Histogram of the estimated mean from current signals mapped to an example m6A-modified genomic location (chr10:128548315, GGACT) across all reads in the training data, comparing Nanopolish (left) and SegPore (right). The x-axis represents current in picoamperes (pA).

(B) Histogram of the estimated mean from current signals mapped to the GGACT motif at all annotated m6A-modified genomic locations in the training data, again comparing Nanopolish (left) and SegPore (right). The x-axis represents current in picoamperes (pA).”

(25) Page 18 “SegPore's results show a more pronounced bimodal distribution in the raw signal segment mean, indicating clearer separation of modified and unmodified signals.”

Without knowing the correct values around the target kmer (like Figure 4B), just the more defined bimodal distribution could also indicate the (wrongful) assignment of neighbouring kmer values to this kmer instead, hence this statement lacks some needed support, this is just one interpretation of the possible reasons.

Thank you for the comment. We have added concrete values to Figures 3A and 3B to support this point. Both peaks fall within a reasonable range: the unmodified peak (125 pA) is approximately 1.17 pA away from its reference value of 123.83 pA, and the modified peak (118 pA) is around 7 pA away from the unmodified peak. This shift is consistent with expected signal changes due to RNA modifications (usually less than 10 pA), and the magnitude of the difference suggests that the observed bimodality is more likely caused by true modification events rather than misalignment.

(26) Page 18 “Furthermore, when pooling all reads mapped to m6A-modified locations at the GGACT motif, SegPore showed prominent peaks (Fig. 3B), suggesting reduced noise and improved modification detection.”

I don't think the prominent peaks directly suggest improved detection, this statement is a tad overreaching.

We revised the sentense to the following:

“SegPore exhibited more distinct peaks (Fig. 3B), indicating reduced noise and potentially enabling more reliable modification detection”.

(27) Page18 “(2) direct m6A predictions from SegPore's Gaussian Mixture Model (GMM), which is limited to the six selected 5mers.”

The 'six selected' refers to what exactly? Also, 'why' this is limited to them is also unclear as it is, and it probably would become clearer if it is clearly defined what this refers to.

It is explained the page 16 in the SegPore’s workflow in the original manuscript as follows:

“A key component of SegPore is the 5mer parameter table, which specifies the mean and standard deviation for each 5mer in both modified and unmodified states (Fig. 2A1A). Since the peaks (representing modified and unmodified states) are separable for only a subset of 5mers, SegPore can provide modification parameters for these specific 5mers. For other 5mers, modification state predictions are unavailable.”

e select a small set of 5mers that show clear peaks (modified and unmodified 5mers) in GMM in the m6A site-level data analysis. These 5mers are provided in Supplementary Fig. S2C, as explained in the section “m6A site level benchmark” in the Material and Methods (page 12 in the original manuscript).

“…transcript locations into genomic coordinates. It is important to note that the 5mer parameter table was not re-estimated for the test data. Instead, modification states for each read were directly estimated using the fixed 5mer parameter table. Due to the differences between human (Supplementary Fig. S2A) and mouse (Supplementary Fig. S2B), only six 5mers were found to have m6A annotations in the test data’s ground truth (Supplementary Fig. S2C). For a genomic location to be identified as a true m6A modification site, it had to correspond to one of these six common 5mers and have a read coverage of greater than 20. SegPore derived the ROC and PR curves for benchmarking based on the modification rate at each genomic location….”

We have updated the sentence as follows to increase clarity:

“which is limited to the six selected 5mers that exhibit clearly separable modified and unmodified components in the GMM (see Materials and Methods for details).”

(28) Page 19, Figure 4C, the blue 'Unmapped' needs further explanation. If this means the segmentation+alignment resulted in simply not assigning any segment to a kmer, this would indicate issues in the resulting mapping between raw data and kmers as the data that probably belonged to this kmer is likely mapped to a neighbouring kmer, possibly introducing a bimodal distribution there.

This is due to deletion event in the full alignment algorithm. See Page 8 of SupplementaryNote1:

During the traceback step of the dynamic programming matrix, not every 5mer in the reference sequence is assigned a corresponding raw signal fragment—particularly when the signal’s mean deviates substantially from the expected mean of that 5mer. In such cases, the algorithm considers the segment to be generated by an unknown 5mer, and the corresponding reference 5mer is marked as unmapped.

(29) Page 19, “For six selected m6A motifs, SegPore achieved an ROC AUC of 82.7% and a PR AUC of 38.7%, earning the third-best performance compared with deep leaning methods m6Anet and CHEUI (Fig. 3D).”

How was this selection of motifs made, are these related to the six 5mers in the middle of Supplementary Figure S2? Are these the same six as on page 18? This is not clear to me.

It is the same, see the response to point 27.

(30) Page 21 “Biclustering reveals that modifications at the 6th, 7th, and 8th genomic locations are specific to certain clusters of reads (clusters 4, 5, and 6), while the first five genomic locations show similar modification patterns across all reads.”

This reads rather confusingly. Both the '6th, 7th, and 8th genomic locations' and 'clusters 4,5,6' should be referred to in clearer terms. Either mark them in the figure as such or name them in the text by something that directly matches the text in the figure.

We have added labels to the clusters and genomic locations Figure 4C, and revised the sentence as follows:

“Biclustering reveals that modifications at g6 are specific to cluster C4, g7 to cluster C5, and g8 to cluster C6, while the first five genomic locations (g1 to g5) show similar modification patterns across all reads.”

(31) Page 21, “We developed a segmentation algorithm that leverages the jiggling property in the physical process of DRS, resulting in cleaner current signals for m6A identification at both the site and single-molecule levels.”

Leverages, or just 'takes into account'?

We designed our HHMM specifically based on the jiggling hypothesis, so we believe that using the term “leverage” is appropriate.

(32) Page 21, “Our results show that m6Anet achieves superior performance, driven by SegPore's enhanced segmentation.”

Superior in what way? It barely improves over Nanopolish in Figure 3C and is outperformed by other methods in Figure 3D. The segmentation may have improved but this statement says something is 'superior' driven by that 'enhanced segmentation', so that cannot refer to the segmentation itself.

We revise it as follows in the revised manuscript:

”Our results demonstrate that SegPore’s segmentation enables clear differentiation between m6A-modified and unmodified adenosines.”

(33) Page 21, “In SegPore, we assume a drastic change between two consecutive 5mers, which may hold for 5mers with large difference in their current baselines but may not hold for those with small difference.”

The implications of this assumption don't seem highlighted enough in the work itself and may be cause for falsely discovering bi-modal distributions. What happens if such a 5mer isn't properly split, is there no recovery algorithm later on to resolve these cases?

We agree that there is a risk of misalignment, which can result in a falsely observed bimodal distribution. This is a known and largely unavoidable issue across all methods, including deep neural network–based methods. For example, many of these models rely on a CTC (Connectionist Temporal Classification) layer, which implicitly performs alignment and may also suffer from similar issues.

Misalignment is more likely when the current baselines of neighboring k-mers are close. In such cases, the model may struggle to confidently distinguish between adjacent k-mers, increasing the chance that signals from neighboring k-mers are incorrectly assigned. Accurate baseline estimation for each k-mer is therefore critical—when baselines are accurate, the correct alignment typically corresponds to the maximum likelihood.

We have added the following sentence to the discussion to acknowledge this limitation:

“As with other RNA modification estimation methods, SegPore can be affected by misalignment errors, particularly when the baseline signals of adjacent k-mers are similar. These cases may lead to spurious bimodal signal distributions and require careful interpretation.”

(34) Page 21, “Currently, SegPore models only the modification state of the central nucleotide within the 5mer. However, modifications at other positions may also affect the signal, as shown in Figure 4B. Therefore, introducing multiple states to the 5mer could help to improve the performance of the model.”

The meaning of this statement is unclear to me. Is SegPore unable to combine the information of overlapping kmers around a possibly modified base (central nucleotide), or is this referring to having multiple possible modifications in a single kmer (multiple states)?

We mean there can be modifications at multiple positions of a single 5mer, e.g. C m5C m6A m7G T. We have revised the sentence to:

“Therefore, introducing multiple states for a 5mer to accout for modifications at mutliple positions within the same 5mer could help to improve the performance of the model.”

(35) Page 22, “This causes a problem when apply DNN-based methods to new dataset without short read sequencing-based ground truth. Human could not confidently judge if a predicted m6A modification is a real m6A modification.”

Grammatical errors in both these sentences. For the 'Human could not' part, is this referring to a single person's attempt or more extensively tested?

Thanks for the comment. We have revised the sentence as follows:

“This poses a challenge when applying DNN-based methods to new datasets without short-read sequencing-based ground truth. In such cases, it is difficult for researchers to confidently determine whether a predicted m6A modification is genuine (see Supplmentary Figure S5).”

(36) Page 22, “…which is easier for human to interpret if a predicted m6A site is real.”

"a" human, but also this probably meant to say 'whether' instead of 'if', or 'makes it easier'.

Thanks for the advice. We have revise the sentence as follows:

“One can generally observe a clear difference in the intensity levels between 5mers with an m6A and those with a normal adenosine, which makes it easier for a researcher to interpret whether a predicted m6A site is genuine.”

(37) Page 22, “…and noise reduction through its GMM-based approach…”

Is the GMM providing noise reduction or segmentation?

Yes, we agree that it is not relevant. We have removed the sentence in the revised manuscript as follows:

“Although SegPore provides clear interpretability and noise reduction through its GMM-based approach, there is potential to explore DNN-based models that can directly leverage SegPore's segmentation results.”

(38) Page 23, “SegPore effectively reduces noise in the raw signal, leading to improved m6A identification at both site and single-molecule levels…”

Without further explanation in what sense this is meant, 'reduces noise' seems to overreach the abilities, and looks more like 'masking out'.

Following the reviewer’s suggestion, we change it to ‘mask out'’ in the revised manuscript.

“SegPore effectively masks out noise in the raw signal, leading to improved m6A identification at both site and single-molecule levels.”

Reviewer #3 (Recommendations for the authors):

I recommend the publication of this manuscript, provided that the following comments (and the comments above) are addressed.

In general, the authors state that SegPore represents an improvement on existing software. These statements are largely unquantified, which erodes their credibility. I have specified several of these in the Minor comments section.

Page 5, Preprocessing: The authors comment that the poly(A) tail provides a stable reference that is crucial for the normalisation of all reads. How would this step handle reads that have variable poly(A) tail lengths? Or have interrupted poly(A) tails (e.g. in the case of mRNA vaccines that employ a linker sequence)?

We apologize for the confusion. The poly(A) tail–based normalization is explained in Supplementary Note 1, Section 3.

As shown in Author response image 1 below, the poly(A) tail produces a characteristic signal pattern—a relatively flat, squiggly horizontal line. Due to variability between nanopores, raw current signals often exhibit baseline shifts and scaling of standard deviations. This means that the signal may be shifted up or down along the y-axis and stretched or compressed in scale.

Author response image 1.

The normalization remains robust with variable poly(A) tail lengths, as long as the poly(A) region is sufficiently long. The linker sequence will be assigned to the adapter part rather than the poly(A) part.

To improve clarity in the revised manuscript, we have added the following explanation:

“Due to inherent variability between nanopores in the sequencing device, the baseline levels and standard deviations of k-mer signals can differ across reads, even for the same transcript. To standardize the signal for downstream analyses, we extract the raw current signal segments corresponding to the poly(A) tail of each read. Since the poly(A) tail provides a stable reference, we normalize the raw current signals across reads, ensuring that the mean and standard deviation of the poly(A) tail are consistent across all reads. This step is crucial for reducing…..”

We chose to use the poly(A) tail for normalization because it is sequence-invariant—i.e., all poly(A) tails consist of identical k-mers, unlike transcript sequences which vary in composition. In contrast, using the transcript region for normalization can introduce biases: for instance, reads with more diverse k-mers (having inherently broader signal distributions) would be forced to match the variance of reads with more uniform k-mers, potentially distorting the baseline across k-mers.

Page 7, 5mer parameter table: r9.4_180mv_70bps_5mer_RNA is an older kmer model (>2 years). How does your method perform with the newer RNA kmer models that do permit the detection of multiple ribonucleotide modifications? Addressing this comment is crucial because it is feasible that SegPore will underperform in comparison to the newer RNA base caller models (requiring the use of RNA004 datasets).

Thank you for highlighting this important point. For RNA004, we have updated SegPore to ensure compatibility with the latest kit. In our revised manuscript, we demonstrate that the translocation-based segmentation hypothesis remains valid for RNA004, as supported by new analyses presented in the supplementary Figure S4.

Additionally, we performed a new benchmark with f5c and Uncalled4 in RNA004 data in the revised manuscript (Table 2), where SegPore exhibit a better performance than f5c and Uncalled4.

We agree that benchmarking against the latest Dorado models—specifically rna004_130bps_hac@v5.1.0 and rna004_130bps_sup@v5.1.0, which include built-in modification detection capabilities—would provide valuable context for evaluating the utility of SegPore. However, generating a comprehensive k-mer parameter table for RNA004 requires a large, well-characterized dataset. At present, such data are limited in the public domain. Additionally, Dorado is developed by ONT and its internal training data have not been released, making direct comparisons difficult.

Our current focus is on improving raw signal segmentation quality, which are upstream tasks critical to many downstream analyses, including RNA modification detection. Future work may include benchmarking SegPore against models like Dorado once appropriate data become available.

The Methods and Results sections contain redundant information - please streamline the information in these sections and reduce the redundancy. For example, the benchmarking section may be better situated in the Results section.

Following your advice, we have removed redundant texts about the Segmentation benchmark from Materials and Methods in the revised manuscript.

Minor comments

(1) Introduction

Page 3: "By incorporating these dynamics into its segmentation algorithm...". Please provide an example of how motor protein dynamics can impact RNA translocation. In particular, please elaborate on why motor protein dynamics would impact the translocation of modified ribonucleotides differently to canonical ribonucleotides. This is provided in the results, but please also include details in the Introduction.

Following your advice, we added one sentence to explain how the motor protein affect the translocation of the DNA/RNA molecule in the revised manuscript.

“This observation is also supported by previous reports, in which the helicase (the motor protein) translocates the DNA strand through the nanopore in a back-and-forth manner. Depending on ATP or ADP binding, the motor protein may translocate the DNA/RNA forward or backward by 0.5-1 nucleotides.”

As far as we understand, this translocation mechanism is not specific to modified or unmodified nucleotides. For further details, we refer the reviewer to the original studies cited.

Page 3: "This lack of interpretability can be problematic when applying these methods to new datasets, as researchers may struggle to trust the predictions without a clear understanding of how the results were generated." Please provide details and citations as to why researchers would struggle to trust the predictions of m6Anet. Is it due to a lack of understanding of how the method works, or an empirically demonstrated lack of reliability?

Thank you for pointing this out. The lack of interpretability in deep learning models such as m6Anet stems primarily from their “black-box” nature—they provide binary predictions (modified or unmodified) without offering clear reasoning or evidence for each call.

When we examined the corresponding raw signals, we found it difficult to visually distinguish whether a signal segment originated from a modified or unmodified ribonucleotide. The difference is often too subtle to be judged reliably by a human observer. This is illustrated in the newly added Supplementary Figure S5, which shows Nanopolish-aligned raw signals for the central 5mer GGACT in Figure 4B, displayed both uncolored and colored by modification state (according to the ground truth).

Although deep neural networks can learn subtle, high-dimensional patterns in the signal that may not be readily interpretable, this opacity makes it difficult for researchers to trust the predictions—especially in new datasets where no ground truth is available. The issue is not necessarily an empirically demonstrated lack of reliability, but rather a lack of transparency and interpretability.

We have updated the manuscript accordingly and included Supplementary Figure S5 to illustrate the difficulty in interpreting signal differences between modified and unmodified states.

Page 3: "Instead of relying on complex, opaque features...". Please provide evidence that the research community finds the figures generated by m6Anet to be difficult to interpret, or delete the sections relating to its perceived lack of usability.

See the figure provided in the response to the previous point. We added a reference to this figure in the revised manuscript.

“Instead of relying on complex, opaque features (see Supplementary Figure S5), SegPore leverages baseline current levels to distinguish between…..”

(2) Materials and Methods

Page 5, Preprocessing: "We begin by performing basecalling on the input fast5 file using Guppy, which converts the raw signal data into base sequences.". Please change "base" to ribonucleotide.

Revised as requested.

Page 5 and throughout, please refer to poly(A) tail, rather than polyA tail throughout.

Revised as requested.

Page 5, Signal segmentation via hierarchical Hidden Markov model: "...providing more precise estimates of the mean and variance for each base block, which are crucial for downstream analyses such as RNA modification prediction." Please specify which method your HHMM method improves upon.

Thank you for the suggestion. Since this section does not include a direct comparison, we revised the sentence to avoid unsupported claims. The updated sentence now reads:

"...providing more precise estimates of the mean and variance for each base block, which are crucial for downstream analyses such as RNA modification prediction."

Page 10, GMM for 5mer parameter table re-estimation: "Typically, the process is repeated three to five times until the 5mer parameter table stabilizes." How is the stabilisation of the 5mer parameter table quantified? What is a reasonable cut-off that would demonstrate adequate stabilisation of the 5mer parameter table?

Thank you for the comment. We assess the stabilization of the 5mer parameter table by monitoring the change in baseline values across iterations. If the absolute change in baseline values for all 5mers is less than 1e-5 between two consecutive iterations, we consider the estimation to have stabilized.

Page 11, M6A site level benchmark: why were these datasets selected? Specifically, why compare human and mouse ribonuclotide modification profiles? Please provide a justification and a brief description of the experiments that these data were derived from, and why they are appropriate for benchmarking SegPore.

Thank you for the comment. These data are taken from a previous benchmark studie about m6A estimation from RNA002 data in the literature (https://doi.org/10.1038/s41467-023-37596-5). We think the data are appropreciate here.

Thank you for the comment. The datasets used were taken from a previous benchmark study on m6A estimation using RNA002 data (https://doi.org/10.1038/s41467-023-37596-5). These datasets include human and mouse transcriptomes and have been widely used to evaluate the performance of RNA modification detection tools. We selected them because (i) they are based on RNA002 chemistry, which matches the primary focus of our study, and (ii) they provide a well-characterized and consistent benchmark for assessing m6A detection performance. Therefore, we believe they are appropriate for validating SegPore.

(3) Results

Page 13, RNA translocation hypothesis: "The raw current signals, as shown in Fig. 1B...". Please check/correct figure reference - Figure 1B does not show raw current signals.

Thank you for pointing this out. The correct reference should be Figure 2B. We have updated the figure citation accordingly in the revised manuscript.

Page 19, m6A identification at the site level: "For six selected m6A motifs, SegPore achieved an ROC AUC of 82.7% and a PR AUC of 38.7%, earning the third best performance compared with deep leaning methods m6Anet and CHEUI (Fig. 3D)." SegPore performs third best of all deep learning methods. Do the authors recommend its use in conjunction with m6Anet for m6A detection? Please clarify in the text.

This sentence aims to convey that SegPore alone can already achieve good performance. If interpretability is the primary goal, we recommend using SegPore on its own. However, if the objective is to identify more potential m6A sites, we suggest using the combined approach of SegPore and m6Anet. That said, we have chosen not to make explicit recommendations in the main text to avoid oversimplifying the decision or potentially misleading readers.

Page 19, m6A identification at the single molecule level: "one transcribed with m6A and the other with normal adenosine". I assume that this should be adenine? Please replace adenosine with adenine throughout.

Thank you for pointing this out. We have revised the sentence to use "adenine" where appropriate. In other instances, we retain "adenosine" when referring specifically to adenine bound to a ribose sugar, which we believe is suitable in those contexts.

Page 19, m6A identification at the single molecule level: "We used 60% of the data for training and 40% for testing". How many reads were used for training and how many for testing? Please comment on why these are appropriate sizes for training and testing datasets.

In total, there are 1.9 million reads, with 1.14 million used for training and 0.76 million  for testing (60% and 40%, respectively). We chose this split to ensure that the training set is sufficiently large to reliably estimate model parameters, while the test set remains substantial enough to robustly evaluate model performance. Although the ratio was selected somewhat arbitrarily, it balances the need for effective training with rigorous validation.

(4) Discussion

Page 21: "We believe that the de-noised current signals will be beneficial for other downstream tasks." Which tasks? Please list an example.

We have revised the text for clarity as follows:

“We believe that the de-noised current signals will be beneficial for other downstream tasks, such as the estimation of m5C, pseudouridine, and other RNA modifications.”

Page 22: "One can generally observe a clear difference in the intensity levels between 5mers with a m6A and normal adenosine, which is easier for human to interpret if a predicted m6A site is real." This statement is vague and requires qualification. Please reference a study that demonstrates the human ability to interpret two similar graphs, and demonstrate how it relates to the differences observed in your data.

We apologize for the confusion. We have revised the sentence as follows:

“One can generally observe a clear difference in the intensity levels between 5mers with an m6A and those with a normal adenosine, which makes it easier for a researcher to interpret whether a predicted m6A site is genuine.”

We believe that Figures 3A, 3B, and 4B effectively illustrate this concept.

Page 23: How long does SegPore take for its analyses compared to other similar tools? How long would it take to analyse a typical dataset?

We have added run-time statistics for datasets of varying sizes in the revised manuscript (see Supplementary Figure S6). This figure illustrates SegPore’s performance across different data volumes to help estimate typical processing times.

(5) Figures

Figure 4C. Please number the hierachical clusters and genomic locations in this figure. They are referenced in the text.

Following your suggestion, we have labeled the hierarchical clusters and genomic locations in Figure 4C in the revised manuscript.

In addition, we revised the corresponding sentence in the main text as follows: “Biclustering reveals that modifications at g6 are specific to cluster C4, g7 to cluster C5, and g8 to cluster C6, while the first five genomic locations (g1 to g5) show similar modification patterns across all reads.”

Author response:

We thank the Reviewers and Editors for their time and insightful comments. We are encouraged by their positive assessment and we look forward to addressing the points raised. Areas of primary concern include (1) the use of high concentrations in peptide experiments; (2) improvement of the presentation and discussion of the results; and (3) clarification of the impact of surface adsorption on the mass photometry analyses.

Regarding (1), we will better explain why some experiments with isolated disordered N-terminal extension were necessarily carried out at high concentrations, in order to demonstrate the potential for these peptides to weakly self-associate. While much lower nucleocapsid protein concentrations are present in the cytosol on average, and are used in our ribonucleoprotein assembly experiments, there are two important physiologically relevant cases where high local concentrations do occur: First, high effective concentrations of tethered disordered N-terminal extensions exist locally in the volume sampled by individual ribonucleoprotein complexes, and, second, high nucleocapsid concentrations are prevalent in its macromolecular condensates. Thus, weak interactions of N-terminal extensions can play a critical role strengthening fuzzy ribonucleoprotein complexes and also altering condensate properties, both of which were confirmed in our experiments. Nonetheless, we do not expect the observed fibrillar state of the concentrated isolated N-terminal peptide to be physiologically relevant, since physiologically they will always remain tethered to the full-length protein impeding fibrillar superstructures.

(2) We are grateful for the Reviewers’ suggestions to enhance the clarity and accessibility of our findings and to streamline the presentation. We intend to tighten up the text and improve figures throughout, and add discussion points, as proposed.

(3) We plan to add an analysis of the extent that irreversible surface adsorption decreases solute concentration in mass photometry, and discuss why this has negligible impact on the conclusions drawn under our experimental conditions.In summary, we agree these points all provide opportunities to strengthen the manuscript further and we are glad to revise our manuscript accordingly.

Author response:

The following is the authors’ response to the original reviews

Recommendations for the Authors:

Reviewer #1:

We think that this manuscript brings an important contribution that will be of interest in the areas of statistical physicists, (microbiota) ecology, and (biological) data science. The evidence of their results is solid and the work improves the state-of-the-art in terms of methods. We have a few concerns that, in our opinion, the authors should address.

Major concerns:

(1) While the paper could be of interest for the broad audience of e-Life, the way it is written is accessible mainly to physicists. We encourage the authors to take the broad audience into account by i) explaining better the essence of what is being done at each step, ii) highlighting the relevance of the method compared to other methods, iii) discussing the ecological implications of the results.

Examples on how to approach i) include: Modify or expand Figure 1 so that non-familiar readers can understand the summary of the work (e.g. with cartoons representing communities, diseased states and bacterial interactions and their relationship with the inference method); in each section, summarize at the beginning the purpose of what is going to be addressed in this section, and summarize at the end what the section has achieved; in Figure 2, replace symbols by their meaning as much as possible-the same for Figure 1, at the very least in the figure caption.

Example on how to approach ii): Since the authors aim to establish a bridge between disordered systems and microbiome ecology, it could be useful to expand a bit the introduction on disordered systems for biologists/biophysicists. This could be done with an additional text box, which could also highlight the advantages of this approach in comparison to other techniques (e.g. model-free approaches can also classify healthy and diseased states).

Example on how to approach iii): The authors could discuss with more depth the ecological implications of their results. For example, do they have a hypothesis on why demographic and neutral effects could dominate in healthy patients?

We thank the reviewer for the observations. Following the suggestion in the revised version, each section outlines the goal of what will be addressed in that section, and summarizes what we have achieved at the end; We also updated Figure 1 and Figure 2.

(i) For figure 1, we expanded and hopefully made more clear how we conceptualize the problem, use the data, andestablish our method. In Figure 2, we enriched the y labels of each panel with the name associated with the order parameter.

(ii) We thank the reviewer for helping us improve the readability of the introductory part, thus providing moreinsights into disordered systems techniques for a broader audience. We have added a few explanations at the end of page 2 – to explain the advantages of such methodology compared to other strategies and models.

(iii) We thank the reviewer for raising the need for a more in-depth ecological discussion of our results. A simple wayto understand why neutral effects may dominate in healthy patients is the following. Neutrality implies that species differences are mainly shaped by stochastic processes such as demographic noise, with species treated as different realizations of the same underlying stochastic ecological dynamics. In our analysis, we observe that healthy individuals tend to exhibit highly similar microbial communities, suggesting that the compositional variability among their microbiomes is compatible—at least in part—with the fluctuations expected from demographic stochasticity alone. In contrast, patients with the disease display significantly more heterogeneous microbial compositions. The diversity and structure of their gut communities cannot be satisfactorily explained by neutral demographic fluctuations alone.

This discrepancy implies that additional deterministic forces—such as altered ecological interactions—are driving the divergence observed in dysbiotic states. In diseased individuals, the breakdown of such interactions leads to a structurally distinct regime that may correspond to a phase of marginal stability, as indicated by our theoretical modeling. This shift marks a transition from a community governed by neutrality and demographic noise to one dominated by non-neutral ecological forces (as depicted in Figure 4). We added these comments in the discussion section of the revised manuscript.

(2) Taking into account the broader audience, we invite the authors to edit the abstract, as it seems to jump from one ecological concept to another without explicitly communicating what is the link between these concepts. From the first two sentences, the motivation seems to be species diversity, but no mention of diversity comes after the second sentence. There is no proper introduction/definition of what macroecological states are. After that, the authors switch to healthy and unhealthy states, without previously introducing any link between gut microbiota states and the host’s health (which perhaps could be good in the first or second sentence, although other framings can be as valid). After that, interactions appear in the text and are related to instability, but the reader might not know whether this is surprising or if healthy/unhealthy states are generally related to stability.

We pointed out a few examples, but the authors could extend their revision on i), ii) and iii) beyond such specific comments. In our opinion, this would really benefit the paper.

In response to the reviewer’s concern about conceptual clarity and structure, we substantially revised the abstract to improve its accessibility and logical flow. In the revised abstract, we now clearly link species diversity to microbiome structure and function from the outset, addressing initial confusion. We provide a concise definition of ”macroecological states,” framing them as reproducible statistical patterns reflecting community-level properties. Additionally, the revised version explicitly connects gut microbiome states to host health earlier, resolving the previous abrupt shift in focus. Finally, we conclude by highlighting how disordered systems theory advances our understanding of microbiome stability and functioning, reinforcing the novelty and broader significance of our approach. Overall, the revised abstract better serves a broad interdisciplinary audience, including readers unfamiliar with the technicalities of disordered systems or microbial ecology, while preserving the scientific depth and accuracy of our work

(3) The connection with consumer-resource (CR) models is quite unusual. In Equation (12), why do the authors assume that the consumption term does not depend on R? This should be addressed, since this term is usually dependent on R in microbial ecology models.

In case this is helpful, it is known that the symmetric Lotka-Volterra model emerges from time-scale separation in the MacArthur model, where resources reproduce logistically and are consumed by other species (e.g., plants eaten by herbivores). Consumer-resource models form a broad category, while the MacArthur model is a specific case featuring logistic resource growth. For microbes, a more meaningful justification of the generalized Lotka-Volterra (GLV) model from a consumer-resource perspective involves the consumer-resource dynamics in a chemostat, where time-scale separation is assumed and higher-order interactions are neglected. See, for example: a) The classic paper by MacArthur: R. MacArthur. Species packing and competitive equilibrium for many species. Theoretical Population Biology, 1(1):1-11, 1970. b) Recent works on time-scale separation in chemostat consumer-resource models: Anna Posfai et al., PRL, 2017 Sireci et al., PNAS, 2023 Akshit Goyal et al., PRX-Life, 2025

We thank the reviewer for the observation. We apologize for the typo that appeared in the main text and that we promptly corrected. The Consumers-Resources model we had in mind is the classical case proposed by MacArthur, where resources are self-regulated according to a logistic growth mechanism, which leads to the generalized LotkaVolterra model we employ in our work.

Minor concerns:

(1) The title has a nice pun for statistical physicists, but we wonder if it can be a bit confusing for the broader audience of e-Life. Although we leave this to the author’s decision, we’d recommend considering changing the title, making it more explicit in communicating the main contribution/result of the work.

Following the reviewer’s suggestion, we have introduced an explanatory subtitle: “Linking Species Interactions to Dysbiosis through a Disordered Lotka-Volterra Framework”.

(2) Review the references - some preprints might have already been published: Pasqualini J. 2023, Sireci 2022, Wu 2021.

We thank the reviewer for pointing our attention to this inaccuracy. We updated the references to Pasqualini and Sireci papers. To our knowledge, Wu’s paper has appeared as an arXiv preprint only.

(3) Species do not generally exhibit identical carrying capacities (see Grilli, Nat. Commun., 2020; some taxa are generally more abundant than others. The authors could discuss whether the model, with the inferred parameters, can accurately reproduce the distribution of species’ mean abundances.

We thank the reviewer for this insightful comment. As discussed in the revised manuscript (lines 294–299), our current model does not accurately reproduce the empirical species abundance distribution (SAD). This limitation stems from the assumption of constant carrying capacities across species. While empirical observations (e.g., Grilli et al., Nat. Commun., 2020 [1]) show heterogeneous mean abundances often following power-law or log-normal distributions. However, our model assumes constant carrying capacity, resulting in SADs devoid of fat tails, which diverge from empirical data.

This simplification is implemented to maintain the analytical tractability of the disordered generalized Lotka-Volterra (dGLV) framework, a common approach also found in prior works such as Bunin (2017) and Barbier et al. (2018) [2, 3]. Introducing heterogeneity in carrying capacities, such as drawing them from a log-normal distribution, or switching to multiplicative (rather than demographic) noise, could indeed produce SADs that better align with empirical data. Nevertheless, implementing changes would significantly complicate the analytical treatment.

We acknowledge these directions as promising avenues for future research. They could help enhance the empirical realism of the model and its capacity to capture observed macroecological patterns while posing new theoretical challenges for disordered systems analysis

(4) A substantial number of cited works (Grilli, Nat. Commun., 2020; Zaoli & Grilli, Science Advances, 2021; Sireci et al., PNAS, 2023; Po-Yi Ho et al., eLife, 2022) suggest that environmental fluctuations play a crucial role in shaping microbiome composition and dynamics. Is the authors’ analysis consistent with this perspective? Do they expect their conclusions to remain robust if environmental fluctuations are introduced?

We thank the reviewer for stressing this point. The introduction of environmental fluctuations in the model formally violates detailed balance, thereby preventing the definition of an energy function. To date, no study has integrated random interactions together with both demographic and environmental noise within a unified analytical framework. This is certainly a highly promising direction that some of the authors are already exploring. However, given the inherently out-of-equilibrium nature of the system and the absence of a free energy, we would need to adopt a Dynamical Mean-Field Theory formalism and eventually analyze the corresponding stationary equations to be solved self-consistently. We added, however, a brief note in the Discussion section.

(5) The term “order parameters“ may not be intuitive for a biological audience. In any case, the authors should explicitly define each order parameter when first introduced.

We thank the reviewer for the comment. We introduced the names of the order parameters as soon as they are introduced, along with a brief explanation of their meaning that may be accessible to an audience with biological background.

(6) Line 242: Should ψU be ψD?

We thank the reviewer for the observation. We corrected the typo.

(7) Given that the authors are discussing healthy and diseased states and to avoid confusion, the authors could perhaps use another word for ’pathological’ when they refer to dynamical regimes (e.g., in Appendix 2: ’letting the system enter the pathological regime of unbounded growth’).

We thank the reviewer for the helpful comment. As suggested, we used the term “unphysical” instead of “pathological” where needed.

Reviewer #2:

(1) A technical point that I could not understand is how the authors deal with compositional data. One reason for my confusion is that the order parameters h and q0 are fixed n data to 1/S and 1/S2, and thus I do not see how they can be informative. Same for carrying capacity, why is it not 1 if considering relative abundance?

We thank the reviewer for raising this point. We acknowledge that the treatment of compositional data and the interpretation of order parameters h and q0 were not sufficiently clarified in the manuscript. Additionally, there was an imprecision in the text regarding the interpretation of these parameters.

As defined in revised Eq. (4) of the manuscript, h and q0 are to be averaged over the entire dataset, summing across samples α. Specifically, and , where S_α is the number of species present in sample α and is the average over samples. These parameters are therefore informative, as they encapsulate sample-level ecological diversity, and their variation reflects biological differences between healthy and diseased states. For instance, Pasqualini et al., 2024 [4] reported significant differences in these metrics between health conditions, thereby supporting their ecological relevance.

Regarding carrying capacities, we clarify that although we work with relative abundance data (i.e., compositional data), we do not fix the carrying capacity K to 1. Instead, we set K to the maximum value of xi (relative abundance) within each sample, to preserve compatibility with empirical data and allow for coexistence. While this remains a modeling assumption, it ensures better ecological realism within the constraints of the disordered GLV framework.

(2) Obviously I’m missing something, so it would be nice to clarify in simple terms the logic of the argument. I understand that Lagrange multipliers are going to be used in the model analysis, and there are a lot of technical arguments presented in the paper, but I would like a much more intuitive explanation about the way the data can be used to infer order parameters if those are fixed by definition in compositional data.

We thank the reviewer for the observation. The order parameters can be measured directly from the data, even in the presence of compositionality, as explained above. We can connect those parameters with the theory even for compositional data, because the only effect of adding the compositionality constraint is to shift the linear coefficient in the Hamiltonian, which corresponds to shifting the average interaction µ. However, the resulting phase diagram is mostly affected by the variance of the interactions σ2 (as µ is such that we are in the bounded phase).

(3) Another point that I did not understand comes from the fact that the authors claim that interaction variance is smaller in unhealthy microbiomes. Yet they also find that those are closer to instability, and are more driven by niche processes. I would have expected the opposite to be true, more variance in the interactions leading to instability (as in May’s original paper for instance). Is this apparent paradox explained by covariations in demographic stochasticity (T) and immigration rate (lambda)? If so, I think it would be very useful to comment on that.

As Altieri and coworkers showed in their PRL (2021) [5], the phase diagram of our model differs fundamentally from that of Biroli et al. (2018) [6]. In the latter, the intuitive rule – greater interaction variance yields greater instability – indeed holds. For the sake of clarity, we have attached below the resulting phase diagram obtained by Altieri et al.

The apparent paradox arises because the two phase diagrams are tuned by different parameters. Consequently, even at low temperature and with weak interaction variance, our system may sit nearer to the replica-symmetrybreaking (RSB) line.

Fig. 3 in the main text it is not a (σ,T) phase diagram where all other parameters are kept constant. Rather, it is a plot of the inferred σ and T parameters from the data (without showing the corresponding µ).

To capture the full, non-trivial influence of all parameters on stability, we studied the so-called “replicon eigenvalue” in the RS (i.e. single equilibrium) approximation. This leading eigenvalue measures how close a given set of inferred parameters – and hence a microbiome – is to the RSB threshold. For a visual representation of these findings, refer to Figure 4.

Author response image 1.

(4) What do the empirical SAD look like? It would be nice to see the actual data and how the theoretical SADs compare.

The empirical species abundance distributions (SADs) analyzed in our study are presented and discussed in detail in Pasqualini et al., 2024 [4]. Given the overlap in content, we chose not to reproduce these figures in the current manuscript to avoid redundancy.

As we also clarify in the revised text, the theoretical SAD is derived from the disordered generalized Lotka-Volterra (dGLV) model in the unique fixed point phase typically exhibit exponential tails. These distributions do not match the heavier-tailed patterns (e.g., log-normal or power-law-like) observed in empirical microbiome data. This discrepancy stems from the simplifying assumptions of the dGLV framework, including the use of constant carrying capacities and demographic noise.

In the revised manuscript, we have added a brief discussion in the revised manuscript to explicitly acknowledge this limitation and emphasize it as a direction for future refinement of the model, such as incorporating heterogeneous carrying capacities or exploring alternative noise structures.

(5) Some typos: often “niche” is written “nice”.

We thank the reviewer for this suggestion. After inspecting the text, we corrected the reported typos.

Reviewer #3:

Major comments:

(1) In the S3 text, the authors say that filtered metagenomic reads were processed using the software Kaiju. The description of the pipeline does not mention how core genes were selected, which is often a crucial step in determining the abundance of a species in a metagenomic sample. In addition, the senior author of this manuscript has published a version of Kaiju that leverages marker genes classification methods (deemed Core-Kaiju), but it was not used for either this manuscript or Pasqualini et al. (2014; Tovo et al., 2020). I am not suggesting that the data necessarily needs to be reprocessed, but it would be useful to know how core genes were chosen in Pasqualini et al. and why Core-Kaiju was not used (2014).

Prior to the current manuscript and the PLOS Computational Biology paper by Pasqualini et al. [4], we applied the core-Kaiju protocol to the same dataset used in both studies. However, this tool was originally developed and validated using general catalogs of culturable organisms, not specifically tuned for gut microbiomes. As a result, we have realized that in many samples Core Kajiu would filter only very few species (in some samples, the number of identified species was as low as 5–10), undermining the reliability of the analysis. Due to these limitations, we opted to use the standard Kaiju version in our work. We are actively developing an improved version of the core-Kaiju protocol that will overcome the discussed limitations and preliminary results (not shown here) indicate the robustness of the obtained patterns also in this case.

(2) My understanding of Pasqualini et al. was that diseased patients experienced larger fluctuations in abundance, while in this study, they had smaller fluctuations (Figure 3a; 2024). Is this a discrepancy between the two models or is there a more nuanced interpretation?

We thank the reviewer for the observation. This is only an apparent discrepancy, as the term fluctuation has different meanings in the two contexts. The fluctuations referred to by the reviewer correspond to a parameter of our theory—namely, noise in the interactions. Conversely, in Pasqualini et al. σ indicates environmental fluctuations. Nevertheless, there is no conceptual discrepancy in our results: in both studies, unhealthy microbiomes were found to be less stable. In fact, also in this study, notably Fig. 4, shows that unhealthy microbiomes lie closer to the RSB line, a phenomenon that is also associated with enhanced fluctuations.

(3) Line 38-41: It would be helpful to explicitly state what “interaction patterns” are being referenced here. The final sentence could also be clarified. Do microbiomes “host“ interactions or are they better described as a property (“have”, “harbor”). The word “host” may confuse some readers since it is often used to refer to the human host. I am also not sure what point is being made by “expected to govern natural ones”. There are interactions between members of a microbiome; experimental studies have characterized some of these interactions, which we expect to relate in some way to interactions in nature. Is this what the authors are saying?

Thanks. We agree that this sentence was not clear. Indeed, we are referring to pairwise species interactions and not to host-microbiome interactions. We have rewritten this part in the following way: In fact, recent work shows that the network-level properties of species-species interactions —for example, the sign balance, average strength, and connectivity of the inferred interaction matrix— shift systematically between healthy and dysbiotic gut communities (see for instance, [7, 8]). Pairwise species interactions have been quantified in simplified in-vitro consortia [9, 10]; we assume that the same classes of interactions also operate—albeit in a more complex form—in the native gut microbiome.

(4) Line 43: I appreciate that the authors separated neutral vs. logistic models here.

(5) Lines 51-75: The framing here is well-written and convincing. Network inference is an ongoing, active subject in ecology, and there is an unfortunate focus on inferring every individual interaction because ecologists with biology backgrounds are not trained to think about the problem in the language of statistical physics.

We thank the reviewer for these positive comments.

(6) Line 87: Perhaps I’m missing something obvious, but I don’t see how ρi sets the intrinsic timescale of the dynamics when its units are 1/(time*individuals), assuming the dimensions of ri are inverse time.

We thank the reviewer for the observation. We corrected this phrase in the main text.

(7) Lines 189-190: “as close as possible to the data” it would aid the reader if you specified the criteria meant by this statement.

We thank the reviewer for the observation. We removed the sentence, as it introduced some redundancy in our argument. In the subsequent text, the proposed method is exposed in details.

(8) Line 198: It would aid the reader if you provided some context for what the T - σ plane represents.

We thank the referee for the helpful indication. Indeed, we have better clarified the mutual role of the demographic noise amplitude and strength of the random interaction matrix, as theoretically predicted in the PRL (2021) by Altieri and coworkers [5]. Please, find an additional paragraph on page 6 of the resubmitted version.

(9) Line 217: Specifying what is meant by “internal modes“ would aid the typical life science reader.

We thank the reviewer for the suggestion. Recognizing that referring to “internal modes” to describe the SAD shape in that context might cause confusion, we replaced “internal modes“ with “peaks”.

(10) Line 219: Some additional justification and clarification are needed here, as some may think of “m“ as being biomass.

We added a sentence to better explain this concept. “In classical and quantum field theory, the particle-particle interaction embedded in the quadratic term is typically referred to as a mass source. In the context of this study, captures quadratic fluctuations of species abundances, as also appearing in the expression of the leading eigenvalue of the stability matrix.”

Minor comments:

(1) I commend the authors for removing metagenomic reads that mapped to the human genome in the preprocessing stage of their pipeline. This may seem like an obvious pre-processing step, but it is unfortunately not always implemented.

We thank the referee for pointing this potential issue. The data used in this work, as well as the bioinformatic workflow used to generate them has been described in detail in Pasqualini et al., 2024 [4]. As one of the main steps for preprocessing, we remove reads mapping to the human genome.

(2) Line 13: “Bacterial“ excludes archaea, and while you may not have many high-abundance archaea in your human gut data, this sentence does not specify the human gut. Usually, this exclusion is averted via the term “microbial“, though sometimes researchers raise objections to the term when the data does not include fungal members (e.g., all 16S studies).

We thank the reviewer for this suggestion. As to include archaeal organisms, we adopt the term “microbial“ instead of “bacterial“.

(3) Line 18: This manuscript is being submitted under the “Physics of Living Systems“ tract, but it may be useful to explicitly state in the Abstract that disordered systems are a useful approach for understanding large, complex communities for the benefit of life science researchers coming from a biology background.

Thank. We have modified the abstract following this suggestion.

(4) Line 68: Consider using “adapted“ or something similar instead of “mutated“ if there is no specific reason for that word choice.

We thank the reviewer for this suggestion, which was implemented in the text.

(5) Line 111: It would be useful to define annealed and quenched for a general life science audience.

We thank the reviewer for this suggestion. In the “Results” section, we have opted for “time-dependent disordered interactions” to reach a broader audience and avoid any jargon. Moreover, in the Discussion we added a detailed footnote: “In contrast to the quenched approximation, the annealed version assumes that the random couplings are not fixed but instead fluctuate over time, with their covariance governed by independent Ornstein–Uhlenbeck processes.”

(6) Line 124: Likewise for the replicon sector.

We thank the reviewer for the suggestion. We added a footnote on page 4, after the formula, to highlight the physical intuition behind the introduction of the replicon mode.

“The replicon eigenvalue refers to a particular type of fluctuation around the saddle-point (mean-field) solution within the replica framework. When the Hessian matrix of the replicated free energy is diagonalized, fluctuations are divided into three sectors: longitudinal, anomalous, and replicon. The replicon mode is the most sensitive to criticality signaling – by its vanishing trend – the emergence of many nearly-degenerate states. It essentially describes how ‘soft’ the system is to microscopic rearrangements in configuration space.”

(7) Figure 2: It would be helpful to include y-axis labels for each order parameter alongside the mathematical notation.

We thank the reviewer for this suggestion. Now the y-axis of Figure 2 includes, along the mathmetical symbol, the label of the represented quantities.

(8) Line 242: Subscript “U” is used to denote “Unhealthy” microbiomes, but “D” is used to denote “Diseased” in Figs. 2 and 3 (perhaps elsewhere as well).

We thank the reviewer for this observation. After checking the various subscripts in the text, coherently with figure 2 and 3, we homogenized our notation, adopting the subscript “D“ for symbols related to the diseased/unhealthy condition.

(9) Line 283: “not to“ should be “not due to“

We thank the reviewer for this suggestion. After inspecting the text, we corrected the reported error.

(10) Equations 23, 34: Extra “=“ on the RHS of the first line.

We consistently follow the same formatting across all the line breaks in the equations throughout the text.

We are thus resubmitting our paper, hoping to have satisfactorily addressed all referees’ concerns.

References

(1) Jacopo Grilli. Macroecological laws describe variation and diversity in microbial communities. Nature communications, 11(1):4743, 2020.

(2) Guy Bunin. Ecological communities with lotka-volterra dynamics. Physical Review E, 95(4):042414, 2017.

(3) Matthieu Barbier, Jean-Franc¸ois Arnoldi, Guy Bunin, and Michel Loreau. Generic assembly patterns in complex ecological communities. Proceedings of the National Academy of Sciences, 115(9):2156–2161, 2018.

(4) Jacopo Pasqualini, Sonia Facchin, Andrea Rinaldo, Amos Maritan, Edoardo Savarino, and Samir Suweis. Emergent ecological patterns and modelling of gut microbiomes in health and in disease. PLOS Computational Biology, 20(9):e1012482, 2024.

(5) Ada Altieri, Felix Roy, Chiara Cammarota, and Giulio Biroli. Properties of equilibria and glassy phases of the random lotka-volterra model with demographic noise. Physical Review Letters, 126(25):258301, 2021.

(6) Giulio Biroli, Guy Bunin, and Chiara Cammarota. Marginally stable equilibria in critical ecosystems. New Journal of Physics, 20(8):083051, 2018.

