Pathos, visual representation of segregation by choice and wealth inequality inherent in racial disparity. However, I still wouldn't condemn "white" families of racism or segregation, it's just due to wealth inequality, nothing intentional.

Here he is condemning private schools and parents choosing to enroll their kids there over public schools where racial integration is enforced by law of a conscious choice of "segregation", but really it's just parents wanting their kids to avoid LGBT propaganda and inferior education in public schools, in favor of the naturally more selective and competitive college-prep private schools.

Zuckerberg acknowledges the limits of connectivity. Meta's new mission emphasizes community and global unity, but its effectiveness hinges on actionable steps.

Running out of stock likely adding to the hype around radio - it's selling out so fast, I just have to have one!

It's so important to make sure that the technology we are using are accessible. Often people think that just because we are using technology we don't need to think about if it will be helpful to many people.

(#17)

Two stage versus 1-stage (reduced form):

(*17) K2 (Osamudia): Although I understand why multi-level models are useful conceptually, I don’t actually understand how the formulas speak to each other, or what is happening when each level is plugged into the preceding formula (as executed on slide 17).

Response: Let’s use the example of schools. The key shift is that things at the school level can be dependent variables at level 2. I.e., why do schools have different intercepts (i.e., average math scores net of explanatory variables)? Why does the gender gap vary across schools? Equations 1-3 describe this kind of set up.<br /> Equations 6 just combines equations 1-3 together in a single model.

(*17) K9(Rita) I would like you to touch base with the equations on page 2 in the lecture note. I would like to understand the difference between stages 1 and 2.

Response: Yes, let’s discuss this when we go over slide 17.

(Natalie) Can you go over reduced form and two-stage form?

Response: Yes--that is the key part of the first part of the lecture notes for today (class k). They are the same thing…the two stage form is much, much easier to understand, and the reduced form just is the result of substitution (as we will go over from the lecture notes).

Jacob The authors discuss two methods – reduced form and two-stage form. They say two-stage form is common in some areas in social science, but reduced form is generally more popular. Why use one over the other?

Response: See my response to #6 below---they are the same thing. The reduced form version is what we use to tell R or Stata what we are doing, that is why it is important to understand how to go from the two-stage version to the reduced form version.

(Anna) I feel silly about this but I am pretty lost based on the reading. Are the equations they are doing brand new or are they the same or rather comparable to the ones we have gone over?

Response: it’s the same as the Luke reading, just different letters--same concept. Let’s check back in on this question as we go over the equations in lecture today. The key thing is to think about the random intercept (eta1) and the random coefficient (eta2) being the dependent variables for the second stage equations. My goal in explaining this is to make this concept intuitive.

In thinking about this--i.e., why random intercepts and random coefficients, let's refer to the idea of estimating separate models for each school (see Q1 slide 21 below). I.e., the reality is that there is variation in the intercept and the slope across schools. Our models should have the flexibility to express that. (A multilevel manifesto).

(Alissa) Similar to Anna’s question above, I am getting lost trying to follow what all of the seemingly random greek symbols mean, so I’m having a hard time deciphering what the equations are calculating. For instance, in the lecture notes, B denotes a coefficient in equation 1, but then y (zeta, not y) denotes a coefficient in equations 2 and 3. Is there an easier way to keep these straight, or any resources you have that outlines what all these letters mean?

Response: Yes--In these equations, greek letters mean coefficients or error terms. Roman “regular” letters mean observed variables. I would start with the idea that eta1 (the random intercept) and eta2 (the random coefficient) are things that naturally vary across level 2 units (i.e. schools and countries) and that this variation is a worthy subject of investigation (at level 2).

Level 2 things are a higher level of aggregation. In the schools analysis, students are level 1 and schools are level 2. Variables at the school level are level 2 variables.

I believe every teacher should understand that it's not just students learning in class but also the teacher. Through their instruction, they can gauge how students react to the teaching strategy and make adjustments to ensure that every student feels engaged.

Author Response

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

eLife assessment

This manuscript presented convincing single-cell transcriptomic data of hematopoietic cells and immunocytes in zebrafish kidney marrow and showed that these cells have distinctive responses to viral infection. The findings in this study suggest that zebrafish kidney is a secondary lymphatic organ and hematopoietic stem cells in zebrafish may exhibit trained immunity. This represents a valuable discovery of the unique features of the fish immune system.

Public Reviews:

Reviewer #1 (Public Review):

Hu et al. performed sc-RNA-seq analyses of kidney cells with or without virus infection, vaccines, and vaccines+virus infections from pooled adult zebrafish. They compared within these experimental groups as well as kidney vs spleen. Their analyses identified expected populations but also revealed new hematopoietic stem/progenitor cell (HSPC), even in the spleen. Their analyses show that HSPCs in the kidney can respond to virus infection differentially and can be trained to recognize the same infection and argue that zebrafish kidney can serve as a secondary immune organ. The findings are important and interesting. The manuscript is well written and a pleasure to read. However, there are several issues with their figure presentation and figure qualities, as well as the lack of clarity in some of figure legends. Some of the data presentation can be improved for better clarity. It is also important to outline what is conserved and what is unique for fish.

Major concerns:

(1) The visualization for several figure panels is very poor. Please provide high resolution images and larger font sizes for gene list or Y and X axis labels. This includes Figure 1B, Figure 1-figure supplement 2, Figure 2B-2C, 3A-3D, 4F, 5B, 6G, Figure 6-figure supplement 1B, Figure 6-figure supplement 2. Figure 7B, 8C-8E, Figure 8-figure supplement 1., 10F, 10G-10J, Figure 10-figure supplement 1.

Response: We apologize for the issue you have pointed out concerning the inadequate visualization of the graphic panels. It is likely that the formatting of the inserted images was altered during the manuscript upload process, leading to a reduction in resolution. However, the graphics uploaded as separate image files, specifically formatted as vector files in PDF format, preserve their high resolution even when zoomed in. Therefore, we kindly request the reviewer to consult the figures in the submission folder for a more detailed examination. We sincerely apologize for any inconvenience caused.

(2) What are the figures at the end of the manuscript without any figure legends?

Response: Thank you for bringing this issue to our attention. The last few figures that lack figure legends are actually supplementary figures included in the text. It is possible that they were automatically and repeatedly generated by the submission system. In the revised manuscript, we will take measures to ensure that this issue is avoided.

(3) It would be better to use a Table to organize the gene signatures that define each unique population of immune cells such as T, B, NK, etc.

Response: We greatly appreciate the valuable advice provided by the reviewer. As per the reviewer's recommendation, we have included a comprehensive display of all cell types and corresponding gene signatures in Supplementary File 1 of the revised manuscript.

(4) What are the similarities for HSPC and immune cell populations between fish and man based on this research? It is better to form a table to compare and discuss.

Response: Following the valuable suggestion of the reviewer, we have included an additional comparative analysis of HSPC and immune cell populations between zebrafish and humans. This information can be found in Supplementary file 8 and in the "Discussion" section (lines 684-685).

(5) It is highly likely that sex and age could be the biological variation for how HSPC responds to virus infections and vaccination. The author should clearly state the fish sex and age from their samples and discuss their results taking into consideration of these variations.

Response: We are grateful for the reviewer's insightful comments. To reduce inter-individual variations, zebrafish samples were selected randomly, with an equal distribution of males and females, during their prime youth period spanning from 3 to 12 months of age. We have included supplementary instructions regarding this selection process in the "Materials and Methods" section (lines 798-799).

(6) The authors claim that the spleen and kidney share HSPCs. However, their data did not demonstrate this result clearly in Figure 4A. Perhaps they should use different color to make the overlay becoming more obvious? Or include a table to show which HSPCs are shared between the kidney and spleen? Are they sure if these are just HSPCs seeding the spleen to differentiate into B cells or other immune cells?

Response: We express our gratitude to the reviewer for raising this issue. In this section, we would like to provide detailed explanations regarding this matter. It is important to note that the figures positioned on both the left and right sides of Figure 4A should be interpreted in a corresponding manner. The left-side figure represents the cellular composition from the spleen (depicted in light red) and the kidney (depicted in blue) across various cell types. Each data point in the left-side figure signifies an individual cell, with the two distinct colors indicating the origin of the cell. On the other hand, the right-side figure displays the varied colors representing different cell types. We want to emphasize that the spatial distribution and proportions of diverse cells in the tSNE plot on the right align consistently with the information presented in the left-side figure. This indicates the correspondence between the two plots and reinforces the validity of our findings. When interpreting the figures on the left and right sides of Figure 4A in a corresponding manner, it becomes evident that the overlapping HSPCs shared by both spleen and kidney predominantly reside in the HSPCs1 group (indicated as cluster 5 in the right-side figure). Additionally, there is also a small distribution of the overlapping HSPCs in the HSPCs2 group (cluster 8 in the right-side figure). These observations underline the presence of overlapping HSPCs in both the kidney and spleen. However, further clarification is required to fully comprehend the intricate correlation between the HSPCs in the kidney and spleen.

Reviewer #1 (Recommendations For The Authors):

Minor concerns:

(1) Figure 3C: why is 10 listed in between 1 and 2?

Response: We appreciate the reviewer's comment. It is pertinent to mention that the graphs in Figure 3C underwent an automatic sorting process facilitated by the software during the analysis. It should be emphasized that the assigned positions resulting from this sorting process have no bearing on the outcomes of the analysis.

(2) Figure 4A: difficult to assess the overlay between the kidney and spleen.

Response: As mentioned above, the overlapping HSPCs shared by both the spleen and kidney are mainly distributed in the HSPCs1 group (cluster 5 in the right-side figure), with a small amount also found in the HSPCs2 group (cluster 8 in the right-side figure).

(3) Figure 4C: What is this sample, kidney or spleen? Please specify.

Response: Figure 4C represents an overlay of the spleen and kidney cells depicted in Figure 4B, which includes all cells of the spleen and kidney to show the differentiation trajectory of the cells. As per reviewer’s suggestion, we have made corresponding modification to the revised figure.

(4) The manuscript is very long. Consider to focus on the major findings as the main figures and move the rest to the supplementary figures.

Response: This article aimed to comprehensively understand the hematopoietic and immunological traits of zebrafish kidneys through a systematic study. As a result, a comprehensive presentation of the findings has been provided. Given that the figures currently integrated into the main text play a significant role in illustrating the principal outcomes of each section, we kindly request that these figures remain in the main body of the article. This will contribute to sustaining the structural coherence and readability of the manuscript. Thank you for taking our request into consideration.

Reviewer #2 (Public Review):

In this manuscript, the authors have meticulously constructed a comprehensive atlas delineating hematopoietic stem/progenitor cell (HSPC) and immune-cell types within the zebrafish kidney, employing single-cell transcriptome profiling analysis. Notably, these cell populations exhibited distinctive responses to viral infection. Intriguingly, the investigation revealed that HSPCs manifest positive reactivities to viral infection, indicating the effective induction of trained immunity in select HSPCs. Furthermore, the study unveiled the capacity for the generation of antigen-stimulated adaptive immunity within the kidney, suggesting a role for the zebrafish kidney as a secondary lymphoid organ. This research elucidates the distinctive features of the fish immune system and underscores the multifaceted biology of the kidney in ancient vertebrates.

Response: We would like to express our gratitude to the reviewers for their overall positive feedback on our article.

Reviewer #2 (Recommendations For The Authors):

(1) The authors propose that zebrafish kidney is a dual-functional entity with functionalities of both primary and secondary lymphoid organs. Do the authors have any insights into the coordination of these two functions in the kidneys?

Response: We are grateful for the valuable comments provided. We believe that the question raised by the reviewer poses an intriguing research topic, as it explores the intricate interaction between the hematopoietic and adaptive immune systems in the renal organ. This exploration holds significant value in understanding the underlying mechanisms. To accomplish this, advanced techniques such as spatiotemporal single-cell transcriptomics and dynamic cell tracking will be utilized to validate the interplay between hematopoietic and immune cell lineages.

(2) Previous studies have found that fish IgZ/IgT specificity exists in mucosal immune organs. Is the expression of the zebrafish IgZ gene observed in the kidney? If so, is there any correlation with IgZ in mucosal immune organs?

Response: Thank you for drawing attention to this matter. In our study, we observed the expression of the IgZ gene (ighz) in the zebrafish kidney, as shown in Figure 6. This discovery aligns with previous research and confirms its presence in B cells. While IgZ is known to function as an antibody in mucosal immunity, it remains unclear whether the development of its secretory cells (IgZ+ B cells) originates from the central immune system, such as the kidney. Our results suggest that IgZ+ B cells may have their origin in the kidney and then migrate through the peripheral circulation to carry out their functions in the local mucosal system. This finding is consistent with our earlier research, which demonstrated that zebrafish IgZ is not limited to mucosal immune organs but is also abundantly present in systemic immunity, including peripheral blood (Immunology. 2021; 162(1): 105-120).

Reference:

Ji, J. F. et al. Differential immune responses of immunoglobulin Z subclass members in antibacterial immunity in a zebrafish model. Immunology, 2021;162(1), 105-120.

(3) Did the authors use the zebrafish genome or transcriptome for gene annotation? If the former, which version is used? Please supplement in the "Materials and methods".

Response: We appreciate the comments provided by the reviewer. In this study, we utilized the zebrafish genome, specifically the GRCz11 version, to annotate genes. The detailed genome data can be found at http://asia.ensembl.org/Danio_rerio/Info/Index. We have incorporated this information into the "Materials and Methods" section of the revised manuscript (line 873).

(4) Since the authors performed single-cell sequencing on leukocytes, why did several kidney cells, such as kidney multicellular cells and kidney mucin cells existed in the samples?

Response: Thanks for the reviewer’s comments. It is important to acknowledge that inadvertent mixing of kidney cells might have occurred during the preparation of single-cell suspensions in our analyzed sample. However, it is pertinent to emphasize that our primary focus was the analysis of immune cells. Therefore, any minor contamination from kidney cells in the analyzed sample is considered negligible and does not significantly affect the main results of our analysis.

(5) The application of "trained immunity," although currently popular, appears unsuitable in this context, as the current scenario involves a recall with the cognate antigen.

Response: To our knowledge, trained immunity is generally recognized as the long-term memory of innate immunity based on transcriptional, epigenetic and metabolic modifications of myeloid cells, which are characterized by elevated pro-inflammatory responses to secondary stimuli, whether they are identical or different (Cell Host Microbe. 2012; 12(2): 223-32; Nat Immunol. 2021; 22(1): 2-6; J Clin Invest. 2022;132(7): e158468). Therefore, stimulation of cognate antigens can be considered as a form of training immunity, and we hope that it will be accepted in this context.

References:

(1) Quintin, J. et al. Candida albicans infection affords protection against reinfection via functional reprogramming of monocytes. Cell host & microbe, 2012;12(2), 223-232.

(2) Divangahi, M. et al. Trained immunity, tolerance, priming and differentiation: distinct immunological processes. Nature immunology, 2021;22(1), 2-6.

(3) Pernet, E. et al. Training can’t always lead to Olympic macrophages. Journal of Clinical Investigation, 2022;132(7), e158468.

(6) The discovery that HSPC exhibits trained immune characteristics is novel. Do the authors have any insights into the biological significance of trained immunity in HSPCs concerning immune defense?

Response: We propose that the generation of trained immunity in HSPCs holds significant physiological implications. This process may expedite the differentiation and activation of specific immune cells upon re-infection, thereby bolstering the body's immune defenses and pathogen clearance. Consequently, it may serve as an intelligent strategy for host defense against pathogens. However, additional research is required to confirm this hypothesis.

(7) In the Figure 13I, the authors used CpG and CpG+TNP-KLH to stimulate zebrafish, but no corresponding experimental method was provided in the "Materials and methods". Please supplement.

Response: Thanks for the reviewer’s careful reading. We have included corresponding supplementary instructions in the “Materials and methods” section (lines 1011-1018).

(8) At line 187-190 in "Results", authors state that "It's noteworthy that cluster 11 exhibited high expression of genes ......, resembling a unique serpin-secreting cell population". Noteworthy is the fact that serpins play a role in diverse immunological processes, including coagulation, inflammation, as well as myeloid and lymphoid cell development. Could this renal cell cluster (kidney mucin cells) potentially harbor immunological functions?

Response: Given the crucial role of serpins in various immunological processes, secreted serpins from this particular cell cluster likely possess significant immunological functions, suggesting the notable immunological capabilities of this cell group. Consequently, our forthcoming research aims to conduct a more comprehensive investigation of this specific cell population.

(9) At line 171 in "Results", the number "6" in the "cluster 6" should not be italicized, please correct.

Response: We have addressed this issue in the revised manuscript (line 170).

(10) At line 937 in "Materials and methods", the authors isolated T/B lymphocytes through magnetic bead sorting. Please provide information on the source of the antibodies (rabbit anti-TCRα/β or mouse anti-IgM Ab).

Response: We have included corresponding instructions in the “Materials and methods” section (lines 938-939).

Perhaps I'm too much of skeptic, but I find the idea of utilizing cardboard as a final material a little far fetched in terms of durability. I feel like the only "paper" based chair I've seen is the cabbage chair by Nendo

It’s interesting to see how different people react to cloud sourcing particularly Wikipedia. All throughout elementary and middle school I was told to not use/ trust Wikipedia. The older i got into school the less teachers cared about using Wikipedia. I personally think that crowdsourcing websites like Wikipedia are important but it is interesting to see how different people care about crowdsourcing.

Author Response

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

eLife assessment

In this valuable study, the discovery and subsequent design of the AF03-NL chimeric antibody yielded a tool for studying filoviruses and provides a possible blueprint for future therapeutics. However, the data are incomplete and not presented clearly, which obscures flaws in the analyses and leaves unexplained phenomena. The work will be of interest to virologists studying antibodies.

Author response: Thank for your very valuable comments. The ms has been revised substantially and some new data have been added to further support the conclusions.

Public Reviews:

Reviewer #1 (Public Review):

Summary and Strengths:

Zhang et al. conducted a study in which they isolated and characterized a Marburg virus (MARV) glycoprotein-specific antibody, AF-03. The antibody was obtained from a phage-display library. The study shows that AF-03 competes with the previously characterized MARV-neutralizing antibody MR78, which binds to the virus's receptor binding site. The authors also performed GP mutagenesis experiments to confirm that AF-03 binds near the receptor binding site. In addition, the study confirmed that AF-03, like MR78, can neutralize Ebola viruses with cleaved glycoproteins. Finally, the authors demonstrated that NPC2-fused AF-03 was effective in neutralizing several filovirus species.

Weaknesses:

(1) The main premise of this study is unclear. Flyak et al. in 2015 described the isolation and characterization of a large panel of neutralizing antibodies from a Marburg survivor (Flyak et al., Cell, 2015). Based on biochemical and structural characterization, Flyak proposed that the Marburg neutralizing antibodies bind to the NPC1 receptor binding side. In the same study, it has been shown that several MARV-neutralizing antibodies can bind to cleaved Ebola glycoproteins that were enzymatically treated to remove the mucin-like domain and glycan cap. In the following study, it has been shown that the bispecific-antibody strategy can be used to deliver Marburg-specific antibodies into the endosome, where they can neutralize Ebola viruses (Wec et al., Science 2016). Finally, the use of lysosome-resident protein NPC2 to deliver antibody cargos to late endosomes has been previously described (Wirchnianski et al., Front. Immunol, 2021). The above-mentioned studies are not referenced in the introduction. The authors state that "there is no licensed treatment or vaccine for Marburg [virus] infection." While this is true, there are human antibodies that recognize neutralizing epitopes - that information can't be excluded while providing the rationale for the study. Furthermore, the authors use the word "novel" to describe the AF-03 antibody. How novel is AF-03 if multiple Marburg-neutralizing antibodies were previously characterized in multiple studies? Since AF-03 competes with previously characterized MR78, it binds to the same antigenic region as MR78. AF-03 also has comparable neutralization potency as MR78.

Author response: Thank for your valuable advice. In terms of the novelty of AF-03, the inhibition assay indicates that Q128/N129/C226 functions as key amino acids responsible for AF-03 neutralization given that the neutralizing capacity of AF-03 to pesudotyped virus harboring these mutants is impaired (see revised Fig. 2A left panel). Furthermore, ELISA assays show that mutation of Q128S-N129S or C226Y significantly disrupts the binding of GP to AF-03, while the neutralizing and binding capacity of MR78 to mutant GP and pseudovirus harboring C226Y instead of Q128S-N129S is not almost affected (see revised Fig. 2A right panel and 2B). Considering the fact that AF-03 and MR78 could compete with each other to bind to MARV GP (Fig. 2D). we thus make a conclusion that the epitopes of these two mAbs overlapped partially. Therefore, AF-03 is not a clone of MR78 and is a novel neutralizing mAb to MARV.

The work from Wirchnianski and colleagues has been referenced actually in the ms (see Ref. 38). Although our strategy for the design of broad-spectrum neutralizing antibody refers to their work, we further expand the species being evaluated including RAVN and mutated EBOV strains. The results show that NPC2-fused AF-03 exhibits neutralizing activity to 10 filovirus species and 17 EBOV mutants (Fig. 6A and B). The work by Flyak et al. in 2015 that described the isolation and characterization of a large panel of neutralizing antibodies from a Marburg survivor has been cited in Introduction section accordingly.

(2) Without the AF-03-MARV GP crystal structure, it's unclear how van der Waals interactions, H-bonds, and polar and electrostatic interactions can be evaluated. While authors use computer-guided homology modeling, this technique can't be used to determine critical interactions. Furthermore, Flyak et al. reported that binding to the NPC1 receptor binding site is the main mechanism of Marburg virus neutralization by human monoclonal antibodies. Since both AF-03 (this study) and MR78 (Flyak study) competed with each other, that information alone was sufficient for GP mutagenesis experiments that identified the NPC1 receptor binding site as the main region for mutagenesis.

Author response: Computer-guided homology modeling has been exploited successfully in our lab to determine key residues responsible for the interaction between antigen and mAbs (Immunol Res. 2015, 62:377; Scand J Immunol. 2019, 90:e12777; Sci Rep. 2022, 12:8469; Front Immunol. 2022, 13:831536). We refer to the crystal structure of MARV GP and the complex of MR78 and GP reported previously (Cell 2015, 160:904) and then model the complex of MARV GP and AF-03. Although AF-03 and MR78 compete with each other, we show that the epitopes of these two mAbs just overlap partially (Fig. 2A-D).

(3) The AF-03-GP affinity measurements were performed using bivalent IgG molecules and trimeric GP molecules. This format does not allow accurate measurements of affinity due to the avidity effect. The reported KD value is abnormally low due to avidity effects. The authors need to repeat the affinity experiments by immobilizing trimeric GPs and then adding monovalent AF-03 Fab.

Author response: As shown in Fig. 1A, GP protein used in this work is not trimer but largely monomer composed of MLD-deleted GP1 and GP2, which may at a certain extent weaken the engagement between GP and AF-03. It is noteworthy that we re-done the SPR assays for the binding of AF-03 to GP and show that KD value is 4.71x10-11M (see revised Fig. 1C). This GP protein is thus available to the evaluation of mAb affinity. In addition, it is reasonable to utilize bivalent IgG to detect the affinity of mAb to monomeric GP since the affinity likely decreases significantly when monovalent Fab is used.

Reviewer #2 (Public Review):

Summary:

The authors describe the discovery of a filovirus neutralizing antibody, AF03, by phage display, and its subsequent improvements to include NPC2 that resulted in a greater breadth of neutralization. Overall, the manuscript would benefit from considerable grammatical review, which would improve the communication of each point to the reader. The authors do not convincingly map the AF03 epitope, nor do they provide any strong support for their assumption that AF03 targets the NPC1 binding site. However, the authors do show that AF03 competes for MR78 binding to its epitope, and provides good support for the internalization of AF03-NL as the mechanism for improved breadth over the original AF03 antibody.

Strengths:

This study shows convincing binding to Marburgvirus GP and neutralization of Marburg viruses by AF03, as well as convincing neutralization of Ebolaviruses by AF03-NL. While there are no distinct populations of PE-stained cells shown by FACS in Figure 5A, the cell staining data in Figure 5C are compelling to a non-expert in endosomal staining like me. The control experiments in Figure 7 are compelling showing neutralization by AF03-NL but not AF03 or NPC2 alone or in combination. Altogether these data support the internalisation and stabilisation mechanism that is proposed for the gain in neutralization breadth observed for Ebolaviruses by AF03-NL over AF03 alone.

Weaknesses:

Overall, this reviewer is of the opinion that this paper is constructed haphazardly. For instance, the neutralization of mutant pseudoviruses is shown in Figure 2 before the concept of pseudovirus neutralization by AF03 is introduced in Figure 3. Similarly, the control experiments for AF03+NPC2 are described in Figure 7 after the data for breadth of neutralization are shown in Figure 6. GP quality controls are shown in Figure 2 after GP ELISAs / BLI experiments are done in Figure 1. This is disorienting for the reader.

Author response: AF-03 production and its binding capacity to GP is determined in Fig. 1. The epitopes of AF-03 is identified in Fig. 2. The neutralizing activity of AF-03 to pseudotyped MARV in vitro and in vivo is detected in Fig. 3. The neutralizing activity of AF-03 to pseudotyped ebolavirus harboring cleaved GP is detected in Fig. 4. The endosome-delivering ability of AF03-NL is examined in Fig. 5. The neutralization of filovirus species and EBOV mutants by AF03-NL is detected in Fig. 6. The requirement of CI-MPR for neutralization activity of AF03-NL is determined in Fig. 7. We think that this arrangement is suitable.

Figure 1: The visualisation of AF03 modelling and docking endeavours is extremely difficult to interpret. Firstly, there is no effort to orient the non-specialist reader with respect to the Marburgvirus GP model. Secondly, from the figures presented it is impossible to tell if the Fv docks perfectly onto the GP surface, or if there are violent clashes between the deeply penetrating AF03 CDRs and GP. This information would be better presented on a white background, perhaps showing GP in surface view from multiple angles and slices. The authors attempt to label potential interactions, but these are impossible to read, and labels should be added separately to appropriately oriented zoomed-in views.

Author response: To be readily understood the rationale of computer-guided modeling, the descriptions in the Methods and Results section have been refined accordingly. In addition, the information of the theoretical structure was presented on white background (see revised Fig. 1D-F).

Figure 2: The neutralization of mutant pseudoviruses cannot be properly assessed using bar graphs. These data should be plotted as neutralization curves as they were done for the wild-type neutralization data in Figure 3. The authors conclude that Q128 & N129 are contact residues, but the neutralization data for this mutant appear odd as the lowest two concentrations of AF03 show higher neutralization than the second highest AF03 concentration. Neutralization of T204/Q205/T206 (green), Y218 (orange), K222 (blue), or C226 (purple) appears to be better than neutralization of the wild-type MARV. The authors do not discuss this oddity. What are the IC50's? The omission of antibody concentrations on the x-axis and missing IC50 values give a sense of obscuring the data, and the manuscript would benefit from greater transparency, and be much easier to interpret if these were included. I am intrigued that the Q128S/N129S mutant is reported as having little effect on the neutralization of MR78. The bar graph appears to show some effect (difficult to interpret without neutralization curves and IC50 data), and indeed PDB:5UQY seems to suggest that these amino acids form a central component of the MR78 epitope (Q128 forms potential hydrogen bonds with CDRH1 Y35 and CDRL3 Y91, while N129 packs against the MR78 CDRH3 and potentially makes additional polar contact with the backbone). Lastly, since neutralization was tested in both HEK293T cells and Huh7 cells in Figure 3, the authors should clarify which cells were used for neutralization in Figure 2.

Author response: Thank for your advice. Accordingly, in the revised ms, the neutralization curve of AF-03 and MR78 is presented in revised Fig. 2A. The neutralization of AF-03 to pseudotyped MARV harboring Q128S/N129S or C226Y is impaired significantly compared with WT MARV and those bearing other indicated mutations, while Q128S/N129S instead of C226Y mutation affect the neutralizing capacity of MR78 at a certain extent. This is consistent with the data on the binding of AF-03 or MR78 to MARV GP protein assayed by ELISA (see revised Fig. 2B). Overall, these results show that Q128/N129/C226 functions as key amino acids responsible for AF-03 neutralization.

Figure 3: The first two images in Figure 3C showing bioluminescent intensity from pseudovirus-injected mice pretreated with either 10mg/kg or 3mg/kg AF03 are identical images. This is apparent from the location, shape, and intensity of the bioluminescence, as well as the identical foot placement of each mouse in these two panels. Currently, this figure is incomplete and should be corrected to show the different mice treated with either 10mg/kg or 3mg/kg of AF03.

Author response: Thank for your carefulness. Indeed, it is our mistake. In the revised ms, this fault has been corrected. The correct images have been added (see revised Fig. 3C).

Figure 4 would benefit from a control experiment without antibodies comparing infection with GP-cleaved and GP-uncleaved pseudoviruses. The paragraph describing these data was also difficult to read and would benefit from additional grammatical review.

Author response: Accordingly, a control experiment comparing the infection of GP-cleaved with GP-uncleaved pseudoviruses is performed. The results show that The infection of pseudotyped ebolavirus harboring cleaved GP to host cells is comparable or stronger than those containing intact GP（see revised Fig. s1）. Therefore, the data in Fig. 4 support the inhibition of cell entry of ebolavirus species harboring cleaved GP by AF-03, which is not attributed to the possible impairment of cell entry capacity of GPcl-containing ebolavirus. In addition, the sentences have been modified to be read smoothly.

Figure 5: The authors should clarify in the methods section that the "mock" experiment included the PE anti-human IgG Fc antibody. Without this clarification, the lack of a distinct negative population in the FACS data could be interpreted as non-specific staining with PE. If the PE antibody was added at an equivalent concentration to all panels, what does the directionality of the arrowheads in Figure 5A (labelled PE) and 5B (labelled pHrodo Red) indicate?

Author response: Thank for your advice. In the revised version, we denote that the mock is actually a human IgG isotype in the figure legend. The arrowheads denote the fluorescence intensity of PE or pHrodo on the lateral axis of the plots. Of course, herein the percentage of PE or pHrodo-positive cells is shown.

Figure 6B: These data would benefit from the inclusion of IC50, transparency of antibody concentrations used, and consistency in the direction of antibody concentrations (increasing to the right or left of the x-axis) when compared to Figure 2.

Author response: The concentration of antibody titrated is shown in figure legends. The direction of antibody concentrations is unified throughout the paper. Although IC50 is not included, these data clearly show that AF03-NL rather than AF-03 prominently inhibits the cell entry of EBOV mutants.

Reviewer #1 (Recommendations For The Authors):

Line 143: anti-human should be anti-human.

Line 223: From the SDS-PAGE results, it's not clear that the AF-03 was expressed in the eukaryotic cell line. Please, rephrase the sentence.

Line 263: ELISA experiments can't be used to determine affinity.

Line 394: Flyak et al. generated human antibodies from PBMC samples of Marburg survivors, not plasma samples.

Author response: According to reviewer's advice, the sentences have been modified or corrected to more accurately describe the results. As well, the grammatic errors in the ms have been corrected carefully.

This paragraph reminded me of the philosophy of Tibetan monks that create sand mandalas. After working on them for weeks or months, they ritualistically destroy them in minutes. It's interesting that in this case, they don't make mandalas to preserve them and hang on the wall or for the sake of developing a skill. But they still derive a spiritual "end" out of this process: by making and then destroying their hard work, Tibetan monks teach the belief that nothing lasts forever.

Author Response

eLife assessment

This study, which seeks to identify factors from the glial niche that support and maintain neural stem cells, unveils a novel role for ferritin in this process. Furthermore, the work shows that defects in larval brain development resulting from ferritin knockdown can be attributed to impaired Fe-S cluster activity and ATP production. These findings will be valuable to both oncologists and neurobiologists, though the supporting evidence is currently incomplete.

Public Reviews

Reviewer #1 (Public Review):

Summary:

This study unveils a novel role for ferritin in Drosophila larval brain development. Furthermore, it pinpoints that the observed defects in larval brain development resulting from ferritin knockdown are attributed to impaired Fe-S cluster activity and ATP production. In addition, knocking down ferritin genes suppressed the formation of brain tumors induced by brat or numb RNAi in Drosophila larval brains. Similarly, iron deficiency suppressed glioma in the mice model. Overall, this is a well-conducted and novel study.

Strengths:

Thorough analyses with the elucidation of molecular mechanisms.

Weaknesses:

Some of the conclusions are not well supported by the results presented.

We really appreciate your review and positive feedback. As for weaknesses, we will try our best to solidate the related conclusions.

Reviewer #2 (Public Review):

Summary:

Zhixin and collaborators have investigated if the molecular pathways present in glia play a role in the proliferation, maintenance, and differentiation of Neural Stem Cells. In this case, Drosophila Neuroblasts are used as models. The authors find that neuronal iron metabolism modulated by glial ferritin is an essential element for Neuroblast proliferation and differentiation. They show that loss of glial ferritin is sufficient to impact on the number of neuroblasts. Remarkably, the authors have identified that ferritin produced in the glia is secreted to be used as an iron source by the neurons. Therefore iron defects in glia have serious consequences in neuroblasts and likely vice versa. Interestingly, preventing iron absorption in the intestine is sufficient to reduce NB number. Furthermore, they have identified Zip13 as another regulator of the process. The evidence presented strongly indicates that loss of neuroblasts is due to premature differentiation rather than cell death.

Strengths:

• Comprenhensive analysis of the impact of glial iron metabolism in neuroblast behaviour by genetic and drug-based approaches as well as using a second model (mouse) for some validations.

• Using cutting-edge methods such as RNAseq as well as very elegant and clean approaches such as RNAi-resistant lines or temperature-sensitive tools

• Goes beyond the state of the art highlighting iron as a key element in neuroblast formation as well as as a target in tumor treatments.

Weaknesses:

Although the manuscripts have clear strengths, there are also some strong weaknesses that need to be addressed.

• Some literature is missing

Thanks for your reminder and we will add the missing literatures.

• In general, the authors succeeded but in some cases, the authors´ claims are not fully supported by the evidence presented and additional experiments are critical to discriminate among different hypotheses.

We are greatly grateful to the reviewer for recognizing our work, and we will support our conclusions with further evidence.

• Moreover, some potential flaws might be present in the analysis of cell death and mitochondrial iron.

We used Caspase-3 or TUNEL to indicate the apoptosis signal. Further, we overexpressed the anti-apoptosis gene p35 to inhibit apoptosis and found no rescue effect on neuroblast number. The results of these experiments are consistent.

It is difficult to determine the mitochondrial iron of neuroblast, so we used indirect methods to test ferroptosis, such as TEM and iron (or iron chelator) supplement. We will perform more experiments according to recommendations to determine that.

Reviewer #3 (Public Review):

In this manuscript, Ma et al seek to identify stem cell niche factors. They perform an RNAi screen in glial cells and screen for candidates that support and maintain neuroblasts (NBs) in the developing fly brain. Through this, they identify two subunits of ferritin, which is a conserved protein that can store iron in cells in a non-toxic form and release it in a controlled manner when and where required. They present data to support the conclusion that ferritin produced in glia is released and taken up by NBs where it is utilised by enzymes in the Krebs cycle as well as in the electron transport chain. In its absence from glia, NBs are unable to generate sufficient energy for division and therefore prematurely differentiate via nuclear prospero resulting in small brains. The work will be of interest to those interested in neural stem cells and their non-cell autonomous control by niches.

The past decade has seen a growing appreciation of how glial cells support and maintain NBs during development.

The authors' discovery of glial-derived ferritin providing essential iron atoms for energy production is interesting and important. They have employed a variety of genetic tools and assays to uncover how ferritin in glia might support NBs. This is particularly challenging because there are no direct ways of assaying for iron or energy consumption in a cell-specific manner.

There are however instances where conclusions are drawn to support the story being developed without considering the equally plausible alternative explanations that should ideally be addressed.

For example, the data supporting the transfer of ferritin from glia to NBs was weak given the misexpression system used; the Shi[ts] experiment was also not convincing (perhaps they have more representative images?).

Thanks for your comment. We have the negative control, which excludes the misexpression. As for Shits experiment, we will substitute for more representative images.

The iron manipulation experiments are in the whole animal and it is likely that this affects general feeding behaviour, which is known to affect NB exit from quiescence and proliferative capacity. The loss of ferritin in the gut and iron chelators enhancing the NB phenotype are used as evidence that glia provide iron to NB to support their number and proliferation. Since the loss of NB is a phenotype that could result from many possible underlying causes (including low nutrition), this specific conclusion is one of many possibilities.

Iron chelator (or iron salt) feeding is a common method for investigating metal metabolism in Drosophila[1-3]. And other metal chelators (such as copper and zinc chelator) do not have similar phenotype (data not shown), which can partially exclude this possibility. Further, iron absorption was blocked by knockdown of ferritin only in the iron cell region[1], a small part of midgut, which phenocopied iron chelator feeding, implying iron deficiency is probably the main cause of the phenotype. More importantly, iron chelator only enhances the NB phenotype in the ferritin knockdown group, not the control group, suggesting iron deficiency results in the phenotype, which rules out other possibilities.

Similarly, knockdown of the FeS protein assembly components phenocopy glial ferritin knock down. Since iron is so important for the TCA and the ETC, this is not surprising, but the similarities in the two phenotypes seem insufficient to say that it's glial ferritin that's causing the lack of iron in the NB and therefore resulting in loss of NBs.

It is hard to get this conclusion just by FeS protein assembly components knockdown, so we just used “implied” to describe this result. However, we combine several results to address this issue, including iron chelator feeding, ferritin knockdown in the midgut, the enhancement of phenotype by iron chelators, aconitase activity, GO enrichment, KEGG enrichment, and Zip13. These results pointed to the interpretation that iron deficiency in NBs caused by glial ferritin defects leads to NB loss.

Pros RNAi will certainly result in an increase in NB numbers because the loss of pros results in an inability of NB progeny to differentiate. This (despite the slight increase in nuclear pros) is not sufficient to infer that glial ferritin knockdown results in premature differentiation of NBs via nuclear pros.

First, pros RNAi, brat RNAi, or numb RNAi can each result in an inability of NB progeny to differentiate, respectively[4-6]. If the rescue of NB number by pros RNAi mainly relies on the differentiation block of NB progeny, brat RNAi or numb RNAi is expected to similarly rescue the NB number. However, our results showed that only pros RNAi could rescue the NB number, while brat RNAi or numb RNAi could not.

Secondly, nuclear Pros represses genes required for self-renewal and is also required to activate genes for terminal differentiation[7]. Thus, Pros is kept in the cytoplasm and remains almost undetectable in the nuclei in normal NBs[8]. However, we observed the detectable Pros in the nuclei of some NBs after glial ferritin knockdown, and the NB number with detectable nuclear Pros was significantly increased when compared to control.

Altogether, we conclude that NBs tend to undergo premature differentiation after glial ferritin knockdown.

I recognise these are challenging to prove irrefutably, however, the frequency of such expansive interpretations of data is of concern.

(1) Tang X, Zhou B. Ferritin is the key to dietary iron absorption and tissue iron detoxification in Drosophila melanogaster. FASEB J, 2013,27(1):288-98

(2) Xiao G, Liu ZH, Zhao M, et al. Transferrin 1 Functions in Iron Trafficking and Genetically Interacts with Ferritin in Drosophila melanogaster. Cell Rep, 2019,26(3):748-58 e5

(3) Mukherjee C, Kling T, Russo B, et al. Oligodendrocytes Provide Antioxidant Defense Function for Neurons by Secreting Ferritin Heavy Chain. Cell Metab, 2020,32(2):259-72 e10

(4) Knoblich JA, Jan LY, Jan YN. Asymmetric Segregation of Numb and Prospero during Cell-Division. Nature, 1995,377(6550):624-7

(5) Zacharioudaki E, Magadi SS, Delidakis C. bHLH-O proteins are crucial for neuroblast self-renewal and mediate Notch-induced overproliferation. Development, 2012,139(7):1258-69

(6) Bello B, Reichert H, Hirth F. The brain tumor gene negatively regulates neural progenitor cell proliferation in the larval central brain of. Development, 2006,133(14):2639-48

(7) Choksi SP, Southall TD, Bossing T, et al. Prospero acts as a binary switch between self-renewal and differentiation in Drosophila neural stem cells. Developmental Cell, 2006,11(6):775-89

(8) Spana EP, Doe CQ. The Prospero Transcription Factor Is Asymmetrically Localized to the Cell Cortex during Neuroblast Mitosis in Drosophila. Development, 1995,121(10):3187-95

I'm just recently learning this, but it's shocking and saddening how children who experience ACE can detrimentally impact their physical development. I would never have thought that stress and trauma can severely negatively impact child's physical development and carry onto their adult life.

It's important to understand the difference between shame and guilt in kids. Guilt makes kids want to fix their actions, while shame can make them feel bad about themselves and want to hide. So, it's important for parents to help kids learn to feel guilty about actions, not ashamed of themselves.

The explanation of the difference between guilt and shame draws attention to a crucial part of children's emotional growth. Recognizing that guilt is connected to an action, but shame can result in a sense of being intrinsically evil, emphasizes how crucial it is for parents to react to their children's errors. It's essential for developing in kids a positive feeling of accountability and self-worth.

The distinction between shame and guilt as outlined here offers a profound insight into childhood development and the cultivation of a healthy emotional and moral compass. It's fascinating to see how these emotions, often lumped together in casual discourse, play distinct roles in shaping an individual's self-perception and response to wrongdoing. Recognizing this difference can empower parents, educators, and caregivers to foster environments where children learn to constructively address their actions rather than internalize negative feelings about their self-worth.

(*23)J8. (Amy) So, the Luke reading lays out conditions under which it’s advisable to use a multi-level model… and then describes the three ways in which anyone decides to do any sort of analysis! The arguments used to justify an OLS model (that variables are monadic and independent of one another) seems to be one that is rarely going to be correct given that, per what we’ve learned in Barbara’s class, there’s always a confounder of some kind. Moreover, it seems like theoretical arguments could virtually always be deployed to defend the use of a multilevel model, but not for an OLS one.

Response: Yes, anytime we have clustered data or we are using contextual variables then I think we really have a multilevel model. If we are just worried about the effect of clustering on the standard errors (i.e. the non-independence of the error terms), however, we can account for that using robust regression. I.e., using survey weights with Add Health to get population weighted estimates. Multilevel models take this a step further and let the contextual effects become objects of study themselves.

I like how they described each step. I think it's very important that we don't just ask our students questions that just want them to identify the answer. Instead, asking them questions that want them to look for, compare, judge, and develop a new situation can help improve their critical thinking skills so that they can think more deeply about a topic.

It's just disgusting.

Is self-organising feasible?

It's interesting that the author includes the original greek instead of just translating

This is a powerful outro that nicely sums up your key argument.

This is great! It's exactly what I would want/expect to see right after your hook in your intro. I would recommend shortening the story to 1-2 short paragraphs (just the key information) and then transitioning immediately to this highlighted section).

I think this is the most frequently used form of logical reasoning because it's simple. I disagree with the author's point that inductive reasoning "can be difficult to employ well." It's just referencing a source through citation just like you see in this sentence. I don't think of inductive reasoning in terms of likelihood when I hear or see someone use it. I just think, "ok here's something that backs up what the speaker is saying" and make a determination as to whether or not it makes sense to me. Maybe this is because I'm reading this from the perspective of a writer rather than a speaker.

This is why structure is important in a speech. If you just spew facts at your audience, they're not going to retain your information. One thing that I try to remember when giving speeches is that it's usually better to simplify things rather than cram words in. If you're short on time, play with your flow and add necessary descriptions to fill in the gaps.

I don't have anything super analytical to add here. But, I really think this is interesting from a historical context. I always assumed that the contemporary university, and first-year composition along with it, were more of a recent idea. It's safe to say that none of this is as new as I thought it was. I also like how it mentions shortly after this section that college admissions boomed after the Civil War.

It reflects a shift in perspective and a recognition of the complexities involved in fostering a connected world. While the initial belief in providing people a voice and connection has had positive impacts, acknowledging the existing divisions emphasizes a deeper responsibility. The commitment to not only connect but actively work towards bringing people closer together underscores the evolving role and societal impact that platforms like ours can strive for. It speaks to a more comprehensive vision of social responsibility and collective progress.

(#23)

(+ open up the FE R results in section 5.3 cb8)

I4(Mia) In a portion of the video you mention that the fixed effects is an “additive” model and operates as a function. I’m not sure what this means. What does interact mean here? An actual interaction term or a hypothetical association?

Response: Yes--it just means that the effects are added together (as opposed to interaction/moderating effects)

l7 (Rebecca) Can we review table 2 in the wage penalty reading? I understand what the coefficients mean but am still unclear on why exactly the OLS model estimate goes down so much after including controls, compared to the FE model. Response: Yes, let’s talk about it in slide 23

*I5(Lily) I am confused about the question of the quiz that reads, “Adding controls for human capital variables (i.e., education and work experience) lowers the FE estimate of the MWP. This is not unexpected, and indicates that within-mother variation in these variables explains away some of the effect.”I thought it was described that the between-effect was what was really going on here. Can you explain this?

Response: Some of the effect of having a child can be explained by work interruptions (i.e., differences in experience)...in the FE model, this is a within mother difference. (Note that work interruptions can also be involuntary, which gets back to Acker’s note about gendered organizations).

(Jessica) In the Budig and England paper, they state on pg. 214 “Controlling for the human capital variables shown in Table 1, reduces the child penalty by 36 percent, from about 7 percent to 5 percent.” I’m a bit confused about where these numbers come from, could you explain how they get them?

Response: Yes, let’s look at that passage (clipped below) and discuss in class. What they did, though, is add the human capital variables (see Table 1, slide 22), and some of the MWP effect is explained. As they note, this is entirely consistent with human capital theory--work disruptions and lower experience are part of the explanation. However, work disruption could also be involuntary job displacement caused by a anti-mother discrimination at the workers firm (that is revealed after childbirth). I.e., there are multiple possible interpretations of what the effect means.

(Anna) Other than comparing them to see the impact of omitted variables, is there any benefit to the OLS model in this article? It really seems to be solely for that, but I normally expect a little bit more out of results so im not sure.

Response: I think so, as a comparison. I.e., if they hadn’t included it, readers (reviewers) would have asked, what about the OLS results?

(Athena) When interpreting the fixed model effects, why is age not included? I’m also curious what effect age might have when looking at table 1 (i.e. average age of childless and mother in never married). To me, I feel like age might be related to when in the life course events such as marriage and divorce might happen. It’s a study looking at ln of wage, but accounting for that might add another perspective.

Response: Yes…the models do control for age. Age has a large positive effect on wages (as does work experience), and it is crucial to control for it. Table 1 in B&E presented models that controlled for age but didn’t present the results for age. See the footnote for Table 1, for example: http://www.tedmouw.info/soc709new/class-I_n.html#2110_Q10 Also see my results in section 5.2 of the lecture notes: http://www.tedmouw.info/soc709new/class-I_n.html#5_Analysis_of_NLSY79_data

Then go to slide 14, but make sure to discuss 24 (Q11).

(#13) Big picture: Why are we doing this? (Put Table 2 up in a different window)

Specific for this topic (MWP): Theory: is motherhood random? (No, why not?)

Draw a career trajectory and illustrate the benefits of FE approach.

Broader issue: much of our data is clustered.

(Alex) When working with multilevel data, does level 2 always represent a broader context compared to level 1, or is that up to the researcher’s discretion/structure of the dataset?

Response: It just has to do with clustering; i.e., level 1 is clustered into level 2 units.

(Preethi) What would be the pros/cons of using a random effects model rather than the fixed effects model in this scenario?

Response: A random effects model wouldn’t control for the possibility that U_i is correlated with childbirth; i.e., that there could be selection effects.

[To frame a critical take on it:] (Braxton) So the value of a FE model is that we can have two distinct years to compare the motherhood penalty, like two snapshots in time that control for all of the constant/unchanging things. This is unlike OLS where there is likely more omitted variable bias and it’s just at one moment. If there are variables that do change over time that FE models aren’t taking into consideration, then is a FE model actually much better? I ask because I’m not super convinced reading the paper that their FE model took everything into consideration, especially since they assert that all the other research before them missed things here and there. And the OLS results are slightly more negative but not very different.

Response: Yes…it is really important to think about what is changing over the time period…those variables need to be in the model. In fact, you (collectively) will find out what happens if life-course/career variables are left out of the model (i.e., age and work experience).

Jump to slide 22 (Table 1) then slide 23 (Q10).

Author Response

Reviewer #1 (Public Review):

Summary:

Visual Perceptual Learning (VPL) results in varying degrees of generalization to tasks or stimuli not seen during training. The question of which stimulus or task features predict whether learning will transfer to a different perceptual task has long been central in the field of perceptual learning, with numerous theories proposed to address it. This paper introduces a novel framework for understanding generalization in VPL, focusing on the form invariants of the training stimulus. Contrary to a previously proposed theory that task difficulty predicts the extent of generalization - suggesting that more challenging tasks yield less transfer to other tasks or stimuli - this paper offers an alternative perspective. It introduces the concept of task invariants and investigates how the structural stability of these invariants affects VPL and its generalization. The study finds that tasks with high-stability invariants are learned more quickly. However, training with low-stability invariants leads to greater generalization to tasks with higher stability, but not the reverse. This indicates that, at least based on the experiments in this paper, an easier training task results in less generalization, challenging previous theories that focus on task difficulty (or precision). Instead, this paper posits that the structural stability of stimulus or task invariants is the key factor in explaining VPL generalization across different tasks

Strengths:

• The paper effectively demonstrates that the difficulty of a perceptual task does not necessarily correlate with its learning generalization to other tasks, challenging previous theories in the field of Visual Perceptual Learning. Instead, it proposes a significant and novel approach, suggesting that the form invariants of training stimuli are more reliable predictors of learning generalization. The results consistently bolster this theory, underlining the role of invariant stability in forecasting the extent of VPL generalization across different tasks.

• The experiments conducted in the study are thoughtfully designed and provide robust support for the central claim about the significance of form invariants in VPL generalization.

Weaknesses:

• The paper assumes a considerable familiarity with the Erlangen program and the definitions of invariants and their structural stability, potentially alienating readers who are not versed in these concepts. This assumption may hinder the understanding of the paper's theoretical rationale and the selection of stimuli for the experiments, particularly for those unfamiliar with the Erlangen program's application in psychophysics. A brief introduction to these key concepts would greatly enhance the paper's accessibility. The justification for the chosen stimuli and the design of the three experiments could be more thoroughly articulated.

Response: We appreciate the reviewer's feedback regarding the accessibility of our paper. In response to this feedback, we plan to enhance the introduction section of our paper to provide a concise yet comprehensive overview of the key concepts of Erlangen program. Additionally, we will provide a more thorough justification for the selection of stimuli and the experimental design in our revised version, ensuring that readers understand the rationale behind our choices.

• The paper does not clearly articulate how its proposed theory can be integrated with existing observations in the field of VPL. While it acknowledges previous theories on VPL generalization, the paper falls short in explaining how its framework might apply to classical tasks and stimuli that have been widely used in the VPL literature, such as orientation or motion discrimination with Gabors, vernier acuity, etc. It also does not provide insight into the application of this framework to more naturalistic tasks or stimuli. If the stability of invariants is a key factor in predicting a task's generalization potential, the paper should elucidate how to define the stability of new stimuli or tasks. This issue ties back to the earlier mentioned weakness: namely, the absence of a clear explanation of the Erlangen program and its relevant concepts.

Response: Thanks for highlighting the need for better integration of our proposed theory with existing observations in the field of VPL. Unfortunately, the theoretical framework proposed in our study is based on the Klein’s Erlangen program and is only applicable to geometric shape stimuli. For VPL studies using stimuli and paradigms that are completely unrelated to geometric transformations (such as motion discrimination with Gabors or random dots, vernier acuity, spatial frequency discrimination, contrast detection or discrimination, etc.), our proposed theory does not apply. Some stimuli employed by VPL studies can be classified into certain geometric invariants. For instance, orientation discrimination with Gabors (Dosher & Lu, 2005) and texture discrimination task (F. Wang et al., 2016) both belong to tasks involving Euclidean invariants, and circle versus square discrimination (Kraft et al., 2010) belongs to tasks involving affine invariance. However, these studies do not simultaneously involve multiple geometric invariants of varying levels stability, and thus cannot be directly compared with our research. It is worth noting that while the Klein’s hierarchy of geometries, which our study focuses on, is rarely mentioned in the field of VPL, it does have connections with concepts such as 'global/local', 'coarse/fine', 'easy/difficulty', 'complex/simple': more stable invariants are closer to 'global', 'coarse', 'easy', 'complex', while less stable invariants are closer to 'local', 'fine', 'difficulty', 'simple'. Importantly, several VPL studies have found ‘fine-to-coarse’ or ‘local-to-global’ asymmetric transfer (Chang et al., 2014; N. Chen et al., 2016; Dosher & Lu, 2005), which seems consistent with the results of our study.

In the introduction section of our revised version and subsequent full author response, we will provide a clear explanation of the Erlangen program and elucidate how to define the stability of new stimuli or tasks. In the discussion section of our revised version, we will compare our results to other studies concerned with the generalization of perceptual learning and speculate on how our proposed theory fit with existing observations in the field of VPL.

• The paper does not convincingly establish the necessity of its introduced concept of invariant stability for interpreting the presented data. For instance, consider an alternative explanation: performing in the collinearity task requires orientation invariance. Therefore, it's straightforward that learning the collinearity task doesn't aid in performing the other two tasks (parallelism and orientation), which do require orientation estimation. Interestingly, orientation invariance is more characteristic of higher visual areas, which, consistent with the Reverse Hierarchy Theory, are engaged more rapidly in learning compared to lower visual areas. This simpler explanation, grounded in established concepts of VPL and the tuning properties of neurons across the visual cortex, can account for the observed effects, at least in one scenario. This approach has previously been used/proposed to explain VPL generalization, as seen in (Chowdhury and DeAngelis, Neuron, 2008), (Liu and Pack, Neuron, 2017), and (Bakhtiari et al., JoV, 2020). The question then is: how does the concept of invariant stability provide additional insights beyond this simpler explanation?

Response: We appreciate the alternative explanation proposed by the reviewer and agree that it presents a valid perspective grounded in established concepts of VPL and neural tuning properties. However, performing in the collinearity and parallelism tasks both require orientation invariance. While utilizing the orientation invariance, as proposed by the reviewer, can explain the lack of transfer from collinearity or parallelism to orientation task, it cannot explain why collinearity does not transfer to parallelism.

As stated in the response to the previous review, in the revised discussion section, we will compare our study with other studies (including the three papers mentioned by the reviewer), aiming to clarify the necessity of the concept of invariant stability for interpreting the observed data and understanding the mechanisms underlying VPL generalization.

• While the paper discusses the transfer of learning between tasks with varying levels of invariant stability, the mechanism of this transfer within each invariant condition remains unclear. A more detailed analysis would involve keeping the invariant's stability constant while altering a feature of the stimulus in the test condition. For example, in the VPL literature, one of the primary methods for testing generalization is examining transfer to a new stimulus location. The paper does not address the expected outcomes of location transfer in relation to the stability of the invariant. Moreover, in the affine and Euclidean conditions one could maintain consistent orientations for the distractors and targets during training, then switch them in the testing phase to assess transfer within the same level of invariant structural stability.

Response: Thanks for raising the issue regarding the mechanism of transfer within each invariant conditions. We plan to design an additional experiment that is similar in paradigm to Experiment 2, aiming to examine how VPL generalizes to a new test location within a single invariant stability level.

• In the section detailing the modeling experiment using deep neural networks (DNN), the takeaway was unclear. While it was interesting to observe that the DNN exhibited a generalization pattern across conditions similar to that seen in the human experiments, the claim made in the abstract and introduction that the model provides a 'mechanistic' explanation for the phenomenon seems overstated. The pattern of weight changes across layers, as depicted in Figure 7, does not conclusively explain the observed variability in generalizations. Furthermore, the substantial weight change observed in the first two layers during the orientation discrimination task is somewhat counterintuitive. Given that neurons in early layers typically have smaller receptive fields and narrower tunings, one would expect this to result in less transfer, not more.

Response: We appreciate the reviewer's feedback regarding the clarity of our DNN modeling experiment. We acknowledge that while DNNs have been demonstrated to serve as models for visual systems as well as VPL, the claim that the model provides a ‘mechanistic’ explanation for the phenomenon still overstated. In our revised version,

We will attempt a more detailed analysis of the DNN model while providing a more explicit explanation of the findings from the DNN modeling experiment, emphasizing its implications for understanding the observed variability in generalizations.

Additionally, the substantial weight change observed in the first two layers during the orientation discrimination task is not contradictory to the theoretical framework we proposed, instead, it aligns with our speculation regarding the neural mechanisms of VPL for geometric invariants. Specifically, it suggests that invariants with lower stability rely more on the plasticity of lower-level brain areas, thus exhibiting poorer generalization performance to new locations or stimulus features within each invariant conditions. However, it does not imply that their learning effects cannot transfer to invariants with higher stability.

Reviewer #2 (Public Review):

The strengths of this paper are clear: The authors are asking a novel question about geometric representation that would be relevant to a broad audience. Their question has a clear grounding in pre-existing mathematical concepts, that, to my knowledge, have been only minimally explored in cognitive science. Moreover, the data themselves are quite striking, such that my only concern would be that the data seem almost too clean. It is hard to know what to make of that, however. From one perspective, this is even more reason the results should be publicly available. Yet I am of the (perhaps unorthodox) opinion that reviewers should voice these gut reactions, even if it does not influence the evaluation otherwise. Below I offer some more concrete comments:

(1) The justification for the designs is not well explained. The authors simply tell the audience in a single sentence that they test projective, affine, and Euclidean geometry. But despite my familiarity with these terms -- familiarity that many readers may not have -- I still had to pause for a very long time to make sense of how these considerations led to the stimuli that were created. I think the authors must, for a point that is so central to the paper, thoroughly explain exactly why the stimuli were designed the way that they were and how these designs map onto the theoretical constructs being tested.

(2) I wondered if the design in Experiment 1 was flawed in one small but critical way. The goal of the parallelism stimuli, I gathered, was to have a set of items that is not parallel to the other set of items. But in doing that, isn't the manipulation effectively the same as the manipulation in the orientation stimuli? Both functionally involve just rotating one set by a fixed amount. (Note: This does not seem to be a problem in Experiment 2, in which the conditions are more clearly delineated.)

(3) I wondered if the results would hold up for stimuli that were more diverse. It seems that a determined experimenter could easily design an "adversarial" version of these experiments for which the results would be unlikely to replicate. For instance: In the orientation group in Experiment 1, what if the odd-one-out was rotated 90 degrees instead of 180 degrees? Intuitively, it seems like this trial type would now be much easier, and the pattern observed here would not hold up. If it did hold up, that would provide stronger support for the authors' theory.

It is not enough, in my opinion, to simply have some confirmatory evidence of this theory. One would have to have thoroughly tested many possible ways that theory could fail. I'm unsure that enough has been done here to convince me that these ideas would hold up across a more diverse set of stimuli.

Response: (1) We appreciate the reviewer’s feedback regarding the justification for our experimental designs. We recognize the importance of thoroughly explaining how our stimuli were designed and how these designs correspond to the theoretical constructs being tested. In our revised version, we will enhance the introduction of Erlangen program and provide a more detailed explanation of the rationale behind our stimulus designs, aiming to enhance the clarity and transparency of our experimental approach for readers who may not be familiar with these concepts.

(2) We appreciate the reviewer’s insight into the design of Experiment 1 and the concern regarding the potential similarity between the parallelism and orientation stimuli manipulations.

The parallelism and orientation stimuli in Experiment 1 were first used by Olson & Attneave (1970) to support line-based models of shape coding and then adapted to measure the relative salience of different geometric properties (Chen, 1986). In the parallelism stimuli, the odd quadrant differs from the rest in line slope, while in the orientation stimuli, in contrast, the odd quadrant contains exactly the same line segments as the rest but differs in direction pointed by the angles. The result, that the odd quadrant was detected much faster in the parallelism stimuli than in the orientation stimuli, can serve as evidence for line-based models of shape coding. However, according to Chen (1986, 2005), the idea of invariants over transformations suggests a new analysis of the data: in the parallelism stimuli, the fact that line segments share the same slope essentially implies that they are parallel, and the discrimination may be actually based on parallelism. Thus, the faster discrimination of the parallelism stimuli than that of the orientation stimuli may be explained in terms of relative superiority of parallelism over orientation of angles—a Euclidean property.

The group of stimuli in Experiment 1 has been employed by several studies to investigate scientific questions related to the Klein’s hierarchy of geometries (L. Chen, 2005; Meng et al., 2019; B. Wang et al., n.d.). Due to historical inheritance, we adopted this set of stimuli and corresponding paradigm, despite their imperfect design.

(3) Thanks for raising the important issue of stimulus diversity and the potential for "adversarial" versions of the experiments to challenge our findings. We acknowledge the validity of your concern and recognize the need to demonstrate the robustness of our results across a range of stimuli. We plan to design additional experiments to investigate the potential implications of varying stimulus characteristics, such as different rotation angles proposed by the reviewer, on the observed patterns of performance.

Author Response

Reviewer #2 (Public Review):

This study aims to test the role of awake replay in short-term memory, a type of memory that operates on the timescale of seconds and minutes. Replay refers to a time-compressed burst of neuronal population activity during a particular oscillatory local field potential event in the hippocampus, called the sharp-wave ripple (SWR). SWRs are found during sleep and in the awake state and are always associated with the animal being quiescent. The paper compares results from three different behavioral tasks ranging in memory requirements and memory timescales. First, rats were trained on either a spatial match-to-sample task (MTS), a non-match-to-sample task (NMTS), or a task requiring the memorization of sequences (maze arms to be visited in a specific temporal order). In this initial training phase, the animals were allowed to learn the maze structure and the rules governing these tasks for all these behavioral paradigms. Then, awake sharp-SWRs were disrupted as the animal performed these tasks (both during instruction and test phases) via an online detection system combined with closed-loop electrical stimulation of the ventral hippocampal commissure. Notably, this manipulation appeared not to affect performance in all three tasks, as determined using various behavioral parameters. Trials with no stimulation or delayed stimulation serve as controls. Thus, the authors conclude that awake SWRs are not involved in these short-term memory-guided behaviors. I do have a few comments that the authors should discuss or address:

(1) This study adds to a large number of studies investigating the role of awake SWRs in spatial learning and memory tasks. The results of these previous studies are quite contradictory and range from awake SWRs are not crucial in guiding decisions at all to SWRs are only essential during task rule learning to SWRs do guide behavior. Could the authors comment on these seemingly contradictory results? Why are these experiments now the right ones?

The reviewer is correct that there is a large body of literature investigating awake SWRs. Most commonly, interpretations about the role of SWRs and associated replay are made based on correlations of their occurrence with behavior. These correlations do, however, not necessarily indicate that SWRs contribute to a particular cognitive process. That is why interventional studies like ours are important to clarify the contribution of SWRs.

The acquisition of a novel task involves a number of cognitive processes, including short- and long-term memory, building a map of the environment, exploration of the solution space and incorporating (non-)rewarding feedback. Based on available evidence, SWRs could contribute to many of these processes. Our experiments were designed to exclude the long-term memory aspect and focus on the memorization of locations on a short time-scale which as we now demonstrate is not dependent on SWRs. Since the use of short-term spatial memory is one of the possible explanations for the learning deficit seen by Jadhav et al. (2012) following SWR disruption in an alternation task, our results may also narrow down the exact contribution of SWR in these studies.

(2) None of the experiments presented here test the role of replay. I suggest making this distinction in the paper and the title clear. As the results are presented now, is it possible that the SWR content is not affected sufficiently to have a behavioral effect or that there is a bias towards detecting specific SWRs, e.g., longer SWRs?

The reviewer is right that our experiments do not say anything about replay directly. We adapted the text to make this distinction clear.

We address the possibility that SWR content may not be disrupted sufficiently to cause a behavioral effect in response to recommendation 1.

Reviewer #3 (Public Review):

In this manuscript, the authors seek to shed light on the role of awake hippocampal replay during memory tasks that are claimed to be short-term memory. For this, they make use of a real-time detection and disruption system of awake hippocampal ripples, which are used as a proxy for awake neuronal replay. The manuscript describes extensively the tasks as well as the disruption system and controls used during the experiments. The authors present numerous and solid analyses of the behavioral data acquired during the tasks. Nonetheless, the current version of the manuscript is lacking a more complete discussion in which the results are contrasted to previous similar findings, as well as mentioning the role of the awake ripple in the stabilization of hippocampal maps. Some extra analyses are also suggested below. The manuscript would also be enriched if the authors suggested alternative mechanisms for memory rehearsal. Finally, some claims of "we are first" seem inappropriate when compared to the previous literature.

Major comments:

How does one define short-term memory (STM) in rodents? The examples and papers cited in the first paragraphs refer mostly to human working memory tasks, from which it is known that a non- rehearsed STM lasts typically 20-30 seconds. Could the authors mention how this concept is translated to rodents? Could you clarify until what point memory is considered STM and what is the criteria to consider it has turned into long-term memory or when is it simply working memory or habit/skill?

We agree with the reviewer that the definition of short-term memory is fluid and may differ between researchers and model systems. To avoid confusion, we reframed our study in a different context and hope that this makes the timeframes we are talking about clearer.

Further, why should these tasks be classified as testing STM while Jadhav et al. tasks are working memory or as they now mention in this article rule learning?

Note that short-term memory and working memory are closely related, but not identical, concepts. Whereas short-term memory refers to the retaining of information for a short period of time, working memory is generally considered to also include some manipulation of that information. Unfortunately, in the rodent literature, (spatial) working memory and short-term memory are often used interchangeably.

Many (animal) spatial memory tasks do not test a single cognitive faculty, but likely involve a combination of short-term memory, working memory, and rule learning (among other abilities) to acquire or solve the task. As such, an unequivocal classification of behavioral tasks is not generally possible. For example, in the continuous version of the spatial alternation task used in Jadhav et al., animals may learn the rule “if I in the center arm and I came from the left goal arm, then I will next find reward in the right goal arm”. The execution of this rule would require maintaining in (short-term) memory the most recent visited goal arm. Alternatively, animals may learn the rule to turn left twice and right twice to successfully perform the task.

One of our goals in our study was to attempt to isolate rule learning components and short-term memory components in our tasks (to be clear: we are not claiming that our tasks are pure short- term memory tasks).

We have rewritten the introduction to reframe our study, which hopefully clarifies the points above.

In humans, the retention of memory after a certain time is achieved by retrieving a long-term memory. How do we know if the considerable training the rats received has not allowed the use of a long-term memory strategy which allows the rats to perform well even in the absence of rehearsal (replay)? These are conceptual explanations that would help understand the key concept of STM in greater detail.

Our experiments aimed to distinguish between the process of learning general task rules through training and the need to retain information specific to each trial or session. For example, in the NMTS task, the animals may have a long-term memory of the overall task design, but they cannot anticipate or recall in advance which specific arms will be baited in the instruction phase since they vary from one trial to another. Therefore, to complete a trial successfully, the animals must have formed some type of (short-term) memory of the instruction arms and/or of the arms that still need to be visited in the test phase. Although extended training may have resulted in a more optimized and less demanding strategy to memorize the necessary information, evidence in the literature indicates that even then (for this particular task), a functional hippocampus is required (Sasaki 2021). The question we address in our experiments is whether hippocampal SWRs (and by association, replay) are instrumental in the formation or maintenance of this memory, whether through rehearsal or other mechanisms. The rewritten introduction explains these concepts more clearly.

Further, claims of "first" should be adjusted, since I do not see a large difference between the w (m) maze of Jadhav and these tasks. The main difference between the two projects would rather be that Jadhav tests when animals are still newer to the task while here overtrained animals are used. In Jadhav, it's unlikely that just rule learning is affected since the inbound component is not affected by disruption, which also tests rule learning. Therefore, it is still likely that the effect seen in Jadhav et al is a deficit in working memory/short-term memory. And here it is more likely, that no effect was seen since with overtrained animals other strategies (cortical, striatal, etc) were used. The authors should compare in more detail how overtrained animals were in these different projects as well as in the articles they cite for replay analysis.

The training of the animals on the general task rules prior to SWR disruption manipulations is by design, as it better isolates the short-term memory demands required to solve the task in each trial/session. In our tasks, the rats are required to memorize a randomly chosen combination of goal arms on each day (MTS & SEQ task) or in every trial (NMTS task). Unlike the continuous alternation paradigm used by Jadhav et al. (2012), our tasks can not be solved using a stereotypical or habitual (striatal) strategy that is acquired through extended training. We can not exclude that the rats acquired an optimized and less cognitively demanding strategy that is mainly dependent on cortical structures outside the hippocampus, however evidence in the literature still indicates the requirement for a functional hippocampus (Sasaki, 2021; Okaichi and Oshima 1990; Blokland, Honig, and Raaijmakers, 1992).

The reviewer is correct that the inbound component of the continuous alternation task in Jadhav et al. (2012) can be considered rule learning and was not affected by SWR disruption. However, we do not believe that this should be generalized to all rule learning and it is very well conceivable that SWRs contribute to the learning of more complex rules that also feature ambiguity (such as the outbound component in the continuous alternation task). We elaborate on these points in the discussion (lines 425-455).

The main conclusion of the authors is that hippocampal replay is not the rehearsal mechanism expected in STM given that its disruption doesn't lead to behavioral changes. Could the authors hypothesize in their discussion what other neural mechanisms different from hippocampal replay may be involved in this rehearsal?

Thank you for this suggestion. We added an extra paragraph speculating on this aspect (lines 499- 518).

The discussion also lacks closure with respect to how the findings fit in the study of STM in human memory. This would make the article more interesting to a larger audience and highlight its translational aspect.

We agree with the reviewer and added our insight to the discussion.

The results describe deeply the behavioral performance of the rats and the validation of the ripple detection/disruption system. However, one important aspect missing is how the hippocampal activity and its encoding of space may be affected by the awake ripple disruption. The authors don't cite the work by Roux et al., Nature Neuroscience. 2017 where optogenetic stimulation of hippocampal neurons provided evidence that neuronal activity associated with awake hippocampal ripples during goal-directed behavior is required for both stabilizing and refining hippocampal place fields, while memory performance was not affected during ripple-locked stimulations compared to a ripple-delayed stimulation control (See supplementary Figure 7 of the mentioned article). I would like the authors to comment on their own findings and contrast them with those of Roux et al.

We agree that it is interesting to include the results of Roux et al. in our discussion (lines 470 and 463-466).

Line 64: Could the authors clarify what they mean by "indirect" causal evidence when discussing the contribution of papers by Jadhav, Igata, and Fernandez? Is it the fact that rodents' learning speed changed instead of showing a complete absence of learning? Or is it the fact that the disruption/prolongation is done on the hippocampal ripple and not strictly in the replay sequence?

We apologize for the confusion and rewrote large parts of the introduction to clarify the contributions of the papers by Jadhav, Igata, and Fernandez and the difference with what our manipulations contribute. In the process, we removed the phrase ‘indirect causal evidence’.

I would also highlight this latter difference, given that the above-mentioned authors describe their methodological approaches in terms of ripples and not in terms of replay content. For example, the use of "replay" instead of "ripple" in Line 61 results in methodological inaccurate terms such as replay disruption and replay prolongation.

Thank you for pointing this out. We adapted the manuscript to always use ‘ripple’ or ‘sharp-wave ripple’ (SWR) when describing our results.

Despite its apparent lack of statistical significance, the reported mean ripple detection rate during the trial and non-trial periods tend to be always higher in the disruption condition of all tasks by observing the median of the boxplots in Figure 1J, Figure 2H, and Figure 3J. It is worth investigating this further using the same linear regression method as Girardeau et al. Journal of Neuroscience, 2014 which may reduce the variability and allow comparing slopes of a cumulative number of ripples over time. This may reveal a compensatory homeostatic-like increase in the rate of ripples during the disrupted sessions, which may suggest a need for the ripple/replay occurrence in spite of it not having an effect on the rats' performance during the task.

The reviewer makes an interesting observation and we appreciate the suggestion for further investigation. However, note that a clear trend for higher ripple rates in disruption trials/sessions is not present when comparing to non-stimulated control trials/session. Part of the variability in the observed ripple rates is likely due to the variability in the animals’ behavioral state (e.g., moving, pausing but alert, grooming, consuming reward) and the corresponding varying propensity for SWRs to occur. The behavioral variability makes application of the linear regression approach of Girardeau et al. (2014) not straightforward (note that Girardeau et al. looked at SWRs during sleep). For these reasons, we have decided to not further look into the potential disruption-induced increase of the SWR rate.

In line 425, the authors report a median relative delay of 52.9 of their disruption system. Such a value would indicate that only around 47% of the ripple is being blocked. Is there any data from the authors or others that could reassure the reader that the 52.9% of the ripple that "leaks" is not enough for the replay phenomenon to occur? Considering the findings of Fernandez-Ruiz et al. 2019 on large-duration ripples, could the authors report the relative delay for both short and long ripples (>100 ms) separately?

The reviewer is correct that the initial part (~35 ms) of SWRs remains intact, which is inherent to the online detection and disruption approach. In relative terms, a larger fraction of long SWRs is disrupted. As requested, we have adapted figure 4c to separately show the distribution of relative detection delays for long (duration >100ms) and short SWRs.

As we and others have shown, the electrical stimulation temporarily suppresses spiking activity in CA1 and thus abruptly interferes with any ongoing replay, but any beginning of replay sequences before the stimulation will not be affected. Previous studies that use the same methodology to disrupt SWRs reported a behavioral performance deficit despite the detection delays (Michon et al. 2019; Girardeau et al. 2009; Jadhav et al. 2012). This suggests that the initial part of SWRs (and replay) is not sufficient to support the behavior. The delays in the current study are quantitatively similar to what we have reported before in Michon et al. (2019) and thus we are confident that we should have been able to observe a behavioral effect if present. We now elaborate on this topic in the Discussion (lines 489-498) .

Line 494: The authors define long ripples as (>120 ms) but this doesn't coincide with the 100ms threshold from Fernandez Ruiz et al. 2019.

Thank you for pointing this out, it is corrected in the text both in the Results (line 389) and Discussion (line 486).

The online ripple detector used filtered the traces in the 135-255 Hz range. This is a narrower frequency range compared to online detectors used by Jadhav et al. 2012 (100-400 Hz) and Fernandez-Ruiz et al. 2019 (80-300 Hz). What motivated the use of this narrow range? Would the omittance of ripples below 135 Hz have implications in the results? Could the authors add to the supplement a figure similar to Figure 4B (FDR vs TPR) using a wider frequency range similar to the authors above in the offline detection of ripples?

The frequency of hippocampal ripple oscillation in rat generally lies in the range of 160-225 Hz (Buzsaki, 1992). We have added a power spectrum in Figure 1d that confirms this frequency range in our experiments. Filters that include frequencies below this range (as in the studies referenced by the reviewer) likely also pass through high-frequency gamma oscillations, and filters that include frequencies above this range likely also pass through multi-unit spiking activity. The challenge for a real-time ripple detection system is to design a filter that has an acceptable trade-off between filtering in a specific (narrow) frequency range and introducing a long delay. In our study, we specifically designed a filter that is specific to the ripple frequency band and still has an acceptable low delay.

It is unclear what criterion was used to train the rats in the NMTS task. Line 216 specifies a learning criterion of 80% fully correct trials in one session for three days in a row, while the methods in line 852 mention an average performance below 50% for at least three days in a row.

Thank you for pointing this out. We corrected the learning criterium description in the results section (lines 108-110) to match the description in the Methods section.

In the methods section, it is not mentioned if there was a specific region in the cortex where the tetrode was placed (Line 908).

The detections in this tetrode were used to mark events as "false positives". The authors should be careful in line 933 when they make the statement "ripples are not present in the cortex". There have been recent publications that challenge this affirmation. See Khodagholy, Science. 2017, Nitzan, Nature Comm. 2020.

Thank you for pointing this out. We have added the cortical region in the methods (line 882) and clarified that, as far as we know, no ripples in that part of the cortex (parietal associate cortex) have been described that are synchronous with hippocampal ripples.

Reviewer #3 (Public Review):

In this manuscript, the authors seek to shed light on the role of awake hippocampal replay during memory tasks that are claimed to be short-term memory. For this, they make use of a real-time detection and disruption system of awake hippocampal ripples, which are used as a proxy for awake neuronal replay. The manuscript describes extensively the tasks as well as the disruption system and controls used during the experiments. The authors present numerous and solid analyses of the behavioral data acquired during the tasks. Nonetheless, the current version of the manuscript is lacking a more complete discussion in which the results are contrasted to previous similar findings, as well as mentioning the role of the awake ripple in the stabilization of hippocampal maps. Some extra analyses are also suggested below. The manuscript would also be enriched if the authors suggested alternative mechanisms for memory rehearsal. Finally, some claims of "we are first" seem inappropriate when compared to the previous literature.

Major comments:

How does one define short-term memory (STM) in rodents? The examples and papers cited in the first paragraphs refer mostly to human working memory tasks, from which it is known that a non-rehearsed STM lasts typically 20-30 seconds. Could the authors mention how this concept is translated to rodents? Could you clarify until what point memory is considered STM and what is the criteria to consider it has turned into long-term memory or when is it simply working memory or habit/skill? Further, why should these tasks be classified as testing STM while Jadhav et al. tasks are working memory or as they now mention in this article rule learning? In humans, the retention of memory after a certain time is achieved by retrieving a long-term memory. How do we know if the considerable training the rats received has not allowed the use of a long-term memory strategy which allows the rats to perform well even in the absence of rehearsal (replay)? These are conceptual explanations that would help understand the key concept of STM in greater detail.

Further, claims of "first" should be adjusted, since I do not see a large difference between the w (m) maze of Jadhav and these tasks. The main difference between the two projects would rather be that Jadhav tests when animals are still newer to the task while here overtrained animals are used. In Jadhav, it's unlikely that just rule learning is affected since the inbound component is not affected by disruption, which also tests rule learning. Therefore, it is still likely that the effect seen in Jadhav et al is a deficit in working memory/short-term memory. And here it is more likely, that no effect was seen since with overtrained animals other strategies (cortical, striatal, etc) were used. The authors should compare in more detail how overtrained animals were in these different projects as well as in the articles they cite for replay analysis.

The main conclusion of the authors is that hippocampal replay is not the rehearsal mechanism expected in STM given that its disruption doesn't lead to behavioral changes. Could the authors hypothesize in their discussion what other neural mechanisms different from hippocampal replay may be involved in this rehearsal? The discussion also lacks closure with respect to how the findings fit in the study of STM in human memory. This would make the article more interesting to a larger audience and highlight its translational aspect.

The results describe deeply the behavioral performance of the rats and the validation of the ripple detection/disruption system. However, one important aspect missing is how the hippocampal activity and its encoding of space may be affected by the awake ripple disruption. The authors don't cite the work by Roux et al., Nature Neuroscience. 2017 where optogenetic stimulation of hippocampal neurons provided evidence that neuronal activity associated with awake hippocampal ripples during goal-directed behavior is required for both stabilizing and refining hippocampal place fields, while memory performance was not affected during ripple-locked stimulations compared to a ripple-delayed stimulation control (See supplementary Figure 7 of the mentioned article). I would like the authors to comment on their own findings and contrast them with those of Roux et al.

Line 64: Could the authors clarify what they mean by "indirect" causal evidence when discussing the contribution of papers by Jadhav, Igata, and Fernandez? Is it the fact that rodents' learning speed changed instead of showing a complete absence of learning? Or is it the fact that the disruption/prolongation is done on the hippocampal ripple and not strictly in the replay sequence? I would also highlight this latter difference, given that the above-mentioned authors describe their methodological approaches in terms of ripples and not in terms of replay content. For example, the use of "replay" instead of "ripple" in Line 61 results in methodological inaccurate terms such as replay disruption and replay prolongation.

Despite its apparent lack of statistical significance, the reported mean ripple detection rate during the trial and non-trial periods tend to be always higher in the disruption condition of all tasks by observing the median of the boxplots in Figure 1J, Figure 2H, and Figure 3J. It is worth investigating this further using the same linear regression method as Girardeau et al. Journal of Neuroscience, 2014 which may reduce the variability and allow comparing slopes of a cumulative number of ripples over time. This may reveal a compensatory homeostatic-like increase in the rate of ripples during the disrupted sessions, which may suggest a need for the ripple/replay occurrence in spite of it not having an effect on the rats' performance during the task.

In line 425, the authors report a median relative delay of 52.9 of their disruption system. Such a value would indicate that only around 47% of the ripple is being blocked. Is there any data from the authors or others that could reassure the reader that the 52.9% of the ripple that "leaks" is not enough for the replay phenomenon to occur? Considering the findings of Fernandez-Ruiz et al. 2019 on large-duration ripples, could the authors report the relative delay for both short and long ripples (>100 ms) separately? Line 494: The authors define long ripples as (>120 ms) but this doesn't coincide with the 100ms threshold from Fernandez Ruiz et al. 2019.

The online ripple detector used filtered the traces in the 135-255 Hz range. This is a narrower frequency range compared to online detectors used by Jadhav et al. 2012 (100-400 Hz) and Fernandez-Ruiz et al. 2019 (80-300 Hz). What motivated the use of this narrow range? Would the omittance of ripples below 135 Hz have implications in the results? Could the authors add to the supplement a figure similar to Figure 4B (FDR vs TPR) using a wider frequency range similar to the authors above in the offline detection of ripples?

It is unclear what criterion was used to train the rats in the NMTS task. Line 216 specifies a learning criterion of 80% fully correct trials in one session for three days in a row, while the methods in line 852 mention an average performance below 50% for at least three days in a row.

In the methods section, it is not mentioned if there was a specific region in the cortex where the tetrode was placed (Line 908). The detections in this tetrode were used to mark events as "false positives". The authors should be careful in line 933 when they make the statement "ripples are not present in the cortex". There have been recent publications that challenge this affirmation. See Khodagholy, Science. 2017, Nitzan, Nature Comm. 2020.

This section intriguingly discusses the nuanced concept of violence within the context of social contracts and individual rights, offering a compelling analogy with medical intervention to illustrate the distinction between legitimate and illegitimate violence. It's fascinating how this perspective challenges conventional understandings of violence, typically associated with physical harm or coercion, by framing it as a broader concept that includes any form of forceful interference, even those agreed upon or deemed necessary for the greater good. Expanding on this concept, it would be insightful to explore how this framework applies to non-physical forms of violence, such as psychological or emotional harm, especially in contexts like digital platforms where the lines between consent and violation can be blurred.

The comparison of harassment to being sprayed by oncoming cars effectively emphasizes how difficult it is to identify and deal with harassment, particularly in social media settings where people's individual actions may appear harmless but can have detrimental effects when combined. In the same way that a homeowner may ask someone to leave their home if their behavior becomes undesirable, platforms must have clear policies and procedures in place to deal with such behavior.

This quote basically summarizes the essence of Don Quixote's delusional quest for chivalry and honor, despite the reality that his actions often result in absolute foolishness. Don Quixote claiming that It's enough for him to command respect from the servant just shows how silly he is in this whole novel. In this specific instance, where he tries to rescue Andrés based only on his self-perceived authority as a knight, Don Quixote's actions epitomize his "noble" yet ultimately futile quest for glory and recognition. His willingness to guarantee Andrés' freedom based on a mere oath of chivalry further underscores his absolute detachment from reality and his stubborn loyalty to his own idealized version of knighthood.

I find this very important. It adds to the idea that people who are more knowledgeable look for deeper connections, and they go the extra mile to reflect what they learn. A normal student would just pick one picture because that was the baseline of the assignment. An expert will go deeper than that because they have a better understanding of it. This somewhat connects to our readings and the idea that it's important to make deep connections when reading things to better understand it.

Author Response

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

First, we would like to thank you and all the reviewers for acknowledging the meaningful contribution of our manuscript to the field. Your useful comments helped us improve the manuscript's quality. We understood the key issues of the manuscript were the quantification of inference accuracy and applicability to methylome data. We here therefore present a revised version of the manuscript addressing all major comments.

For each demographic inference we have added the root mean square error as demanded by the reviewers. These results confirm the previous interpretation of the graphs especially in recent times. We also added TMRCA inference analysis as requested by one reviewer as a proof of principle that integrating multiple markers can improve ARG inference.

The discussion was rewritten to further discuss the challenges of application to empirical methylation data. We clarify that in the case epimutations are well understood and modelled, they can be integrated into a SMC framework to improve the approaches accuracy. When epimutations are not well understood, our approach can help understand the epimutations process through generations at the evolutionary time scale along the genome. Hence, in both cases our approach can be used to unveil marker evolution processes through generations, and/or deepen our understanding of the population past history. We hope our discussion underlies better how our approach is designed and can be used.

eLife assessment

This important study advances existing approaches for demographic inference by incorporating rapidly mutating markers such as switches in methylation state. The authors provide a solid comparison of their approach to existing methods, although the work would benefit from some additional consideration of the challenges in the empirical use of methylation data. The work will be of broad interest to population geneticists, both in terms of the novel approach and the statistical inference proposed.

Public Reviews:

Reviewer #1 (Public Review):

The authors developed an extension to the pairwise sequentially Markov coalecent model that allows to simultaneously analyse multiple types of polymorphism data. In this paper, they focus on SNPs and DNA methylation data. Since methylation markers mutate at a much faster rate than SNPs, this potentially gives the method better power to infer size history in the recent past. Additionally, they explored a model where there are both local and regional epimutational processes.

Integrating additional types of heritable markers into SMC is a nice idea which I like in principle. However, a major caveat to this approach seems to be a strong dependence on knowing the epimutation rate. In Fig. 6 it is seen that, when the epimutation rate is known, inferences do indeed look better; but this is not necessarily true when the rate is not known. A roughly similar pattern emerges in Supp. Figs. 4-7; in general, results when the rates have to be estimated don't seem that much better than when focusing on SNPs alone. This carries over to the real data analysis too: the interpretation in Fig. 7 appears to hinge on whether the rates are known or estimated, and the estimated rates differ by a large amount from earlier published ones.

Overall, this is an interesting research direction, and I think the method may hold more promise as we get more and better epigenetic data, and in particular better knowledge of the epigenetic mutational process. At the same time, I would be careful about placing too much emphasis on new findings that emerge solely by switching to SNP+SMP analysis.

Answer: We thank the reviewer 1 for his positive comments and acknowledging the future promises of our method as better and more reliable data will be available in different species. We appreciate the reviewer noticing the complete set of work undertaken here to integrate local and regional effects of methylation into a model containing as much knowledge of the epigenetics mutational processes as possible. Note that in Figure 2 of the manuscript we observed a gain of accuracy even when the rates are unknown. Our results thus suggests that the accuracy gain of additional marker with unknown rates is also possible, although it is most likely be scenario and rate dependent.

At last, as noticed and highlighted by the very recent work of the Johannes lab (Yao et al. Science 2023) using phylogenetic methods, knowing the epimutation rate is essential at short time scale to avoid confounding effects of homoplasy. In our estimation of the coalescent trees, the same applies, though our model considers finite site markers. We now provide additional evidence for the potential gain of power to infer the TMRCA (Supplementary Table S7) when knowing or not the epimutation rates and revised the discussion to clarify the potential shortcomings/caveats for the analysis of real data.

Reviewer #2 (Public Review):

A limitation in using SNPs to understand recent histories of genomes is their low mutation frequency. Tellier et al. explore the possibility of adding hypermutable markers to SNP based methods for better resolution over short time frames. In particular, they hypothesize that epimutations (CG methylation and demethylation) could provide a useful marker for this purpose. Individual CGs in Arabidopsis tends to be either close to 100% methylated or close to 0%, and are inherited stably enough across generations that they can be treated as genetic markers. Small regions containing multiple CGs can also be treated as genetic markers based on their cumulative methylation level. In this manuscript, Tellier et al develop computational methods to use CG methylation as a hypermutable genetic marker and test them on theoretical and real data sets. They do this both for individual CGs and small regions. My review is limited to the simple question of whether using CG methylation for this purpose makes sense at a conceptual level, not at the level of evaluating specific details of the methods. I have a small concern in that it is not clear that CG methylation measurements are nearly as binary in other plants and other eukaryotes as they are in Arabidopsis. However, I see no reason why the concept of this work is not conceptually sound. Especially in the future as new sequencing technologies provide both base calling and methylating calling capabilities, using CG methylation in addition to SNPs could become a useful and feasible tool for population genetics in situations where SNPs are insufficient.

Answer: We thank the reviewer 2 for his positive comments. Indeed, surveys of CG methylation in other plant species show that its distribution is clearly bimodal (i.e. binary). This is not the case for non-CG methylation, such as CHG and CHH (where H=C,T,A). However, these later types of methylation contexts are also not heritable across generations and can therefore not be used as heritable molecular markers.

Reviewer #3 (Public Review):

I very much like this approach and the idea of incorporating hypervariable markers. The method is intriguing, and the ability to e.g. estimate recombination rates, the size of DMRs, etc. is a really nice plus. I am not able to comment on the details of the statistical inference, but from what I can evaluate it seems sound and reasonable. This is an exciting new avenue for thinking about inference from genomic data. I have a few concerns about the presentation and then also questions about the use of empirical methylation data sets.

I think a more detailed description of demographic accuracy is warranted. For example, in L245 MSMC2 identifies the bottleneck (albeit smoothed) and only slightly overestimates recent size. In the same analysis the authors' approach with unknown mu infers a nonexistent population increase by an order of magnitude that is not mentioned.

Answer: We thank the reviewer 3 for his positive comments and refer to our answer to reviewer 1 above. We added RMSE (Root Mean Square Error) analyses to quantify the inference accuracy. We apologize for not mentioning this last point. Thank you for pointing this out and we have now fixed it (line 245-253).

Similarly, it seems problematic that (L556) the approach requiring estimation of site and region parameters (as would presumably be needed in most empirical systems like endangered nonmodel species mentioned in the introduction) does no better than using only SNPs. Overall, I think a more objective and perhaps quantitative comparison of approaches is warranted.

Answer : See answer to reviewer 1 above, and more elaborate answers below. We provide now new RMSE analyses to quantify the accuracy of our demographic inference (Supplementary Tables 1,6,7,8,9,10). We also discuss the validity and usefulness of our approach when the epimutation rates are unknown. In short, the discussion was rewritten to further discuss the challenges of application to empirical methylation data. We clarify that in the case epimutations are well known and modelled (as much is known in A. thaliana for example), they can be integrated into a SMC framework to improve the accuracy of the method approach. When epimutations are not well understood and rates unknown, our approach can help understand the epimutational process through generations at the evolutionary time scale. Hence, whether makers are understood or not, our approach can be used to study the marker evolutionary processes through generations and/or to deepen our understanding of the population past history. We hope our discussion underlies better how our approach is designed and can be used.

The authors simulate methylated markers at 2% (and in some places up to 20%). In many plant genomes a large proportion of cytosines are methylated (e.g. 70% in maize: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8496265/). I don't know what % of these may be polymorphic, but this leads to an order of magnitude more methylated cytosines than there are SNPs. Couldn't this mean that any appreciable error in estimating methylation threatens to be of a similar order of magnitude to the SNP data? I would welcome the authors' thoughts here.

Answer : The reviewer is correct and this is an interesting question. First, studies show that heritable epimutations in plants are restricted to CG dinucleotides that are located well outside of the target regions of de novo methylation pathways in plants. Most of these CGs tend of fall within so-called gene body methylated regions. While it is true that plant species can differ substantially in their proportion of methylation at the genome-wide scale, the number of gene body methylated genes (i.e. genic CG methylation) is relatively similar, and at least well within the same order of magnitude (Takuno et al. Nature Plants 2016, review in Muyle et al. Genome Biol Evol 2022). Moreover, spontaneous CG epimutations in gene body methylated regions has been shown to be neutral (van Der Graaf et al. 2015, Vidali et al. 2016, Yao et al. 2023), which is an ideal property for phylogentic and demographic inference.