(7) Amir Bashan, Travis E Gibson, Jonathan Friedman, Vincent J Carey, Scott T Weiss, Elizabeth L Hohmann, and Yang-Yu Liu. Universality of human microbial dynamics. Nature, 534(7606):259–262, 2016.

(8) Marcello Seppi, Jacopo Pasqualini, Sonia Facchin, Edoardo Vincenzo Savarino, and Samir Suweis. Emergent functional organization of gut microbiomes in health and diseases. Biomolecules, 14(1):5, 2023.

(9) Jared Kehe, Anthony Ortiz, Anthony Kulesa, Jeff Gore, Paul C Blainey, and Jonathan Friedman. Positive interactions are common among culturable bacteria. Science advances, 7(45):eabi7159, 2021.

(10) Ophelia S Venturelli, Alex V Carr, Garth Fisher, Ryan H Hsu, Rebecca Lau, Benjamin P Bowen, Susan Hromada, Trent Northen, and Adam P Arkin. Deciphering microbial interactions in synthetic human gut microbiome communities. Molecular systems biology, 14(6):e8157, 2018.

Author response:

We gratefully acknowledge the comments on our manuscript and the time you took to read and understand our work. Nevertheless, it is the opinion of these authors that the evidence provided in the submitted paper is strong and we performed multiple replicates of the experiments. In particular, gene deletion and complementation is the accepted gold standard for studies in physiology. In the isoleucine auxotroph (IMaux) strain carrying an ilvG deletion, growth is only possible if ilvG is reintroduced on a plasmid and induced. Additionally, isotopic labeling clearly demonstrates the activity of the proposed pathway. Regardless, we agree with the reviewers that the paper and the scientific community would benefit from an in vitro characterization of the promiscuity of IlvG, so we will perform this experiment and resubmit the paper for further revision, and in this revision also provide more detail on the replicates performed.

Author response:

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

Reviewer #1 (Public Review):

Summary:

The study explored the biomechanics of kangaroo hopping across both speed and animal size to try and explain the unique and remarkable energetics of kangaroo locomotion.

Strengths:

The study brings kangaroo locomotion biomechanics into the 21st century. It is a remarkably difficult project to accomplish. There is excellent attention to detail, supported by clear writing and figures.

Weaknesses:

The authors oversell their findings, but the mystery still persists. 

The manuscript lacks a big-picture summary with pointers to how one might resolve the big question.

General Comments

This is a very impressive tour de force by an all-star collaborative team of researchers. The study represents a tremendous leap forward (pun intended) in terms of our understanding of kangaroo locomotion. Some might wonder why such an unusual species is of much interest. But, in my opinion, the classic study by Dawson and Taylor in 1973 of kangaroos launched the modern era of running biomechanics/energetics and applies to varying degrees to all animals that use bouncing gaits (running, trotting, galloping and of course hopping). The puzzling metabolic energetics findings of Dawson & Taylor (little if any increase in metabolic power despite increasing forward speed) remain a giant unsolved problem in comparative locomotor biomechanics and energetics. It is our "dark matter problem".

Thank you for the kind words.

This study is certainly a hop towards solving the problem. But, the title of the paper overpromises and the authors present little attempt to provide an overview of the remaining big issues. 

We have modified the title to reflect this comment.  “Postural adaptations may contribute to the unique locomotor energetics seen in hopping kangaroos”

The study clearly shows that the ankle and to a lesser extent the mtp joint are where the action is. They clearly show in great detail by how much and by what means the ankle joint tendons experience increased stress at faster forward speeds.

Since these were zoo animals, direct measures were not feasible, but the conclusion that the tendons are storing and returning more elastic energy per hop at faster speeds is solid. The conclusion that net muscle work per hop changes little from slow to fast forward speeds is also solid. 

Doing less muscle work can only be good if one is trying to minimize metabolic energy consumption. However, to achieve greater tendon stresses, there must be greater muscle forces. Unless one is willing to reject the premise of the cost of generating force hypothesis, that is an important issue to confront. Further, the present data support the Kram & Dawson finding of decreased contact times at faster forward speeds. Kram & Taylor and subsequent applications of (and challenges to) their approach supports the idea that shorter contact times (tc) require recruiting more expensive muscle fibers and hence greater metabolic costs. Therefore, I think that it is incumbent on the present authors to clarify that this study has still not tied up the metabolic energetics across speed problems and placed a bow atop the package. 

Fortunately, I am confident that the impressive collective brain power that comprises this author list can craft a paragraph or two that summarizes these ideas and points out how the group is now uniquely and enviably poised to explore the problem more using a dynamic SIMM model that incorporates muscle energetics (perhaps ala' Umberger et al.). Or perhaps they have other ideas about how they can really solve the problem.

You have raised important points, thank you for this feedback. We have added a limitations and considerations section to the discussion which highlights that there are still unanswered questions. Line 311-328

Considerations and limitations

“First, we believe it is more likely that the changes in moment arms and EMA can be attributed to speed rather than body mass, given the marked changes in joint angles and ankle height observed at faster hopping speeds. However, our sample included a relatively narrow range of body masses (13.7 to 26.6 kg) compared to the potential range (up to 80 kg), limiting our ability to entirely isolate the effects of speed from those of mass. Future work should examine a broader range of body sizes. Second, kangaroos studied here only hopped at relatively slow speeds, which bounds our estimates of EMA and tendon stress to a less critical region. As such, we were unable to assess tendon stress at fast speeds, where increased forces would reduce tendon safety factors closer to failure. A different experimental or modelling approach may be needed, as kangaroos in enclosures seem unwilling to hop faster over force plates. Finally, we did not determine whether the EMA of proximal hindlimb joints (which are more difficult to track via surface motion capture markers) remained constant with speed. Although the hip and knee contribute substantially less work than the ankle joint (Fig. 4), the majority of kangaroo skeletal muscle is located around these proximal joints. A change in EMA at the hip or knee could influence a larger muscle mass than at the ankle, potentially counteracting or enhancing energy savings in the ankle extensor muscle-tendon units. Further research is needed to understand how posture and muscles throughout the whole body contribute to kangaroo energetics.”

Additionally, we added a line “Peak GRF also naturally increased with speed together with shorter ground contact durations (Fig. 2b, Suppl. Fig 1b)” (line 238) to highlight that we are not proposing that changes in EMA alone explain the full increase in tendon stress. Both GRF and EMA contribute substantially (almost equally) to stress, and we now give more equal discussion to both. For instance, we now also evaluate how much each contributes: “If peak GRF were constant but EMA changed from the average value of a slow hop to a fast hop, then stress would increase 18%, whereas if EMA remained constant and GRF varied by the same principles, then stress would only increase by 12%. Thus, changing posture and decreasing ground contact duration both appear to influence tendon stress for kangaroos, at least for the range of speeds we examined” (Line 245-249)

We have added a paragraph in the discussion acknowledging that the cost of generating force problem is not resolved by our work, concluding that “This mechanism may help explain why hopping macropods do not follow the energetic trends observed in other species (Dawson and Taylor 1973, Baudinette et al. 1992, Kram and Dawson 1998), but it does not fully resolve the cost of generating force conundrum” Line 274-276.

I have a few issues with the other half of this study (i.e. animal size effects). I would enjoy reading a new paragraph by these authors in the Discussion that considers the evolutionary origins and implications of such small safety factors. Surely, it would need to be speculative, but that's OK.

We appreciate this comment from the reviewer, however could not extend the study to discuss animal size effects because, as we now note in the results: “The range of body masses may not be sufficient to detect an effect of mass on ankle moment in addition to the effect of speed.” Line 193

Reviewer #2 (Public Review):

Summary

This is a fascinating topic that has intrigued scientists for decades. I applaud the authors for trying to tackle this enigma. In this manuscript, the authors primarily measured hopping biomechanics data from kangaroos and performed inverse dynamics. 

While these biomechanical analyses were thorough and impressively incorporated collected anatomical data and an Opensim model, I'm afraid that they did not satisfactorily address how kangaroos can hop faster and not consume more metabolic energy, unique from other animals.  Noticeably, the authors did not collect metabolic data nor did they model metabolic rates using their modelling framework. Instead, they performed a somewhat traditional inverse dynamics analysis from multiple animals hopping at a self-selected speed.

In the current study, we aimed to provide a joint-level explanation for the increases of tendon stress that are likely linked to metabolic energy consumption.

We have now included a limitations section in the manuscript (See response to Rev 1). We plan to expand upon muscle level energetics in the future with a more detailed musculoskeletal model.

Within these analyses, the authors largely focused on ankle EMA, discussing its potential importance (because it affects tendon stress, which affects tendon strain energy, which affects muscle mechanics) on the metabolic cost of hopping. However, EMA was roughly estimated (CoP was fixed to the foot, not measured) and did not detectibly associate with hopping speed (see results Yet, the authors interpret their EMA findings as though it systematically related with speed to explain their theory on how metabolic cost is unique in kangaroos vs. other animals

As noted in our methods, EMA was not calculated from a fixed centre of pressure (CoP). We did fix the medial-lateral position, owing to the fact that both feet contacted the force plate together, but the anteroposterior movement of the CoP was recorded by the force plate and thus allowed to move. We report the movement (or lack of movement) in our results. The anterior-posterior axis is the most relevant to lengthening or shortening the distance of the ‘out-lever’ R, and thereby EMA. It is necessary to assume fixed medial-lateral position because a single force trace and CoP is recorded when two feet land on the force plate. The mediallateral forces on each foot cancel out so there is no overall medial-lateral movement if the forces are symmetrical (e.g. if the kangaroo is hopping in a straight path and one foot is not in front of the other). We only used symmetrical trials so that the anterior-posterior movement of the CoP would be reliable. We have now added additional details into the text to clarify this

Indeed, the relationship between R and speed (and therefore EMA and speed) was not significant. However, the significant change in ankle height with speed, combined with no systematic change in COP at midstance, demonstrates that R would be greater at faster speeds. If we consider the nonsignificant relationship between R and speed to indicate that there is no change in R, then these two results conflict. We could not find a flaw in our methods, so instead concluded that the nonsignificant relationship between R and speed may be due to a small change in R being undetectable in our data. Taking both results into account, we believe it is more likely that there is a non-detectable change in R, rather than no change in R with speed, but we presented both results for transparency. We have added an additional section into the results to make this clearer (Line 177-185) “If we consider the nonsignificant relationship between R (and EMA) and speed to indicate that there is no change in R, then it conflicts with the ankle height and CoP result. Taking both into account, we think it is more likely that there is a small, but important, change in R, rather than no change in R with speed. It may be undetectable because we expect small effect sizes compared to the measurement range and measurement error (Suppl. Fig. 3h), or be obscured by a similar change in R with body mass. R is highly dependent on the length of the metatarsal segment, which is longer in larger kangaroos (1 kg BM corresponded to ~1% longer segment, P<0.001, R²=0.449). If R does indeed increase with speed, both R and r will tend to decrease EMA at faster speeds.”

These speed vs. biomechanics relationships were limited by comparisons across different animals hopping at different speeds and could have been strengthened using repeated measures design

There is significant variation in speed within individuals, not just between individuals. The preferred speed of kangaroos is 2-4.5 m/s, but most individuals showed a wide speed range within this. Eight of our 16 kangaroos had a maximum speed that was 1-2m/s faster than their slowest trial. Repeated measures of these eight individuals comprises 78 out of the 100 trials.   It would be ideal to collect data across the full range of speeds for all individuals, but it is not feasible in this type of experimental setting. Interference with animals such as chasing is dangerous to kangaroos as they are prone to adverse reactions to stress. We have now added additional information about the chosen hopping speeds into the results and methods sections to clarify this “The kangaroos elected to hop between 1.99 and 4.48 m s^-1, with a range of speeds and number of trials for each individual (Suppl. Fig. 9).”  (Line 381-382)

There are also multiple inconsistencies between the authors' theory on how mechanics affect energetics and the cited literature, which leaves me somewhat confused and wanting more clarification and information on how mechanics and energetics relate

We thank the reviewer for this comment. Upon rereading we now understand the reviewers position, and have made substantial revisions to the introduction and discussion (See comments below) 

My apologies for the less-than-favorable review, I think that this is a neat biomechanics study - but am unsure if it adds much to the literature on the topic of kangaroo hopping energetics in its current form.

Again we thank the reviewer for their time and appreciate their efforts to strengthen our manuscript.

Reviewer #3 (Public Review):

Summary:

The goal of this study is to understand how, unlike other mammals, kangaroos are able to increase hopping speed without a concomitant increase in metabolic cost. They use a biomechanical analysis of kangaroo hopping data across a range of speeds to investigate how posture, effective mechanical advantage, and tendon stress vary with speed and mass. The main finding is that a change in posture leads to increasing effective mechanical advantage with speed, which ultimately increases tendon elastic energy storage and returns via greater tendon strain. Thus kangaroos may be able to conserve energy with increasing speed by flexing more, which increases tendon strain.

Strengths:

The approach and effort invested into collecting this valuable dataset of kangaroo locomotion is impressive. The dataset alone is a valuable contribution.

Thank you!

Weaknesses:

Despite these strengths, I have concerns regarding the strength of the results and the overall clarity of the paper and methods used (which likely influences how convincingly the main results come across).

(1) The paper seems to hinge on the finding that EMA decreases with increasing speed and that this contributes significantly to greater tendon strain estimated with increasing speed. It is very difficult to be convinced by this result for a number of reasons:

It appears that kangaroos hopped at their preferred speed. Thus the variability observed is across individuals not within. Is this large enough of a range (either within or across subjects) to make conclusions about the effect of speed, without results being susceptible to differences between subjects? 

Apologies, this was not clear in the manuscript. Kangaroos hopping at their preferred speed means we did not chase or startle them into high speeds to comply with ethics and enclosure limitations. Thus we did not record a wide range of speeds within the bounds of what kangaroos are capable of in the wild (up to 12 m/s), but for the range we did measure (~2-4.5 m/s), there is a large amount of variation in hopping speed within each individual kangaroo. Out of 16 individuals, eight individuals had a difference of 1-2m/s between their slowest and fastest trials, and these kangaroos accounted for 78 out of 100 trials. Of the remainder, six individuals had three for fewer trials each, and two individuals had highly repeatable speeds (3 out of 4, and 6 out of 7 trials were within 0.5 m/s). We have now removed the terminology “preferred speed” e.g line 115. We have added additional information about the chosen hopping speeds into the results and methods, including an appendix figure “The kangaroos elected to hop between 1.99 and 4.48 m s^-1, with a range of speeds and number of trials for each individual (Suppl. Fig. 9).” (Line 381-382)

In the literature cited, what was the range of speeds measured, and was it within or between subjects?

For other literature, to our knowledge the highest speed measured is ~9.5m/s (see supplementary Fig1b) and there were multiple measures for several individuals (see methods Kram & Dawson 1998). 

Assuming that there is a compelling relationship between EMA and velocity, how reasonable is it to extrapolate to the conclusion that this increases tendon strain and ultimately saves metabolic cost?  They correlate EMA with tendon strain, but this would still not suggest a causal relationship (incidentally the p-value for the correlation is not reported). 

The functions that underpin these results (e.g. moment = GRF*R) come from physical mechanics and geometry, rather than statistical correlations. Additionally, a p-value is not appropriate in the relationship between EMA and stress (rather than strain) because the relationship does not appear to be linear. We have made it clearer in the discussion that we are not proposing that entire change in stress is caused by changes in EMA, but that the increase in GRF that naturally occurs with speed will also explain some of the increase in stress, along with other potential mechanisms. The discussion has been extensively revised to reflect this. 

Tendon strain could be increasing with ground reaction force, independent of EMA. Even if there is a correlation between strain and EMA, is it not a mathematical necessity in their model that all else being equal, tendon stress will increase as ema decreases? I may be missing something, but nonetheless, it would be helpful for the authors to clarify the strength of the evidence supporting their conclusions.

Yes, GRF also contributes to the increase in tendon stress in the mechanism we propose (Suppl. Fig. 8), see the formulas in Fig 6, and we have made this clearer in the revised discussion (see above comment).  You are correct that mathematically stress is inversely proportional to EMA, which can be observed in Fig. 7a, and we did find that EMA decreases. 

The statistical approach is not well-described. It is not clear what the form of the statistical model used was and whether the analysis treated each trial individually or grouped trials by the kangaroo. There is also no mention of how many trials per kangaroo, or the range of speeds (or masses) tested. 

The methods include the statistical model with the variables that we used, as well as the kangaroo masses (13.7 to 26.6 kg, mean: 20.9 ± 3.4 kg). We did not have sufficient within individual sample size to use a linear mixed effect model including subject as a random factor, thus all trials were treated individually. We have included this information in the results section. 

We have now moved the range of speeds from the supplementary material to the results and figure captions. We have added information on the number of trials per kangaroo to the methods, and added Suppl. Fig. 9 showing the distribution of speeds per kangaroo.

We did not group the data e.g. by using an average speed per individual for all their trials, or by comparing fast to slow groups for statistical analysis (the latter was only for display purposes in our figures, which we have now made clearer in the methods statistics section). 

Related to this, there is no mention of how different speeds were obtained. It seems that kangaroos hopped at a self-selected pace, thus it appears that not much variation was observed. I appreciate the difficulty of conducting these experiments in a controlled manner, but this doesn’t exempt the authors from providing the details of their approach.

Apologies, this was not clear in the manuscript. Kangaroos hopping at their preferred speed means we did not chase or startle them into high speeds to comply with ethics and enclosure limitations. Thus we did not record a wide range of speeds within the bounds of what kangaroos are capable of in the wild (up to 12 m/s). We have now removed the terminology “preferred speed” e.g. line 115. We have added additional information about the chosen hopping speeds into the results and methods, including an appendix figure (see above comment). (Line 381-382)

Some figures (Figure 2 for example) present means for one of three speeds, yet the speeds are not reported (except in the legend) nor how these bins were determined, nor how many trials or kangaroos fit in each bin. A similar comment applies to the mass categories. It would be more convincing if the authors plotted the main metrics vs. speed to illustrate the significant trends they are reporting.

Thank you for this comment. The bins are used only for display purposes and not within the statistical analysis. We have clarified this in the revised manuscript: “The data was grouped into body mass (small 17.6±2.96 kg, medium 21.5±0.74 kg, large 24.0±1.46 kg) and speed (slow 2.52±0.25 m s^-1, medium 3.11±0.16 m s^-1, fast 3.79±0.27 m s^-1) subsets for display purposes only”. (Line 495-497)

(2) The significance of the effects of mass is not clear. The introduction and abstract suggest that the paper is focused on the effect of speed, yet the effects of mass are reported throughout as well, without a clear understanding of the significance. This weakness is further exaggerated by the fact that the details of the subject masses are not reported.

Indeed, the primary aim of our study was to explore the influence of speed, given the uncoupling of energy from hopping speed in kangaroos. We included mass to ensure that the effects of speed were not driven by body mass (i.e.: that larger kangaroos hopped faster). Subject masses were reported in the first paragraph of the methods, albeit some were estimated as outlined in the same paragraph.

(3) The paper needs to be significantly re-written to better incorporate the methods into the results section. Since the results come before the methods, some of the methods must necessarily be described such that the study can be understood at some level without turning to the dedicated methods section. As written, it is very difficult to understand the basis of the approach, analysis, and metrics without turning to the methods.

The methods after the discussion is a requirement of the journal. We have incorporated some methods in the results where necessary but not too repetitive or disruptive, e.g. Fig. 1 caption, and specifying we are only analysing EMA for the ankle joint

Reviewing Editor (Recommendations For The Authors):

Below is a list of specific recommendations that the authors could address to improve the eLife assessment:

(1) Based on the data presented and the fact that metabolic energy was not measured, the authors should temper their conclusions and statements throughout the manuscript regarding the link between speed and metabolic energy savings. We recommend adding text to the discussion summarizing the strengths and limitations of the evidence provided and suggesting future steps to more conclusively answer this mystery.

There is a significant body of work linking metabolic energy savings to measured increases in tendon stress in macropods. However, the purpose of this paper was to address the unanswered questions about why tendon stress increases. We found that stress did not only increase due to GRF increasing with speed as expected, but also due to novel postural changes which decreased EMA. In the revised manuscript, we have tempered our conclusions to make it clearer that it is not just EMA affecting stress, and added limitations throughout the manuscript (see response to Rev 1). 

(2) To provide stronger evidence of a link between speed, mechanics, and metabolic savings the authors can consider estimating metabolic energy expenditure from their OpenSIM model. This is one suggestion, but the authors likely have other, possibly better ideas. Such a model should also be able to explain why the metabolic rate increases with speed during uphill hopping.

Extending the model to provide direct metabolic cost estimates will be the goal of a future paper, however the models does not have detailed muscle characteristics to do this in the formulation presented here. It would be a very large undertaking which is beyond the scope of the current manuscript. As per the comment above, the results of this paper are not reliant on metabolic performance. 

(3) The authors attempt to relate the newly quantified hopping biomechanics to previously published metabolic data. However, all reviewers agree that the logic in many instances is not clear or contradictory. Could one potential explanation be that at slow speeds, forces and tendon strain are small, and thus muscle fascicle work is high? Then, with faster speeds, even though the cost of generating isometric force increases, this is offset by the reduction in the metabolic cost of muscular work. The paper could provide stronger support for their hypotheses with a much clearer explanation of how the kinematics relate to the mechanics and ultimately energy savings.

In response to the reviewers comments, we have substantially modified the discussion to provide clearer rationale.

(4) The methods and the effort expended to collect these data are impressive, but there are a number of underlying assumptions made that undermine the conclusions. This is due partly to the methods used, but also the paper's incomplete description of their methods. We provide a few examples below:

It would be helpful if the authors could speak to the effect of the limited speeds tested and between-animal comparisons on the ability to draw strong conclusions from the present dataset. ·

Throughout the discussion, the authors highlight the relationship between EMA and speed. However, this is misleading since there was no significant effect of speed on EMA. Speed only affected the muscle moment arm, r. At minimum, this should be clarified and the effect on EMA not be overstated. Additionally, the resulting implications on their ability to confidently say something about the effect of speed on muscle stress should be discussed. 

We have now provided additional details, (see responses above) to these concerns. For instance, we added a supplementary figure showing the speed distribution per individual. The primary reviewer concern (that each kangaroo travelled at a single speed) was due to a miscommunication around the terminology “preferred” which has now been corrected. 

We now elaborate in the results why we are not very concerned that EMA is insignificant. The statistical insignificance of EMA is ultimately due to the insignificance of the direct measurement of R, however, we now better explain in the results why we believe that this statistical insignificance is due to error/noise of the measurement which is relatively large compared to the effect size. Indirect indications of how R may increase with speed (via ankle height from the ground) are statistically significant. Lines 177-185. 

We consider this worth reporting because, for instance, an 18% change in EMA will be undetectable by measurement, but corresponds to an 18% change in tendon stress which is measurable and physiologically significant (safety factor would decrease from 2 to 1.67).  We presented both significant and insignificant results for transparency. 

We have also discussed this within a revised limitations section of the manuscript (Line 311328). 

Reviewer #1 (Recommendations For The Authors):

Title: I would cut the first half of the title. At least hedge it a bit. "Clues" instead of "Unlocking the secrets".

We have revised the title to: “Postural adaptations may contribute to the unique locomotor energetics seen in hopping kangaroos”

In my comments, ... typically indicates a stylistic change suggested to the text.

Overall, the paper covers speed and size. Unfortunately, the authors were not 100% consistent in the order of presenting size then speed, or speed then size. Just choose one and stick with it.

We have attempted to keep the order of presenting size and speed consistent, however there are several cases where this would reduce the readability of the manuscript and so in some cases this may vary. 

One must admit that there is a lot of vertical scatter in almost all of the plots. I understand that these animals were not in a lab on a treadmill at a controlled speed and the animals wear fur coats so marker placements vary/move etc. But the spread is quite striking, e.g. Figure 5a the span at one speed is almost 10x. Can the authors address this somewhere? Limitations section?

The variation seen likely results from attempting to display data in a 2D format, when it is in fact the result of multiple variables, including speed, mass, stride frequency and subject specific lengths. Slight variations in these would be expected to produce some noise around the mean, and I think it’s important to consider this while showing the more dominant effects. 

In many locations in the manuscript, the term "work" is used, but rarely if ever specified that this is the work "per hop". The big question revolves around the rate of metabolic energy consumption (i.e. energy per time or average metabolic power), one must not forget that hop frequency changes somewhat across speed, so work per hop is not the final calculation.

Thank you for this comment. We have now explicitly stated work per hop in figure captions and in the results (line 208). The change in stride frequency at this range of speeds is very small, particularly compared to the variance in stride frequency (Suppl. Fig. 1d), which is consistent with other researchers who found that stride frequency was constant or near constant in macropods at analogous speeds (e.g. Dawson and Taylor 1973, Baudinette et al. 1987). 

Line 61 ....is likely related.

Added “likely” (line 59)

Line 86 I think the Allen reference is incomplete. Wasn't it in J Exp Biology?

Thank you. Changed. 

Line 122 ... at faster speeds and in larger individuals.

Changed: “We hypothesised that (i) the hindlimb would be more crouched at faster speeds, primarily due to the distal hindlimb joints (ankle and metatarsophalangeal), independent of changes with body mass” (Line 121-122).

Line 124 I found this confusing. Try to re-word so that you explain you mean more work done by the tendons and less by the ankle musculature.

Amended: “changes in moment arms resulting from the change in posture would contribute to the increase in tendon stress with speed, and may thereby contribute to energetic savings by increasing the amount of positive and negative work done by the ankle without requiring additional muscle work” (Line 123)

Line 129 hopefully "braking" not "breaking"!

Thank you. Fixed. (Line 130)

Line 129 specify fore-aft horizontal force.

Added "fore-aft" to "negative fore-aft horizontal component" (Line 130-131)

Line 130 add something like "of course" or "naturally" since if there is zero fore-aft force, the GRF vector of course must be vertical. 

Added "naturally" (Line 132)

Line 138 clarify that this section is all stance phase. I don't recall reading any swing phase data.

Changed to: "Kangaroo hindlimb stance phase kinematics varied…" (Line 141)

Line 143 and elsewhere. I found the use of dorsiflexion and plantarflexion confusing. In Figure 3, I see the ankle never flexing more than 90 degrees. So, the ankle joint is always in something of a flexed position, though of course it flexes and extends during contact. I urge the authors to simplify to flextion/extension and drop the plantar/dorsi.

We have edited this section to describe both movements as greater extension (plantarflexion). (Line 147). We have further clarified this in the figure caption for figure 3.  

Line 147 ...changes were…

Fixed, line 150

Line 155 I'm a bit confused here. Are the authors calculating some sort of overall EMA or are they saying all of the individual joint EMAs all decreased?

Thank you, we clarified that it is at the ankle. Line 158

Line 158 since kangaroos hop and are thus positioned high and low throughout the stance phase, try to avoid using "high" and "low" for describing variables, e.g. GRF or other variables. Just use "greater/greatest" etc.

Thanks for this suggestion. We have changed "higher" into "greater" where appropriate throughout the manuscript e.g. line 161

Lines 162 and 168 same comment here about "r" and "R". Do you mean ankle or all joints?

Clarified that it is the gastrocnemius and plantaris r, and the R to the ankle. (Lines 164-165)

Line 173 really, ankle height?

Added: ankle height is "vertical distance from the ground". Line 177

Line 177 is this just the ankle r?

Added "of the ankle" line 158 and “Achilles” line 187 

Line 183 same idea, which tendon/tendons are you talking about here?

Added "Achilles" to be more clear (Line 187)

Line 195 substitute "converted" for "transferred".

Done (Line 210)

Line 223 why so vague? i.e. why use "may"? Believe in your data. ...stress was also modulated by changes....

Changed "may" to "is"

Line 229 smaller ankle EMA (especially since you earlier talked about ankle "height").

Changed “lower” to “smaller” Line 254

Line 2236 ...and return elastic energy…

Added "elastic" line 262

Line 244 IMPORTANT: Need to explain this better! I think you are saying that the net work at the ankle is staying the same across speed, BUT it is the tendons that are storing and returning that work, it's not that the muscles are doing a lot of negative/positive work.

Changed: “The consistent net work observed among all speeds suggests the ankle extensor muscle-tendon units are performing similar amounts of ankle work independent of speed, which would predominantly be done by the tendon.” Line 270-272)

Line 258-261 I think here is where you are over-selling the data/story. Although you do say "a" mechanism (and not "the" mechanism, you still need to deal with the cost of generating more force and generating that force faster.

We removed this sentence and replaced it with a discussion of the cost of generating force hypothesis, and alternative scenarios for the how force and metabolics could be uncoupled. 

Line 278 "the" tendon? Which tendon?

Added "Achilles"

Line 289. I don't think one can project into the past.

Changed “projected” to "estimated"

Line 303 no problem, but I've never seen a paper in biology where the authors admit they don't know what species they were studying!

Can’t be helped unfortunately. It is an old dataset and there aren’t photos of every kangaroo. Fortunately, from the grey and red kangaroos we can distinguish between, we know there are no discernible species effects on the data. 

Lines 304-306 I'm not clear here. Did you use vertical impulse (and aerial time) to calculate body weight? Or did you somehow use the braking/propulsive impulse to calculate mass? I would have just put some apples on the force plate and waited for them to stop for a snack.

Stationary weights were recorded for some kangaroos which did stand on the force plate long enough, but unfortunately not all of them were willing to do so. In those cases, yes, we used impulse from steady-speed trials to estimate mass. We cross-checked by estimated mass from segment lengths (as size and mass are correlated). This is outlined in the first paragraph of the methods.

Lines 367 & 401 When you use the word "scaled" do you mean you assumed geometric similarity?

No, rather than geometric scaling, we allowed scaling to individual dimensions by using the markers at midstance for measurements. We have amended the paragraph to clarify that the shape of the kangaroo changes and that mass distribution was preserved during the shape change (line 441-446) 

Lines 381-82 specify "joint work"

Added "joint work"  (Line 457)

Figure 1 is gorgeous. Why not add the CF equation to the left panel of the caption?

We decided to keep the information in the figure caption. “Total leg length was calculated as the sum of the segment lengths (solid black lines) in the hindlimb and compared to the pelvisto-toe distance (dashed line) to calculate the crouch factor”

Figure 2 specify Horizontal fore-aft.

Done

Figure 3g I'd prefer the same Min. Max Flexion vertical axis labels as you use for hip & knee.

While we appreciate the reviewer trying to increase the clarity of this figure, we have left it as plantar/dorsi flexion since these are recognised biomechanical terms. To avoid confusion, we have further defined these in the figure caption “For (f-g), increased plantarflexion represents a decrease in joint flexion, while increased dorsiflexion represents increased flexion of the joint.”

Figure 4. I like it and I think that you scaled all panels the same, i.e. 400 W is represented by the same vertical distance in all panels. But if that's true, please state so in the Caption. It's remarkable how little work occurs at the hip and knee despite the relatively huge muscles there.

Is it true that the y axes are all at the same scale. We have added this to the caption. 

Figure 5 Caption should specify "work per hop".

Added

Figure 7 is another beauty.

Thank you!

Supplementary Figure 3 is this all ANKLE? Please specify.

Clarified that it is the gastrocnemius and plantaris r, and the R to the ankle.

Reviewer #2 (Recommendations For The Authors):

To 'unlock the secrets of kangaroo locomotor energetics' I expected the authors to measure the secretive outcome variable, metabolic rate using laboratory measures. Rather, the authors relied on reviewing historic metabolic data and collecting biomechanics data across different animals, which limits the conclusions of this manuscript.

We have revised to the title to make it clearer that we are investigating a subset of the energetics problem, specifically posture. “Postural adaptations may contribute to the unique locomotor energetics seen in hopping kangaroos.” We have also substantially modified the discussion to temper the conclusions from the paper. 

After reading the hypothesis, why do the authors hypothesize about joint flexion and not EMA? Because the following hypothesis discusses the implications of moment arms on tendon stress, EMA predictions are more relevant (and much more discussed throughout the manuscript).

Ankle and MTP angles are the primary drivers of changes in r, R & thus, EMA. We used a two part hypothesis to capture this. We have rephased the hypotheses: “We hypothesised that (i) the hindlimb would be more crouched at faster speeds, primarily due to the distal hindlimb joints (ankle and metatarsophalangeal), independent of changes with body mass, and (ii) changes in moment arms resulting from the change in posture would contribute to the increase in tendon stress with speed, and may thereby contribute to energetic savings by increasing the amount of positive and negative work done by the ankle without requiring additional muscle work.”

If there were no detectable effects of speed on EMA, are kangaroos mechanically like other animals (Biewener Science 89 & JAP 04) who don't vary EMA across speeds? Despite no detectible effects, the authors state [lines 228-229] "we found larger and faster kangaroos were more crouched, leading to lower ankle EMA". Can the authors explain this inconsistency? Lines 236 "Kangaroos appear to use changes in posture and EMA". I interpret the paper as EMA does not change across speed.

Apologies, we did not sufficiently explain this originally. We now explain in the results our reasoning behind our belief that EMA and R may change with speed. “If we consider the nonsignificant relationship between R (and EMA) and speed to indicate that there is no change in R, then it conflicts with the ankle height and CoP result. Taking both into account, we think it is more likely that there is a small, but important, change in R, rather than no change in R with speed. It may be undetectable because we expect small effect sizes compared to the measurement range and measurement error (Suppl. Fig. 3h), or be obscured by a similar change in R with body mass. R is highly dependent on the length of the metatarsal segment, which is longer in larger kangaroos (1 kg BM corresponded to ~1% longer segment, P<0.001, R²=0.449). If R does indeed increase with speed, both R and r will tend to decrease EMA at faster speeds.” (Line 177-185)

Lines 335-339: "We assumed the force was applied along phalanx IV and that there was no medial or lateral movement of the centre of pressure (CoP)". I'm confused, did the authors not measure CoP location with respect to the kangaroo limb? If not, this simple estimation undermines primary results (EMA analyses).

We have changed "The anterior or posterior movement of the CoP was recorded by the force plate" to read: "The fore-aft movement of the CoP was recorded by the force plate within the motion capture coordinate system" (Line 406-407) and added more justification for fixing the CoP movement in the other axis: “It was necessary to assume the CoP was fixed in the mediallateral axis because when two feet land on the force plate, the lateral forces on each foot are not recorded, and indeed cancel if the forces are symmetrical (i.e. if the kangaroo is hopping in a straight path and one foot is not in front of the other). We only used symmetrical trials to ensure reliable measures of the anterior-posterior movement of the CoP.” (Line 408-413)

The introduction makes many assertions about the generalities of locomotion and the relationship between mechanics and energetics. I'm afraid that the authors are selectively choosing references without thoroughly evaluating alternative theories. For example, Taylor, Kram, & others have multiple papers suggesting that decreasing EMA and increasing muscle force (and active muscle volume) increase metabolic costs during terrestrial locomotion. Rather, the authors suggest that decreasing EMA and increasingly high muscle force at faster speeds don't affect energetics unless muscle work increases substantially (paragraph 2)? If I am following correctly, does this theory conflict with active muscle volume ideas that are peppered throughout this manuscript?

Yes, as you point out, the same mechanism does lead to different results in kangaroos vs humans, for instance, but this is not a contradiction. In all species, decreasing EMA will result in an increase in muscle force due to less efficient leverage (i.e. lower EMA) of the muscles, and the muscle-tendon unit will be required to produce more force to balance the joint moment. As a consequence, human muscles activate a greater volume in order for the muscle-tendon unit to increase muscle work and produce enough force. We are proposing that in kangaroos, the increase in work is done by the achilles tendon rather than the muscles. Previous research suggests that macropod ankle muscles contract isometrically or that the fibres do not shorten more at faster speeds i.e. muscle work does not increase with speed. Instead, the additional force seems to come from the tendon storing and subsequently returning more strain energy (indicated by higher stress). We found that the increase in tendon stress comes from higher ground force at faster speeds, and from it adopting a more crouched posture which increases the tendons’ stresses compared to an upright posture for a given speed (think of this as increasing the tendon’s stress capacity). We have substantially revised the discussion to highlight this.

Similarly, does increased gross or net tendon mechanical energy storage & return improve hopping energetics? Would more tendon stress and strain energy storage with a given hysteresis value also dissipate more mechanical energy, requiring leg muscles to produce more net work? Does net or gross muscle work drive metabolic energy consumption?

Based on the cost of generating force hypothesis, we think that gross muscle work would be linked to driving metabolic energy consumption. Our idea here is that the total body work is a product of the work done by the tendon and the muscle combined. If the tendon has the potential to do more work, then the total work can increase without muscle work needing to increase.

The results interpret speed effects on biomechanics, but each kangaroo was only collected at 1 speed. Are inter-animal comparisons enough to satisfy this investigation?

We have added a figure (Suppl Fig 9) to demonstrate the distribution of speed and number of trials per kangaroo. We have also removed "preferred" from the manuscript as this seems to cause confusion. Most kangaroos travelled at a range of “casual” speeds.

Abstract: Can the authors more fully connect the concept of tendon stress and low metabolic rates during hopping across speeds? Surely, tendon mechanics don't directly drive the metabolic cost of hopping, but they affect muscle mechanics to affect energetics.

Amended to: " This phenomenon may be related to greater elastic energy savings due to increasing tendon stress; however, the mechanisms which enable the rise in stress, without additional muscle work remain poorly understood." (Lines 25-27).

The topic sentence in lines 61-63 may be misleading. The ensuing paragraph does not substantiate the topic sentence stating that ankle MTUs decouple speeds and energetics.

We added "likely" to soften the statement. (Line 59)

Lines 84-86: In humans, does more limb flexion and worse EMA necessitate greater active muscle volume? What about muscle contractile dynamics - See recent papers by Sawicki & colleagues that include Hill-type muscle mechanics in active muscle volume estimates.

Added: “Smaller EMA requires greater muscle force to produce a given force on the ground, thereby demanding a greater volume of active muscle, and presumably greater metabolic rates than larger EMA for the same physiology”. (Line 80-82)

Lines 106: can you give the context of what normal tendon safety factors are?

Good idea. Added: "far lower than the typical safety factor of four to eight for mammalian tendons (Ker et al. 1988)." Line 106-107

I thought EMA was relatively stable across speeds as per Biewener [Science & JAP '04]. However the authors gave an example of an elephant to suggest that it is typically inversely related to speed. Can the authors please explain the disconnect and the most appropriate explanation in this paragraph?

Knee EMA in particular changed with speed in Biewener 2004. What is “typical” probably depends on the group of animals studied; e.g., cursorial quadrupedal mammals generally seem to maintain constant EMA, but other groups do not.

These cases are presented to show a range of consequences for changing EMA (usually with mass, but sometimes with speed). We have made several adjustments to the paragraph to make this clearer. Lines 85-93.

The results depend on the modeled internal moment arm (r). How confident are the authors in their little r prediction? Considering complications of joint mechanics in vivo including muscle bulging. Holzer et al. '20 Sci Rep demonstrated that different models of the human Achilles tendon moment arm predict vastly different relationships between the moment arm and joint angle.

Our values for r and EMA closely align with previous papers which measured/calculate these values in kangaroos, such as Kram 1998, and thus we are confident in our interpretation.  

This is a misleading results sentence: Small decreases in EMA correspond to a nontrivial increase in tendon stress, for instance, reducing EMA from 0.242 (mean minimum EMA of the slow group) to 0.206 (mean minimum EMA of the fast group) was associated with an ~18% increase in tendon stress. The authors could alternatively say that a ~15% decrease in EMA was associated with an ~18% increase in tendon stress, which seems pretty comparable.

Thank you for pointing this out, it is important that it is made clearer. Although the change in relative magnitude is approximately the same (as it should be), this does not detract from the importance. The "small decrease in EMA" is referring to the absolute values, particularly in respect to the measurement error/noise. The difference is small enough to have been undetectable with other methods used in previous studies. We have amended the sentence to clarify this.

It now reads: “Subtle decreases in EMA which may have been undetected in previous studies correspond to discernible increases in tendon stress. For instance, reducing EMA from 0.242 (mean minimum EMA of the slow group) to 0.206 (mean minimum EMA of the fast group) was associated with an increase in tendon stress from ~50 MPa to ~60 MPa, decreasing safety factor from 2 to 1.67 (where 1 indicates failure), which is both measurable and physiologically significant.” (Line 195-200)

Lines 243-245: "The consistent net work observed among all speeds suggests the ankle extensors are performing similar amounts of ankle work independent of speed." If this is true, and presumably there is greater limb work performed on the center of mass at faster speeds (Donelan, Kram, Kuo), do more proximal leg joints increase work and energy consumption at faster speeds?

The skin over the proximal leg joints (knee and hip) moves too much to get reliable measures of EMA from the ratio of moment arms. This will be pursued in future work when all muscles are incorporated in the model so knee and hip EMA can be determined from muscle force.

We have added limitations and considerations paragraph to the manuscript: “Finally, we did not determine whether the EMA of proximal hindlimb joints (which are more difficult to track via surface motion capture markers) remained constant with speed. Although the hip and knee contribute substantially less work than the ankle joint (Fig. 4), the majority of kangaroo skeletal muscle is located around these proximal joints. A change in EMA at the hip or knee could influence a larger muscle mass than at the ankle, potentially counteracting or enhancing energy savings in the ankle extensor muscle-tendon units. Further research is needed to understand how posture and muscles throughout the whole body contribute to kangaroo energetics.” (Line 321-328)

Lines 245-246: "Previous studies using sonomicrometry have shown that the muscles of tammar wallabies do not shorten considerably during hops, but rather act near-isometrically as a strut" Which muscles? All muscles? Extensors at a single joint?

Added "gastrocnemius and plantaris" Line 164-165

Lines 249-254: "The cost of generating force hypothesis suggests that faster movement speeds require greater rates of muscle force development, and in turn greater cross-bridge cycling rates, driving up metabolic costs (Taylor et al. 1980, Kram and Taylor 1990). The ability for the ankle extensor muscle fibres to remain isometric and produce similar amounts of work at all speeds may help explain why hopping macropods do not follow the energetic trends observed in quadrupedal species." These sentences confuse me. Kram & Taylor's cost of force-generating hypothesis assumes that producing the same average force over shorter contact times increases metabolic rate. How does 'similar muscle work' across all speeds explain the ability of macropods to use unique energetic trends in the cost of force-generating hypothesis context?

Thank you for highlighting this confusion. We have substantially revised the discussion clarify where the mechanisms presented deviate from the cost of generating force hypothesis. Lines 270-309

Reviewer #3 (Recommendations For The Authors):

In addition to the points described in the public review, I have additional, related, specific comments:

(1) Results: Please refer to the hypotheses in the results, and relate the the findings back to the hypotheses.

We now relate the findings back to the hypotheses 

Line 142 “In partial support of hypothesis (i), greater masses and faster speeds were associated with more crouched hindlimb postures (Fig. 3a,c).”.

Lines 205-206: “The increase in tendon stress with speed, facilitated in part by the change in moment arms by the shift in posture, may explain changes in ankle work (c.f. Hypothesis (ii)).” 

(2) Results: please provide the main statistical results either in-line or in a table in the main text.

We (the co-authors) have discussed this at length, and have agreed that the manuscript is far more readable in the format whereby most statistics lie within the supplementary tables, otherwise a reader is met with a wall of statistics. We only include values in the main text when the magnitude is relevant to the arguments presented in the results and discussion.

(3) Line 140: Describe how 'crouched' was defined.

We have now added a brief definition of ‘Crouch factor’ after the figure caption. (Line 143) (Fig. 3a,c; where crouch factor is the ratio of total limb length to pelvis to toe distance).

(4) Line 162: This seems to be a main finding and should be a figure in the main text not supplemental. Additionally, Supplementary Figures 3a and b do not show this finding convincingly There should be a figure plotting r vs speed and r vs mass.

The combination of r and R are represented in the EMA plot in the main text. The r and R plots are relegated to the supplementary because the main text is already very crowded.  Thank you for the suggestion for the figure plotting r and R versus speed, this is now included as Suppl. Fig. 3h

(5) Line 166: Supplementary Figure 3g does not show the range of dorsiflexion angles as a function of speed. It shows r vs dorsiflexion angle. Please correct.

Thanks for noticing this, it was supposed to reference Fig 3g rather than Suppl Fig 3g in the sentence regarding speed. We have fixed this, Line 170. 

We had added a reference to Suppl Fig 3 on Line 169 as this shows where the peak in r with ankle angle occurs (114.4 degrees).

(6) Line 184: Where are the statistical results for this statement?

The relationship between stress and EMA does not appear to be linear, thus we only present R^{^}2 for the power relationship rather than a p-value. 

(7) Line 192: The authors should explain how joint work and power relate/support the overall hypotheses. This section also refers to Figures 4 and 5 even though Figures 6 and 7 have already been described. Please reorganize.

We have added a sentence at the end of the work and power section to mention hypothesis (ii) and lead into the discussion where it is elaborated upon. 

“The increase in positive and negative ankle work may be due to the increase in tendon stress rather than additional muscle work.” Line 219-220 We have rearranged the figure order.

(8) The statistics are not reported in the main text, but in the supplementary tables. If a result is reported in the main text, please report either in-line or with a table in the main text.

We leave most statistics in the supplementary tables to preserve the readability of the manuscript. We only include values in the main text when the magnitude is relevant to the arguments raised in the results and discussion.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review):

Summary:

This is a contribution to the field of developmental bioelectricity. How do changes of resting potential at the cell membrane affect downstream processes? Zhou et al. reported in 2015 that phosphatidylserine and K-Ras cluster upon plasma membrane depolarization and that voltage-dependent ERK activation occurs when constitutively active K-RasG12V mutants are overexpressed. In this paper, the authors advance the knowledge of this phenomenon by showing that membrane depolarization up-regulates mitosis and that this process is dependent on voltage-dependent activation of ERK. ERK activity's voltage-dependence is derived from changes in the dynamics of phosphatidylserine in the plasma membrane and not by extracellular calcium dynamics. This paper reports an interesting and important finding. It is somewhat derivative of Zhou et al., 2015. (https://www.science.org/doi/full/10.1126/science.aaa5619). The main novelty seems to be that they find quantitatively different conclusions upon conducting similar experiments, albeit with a different cell line (U2OS) than those used by Zhou et al. Sasaki et al. do show that increased K+ levels increase proliferation, which Zhou et al. did not look at. The data presented in this paper are a useful contribution to a field often lacking such data.

Strengths:

Bioelectricity is an important field for areas of cell, developmental, and evolutionary biology, as well as for biomedicine. Confirmation of ERK as a transduction mechanism and a characterization of the molecular details involved in the control of cell proliferation are interesting and impactful.