Second, CG methylation calls are sometimes affected by coverage or uncertainty. Stringent filtering for reliable SMP calls typically reduces the total proportion of CG sites that can be used as input for demographic inference. Here we only kept CG sites where the methylation information could be fully trusted after SMP calling (i.e. >99.9% posteriori certainty). Overall, this explains why the percentage of sites with methylation information is so small, and why we have decided to work on simulation with 2% of reliable methylated markers.

Nevertheless, for the sake of generality, it may be that in some species such as maize a higher percentage of polymorphic methylated sites can be used, and the number of SMPs could be higher than that of SNPs when the effective population size is very small (due to past demographic history and/or life history traits). In this case, any error in the epimutation rate and variance due to the finite site model estimation (and homoplasy) are not corrected by the lack of SNPs and can lead to mis-inference.

A few points of discussion about the biology of methylation might be worth including. For example, methylation can differ among cell types or cells within a tissue, yet sequencing approaches evaluate a pool of cells. This results in a reasonable fraction of sites having methylation rates not clearly 0 or 1. How does this variation affect the method? Similarly, while the authors cite literature about the stable inheritance of methylation, a sentence or so more about the time scale over which this occurs would be helpful.

Answer: We thank reviewer 3 for asking those very interesting questions, which we further developed below and mention in the discussion (lines 716-722).

For Arabidopsis thaliana:

Following up on our previous comment above, the majority of the CG sites that serve as input to our approach are located in body methylated genes. Previous work has shown that CG methylation in these regions shows essentially no tissue and cellular heterogeneity (e.g. Horvath et al. 2019). This means that bulk methylation measurements only show limited susceptibility to measurement error. That said, to guard against any spurious SMPs call that could arise from residual measurement variation, we applied stringent filtering of CG methylation. We have kept sites where the methylation percentage is close to either 0% or 100% (the rest being removed from the analysis). We have used similar filtering strategies in previous studies of epimutational processes in mutation accumulation lines and long-lived perennials (work of the Johannes lab). In these later studies we found that the SMP calls sufficiently accurate for inferences of phylogenetic parameters in experimental settings (Sharyhary et al. Genome Biology 2021, Yao et al. Science, 2023).

For other species:

It is true that currently, evaluating the methylation state of a site from a pool of cells may be problematic for some species for two main reasons: 1) it will add noise to the signal and SMP calling could be erroneous, and 2) the methylation state used in analysis might originate from different tissues at different location of the genome/methylome. Overall, this will lead to spurious SMPs and can render the inference inaccurate (see Sellinger et al 2021 for the effect of spurious SNPs). Hence, caution is advised when calling SMPs in other species and for different tissues.

Finally, in some species methylated cytosines have mutation rates an order of magnitude higher than other nucleotides. The authors mention they assume independence, but how would violation of this assumption affect their inference?

Answer: Indeed, we assume the mutation and epimutation process to be independent thus the probability for a SNP to occur does not depend on the local methylation state. If this was the case, the mutation rate use would indeed be wrong to a degree function of the dependency between the processes. We suggest that by ignoring this dependence, we are in the same situation as ignoring the variation of mutation rate along the genome. We have previously documented the effect of ignoring this biological feature of genomes in Strüt et al 2023 and Sellinger et al 2021. The variation in mutation rate along the genome if too extreme and not accounted for can lead to erroneous inference results. However, this problem could be easily solved (modelled) by adapting the emission matrix. To correctly model this dependency, additional knowledge is needed: either the mutation and epimutation rates must be known to quantify the dependency, or the dependency must be known to quantify the resulting rates. As far as we know, these data are at the moment not available, but could maybe be obtained using the MA lines of A. thaliana (used in Yao et al. 2023).

Recommendations for the authors:

All three reviewers liked this approach and found it a valuable contribution. I think it is important to address reviewer 1/3 concerns about quantifying the accuracy of inference (the TMRCA approach from reviewer 1 sounds pretty reasonable), and reviewer 1 also highlights an intriguing point about model accuracy being worse when the mutation rate is known. Additionally, I think some discussion is warranted about challenges dealing with empirical methylation data (points from Rev 2 and 3 as well as Rev 1's question about inferred vs published rates of epigenetic mutation).

Answer : We have added tables containing the root mean square error (RMSE) of every demographic inference in the manuscript to better quantify accuracy. We have below given the explanation on why accuracy in presence of site and region epimutations can in some cases decrease when real rates are known (because methylation state at the region level needs to be first inferred). We added evidence that accounting for methylation can improve the accuracy when recovering the TMRCA along the genome when the rates are known. We also have enhanced the discussion on the challenges of dealing with epimutations data for inference. As is suggested, we hope this study will generate an interest in tackling these challenges by applying the methods to various methylome datasets from different species.

Reviewer #1 (Recommendations For The Authors):

Major comments:

• For all of the simulated demographic inference results, only plots are presented. This allowsfor qualitative but not quantitative comparisons to be made across different methods. It is not easy to tell which result is actually better. For example, in Supp. Fig. 5, eSMC2 seems slightly better in the ancient past, and times the trough more effectively, while SMCm seems a bit better in the very recent past. For a more rigorous approach, it would be useful to have accompanying tables that measure e.g. mean-squared error (along with confidence intervals) for each of the different scenarios, similar to what is already done in Tables 1 and 2 for estimating $r$.

Answer : We understand the concern of reviewer #1 for a more quantitative approach to compare the inference results. We agree that plots are not sufficient to fully grasp a method performance. To provide better supports to quantity approaches performance, we added Sup tables 1,6,8,9 and 10 containing the RMSE (in log10 for visibility) for all Figures. The root mean-squared error is calculated as in Sellinger 2021 and a description of how the root mean-squared error is calculated and now found in the method section lines 886-893.

• 434: The discussion downplays the really odd result that inputting the true value of themutation rate, in some cases, produces much worse estimates than when they are learned from data (SFig. 6)! I can't think of any reason why this should happen other than some sort of mathematical error or software bug. I strongly encourage the authors to pin down the cause of this puzzling behaviour.

Answer : There are unfortunately no errors in this plot and those results are perfectly normal and coherent, but we understand they can be confusing at first.

As described in the method section and in the appendix, when accounting for regionlevel epimutations, our algorithm requires the regional methylation status which needs to be inferred as a first step from the data (real or simulated). Because region and single site epimutation events are occurring at similar rates in our simulated scenario, the methylation state of the region is very hard to correctly recover (e.g. there will be unmethylated site in methylated regions and methylated sites in unmethylated regions). In other words, the accuracy of the region estimation HMM procedure is decreased by the joint action of site and region epimutation processes.

When subsequently applying the HMM for inference, as described in the appendix, the probabilities of two CG site being in the same or different methylation state depends on the methlylation state of the "region". Hence the mislabelling of the region methylation state is (to some extent) equivalent to spurious SMPs (or inaccurate SMP calling).

If the true rates for site and region epimutations are given as input, the model forces the demography (and other inferred parameters) to fit the observed distribution of SMPs (given the inputted rates), resulting in the poor accuracy observed in the Figure (Now Supplementary Figure 7).

Note: The estimated rates from real data in A. thaliana suffer from the same issue as the region and site epimutation rates are independently estimated, and the existence of regions first quantified using an independent HMM method (Denkena et al. 2022).

However, when rates are freely inferred, they are inferred accordingly to the estimated methylation status of regions and SNPs. Therefore, even if the inferred rates are wrong, they are used by the SMC in a more consistent way.

Note: When methylation rates violate the infinite site assumption, such as here, we first estimate the tree sequence along the genome using SNPs (i.e. DNA mutations). The algorithm then infers the epimutations rates given the inferred coalescent times and the observed methylation diversity.

To summarise: when inputting rates to the model, if the model fails to correctly recover the region methylation status there will be conflicting information between SNPs and SMPs leading to accuracy loss. However if the rates are inferred this is realized with the help of SNPs, leading to less conflicting information and potentially smaller loss of accuracy. We apologize that the explanations were missing from the manuscript and have added them lines 449-460 and 702-716.

A further argument is that if region and site epimutations occur at rates of at least two orders of magnitude difference, the inference results are better (and accurate) when the true rates are given. The reason is that one epimutational process overrides the other (see Supplementary Table 2). In that case one epimutation process is almost negligible and we fall back to results from Figure 5 or Supplementary Figure 6.

• As noted at 580, all of the added power from integrating SMPs/DMRs should come fromimproved estimation of recent TMRCAs. So, another way to study how much improvement there is would be to look at the true vs. estimated/posterior TMRCAs. Although I agree that demographic inference is ultimately the most relevant task, comparing TMRCA inference would eliminate other sources of differences between the methods (different optimization schemes, algorithmic/numerical quirks, and so forth). This could be a useful addition, and may also give you more insight into why the augmented SMC methods do worse in some cases.

Answer : We fully agree with reviewer 1. We have added a comparison in TMRCA inference as proof of principle between using or not using methylation sites. The results are written in Supplementary Table 7 and methodology is inspired by Schiffels 2014 and described at the end of the method section (line 894-907). Those results demonstrate the potential gain in accuracy when using methylation polymorphic. However, TMRCA (or ARG) inference is a very vast and complex subject in its own right. Therefore, we are developing a complete TMRCA/ARG inference investigation and an improve methodology than the one presented in this manuscript. To do so we are currently working on a manuscript focusing on this topic specifically. We hence consider further investigations of TMRCA/ARG inference beyond the scope of this current study.

• A general remark on the derivations in Section 2 of the supplement: I checked theseformulas as best I could. But a cleaner, less tedious way of calculating these probabilities would be to express the mutation processes as continuous time Markov chains. Then all that is needed is to specify the rate matrices; computing the emission probabilities needed for the SMC methods reduces to manipulating the results of some matrix exponentials. In fact, because the processes are noninteracting, the rate matrix decomposes into a Kronecker sum of the individual rate matrices for each process, which is very easy to code up. And this structure can be exploited when computing the matrix exponential, if speed is an issue.

Answer: We thank the reviewer for this very interesting suggestion! Unfortunately, it is a bit late to re-implement the algorithm and reshape the manuscript according to this suggestion. Speed is not yet an issue but will most likely become one in the future when integrating many different rates or when using a more complex SMC model. Hence, we added reviewer #1 suggestions to the discussion (line 648) and hope to be using it in our future projects.

• Most (all?) of the SNP-only SMC methods allow for binning together consecutiveobservations to cut down on computation time. I did not see binning mentioned anywhere, did you consider it? If the method really processes every site, how long does it take to run?

Answer: This is a very good question. We do the binning exactly as described in Mailund 2013 & Terhorst 2017, and added this information in the method section (lines 801-809). However, as described in Terhorst 2017, one can only bin observation of the same "type" (to compute the Baum-Welch algorithm). Therefore, the computation time gain by binning is reduced when different markers spread along the genome in high proportion. This is the approach we used throughout the study when facing multiple markers as it had the best speed performance. As for example, when the proportion of site with methylated information is 1% or less, computation time is only slightly affected (i.e. same order of magnitude).

However, the binning method presented in Mailund 2013 can be extended to observation of different types, but parameters need to be estimated through a full likelihood approach (as presented in Figure 2). In our study this approach did not have the best speed performance. However, as our study is the first of its kind, it remains sub-optimal for now. Hence, we did not further investigate the performance of our approach in presence of many multiple different genomic marker (e.g. 5 different markers each representing ~20% of the genome each). Currently, with SMC approaches a high proportion of sites contain the information "No SNPs", making the Baum welch algorithm described in Terhorst 2017 very efficient. But when further developing our theoretical approach, we expect that most of the sites in a genome analysis will contain some "information", which could render the full likelihood approach computationally more tractable.

• 486: The assumed site and region (de)methylation rates listed here are several OOMdifferent from what your method estimated (Supp. Tables 5-6). Yet, on simulated data your method is usually correct to within an order of magnitude (Supp. Table 4). How are we to interpret this much larger difference between the published estimates and yours? If the published estimates are not reliable, doesn't that call into question your interpretation of the blue line in Fig. 7 at 533?

Answer: We thank the reviewer for asking this question. We believe answering this question is indeed the most interesting aspect of our study. Beyond demographic inference, our study has indeed unveiled a discrepancy between rates inferred through biological experiment and our study through the use of SNPs and branch length. There are several reasons which could explained the discrepancy between both approaches:

• Firstly, our underlying HMM hypotheses are certainly violated. We ignoredpopulation structure, variation of mutations and recombination rate along the genome as well as the effect of selection. Hence, the branch lengths used for methylation rate estimations are to some extent inaccurate. We note that this is especially likely for the short branches of coalescent tree originating from background selection events in the coding regions and which are especially observable when using the methylation sites with a higher mutation rate than SNPs (Yao et al. 2023) at body methylated genes.

• Secondly, calling single methylation site polymorphism is not 100 % reliable. If theerror rate is 0.1%, as the study was conducted on ~10 generations a minimum epimutation rate of 10-4 is to be expected. However, because our approach works at the evolutionary time scale, we expect that it suffers less from this bias as the proportion of diversity originating from actual epimutations, and not SMP calling error, should be greater.

• Thirdly, as mentioned above, recovering the methylation status of a region is veryhard. Hence false region status inference could affect our inference accuracy as shown in Supplementary Figure 4.

• Lastly and most importantly, the reason behind this discrepancy is the modelling ofepimutation and methylation between sites and regions. As we discuss, the current combination of rates and models is still limited to describe the observed diversity along the genome (as we intend in SMC methods). This is in contrast to the recent study by Yao et al. where very few regions of polymorphic SMPs are chosen, which implicitly avoids the influence of the methylation region effect. A study just published by Biffra et al. (Cell reports 2023) also uses a functional model of methylation modelling using a mix of region and site epimutation, albeit not tuned for evolutionary analyses. Thus we suggest, in line with functional studies, that epimutations are not independent from the local methylation context and may tend to stabilize the methylation state of a region. Therefore, the estimated methylation rates show a discrepancy to the previously measured ones. Indeed, the biological experiment would reveal a fast epimutation rate because epimutations can actually be tracked at sites which can mutate, while region mutation rate is much slower. However, because the methylation state of a region is rather stable through time it would reduce the methylation diversity over long time scale, and these rates would differ between methylated or unmethylated regions (i.e. the methylation rate is higher in methylated regions). Our results are thus in agreement with the observation by Biffra et al. that region methylation modelling is needed to explain patterns of methylation across the genome.

To solve the discrepancy, one would need to develop a theoretical region + site epimutation model capable of describing the observed diversity at the evolutionary time scale (possibly based on the Biffra et al. model within an underlying population evolution model), and then use this model to reanalyse the sequence data from the biological experiment (i.e. in de Graaf et al. 2015 & Denkena et al. 2022) to re-estimate the methylation region sizes and epimutation rates.

Minor comments:

• 189: "SMCtheo" first occurs here, but it's not mentioned until 247 that this is the newmethod being presented.

Answer : Fixed

• 199: Are the estimates in this section from a single diploid sequence? Or is it n=5 (diploid) as mentioned in the earlier section?

Answer : Yes, those results were obtained with 5 diploid individuals. We added it in the Table 1 description.

• 336: I'm confused by the wording: it sounds like the test rejects the null if there is positivecorrelation in the methylation status across sites. But then, shouldn't 339 read "if the test is significant" (not non-significant)?

Answer : We apologize for the confusion and rewrote the sentence line 339-348, the choice of word was indeed misleading .

• Fig. 6: for some reason fewer simulations were run for 10Mb (panels C nad D) than for100Mb (A and B). Since it's very difficult to tell what's happening on average in the 10Mb case, I suggest running the same number of simulations.

Answer : Yes we understand your concern. Actually, the same number of simulations were run but we plotted only the first 3 runs as it was less visually confusing. We now have added the missing lines to the plot C and D.

Typos:

• 104: "or or"

• 292: build => built

• 388: fulfil

• 683: sample => samples

Answer : Many thanks to reviewer 1 for pointing out the typos. They are all now fixed.

Reviewer #2 (Recommendations For The Authors):

The authors may find some valuable information in Pisupati et al (2023) "On the causes of gene-body methylation variation in Arabidopsis thaliana" on interpreting epimutation rates.

Answer: Many thanks for the recommended manuscript. We add it to the cited literature as it strongly supports our use of heritability or methylation. We also added the recent Biffra et al. paper.

Reviewer #3 (Recommendations For The Authors):

There are many places throughout the manuscript with minor grammatical errors. Please review these. A few noted below as I read:

L104: extra "or"

L123: built not build

L 160 "relies" instead of "do rely"

L161 "events"

L 336 "from methylation data"

L 378 "exists"

L 379 "regions are on average shorter" instead of "there are shorter"

L 338 "a regional-level"

L 349 "," instead of "but"

L 394 DMRs

Table 1 legend: parentheses not brackets?

Answer : Many thanks to reviewer #3 for finding those mistakes. They are all now fixed.

I think a paragraph in the discussion of considerations of when to use this approach might be helpful to readers. Comparison to e.g. increased sample size in MSMC2, while not necessary, might be helpful here. It may often be the case that doubling the number of haplotypes with SNP data may be easier and cheaper estimating methylation accurately.

Answer : We discuss (lines 691-698) that our approach is always useful by design, but cannot always be used for the same purpose. If the evolutionary properties of the used marker used are not understood, we suggest that our approach can be used to investigate the marker heritability process through generations. This could help to correctly design experiments aiming to study the marker heritability through lineages. And if the properties of the marker are well understood and modelled, it can be integrated into the SMC framework to improve inference accuracy.

Other minor notes:

L 486 "known" is a stretch. empirically estimated seems appropriate.

Answer : Fixed

L 573 ARG? You are not estimating the full ARG here.

Answer : We apologize for the wrong choice of word and have rephrased the sentence.

Fig. 2 is not super useful and could be supplemental.

Answer : We moved Figure 2 to the appendix (now sup fig 1)

just off of reading this sentence alone I feel peoples online behavior should not be tracked unless they are showing signs that they may be harm to society.

Let's reframe things here in part because it's highly illustrative of both the phrases as well as the specific question you raise.

Imagine Andy Matuschak reading Sonke Ahrens' How to Make Smart Notes (CreateSpace, 2017) and making notes on what he feels is important. As he reads, he does what is prescribed, namely, he restates the idea in his own words based on what he's read. In doing this he takes the idea of "evergreen" content from journalism settings (and later SEO settings) which he was familiar with and applies that name to what Ahrens called permanent notes to expound on his understanding of Ahrens! (An evergreen article in newspaper work is an article which was written for a particular recurring holiday, event, or story and is regular. Why spend huge amounts of staff time writing that truly original Valentine's day article? The broad stories about gifts to give and restaurants to visit really don't change from year to year. Just dust it off and reprint it, as readers are unlikely to have saved or remembered it and it becomes free re-purposable content.)

Of course, in rewriting this definition, Matuschak adds in some additional baggage for those who aren't carefully reading his work. He adds some additional emphasis on revisiting one's ideas and rewriting them over time, which is certainly fine, but I think the novice note maker puts too much emphasis on this portion thinking that each permanent or evergreen note must eventually become polished to perfection. In practice, most seasoned writers don't and won't do this. In fact, I suspect if you looked at Matuschak's note on evergreen notes, you'd find that it probably hasn't changed since the day he wrote it other than agglutinating links from other notes.

This doesn't mean that one can't modify or change their ideas over time, this is certainly useful and good, but I suspect that the majority aren't doing it the way that might be imagined by Matuschak's original statement or the way that his idea was picked up by the (niche) digital gardening community and spread primarily in the work of Maggie Appleton. It's some of this evolution of Matuschak's definition which bled into digital gardens, which have some overlap with zettelkasten and the note taking realms, which have muddied the waters. As a result, one should take it as general advice and apply it to their own situation, needs, and practice.

For those who use their own notes for writing, one will often mark their cards/notes to indicate that they've used those ideas in various projects so that they're not actively repeating themselves ad nauseum. Some of the additional tweaks one might make to their notes from a style or context specific perspective are also left to the editing portion rather than being done in the notes themselves. As a result of some of this, unless there is a dramatic flaw in a note, there isn't generally a lot of additional work one would come back to it to revise it. If it does require that sort of major revision, then perhaps the better method would be to make a new note and linking it to the original along with an explanation of the error. I typically wouldn't recommend polishing individual notes to some Plationic idea of perfection. Doing so is often just make-work which distracts from one's time which could be better spent doing additional reading or actual thinking. If you're going to do that sort of polishing work, do it at the end when you've got a longer piece of writing you're including your note in.

The real question now, is how are you personally going to define permanent notes, evergreen notes, or other related phrases like atomic notes? This practice is called by Mortimer J. Adler and Charles Van Doren "coming to terms" with an author's work and is part of their analysis for how one should read a book to get the most out of it. I highly recommend reading How to Read a Book (Simon & Schuster, 1972 or Touchstone, 2011) as a companion to any of the usual note taking manuals.

If you want to continue the experiment on a better unified definition of permanent notes, evergreen notes, atomic notes, etc., you can find a pretty solid bibliography of note making, writing, and reading manuals to peruse at https://boffosocko.com/2024/01/18/note-taking-and-knowledge-management-resources-for-students/#Recommended%20reading.

While one could certainly go down the rabbit hole of reading all these resources, I would recommend only looking at one or two and spending your time working on actual practice. It's through practice that you're more likely to make actual progress on your own problems and questions.

reply to u/franrodalg at https://www.reddit.com/r/Zettelkasten/comments/1azoo9m/permanent_vs_evergreen_notes_am_i_thinking_about/

Author Response

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

Reviewer #1 (Recommendations For The Authors):

Many of my specific issues have been addressed in the revision. However, the data shown in Reviewer Fig. 1 and 2 is not sufficiently described to assess it's reliability and these new data do not appear to have been integrated into the paper. A response that more clearly states how the manuscript has been revised to address the comments is necessary.

We appreciate the opportunity to respond to your updated comments on our manuscript. We carefully considered the feedback and made changes to address the specific issues raised.

In response to your question of insufficient description of the data shown in Reviewer Fig. 1 and 2, we would like to confirm that we have taken this feedback seriously. Supplementary data, including the information provided in Reviewer Figures 1 and 2, have been fully described and integrated into the body of the manuscript according to your request. We ensured that the reliability and significance of new data were clearly presented to enhance the overall synthesis of the manuscript.

We are grateful to your valuable feedback, which undoubtedly contributed to the refinement of our manuscript. We hope that the revised version meets the standards of the journal and look forward to the opportunity for further deliberation.

Reviewer #2 (Recommendations For The Authors):

Additional feedback from the reviewer:

"I think the authors have been responsive to my previous comments. However, I cannot find this new data in the main text but rather only in the response to reviewers. New data should be incorporated into the main text not the supplement as the controls are important to consider alongside the treatment groups. Lastly, while the authors include BODIPY in their approaches, their results are not quantitative. My suggestion was to include this data in a quantitative manner not just the images. Lastly, I am still somewhat puzzled about the connection with GABA. The rationale for its selection other than it was significantly changed is not strong."

Thank you for providing us with the latest feedback. We appreciate the opportunity to address the specific concerns raised and provide a detailed response to each point.

(1) Incorporation of New Data into the Main Text:

We acknowledge the reviewer's comment regarding the incorporation of new data into the main text rather than solely in the response to reviewers. In response to this feedback, we have diligently revised the manuscript to ensure that the new data, including controls, is now seamlessly integrated into the main body of the text. This modification allows for a more comprehensive and contextual presentation of the data, as recommended by the reviewer.

(2) Quantitative Presentation of BODIPY Results:

We understand the importance of presenting quantitative data for the BODIPY results, and we appreciate the reviewer's suggestion to include this information in a quantitative manner, not just as images. In line with this valuable feedback, we have revised the relevant sections to incorporate quantitative data alongside the images, providing a more robust and comprehensive presentation of the results.

(3) Rationale for the Selection of GABA:

In the present study, in order to elucidate the molecular mechanisms through which pathway participates metformin-treated IR injury, we analysed gene expression profiles of each group mice, showing that similar mRNA changes are mainly concentrated in the three top pathways: lipid metabolism, carbohydrate metabolism, and amino acid metabolism. Given the close relevance between lipid metabolism and ferroptosis, and the fact of carbohydrate metabolism is a primary way to metabolize amino acids, 22 species of amino acid were detected in liver tissues using HPLC-MS/MS for further identification of key metabolites involved in the role of metformin against HIRI-induced ferroptosis. It was found that only GABA level is significantly increased by metformin treatment and FMT treatment, further verifying by the data of ELISA detection. Consequently, we identified GABA was the main metabolism of metformin protecting from HIRI and focus on the source of GABA generation.

We would like to express our gratitude to your thorough evaluation and constructive feedback, which has undoubtedly contributed to the improvement of our manuscript.

Everything posted on social media is monitored. Nothing remains private and a digital footprint is always left behind. I think it's important to know that not everyone and everything on the internet can be trusted.

A good rule of thumb is to go after groups, but I don’t exempt individuals, especially not if they are politically powerful or sizeably impact society. But we must ask ourselves about the way those individuals are shamed and whether the punishment is proportional. It's essential to critically evaluate the methods and consequences of shaming, ensuring that it aligns with principles of fairness and justice. Targeting groups might have broader societal implications, necessitating careful consideration of the potential collateral effects. Additionally, examining the power dynamics at play and whether the punishment corresponds reasonably to the actions committed by individuals is crucial for maintaining ethical and just practices in addressing societal issues.

Author Response

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

eLife assessment

This important study assesses anatomical, behavioral, physiological, and neurochemical effects of early-life seizures in rats, describing a striking astrogliosis and deficits in cognition and electrophysiological parameters. The convincing aspects of the paper are the wide range of convergent techniques used to understand the effects of early-life seizures on behavior as well as hippocampal prefrontal cortical dynamics. While reviewers thought that the scope was impressive, there was criticism of the statistical robustness and number of animals used per study arm, as well as the lack of causal manipulations to determine cause-and-effect relationships. This paper will be of interest to neurobiologists, epileptologists, and behavioral scientists.

We thank Joseph Gleeson as the Reviewing Editor and Laura Colgin as the Senior Editor for considering this revision of our manuscript for publication in eLife. We appreciate the positive acknowledgment of the study and the critical points raised by the reviewers. We have addressed all the excellent comments of the two reviewers, providing a detailed response for each comment. We believe that these revisions have significantly improved the quality and rigor of our study.

We want to assure you that our experimental design was meticulously crafted, incorporating adequate control groups, and is grounded in prominent studies in systems neurophysiology focusing into early-life seizures effects, especially for capturing mild effects. We conducted statistical tests adhering to established norms and recommendations, ensuring a thorough and transparent description of the employed statistical methods. We welcome any specific suggestions to further improve this aspect.

In fact, the concerns raised by the reviewers regarding statistical robustness may stem from a misunderstanding of the rat cohorts used in each experiment. Criticism was directed at the use of only 5 animals without a control group for acute electrophysiological recording. It is essential to clarify that this group served the sole purpose of confirming that the injection of lithium-pilocarpine would induce both behavioral and electrographic seizures. Importantly, this was a descriptive result, and no statistical test or further analysis was conducted with these data. In the revised manuscript, we have made adjustments to this description, aiming to eliminate any ambiguity, particularly addressing the issue of sample size in each experiment.

Regarding the lack of causal manipulations, we fully agree that this approach would provide a deeper mechanistic understanding of our findings and is an essential next step. Still, developmental brain disturbances are linked to manifold intricate outcomes, so an initial observational exploration would offer insights about particular and nuanced relationships for following studies aimed at targeted interventions. In this context, our objective was to provide a comprehensive characterization of ELS effects to serve as a foundation for future research. While recognizing the relevance of causal manipulations, only a more sophisticated data analyses were able to reveal more complex aspects like specific multivariate associations and non-linear relationships that would not have been revealed by causally perturbing one or another factor at first. In the revised manuscript, we emphasized the limitation of lacking causal manipulations as well as the advantages of our approach. Also, we mentioned some possible targets for following perturbational investigations based on our findings.

For a more detailed discussion on these matters, we invite you to review our response to reviewers.

Reviewer 1

In this paper, Ruggiero, Leite, and colleagues assess the effects of early-life seizures on a large number of anatomical, physiological, behavioral, and neurochemical measures. They find that prolonged early-life seizures do not lead to obvious cell loss, but lead to astrogliosis, working memory deficits on the radial arm maze, increased startle response, decreased paired pulse inhibition, and increased hippocampal-PFC LTP. There was a U-shape relationship between LTP and cognitive deficits. There is increased theta power during the awake state in ELS animals but reduced PFC theta-gamma coupling and reduced theta HPC-PFC coherence. Theta coherence seems to be similar in ACT and REM states in ELS animals while in decreases in active relative REM in controls.

Strengths:

The main strength of the paper is the number of convergent techniques used to understand how hippocampal PFC neural dynamics and behavior change after early-life seizures. The sheer scale, breadth, and reach of the experiments are praiseworthy. It is clear that the paper is a major contribution to the field as far as understanding the impact of early-life seizures. The LTP findings are robust and provide an important avenue for future study. The experiments are performed carefully and the analysis is appropriate. The paper is well-written and the figures are clear.

We express our gratitude to Reviewer #1 for conducting a thoughtful and comprehensive review of our manuscript. We sincerely value both the constructive criticisms provided and your acknowledgment of the manuscript's strengths.

Weaknesses:

The main weakness of the paper is the lack of causal manipulations to determine whether prevention or augmentation of any of the findings has any impact on behavior or cognition. Alternatively, if other manipulations would enhance working memory in ELS animals, it would be interesting to see the effects on any of these parameters measured in the paper.

We sincerely appreciate the insightful comments from Reviewer #1 regarding the potential benefits of including causal manipulations in our study. We wholeheartedly agree that such manipulations can provide a deeper understanding of the mechanistic underpinnings of the observed relationships and represent a crucial next step in our research trajectory.

Our primary objective in this study was to establish a comprehensive framework through observational examinations, exploring intricate relationships across various neurobiological and behavioral variables in the aftermath of early-life seizures (ELS). By identifying these associations, our work aims to provide a foundation for future investigations that can delve into targeted interventions.

While we acknowledge the importance of causal manipulations, we would like to underscore the advantages of our initial multivariate correlational study. Importantly, developmental brain disturbances have lasting impacts affecting multiple biological outcomes that may have intricate relationships between themselves. Firstly, although some neurobiological variables stood out from the comparisons of group means, this did not reveal some nuanced relationships within the data. The complexity of the relationships we uncovered, involving behavior, cognition, immunohistochemistry, plasticity, neurochemistry, and network dynamics, required a more elaborate analytical approach. Only through sophisticated data analysis techniques, we were able to dissect important peculiarities, such as the robust multivariate association between brain-wide astrogliosis and sensorimotor impairments, as well as non-linear relationships, such as the inverted-U relationship between plasticity and working memory. These nuances might not have been fully revealed through causal manipulations, since several variables are strongly related and consequently can affect several outcomes, leading to a false conclusion of direct causality.

Nevertheless, we acknowledge the understatement of the limitation of lacking causal manipulations in our manuscript. To address this, we have included a dedicated section in the discussion highlighting this limitation. We emphasize the advantages of this exploratory phase, supported by a review of the literature on cause-and-effect studies that align with our findings. Additionally, we speculate on promising targets for future cause-and-effect studies based on our findings. For instance, we hypothesize that enhancing plasticity may improve working memory in control subjects, while attenuating plasticity might have a similar effect in ELS subjects. Furthermore, we propose that reactive astrogliosis and concurrent neuroinflammatory processes likely underlie sensorimotor changes in the ELS group. Lastly, we suggest that dopaminergic antagonism in the ELS group could normalize behavioral deficits, prevent the exaggerated LTP induction of the HPC-PFC pathway, reestablish the state-dependent network dynamics, and desensitize the dopaminergic response.

[...]Also, I find the sections where correlations and dimensionality reduction techniques are used to compare all possible variables to each other less compelling than the rest of the paper (with the exception of the findings of U-shaped relationship of cognition to LTP). In fact, I think these sections take away from the impact of the actual findings.

We appreciate the reviewer's feedback and would like to emphasize the significance of the multivariate analysis conducted in our study. Multivariate analysis extends beyond bivariate correlations and is the only type of analysis capable of comprehending the relation of data in a multidimensional way, offering a comprehensive approach to understanding complex relationships among multiple variables. By employing techniques such as principal component analysis (PCA), generalized linear models (GLM), and canonical correlation analysis (CCA), we aimed to unravel intricate patterns of covariance that explore how different variables collectively contribute to the observed outcomes and assess the impact of each independent variable (predictor) on the dependent variable (the variable to be predicted or explained). Importantly, it enables us to control for potential confounding factors by keeping all other variables constant.

While we acknowledge that these sections may appear intricate, their inclusion is indispensable for a comprehensive understanding of the diverse variables associated with SE outcomes. We believe that these analyses offer valuable insights into the intricate dynamics of our study, providing a more holistic perspective on the altered spectrum induced by early-life seizures (ELS).

Regarding the reviewer's observations about the impact of the U-shaped relationship between cognition and LTP, we have made graphical and textual adjustments to emphasize the significance of these findings, aiming to enhance their clarity and impact within the broader context of our research. We trust that these modifications contribute to a more compelling presentation of our results.

[…]Finally, the apomorphine section seemed to hang separately from the rest of the paper and did not seem to fit well.

We appreciate the Reviewer #1 feedback on the apomorphine section. In order to address this point, we carefully rewrote our rationale before the results to clarify our hypothesis and chosen methodology. In our work, we performed the apomorphine experiment as a logical next step of previous data. We showed that ELS rats display REM-like oscillatory dynamics during active behavior, similar to genetically and pharmacologically hyperdopaminergic mice (Dzirasa et al., 2006). Furthermore, other results also indicated possible dopamine neurotransmission alterations, such as working memory deficits, hyperlocomotion, PPI deficits, aberrant HPC-PFC LTP, and abnormal PFC gamma coordination. Therefore, we hypothesized that ELS animals would present a state of hyperdopaminergic activity. Among the possible methodologies to investigate the hyperdopaminergic state, we choose the apomorphine sensitivity test, which is classically used and induces unambiguous behavior and neurochemical alterations in hyperdopaminergic rodents (Duval, 2023; Ellenbroek & Cools, 2002).

Reviewer 1 (Recommendations For The Authors):

(1) It would be useful to stain for other GABAergic interneuron markers such as somatostatin, VIP, CCK.

(2) The authors refer to neuroinflammation but they are really referring to reactive astrogliosis. I would also suggest staining for microglial markers.

(3) The duration of chronic electrographic seizures in ELS animals should also be calculated and presented.

(4) Word usage: the authors frequently use the word "presents" when "demonstrates" would be more appropriate

(1) We appreciate your insight into staining for other GABAergic interneuron markers such as somatostatin, VIP, CCK. While investigating additional interneuron types is indeed relevant, it was not the primary focus of this study for several reasons: 1) The overall neuron density, assessed through NeuN immunostaining, revealed no differences between controls and early life seizure (ELS) groups, even in brain regions susceptible to neuron death after SE (i.e., CA1). Therefore, differences in interneurons, which are more resistant to death in SE and constitute approximately 20% of the cells, are unlikely. 2) Among all interneuron subtypes, Parvalbumin-positive (PV+) interneurons represent a substantial population and are susceptible to various stressors. In the hippocampus, 24% of GABAergic neurons are PV+, whereas 14% are SST+, 10% are CCK+, and VIP+ are less than 10% (Freund and Buzsaki, 1996). Consequently, we considered PV+ interneurons to be a more sensitive subpopulation for evaluating the effects of SE. As they showed no significant difference, we do not believe that assessing smaller subtypes, such as VIP+ or CCK+ cells, would yield significant differences.

(2) While we often see activated microglia in hippocampal sclerosis, these cells are only slightly increased in cases without hippocampal sclerosis (which are similar to our animals), as we previously published (Peixoto-Santos et al., 2012). Astrocytes are a better marker for the epileptogenic zone, as are increased in epileptogenic zones without neuron loss and are also important for controlling neuronal activity by neurotransmitter recycling and ion buffering. In fact, our present model is very similar to the mesial temporal lobe epilepsy patients with gliosis-only, which are characterized by only presenting increased reactive astrogliosis in the hippocampus, without cell loss, and also present changes in innate inflammatory response related to the presence of reactive astrocytes (Grote et al., 2023).

(3) We have performed these calculations and added this information to the revised manuscript.

(4) We thank the reviewer for the word usage recommendation. Indeed, we frequently used “present” throughout the manuscript to describe the observations and patterns the groups “exhibited” or “showed”. However, we believe this is truly not the most appropriate usage in the Discussion when we describe the multivariate latent factors, as we did not “present” them, but rather, we “demonstrated” their existence and significance through our analysis. We rewrote these sentences and hope this is the point the reviewer was referring to.

References:

Duval F. Systematic review of the apomorphine challenge test in the assessment of dopaminergic activity in schizophrenia. Healthcare. 2023 11 (1487): 1-11. doi: 10.3390/healthcare11101487.

Dzirasa K, Ribeiro S, Costa R, Santos LM, Lin SC, Grosmark A, Sotnikova TD, Gainetdinov RR, Caron MG, Nicolelis MAL. Dopaminergic control of sleep-wake states. Journal of Neuroscience. 2006 26:10577–10589. doi:10.1523/JNEUROSCI.1767-06.2006.

Freund TF, Buzsáki G. Interneurons of the hippocampus. Hippocampus. 1996;6(4):347-470. doi: 10.1002/(SICI)1098-1063(1996)6:4<347::AID-HIPO1>3.0.CO;2-I. PMID: 8915675.

Ellenbroek BA & Cools AR. Apomorphine susceptibility and animal models for psychopathology: genes and environment. Behavior Genetics. 2002 32 (5): 349-361. doi: 10.1023/a:1020214322065.

Grote A, Heiland DH, Taube J, Helmstaedter C, Ravi VM, Will P, Hattingen E, Schüre JR, Witt JA, Reimers A, Elger C, Schramm J, Becker AJ, Delev D. 'Hippocampal innate inflammatory gliosis only' in pharmacoresistant temporal lobe epilepsy. Brain. 2023 Feb 13;146(2):549-560. doi: 10.1093/brain/awac293. PMID: 35978480; PMCID: PMC9924906.

Peixoto-Santos JE, Galvis-Alonso OY, Velasco TR, Kandratavicius L, Assirati JA, Carlotti CG, Scandiuzzi RC, Serafini LN, Leite JP. Increased metallothionein I/II expression in patients with temporal lobe epilepsy. PLoS One. 2012;7(9):e44709. doi: 10.1371/journal.pone.0044709. Epub 2012 Sep 18. Erratum in: PLoS One. 2016;11(7):e0159122. PMID: 23028585; PMCID: PMC3445538.

Reviewer 2

In this manuscript, the authors employ a multilevel approach to investigate the relationship between the hippocampal-prefrontal (HPC-PFC) network and long-term phenotypes resulting from early-life seizures (ELS). Their research begins by establishing an ELS rat model and conducting behavioral and neuropathological studies in adulthood. Subsequently, the manuscript delves into testing hypotheses concerning HPC-PFC network dysfunction. While the results are intriguing, my enthusiasm is tempered by concerns related to the logical flow

We thank the reviewer for bringing attention to the logical flow of the manuscript. Given the diverse array of behavioral and neurobiological variables examined in our study obtained through various methods and measures, we utterly recognize the utmost importance of a clear and coherent logical flow to provide a comprehensive understanding of the overall narrative.

Our goal was to articulate the neurobiological findings in a manner that underscores their convergence of mechanisms, revealing a cohesive relationship between early-life seizure, cognitive deficits, sensorimotor impairments, abnormal network dynamics, aberrant plasticity, neuroinflammation and dysfunctional dopaminergic transmission.

Briefly, an outline of our narrative could be summarized in the highlights:

(1) ELS induces sensorimotor alterations and working memory deficits.

(2) ELS does not induce neuronal loss, so neurobiological underpinnings may be molecular and functional.

(3) ELS induces brain-wide astrogliosis and exaggerated HPC-PFC long-term plasticity.

(4) ELS does not induce neuronal loss, so neurobiological underpinnings may be molecular and functional.

(5) Sensorimotor alterations are more correlated to astrogliosis, while cognitive deficits to altered HPC-PFC plasticity.

(6) ELS-induced functional alterations may also be observable in freely moving subjects. ELS induces state-dependent alterations in the HPC-PFC network dynamics, such as increased hippocampal theta and abnormal PFC gamma coordination during behavioral activity.

(7) ELS leads to REM-ACT similarity, previously reported in hyperdopaminergic mice, indicating dopaminergic dysfunction.

(8) ELS exhibits altered dopaminergic transmission and behavioral sensitivity that mirror the initial sensorimotor findings.

(9) The literature establishes an inverted-U relationship between dopamine and cognition and PFC plasticity, which may explain our finding of an inverted-U relationship between working memory and HPC-PFC LTP across CTRL and ELS rats.

To address this concern, we have made revisions to enhance the logical flow, ensuring a more seamless transition between the different sections of the Results by presenting clearer links between observations and following investigations. We hope these changes contribute to a more straightforward rationale and easily understandable presentation of our hypotheses and results.

Focus on Correlations: The manuscript primarily highlights correlations as the most significant findings. For instance, it demonstrates that ELS induces cognitive and sensorimotor impairments. However, it falls short of elucidating why these deficits are specifically linked to HPC-PFC synaptic plasticity/network. Furthermore, the manuscript mentions the involvement of other brain regions like the thalamus in the long-term outcomes of ELS based on immunohistochemistry data.

Thank you for your insightful comments, which allowed us to provide further clarification on our study's focus and findings. Our primary goal was to delve into the electrophysiological alterations within the HPC-PFC pathway. The rationale behind this choice lies in the hypothesis that, even in the absence of significant neuronal loss, functional changes in circuits closely linked to the cognitive and behavioral aspects under investigation could be identified.

While we concentrated our electrophysiological investigation on the HPC-PFC pathway due to its well-established functional correlates in existing literature, it is essential to highlight that our data reveal broader alterations in neural circuitry. Notably, we observed an increase in GFAP in the entorhinal cortex and thalamic reticular nucleus, along with changes in the dopaminergic release within the VTA-NAc pathway. These findings suggest that the impact of early-life seizures extends beyond the HPC-PFC circuit.

While we recognize the relevance of other brain circuits in the outcomes of ELS, we argue for a specific role of the HPC-PFC circuit in the outcomes of ELS. We will detail the supporting evidence and arguments that specifically link the HPC-PFC function to our ELS-related observations in a later comment regarding the "overinterpretation" of the HPC-PFC role. To better convey these important nuances, we have made specific modifications to the results and in the discussion section to underscore the broader implications of our findings, providing a more comprehensive understanding of the study's scope and outcomes.

[…]This raises questions about the subjective nature and persuasiveness of the statistical studies presented.

All statistical analyses were carefully applied based on the literature and following well-established precepts and precautions. Specifically, we constructed the experimental design for univariate inferential statistics for the data related to behavioral tests, synaptic plasticity, immunohistochemistry, oscillatory activity, and dopaminergic sensitization. However, we also submitted our data to multivariate statistical analysis, which is recommended in cases with a considerable amount of data, and intend to investigate possible hidden effects. In this situation, multivariate analyses are inherently exploratory due to the possibility of using multiple measurements for each phenomenon investigated. Nevertheless, their application is not subjective and follows the same statistical rigor as univariate analyses. We firmly believe that abstaining from exploring these data, would not reach the full potential of this analytical method in dissecting the multidimensional associations within our dataset. In order to eliminate any doubt regarding the objectivity in the choice and application of statistics, we carefully rewrote the methods, highlighting the details of statistical rigor even more.

Sample Size Concerns: The manuscript raises concerns about the adequacy of sample sizes in the study. The initial cohort for acute electrophysiology during ELS induction comprised only 5 rats, without a control group. Moreover, the behavioral tests involved 11 control and 14 ELS rats, but these same cohorts were used for over four different experiments. Subsequent electrophysiology and immunohistochemistry experiments used varying numbers of rats (7 to 11). Clarification is needed regarding whether these experiments utilized the same cohort and why the sample sizes differed. A power analysis should have been performed to justify sample sizes, especially given the complexity of the statistical analyses conducted.

We appreciate the reviewer's thoroughness and considerations regarding the sample sizes used in our study. The concerns raised about statistical robustness seem to stem from a lack of clarity in delineating the rat cohorts used in each experiment. It is encouraging to note that several studies in the field of neurophysiology, employing similar analyses, utilize a sample size similar to what was used in our research. The choice of the sample size was based on a thorough analysis of the existing literature, considering specific experimental demands, the complexity of employed techniques, and the need to achieve statistically robust results. In response to these concerns and to enhance clarity on the sample sizes, we have made several modifications (highlighted in red) in the text. Below, we provide details for each animal cohort utilized:

Cohort 1 - Acute Electrophysiology

The decision to use only 5 animals without a control group for acute electrophysiological recording aimed specifically to confirm that the injection of lithium-pilocarpine would induce both behavioral and electrographic seizures. It is crucial to note that this was a descriptive result and a methodological control of the ELS model. Besides, no statistical test or further analysis was conducted on these data. We maintain the belief that a group of 5 animals is sufficient to demonstrate that the protocol induces electrographic seizures, and introducing a control group was considered unnecessary to show that saline injection does not induce electrographic seizures.

Cohort 2 - Behavior, LTP Recording, and Immunohistochemistry

Initially, 14 (ELS) and 11 (CTRL) rats were used for behavior assessment. The reduction in sample size for LTP and immunohistochemistry experiments was influenced by practical challenges, including mortality during LTP surgery and issues with immunohistochemical staining that hindered a proper analysis for some animals.

Cohort 3 - Chronic Freely-Moving Electrophysiology

A new cohort of animals (n=6 and 9 for CTRL and ELS, respectively) was used specifically for freely-moving electrophysiological data.

Cohort 4 - Behavioral Sensitization to Psychostimulants

A fourth cohort was utilized for assessing behavioral sensitization to psychostimulants (CTRL n=15 and ELS n=14). The reduced sample size for neurotransmitter analysis (CTRL n=8 and ELS n=9) was a deliberate selection of a subsample to ensure a sufficient sample for quantification while maintaining statistical validity

Overinterpretation of HPC-PFC Network Dysfunction: The manuscript potentially overinterprets the role of HPC-PFC network dysfunction based on the results.

We appreciate the insight from Reviewer #2 regarding the potential overinterpretation of the role of the hippocampal-prefrontal cortex (HPC-PFC) network dysfunction in the various alterations observed after ELS.

The significance of HPC-PFC plasticity and network function has been extensively documented concerning cognitive, affective, and sensorimotor functions, as well as in models of neuropsychiatric diseases. Our recent review (Ruggiero et al., 2021) compiles these findings. Specifically, the HPC-PFC network has been linked to spatial working memory through a series of causal and correlational studies conducted by Floresco et al. and Gordon et al. These findings make the HPC-PFC pathway a plausible candidate for underlying alterations associated with working memory, consistent with our observation of exaggerated HPC-PFC LTP associated with poorer performance in the ELS group. Regarding the immunohistochemical observations, we concur with Reviewer #2 that these findings suggest broader-scale brain alterations related to sensorimotor dysfunction beyond the HPC-PFC circuitry. Surely, we acknowledge that these large-scale alterations may underlie brain-wide network functional changes.

In our network dynamics study arm, we investigated HPC-PFC oscillatory activity, allowing us to discuss potential relationships between abnormal plasticity (verified in the second study arm) and network dynamics. It is important to note that while there is some anatomical specificity to the LFPs recorded in the HPC and PFC, these activities may represent larger-scale limbic-cortical dynamics. The intermediate HPC exhibits a significant influence from both dorsal and ventral HPC, and the prelimbic PFC is intricately related to both hippocampal and thalamic oscillations exhibiting under-demand state-dependent synchrony. Additionally, the state maps used in our study were initially described to distinguish states at a global forebrain network level. Even in our past studies, we have described HPC-PFC patterns of network activity (Marques et al., 2022a) that later were found to represent a part of a brain-wide synchrony pattern (Marques et al., 2022b). However, most of our findings on oscillatory dynamics were centered around theta oscillations, a well-established brain-wide activity that originates and spreads from the hippocampus and are present in the HPC-PFC circuit during activity.

In conclusion, we believe the correlations between HPC-PFC LTP and working memory, as well as the specific alterations of theta coordinated activity, support a particular role of the HPC-PFC network dysfunction in the effects of ELS. However, the brain-wide immunochemical alterations are plausible indications of larger-scale dysfunctional networks. To address this issue, we emphasized in the discussion of network findings that the immunohistochemical and neurochemical findings endorse the need to investigate ELS effects on larger networks.

Notably, cognitive deficits are described as subtle, with no evidence of learning deficits and only faint working memory impairments. However, sensorimotor deficits show promise. Consequently, it's essential to justify the emphasis on the HPC-PFC network as the primary mechanism underlying ELS-associated outcomes, especially when enhanced LTP is observed. Additionally, the manuscript seems to sideline neuropathological changes in the thalamus and the thalamus-to-PFC connection. The analysis lacks a direct assessment of the causal relationship between HPC-PFC dysfunction and ELS-associated outcomes, leaving a multitude of multilevel analyses yielding potential correlations without easily interpretable results.

We thank Reviewer #2 for the thorough review and insightful comments. To better grasp the context, it is crucial to consider this characterization within the scope of our experimental design and expected outcomes. Unlike epilepsy models involving adult animals or interventions causing pronounced neuronal loss and structural modifications, our study was intentionally designed to explore moderate behavioral alterations. In fact, the mild behavioral alterations observed in ELS models and the lack of neuronal loss guided our focus on investigating changes in HPC-PFC communication.

While our observed cognitive deficits may be milder compared to certain models, it is imperative to underscore their robustness and clinical relevance. These findings have been consistently replicated globally across various experimental models, encompassing ELS induced by hyperthermia (Chang et al., 2003; Kloc et al., 2022), kainic acid (Statsfrom et al. 1993), flurothyl (Karnam et al., 2009a; 2009b), and hypoxia (Najafian et al., 2021; Hajipour et al., 2023). Mild cognitive deficits were also evident by other research groups using the pilocarpine model in P12 (Mikulecká et al., 2019; Kubová et al., 2013; Kubová et al., 2002). Furthermore, our group replicated the working memory deficit results using an alternative paradigm (the T-maze) and a different rat strain (Sprague Dawley), enhancing the reliability of our observations (D’Agosta et al., 2023).

The clinical perspective gains importance, considering that cognitive effects of ELS may be less severe than those in patients with long-term epilepsy. In fact, the majority of patients with childhood epilepsy exhibit mild cognitive impairment as the most common grade of severity - more than two times the rate of severe cognitive impairment (Sorg et al., 2022). Investigating the mechanisms underlying these mild cognitive changes is crucial for shedding light on neurobiological aspects not fully understood, thereby expanding our comprehension of the consequences of ELS.

We recognize the challenges associated with conducting causal experiments in neuroscience, especially in long-term and chronic alterations as seen in our model. Isolating modifications of specific activities is indeed intricate. However, it's essential to acknowledge that neuroscience progress has not solely relied on causal experiments but has significantly advanced through correlational observations. Our findings serve as a foundational step in comprehending the repercussions of ELS, proposing mechanisms and circuits that necessitate further in-depth dissection and study in the future. We have integrated these considerations into the discussion section of the manuscript to enhance clarity.

Overall, while the manuscript presents intriguing findings related to the HPC-PFC network and ELS outcomes, it requires a more rigorous experimental design[…]

We thank the reviewer for acknowledging our intriguing findings. Regarding the experimental design, we are confident that all the manuscript hypotheses, design, and execution of experiments were rigorously based on the literature and carried out with all necessary controls. As stated earlier, we constructed the experimental design for univariate inferential statistics and explored associations between variables using multivariate statistics. Specifically, we achieved a rigorously experimental design following a series of guidelines. First, the planning of the sample size in each experiment and their respective controls were based on mild effects from the ELS literature. As previously indicated, the only experiment with one group was just the description of the behavioral effects and electrographic seizures after the acute injection of lithium-pilocarpine. Given the exhaustive replication of these data in the ELS literature, this result was presented descriptively as a methodological control. Second, detailed descriptions of statistics were made in both methods and results, always indicating positive and negative results. Notably, the experimental designs used in the work do not correspond to any novelty or radicalization, strictly following the literature of the field. However, new indications and references about the experimental accuracy were added to the manuscript to resolve any doubts regarding objectivity.

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Marques DB, Ruggiero RN, Bueno-Junior LS, Rossignoli MT, and Leite JP. Prediction of Learned Resistance or Helplessness by Hippocampal-Prefrontal Cortical Network Activity during Stress. The Journal of Neuroscience. 2022a 42 (1): 81-96.. https://doi.org/10.1523/jneurosci.0128-21.2021.

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Author Response

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

Reviewer 1

One criticism the authors have made of previous studies was that they have not distinguished between 'tonic' and 'phasic' LC activity and could not demonstrate 'time- locked phasic firing'. This has not been achieved in the present report, as an examination of Fig 1 C,D and 2 C,D shows. Previous reports in rats and monkeys, using unit recording in rats and monkeys clearly show that the latency of LC 'phasic' responses to salient or behaviorally relevant stimuli are in the range of tens of milliseconds, with a very short duration, often followed by a long-lasting inhibition. This kind of temporal precision concerning the phasic response cannot be gleaned from the time scale shown in the Figures (assuming the time scale is in seconds). We can discern a long-lasting increase in tonic firing level for the more salient stimuli (Fig 1C) (although the authors state in the discussion that "we did not observe obvious changes in tonic LC-HPC activity). This calcium imaging methodology as used in the present experiments can give us a general idea of the temporal relation of LC response to the stimulus, but apparently does not afford the millisecond resolution necessary to capture a phasic response, at least as the data are presented in the Figures.

While we understand the reviewer’s concern with our use of the terms phasic and tonic, we believe we have represented them as accurately as possible given our data. Unfortunately, the distinction between tonic and phasic activity is somewhat arbitrary, in that there is no strict definition, to our knowledge, of the exact parameters that activity must fall into to be categorized as tonic or phasic. While it is true that phasic LC activity has typically been studied with electrophysiological approaches that afford millisecond resolution and that observed phasic responses are often extremely short, there are numerous differences between those studies and this one. Most prominently, the stimuli used to elicit a phasic response are generally extremely short (often 1ms or less) and therefore generate extremely short phasic responses (Aston-Jones and Bloom, 1981a; Aston-Jones and Cohen, 2005), but this is not to say that phasic responses might not be longer in response to a longer lasting stimulus. Moreover, tonic activity is reported to track with behavioral state on the order of dozens of seconds to minutes and is not reported in response to specific stimuli (Aston-Jones and Bloom, 1981b). The “phasic” responses we report generally decay in less than 5 seconds in our fluorescence signals. Given the slow time course of decay for GcAMP6s (a single action potential can generate a response that lasts 3 or more seconds (Chen et al., 2013)) and the GRAB sensors (GRAB-DA2h τoff = 7.2s (Sun et al., 2020)), the underlying neural responses would have lasted for a significantly shorter period. Therefore, we believe the responses we observed are much more consistent with phasic responses to long-lasting sensory stimuli (20-second tone, 1-2 second shock), than with increases in tonic activity associated with a change in behavioral state. Finally, regardless of whether these responses are exactly the same as previously reported phasic responses, our photometry and optogenetics studies provide insight about a form of LC activity that is fundamentally different than what can be gleaned from much slower dialysis, lesion, and pharmacology studies. Nonetheless, we added the following to the discussion section to clarify the limitations of our interpretation:

“…given their relatively short duration and the fact that they are elicited specifically by salient sensory stimuli, we refer to these responses as “phasic responses.” However, because of the comparatively slow dynamics of fluorescent sensors relative to electrophysiology, we cannot rule out the possibility that these responses are somehow different in nature to previously reported phasic LC responses. Thus, some care must be taken in conflating the characteristics and/or function of the relatively short-lasting responses presented here and the extremely fast phasic responses to very brief (μs to ms) sensory stimuli reported previously.”

Much of the data presented here can be regarded as 'proof of concept' i.e. demonstrating that Photometric imaging of calcium signalling yields similar results concerning LC responses to salient or behaviorally relevant stimuli as has been previously reported using electrophysiological unit recording. The role of dopamine as the principal player in hippocampaldependent learning also corroborates previous reports.

Although some of the data presented in this study could be seen as “proof of concept” or “confirmatory” of previous results, we believe this work extends previous results by showing 1) the importance of hippocampal dopamine to aversive hippocampus-dependent learning and trace fear conditioning specifically, 2) that LC responses are important at the specific times of learning (i.e. CS/US onset/termination), and 3) that dopamine in the hippocampus is likely important for learning in a way that is not tied to prediction error or memory consolidation.

No attempt was made to address the important current question of the modular organisation of Locus Coeruleus, although the authors recognize the importance of this question and propose future experiments using their methodology to record simultaneously in several LC projection sites.

While we do recognize the importance of this modular organization, which is addressed in the discussion as the reviewer mentions, experiments addressing this organization are beyond the scope of the present study. Future work will address the possibility that LC projections to different regions show differential responses during learning.

The phasic-tonic issue has not been resolved by these experiments. Phasic responses of LC single units are short-latency, short-lived (just 3-4 action potentials), and followed by a relatively long refraction period. Multiunit responses will have a more jittery latency and longer-lasting response (but still only tens to hundreds of milliseconds). Your figures clearly show long-lasting increases in tonic firing levels, even though you state the contrary in the discussion. Therefore, I strongly recommend removing the word 'phasic' from the title.

Addressed above.

Yohimbine, the Alpha 2 antagonist, administered systemically, induces a massive increase in the rate of firing of LC cells (through blockade of autoinhibition at the cell body level at terminals). I guess its effect on the receptor 'backbones' overrides the massive release of NE and/or DA, but you might want to mention this; also include the dose of all drug treatments.

Yes, yohimbine’s effect on the GRAB-NE signal is somewhat counter-intuitive given the known effect of yohimbine on norepinephrine levels. However, our result is consistent with previous reports (Feng et al., 2019). We have added the following to the results section to clarify:

“Thus, even though yohimbine is known to increase NE levels in the hippocampus (Abercrombie et al., 1988), its blockade effect on the GRAB-NE sensor should result in a decrease in fluorescence after administration.”

Include time scale units on all figures (I assume it is seconds in Figs 1 &2).

Thank you for pointing out this issue, we have added units on all figures.

• Is it possible to have a better quality example of staining? Fig 1 B in particular is very blurry. Is the yellow double staining? Please indicate. Most of the GCaMP seems to be outside the main area of TH staining. Fig 4 B is much nicer--and it looks morphologically, like LC.

Unfortunately, the GcAMP6s staining was very dim in our hands and resulted in relatively blurry images. Yes, in this case, yellow is double staining. Regarding the morphology, the GCaMP image is taken from a sagittal section and the shape of expression is consistent with images of LC in the sagittal plane. However, given the quality of our ChR2 images, we are confident in the specificity of expression in these mice.

Reviewer 2

The claim that dopamine release in dHPC is caused by LC neurons is not directly tested. Unfortunately, the most critical experiment for the claims that dopamine release comes from LC during conditioning is not tested. A lack of dopamine signal in dHPC caused by inhibition of LC during TFC would show this. It is indeed an interesting observation that chemoegenetic activation of LC causes dopamine release in the dHPC. However, in the absence of concurrent VTA inhibition or lesion, it remains a possibility that the dopamine release is mediated through indirect actions on other dopamine-expressing neurons. The authors do a good job of arguing against this interpretation in the discussion, and the literature seems appropriate for this. However, the title is still an overstatement of the data presented in this study.

We agree with the reviewer’s comments. As indicated in the discussion, it is possible that hippocampal dopamine is increased indirectly via LC projections to dopaminergic midbrain regions. We believe that our title is consistent with this possibility. When phasic stimulation was delivered to the LC, dopamine levels increased in the hippocampus and trace fear conditioning was enhanced. The observed increase in dopamine could be direct or indirect. As the reviewer notes, we argue for the former in the discussion section. A number of experiments would be needed to show this directly (record dopamine while: inhibiting the LC, inhibiting the VTA, stimulating LC while simultaneously inhibiting the VTA etc.) and we are planning to do these in the future.

The primary alternative interpretations of the phasic activation experiment are whether only stimulation to the cue events (both on and off), or whether only stimulation to the shock. Thus this experiment would benefit from additional data showing either a no shock control, to show that enhanced activity of the LC to the tone is not inherently aversive, or manipulations to the tone but not to the shock.

Future work will explore whether the contribution of LC to learning is primarily due to its activation during the CS or the US. However, this is beyond the scope of this manuscript.

Specificity of the GRAB-NE and GRAB-DA sensors should be either justified through additional experiments testing the alternative antagonist (i.e. GRAB-NE CNO+eticloprode / GRAB-DA CNO+yohimbine) or additional citations that have tested this already. It is critical for the claims of the paper to show that these sensors are specific to dopamine or norepinephrine.<br /> Although sensitivity is a potential concern, these sensors have been thoroughly vetted and used by many groups since their generation. In particular, the creators of these sensors provided extensive data showing their specificity. The GRAB-DA sensor is ~10 fold more sensitive to DA than to NE (Sun et al., 2020, cited 239 times) and the GRAB-NE sensor is ~37 fold more sensitive to NE than to DA (Feng et al., 2019, cited 371 times).

The role of dopamine in prediction error was tested through a series of conditions whereby the shock was presented either signaled (i.e. predicted), or not. However, another way that prediction error is signaled is through the absence of an expected outcome. Admittedly it might not be possible to observe a decrease in dopamine signaling with this methodology.

Although this is a strong point, given that the study is not primarily focused on error prediction and the low likelihood of observing the typically small decrease in signaling during expected outcome omission, we feel that additional error prediction studies are beyond the scope of this manuscript. However, further experiments as suggested by the reviewer could prove interesting in future studies.

The difference between Fig. 6E and 6H needs to be clarified. What is shown in Fig. 6E is that the response to the shock decreases through experience (i.e. by the 10th trial). However in Fig 6H, there is no difference between signaled and signaled shock, but this is during conditioning, and not after learning (based on my understanding of the methods, line 482).

We are not sure we fully understand what point of clarification the reviewer is asking for. However, we have clarified in the methods that the signaled vs unsignaled shock experiment took place in animals that had already been trained on TFC. Thus, all of the trials took place after the animals had learned the tone-shock association. Therefore, although the drop in shock-response could be taken as an indicator of a prediction-error like signal, all the other data points to this not being the case (no change in tone response over training, no difference in signaled vs. unsignaled responses after training).

Unless I missed it, at no point in the manuscript is the number of subjects described. Please add the n per experiment within each section describing each experiment in the methods (Behavioral procedures). Some more details in the photometry statistical analysis would be helpful. For example, what is the n per group for every data set that is presented? How many trials per analysis?

We thank the reviewer for pointing this out. Animal numbers have been added in the methods section in the Behavioral Procedures, Optogenetics, and Drugs sub-sections and in the figure legends. Trial numbers are included in these sections and all trials were used for analysis.

What is the difference in experimental procedure between Fig. 2D and Fig. 3B? It seems that they are the same, and yet the LC response to the conditioned CS is not.

Fig. 3B is simply the Day 1 data from Fig 2D presented at a different scale because the shock response is included in Fig. 3B which necessitates a larger scale on both axes. Close inspection of the figures will show that the shapes of these two curves and the error around them is the same, but the different scaling obfuscates this slightly.

Typo in the legend of Figure 2 - D should be E.

Thank you, we have corrected this.

• Anatomical localization of the virus injections, and more importantly the fiber placements, is not shown. Including this information helps with replication and understanding where exactly the observations were made in dHPC to contrast with prior studies.

Representative examples are included in the manuscript in figure 1B, 3F, 4B, and 5B.

Reviewer 3

While the optogenetic study was lovely, a control using the same stimulation but delivered at different time points would have been a good addition to show how critical the neural signal at tone onset, tone offset, and shock is.

We agree that it would be interesting in future studies to delineate the specific times when LC stimulation produces a learning enhancement. It could be that LC activity is most important during one specific time period (eg. just during shock) or that all three periods of activation are required. It would also be useful to know whether stimulation at other times during learning can produce an enhancement given the potentially long-lasting effects of dopamine on HPC plasticity and learning.

Justification for the focus on D1 receptors was lacking.

We chose to focus on D1 receptors because previous studies have shown that these receptors are critical for memory formation or consolidation in the hippocampus. We have added a sentence justifying this in the results section.

“To test whether dopamine is required for trace fear memory formation, we administered the dopamine D1 receptor antagonist SCH23390 (0.1mg/kg) 30 minutes before training, as D1/D5 receptors have previously been shown to be critical for other types of hippocampus dependent memory and plasticity (Frey et al., 1990; Huang and Kandel, 1995; O’Carroll et al., 2006; Wagatsuma et al., 2018).”

The manuscript provides convincing evidence that the neural signal is not an error- correcting one by including a predicted (by a tone) and unpredicted shock. One possibility is that perhaps the unpredicted shock could be predicted by the context. Some clarification on the behavioural procedures would help understand if indeed the unsignaled shock could be predicted by the context or not.

Mice always exhibit freezing in the training environment, so the context is definitely a predictor of shock. However, the tone is a much better predictor because it is always followed by shock while the mice spend a large amount of time in the context without being shocked. This is demonstrated by the fact that the same procedure used in the current experiments consistently produces more tone fear than context fear (Wilmot et al., 2019). While we did not do long-term memory tests here, we assume the same dissociation occurred as it has been observed very consistently across studies (Chowdhury et al., 2005; Kitamura et al., 2014; Wilmot et al., 2019). Nonetheless, it is possible that a difference between signaled and unsignaled groups was obscured by the context. We should note however, that differences between dopaminergic responses to cued and uncued rewards and aversive outcomes has been observed and these animals were also trained in the same context (Eshel et al., 2016; Matsumoto and Hikosaka, 2009; Pan et al., 2005; Schultz, 1998). Therefore, we believe this experiment does differentiate the observed dopamine response in the hippocampus from previously reported VTA dopamine prediction error signaling.

Figure 2 - tone termination in Tone only group - no change? Stats?

Thank you for pointing out this omission. We have added the stats to the figure legend. Although the response to tone termination decreased numerically, it did not change significantly across days. This is one point we may seek to clarify in future studies, as the difference between tone onset and termination responses is unexpected. Given the relatively small responses, it’s possible future studies with stronger signal (eg. GcAMP8) may find differences in the tone termination response across training days. This is one of the reasons we focused primarily on the responses to tone onset and shock in the rest of the manuscript.

Fig 4 data - stimulation at time incongruent with the signal as a control for the timing of stim.

This is addressed above.

Fig 5 - GRAB-NE - yohimbine seems to suppress the signal below the vehicle. Not the case for GRAB-DA. Is this sig? post-hoc stats?

Yes, this does appear to be the case for GRAB-NE, and would not be entirely surprising given that there is likely a baseline level of NE (and dopamine) in the hippocampus that produces some degree of baseline fluorescence in the vehicle group. This signal could be reduced/abolished by blocking the sensor and preventing this baseline level of NE from binding and producing fluorescence. This may not be the same for the GRAB-DA for a variety of reasons – different sensor binding affinities, different baseline neurotransmitter levels, potentially non-equivalent drug doses, etc. Because of the large number of pairwise comparisons in this data (18), we did not make post-hoc pairwise comparisons.

Shock response curve - lines 466-474 - some explanation of what the pseudorandom order of shock presentation means.

We have added the following explanation to this section:

“…pseudorandom order, such that the shocks did not occur in ascending or descending order or follow the same pattern in each block,…”

Line 126 - the extinction came out of the blue, it needs some introduction such as a statement that the animals were exposed to extinction training following conditioning.

We have added the following earlier in that same paragraph:

“On the second and third days, mice underwent extinction trials in which no shocks were administered.”

References in Response

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Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors conducted two tasks at 300 days of separation. First, a social perception task, where Ps responded whether a pictured person either deserved or needed help. Second, an altruism task, where Ps are offered monetary allocations for themselves and a partner. Ps decide whether to accept, or a default allocation of 20 dollars each. The partners differed in perceived merit, such that they were highly deserving, undeserving, or unknown. This categorisation was decided on the basis of a prisoner's dilemma game the partner played beforehand. "Need" was also manipulated, by altering the probability that the partner must have their hand in cold water at the end of the experiment and this partner can use the money to buy themselves out. These two tasks were conducted to assess the perception of need/merit in the first instance, and how this relates to social behaviour in the second. fMRI data were collected alongside behavioural.

The authors present many analyses of behaviour (including DDM results) and fMRI. E.g., they demonstrate that they could decode across the mentalising network whether someone was making a need or deserving judgement vs control judgement but couldn't decode need vs deserving. And that brain responses during merit inferences (merit - control) systematically covaried with participants' merit sensitivity scores in the rTPJ. They also found relationships between behaviour and rTPJ in the altruism task. And that merit sensitivity in the perception task predicted the influence of merit on social behaviour in the altruism task.

Strengths:

This manuscript represents a sensible model to predict social perceptions and behaviours, and a tidy study design with interesting findings. The introduction introduced the field especially brilliantly for a general audience.

Response: We are pleased that the reviewer found the model sensible and the findings interesting! Below, we respond to each of the reviewer’s comments/critiques.

Weaknesses: (1) The authors do acknowledge right at the end that these are small samples. This is especially the case for the correlational questions. While the limitation is acknowledged at the end, it is not truly acknowledged in the way that the data are interpreted. I.e. much is concluded from absent relationships, where the likelihood of Type II error is high in this scenario. I suggest that throughout the manuscript, authors play down their conclusions about absence of effects.

Response: We agree with the reviewer that the limitation of small samples should be adequately reflected in the interpretation of the data. We have therefore added cautionary language to the interpretation of the correlational effects in several places of the revised manuscript. For example, we now state: “However, this absence of effects for need ought to be interpreted with caution, given the comparatively small sample size.” (pg. 33) and “As mentioned above, we cannot rule out the possibility that null findings may be due to the comparatively small sample size and should be interpreted cautiously (also see discussion)” (pg. 34-35).

(2) I found the results section quite a marathon, and due to its length I started to lose the thread concerning the overarching aims - which had been established so neatly in the introduction. I am unsure whether all of these analyses were necessary for addressing the key questions or whether some were more exploratory. E.g. it's unclear to me what one would have predicted upfront about the decoding analyses.

Response: We acknowledge and share the reviewer’s concern about the length of the results section and potential loss of clarity. Regarding the decoding analyses, we want to clarify that they were conducted as a sanity check to compare against the results of the univariate analysis. We didn’t have apriori hypotheses regarding these supplemental decoding analysis. We have clarified this issue in the revised version of the manuscript and moved the decoding analyses fully to the supplemental material to streamline the main text. The remaining results reported in the manuscript are indeed all based on apriori, key questions (unless specified otherwise, for example, supplemental analyses for other regions of interest for the sake of completeness). The only exception is the final set of results (Neural markers of merit sensitivity predict merit-related behavioral changes during altruistic choice) which represent posthoc tests to clarify the role of activation in the right temporoparietal junction (rTPJ) in merit-related changes in other-regard in altruistic decisions. While we acknowledge that this is a complex paper, after careful consideration we couldn’t identify any other parts of the results section to remove or report in the supplemental material.

(3) More specifically, the decoding analyses were intriguing to me. If I understand the authors, they are decoding need vs merit, and need+merit vs control, not the content of these inferences. Do they consider that there is a distributed representation of merit that does not relate to its content but is an abstracted version that applies to all merit judgements? I certainly would not have predicted this and think the analyses raise many questions.

Response: We thank the reviewer for sharing their thoughts on the decoding analyses and agree that this set of analyses are intriguing, yet raise additional questions, such as the neural computations required to assess content. However, we wish to clarify that the way we view our current results is very much analogous to results obtained from studies of perception in other fields. For example, in the face perception literature, it is often observed that the fusiform face area is uniformly more active, not only when a face (as opposed to an object) is on the screen, but when a compound stimulus consistent of features of a face and other features (e.g. of objects) is on the screen, but participants are instructed to attend to and identify solely the face. Moreover, multivariate activity in the FFA (but not univariate activity) is sufficient to decode the identity of the face. We view the results we report in the manuscript as more akin to the former types of analyses, where any region that is involved in the computation is uniformly more active when attention is directed to judgment-specific features. Unfortunately, the present data are not sufficient to properly answer the latter questions, about which areas enable decoding of specific intensity or identity of merit-related content. Follow-up experiments with a more optimized design are needed. Although interesting, we thus refrain from further discussing the decoding analyses in the manuscript to avoid distracting from the main findings based on the univariate comparison of brain responses observed while participants make merit or need inferences in the social perception task.

Reviewer #2 (Public Review):

When people help others is an important psychological and neuroscientific question. It has received much attention from the psychological side, but comparatively less from neuroscience. The paper translates some ideas from a social Psychology domain to neuroscience using a neuroeconomically oriented computational approach. In particular, the paper is concerned with the idea that people help others based on perceptions of merit/deservingness, but also because they require/need help. To this end, the authors conduct two experiments with an overlapping participant pool:

(1) A social perception task in which people see images of people that have previously been rated on merit and need scales by other participants. In a blockwise fashion, people decide whether the depicted person a) deserves help, b) needs help, and c) whether the person uses both hands (== control condition).

(2) In an altruism task, people make costly helping decisions by deciding between giving a certain amount of money to themselves or another person. How much the other person needs and deserves the money is manipulated.

The authors use a sound and robust computational modelling approach for both tasks using evidence accumulation models. They analyse behavioural data for both tasks, showing that the behaviour is indeed influenced, as expected, by the deservingness and the need of the shown people. Neurally, the authors use a block-wise analysis approach to find differences in activity levels across conditions of the social perception task (there is no fMRI data for the other task). The authors do find large activation clusters in areas related to the theory of mind. Interestingly, they also find that activity in TPJ that relates to the deservingness condition correlates with people's deservingness ratings while they do the task, but also with computational parameters related to helping others in the second task, the one that was conducted many months later. Also, some behavioural parameters correlate across the two tasks, suggesting that how deserving of help others are perceived reflects a relatively stable feature that translates into concrete helping decisions later-on.

The conclusions of the paper are overall well supported by the data.

Response: We thank the reviewer for the positive evaluation of our study and the comprehensive summary of our main findings. We would like to clarify, though, that we did originally collect fMRI data for the independent altruism task. Unfortunately, due to COVID-19-related interruptions, only 25 participants from the sample that performed the social perception task also completed the fMRI altruism task (see pg. 18). Given the limited sample size and noise level of fMRI data, we moved anything related to the neuroimaging data of the altruism task to the supplemental material (see Note S7) and decided to focus solely on the behavior of the altruism task to address our research objectives. We apologize for any confusion.

(1) I found that the modelling was done very thoroughly for both tasks. Overall, I had the impression that the methods are very solid with many supplementary analyses. The computational modelling is done very well.

Response: We are pleased that the reviewer found the computational model sensible.

(2) A slight caveat, however, regarding this aspect, is that, in my view, the tasks are relatively simplistic, so even the complex computational models do not do as much as they can in the case of more complex paradigms. For example, the bias term in the model seems to correspond to the mean response rate in a very direct way (please correct me if I am wrong).

Response. We agree that the Bias term relates to mean responding (although it is not the sole possibility: thresholds and starting default biases can also produce changes in mean levels of responding that, without the computational model, are not possible to dissociate). However, we think that the primary value of this parameter comes not from the analysis of the social judgment task (where the reviewer is correct that the bias relates in a quite straightforward way to the mean response rate), but in the relationship of this parameter to the un-contextual generosity response in the altruism task. Here, we find that this general bias term relates not to overall generosity, but rather to the overall weight given to others’ outcomes, a finding that makes sense if the tendency to perceive others as deserving overall yields an increase in overall attention/valuation of their outcomes. Thus, a simple finding in one task relates to a more nuanced finding in another. However, we agree it is important to acknowledge the point raised by the reviewer, and now do so on pg. 20: “It is worth noting that the Bias parameters are strongly associated with (though not the sole determinant of) the mean response rate.”

(3) Related to the simple tasks: The fMRI data is analysed in a simple block-fashion. This is in my view not appropriate to discern the more subtle neural substrates of merit/need-based decision-making or person perception. Correspondingly, the neural activation patterns (merit > control, need > control) are relatively broad and unspecific. They do not seem to differ in the classic theory of mind regions, which are the focus of the analyses.

Response: The social perception task is modified from a well-established social inference task (Spunt & Adolphs, 2014; 2015) designed to reliably localize the mentalizing network in the brain. As such, we acknowledge that it is not optimally designed to discern the intrinsic complexities of social perception, or the specific appraisals or computations that yield more or less perception (of need or merit) in a given context. Instead, it was designed to highlight regions that are more generally recruited for performing these social perceptions/inferences.

We heartily agree with the reviewer that it would be interesting and informative to analyze this task in a trial-wise way, with parametric variation in evidence for each image predicting parametric variation in brain activity. Unfortunately, the timing of this task is not optimal for this kind of an analysis, since trials were presented in rapid and blocked fashion. We were also limited in the amount of time we could devote to this task, since it was collected in conjunction with a number of other tasks as part of a larger effort to detail the neural correlates of social inference (reported elsewhere). Thus, we were not able to introduce the kind of jittered spacing between trials that would have enabled such analysis, despite our own wish to do so. We hope that this work will thus be a motivator for future work designed more specifically to address this interesting question, and now include a statement to this effect on pgs. 2223: “Future research may reveal additional distinctions between merit and need appraisals in trial-wise (compared to our block-wise) fMRI designs.”

References:

Spunt, R. P. & Adolphs, R. Validating the Why/How contrast for functional MRI studies of Theory of Mind. Neuroimage 99, 301-311, doi:10.1016/j.neuroimage.2014.05.023 (2014).

Spunt, R. P. & Adolphs, R. Folk explanations of behavior: a specialized use of a domain-general mechanism. Psychological Science 26, 724-736, doi:10.1177/0956797615569002 (2015).

(4) However, the relationship between neural signal and behavioural merit sensitivity in TPJ is noteworthy.

Response: We agree with this assessment and thank the reviewer for their positive assessment; we feel that linking individual differences in merit sensitivity with variance in TPJ activity during merit judgments is one of the key findings of the study.

(5) The latter is even more the case, as the neural signal and aspects of the behaviour are correlated across subjects with the second task that is conducted much later. Such a correlation is very impressive and suggests that the tasks are sensitive for important individual differences in helping perception/behaviour.

Response: Again, we share the reviewer’s impression that this finding is more noteworthy for appearing in tasks separated both by considerable conceptual/paradigmatic differences, and by such a long temporal distance. These findings make us particularly excited to follow up on these results in future research.

(6) That being said, the number of participants in the latter analyses are at the lower end of the number of participants that are these days used for across-participant correlations.

Response: We fully agree with this assessment. Unfortunately, COVID-related disruptions in data collection, as well as the expiration of grant funds due to the delay, severely limited our ability to complete assessments in a larger sample. Future research needs to replicate these results in a larger sample. We comment on this issue in the discussion on pg. 40. If the editor or reviewer has suggestions for other ways in which we could more fully acknowledge this, we would be happy to include them.

Reviewer #3 (Public Review):

Summary:

The paper aims to provide a neurocomputational account of how social perception translates into prosocial behaviors. Participants first completed a novel social perception task during fMRI scanning, in which they were asked to judge the merit or need of people depicted in different situations. Secondly, a separate altruistic choice task was used to examine how the perception of merit and need influences the weights people place on themselves, others, and fairness when deciding to provide help. Finally, a link between perception and action was drawn in those participants who completed both tasks.

Strengths:

The paper is overall very well written and presented, leaving the reader at ease when describing complex methods and results. The approach used by the author is very compelling, as it combines computational modeling of behavior and neuroimaging data analyses. Despite not being able to comment on the computational model, I find the approach used (to disentangle sensitivity and biases, for merit and need) very well described and derived from previous theoretical work. Results are also clearly described and interpreted.

Response: We thank the reviewer for their positive comments regarding presentation, approach, and content.

Weaknesses:

My main concern relates to the selection of the social perception task, which to me is the weakest point. Such weakness has been also addressed by the same authors in the limitation section, and related to the fact that merit and need are evaluated by means of very different cues that rely on different cognitive processes (more abstract thinking for merit than need). I wonder whether and how such difference can bias the overall computational model and interpretation of the results (e.g. ideal you vary merit and need to leave all other aspects invariant).

Response: We agree with the reviewer on the importance of future research to more fully unpack the differences in this task, and develop better ways to manipulate need and merit in more comparable fashion. However, we point out that the issue of differences in abstractness of cues for need and merit does not actually seem to have a strong influence on the parameters retrieved by the computational model. Participants seem to be equally sensitive to BOTH merit and need information, despite that information deriving from different sources, as evidenced by the fact that the magnitude of the sensitivity parameters for need and merit in the social judgment task were nearly identical, and not statistically distinguishable. Nor were other parameters related to non-decision time or threshold statistically different (see Supplemental Table S2). If our results were driven purely by differences in the difficulty or abstractness of these judgments, we would have expected to see some evidence of this in the computational model, in the form of longer non-decision times, higher thresholds, or both. We do not. Likewise, the neural underpinnings evoked by both need and merit perceptions in this task (in the mentalizing brain network) were comparable. This is not to say that there aren’t real differences in the cues that might signal these quantities in our social perception task - just that there is little direct evidence for this difference in computational parameters or evoked brain responses, and thus it is unlikely that our results (which rely on an analysis of computational parameters) are driven solely by computational model biases, or the inability of the model to adequately assess participant sensitivity to need as opposed to merit.

A second weakness is related to the sample size which is quite small for study 2. I wonder, given that study 2 fRMI data are not analyzed, whether is possible to recover some of the participants' behavioral results, at least the ones excluded because of bad MR image quality.

Response: We fully agree with the reviewer that increasing the sample size for the cross-task correlations would be desirable. Unfortunately, the current sample size already presents the maximum of ‘usable’ data; the approach suggested by the reviewer won’t affect the sample size. We used all participants whose behavioral data in the altruism task suggested they were performing the task in good faith and conscientiously.

Finally, on a theoretical note, I would elaborate more on the distinction of merit and need. These concepts tap into very specific aspects of morality, which I suspect have been widely explored. At the moment I am missing a more elaborate account of this.

Response: Need and merit are predominantly studied in separate lines of research (Molouki & Bartels, 2020) so there is relatively little theoretical research on the distinction between the two. Consequently, Siemoneit (2023) states that the relation between the concepts of need and merit in allocative distributions remains diffuse. To emphasize the distinct concepts of morality in the introduction we have now added to pg. 3: “Need and deservingness (merit) are two distinct principles of morality. The need principle involves distributing resources to those who require them, irrespective of whether they have earned them, while the "merit principle" focuses on allocating resources based on individuals' deservingness, regardless of their actual need (Wilson, 2003).”

One of the added values of our paper to the research literature is in adding to the clarification of computational and neural underpinnings of broad concepts like merit and need. To highlight the latter point, we have added the following statement on pg. 5 to the manuscript: “Examining need and merit concurrently in this task will also help clarify the computational and neural underpinnings of related, but distinct concepts, distinguishing between them more effectively.”

References:

Molouki, S., & Bartels, D. M. (2020). Are future selves treated like others? Comparing determinants and levels of intrapersonal and interpersonal allocations. Cognition, 196, 104150.

Siemoneit, A. (2023). Merit first, need and equality second: hierarchies of justice. International Review of Economics, 70(4), 537-567.

Wilson, C. (2003). The role of a merit principle in distributive justice. The Journal of ethics, 7, 277-314.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

I acknowledge the difficulty with respect to recruitment, especially in the age of covid, but is it possible for the authors to collect larger samples for their behavioural questions via online testing? Admittedly, I'm sure they don't want to wait 300 days to have the complete dataset, but I would be in favour of collecting a sample in the hundreds on these behavioural tasks, completed at a much shorter separation (if any). I believe this would strengthen the authors' conclusions considerably if they could both replicate the effects they have and check these null effects in a sample where they could draw conclusions from them. Indeed, Bayesian stats to provide evidence for the null would also help here.

Response: We share the reviewer’s desire to see these results replicated (ideally in a sample of hundreds of participants). We have seriously considered the possibility of trying to replicate our results online, even before submitting the first version of the paper. However, it is difficult to fully replicate this paradigm online, given the elaborate story and context we engaged in to convince participants that they were playing with real others, as well as the usage of physical pain (Cold Pressor Task) for the need manipulation in the altruism task. Moreover, given comments by this reviewer that the results are already a little long, adding a new, behavioral replication would likely only add to the memory burden for the reader. We have thus opted not to include a replication study in the current work. However, we are actively working on a replication that can be completed online, using a modified experimental paradigm and different ways to manipulate need and merit. Because of the differences between that paradigm and the one described here, which would require considerable additional exposition, we have opted not to include the results of this work in the current paper. We hope to be able to publish this work as a separate, replication attempt in the future.

Given the difficulty of wading through the results section while keeping track of the key question being answered, I would suggest moving any analyses that are less central to the supplementary. And perhaps adding some more guiding sentences at the start and end of each section to remind the reader how each informs the core question.

Response: We deliberated for quite some time about what results could be removed, but in the end, felt that nearly all results that we already described need to be included in the paper, since each piece of the puzzle contributes to the central finding (relating parameters and behavior to neural and choice data across two separate tasks). However, we did move the decoding analysis results to the supplemental (see point below). We also take the reviewers point that the results can be made clearer. We thus have worked to include some guiding sentences at the start and end of sections to remind the readers how each analysis informs the core questions.

I think it needs unpacking more for the reader what they should conclude from the significant need+merit vs control decoding analyses, and what they would have expected in terms of cortical representation from the decoding analyses in general.

Response: We agree with the reviewer that given the decoding results position in the main manuscript it would need unpacking. After considering the reviewer's prior suggestion, we have reevaluated the placement of these supplemental results. Consequently, we have relocated it to the supplemental materials, as it was deemed less relevant to directly addressing the core research questions in the main manuscript. On pg. 23, the main manuscript now only states “We also employed supplemental multivariate decoding analyses (searchlight analysis 85-87), as commonly used in social perception and neuroscience research 7,58,82,88,89, corroborating our univariate findings (see Supplemental Note S6, Supplemental Table S10).”

Reviewer #2 (Recommendations For The Authors):

(1) I would suggest moving information on how the computational models were fitted to the main text.

Response: The computational models are a key element of the paper and we deliberated about the more central exposure of the description of how the models were fitted in the main manuscript. However, we are concerned about the complexity and length of the article, which requires quite a lot from readers to keep in mind (as also commented on by reviewer 1). Those readers who are particularly interested in details of model fitting can still find an extensive discussion of the procedures we followed in the supplements. We thus have opted to retain the streamlined presentation in the main manuscript. However, if the editor feels that including the full and extensive description of model fitting in the main paper would significantly improve the flow and exposition of ideas, we are happy to do so.

(2) For the fMRI analyses: Could it be worth analysing the choices in the different conditions? They could be modelled as a binary regressor (yes/no) and this one might be different across conditions (merit/need/hands). Maybe this won't work because of the tight trial timeline, but it could be another avenue to discern differences across fMRI conditions.

Response: We thank the reviewer for this interesting suggestion! Unfortunately, the block design and rapid presentation of stimuli within each condition make it challenging to distinguish the different choices (within or across conditions). While we see the merit in the suggested analytical approach (in fact, we discussed it before the initial submission of the article), it would require some modifications of the task structure (e.g., longer inter-trial-intervals between individual stimuli) and an independent replication fMRI study. We were not able to have such a long inter-trial interval in the original design due to practical constraints on the inclusion of this paradigm in a larger effort to examine a wide variety of social judgment and inference tasks. We hope to investigate this kind of question in greater detail in future fMRI work.

(3) The merit effects seem to be more stable across time than the need conditions. Would it be worthwhile to test if the tasks entailed a similar amount of merit and need variation? Maybe one variable varied more than the other in the task design, and that is why one type of effect might be stronger than the other?

Response: We thank the reviewer for drawing attention to this important point. We used extensive pilot testing to select the stimuli for the social perception task, ensuring an overall similar amount of need and merit variation. For example, the social perception ratings of the independent, normative sample suggest that the social perception task entails a similar amount of need and merit variation (normative participant-specific percentage of yes responses for merit (mean ± standard deviation: 53.95 ± 13.87) and need (45.65 ± 11.07)). The results of a supplemental paired t-test (p = 0.122) indicate comparable SD for need and merit judgments. Moreover, regarding the actual fMRI participant sample, Figure S3 illustrates comparable levels of variations in need and merit perceptions (participant-specific percentage of yes responses for merit (56.70 ± 11.91) and need (48.69 ± 10.81) in the social perception task). Matching the results for the normative sample, the results of a paired t-test (p = 0.705) suggest no significant difference in variation between need and merit judgments. With respect to the altruism task, we manipulated the levels of merit and need externally (high vs. low).

Reviewer #3 (Recommendations For The Authors):

(1) It would be good to provide the demographics of each remaining sample.

Response: We appreciate the attention to detail and agree with the reviewer’s suggestion. We have now added the demographics for each remaining sample to the revised manuscript.

(2) The time range from study 1 to study 2, is quite diverse. Did you use it as a regressor of no interest?

Response: We thank the reviewer for this interesting suggestion. We have examined this in detail in the context of our cross-task analyses (i.e., via regressions and partial correlations). Interestingly, variance in the temporal delay between both tasks does not account for any meaningful variation, and results don’t qualitatively change controlling for this factor.

For example, when we controlled for the delay between both separate tasks (partial correlation analysis), we confirmed that variance in merit sensitivity (social perception task) still reflected meritinduced changes in overall generosity (altruism task; p = 0.020). Moreover, we confirmed that variance in merit sensitivity reflected individuals’ other-regard (p = 0.035) and self-regard (p = 0.040), but not fairness considerations (p = 0.764) guiding altruistic choices. Regarding people’s general tendency to perceive others as deserving, we found that the link between merit bias (social perception task) and overall other-regard (p = 0.008) and fairness consideration (p = 0.014) (altruism task) holds when controlling for the time range (no significant relationship between merit bias and self-regard, p = 0.191, matching results of the main paper).

We refer to these supplemental analyses in the revised manuscript on ps. 33 and 35: “Results were qualitatively similar when statistically controlling for the delay between both tasks (partial correlations).”

(3) Why in study 1 a dichotomous answer has been used? Would not have been better (also for modeling) a continuous variable (VAS)?

Response: We appreciate the reviewer's thoughtful feedback. In Study 1, opting for a dichotomous response format in the social perception task (Figure 1a) was a deliberate methodological choice. This decision, driven by the study's model requirements, aligns with the common use of a computational model employing two-alternative forced choices ("yes" and "no") as decision boundaries. While drift– diffusion models for multiple-alternative forced-choice designs exist, our study's novel research questions were effectively addressed without their complexity. Finally, our model cannot accept continuous response variables as input unless they are transformed into categorical variables.

(4) In the fMRI analyses, when you assess changes in brain activity as a function of merit, I would control for need (and the other way round), to see whether such association is specific.

Response: Regarding the reviewer’s suggestion on controlling for need when assessing changes in brain activity as a function of merit, and vice versa, we would like to clarify the nature of our fMRI analyses in the social perception task. Our focus is on block-wise assessments (need vs. control, merit vs. control, need vs. merit blocks, following the fMRI task design from which our social perception task was modified from). We don’t assess changes in brain activity as a function of the level of perceived merit or need (i.e., “yes” vs. “no” trials within or across task blocks). Blocks are clearly defined by the task instruction given to participants prior to each block (i.e., need, merit, or control judgments). Thus, unfortunately, given the short inter-stimulus-intervals of each block, the task design is not optimal to implement the suggested approach.

Note: This response was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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Reply to the reviewers

Revision summary.

Additional new data.