Weaknesses:

The authors lean heavily on the assumption that the Nernst equation is an accurate predictor of membrane potential based on K+ level. This is a large oversimplification that undermines the author's conclusions, most glaringly in Figure 2C. The author's conclusions should be weakened to reflect that the activity of voltage gated ion channels and homeostatic compensation are unaccounted for.

We appreciate the reviewer’s thoughtful comment regarding our reliance on the Nernst equation to estimate membrane potential. We agree that the Nernst equation is a simplification and does not account for the activity of other ions, voltage-gated channels, or homeostatic compensation mechanisms. To address this concern, we conducted electrophysiological experiments in which the membrane potential was directly controlled using the perforated patch-clamp technique (Fig. 3). Under these conditions, we also monitored the membrane potential and confirmed that there was negligible drift within 20 minutes of perfusion with 145 mM K^⁺ (only a 1–5 mV change). These results suggest that the influence of voltage-gated channels and homeostatic compensation is minimal in our experimental setup. We revised the manuscript to clarify these limitations and to present our conclusions more cautiously in light of this point.

“A potential limitation of extracellular K^⁺-based approaches is their reliance on the Nernst equation to estimate membrane potential, which oversimplifies the actual situation by neglecting voltage-gated ion channel activity and compensatory mechanisms. To directly address this concern, we measured membrane potential using the perforated patch-clamp technique and confirmed that the potential was stable during perfusion with 145 mM K^⁺ (only a 1–5 mV drift within 20 min). Moreover, we used a voltage clamp to precisely control the membrane potential and demonstrated that ERK activity was directly regulated by the voltage itself, excluding the influence of other secondary factors. An additional strength of electrophysiology is its ability to examine the effects of repolarization, which is difficult to assess with conventional perfusion-based methods owing to slow solution exchange.”

There are grammatical tense errors are made throughout the paper (ex line 99 "This kinetics should be these kinetics")

We thank the reviewer for pointing out the grammatical errors. We carefully revised the entire manuscript.

Line 71: Zhou et al. use BHK, N2A, PSA-3 cells, this paper uses U2OS (osteosarcoma) cells. Could that explain the differences in bioelectric properties that they describe? In general, there should be more discussion of the choice of cell line. Why were U2OS cells chosen? What are the implications of the fact that these are cancer cells, and bone cancer cells in particular? Does this paper provide specific insights for bone cancers? And crucially, how applicable are findings from these cells to other contexts?

We thank the reviewer for this valuable comment regarding the choice of cell line. We selected U2OS cells primarily because they are well suited for live-cell FRET imaging. We did not use BHK, N2A, or PSA-3 cells, and therefore it is difficult for us to provide a clear comparison with the specific bioelectric properties reported in Zhou et al. Nevertheless, we agree that cancer cell lines, including U2OS, may exhibit bioelectric properties that differ from those of non-cancerous cells. While this could be a potential limitation, we are inclined to consider voltage-dependent ERK activation to be a fundamental and generalizable phenomenon, not restricted to osteosarcoma cells. The key components of this pathway—phosphatidylserine, Ras, MAPK (including ERK)—are expressed in essentially all mammalian cells. In support of our view, we observed voltage-dependent ERK activation not only in U2OS cells but also in HeLa, HEK293, and A431 cells. These results strongly suggest that the mechanism we describe is not cell-type specific but rather a universal feature of mammalian cells. In the revised Discussion, we expanded our rationale to choose U2OS cells, while addressing the potential implications of using a cancer-derived cell line. 

“In this study, we primarily used U2OS cells because their flat morphology makes them suitable for live-cell FRET imaging. Although cancer cell lines, including U2OS, may display bioelectric properties that differ from those of noncancerous cells, our findings raise the possibility that voltage-dependent ERK activation is a fundamental and broadly applicable phenomenon rather than a feature specific to osteosarcoma cells. This conclusion is supported by the fact that essential components of this pathway, namely phosphatidylserine, Ras, and MAPK (including ERK), are ubiquitously expressed in mammalian cells. Consistent with this finding, we observed voltage-dependent ERK activation across multiple cell lines: U2OS, HeLa, HEK293, and A431 cells (Fig.S2). These observations indicate that the mechanism we describe is not cell-type-restricted, but rather a universal property of mammalian cells.”

Line 115: The authors use EGF to calibrate 'maximal' ERK stimulation. Is this level near saturation? Either way is fine, but it would be useful to clarify.

We thank the reviewer for raising this important point. The YFP/CFP ratio obtained after EGF stimulation is generally considered to represent saturation levels detectable by EKAREV imaging. However, we acknowledge that it remains uncertain whether 10 ng/mL EGF induces the absolute maximal ERK activity in all contexts. To clarify this point, we revised the manuscript (result) text as follows:

“To normalize variation among cells, cells were stimulated with EGF (10 ng/mL) at the end of the experiment, which presumably yielded a near-saturated YFP/CFP value (ERK activity). This value was used to determine the maximum ERK activity in each cell”

Line 121: Starting line 121 the authors say "Of note, U2OS cells expressed wild-type K-Ras but not an active mutant of K-Ras, which means voltage dependent ERK activation occurs not only in tumor cells but also in normal cells". Given that U2OS cells are bone sarcoma cells, is it appropriate to refer to these as 'normal' cells in contrast to 'tumor' cells?

We thank the reviewer for pointing this out. We agree that it is not appropriate to contrast U2OS cells with “normal” cells, since they are sarcoma-derived. To address this point, we revised the sentence to weaken the claim and avoid the misleading terminology.

“Importantly, as U2OS cells express wild-type K-Ras rather than an oncogenic mutant (16), our results raise the possibility that voltage-dependent ERK activation may also occur in non-transformed cells.”

Line 101: These normalizations seem reasonable, the conclusions sufficiently supported and the requisite assumptions clearly presented. Because the dish-to-dish and cell-to-cell variation may reflect biologically relevant phenomena it would be ideal if non-normalized data could be added in supplemental data where feasible.

We thank the reviewer for this helpful suggestion. As recommended, we added representative non-normalized data in the Supplemental Figure S1, which illustrates the non-normalized variation across cells and dishes.

Figure 2C is listed as Figure 2D in the text

There is no Figure 2F (Referenced in line 148)

We thank the reviewer for pointing out these errors. The incorrect figure citations were corrected.

Reviewer #2 (Public review):

Sasaki et al. use a combination of live-cell biosensors and patch-clamp electrophysiology to investigate the effect of membrane potential on the ERK MAPK signaling pathway, and probe associated effects on proliferation. This is an effect that has long been proposed, but a convincing demonstration has remained elusive, because it is difficult to perturb membrane potential without disturbing other aspects of cell physiology in complex ways. The time-resolved measurements here are a nice contribution to this question, and the perforated patch clamp experiments with an ERK biosensor are fantastic - they come closer to addressing the above difficulty of perturbing voltage than any prior work. It would have been difficult to obtain these observations with any other combination of tools.

However, there are still some concerns as detailed in specific comments below:

Specific comments:

(1) All the observations of ERK activation, by both high extracellular K+ and voltage clamp, could be explained by cell volume increase (more discussion in subsequent comments). There is a substantial literature on ERK activation by hypotonic cell swelling (e.g. https://doi.org/10.1042/bj3090013, https://doi.org/10.1002/j.1460-2075.1996.tb00938.x, among others). Here are some possible observations that could demonstrate that ERK activation by volume change is distinct from the effects reported here:

(i) Does hypotonic shock activate ERK in U2OS cells?

(ii) Can hypotonic shock activate ERK even after PS depletion, whereas extracellular K+ cannot?

(iii) Does high extracellular K+ change cell volume in U2OS cells, measured via an accurate method such as fluorescence exclusion microscopy?

(iv) It would be helpful to check the osmolality of all the extracellular solutions, even though they were nominally targeted to be iso-osmotic.

(2) Some more details about the experimental design and the results are needed from Figure 1:

(i) For how long are the cells serum-starved? From the Methods section, it seems like the G1 release in different K+ concentration is done without serum, is this correct? Is the prior thymidine treatment also performed in the absence of serum?

(ii) There is a question of whether depolarization constitutes a physiologically relevant mechanism to regulate proliferation, and how depolarization interacts with other extracellular signals that might be present in an in vivo context. Does depolarization only promote proliferation after extended serum starvation (in what is presumably a stressed cell state)? What fraction of total cells are observed to be mitotic (without normalization), and how does this compare to the proliferation of these cells growing in serum-supplemented media? Can K+ concentration tune proliferation rate even in serum-supplemented media?

(3) In Figure 2, there are some possible concerns with the perfusion experiment:

(i) Is the buffer static in the period before perfusion with high K+, or is it perfused? This is not clear from the Methods. If it is static, how does the ERK activity change when perfused with 5 mM K+? In other words, how much of the response is due to flow/media exchange versus change in K+ concentration?

(ii) Why do there appear to be population-average decreases in ERK activity in the period before perfusion with high K+ (especially in contrast to Fig. 3)? The imaging period does not seem frequent enough for photobleaching to be significant.

(4) Figure 3 contains important results on couplings between membrane potential and MAPK signaling. However, there are a few concerns:

(i) Does cell volume change upon voltage clamping? Previous authors have shown that depolarizing voltage clamp can cause cells to swell, at least in the whole-cell configuration: https://www.cell.com/biophysj/fulltext/S0006-3495(18)30441-7 . Could it be possible that the clamping protocol induces changes in ERK signaling due to changes in cell volume, and not by an independent mechanism?

(ii) Does the -80 mV clamp begin at time 0 minutes? If so, one might expect a transient decrease in sensor FRET ratio, depending on the original resting potential of the cells. Typical estimates for resting potential in HEK293 cells range from -40 mV to -15 mV, which would reach the range that induces an ERK response by depolarizing clamp in Fig. 3B. What are the resting potentials of the cells before they are clamped to -80 mV, and why do we not see this downward transient?

(5) The activation of ERK by perforated voltage clamp and by high extracellular K+ are each convincing, but it is unclear whether they need to act purely through the same mechanism - while additional extracellular K+ does depolarize the cell, it could also be affecting function of voltage-independent transporters and cell volume regulatory mechanisms on the timescales studied. To more strongly show this, the following should be done with the HEK cells where there is already voltage clamp data:

(i) Measure resting potential using the perforated patch in zero-current configuration in the high K+ medium. Ideally this should be done in the time window after high K+ addition where ERK activation is observed (10-20 minutes) to minimize the possibility of drift due to changes in transporter and channel activity due to post-translational regulation.

(ii) Measure YFP/CFP ratio of the HEK cells in the high K+ medium (in contrast to the U2OS cells from Fig. 2 where there is no patch data).

(iii) The assertion that high K+ is equivalent to changes in Vmem for ERK signaling would be supported if the YFP/CFP change from K+ addition is comparable to that induced by voltage clamp to the same potential. This would be particularly convincing if the experiment could be done with each of the 15 mM, 30 mM, and 145 mM conditions.

(6) Line 170: "ERK activity was reduced with a fast time course (within 1 minute) after repolarization to -80 mV." I don't see this in the data: in Fig. 3C, it looks like ERK remains elevated for > 10 min after the electrical stimulus has returned to -80 mV

Comments on revisions:

The authors have done a good job addressing the comments on the previous submission.

Reviewer #3 (Public review):

Summary:

This paper demonstrates that membrane depolarization induces a small increase in cell entry into mitosis. Based on previous work from another lab, the authors propose that ERK activation might be involved. They show convincingly using a combination of assays that ERK is activated by membrane depolarization. They show this is Ca2+ independent and is a result of activation of the whole K-Ras/ERK cascade which results from changed dynamics of phosphatidylserine in the plasma membrane that activates K-Ras. Although the activation of the Ras/ERK pathway by membrane depolarization is not new, linking it to an increase in cell proliferation is novel.

Strengths

A major strength of the study is the use of different techniques - live imaging with ERK reporters, as well as Western blotting to demonstrate ERK activation as well as different methods for inducing membrane depolarization. They also use a number of different cell lines. Via Western blotting the authors are also able to show that the whole MAPK cascade is activated.

Weaknesses

A weakness of the study is the data in Figure 1 showing that membrane depolarization results in an increase of cells entering mitosis. There are very few cells entering mitosis in their sample in any condition. This should be done with many more cells to increase the confidence in the results. The study also lacks a mechanistic link between ERK activation by membrane depolarization and increased cell proliferation.

The authors did achieve their aims with the caveat that the cell proliferation results could be strengthened. The results, for the most par,t support the conclusions.

This work suggests that alterations in membrane potential may have more physiological functions than action potential in the neural system as it has an effect on intracellular signalling and potentially cell proliferation.

In the revised manuscript, the authors have now addressed the issues with Figure 1, and the data presented are much clearer. They did also attempt to pinpoint when in the cell cycle ERK is having its activity, but unfortunately, this was not conclusive.

Reviewer #2 (Recommendations for the authors):

Small issues:

Fig. 1A. Please add a mark on the timeline showing when the K+ concentration is changed. Also, please add a time axis that matches the time axis in (C), so readers can know when in C the medium was changed.

1B caption: unclear what "the images were 20 min before and after cytokinesis" means, given that the images go from -30 min to +20 min. Maybe the authors mean, "the indicated times are measured relative to cytokinesis."

Thank you for bringing these points to our attention that can confuse readers. We revised the figure legend.

Line 214: nonoclusters --> nanoclusters

Line 475: 10 mm -> 10 ¥mum

Corrected.

Author response:

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

Reviewer #1 (Public review):

Summary:

This paper presents results from four independent experiments, each of which tests for rhythmicity in auditory perception. The authors report rhythmic fluctuations in discrimination performance at frequencies between 2 and 6 Hz. The exact frequency depends on the ear and experimental paradigm, although some frequencies seem to be more common than others.

Strengths:

The first sentence in the abstract describes the state of the art perfectly: "Numerous studies advocate for a rhythmic mode of perception; however, the evidence in the context of auditory perception remains inconsistent". This is precisely why the data from the present study is so valuable. This is probably the study with the highest sample size (total of > 100 in 4 experiments) in the field. The analysis is very thorough and transparent, due to the comparison of several statistical approaches and simulations of their sensitivity. Each of the experiments differs from the others in a clearly defined experimental parameter, and the authors test how this impacts auditory rhythmicity, measured in pitch discrimination performance (accuracy, sensitivity, bias) of a target presented at various delays after noise onset.

Weaknesses:

(1) The authors find that the frequency of auditory perception changes between experiments. I think they could exploit differences between experiments better to interpret and understand the obtained results. These differences are very well described in the Introduction, but don't seem to be used for the interpretation of results. For instance, what does it mean if perceptual frequency changes from between- to within-trial pitch discrimination? Why did the authors choose this experimental manipulation? Based on differences between experiments, is there any systematic pattern in the results that allows conclusions about the roles of different frequencies? I think the Discussion would benefit from an extension to cover this aspect.

We believe that interpreting these differences remains difficult and a precise, detailed (and possibly mechanistic) interpretation is beyond the goal of the present study. The main goal of this study was to explore the consistency and variability of effects across variations of the experimental design and samples of participants. Interpreting specific effects, e.g. at particular frequencies, would make sense mostly if differences between experiments have been confirmed in a separate reproduction. Still, we do provide specific arguments for why differences in the outcome between different experiments, e.g. with and without explicit trial initialization by the participants, could be expected. See lines 91ff in the introduction and 786ff in the discussion.

(2) The Results give the impression of clear-cut differences in relevant frequencies between experiments (e.g., 2 Hz in Experiment 1, 6 Hz in Exp 2, etc), but they might not be so different. For instance, a 6 Hz effect is also visible in Experiment 1, but it just does not reach conventional significance. The average across the three experiments is therefore very useful, and also seems to suggest that differences between experiments are not very pronounced (otherwise the average would not produce clear peaks in the spectrum). I suggest making this point clearer in the text.

We have revised the conclusions to note that the present data do not support clear cut differences between experiments. For this reason we also refrain from detailed interpretations of specific effects, as suggested by this reviewer in point 1 above.

(3) I struggle to understand the hypothesis that rhythmic sampling differs between ears. In most everyday scenarios, the same sounds arrive at both ears, and the time difference between the two is too small to play a role for the frequencies tested. If both ears operate at different frequencies, the effects of the rhythm on overall perception would then often cancel out. But if this is the case, why would the two ears have different rhythms to begin with? This could be described in more detail.

This hypothesis was not invented by us, but in essence put forward in previous work. The study by Ho et al. CurrBiol 2017 has reported rhythmic effects at different frequencies in the left and right ears, and we here tried to reproduce these effects. One could speculate about an ear-difference based on studies reporting a right-ear advantage in specific listening tasks, and the idea that different time scales of rhythmic brain activity may be specifically prevail in the left and right cortical hemispheres; hence it does not seem improbable that there could be rhythmic effects in both ears at different frequencies. We note this in the introduction, l. 65ff.

Reviewer #2 (Public review):

Summary:

The current study aims to shed light on why previous work on perceptual rhythmicity has led to inconsistent results. They propose that the differences may stem from conceptual and methodological issues. In a series of experiments, the current study reports perceptual rhythmicity in different frequency bands that differ between different ear stimulations and behavioral measures.

The study suggests challenges regarding the idea of universal perceptual rhythmicity in hearing.

Strengths:

The study aims to address differences observed in previous studies about perceptual rhythmicity. This is important and timely because the existing literature provides quite inconsistent findings. Several experiments were conducted to assess perceptual rhythmicity in hearing from different angles. The authors use sophisticated approaches to address the research questions.

Weaknesses:

(1) Conceptional concerns:

The authors place their research in the context of a rhythmic mode of perception. They also discuss continuous vs rhythmic mode processing. Their study further follows a design that seems to be based on paradigms that assume a recent phase in neural oscillations that subsequently influence perception (e.g., Fiebelkorn et al.; Landau & Fries). In my view, these are different facets in the neural oscillation research space that require a bit more nuanced separation. Continuous mode processing is associated with vigilance tasks (work by Schroeder and Lakatos; reduction of low frequency oscillations and sustained gamma activity), whereas the authors of this study seem to link it to hearing tasks specifically (e.g., line 694). Rhythmic mode processing is associated with rhythmic stimulation by which neural oscillations entrain and influence perception (also, Schroeder and Lakatos; greater low-frequency fluctuations and more rhythmic gamma activity). The current study mirrors the continuous rather than the rhythmic mode (i.e., there was no rhythmic stimulation), but even the former seems not fully fitting, because trials are 1.8 s short and do not really reflect a vigilance task. Finally, previous paradigms on phase-resetting reflect more closely the design of the current study (i.e., different times of a target stimulus relative to the reset of an oscillation). This is the work by Fiebelkorn et al., Landau & Fries, and others, which do not seem to be cited here, which I find surprising. Moreover, the authors would want to discuss the role of the background noise in resetting the phase of an oscillation, and the role of the fixation cross also possibly resetting the phase of an oscillation. Regardless, the conceptional mixture of all these facets makes interpretations really challenging. The phase-reset nature of the paradigm is not (or not well) explained, and the discussion mixes the different concepts and approaches. I recommend that the authors frame their work more clearly in the context of these different concepts (affecting large portions of the manuscript).

Indeed, the paradigms used here and in many similar previous studies incorporate an aspect of phase-resetting, as the presentation of a background noisy may effectively reset ongoing auditory cortical processes. Studies trying to probe for rhythmicity in auditory perception in the absence any background noise have not shown any effect (Zoefel and Heil, 2013), perhaps because the necessary rhythmic processes along auditory pathways are only engaged when some sound is present. We now discuss these points, and also acknowledge the mentioned studies in the visual system; l. 57.

(2) Methodological concerns:

The authors use a relatively unorthodox approach to statistical testing. I understand that they try to capture and characterize the sensitivity of the different analysis approaches to rhythmic behavioral effects. However, it is a bit unclear what meaningful effects are in the study. For example, the bootstrapping approach that identifies the percentage of significant variations of sample selections is rather descriptive (Figures 5-7). The authors seem to suggest that 50% of the samples are meaningful (given the dashed line in the figure), even though this is rarely reached in any of the analyses. Perhaps >80% of samples should show a significant effect to be meaningful (at least to my subjective mind). To me, the low percentage rather suggests that there is not too much meaningful rhythmicity present. 

We note that there is no clear consensus on what fraction of experiments should be expected or how this way of quantifying effects should be precisely valued (l. 441ff). However, we now also clearly acknowledge in the discussion that the effective prevalence is not very high (l. 663).

I suggest that the authors also present more traditional, perhaps multi-level, analyses: Calculation of spectra, binning, or single-trial analysis for each participant and condition, and the respective calculation of the surrogate data analysis, and then comparison of the surrogate data to the original data on the second (participant) level using t-tests. I also thought the statistical approach undertaken here could have been a bit more clearly/didactically described as well.

We here realize that our description of the methods was possibly not fully clear. We do follow the strategy as suggested by this reviewer, but rather than comparing actual and surrogate data based on a parametric t-test, we compare these based on a non-parametric percentile-based approach. This has the advantage of not making specific (and possibly not-warranted) assumptions about the distribution of the data. We have revised the methods to clarify this, l. 332ff. 

The authors used an adaptive procedure during the experimental blocks such that the stimulus intensity was adjusted throughout. In practice, this can be a disadvantage relative to keeping the intensity constant throughout, because, on average, correct trials will be associated with a higher intensity than incorrect trials, potentially making observations of perceptual rhythmicity more challenging. The authors would want to discuss this potential issue. Intensity adjustments could perhaps contribute to the observed rhythmicity effects. Perhaps the rhythmicity of the stimulus intensity could be analyzed as well. In any case, the adaptive procedure may add variance to the data.

We have added an analysis of task difficulty to the results (new section “Effects of adaptive task difficulty“) to address this. Overall we do not find systematic changes in task difficulty across participants for most of the experiments, but for sure one cannot rule out that this aspect of the design also affects the outcomes.  Importantly, we relied on an adaptive task difficulty to actually (or hopefully) reduce variance in the data, by keeping the task-difficulty around a certain level. Give the large number of trials collected, not using such an adaptive produce may result in performance levels around chance or near ceiling, which would make impossible to detect rhythmic variations in behavior. 

Additional methodological concerns relate to Figure 8. Figures 8A and C seem to indicate that a baseline correction for a very short time window was calculated (I could not find anything about this in the methods section). The data seem very variable and artificially constrained in the baseline time window. It was unclear what the reader might take from Figure 8.

This figure was intended mostly for illustration of the eye tracking data, but we agree that there is no specific key insight to be taken from this. We removed this. 

Motivation and discussion of eye-movement/pupillometry and motor activity: The dual task paradigm of Experiment 4 and the reasons for assessing eye metrics in the current study could have been better motivated. The experiment somehow does not fit in very well. There is recent evidence that eye movements decrease during effortful tasks (e.g., Contadini-Wright et al. 2023 J Neurosci; Herrmann & Ryan 2024 J Cog Neurosci), which appears to contradict the results presented in the current study. Moreover, by appealing to active sensing frameworks, the authors suggest that active movements can facilitate listening outcomes (line 677; they should provide a reference for this claim), but it is unclear how this would relate to eye movements. Certainly, a person may move their head closer to a sound source in the presence of competing sound to increase the signal-to-noise ratio, but this is not really the active movements that are measured here. A more detailed discussion may be important. The authors further frame the difference between Experiments 1 and 2 as being related to participants' motor activity. However, there are other factors that could explain differences between experiments. Self-paced trials give participants the opportunity to rest more (inter-trial durations were likely longer in Experiment 2), perhaps affecting attentional engagement. I think a more nuanced discussion may be warranted.

We expanded the motivation of why self-pacing trials may effectively alter how rhythmic processes affect perception, and now also allude to attention and expectation related effects (l. 786ff). Regarding eye movements we now discuss the results in the light of the previously mentioned studies, but again refrain from a very detailed and mechanistic interpretation (l. 782).

Discussion:

The main data in Figure 3 showed little rhythmicity. The authors seem to glance over this fact by simply stating that the same phase is not necessary for their statistical analysis. Previous work, however, showed rhythmicity in the across-participant average (e.g., Fiebelkorn's and similar work). Moreover, one would expect that some of the effects in the low-frequency band (e.g., 2-4 Hz) are somewhat similar across participants. Conduction delays in the auditory system are much smaller than the 0.25-0.5 s associated with 2-4 Hz. The authors would want to discuss why different participants would express so vastly different phases that the across-participant average does not show any rhythmicity, and what this would mean neurophysiologically.

We now discussion the assumptions and implications of similar or distinct phases of rhythmic processes within and between participants (l. 695ff). In particular we note that different origins of the underlying neurophysiological processes eventually may suggest that such assumptions are or a not warranted.  

An additional point that may require more nuanced discussion is related to the rhythmicity of response bias versus sensitivity. The authors could discuss what the rhythmicity of these different measures in different frequency bands means, with respect to underlying neural oscillations.

We expanded discussion to interpret what rhythmic changes in each of the behavioral metric could imply (l. 706ff).

Figures:

Much of the text in the figures seems really small. Perhaps the authors would want to ensure it is readable even for those with low vision abilities. Moreover, Figure 1A is not as intuitive as it could be and may perhaps be made clearer. I also suggest the authors discuss a bit more the potential monoaural vs binaural issues, because the perceptual rhythmicity is much slower than any conduction delays in the auditory system that could lead to interference.

We tried to improve the font sizes where possible, and discuss the potential monaural origins as suggested by other reviewers. 

Reviewer #3 (Public review):

Summary:

The finding of rhythmic activity in the brain has, for a long time, engendered the theory of rhythmic modes of perception, that humans might oscillate between improved and worse perception depending on states of our internal systems. However, experiments looking for such modes have resulted in conflicting findings, particularly in those where the stimulus itself is not rhythmic. This paper seeks to take a comprehensive look at the effect and various experimental parameters which might generate these competing findings: in particular, the presentation of the stimulus to one ear or the other, the relevance of motor involvement, attentional demands, and memory: each of which are revealed to effect the consistency of this rhythmicity.

The need the paper attempts to resolve is a critical one for the field. However, as presented, I remain unconvinced that the data would not be better interpreted as showing no consistent rhythmic mode effect. It lacks a conceptual framework to understand why effects might be consistent in each ear but at different frequencies and only for some tasks with slight variants, some affecting sensitivity and some affecting bias.

Strengths:

The paper is strong in its experimental protocol and its comprehensive analysis, which seeks to compare effects across several analysis types and slight experiment changes to investigate which parameters could affect the presence or absence of an effect of rhythmicity. The prescribed nature of its hypotheses and its manner of setting out to test them is very clear, which allows for a straightforward assessment of its results

Weaknesses:

There is a weakness throughout the paper in terms of establishing a conceptual framework both for the source of "rhythmic modes" and for the interpretation of the results. Before understanding the data on this matter, it would be useful to discuss why one would posit such a theory to begin with. From a perceptual side, rhythmic modes of processing in the absence of rhythmic stimuli would not appear to provide any benefit to processing. From a biological or homeostatic argument, it's unclear why we would expect such fluctuations to occur in such a narrow-band way when neither the stimulus nor the neurobiological circuits require it.

We believe that the framework for why there may be rhythmic activity along auditory pathways that shapes behavioral outcomes has been laid out in many previous studies, prominently here (Schroeder et al., 2008; Schroeder and Lakatos, 2009; Obleser and Kayser, 2019). Many of the relevant studies are cited in the introduction, which is already rather long given the many points covered in this study. 

Secondly, for the analysis to detect a "rhythmic mode", it must assume that the phase of fluctuations across an experiment (i.e., whether fluctuations are in an up-state or down-state at onset) is constant at stimulus onset, whereas most oscillations do not have such a total phase-reset as a result of input. Therefore, some theoretical positing of what kind of mechanism could generate this fluctuation is critical toward understanding whether the analysis is well-suited to the studied mechanism.

In line with this and previous comments (by reviewer 2) we have expanded the discussion to consider the issue of phase alignment (l. 695ff). 

Thirdly, an interpretation of why we should expect left and right ears to have distinct frequency ranges of fluctuations is required. There are a large number of statistical tests in this paper, and it's not clear how multiple comparisons are controlled for, apart from experiment 4 (which specifies B&H false discovery rate). As such, one critical method to identify whether the results are not the result of noise or sample-specific biases is the plausibility of the finding. On its face, maintaining distinct frequencies of perception in each ear does not fit an obvious conceptual framework.

Again this point was also noted by another reviewer and we expanded the introduction and discussion in this regard (l. 65ff).

Reviewer #1 (Recommendations for the authors):

(1) An update of the AR-surrogate method has recently been published (https://doi.org/10.1101/2024.08.22.609278). I appreciate that this is a lot of work, and it is of coursee up to the authors, but given the higher sensitivity of this method, it might be worth applying it to the four datasets described here.

Reading this article we note that our implementation of the AR-surrogate method was essentially as suggested here, and not as implemented by Brookshire. In fact we had not realized that Brookshire had apparently computed the spectrum based on the group-average data. As explained in the Methods section, as now clarified even better, we compute for each participant the actual spectrum of this participant’s data, and a set of surrogate spectra. We then perform a group-average of both to compute the p-value of the actual group-average based on the percentile of the distribution of surrogate averages. This send step differs from Harris & Beale, which used a one-sided t-test. The latter is most likely not appropriate in a strict statistical sense, but possibly more powerful for detecting true results compared to the percentile-based approach that we used (see l. 332ff).

(2) When results for the four experiments are reported, a reminder for the reader of how these experiments differ from each other would be useful.

We have added this in the Results section.

"considerable prevalence of differences around 4Hz, with dual‐task requirements leading to stronger rhythmicity in perceptual sensitivity". There is a striking similarity to recently published data (https://doi.org/10.1101/2024.08.10.607439 ) demonstrating a 4-Hz rhythm in auditory divided attention (rather than between modalities as in the present case). This could be a useful addition to the paragraph.

We have added a reference to this preprint, and additional previous work pointing in the same direction mentioned in there.  

(3) There are two typos in the Introduction: "related by different from the question", and below, there is one "presented" too much.

These have been fixed.

Reviewer #3 (Recommendations for the authors):

My major suggestion is that these results must be replicated in a new sample. I understand this is not simple to do and not always possible, but at this point, no effect is replicated from one experiment to the next, despite very small changes in protocol (especially experiment 1 vs 2). It's therefore very difficult to justify explaining the different effects as real as opposed to random effects of this particular sample. While the bootstrapping effects show the level of consistency of the effect within the sample studied, it can not be a substitute for a true replication of the results in a new sample.

We agree that only an independent replication can demonstrate the robustness of the results. We do consider experiment 1 a replication test of Ho et al. CurrBiol 2017, which results in different results than reported there. But more importantly, we consider the analysis of ‘reproducibility’ by simulating participant samples a key novelty of the present work, and want to emphasize this over the within-study replication of the same experiment.  In fact, in light of the present interpretation of the data, even a within-study replication would most likely not offer a clear-cut answer. 

As I said in the public review, the interpretation of the results, and of why perceptual cycles in arhythmic stimuli could be a plausible theory to begin with, is lacking. A conceptual framework would vastly improve the impact and understanding of the results.

We tried to strengthen the conceptual framework in the introduction. We believe that this is in large provided by previous work, and the aim of the present study was to explore the robustness of effects and not to suggest and discover novel effects. 

Minor comments:

(1) The authors adapt the difficulty as a function of performance, which seems to me a strange choice for an experiment that is analyzing the differences in performance across the experiment. Could you add a sentence to discuss the motivation for this choice?

We now mention the rationale in the Methods section and in a new section of the Results. There we also provide additional analyses on this parameter.

(2) The choice to plot the p-values as opposed to the values of the actual analysis feels ill-advised to me. It invites comparison across analyses that isn't necessarily fair. It would be more informative to plot the respective analysis outputs (spectral power, regression, or delta R2) and highlight the windows of significance and their overlap across analyses. In my opinion, this would be more fair and accurate depiction of the analyses as they are meant to be used.

We do disagree. As explained in the Methods (l. 374ff): “(Showing p-values) … allows presenting the results on a scale that can be directly compared between analysis approaches, metrics, frequencies and analyses focusing on individual ears or the combined data. Each approach has a different statistical sensitivity, and the underlying effect sizes (e.g. spectral power) vary with frequency for both the actual data and null distribution. As a result, the effect size reaching statistical significance varies with frequency, metrics and analyses.” 

The fact that the level of power (or R2 or whatever metric we consider) required to reach significance differs between analyses (one ear, both ears), metrics (d-prime, bias, RT) and between analyses approaches makes showing the results difficult, as we would need a separate panel for each of those. This would multiply the number of panels required e.g. for Figure 4 by 3, making it a figure with 81 axes. Also neither the original quantities of each analysis (e.g. spectral power) nor the p-values that we show constitute a proper measure of effect size in a statistical sense. In that sense, neither of these is truly ideal for comparing between analyses, metrics etc. 

We do agree thought that many readers may want to see the original quantification and thresholds for statistical significance. We now show these in an exemplary manner for the Binned analysis of Experiment 1, which provides a positive result and also is an attempt to replicate the findings by  Ho et al 2017. This is shown in new Figure 5. 

(3) Typo in line 555 (+ should be plus minus).

(4) Typo in line 572: "Comparison of 572 blocks with minus dual task those without"

(5) Typo in line 616: remove "one".

(6) Line 666 refers to effects in alpha band activity, but it's unclear what the relationship is to the authors' findings, which peak around 6 Hz, lower than alpha (~10 Hz).

(7) Line 688 typo, remove "amount of".

These points have been addressed.  

(8) Oculomotor effect that drives greater rhythmicity at 3-4 Hz. Did the authors analyze the eye movements to see if saccades were also occurring at this rate? It would be useful to know if the 3-4 Hz effect is driven by "internal circuitry" in the auditory system or by the typical rate of eye movement.

A preliminary analysis of eye movement data was in previous Figure 8, which was removed on the recommendation of another review.  This showed that the average saccade rate is about 0.01 saccade /per trial per time bin, amounting to on average less than one detected saccade per trial. Hence rhythmicity in saccades is unlikely to explain rhythmicity in behavioral data at the scale of 34Hz. We now note this in the Results.

Obleser J, Kayser C (2019) Neural Entrainment and Attentional Selection in the Listening Brain. Trends Cogn Sci 23:913-926.

Schroeder CE, Lakatos P (2009) Low-frequency neuronal oscillations as instruments of sensory selection. Trends Neurosci 32:9-18.

Schroeder CE, Lakatos P, Kajikawa Y, Partan S, Puce A (2008) Neuronal oscillations and visual amplification of speech. Trends Cogn Sci 12:106-113.

Zoefel B, Heil P (2013) Detection of Near-Threshold Sounds is Independent of EEG Phase in Common Frequency Bands. Front Psychol 4:262.

Author response:

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

Reviewer #1 (Public review):

Summary:

This is an interesting study characterizing and engineering so-called bathy phytochromes, i.e., those that respond to near infrared (NIR) light in the ground state, for optogenetic control of bacterial gene expression. Previously, the authors have developed a structure-guided approach to functionally link several light-responsive protein domains to the signaling domain of the histidine kinase FixL, which ultimately controls gene expression. Here, the authors use the same strategy to link bathy phytochrome light-responsive domains to FixL, resulting in sensors of NIR light. Interestingly, they also link these bathy phytochrome light-sensing domains to signaling domains from the tetrathionate-sensing SHK TtrS and the toluene-sensing SHK TodS, demonstrating the generality of their protein engineering approach more broadly across bacterial two-component systems.

This is an exciting result that should inspire future bacterial sensor design. They go on to leverage this result to develop what is, to my knowledge, the first system for orthogonally controlling the expression of two separate genes in the same cell with NIR and Red light, a valuable contribution to the field.

Finally, the authors reveal new details of the pH-dependent photocycle of bathy phytochromes and demonstrate that their sensors work in the gut - and plant-relevant strains E. coli Nissle 1917 and A. tumefaciens.

Strengths:

(1) The experiments are well-founded, well-executed, and rigorous.

(2) The manuscript is clearly written.

(3) The sensors developed exhibit large responses to light, making them valuable tools for ontogenetic applications.

(4) This study is a valuable contribution to photobiology and optogenetics.

We thank the reviewer for the positive verdict on our manuscript.

Weaknesses:

(1) As the authors note, the sensors are relatively insensitive to NIR light due to the rapid dark reversion process in bathy phytochromes. Though NIR light is generally non-phototoxic, one would expect this characteristic to be a limitation in some downstream applications where light intensities are not high (e.g., in vivo).

We principally concur with this reviewer’s assessment that delivery of light (of any color) into living tissue can be severely limited by absorption, reflection, and scattering. That notwithstanding, at least two considerations suggest that in-vivo deployment of the pNIRusk setups we presently advance may be feasible.

First, while the pNIRusk setups are indeed less light-sensitive compared to, e.g., our earlier redlight-responsive pREDusk and pDERusk setups (see Meier et al. Nat Commun 2024), we note that the overall light fluences required for triggering them are in the range of tens of µW per cm₂. By contrast, optogenetic experiments in vivo, in particular in the neurosciences, often employ light area intensities on the order of mW per cm₂ and above. Put another way, compared to the optogenetic tools used in these experiments, the pNIRusk setups are actually quite sensitive to light.

Second, sensitivity to NIR light brings the advantage of superior tissue penetration, see data reported by Weissleder Nat Biotech 2001 and Ash et al. Lasers Med Sci 2017 (both papers are cited in our manuscript). Based on these data, the intensity of blue light (450 nm) therefore falls off 5-10 times more strongly with penetration depth than that of NIR light (800 nm).

We have added a brief treatment of these aspects in the Discussion section.

(2) Though they can be multiplexed with Red light sensors, these bathy phytochrome NIR sensors are more difficult to multiplex with other commonly used light sensors (e.g., blue) due to the broad light responsivity of the Pfr state. This challenge may be overcome by careful dosing of blue light, as the authors discuss, but other bacterial NIR sensing systems with less cross-talk may be preferred in some applications.

The reviewer is correct in noting that, at least to a certain extent, the pNIRusk systems also respond to blue light owing to their Soret absorbance bands (see Fig. 1). That said, we note two points:

First, a given photoreceptor that preferentially responds to certain wavelengths, e.g., 700 nm in the case of conventional bacterial phytochromes (BphP), generally absorbs shorter wavelengths to some degree as well. Absorption of these shorter wavelengths suffices for driving electronic and/or vibronic transitions of the chromophore to higher energy levels which often give rise to productive photochemistry and downstream signal transduction. Put another way, a certain response of sensory photoreceptors to shorter wavelengths is hence fully expected and indeed experimentally borne out, as for instance shown by Ochoa-Fernandez et al. in the so-called PULSE setup (Nat Meth 2020, doi: 10.1038/s41592-020-0868-y).

Second, known BphPs share similar Pr and Pfr absorbance spectra. We therefore expect other BphP-based optogenetic setups to also respond to blue light to some degree. Currently, there are insufficient data to gauge whether individual BphPs systematically differ in their relative sensitivity to blue compared to red or NIR light. Arguably, pertinent experiments may be an interesting subject for future study.

Reviewer #2 (Public review):

Summary:

In this manuscript, Meier et al. engineer a new class of light-regulated two-component systems. These systems are built using bathy-bacteriophytochromes that respond to near-infrared (NIR) light. Through a combination of genetic engineering and systematic linker optimization, the authors generate bacterial strains capable of selective and tunable gene expression in response to NIR stimulation. Overall, these results are an interesting expansion of the optogenetic toolkit into the NIR range. The cross-species functionality of the system, modularity, and orthogonality have the potential to make these tools useful for a range of applications.

Strengths:

(1) The authors introduce a novel class of near-infrared light-responsive two-component systems in bacteria, expanding the optogenetic toolbox into this spectral range.

(2) Through engineering and linker optimization, the authors achieve specific and tunable gene expression, with minimal cross-activation from red light in some cases.

(3) The authors show that the engineered systems function robustly in multiple bacterial strains, including laboratory E. coli, the probiotic E. coli Nissle 1917, and Agrobacterium tumefaciens.

(4) The combination of orthogonal two-component systems can allow for simultaneous and independent control of multiple gene expression pathways using different wavelengths of light.

(5) The authors explore the photophysical properties of the photosensors, investigating how environmental factors such as pH influence light sensitivity.

Weaknesses:

(1) The expression of multi-gene operons and fluorescent reporters could impose a metabolic burden. The authors should present data comparing optical density for growth curves of engineered strains versus the corresponding empty-vector control to provide insight into the burden and overall impact of the system on host viability and growth.

In response to this comment, we have recorded growth kinetics of bacteria harboring the pNIRusk-DsRed plasmids or empty vectors under both inducing (i.e., under NIR light) and noninducing conditions (i.e., darkness). We did not observe systematic differences in the growth kinetics between the different cultures, thus suggesting that under the conditions tested there is no adverse effect on cell viability.

We include the new data in Suppl. Fig. 5c-d and refer to them in the main text.

(2) The manuscript consistently presents normalized fluorescence values, but the method of normalization is not clear (Figure 2 caption describes normalizing to the maximal fluorescence, but the maximum fluorescence of what?). The authors should provide a more detailed explanation of how the raw fluorescence data were processed. In addition, or potentially in exchange for the current presentation, the authors should include the raw fluorescence values in supplementary materials to help readers assess the actual magnitude of the reported responses.

We appreciate this valid comment and have altered the representation of the fluorescence data. All values for a given fluorescent protein (i.e., either DsRed or YPet) across all systems are now normalized to a single reference value, thus enabling direct comparison between experiments.

(3) Related to the prior point, it would be useful to have a positive control for fluorescence that could be used to compare results across different figure panels.

As all data are now normalized to the same reference value, direct comparison across all figures is enabled.

(4) Real-time gene expression data are not presented in the current manuscript, but it would be helpful to include a time-course for some of the key designs to help readers assess the speed of response to NIR light.

In response to this comment, we include in the revised manuscript induction kinetics of bacterial cultures bearing pNIRusk upon transfer to inducing NIR-light conditions. To this end, aliquots were taken at discrete timepoints, transcriptionally and translationally arrested, and analyzed for optical density and DsRed reporter fluorescence after allowing for chromophore maturation.

We include the new data in Suppl. Fig. 5e and refer to them in the manuscript.

Moreover, we note that the experiments in Agrobacterium tumefaciens used a luciferase reporter thus enabling the continuous monitoring of the light-induced expression kinetics. These data (unchanged in revision) are to be found in Suppl. Fig. 9.

Reviewer #3 (Public review):

Summary:

This paper by Meier et al introduces a new optogenetic module for the regulation of bacterial gene expression based on "bathy-BphP" proteins. Their paper begins with a careful characterization of kinetics and pH dependence of a few family members, followed by extensive engineering to produce infrared-regulated transcriptional systems based on the authors' previous design of the pDusk and pDERusk systems, and closing with characterization of the systems in bacterial species relevant for biotechnology.

Strengths:

The paper is important from the perspective of fundamental protein characterization, since bathyBphPs are relatively poorly characterized compared to their phytochrome and cyanobacteriochrome cousins. It is also important from a technology development perspective: the optogenetic toolbox currently lacks infrared-stimulated transcriptional systems. Infrared light offers two major advantages: it can be multiplexed with additional tools, and it can penetrate into deep tissues with ease relative to the more widely used blue light-activated systems. The experiments are performed carefully, and the manuscript is well written.

Weaknesses:

My major criticism is that some information is difficult to obtain, and some data is presented with limited interpretation, making it difficult to obtain intuition for why certain responses are observed. For example, the changes in red/infrared responses across different figures and cellular contexts are reported but not rationalized. Extensive experiments with variable linker sequences were performed, but the rationale for linker choices was not clearly explained. These are minor weaknesses in an overall very strong paper.

We are grateful for the positive take on our manuscript.

Reviewer #1 (Recommendations for the authors):

(1) As eLife is a broad audience journal, please define the Soret and Q-bands (line 125).

We concur and have added labels in fig. 1a that designate the Soret and Q bands.

(2) The initial (0) Ac design in Figure 2b is activated by NIR and Red light, albeit modestly. The authors state that this construct shows "constant reporter fluorescence, largely independent of illumination" (line 167). This language should be changed to reflect the fact that this Ac construct responds to both of these wavelengths.

Agreed. We have amended the text accordingly.

(3) pNIRusk Ac 0 appears to show a greater light response than pNIRusk Av -5. However, the authors claim that the former is not light-responsive and the latter is. This conclusion should be explained or changed.

The assignment of pNIRusk Av-5 as light-responsive is based on the relative difference in reporter fluorescence between darkness and illumination with either red or NIR light. Although the overall fluorescence is much lower in Av-5 than for Av-0, the relative change upon illumination is much more pronounced. We add a statement to this effect to the text.

(4) The authors state that "when combining DmDERusk-Str-YPet with AvTod+21-DsRed expression rose under red and NIR light, respectively, whereas the joint application of both light colors induced both reporter genes" (lines 258-261). In contrast, Figure 3c shows that application of both wavelengths of light results in exclusive activation of YPet expression. It appears the description of the data is wrong and must be corrected. That said, this error does not impact their conclusion that two separate target genes can be independently activated by NIR and red light.

We thank the reviewer for catching this error which we have corrected in the revised manuscript.

(5) Line 278: I don't agree with the authors' blanket statement that the use of upconversion nanoparticles is a "grave" limitation for NIR-light mediated activation of bacterial gene expression in vivo. The authors should either expound on the severity of the limitation or use more moderate language.

We have replaced the word ‘grave’ by ‘potential’ and thereby toned down our wording.

Reviewer #2 (Recommendations for the authors):

(1) Please include a discussion on the expected depth penetration of different light wavelengths. This is most relevant in the context of the discussion about how these NIR systems could be used with living therapeutics.

Given the heterogeneity of biological tissue, it is challenging to state precise penetration depths for different wavelengths of light. That said, blue light for instance is typically attenuated by biological tissue around 5 to 10 times as strongly as near-infrared light is.

We have expanded the Discussion chapter to cover these aspects.

(2) It would be helpful for Figure 2C (or supplementary) to also include the response to blue light stimulation.

We agree and have acquired pertinent data for the blue-light response. The new data are included in an updated Fig. 2c. Data acquired at varying NIR-light intensities, originally included in Fig. 2c, have been moved to Suppl. Fig. 5a-b.

(3) In Figure 4A, data on the response of E. coli Nissle to blue and red light are missing. Including this would help identify whether the reduced sensitivity to non-NIR wavelengths observed in the E. coli lab strain is preserved in the probiotic background.

In response to this comment, we have acquired pertinent data on E. coli Nissle. While the results were overall similar to those in the laboratory strain, the response to blue and NIR light was yet lower in the Nissle bacteria which stands to benefit optogenetic applications.

We have updated Fig. 4a accordingly. For clarity, we only show the data for AvNIRusk in the main paper but have relegated the data on AcNIRusk to Suppl. Fig. 8. (Note that this has necessitated a renumbering of the subsequent Suppl. Figs.)

(4) On many of the figures, there are thin gray lines that appear between the panels that it would be nice to eliminate because, in some cases, they cut through words and numbers.

The grey lines likely arose from embedding the figures into the text document. In the typeset manuscript, which has become available on the eLife webpage in the meantime, there are no such lines. That said, we will carefully check throughout the submission/publishing/proofing process lest these lines reappear.

(5) Page 7, line 155: "As not least seen" typo or awkward phrasing.

We have restructured the sentence and thereby hopefully clarified the unclear phrasing.

(6) Page 7, line 167: It does not appear to be the case that the initial pNIRusk designs show constant fluorescence that is largely independent of illumination. AcNIRusk shows an almost twofold change from dark to NIR. Reword this to avoid confusion.

We concur with this comment, similar to reviewer #1’s remark, and have adjusted the text accordingly.

(7) Page 8, line 174: Related to the previous point, AvNIRusk has one design that is very minimally light switchable (-5), so stating that six light switchable designs have been identified is also confusing.

As stated in our response to reviewer #1 above, the assignment of AvNIRusk-5 as light-switchable is based on the relative fluorescence change upon illumination. We have added an explanation to the text.

(8) Page 10, line 228-229: I was not able to find the data showing that expression levels were higher for the DmTtr systems than the pREDusk and pNIRusk setups. This may be an issue related to the normalization point. It was not clear to me how to compare these values.

We apologize for the initially unclear representation of the data. In response to this reviewer’s general comments above, we have now normalized all fluorescence values to a single reference value, thus allowing their direct comparison.

(9) Page 12, line 264: "finer-grained expression control can be exerted..." Either show data or adjust the language so that it is clear this is a prediction.

True, we have replaced ‘can’ by ‘could’.

(10) Page 25, line 590: CmpX13 cells have a reference that is given later, but it should be added where it first appears.

Agreed, we have added the reference in the indicated place.

(11) Page 25, line 592: define LB/Kan.

We had already defined this abbreviation further up but, for clarity, we have added it again in the indicated position.

(12) Page 40, line 946: "normalized by" rather than "to".

We have implemented the requested change in the indicated and several other positions of the manuscript.

(13) Figures 2C, 3C, and similar plots in the supplementary material would benefit from having a legend for the colors.

We agree and have added pertinent legends to the corresponding main and supplementary figures.

(14) As a reader, I had some trouble following all the acronyms. This is at the author's discretion, but I would eliminate ones that are not strictly essential (e.g. MTP for microtiter plate; I was unable to identify what "MCS" meant; look for other opportunities to remove acronyms).

In the revised manuscript, we have defined the abbreviation ‘MCS’ (for ‘multiple-cloning site’) upon first occurrence. We have decided to retain the abbreviation ‘MTP’ in the text.

(15) Could the authors briefly speculate on why A. tumefaciens activation with red light might occur?

While we can but speculate as to the underlying reasons for the divergent red-light response in A. tumefaciens, we discuss possible scenarios below.

Commonly, two-component systems (TCS) exhibit highly cooperative and steep responses to signal. As a consequence, even small differences in the intracellular amounts of phosphorylated and unphosphorylated response regulator (RR) can give to significantly changed gene-expression output. Put another way, the gene-expression output need not scale linearly with the extent of RR phosphorylation but, rather, is expected to show nonlinear dependence with pronounced thresholding effects.

Differences in the pertinent RR levels can for instance arise from variations in the expression levels of the pNIRusk system components between E. coli and A. tumefaciens. Moreover, the two bacteria greatly differ in their two-component-system (TCS) repertoire. Although TCSs are commonly well insulated from each other, cross-talk with endogenous TCSs, even if limited, may cause changes in the levels of phosphorylated RR and hence gene-expression output. In a similar vein, the RR can also be phosphorylated and dephosphorylated non-enzymatically, e.g., by reaction with high-energy anhydrides (such as acetyl phosphate) and hydrolysis, respectively. Other potential origins for the divergent red-light response include differences in the strength of the promoters driving expression of the pNIRusk system components and the fluorescent/luminescent reporters, respectively.

(16) It would be helpful for the authors to briefly explain why they needed to switch to luminescence from fluorescence for the A. tumeraciens studies.

While there was no strict necessity to switch from the fluorescence-based system used in E. coli to a luminescence-based system in A. tumefaciens, we opted for luminescence based on prior experience with other Alphaproteobacteria (e.g., 10.1128/mSystems.00893-21), where luminescence offered significant advantages. Specifically, it provides essentially background-free signal detection and greater sensitivity for monitoring gene expression. In addition, as demonstrated in Suppl. Fig. 9c and d, the luminescence system enables real-time tracking of gene expression dynamics, which further supported its use in our experimental setup (see our response to reviewer #2’s general comments).

(17) This is a very minor comment that the authors can take or leave, but I got hung up on the word "implement" when it appeared a few times in the manuscript because I tended to read it as "put a plan into place" rather than its other meaning.

In the abstract, we have replaced one instance of the word ‘implement’ by ‘instrument’.

(18) The authors should include the relevant constructs on AddGene or another public strainsharing service.

We whole-heartedly subscribe to the idea of freely sharing research materials with fellow scientists. Therefore, we had already deposited the most relevant AvNIRusk in Addgene, even prior to the initial submission of the manuscript (accession number 235084). In the meantime, we have released the deposition, and the plasmid can be obtained from Addgene since May 15_th of this year.

Reviewer #3 (Recommendations for the authors):

Suggestion for improvement:

This paper relies heavily on variations in linker sequences to shift responses. I am familiar with prior work from the Moglich lab in which helical linkers were employed to shift responses in synthetic two-component systems, with interesting periodicity in responses with every 7 residues (as expected for an alpha helix) and inversion of responses at smaller linker shifts. There is no mention in this paper whether their current engineering follows a similar rationale, what types of linkers are employed (e.g. flexible vs helical), and whether there is an interpretation for how linker lengths alter responses. Can you explain what classes of linker sequences are used throughout Figures 2 and 3, and whether length or periodicity affects the outcome? This would be very helpful for readers who are new to this approach, or if the rationale here differs from the authors' prior work.

The PATCHY approach employed at present followed a closely similar rationale as in our previous studies. That is, linkers were extended/shortened and varied in their sequence by recombining different fragments of the natural linkers of the parental receptors, i.e., the bacteriophytochrome and the FixL sensor histidine kinase, respectively. We have added a statement to this effect in the text and a reference to Suppl. Fig. 3 which illustrates the principal approach.

Compared to our earlier studies, we isolated fewer receptor variants supporting light-regulated responses, despite covering a larger sequence space. Owing to the sparsity of the light-regulated variants, an interpretation of the linker properties and their correlation with light-regulated activity is challenging. Although doubtless unsatisfying from a mechanistic viewpoint, we therefore refrain from a pertinent discussion which would be premature and speculative at this point. As the reviewer raises a valid and important point, we have expanded the text by referring to our earlier studies and the observed dependence of functional properties on linker composition.

It is sometimes difficult to intuit or rationalize the differences in red/IR sensitivity across closely related variants. An important example appears in Figure 3C vs 3B. I think the AvTod+21 in 3B should be the equivalent to the DsRed response in the second column of 3C (AvTod+21 + DmDERusk), except, of course, that the bacteria in 3C carry an additional plasmid for the DERusk system. However, in 3B, the response to red light is substantial - ~50% as strong as that for IR, whereas in 3C, red light elicits no response at all. What is the difference? The reason this is important is that the AvTod+21 and DMDERusk represent the best "orthogonal" red and infrared light responses, but this is not at all obvious from 3B, where AvTod+21 still causes a substantial (and for orthogonality, undesirable) response under red light. Perhaps subtle differences in expression level due to plasmid changes cause these differences in light responses? Could the authors test how the expression level affects these responses? The paper would be greatly improved if observations of the diverse red/IR responses could be rationalized by some design criteria.

As noted above in our response to reviewer #2, we have now normalized all fluorescence readings to joint reference values, thus allowing a better comparison across experiments.

The reviewer is correct in noting that upon multiplexing, the individual plasmid systems support lower fluorescence levels than when used in isolation. We speculate that the combination of two plasmids may affect their copy numbers (despite the use of different resistance markers and origins of replications) and hence their performance. Likewise, the cellular metabolism may be affected when multiple plasmids are combined. These aspects may well account for the absent red-light response in AvTod+21 in the multiplexing experiments which is – indeed – unexpected. As, at present, we cannot provide a clear rationalization for this effect, we recommend verifying the performance of the plasmid setups when multiplexing.

The paper uses "red" and "infrared" to refer to ~624 nm and ~800 nm light, respectively. I wonder whether it might be possible to shift these peak wavelengths to obtain even better separation for the multiplexing experiments. Perhaps shifting the specific red wavelength could result in better separation between DERusk and AvTod systems, for example? Could the authors comment on this (maybe based on action spectra of their previously developed tools) or perhaps test a few additional stimulation wavelengths?

The choice of illumination wavelengths used in these experiments is dictated by the LED setups available for illumination of microtiter plates. On the one hand, we are using an SMD (surface-mount device) three-color LED with a fixed wavelength of the red channel around 624 nm (see Hennemann et al., 2018). On the other hand, we are deploying a custom-built device with LEDs emitting at around 800 nm (see Stüven et al., 2019 and this work). Adjusting these wavelengths is therefore challenging, although without doubt potentially interesting.

To address this reviewer comment, we have added a statement to the text that the excitation wavelengths may be varied to improve multiplexed applications.

Additional minor comments:

(1) Figure 2C: It would be very helpful to place a legend on the figure panel for what the colors indicate, since they are unique to this panel and non-intuitive.

This comment coincides with one by reviewer #2, and we have added pertinent legends to this and related supplementary figures.

(2) Figure 3C: it is not obvious which system uses DsRed and which uses YPet in each combination, since the text indicates that all combinations were cloned, and this is not clearly described in the legend. Is it always the first construct in the figure legend listed for DsRed and the second for YPet?

For clarification, we have revised the x-axis labels in Fig. 3C. (And yes, it is as this reviewer surmises: the first of the two constructs harbored DsRed and the second one YPet.)

Author response:

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

Reviewer #1 (Public Review):

This is an interesting study of the nature of representations across the visual field. The question of how peripheral vision differs from foveal vision is a fascinating and important one. The majority of our visual field is extra-foveal yet our sensory and perceptual capabilities decline in pronounced and well-documented ways away from the fovea. Part of the decline is thought to be due to spatial averaging (’pooling’) of features. Here, the authors contrast two models of such feature pooling with human judgments of image content. They use much larger visual stimuli than in most previous studies, and some sophisticated image synthesis methods to tease apart the prediction of the distinct models.

More importantly, in so doing, the researchers thoroughly explore the general approach of probing visual representations through metamers-stimuli that are physically distinct but perceptually indistinguishable. The work is embedded within a rigorous and general mathematical framework for expressing equivalence classes of images and how visual representations influence these. They describe how image-computable models can be used to make predictions about metamers, which can then be compared to make inferences about the underlying sensory representations. The main merit of the work lies in providing a formal framework for reasoning about metamers and their implications, for comparing models of sensory processing in terms of the metamers that they predict, and for mapping such models onto physiology. Importantly, they also consider the limits of what can be inferred about sensory processing from metamers derived from different models.

Overall, the work is of a very high standard and represents a significant advance over our current understanding of perceptual representations of image structure at different locations across the visual field. The authors do a good job of capturing the limits of their approach and I particularly appreciated the detailed and thoughtful Discussion section and the suggestion to extend the metamer-based approach described in the MS with observer models. The work will have an impact on researchers studying many different aspects of visual function including texture perception, crowding, natural image statistics, and the physiology of low- and mid-level vision.

The main weaknesses of the original submission relate to the writing. A clearer motivation could have been provided for the specific models that they consider, and the text could have been written in a more didactic and easy-to-follow manner. The authors could also have been more explicit about the assumptions that they make.

Thank you for the summary. We appreciate the positives noted above. We address the weaknesses point by point below.

Reviewer #2 (Public Review):

Summary

This paper expands on the literature on spatial metamers, evaluating different aspects of spatial metamers including the effect of different models and initialization conditions, as well as the relationship between metamers of the human visual system and metamers for a model. The authors conduct psychophysics experiments testing variations of metamer synthesis parameters including type of target image, scaling factor, and initialization parameters, and also compare two different metamer models (luminance vs energy). An additional contribution is doing this for a field of view larger than has been explored previously

General Comments

Overall, this paper addresses some important outstanding questions regarding comparing original to synthesized images in metamer experiments and begins to explore the effect of noise vs image seed on the resulting syntheses. While the paper tests some model classes that could be better motivated, and the results are not particularly groundbreaking, the contributions are convincing and undoubtedly important to the field. The paper includes an interesting Voronoi-like schematic of how to think about perceptual metamers, which I found helpful, but for which I do have some questions and suggestions. I also have some major concerns regarding incomplete psychophysical methodology including lack of eye-tracking, results inferred from a single subject, and a huge number of trials. I have only minor typographical criticisms and suggestions to improve clarity. The authors also use very good data reproducibility practices.

Thank you for the summary. We appreciate the positives noted above. We address the weaknesses point by point below.

Specific Comments

Experimental Setup

Firstly, the experiments do not appear to utilize an eye tracker to monitor fixation. Without eye tracking or another manipulation to ensure fixation, we cannot ensure the subjects were fixating the center of the image, and viewing the metamer as intended. While the short stimulus time (200ms) can help minimize eye movements, this does not guarantee that subjects began the trial with correct fixation, especially in such a long experiment. While Covid-19 did at one point limit in-person eye-tracked experiments, the paper reports no such restrictions that would have made the addition of eye-tracking impossible. While such a large-scale experiment may be difficult to repeat with the addition of eye tracking, the paper would be greatly improved with, at a minimum, an explanation as to why eye tracking was not included.

Addressed on pg. 25, starting on line 658.

Secondly, many of the comparisons later in the paper (Figures 9,10) are made from a single subject. N=1 is not typically accepted as sufficient to draw conclusions in such a psychophysics experiment. Again, if there were restrictions limiting this it should be discussed. Also (P11) Is subject sub-00 is this an author? Other expert? A naive subject? The subject’s expertise in viewing metamers will likely affect their performance.

Addressed on pg. 14, starting on line 308.

Finally, the number of trials per subject is quite large. 13,000 over 9 sessions is much larger than most human experiments in this area. The reason for this should be justified.

In general, we needed a large number of trials to fit full psychometric functions for stimuli derived for both models, with both types of comparison, both initializations, and over many target images. We could have eliminated some of these, but feel that having a consistent dataset across all these conditions is a strength of the paper.

In addition to the sentence on pg. 14, line 318, a full enumeration of trials is now described on pg. 23, starting on line 580.

Model

For the main experiment, the authors compare the results of two models: a ’luminance model’ that spatially pools mean luminance values, and an ’energy model’ that spatially pools energy calculated from a multi-scale pyramid decomposition. They show that these models create metamers that result in different thresholds for human performance, and therefore different critical scaling parameters, with the basic luminance pooling model producing a scaling factor 1/4 that of the energy model. While this is certain to be true, due to the luminance model being so much simpler, the motivation for the simple luminance-based model as a comparison is unclear.

The use of simple models is now addressed on pg. 3, starting on line 98, as well as the sentence starting on pg. 4 line 148: the luminance model is intended as the simplest possible pooling model.

The authors claim that this luminance model captures the response of retinal ganglion cells, often modeled as a center-surround operation (Rodieck, 1964). I am unclear in what aspect(s) the authors claim these center-surround neurons mimic a simple mean luminance, especially in the context of evidence supporting a much more complex role of RGCs in vision (Atick & Redlich, 1992). Why do the authors not compare the energy model to a model that captures center-surround responses instead? Do the authors mean to claim that the luminance model captures only the pooling aspects of an RGC model? This is particularly confusing as Figures 6 and 9 show the luminance and energy models for original vs synth aligning with the scaling of Midget and Parasol RGCs, respectively. These claims should be more clearly stated, and citations included to motivate this. Similarly, with the energy model, the physiological evidence is very loosely connected to the model discussed.

We have removed the bars showing potential scaling values measured by electrophysiology in the primate visual system and attempted to clarify our language around the relationship between these models and physiology. Our metamer models are only loosely connected to the physiology, and we’ve decided in revision not to imply any direct connection between the model parameters and physiological measurements. The models should instead be understood as loosely inspired by physiology, but not as a tool to localize the representation (as was done in the Freeman paper).

The physiological scaling values are still used as the mean of the priors on the critical scaling value for model fitting, as described on pg. 27, starting on line 698.

Prior Work:

While the explorations in this paper clearly have value, it does not present any particularly groundbreaking results, and those reported are consistent with previous literature.The explorations around critical eccentricity measurement have been done for texture models (Figure 11) in multiple papers (Freeman 2011, Wallis, 2019, Balas 2009). In particular, Freeman 20111 demonstrated that simpler models, representing measurements presumed to occur earlier in visual processing need smaller pooling regions to achieve metamerism. This work’s measurements for the simpler models tested here are consistent with those results, though the model details are different. In addition, Brown, 2023 (which is miscited) also used an extended field of view (though not as large as in this work). Both Brown 2023, and Wallis 2019 performed an exploration of the effect of the target image. Also, much of the more recent previous work uses color images, while the author’s exploration is only done for greyscale.

We were pleased to find consistency of our results with previous studies, given the (many) differences in stimuli and experimental conditions (especially viewing angle), while also extending to new results with the luminance model, and the effects of initialization. Note that only one of the previous studies (Freeman and Simoncelli, 2011) used a pooled spectral energy model. Moreover, of the previous studies, only one (Brown et al., 2023) used color images (we have corrected that citation - thanks for catching the error).

Discussion of Prior Work:

The prior work on testing metamerism between original vs. synthesized and synthesized vs. synthesized images is presented in a misleading way. Wallis et al.’s prior work on this should not be a minor remark in the post-experiment discussion. Rather, it was surely a motivation for the experiment. The text should make this clear; a discussion of Wallis et al. should appear at the start of that section. The authors similarly cite much of the most relevant literature in this area as a minor remark at the end of the introduction (P3L72).

The large differences we observed between comparison types (original vs synthesized, compared to synthesized vs synthesized) surprised us. Understanding such difference was not a primary motivation for the work, but it is certainly an important component of our results. In the introduction, we thought it best to lay out the basic logic of the metamer paradigm for foveated vision before mentioning the complications that are introduced in both the Wallis and Brown papers (paragraph beginning p. 3, line 109). Our results confirm and bolster the results of both of those earlier works, which are now discussed more fully in the Introduction (lines 109 and following).

White Noise: The authors make an analogy to the inability of humans to distinguish samples of white noise. It is unclear however that human difficulty distinguishing samples of white noise is a perceptual issue- It could instead perhaps be due to cognitive/memory limitations. If one concentrates on an individual patch one can usually tell apart two samples. Support for these difficulties emerging from perceptual limitations, or a discussion of the possibility of these limitations being more cognitive should be discussed, or a different analogy employed.

We now note the possibility of cognitive limits on pg. 8, starting on line 243, as well as pg. 22, line 571. The ability of observers to distinguish samples of white noise is highly dependent on display conditions. A small patch of noise (i.e., large pixels, not too many) can be distinguished, but a larger patch cannot, especially when presented in the periphery. This is more generally true for textures (as shown in Ziemba and Simoncelli (2021)). Samples of white noise at the resolution used in our study are indistinguishable.

Relatedly, in Figure 14, the authors do not explain why the white noise seeds would be more likely to produce syntheses that end up in different human equivalence classes.

In figure 14, we claim that white noise seeds are more likely to end up in the same human equivalence classes than natural image seeds. The explanation as to why we think this may be the case is now addressed on pg. 19, starting on line 423.

It would be nice to see the effect of pink noise seeds, which mirror the power spectrum of natural images, but do not contain the same structure as natural images - this may address the artifacts noted in Figure 9b.

The lack of pink noise seeds is now addressed on pg. 19, starting on line 429.

Finally, the authors note high-frequency artifacts in Figure 4 & P5L135, that remain after syntheses from the luminance model. They hypothesize that this is due to a lack of constraints on frequencies above that defined by the pooling region size. Could these be addressed with a white noise image seed that is pre-blurred with a low pass filter removing the frequencies above the spatial frequency constrained at the given eccentricity?

The explanation for this is similar to the lack of pink noise seeds in the previous point: the goal of metamer synthesis is model testing, and so for a given model, we want to find model metamers that result in the smallest possible critical scaling value. Taking white noise seed images and blurring them will almost certainly remove the high frequencies visible in luminance metamers in figure 4 and thus result in a larger critical scaling value, as the reviewer points out. However, the logic of the experiments requires finding the smallest critical scaling value, and so these model metamers would be uninformative. In an early stage of the project, we did indeed synthesize model metamers using pink noise seeds, and observed that the high frequency artifacts were less prominent.

Schematic of metamerism: Figures 1,2,12, and 13 show a visual schematic of the state space of images, and their relationship to both model and human metamers. This is depicted as a Voronoi diagram, with individual images near the center of each shape, and other images that fall at different locations within the same cell producing the same human visual system response. I felt this conceptualization was helpful. However, implicitly it seems to make a distinction between metamerism and JND (just noticeable difference). I felt this would be better made explicit. In the case of JND, neighboring points, despite having different visual system responses, might not be distinguishable to a human observer.

Thanks for noting this – in general, metamers are subthreshold, and for the purpose of the diagram, we had to discretize the space showing metameric regions (Voronoi regions) around a set of stimuli. We’ve rewritten the captions to explain this better. We address the binary subthreshold nature of the metamer paradigm in the discussion section (pg. 19, line 438).

In these diagrams and throughout the paper, the phrase ’visual stimulus’ rather than ’image’ would improve clarity, because the location of the stimulus in relation to the fovea matters whereas the image can be interpreted as the pixels displayed on the computer.

We agree and have tried to make this change, describing this choice on pg. 3 line 73.

Other

The authors show good reproducibility practices with links to relevant code, datasets, and figures.

Reviewer #1 (Recommendations For The Authors):

In its current form, I found the introduction to be too cursory. I felt that the article would benefit from a clearer motivation for the two models that are considered as the reader is left unclear why these particular models are of special scientific significance. The luminance model is intended to capture some aspects of retinal ganglion cells response characteristics and the spectral energy model is intended to capture some aspects of the primary visual cortex. However, one can easily imagine models that include the pooling of other kinds of features, and it would be helpful to get an idea of why these are not considered. Which aspects of processing in the retina and V1 are being considered and which are being left out, and why? Why not consider representations that capture even higher-order statistical structure than those covered by the spectral energy model (or even semantics)? I think a bit of rewriting with this in mind could improve the introduction.

Along similar lines, I would have appreciated having the logic of the study explained more explicitly and didactically: which overarching research question is being asked, how it is operationalised in the models and experiments, and what are the predictions of the different models. Figures 2 and 3 are certainly helpful, but I felt further explanations would have made it easier for the reader to follow. Throughout, the writing could be improved by a careful re-reading with a view to making it easier to understand. For example, where results are presented, a sentence or two expanding on the implications would be helpful.

I think the authors could also be more explicit about the assumptions they make. While these are obviously (tacitly) included in the description of the models themselves, it would be helpful to state them more openly. To give one example, when introducing the notion of critical scaling, on p.6 the authors state as if it is a self-evident fact that "metamers can be achieved with windows whose size is matched to that of the underlying visual neurons". This presumably is true only under particular conditions, or when specific assumptions about readout from populations of neurons are invoked. It would be good to identify and state such assumptions more directly (this is partly covered in the Discussion section ’The linking proposition underlying the metamer paradigm’, but this should be anticipated or moved earlier in the text).

We agree that our introduction was too cursory and have reworked it. We have also backed off of the direct comparison to physiology and clarified that we chose these two as the simplest possible pooling models. We have also added sentences at the end of each result section attempting to summarize the implication (before discussing them fully in the discussion). Hopefully the logic and assumptions are now clearer.

There are also some findings that warrant a more extensive discussion. For example, what is the broader implication of the finding that original vs. synthesised and synthesised vs. synthesised comparisons exhibit very different scaling values? Does this tell us something about internal visual representations, or is it simply capturing something about the stimuli?

We believe this difference is a result of the stimuli that are used in the experiment and thus the synthesis procedure itself, which interacts with the model’s pooled image feature. We have attempted to update the relevant figures and discussions to clarify this, in the sections starting on pg 17 line 396 and pg. 19 line 417.

At some points in the paper, a third model (’texture model’) creeps into the discussion, without much explanation. I assume that this refers to models that consider joint (rather than marginal) statistics of wavelet responses, as in the famous Portilla & Simoncelli texture model. However, it would be helpful to the reader if the authors could explain this.

Addressed on pg. 3, starting on line 94.

Minor corrections.

Caption of Figure 3: ’top’ and ’bottom’ should be ’left’ and ’right’

Line 177: ’smallest tested scaling values tested’. Remove one instance of ’tested’

Line 212: ’the images-specific psychometric functions’ -> ’image-specific’

Line 215: ’cloud-like pink noise’. It’s not literally pink noise, so I would drop this.

Line 236: ’Importantly, these results cannot be predicted from the model, which gives no specific insight as to why some pairs are more discriminable than others’. The authors should specify what we do learn from the model if it fails to provide insight into why some image pairs are more discriminable than others.

Figure 9: it might be helpful to include small insets with the ’highway’ and ’tiles’ source images to aid the reader in understanding how the images in 9B were generated.

Table 1 placement should be after it is first referred to on line 258.

In the Discussion section "Why does critical scaling depend on the comparison being performed", it would be helpful to consider the case where the two model metamers *are* distinguishable from each other even though each is indistinguishable from the target image. I would assume that this is possible (e.g., if the target image is at the midpoint between the two model images in image space and each of the stimuli is just below 1 JND away from the target). Or is this not possible for some reason?

Regarding line 236: this specific line has been removed, and the discussion about this issue has all been consolidated in the final section of the discussion, starting on pg. 19 line 438.

Regarding the final comment: this is addressed in the paragraph starting on pg. 16 line 386. To expand upon that: the situation laid out by the reviewer is not possible in our conceptualization, in which metamerism is transitive and image discriminability is binary. In order to investigate situations like the one laid out by the reviewer, one needs models whose representations have metric properties, i.e., which allow you to measure and reason about perceptual distance, which we refer to in the paragraph starting on pg. 20 line 460. We also note that this situation has not been observed in this or any other pooling model metamer study that we are aware of. All other minor changes have been addressed.

Reviewer #2 (Recommendations For The Authors):

Original image T should be marked in the Voronoi diagrams.

Brown et al is miscited as 2021 should be ACM Transactions on Applied Perception 2023.

Figure 3 caption: models are left and right, not top and bottom.

Thanks, all of the above have been addressed.

References

BrownReral Encoding, in the Human Visual System. ACM Transactions on Applied Perception. 2023 Jan; 20(1):1–22.http://dx.doi.org/10.1145/356460, Dutell V, Walter B, Rosenholtz R, Shirley P, McGuire M, Luebke D. Efficient Dataflow Modeling of Periph-5, doi: 10.1145/3564605.

Freeman Jdoi: 10.1038/nn.2889, Simoncelli EP. Metamers of the ventral stream. Nature Neuroscience. 2011 aug; 14(9):1195–1201..

Ziemba CMnications. 2021 jul; 12(1)., Simoncelli EP. Opposing Effects of Selectivity and Invariance in Peripheral Vision. Nature Commu-https://doi.org/10.1038/s41467-021-24880-5, doi: 10.1038/s41467-021-24880-5.

Author response:

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

Reviewer #1 (Public Review):

(1) The authors make fairly strong claims that "arousal-related fluctuations are isolated from neurons in the deep layers of the SC" (emphasis added). This conclusion is based on comparisons between a "slow drift axis", a low-dimensional representation of neuronal drift, and other measures of arousal (Figures 2C, 3) and motor output sensitivity (Figures 2B, 3B). However, the metrics used to compare the slow-drift axis and motor activity were computed during separate task epochs: the delay period (600-1100 ms) and a perisaccade epoch (25 ms before and after saccade initiation), respectively. As the authors reference, deep-layer SC neurons are typically active only around the time of a saccade. Therefore, it is not clear if the lack of arousal-related modulations reported for deep-layer SC neurons is because those neurons are truly insensitive to those modulations, or if the modulations were not apparent because they were assessed in an epoch in which the neurons were not active. A potentially more valuable comparison would be to calculate a slow-drift axis aligned to saccade onset. 

The reviewer makes an important point that the calculation of an axis can depend critically on the time window of neuronal response. We find when considering this that the slow drift axis is less sensitive to this issue because it is calculated on time-averaged activity over multiple trials. In previous work we found that slow drift calculated on the stimulus evoked response in V4 was very well aligned to slow drift calculated on pre-stimulus spontaneous activity (Cowley et al, Neuron, 2020, Supplemental Figure 3A and 3B). To address this issue in the present data, we compared the axis computed for an example session for neural activity during the delay period and neural activity aligned to saccade onset. As shown new Figure 2 – figure supplement 1 in the revised manuscript, we found a similar lack of arousal-related modulations for deep-layer SC neurons when slow drift was computed using the saccade epoch (25ms before to 25ms after the onset of the saccade). Figure 2 – figure supplement 1A shows loadings for the SC slow drift axis when it was computed using spiking responses during the delay period (as in the main manuscript analysis). In contrast, Figure 2 – figure supplement 1B shows loadings from the same session when the SC slow drift axis was computed using spiking responses during the saccade epoch. The plots are highly similar and in both cases the loadings were weaker for neurons recorded from channels at the bottom of the probe which have a higher motor index. Finally, we found that projections onto the SC slow drift axis for this session were strongly correlated when the slow drift axis was computed using spiking responses during the delay period and the saccade epoch (r = 0.66, p < 0.001, Figure 1C). Taken together, these results suggest that arousal-related modulations are less evident in deep-layer SC neurons irrespective of whether slow drift was computed during the delay or saccade epoch (see also Public Reviews, Reviewer 1, Point 2).

(2) More generally, arousal-related signals may persist throughout multiple different epochs of the task. It would be worthwhile to determine whether similar "slow-drift" dynamics are observed for baseline, sensory-evoked, and saccade-related activity. Although it may not be possible to examine pupil responses during a saccade, there may be systematic relationships between baseline and evoked responses. 

Similar to the point above, slow drift dynamics tend to be similar across different response epochs because they are averaged across many trials and seem to tap into responsivity trends that are robust across epochs. As shown in Author response image 1 below, and the Figure 2 – figure supplement 1 in the revised manuscript, similar dynamics were observed when the SC slow drift axis was computed using spiking responses during the baseline, delay, visual and saccade epochs. We did not investigate differences between baseline and evoked pupil responses in the current paper. However, these effects were characterized in one of our previous papers that focused exclusively on the relationship between slow drift and eye-related metrics (Johnston et al., 2022, Cereb. Cortex, Figure 6). In this previous work, we found a negative correlation between baseline and evoked pupil size. Both variables were significantly correlated with slow drift, the only difference being the sign of the correlation.

Author response image 1.

(A-C) Dynamics of slow drift for three example sessions when the SC slow drift axis was computed using spiking responses during the baseline, delay, visual and saccade epochs. Baseline = 100ms before the onset of the target stimulus; Delay = 600 to 1100ms after the offset of the target stimulus; Stim = 25ms to 125ms after the onset of the target stimulus; Sac = 25ms before to 25ms after the onset of the saccade.

Johnston R, Snyder AC, Khanna SB, Issar D, Smith MA (2022) The eyes reflect an internal cognitive state hidden in the population activity of cortical neurons. Cereb Cortex 32:3331–3346.

(3) The relationships between changes in SC activity and pupil size are quite small (Figures 2C & 5C). Although the distribution across sessions (Figure 2C) is greater than chance, they are nearly 1/4 of the size compared to the PFC-SC axis comparisons. Likewise, the distribution of r2 values relating pupil size and spiking activity directly (Figure 5) is quite low. We remain skeptical that these drifts are truly due to arousal and cannot be accounted for by other factors. For example, does the relationship persist if accounting for a very simple, monotonic (e.g., linear) drift in pupil size and overall firing rate over the course of an individual session? 

Firstly, it is important to note that the strength of the relationship between projections onto the SC slow drift axis and pupil size (r² = 0.06) is within the range reported by Joshi et al. (2016, Neuron, Figure 3). They investigated the median variance explained between the spiking responses of individual SC neurons and pupil size and found it to be approximately 0.02 across sessions. Secondly, our statistical approach of testing the actual distribution of r² values against a shuffled distribution was specifically designed to rule out the possibility that the relationship between SC spiking responses and pupil size occurred due to linear drifts. The shuffled distribution in Figure 2C of the main manuscript represents the variance that can be explained by one session’s slow drift correlated with another session’s pupil, which would contain effects that occurred due to linear drifts alone. That the actual proportion of variance explained was significantly greater than this distribution suggests that the relationship between projections onto the SC slow drift axis and pupil size reflects changes in arousal rather than other factors related to linear drifts.

Joshi S, Li Y, Kalwani RM, Gold JI (2016) Relationships between Pupil Diameter and Neuronal Activity in the Locus Coeruleus, Colliculi, and Cingulate Cortex. Neuron 89:221–234.

(4) It is not clear how the final analysis (Figure 6) contributes to the authors' conclusions. The authors perform PCA on: (i) residual spiking responses during the delay period binned according to pupil size, and (ii) spiking responses in the saccade epoch binned according to target location (i.e., the saccade tuning curve). The corresponding PCs are the spike-pupil axis and the saccade tuning axis, respectively. Unsurprisingly, the spikepupil axis that captures variance associated with arousal (and removes variance associated with saccade direction) was not correlated with a saccade-tuning axis that captures variance associated with saccade direction and omits arousal. Had these measures been related it would imply a unique association between a neuron's preferred saccade direction and pupil control- which seems unlikely. The separation of these axes thus seems trivial and does not provide evidence of a "mechanism...in the SC to prevent arousal-related signals interfering with the motor output." It remains unknown whether, for example, arousal-related signals may impact trial-by-trial changes in neuronal gain near the time of a saccade, or alter saccade dynamics such as acceleration, precision, and reaction time. 

The reviewer makes a good point, and we agree that more evidence is needed to determine if the separation of the pupil size axis and saccade tuning axis is the mechanism through which cognitive and arousal-related signals can be intermixed in the SC. In the revised manuscript (lines 679-682), we have raised this as a possible explanation that necessitates further study rather than stating definitively that it is the exact mechanism through which these signals are kept separate. Our analysis here is similar to the one from Smoulder et al (2024, Neuron, Fig. 2F), in which the interactions between reward signals and target tuning in M1 were examined (and found to be orthogonal). While we agree with the reviewer that it may seem “trivial” for these axes to be orthogonal, it does not have to be so. If, for example, neural tuning curves shifted with changes in pupil size through gain changes that revealed tuning or affected tuning curve shape, there could be projections of the pupil axis onto the target tuning axis. Thus, while we agree with the reviewer that it appears sensible for these two axes to be orthogonal, our result is nonetheless a novel finding. We have edited the text in our revised manuscript, however, to make sure the nuance of this point is conveyed to the reader.

Smoulder AL, Marino PJ, Oby ER, Snyder SE, Miyata H, Pavlovsky NP, Bishop WE, Yu BM, Chase SM, Batista AP. A neural basis of choking under pressure. Neuron. 2024 Oct 23;112(20):3424-33.

Reviewer #2 (Public Review):

(1) The greatest weakness in the present research is the fact that arousal is a functionally less important non-motoric variable. The authors themselves introduce the problem with a discussion of attention, which is without any doubt the most important cognitive process that needs to be functionally isolated from oculomotor processes. Given this introduction, one cannot help but wonder, why the authors did not design an experiment, in which spatial attention and oculomotor control are differentiated. Absent such an experiment, the authors should spend more time explaining the importance of arousal and how it could interfere with oculomotor behavior. 

Although attention does represent an important cognitive process, we did not design an experiment in which attention and oculomotor control are differentiated because attention does not appear to be related to slow drift. In our first paper that reported on this phenomenon, we investigated the effects of spatial attention on slow fluctuations in neural activity by cueing the monkeys to attend to a stimulus in the left or right visual field in a block-wise manner. Each block lasted ~20 minutes and we found that slow drift did not covary with the timing of cued blocks (see Figure 4A, Cowley et al., 2020, Neuron). Furthermore, there is a large body of work showing that arousal also impacts motor behavior leading to changes in a range of eye-related metrics (e.g., pupil size, microsaccade rate and saccadic reaction time - for review, see Di Stasi et al. 2013, Neurosci. Biobehav. Rev.). We also note that the terms attention and arousal are often used in nonspecific and overlapping ways in the literature, adding to some potential confusion here. Nonetheless, pupil-linked arousal is an important variable that impacts motor performance. This has now been stated clearly in the Introduction of the revised manuscript (lines 108-114) to address the reviewer’s concerns and highlight the importance of studying how precise fixation and eye movements are maintained even in the presence of signals related to ongoing changes in brain state. 

Cowley BR, Snyder AC, Acar K, Williamson RC, Yu BM, Smith MA (2020) Slow Drift of Neural Activity as a Signature of Impulsivity in Macaque Visual and Prefrontal Cortex. Neuron 108:551-567.e8.

(2) In this context, it is particularly puzzling that one actually would expect effects of arousal on oculomotor behavior. Specifically, saccade reaction time, accuracy, and speed could be influenced by arousal. The authors should include an analysis of such effects. They should also discuss the absence or presence of such effects and how they affect their other results. 

As described above, several studies across species have demonstrated that arousal impacts motor behavior e.g., saccade reaction time, saccade velocity and microsaccade rate (for review, see Di Stasi et al. 2013, Neurosci. Biobehav. Rev.). This has been clarified in the Introduction of the revised manuscript to address the reviewer's concerns (lines 108-114). Our prior work (Johnston et al, Cerebral Cortex, 2022) shows that slow drift impacts several types of oculomotor behavior. Overall, these studies highlight the impact of arousal on eye movements as a robust effect, and support the present investigation into arousal and oculomotor control signals. While we agree reaction time, accuracy, and speed all can be influenced by arousal depending on task demands, the present study is focused on the connection between slow fluctuations in neural activity, linked to arousal, and different subpopulations of SC neurons. 

Di Stasi LL, Catena A, Cañas JJ, Macknik SL, Martinez-Conde S (2013) Saccadic velocity as an arousal index in naturalistic tasks. Neurosci Biobehav Rev 37:968–975.

Johnston R, Snyder AC, Khanna SB, Issar D, Smith MA (2022) The eyes reflect an internal cognitive state hidden in the population activity of cortical neurons. Cereb Cortex 32:3331–3346.

(3) The authors use the analysis shown in Figure 6D to argue that across recording sessions the activity components capturing variance in pupil size and saccade tuning are uncorrelated. however, the distribution (green) seems to be non-uniform with a peak at very low and very high correlation specifically. The authors should test if such an interpretation is correct. If yes, where are the low and high correlations respectively? Are there potentially two functional areas in SC? 

We agree with the reviewer that our actual data distribution was non-uniform. We examined individual sessions with high and low variance explained and did not find notable differences. One source of this variation has to do with session length. Longer sessions in principle should have a chance distribution of variance explained closer to zero because they contained more time bins. Given that we had no specific hypothesis for a non-uniform distribution, we have simply displayed the full distribution of values in our figure and the statistical result of a comparison to a shuffled distribution.

Reviewer #3 (Public Review):

(1) However, I am concerned about two main points: First, the authors repeatedly say that the "output" layers of the SC are the ones with the highest motor indices. This might not necessarily be accurate. For example, current thresholds for evoking saccades are lowest in the intermediate layers, and Mohler & Wurtz 1972 suggested that the output of the SC might be in the intermediate layers. Also, even if it were true that the high motor index neurons are the output, they are very few in the authors' data (this is also true in a lot of other labs, where it is less likely to see purely motor neurons in the SC). So, this makes one wonder if the electrode channels were simply too deep and already out of the SC? In other words, it seems important to show distributions of encountered neurons (regardless of the motor index) across depth, in order to better know how to interpret the tails of the distributions in the motor index histogram and in the other panels of Figure Supplement 1. I elaborate more on these points in the detailed comments below. 

The reviewer makes a good point about the efferent signals from SC. It is true that electrical thresholds are often lowest in intermediate layers, though deep layers do project to the oculomotor nuclei (Sparks, 1986; Sparks & Hartwich-Young, 1989) and often intermediate and deep layers are considered to function together to control eye movements (Wurtz & Albano, 1980). As suggested by the reviewer, we have edited the text throughout the manuscript to say that slow drift was less evident in SC neurons with a higher motor index, as well as included the above references and points about the intermediate and deep layers (Lines 73-81). Aside from the question of which layers of the SC function as the “motor output”, the reviewer raises a separate and important question – are our deep recordings still in SC. Here, we can say definitively that they are. We removed neurons if they did not exhibit elevated (above baseline) firing rates during the visual or saccade epochs of the MGS task (see Methods section on “Exclusion criteria”). All included neurons possessed a visual, visuomotor or motor response, consistent with the response properties of neurons in the SC. In addition, we found a number of neurons well above the bottom of the probe with strong motor responses and minimal loadings onto the slow drift axis (see Figure 2 – figure supplement 1A), consistent with the reviewer’s comment that intermediate layer neurons are tuned for movement and play a role in saccade production.

Mohler CW, Wurtz RH. Organization of monkey superior colliculus: intermediate layer cells discharging before eye movements. Journal of neurophysiology. 1976 Jul 1;39(4):722-44.

Sparks DL. Translation of sensory signals into commands for control of saccadic eye movements: role of primate superior colliculus. Physiol Rev. 1986 Jan;66(1):118-71. doi: 10.1152/physrev.1986.66.1.118. PMID: 3511480.

Sparks DL, Hartwich-Young R. The deep layers of the superior colliculus. Reviews of oculomotor research. 1989 Jan 1;3:213-55.

Wurtz RH, Albano JE. Visual-motor function of the primate superior colliculus. Annu Rev Neurosci. 1980;3:189-226. doi: 10.1146/annurev.ne.03.030180.001201. PMID: 6774653.

(2) Second, the authors find that the SC cells with a low motor index are modulated by pupil diameter. However, this could be completely independent of an "arousal signal". These cells have substantial visual responses. If the pupil diameter changes, then their activity should be influenced since the monkey is watching a luminous display. So, in this regard, the fact that they do not see "an arousal signal" in most motor neurons (through the pupil diameter analyses) is not evidence that the arousal signal is filtered out from the motor neurons. It could simply be that these neurons simply do not get affected by the pupil diameter because they do not have visual sensitivity. So, even with the pupil data, it is still a bit tricky for me to interpret that arousal signals are excluded from the "output layers" of the SC. 

The reviewer makes an important point about the SC’s visual responses. Neurons with a low motor index are, conversely, likely to have a stronger visual response index. However, we do not believe that changes in luminance can explain why the correlation between SC spiking response and pupil size is weaker for neurons with a lower motor index. Firstly, the changes in pupil size observed in the current paper and our previous work are slow and occur on a timescale of minutes (Cowley et al., 2020, Neuron) and are correlated with eye movement measures such as reaction time and microsaccade rate (Johnston et al., 2022, Cerebral Cortex). This is in stark contrast to luminance-evoked changes in pupil size that occur on a timescale of less than a second. Secondly, as shown the new Figure 5 – figure supplement 1 in the revised manuscript, very similar results were found when SC spiking responses were correlated with pupil size during the baseline period, when only the fixation point was on the screen. Although the luminance of the small peripheral target stimulus can result in small luminance-evoked changes in pupil size, no changes in luminance occurred during the baseline period which was defined as 100ms before the onset of the target stimulus. In Figure 2 – figure supplement 1 and Author response image 1 above, we show that slow drift is the same whether calculated on the baseline response, delay period, or peri-saccadic epoch. Thus, the measurement of slow drift is insensitive to the precise timing of the selection of both the window for the spiking response and the window for the pupil measurement. If luminance were the explanation for the slow changes in firing observed in visually responsive SC neurons, it would require those neurons to exhibit robust, sustained tuned responses to the small changes in retinal illuminance induced by the relatively small fluctuations in pupil size we observed from minute to minute. We are aware of no reports of such behavior in visually-responsive neurons in SC. We have included these analyses and this reasoning in the revised manuscript on lines 478-495.

Reviewer#1 (Recommendations for the author):

(1) It would be useful to provide line numbers in subsequent manuscripts for reviewers.

Line numbers have been added in the revised version of the manuscript.

(2) Page #6; last sentence: "...even impact processing at the early to mid stages of the visuomotor transformation, without leading to unwanted changes in motor output." I do not believe the authors have provided evidence that arousal levels were not associated with changes in motor output.

As suggested by Reviewer 3 (see Public Reviews, Reviewer 3, Point 2), we have edited the text throughout the manuscript to say that slow drift was less evident in SC neurons with a higher motor index. This sentence in the revised manuscript now reads:

“This provides a potential mechanism through which signals related to cognition and arousal can exist in the SC, and even impact processing at the early to mid stages of the visuomotor transformation, without leading to unwanted changes in SC neurons that are linked to saccade execution.”

(3) Page #8; last paragraph: Although deep-layer SC neurons may not have been obtained during every recording session, a summary of the motor index scores observed along the probe across sessions would be useful to confirm their assumptions. 

See Author response image 2 below which shows the motor index of each recoded SC neuron on the x-axis and session number on the y-axis. The points are colored by to the squared factor loading which represents the variance explained between the response a neuron and the slow drift axis (see Figure 3B of the main manuscript). You can see from this plot that neurons with a stronger component loading (shown in teal to yellow) typically have a lower motor index whereas the opposite is true for neurons with a weaker component loading (shown in dark blue).

Author response image 2.

Scatter plot showing the motor index of each recorded neuron along with the session number in which it was recorded. The points are colored by to the squared factor loading for each neuron along the slow drift axis. Note that loadings above 0.5 (33 data points in total) have been thresholded at 0.5 so that we could effectively use the color range to show all of the slow drift axis loadings.

(4) Page #10; first paragraph: The authors should state the time window of the delay period used, since it may be distinct from the pupil analysis (first 200ms of delay). 

This has been stated in the revised version of the manuscript. The sentence now reads:

“We first asked if arousal-related fluctuations are present in the SC. As in previous studies that recorded from neurons in the cortex (Cowley et al., 2020), we found that the mean spiking responses of individual SC neurons during the delay period (chosen at random on each trial from a uniform distribution spanning 600-1100ms, see Methods) fluctuated over the course of a session while the monkeys performed the MGS task (Figure 2A, left).”

(5) Page #10; second paragraph: Extra period at the end of a sentence: " most variance in the data..". 

Fixed in the revised version of the manuscript.

(6) Page #12: "between projections onto the SC slow drift axis and mean pupil size during the first 200ms of the delay period when a task-related pupil response could be observed." What criteria was used to determine whether a task-related pupil response was observed? 