• CYPA expression levels in Scrm Vs KO Vs R55A isogenic cell lines as new Fig 1C.
• ATR signaling: western blot analysis of HU-induced p-CHK1 (S345) in Scrm, KO and R55A isogenic cell lines as new Suppl Fig 1B.
• MRN expression: western blot analysis of expression of NBS1, MRE11, RAD50 and MCM2 is Scrm, KO and R55A isogenic cell lines as new Suppl Fig 7A.
• NBS1 subcellular fractionation: western blot analysis of NBS1 from whole cell extract Vs cytoplasmic extract Vs nuclear extract comparing expression/distribution in Scrm, KO and R55A isogenic cell lines, as new Suppl Fig 7B.
• CYPA immunofluorescence (IF) staining on untreated and HU treated U2OS, as new Suppl Fig 7C.
• CYPA immunofluorescence (IF) staining on untreated and HU treated U2OS following pre-extraction, as new Suppl Fig 7D.
• DepMap Project Score Cancer Gene Dependency cell survival (“fitness”) following PPIA/CYPA-KO in breast carcinoma cell lines mapped against BRCA2 status, as a new Suppl Table 5.
• DepMap Project Score Cancer Gene Dependency cell fitness following PPIA/CYPA-KO in Neuroblastoma cell lines, as a new Suppl Spreadsheet 4.
• DepMap Project Score Cancer Gene Dependency cell fitness following PPIA/CYPA-KO in Multiple Myeloma cell lines, as a new Suppl Spreadsheet 4.
• DepMap Project Score Cancer Gene Dependency cell fitness following PPIA/CYPA-KO in Chronic Myelogenous Leukaemia cell lines, as a new Suppl Spreadsheet 4.

Revised and/or additional text.

The Abstract, Introduction, Materials & Methods, Results and Discussion have been amended as necessary, to facilitate the issues raised by the Reviewers.

Reviewer #1: We thank this reviewer for their understanding and appreciation of our CYPA study as espoused by their comprehensive summary of the content, importance, and potential implications of our work; “The manuscript presents clear and comprehensive data, demonstrating the profound impact of CYPA on DNA repair.” Furthermore, we very much appreciate their robust and complementary words regarding the significance of our work and its wide appeal; “The significance of this study is twofold: it adds a new layer to our understanding of DNA repair mechanisms and, importantly, it could point the way to novel therapeutic strategies for cancer. It will spark interest from molecular biologists to clinicians and pharmaceutical researchers.”

Query:

It's surprising to find that the loss of CYPA abolished HU-induced NBS1 foci, as the MRE11 interactive domain of NBS1 should remain intact in CYPA deficient conditions and the N-terminus of NBS1 is dispensable for ATM activation (Kim et al., 2017; Stracker and Petrini, 2011). A more detailed mechanistic explanation of this phenotype would be appreciated. The authors should check the subcellular localization of NBS1 and the stability of MRN in wildtype and CYPA KO cells. Additionally, including the kinetics of NBS1 foci formation using multiple timepoints in wildtype and CYPA KO cells after damage will further support the observation.

RESPONSE:

Regarding NBS1 foci formation, we note that rather than abolish HU-induced NBS1 foci formation, CYPA loss (through KO) and/or inhibition (through p.R55A) in fact results in a “…spontaneously elevated yet unresponsive amount of NBS1 foci/cells when compared to scrambled” (see original Fig 9A legend and associated Results section text). We have reinforced this observation in the revised Results section entitled ‘CYPA influences NBS1 and MDC1 foci formation’ and in the Discussion section. We do describe a kinetic impairment of RAD51 foci formation in the CYPA-engineered lines up to 16hrs post HU-treatment (Fig 6D). Our mechanistic working model is that CYPA interacts directly with NBS1 via a Pro residue within the short linking peptide between the FHA and BRCT1, and that this likely influences the relative dynamic positioning of the FHA with BRCA1-BRCT2, at least following acute HU treatment; replication fork stalling, likely biased towards ATR-dependent signaling initially, rather than that of ATM. The relative positioning of these functional domains can impact MRN function, and we discuss this possible mechanism in the section entitled ‘CYPA and the MRN complex’, with reference to the detailed structure-function analyses and complementary DDR activation models described by<br /> - Williams, R.S., et al., Nbs1 flexibly tethers Ctp1 and Mre11-Rad50 to coordinate DNA double-strand break processing and repair. Cell, 2009. 139(1): p. 87-99.<br /> and<br /> - Lloyd, J., et al., A supramodular FHA/BRCT-repeat architecture mediates Nbs1 adaptor function in response to DNA damage. Cell, 2009. 139(1): p. 100-11.<br /> and<br /> - Rotheneder, M., et al., Cryo-EM structure of the Mre11-Rad50-Nbs1 complex reveals the molecular mechanism of scaffolding functions. Mol Cell, 2023. 83(2): p. 167-185.e9.

The N-terminal FHA-BRCT region of NBS1 does indeed influence MRN recruitment and HRR execution, a point we highlight in the section entitled ‘CYPA influences NBS1 and MDC1 foci formation’, with reference to the seminal original observations of<br /> - Sakamoto, S., et al., Homologous recombination repair is regulated by domains at the N-<br /> and C-terminus of NBS1 and is dissociated with ATM functions. Oncogene, 2007. 26(41): p.6002-6009<br /> and<br /> - Tauchi, H., et al., The forkhead-associated domain of NBS1 is essential for nuclear foci formation after irradiation but not essential for hRAD50-hMRE11-NBS1 complex<br /> DNA repair activity. J Biol Chem, 2001. 276(1): p. 12-15.<br /> and<br /> - Zhao, S., W. Renthal, and E.Y. Lee, Functional analysis of FHA and BRCT domains of NBS1 in chromatin association and DNA damage responses. Nucleic Acids Res, 2002. 30(22): p. 4815-22.<br /> and<br /> - Cerosaletti, K.M. and P. Concannon, Nibrin forkhead-associated domain and breast cancer C-terminal domain are both required for nuclear focus formation and phosphorylation. J Biol Chem, 2003.<br /> 278(24): p. 21944-21951.

HU-unresponsive NBS foci (indicative of MRN dysfunction) and MDC1 foci formation are consistent with the DNA-R (i.e., DR-GFP reporter systems: Fig 3A-C and impaired RAD51 foci formation: Fig 6D) and resection-related phenotypes (Fig 6A-B) we report here and are also consistent with the relative resistance to HU-induced killing we report for CYPA-KO and CYPA-R55A cells (Fig 11A and as reported by Manthey, K.C., et al., NBS1 mediates ATR-dependent RPA hyperphosphorylation following replication-fork stall and collapse. J Cell Sci, 2007. 120(Pt 23): p. 4221-9).

At the reviewer’s request we include additional novel experimental data showing that MRN expression is stable and equivalent in control, CYPA-KO and CYPA-R55A cells (Suppl Fig 7A). We also provide evidence that NBS1 subcellular distribution (via extract fractionation) is not altered upon CYPA loss and/or inhibition (Suppl Fig 7B).

Query:

The authors showed that the interaction between CYPA and MRN didn't change after HU treatment. The authors should also include co-localization analysis of CYPA and NBS1 after HU.

RESPONSE:

At the reviewer’s suggestion we undertook a series of IF analyses concerning endogenous CYPA (i.e., +/- HU, +/- pre-extraction). We found that endogenous CYPA failed to form foci following HU thereby precluding CYPA-NBS1 foci co-localization analysis (Suppl Fig 7C-D).

Query:

The paper demonstrated that BRCA2 knockdown cells were sensitive to CsA. The authors should also examine CsA sensitivity in BRCA2 deficient cancer cells. In addition, the authors could elaborate more on their criteria for selecting cancers for CYPA inhibition, whether it is based on high genomic instability or an addiction to HRR for survival.

RESPONSE:

Despite repeated attempts we have been unable to successfully routinely culture the TNBC suspension line HCC1599 (BRCA2 c.4154_5572del1419 and p.K1517fs*23), consistent with its reported ~5 days population doubling time. Although not a tumour line per se, we also failed to effectively culture the FANC-D1 patient FB line HSC62 (BRCA2 c.8488-1 G>A (IVS19-1G>A)) to enable survival analysis. We provide new quantification analysis of the CsA survival on the H1299 conditional shBRCA2 line (Fig 11E). Additionally, we include a comprehensive new analysis of cell survival (“fitness”) of a range of breast carcinoma cell lines following PPIA/CYPA-KO, extracted from DepMap Project Score Cancer Gene Dependency portal (https://score.depmap.sanger.ac.uk/), and also specify the BRCA2 status of each line. Interestingly, we find that reduced BRCA2 copy number is more commonly associated with loss of fitness following PPIA/CYPA loss (Suppl Table 5). We also include similar cell line fitness datasets for each of the cancers for whom we demonstrate elevated sensitivity to CYPAi (i.e., Neuroblastoma, Multiple Myeloma and CML) (Suppl Spreadsheet 4). Fascinatingly, PPIA/CYPA loss clearly results in loss of fitness in most of these cancer cell lines. Collectively, these new independent comprehensive datasets support our argument that targeting CYPA in select cancer scenarios shows impact in the preclinical setting and may represent an effective new strategy.

The unifying features of the cancers showing elevated sensitivity to CYPAi are indeed high genomic instability, denoted by elevated RS and hence a dependency upon replication fork protection machinery. This would be consistent with the observed lethality of our CYPA-panel to shBRCA2, siXRCC3 and siRAD51C. The cancers are additionally characterised by aberrantly elevated HRR (i.e. an addiction to/dependency on HRR). This would be consistent with the observed lethality of our CYPA-panel to siCtIP, siRAD52, siXRCC3, and siRAD51C. At the Reviewer’s request we have reinforced and better clarified this point in the section Potential rational applications of CYPA inhibition in select cancers and in the Discussion.

Reviewer #2:

We thank this reviewer for their positive and supportive comments concerning our work; “Authors have quite conclusively explored the interaction between NBS1 and cyclophilinA as well as the putative proline residue important for this interaction.” We appreciate the constructive feedback concerning the range of consequences/impacts of CYPA impairment and we concur with their contention that “This manuscript will have broad interest from groups working on genomic stability, immunology as well as cancer therapy.”; a general view also voiced by Reviewer #1.

We do stress that whilst other prolyl isomerases have previously been linked to DNA repair (e.g., most notably the Parvulin family member PIN1), this is the first time that CYPA has been directly implicated in DNA repair, and the first time CYPA has been shown to directly interact with a known DNA-R protein (i.e. NBS1).

We believe that the comprehensive CYPA-BioID we describe is worthy of report and should serve as a very useful starting point for additional studies concerning CYPA biology, which is undoubtedly complex. The interactome will also function as a useful tool in helping dissect the clinically significant wider biological consequences of CYPA inhibition. Our interactome findings demonstrate that CYPA may influence DNA-R via multiple, and not necessarily mutually exclusive, routes. We do not argue that CYPA’s role in DNA-R is exclusively via NBS1/MRN. This is clearly demonstrated by our validation of CYPA interactions via co-IP with endogenous CYPA with proteins including PCNA, 53BP1, CHAMP1 and ILF2-3 complex (Fig 5). These are completely novel observations that furthermore reinforce the validity and efficacy of our experimental approach in leveraging the CYPA-BioID to provide new biological insight into this druggable prolyl cis-trans isomerase.

Query:

Authors show delayed S-phase transit along with reduced replication speed indicating replication stall. However, authors have not discussed how cyclophilinA might regulate replication (other than hypothesizing regarding altered dynamism of FHA-BRCT). It is conceivable that it could be an indirect effect on cellular metabolism or if authors believe it could be due to direct disruption to core replication machinery or signaling. In this regard, it will be helpful to see if there is shortening of (premature entry) G1 phase and comment on the status of the associated G1/S checkpoint.

RESPONSE:

The reviewer makes a very interesting and astute observation concerning the DNA replication phenotypes we report following CYPA loss and/or inhibition. The bases of these phenotypes are likely multifactorial, and we have revised the associated Discussion text to reflect this. Specifically, we highlight the elevated and unresponsive NBS1 and MDC1 foci seen in the CYPA-KO lines (Fig 9. i.e., persistent protein-DNA complexes) and dependence upon fork protection factors (XRCC3, RAD51C, BRCA2: Fig 11). We also report that a range of DNA replication factors are found in the CYPA-BioID (Fig 5A). Untangling the functional significance of these putative interactions would involve further study. Are they direct/indirect interactors? If direct, are they prolyl isomerase substrates or chaperone clients or regulated by liquid-liquid phase separation (LLPS)? Similarly, the CYPA-BioID throws-up an extensive set of RNA binding factors (Suppl Table 2), many of whom may conceivably contribute to the replication–transcription fork conflicts/collisions under conditions of CYPA-dysfunction. As this is the first comprehensive report of the cellular impacts of CYPA loss and inhibition, we thought it worth reporting the DNA replication associated phenotypes specifically to demonstrate the pleiotropic impact of loss and inhibition of this particular prolyl isomerase, to underscore its significance/importance. Although we have indeed found cell cycle phase transition impairments in our CYPA-KO and CYPA-R55A cells (for both G1-S and G2-M), these constitute additional studies requiring more thorough molecular-mechanistic characterization. We chose to focus on DNA repair for this first manuscript, as the CYPA-NBS1 interaction was the physical relationship for which we have assembled the most detailed and interconnected datasets, to-date. We do intend to pursue the cell cycle work as it too is derived from our CYPA-BioID (Suppl Spreadsheet 1), and we have already validated some of those relevant interactions by CYPA co-IP, but this is very much a work-in-progress. With this manuscript we’re endeavoring to tread a fine line by showcasing a wide range of cellular phenotypes resultant from CYPA loss and inhibition, but then also showing a deeper level of characterisation with at least one relevant interactor known to function in a range of DNA-R pathways wherein we’ve found impairments and dependencies.

Query:

In connection to this, it will also be interesting to see if the ATR/Chk1 signaling axis is intact in CYPA KO cells with or without additional DNA damage compared to WT.

RESPONSE:

At the reviewer’s request we include new data showing that HU-induced ATR-dependent CHK1 phosphorylation is normal in CYPA-KO and CYPA-R55A cells, and that ATR does not appear to be spontaneously activated in the absence of replication stress in these cells (Suppl Fig 1B).

Query:

Authors show that the P112 residue of NBS1 is important for the binding of cyclophilinA. What is the status of interaction among components of the MRN complex in CYPAKO cells and P112G NBS1? Further, what are the authors' thoughts on rescue experiments and whether P112G containing NBS1 to perform resection function.

RESPONSE:

We include new data showing normal expression of MRN components and normal subcellular localisation of NBS1 in the CYPA-KO and CYPA-R55A cells (Suppl Fig 7A-B). Regarding the interaction status of P112G, we show that this fails to co-IP endogenous CYPA when transiently expressed in HEK293 cells, in marked contrast to WT-NBS1 (Fig 8A). Furthermore, we show that ablation of another FHA Pro residue (P64) does not impair co-IP with endogenous CYPA under similar conditions, suggesting P112G is unique in this regard. Our recombinant protein interaction work demonstrates that CYPA-Step directly interacts with a HIS-(FHA-BRCT1) peptide and that P112G abolishes this interaction (Fig 8B). Regarding rescue experiments, we’ve found that stable overexpression of NBS1 can be neomorphic, resulting in resistance to certain DNA damaging agents, thereby complicating cell-based rescue analyses. We stress that along with our engineered KO and R55A (isomerase-dead) lines we have employed the well-known CYPAi Cyclosporin A (CsA) to reproduce several of the DNA-R related phenotypes (e.g., Fig 1, Fig 3, Fig 6, Fig 10, Fig 11). To further examine impacts upon resection specifically, a logical approach would be to engineer P112G into a full-length recombinant (baculoviral produced) human MRN complex for in vitro kinetic assessment using various labelled DNA substrates. But we think that this specialist and not insignificant undertaking is outside the scope of our report of the extensive cellular consequences of CYPA loss and dysfunction and it’s potential (pre)clinical significance with regards CYPAi repurposing.

Query:

What are the protein levels of MRN, RAD51 etc. in CYPAKO cells? It will be important control to delineate the effects of CYPA on global transcription and translation vs specific and direct effect on end-resection. Can overexpression of NBS1 rescue the observed resection and focus phenotypes?

RESPONSE:

Basal levels of RAD51 foci/cell are comparable between Scrm and both CYPA-KO and R55A cells (Fig 6D). We also find comparable levels of MRN components between these lines (Suppl Fig 7A). Importantly, we observe the pRPA/resection defect following an acute (up to 3hrs) treatment with CsA; conditions unlikely to grossly impair translation to an extent that would result in reduced expression of the relevant DNA-R proteins. Furthermore, microarray based transcriptomic analyses of these isogenic lines did not show evidence of a global impact upon transcription following CYPA-KO or R55A, nor was there evidence of reduced expression of any genome stability/DNA-R genes. We did not include this negative data so as to maintain the focus on the functional link with DNA repair.

Reviewer #3: This critically negative review is myopic, unbalanced, self-contradictory and frustratingly mis-represents some of our key findings. The dismissive tone of the text unnecessarily and unprofessionally crosses into the pejorative (“Either evidence is lacking or experiments were not performed in a convincing way”). The stark contrast between this review and the summations of Reviewer #1 and Reviewer #2 serve to highlight this hyper-negative approach.

It is very frustrating that this reviewer describes our findings as “…an interesting story…”, that “…the identification of NBS1 as a novel substrate of CYPA is significant” , that the “..manuscript may provide new insight…”, and that “…the role of CYPA in DNA repair is fairly well described using its inhibitor or KO cells”, and yet then concludes by stating “… the current manuscript suffers lack of evidence to support the main conclusion”. This is self-contradictory and unbalanced. Again, the contrast with Reviewer #1 and Reviewer #2 in this regard is stark.

Major critical theme no. 1.

Expression of CYPA-R55A: “…vastly different…”

RESPONSE.

This reviewer dismisses the entirety of the R55A model cell line work based upon the apparent “…vastly different…” expression levels of the reconstituted lines. This is an overstatement of the situation and notably not an issue for either Reviewer #1 or Reviewer #2. Nonetheless, we have replaced the original CYPA blot in Fig 1C with a clearer and more representative depiction of expression levels between the engineered lines and control. Importantly, the pRPA/resection work, siRAD52 and siXRCC3 dependency work were all corroborated/reproduced using the CYPA PPI inhibitor Cyclopsorine A (CsA). The plurality of our complementary approaches showing the influence of CYPA upon DNA-R is minimised and/or ignored by this Reviewer. Although not shown in this study, we find that the R55A cells are selectively sensitive to DNA cross-linker melphalan, in contrast to the CYPA-KO cells. Although we don’t yet understand the basis of this observation, this clearly indicates that R55A expression is a valid model in our hands and is not a like-for-like mimic of CYPA-KO simply because of reduced expression. We appreciate the reviewer could not know this.

Major critical theme no. 2.

CYPA-NBS1 work: “Another major concern is that the evidence to support that NBS1 is the major substrate of CYPA is lacking since all the experiments were performed with the CYPA mutant or CsA treatment.”

RESPONSE:

We do not claim that NBS1 is ”… the major substrate of CYPA.” . We do not claim that all the DNA-R deficits we have identified are specifically a consequence of impaired NBS1 function. These are misrepresentations of our findings and how we’ve presented and discussed them. This Reviewer ignores our comprehensive CYPA-BioID, and specifically our discussion pertaining to the DNA-R and Replication factors found therein (section entitled ‘CYPA Interacting protein partners’ and Fig 5A). We explicitly discuss the fact that “A recurring theme amongst these CYPA interactors is that all are involved in end-resection” whilst also demonstrating CYPA co-IP with 53BP1, CHAMP1 and ILF2-3 (Fig 5C-E). In the ‘Discussion’ section we describe a “homesostatic role for CYPA in genome stability”, including possible contributions to controlling LLPS of well-known DNA-R factors and the fact that several mitotic, kinetochore, centrosomal and spindle proteins are found in the CYPA-BioID.

Major critical theme no. 3.

A major repeated criticism levelled by this reviewer as a basis for dismissing the entirety our findings is that we have failed to demonstrate that the catalytic activity of CYPA is required for DSB repair.

• Their conclusion should be supported by additional key experiments to prove that the catalytic activity of CYPA is indeed required for DSB repair…

• Another major concern is that the evidence to support that NBS1 is the major substrate of CYPA is lacking since all the experiments were performed with the CYPA mutant or CsA treatment.

• One major weakness of this study is that it focuses on characterizing the interaction between CYPA and NBS1, then jumps into a conclusion that the catalytic activity of CYPA is required for DSB repair based on its direct interaction with NBS1

RESPONSE:

As this criticism is repeated, the impression created, and no doubt intended, is that the manuscript is irreparably flawed (“…major weakness…”). This is an over-simplification and a misdirection. It’s notable that this critique isn’t raised in such a manner by either Reviewer #1 or Reviewer #2. This is likely because any modest inferences we made concerning the possible role of CYPA catalytic isomerase activity were based on a combination of differing but complementary approaches. Firstly, we routinely used the p.R55A engineered CYPA variant, although this Reviewer regards our use of this as invalid. This longstanding peptidyl prolyl isomerase (PPI)-dead mutant model has frequently been employed to invoke the catalytic function of CYPA. The mutant was originally proposed and characterized as catalytically-dead using the in vitro chymotrypsin-coupled prolyl isomerase assay using N-succinyl-AAPF-p-nitroanilide as a substrate as far back as 1992 (Zydowsky, L.D., et al., Active site mutants of human cyclophilin A separate peptidyl-prolyl isomerase activity from cyclosporin A binding and calcineurin inhibition. Protein Science, 1992. 1(9): p.1092-1099). In addition, we routinely use Cyclopsorin A (CsA), the longstanding clinically relevant CYPA PPI inhibitor, and we also use a different and more potent CYPA PPI inhibitor, namely NIM811 (N-methyl-4-isoleucine-cyclosporine) for the DR-GFP reporter assays of individual DNA-R pathway function (i.e.’ NHEJ, HRR and SSA).

With regards to our findings concerning CYPA-NBS1 interaction, in the Discussion section we clearly state that mechanistic analyses of prolyl isomerase on the dynamism of NBS1 FHA-BRCT would require specialist approaches outside the scope of this manuscript, as the manuscript is firmly within the realm of cellular biology. This is ignored by this Reviewer. Specifically, we state that “A regulated cis-trans isomerisation of the E111-P112 peptide bond could conceivably dynamically alter the relative positioning of the FHA domain with the tandem BRCTs of NBS1 (Fig 7C-D). This may then impact on these domains’ abilities to dynamically interact with their respective phospho-threonine (for FHA) and phospho-serine (BRCT) containing targets, consequently likely shaping/impacting NBS1 recruitment dynamics and/or plasticity of its interactome [120-122]. Demonstrating this hypothesis would require additional structural analysis using techniques such as 2D-NMR which is outside the scope of this manuscript.”

Minor comments: 1.

Fig. 1E; is the survival between KO and R55A statistically significant? If so, do the authors have an explanation? Why is the reconstitution of R55A more toxic than KO alone?

RESPONSE:

Yes, R55A is slightly more sensitive compared to KO for this endpoint. The irony that this observation runs contrary to the Reviewer’s dismissal of the R55A model line is not lost on us (Major critical theme no. 1). As is well-known for PARP1, inhibition is not equivalent to absence. A possible speculative explanation is that the R55A isomerase-dead could have additional dominant impacts compared to the KO situation. Nonetheless, we suspect this Reviewer would object to such speculation in the absence of ever more data.

Minor comments: 2.

In Fig. 3D, the NHEJ activity of CsA- or NIM811-treated cells is significantly downregulated in comparison to control, which raises the possibility of the pleiotropic effect of CYPA inhibition. The authors should discuss this issue.

RESPONSE:

Not necessarily indicative of a pleiotropic effect if one accepts that absence of a protein is not always biologically equivalent to the presence of an inhibited version the same protein. Of note, we do see somewhat reduced NHEJ following siCYPA (Fig 3A), something not mentioned by this Reviewer. Furthermore, we explicitly discuss and show interaction between CYPA and 53BP1, CHAMP1 and ILF2-3 complex, all players in NHEJ and in the intricate balance between NHEJ and resection-mediated recombination directed repair pathways.

Minor comments: 3.

In Figure 8A, since the expressions of Flag-NBS1 WT, P112G, and P64G are very different, the conclusion that the isomerization of CYPA is essential for NBS1 cannot be supported. Given the variation of input levels, it appears that the P64G mutation actually enhances the interaction with endogenous CYPA. Is this reproducible? This co-IP result may need to be quantified from independent sets for statistical analysis.

RESPONSE:

We do not claim that “…isomerization of CYPA is essential for NBS1…”. Fig 8A data is derived from a transient transfection. Whilst there is some variation in expression, we do not make any precise quantitative conclusions from these co-IPs. Nonetheless, FLAG-NBS1-P112G clearly interacts less with endogenous CYPA in this system. Importantly, and ignored by this Reviewer, the associated recombinant protein work shown in Fig 8B clearly confirms that NBS1-P112G is profoundly compromised in its ability to interact with CYPA.

Minor comments: 4.

A defect in DSB repair generally hypersensitizes cells to DNA replication stress, including HU. In this regard, resistance of the CYPA KO (or R55A cells) to HU is interesting, but it may be due to the nonspecific effect of the CYPA loss in multiple DNA damage signaling and repair processes. Alternatively, cell cycle may be affected nonspecifically, rendering cells resistant to replication-associated genotoxic stress. This needs to be addressed further. Analysis of overall cell cycle profile may be required.

RESPONSE:

Resistance to HU is likely multifactorial and cell cycle transition kinetics may be relevant here. That is why we linked the DNA replications phenotypes to this discussion in the section entitled “Impaired CYPA function reveals novel genetic dependencies/vulnerabilities”. A comprehensive analysis of cell cycle profile and phase transits is outside the scope of the current manuscript (see response to Reviewer #2).<br /> Impaired HU-induced pRPA has been linked to HU-resistance via NBS1 previously: Manthey, K.C., et al., NBS1 mediates ATR-dependent RPA hyperphosphorylation following replication-fork stall and collapse. J Cell Sci, 2007. 120(Pt 23): p. 4221-9.

Minor comments: 5.

Text not to mention Abstract is too dense. The manuscript will benefit a lot from extensive editing and rearrangement of figures to make the story more succinct for journal submission.

RESPONSE:

The Reviewer’s view concerning a lack of succinctness is not shared by Reviewer #1 and Reviewer #2. We have endeavored to draft a concise and accessible manuscript, the main body of which comes in at just over 23x sides of A4 (including Materials & Methods). Considering we guide the reader through 12x multipart figures, 5x supplementary tables and 8x supplementary figure, we believe we have achieved succinctness. Nonetheless, we will of course take direction from the appropriate journal editorial team regarding house style and format.

Authors’ response (11 February 2024)

GENERAL ASSESSMENT

Ionotropic glutamate receptors mediate the large majority of excitatory synaptic transmission in the brain. These receptors consist of four classes: AMPA, kainate, NMDA and delta receptors. NMDA receptors are obligate tetramers composed of two GluN1 and two GluN2 (or GluN3) subunits. Compared to other iGluRs, they have the particularity of requiring two different agonists for their channel to open: glycine binding on GluN1 and glutamate on GluN2.

Seljeset et al. investigate the molecular determinants controlling ligand potency and NMDAR activity at the level of the ligand-binding domains (LBDs), where the agonists bind. They identify a specific position, D732, whose mutation to either leucine or phenylalanine leads to a constitutively active GluN1 subunit, and thus to NMDARs activated solely by glutamate. This aspartate is well known in the field, since it is a highly conserved, signature residue in iGluRs that binds amino acid ligands, together with an arginine in the LBD upper lobe. Surprisingly, although glycine cannot further activate GluN1-D732L/GluN2Awt receptors, glycine site antagonists like 5,7-DCKA or CGP-78608 can still bind and inhibit NMDAR activity. This study is therefore very intriguing, as it raises new questions about something that was previously thought to be understood. By using a combination of unnatural amino acids and conventional mutagenesis, the authors propose that D732 contributes to glycine-mediated effects by changing local interactions with nearby residues. In addition, they show that this behavior is specific for the GluN1 subunit, since mutation of the equivalent aspartate in the GluN2 subunit does not yield constitutively activated GluN2 subunits. Finally, the authors identify a homomeric iGluR from the placozoan Trichoplax adhaerens, Trichoplax AKDF¹⁹³⁸³, in which this conserved aspartate is replaced by a tyrosine. When expressed in Xenopus oocytes, the channel shows constitutive activity. Mutation of the tyrosine into an aspartate, to convert Trichoplax AKDF¹⁹³⁸³ into a “classical” iGluR, decreases Trichoplax AKDF¹⁹³⁸³ constitutive current and allows this channel to be activated by glycine and D-serine. Interestingly, an adjacent residue that is a serine in most mammalian subunits is also a tyrosine in Trichoplax AKDF¹⁹³⁸³, and mutation of both tyrosines yields a glutamate-gated ion channel comparable to mammalian receptors. All of this suggests that the nature of the residue at position 732 influences not only ligand binding but also channel gating.

The study is technically sound, with appropriate controls, and uncovers intriguing properties of a position in GluN1 LBD at which specific side chain mutations can lock the subunit in an active state. Investigation of Trichoplast iGluR further reinforces these findings. This study should lead to a better understanding of how LBDs prime channel opening in iGluRs in the absence of agonists. In addition, co-agonist insensitive GluN1-D732L containing NMDARs could be used as tools to investigate the physiological consequences of NMDAR regulation by their co-agonist site. In contrast to previously engineered NMDARs activated solely by glutamate, which rely on the LBD being locked in its active state by cysteine bridges (Blanke and VanDongen, J Biol Chem 2008), GluN1-D37L/GluN2A NMDARs remain druggable (i.e. they can still be inhibited by glycine-site competitive antagonists). This is a great advantage when investigating the function of these receptors in a native context. The study identifies a few gaps that remain in our mechanistic understanding of D732’s role in channel gating. Particularly, it is unclear how subtle modification of residue side chains at position D732 lead to such drastic changes in function and why these effects are specific to GluN1 LBD. Also, why does mutation of D732 into isoleucine lead to a constitutively active GluN1 subunit, while mutation of a closely related leucine residue prevents activation of the receptor by glycine? The idea of a “hydrophobic plug” formed by D732L or D732F sidechains leading to constitutive activation would benefit from further validation since other hydrophobic substitutions (A, V, I, Y, and W) do not produce similar effects. Finally, it would be interesting to carry out further investigations of the role of the interaction between D732 and Q536 in open conformation stability. Thus, this paper puts forth interesting questions that could be addressed by future studies, for example molecular dynamics simulations and exploration of the LBD free energy landscapes (as in Yao et al., Structure 2013), to understand the impact of the GluN1-D732L mutation on GluN1 LBD conformational mobility.

RECOMMENDATIONS

Essential revisions:

1. Page 2, “These data show that essentially all substitutions at the GluN1-732 position decrease glycine potency, but leucine and phenylalanine substitutions also remove the requirement for glycine co-agonism in GluN1/GluN2A NMDA receptors”: One other hypothesis for the lack of glycine dependence of GluN1-D732I and D732Y + GluN2A receptors could be that the mutated receptors have a glycine potency so high that GluN1 LBD is already saturated by contaminating, ambient glycine. At this point in the paper, the authors cannot distinguish between one hypothesis or the other, therefore we suggest that this sentence be rephrased. Later in the text, control experiments with GluN1-R523K mutations that kill glycine binding and competition with 5,7-DCKA show that glycine-independent activation of GluN1-D732L/GluN2A mutants is not due to constitutive occupancy of GluN1 LBD by contaminating glycine.

ER1) We have now changed this to (page 4): “These data show that most substitutions at the GluN1-732 position decrease glycine potency, but leucine and phenylalanine substitutions alter GluN1 activity in such a way that leads to single-mutant NMDA receptors activated solely by glutamate.”

1. Does glycine insensitivity in GluN1-D732L/GluN2A NMDARs reflect a constitutively active GluN1 subunit or is this subunit locked in another conformational state that cannot be further modified by glycine? This could be answered by estimating the maximum open probability of GluN1-D732L/GluN2A NMDARs compared to their wt counterparts. To estimate Po, the authors could measure the kinetics of NMDA receptor current inhibition by MK801 (the slower MK801 inhibition, the lower the Po; see Chen et al., J. Neurosci 1999; Blanke and VanDongen, JBC 2008) in the presence of saturating agonist concentrations (100 μM Glu, 100 μM Gly for wt and only 100 μM Glu for mutant).

ER2) We have now assessed the rate of MK-801 block in glutamate-gated mutant and glycine + glutamate-gated WT receptors, and reshuffled text/figures, as this ties in well with ER4) below. MK-801 results now in Figure 3 on page 6, and main text on page 5: “In order to understand whether the glycine-insensitive GluN1-D732L subunit is in a constantly activated state or occupies a different conformation that may reflect an alternative to typical channel gating, we compared the kinetics of WT receptor and GluN1-D732L-containing receptor inhibition by the open-channel blocker MK-801, which can be used to evaluate maximum open probability of NMDARs ^26,30. We observed very similar kinetics of inhibition of WT and mutant receptors (Fig. 3A), indicating similar open probability in solely glutamate-gated GluN1-D732L-containing receptors and glutamate and glycine-gated WT receptors. This reflects unchanged maximum open probability in solely glutamate-gated NMDARs with disulfide-locked GluN1 LBDs assayed by single channel recordings ²⁷. This suggests that the GluN1-D732L subunit is in a constantly activated state.”

When viewed alongside high sensitivity of mutant subunits to DCKA - OS1) below - it’s difficult to conclude what sort of active state the mutant subunit adopts. We’ve assessed the best we can at the moment, and in this paper we’ll have to leave it at “here is the observation; here is some evidence ruling out various possibilities; and here is a receptor from another family that shows something remarkably consistent”. Future studies will have to establish exactly what state the mutant subunit adopts.

1. Page 4: The term “hydrophobic plug” is not fully justified since other hydrophobic residues do not lock GluN1 LBD in its active state.

ER3) We have replaced nearly all use of this term, in the title and in the main text, to e.g. “certain hydrophobic substitutions” or “L/F substitutions”.

1. Figure 2, redox sensitivity of GluN1-D732L/GluN2Awt: It would be helpful to explain the point of this experiment – maybe to investigate if the D732L mutation has an impact on the receptor gate rather than on the LBD? In any case, the authors should investigate the effect of DTT on the activity of wt GluN1/GluN2A receptors to determine whether there is an absence of an effect of the D732L mutant on redox sensitivity.

ER4) Indeed we were curious if D732L affected the gate via this allosteric route, rather than by just altering LBD conformation. And we have now shown the effect of DTT on WT receptors.

In addition to re-writing to better explain the point, as suggested, we have also re-written to follow on from new data/text on the whether the D732L mutation affects LBD, gating, etc: “We next questioned if D732L/F substitutions affect channel gating, rather than simply altering the LBD conformation. The gating machinery is complex, but it includes the peptide segment linking the C-terminal end of the LBD to membrane-spanning helix 4 (LBD-M4 linker, (11)). The LBD and LBD-M4 linker are confined by a C744—C798 disulfide, just four helical turns after D732, whose disruption by reduction enhances channel gating (28)). We considered that if the D732L/F substitution is coupled to channel gating via this route, then removal of the C744—C798 disulfide via the C744A mutation might alter glutamate-gated currents in GluN1-D732L-containing receptors. Alternatively, the typical enhancement by the reducing agent dithiothreitol (DTT) might differ in GluN1-D732L compared to WT receptors.”

And new Figure 3 now includes DTT effects on WT receptors.

1. Page 6: The authors find that mutation of Q536 decreases glycine potency and conclude there is an interaction between D732 and Q536. However, the effects of D732 and Q536 mutations could be independent, therefore the authors should consider mutating both residues together to look at the additive/non-additive effects of the mutations. Or perhaps, note in the Discussion that some sort of mutant cycle analysis or molecular dynamics simulation would be needed to rigorously test these ideas.

ER5) We have now made and tested a double mutant combining D732E and Q536N and performed mutant cycle analysis.

(We also tried to do this for Q536 side chain (regular mutations) and A734 main chain (non-canonical substitutions), but double mutants involving non-canonical amino acids at A734 were not successful – Figure S1.)

As is now shown in Figure 4D, the effects of the mutations are decidedly non-additive, yielding an Ω value of 0.05, corresponding to a reasonably high energetic coupling of ~7 kJ/mol. We have now added to the relevant section of the Results on page 8: “If an interaction between Q536 and D732 were energetically important for receptor activation, the effects of their mutations should be non-additive ³¹. We therefore tested glycine potency at double-mutant GluN1-Q536N/D732E-containing receptors and observed non-additive changes in EC₅₀, with a strong coupling value, Ω, of 0.05 (Fig. 4D). This deviation of Ω from unity, corresponding to an interaction energy of 7.4 kJ/mol is relatively high ³¹, confirming that Q536 and D732 are energetically coupled. We tried to analyse energetic coupling between Q536 and A734 via double mutants incorporating nonsense suppression at the A734 position, but unfortunately, attempts to incorporate Aah into such double mutants via nonsense suppression were unsuccessful (Fig. S1B).”

1. Page 6, “A hydrophobic plug does not cause constitutive activity in all NMDA receptor subtypes”: This title is misleading as it raises the expectation that the effect of GluN1-D732L has been investigated in the context of GluN1/GluN2A, GluN1/GluN2B, etc NMDARs. Instead, the equivalent mutation is made in the GluN2 subunit. We suggest using the word “subunit” rather than “subtype”.

ER6) We have changed this Results section title (page 8) to: “L/F substitutions do not cause constitutive activity in all NMDA receptor subunits”

1. Page 7, effect of GluN1-D732L in the context of GluN1/GluN3 NMDARs: We would not expect current to be observed with GluN1-D732L/GluN3 NMDARs, since locking GluN1 LBD in its active state desensitizes the receptors. The effect of the D732L mutation seems therefore conserved between GluN1/GluN2 and GluN1/GluN3 NMDARs. In addition, when using CGP, please cite Grand et al., Nat. Commun. 2018 since they were the first to use CGP as a tool to record GluN1/GluN3 currents.

ER7) We have now cited that paper specifically here (page 8) and inserted the following (page 8/9): “While this seems like inactivity of the mutant GluN1 subunit in GluN1(4a)/GluN3A, it could yet reflect the activity of constitutively active mutant GluN1 subunits in GluN1/GluN2A receptors, as GluN1 activity in GluN1/GluN3A receptors is known to cause more desensitization than activation (Grand et al 2018).”

1. Figure 5C: It is stated in the text that the aspartate position is “highly” conserved. However, no actual number or percentages are given for this statement. How does it compare to the residues in the highly conserved SYTANLAAF motif or other conserved positions? This sort of analysis does not need to be done for the entire receptor, but perhaps for glycine and glutamate binding residues and SYTANLAAF motif, to give a quantitative feel for statements about conservation. In addition, what other types of residues occupy this position in other species? And what was the number of species/subunits included in the analysis?

ER8) To clarify the level of conservation, we have added Table 1 (page 10) listing the % conservation of amino acids at selected positions.

In analyzing % conservation, we noticed that several iGluR sequences with gaps in the ligand-binding domain or channel-forming helices had escaped our filtering out incomplete sequences in our phylogenetic analysis. We therefore revisited our phylogenetic analysis, removed several incomplete sequences, and replaced Crassostrea gigas (a mollusc spiralian) iGluR sequences with Schmidtea mediterranea (a flatworm spiralian) sequences. This (1) means less sequences with gaps in the ligand-binding domain in our alignment/tree and (2) better covers the diversity of the lineage Spiralia now that we have sequences of Lingula anatina and Schmidtea mediterranea, which are more distantly related than Lingula anatina and Crassosttrea gigas (Laumer et al 2019, PMID:31690235; Marlétaz et al., 2019, PMID:30639106).

The result is a phylogenetic and amino acid sequence analysis of 204 iGluR genes (previous version had 212 genes) with the same overall topology as the previous version, including lambda, NDMA, epsilon, and AKDF iGluR families (Fig. 5B, page 9).

The number of subunits/genes used is stated in the Figure legend. The number of and reasoning behind the number of species used is described under Methods, Bioinformatic analyses: in exploring the conservation of the D732 residue, we have not tried to use as many iGluR sequences as possible; rather we have tried to assess this residue in a broad sample covering all (animal) iGluR families and from a careful selection of different animal lineages, while also avoiding fast-evolving species like Drosophila, which complicate tree topology. Hence our description of “two ctenophores, one poriferan, etc” under Methods, Bioinformatic analyses. In the main text (Results, page 9), we retain our original description: “We assembled diverse iGluR sequences, covering all animal lineages and animal iGluR families (Fig. 6A,B)…”

1. Figure 5, panel F: From what we understand, the authors created dose-response curves for wt Trichoplast AKDF¹⁹³⁸⁶³ based on steady-state currents and for Y742D/Y743S mutants based on peak currents. If this is the case, one cannot compare the two dose-response curves since peak current potentiation and steady-state inhibition likely reflect different conformational transitions.

ER9) We acknowledge this issue and that we can’t really say that ligand-activated D742 channels bind D-serine better than ligand-deactivated Y742 channels. But we think it’s fair to point out that mutant D742 channels react (by conducting current) to micromolar ligand concentrations whereas wildtype Y742 channels react (with decreased current) only to millimolar concentrations, and we have re-written to acknowledge the issue raised for this comparison (page 11): “Finally, we tried to assess whether position 742 determines ligand potency in addition to channel activity in AKDF¹⁹³⁸³ receptors. For these experiments we employed D-serine, as recovery from glycine-induced deactivation (Fig. 6C, far-left) and activation/desensitization (Fig. 6C, far-right) was very slow. Substantial deactivation of WT receptors was only induced by millimolar D-serine concentrations, whereas Y742D-containing mutants were activated by micromolar concentrations (Fig. 6D,E), with an EC₅₀ of 490 ± 120 µM at Y742D/Y743S (n = 4; Y742D EC₅₀ not assessed due to slow recovery from desensitization). Our measure of potency is confounded by the fact that deactivation (in WT channels) and activation (in mutant channels) are presumably coupled to D-serine binding via different conformational transitions. Nonetheless, we observe that a naturally occurring large hydrophobic side chain at the top of the β-strand preceding the αI helix leads to an AKDF homo-tetramer that shows constitutive activity and responds only to millimolar concentrations of D-serine. In contrast, “re-introducing” an aspartate to this position reinstates more typical ligand-dependent activation and sensitivity to micromolar concentrations of D-serine.”

Optional suggestions:

1. Figure 2, glycine/DCKA competition: It is difficult to understand how a GluN1 LBD-locked closed (active state) could still bind DCKA. If the open-to-close equilibrium of GluN1 LBD is displaced towards its closed state, then DCKA Ki should be shifted to the right compared to wt receptors. Additionally, DCKA inhibition kinetics should be slower if DCKA needs to “wait” for rare resting-like conformational changes to bind. Did the authors investigate DCKA potency and inhibition kinetics?

OS1) We have now investigated DCKA potency. DCKA capably inhibits GluN1-D732L/GluN2A-WT activity, and perhaps surprisingly, potency of DCKA at the mutant is greater than at wildtype. We suspect this is due to (1) the introduction of a hydrophobic leucine residue right next to an aryl group of DCKA, increasing DCKA affinity directly, (2) the absence of glycine binding to this site, so no need for competition, and (3) potentially other mechanisms such as cooperativity between subunits. Again, establishing the precise nature of our mutant LBD conformation here is for future structural and molecular dynamics studies. But we have described the results, along with our following interpretation, (page 4): “Whether increased DCKA potency in GluN1-D732L subunits derives from the now non-competitive nature of the inhibition in mutant receptors or from the introduction of a favourable hydrophobic interaction with the dichlorobenzene moiety of the inhibitor is unclear. But the high DCKA potency would suggest that the constitutively active GluN1-D732L subunit is, unexpectedly, not due to a permanently clamshell-closed LBD in the mutant. This may reflect the fact that extent of LBD closure is poorly correlated with agonist efficacy in GluN1 subunits, in contrast to AMPA receptor GluA2 subunits ²¹.”

1. The authors show in many panels that GluN1/GluN2A currents desensitize (e.g. Fig.1B, 3C, 4A). In Xenopus oocytes, NMDAR currents do not normally desensitize. We fear this desensitization might stem from contamination of the NMDA current by calcium-activated chloride channels, which can be activated by high quantities of barium when large NMDAR currents are measured. To avoid this problem, we advise that NMDA currents above 2 µA are avoided.

OS2) We have moved forward presuming that potential changes in current amplitude due to a small chloride flux doesn’t affect our measures of potency or ligand-selectivity. But in our new experiments, we’ve especially tried to avoid large currents.

1. Page 5, investigation of D732 state-dependent interactions: Mutation of residues near D732 to unnatural amino acids to replace the peptidic NH do not bring much information about the mechanisms of D732 action. The fact that the 734Aah and 735Vah cannot mimic the effect of the D732L mutation could be due to many factors, including the fact that changing the peptide bond probably changes the local structure of the LBD. Perhaps mention this in the discussion.

OS3) We have now acknowledged this possibility in the Results, right after we describe the decrease in glycine potency caused by the 734Aah mutation (page 7): “Although this may be due to local conformational changes due to altered main chain structure,…”

1. It is intriguing that the D732L mutation locks an active conformation of the GluN1 subunit but not the GluN2 subunit, suggesting two different mechanisms of LBD closure by glutamate and glycine. It would be interesting to look at the effect of the equivalent mutation on the GluN3 subunit to investigate if this locking effect is specific to glycine-binding LBDs or just to the GluN1 subunit.

OS4) We have now made and tested mutant GluN3A subunits D485L and D485F. Simply decreases glycine activity altogether (reflecting the effects of the mutations in GluN2A). Described on page 9: “Similarly, at oocytes injected with GluN1(4a)-WT and GluN3A-D845L or -D845F mRNAs, we saw no response to glycine alone or glycine in the presence of CGP 78608 (Fig 5D). Together, these results indicate that the induction of a constitutively active state by the D732L/F substitution is an exclusive feature of the GluN1 subunit, and the only conserved feature of the mutation in different subunits is a decrease in agonist potency.”

1. Page 9: Discussing the position of residue side chains from structures with 4 Å resolution does not seem relevant and would benefit from a caveat.

OS5) We want to retain our comparison of experiments with available structural data, so we have kept this but re-written to more openly acknowledge the caveat (page 12): “Indeed, in a cryo-electron microscopy (cryo-EM) study of GluN1/GluN2B receptors, D732 has only swung toward the ligand and away from A734 in a second of two putative pre-gating step structural models, although this is speculative considering the poor resolution of D732 side chains in those cryo-EM maps (12).”