This was chosen based on the results of a previous study in our lab that used the same memory-guided saccade task to investigate the relationship between slow drift and changes in based and evoked pupil size (see Johnston et al., 2022, Cereb. Cortex, Figure 6B). The period was chosen based on plotting the average pupil size aligned on different trial epochs. As we show in Figure 5-figure supplement 3 above, the pupil interactions with slow drift did not depend on the particular time window of the pupil we chose.  

(7) Page #14; Figure 2A: The axes for the individual channels are strangely floating and quite different from all other figures. Please label the channel in the figure legend that was used as an example of the projected values onto the slow drift axis.

The figure has been changed in the revised version of the manuscript so that the tick mark denoting zero residual spikes per second is on the top layer of each plot. A scale bar was chosen instead of individual axes to reduce clutter in the figure as it was used to demonstrate how slow drift was computed. Residual spiking responses from all neurons were projected on the slow drift axis to generate the scatter plot in the bottom right-hand corner of Figure 2A. There is no single neuron to label.

(8) Page #16: "These results demonstrate that even though arousal-related fluctuations are present in the SC, they are isolated from deep-layer neurons that elicit a strong saccadic response and presumably reside closer to the motor output." In line with our major comments, lack of arousal-related activity during the delay period is meaningless for deep-layer SC neurons that are generally inactive during this time. It does not imply that there is no arousal signal! 

Addressed in Public Reviews, Reviewer 1, Point 1 & 2. We found a similar lack of arousal-related modulations reported for deep-layer SC neurons when slow drift was computed using the saccade epoch (Figure 1 above). In addition, similar dynamics were observed when the SC slow drift axis was computed using spiking responses during the baseline, delay, visual and saccade period (Figure 2).

(9) Page #18: "These findings provide additional support for the hypothesis that arousalrelated fluctuations are isolated from neurons in the deep layers of the SC." The same criticism from above applies.

Addressed in Public Reviews, Reviewer 1, Point 1 & 2.

(10) Page #20; paragraph 3: "Taken together, the findings outlined above..." Would be useful to be more specific when referring to "activity" ; e.g., "...these neurons did not exhibit large fluctuations in delay-period activity over time".

This sentence has been changed in the revised manuscript in light of the reviewer’s comments. It now reads:

“In addition to being more weakly correlated with pupil size, the spiking responses of these neurons did not exhibit large fluctuations over time (Figure 2), and when considering the neuronal population as a whole, explained less variance in the slow drift axis when it was computed using population activity in the SC (Figure 3) and PFC (Figure 4).”

Reviewer #3 (Recommendations for the author):

The paper is clear and well-written. However, I am concerned about two main points: 

(1) First, the authors repeatedly say that the "output" layers of the SC are the ones with the highest motor indices. This might not necessarily be accurate. For example, current thresholds for evoking saccades are lowest in the intermediate layers, and Mohler & Wurtz 1972 suggested that the output of the SC might be in the intermediate layers. Also, even if it were true that the high motor index neurons are the output, they are very few in the authors' data (this is also true in a lot of other labs, where it is less likely to see purely motor neurons in the SC). So, this makes one wonder if the electrode channels were simply too deep and already out of the SC. In other words, it seems important to show distributions of encountered neurons (regardless of motor index) across depth, in order to better know how to interpret the tails of the distributions in the motor index histogram and in the other panels of the figure supplement 1. I elaborate more on these points in the detailed comments below. 

Addressed in Public Reviews, Reviewer 3, Point 1.

(2) Second, the authors find that the SC cells with a low motor index are modulated by pupil diameter. However, this could be completely independent of an "arousal signal". These cells have substantial visual responses. If the pupil diameter changes, then their activity should be influenced since the monkey is watching a luminous display. So, in this regard, the fact that they do not see "an arousal signal" in most motor neurons (through the pupil diameter analyses) is not evidence that the arousal signal is filtered out from the motor neurons. It could simply be that these neurons simply do not get affected by the pupil diameter because they do not have visual sensitivity. So, even with the pupil data, it is still a bit tricky for me to interpret that arousal signals are excluded from the "output layers" of the SC. 

Addressed in Public Reviews, Reviewer 3, Point 2.

(3) I think that a remedy to the first point above is to change the text to make it a bit more descriptive and less interpretive. For example, just say that the slow drifts were less evident among the neurons with high motor index. 

We thank the reviewer for this suggestion (see Public Reviews, Reviewer 3, Point 1).

(4) For the second point, I think that it is important to consider the alternative caveat of different amounts of light entering the system. Changes in light level caused by pupil diameter variations can be quite large. 

We thank the reviewer for this suggestion (see Public Reviews, Reviewer 3, Point 2).

(5) Line 31: I'm a bit underwhelmed by this kind of statement. i.e. we already know that cognitive processes and brain states do alter eye movements, so why is it "critical" that high precision fixation and eye movements are maintained? And, isn't the next sentence already nulling this idea of criticality because it does show that the brain state alters the SC neurons? In fact, cognitive processes are already known to be most prevalent in the intermediate and deep layers of the SC. 

It seems clear that while cognitive state does affect eye movements, it is desirable to have some separation between cognitive state and eye movement control. Covert attention, for instance, is precisely a situation where eye movement control is maintained to avoid overt saccades to the attended stimulus, and yet there are clear indications of attention’s impact on microsaccades and fixation. We stand by our statement that an important goal of vision is to have precise fixation and movements of the eye, and yet at the same time the eyes are subject to numerous influences by cognitive state.

(6) Line 65: it is better to clarify that these are "functional layers" because there are actually more anatomical layers. 

We have edited this sentence in the revised version of the manuscript so that it now reads:

“The role of these projections in the visuomotor transformation depends on the functional layer of the SC in which they terminate”.

(7) Line 73: this makes it sound like only the deepest layers are topographically organized, which is not true. Also, as early as Mohler & Wurtz, 1972, it was suggested that the intermediate layers have the biggest impacts downstream of the SC. This is also consistent with electrical microstimulation current thresholds for evoking saccades from the SC. 

We have addressed the reviewers’ comments about the intermediate layers having the biggest impact downstream of the SC in Public Reviews, Reviewer 3, Point 1. Furthermore, line 73 has been changed in the revised manuscript so that it now reads:

“As is the case for neurons in the superficial and intermediate layers, they [SC motor neurons] form a topographically organized map of visual space (White et al. 2017; Robinson 1972; Katnani and Gandhi 2011)”.  

(8) Line 100: there is an analogous literature regarding the question of why unwanted muscle contractions do not happen. Specifically, in the context of why SC visual bursts do not automatically cause saccades (which is a similar problem to the ones you mention about cognitive signals interfering by generating unwanted eye movements), both Jagadisan & Gandhi, Curr Bio, 2022 and Baumann et al, PNAS, 2023 also showed that SC population activity not only has different temporal structure (Jagadisan & Gandhi) but also occupy different subspaces (Baumann et al) under these two different conditions (visual burst versus saccade burst). This is conceptually similar to the idea that you are mentioning here with respect to arousal. So, it is worth it to mention these studies here and again in the discussion. 

We are grateful to the reviewer for these suggestions and have included text in the Introduction (Lines 125-128) and Discussion (Lines 678-682) of the revised manuscript along with the references cited above.

(9) Line 147: as mentioned above, it is now generally accepted that there are quite a few "pure" motor neurons in the SC. This is consistent with what you find. E.g. Baumann et al., 2023. And, again see Mohler and Wurtz in the 1970's. So, I wonder how useful it is to go too much into this idea of the deeper motor neurons (e.g. the correlations in the other panels of the Figure 1 supplement). 

This is related to the reviewer’s comment that the output of the SC might be in the intermediate layers. This concern has been addressed in Public Reviews, Reviewer 3, Point 1.

(10) Figure 1 should say where the RF was for the shown spike rasters. i.e. were these the same saccade target across trials? And where was that location relative to the RF? It would help also in the text to say whether the saccade was always to the RF center or whether you were randomizing the target location. 

We centered the array of saccade targets using the microstimulation-evoked eye movement for SC (see Methods section “Memory-guided saccade task”) to find the evoked eccentricity, and then used saccade targets with equal spacing of 45 degrees starting at zero (rightward saccade target). We did not do extensive RF mapping beyond this microstimulation centering. In Figure 1, the spike rasters are shown for a target that was visually identified to be within the neuron’s RF based on assessing responses to all 8 target angles. We have added information about this to the figure caption.

(11) Line 218: but were there changes in the eye movement statistics? For example, the slow drift eye movements during fixation? Or even the microsaccades? 

Addressed in Public Reviews, Reviewer 2, Point 2.  

(12) Line 248: shuffling what exactly? I think that more explanation would be needed here. 

Addressed in Public Reviews, Reviewer 1, Point 3.  

(13) Line 263: but isn't this reflecting a sensory transient in the pupil diameter, since the target just disappeared? 

Addressed in Public Reviews, Reviewer 3, Point 2.  

(14) Line 271: I suspect that slow drift eye movements (in between microsaccades) would show higher correlations. Not sure how well you can analyze those with a video-based eye tracker. 

We agree that fixational drift would be a worthwhile metric, but it is not one we have focused on here and to our knowledge does require higher precision tracking. 

(15) Line 286: again, see above about similar demonstrations with respect to the visual and motor burst intervals, which clearly cause the same problem (even stronger) as the one studied here. 

See reply, including Figure 2.

(16) Line 330: again, I'm not sure deeper necessarily automatically means closer to the output. For example, current thresholds for evoked saccades grow higher as you go deeper. Maybe the authors can ask their colleague Neeraj Gandhi about this point specifically, just to be safe. Maybe the safest would be to remain descriptive about the data, and just say something like: arousal-related fluctuations were absent in our deepest recorded sites. 

Addressed in Public Reviews, Reviewer 3, Point 1.

(17) Line 332: likewise, statements like this one here would be qualified if the output was the intermediate layers......anyway if I understand what I read so far in the paper, the signal will be anyway orthogonal to the motor burst population subspace. So, maybe there's no need to emphasize that it goes away in the very deepest layers. 

See reply above, Public Reviews, Reviewer 1, Point 4.

(18) Figure 3A: related to the above, I think one issue could be that the deeper contacts might already be out of the SC. Maybe some cell count distribution from each channel should help in this regard. i.e. were you finding way fewer saccade-related neurons in the deepest channels (even though the few that you found were with high motor index)? If so, then wouldn't this just mean that the channel was too deep? I think there needs to be an analysis like this, to convince readers that the channels were still in the SC. Ideally, electrical stimulation current thresholds for evoking saccades at different depths would be tested, but I understand that this can be difficult at this stage. 

Addressed in Public Reviews, Reviewer 3, Point 1.

(19) I keep repeating this because in general, cognitive effects are stronger in the intermediate/deeper layers than in the superficial layers. If these interfere with eye movements like arousal, then why should arousal be different?

Few studies have investigated the effects of attention on “pure” movement SC neurons that only discharge during a saccade. One study, which we cited in Introduction (Ignashchenkova et al., 2004, Nat. Neurosci.), found significant differences in spiking responses between trials with and without attentional cueing for visual and visuomotor neurons. No significant difference was found for motor neurons, consistent with our hypothesis that signals related to cognition and arousal are kept separate from saccade-related signals in the SC.

(20) The problem with Figure 5 and its related text is that the neurons with low motor index are additionally visual. So, of course, they can be modulated if the pupil diameter changes!

Addressed in Public Reviews, Reviewer 3, Point 2.  

(21) I had a hard time understanding Figure 6. 

See reply above, Public Reviews, Reviewer 1, Point 4.

(22) Line 586: these cells have more visual responses and will be affected by the amount of light entering the eye. 

Addressed in Public Reviews, Reviewer 3, Point 2.

Author response:

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

Reviewer #1 (Public Review)：

(1) Glycogen biosynthesis typically involves several enzymes. In this context, could the authors comment on the effect of overexpressing a single enzyme - especially a mutant version - on the structure or quality of the glycogen synthesized?

While quantitative molecular weight analysis of synthesized glycogen was not performed, we documented changes in glycogen particle morphology. GYSmut overexpression resulted in significantly enlarged singular glycogen granules, suggesting potential high molecular mass, while GYS-GYG co-overexpression in MSCs (GYG being the essential enzyme for glycogen synthesis initiation) produced a diffuse glycogen distribution pattern rather than particulate structures. We have incorporated this result as new Figure S2C.

These results suggest that overexpression of specific glycogen-metabolizing enzymes significantly influences glycogen structure. Consequently, targeted modulation of glycogen architecture and properties through key enzymes represents a potential avenue for future investigation.

(2) Regarding the in vitro starvation experiments (Figure 2C), what oxygen conditions (pO₂) were used? Are these conditions physiologically relevant and representative of the in vivo lung microenvironment?

Our in vitro starvation experiments (Figure 3C) were conducted under normoxic (21%). The oxygen concentration in human lungs is physiologically lower than atmospheric levels, with healthy individuals exhaling air containing approximately 16% oxygen (Thalakkotur Lazar Mathew, Diagnostics 2015). To our knowledge, direct measurements of alveolar oxygen concentration in pulmonary fibrosis are rare. Therefore, to evaluate the performance of GYSmut under hypoxic conditions, in the revised manuscript, Figure S2 has been augmented to include assessment of cell performance under combined hypoxia （oxygen concentration < 5%）and nutrient deprivation stress, which further corroborate the superiority of the GYSmut group over the control under different oxygen concentrations. 

(3) In the in vitro model, how many hours does it take for the intracellular glycogen reserve to be completely depleted under starvation conditions?

While quantitative cell viability data were recorded up to 72 hours post-implantation (Fig 3C), we observed cell viability at approximately 96 hours. We noticed that the presence of glycogen particles exhibited a correlation with sustained cell viability. However, reliable quantitative assessment of glycogen became increasingly challenging upon significant depletion of viable cells, thereby limiting our measurements during later time points.

(4) For the in vivo model, is there a quantitative analysis of the survival kinetics of the transplanted cells over time for each group? This would help to better assess the role and duration of glycogen stores as an energy buffer after implantation.

We tracked the in vivo distribution and persistence of implanted MSCs using enzymatic activity quantification assays (using Gluc luciferase assay) and live animal imaging (using Akaluc luciferase). The revised manuscript includes quantitative analysis of the in vivo fluorescence imaging data, which has been supplemented as Figure S4. Glycogen-engineered MSCs and control cells were quantitatively assessed at three discrete time points post-implantation. This quantification revealed a transient divergence in cell viability between the experimental and control groups around day 7. However, fluorescence in both cohorts subsequently declined to similar levels over the extended observation period.

(5) Finally, the study was performed in male mice only. Could sex differences exist in the efficacy or metabolism of the engineered MSCs? It would be helpful to discuss whether the approach could be expected to be similarly effective in female subjects.

We appreciate the reviewer’s important question regarding potential sex differences. Our study used male mice based on three key considerations: 1) Clinical Relevance: Idiopathic pulmonary fibrosis (IPF) shows significant male predominance, with diagnosis rates 3.5-fold higher in men (37.8% vs 10.6%, p<0.0001) and greater diagnostic confidence (Assayag et al., Thorax 2020). 2) Model Consistency: The bleomycin model (our chosen method) demonstrates more consistent fibrotic responses in male mice (Gul et al., BMC Pulm Med 2023). 3) Biological Rationale:

Estrogen’s protective effects in females may confound therapeutic assessments (cited in Assayag et al.).

We fully acknowledge this limitation and will include female subjects in subsequent translational studies. The therapeutic principle should theoretically apply to both sexes, but we agree this requires experimental validation.

(6) The number of mice for each group and time point should be specified.

The manuscript text has been revised to enhance clarity, and the number of mice for each group and time point has been specified (line 170 to 182).

Reviewer #2 (Public Review):

(4) Inconsistencies in In Vivo Data: There is a discrepancy between the number of animals shown in the figures and the graph (three individuals vs. five animals), as well as missing details on how luciferase signal intensity was quantified, requiring further clarification.

To assess MSC survival in vivo, we employed two strategies utilizing distinct luciferases optimized for specific detection modalities. MSC viability was quantified ex vivo through Gaussia luciferase (Gluc) activity, leveraging its high sensitivity and established commercial assay kits (n = 3 mice per group per time point). For non-invasive longitudinal tracking within living animals, MSC distribution and viability were monitored via in vivo bioluminescence imaging using Akaluc luciferase, selected for its superior tissue penetration and sensitivity in situ (n = 5 mice per group).The manuscript text has been revised to enhance clarity, and the experiment protocols for luciferase signal detection and quantification has been added into Methods.

（1) (2) (3) (5):

We fully agree that further investigation into the functional consequences of glycogen engineering in MSCs – encompassing core cellular functions, immunomodulatory properties, and associated signaling pathways – is important to fully elucidate the underlying mechanisms. Cellular metabolism is intrinsically intertwined with diverse physiological processes. Consequently, we believe that glycogen engineering exerts multifaceted effects on MSCs, likely extending beyond the modulation of any single specific pathway. Studying the metabolic perturbation induced by such engineering approaches in mammalian cells represents an interesting field. The exploration of these aspects remains an long-term research objective within our group.

Reviewer #2 (Recommendations for the authors):

(6) Clarification of Data in the Murine Model:

In Figure 4B, there is a discrepancy between the number of animals shown in the image (five) and those represented in the graph (three). This discrepancy needs clarification. Additionally, the study lacks information regarding the intensity of the signal in the luciferase assays. It is unclear how luciferase expression in the mice was quantified, and providing this detail would enhance the understanding of the data presented.

We sincerely appreciate these valuable suggestions. We have revised the relevant text for greater clarity. Figure 4B and Figure 4C present results from two distinct experimental approaches, each employing different luciferase reporters and measurement methodologies, and different num of mice were used in these two experiments.

Quantitative data derived from the in vivo bioluminescence imaging has been supplemented as Figure S4. The experiment protocols for luciferase signal detection and quantification has been added into Methods.

To other recommendations of reviewer 2：

We sincerely appreciate your valuable insights, which demonstrate your deep expertise. We fully agree that beyond nutrient availability, factors such as reactive oxygen species (ROS) and the immune microenvironment are also critical limitations affecting the survival and therapeutic efficacy of implanted MSCs.

We propose that glycogen engineering exerts broad effects on MSCs. These effects manifest as changes in multiple cellular characteristics, including proliferation, differentiation, surface marker expression, antioxidant capacity, and immunomodulatory activity – all crucial factors for the therapeutic purpose of MSCs.

We believe these changes likely involve complex networks of interconnected regulatory factors. The underlying mechanisms might be clarified through proteomic and metabolomic profiling.

However, comprehensively investigating these interconnected aspects requires significant time and resources. Some components of this research extend beyond the current scope of our project. Nevertheless, exploring these mechanisms remains an important objective, and we will actively work to investigate them further in our ongoing studies.

Author response

We would like to thank the editors and two reviewers for the assessment and the constructive feedback on our manuscript, “Toward Robust Neuroanatomical Normative Models: Influence of Sample Size and Covariates Distributions”. We appreciate the thorough reviews and believe the constructive suggestions will substantially strengthen the clarity and quality of our work. We plan to submit a revised version of the manuscript and a full point-by-point response addressing both the public reviews and the recommendations to the authors. 

Reviewer 1. 

In revision, we plan to address the reviewer’s comments by: (i) strengthen the interpretation of model fit through reporting the proportion of healthy controls within and outside the extreme percentile bounds; (ii) adding age-resolved overlays of model-derived percentile curves compared to those from the full reference cohort for key sample sizes and regions; (iii) quantifying age-distribution alignment between train and test set; and (iv) summarizing model performance as a joint function of age-distribution alignment and sample size.

Reviewer 2. 

In the revised manuscript, we will (i) expand the Discussion to more clearly outline the trade-offs between simple regression frameworks and hierarchical models for normative modeling (e.g., scalability, handling of multi-site variation, computational considerations), and discuss alternative approaches and harmonization as important directions for multi-site settings; (ii) contextualize OASIS-3 vs AIBL differences by quantifying train– test age-alignment across sampling strategies and emphasize that skewness should be interpreted relative to the target cohort’s alignment rather than absolute numbers. (iii) reassess sex-imbalance effects by reporting expected age distributions per condition and re-evaluate sex effects while controlling for age; (iv) investigate the apparent dip at n≈300 dip by increasing sub-sampling seeds, testing neighboring sample sizes, and using an alternative age-binning scheme to clarify the observed artifact; (v) clarify potential divergence between tOC separation and global fit under discrepancies in demographic distributions and relate tOC to age-alignment distance; (vi)  reframe the sample-size guidance in terms of distributional alignment rather than an absolute n.

Author response:

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

Reviewer #1 (Public review): 

Summary:

The authors describe the degradation of an intrinsically disordered transcription factor (LMO2) via PROTACs (VHL and CRBN) in T-ALL cells. Given the challenges of drugging transcription factors, I find the work solid and a significant scientific contribution to the field. 

Strengths: 

(1) Validation of LMO2 degradation by starting with biodegraders, then progressing to chemical degrades. 

(2)interrogation of the biology and downstream pathways upon LMO2 degradation (collateral degradation §

(3) Cell line models that are dependent/overexpression of LMO2 vs LMO2 null cell lines. 

(4) CRBN and VHL-derived PROTACs were synthesized and evaluated. 

Weaknesses: 

(1) The conventional method used to characterize PROTACs in the literature is to calculate the DC50 and Dmax of the degraders, I did not find this information in the manuscript. 

As noted in the reply to referee’s point 4 below, our first generation compounds are not highly potent. The DC₅₀ values have been computed specifically using Western blot reflected in the data shown in Fig. 2. The revised version Supplementary Fig. S3 shows these quantified Western blot data from a time course of treating KOPT-K1 cells with either Abd-CRBN and Abd-VHL, where the 24 hour blot data are shown in Figure 2, G and E, and the quantified data from each 24 hour treatment are quantified in Supplementary Fig. S3). With these data, the DC₅₀ values 9 μM for Abd-CRBN and 15 μM Abd-VHL), included in in the main text and the Supplementary Fig. S3 figure legend.

In addition, the loss of signal of the LMO2-Rluc reporter protein from PROTAC treated cells shown in Fig. 2M has been used to calculate a half-point of degradation; although strictly not DC₅₀, as it measures a reporter protein, this yielded values are 10 μM for Abd-CRBN and 9 μM Abd-VHL. 

(2) The proteomics data is not very convincing, and it is not clear why LMO2 does not show in the volcano plot (were higher concentrations of the PROTAC tested? and why only VHL was tested and not CRBN-based PROTAC?).

Due to the relatively small size of the LMO2 protein, it is challenging to produce enough unique peptides for reliable identification, especially to distinguish some proteins in the LMO2 complex.  

(3) The correlation between degradation potency and cell growth is not well-established (compare Figure 4C: P12-Ichikawa blots show great degradation at 24 and 48 hrs, but it is unclear if the cell growth in this cell line is any better than in PF-382 or MOLT-16) - Can the authors comment on the correlation between degradation and cell growth?  

In this study (Fig. 4) we did not aim to compare the effect of LMO2 loss on cell growth among LMO2 positive cells. Rather, we aimed to evaluate the LMO2 importance for cell growth in LMO2-expressing T-ALL cells compared to non-expressing cells and to correlate the loss of the protein with this effect on the cell growth. In addition, the treatment of cells with the LMO2 compounds did now show an effect to LMO2 negative cells until at least 48 hours of treatment indicating that low toxicity of our PROTAC compounds and providing correlation between LMO2 loss and cell growth. 

(4) The PROTACs are not very potent (double-digit micromolar range?) - can the authors elaborate on any challenges in the optimization of the degradation potency? 

The Abd methodology to use intracellular domain antibodies to screen for compounds that bind to intrinsically disordered proteins such as the LMO2 transcription factors offers a tractable approach to hard drug targets but, in so doing, creates challenging factors to improve the potency that are not the same as those targets for which structural data are available. LMO2 is an intrinsically disordered protein, for which soluble recombinant protein is not readily available to identify the binding pocket of compounds. The potency has so far been optimized solely based on the different moieties substituted in cell-based SAR studies (http://advances.sciencemag.org/cgi/content/full/7/15/eabg1950/DC1) and all new compounds were tested with BRET assays. Thus, currently optimization of the degradation potency (including properties such as improved solubility) for the LMO2-binding compounds relies on chemical modification the three areas of the compounds indicated in Fig. 2 B,C.  

(5) The authors mentioned trying six iDAb-E3 ligase proteins; I would recommend listing the E3 ligases tried and commenting on the results in the main text. 

The six chimaeric iDAb-E3 ligase proteins involved one anti-LMO2 iDAb and three different E3 ligase where either fused at the N- or the C-terminus of the VH (giving six protein formats). These six fusion proteins were described in the text referring to the degrader studies described in Supplementary Fig. 1. 

Reviewer #2 (Public review): 

Summary: 

Sereesongsaeng et al. aimed to develop degraders for LMO2, an intrinsically disordered transcription factor activated by chromosomal translocation in T-ALL. The authors first focused on developing biodegraders, which are fusions of an anti-LMO2 intracellular domain antibody (iDAb) with cereblon. Following demonstrations of degradation and collateral degradation of associated proteins with biodegraders, the authors proceeded to develop PROTACs using antibody paratopes (Abd) that recruit VHL (Abd-VHL) or cereblon (Abd-CRBN). The authors show dose-dependent degradation of LMO2 in LMO2+ T-ALL cell lines, as well as concomitant dose-dependent degradation of associated bHLH proteins in the DNA-binding complex. LMO2 degradation via Abd-VHL was also determined to inhibit proliferation and induce apoptosis in LMO2+ T-ALL cell lines. 

Strengths: 

The topic of degrader development for intrinsically disordered proteins is of high interest, and the authors aimed to tackle a difficult drug target. The authors evaluated methods, including the development of biodegraders, as well as PROTACs that recruit two different E3 ligases. The study includes important chemical control experiments, as well as proteomic profiling to evaluate selectivity. 

Weaknesses: 

The overall degradation is relatively weak, and the mechanism of potential collateral degradation is not thoroughly evaluated

The purpose of the study was to evaluate effects of LMO2 degraders. The mechanism of the observed collateral degradation could not be investigated directly within the scope of our study. In the main text, discussed two possible, not exclusive, explanations. One being that our work (and previously published, cited work) indicates that the DNA-binding bHLH proteins have relatively short half file (Supplementary Fig. S12) and may therefore be subject to normal turnover when the LMO2, which is in the complex, turns over. Further, the known structure of the LMO2-bHLH interactions (from Omari et al, doi: 10.1016/j.celrep.2013.06.008) was also examined for the location of lysines in the TAL1 & E47 partners (Supplementary Fig. S11). It is possible that their local association with the LMO2-E3-ligase complex created by the PROTAC interaction, could cause their concurrent degradation. Mutagenesis and structural analysis would be needed to establish this point.

In addition, experiments comparing the authors' prior work with their anti-LMO2 iDAb or Abl-L are lacking, which would improve our understanding of the potential advantages of a degrader strategy for LMO2.  

A major motivation behind developing the Antibody-derived (Abd) method to select compounds, which are surrogates of the antibody paratope, is because using iDAbs directly as inhibitors requires the development of delivery technologies for these macromolecules, as protein directly or as vectors or mRNA for their expression. Ultimately, high affinity anti-LMO2 iDAbs should directly be used as tractable inhibitors when delivery methods redeveloped. In the meantime, Abd compounds were envisaged as being surrogates suitable for development into reagents, and potentially drugs, by medicinal chemistry. We evaluated selected first generation LMO2-binding Abd compounds previously, finding their ability to interfere with LMO2-iDAb BRET signal to EC_max about 50% but these compounds do not have potency to have an effect on the interaction of LMO2 with a non-mutated iDAb (nM affinity). These data indicated that efficacy improvement for the PROTACs was needed. In addition, in the current study, we observed viability effects in T-ALL lines at high concentrations (20 μM) irrespective of LMO2 expression (Supplementary Fig. S 2A, B) These data indicated that efficacy improvement was needed and potentially converting the degraders (PROTACs) would add to in-cell potency. By adding the E3 ligase ligands, we found the toxicity of non-LMO2 expressing Jurkat was significantly reduced (Supplementary Fig. S 2E, F). 

Reviewer #2 (Recommendations for the authors): 

Suggestions for additional experiments: 

(1) The data presented is primarily focused on demonstrating targeted degradation of LMO2, with a focus on phenotypes such as proliferation and apoptosis. In this manuscript, there are limited comparative evaluations of anti-LMO2 iDAb or Abl-L to show the potential benefits of a degrader approach to their previously described work, as well as why targeted degradation is in fact, advantageous. For example, the authors' previous work has shown that anti-LMO2 iDAb inhibits tumor growth in a mouse transplantation model. Comparisons in vitro would be supportive of the importance of continued degrader optimization/development.  

we have previously shown that an anti-LMO2 scFv inhibits tumour growth in a mouse model but this work used an expressed scFv antibody that binds to LMO2 in nM range. The Abd compounds are much lower potency that the antibody and, because recombinant LMO2 is difficult to work with, we could only evaluate interactions of compounds with LMO2 in cell-based assays like BRET (LMO2-iDAb BRET). In this cell-based assay, the first generation Abd compounds do not have sufficient potency to block LMO2-iDAb interaction unless the affinity of the iDAb is reduced to sub-μM. The justification for proceeding on the degrader process rather than just using the protein-protein interaction (PPI) inhibition was based largely around the low potency of the first generation PPI compounds in cell assays and that incorporation protein degradation with PPI inhibition would enhance the efficacy.

In addition, the viability experiments are also very short-term; is there a reason why the authors did not carry out these experiments for 3-5 days to fully understand the impacts on proliferation? 

In Supplementary Fig. S5, we did show assays up to 3 days. In KOPT-K1 (LMO2+), the LMO2 levels were reduced during the time course of this assay (from a single compound dose at time zero) (Supplementary Fig S 5A, B). We also show CellTitreGlo assays up to 3 days and, with these second generation compounds, we observed sustained effects on KOPT-K1 (LMO2+) but low non-DMSO toxicity in Jurkat (LMO2-) (revised version Supplementary (Fig S5 C, D).

(2) The potential mechanism of collateral degradation is interesting and important in evaluating the on-target responses and consequences of degrading LMO2. At this time, the data supporting collateral degradation is limited and would be strengthened by showing that it is not due to a change in mRNA levels and not due to complex dissociation. Overall, the kinetics and depth of loss of complex members such as E47 in Figure 3 appear more substantial than LMO2 itself, and as presented, collateral degradation is not effectively demonstrated. In addition, to aid in the readers' assessments, additional background and references around the roles of TAL1 and E47 would be helpful. For example, structurally, where do they (and other associated proteins that are not degraded) fit in the complex? 

We have responded above in relation to the Public Review Comments and note that a structure of the complex was in submitted version (now revised version Supplementary Fig. S11). 

(3) In Figure 1A, the blots show decreased levels of endogenous CRBN with iDAB-CRBN. Is this a known consequence of this approach in these cell lines? Does the partial recovery of endogenous CRBN in KOPTK1 cells have any indication of iDAB-CRBN levels? 

We cannot be sure why the endogenous level of CRBN decreases in doxycycline treated cells. It has been shown (DOI:10.1371/journal.pone.0064561) that doxycycline used in the inducible expression system (and its derivatives), such as the lentivirus we used, has an effect to gene expression patterns, which can be increase or decrease expression. Although the published study did not examine CRBN expression, the effect might explain the CRBN expression decrease on doxycycline addition and remains the same level after that. 

(4) In Figure S7, the authors do not fully explain the results and why there is minimal rescue with epoxomicin (S7A) or MLN4924 (S7J). This could indicate an alternative mechanism of degradation and loss at play, given the lack of rescue. Can the authors comment on this discrepancy, and have they looked autophagy inhibitor or other agents to achieve the chemical rescue? 

In the experiments such as in revised version Supplementary Fig. S6, we used KOPT-K1 cells with a single concentration of the inhibitors and the cells may less susceptible to the epoxomicin (0.8 μM) but lenalidomide and free thalidomide restored the LMO2 levels fully. In the main text Fig. 3D, we also showed that including epoxomicin and thalidomide with the Abd-CRBN in KOPT-K1 and CCRF-CEM restore LMO2 levels, supporting the conclusion that the main mechanism of degradation is through ubiquitination proteosomal route.

(5) For the proteomics data, it would be helpful to have the proteins in yellow highlighted to have them noted in 5D and 5E. In addition, can the authors comment on why LMO2 or their collateral targets are not confirmed in the table? Furthermore, 5C is difficult to interpret; if there are no significantly changing proteins in the Jurkat cells, why are there pathways that are identified? 

As mentioned in reply to referee 1, due to the relatively small size of the LMO2 protein, it is challenging to produce enough unique peptides for reliable identification, especially to distinguish some proteins in the LMO2 complex where expression levels are low.

Author response

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

Reviewer #1 (Public Review):

Confirmation of daf-7::GFP data and inheritance beyond F2

Reviewer suggested confirming daf-7::GFP molecular marker data and testing inheritance beyond the F2 generation to further strengthen the findings.

We agree these experiments would provide valuable mechanistic insights into the molecular basis of transgenerational inheritance. However, our study was specifically designed as a reproducibility study focusing on the central controversy regarding F2 inheritance (Gainey et al. vs. Murphy lab findings). The daf-7::GFP molecular marker experiments, while important for understanding mechanisms, represent a different research question requiring extensive additional resources and expertise beyond the scope of this validation study. Our primary goal was to provide independent confirmation of the disputed F2 inheritance using standardized behavioral assays. It is our hope that future work will pursue these important mechanistic validations.

"Exhaustive attempts" language

Reviewer disagreed with characterizing Gainey et al.'s efforts as "exhaustive attempts" since they modified the original protocol.

We revised this statement in the Results and Discussion to more accurately reflect the experimental situation: "In contrast, Gainey et al. (2025), representing the Hunter group, reported that while parental and F1 avoidance behaviors were evident, transgenerational inheritance was not reliably observed beyond the F1 generation under their experimental conditions."

Importance of sodium azide

Reviewer suggested including more discussion about the recent findings on the importance of sodium azide in the assay, referencing the Murphy group's response paper.

We have prominently highlighted the critical role of sodium azide in our Introduction with strengthened language that emphasizes its importance for resolving the scientific controversy: "Critically, Kaletsky et al. (2025) demonstrated that omission of sodium azide during scoring can completely abolish detection of inherited avoidance, revealing that this key methodological difference may explain the conflicting results between laboratories. The use of sodium azide to immobilize worms at the moment of initial bacterial choice appears essential for capturing the inherited behavioral response. These findings highlight how seemingly minor methodological variations can dramatically impact detection of transgenerational inheritance and underscore the need for independent replication using standardized protocols."

Protocol fidelity statement

Reviewer requested a more direct statement clarifying that we followed the Murphy group protocol, noting that we made some modifications.

We followed the core Murphy lab protocol with two evidence-based optimizations that preserve the essential experimental elements: 1) We used 400 mM sodium azide instead of 1 M based on preliminary data showing the higher concentration caused premature paralysis before worms could make behavioral choices, and 2) We used liquid NGM buffer instead of M9 to maintain chemical consistency with the solid NGM plates used for worm culture, minimizing potential osmotic stress. These modifications improved experimental reliability while maintaining the critical components: sodium azide immobilization, bacterial lawn density standardization (OD₆₀₀ = 1.0), and synchronized scoring conditions that are essential for detecting inherited avoidance.

Overstated dilution claim

Reviewer noted that the statement about "gradual decrease" in avoidance strength was overstated and didn't reflect the actual data presented in the manuscript.

We removed this statement.

Environmental variables phrasing

Reviewer found the sentence about environmental variables unclear, noting that Gainey et al. didn't actually acknowledge variability but saw it as indicating error or stochastic processes.

We refined this statement for greater precision and clarity: "This underscores the assay's sensitivity to environmental variables, such as synchronization method and bacterial lawn density. This highlights the importance of consistency across experimental setups and support the view that context-dependent variation may underlie previously reported discrepancies."

Reviewer #2 (Public Review):

Reagent sourcing

Reviewer suggested listing the sources of media ingredients with company names and catalog numbers, as this might be important for reproducibility.

To ensure complete reproducibility, we created a comprehensive Table S3 listing all reagents, suppliers, and catalog numbers used in our experiments. This detailed information enables exact replication of our experimental conditions and addresses potential variability that might arise from different reagent sources between laboratories.

Reviewer #3 (Public Review):

Raw data transparency

Reviewer noted that while a spreadsheet with choice assay results was provided, the individual raw data from assays was not included, which would be helpful for assessing sample sizes.

We now provide complete experimental transparency through Table S2, which contains individual choice indices from all 138 assays conducted across four independent trials. This comprehensive dataset allows full assessment of our experimental outcomes, statistical robustness, and reproducibility while enabling other researchers to perform independent statistical analyses.

F1/F2 assay disparity

Reviewer questioned whether the higher number of F2 assays compared to F1 represented truly independent assays, asking if multiple F2 assays were performed from offspring of one F1 plate (which would not represent independent assays).

We clarified this important statistical consideration in Methods (Transgenerational Testing): "Each behavioral assay was conducted using animals from a biologically independent growth plate. While F2 plates were derived from pooled embryos from multiple F1 parents, each assay represents an independent biological replicate with no reuse of animals across assays. F2 assays (n=45) exceeded F1 assays (n=20) due to PA14-induced fecundity reduction in trained worms, limiting the number of viable F1 progeny. The higher number of F2 assays reflects the greater reproductive success of healthy F1 animals and provides additional statistical power for population-level behavioral comparisons." We also enhanced our Controls section to clarify that "Our experimental design employed population-level comparisons across generations using unpaired statistical analyses, with no attempt to track individual lineages across generations."

Methodological variations overstatement

Reviewer felt the Introduction overstated the findings by suggesting the authors "address potential methodological variations," when they only used one assay setup throughout.

We have corrected the Introduction to accurately reflect our study design and scope: "Here, we adapted the protocol established by the Murphy group, maintaining the critical use of sodium azide to paralyze worms at the time of choice, to test whether parental exposure to PA14 elicits consistent avoidance in subsequent generations. Our study specifically focuses on the transmission of learned avoidance through the F2 generation, beyond the intergenerational (F1) effect, because this is where divergence between published studies begins."

Reviewer #1 (Recommendations for the authors):

Worm numbers

Reviewer noted that information about the number of worms used should be included in the training and choice assay methods section rather than separated.

We clarified worm numbers and sample sizes in the Methods (Controls and Additional Considerations): "Each individual assay averaged 62 ± 43 animals (range: 15-150 worms per assay), with a total of 138 assays conducted across four independent experimental trials. The variation in worm numbers per assay reflects natural variation in worm recovery and immobilization efficiency during choice assays. We conducted an average of 8.5 assays per condition during each of the four replicates."

Figure 1 legend and consistency

Reviewer identified several issues: inconsistent terminology ("treated" vs "trained"), incorrect statistical test naming, missing p-value annotations, and need for consistency between figure and legend. We have systematically addressed all figure consistency and statistical annotation issues:

Replaced inconsistent "treated" terminology with "trained" throughout

Corrected the statistical test description to accurately reflect our analysis: "Kruskal-Wallis oneway ANOVA followed by Dunn's post hoc" which properly corresponds to the statistical tests detailed in Table S1

Added explicit p-value annotations in the figure legend: "*p<0.05, **p<0.01 means and SEM shown (see Table S1 for statistics and Table S2 for raw data)"

Ensured consistent terminology between figure and legend

NGM vs. M9 buffer

Reviewer questioned whether we used NGM buffer or M9 buffer for washing steps, noting that NGM isn't usually referred to as "buffer."

We have prominently featured and thoroughly clarified our rationale for using liquid NGM buffer in the Methods (Synchronization of Worms section). The explanation now appears upfront in the methods: "We used liquid NGM buffer instead of M9 buffer (as specified in the original Murphy protocol) to maintain chemical consistency with the solid NGM culture plates. This modification minimizes potential osmotic stress since liquid NGM matches the pH (6.0) and ionic composition of the growth medium, whereas M9 buffer has a different pH (7.0) and ionic profile." We provide detailed chemical differences and explain that this modification maintains consistency with culture conditions while preserving essential experimental procedures.

Grammar/typos

Reviewer noted that the manuscript needed thorough proofreading to address grammatical errors and typographical mistakes.

We have conducted comprehensive proofreading and editing throughout the manuscript to resolve grammatical and typographical errors. Specific improvements include: clarified sentence structure in the Introduction and Results sections, corrected technical terminology consistency, improved figure legend clarity, and enhanced overall readability while maintaining scientific precision.

Sodium azide concentration

Reviewer noted that our sodium azide concentration differed from the Moore paper and requested comment on this difference.

We have included explicit justification for our sodium azide concentration choice in the Methods (Training and Choice Assay): "We used 400 mM sodium azide rather than the 1 M concentration reported by Moore et al. (2019) because preliminary trials showed that higher concentrations caused premature paralysis before worms could reach either bacterial spot, potentially biasing choice measurements. The 400 mM concentration provided sufficient immobilization while preserving the behavioral choice window."

Reviewer #2 (Recommendations for the authors):

Comparative reagent analysis

Reviewer suggested creating a supplemental table comparing reagent sources between our study, Gainey et al., and Murphy et al., proposing that media ingredient differences might explain the discrepancies.

While direct reagent comparison between laboratories was beyond the scope of this validation study, we recognize this as an important consideration for understanding experimental variability. Our comprehensive reagent sourcing information (Table S3) provides the foundation for future comparative studies. We encourage collaborative efforts to systematically compare reagent sources across laboratories, as media component differences could contribute to the experimental variability observed between research groups. Such analyses would be valuable for establishing standardized protocols across the field.

Conclusion

We hope that these revisions satisfactorily address the reviewers’ concerns. We believe these improvements significantly strengthened the manuscript's contribution to resolving this important scientific controversy.

We thank the reviewers again for their invaluable insights and constructive feedback, which have substantially improved the quality and impact of our work.

Author response:

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

Reviewer #1 (Public review):

The authors present MerQuaCo, a computational tool that fills a critical gap in the field of spatial transcriptomics: the absence of standardized quality control (QC) tools for image-based datasets. Spatial transcriptomics is an emerging field where datasets are often imperfect, and current practices lack systematic methods to quantify and address these imperfections. MerQuaCo offers an objective and reproducible framework to evaluate issues like data loss, transcript detection variability, and efficiency differences across imaging planes.

Strengths:

(1) The study draws on an impressive dataset comprising 641 mouse brain sections collected on the Vizgen MERSCOPE platform over two years. This scale ensures that the documented imperfections are not isolated or anecdotal but represent systemic challenges in spatial transcriptomics. The variability observed across this large dataset underscores the importance of using sufficiently large sample sizes when benchmarking different image-based spatial technologies. Smaller datasets risk producing misleading results by over-representing unusually successful or unsuccessful experiments. This comprehensive dataset not only highlights systemic challenges in spatial transcriptomics but also provides a robust foundation for evaluating MerQuaCo's metrics. The study sets a valuable precedent for future quality assessment and benchmarking efforts as the field continues to evolve.

(2) MerQuaCo introduces thoughtful metrics and filters that address a wide range of quality control needs. These include pixel classification, transcript density, and detection efficiency across both x-y axes (periodicity) and z-planes (p6/p0 ratio). The tool also effectively quantifies data loss due to dropped images, providing tangible metrics for researchers to evaluate and standardize their data. Additionally, the authors' decision to include examples of imperfections detectable by visual inspection but not flagged by MerQuaCo reflects a transparent and balanced assessment of the tool's current capabilities.

Weaknesses:

(1) The study focuses on cell-type label changes as the main downstream impact of imperfections. Broadening the scope to explore expression response changes of downstream analyses would offer a more complete picture of the biological consequences of these imperfections and enhance the utility of the tool.

Here, we focused on the consequences of imperfections on cell-type labels, one common use for spatial transcriptomics datasets. Spatial datasets are used for so many other purposes that there are almost endless ways in which imperfections could impact downstream analyses. It is difficult to see how we might broaden the scope to include more downstream effects, while providing enough analysis to derive meaningful conclusions, all within the scope of a single paper. Existing studies bring some insight into the impact of imperfections and we expect future studies will extend our understanding of consequences in other biological contexts.

(2) While the manuscript identifies and quantifies imperfections effectively, it does not propose post-imaging data processing solutions to correct these issues, aside from the exclusion of problematic sections or transcript species. While this is understandable given the study is aimed at the highest quality atlas effort, many researchers don't need that level of quality to compare groups. It would be important to include discussion points as to how those cut-offs should be decided for a specific study.

Studies differ greatly in their aims and, as a result, the impact of imperfections in the underlying data will differ also, preventing us from offering meaningful guidance on how cut-offs might best be identified. Rather, our aim with MerQuaCo was to provide researchers with tools to generate information on their spatial datasets, to facilitate downstream decisions on data inclusion and cut-offs.

(3) Although the authors demonstrate the applicability of MerQuaCo on a large MERFISH dataset, and the limited number of sections from other platforms, it would be helpful to describe its limitations in its generalizability.

In figure 9, we addressed the limitations and generalizability of MerQuaCo as best we could with the available datasets. Gaining deep insight into the limitations and generalizability of MerQuaCo would require application to multiple large datasets and, to the best of our knowledge, these datasets are not available.

Reviewer #2 (Public review):

The authors present MerQuaCo, a computational tool for quality control in image-based spatial transcriptomic, especially MERSCOPE. They assessed MerQuaCo on 641 slides that are produced in their institute in terms of the ratio of imperfection, transcript density, and variations of quality by different planes (x-axis).

Strengths:

This looks to be a valuable work that can be a good guideline of quality control in future spatial transcriptomics. A well-controlled spatial transcriptomics dataset is also important for the downstream analysis.

Weaknesses:

The results section needs to be more structured.

We have split the ‘Transcript density’ subsection of the results into 3 new subsections.

Reviewer #3 (Public review):

MerQuaCo is an open-source computational tool developed for quality control in imagebased spatial transcriptomics data, with a primary focus on data generated by the Vizgen MERSCOPE platform. The authors analyzed a substantial dataset of 641 freshfrozen adult mouse brain sections to identify and quantify common imperfections, aiming to replace manual quality assessment with an automated, objective approach, providing standardized data integrity measures for spatial transcriptomics experiments.

Strengths:

The manuscript's strengths lie in its timely utility, rigorous empirical validation, and practical contributions to methodology and biological discovery in spatial transcriptomics.

Weaknesses:

While MerQuaCo demonstrates utility in large datasets and cross-platform potential, its generalizability and validation require expansion, particularly for non-MERSCOPE platforms and real-world biological impact.

We agree that there is value in expanding our analyses to non-Merscope platforms, to tissues other than brain, and to analyses other than cell typing. The limiting factor in all these directions is the availability of large enough datasets to probe the limits of MerQuaCo. We look forward to a future in which more datasets are available and it’s possible to extend our analyses

Reviewer #1(Recommendation for the Author):

(1) To better capture the downstream impacts of imperfections, consider extending the analysis to additional metrics, such as specificity variation across cell types, gene coexpression, or spatial gene patterning. This would deepen insights into how these imperfections shape biological interpretations and further demonstrate the versatility of MerQuaCo.