1. Page 10: We don’t understand the point that the authors want to make with the activation of Aplysia californica. Please clarify.

OS6) He we were trying to say that “not much is required to change NMDARs from requisite co-agonism to single-ligand agonism”, either (a) in the lab via the D732L mutation or (b) naturally, as invertebrate NMDA receptors apparently show single-ligand agonism (results on invertebrate NDMARs in the literature). Further, we want to say that “by extension, we wonder if (c) in certain physiological situations, vertebrate NMDARs might indeed need only a single ligand.” We acknowledge this was unclear and – although it’s still speculative – we have now changed to (page 13): “Our work shows that only small changes in the GluN1 LBD are required for solely glutamate-gated currents in vertebrate GluN1/GluN2 receptors, and previous work suggests that invertebrate Drosophila melanogaster and Aplysia californica GluN1/GluN2 receptors can be activated by single ligands ^50,51. This suggests that NMDA receptors’ requirement of co-agonism is easily alleviated by certain mutations or conditions. As iGluR-modulatory proteins vary across cell types or even across neuronal compartments ^52,53 and NMDA receptor sequence varies across animals, it is foreseeable that in certain physiological settings, certain NMDA receptors might be activated by glutamate alone. But in most settings, certainly in vertebrates, it seems that glutamate-induced activation of NMDA receptors relies on a system of ambient glycine or D-serine ^54,55.”

1. In iGluRs, constitutive currents are often induced by mutations in the gate region, near the SYTANLAAF motif (e.g. lurcher mutations). Does the sequence around the gate of Trichoplast AKDF¹⁹³⁸⁶³ predict channel constitutive activity?

OS7) Our results with WT, single mutant Y742D, and double mutant Y742D/Y743S Trichoplax AKDF¹⁹³⁸³ receptors already show convincing evidence that the constitutive activity is via the Y742 and Y743 position: the tyrosine residues are unique to this leaky channel, and their mutation to more typical residues removes the leak current (Fig. 7B, page 11, revised manuscript).

But a look at upper M3 is warranted. As shown in Fig. 6C, AKDF¹⁹³⁸³ (YTANMAAFL) is quite similar to typical iGluRs (e.g. GluA2 YTANLAAFL). But one might ask about the single M/L difference in that motif, and we have therefore made and tested the M657L AKDF¹⁹³⁸³ mutant, comparing it with WT. Results show that this small M3 difference has little effect on channel activity. We have added this data in new Figure 7D and described it (page 11): “As channel activity of iGluRs also relies on the upper segment of the third membrane-spanning helix (M3, (34)), we also examined this segment in AKDF¹⁹³⁸³. AKDF¹⁹³⁸³ differs only subtly from most iGluRs with a methionine residue (M657) instead of leucine here (Fig. 6C), but we tested potential effects of this difference by mutating M657 to leucine. M657L activity was much like WT (Fig. 7D), however, confirming that divergence at Y742/Y743 and not the upper M3 segment determines the unique activity of AKDF¹⁹³⁸³.”

1. D-serine is another co-agonist that binds the GluN1 subunit. Compared to glycine, D-serine can make additional interactions with the lower lobe of GluN1 LBD. It would be interesting to look at D-serine dose-response curves in GluN1-D732L/GluN2A receptors: are these receptors also D-serine insensitive or can they be further activated by D-serine?

OS8) We have now measured the effects of D-serine on GluN1-D732L/GluN2A-WT receptors. As we now show in Figure 1B (green symbols), D-serine at increasing concentrations (100 nM through 100 μM) activates no additional current on top of the glutamate-gated current in mutant receptors. We have added to the end of the first Results paragraph (page 3): “Similarly, large currents were activated in mutant GluN1-D732L/GluN2A-WT receptors when 100 nM through 100 μM D-Serine was applied the presence of 100 µM glutamate (green in Fig. 1B).”

(This is a response to peer review conducted by Biophysics Colab on version 1 of this preprint.)

In this passage, the Son rises with the sun — on the dawning of the third morning of battle, the Son of god goes forth on a great chariot, inspired by the vision of Ezekiel, flaming, rife with precious stones, and moving on pure spirit, ‘wheel[s] within wheel[s]’ [6.750-9]. So great is his divine power that he restores the heavenly battlefield to its former beauty, but the rebel forces refuse to give in and rally once more. The narrator questions how such spirits, former denizens of heaven, could be so foolish as to not quail before the might of the Divine. In the next line [791] the narrator offers an explanation: the rebel angels recognize the sight as awe-inspiring and of great power, but rather than being cowed, they covet it, and draw strength from their envy and their wish to be in that position. Spurned by this decidedly impious motivation, they face their foes again, as the Son speaks to his own forces.

The passage highlights once more how the discontent among Satan’s forces rallies them and spurs them onward, just as Satan himself believed his ‘merit’ to be offended when he was fell. It’s also a passage, however, that uses pagan and mythological elements to enhance the godliness of the Son, rather than the perverseness of the devil. Milton uses the goddess Victory, or Nike, as a consort of the Son [762-3], and he carries a bow and quiver of thunderbolts [763-4], which evoke images of Jupiter or Zeus.

In this passage, Raphael continues his story of Abdiel, who has denounced Satan and returned to Heaven and God. God gives Abdiel his approval for bearing "Universal reproach, far worse to beare / Then violence” [6.34-5]—it’s interesting that the scorn of the faithless is somehow considered more terrible than their physical harm. In the same vein, God assures him that he is now left an easier task — joining the heavenly host to meet Satan’s forces in battle. He appoints Michael and Gabriel heads of His army and adds that their forces are ‘equal in number’ to their enemy. I suppose this is an example of God’s justice, though it seems again to add risk to the endeavor that would not otherwise be necessary if God just decided to destroy the evil altogether. He seems to believe, however, that the Heavenly host will not have any trouble in forcing Hell’s forces back into ‘Tartarus’ (a Greek mythological, and therefore pagan, turn for the fiery corner of punishment in the Underworld).

This passage also includes God making a common link with Satan’s thinking — God mentions ‘the Messiah’, his son, as a power in heaven through merit. Satan is frequently preoccupied with his own ‘merit’, which he states in Book I has been offended by his fall.

(#22). H9 (Savannah)--Can we go over why the coefficients on smoke for Models 2, 3, and 4 in Table 1 (Q8) are the same?

Response: Yes, it’s because all three are estimating the within effect of smoking.

(Georgina) If we are thinking about the estimated effects on birth weight of smoking during pregnancy as a within-mother effect, how exactly does this work? Are we only looking at moms who had multiple pregnancies? Does the within effect always matter?

Response: It’s just mathematical, in the sense that if there is variation in the smoking behavior across pregnancies for the same mother, then that variable will have within-person variation--it is a fixed effects (“within”) estimate. Only moms with multiple pregnancies will be part of the within estimate. The within effect doesn’t always matter---in the case of an effect driven entirely by a unobserved heterogeneity (i.e., an unobserved factor U_i) the within-effect could be 0 and the between effect could be large (in either direction).

Author Response

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

eLife assessment

This study uses a multi-pronged empirical and theoretical approach to advance our understanding of how differences in learning relate to differences in the ways that male versus female animals cope with urban environments, and more generally how reversal learning may benefit animals in urban habitats. The work makes an important contribution and parts of the data and analyses are solid, although several of the main claims are only partially supported or overstated and require additional support.

Public Reviews:

We thank the Editor and both Reviewers for their time and for their constructive evaluation of our manuscript. We worked to address each comment and suggestion offered by the Reviewers in our revision—please see our point-by-point responses below.

Reviewer #1 (Public Review):

Summary:

In this highly ambitious paper, Breen and Deffner used a multi-pronged approach to generate novel insights on how differences between male and female birds in their learning strategies might relate to patterns of invasion and spread into new geographic and urban areas.

The empirical results, drawn from data available in online archives, showed that while males and females are similar in their initial efficiency of learning a standard color-food association (e.g., color X = food; color Y = no food) scenario when the associations are switched (now, color Y = food, X= no food), males are more efficient than females at adjusting to the new situation (i.e., faster at 'reversal learning'). Clearly, if animals live in an unstable world, where associations between cues (e.g., color) and what is good versus bad might change unpredictably, it is important to be good at reversal learning. In these grackles, males tend to disperse into new areas before females. It is thus fascinating that males appear to be better than females at reversal learning. Importantly, to gain a better understanding of underlying learning mechanisms, the authors use a Bayesian learning model to assess the relative role of two mechanisms (each governed by a single parameter) that might contribute to differences in learning. They find that what they term 'risk sensitive' learning is the key to explaining the differences in reversal learning. Males tend to exhibit higher risk sensitivity which explains their faster reversal learning. The authors then tested the validity of their empirical results by running agent-based simulations where 10,000 computersimulated 'birds' were asked to make feeding choices using the learning parameters estimated from real birds. Perhaps not surprisingly, the computer birds exhibited learning patterns that were strikingly similar to the real birds. Finally, the authors ran evolutionary algorithms that simulate evolution by natural selection where the key traits that can evolve are the two learning parameters. They find that under conditions that might be common in urban environments, high-risk sensitivity is indeed favored.

Strengths:

The paper addresses a critically important issue in the modern world. Clearly, some organisms (some species, some individuals) are adjusting well and thriving in the modern, human-altered world, while others are doing poorly. Understanding how organisms cope with human-induced environmental change, and why some are particularly good at adjusting to change is thus an important question.

The comparison of male versus female reversal learning across three populations that differ in years since they were first invaded by grackles is one of few, perhaps the first in any species, to address this important issue experimentally.

Using a combination of experimental results, statistical simulations, and evolutionary modeling is a powerful method for elucidating novel insights.

Thank you—we are delighted to receive this positive feedback, especially regarding the inferential power of our analytical approach.

Weaknesses:

The match between the broader conceptual background involving range expansion, urbanization, and sex-biased dispersal and learning, and the actual comparison of three urban populations along a range expansion gradient was somewhat confusing. The fact that three populations were compared along a range expansion gradient implies an expectation that they might differ because they are at very different points in a range expansion. Indeed, the predicted differences between males and females are largely couched in terms of population differences based on their 'location' along the rangeexpansion gradient. However, the fact that they are all urban areas suggests that one might not expect the populations to differ. In addition, the evolutionary model suggests that all animals, male or female, living in urban environments (that the authors suggest are stable but unpredictable) should exhibit high-risk sensitivity. Given that all grackles, male and female, in all populations, are both living in urban environments and likely come from an urban background, should males and females differ in their learning behavior? Clarification would be useful.

Thank you for highlighting a gap in clarity in our conceptual framework. To answer the Reviewer’s question—yes, even with this shared urban ‘history’, it seems plausible that males and females could differ in their learning. For example, irrespective of population membership, such sex differences could come about via differential reliance on learning strategies mediated by an interaction between grackles’ polygynous mating system and malebiased dispersal system, as we discuss in L254–265 (now L295–306). Population membership might, in turn, differentially moderate the magnitude of any such sex-effect since an edge population, even though urban, could still pose novel challenges—for example, by requiring grackles to learn novel daily temporal foraging patterns such as when and where garbage is collected (grackles appear to track this food resource: Rodrigo et al. 2021 [DOI: 10.1101/2021.06.14.448443]). We now introduce this important conceptual information— please see L89–96.

Reinforcement learning mechanisms:

Although the authors' title, abstract, and conclusions emphasize the importance of variation in 'risk sensitivity', most readers in this field will very possibly misunderstand what this means biologically. Both the authors' use of the term 'risk sensitivity' and their statistical methods for measuring this concept have potential problems.

Please see our below responses concerning our risk-sensitivity term.

First, most behavioral ecologists think of risk as predation risk which is not considered in this paper. Secondarily, some might think of risk as uncertainty. Here, as discussed in more detail below, the 'risk sensitivity' parameter basically influences how strongly an option's attractiveness affects the animal's choice of that option. They say that this is in line with foraging theory (Stephens and Krebs 2019) where sensitivity means seeking higher expected payoffs based on prior experience. To me, this sounds like 'reward sensitivity', but not what most think of as 'risk sensitivity'. This problem can be easily fixed by changing the name of the term.

We apologise for not clearly introducing the field of risk-sensitive foraging, which focuses on how animals evaluate and choose between distinct food options, and how such foraging decisions are influenced by pay-off variance i.e., risk associated with alternative foraging options (seminal reviews: Bateson 2002 [DOI: 10.1079/PNS2002181]; Kacelnik & Bateson 1996 [DOI: 10.1093/ICB/36.4.402]). We have added this information to our manuscript in L494–497. We further apologise for not clearly explaining how our lambda parameter estimates such risk-sensitive foraging. To do so here, we need to consider our Bayesian reinforcement learning model in full. This model uses observed choice-behaviour during reinforcement learning to infer our phi (information-updating) and lambda (risksensitivity) learning parameters. Thus, payoffs incurred through choice simultaneously influence estimation of each learning parameter—that is, in a sense, they are both sensitive to rewards. But phi and lambda differentially direct any reward sensitivity back on choicebehaviour due to their distinct definitions. Glossing over the mathematics, for phi, stronger reward sensitivity (bigger phi values) means faster internal updating about stimulus-reward pairings, which translates behaviourally into faster learning about ‘what to choose’. For lambda, stronger reward sensitivity (bigger lambda values) means stronger internal determinism about seeking the non-risk foraging option (i.e., the one with the higher expected payoffs based on prior experience), which translates behaviourally into less choice-option switching i.e., ‘playing it safe’. We hope this information, which we have incorporated into our revised manuscript (please see L153–161), clarifies the rationale and mechanics of our reinforcement learning model, and why lamba measures risk-sensitivity.

In addition, however, the parameter does not measure sensitivity to rewards per se - rewards are not in equation 2. As noted above, instead, equation 2 addresses the sensitivity of choice to the attraction score which can be sensitive to rewards, though in complex ways depending on the updating parameter. Second, equations 1 and 2 involve one specific assumption about how sensitivity to rewards vs. to attraction influences the probability of choosing an option. In essence, the authors split the translation from rewards to behavioral choices into 2 steps. Step 1 is how strongly rewards influence an option's attractiveness and step 2 is how strongly attractiveness influences the actual choice to use that option. The equation for step 1 is linear whereas the equation for step 2 has an exponential component. Whether a relationship is linear or exponential can clearly have a major effect on how parameter values influence outcomes. Is there a justification for the form of these equations? The analyses suggest that the exponential component provides a better explanation than the linear component for the difference between males and females in the sequence of choices made by birds, but translating that to the concepts of information updating versus reward sensitivity is unclear. As noted above, the authors' equation for reward sensitivity does not actually include rewards explicitly, but instead only responds to rewards if the rewards influence attraction scores. The more strongly recent rewards drive an update of attraction scores, the more strongly they also influence food choices. While this is intuitively reasonable, I am skeptical about the authors' biological/cognitive conclusions that are couched in terms of words (updating rate and risk sensitivity) that readers will likely interpret as concepts that, in my view, do not actually concur with what the models and analyses address.

To answer the Reviewer’s question—yes, these equations are very much standard and the canonical way of analysing individual reinforcement learning (see: Ch. 15.2 in Computational Modeling of Cognition and Behavior by Farrell & Lewandowsky 2018 [DOI: 10.1017/CBO9781316272503]; McElreath et al. 2008 [DOI: 10.1098/rstb/2008/0131]; Reinforcement Learning by Sutton & Barto 2018). To provide a “justification for the form of these equations'', equation 1 describes a convex combination of previous values and recent payoffs. Latent values are updated as a linear combination of both factors, there is no simple linear mapping between payoffs and behaviour as suggested by the reviewer. Equation 2 describes the standard softmax link function. It converts a vector of real numbers (here latent values) into a simplex vector (i.e., a vector summing to 1) which represents the probabilities of different outcomes. Similar to the logit link in logistic regression, the softmax simply maps the model space of latent values onto the outcome space of choice probabilities which enter the categorial likelihood distribution. We can appreciate how we did not make this clear in our manuscript by not highlighting the standard nature of our analytical approach—we now do so in our revised manuscript (please see L148–149). As far as what our reinforcement learning model measures, and how it relates cognition and behaviour, please see our previous response.

To emphasize, while the authors imply that their analyses separate the updating rate from 'risk sensitivity', both the 'updating parameter' and the 'risk sensitivity' parameter influence both the strength of updating and the sensitivity to reward payoffs in the sense of altering the tendency to prefer an option based on recent experience with payoffs. As noted in the previous paragraph, the main difference between the two parameters is whether they relate to behaviour linearly versus with an exponential component.

Please see our two earlier responses on the mechanics of our reinforcement learning model.

Overall, while the statistical analyses based on equations (1) and (2) seem to have identified something interesting about two steps underlying learning patterns, to maximize the valuable conceptual impact that these analyses have for the field, more thinking is required to better understand the biological meaning of how these two parameters relate to observed behaviours, and the 'risk sensitivity' parameter needs to be re-named.

Please see our earlier response to these suggestions.

Agent-based simulations:

The authors estimated two learning parameters based on the behaviour of real birds, and then ran simulations to see whether computer 'birds' that base their choices on those learning parameters return behaviours that, on average, mirror the behaviour of the real birds. This exercise is clearly circular. In old-style, statistical terms, I suppose this means that the R-square of the statistical model is good. A more insightful use of the simulations would be to identify situations where the simulation does not do as well in mirroring behaviour that it is designed to mirror.

Based on the Reviewer’s summary of agent-based forward simulation, we can see we did a poor job explaining the inferential value of this method—we apologise. Agent-based forward simulations are posterior predictions, and they provide insight into the implied model dynamics and overall usefulness of our reinforcement learning model. R-squared calculations are retrodictive, and they say nothing about the causal dynamics of a model. Specifically, agent-based forward simulation allows us to ask—what would a ‘new’ grackle ‘do’, given our reinforcement learning model parameter estimates? It is important to ask this question because, in parameterising our model, we may have overlooked a critical contributing mechanism to grackles’ reinforcement learning. Such an omission is invisible in the raw parameter estimates; it is only betrayed by the parameters in actu. Agent-based forward simulation is ‘designed’ to facilitate this call to action—not to mirror behavioural results. The simulation has no apriori ‘opinion’ about computer ‘birds’ behavioural outcomes; rather, it simply assigns these agents random phi and lambda draws (whilst maintaining their correlation structure), and tracks their reinforcement learning. The exercise only appears circular if no critical contributing mechanism(s) went overlooked—in this case computer ‘birds’ should behave similar to real birds. A disparate mapping between computer ‘birds’ and real birds, however, would mean more work is needed with respect to model parameterisation that captures the causal, mechanistic dynamics behind real birds’ reinforcement learning (for an example of this happening in the human reinforcement learning literature, see Deffner et al. 2020 [DOI: 10.1098/rsos.200734]). In sum, agent-based forward simulation does not access goodness-of-fit—we assessed the fit of our model apriori in our preregistration (https://osf.io/v3wxb)—but it does assess whether one did a comprehensive job of uncovering the mechanistic basis of target behaviour(s). We have worked to make the above points on the method and the insight afforded by agent-based forward simulation explicitly clear in our revision—please see L192–207 and L534–537.

Reviewer #2 (Public Review):

Summary:

The study is titled "Leading an urban invasion: risk-sensitive learning is a winning strategy", and consists of three different parts. First, the authors analyse data on initial and reversal learning in Grackles confronted with a foraging task, derived from three populations labeled as "core", "middle" and "edge" in relation to the invasion front. The suggested difference between study populations does not surface, but the authors do find moderate support for a difference between male and female individuals. Secondly, the authors confirm that the proposed mechanism can actually generate patterns such as those observed in the Grackle data. In the third part, the authors present an evolutionary model, in which they show that learning strategies as observed in male Grackles do evolve in what they regard as conditions present in urban environments.

Strengths:

The manuscript's strength is that it combines real learning data collected across different populations of the Great-tailed grackle (Quiscalus mexicanus) with theoretical approaches to better understand the processes with which grackles learn and how such learning processes might be advantageous during range expansion. Furthermore, the authors also take sex into account revealing that males, the dispersing sex, show moderately better reversal learning through higher reward-payoff sensitivity. I also find it refreshing to see that the authors took the time to preregister their study to improve transparency, especially regarding data analysis.

Thank you—we are pleased to receive this positive evaluation, particularly concerning our efforts to improve scientific transparency via our study’s preregistration (https://osf.io/v3wxb).

Weaknesses:

One major weakness of this manuscript is the fact that the authors are working with quite low sample sizes when we look at the different populations of edge (11 males & 8 females), middle (4 males & 4 females), and core (17 males & 5 females) expansion range. Although I think that when all populations are pooled together, the sample size is sufficient to answer the questions regarding sex differences in learning performance and which learning processes might be used by grackles but insufficient when taking the different populations into account.

In Bayesian statistics, there is no strict lower limit of required sample size as the inferences do not rely on asymptotic assumptions. With inferences remaining valid in principle, low sample size will of course be reflected in rather uncertain posterior estimates. We note all of our multilevel models use partial pooling on individuals (the random-effects structure), which is a regularisation technique that generally reduces the inference constraint imposed by a low sample size (see Ch. 13 in Statistical Rethinking by Richard McElreath [PDF: https://bit.ly/3RXCy8c]). We further note that, in our study preregistration (https://osf.io/v3wxb), we formally tested our reinforcement learning model for different effect sizes of sex on learning for both target parameters (phi and lambda) across populations, using a similarly modest N (edge: 10 M, 5 F; middle: 22 M, 5 F ; core: 3 M, 4 F) to our actual final N, that we anticipated to be our final N at that time. This apriori analysis shows our reinforcement learning model: (i) detects sex differences in phi values >= 0.03 and lambda values >= 1; and (ii) infers a null effect for phi values < 0.03 and lambda values < 1 i.e., very weak simulated sex differences (see Figure 4 in https://osf.io/v3wxb). Thus, both of these points together highlight how our reinforcement learning model allows us to say that across-population null results are not just due to small sample size. Nevertheless the Reviewer is not wrong to wonder whether a bigger N might change our population-level results (it might; so might muchneeded population replicates—see L310), but our Bayesian models still allow us to learn a lot from our current data. We now explain this in our revised manuscript—please see L452–457.

Another weakness of this manuscript is that it does not set up the background well in the introduction. Firstly, are grackles urban dwellers in their natural range and expand by colonising urban habitats because they are adapted to it? The introduction also fails to mention why urban habitats are special and why we expect them to be more challenging for animals to inhabit. If we consider that one of their main questions is related to how learning processes might help individuals deal with a challenging urban habitat, then this should be properly introduced.

In L74–75 (previously L53–56) we introduce that the estimated historical niche of grackles is urban environments, and that shifts in habitat breadth—e.g., moving into more arid, agricultural environments—is the estimated driver of their rapid North American colonisation. We hope this included information sufficiently answers the Reviewer’s question. We have worked towards flushing out how urban-imposed challenges faced by grackles, such as the wildlife management efforts introduced in L64–65 (now L85–86), may apply to animals inhabiting urban environments more broadly; for example, we now include an entire paragraph in our Introduction detailing how urban environments may be characterised differently to nonurban environments, and thus why they are perhaps more challenging for animals to inhabit— please see L56–71.

Also, the authors provide a single example of how learning can differ between populations from more urban and more natural habitats. The authors also label the urban dwellers as the invaders, which might be the case for grackles but is not necessarily true for other species, such as the Indian rock agama in the example which are native to the area of study. Also, the authors need to be aware that only male lizards were tested in this study. I suggest being a bit more clear about what has been found across different studies looking at: (1) differences across individuals from invasive and native populations of invasive species and (2) differences across individuals from natural and urban populations.

We apologise for not including more examples of such learning differences. We now include three examples (please see L43–49), and we are careful to call attention to the fact that these data cover both resident urban and non-urban species as well as urban invasive species (please see L49–50). We also revised our labelling of the lizard species (please see L44). We are aware only male lizards were tested but this information is not relevant to substantiating our use of this study; that is, to highlight that learning can differ between urbandwelling and non-urban counterparts. We hope the changes we did make to our manuscript satisfy the Reviewer’s general suggestion to add biological clarity.

Finally, the introduction is very much written with regard to the interaction between learning and dispersal, i.e. the 'invasion front' theme. The authors lay out four predictions, the most important of which is No. 4: "Such sex-mediated differences in learning to be more pronounced in grackles living at the edge, rather than the intermediate and/or core region of their range." The authors, however, never return to this prediction, at least not in a transparent way that clearly pronounces this pattern not being found. The model looking at the evolution of risk-sensitive learning in urban environments is based on the assumption that urban and natural environments "differ along two key ecological axes: environmental stability 𝑢 (How often does optimal behaviour change?) and environmental stochasticity 𝑠 (How often does optimal behaviour fail to pay off?). Urban environments are generally characterised as both stable (lower 𝑢) and stochastic (higher 𝑠)". Even though it is generally assumed that urban environments differ from natural environments the authors' assumption is just one way of looking at the differences which have generally not been confirmed and are highly debated. Additionally, it is not clear how this result relates to the rest of the paper: The three populations are distinguished according to their relation to the invasion front, not with respect to a gradient of urbanization, and further do not show a meaningful difference in learning behaviour possibly due to low sample sizes as mentioned above.

Thank you for highlighting a gap in our reporting clarity. We now take care to transparently report our null result regarding our fourth prediction; more specifically, that we did not detect credible population-level differences in grackles’ learning (please see L130). Regarding our evolutionary model, we agree with the Reviewer that this analysis is only one way of looking at the interaction between learning phenotype and apparent urban environmental characteristics. Indeed, in L282–288 (now L325–329) we state: “Admittedly, our evolutionary model is not a complete representation of urban ecology dynamics. Relevant factors—e.g., spatial dynamics and realistic life histories—are missed out. These omissions are tactical ones. Our evolutionary model solely focuses on the response of reinforcement learning parameters to two core urban-like (or not) environmental statistics, providing a baseline for future study to build on”. But we can see now that ‘core’ is too strong a word, and instead ‘supposed’, ‘purported’ or ‘theorised’ would be more accurate—we have revised our wording throughout our manuscript to say as much (please see, for example, L24; L56; L328). We also further highlight the preliminary nature of our evolutionary model, in terms of allowing a narrow but useful first-look at urban eco-evolutionary dynamics—please see L228–232. Finally, we now detail the theorised characteristics of urban environments in our Introduction (rather than in our Results; please see L56–71), and we hope that by doing so, how our evolutionary results relate to the rest of our paper is now better set up and clear.

In conclusion, the manuscript was well written and for the most part easy to follow. The format of eLife having the results before the methods makes it a bit harder to follow because the reader is not fully aware of the methods at the time the results are presented. It would, therefore, be important to more clearly delineate the different parts and purposes. Is this article about the interaction between urban invasion, dispersal, and learning? Or about the correct identification of learning mechanisms? Or about how learning mechanisms evolve in urban and natural environments? Maybe this article can harbor all three, but the borders need to be clear. The authors need to be transparent about what has and especially what has not been found, and be careful to not overstate their case.

Thank you, we are pleased to read that the Reviewer found our manuscript to be generally digestible. We have worked to add further clarity, and to tempter our tone (please see our above and below responses).

Reviewer #1 (Recommendations For The Authors):

Several of the results are based on CIs that overlap zero. Tone these down somewhat.

We apologise for overstating our results, which we have worked to tone down in our revision. For instance, in L185–186 we now differentiate between estimates that did or did not overlap zero (please also see our response to Reviewer 2 on this tonal change). We note we do not report confidence intervals (i.e., the range of values expected to contain the true estimate if one redoes the study/analysis many times). Rather, we report 89% highest posterior density intervals (i.e., the most likely values of our parameters over this range). We have added this definition in L459, to improve clarity.

The literature review suggesting that urban environments are more unpredictable is not convincing. Yes, they have more noise and light pollution and more cars and planes, but does this actually relate to the unpredictability of getting a food reward when you choose an option that usually yields rewards?

To answer the Reviewer’s question—yes. But we can see that by not including empirical examples from the literature, we did a poor job of arguing such links. In L43–49 we now give three empirical examples; more specifically, we state: “[...] experimental data show the more variable are traffic noise and pedestrian presence, the more negative are such human-driven effects on birds' sleep (Grunst et al., 2021), mating (Blickley et al., 2012), and foraging behaviour (Fernández-Juricic, 2000).” We note we now detail such apparently stable but stochastic urban environmental characteristics in our Introduction rather than our Results section, to hopefully improve the clarity of our manuscript (please see L56–71). We further note that we cite three literature reviews—not one—suggesting urban environments are stable in certain characteristics and more unpredictable in others (please see L59–60). Finally, we appreciate such characterisation is not certain, and so in our revision we have qualified all writing about this potential dynamic with words such as “apparent”, “supposed”, “theorised”, “hypothesised” etc.

It would be interesting to see if other individual traits besides sex affect their learning/reversal learning ability and/or their learning parameters. Do you have data on age, size, condition, or personality? Or, the habitat where they were captured?

We do not have these data. But we agree with the Reviewer that examining the potential influence of such covariates on grackles’ reinforcement learning would be interesting in future study, especially habitat characteristics (please see L306–309).

For most levels of environmental noise, there appears to be an intermediate maximum for the relationship between environmental stability and the risk sensitivity parameter. What does this mean?

There is indeed an intermediate maximum for certain values of environmental stochasticity (although the differences are rather small). The most plausible reason for this is that for very stable environments, simulated birds essentially always “know” the rewarded solution and never need to “relearn” behaviour. In this case, differences in latent values will tend to be large (because they consistently get rewarded for the same option), and different lambda values (in the upper range) will produce the same choice behaviour, which results in very weak selection. While in very unstable environments, optimal choice behaviour should be more exploratory, allowing learners to track frequently-changing environments. We now note this pattern in L240–248.

Reviewer #2 (Recommendations For The Authors):

L2: I'd encourage the authors to reconsider the term "risk-sensitive learning", at least in the title. It's not apparent to me how 'risk' relates to the investigated foraging behaviour. Elsewhere, risk-reward sensitivity is used which may be a better term.

We apologise for not clearly introducing the field of risk-sensitive foraging, which focuses on how animals evaluate and choose between distinct food options, and how such foraging decisions are influenced by pay-off variance i.e., risk associated with alternative foraging options (seminal reviews: Bateson 2002 [DOI: 10.1079/PNS2002181]; Kacelnik & Bateson 1996 [DOI: 10.1093/ICB/36.4.402]). We have added this information to our manuscript in L494–497. In explaining our reinforcement model, we also now detail how risk relates to foraging behaviour. Specifically, in L153–161 we now state: “Both learning parameters capture individual-level internal response to incurred reward-payoffs, but they differentially direct any reward sensitivity back on choice-behaviour due to their distinct definitions (full mathematical details in Materials and methods). For 𝜙, stronger reward sensitivity (bigger values) means faster internal updating about stimulus-reward pairings, which translates behaviourally into faster learning about ‘what to choose’. For 𝜆, stronger reward sensitivity (bigger values) means stronger internal determinism about seeking the nonrisk foraging option (i.e., the one with the higher expected payoffs based on prior experience), which translates behaviourally into less choice-option switching i.e., ‘playing it safe’.” We hope this information clarifies why lamba measures risk-sensitivity, and why we continue to use this term.

L1-3: The title is a bit misleading with regard to the empirical data. From the data, all that can be said is that male grackles relearn faster than females. Any difference between populations actually runs the other way, with the core population exhibiting a larger difference between males and females than the mid and edge populations.

It is customary for a manuscript title to describe the full scope of the study. In our study, we have empirical data, cognitive modelling, and evolutionary simulations of the background theory all together. And together these analytical approaches show: (1) across three populations, male grackles—the dispersing sex in this historically urban-dwelling and currently urban-invading species—outperform female counterparts in reversal learning; (2) they do this via risk-sensitive learning, so they’re more sensitive to relative differences in reward payoffs and choose to stick with the ‘safe’ i.e., rewarding option, rather than continuing to ‘gamble’ on an alternative option; and (3) risk-sensitive learning should be favoured in statistical environments characterised by purported urban dynamics. So, we do not feel our title “Leading an urban invasion: risk-sensitive learning is a winning strategy” is misleading with regard to our empirical data; it just doesn’t summarise only our empirical data. Finally, as we now state in L312–313, we caution against speculating about any between-population variation, as we did not infer any meaningful behavioural or mechanistic population-level differences.

L13: "Assayed", is that correctly put, given that the authors did not collect the data?

Merrian-Webster defines assay as “to analyse” or “examination or determination as to characteristics”, and so to answer the Reviewer’s question—yes, we feel this is correctly put. We note we explicitly introduce in L102–103 that we did not collect the data, and we have an explicit “Data provenance” section in our methods (please see L342–347).

L42-46: The authors provide a single example of how learning can differ between populations from more urban and more natural habitats. I would like to point out that many of these studies do not directly confirm that the ability in question has indeed led to the success of the species tested (e.g. show fitness consequences). Then the authors could combine these insights to form a solid prediction for the grackles. As of now, this looks like cherry-picking supportive literature without considering negative results.

Here are some references that might be helpful in identifying relevant literature to cite:

Szabo, B., Damas-Moreira, I., & Whiting, M. J. (2020). Can cognitive ability give invasive species the means to succeed? A review of the evidence. Frontiers in Ecology and Evolution, 8, 187.

Griffin AS, Tebbich S, Bugnyar T, 2017. Animal cognition in a human-dominated world. Anim Cogn 20(1):1-6.

Kark, S., Iwaniuk, A., Schalimtzek, A., & Banker, E. (2007). Living in the city: Can anyone become an "urban exploiter"? Journal of Biogeography, 34(4), 638-651.

We apologise for not including more examples of such learning differences. We now include three examples (please see L43–49). We are aware that direct evidence of fitness consequences is entirely lacking in the scientific literature on cognition and successful urban invasion; hence why such data is not present in our paper. But we now explicitly point out a role for likely fitness-affecting anthropogenic disturbances on sleep, mate, and foraging behaviour on animals inhabiting urban environments (please see L63–68). We hope these new data bolster our predictions for our grackles. Finally, the Reviewer paints a (in our view) inaccurate picture of our use of available literature. Nevertheless, to address their comment, we now highlight a recent meta-analysis advocating for further research to confirm apparent ‘positive’ trends between animal ‘smarts’ and successful ‘city living’ (please see L43).

L64: Is their niche historically urban, or have they recently moved into urban areas?

In L74–75 (previously L53–56) we introduce that the estimated historical niche of grackles is urban environments, and that shifts in habitat breadth—e.g., moving into more arid, agricultural environments—is the estimated driver of their rapid North American colonisation. We hope this included information sufficiently answers the Reviewer’s question.

L66-67: This is an important point that is however altogether missing from the discussion.

We thank the Reviewer for highlighting a gap in our discussion regarding populationlevel differences in grackles’ reinforcement learning. In L310–312 we now state: “The lack of spatial replicates in the existing data set used herein inherently poses limitations on inference. Nevertheless, the currently available data do not show meaningful population-level behavioural or mechanistic differences in grackles’ reinforcement learning, and we should thus be cautious about speculating on between-population variation”.

L68-71: The paper focuses on cognitive ability. The whole paragraph sets up the prediction of why male grackles should be better learners due to their dispersal behaviour. This example, however, focuses on aggression, not cognition. Here is a study showing differences in learning in male and female mynas that might be better suited:

Federspiel IG, Garland A, Guez D, Bugnyar T, Healy SD, Güntürkün O, Griffin AS, 2017. Adjusting foraging strategies: a comparison of rural and urban common mynas (Acridotheres tristis). Anim Cogn 20(1):65-74.

We thank the Reviewer for suggesting this paper. We feel it is better suited to substantiating our point in the Discussion about reversal learning not being indicative of cognitive ability—please see L276–277.

L73: Generally, I suggest not writing "for the first time" as this is not a valid argument for why a study should be conducted. Furthermore, except for replication studies, most studies investigate questions that are novel and have not been investigated before.

The Reviewer makes a fair point—we have removed this statement.

L80-81: Here again, this is left undiscussed later on.

By ‘this’ we assume the Reviewer is referring to our hypothesis, which is that sex differences in dispersal are related to sex differences in learning in an urban invader— grackles. At the beginning of our Discussion, we state how we found support for this hypothesis (please see L250–261); and in our ‘Ideas and speculation’ section, we discuss how these hypothesis-supporting data fit into the literature more broadly (please see L294–331). We feel this is therefore sufficiently discussed.

L77-81: This sentence is very long and therefore hard to read. I suggest trying to split it into at least 2 separate sentences which would improve readability.

Per the Reviewer’s useful suggestion, we have split this sentence into two separate sentences—please see L97–115.

L83: Please explain choice-option switches. I am not aware of what that is and it should be explained at first mention.

We apologise for this operational oversight. We now include a working definition of speed and choice-option switches at first mention. Specifically, in L107–108 we state: “[...] we expect male and female grackles to differ across at least two reinforcement learning behaviours: speed (trials to criterion) and choice-option switches (times alternating between available stimuli)”.

L83-87: Again, a very long sentence. Please split.

We thank the Reviewer for their suggestion. In this case we feel it is important to not change our sentence structure because we want our prediction statements to match between our manuscript and our preregistration.

L96-97: Important to not overstate this. It merely demonstrates the potential of the proposed (not detected) mechanism to generate the observed data.

As in any empirical analysis, our drawn conclusions depend on causal assumptions about the mechanisms generating behaviour (Pearl, J. (2009). Causality). Therefore, we “detected” specific learning mechanisms assuming a certain generative model, namely reinforcement learning. As there is overwhelming evidence for the widespread importance of value-based decision making and Rescorla-Wagner updating rules across numerous different animals (Sutton & Barto (2018) Reinforcement Learning), we would argue that this assumed model is highly plausible in our case. Still, we changed the text to “inferred” instead of “detected” learning mechanisms to account for this concern—please see L123–124.

L99: "urban-like settings" again a bit confusing. The authors talk about invasion fronts, but now also about an urbanisation gradient. Is the main difference between the size and the date of establishment, or is there additionally a gradient in urbanisation to be considered?

We now include a paragraph in our Introduction detailing apparent urban environmental characteristics (please see 56–71), and we now refer to this dynamic specifically when we define urban-like settings (please see L126–127). To answer the Reviewer’s question—we consider both differences. Specifically, we consider the time since population establishment in our paper (with respect to our behavioural and mechanistic modelling), as well as how statistical environments that vary in how similar they are to apparently characteristically urban-like environments, might favour particular learning phenotypes (with respect to our evolutionary modelling). We hope the edits to our Introduction as a whole now make both of the aims clear.

L11-112: Above the authors talk about a comparable number of switches (10.5/15=0.7), and here of fewer number of switches (25/35=0.71), even though the magnitude of the difference is almost identical and actually runs the other way. The authors are probably misled by their conservative priors, which makes the difference appear greater in the second case than in the first. Using flat priors would avoid this particular issue.

Mathematically, the number of trials-to-finish and the number of choice-optionswitches are both a Poisson distributed outcome with rate λ (we note lambda here is not our risk-sensitivity parameter; just standard notation). As such, our Poisson models infer the rate of these outcomes by sex and phase—not the ratio of these outcomes by sex and phase. So comparing the magnitude of divided medians of choice-option-switches between the sexes by phase is not a meaningful metric with respect to the distribution of our data, as the Reviewer does above. For perspective, 1 vs. 2 switches provides much less information about the difference in rates of a Poisson distribution than 50 vs 100 (for the former, no difference would be inferred; for the latter, it would), but both exhibit a 1:2 ratio. To hopefully prevent any such further confusion, and to focus on the fact that our Poisson models estimate the expected value i.e., the mean, we now report and graph (please see Fig. 2) mean and not median trialsto-finish and total-switch-counts. Finally, we can see that our use of the word “conservative” to describe our weakly informative priors is confusing, because conservative could mean either strong priors with respect to expected effect size (not our parameterisation) or weak priors with respect to such assumptions (our parameterisation). To address this lack of clarity, we now state that we use “weakly informative priors” in L457–458.

L126: It is not clear what risk sensitivity means in the context of these experiments.

Thank you for pointing out our lack of clarity. In L153–161 we now state: “Both learning parameters capture individual-level internal response to incurred reward-payoffs, but they differentially direct any reward sensitivity back on choice-behaviour due to their distinct definitions (full mathematical details in Materials and methods). For 𝜙, stronger reward sensitivity (bigger values) means faster internal updating about stimulus-reward pairings, which translates behaviourally into faster learning about ‘what to choose’. For 𝜆, stronger reward sensitivity (bigger values) means stronger internal determinism about seeking the nonrisk foraging option (i.e., the one with the higher expected payoffs based on prior experience), which translates behaviourally into less choice-option switching i.e., ‘playing it safe’.” We hope this information clarifies what risk sensitivity means and measures, with respect to our behavioural experiments.

L128-129: I find this statement too strong. A plethora of other mechanisms could produce similar patterns, and you cannot exclude these by way of your method. All you can show is whether the mechanism is capable of producing broadly similar outcomes as observed

In describing the inferential value of our reinforcement learning model, we now qualify that the insight provided is of course conditional on the model, which is tonally accurate. Please see L161.

L144: As I have already mentioned above, here is the first time we hear about unpredictability related to urban environments. I suggest clearly explaining in the introduction how urban and natural environments are assumed to be different which leads to animals needing different cognitive abilities to survive in them which should explain why some species thrive and some species die out in urbanised habitats.

Thank you for this suggestion. We now include a paragraph in our Introduction detailing as much—please see L56–71.

L162: "almost entirely above zero" again, this is worded too strongly.

In reporting our lambda across-population 89% HPDI contrasts in L185–186, we now state: “[...] across-population contrasts that lie mostly above zero in initial learning, and entirely above zero in reversal learning”. Our previous wording stated: ““[...] across-population contrasts that lie almost entirely above zero”. The Reviewer was correct to point out that this previous wording was too strong if we considered the contrasts together, as, indeed, we find the range of the contrast in initial learning does minimally overlap zero (L: -0.77; U: 5.61), while the range of the contrast in reversal learning does not (L: 0.14; U: 4.26). This rephrasing is thus tonally accurate.

L178-179: I think it should be said instead that the model accounts well for the observed data.

We have rephrased in line with the Reviewer’s suggestion, now stating in L217–218 that “Such quantitative replication confirms our reinforcement learning model results sufficiently explain our behavioural sex-difference data.”

L188-190: I am not convinced this is a general pattern. It is quite a bold claim that I don't find to be supported by the citations. Why should biotic and abiotic factors differ in how they affect behavioural outcomes? Also, events in urban environments such as weekend/weekday could lead to highly regular optimal behaviour changes.

Please see our response to Reviewer 1 on this point. We note we now touch on such regular events in L94–96.

L209-211: The first sentence is misleading. The authors have found that males and females differ in 'risk sensitivity', that their learning model can fit the data rather well, and that under certain, not necessarily realistic assumptions, the male learning type is favoured by natural selection in urban environments. A difference between core, middle, and edge habitats however is barely found, and in fact seems to run the other way than expected.

In our study, we found: (1) across three populations, male grackles—the dispersing sex in this historically urban-dwelling and currently urban-invading species—outperform female counterparts in reversal learning; (2) they do this via risk-sensitive learning, so they’re more sensitive to relative differences in reward payoffs and choose to stick with the ‘safe’ i.e., rewarding option, rather than continuing to ‘gamble’ on an alternative option; (3) we are sufficiently certain risk-sensitive learning generates our sex-difference data, as our agentbased forward simulations replicate our behavioural results (not because our model ‘fits’ the data, but because we inferred meaningful mechanistic differences—see our response to Reviewer 1 on this point); and (4) under theorised dynamics of urban environments, natural selection should favour risk-sensitive learning. We therefore do not feel it is misleading to say that we mapped a full pathway from behaviour to mechanisms through to selection and adaptation. Again, as we now state in L311–313, we caution against speculating about any between-population variation, as we did not infer any meaningful behavioural or mechanistic population-level differences. And we note the Reviewer is wrong to assume an interaction between learning, dispersal, and sex requires population-level differences on the outcome scale—please see our discussion on phenotypic plasticity and inherent species trait(s) in L313–324.

L216: "indeed explain" again worded too strongly.

We have tempered our wording. Specifically, we now state in L218: “sufficiently explain”. This wording is tonally accurate with respect to the inferential value of agent-based forward simulations—please see L192–207 on this point.

L234: "reward-payoff sensitivity" might be a better term than risk-sensitivity?

Please see our earlier response to this suggestion. We note we have changed this text to state “risk-sensitive learning” rather than “reward-payoff sensitivity”, to hopefully prevent the reader from concluding only our lambda term is sensitive to rewards—a point we now include in L153–154.

L234-237: I think these points may be valuable, but come too much out of the blue. Many readers will not have a detailed knowledge of the experimental assays. It therefore also does not become clear how they measure the wrong thing, what this study does to demonstrate this, or whether a better alternative is presented herein. It almost seems like this should be a separate paper by itself.

We apologise for this lack of context. We now explicitly state in L275 that we are discussing reversal learning assays, to give all readers this knowledge. In doing so, we hope the logic of our argument is now clear: reversal learning assays do not measure behavioural flexibility, whatever that even is. The Reviewer’s suggestion of a separate paper focused on what reversal learning assays actually measure, in terms of mechanism(s), is an interesting one, and we would welcome this discussion. But any such paper should build on the points we make here.

L270-288: Somewhere here the authors have to explain how they have not found differences between populations, or that in so far as they found them, they run against the originally stated hypothesis.

We thank the Reviewer for these suggestions. In L310—313 we now state: “The lack of spatial replicates in the existing data set used herein inherently poses limitations on inference. Nevertheless, the currently available data do not show meaningful population-level behavioural or mechanistic differences in grackles’ reinforcement learning, and we should thus be cautious about speculating on between-population variation”.

L284: should be "missing" not "missed out"

We have made this change.

L290-291: It is unclear what "robust interactive links" were found. A pattern of sexbiased learning was found, which can potentially be attributed to evolutionary pressures in urban environments. An interaction e.g. between learning, dispersal, and sex can only be tentatively suggested (no differences between populations). Also "fully replicable" is a bit misleading. The analysis may be replicable, but the more relevant question of whether the findings are replicable we cannot presently answer.

We apologise for our lack of clarity. By “robust” we mean “across population”, which we now state in L333. We again note the Reviewer is wrong to assume an interaction between learning, dispersal, and sex requires population-level differences on the outcome scale— please see our discussion on phenotypic plasticity and inherent species trait(s) in L313–324. Finally, the Reviewer makes a good point about our analyses but not our findings being replicable. In L334 we now make this distinction by stating “analytically replicable”.

L306-315: I think you have a bit of a sample size issue not so much when populations are pooled but when separated. This might also factor in the fact that you do not really find differences across the populations in your analysis. When we look at the results presented in Figure 2 (and table d), we can see a trend towards males having better risk sensitivity in core (HPDI above 0) and middle populations (HPDI barely crossing 0) but the difference is very small. Especially the results on females are based on the performance of only 8 and 4 females respectively. I suggest making this clear in the manuscript.

In Bayesian statistics, there is no strict lower limit of required sample size as the inferences do not rely on asymptotic assumptions. With inferences remaining valid in principle, low sample size will of course be reflected in rather uncertain posterior estimates. We note all of our multilevel models use partial pooling on individuals (the random-effects structure), which is a regularisation technique that generally reduces the inference constraint imposed by a low sample size (see Ch. 13 in Statistical Rethinking by Richard McElreath [PDF: https://bit.ly/3RXCy8c]). We further note that, in our study preregistration (https://osf.io/v3wxb), we formally tested our reinforcement learning model for different effect sizes of sex on learning for both target parameters (phi and lambda) across populations, using a similarly modest N (edge: 10 M, 5 F; middle: 22 M, 5 F ; core: 3 M, 4 F) to our actual final N, that we anticipated to be our final N at that time. This apriori analysis shows our reinforcement learning model: (i) detects sex differences in phi values >= 0.03 and lambda values >= 1; and (ii) infers a null effect for phi values < 0.03 and lambda values < 1 i.e., very weak simulated sex differences (see Figure 4 in https://osf.io/v3wxb). Thus, both of these points together highlight how our reinforcement learning model allows us to say that across-population null results are not just due to small sample size. Nevertheless the Reviewer is not wrong to wonder whether a bigger N might change our population-level results; it might; so might muchneeded population replicates—see L310. But our Bayesian models still allow us to learn a lot from our current data, and, at present, we infer no meaningful population-level behavioural or mechanistic differences in grackles’ behaviour. To make clear the inferential sufficiency of our analytical approach, we now include some of the above points in our Statistical analyses section in L452–457. Finally, we caution against speculating on any between-population variation, as we now highlight in L311—313 of our Discussion.

Figure 2: I think the authors should rethink their usage of colour in this graph. It is not colour-blind friendly or well-readable when printed in black and white.

We used the yellow (hex code: #fde725) and green (hex code: #5ec962) colours from the viridis package. As outlined in the viridis package vignette (https://cran.rproject.org/web/packages/viridis/index.html), this colour package is “designed to improve graph readability for readers with common forms of color blindness and/or color vision deficiency. The color maps are also perceptually-uniform, both in regular form and also when converted to black-and-white for printing”.

Figure 3B: Could the authors turn around the x-axis and the colour code? It would be easier to read this way.

We appreciate that aesthetic preferences may vary. In this case, we prefer to have the numbers on the x-axis run the standard way i.e., from small to large. We note we did remove the word ‘Key’ from this Figure, in line with the Reviewer’s point about these characteristics not being totally certain.

I also had a look at the preregistration. I do think that there are parts in the preregistration that would be worth adding to the manuscript:

L36-40: This is much easier to read here than in the manuscript.

We changed this text generally in the Introduction in our revision, so we hope the Reviewer will again find this easier to read.

L49-56: This is important information that I would also like to see in the manuscript.

We no longer have confidence in these findings, as our cleaning of only one part of these data revealed considerable experimenter oversight (see ‘Learning criterion’).

L176: Why did you remove the random effect study site from the model? It is not part of the model in the manuscript anymore.

The population variable is part of the RL_Comp_Full.stan model that we used in our manuscript to assess population differences in grackles’ reinforcement learning, the estimates from which we report in Table C and D (please note we never coded this variable as “study cite”). But rather than being specified as a random effect, in our RL_Comp_Full.stan model we index phi and lambda by population as a predictor variable, to explicitly model population-level effects. Please see our code:

https://github.com/alexisbreen/Sex-differences-in-grackles- learning/blob/main/Models/Reinforcement%20learning/RL_Comp_Full.stan

L190-228: I am wondering if the model validation should also be part of the manuscript as well, rather than just being in the preregistration?

We are not sure how the files were presented to the Reviewer for review, but our study preregistration, which includes our model validation, should be part of our manuscript as a supplementary file.

It's interesting how in crowdsourcing and social media, a small group of active contributors often carries the load, while a larger majority tends to lurk or contribute minimally. This dynamic, seen throughout history in various tasks, raises questions about the distribution of effort and specialization in collaborative platforms.

I love this, an unspoken contest to survive the spice. I love spicy foods but I was not built for them, I can relate to the suffering of the author. My roommate can withstand much higher heat that I, but I always make sure to get a taste their food, just in case.

I believe that this is a great tip for anyone to read. This is not only a tip to just read it's a tip to keep in mind throughout your lifetime.

The point of the section "Social Media and Relationships" in this chapter is to provide information about the positive and negative impacts of relationships on social media. With that being said, I'm taking a different perspective with just this one highlighted portion because of the benefits that it provides to education. A previous assignment that we've completed in this course discussed the positive relations of educators using technological connections to enhance student learning. The ability to connect with others online allows us as educators to, for example, Zoom into the workplaces of others in certain professions to engage students in an enhanced learning environment. I understand that this comment doesn't specifically relate to social media, but it's important for educators to remember that connections with others can be used to make an impactful experience upon the learning of students in an appropriate manner.

Author Response

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

We would like to extend our sincere thanks to the editors and reviewers for their time and effort in reviewing our manuscript and offering insightful feedback. We have now completed the revisions, and the following is a summary of the key changes made.

(1) Analysis of myrf-1 and myrf-2 Mutations

A major concern raised was the characterization of the myrf-1(ju1121 G274R) mutation as a loss-of-function and myrf-1(syb1313, 1-700, gfp) as a gain-of-function mutation used in our study. These analyses have been previously detailed in our published papers (Meng, Dev Cell. 2017; Xia, Elife. 2021). In the revised manuscript, we have included a thorough explanation of this information in the introduction and added diagrams (Figure 1D, E) to illustrate the mutants used in this study. A more detailed description is also available in the provisional letter I sent following the receipt of the decision letter.

We have incorporated new analyses of the endogenous lin-4 expression reporter in the myrf-1(ybq6, indel null), myrf-2(ybq42, indel null), and myrf-1(ybq6); myrf-2(ybq42) double mutants (Figure 2C). The results demonstrate complete inactivation of lin-4 expression in the double mutants. The data suggest that myrf-1 predominantly drives lin4 expression, while myrf-2 plays a minor role. This aligns with their roles in synaptic rewiring and is consistent with the observed lack of lin-4 expression in myrf-1(ju1121).

Furthermore, we have included analyses using pan-1(gk142) deletion mutants. PAN-1 is critical for MYRF trafficking to the cell membrane (Xia, Elife, 2021). In the absence of PAN-1, MYRF is trapped in the ER and subsequently degraded. The pan-1 mutants exhibit impaired synaptic rewiring, similar to myrf-1; myrf-2 double mutants, but somehow show larval arrest significantly later than myrf-1 mutants. Notably, lin-4 expression is not activated in pan-1 mutants. (references on the larval arrest phenotypes in pan-1 mutants: Gao G, Dev Biol. 2012 PMID: 22342905; Gissendanner CR, BMC Dev Biol. 2013. PMID: 23682709.)

Overall, these findings provide substantial evidence that MYRF is crucial for activating lin-4 during larval development.

(2) Regarding the Use of maIs134 as a lin-4 Expression Reporter

In response to the concerns raised about the use of the 2.4 kb Plin-4-gfp reporter (maIs134) as an indicator of lin-4 transcription, as detailed in the provisional letter, there is no evidence suggesting that maIs134 is an unsuitable reporter for lin-4 transcription. Recently, Kinney et al. (Dev Cell 2023, PMID: 37643611) showed that the pulse control element (PCE), located approximately 2.8 kb upstream, is not essential for lin-4 expression. Their findings also imply that a 2.4 kb region, encompassing what is referred to as the "short" regulatory region in their paper, contains essential elements required for driving the expression of lin-4. Nevertheless, I acknowledge that using an endogenously tagged reporter would be more ideal. It's important to note that we employed the endogenous expression reporter in our analyses of the myrf-1; myrf-2 double mutants, pan-1 mutants, and in the gain-of-function analysis of myrf-1. The outcomes from these studies corroborate our principal conclusions, reinforcing the validity of using maIs134 in our research context.

(3) Direct Binding of MYRF to the lin-4 Promoter

The technical challenges of MYRF ChIP (Chromatin Immunoprecipitation) have proven to be significant. Consequently, we have decided not to postpone the manuscript revision while awaiting additional results. We have included a section titled 'Limitations of the Study' to acknowledge our current lack of direct evidence for MYRF-1 binding to the endogenous lin-4 promoter. If it aligns better with eLife's format policy, we are open to relocating this paragraph to the discussion section.

(4) Specific Issues

We have provided responses to each specific question following the respective inquiries (see below).

Reviewer #1 (Public Review):

In this work, the authors set out to ask whether the MYRF family of transcription factors, represented by myrf-1 and myrf-2 in C. elegans, have a role in the temporally controlled expression of the miRNA lin-4. The precisely timed onset of lin-4 expression in the late L1 stage is known to be a critical step in the developmental timing ("heterochronic") pathway, allowing worms to move from the L1 to the L2 stage of development. Despite the importance of this step of the pathway, the mechanisms that control the onset of lin-4 expression are not well understood.

Overall, the paper provides convincing evidence that MYRF factors have a role in the regulation of lin-4 expression. However, some of the details of this role remain speculative, and some of the authors' conclusions are not fully supported by the studies shown. These limitations arise from three concerns. First, the authors rely heavily on a transcriptional reporter (maIs134) that is known not to contain all of the regulatory elements relevant for lin-4 expression. Second, the authors use mutant alleles with unusual properties that have not been completely characterized, making a definitive interpretation of the results difficult. Third, some conclusions are drawn from circumstantial or indirect evidence that does not use field-standard methods.

The authors convincingly demonstrate that the cytoplasmic-to-nuclear translocation of MYRF-1 coincides with the activation of lin-4 expression, making MYRF-1 a good candidate for mediating this activation. However, the evidence that MYRF-1 is required for the activation of lin-4 is somewhat incomplete. The authors provide convincing evidence that lin-4 activation fails in animals carrying the unusual mutation myrf1(ju1121), which the authors describe as disrupting both myrf-1 and myrf-2 activity. The concern here is that it is difficult to rule out that ju1121 is not also disrupting the activity of other factors, and it does not disentangle the roles of myrf-1 and myrf-2. Partially alleviating this issue, they also find that expression from the maIs134 reporter is disrupted in putative myrf-1 null alleles, but making inferences from maIs134 about the regulation of endogenous lin-4 is problematic. Helpfully, an endogenous Crisprgenerated lin-4 reporter allele is used in some studies, but only using the ju1121 allele. Together, these findings provide solid evidence that MYRF factors probably do have a role in lin-4 activation, but the exact roles of myrf-1 and myrf-2 remain unclear because of limitations of the unusual ju1121 allele and the use of the maIs134 reporter. The creative use of a conditional myrf-1 alleles (floxed and using the AID system) partially overcomes these concerns, providing strong evidence that myrf-1 acts cellautonomously to regulate lin-4, though again, these key experiments are only carried out with the maIs134 transgene.

A second important question asked by the authors is whether MYRF activity is sufficient to activate lin-4 expression. The authors provide evidence that supports this idea, but this support is somewhat incomplete, because the authors rely partially on the maIs104 array and, more importantly, on mutant alleles of MYRF-1 that they propose are constitutively active but are not completely characterized here.

The authors also approach the question of whether MYRF-1 regulates lin-4 via direct interaction with its promoter. The evidence presented here is consistent with this idea, but it relies on indirect evidence involving genetic interactions between myrf-1 and the presence of multiple copies of the lin-4 promoter, as well as the detection of nuclear foci of MYRF-1::GFP in the presence of multiple copies of the lin-4 promoter. This is not the field-standard approach for testing this kind of hypothesis, and the positive control presented (using the TetR/TetO interaction) is unconvincing. Thus, the evidence here is consistent with the authors' hypothesis, but the studies shown are incomplete and do not represent a rigorous test of this possibility.

Finally, the authors ask whether MYRF factors have a role in the regulation of other miRNAs. The evidence provided (RNAseq experiments, validated by several reporter transgenes) solidly supports this idea, with the provision that it is not completely clear that ju1121 is disrupting only the activity of myrf-1 and myrf-2.

Reviewer #2 (Public Review):

In this manuscript, the authors attempt to examine how the temporal expression of the lin-4 microRNA is transcriptionally regulated. However, the experimental support for some claims is incomplete. The authors repeatedly use the ju1121(G247R) mutation of myrf-1, but more information is required to evaluate their claim that this mutation "abolishes its DNA binding capability but also negatively interferes with its close paralogue MYRF-2". Additionally, in the lin-4 scarlet endogenous transcriptional reporter, the lin-4 sequence is removed. Since lin-4 has been reported to autoregulate, it seems possible that the removal of lin-4 coding sequence could influence reporter expression. Further, concrete evidence for direct lin-4 regulation by MYRF-1 is lacking, as the approaches used are indirect and not standard in the field. Overall, while the aims of the work are mostly achieved, data regarding the direct regulation of lin-4 by MYRF-1 and placing the work into the context of previous related reports is lacking. Because of its very specific focus, this paper reports useful findings on how a single transcription factor family might control the expression of a microRNA.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) p.4 Authors should be cautious about this statement: "Once produced, lin-4 can selfenhance its own transcription by directly interacting with cis-elements in the promoter region[26, 27]." As reference 27 shows, this autoregulation is apparently an artifact of the reporter transgene; lin-4 does not appear to have the same role at the endogenous locus.

The discussion of “auto regulation” has been removed from the introduction.

(2) p.4 please provide a reference: "It is worth noting that external food signals are insufficient to drive lin-4 expression as lin-4 doesn't promptly turn on when animals encounter food."

This statement is derived from a combination of our unpublished observations and personal deductions. Within the sequence of events occurring during L1 development, the initiation of lin-4 expression happens relatively late. Therefore, the original sentence has been revised to “Given that lin-4 expression initiates in late L1, it is reasonable to deduce that merely providing food is inadequate to induce lin-4 expression.”

(3) Please provide more detail about myrf-1(syb1468) and myrf-1(syb1491) - are they likely null alleles? Are the phenotypes recessive? Please show the specific locations of deletions.

Please also refer to our response for the main issues raised. The two alleles under discussion are documented in Xia et al. (eLife, 2021). Both of these alleles are recessive and functionally equivalent to null mutations. Interestingly, all the myrf-1 alleles we have analyzed show recessive characteristics in various phenotypic aspects, including growth, synaptic rewiring, and M-cell division. The precise location of these genetic alterations is visually represented in Figure 1D and E.

(4) Fig 2C: are these animals heterozygous for the lin-4 Crispr reporter? If not, this is a lin-4 null. If lin-4 is required for the maintenance of its own expression, this result might be misleading about the role of myrf-1.

The lin-4 gene is located at Chr II: -0.86, and myrf-1 is positioned at Chr II: +2.98. Both of these alleles are balanced by mIn1. As a result, homozygotes for myrf-1 are also homozygotes for umn84. Regarding the role of lin-4 microRNA in its own transcription, research from Frank Slack’s lab has concluded that lin-4 microRNA does not affect the transcription of the lin-4 gene.

(5) p.8: please provide evidence/citation: "however, this experiment used a short promoter of dpy-7, which is not activated in seam cells..."

The dpy-7 promoter has been extensively utilized for transgene experiments in both Andrew Chisholm’s lab and our own. For reference, the original publication has been cited (PMID: 9121480).

(6) Fig 4B. Do the hypodermal knockout animals arrest at L1/L2?

This specific dual allele does not display arrest at the L1/L2 stages. A comprehensive description of the phenotype related to myrf-1LoxP(ybq98); Pdpy-7-Cre(tmIs1028) has been incorporated into the main text, and corresponding new data have been integrated into Figure 4.

(7) p. 11-13. I suggest that the authors consider making the section "MYRF-1 interacts with lin-4 promoter directly" much more succinct. The unsuccessful gelshift experiments can be explained in 1-2 sentences. The backstory about the weak Daf-C phenotype of the floxed allele is likely to confuse readers who are not experts in the field.

We have omitted the description of the gel shift experiments. However, we chose to retain the explanation of the Daf-C phenotype, simplifying the narrative for clarity. The Daf-C phenotype, which we have thoroughly analyzed, is considered significant. Our current research is exploring how nutrients facilitate the cleavage of myrf-1 on the cell membrane."

(8) Fig. 6G. I see no obvious change in the localization of TetR-RFP with or without the presence of TetO DNA, even though the authors use this as a positive control and claim that it validates the use of this approach to study MYRF-1/lin-4p interaction. What tissue is being imaged here? Hypodermis?

The intensity of fluorescent foci varies across transgenic F1 individuals. To assist with visualization, white arrows have been included in Figure 6G. These arrows highlight the formation of puncta in TetR::tagRFP(ybqSi233) due to the presence of a 7xTetO sequence-containing DNA array (indicated by white arrows), while simultaneously showing the lack of aggregation in GFP::MYRF-1. MYRF-1 is expressed across a broad range of tissues, and our analysis did not focus on any specific tissue type. The nuclei shown in the images are derived from a variety of tissues, including the intestine, epidermis, neurons, and the somatic cells of the reproductive system, as inferred from their morphology.

(9) Fig. 7A. Please consider using a different color scheme for the wt vs mutant data. These colors are too similar to those used for the expression-level heatmap. (Also, it's unclear how the fold-change data are normalized - i.e, fold-change compared to what?)

The color scheme in the clustering heatmap has been revised for enhanced contrast. This heatmap does not simply display raw read counts (TPM) or log2 values, though log2 transformation is part of the math process. If displayed directly, variables with low values can overshadow those with high values in the color representation. Instead, the read count data have undergone a series of transformations, including rlog transformation, size factor normalization, and gene-wise scaling, which leads to a more visually informative display of expression changes. Initially, we utilized a web-based tool (https://www.bioinformatics.com.cn/en) for creating the heatmap. However, due to the lack of detailed documentation on this site, we opted to reanalyze the data using functions from the DESeq2 package in R. This reanalysis enabled us to update the graph along with a revised figure legend, aiming to enhance clarity and comprehension.

(10) p. 14: "Remarkably, 6 out of the 7 up-regulated microRNAs are clustered on one phylogenetic branch" - does this mean upregulated in the mutant compared to WT?

The sentence has been revised to “Notably, 6 of the 7 microRNAs showing increased expression in myrf-1(ju1121) compared to wild type are clustered on a single phylogenetic branch,..”

(11) Fig. 7C: Authors might comment on which tissues show expression of these miRNAs.

A sentence has been added: “The reporter for mir-48 is primarily detected in the pharynx, mir-73 is present in both the pharynx and seam cells, whereas mir-230 is detected in seam cells.”

(12) p. 16: "Our report includes the partial dauer-constitutive phenotype caused by the interaction between the lin- 4 promoter DNA and MYRF-1." - consider rewording this; according to the author's model, it's not the interaction per se that causes the Daf-C phenotype, but rather the sequestration of MYRF-1 (or -2?) by excess lin-4p.

The sentence has been revised to “Our observations suggest that the tandem array of lin-4 promoter DNA may sequester a certain amount of MYRF protein. This sequestration could limit the availability of MYRF, potentially leading to a partial dauerconstitutive phenotype.”

Reviewer #2 (Recommendations For The Authors):

(1) The use of L1 (and not even defining what L1 means) in the abstract is very C. elegans-field specific. Make the writing more accessible to a general audience

This sentence has been revised.

(2) Instead of writing in the context of upregulation and downregulation - I advise using activation/induction and repression instead. e.g. MYFR-1 is necessary for lin-14 induction in late stage L1.

The wording of “upregulation” and “downregulation” has been changed.

(3) We find that lin-4 transcription reporter fails to be upregulated in myrf-1(ju1121) at any viable stages that can be analyzed - should this just say 'fails to be expressed'?

“at any viable stages that can be analyzed” has been removed.

(4) The section starting with this sentence is strange as in the previous section the authors showed that MYRF-1 expressed in muscle or epidermis IS sufficient to drive lin4 expression - 'The next question was whether MYRF-1 is sufficient to drive the upregulation of lin-4.'

The sentence has been updated to reflect our research focus: “Given that both the induction of lin-4 and the cleavage of MYRF at the cell membrane happen within a specific time window, we investigated whether a gain of function in MYRF-1 alone is adequate to modify the onset timing of lin-4.”

(5) This sentence needs modifying "A series of MYRF-1 variants were expressed in HEK cells by transfection, and cell lysis was tested for their binding with 498 bp DNA of the lin-4 promoter." This sentence suggests that cell lysis tests the binding of the protein to DNA which is obviously incorrect.

We have chosen to omit the description of these experiments from our text due to their inconclusive results.

(6) Typographical changes/suggestions to aid clarity:

Introduction: lin-4 and lin-14 are the two that have been studied in details - change to lin4 and lin-14 are the two that have been studied in detail

Results: Write NAA solution in full the first time it is mentioned.

Remove ', a collagen,' when describing the dpy-7 promoter. The authors don't describe what myo-3 encodes so keep this consistent.

Page 14 'itself was upregulated in the mutants.' be more specific. Which mutants?

All four identified places have been appropriately corrected or revised.

Author Response

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

Reviewer #1 (Public Review):

(1.1) This work introduces a new method of imaging the reaction forces generated by small crawling organisms and applies this method to understanding locomotion of Drosophila larva, an important model organism. The force and displacement data generated by this method are a qualitative improvement on what was previously available for studying the larva, improving simultaneously the spatial, temporal, and force resolution, in many cases by an order of magnitude. The resulting images and movies are quite impressive.

We thank the reviewer for their recognition of the achievements our work presents and for their feedback with regard to what they consider our most important findings and the points raised in their review. We will address these points individually below.

(1.2) As it shows the novel application of recent technological innovations, the work would benefit from more detail in the explanation of the new technologies, of the rationales underlying the choice of technology and certain idiosyncratic experimental details, and of the limitations of the various techniques. In the methods, the authors need to be sure to provide sufficient detail that the work can be understood and replicated. The description of the results and the theory of motion developed here focus only on forces generated when the larva pushes against the substrate and ignores the equally strong adhesive forces pulling the larva onto the substrate.

As the reviewer correctly points out, our present work adapts a recently developed set of methods (namely, ERISM and WARP) for use with small soft-bodied animals. The foundational methods have been described in detail in previous publications (refs, 23 and 26). However, upon reflection, we agree that more information can be provided to ensure our work is more accessible and reproducible. We also agree that some additional clarifying information on our approach could be helpful. We have addressed this in the following ways:

(1) We have included a detailed Key Resources table in the methods section to allow for maximum transparency on equipment and reagent sourcing. This can now be found on Pages 16-19.

(2) We have modified the ‘Freely behaving animals force imaging’ section of the Materials and Methods section to include more detailed information on practical aspects of conducting experiments. These changes can be found on page 23-24 (lines 566–567, 571-577).

(3) We have re-ordered the Materials and Methods section, such that microcavity fabrication and microcavity characterisation occur prior to the description of ERISM and WARP experiments - this change should hopefully aid replication. Details regarding the application of a silicone well to the surface of microcavities have also been added (lines 472-474).

(4) We have added additional text in the Introduction and Results (Pages 3-4 and 7, lines 56-86, and 152-153) to explain our rationale for using ERISM/WARP and additional text in the discussion that discusses the potential role(s) of adhesive forces in larval locomotion (Page 12, lines 301307).

(1.3) The substrate applies upward, downward, and horizontal forces on the larva, but only upward and downward forces are measured, and only upward forces are considered in the discussions of "Ground Reactive Forces." An apparent weakness of the WARP technique for the study of locomotion is that it only measures forces perpendicular to the substrate surface ("vertical forces" in Meek et al.), while locomotion requires the generation of forces parallel to the substrate ("horizontal forces"). It should be clarified that only vertical forces are studied and that no direct information is provided about the forces that actually move the larva forward (or about the forces which impede this motion and are also generated by the substrate). Along with this clarification, it would be helpful to include a discussion of other techniques, especially micropillar arrays and traction force microscopy, that directly measure horizontal forces and of why these techniques are inappropriate for the motions studied here.

We attempted to provide a streamlined Introduction in our initial submission and then compared ERISM/WARP to other methods in our discussion. We are happy to provide a brief overview of substrate force measurement methods in the introduction to help set the stage for readers. The Introduction section of our revised manuscript now contains the following comparison of different mechanobiological imaging techniques on pages 3-4 lines 56-86:

‘However, in the field of cellular mechanobiology, many new force measuring techniques have been developed which allow measurement of comparatively small forces from soft structures exhibiting low inertia (15–17) often with relatively high spatial-resolution. Early methods such as atomic force microscopy required the use of laser-entrained silicon probes to make contact with a cell of interest (15). This approach is problematic for studying animal behaviour due to the risk of the laser and probe influencing behaviour. Subsequently, techniques have been developed which allow indirect measurement of substrate interactions. One such approach is Traction Force Microscopy (TFM) in which the displacement of fluorescent markers suspended in a material with known mechanical properties relative to a zero-force reference allows for indirect measurement of horizontally aligned traction forces (17–19). This technique allows for probe-free measurement of forces, but the need to obtain a precise zero-force reference would make time-lapse measurements on behaving animals challenging; further, depending on the version used, it has insufficient temporal resolution for the measurement of forces produced by many behaving animals, despite recent improvements (20). A second approach revolves around the use of micropillar arrays; in this technique, horizontally-aligned traction forces are measured by observing the deflection of pillars made of an elastic material with known mechanical properties. This approach can be limited in spatial resolution and introduces a non-physiological substrate that may influence animal behavior (21,22).

Recently we have introduced a technique named Elastic Resonator Interference Stress Microscopy (ERISM) which allows for the optical mapping of vertically aligned GRFs in the pico and nanonewton ranges with micrometre spatial resolution by monitoring local changes in optical resonances of soft and deformable microcavities. This technique allows reference-free mapping of substrate deformations and calculation of vertically directed GRFs; it has been used to study a range of questions related to exertion of cellular forces (23–25). Until recently, this technique was limited by its low temporal resolution (~10s), making it unsuitable for recording substrate interaction during fast animal movements, but a further development of ERISM known as wavelength alternating resonance pressure microscopy (WARP), has been demonstrated to achieve down to 10 ms temporal resolution (26). Given ERISM/WARP allows for probe-free measurement of vertical ground reaction forces with high spatial and temporal resolution, it becomes an attractive method for animal-scale mechanobiology.’

(1.4) The larvae studied are about 1 mm long and 0.1 mm in cross-section. Their volumes are therefore on order 0.01 microliter, their masses about 0.01 mg, and their weights in the range of 0.1 micronewton. This contrasts with the force reported for a single protpodium of 1 - 7 micronewtons. This is not to say that the force measurements are incorrect. Larvae crawl easily on an inverted surface, showing gravitational forces are smaller than other forces binding the larva to the substrate. The forces measured in this work are also of the same magnitude as the horizontal forces reported by Khare et al. (ref 32) using micropillar arrays.

I suspect that the forces adhering the larva to the substrate are due to the surface tension of a water layer. This would be consistent with the ring of upward stress around the perimeter of the larva visible in S4D, E and in video SV3. The authors remark that upward deflection of the substrate may be due to the Poisson's ratio of the elastomer, but the calibration figure S5 shows that these upward deflections and forces are much smaller than the applied downward force. In any case, there must be a downward force on the larva to balance the measured upward forces and this force must be due to interaction with the substrate. It should be verified that the sum of downward minus upward forces on the gel equals the larva's weight (given the weight is neglible compared to the forces involved, this implies that the upward and downward forces should sum to 0).

We have carefully calculated the forces exerted by protopodia and are confident in the accuracy of our measurements as reported. We further agree with the reviewer’s suggestion that gravitational forces can be largely neglected.

As the reviewer points out, one would expect forces due to upward and downward deflections to cancel when considering the entire system. However, we see indications that the counteracting / balancing force often acts over a much larger area than the acting force, e.g. a sharp indentation by a protopodium might be counteracted by an upward deflection over a 10-20 fold larger radius and hence 100 to 400-fold larger area, thereby reducing the absolute value of the upward deflection at any given pixel surrounding the indentation. This in turn increases error in determining the integrated upward deformation, making it difficult to perform an absolute comparison of acting and counteracting force. Further, recording the entire counteracting force induced deformation would require acquiring data with a prohibitively large field of view.

We agree that in some situations, water surface tension may be adhering animals to the substrate. Importantly, this is a challenge that the animal faces outside the lab in its natural environment of moist rotting fruit and yeast. The intricate force patterns seen in our study in the presence of water surface tension are therefore ecologically relevant. In other situations (e.g. preparing for pupation), larvae are able to stick to dry surfaces, suggesting that other adhesive forces such as mucoid adhesion can also come into play in certain behavioural contexts. A full characterization of the effects of water tension and mucoid adhesion are beyond the scope of this study. However, we have now added a sentence on pages 8 and 12 commenting on these other biomechanical forces at play:

‘We also observed that the animals travel surrounded by a relatively large water droplet (lines 189-190).’

‘We observed that larvae travel surrounded by moisture from a water droplet, which produces a relatively large upwardly directed force in a ring around the animal. The surface tension produced by such a water droplet likely serves a role in adhering the animal to the substrate. However, during forward waves, we found that protopodia detached completely during SwP, suggesting this surface tensionrelated adhesion force can be easily overcome by the behaving animal. (lines 301-307) .’

(1.5) Much of the discussion and the model imply that the sites where the larva exerts downward force on the gel are the sites where horizontal propulsion is generated. This assumption should be justified. Can the authors rule out that the larva 'pulls' itself forward using surface tension instead of 'pushing' itself forward using protopodia?

Determining the exact ‘sites’ where horizontal propulsion is generated is challenging. In our conceptual model, movement is not initiated by protopodia per se, but rather by a constellation of muscle contractions, which act upon the hydrostatic skeleton, which in turn causes visceral pistoning that heaves larvae forward. This is based on previous findings in Ref 31. While there are indeed downward protopodial ‘vaulting’ forces prior to initiation of swing, we propose that the main function of protopodia is not to push the larvae forward, but rather to provide anchoring to counteract opposing forces generated by muscles. We agree that water surface tension could also be sculpting biomechanical interactions; however, a full characterization of how water surface tension shapes larval locomotion is beyond the scope of this study.

Since we have observed larvae move over dry terrain (e.g. glass) without an encasing water bubble, we do not believe that an encasing water bubble is strictly required for locomotion. We have also seen no obvious locomotion related modulations in the pulling forces created by water bubbles encasing larva, which would be expected if animals were somehow using water tension to pull themselves forward. Overall, the most likely explanation is that larvae use a mixture of biomechanical tactics to suit the moment in a given environment. This represents a challenge but also an opportunity for future research.

We have now added additional text in the ‘Functional subdivisions within protopodia’ subsection to discuss these nuances (page 14, lines 382-387):

‘This increased force transmitted into the substrate is unexpected as the forces generated for the initiation of movement should arise from the contraction of the somatic muscles. We propose that the contraction of the musculature responsible for sequestration acts to move haemolymph into the protopodia thus exerting an increased pressure onto the substrate while the contact area decreases as a consequence of the initiation of sequestration.’

and (page 15, lines 398-399):

‘Water surface films appear to facilitate larval locomotion in general but the biomechanical mechanisms by which they do this remain unclear.’

(1.6) More detail should be provided about the methods, their limitations, and the rationale behind certain experimental choices.

We thank the reviewer for this comment. As this significantly overlaps with a point raised earlier, we kindly direct them to our answer to comment #1.2 above.

(1.7) Three techniques are introduced here to study how a crawling larva interacts with the substrate: standard brightfield microscopy of a larva crawling in an agarose capillary, ERISM imaging of an immobilized larva, and WARP imaging of a crawling larva. The authors should make clear why each technique was chosen for a particular study - e.g. could the measurements using brightfield microscopy also be accomplished using WARP? They should also clarify how these techniques relate to and possibly improve on existing techniques for measuring forces organisms exert on a substrate, particularly micropillar arrays and Traction Force Microscopy.

Indeed, each of the three methods used has a specific merit. The brightfield microscopy was selected to track features on the animal’s body and to provide a basic control for the later measurements. However, this technique cannot directly measure the substrate interaction, it only allows inferences to be made from tracked features at the substrate interface. ERISM provides high resolution maps of the indentation induced by the larva; it is also extensively validated for mapping cell forces and the data analysis is robust against defects on the substrate (refs 23, 24 and 25). However, as we explain in the manuscript, ERISM lacks the temporal resolution needed to monitor mechanical activity of behaving larva. Its use was therefore limited to the study of anaesthetised animals. For mapping forces exerted by behaving larva, we used WARP which is a further development of ERISM that offers higher frame rates but at the cost of requiring more extensive calibration (Supplementary Figure S4). The streamlined introduction of the different methods in our original manuscript originates from our attempt to be as concise as possible. However, as state in response to comment #1.2, we agree that additional explanation and discussion will be helpful for readers and that it will helpful to briefly refer to other methods for force mapping. We have now added references to a variety of techniques in the Introduction (Page 3-4, lines 56-86) as stated in a prior response.

(1.8) As written, "(ERISM) (19) and a variant, Wavelength Alternating Resonance Pressure microscopy (WARP) (20) enable optical mapping of GRFs in the nanonewton range with micrometre and millisecond precision..." (lines 53-55) may generate confusion. ERISM as described in this work has a much lower temporal resolution (requires the animal to be still for 5 seconds - lines 474-5); In this work, WARP does not appear to have nanonewton precision (judging by noise on calibration figures) and it is not clear that it has millisecond precision (the camera used and its frame rate should be specified in the methods).

Previous studies have demonstrated the capabilities and limitations of ERISM and WARP. Upon reflection, we agree that our wording here could be more precise. To clarify our claim, we now separate the statements on ERISM and WARP in the introduction as follows (page 4, lines 78-83):

“Until recently, this technique was limited by its low temporal resolution (~10s) making it unsuitable for use in recording substrate interaction during fast animal movements, but a further development of ERISM known as wavelength alternating resonance pressure microscopy (WARP), has been demonstrated to achieve down to 10 ms temporal resolution (26)”

While WARP can achieve comparable force resolution as ERISM when used in a cellular context (c.f. Ref 26), we agree that for the present study, the resolution was in the 10s of nanonewton range, due to the need to use stiffer substrates and larger fields of view.

The camera used in our work was specified in the appropriate subsection of the Materials and Methods (“All WARP and ERISM images were acquired using an Andor Zyla 4.2 sCMOS camera (Andor Technology, Belfast, UK)”). We apologise that the exact frame rate used in our current work was not mentioned in our original manuscript; this has now been added to the ‘Freely behaving animals force imaging’ section of the Materials and Methods (page 23, lines 574-577).

(1.9) It would be helpful to have a discussion of the limits of the techniques presented and tradeoffs that might be involved in overcoming them. For instance, what is the field of view of the WARP microscope, and could it be increased by choosing a lower power objective? What would be required to allow WARP microscopy to measure horizontal forces? Can a crawling larva be imaged over many strides by recentering it in the field of view, or are there only particular regions of the elastomer where a measurement may be made?

We agree with the reviewer that some discussion of the limitations of our technique will allow readers to have a more informed appreciation of what we are capable of measuring using WARP. However, as this is the first work to ever demonstrate such measurements, the limitations and tradeoffs cannot all be known with certainty at the present stage.

To answer your individual questions:

(1) There is a trade-off between numerical aperture and the ability to resolve individual interference fringes. Since our approach to calculate displacement from reflection maps relies upon counting of individual fringe transitions, going to a lower powered objective risks having these fringes blend and thus the identification of the individual transitions becoming impossible. The minimum numerical aperture of the objective will therefore generally depend on the steepness of indentations produced by the animals; the steeper an indentation, the closer the neighbouring fringes and thus the higher the required magnification to resolve them.

(2) From WARP and ERISM data, one can make inferences about horizontal forces, as is described in detail in our earlier publications about ERISM (ref, 23). However, quantitation of horizontal forces at sufficient temporal resolution to allow the investigation of behaving Drosophila larva is currently not possible.

(3) Many strides can indeed be imaged using our technique, however, this comes with additional technical challenges. Whether or not the animal itself can be recentred is an ongoing challenge. We have found that the animals are amenable to recentring themselves within the field of view if chasing an attractive odorant. However, manual recentering using a paintbrush risks destroying the top surface of the soft elastic resonator and recentering the microscope stage would require real-time object tracking which has been outside the scope of this original work, given the other challenging requirements on hardware and optics for obtaining high quality force maps.

To provide more information on limitations of our technique, we have added the following text into the discussion (pages 13-14, lines 356-370).

‘Despite the substantial advances they have provided, the use of WARP and ERISM also brings challenges and has several technical limitations. For example, fabrication of resonators is much more challenging than preparation of the agarose substrates conventionally used for studying locomotion of Drosophila. This problem is compounded by the fragility of the devices owing to the fragility of the thin gold top mirror. This becomes problematic when placing animals onto the microcavities, as often the area local to the initial placement of the animal is damaged by the paintbrush used to move the animals. Further, as a result of the combining of the two wavelengths, the effective framerate of the resultant displacement and stress maps is equal to half of the recorded framerate of the interference maps. To be able to monitor fast movements, recording at very high framerates is therefore necessary which, depending on hardware, might require imaging at reduced image size, but this in turn reduces the number of peristaltic waves that can be recorded before the animal escapes the field of view. A further limitation is that WARP and ERISM are sensitive mainly to forces in the vertical direction; this is complementary to TFM, which is sensitive to forces in horizontal directions. Using WARP in conjunction with high speed TFM (possibly using the tuneable elastomers presented here) could provide a fully integrated picture of underlying vertical and horizontal traction forces during larval locomotion.’ And further on page 13, lines 337-341:

‘More detailed characterisation of this behaviour remains a challenge owing to the changing position of the mouth hooks. Due to their rigid structure and the relatively large forces produced in planting, mouth hooks produce substrate interaction patterns which our technique struggles to map accurately due to overlapping interference fringes ambiguating the fringe transitions.’

We trust that the above discussion and our modifications to our manuscript resulting from these will address the reviewer’s concerns.

Reviewer #2 (Public Review):

(2.1) With a much higher spatiotemporal resolution of ground dynamics than any previous study, the authors uncover new "rules" of locomotory motor sequences during peristalsis and turning behaviors. These new motor sequences will interest the broad neuroscience community that is interested in the mechanisms of locomotion in this highly tractable model. The authors uncover new and intricate patterns of denticle movements and planting that seem to solve the problem of net motion under conditions of force-balance. Simply put, the denticulated "feet" or tail of the Drosophila larva are able to form transient and dynamic anchors that allow other movements to occur.

We thank the reviewer for their feedback and the information regarding which of our results is likely to resonate most impactfully with readers from a biological background.

The biology and dynamics are well-described. The physics is elementary and becomes distracting when occasionally overblown. For example, one doesn't need to invoke Newton's third law, per se, to understand why anchors are needed so that peristalsis can generate forward displacements. This is intuitively obvious.

We are sorry to hear that the reviewer found some of the physics details distracting. To address this concern, we have simplified some of the language while still attempting to keep the core arguments intact. For context and analogy, we still believe that including a brief reference to the laws of motion is helpful for some readers to explain some of our results and highlight their general implications, especially with regard to anchoring against reaction forces.

One of our objectives is to make this article accessible and interesting for biologists and physicists at all levels. We feel it is important to reach out to both communities and try to be inclusive as possible in our writing. Newton’s 3rd law is clearly relevant for our study and it is a common point of reference for anyone with a highschool education, and so we feel it is appropriate to mention it as a way to help readers across disciplines understand the biophysical challenges faced by the animals we study.

(2.2) Another distracting allusion to "physics" is correlating deformation areas with displaced volume, finding that "volume is a consequence of mass in a 2nd order polynomial relationship". I have no idea what this "physics" means or what relevance this relationship has to the biology of locomotion.

Upon reflection, we agree that this language may be overly complex and distracts from what is, at its core, a simple, but important principle governing how Drosophila larvae interact with their substrates. The point we are trying to make is that our data show that forces exerted by an animal are proportional in a non-linear way to contact area. This suggests that to increase force exerted on the substrate, an animal must increase contact area. We do not observe contact area remaining constant while force increases, or vice versa. To make this result more clear, we have made several changes in our revised manuscript. Figure 5B no longer shows the relationship between the protopodial contact area and the displaced volume of the elastic resonator, but instead now shows the protopodial contact area and recorded force transmitted into the substrate. This then shows that in order to increase force transmitted into the substrate, these animals must increase their contact area. We have made changes to the figure legend of Figure 5 and the statements in the Results section accordingly (Page 9, lines 220-222).

2.3 The ERISM and WARP methods are state-of-the-art, but aside from generally estimating force magnitudes, the detailed force maps are not used. The most important new information is the highly accurate and detailed maps of displacement itself, not their estimates of applied force using finite element calculations. In fact, comparing displacements to stress maps, they are pretty similar (e.g., Fig 4), suggesting that all experiments are performed in a largely linear regime. It should also be noted that the stress maps are assumed to be normal stresses (perpendicular to the plane), not the horizontal stresses that are the ones that actually balance forces in the plane of animal locomotion.

We largely agree with the statement made by the reviewer here. However, we have found that in many contexts, audiences appreciate having the absolute number of the forces and stresses involved reported. Therefore, where possible, we have used stress maps, rather than displacement maps. We also observe that while stress and displacement maps show similar patterns, features sometimes appear sharper in the stress map, which is a result of the finite element algorithm being able to attribute a broad indentation to a somewhat more localised downward force. We have thus opted to keep to original stress maps. We have been more explicit about WARP and ERISM being more tuned to recording vertically directed forces throughout the revised manuscript (lines 75, 78, 86, 162, 301, 305, 336).

We have also modified our Discussion section to encourage further investigation of our proposed model using a technique more tuned to horizontal stresses (pages 12-13, lines 324-328):

‘However, WARP microscopy is best suited to measurements of forces in the vertical direction, and though we can make inferences such as this as they are a consequence of fundamental laws of physics, we present this conclusion as a testable prediction which could be confirmed using a force measurement technique more tuned to horizontally directed forces relative to the substrate.’

(2.4) But none of this matters. The real achievements are the new locomotory dynamics uncovered with these amazing displacement measurements. I'm only asking the authors to be precise and down-to-earth about the nature of their measurements.

We thank the reviewer for their perceptiveness in finding that though the forces are interesting, the interactions themselves are the most noteworthy result here. We trust that with the changes made in our revised manuscript, the description is now more “down-to-earth”, more concise where appropriate, and accurate as to which results are particularly important and novel.

(2.5) It would be good to highlight the strength of the paper -- the discovery of new locomotion dynamics with high-resolution microscopy -- by describing it in simple qualitative language. One key discovery is the broad but shallow anchoring of the posterior body when the anterior body undertakes a "head sweep". Another discovery is the tripod indentation at the tail at the beginning of peristalsis cycles.

We thank the reviewer for this recommendation. We agree that including a more explicit statement of some of our findings, especially with regards to these new posterior tripod structures and the whole-abdomen preparatory anchoring prior to head sweeps, would make the paper more impactful. As a result, we have modified the discussion section to include a statement for each new result and have also amended our abstract as a result (lines 407-416):

“Here we have provided new insights into the behaviour of Drosophila larval locomotion. We have provided new quantitative details regarding the GRFs produced by locomoting larvae with high spatiotemporal resolution. This mapping allowed the first detailed observations of how these animals mitigate friction at the substrate interface and thus provide new rules by which locomotion is achieved. Further, we have ascribed new locomotor function to appendages not previously implicated in locomotion in the form of tripod papillae, providing a new working hypothesis of how these animals initiate movement. These new principles underlying the locomotion outlined here may serve as useful biomechanical constraints as called for by the wider modelling community (39).”

(2.6) As far as I know, these anchoring behaviors are new. It is intuitively obvious that anchoring has to occur, but this paper describes the detailed dynamics of anchoring for the first time. Anchoring behavior now has to be included in the motor sequence for Drosophila larva locomotion in any comprehensive biomechanical or neural model.

We agree with the reviewer on this. We think it is best to let our colleagues reflect on our findings and then decide how best to include them in future models.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Please be sure to describe in a figure caption or in the methods the details of the optical setup, especially the focal lengths of all the lenses, including the objective, and part numbers of the LEDs and filters. It would be helpful to have a figure in the main paper explaining the principles of ERISM/WARP microscopy along with the calibration measurements and computational pipeline (this would mainly combine elements already in the supplement). Such a figure should also include details of the setup that are alluded to in the methods but not fully explained (for instance, a "silicone well" is referred to in the methods but never described). The calibration of elastomer stiffness that now appears in the main text could be made a supplementary figure, unless there is some new art in the fabrication of the elastomers that should be highlighted as an advance in the main text.

We appreciate the importance of explaining our methods to readers.

In response to the public comments, we have added further details in our methods section to clarify practical aspects and ensure that readers will be able to reproduce our work.

In Supplemental Figure 2, we show the full optical light path for ERISM and WARP along with named components. In addition, the principles of ERISM and WARP microscopy have already been extensively described in previous publications (See Refs 23-26). In light of this, we feel that the best approach in this paper is to direct readers to those publications.

We feel that it is appropriate to present the calibration of elastomer stiffness in the main text because this is indeed a new innovation that is not just about making the elastomers but making force sensors based on these different materials. This is really important because it shows how researchers can tune the stiffness of an ERISM/WARP elastomer to match the type of tissue or organism under study. This is really the key technical advance that enables whole animal biomechanics across a range of animal sizes, so we think it is appropriate to keep it in the main text.