These are compelling ideas, but we are unable to study so many possible downstream impacts in sufficient depth in a single study. Insights into these topics will likely come from future studies.

(2) In Figure 7 legend, panel label (D) is repeated thus panels E-F are mislabelled. 

We have corrected this error.

(3) Ensure that the image quality is high for the figures. 

We will upload Illustrator files, ensuring that images are at full resolution.

Reviewer #2 (Recommendation for the Author):

(1) A result subsection "Transcript density" looks too long. Please provide a subsection heading for each figure. 

We have split this section into 3 with new subheadings.

(2) The result subsection title "Transcript density" sounds ambiguous. Please provide a detailed title describing what information this subsection contains. 

We have renamed this section ‘Differences in transcript density between MERSCOPE experiments’.

Minor: 

(1) There is no explanation of the black and grey bars in Figure 2A.

We have added information to the figure legend, identifying the datasets underlying the grey and black bars.

(2) In the abstract, the phrase "High-dimension" should be "High-dimensional". 

We have changed ‘high-dimension’ to ‘high-dimensional’.

(3) In the abstract, "Spatial results" is an unclear expression. What does it stand for? 

We have replaced the term ‘spatial results’ with ‘the outputs of spatial transcriptomics platforms’.

Reviewer #3 (Recommendation for the Author):

(1) While the tool claims broad applicability, validation is heavily centered on MERSCOPE data, with limited testing on other platforms. The authors should expand validation to include more diverse platforms and add a small analysis of non-brain tissue. If broader validation isn't feasible, modify the title and abstract to reflect the focus on the mouse brain explicitly.

We agree that expansion to other platforms is desirable, but to the best of our knowledge sufficient datasets from other platforms are not available. In the abstract, we state that ‘… we describe imperfections in a dataset of 641 fresh-frozen adult mouse brain sections collected using the Vizgen MERSCOPE.’

(2) The impact of data imperfections on downstream analysis needs a more comprehensive evaluation. The authors should expand beyond cluster label changes to include a) differential expression analysis with simulated imperfections, b) impact on spatial statistics and pattern detection, and c) effects on cell-cell interactions. 

Each of these ideas could support a substantial study. We are unable to do them justice in the limited space available as an addition to the current study.

(3) The pixel classification workflow and validation process need more detailed documentation. 

The methods and results together describe the workflow and validation in depth. We are unclear what details are missing.

(4) The manuscript lacks comparison to existing. QC pipelines such as Squidpy and Giotto. The authors should benchmark MerQuaCo against them and provide integration options with popular spatial analysis tools with clear documentation.

To the best of our knowledge, Squidpy and Giotto lack QC benchmarks, certainly of the parameters characterized by MerQuaCo. Direct comparison isn’t possible.

Author response:

The following is the authors’ response to the original reviews

General Statements:

In our manuscript, we demonstrate for the first time that RNA Polymerase I (Pol I) can prematurely release nascent transcripts at the 5' end of ribosomal DNA transcription units in vivo. This achievement was made possible by comparing wild-type Pol I with a mutant form of Pol I, hereafter called SuperPol previously isolated in our lab (Darrière at al., 2019). By combining in vivo analysis of rRNA synthesis (using pulse-labelling of nascent transcript and cross-linking of nascent transcript - CRAC) with in vitro analysis, we could show that Superpol reduced premature transcript release due to altered elongation dynamics and reduced RNA cleavage activity. Such premature release could reflect regulatory mechanisms controlling rRNA synthesis. Importantly, This increased processivity of SuperPol is correlated with resistance with BMH-21, a novel anticancer drugs inhibiting Pol I, showing the relevance of targeting Pol I during transcriptional pauses to kill cancer cells. This work offers critical insights into Pol I dynamics, rRNA transcription regulation, and implications for cancer therapeutics.

We sincerely thank the three reviewers for their insightful comments and recognition of the strengths and weaknesses of our study. Their acknowledgment of our rigorous methodology, the relevance of our findings on rRNA transcription regulation, and the significant enzymatic properties of the SuperPol mutant is highly appreciated. We are particularly grateful for their appreciation of the potential scientific impact of this work. Additionally, we value the reviewer’s suggestion that this article could address a broad scientific community, including in transcription biology and cancer therapy research. These encouraging remarks motivate us to refine and expand upon our findings further.

All three reviewers acknowledged the increased processivity of SuperPol compared to its wildtype counterpart. However, two out of three questions our claims that premature termination of transcription can regulate ribosomal RNA transcription. This conclusion is based on SuperPol mutant increasing rRNA production. Proving that modulation of early transcription termination is used to regulate rRNA production under physiological conditions is beyond the scope of this study. Therefore, we propose to change the title of this manuscript to focus on what we have unambiguously demonstrated:

“Ribosomal RNA synthesis by RNA polymerase I is subjected to premature termination of transcription”.

Reviewer 1 main criticisms centers on the use of the CRAC technique in our study. While we address this point in detail below, we would like to emphasize that, although we agree with the reviewer’s comments regarding its application to Pol II studies, by limiting contamination with mature rRNA, CRAC remains the only suitable method for studying Pol I elongation over the entire transcription units. All other methods are massively contaminated with fragments of mature RNA which prevents any quantitative analysis of read distribution within rDNA.  This perspective is widely accepted within the Pol I research community, as CRAC provides a robust approach to capturing transcriptional dynamics specific to Pol I activity. 

We hope that these findings will resonate with the readership of your journal and contribute significantly to advancing discussions in transcription biology and related fields.

Description of the planned revisions:

Despite numerous text modification (see below), we agree that one major point of discussion is the consequence of increased processivity in SuperPol mutant on the “quality” of produced rRNA. Reviewer 3 suggested comparisons with other processive alleles, such as the rpb1-E1103G mutant of the RNAPII subunit (Malagon et al., 2006). This comparison has already been addressed by the Schneider lab (Viktorovskaya OV, Cell Rep., 2013 - PMID: 23994471), which explored Pol II (rpb1-E1103G) and Pol I (rpa190-E1224G). The rpa190-E1224G mutant revealed enhanced pausing in vitro, highlighting key differences between Pol I and Pol II catalytic ratelimiting steps (see David Schneider's review on this topic for further details).

Reviewer 2 and 3 suggested that a decreased efficiency of cleavage upon backtracking might imply an increased error rate in SuperPol compared to the wild-type enzyme. Pol I mutant with decreased rRNA cleavage have been characterized previously, and resulted in increased errorrate. We already started to address this point. Preliminary results from in vitro experiments suggest that SuperPol mutants exhibit an elevated error rate during transcription. However, these findings remain preliminary and require further experimental validation to confirm their reproducibility and robustness. We propose to consolidate these data and incorporate into the manuscript to address this question comprehensively. This could provide valuable insights into the mechanistic differences between SuperPol and the wild-type enzyme. SuperPol is the first pol I mutant described with an increased processivity in vitro and in vivo, and we agree that this might be at the cost of a decreased fidelity.

Regulatory aspect of the process:

To address the reviewer’s remarks, we propose to test our model by performing experiments that would evaluate PTT levels in Pol I mutant’s or under different growth conditions. These experiments would provide crucial data to support our model, which suggests that PTT is a regulatory element of Pol I transcription. By demonstrating how PTT varies with environmental factors, we aim to strengthen the hypothesis that premature termination plays an important role in regulating Pol I activity.

We propose revising the title and conclusions of the manuscript. The updated version will better reflect the study's focus and temper claims regarding the regulatory aspects of termination events, while maintaining the value of our proposed model.

Description of the revisions that have already been incorporated in the transferred manuscript:

Some very important modifications have now been incorporated:

Statistical Analyses and CRAC Replicates:

Unlike reviewers 2 and 3, reviewer 1 suggests that we did not analyze the results statistically. In fact, the CRAC analyses were conducted in biological triplicate, ensuring robustness and reproducibility. The statistical analyses are presented in Figure 2C, which highlights significant findings supporting the fact WT Pol I and SuperPol distribution profiles are different. We CRAC replicates exhibit a high correlation and we confirmed significant effect in each region of interest (5’ETS, 18S.2, 25S.1 and 3’ ETS, Figure 1) to confirm consistency across experiments. We finally took care not to overinterpret the results, maintaining a rigorous and cautious approach in our analysis to ensure accurate conclusions.

CRAC vs. Net-seq:

Reviewer 1 ask to comment differences between CRAC and Net-seq. Both methods complement each other but serve different purposes depending on the biological question on the context of transcription analysis. Net-seq has originally been designed for Pol II analysis. It captures nascent RNAs but does not eliminate mature ribosomal RNAs (rRNAs), leading to high levels of contamination. While this is manageable for Pol II analysis (in silico elimination of reads corresponding to rRNAs), it poses a significant problem for Pol I due to the dominance of rRNAs (60% of total RNAs in yeast), which share sequences with nascent Pol I transcripts. As a result, large Net-seq peaks are observed at mature rRNA extremities (Clarke 2018, Jacobs 2022). This limits the interpretation of the results to the short lived pre-rRNA species. In contrast, CRAC has been specifically adapted by the laboratory of David Tollervey to map Pol I distribution while minimizing contamination from mature rRNAs (The CRAC protocol used exclusively recovers RNAs with 3′ hydroxyl groups that represent endogenous 3′ ends of nascent transcripts, thus removing RNAs with 3’-Phosphate, found in mature rRNAs). This makes CRAC more suitable for studying Pol I transcription, including polymerase pausing and distribution along rDNA, providing quantitative dataset for the entire rDNA gene.

CRAC vs. Other Methods:

Reviewer 1 suggests using GRO-seq or TT-seq, but the experiments in Figure 2 aim to assess the distribution profile of Pol I along the rDNA, which requires a method optimized for this specific purpose. While GRO-seq and TT-seq are excellent for measuring RNA synthesis and cotranscriptional processing, they rely on Sarkosyl treatment to permeabilize cellular and nuclear membranes. Sarkosyl is known to artificially induces polymerase pausing and inhibits RNase activities which are involved in the process. To avoid these artifacts, CRAC analysis is a direct and fully in vivo approach. In CRAC experiment, cells are grown exponentially in rich media and arrested via rapid cross-linking, providing precise and artifact-free data on Pol I activity and pausing.

Pol I ChIP Signal Comparison:

The ChIP experiments previously published in Darrière et al. lack the statistical depth and resolution offered by our CRAC analyses. The detailed results obtained through CRAC would have been impossible to detect using classical ChIP. The current study provides a more refined and precise understanding of Pol I distribution and dynamics, highlighting the advantages of CRAC over traditional methods in addressing these complex transcriptional processes.

BMH-21 Effects:

As highlighted by Reviewer 1, the effects of BMH-21 observed in our study differ slightly from those reported in earlier work (Ref Schneider 2022), likely due to variations in experimental conditions, such as methodologies (CRAC vs. Net-seq), as discussed earlier. We also identified variations in the response to BMH-21 treatment associated with differences in cell growth phases and/or cell density. These factors likely contribute to the observed discrepancies, offering a potential explanation for the variations between our findings and those reported in previous studies. In our approach, we prioritized reproducibility by carefully controlling BMH-21 experimental conditions to mitigate these factors. These variables can significantly influence results, potentially leading to subtle discrepancies. Nevertheless, the overall conclusions regarding BMH-21's effects on WT Pol I are largely consistent across studies, with differences primarily observed at the nucleotide resolution. This is a strength of our CRAC-based analysis, which provides precise insights into Pol I activity.

We will address these nuances in the revised manuscript to clarify how such differences may impact results and provide context for interpreting our findings in light of previous studies.

Minor points:

Reviewer #1:

In general, the writing style is not clear, and there are some word mistakes or poor descriptions of the results, for example: 

On page 14: "SuperPol accumulation is decreased (compared to Pol I)". 

On page 16: "Compared to WT Pol I, the cumulative distribution of SuperPol is indeed shifted on the right of the graph." 

We clarified and increased the global writing style according to reviewer comment.

There are also issues with the literature, for example: Turowski et al, 2020a and Turowski et al, 2020b are the same article (preprint and peer-reviewed). Is there any reason to include both references? Please, double-check the references.  

This was corrected in this version of the manuscript.

In the manuscript, 5S rRNA is mentioned as an internal control for TMA normalisation. Why are Figure 1C data normalised to 18S rRNA instead of 5S rRNA? 

Data are effectively normalized relative to the 5S rRNA, but the value for the 18S rRNA is arbitrarily set to 100%.

Figure 4 should be a supplementary figure, and Figure 7D doesn't have a y-axis labelling. 

The presence of all Pol I specific subunits (Rpa12, Rpa34 and Rpa49) is crucial for the enzymatic activity we performed. In the absence of these subunits (which can vary depending on the purification batch), Pol I pausing, cleavage and elongation are known to be affected. To strengthen our conclusion, we really wanted to show the subunit composition of the purified enzyme. This important control should be shown, but can indeed be shown in a supplementary figure if desired.

Y-axis is figure 7D is now correctly labelled

In Figure 7C, BMH-21 treatment causes the accumulation of ~140bp rRNA transcripts only in SuperPol-expressing cells that are Rrp6-sensitive (line 6 vs line 8), suggesting that BHM-21 treatment does affect SuperPol. Could the author comment on the interpretation of this result? 

The 140 nt product is a degradation fragment resulting from trimming, which explains its lower accumulation in the absence of Rrp6. BMH21 significantly affects WT Pol I transcription but has also a mild effect on SuperPol transcription. As a result, the 140 nt product accumulates under these conditions.

Reviewer #2:

pp. 14-15: The authors note local differences in peak detection in the 5'-ETS among replicates, preventing a nucleotide-resolution analysis of pausing sites. Still, they report consistent global differences between wild-type and SuperPol CRAC signals in the 5'ETS (and other regions of the rDNA). These global differences are clear in the quantification shown in Figures 2B-C. A simpler statement might be less confusing, avoiding references to a "first and second set of replicates" 

According to reviewer, statement has been simplified in this version of the manuscript.

Figures 2A and 2C: Based on these data and quantification, it appears that SuperPol signals in the body and 3' end of the rDNA unit are higher than those in the wild type. This finding supports the conclusion that reduced pausing (and termination) in the 5'ETS leads to an increased Pol I signal downstream. Since the average increase in the SuperPol signal is distributed over a larger region, this might also explain why even a relatively modest decrease in 5'ETS pausing results in higher rRNA production. This point merits discussion by the authors. 

We agree that this is a very important discussion of our results. Transcription is a very dynamic process in which paused polymerase is easily detected using the CRAC assay. Elongated polymerases are distributed over a much larger gene body, and even a small amount of polymerase detected in the gene body can represent a very large rRNA synthesis. This point is of paramount importance and, as suggested by the reviewer, is now discussed in detail.

A decreased efficiency of cleavage upon backtracking might imply an increased error rate in SuperPol compared to the wild-type enzyme. Have the authors observed any evidence supporting this possibility? 

Reviewer suggested that a decreased efficiency of cleavage upon backtracking might imply an increased error rate in SuperPol compared to the wild-type enzyme. We thank Reviewer #2 to point it as in our opinion, this is an important point what should be added to the manuscript. We have now included new data (panels 5G, 5H and 5I) in the manuscript showing that SuperPol in vitro exhibits an increased error rate compared to the WT enzyme. From these results obtained in vitro, we concluded that SuperPol shows reduced nascent transcript cleavage, associated with more efficient transcript elongation, but to the detriment of transcriptional fidelity.

pp. 15 and 22: Premature transcription termination as a regulator of gene expression is welldocumented in yeast, with significant contributions from the Corden, Brow, Libri, and Tollervey labs. These studies should be referenced along with relevant bacterial and mammalian research. 

According to reviewer suggestion, we referenced these studies.

p. 23: "SuperPol and Rpa190-KR have a synergistic effect on BMH-21 resistance." A citation should be added for this statement. 

This represents some unpublished data from our lab. KR and SuperPol are the only two known mutants resistant to BMH-21. We observed that resistance between both alleles is synergistic, with a much higher resistance to BMH-21 in the double mutant than in each single mutant (data not shown). Comparing their resistance mechanisms is a very important point that we could provide upon request. This was added to the statement.

p. 23: "The released of the premature transcript" - this phrase contains a typo 

This is now corrected.

Reviewer #3:

Figure 1B: it would be opportune to separate the technique's schematic representation from the actual data. Concerning the data, would the authors consider adding an experiment with rrp6D cells? Some RNAs could be degraded even in such short period of time, as even stated by the authors, so maybe an exosome depleted background could provide a more complete picture. Could also the authors explain why the increase is only observed at the level of 18S and 25S? To further prove the robustness of the Pol I TMA method could be good to add already characterized mutations or other drugs to show that the technique can readily detect also well-known and expected changes. 

The precise objective of this experiment is to avoid the use of the Rrp6 mutant. Under these conditions, we prevent the accumulation of transcripts that would result from a maturation defect. While it is possible to conduct the experiment with the Rrp6 mutant, it would be impossible to draw reliable conclusions due to this artificial accumulation of transcripts.

Figure 1C: the NTS1 probe signal is missing (it is referenced in Figure 1A but not listed in the Methods section or the oligo table). If this probe was unused, please correct Figure 1A accordingly. 

We corrected Figure 1A.  

Figure 2A: the RNAPI occupancy map by CRAC is hard to interpret. The red color (SuperPol) is stacked on top of the blue line, and we are not able to observe the signal of the WT for most of the position along the rDNA unit. It would be preferable to use some kind of opacity that allows to visualize both curves. Moreover, the analysis of the behavior of the polymerase is always restricted to the 5'ETS region in the rest of the manuscript. We are thus not able to observe whether termination events also occur in other regions of the rDNA unit. A Northern blot analysis displaying higher sizes would provide a more complete picture. 

We addressed this point to make the figure more visually informative. In Northern Blot analysis, we use a TSS (Transcription Start Site) probe, which detects only transcripts containing the 5' extremity. Due to co-transcriptional processing, most of the rRNA undergoing transcription lacks its 5' extremity and is not detectable using this technique. We have the data, but it does not show any difference between Pol I and SuperPol. This information could be included in the supplementary data if asked.

"Importantly, despite some local variations, we could reproducibly observe an increased occupancy of WT Pol I in 5'-ETS compared to SuperPol (Figure 1C)." should be Figure 2C. 

Thanks for pointing out this mistake. It has been corrected.

Figure 3D: most of the difference in the cumulative proportion of CRAC reads is observed in the region ~750 to 3000. In line with my previous point, I think it would be worth exploring also termination events beyond the 5'-ETS region. 

We agree that such an analysis would have been interesting. However, with the exception of the pre-rRNA starting at the transcription start site (TSS) studied here, any cleaved rRNA at its 5' end could result from premature termination and/or abnormal processing events. Exploring the production of other abnormal rRNAs produced by premature termination is a project in itself, beyond this initial work aimed at demonstrating the existence of premature termination events in ribosomal RNA production.

Figure 4: should probably be provided as supplementary material. 

As l mentioned earlier (see comments), the presence of all Pol I specific subunits (Rpa12, Rpa34 and Rpa49) is crucial for the enzymatic activity we performed. This important control should be shown, but can indeed be shown in a supplementary figure if desired.

"While the growth of cells expressing SuperPol appeared unaffected, the fitness of WT cells was severely reduced under the same conditions." I think the growth of cells expressing SuperPol is slightly affected. 

We agree with this comment and we modified the text accordingly.

Figure 7D: the legend of the y-axis is missing as well as the title of the plot. 

Legend of the y-axis and title of the plot are now present.

The statements concerning BMH-21, SuperPol and Rpa190-KR in the Discussion section should be removed, or data should be provided.

This was discussed previously. See comment above.

Some references are missing from the Bibliography, for example Merkl et al., 2020; Pilsl et al., 2016a, 2016b. 

Bibliography is now fixed

Description of analyses that authors prefer not to carry out:

Does SuperPol mutant produces more functional rRNAs ?

As Reviewer 1 requested, we agree that this point requires clarification.. In cells expressing SuperPol, a higher steady state of (pre)-rRNAs is only observed in absence of degradation machinery suggesting that overproduced rRNAs are rapidly eliminated. We know that (pre)rRNas are unable to accumulate in absence of ribosomal proteins and/or Assembly Factors (AF). In consequence, overproducing rRNAs would not be sufficient to increase ribosome content. This specific point is further address in our lab but is beyond the scope of this article.

Is premature termination coupled with rRNA processing 

We appreciate the reviewer’s insightful comments. The suggested experiments regarding the UTP-A complex's regulatory potential are valuable and ongoing in our lab, but they extend beyond the scope of this study and are not suitable for inclusion in the current manuscript.

Author response:

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

Reviewer #1 (Public review):

Summary:

Liu et al., present glmSMA, a network-regularized linear model that integrates single-cell RNA-seq data with spatial transcriptomics, enabling high-resolution mapping of cellular locations across diverse datasets. Its dual regularization framework (L1 for sparsity and generalized L2 via a graph Laplacian for spatial smoothness) demonstrates robust performance of their model and offers novel tools for spatial biology, despite some gaps in fully addressing spatial communication.

Overall, the manuscript is commendable for its comprehensive benchmarking across different spatial omics platforms and its novel application of regularized linear models for cell mapping. I think this manuscript can be improved by addressing method assumptions, expanding the discussion on feature dependence and cell type-specific biases, and clarifying the mechanism of spatial communication.

The conclusions of this paper are mostly well supported by data, but some aspects of model developmentand performance evaluation need to be clarified and extended.

We are thankful for the positive comments and have made changes following the reviewer's advice, as detailed below.

(1) What were the assumptions made behind the model? One of them could be the linear relationship between cellular gene expression and spatial location. In complex biological tissues, non-linear relationships could be present, and this would also vary across organ systems and species. Similarly, with regularization parameters, they can be tuned to balance sparsity and smoothness adequately but may not hold uniformly across different tissue types or data quality levels. The model also seems to assume independent errors with normal distribution and linear additive effects - a simplification that may overlook overdispersion or heteroscedasticity commonly observed in RNA-seq data.

Thank you for this comment. We acknowledge that the non-linear relationships can be present in complex tissues and may not be fully captured by a linear model. 

Our choice of a linear model was guided by an investigation of the relationship in the current datasets, which include intestinal villus, mouse brain, and fly embryo.There is a linear correlation between expression distance and physical distance [Nitzan et al]. Within a given anatomical structure, cells in closer proximity exhibit more similar expression patterns (Fig. 3c). In tissues where non-linear relationships are more prevalent—such as the human PDAC sample—our mapping results remain robust. We acknowledge that we have not yet tested our algorithm in highly heterogeneous regions like the liver, and we plan to include such analyses in future work if necessary.

Regarding the regularization parameters, we agree that the balance between sparsity and smoothness is sensitive to tissue-specific variation and data quality. In our current implementation, we explored a range of values to find robust defaults. Supplementary Figure 7 illustrates the regularization path for cell assignment in the fly embryo.  

The choice of L1 and L2 regularization parameters is crucial for balancing sparsity and smoothness in spatial mapping. 

For Structured Tissues (brain):

Moderate L1 to ensure cells are localized.

Small to moderate L2 to maintain local smoothness without blurring distinct regions.

For Less Structured (PDAC):

Slightly lower L1 to allow cells to be associated with multiple regions if boundaries are ambiguous.

Higher L2 to stabilize mappings in noisy or mixed regions.

(2) The performance of glmSMA is likely sensitive to the number and quality of features used. With too few features, the model may struggle to anchor cells correctly due to insufficient discriminatory power, whereas too many features could lead to overfitting unless appropriately regularized. The manuscript briefly acknowledges this issue, but further systematic evaluation of how varying feature numbers affect mapping accuracy would strengthen the claims, particularly in settings where marker gene availability is limited. A simple way to show some of this would be testing on multiple spatial omics (imaging-based) platforms with varying panel sizes and organ systems. Related to this, based on the figures, it also seems like the performance varies by cell type. What are the factors that contribute to this? Variability in expression levels, RNA quantity/quality? Biases in the panel? Personally, I am also curious how this model can be used similarly/differently if we have a FISH-based, high-plex reference atlas. Additional explanation around these points would be helpful for the readers.

Thank you for this thoughtful comment. The performance of our method is indeed sensitive to the number and quality of selected features. To optimize feature selection, we employed multiple strategies, including Moran’s I statistic, identification of highly variable genes, and the Seurat pipeline to detect anchor genes linking the spatial transcriptomics data with the reference atlas. The number of selected markers depends on the quality of the data. For highquality datasets, fewer than 100 markers are typically sufficient for prediction. To select marker genes, we applied the following optional strategies:

(1) Identifying highly variable genes (HVGs).

(2) Calculating Moran’s I scores for all genes to assess spatial autocorrelation.

(3) Generating anchor genes based on the integration of the reference atlas and scRNA-seq data using Seurat.

We evaluated our method across diverse tissue types and platforms—including Slide-seq, 10x Visium, and Virtual-FISH—which represent both sequencing-based and imaging-based spatial transcriptomics technologies. Our model consistently achieved strong performance across these settings. It's worth noting that the performance of other methods, such as CellTrek [Wei et al] and novoSpaRc [Nitzan et al], also depends heavily on feature selection. In particular, performance degrades substantially when fewer features are used. For fair comparison across different methods, the same set of marker genes was used. Under this condition, our method outperformed the others based on KL divergence (Fig. 2b, Fig. 5g). 

To assess the effect of marker gene quantity, we randomly selected subsets of 2,000, 1500, 1,000, 700, 500, and 200 markers from the original set. As the number of markers decreases, mapping performance declines, which is expected due to the reduction in available spatial information. This result underscores the general dependence of spatial mapping accuracy on both the number and quality of informative marker genes (Supplementary Fig. 10).

We do not believe that the observed performance is directly influenced by cell type composition. Major cell types are typically well-defined, and rare cell types comprise only a small fraction of the dataset. For these rare populations, a single misclassification can disproportionately impact metrics like KL divergence due to small sample size. However, this does not necessarily indicate a systematic cell type–specific bias in the mapping. We incorporated a high-resolution Slide-seq dataset from the mouse hippocampus to evaluate the influence of cell type composition on the algorithm’s performance [Stickels et al., 2020]. Most cell types within the CA1, CA2, CA3, and DG regions were accurately mapped to their original anatomical locations (Fig. 5e, f, g).

(3) Application 3 (spatial communication) in the graphical abstract appears relatively underdeveloped. While it is clear that the model infers spatial proximities, further explanation of how these mappings translate into insights into cell-cell communication networks would enhance the biological relevance of the findings.

Thank you for this valuable feedback. We agree that further elaboration on the connection between spatial proximity and cell–cell communication would enhance the biological interpretation of our results. While our current model focuses on inferring spatial relationships,  we may provide some cell-cell communications in the future.

(4) What is the final resolution of the model outputs? I am assuming this is dictated by the granularity of the reference atlas and the imposed sparsity via the L1 norm, but if there are clear examples that would be good. In figures (or maybe in practice too), cells seem to be assigned to small, contiguous patches rather than pinpoint single-cell locations, which is a pragmatic compromise given the inherent limitations of current spatial transcriptomics technologies. Clarification on the precise spatial scale (e.g., pixel or micrometer resolution) and any post-mapping refinement steps would be beneficial for the users to make informed decisions on the right bioinformatic tools to use.

Thank you for the comment. For each cell, our algorithm generates a probability vector that indicates its likely spatial assignment along with coordinate information. In our framework, each cell is mapped to one or more spatial spots with associated probabilities. Depending on the amount of regularization through L1 and L2 norms, a cell may be localized to a small patch or distributed over a broader domain (Supplementary Fig. 5 & 7). For the 10x Visium data, we applied a repelling algorithm to enhance visualization [Wei et al]. If a cell’s original location is already occupied, it is reassigned to a nearby neighborhood to avoid overlap. The users can also see the entire regularization path by varying the penalty terms. 

Nitzan M, Karaiskos N, Friedman N, Rajewsky N. Gene expression cartography. Nature. 2019;576(7785):132-137. doi:10.1038/s41586-019-1773-3

Wei, R. et al. (2022) ‘Spatial charting of single-cell transcriptomes in tissues’, Nature Biotechnology, 40(8), pp. 1190–1199. doi:10.1038/s41587-022-01233-1.

Stickels, R.R. et al. (2020) ‘Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-SEQV2’, Nature Biotechnology, 39(3), pp. 313–319. doi:10.1038/s41587-020-0739-1. 

Reviewer #2 (Public review):

Summary:

The author proposes a novel method for mapping single-cell data to specific locations with higher resolution than several existing tools.

Strengths:

The spatial mapping tests were conducted on various tissues, including the mouse cortex, human PDAC, and intestinal villus.

Weakness:

(1) Although the researchers claim that glmSMA seamlessly accommodates both sequencing-based and image-based spatial transcriptomics (ST) data, their testing primarily focused on sequencingbased ST data, such as Visium and Slide-seq. To demonstrate its versatility for spatial analysis, the authors should extend their evaluation to imaging-based spatial data.

Thank you for the comment. We have tested our algorithm on the virtual FISH dataset from the fly embryo, which serves as an example of image-based spatial omics data (Fig. 4c). However, such datasets often contain a limited number of available genes. To address this, we will conduct additional testing on image-based data if needed. The Allen Brain Atlas provides high-quality ISH data, and we can select specific brain regions from this resource to further evaluate our algorithm if necessary [Lein et al]. Currently, we plan to focus more on the 10x Visium platform, as it supports whole-transcriptome profiling and offers a wide range of tissue samples for analysis.

(2) The definition of "ground truth" for spatial distribution is unclear. A more detailed explanation is needed on how the "ground truth" was established for each spatial dataset and how it was utilized for comparison with the predicted distribution generated by various spatial mapping tools.

Thank you for the comment. To clarify how ground truth is defined across different tissues, we provided the following details. Direct ground truth for cell locations is often unavailable in scRNA-seq data due to experimental constraints. To address this, we adopted alternative strategies for estimating ground truth in each dataset:

10x Visium Data: We used the cell type distribution derived from spatial transcriptomics (ST) data as a proxy for ground truth. We then computed the KL divergence between this distribution and our model's predictions for performance assessment.

Slide-seq Data: We validated predictions by comparing the expression of marker genes between the reconstructed and original spatial data.

Fly Embryo Data: We used predicted cell locations from novoSpaRc as a reference for evaluating our algorithm.

These strategies allowed us to evaluate model performance even in the absence of direct cell location data. In addition, we can apply multiple evaluation strategies within a single dataset.

(3) In the analysis of spatial mapping results using intestinal villus tissue, only Figure 3d supports their findings. The researchers should consider adding supplemental figures illustrating the spatial distribution of single cells in comparison to the ground truth distribu tion to enhance the clarity and robustness of their investigation.

Thank you for the comment. In the intestinal dataset, only six large domains were defined. As a result, the task for this dataset is relatively simple—each cell only needs to be assigned to one of the six domains. As the intestinal villus is a relatively simple tissue, most existing algorithms performed well on it. For this reason, we did not initially provide extensive details in the main text.

(4) The spatial mapping tests were conducted on various tissues, including the mouse cortex, human PDAC, and intestinal villus. However, the original anatomical regions are not displayed, making it difficult to directly compare them with the predicted mapping results. Providing ground truth distributions for each tested tissue would enhance clarity and facilitate interpretation. For instance, in Figure 2a and  Supplementary Figures 1 and 2, only the predicted mapping results are shown without the corresponding original spatial distribution of regions in the mouse cortex. Additionally, in Figure 3c, four anatomical regions are displayed, but it is unclear whether the figure represents the original spatial regions or those predicted by glmSMA. The authors are encouraged to clarify this by incorporating ground truth distributions for each tissue.

Thank you for the comment. To improve visualization, we included anatomical structures alongside the mapping results in the next version, wherever such structures are available (e.g., mouse brain cortex, human PDAC sample, etc.). Major cell type assignments for the PDAC samples, along with anatomical structures, are shown in Supplementary Figure 9. Most of these cell types were correctly mapped to their corresponding anatomical regions.

(5) The cell assignment results from the mouse hippocampus (Supplementary Figure 6) lack a corresponding ground truth distribution for comparison. DG and CA cells were evaluated solely based on the gene expression of specific marker genes. Additional analyses are needed to further validate the robustness of glmSMA's mapping performance on Slide-seq data from the mouse hippocampus.

Thank you for the comment. The ground truth for DG and CA cells was not available. To better evaluate the model's performance, we computed the KL divergence between the original and predicted cell type distributions, following the same approach used for the 10x Visium dataset. We identified a higher-quality dataset for the mouse hippocampus and used it to evaluate our algorithm. Additionally, we employed KL divergence as an alternative strategy to validate and benchmark our results (Fig. 5e, f, g). Most CA cells, including CA1, CA2, and CA3 principal cells, were correctly assigned back to the CA region. Dentate principal cells were accurately mapped to the DG region (Fig. 5e, f).

(6) The tested spatial datasets primarily consist of highly structured tissues with well-defined anatomical regions, such as the brain and intestinal villus. Anatomical regions are not distinctly separated, such as liver tissue. Further evaluation of such tissues would help determine the method's broader applicability.

Thank you for the insightful comment. We agree that many spatial datasets used in our study are from tissues with well-defined anatomical regions. To address the applicability of glmSMA in tissues without clearly separated anatomical structures, we applied glmSMA to the Drosophila embryo, which represents a tissue with relatively continuous spatial patterns and lacks well-demarcated anatomical boundaries compared to organs like the brain or intestinal villus.

Despite this less structured spatial organization, glmSMA demonstrated robust performance in the fly embryo, accurately mapping cells to their correct spatial spots based on gene expression profiles. This result indicates that glmSMA is not strictly limited to highly structured tissues and can generalize to tissues with more continuous or gradient-like spatial architectures. These results suggest that glmSMA has broader applicability beyond highly compartmentalized tissues.

Lein, E., Hawrylycz, M., Ao, N. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168–176 (2007). https://doi.org/10.1038/nature05453

Reviewer #3 (Public review):

The authors aim to develop glmSMA, a network-regularized linear model that accurately infers spatial gene expression patterns by integrating single-cell RNA sequencing data with spatial transcriptomics reference atlases. Their goal is to reconstruct the spatial organization of individual cells within tissues, overcoming the limitations of existing methods that either lack spatial resolution or sensitivity.

Strengths:

(1) Comprehensive Benchmarking:

Compared against CellTrek and Novosparc, glmSMA consistently achieved lower Kullback-Leibler divergence (KL divergence) scores, indicating better cell assignment accuracy.

Outperformed CellTrek in mouse cortex mapping (90% accuracy vs. CellTrek's 60%) and provided more spatially coherent distributions.

(2) Experimental Validation with Multiple Real-World Datasets:

The study used multiple biological systems (mouse brain, Drosophila embryo, human PDAC, intestinal villus) to demonstrate generalizability.

Validation through correlation analyses, Pearson's coefficient, and KL divergence support the accuracy of glmSMA's predictions.

We thank reviewer #3 for their positive feedback and thoughtful recommendations.

Weaknesses:

(1) The accuracy of glmSMA depends on the selection of marker genes, which might be limited by current FISH-based reference atlases.

We agree that the accuracy of glmSMA is influenced by the selection of marker genes, and that current FISH-based reference atlases may offer a limited gene set. To address this, we incorporate multiple feature selection strategies, including highly variable genes and spatially informative genes (e.g., via Moran’s I), to optimize performance within the available gene space. As more comprehensive reference atlases become available, we expect the model’s accuracy to improve further.

(2) glmSMA operates under the assumption that cells with similar gene expression profiles are likely to be physically close to each other in space which not be true under various heterogeneous environments.

Thank you for raising this important point. We agree that glmSMA operates under the assumption that cells with similar gene expression profiles tend to be spatially proximal, and this assumption may not strictly hold in highly heterogeneous tissues where spatial organization is less coupled to transcriptional similarity.

To address this concern, we specifically tested glmSMA on human PDAC samples, which represent moderately heterogeneous environments characterized by complex tumor microenvironments, including a mixture of ductal cells, cancer cells, stromal cells, and other components. Despite this heterogeneity, glmSMA successfully mapped major cell types to their expected anatomical regions, demonstrating that the method is robust even in the presence of substantial cellular diversity and spatial complexity.

This result suggests that while glmSMA relies on the assumption of spatialtranscriptomic correlation, the method can tolerate a reasonable degree of spatial heterogeneity without a significant loss of performance. Nevertheless, we acknowledge that in extremely disorganized or highly mixed tissues where transcriptional similarity is decoupled from spatial proximity, the performance may be affected.

Author response:

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

Reviewer #1 (Public review):

Summary:

This study provides a comprehensive single-cell and multiomic characterization of trabecular meshwork (TM) cells in the mouse eye, a structure critical to intraocular pressure (IOP) regulation and glaucoma pathogenesis. Using scRNA-seq, snATAC-seq, immunofluorescence, and in situ hybridization, the authors identify three transcriptionally and spatially distinct TM cell subtypes. The study further demonstrates that mitochondrial dysfunction, specifically in one subtype (TM3), contributes to elevated IOP in a genetic mouse model of glaucoma carrying a mutation in the transcription factor Lmx1b. Importantly, treatment with nicotinamide (vitamin B3), known to support mitochondrial health, prevents IOP elevation in this model. The authors also link their findings to human datasets, suggesting the existence of analogous TM3-like cells with potential relevance to human glaucoma.

Strengths:

The study is methodologically rigorous, integrating single-cell transcriptomic and chromatin accessibility profiling with spatial validation and in vivo functional testing. The identification of TM subtypes is consistent across mouse strains and institutions, providing robust evidence of conserved TM cell heterogeneity. The use of a glaucoma model to show subtype-specific vulnerability, combined with a therapeutic intervention-gives the study strong mechanistic and translational significance. The inclusion of chromatin accessibility data adds further depth by implicating active transcription factors such as LMX1B, a gene known to be associated with glaucoma risk. The integration with human single-cell datasets enhances the potential relevance of the findings to human disease.

We thank the reviewers for their thorough reading of our manuscript and helpful comments.

Weaknesses:

(1) Although the LMX1B transcription factor is implicated as a key regulator in TM3 cells, its role in directly controlling mitochondrial gene expression is not fully explored. Additional analysis of motif accessibility or binding enrichment near relevant target genes could substantiate this mechanistic link. 

We show that the Lmx1b mutation induces mitochondrial dysfunction with mitochondrial gene expression changes but agree with the referee in that we do not show direct regulation of mitochondrial genes by LMX1B. Emerging data suggest that LMX1B regulates the expression of mitochondrial genes in other cell types [1, 2] making the direct link reasonable. Future work that is beyond the scope of the current paper will focus on sequencing cells at earlier timepoints to help distinguish gene expression changes associated with the V265D mutation from those secondary to ongoing disease and elevated IOP. Additional studies, including ATAC seq at more ages, ChIP-seq and/or Cut and Run/Tag (in TM cells) will be necessary to directly investigate LMX1B target genes.

As we studied adult mice, mitochondrial gene expression changes could be secondary to other disease induced stresses. Because we did not intend to say we have shown a direct link, we have now added a sentence to the discussion ensure clarity. 

Lines 932-934: “Although our studies show a clear effect of the Lmx1b mutation on mitochondria, future studies are needed to determine if LMX1B directly modulates mitochondrial genes in V265D mutant TM cells”

(2) The therapeutic effect of vitamin B3 is clearly demonstrated phenotypically, but the underlying cellular and molecular mechanisms remain somewhat underdeveloped - for instance, changes in mitochondrial function, oxidative stress markers, or NAD+ levels are not directly measured. 

We agree that further experiments towards a fuller mechanistic understanding of vitamin B3’s therapeutic effects are needed. Such experiments are planned but are beyond the scope of this paper, which is already very large (7 Figures and 16 Supplemental Figures).

(3) While the human relevance of TM3 cells is suggested through marker overlap, more quantitative approaches, such as cell identity mapping or gene signature scoring in human datasets, would strengthen the translational connection.

We appreciate the reviewer’s suggestion and agree that additional quantitative analyses will further strengthen the translational relevance of TM3 cells. It is not yet clear if humans have a direct TM3 counterpart or if TM cell roles are compartmentalized differently between human cell types. We are currently limited in our ability to perform these comparative analyses. Specifically, we were unable to obtain permission to use the underlying dataset from Patel et al., and our access to the Van Zyl et al. dataset was through the Single Cell Portal, which does not support more complex analyses (ex. cell identity mapping or gene signature scoring). Differences between human studies themselves also affect these comparisons. Future work aimed at resolving differences and standardizing human TM cell annotations, as well as cross species comparisons are needed (working groups exist and this ongoing effort supports 3 human TM cell subtypes as also reported by Van Zyl). This is beyond what we are currently able to do for this paper. We present a comprehensive assessment using readily available published resources.

Reviewer #2 (Public review):

Summary:

This elegant study by Tolman and colleagues provides fundamental findings that substantially advance our knowledge of the major cell types within the limbus of the mouse eye, focusing on the aqueous humor outflow pathway. The authors used single-cell and single-nuclei RNAseq to very clearly identify 3 subtypes of the trabecular meshwork (TM) cells in the mouse eye, with each subtype having unique markers and proposed functions. The U. Columbia results are strengthened by an independent replication in a different mouse strain at a separate laboratory (Duke). Bioinformatics analyses of these expression data were used to identify cellular compartments, molecular functions, and biological processes. Although there were some common pathways among the 3 subtypes of TM cells (e.g., ECM metabolism), there also were distinct functions. For example:

TM1 cell expression supports heavy engagement in ECM metabolism and structure, as well as TGFb2 signaling.

TM2 cells were enriched in laminin and pathways involved in phagocytosis, lysosomal function, and antigen expression, as well as End3/VEGF/angiopoietin signaling.

TM3 cells were enriched in actin binding and mitochondrial metabolism.

They used high-resolution immunostaining and in situ hybridization to show that these 3 TM subtypes express distinct markers and occupy distinct locations within the TM tissue. The authors compared their expression data with other published scRNAseq studies of the mouse as well as the human aqueous outflow pathway. They used ATAC-seq to map open chromatin regions in order to predict transcription factor binding sites. Their results were also evaluated in the context of human IOP and glaucoma risk alleles from published GWAS data, with interesting and meaningful correlations. Although not discussed in their manuscript, their expression data support other signaling pathways/ proteins/ genes that have been implicated in glaucoma, including: TGFb2, BMP signaling (including involvement of ID proteins), MYOC, actin cytoskeleton (CLANs), WNT signaling, etc.

In addition to these very impressive data, the authors used scRNAseq to examine changes in TM cell gene expression in the mouse glaucoma model of mutant Lmxb1-induced ocular hypertension. In man, LMX1B is associated with Nail-Patella syndrome, which can include the development of glaucoma, demonstrating the clinical relevance of this mouse model. Among the gene expression changes detected, TM3 cells had altered expression of genes associated with mitochondrial metabolism. The authors used their previous experience using nicotinamide to metabolically protect DBA2/J mice from glaucomatous damage, and they hypothesized that nicotinamide supplementation of mutant Lmx1b mice would help restore normal mitochondrial metabolism in the TM and prevent Lmx1b-mediated ocular hypertension. Adding nicotinamide to the drinking water significantly prevented Lmxb1 mutant mice from developing high intraocular pressure. This is a laudable example of dissecting the molecular pathogenic mechanisms responsible for a disease (glaucoma) and then discovering and testing a potential therapy that directly intervenes in the disease process and thereby protects from the disease.

Strengths:

There are numerous strengths in this comprehensive study including:

Deep scRNA sequencing that was confirmed by an independent dataset in another mouse strain at another university.

Identification and validation of molecular markers for each mouse TM cell subset along with localization of these subsets within the mouse aqueous outflow pathway.

Rigorous bioinformatics analysis of these data as well as comparison of the current data with previously published mouse and human scRNAseq data.

Correlating their current data with GWAS glaucoma and IOP "hits".

Discovering gene expression changes in the 3 TM subgroups in the mouse mutant Lmx1b model of glaucoma.

Further pursuing the indication of dysfunctional mitochondrial metabolism in TM3 cells from Lmx1b mutant mice to test the efficacy of dietary supplementation with nicotinamide. The authors nicely demonstrate the disease modifying efficacy of nicotinamide in preventing IOP elevation in these Lmx1b mutant mice, preventing the development of glaucoma. These results have clinical implications for new glaucoma therapies.

We thank the reviewer for these generous and thoughtful comments on the strengths of this study.

Weaknesses:

(1) Occasional over-interpretation of data. The authors have used changes in gene expression (RNAseq) to implicate functions and signaling pathways. For example: they have not directly measured "changes in metabolism", "mitochondrial dysfunction" or "activity of Lmx1b".

We thank the reviewer for this feedback. We did not intend to overstate and agree. Our gene expression changes support, but do not by themselves prove, metabolic disturbances. We had felt that this was obvious and did not want to clutter the text. We have revised the manuscript to clarify that our conclusions about metabolic changes and LMX1B activity are based on gene expression patterns rather than direct functional assays and have added EM data (see below under “Recommendations for the authors”).

We have also added the following to the results:

Lines 715-721: “Although the documented gene expression changes strongly suggest metabolic and mitochondrial dysfunction, they do not directly prove it. Using electron microscopy to directly evaluate mitochondria in the TM, we found a reduction in total mitochondria number per cell in mutants (P = 0.015, Figure 6G). In addition, mitochondria in mutants had increased area and reduced cristae (inner membrane folds) in mutants consistent with mitochondrial swelling and metabolic dysfunction (all P < 0.001 compared to WT, Figure 6G-H).”

More detailed EM and metabolic studies are underway but are beyond the scope of this paper.

(2) In their very thorough data set, there is enrichment of or changes in gene expression that support other pathways that have been previously reported to be associated with glaucoma (such as TGFb2, BMP signaling, actin cytoskeletal organization (CLANs), WNT signaling, ossification, etc. that appears to be a lost opportunity to further enhance the significance of this work.

We appreciate the reviewer’s suggestions for enhancing the relevance of our work, we had not initially discussed this due to length concerns. We have now incorporated some of this information into the manuscript (see below under “Recommendations for the authors”).

Reviewer #3 (Public review):

Summary: In this study, the authors perform multimodal single-cell transcriptomic and epigenomic profiling of 9,394 mouse TM cells, identifying three transcriptionally distinct TM subtypes with validated molecular signatures. TM1 cells are enriched for extracellular matrix genes, TM2 for secreted ligands supporting Schlemm's canal, and TM3 for contractile and mitochondrial/metabolic functions. The transcription factor LMX1B, previously linked to glaucoma, shows the highest expression in TM3 cells and appears to regulate mitochondrial pathways. In Lmx1bV265D mutant mice, TM3 cells exhibit transcriptional signs of mitochondrial dysfunction associated with elevated IOP. Notably, vitamin B3 treatment significantly mitigates IOP elevation, suggesting a potential therapeutic avenue.

This is an excellent and collaborative study involving investigators from two institutions, offering the most detailed single-cell transcriptomic and epigenetic profiling of the mouse limbal tissues-including both TM and Schlemm's canal (SC), from wild-type and Lmx1bV265D mutant mice. The study defines three TM subtypes and characterizes their distinct molecular signatures, associated pathways, and transcriptional regulators. The authors also compare their dataset with previously published murine and human studies, including those by Van Zyl et al., providing valuable crossspecies insights.

Strengths: 

(1) Comprehensive dataset with high single-cell resolution

(2) Use of multiple bioinformatic and cross-comparative approaches

(3) Integration of 3D imaging of TM and SC for anatomical context

(4) Convincing identification and validation of three TM subtypes using molecular markers.

We thank the reviewer for their comments on the strengths of this study.

Weaknesses:

(1) Insufficient evidence linking mitochondrial dysfunction to TM3 cells in Lmx1bV265D mice: While the identification of TM3 cells as metabolically specialized and Lmx1b-enriched is compelling, the proposed link between Lmx1b mutation and mitochondrial dysfunction remains underdeveloped. It is unclear whether mitochondrial defects are a primary consequence of Lmx1b-mediated transcriptional dysregulation or a secondary response to elevated IOP. Additional evidence is needed to clarify whether Lmx1b directly regulates mitochondrial genes (e.g., via ChIP-seq, motif analysis, or ATAC-seq), or whether mitochondrial changes are downstream effects.

We agree and refer the reviewer to our responses to the other referees including Reviewer 1, Comment 1 and Reviewer 2 comments 1 and 17. As noted there, these mechanistic questions are the focus of ongoing and future studies. We have revised the text where appropriate to ensure it accurately reflects the scope of our current data.

(2) Furthermore, the protective effects of nicotinamide (NAM) are interpreted as evidence of mitochondrial involvement, but no direct mitochondrial measurements (e.g., immunostaining, electron microscopy, OCR assays) are provided. It is essential to validate mitochondrial dysfunction in TM3 cells using in vivo functional assays to support the central conclusion of the paper. Without this, the claim that mitochondrial dysfunction drives IOP elevation in Lmx1bV265D mice remains speculative. Alternatively, authors should consider revising their claims that mitochondrial dysfunction in these mice is a central driver of TM dysfunction.

We again refer the reviewer to our other response including Reviewer 1, Comment 1 and Reviewer 2 comments 1 and 17.

(3) Mechanism of NAM-mediated protection is unclear: The manuscript states that NAM treatment prevents IOP elevation in Lmx1bV265D mice via metabolic support, yet no data are shown to confirm that NAM specifically rescues mitochondrial function. Do NAM-treated TM3 cells show improved mitochondrial integrity? Are reactive oxygen species (ROS) reduced? Does NAM also protect RGCs from glaucomatous damage? Addressing these points would clarify whether the therapeutic effects of NAM are indeed mitochondrial.

We refer the reviewer to our response to Reviewer 1, Comment 2.

(4) Lack of direct evidence that LMX1B regulates mitochondrial genes: While transcriptomic and motif accessibility analyses suggest that LMX1B is enriched in TM3 cells and may influence mitochondrial function, no mechanistic data are provided to demonstrate direct regulation of mitochondrial genes. Including ChIP-seq data, motif enrichment at mitochondrial gene loci, or perturbation studies (e.g., Lmx1b knockout or overexpression in TM3 cells) would greatly strengthen this central claim.

We refer the reviewer to our response to Reviewer 1, Comment 1.

(5) Focus on LMX1B in Fig. 5F lacks broader context: Figure 5F shows that several transcription factors (TFs)-including Tcf21, Foxs1, Arid3b, Myc, Gli2, Patz1, Plag1, Npas2, Nr1h4, and Nfatc2exhibit stronger positive correlations or motif accessibility changes than LMX1B. Yet the manuscript focuses almost exclusively on LMX1B. The rationale for this focus should be clarified, especially given LMX1B's relatively lower ranking in the correlation analysis. Were the functions of these other highly ranked TFs examined or considered in the context of TM biology or glaucoma? Discussing their potential roles would enhance the interpretation of the transcriptional regulatory landscape and demonstrate the broader relevance of the findings.

Our analysis (Figure 5F) indicates that Lmx1b is the transcription factor most strongly associated with its predicted target gene expression across all TM cells, as reflected by its highest value along the X-axis. While other transcription factors exhibit greater motif accessibility (Y-axis), this likely reflects their broader expression across TM subtypes. In contrast, Lmx1b is minimally expressed in TM1 and TM2 cells, which may account for its lower motif accessibility overall (motifs not accessible in cells where Lmx1b is not / minimally expressed).

Our emphasis on LMX1B is further supported by its direct genetic association with glaucoma. In contrast, the other transcription factors lack clear links to glaucoma and are supported primarily by indirect evidence. Nonetheless, we agree that the transcription factors highlighted in our analysis are promising candidates for future investigation. However, to maintain focus on the central narrative of this study, we have chosen not to include an extended discussion of these additional genes.

(6) In abstract, they say a number of 9,394 wild-type TM cell transcriptomes. The number of Lmx1bV265D/+ TM cell transcriptomes analyzed is not provided. This information is essential for evaluating the comparative analysis and should be clearly stated in the Abstract and again in the main text (e.g., lines 121-123). Including both wild-type and mutant cell counts will help readers assess the balance and robustness of the dataset.

We thank the reviewer for noticing this oversight and have added this value to the abstract and results section. 

Lines 41 and 696: 2,491 mutant TM cells.  

(7) Did the authors monitor mouse weight or other health parameters to assess potential systemic effects of treatment? It is known that the taste of compounds in drinking water can alter fluid or food intake, which may influence general health. Also, does Lmx1bV265D/+ have mice exhibit non-ocular phenotypes, and if so, does nicotinamide confer protection in those tissues as well? Additionally, starting the dose of the nicotinamide at postnatal day 2, how long the mice were treated with water containing nicotinamide, and after how many days or weeks IOP was reduced, and how long the decrease in the IOP was sustained.

Water intake was monitored in both treatment groups, and dosing was based on the average volume consumed by adult mice (lines 1017–1018, young pups do not drink water and so drug is largely delivered through mothers’ milk until weaning and so we do not know an accurate dose for young pups). Mouse health was assessed throughout the experiment through regular monitoring of body weight and general condition.

Depending on genetic context, Lmx1b mutations can cause kidney disease and impact other systems. Non-ocular phenotypes were not the focus of this study and were not characterized.

We added a comment to the method to clarify the NAM treatment timeline. NAM was administered continuously in the drinking water starting at P2 and maintained throughout the experiment. IOP was measured beginning at 2 months and then at monthly time points. NAM lessened IOP at 2 and 3 months. We terminated IOP assessment at 3 months.

Lines 1028-1029: “Treatment was started at postnatal day 2 and continued throughout the experiment.”

(8) While the IOP reduction observed in NAM-treated Lmx1bV265D/+ mice appears statistically significant, it is unclear whether this reflects meaningful biological protection. Several untreated mice exhibit very high IOP values, which may skew the analysis. The authors should report the mean values for IOP in both untreated and NAM-treated groups to clarify the magnitude and variability of the response.

We have added supplemental table 7 with the statistical information. Regarding the high IOP values observed in a subset of untreated V265D mutant mice, we consistently detect individual mutant eyes with IOPs exceeding 30 mmHg across independent cohorts and time points [3-5]. It is important to note that IOP is subject to fluctuation and in disease states such as glaucoma, circadian rhythms can be disrupted with stochastic and episodic IOP spikes throughout the day. This may be occurring in those untreated mice. This is also why we strive to use sample sizes of 40 or more. Additionally, we observe that some mutant eyes with IOPs measured within the normal range have anterior chamber deepening (ACD) - a persistent anatomical change associated with sustained or recurrent high IOP that stretches the cornea and may posteriorly displace the lens. This suggests mutant mice experience transient IOP elevations that are not always captured at a single time point due to the stochastic nature of these fluctuations. To account for this, we include ACD as an additional readout alongside IOP measurements. The reduction in ACD observed in NAM-treated mice provides independent evidence supporting the biological relevance of NAM-mediated IOP reduction.   

(9) Additionally, since NAM has been shown to protect RGCs in other glaucoma models directly, the authors should assess whether RGCs are preserved in NAM-treated Lmx1b V265D/+ mice. Demonstrating RGC protection would support a synergistic effect of NAM through both IOP reduction and direct neuroprotection, strengthening the translational relevance of the treatment.

We again thank the referee. We note the possibility of dual IOP protection and neuroprotection in the manuscript (lines 961–963). The goal of the present study, however, was to determine mechanisms underlying IOP elevation in patients with LMX1B variants. Therefore, we limited our focus to IOP elevation (LMX1B is expressed in the TM but not RGCs). Studies of the RGCs and optic nerve in V265D mutant mice treated with NAM take considerable effort but are underway. They will be reported in a subsequent manuscript. Initial data support protection, but that is a work in progress.  

Additionally, we recently reported a similar pattern of IOP protection to that reported here using pyruvate - in experiments where we analyzed the optic nerve as the focus of the study was assessment of pyruvate as a resilience factor against high genetic risk of glaucoma [4]. In that case, there was statistically significant protection from glaucomatous optic nerve damage, arguing for translational relevance again with a possible synergistic effect through both IOP reduction and direct neuroprotection.

(10) Can the authors add any other functional validation studies to explore to understand the pathways enriched in all the subtypes of TM1, TM2, and TM3 cells, in addition to the ICH/IF/RNAscope validation?

We agree with the reviewer on the importance of further functional validation of pathways active in TM cell subtypes that influence IOP. However, comprehensive investigation of the pathways active in subtypes need to be in future studies. It is beyond the scope of his already large paper.

(11) The authors should include a representative image of the limbal dissection. While Figure S1 provides a schematic, mouse eyes are very small, and dissecting unfixed limbal tissue is technically challenging. It is also difficult to reconcile the claim that the majority of cells in the limbal region are TM and endothelium. As shown in Figure S6, DAPI staining suggests a much higher abundance of scleral cells compared to TM cells within the limbal strip. Additional clarification or visual evidence would help validate the dissection strategy and cellular composition of the captured region.

We appreciate the reviewer’s suggestion and have added additional images to Figure S1 to show our limbal strip dissection. However, we clarify that we do not intend to suggest that TM and endothelial cells are the most abundant populations in these dissected strips.  When we say “are enriched for drainage tissues” we mean in comparison to dissecting the anterior segment as a whole. We have clarified this in the text. In fact, epithelial cells (primarily from the cornea) constituted the largest cluster in our dataset (Figure 1A). Additionally, to avoid misinterpretation, we generally refrain from drawing conclusions about the relative abundance of cell types based on sequencing data. Single-cell and single nucleus RNA sequencing results are sensitive to technical factors that alter cell proportions depending on exact methodological details. In our study, TM cells comprised 24.4% of the single-cell dataset and 11.8% of the single-nucleus dataset, illustrating the impact of methodological variability. 

Lines 163-164: “Individual eyes were dissected to isolate a strip of limbal tissue, which is enriched for TM cells in comparison to dissecting the anterior segment as a whole.”

Reviewer #1 (Recommendations for the authors):

To enhance the reproducibility and transparency of the findings presented in this study, we strongly recommend that the authors make all analysis scripts and computational tools publicly available.

We agree with the reviewer’s emphasis on transparency and are currently building a GitHub page to share our scripts. However, we did not develop any new tools for this study. All tools that we used are publicly available and provided in our methods section. All data will be available as raw data and through the Broad Institute’s Single Cell Portal.

Reviewer #2 (Recommendations for the authors):

The authors are to be commended for a well-written presentation of high-quality data, their comparisons of datasets (other mouse and human scRNAseq data), correlation with clinical glaucoma risk alleles, and curative therapy for the mouse model of Lmx1b glaucoma. There are several minor suggestions that the authors might consider to further improve their manuscript:

(1) Lines 42-43: Although their data strongly support the role of mitochondrial dysfunction in Lmx1b glaucoma, they might want to soften their conclusion "supports a primary role of mitochondrial dysfunction within TM3 cells initiating the IOP elevation that causes glaucoma".

With the inclusion of EM data supporting mitochondrial dysfunction in Lmx1b mutant TM cells, we have revised this sentence to more accurately reflect our findings.

Lines 42-44 (previously lines 42-43): “Mitochondria in TM cells of V265D/+ mice are swollen with a reduced cristae area, further supporting a role for mitochondrial dysfunction in the initiation of IOP elevation in these mice.”

(2) Figure 1: Why is the shape of the "TM containing" cluster in 1A so different than the cluster shown in 1B?

We isolated cells from the 'TM-containing' cluster and performed unbiased reclustering, which alters their positioning in UMAP space. The figure legend has been updated to clarify this point.

Lines 143-144 “A separate UMAP representation of the trabecular meshwork (TM) containing cluster following subclustering.”

(3) Line 160: change "data was" to "data were"

Corrected

(4) S4 Fig C: Please comment on why the Columbia and Duke heatmaps for TM3 are not as congruent as the heatmaps for TM1 and TM2.

We cannot definitively determine the reason for this. However, differences in tissue processing techniques between the Columbia and Duke preparations may contribute. Such variations have been shown to affect cellular transcriptomes in certain contexts. It is possible that TM3 cells are more susceptible to these effects than others. We have added a statement addressing this point to the figure legend.

Lines 238-240: “Because tissue processing techniques can alter gene expression [52], the heatmap variation between institutes likely reflects differences in processing techniques (Methods) and suggests that TM3 cells are more susceptible to these effects than other cell types.”

(5) S9 Fig: It is very difficult to see any staining for TM1 CHIL1 (2nd panel), TM2 End3 (2nd panel), and TM3 Lypd1 (both panels)

We apologize for the difficulty in visualizing these panels. To improve clarity, we have increased the brightness of all relevant marker signals, within standard bounds, to facilitate easier interpretation.

(6) Line 380: "are significantly higher"; since statistical analysis was not reported, please do not use "significantly"

Done

(7) The authors should consider discussing several of their findings that agree with published literature. For example:

Figure 3B: "Wnt protein binding" (PMID: 18274669), "TGFb "binding" (numerous references), "integrin binding" (work of Donna Peters), "actin binding"/"actin filament binding"/"actin filament bundle" (CLANs references)

S10 Fig c: "ossification" (work of Torretta Borres)

S11 Fig A: ID2/ID3 (PMID: 33938911); (B) BMP4 (PMID: 17325163)

S12 Fig A: MYOC in TM1 cells (numerous references)

We appreciate the reviewer’s diligent review and comments regarding these pathways. We have added a comment to the discussion regarding the agreement of these pathways.

Lines 855-858: In addition, the expression of genes that we document generally agrees with the literature. For example, the following genes and signaling molecules have been reported in TM cells, WNT signaling [78], TGF-β signaling [79-85], integrin binding [86-88], actin cytoskeletal networks [89], calcification genes [90, 91], and Myocilin [91-94].

(8) Line 541: was confocal microscopy used to measure the "3D shapes" of nuclei or was this done with a single image to determine sphericity?

This analysis was performed using confocal microscopy and 3D reconstructed models of the TM nuclei. We have added text to clarify this in the figure legend 

Lines 553-556: “To rigorously assess whether TM1 nuclei are more spherical, we analyzed their reconstructed 3D shapes from whole mounts images by confocal microscopy, comparing them to TM3 nuclei using the ‘Sphericity’ tool in Imaris.”

(9) Line 545: please add a close parentheses after "scoring 1"

Done

(10) S15 Fig: (A) There does not appear to be "good agreement" (line 653) between the datasets for TM1. (C) please provide a better explanation on how to interpret these "Confusion Matrix" results.

We understand the referee's concern, the patterns likely appear different to the referee due to limited sampling in snRNA-seq data. Based on our results, TM1 seems particularly susceptible, possibly because these cells do not tolerate the isolation process as well. Although we are confident that TM1 shows good agreement between the two techniques based on our experience, we have revised the language in the text to “generally” to reflect this nuance.

Lines 633-635 (previously line 653): The generated clusters and their marker genes generally agreed with our scRNA-seq analyses (Fig 5A-B, S15A Fig).

We have also added additional clarification for how to interpret the Confusion Matrix. 

Lines 669-672: “Colors indicate the fraction of cells identified in each ATAC cluster (row) which are also identified in each RNA cell type (columns), where darker colors represent stronger correspondence between RNA and ATAC clusters.”

(11) Line 676: The transition from discussing the sc/snRNAseq data to the work in Lmx1b mutant mice is quite abrupt and could use a better transition to introduce this metabolism work.

We have revised this transition for improved flow but prefer to keep all transitions brief due to the paper's length.

Lines 691-694 (previously line 676): To evaluate the utility of our new TM cell atlas, we used it to examine how Lmx1b mutations affect the TM cell transcriptome and to identify potential mechanisms underlying IOP elevation. We selected LMX1B because it causes IOP elevation and glaucoma in humans and was identified as a highly active transcription factor in our TM cell dataset.

(12) Lines 696-697: It appears counter-intuitive that upregulation of ubiquitin pathways would lead to proteostasis (proteosome protein degradation requires ubiquination).

We have clarified that the protein tagging pathway was significantly upregulated. However, polyubiquitin precursor itself was downregulated. In general, the statistical significance of the protein tagging pathway suggests perturbation of the system tagging proteins for degradation. We have clarified this in the text. 

Lines 711-714 (previously lines 696-697): “In addition, mutant TM3 cells showed an upregulation of protein tagging genes. However, there is a downregulation of the polyubiquitin precursor gene (Ubb, P = 4.5E-30), indicating a general dysregulation of pathways that tag proteins for degradation.”

(13) Line 715: Please justify why "perturbed metabolism" was chosen to pursue vs the other differentially expressed pathways

We chose to narrow our focus on TM3 cells because of the enrichment for Lmx1b expression.Most pathways identified in our analysis of TM3 cells implicate mitochondrial metabolism.Therefore, we chose to further explore this avenue. We clarified that perturbed metabolism was the strongest gene expression signature in the text. 

Lines 753-754 (previously line 715): “Our findings most strongly implicate perturbed metabolism within TM3 cells as responsible for IOP elevation in an Lmx1b glaucoma model.”

(14) Line 759: The authors clearly demonstrate that Lmx1b is most expressed in TM3 cells; however, they did not demonstrate that "Lmx1b was most active"

ATAC analysis showed that Lmx1b was most active in TM cells overall. We inferred its activity in TM3 because Lmx1b is most enriched in that subtype. This has been clarified in the text.

Lines 799-800 (previously line 759): “More specifically, we demonstrate that Lmx1b is the most active TM cell TF and is enriched in TM3 cells,…”

(15) Lines 830-835: Please include references documenting increased TGFβ2 concentrations in POAG aqueous humor and TM, effects of TGFβ2 on TM ECM deposition, and TGFβ2 induced ocular hypertension ex vivo and in vivo.

Done.

(16) Line 875: The authors provide no direct evidence for enhances "oxidative stress" in Lmx1b TM3 cells

The mitochondrial abnormalities and changed pathways support oxidative stress, but we have not directly tested this. Experiments are currently underway to evaluate its role, but these additional analyses are beyond the scope of this paper. We removed oxidative stress from the sentence.

Lines 920-922 (previously line 875): “Importantly, in heterozygous mutant V265D/+ mice, TM3 cells had pronounced gene expression changes that implicate mitochondrial dysfunction, but that were absent or much lower in other cells including TM1 and TM2.”

(17) Line 880: Similarly, the authors have not directly assessed effects on metabolism in TM3 cells; they only have shown changes in the expression of mitochondrial genes that may affect metabolism

We have no way to specifically isolating TM3 cells to test this. Future work is underway to test this more broadly in isolated TM cells but is beyond the scope of this is already large paper. Considering our gene expression data and the addition of supporting EM data, we have qualified the text.

Lines 930-931 (previously 880): “Our data extend these published findings by showing that inheritance of a single dominant mutation in Lmx1b similarly affects mitochondria in TM cells.”

(18) Line 892: What markers were used to detect "cell stress"?

We have revised the text. Although our RNA data show stress gene changes, characterization of these markers is beyond the scope of the current study and will be included in a subsequent paper.

Lines 945-948 (previously line 892): “However, these processes were not limited to TM3 cells or even to cell types that express detectable Lmx1b, suggesting that they are secondary damaging processes that are subsequent to the initiating, Lmx1b-induced perturbations in TM3 cells.”

Additional author driven change

While revising and reviewing our data, we identified a coding error that resulted in the WT and V265D mutant group labels being switched in Figure 6. Importantly, the significance of the differentially expressed genes (DEGs), the implicated biological pathways, and the interpretation of pathway directionality in the manuscript remain accurate. The only issue was the incorrect labeling in the figure. We have corrected the labels in Figure 6 to accurately reflect the data. As noted above, all data and code will be made available to ensure full reproducibility of our results.

References

(1) Doucet-Beaupre H, Gilbert C, Profes MS, Chabrat A, Pacelli C, Giguere N, et al. Lmx1a and Lmx1b regulate mitochondrial functions and survival of adult midbrain dopaminergic neurons. Proc Natl Acad Sci U S A. 2016;113(30):E4387-96. Epub 2016/07/14. doi: 10.1073/pnas.1520387113. PubMed PMID: 27407143; PubMed Central PMCID: PMCPMC4968767.

(2) Jimenez-Moreno N, Kollareddy M, Stathakos P, Moss JJ, Anton Z, Shoemark DK, et al. ATG8-dependent LMX1B-autophagy crosstalk shapes human midbrain dopaminergic neuronal resilience. J Cell Biol. 2023;222(5). Epub 2023/04/05. doi: 10.1083/jcb.201910133. PubMed PMID: 37014324; PubMed Central PMCID: PMCPMC10075225.

(3) Cross SH, Macalinao DG, McKie L, Rose L, Kearney AL, Rainger J, et al. A dominantnegative mutation of mouse Lmx1b causes glaucoma and is semi-lethal via LDB1mediated dimerization [corrected]. PLoS Genet. 2014;10(5):e1004359. Epub 2014/05/09. doi: 10.1371/journal.pgen.1004359. PubMed PMID: 24809698; PubMed Central PMCID: PMCPMC4014447.

(4) Li K, Tolman N, Segre AV, Stuart KV, Zeleznik OA, Vallabh NA, et al. Pyruvate and related energetic metabolites modulate resilience against high genetic risk for glaucoma. Elife. 2025;14. Epub 2025/04/24. doi: 10.7554/eLife.105576. PubMed PMID: 40272416; PubMed Central PMCID: PMCPMC12021409.

(5) Tolman NG, Balasubramanian R, Macalinao DG, Kearney AL, MacNicoll KH, Montgomery CL, et al. Genetic background modifies vulnerability to glaucoma-related phenotypes in Lmx1b mutant mice. Dis Model Mech. 2021;14(2). Epub 2021/01/20. doi: 10.1242/dmm.046953. PubMed PMID: 33462143; PubMed Central PMCID: PMCPMC7903917.

Author response:

Below we outline our provisional responses to the major points raised in the public reviews, and our planned revisions:

(1) Mechanistic model of how ZDHHC18/MARCH8 engage the cGAS–DNA condensate (Reviewer #1 & #2

We will add a dedicated subsection and a working-model figure describing our current view: IDRs of ZDHHC18 (Golgi) and MARCH8 (endosomes) engage pre-formed cGAS–DNA condensates at organelle membranes, and thereby tune cGAS activity through PTMs. We will explicitly discuss bridge-like versus allosteric modes by perform additional LLPS experiment (e.g. FRAP assay) to detect any IDR-driven changes in condensate properties, and explain how these scenarios fit our data.

(2) Selectivity beyond ZDHHC18/MARCH8 (Reviewer #1)

We will expand the text to explain existing evidence indicating that, in addition to ZDHHC18 or MARCH8, other post-translational modification (PTM) enzymes and/or membrane-associated scaffolds may also modulate cGAS. We will summarize our current datasets that support this possibility and outline how this selectivity relates to organelle identity.

(3) Why membrane association suppresses cGAS activity (Reviewer #1)

We will provide a concise mechanistic rationale—integrating our published work—to explain how membrane-proximal sequestration can limit cGAS catalysis despite cGAS–DNA coexistence within condensates. Specifically, we will discuss (i) IDR-dependent changes in condensate properties, and (ii) PTMs by ZDHHC18/MARCH8 that allosterically reduce catalytic efficiency; we will clearly cross-reference our prior publications that bear on these points.

(4) Reconciling Fig. S7 (DNA-dependent binding) with Fig. 5 (recruitment to IDR droplets) (Reviewer #2)

We will add text to clarify experimental context and readouts to prove that there is no real contradiction between Fig. S7 and Fig. 5. In the experiment shown in Fig. 5, PEG (a macromolecular crowding agent) was added to the system, which facilitates the formation of IDR phase-separated droplets. Under these conditions, cGAS partitions into the IDR condensates, leading to the observed recruitment. In contrast, Fig. S7 examines the direct physical interaction between cGAS and the IDRs using biochemical pull-down assays and shows that no direct interaction occurs in the absence of DNA. These two results reflect different experimental contexts and are therefore not mutually exclusive.

(5) Planned additional tests to address specificity and mechanism (Reviewer #2)

DNA pull-down: to test whether IDRs alter cGAS–DNA affinity, we will compare cGAS binding to DNA with/without MEMCA IDRs (and with charged-residue mutants).

Domain mapping: to determine which region of cGAS engages MEMCA IDRs, we will map binding using cGAS N-terminus/core-domain truncations and key surface mutants.

Physiological in vitro LLPS: we will repeat cGAS–DNA–IDR LLPS assays under physiological buffer conditions and report partition coefficients, FRAP, and phase diagrams to ensure physiological relevance.

(6) Image clarity and data presentation (Reviewer #2):

We will improve image resolution, add zoomed-in insets with organelle markers, and provide more significant Cy5-ISD signal.

(7) Nuclear localization of cGAS and system considerations (Reviewer #3)

We will explicitly document the nuclear signal of cGAS observed in our confocal experiments, detail the cell lines and expression systems used. We will also clarify cGAS nuclear localization in the cell lines used.

(8) Endogenous validation and cell line consistency (Reviewer #3):

We will perform experiments in primary cells (knockout macrophages) to address the concern of relying on overexpression.

(9) Language and grammar (Reviewer #3):

We will thoroughly revise the manuscript for grammar and clarity.

Together, these planned revisions will strengthen the mechanistic basis of our findings and provide direct evidence for the physiological role of organelle-tethered IDRs in regulating cGAS activity.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public review):

(1) Their first major claim is that fluid flows alone must be quite strong in order to fragment the cyanobacterial aggregates they have studied. With their rheological chamber, they explicitly show that energy dissipation rates must exceed "natural" conditions by multiple orders of magnitude in order to fragment lab strain colonies, and even higher to disrupt natural strains sampled from a nearby freshwater lake. This claim is well-supported by their experiments and data.

We thank the reviewer for this positive comment. We fully agree, as our fragmentation experiments on division-formed colonies clearly demonstrate their strong mechanical resistance in naturally occurring flows.

(2) The authors then claim that the fragmentation of aggregates due to fluid flows occurs through erosion of small pieces. Because their experimental setup does not allow them to explicitly observe this process (for example, by watching one aggregate break into pieces), they implement an idealized model to show that the nature of the changes to the size histogram agrees with an erosion process. However, in Figure 2C there is a noticeable gap between their experiment and the prediction of their model. Additionally, in a similar experiment shown in Figure S6, the experiment cannot distinguish between an idealized erosion model and an alternative, an idealized binary fission model where aggregates split into equal halves. For these reasons, this claim is weakened.

The two idealized models of colony fragmentation, namely erosion of single cells and fragmentation into equal sizes (or binary fission), lead to distinguishable final size distributions. We believe that our experiments for division-formed colonies support the hypothesis of the erosion mechanism. Specifically, Figure 2E shows that colony fragmentation resulted in a decrease of large colonies and a strong increase of single cells and dimers (two cells). In our view, the strong increase of single cells and dimers provides quite convincing (but indirect) evidence supporting the erosion mechanism. This is described on lines 112-121. To further address the reviewer’s concern, we have included in the revised version of Figure 2 (panels B and D) a direct comparison between these two fragmentation models for large division-formed colonies fragmented at a high dissipation rate of ε = 5.8 m²/s³. Furthermore, we have included the new Supplementary Figure S9, which details the model predictions for the colony size distribution at various time points.

The ideal equal fragments model (i.e., where every fracture event produces two identical fragments with half the original biovolume) does not capture the biovolume transfer from large colonies to single cells, as observed for the experimental results in panel D of Figure 2 and panel E of Figure S9. In contrast, the erosion model, in panel D of Figure 2 and panel D of Figure S9, provides a good prediction of the experimental results within the experimental uncertainty. The different fragmentation models are discussed in lines 226-228 of the revised manuscript and lines 865-873 of the SI.

(3) Their third major claim is that fluid flows only weakly cause cells to collide and adhere in a "coming together" process of aggregate formation. They test this claim in Figure 3, where they suspend single cells in their test chamber and stir them at moderate intensity, monitoring their size histogram. They show that the size histogram changes only slightly, indicating that aggregation is, by and large, not occurring at a high rate. Therefore, they lend support to the idea that cell aggregation likely does not initiate group formation in toxic cyanobacterial blooms. Additionally, they show that the median size of large colonies also does not change at moderate turbulent intensities. These results agree with previous studies (their own citation 25) indicating that aggregates in toxic blooms are clonal in nature. This is an important result and well-supported by their data, but only for this specific particle concentration and stirring intensity. Later, in Figure 5 they show a much broader range of particle concentrations and energy dissipation rates that they leave untested.

We thank the reviewer for this positive comment. We agree that our experimental results show clear evidence that aggregated colonies have a weaker structure in comparison to division-formed colonies, thus supporting the hypothesis that clonal expansion is the main mechanism for colony formation under most natural settings. The range of energy dissipation rates of our experimental setup covers almost entirely the region for which aggregated and division-formed colonies differ in their fragmentation behavior (Zone III of Figure 5). Within this zone, aggregated colonies are fragmented and only the division-formed colonies are able to withstand the hydrodynamic stresses. Furthermore, we show that this fragmentation behavior has a low sensitivity to the total biovolume fraction, as displayed in the Supplementary Figures S2 and S4 and discussed in lines 151-154 and 160-163. We agree that our cone-and-plate setup covers a limited parameter range, and we have added a detailed discussion of these limitations in the revised manuscript, under section Materials and Methods in lines 462-473.

(4) The fourth major result of the manuscript is displayed in Equation 8 and Figure 5, where the authors derive an expression for the ratio between the rate of increase of a colony due to aggregation vs. the rate due to cell division. They then plot this line on a phase map, altering two physical parameters (concentration and fluid turbulence) to show under what conditions aggregation vs. cell division are more important for group formation. Because these results are derived from relatively simple biophysical considerations, they have the potential to be quite powerful and useful and represent a significant conceptual advance. However, there is a region of this phase map that the authors have left untested experimentally. The lowest energy dissipation rate that the authors tested in their experiment seemed to be \dot{epsilon}~1e-2 [m^2/s^3], and the highest particle concentration they tested was 5e-4, which means that the authors never tested Zone II of their phase map. Since this seems to be an important zone for toxic blooms (i.e. the "scum formation" zone), it seems the authors have missed an important opportunity to investigate this regime of high particle concentrations and relatively weak turbulent mixing.

We agree with the reviewer that Zone (II) of Figure 5 is of great importance to dense bloom formation under wind mixing and that this parameter range was not covered by our experiments using a cone-and-plate shear flow. The measuring range of our device was motivated by engineering applications such as artificial mixing of eutrophic lakes using bubble plumes, as well as preliminary experiments which demonstrated that high levels of dissipation rate were required to achieve fragmentation. The range of dissipation rates that can be achieved by the cone-and-plate setup is limited at the lower end by the accumulation of colonies near the stagnation point at the conical tip and at the upper end by the spillage of fluid out of the chamber. We now discuss this measuring range in lines 462-473 of the revised manuscript.

Although our setup does not cover Zone (II), we now refer to recent results in the literature for evidence of aggregation-dominance at Zone (II). The experimental study of Wu et al. (2024) (reference number 64 of the revised manuscript) investigated the formation of Microcystis surface scum layers in wind-mixed mesocosms. Their study identified aggregation of colonies in the scum layer, resulting in increases of colony size at rates faster than cell division. These results agree with our model, and the parameters range investigated fall within the Zone II. We have included in the revised version, lines 328-337, a detailed discussion elucidating the parameter range covered in our experiments and the findings of Wu et al. (2024).

Other items that could use more clarity:

(5) The authors rely heavily on size distributions to make the claims of their paper. Yet, how they generated those size distributions is not clearly shown in the text. Of primary concern, the authors used a correction function (Equation S1) to estimate the counts of different size classes in their image analysis pipeline. Yet, it is unclear how well this correction function actually performs, what kinds of errors it might produce, and how well it mapped to the calibration dataset the authors used to find the fit parameters.

We agree with the reviewer that more details of the correction function should be included. We have included in the revised version of the Supporting Information, in lines 785-796, a more detailed explanation of the correction function. Furthermore, a direct comparison of raw and corrected histograms of the size distribution and its associated uncertainty is presented in the new Supplementary Figure S8.

(6) Second, in their models they use a fractal dimension to estimate the number of cells in the group from the group radius, but the agreement between this fractal dimension fit and the data is not shown, so it is not clear how good an approximation this fractal dimension provides. This is especially important for their later derivation of the "aggregation-to-cell division" ratio (Equation 8)

We agree with the reviewer that more details on the estimation of fractal dimension are needed. The revised version, under Materials and Methods in lines 508-515, now includes the detailed estimation procedure, the number of colonies analysed, and the associated uncertainty.

Reviewer #1 (Recommendations For The Authors):

In light of the weak evidence for claim #2 outlined above, I believe the paper would benefit from a more explicit comparison in Figure 2C of the two models - idealized erosion, and idealized binary fission. With such a comparison, the authors would have stronger footing to claim that one process is more important than the other.

As mentioned in our answer above to comment #2 of public review, we have included in the revised version of Figure 2 (panels B and D) a direct comparison between the erosion and equal fragments (binary fission) models for large division-formed colonies fragmented under ε = 5.8 m²/s³. The comparison is further detailed in the new Supplementary Figure S9 for representative time points. Only the erosion models can recover the biovolume transfer from large colonies to single cells, as observed for the experimental results in Figure 2D and further detailed in Figure S9D. We believe that the revised version of Figure 2 and the new Supplementary Figure S9 provide strong evidence in support of the erosion fragmentation model.

Would the authors comment on their chosen range of experimental dissipation rates? For instance, was their goal more to investigate industrial/engineering applications where the goal is to disrupt the cyanobacteria, but not really typical natural conditions under which the groups might form?

The choice of experimental dissipation rates in our experiment was such that it covers engineering applications such as artificial mixing of eutrophic lakes using bubble plumes. We have now clarified in the Introduction, on lines 37-39, that artificial mixing has been successfully applied in several lakes to suppress cyanobacterial blooms. Furthermore, we have now clarified in the caption of Figure 5 that the bars on the right side indicate typical values of dissipation rates induced by natural wind-mixing, bubble plumes in artificially mixed lakes, and laboratory-scale experiments such as cone-and-plate systems and stirred tanks. The dissipation rates induced by the bubble plumes in artificially mixed lakes could potentially fragment aggregated cyanobacterial colonies and thus disrupt bloom formation. However, our preliminary experiments demonstrated that high levels of dissipation rate were required to achieve fragmentation, therefore we’ve focused on the upper range of values (0.01 to 10 m²/s³).

The dissipation rates generated by the cone-and-plate approach are indeed higher than the dissipation rates under typical natural conditions in lakes. We have now added a detailed discussion of the range of dissipation rates generated by the cone-and-plate approach in the revised manuscript, under section Materials and Methods in lines 462-473, where we also explain that these values are higher than the natural dissipation rates generated by wind action in lakes. However, the more generic insights obtained by our study, shown in Figure 5, are relevant for dissipation rates of natural lakes (e.g., Zone II). Therefore, in our discussion of Figure 5 we have now included the recent findings of Wu et al. (2024) (reference number [64] of the revised manuscript), who studied bloom formation of Microcystis in mesocosm experiments at dissipation rates representative of natural conditions; see also our reply to the next comment.

The authors should consider testing the space of Zone II on their phase map, for instance at very high particle concentrations and even lower rotational speeds, in order to show that their derivations match experiments.

Good point. As mentioned in our answer above to comment #4 of the public review, Zone II lies beyond the measuring range of our experimental setup. Instead, we refer to the recent study of Wu et al. (2024) (reference number [64] of the revised manuscript) which demonstrated that dense scum layers of Microcystis colonies are aggregation-dominated. These mesocosm experiments agree with our model predictions and their parameter range falls within Zone II. We have included in the revised version, lines 328-337, a detailed discussion where we elucidate the parameter range covered in our experiments and compare our predictions for Zone II with the recent findings of Wu et al. (2024).

The authors should show their calibration data and fit for the correction function of equation S1. Additionally, you may consider showing "raw" and "corrected" histograms of the size distribution, to demonstrate exactly what corrections are made.

As mentioned in our answer above to comment #5 of the public review, we have included in the revised version of the Supporting Information the new Supplementary Figure S8, which shows the raw and adjusted histograms of the size distribution, including the associated uncertainties. Furthermore, the correction function is now explained in detail in the new Supporting Information Text in lines 785-796.

The authors might consider commenting on Figure S3 a bit more in the main text. Even at very high dissipation rates, the cyanobacterial groups don't plummet to size 1, but stay in an equilibrium around 10-20x the diameter of a single cell. What might this mean for industrial applications trying to break up the groups?

We agree with the reviewer that further discussion of Figure S3, panels E and F, is warranted. In the revised version of the manuscript, under section Fragmentation of Microcystis colonies occurs through erosion in lines 133-137, we have now included a discussion of this figure. Figure S3F shows that more than 90% of the total biovolume ends up in the category “small colonies” (mostly single cells and dimers); hence, most of the initially large colonies do fragment to single cells or dimers. Only about 5-10% of the biovolume remains as “large colonies” of 10-20 cells. Although it is challenging to draw definitive conclusions about the behavior of these remaining large colonies, as they account for only a minor fraction of the suspension, one hypothesis is that variability in mechanical properties between colonies results in a subset of colonies exhibiting exceptional resistance even to very high dissipation rates (see lines 133-137).

Minor comments:

Typo Caption of Figure 2: Should read [m^2/s^3] for units

Thanks for catching this typo. The units in the caption of Figure 2 has been corrected to [m^2/s^3].

There is no Equation 10 in Materials and Methods as indicated in the rheology section.

We thank the reviewer for pointing out the lack of clarity in this algebraic manipulation. In fact, the yield stress has to be substituted in the current Equation 11 (previously Eq.10), from which the critical dissipation rate must be substituted in Equation 3. The result is the critical colony size (l* = 2.8) mentioned in line 243 of the revised manuscript. The correct equation numbers and algebraic substitutions are now indicated in lines 241-243 of the revised version of the manuscript.

<Reviewer #2 (Public review):

Especially the introduction seems to imply that shear force is a very important parameter controlling colony formation. However, if one looks at the results this effect is overall rather modest, especially considering the shear forces that these bacterial colonies may experience in lakes. The main conclusion seems that not shear but bacterial adhesion is the most important factor in determining colony size. As the importance of adhesion had been described elsewhere, it is not clear what this study reveals about cyanobacterial colonies that was not known before.

We would like to emphasize several key findings that our study reveals about the impacts of fluid flow on cyanobacterial colonies:

(I) Quantification of mechanical strength in cyanobacterial colonies: Our results demonstrate the high mechanical strength of cyanobacterial colonies, as evidenced by the requirement of high shear rates to achieve fragmentation. This is new knowledge, that was not known before for cyanobacterial colonies. To this end, our study highlights the resilience of these colonies against naturally occurring flows and bridges the gap between theoretical assumptions about colony strength and experimentally measured mechanical properties.

(II) The discovery that the mechanical strength of colonies differs between colonies formed by cell division and colonies formed by aggregation. This is again new knowledge, that was not known before for cyanobacterial colonies.

(III) Validation of a hypothesis regarding colony formation: Using a fluid-mechanical approach, we confirm the findings of recent genetic studies (references 25 and 67 of the revised version of the manuscript) which indicated that colony formation occurs predominantly via cell division rather than cell aggregation under natural conditions (except in very dense blooms).

(IV) Practical guidelines for cyanobacterial bloom control: Our findings provide valuable insights into the design of artificial mixing systems applied in several lakes. Artificial mixing of lakes is based on fundamentals of fluid flow, aiming at preventing aggregation of buoyant cyanobacteria in scum layers at the water surface. Our results show that the dissipation rates generated by bubble blumes in artificially mixed lakes can fragment cyanobacterial colonies formed by aggregation, but are not intense enough to cause fragmentation of division-formed colonies (see Figure 5 and lines 348-360).

The agreement between model and experiments is impressive, but the role of the fit parameters in achieving this agreement needs to be further clarified.

The influence of the fit parameters (namely the stickiness α1 and the pairs of colony strength parameters S1,q1,S2,q2) is discussed in the sections Dynamical changes in colony size modelled by a two-category distribution in lines 247-253 and Materials and Methods in lines 559-565. We kept the discussion concise to maintain readability. However, we agree with the reviewer that additional details about the importance of the fit parameters and the sensitivity of the results to these parameters could be beneficial. In the revised version of the section Materials and Methods in lines 560-563, we have included a detailed discussion of the fit parameters.

The article may not be very accessible for readers with a biology background. Overall, the presentation of the material can be improved by better describing their new method.

We apologize for the limited readability of the description of the experimental setup and model used. In the revised version of the manuscript and the SI, we have detailed further the new methods presented here. The modifications include a detailed description of the operating range of the cone-and-plate shear setup (subsection Cone-and-plate shear of the section Materials and Methods, in lines 462-473). Furthermore, we think that incorporation of the recent experimental results of Wu et al. (2024), on lines 331-337 of the manuscript, will appeal to readers with a biology background. Their mesocosm experiments support our model prediction that aggregation is the dominant mechanism for colony formation in region (II) of Figure 5.

Reviewer #2 (Recommendations For The Authors):

(1) The authors seem too modest in claiming technological advance. They should describe the technological advance of combining microscopy with rheometry, in such a way that this invites others to apply this or similar approaches on biological samples. Even though I feel that the advancement of knowledge of this system by their method is relatively modest, there may be more advances in other systems.

We appreciate the positive view of the reviewer towards the importance of this technology and we agree that its advantages should be advertised to researchers investigating similar systems. We have now given more attention to the technological advance of combining microscopic imaging with rheometry in the final paragraph of the Conclusions (lines 386400), where we now also briefly discuss an interesting recent study of marine snow (Song et al. 2023, Song and Rau 2022, reference numbers 70 and 71 of the revised manuscript), which used a similar combination of microscopy and rheometry as in our study. Furthermore, in the Methods section, we now briefly explain how the rheometry can be adjusted to investigate other systems (lines 474-480).

(2) It seems reasonable -also based on what we already know about these aggregates - to assume that the main difference in shear sensitivity between field samples and cultures lies in the production of extracellular polysaccharide substance (EPS). To go beyond what is already known, the study could try to provide more direct and quantitative evidence for EPS involvement. For example, using a chemical quantification of EPS levels, or perturbing EPS levels using digestive enzymes.

We agree with the reviewer that further characterization of the EPS is highly relevant to understand the mechanical strength of colonies. However, we believe that chemical quantification and/or degradation of EPS lies beyond the scope of our article and should be addressed by future studies.

(3) Assuming EPS is indeed the reason for the differences in shear resistance: the authors speculate the reason why the field samples have more EPS lies in chemical composition (Calcium/nitrogen levels). In addition, there could be grazing that is known to promote aggregation (possibly increasing EPS), or just inherent genetic differences between strains. I am not necessarily expecting the authors to explore this direction experimentally, but it seems certainly feasible and would make the final result less speculative.

We agree with the reviewer that there are more biotic and abiotic factors that can influence EPS amount and composition. The influence of grazing and other relevant factors on cell adhesion is discussed in references [26-29], cited in our introduction in lines 50-53. As discussed in our answer to recommendation #2, we believe that a quantitative investigation of these various factors is beyond the scope of this work and should be addressed in future studies.

(4) A cool finding seems to be the critical relative diameter (Fig 2E), a colony size that seems invariant under shear. I was slightly surprised that the authors seem to take little effort to understand this critical diameter mechanistically (for example by predicting it, or experimentally perturbing it). Again, not a necessary requirement, but this is where the study could harness its technological advantage to provide a more quantitative understanding of something that goes beyond the existing knowledge of the system.

We apologize to the reviewer if our descriptions and discussions of Figure 2 were unclear. One of the key conclusions from our experiments is that the critical relative diameter depends on the dissipation rate, as shown in Figure 2F. This dependence is also incorporated into the model through the constitutive equation (2). Furthermore, we expect the mechanical resistance of colonies, quantified by the critical relative diameter, to be affected by other biotic and abiotic factors that influence EPS amount and composition.

(5) The jump from 0.019 to 1.1 m²/s³ seems large. What was the reason for not exploring intermediate values? The authors should also define low, modest and intense dissipation rates more clearly. Currently, they seem somewhat arbitrarily defined, i.e. 0.019 m²/s³ is described as low (methods) and moderate (results). In Fig 2, the authors further talk about low dissipation rates without a quantitative description.

We thank the reviewer for pointing out the lack of clarity in the choice of parameter range and the nomenclature. Regarding the former, the suspension of division-formed colonies of Microcystis strain V163 displayed negligible fragmentation for dissipation rates between 0.019 to 1.1 m²/s³, as seen in Figures S2A and S3A. Due to the low sensitivity of the fragmentation results in this region, we don’t expect change in behavior for intermediate values. Regarding the nomenclature, we have corrected the inconsistencies throughout the text. We have chosen to name the dissipation rate values as: low for values typical of windmixing, moderate for values typical of the core of bubble plumes, and intense for values typical of propellers. Whenever mentioned in the text, the numerical value of dissipation rate is also included to avoid doubt.

(6.) The structure and narrative of the paper can be improved. The article first describes all lab culture experiments and then the model, while the first figure already shows model fits. Perhaps it would be better to first describe the aggregation experiments, to constrain the appropriate terms of the model, and then move to fragmentation.

We appreciate the recommendation of the reviewer regarding the structure. We have chosen to describe first the fragmentation experiments (Fig. 2), as these can be understood without introducing the aggregation effects. In contrast, the steady state results in the aggregation experiments (Fig. 3) come from the balance between aggregation and fragmentation. Therefore, we judged the current order to be more appropriate. The model fits are combined with the experimental results in Figures 2 and 3 to have a concise display. We have ensured that all the concepts required to understand each figure panel are explained prior to their discussion.

(7) The number of data points that go into the histogram needs to be indicated. The main reason is that the authors report the distribution in terms of the biovolume fraction, suggesting the numerical counts are converted into volume. This to me seems like the most sensible parameter, but I could not find how this conversion is calculated (my apologies if I missed it). This seems especially relevant because a single large colony can impact this histogram quite considerably.

We apologize for the lack of clarity in the calibration and conversion steps of the size distribution. As discussed above in the answer to comment #5 of the reviewer #1, more details of the calibration process have been added to the revised version of the Supporting Information Text in lines 785-796. Furthermore, the new Supplementary Figure S8 presents examples of the raw and adjusted size distribution, including the total number of counted colonies per histogram and the associated uncertainties in the concentration and biovolume distributions.

(8) Over the timescales measured here, colonies could start sinking (or floating), possibly in a size-dependent manner, that could lead to a bias due to boundary effects. Did the authors consider this potential artifact?

The sinking or floating of colonies is a relevant process which was taken into account in the choice of our parameter range for the dissipation rate. The minimum dissipation rate used in our experiments ensures that the upward inertial velocity near stagnation is sufficient to counteract the sedimentation of colonies. A detailed discussion of the choice of the parameter range is now included in the revised version of the Materials and Methods in lines 462-473.

(9) "On the one hand, sequencing of the genetic diversity within Microcystis colonies supports the hypothesis that colony formation undernatural conditions is primarily driven by cell division [25]. On the other hand, cell aggregation can occur on a shorter time scale and may offer improved protection against high grazing pressure [26]." This appears somewhat constructed, as what is described as "on the other hand" is not evidence against the genetic diversity.

We agree that the suggested dichotomy in this text appeared somewhat constructed, and we have now removed the wording “on the one hand” and “on the other hand”. The studies from reference [25] demonstrated that the genetic diversity between independent Microcystis colonies is much greater than the diversity within colonies. If cell aggregation was the dominant mechanism, a similar genetic diversity would be observed between and within colonies, which contrasts the findings from reference [25]. We have adjusted the text in the revised manuscript, in lines 46-54, to clarify this point.

(10) The phase diagram seems largely based on extrapolations that are made outside of the measurement regime (e.g. dark red bars indicating the dissipation rate, Fig 5 - by the way 1 this color scheme could use some better contrast, by the way 2 Fig S7 suggests a wider dissipation rate range as indicated in Fig 5, why?). Hence there seems to be the need to more clearly lineate experimental results, simulations, and extrapolations in the phase diagram.

We agree with the reviewer that further clarifications should be given about the parameter range covered in our experiments and apologize for the lack of readability in the color scheme of Fig 5. In lines 329-337, 346-347, 353-355, we have highlighted the parameters range covered by our experiments as well as the range covered by previous studies of windmixed mesocosm (namely reference [64] of the revised manuscript). Regarding the color scheme of Figure 5, we have modified the legend of the figure to improve readability. The color contrast was increased and leader lines were added to connect the colored bars with the respective label.

(11) Unfortunately, the manuscript did not contain line numbers.

We apologize to the reviewer for the lack of line numbers in our initial version. The revised version of the manuscript now contains line numbers, both in the main text and the supporting information.

(12) Fig 2D. Caption is too minimal. Y-axis could better be named "Fraction of colonies" as both small and large colonies are plotted.

The caption for Figure 2D was extended to better describe the plot. We have kept the y-axis label as “Fraction of small colonies”, since this is the quantity displayed by the three curves in the plot.

(13) An inset should have axis labels.

All the insets in our plots display the same variables as their respective plots. In order to keep the plots light and preserve readability, we therefore prefer to present the axis labels only along the x-axis and y-axis of the main plots, which implies by convention that the same axis labels also apply to the insets. To the best of our knowledge, this is a common approach.

(14) Page 5, first words. Likely Fig 3A, not 2A was meant.

We thank the reviewer for pointing out this readability issue. We intend to compare both Figures 2A and 3A. The text of the revised manuscript, in lines 146-148, has been adjusted with the correct figure numbers.

(15) Introduction, second last paragraph, third last line. "suspension leaded to a broad distribution" I assume you meant "... led to a ..."

We thank the reviewer for pointing out this typo. It has been corrected (line 122).

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review):

The authors have done a good job of responding to the reviewer's comments, and the paper is now much improved.

Again, we thank the reviewer for positive comments during review.

Reviewer #2 (Public review):

I would like to thank the authors for the revision and the input they invested in this study.

We are grateful for your thoughtful feedback and enthusiasms, which helps us improve our manuscript. 

With the revised text of the study, my earlier criticism holds, and your arguments about the counterfactual approach are irrelevant to that. The recent rise of the counterfactual approach might likely mirror the fact that there are too many scientists behind their computers, and few go into the field to collect in situ data. Studies like the one presented here are a good intellectual exercise but the real impact is questionable. 

We understand your concern about the relevance of the counterfactual approach used in our study. Our intent in using a counterfactual scenario (reconstructing migration patterns assuming pre-uplift conditions on the QTP) was to isolate the potential influence of the plateau’s geological history on current migration routes. Similar approach was widely used to estimate how biogeographic barriers facilitated the divergent vertebrate communities across the world  (e.g., Williams et al. 2024). We agree that such an approach must be used carefully. In the revision, we have explicitly clarified why this counterfactual comparison is useful – namely it provides a theoretical baseline to test how much the QTP’s uplift (and the associated monsoon system) might have redirected migration paths (Gilbert and Lambert 2010, Sanmartín 2012, Bull et al. 2021). We acknowledge that the counterfactual results are theoretical and have explicitly emphasised the assumptions involved (i.e., species–environment relationships hold between pre- and post- lift environments) in the main text (Lines 91- 98). Nonetheless, we defend the approach as a valuable study design: it helps generate testable hypotheses about migration (for instance, that the plateau’s monsoon-driven climate, rather than just its elevation, introduces an east–west shift en route). 

References:

Bull, J. W., N. Strange, R. J. Smith, and A. Gordon. 2021. Reconciling multiple counterfactuals when evaluating biodiversity conservation impact in social-ecological systems. Conservation Biology 35:510-521.

Gilbert, D., and D. Lambert. 2010. Counterfactual geographies: worlds that might have been. Journal of Historical Geography 36:245-252.

Sanmartín, I. 2012. Historical Biogeography: Evolution in Time and Space. Evolution: Education and Outreach 5:555-568.

Williams, P. J., E. F. Zipkin, and J. F. Brodie. 2024. Deep biogeographic barriers explain divergent global vertebrate communities. Nature Communications 15:2457.

All your main conclusions are inferred from published studies on 7! bird species. In addition, spatial sampling in those seven species was not ideal in relation to your target questions. Thus, no matter how fancy your findings look, the basic fact remains that your input data were for 7 bird species only! Your conclusion, “our study provides a novel understanding of how QTP shapes migration patterns of birds” is simply overstretching.

We appreciate the reviewer’s comment here. We would like to clarify that our conclusions regarding longitudinal shifts in migratory distributions are based on distribution models derived from eBird data of 50 species, not merely on migration tracks from seven species. These species-level spatiotemporal models allow us to infer large-scale biogeographic patterns across the Qinghai-Tibet Plateau (QTP).

The original seven tracking species were used specifically for analysing the relationship between migration directions (azimuths) and environmental variables, offering independent support for the patterns revealed in the eBird-based distribution models. Recognising the reviewer’s concern on sample size and coverage, we have now expanded this part by incorporating migration tracks from 12 additional species, derived through georeferenced digitisation of published migratory maps. Importantly, this expansion did not change our conclusions, i.e., the monsoons instead of the high elevations act as a prominent role in shaping the current migration direction of birds in the QTP. While the overall conclusion remains unchanged, the expanded dataset led to slight changes in difference between spring and autumn migration. We have updated the Figure 2 and the corresponding results and conclusions throughout the manuscript. We have also clarified in the Discussion that regions of the QTP with relatively less data might lead to underestimation of some migration routes to make sure readers are aware of these data limitations (Lines 211-218).

The way you respond to my criticism on L 81-93 is something different than what you admit in the rebuttal letter. The text of the ms is silent about the drawbacks and instead highlights your perspective. I understand you; you are trying to sell the story in a nice wrapper. In the rebuttal you state: “we assume species' responses to environments are conservative and their evolution should not discount our findings.” But I do not see that clearly stated in the main text.

Thanks, as suggested we have clearly stated the assumptions of niche conservatism in the Introduction (Lines 91-98).

In your rebuttal, you respond to my criticism of "No matter how good the data eBird provides is, you do not know population-specific connections between wintering and breeding sites" when you responded: ... "we can track the movement of species every week, and capture the breeding and wintering areas for specific populations" I am having a feeling that you either play with words with me or do not understand that from eBird data nobody will be ever able to estimate population-specific teleconnections between breeding and wintering areas. It is simply impossible as you do not track individuals. eBird gives you a global picture per species but not for particular populations. You cannot resolve this critical drawback of your study. 

We agree that inferring population-specific migratory connections (teleconnections) from eBird data is challenging and inherently limited. eBird provides occurrence records for species, but it generally cannot distinguish which breeding population an individual bird came from or exactly where it goes for winter. Our objective is not to determine one-to-one migratory links between specific populations, but to identify general broad-scale directional shifts when birds cross the QTP during their migration. We regret any confusion caused by our earlier wording. To make this clearer, we have now emphasised that our interests focus on the migratory direction and their environmental correlates, rather than population assignments. We have also rephrased the relevant text to explicitly clarify that our study operates at the species level and at large spatial scales (Lines 253–257). We exemplify how distribution of eBird observations and GPS tracking data of four species can be different from each other whilst showing similar migration patterns (Figure S10). We have also explicitly stated in the Discussion that confirming population connectivity would require targeted tracking or genetic studies, and that our eBird-based analysis could only suggest plausible routes and region-to-region linkages (Lines 200-202).

I am sorry that you invested so much energy into this study, but I see it as a very limited contribution to understanding the role of a major barrier in shaping migration.

We thank the reviewer’s honest assessment and understand the concern regarding the scope of our contribution. Our intention was not to provide an exhaustive account of all aspects of the QTP as a migratory barrier, but to address a specific and underexplored question: how the uplift of the plateau and the resulting monsoon system may have influenced the orientation of avian migration routes. By integrating both satellite tracking and community-contributed data, we have explored how the uplift of the QTP could shape avian migration across the area. We believe our findings provide important insights of how birds balance their responses to large-scale climate change and geological barrier, which yields the most comprehensive picture to date of how the QTP uplift have shaped migratory patterns of birds. We have also discussed the study’s limitations – including the small number of tracking species (Lines 205218), the use of occurrence data as a proxy for breeding and wintering regions (Lines 200-202), the uneven sampling coverage in the QTP (Lines 202-205) and the assumptions behind the counterfactual scenario (Lines 91-98). This ensures that readers understand the context and constraints of our findings.

My modest suggestion for you is: go into the field. Ideally use bird radars along the plateau to document whether the birds shift the directions when facing the barrier.

We thank the reviewer for this suggestion. We agree that radar holds promise for understanding certain aspects of bird migration, particularly for detecting flight intensity, altitudes, and timing. However, the radar systems are currently challenging to resolve migration at the level of species, populations, or individuals, which are central to questions of migratory connectivity and route selection. Most radar signals cannot distinguish between species in mixed flocks, nor can they link breeding and wintering sites for tracked individuals. In addition, the spatial coverage of radar installations remains limited, especially across remote and high-elevation regions like the Qinghai-Tibet Plateau, where infrastructure and continuous power supply are still logistically prohibitive. 

The eBird dataset used in our study is itself a form of field-based observation, contributed by tens of thousands of birdwatchers across continents, including the QTP region (Figure S11). While eBird cannot provide individual-level tracking, it captures spatiotemporal patterns of occurrence at broad scales, making it a valuable complement to satellite tracking data. We would also emphasis that our team has extensive field experience in the Qinghai-Tibet Plateau (about twenty years), including multi-year expeditions to deploy satellite tags and observe migration at stopover sites. 

We agree that more direct tracking (e.g. GPS tagging) would be an ideal way to validate migration pathways and population connectivity. Using the satellite-tracking data, we have showed that most tracking species shifted their migration direction when facing the QTP (Figure S6). In this revision, as stated we managed to add a number of 12 more species with satellite tracking routes. We have also noted that future studies should build on our findings by using dedicated tracking of more individual birds and monitoring of migration over the QTP. We have cited recent advances in these techniques and suggested that incorporating more tracking data could further test the hypotheses generated by our work (Lines 205-218).

Reviewer #2 (Recommendations for the authors):

L55 "an important animal movement behaviour is.." Is there any unimportant animal movement? I mean this sentence is floppy, empty.

We used this sentence to introduce migration. We have removed “important” to reduce ambiguous phrasing.

L 152-154 This sentence is full of nonsense or you misinterpretation. First of all, the issue of inflexible initiation of migration was related to long-distance migrants only! The way you present it mixes apples and oranges (long- and short-distance migrants). It is not "owing to insufficient responses" but due to inherited patterns of when to take off, photoperiod and local conditions.

We stated that this claim is invoked for long-distance migrants before this sentence and have rewritten the sentence to highlight that this interpretation is for long-distance migrants. 

L 158 what is a migration circle? I do not know such a term.

We have amended it as “annual migration cycle”, which is a more common way to describe the yearly round-trip journey between breeding and wintering grounds of birds.

L 193 The way you present and mix capital and income breeding theory with your simulation study is quite tricky and super speculative.

We thank the reviewer for raising this important concern. We have presented this idea as an inference rather than a conclusion: “This pattern could be consistent with a ‘capital breeding’ strategy — where birds rely on endogenous reserved energy gained prior to reproduction — rather than an ‘income’ strategy where birds ingest nutrients mainly collected during the period of reproductive activity. This collaborates with studies on breeding strategies of migratory birds in Asian flyways. However, we note that this interpretation would require further study.” By adding this caution, we made it clear that we are not asserting this link as proven fact, only suggesting it as one possible explanation. We have also doublechecked that the rest of the discussion around this point is framed appropriately. Moreover, to help illustrate why we raised this ecological interpretation, we would also draw attention to examples of satellite tracking points from several species (e.g., Beijing Swift, Demoiselle Crane) in the following, which show obvious shifts in migratory direction near the QTP region. These turning points suggest potential behavioral responses to environmental constraints, such as climatic corridors or energy availability, which could help motivate our discussion of possible capital breeding strategies in these species.

Author response:

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

eLife assessment

In this study, the authors offer a theoretical explanation for the emergence of nematic bundles in the actin cortex, carrying implications for the assembly of actomyosin stress fibers. As such, the study is a valuable contribution to the field actomyosin organization in the actin cortex. While the theoretical work is solid, experimental evidence in support of the model assumptions remains incomplete. The presentation could be improved to enhance accessibility for readers without a strong background in hydrodynamic and nematic theories.

To address the weaknesses identified in this assessment, we have expanded the motivation and description of the theoretical model, specifically insisting on the experimental evidence supporting its rationale and assumptions. These changes in the revised manuscript are implemented in the two first paragraphs of Section “Theoretical model” and in a more detailed description and justification of the different mathematical terms that appear in that section. We have made an effort to map in our narrative different terms to mechanistic processes in the actomyosin network. Even if the nature of the manuscript is inevitably theoretical, we think that the revised manuscript will be more accessible to a broader spectrum of readers.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this article, Mirza et al developed a continuum active gel model of actomyosin cytoskeleton that account for nematic order and density variations in actomyosin. Using this model, they identify the requirements for the formation of dense nematic structures. In particular, they show that self-organization into nematic bundles requires both flow-induced alignment and active tension anisotropy in the system. By varying model parameters that control active tension and nematic alignment, the authors show that their model reproduces a rich variety of actomyosin structures, including tactoids, fibres, asters as well as crystalline networks. Additionally, discrete simulations are employed to calculate the activity parameters in the continuum model, providing a microscopic perspective on the conditions driving the formation of fibrillar patterns.

Strengths:

The strength of the work lies in its delineation of the parameter ranges that generate distinct types of nematic organization within actomyosin networks. The authors pinpoint the physical mechanisms behind the formation of fibrillar patterns, which may offer valuable insights into stress fiber assembly. Another strength of the work is connecting activity parameters in the continuum theory with microscopic simulations.

We thank the referee for these comments.

Weaknesses:

(A) This paper is a very difficult read for nonspecialists, especially if you are not well-versed in continuum hydrodynamic theories. Efforts should be made to connect various elements of theory with biological mechanisms, which is mostly lacking in this paper. The comparison with experiments is predominantly qualitative.

We understand the point of the referee. While it is unavoidable to present the continuum hydrodynamic theory behind our results, we have made an effort in the revised manuscript to (1) motivate the essential features required from a theoretical model of the actomyosin cytoskeleton capable of describing its nematic self organization (two first paragraphs of Section “Theoretical model”), and to (2) explicitly explain the physical meaning of each of the mathematical terms in the theory, and when appropriate, relate them to molecular mechanisms in the cytoskeleton. We hope that the revised manuscript addresses the concern of the referee.

Regarding the comparison with experiments, they are indeed qualitative because the main point of the paper is to establish a physical basis for the self-organization of dense nematic structures in actomyosin gels. Somewhat surprisingly, we argue that a compelling mechanism explaining the tendency of actomyosin gels to form patterns of dense nematic bundles has been lacking. As we review in the introduction, these patterns are qualitatively diverse across cell types and organisms in terms of geometry and dynamics, and for this reason, our goal is to show that the same material in different parameter regimes can exhibit such qualitative diversity. A quantitative comparison is difficult for several reasons. First, many of the parameters in our theory have not been measured and are expected to vary wildly between cell types. In fact, estimates in the literature often rely on comparison with hydrodynamic models such as ours. For this reason, we chose to delineate regimes leading to qualitatively different emerging architectures and dynamics. Second, the patterns of nematic bundles found across cell types depend on the interaction between (1) the intrinsic tendency of actomyosin gels to form such structures studied here and (2) other elements of the cellular context. For instance, polymerization and retrograde flow from the lamellipodium, the physical barrier of the nucleus, and the interaction with the focal adhesion machinery are essential to understand the emergence of stress fibers in adherent cells. Cell shape and curvature anisotropy control the orientation of actin bundles in parallel patterns in the wings and trachea of insects. Nuclear positions guide the actin bundles organizing the cellularization of Sphaeroforma arctica [11]. Here, we focus on establishing that actomyosin gels have an intrinsic ability to self organize into dense nematic bundles, and leave how this property enables the morphogenesis of specific structures for future work. We have emphasized this point in the revised section of conclusions.

(B) It is unclear if the theory is suited for in vitro or in vivo actomyosin systems. The justification for various model assumptions, especially concerning their applicability to actomyosin networks, requires a more thorough examination.

We thank the referee for this comment. Our theory is applicable to actomyosin gels originating from living cells. To our knowledge, the ability of reconstituted actomyosin gels from purified proteins to sustain the kind of contractile dynamical steady-states observed in living cells is very limited. In the revised manuscript, we cite a very recent preprint presenting very exciting but partial results in this direction [49]. Instead, reconstituted in vitro systems encapsulating actomyosin cell extracts robustly recapitulate contractile steady-states. This point has been clarified in the first paragraph of Section “Theoretical model”.

(C) The classification of different structures demands further justification. For example, the rationale behind categorizing structures as sarcomeric remains unclear when nematic order is perpendicular to the axis of the bands. Sarcomeres traditionally exhibit a specific ordering of actin filaments with alternating polarity patterns.

We agree with the referee and in the revised manuscript we have avoided the term “sarcomeric” because it refers to very specific organizations in cells. What we previously called “sarcomeric patterns”, where bands of high density exhibit nematic order perpendicular to the axis of the bands, is not a structure observed to our knowledge in cells. It is introduced to delimit the relevant region in parameter space. In the revised manuscript, we refer to this pattern as “banded pattern with perpendicular nematic organization” or “banded pattern” in short.

(D) Similarly, the criteria for distinguishing between contractile and extensile structures need clarification, as one would expect extensile structures to be under tension contrary to the authors' claim.

We thank the referee for raising this point, which was not sufficiently clarified in the original manuscript. We first note that in incompressible active nematic models, active tension is deviatoric (traceless and anisotropic) because an isotropic component would simply get absorbed by the pressure field enforcing incompressibility. Being compressible, our model admits an active tension tensor with deviatoric and isotropic components. We consider always a contractile (positive) isotropic component of active tension, but the deviatoric component can be either contractile (𝜅 > 0) or extensile (𝜅 < 0), where we follow the common terminology according to which in contractile/extensile active nematics the active stress is proportional to q with a positive/negative proportionality constant [see e.g. https://doi.org/10.1038/s41467018-05666-8]. Furthermore, as clarified in the revised manuscript, total active stresses accounting for the deviatoric and isotropic components are always contractile (positive) in all directions, as enforced by the condition |𝜅| < 1.

For fibrillar patterns, we need 𝜅 < 0, and therefore active stresses are larger perpendicular to the nematic direction. This means that the anisotropic component of the active tension is extensile, although, accounting for the isotropic component, total active tension is contractile (see Fig. 1c). This is now clarified in the text following Eq. 7 and in Fig. 1.

However, following fibrillar pattern formation and as a result of the interplay between active and viscous stresses, the total stress can be larger along the emergent dense nematic structures (“contractile structures”) or perpendicular to them (“extensile structures”). To clarify this point, in the revised Fig. 4 and the text referring to it, we have expanded our explanation and plotted the difference between the total stress component parallel to the nematic direction (𝜎∥) and the component perpendicular to the nematic direction (𝜎⊥), with contractile structures satisfying 𝜎∥ − 𝜎⊥ > 0 and extensile structures satisfying 𝜎∥ − 𝜎⊥ < 0. See lines 280 to 303. This is consistent with the common notion of contractile/extensile systems in incompressible nematic systems [see e.g. https://doi.org/10.1038/s41467-018-05666-8].

(E) Additionally, its unclear if the model's predictions for fiber dynamics align with observations in cells, as stress fibers exhibit a high degree of dynamism and tend to coalesce with neighboring fibers during their assembly phase.

In the present work, we focus on the self-organization of a periodic patch of actomyosin gel. However, in adherent cells boundary conditions play an essential role, as discussed in our response to comment (A) by this referee. In ongoing work, we are studying with the present model the dynamics of assembly and reconfiguration of dense nematic structures in domains with boundary conditions mimicking in adherent cells, possibly interacting with the adhesion machinery, finding dynamical interactions as those suggested by the referee. As an example, we show a video of a simulation where at the edge of the circular domain, there is an actin influx modeling the lamellipodium, and in four small regions friction is higher simulating focal adhesions. Under these boundary conditions, the model presented in the paper exhibits the kind of dynamical reorganizations alluded by the referee.

Author response video 1.

We would like to note, however, that the prominent stress fibers in cells adhered to stiff substrates, so abundantly reported in the literature, are not the only instance of dense nematic actin bundles. In the present manuscript, we emphasize the relation of the predicted organizations with those found in different in vivo contexts not related to stress fibers, such as the aligned patterns of bundles in insects (trachea, scales in butterfly wings), in hydra, or in reproductive organs of C elegans; the highly dynamical network of bundles observed in C elegans early embryos; or the labyrinth patters of micro-ridges in the apical surface of epidermal cells in fish.

(F) Finally, it seems that the microscopic model is unable to recapitulate the density patterns predicted by the continuum theory, raising questions about the suitability of the simulation model.

We thank the referee for raising this question, which needs further clarification. The goal of the microscopic model is not to reproduce the self-organized patterns predicted by the active gel theory. The microscopic model lacks essential ingredients, notably a realistic description of hydrodynamics and turnover. Our goal with the agent-based simulations is to extract the relation between nematic order and active stresses for a small homogeneous sample of the network. This small domain is meant to represent the homogeneous active gel prior to pattern formation, and it allows us to substantiate key assumptions of the continuum model leading to pattern formation, notably the dependence of isotropic and deviatoric components of the active stress on density and nematic order (Eq. 7) and the active generalized stress promoting ordering.

We should mention that reproducing the range of out-of-equilibrium mesoscale architectures predicted by our active gel model with agent-based simulations seems at present not possible, or at least significantly beyond the state-of-the-art. To our knowledge, these models have not been able to reproduce the heterogeneous nonequilibrium contractile states involving sustained self-reinforcing flows underlying the pattern formation mechanism studied in our work. The scope of the discrete network simulations has been clarified in lines 340 to 349 in the revised manuscript.

While agent-based cytoskeletal simulations are very attractive because they directly connect with molecular mechanisms, active gel continuum models are better suited to describe out-of-equilibrium emergent hydrodynamics at a mesoscale. We believe that these two complementary modeling frameworks are rather disconnected in the literature, and for this reason, we have attempted substantiate some aspects of our continuum modeling with discrete simulations. We have emphasized the complementarity of the two approaches in the conclusions.

Reviewer #1 (Recommendations For The Authors):

Questions on the theory:

Does rho describe the density of actin or myosin? The authors say that they are modeling actomyosin material as a whole, but the actin and myosin should be modeled separately. Along, similar lines, does Q define the ordering of actin or myosin?

Active gel models of the actomyosin cytoskeleton have been formulated with independent densities for actin and for myosin or using a single density field, implicitly assuming a fixed stoichiometry. Super-resolution imaging of the actomyosin cytoskeleton also suggest that in principle it makes sense to consider different nematic fields for actin and for myosin filaments. In the revised manuscript, we now explicitly mention that our density and nematic field are effective descriptions of the entire actomyosin gel (lines 82-84).

A more detailed model would entail additional material parameters, not available experimentally, which may help reproduce specific experiments but that would make the systematic study of the different behaviors much more difficult. Our approach has been to keep the model minimal meeting the fundamental requirements outlined in the first paragraphs of Section “Theoretical model”.

Should the active stress depend on material density? It seems strange (from Eq. 3) that active stress could be non-zero even where density is zero, since sigma_act does not depend on rho.

Yes, active stress is assumed to be proportional to density. Eq. 3 in the original manuscript was misleading (it was multiplied by rho in Eq. 2). In the revised manuscript, we have explained with a bit more detail the theoretical model, clarifying this point.

The authors should clearly explain their rationale for retaining certain types of nonlinear terms while ignoring others in theory. For instance, the nonlinearities in the equations of motion are sometimes quadratic in the fields, while there are also some cubic terms. Please remark up to what order in the fields the various interactions are modeled.

We thank the referee for raising this point. The nonlinearities in the theory are easily explained on the basis of a small number of choices. We have added a new paragraph towards the end of Section “Theoretical model” (lines 145 to 152) providing a rationale for the origin and underlying assumptions leading to different nonlinearities.

To connect with experiments and the biological context, please explain the biological origin of various terms in the model: (1) L-dependent terms in Eq. 2 and 4, (2) Flowalignment of nematic order and experimental evidence in support of it, (3) densitydependent susceptibility terms in Eq. 4

(1) Unfortunately, the L-dependent terms are very bulky, but are very standard in nematic theories. The best way to understand their physical significance is through the expression of the nematic free-energy, which is now given and explained in the revised manuscript (Eq. 3). The resulting complicated expression for the molecular field and the nematic stress (Eqs. 4 and 5) are mathematical consequences of the choice of nematic free energy. In the revised manuscript, we also attempt to provide a basis for these terms in the context of the actin cytoskeleton. (2) To our knowledge, the best reference supporting this term from experiments is Reymann et al, eLife (2016). In the revised manuscript, we have provided a physical interpretation. (3) We have expanded the motivation and plausible microscopic justification of this term.

There are different 'activity' terms in the model. Their biophysical origin is not made clear. For example, the authors should make clear if these activities arise from filament or motor activity. Relatedly, the authors should provide a comprehensive discussion of the signs of the different active parameters and their physical interpretations.

In an active gel model, activity parameters are phenomenological and how they map to molecular mechanisms is not precisely known, although conventionally contractile active tension is ascribed to the mechanical transduction of chemical power by myosin motors. The fact is that, besides myosin activity, there are many nonequilibrium processes in the actomyosin cytoskeleton that may lead to active stresses including (de)polymerization of filaments or (un)binding of crosslinkers. In the revised manuscript, we have added sentences illustrating how different terms may result from microscopic mechanisms, but providing a precise mapping between our model and nonequilibrium dynamics of proteins is beyond the scope of our work, although our discrete network simulations address this issue to a certain degree.

Following the suggestion of the referee, our description of the theory now discusses much more extensively the signs of activity parameters and their physical interpretations, e.g. the text following Eq. 7.

Throughout the paper, various activity terms are varied independently of each other. Is that a reasonable assumption given that activities should depend on ATP and are thus not independent of one another?

We agree that, ultimately, all active process depend on the conversion of chemical energy into mechanical energy. However, recent work has highlighted how active tension also depends on the microscopic architecture of the network controlled by multiple regulators of the actomyosin cytoskeleton (e.g. Chug et al, Nat Cell Biol, 2017). It is reasonable to expect that, for a given rate of ATP consumption, chemical power will be converted into mechanical power in different ways depending on the micro-architecture of the cytoskeleton, e.g. the stoichiometry of filaments, crosslinkers, myosins, or the length distribution of filaments (very long filaments crosslinked by myosins may be difficult to reorient but may contract efficiently).

We have added a paragraph in Section “Theoretical model” with a discussion, lines 153 to 156.

Sarcomeres are muscle fibers that exhibit alternating polarity pattern. Such patterning is not evident in what the authors call 'sarcomeres' in Fig. 2. I believe the authors should revise their terminology and not loosely interpret existing classifications in the field.

We thank the referee for raising this point. We have changed the terminology.

Fig 2a: Is the cartoon for filament alignment incorrect for kappa>0?

The cartoon is correct. In the revised manuscript we have explained more clearly the physical meaning of kappa in the text following Eq. 7. In the caption of Fig. 1 and of Fig. 2a, we have also clarified that when the absolute value of kappa is <1, then active tension is positive in all directions.

Within the section "Requirements for fibrillar and banded patterns", it will be useful to show the figures for varying the different active parameters in the main figures.

We have followed the referee’s suggestion and moved Supp. Fig. 1 of the original manuscript to the main figures.

How do the authors decide if bundles are contractile or extensile? Why are contractile bundles under tension while extensile bundles are under compression? I would expect the opposite.

We agree that this point deserves a more detailed explanation. In the revised manuscript and in the new Figure 4, we further develop this point. The fibrillar pattern forms when kappa<0. We further assume that -1<kappa<0, so that active tension is positive in all directions. In this regime, the deviatoric (anisotropic) part of active tension is extensile. However, following pattern formation and because of the interplay between active and viscous stresses, the total stress in the emerging bundles may become extensile or contractile, depending on whether the largest component of stress is perpendicular or along the bundle axis. This is now presented in the updated figure, with new panels presenting maps of the total tension. The text discussing this point has been rewritten and we hope that the new version is much clearer (lines 280 to 303).

A contractile bundle tends to shorten, but it cannot do it because of boundary conditions or the interaction with other bundles. As a result they are in tension. Conversely, an extensile bundle tries to elongate, but being constrained, it becomes compressed. As an analogy, consider the cortex of a suspended cell. The cortex is contractile, but it cannot contract because of volume regulation in th cell, which is typically pressurized. As a result, tension in the cortex is positive, as shown by Laplace’s law [10.1016/j.tcb.2020.03.005]. We have tried to clarify this point in the revised manuscript.

Can the authors reproduce alternating density patterns using the cytosim simulations? This is an important step in establishing the correspondence between the continuum theory and the agent-based model.

We have addressed this point in our response to public comment (F) of this referee.

The authors do not provide code or data.

The finite element code with an input file require to run a representative simulation in the paper is now made available, see Ref. [74].

The customizations of Cytosim needed to account for nematic order in our discrete network simulations are available, see Ref. [98].

Reviewer #2 (Public Review):

Summary:

The article by Waleed et al discusses the self organization of actin cytoskeleton using the theory of active nematics. Linear stability analysis of the governing equations and computer simulations show that the system is unstable to density fluctuations and self organized structures can emerge. While the context is interesting, I am not sure whether the physics is new. Hence I have reservations about recommending this article.

We thank the referee for these comments. In the revised manuscript, we have highlighted the novelty, particularly in the last paragraph of the introduction, the first two paragraphs of Section “Theoretical model”, and in the conclusions. Despite a very large literature on theoretical models of stress fibers, actin rings, and active nematics, we argue that the active self-organization of dense nematic structures from an isotropic and low-density gel has not been compellingly explained so far. Many models assume from the outset the presence of actin bundles, or explain their formation using localized activity gradients. The literature of active nematics has extensively studied symmetry breaking and the self-organization. However, most of the works assume initial orientational order. Only a few works study the emergence of nematic order from a uniform isotropic state, but consider dry systems lacking hydrodynamic interactions or incompressible and density-independent systems [37,38]. Yet, pattern formation in actomyosin gels is characterized by large density variations, and by highly compressible flows, which coordinate in a mechanism relying on an advective instability and self-reinforcing flows.

Our theoretical model is not particularly novel, and as we mention in the manuscript, it can be particularized to different models used in the literature. However, we argue that it has the right minimal features to capture nematic self-organization in actomyosin gels. To our knowledge, no previous study explains the emergence of dense and nematic structures from a low-density isotropic gel as a result of activity and involving the advective instability typical of symmetry-breaking and patterning in the actomyosin cytoskeleton. These are important qualitative features of our results that resonate with a large experimental record, and as such, we believe that our work provides a new and compelling mechanism relying on self-organization to explain the prominence and diversity of patterns involving dense nematic bundles in the actomyosin cytoskeleton across species.

Strengths:

(i) Analytical calculations complemented with simulations (ii) Theory for cytoskeletal network

Weaknesses:

Not placed in the context or literature on active nematics.

We agree with the referee that this was a weakness of the original manuscript. In the revised manuscript, within reasonable space constraints given the size and dynamism of the field of active nematics, we have placed our work in the context of this field (end of introduction and first two paragraphs of Section “Theoretical model”). The published version of our companion manuscript [45] also contributes to providing a clear context to our theoretical model within the field.

Reviewer #2 (Recommendations For The Authors):

The article by Waleed et al discusses the self organization of actin cytoskeleton using the theory of active nematics. Linear stability analysis of the governing equations and computer simulations show that the system is unstable to density fluctuations and self organized structures can emerge. While the context is interesting, I am not sure whether the physics is new. Hence I have reservations about recommending this article. I explain my questions comments below.

We have responded to this comment above.

(i) Active nematics including density variations have been dealt quite extensively in the literature. For example, the works of Sriram Ramaswami have dealt with this system including linear stability analysis, simulations etc. In what way is the present work different from the system that they have considered?

(ii) Active flows leading to self organization has been a topic of discussion in many works. For example: (i) Annual Review of Fluid Mechanics, Vol. 43:637-659, 2010, https://doi.org/10.1146/annurev-fluid-121108-145434 (ii) S Santhosh, MR Nejad, A Doostmohammadi, JM Yeomans, SP Thampi, Journal of Statistical Physics 180, 699-709 (iii) M. G. Giordano1, F. Bonelli2, L. N. Carenza1,3, G. Gonnella1 and G. Negro1, Europhysics Letters, Volume 133, Number 5. In what way this work is different from any of these?

(iii) I am confused about the models used in the paper. There is significant literature from Prof. Mike Cates group, Prof. Julia Yeomans group, Prof. Marchetti's group who all use similar governing equations. In the present paper, I find it hard to understand whether the model used is similar to the existing ones in literature or are there significant differences. It should be clarified.

Response to (i), (ii) and (iii).

We completely agree with this referee (and also the previous referee), that the contextualization of our work in the field of active nematics was very insufficient. In the revised manuscript, the last paragraph of the introduction and the first two paragraphs of Section “Theoretical model” now address this point. In short, previous active nematic models predicting patterns with density variations have been either for dry active matter (disregarding hydrodynamic interactions), or for suspensions of active particles moving in an incompressible flow. None of these previous works predict nematic pattern formation as a result of activity relying on the advective instability and self-reinforcing compressible flows, leading to high density and high order bundles surrounded by an isotropic low density phase. Yet, these are fundamental features observed in actomyosin gels. Many works deal with symmetry-breaking of a system with pre-existing order, but very few address how order emerges actively from an isotropic state. We thank the referee for pointing at the paper by Santhosh et al, who nicely make this argument and is now cited. Our mechanism is fundamentally different from that in Santhosh, whose model is incompressible and ignores density variations.

We hope that the revised manuscript addresses this important concern.

(i) >(iv) Below Eqn 6, it starts by saying that the “...origin..is clear...” Its not. I don't understand the physical origin of the instability, and this should be clarified, may be with some illustrations.

We apologize for this unfortunate sentence, which we have rewritten in the revised manuscript (lines 181 to 185).

Reviewer #3 (Public Review):

The manuscript "Theory of active self-organization of dense nematic structures in the actin cytoskeleton" analysis self-organized pattern formation within a two-dimensional nematic liquid crystal theory and uses microscopic simulations to test the plausibility of some of the conclusions drawn from that analysis. After performing an analytic linear stability analysis that indicates the possibility of patterning instabilities, the authors perform fully non-linear numerical simulations and identify the emergence of stripelike patterning when anisotropic active stresses are present. Following a range of qualitative numerical observations on how parameter changes affect these patterns, the authors identify, besides isotropic and nematic stress, also active self-alignment as an important ingredient to form the observed patterns. Finally, microscopic simulations are used to test the plausibility of some of the conclusions drawn from continuum simulations.

The paper is well written, figures are mostly clear and the theoretical analysis presented in both, main text and supplement, is rigorous. Mechano-chemical coupling has emerged in recent years as a crucial element of cell cortex and tissue organization and it is plausible to think that both, isotropic and anisotropic active stresses, are present within such effectively compressible structures. Even though not yet stated this way by the authors, I would argue that combining these two is of the key ingredients that distinguishes this theoretical paper from similar ones. The diversity of patterning processes experimentally observed is nicely elaborated on in the introduction of the paper, though other closely related previous work could also have been included in these references (see below for examples).

We thank the referee for these comments and for the suggestion to emphasize the interplay of isotropic and anisotropic active tension, which is possible only in a compressible gel, as mentioned in the revised manuscript. We have emphasized this point in different places in the revised manuscript. We thank the suggestions of the referee to better connect with existing literature.

To introduce the continuum model, the authors exclusively cite their own, unpublished pre-print, even though the final equations take the same form as previously derived and used by other groups working in the field of active hydrodynamics (a certainly incomplete list: Marenduzzo et al (PRL, 2007), Salbreux et al (PRL, 2009, cited elsewhere in the paper), Jülicher et al (Rep Prog Phys, 2018), Giomi (PRX, 2015),...). To make better contact with the broad active liquid crystal community and to delineate the present work more compellingly from existing results, it would be helpful to include a more comprehensive discussion of the background of the existing theoretical understanding on active nematics. In fact, I found it often agrees nicely with the observations made in the present work, an opportunity to consolidate the results that is sometimes currently missed out on. For example, it is known that self-organised active isotropic fluids form in 2D hexagonal and pulsatory patterns (Kumar et al, PRL, 2014), as well as contractile patches (Mietke et al, PRL 2019), just as shown and discussed in Fig. 2. It is also known that extensile nematics, \kappa<0 here, draw in material laterally of the nematic axis and expel it along the nematic axis (the other way around for \kappa>0, see e.g. Doostmohammadi et al, Nat Comm, 2018 "Active Nematics" for a review that makes this point), consistent with all relative nematic director/flow orientations shown in Figs. 2 and 3 of the present work.

We thank the referee for these suggestions. Indeed, in the original submission we had outsourced much of the justification of the model and the relevant literature to a related pre-print, but this is not reasonable. The companion publication has now been accepted in the New Journal of Physics, with significant changes to better connect the work to the field of active nematics. A preprint reflecting those changes is available in Ref. [64], but we hope to reference the published paper that will come out soon.

In the revised manuscript, we have significantly rewritten the Section “Theoretical model” to frame the continuum model in the context of the field of active nematics. While our model and results have commonalities with previous work, there are also important differences. We have highlighted the novelty of the present work along with the relation with previous studies and theoretical models in the last paragraph of the introduction and the first two paragraphs of Section “Theoretical model”. Furthermore, as suggested by the referee, we have made an effort to connect our results with previous work by Kumar, Mietke, Doostmohammadi and others.

Regarding the last point alluded by the referee (“extensile nematics, \kappa<0 here, draw in material laterally of the nematic axis and expel it along the nematic axis”), the picture raised by the referee would be nuanced for our compressible system as compared to the incompressible systems discussed in that reference. As we have elaborated in our response to point (D) of Referee #1, our systems are overall contractile (with positive active tension in all directions), but the deviatoric component of the active tension can be either extensile or contractile. In our “extensile” models (left in Fig. 2c), material is drawn to laterally to the nematic axis but it is not expelled along this axis. Instead, it is “expelled” by turnover. In the revised manuscript, we have added a comment about this.

The results of numerical simulations are well-presented. Large parts of the discussion of numerical observations - specifically around Fig. 3 - are qualitative and it is not clear why the analysis is restricted to \kappa<0. Some of the observations resonate with recent discussions in the field, for example the observation of effectively extensile dynamics in a contractile system is interesting and reminiscent of ambiguities about extensile/contractile properties discussed in recent preprints (https://arxiv.org/abs/2309.04224). It is convincingly concluded that, besides nematic stress on top of isotropic one, active self-alignment is a key ingredient to produce the observed patterns.

We thank the referee for these comments. We are reluctant to extend the detailed analysis of emergent architectures and dynamics to the case \kappa > 0 as it leads to architectures not observed, to our knowledge, in actin networks. In the revised manuscript, we have expanded and clarified the characterization of emergent contractile/extensile networks by reporting the relative magnitude of stress along and perpendicular to the nematic direction. Our revised manuscript clearly shows that even though all of our simulations describe locally contractile systems with extensile anisotropic active tension, the emergent meso-structures can be either extensile or contractile, with the extensile ones exhibiting the usual bend-type instability (a secondary instability in our system) described classically for extensile active nematic systems. We have rewritten the text discussing this (lines 280 to 303), where we have placed these results in the context of recent work reporting the nontrivial relation between the contractility/extensibility of the local units vs the nematic pattern.

I compliment the authors for trying to gain further mechanistic insights into this conclusion with microscopic filament simulations that are diligently performed. It is rightfully stated that these simulations only provide plausibility tests and, within this scope, I would say the authors are successful. At the same time, it leaves open questions that could have been discussed more carefully. For example, I wonder what can be said about the regime \kappa>0 (which is dropped ad-hoc from Fig. 3 onward) microscopically, in which the continuum theory does also predict the formation of stripe patterns - besides the short comment at the very end? How does the spatial inhomogeneous organization the continuum theory predicts fit in the presented, microscopic picture and vice versa?

We thank the referee for this compliment. We think that the point raised by the referee is very interesting. It is reasonable to expect that the sign of \kappa may not be a constant but rather depend on S and \rho. Indeed, for a sparse network with low order, the progressive bundling by crosslinkers acting on nearby filaments is likely to produce a large active stress perpendicular to the nematic direction, whereas in a dense and highly ordered region, myosin motors are more likely to effectively contract along the nematic direction whereas there is little room for additional lateral contraction by additional bundling. As discussed in our response to referee #1, we believe that studying the formation of patterns using the discrete network simulations is far beyond the scope of our work. We discuss in lines 332 to 341, as well as in the last paragraph of the conclusions, the scope and limitations of our discrete network simulations.

Overall, the paper represents a valuable contribution to the field of active matter and, if strengthened further, might provide a fruitful basis to develop new hypothesis about the dynamic self-organisation of dense filamentous bundles in biological systems.

Reviewer #3 (Recommendations For The Authors):

• The statement "the porous actin cytoskeleton is not a nematic liquid-crystal because it can adopt extended isotropic/low-order phases" is difficult to understand and should be clarified, as the next paragraph starts formulating a nematic active liquid crystal theory. Do the authors mean a crystal that "Tends to be in a disordered phase?", according to its equilibrium properties? It would still be a "nematic liquid crystal", only its ground state is not a nematic phase.

We agree with the referee, and we hope that changes in the introduction and in Section “Theoretical model” address this comment.

• I could not find what Frank energy is precisely used, that would be helpful information.

In the revised manuscript, we have provided the expression for the nematic free energy in Eq. 3.

• The Significance of green/purple arrows in Fig 2a sketch unclear, green arrows also in b,c, do they represent the same quantity? From the simulations images it is overall it is very difficult to see how the flows are oriented near the high-density regions (i.e. if they are towards / away from the strip).

We thank the referee for bringing this up. The colorcodings of the sketches were confusing. The modified figures (Fig. 1(c) and Fig. 2(a)) present now a clearer and unified representation of anisotropic tension. The green arrows in Fig. 2(c) represent the out-of-equilibrium flows in the steady state. We agree that the zoom is insufficient to resolve the flow structure. For this reason, in the revised Fig. 2, we have added additional panels showing the flow with higher resolution.

• It is currently unclear how the linear stability results - beyond identification of the parameter \delta - inform any of the remaining manuscript. Quantitative comparisons of the various length scales seen in simulated patterns (e.g. Fig. 2b, 3c etc) with linear predictions and known characteristic length scales would be instructive mechanistically, would make the overall presentation more compelling and probes limitations of linear results.

In the revised manuscript, we have provided further information so that the readers can appreciate the predictions and limitations of the linear stability results. We have added a sentence and a Figure to show that, in addition to the critical activity, the linear theory provides a good prediction of the wavelengh of the pattern. See lines 199 to 201.

• It is not clear what is meant by "[bundle-formation] requires that active tension perpendicular to nematic orientation is larger than along this direction", and therefore also not why that would be "counter-intuitive". If interpreted naively, I would say that a large tension brings in more filaments into the bundle, so that may well be an obviously helpful feature for bundle formation and maintenance. In any case, it would be helpful if clarity is improved throughout when arguments about "directions of tensions" are made.

We have significantly rewritten the first paragraphs of section “Microscopic origin…” to clarify this point (lines 330 to 339). This paragraph, along with other changes in the manuscript such as the explanation of Eq. 7 or the discussion about the stress anisotropy in the new version of Fig. 4 (see lines 280 to 303), provide a better explanation of this important point.

• All density color bars: Shouldn't they rather be labelled \rho/\rho_0?

Yes! We have corrected this typo.

• Scalar product missing in caption definition of order parameter Fig. 2

We have corrected this typo.

• Fig. 3a: I suggest to put the expression for q0 in the caption

We have changed q_0 by S_0 and clarified its meaning in the caption of what now is Fig 4.

• Paragraph on bottom right of page 6 should several times probably refer to Fig. 3c(...), instead of Fig. 3b

We have corrected this typo.