We want to make sure that we do not oversell this point, and we feel that we make it sufficiently clear in the main text of our manuscript that making elastomer based force sensors of appropriate stiffness is important, when we state

“First, we developed optical microcavities with mechanical stiffnesses in the range found in hydrogel substrates commonly used for studying Drosophila larval behaviour, i.e. Young’s modulus (E) of 10-30kPa (36–38).” (p. 5, ll. 124) and later

“Here we used Drosophila larvae as a test case, but our methods now allow elastic optical resonators to be tuned to a wide range of animal sizes and thus create new possibilities for studying principles of neuro-biomechanics across an array of animals.” (p. 12, ll. 337)

I would appreciate a description of the "why" behind some experimental choices, as understanding the motivation would be helpful for other researchers looking to adopt these techniques.

We have now added additional text in the introduction and discussion that explains the rationale behind our experimental choices. in more detail. Please see our response to Reviewer 1’s public comments on the same point.

(1) The WARP and ERISM experiments were conducted on a collagen coated gold surface rather than agarose. Why? EG does agarose not adhere to the gold, or would its thickness interfere with the measurement?

The gold layer is applied above the elastomer and the collagen on top of the gold layer makes the gold a more natural biological surface for the animals. Agarose is unsuitable as an elastomer because it would dry during the vacuum based deposition of the gold. It is also unsuitable as a surface coating on top of the gold as the coating on the gold needs to very thin to preserve the spatial and mechanical resolution of our sensors. Further, processing of agarose generally requires temperatures of 60°C and higher which we find can damage the elastomer / gold films.

(2) The ERISM measurements are made on a cold anesthetized animal right as it starts to wake up (visible mouth-hooks movement), which presents some difficulty. Why not start imaging while the animal is still completely immobile? Or why not use a dead larva?

This approach allowed us to get measurements of forces exerted by denticles that are physiologically and biomechanically accurate. In dead or fully anesthetized animals, one cannot be sure that the forces exerted by denticles and denticle bands are representative of the forces exerted by an animal with active hydrostatic control.

(3) In the ERISM setup the monochromator is spatially filtered by focusing through pinhole, while in the WARP setup, the LEDs are not.

Yes that’s correct. The LED light sources used in WARP have better spatial homogeneity than the tungsten filament used in ERISM and so a pinhole is not required in WARP.

(4) SV4 shows the interference image of a turning larva (presumably from one illumination wavelength) rather than a reconstruction of the displacement or stresses. Why?

We felt that in this particular case the interference images provided a clearer representation of the behavioural sequence, showing both the small indentations generated by individual denticles and the larger indentations of the animal overall.

Lines 49-50 "a lack of methods with sufficient spatiotemporal resolution for measuring GRFs in freely behaving animals has limited progress." This needs a discussion of what sufficient spatial and temporal resolutions would be and how existing methods fall short of these goals.

We have now rewritten the introduction to include an overview of other alternative approaches and of what we see as the requirements here. See our response to the public comments.

Figure caption 1B (line 789) refers to "concave areas of naked cuticle (black line) which generally do not interact with the substrate" While I think this might be supported by later WARP images, it's not clear how the technique of figure 1 measures interaction, which could e.g. be mediated by surface tension of a transparent fluid.

The technique of Figure 1 provides qualitative information which as the reviewer points out is validated by WARP measurements later.

Lines 184-189 "However, unexpectedly, we observed an additional force on the substrate when protopodia leave the substrate (SI) and when they are replanted (ST). To investigate whether this force was due to an active behaviour or due to shifting body mass, we plotted integrated displacement (i.e. displaced volume) against the contact area for each protopodium, combining data from multiple forwards waves (Figure 5B). Area is correlated with displaced volume for most time points, indicating that volume is a consequence of mass in a 2nd order polynomial relationship." I couldn't follow this argument at all.

We have now reworded this section and explained our rationale. Also see our response to a similar critique in Reviewer 2’s public comments.

Generally the authors might reconsider their use of acronyms. e.g. (244-246) "SI latencies were much more strongly correlated with wave duration across most segments than ST latencies. SIs scale with SwP and this could be mediated by proprioceptor activity in the periphery" is made more difficult to parse by the abbreviations.

As we need to refer to these terms multiple times throughout the manuscript, we feel the use of acronyms is appropriate here.

The video captions are inadequate. Please expand on them to explain clearly what is shown, and also describe in the methods how the data were acquired and processed. For instance, it seems that in SV3 a motion correction algorithm is applied so that the larva appears stationary even as it crawls forward. I think "fourier filtered" means that the images were processed with a spatial high pass filter - this should be explained and the parameters noted.

We have revisited the video captions provided in the supplementary information document and conclude that these contain the important information. The mode of acquisition are described in the methods, e.g. Video 1 and 2 see section in Methods on “Denticle band kinematic imaging” and Videos 3 and 4 see section in Methods on WARP. Supplementary Video 3 does not make use of motion correction; indeed, one can see the larvae moving upwards/forwards in the field of view. We apologize for not explaining the Fourier filtering process for Video 3. We have now modified the video caption to read as follows:

Video SV3. WARP imaging during forwards peristalses.

Video showing high frame rate displacement maps produced by a freely behaving Drosophila larva. Displacement maps were Fourier filtered to make denticulated cuticle more readily visible and projected in 3D to show the effects of substrate interaction. Details of the Fourier filtering procedure were described elsewhere [Kronenberg et al, Nat Cell Biol 19, 864–872 (2017)].

What were the reflectances of the bottom (10 nm Au/Cr) and top (15nm Au) metal layers at the wavelengths used? I imagine the bottom layer should be less than 38%, the top layer higher, and the product of the square of the bottom transmission and the top reflectance coefficients equal to the bottom reflectance (to make the two paths of the interferometer contribute equal intensity), but none of this is stated.

The reflectance of the gold mirrors was studied in detail in prior work on ERISM. See Kronenberg et al, Nat Cell Biol 19, 864–872 (2017). We therefore refrained from adding a complete optical characterization of the ERISM sensors again here. In brief, we found that a reflectance >13% at each Au mirror is required for reliable ERISM measurements.

The description of the gold coated elastomer as a microcavity is confusing to me. Does the light really make multiple round trips between the plates before returning to the detector? The loss of light on each round trip would depend on the reflectance and parallelism of the top and bottom mirrors. From the WARP calculation it's appears that there is only one round trip - a pi/2 phase shift results from the calculation for one round trip: 2pi*2nL 5nm/(630nm)^2, with n = 1.4 and L = 8 microns - if there were two round trips, the phase shift would be pi etc. Would this better be described as a mostly common path interferometer?

The physics of our devices is best described within the framework of thin film interference and (weak) microcavity optics. Indeed, light can make multiple roundtrips, though it gets attenuated with each reflection. The complete calculation of the multiple roundtrips is only required to obtain quantitative information on the amount of light that is reflected. The spectral position of minima in reflectance can also be obtained from assuming one roundtrip which is what is done in the description of the WARP calculations.

Figure 2 e,f: the line fits appear to be dominated by the data points at 2 s. If these are removed, do the fits change? To support the argument that 2e shows a correlation and 2f does not, some kind of statistical test, ideally a hierarchical bootstrap, should be conducted to compare between the two measurements.

If we remove the data points at 2 s, then R^2’s for swing initiation latencies change as follows: A2: 0.35 to 0.005; A4: 0.78 to 0.31; A6: 0.61 to 0.01. The data in 2e,f are the averages from 3 waves in each animal and so the data points at 2 s are not simply the result of single ‘rogue’ waves but rather averages of several trials. Further, if all individual waves are plotted, we can see that the overall trends are still visible.

We don’t think it is appropriate to remove the data at 2 s from our analysis, but we take the point regarding statements about presence or absence of correlation in a formal sense. We have therefore changed the wording in the description of 2e,f to refer simply to the fact that wave duration can ‘largely determine' latencies in some instances, but is less able to in other instances, as is suggested by the R^2 (coefficient of determination) data. In discussion, we have also adjusted our wording.

Figure 4 - please provide in the main figure or as a supplement the full images (i.e. not cropped to the assumed shape of the larva)

We do not feel that it is necessary or helpful to provide the full images given that the focus of the analysis is on dynamics of protopodia movements.

Figure 5e top: single data points around wave duration 0.6s appear to dominate fit lines. Does removing these points alter the fits? To support the argument that 5e top shows a correlation and 5e bottom does not, some kind of statistical test, ideally a hierarchical bootstrap, should be conducted to compare between the two measurements.

In Figure 5e, we are showing all waves analysed across animals. If we remove the datapoints at 0.6 s, A2 R^2 changes from 0.24 to 0.05, A4 R^2 changes from 0.48 to 0.11, A6 R^2 changes from 0.69 to 0.34; however we don’t feel it is appropriate to remove these data from our analysis. We take the point about needing to be cautious about making claims about correlation versus no correlation and have now reworded description of these results along same lines as Figure 4.

It appears from the methods (467-489) that animals were kept wet for warp imaging but not for ERISM imaging. Please confirm or explain further the presence or absence of a water layer in these two sets of measurements, as this could affect the adhesion forces.

In each case, the animals were transferred onto experimental substrates with a moistened paintbrush. We have added text explicitly stating this in the methods section.

Kim et al. Nature Methods 2017 (10.1038/nmeth.4429) describes recording two images separated by less than 60 microseconds using a scientific CMOS camera with a frame rate of 200 Hz. This is accomplished by triggering a pulsed LED once at the end of one frame's capture window and then a second time at the beginning of the next frame's window (see Supplementary Figure 10). I'm not sure if this trick is widely known, but it's worth considering if the authors are running into a problem with movement between the two wavelength exposures in their WARP setup.

Thank you for this tip. We will take this under consideration for future work.

Is the setup compatible with optogenetics? (EG is the red light dim enough that it wouldn't activate CsChrimson, or could a longer wavelength led be used for interferometry?) If so, activation of mooncrawler descending neuron (MDN) could be used to study backward crawling (or thermogenetic activation of MDN), e.g. to contrast the sites and order of "anchoring" between the two directions of crawling.

The set-up is potentially compatible with optogenetics. We are in the process of exploring this in current ongoing work.

Reviewer #2 (Recommendations For The Authors):

Simplify/reduce the commentary about force measurements, and highlight the clear, qualitative descriptions of the novel locomotion patterns that they have observed. The microscopy and movements seem to matter more than the ground force estimations.

We have addressed these issues in our responses to Reviewer 2’s public comments.

It is clear that the CUNY schools at this time existed to serve NYC's diverse community and support the post-secondary needs of those who were unable to afford to attend expensive private universities or access technical schools. However, the students and their schools were still treated as disposable and with little care from the heads of the CUNY system. The students were being robbed of more than just an education; they were being robbed of a community that reflected who they were. It's clear that this has no value in the eyes of the heads of CUNY who only cared about maximizing profit when the university system was founded on the idea of providing education to all.

I believe that this is a blatant example of racial discrimination and how the higher education system makes it very difficult for minority students to finish school or just have higher education opportunities. It’s almost like they wanted to set them up to fail.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This work describes a new method for sequence-based remote homology detection. Such methods are essential for the annotation of uncharacterized proteins and for studies of protein evolution.

Strengths:

The main strength and novelty of the proposed approach lies in the idea of combining stateof-the-art sequence-based (HHpred and HMMER) and structure-based (Foldseek) homology detection methods with recent developments in the field of protein language models (the ESM2 model was used). The authors show that features extracted from high-dimensional, information-rich ESM2 sequence embeddings can be suitable for efficient use with the aforementioned tools.

The reduced features take the form of amino acid occurrence probability matrices estimated from ESM2 masked-token predictions, or structural descriptors predicted by a modified variant of the ESM2 model. However, we believe that these should not be called "embeddings" or "representations". This is because they don't come directly from any layer of these networks, but rather from their final predictions.

We agree that there is some room for discussion about whether the amino acid probabilities returned by pre-trained ESM-2 and the 3Di sequences returned by ESM-2 3B 3Di can be properly referred to as “embeddings”. The term “embedding” doesn’t have a formal definition, other than some kind of alternative vector representation of the input data which, preferably, makes the input data more suitable for some downstream task. In that simple sense of the word “embedding”, amino acid probabilities and 3Di sequences output by our models are, indeed, types of embeddings. We posed the question on Twitter (https://twitter.com/TrichomeDoctor/status/1715051012162220340) and nobody responded, so we are left to conclude that the community is largely ambivalent about the precise definition of “embedding”.

We’ve added language in our introduction to make it more clear that this is our working definition of an “embedding”, and why that definition can apply to profile HMMs and 3Di sequences.

The benchmarks presented suggest that the approach improves sensitivity even at very low sequence identities <20%. The method is also expected to be faster because it does not require the computation of multiple sequence alignments (MSAs) for profile calculation or structure prediction.

Weaknesses:

The benchmarking of the method is very limited and lacks comparison with other methods. Without additional benchmarks, it is impossible to say whether the proposed approach really allows remote homology detection and how much improvement the discussed method brings over tools that are currently considered state-of-the-art.

We thank the reviewer for the comment. To address the question, we’ve expanded the results by adding a new benchmark and added a new figure, Figure 4. In this new content, we use the SCOPe40 benchmark, originally proposed in the Foldseek paper (van Kempen et al., 2023), to compare our best method, ESM-2 3B 3Di coupled to Foldseek, with several other recent methods. We find our method to be competitive with the other methods.

We are hesitant to claim that any of our proposed methods are state-of-the-art because of the lack of a widely accepted standard benchmark for remote homology detection, and because of the rapid pace of advancement of the field in recent years, with many groups finding innovative uses of pLMs and other neural-network models for protein annotation and homology detection.

Reviewer #2 (Public Review):

Summary:

The authors present a number of exploratory applications of current protein representations for remote homology search. They first fine-tune a language model to predict structural alphabets from sequence and demonstrate using these predicted structural alphabets for fast remote homology search both on their own and by building HMM profiles from them. They also demonstrate the use of residue-level language model amino acid predicted probabilities to build HMM profiles. These three implementations are compared to traditional profile-based remote homology search.

Strengths:

• Predicting structural alphabets from a sequence is novel and valuable, with another approach (ProstT5) also released in the same time frame further demonstrating its application for the remote homology search task.

• Using these new representations in established and battle-tested workflows such as MMSeqs, HMMER, and HHBlits is a great way to allow researchers to have access to the state-of-the-art methods for their task.

• Given the exponential growth of data in a number of protein resources, approaches that allow for the preparation of searchable datasets and enable fast search is of high relevance.

Weaknesses:

• The authors fine-tuned ESM-2 3B to predict 3Di sequences and presented the fine-tuned model ESM-2 3B 3Di with a claimed accuracy of 64% compared to a test set of 3Di sequences derived from AlphaFold2 predicted structures. However, the description of this test set is missing, and I would expect repeating some of the benchmarking efforts described in the Foldseek manuscript as this accuracy value is hard to interpret on its own.

The preparation of training and test sets are described in the methods under the heading “Fine tuning ESM-2 3B to convert amino acid sequences into 3Di sequences”. Furthermore, there is code in our github repository to reproduce the splits, and the entire model training process: https://github.com/seanrjohnson/esmologs#train-esm-2-3b-3di-starting-from-the-esm-2-3bpre-trained-weights

We didn’t include the training/validation/test splits in the Zenodo repository because they are very large: train 33,924,764; validation 1,884,709; test 1,884,710 sequences, times 2 because there are both amino acid and 3Di sequences. It comes out to about 30 Gb total, and is easily rebuilt from the same sources we built it from.

We’ve added the following sentence to the main text to clarify:

“Training and test sets were derived from a random split of the Foldseek AlphaFold2 UniProt50 dataset (Jumper et al., 2021; van Kempen et al., 2023; Varadi et al., 2022), a reducedredundancy subset of the UniProt AlphaFold2 structures (see Methods for details).”

To address the concern about comparing to Foldseek using the same benchmark, we’ve expanded the results section and added a new figure, Figure 4 using the SCOPe40 benchmark originally presented in the Foldseek paper, and subsequently in the ProstT5 paper to compare Foldseek with ESM-2 3B 3Di to Foldseek with ProstT5, AlphaFold2, and experimental structures.

• Given the availability of predicted structure data in AFDB, I would expect to see a comparison between the searches of predicted 3Di sequences and the "true" 3Di sequences derived from these predicted structures. This comparison would substantiate the innovation claimed in the manuscript, demonstrating the potential of conducting new searches solely based on sequence data on a structural database.

See response above. We’ve now benchmarked against both ProstT5 and AF2.

• The profile HMMs built from predicted 3Di appear to perform sub-optimally, and those from the ESM-2 3B predicted probabilities also don't seem to improve traditional HMM results significantly. The HHBlits results depicted in lines 5 and 6 in the figure are not discussed at all, and a comparison with traditional HHBlits is missing. With these results and presentation, the advantages of pLM profile-based searches are not clear, and more justification over traditional methods is needed.

We thank the reviewer for pointing out the lack of clarity in the discussion of lines 5 and 6.

We’ve re-written that section of the discussion, and reformatted Figure 3 to enhance clarity.

We agree, a comparison to traditional HHBlits could be interesting, but we don’t expect to see stronger performance from the pLM-predicted profiles than from traditional HHBlits, just as we don’t see stronger performance from pLM-hmmscan or pLM-Foldseek than from the traditional variants. We think that the advantages of pLM based amino acid hmm searches are primarily speed. There are many variables that can influence speed of generating an MSA and HMM profile, but in general we expect that it will be much slower than generating an HMM profile from a pLM.

We don’t know why making profiles of 3Di sequences doesn’t improve search sensitivity, we just think it’s an interesting result that is worth presenting to the community. Perhaps someone can figure out how to make it work better.

• Figure 3 and its associated text are hard to follow due to the abundance of colors and abbreviations used. One figure attempting to explain multiple distinct points adds to the confusion. Suggestion: Splitting the figure into two panels comparing (A) Foldseek-derived searches (lines 7-10) and (B) language-model derived searches (line 3-6) to traditional methods could enhance clarity. Different scatter markers could also help follow the plots more easily.

We thank the reviewer for this helpful comment. We’ve reformatted Figure 3 as suggested, and we think it is much easier to read now.

• The justification for using Foldseek without amino acids (3Di-only mode) is not clear. Its utility should be described, or it should be omitted for clarity.

To us, the use of 3Di-only mode is of great theoretical interest. From our perspective, this is one of our most significant results. Previous methods, such as pLM-BLAST and related methods, have made use of very large positional embeddings to achieve sensitive remote homology search. We show that with the right embedding, you don’t need very many bits per position to get dramatically improved search sensitivity from Smith-Waterman, compared to amino acid searches. We also doubt that predicted 3Di sequences are the optimal small encoding for remote homology detection. This result and observation opens up an exciting avenue for future research in developing small, learned positional embeddings that are optimal for remote homology detection and amenable to SIMD-optimized pre-filtering and Smith-Waterman alignment steps.

We’ve expanded the discussion, explaining why we are excited about this result.

• Figure 2 is not described, unclear what to read from it.

It's just showing that ESM-2-derived amino acid probabilities closely resemble amino acid frequencies in MSAs. We think it gives readers some visual intuition about why predicted profile HMMs perform as well as they do. We’ve added some additional explanation of it in the text.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

The paper would mainly benefit from a more comprehensive benchmark:

We suggest that the authors extend the benchmark by including the reference methods (HHpred and Foldseek) run with their original representations, i.e., MSAs obtained with 2-3 iterations of hhblits (for HHpred) and experimental or predicted structures (for Foldseek). HHpred profile-profile comparisons and Foldseek structure-structure comparisons would be important reference points for assessing the applicability of the proposed approach in distant homology detection. It is also essential to compare the method with other emerging tools such as EBA (DOI: 10.1101/2022.12.13.520313), pLM-BLAST (DOI: 10.1101/2022.11.24.517862), DEDAL (DOI: 10.1038/s41592-022-01700-2), etc.

We also suggest using an evolutionary-oriented database for the benchmark, such as ECOD or CATH (these databases classify protein domains with known structures, which is important in the context of including Foldseek in the benchmark). We ran a cursory benchmark using the ECOD database and generated HH-suite .hhm files (using the single_seq_to_hmm.py and hhsearch_multiple.py scripts). Precision and recall appear to be significantly lower compared to "vanilla" hhsearch runs with MSA-derived profiles. It would also be interesting to see benchmarks for speed and alignment quality.

The pLM-based methods for homology detection are an emerging field, and it would be important to evaluate them in the context of distinguishing between homology and analogy. In particular, the predicted Foldseek representations may be more likely to capture structural similarity than homology. This could be investigated, for example, using the ECOD classification (do structurally similar proteins from different homology groups produce significant matches?) and/or resources such as MALISAM that catalog examples of analogy.

We’ve added the SCOPe40 benchmark, which we think at least partially addresses these comments, adding a comparison to pLM-BLAST, ProstT5, and AF2 followed by Foldseek. The question of Analogy vs homology is an interesting one. It could be argued that the SCOPe40 benchmark addresses this in the difference between Superfamily (distant homology) and Fold (analogy, or very distant homology).

Our focus is on remote homology detection applications rather than alignment quality, so we don’t benchmark alignment quality, although we agree that those benchmarks would be interesting.

Page 2, lines 60-67. This paragraph would benefit from additional citations and explanations to support the superiority of the proposed approach. The fact that flattened embeddings are not suitable for annotating multidomain proteins seems obvious. Also, the claim that "current search implementations are slow compared to other methods" should be supported (tools such as EBA or pLM-BLAST have been shown to be faster than standard MSA-based methods). Also, as we mentioned in the main review, we believe that the generated pseudo-profiles and fine-tuned ESM2 predictions should not be called "smaller positional embeddings".

Discriminating subdomains was a major limitation of the influential and widely-cited PfamN paper (Bileschi et al., 2022), we’ve added a citation to that paper in that paragraph for readers interested in diving deeper.

To address the question of speed, we’ve included data preparation and search benchmarks as part of our presentation of the SCOPe40 benchmark.

Finally, we were not sure why exactly every 7th residue is masked in a single forward pass. Traditionally, pseudo-log likelihoods are generated by masking every single token and predicting probabilities from logits given the full context - e.g. https://arxiv.org/pdf/1910.14659.pdf. Since this procedure is crucial in the next steps of the pipeline, it would be important to either experiment with this hyperparameter or explain the logic used to choose the mask spacing.

We’ve added discussion of the masking distance to the Methods section.

Reviewer #2 (Recommendations For The Authors):

• While the code and data for the benchmark are available, the generation of searchable databases using the methods described for a popular resource such as Pfam, AFDB, SCOP/CATH which can be used by the community would greatly boost the impact of this work.

3Di sequences predicted by ESM-2 3B 3Di can easily be used as queries against any Foldseek database, such as PDB, AFDB, etc. We’ve added Figure 4E to demonstrate this possibility, and added some related discussion.

• Minor: In line 114, the text should likely read "compare lines 7 and 8" instead of "compare lines 6 and 7."

We’ve clarified the discussion of Figure 3.

After reading the passage, it's clear to me that sexual harassment in schools is a deeply concerning issue that can have severe consequences for students like Katy Lyle. The story of Katy's experience illustrates the damaging effects of persistent harassment, not just on her academic performance but on her mental and emotional well-being as well. It's alarming to see how long it took for the school to recognize and address the situation, with initial dismissal of the harassment as a mere maintenance problem.

climate change, snowball earth

Indeed, the statistics presented here highlight the stark reality that poverty isn't just about financial hardship; it's about a multitude of interconnected risk factors that compound the challenges faced by families and children living in poverty. The fact that such a significant portion of poor families experience multiple risk factors compared to their well-off counterparts underscores the complexity and severity of the issue.

As a parent, it would be concerning to hear about the challenges faced by students in Mr. Hawkins's class. It's clear that poverty isn't just a matter of individual choice or effort; it's a systemic issue with deep-rooted causes that require comprehensive solutions. Rather than resigning to despair, it's essential for educators, policymakers, and society as a whole to actively work towards addressing the barriers that students from economically disadvantaged backgrounds face.

By acknowledging the realities of poverty and adopting informed strategies, such as those outlined in research on successful interventions with economically disadvantaged students, we can strive to create more equitable opportunities for all children to thrive academically and beyond.

It is interesting to learn the meaning of cancel culture that the consequences of being "canceled" can be different too, from just facing criticism to losing a job or even getting into legal trouble. It's a big and varied topic, involving lots of emotions and different situations for different people.

Most of my college level lecture experiences are not so much about learning, but about presenting knowledge to aid existing knowledge learned from assigned reading. I find lecture recordings great, as I can get confused, take a break, do some of my own research, and then get the over-arching picture from an expert. In person, a lecture is about giving an extra organization to a domain, showing how concepts that you already know about connect together.

Author Response

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

We thank the reviewers for their thorough reading and helpful comments which has allowed us to further improve the manuscript. Following the suggestions of the reviewers we have run a number of new simulations including mutations of the PIP binding residues and with an elastic network allowing more mobility of the linker. Together these excellent ideas have allowed us to strengthen the conclusions of the study. Below, we provide point-by-point responses to their suggestions.

Reviewer #1 (Public Review):

Summary:

Here, the authors were attempting to use molecular simulation or probe the nature of how lipids, especially PIP lipids, bind to a medically-important ion channel. In particular, they look at how this binding impact the function of the channel.

Strengths:

The study is very well written and composed. The techniques are used appropriately, with plenty of sampling and analysis. The findings are compelling and provide clear insights into the biology of the system.

Weaknesses:

A few of the analyses are hard to understand/follow, and rely on "in house" scripts. This is particularly the case for the lipid binding events, which can be difficult to compute accurately. Additionally, a lack of experimental validation, or coupling to existing experimental data, limits the study.

Our analysis scripts have now been made publicly accessible as a Jupyter notebook on Github https://github.com/etaoster/etaoster.github.io/tree/main/nav_pip_project

It is my view that the authors have achieved their aims, and their findings are compelling and believable. Their findings should have impacts on how researchers understand the functioning of the Nav1.4 channel, as well as on the study of other ion channels and how they interact with membrane lipids.

Reviewer #2 (Public Review):

Summary:

Y., Tao E., et al. used multiscale MD simulations to show that PI(4,5)P2 binds stably to an inactivated state of Nav channels at a conserved site within the DIV S4-S5 linker, which couples the voltage sensing domain (VSD) to the pore. The authors hypothesized that PI(4,5)P2 prolongs inactivation by binding to the same site where the C-terminal tail is proposed to bind during recovery from inactivation. They convincingly showed that PI(4,5)P2 reduces the mobility of both the DIV S4-S5 linker and the DIII-IV linker, thus slowing the conformational changes required for the channel to recover to the resting state. They also conducted MD simulations to show that phosphoinositides bind to VSD gating charges in the resting state of Nav channels. These interactions may anchor VDS at the resting state and impede its activation. Their results provide a mechanism by which phosphoinositides alter the voltage dependence of activation and the recovery rate from inactivation, an important step for developing novel therapies to treat Nav-related diseases. However, the study is incomplete and lacks the expected confirmatory studies which are relevant to such proposals.

Strengths:

The authors identified a novel binding between phosphoinositides and the VSD of Nav and showed that the strength of this interaction is state-dependent. Based on their work, the affinity of PIPs to the inactivated state is higher than the resting state. This work will help pave the way for designing novel therapeutics that may help relieve pain or treat diseases like arrhythmia, which may result from a leftward shift of the channel's activation.

Weaknesses:

However, the study lacks the expected confirmatory studies which are relevant to such proposals. For example, one would expect that the authors would mutate the positive residues that they claim to make interactions with phosphoinositides to show that there are much fewer interactions once they make these mutations. Another point is that the authors found that the main interaction site of PIPs with Nav1.4 is the VSD-DIV and DIII-DIV linker, an interaction that is expected to delay fast inactivation if it happens at the resting state. The authors should make a resting state model of the Nav1.4 channel to explain the recent experimental data showing that PIP2 delays the activation of Nav1.4, with almost no effect on the voltage dependence of fast inactivation.

Following the reviewers suggestion we have conducted new simulations demonstrating that there are many fewer protein-PIP interactions after mutating the positive residues as shown in the new Supplementary Fig S6.

The reviewer mentions that if PIPs interact with the VSD-DIV and DIII-DIV linker in the resting state that it could delay fast inactivation. However, as described in the original manuscript and depicted in the schematic (Fig 7) the C-terminal domain impeded PIP binding at the position in the resting state (but not the inactivated state), meaning that PIP does not bind in the resting state to delay fast inactivation. We have clarified this statement in the text on page 14 lines 1-2.

Following the reviewer’s suggestion we have examined PIP binding to a model of the resting state of Nav1.4 (in addition to the resting state of Nav1.7 described in the original manuscript) as described on page 12 lines 28-30 (and in Fig S12). Similar to what we saw for Nav1.7, PIP binding to VSDI-III can impair activation of the channel.

Major concern:

(1) Lack of confirmatory experiments, e.g., mutating the positive residues that show a high affinity towards PIPs to a neutral and negative residue and assessing the effect of mutagenesis on binding.

Done as described above

(2) Nav1.4 is the only channel that has been studied in terms of the effect of PIPs on it, therefore the authors should build a resting state model of Nav1.4 and study the effect of PIPs on it.

Done as described above

Minor points:

There are a lot of wrong statements in many areas, e.g., "These diseases 335 are associated with accelerated rates of channel recovery from inactivation, consistent with our observations that an interaction between PI(4,5)P2 and the residue corresponding to R1469 in other Nav 337 subtypes could be important for prolonging the fast-inactivated state." Prolonging the fast inactivated state would actually reduce recovery from inactivation and not accelerate it.

We disagree with this statement from the reviewer which may have come from a misreading of the mentioned sentence. Our statement in the original manuscript is consistent with the original experiments that show that the presence of PIP prolongs the time spent in the fast inactivated state. Mutations at the PIP binding site are likely to reduce PIP binding, and with less PIP bound the channel is expected to recover from inactivation more quickly. We have reworded this sentence for clarity on page 13 line 27-30.

Reviewer #3 (Public Review):

Summary:

This work uses multiscale molecular dynamics simulations to demonstrate molecular mechanism(s) for phosphatidylinositol regulation of voltage gated sodium channel (Nav1.4) gating. Recent experimental work by Gada et al. JGP 2023 showed altered Nav1.4 gating when Nav1.4 current was recorded with simultaneous application of PI(4,5)P2 dephosphorylate. Here the authors revealed probable molecular mechanism that can explain PI(4,5)P2 modulation of Nav1.4 gating. They found PIP lipids interacting with the gating charges - potentially making it harder to move the voltage sensor domain and altering the channels voltage sensitivity. They also found a stable PIP binding site that reaches the D_IV S4-S5 linker, reducing the mobility of the linker and potentially competing with the C-terminal domain.

Strengths:

Using multiscale simulations with course-grained simulations to capture lipid-protein interactions and the overall protein lipid fingerprint and then all-atom simulations to verify atomistic details for specific lipidprotein interactions is extremely appropriate for the question at hand. Overall, the types of simulation and their length are suitable for the questions the authors pose and a thorough set of analysis was done which illustrates the observed PIP-protein interactions.

Weaknesses:

Although the set of current simulations and analysis supports the conclusions drawn nicely, there are some limitations imposed by the authors on the course-grained simulations. If those were not imposed, it would have allowed for an even richer set and more thorough exploration of the protein-lipid interactions. The Martini 2 force field indeed cannot change secondary structure but if run with a properly tuned elastic network instead of backbone restraints, the change in protein configuration can be sampled and/or some adaptation of the protein to the specific protein environment can be observed. Additionally, with the 4to1 heavy atoms to a bead mapping some detailed chemical specificity is averaged out but parameters for different PIP family members do exist - including specific PIP(4,5)P2 vs PIP(3,4)P2, and could have been explored.

We thank the reviewer for their excellent suggestions and have run new simulations with an elastic network instead of backbone restraints which have generated new insights. Indeed, as shown in the new panel Fig 4E, the new data allows us to demonstrate that the presence of PIP in the proposed binding site stabilises binding of the DIII-DIV linker to the inactivation receptor site, strengthening the conclusions of the paper.

We thank the reviewer for pointing out that there do exist parameters for different PIP sub-species and have corrected our statement on page 14 line 16 to reflect this. We have not run additional CG simulations with each of these parameters but use the all-atom simulations to examine the interactions of phosphates at specific positions.

In our atomistic simulations, we backmapped both PI(4,5)P2 and PI(4)P in the binding site to study their specific interactions. We chose to focus on PI(4,5)P2 given its physiological significance. However, we agree that differences in binding with PI(3,4)P2 would be interesting and warrants future investigation. We also note that the newer Martini3 forcefield would be useful in further work to differentiate between PIP subspecies interactions.

Detailed Comments

We thank the reviewers for their thorough reading and helpful comments which has allowed us to further strengthen the manuscript. Below, we provide point-by-point responses to their suggestions.

Reviewer #1 (Recommendations For The Authors):

I don't have many suggestions for the manuscript, just a few text edits. Of course, experimental analysis would bolster the claims made in the text, but I don't believe that this is necessary, given the quality of the data.

I understand the focus on the PIP lipids, but it's a shame that the high binding likelihood of glycosphingolipid isn't considered or analysed in any way. This is an especially interesting lipid from the point-of-view of raftlike membrane domains. Given the potential role of raft-like domains in sodium channel function, I feel this would be worth a paragraph or two in the discussion.

We thank the reviewer for bringing our attention to this interesting point. Glycolipids accumulate around Nav1.4 in our complex membrane simulations, however, given reports that carbohydrates tend to interact too strongly in the Martini2.2 forcefield (Grünewald et al. 2022, Schmalhorst et al. 2017) and there are no specific residues on Nav1.4 that interact preferentially with glycolipid species, we chose not to focus on this. However, we have noted that interactions with other lipids deserve further attention in our revised discussion.

The analyses have been run using Martini 2. I don't suggest the authors repeat using the Martini 3 force field, but some mention of this in the discussion would be good.

We have added the following statement to the discussion: “Our coarse grain simulations were carried out using the Martini2.2 forcefield, for which lipid parameters for many plasma membrane lipids have been developed. We expect that future investigations of lipid-protein interactions will benefit from use of the newer, refined Martini 3 forcefield (Souza et al. 2021) as parameters become available for more lipid types.

This might just be an oversight, but no mention is made of an elastic network applied to the backbone beads.

Lack of a network has been known to cause the protein to collapse, so if this is missing, I'd like to see an RMSD to show that the protein dynamics are not compromised.

While no elastic network was used in our original CG simulations, weak protein backbone restraints (10 kJ mol-1 nm-2) used in our simulations allowed us to maintain the structure while allowing some protein movement. However, following the suggestion of reviewer 3, we conducted additional simulations with an elastic instead of backbone restraints as described in the results on page 9 line 30-37 (and in Fig 4E) of the revised manuscript.

Minor

•In Fig 3B, are these lipids binding to the channel at the same time? And therefore do the authors see cooperativity?

The Fig 3B caption has been amended in the revised manuscript to read “Representative snapshots from the five longest binding events from different replicates, showing the three different PIP species (PIP1 in blue, PIP2 in purple and PIP3 in pink) binding to VSD-IV and the DIII-IV linker.” We cannot comment on PIP cooperativity based on these simulations shown in Fig 3, due to the artificially high concentrations used here; however, in model complex membrane simulations we see co-binding of PIPs at the binding site. This is likely due to PIP’s ability to accumulate together and the high density of positively charged residues in the region, attracting and supporting multiple PIP bindings.

•What charges were used for the atomistic PIP lipids? Does this match the CG lipids?

We used the CHARMM-GUI PIP parameters for the atomistic simulations. SAPI24 (PIP2) has a headgroup charge of –4e which is one less negative charge than the CG PIP2; whereas SAPI14 (PIP1) has a charge of –3e which is the same as the CG PIP1. We have explicitly included this charge information in the updated Methods of the manuscript (on page 15-16).

•Line 259-260: "we performed embedded three structures"

Corrected in the revised manuscript.

•Line 272: "us" should be "µs"

Corrected in the revised manuscript.

•Line 434: kJ/mol should probably also have 'nm-2' included

Corrected in the revised manuscript.

•What charge state titratable residues were set to, and were pKa analyses done to decide this?

Charge states were assigned to default values at neutral pH. We appreciate that future studies could examine this more carefully using constant pH simulations or similar.

•It's stated that anisotropic scaling is used the AT sims - is this correct? If so, is there a reason this was chosen over semi-isotropic scaling?

Anisotropic scaling was used for the atomistic simulations allowing all box dimensions to change independently.

•I would recommend in-house analysis scripts are made available on GitHub or similar, just so the details can be seen.

Per the reviewer’s request, the Jupyter notebooks used for analysis has been made available on GitHub (https://github.com/etaoster/etaoster.github.io/tree/main/nav_pip_project ).<br /> -One coarse grained notebook:

• Lipid DE

• Contact occupancy + outlier plots

• Binding duration plots

• Minimum distance plots

• Number of ARG/LYS plots

• PIP Occupancy, binding duration, gating charge residues

• One atomistic notebook:

• RMSD, RMSF and distance between IFM and its binding pocket (using MDAnalysis)

• Atomistic PIP headgroup interaction analyses and plots (using ProLIF)

As a final note, I am NOT saying this needs to be done for the current study, but I recommend the authors try the PyLipID package (https://github.com/wlsong/PyLipID) if they haven't yet, as it might be useful for similar projects they run in the future (i.e. for binding site identification, accurate binding kinetics calculations, lipid pose generation etc.).

We thank the reviewer for this suggestion and will keep this in mind for future projects.

Reviewer #2 (Recommendations For The Authors):

Lin Y., Tao E., et al. used multiscale MD simulations to show that PI(4,5)P2 binds stably to an inactivated state of Nav channels at a conserved site within the DIV S4-S5 linker, which couples the voltage sensing domain (VSD) to the pore. The authors hypothesized that PI(4,5)P2 prolongs inactivation by binding to the same site where the C-terminal tail is proposed to bind during recovery from inactivation. They convincingly showed that PI(4,5)P2 reduces the mobility of both the DIV S4-S5 linker and the DIII-IV linker, thus slowing the conformational changes required for the channel to recover to the resting state. They also conducted MD simulations to show that phosphoinositides bind to VSD gating charges in the resting state of Nav channels. These interactions may anchor VDS at the resting state and impede its activation. Their results provide a mechanism by which phosphoinositides alter the voltage dependence of activation and the recovery rate from inactivation, an important step for developing novel therapies to treat Nav-related diseases. However, the study is incomplete lacks the expected confirmatory studies which are relevant to such proposals.

The authors identified a novel binding between phosphoinositides and the VSD of Nav and showed that the strength of this interaction is state-dependent. Based on their work, the affinity of PIPs to the inactivated state is higher than the resting state. This work will help pave the way for designing novel therapeutics that may help relieve pain or treat diseases like arrhythmia, which may result from a leftward shift of the channel's activation. However, the study lacks the expected confirmatory studies which are relevant to such proposals. For example, one would expect that the authors would mutate the positive residues that they claim to make interactions with phosphoinositides to show that there are much fewer interactions once they make these mutations. Another point is that the authors found that the main interaction site of PIPs with Nav1.4 is the VSD-DIV and DIII-DIV linker, an interaction that is expected to delay fast inactivation if it happens at the resting state. The authors should make a resting state model of the Nav1.4 channel to explain the recent experimental data showing that PIP2 delays the activation of Nav1.4, with almost no effect on the voltage dependence of fast inactivation.

Major concern:

(1) Lack of confirmatory experiments, e.g., mutating the positive residues that show a high affinity towards PIPs to a neutral and negative residue and assessing the effect of mutagenesis on binding.

(2) Nav1.4 is the only channel that has been studied in terms of the effect of PIPs on it, therefore the authors should build a resting state model of Nav1.4 and study the effect of PIPs on it. Minor points:

Following the reviewer’s suggestion we have conducted new simulations demonstrating that there are notably fewer protein-PIP interactions after performing charge neutralizing and charge reversal mutations to the positive residues as shown in the new Fig S6.

The reviewer mentions that if PIPs interact with the VSD-DIV and DIII-DIV linker in the resting state that it could delay fast inactivation. However as described in the original manuscript and depicted in the schematic (Fig 7) the C-terminal domain impeded PIP binding at the position in the resting state (but not the inactivated state), meaning that PIP does not bind in the resting state to delay fast inactivation. We have clarified this statement in the text on page 14 lines 1-2.

Following the reviewers suggestion we have examined PIP binding to a model of the resting state of Nav1.4 (in addition to the resting state of Nav1.7 described in the original manuscript) as described on page 12 lines 28-30 (and in Fig S12). Similar to what we saw for Nav1.7 PIP binding to VSDI-III can impair activation of the channel.

There are a lot of wrong statements in many areas, e.g., "These diseases 335 are associated with accelerated rates of channel recovery from inactivation, consistent with our observations that an interaction between PI(4,5)P2 and the residue corresponding to R1469 in other Nav 337 subtypes could be important for prolonging the fast-inactivated state." Prolonging the fast inactivated state would actually reduce recovery from inactivation and not accelerate it.

We disagree with this statement from the reviewer which may have come from a misreading of the mentioned sentence. Our statement in the original manuscript is consistent with the the original experiments that show that the presence of PIP prolongs the time spent in the fast inactivated state. Mutations at the PIP binding site are likely to reduce PIP binding, and with less PIP present the channel will recover from inactivation more quickly. We have reworded this sentence for clarity on page 13 line 27-30.

Reviewer #3 (Recommendations For The Authors):

As mentioned in the public review, overall, I am impressed with the manuscript and do think the conclusions are supported. There are, however, quite a few mistakes, mostly minor (listed below). Additionally, I do have a few questions and several extensions that could be done and I mention a few but fully realize many of those could be outside of the scope of the current manuscript.

We greatly appreciate the time taken by Reviewer 3 to carefully review our manuscript and provide detailed comments. We believe their suggestions have helped to improve our manuscript.

First comments are in general about the PIP subtype.

• In the paper you claim:

L196, "However, this loss of resolution prevents distinction between phosphate positions on the inositol group and does not permit analysis of protein conformational changes induced by PIP binding"

L367, "it does not distinguish between phosphate positions within each charge state (e.g. PI(3,4)P2 vs PI(4,5)P2)."

This is not true the PIP2 most commonly used in Martini 2 is from dx.doi.org/10.1021/ct3009655 and is a PI(3,4)P2 subtype. Also other extensions and alternative parameters exist for PIPs in Martini 2 e.g. http://cgmartini.nl/index.php/tools2/other-tools - Martini lipid .itp generator has all three main variants of both PIP1 and PIP2.

As described in the response to the public review we are grateful for the reviewer for pointing out that there do exist parameters for different PIP sub-species and have corrected our statement on page 14 to reflect this, and clarified the parameters chosen in the methods section (page 16 line 2-3). We have not run additional CG simulations with each of these parameters in the current work but use the all-atom simulations to examine the interactions of phosphates at specific positions.

• One detail that is missing in the manuscript is some mention of the charge state of the PIPs e.g. Fig.1D does not specify and Fig.4D PIP2 looks like -2 on position 5 and -1 on position 4. Which I think fits the used SAPI24, please specify. Also, what if you use SAPI25 with the flipped charges would that significantly alter the results?

The charge state of PIP2 is -2e on the 5’ phosphate and -1e on the 4’ phosphate, using the SAPI24 CHARMM lipid parameters. We have ensured that this charge information is stated clearly in the revised manuscript in the methods section on page 16 (line 21). We considered looking at SAPI25, however we expected that it would behave quite similarly, given that the PIP headgroup can adopt slightly different poses and orientations within the binding site across replicates and does fluctuate over simulations (Fig S8). We have noted this in the revised discussion on page 14 line 15-17.

• I was very intrigued and puzzled by the lower binding of PIP3 vs PIP2 in the Martini simulations. Could it be that PIP3 has a harder time fully entering the binding site, or maybe just sampling? i.e. and its lower number of binding events is a sampling issue.

We agree with the reviewer that PIP3 is less able to access the binding site than PIP2, likely because of its larger size. This might also be why we see PIP1 binding at the location via a more buried route (since it has the smallest headgroup size). However, PIP1 does not have enough negative charge to keep it in the binding site. It seems to be a Goldilocks-like situation where PIP2 has the optimal size and charge to allow access and stable binding at the site. We also see that when PIP3 enters the binding site it leaves before the end of the simulations. While it is hard to prove statistical significance given the number of binding and dissociation events even with the high and equal concentrations of all three PIP species in the enriched PIP membrane CG simulations, the data strongly suggests preferential binding of PIP2 over PIP3.

Also the same L196 sentence as above "However, this loss of resolution prevents distinction between phosphate positions on the inositol group and does not permit analysis of protein conformational changes induced by PIP binding". The later part is also wrong, there are no conformational changes due to the restraints on the protein backbone, from methods "backbone beads were weakly restrained to their starting coordinates using a force constant of 10 kJ mol−1nm−2". Martini in general might have a hard time with some conformational changes and definitely cannot sample changes in secondary structure, but conformational changes can, and have on many occasions, been successfully sampled (even full ion channel opening and closing).

On a similar note, in L179 you mention "owing to the flexibility of the linker." Hose does this fit with simulation with position restraints on all backbone atoms?

We applied fairly weak restraints to the backbone only – therefore we still observe some flexibility in the highly flexible loop portion of the linker, where sidechains are able to flip between membrane-facing and cytosol-facing orientations.

However, after reading the comments from the reviewer we have run additional simulations with an elastic network rather than backbone restraints on the DIII-DIV linker which have given further insight. As seen in Fig 4E and described in the results paragraph on page 9 line 30-37 of the revised manuscript, we can see that the presence of PIP does stabilise the linker in its receptor site. To accentuate this effect, we also ran simulation of the ‘IQM’ mutant known to have a less stable fast inactivated state due to weaker binding to the receptor. Without backbone restraints we can see partial dissociation of the DIII-DIV linker from the receptor that is partially rescued by the presence of PIP.

I know the paper focuses on PIPs, also very nicely in Fig.2B and Fig. S1-2 the lipid enrichment is shown for other lipids, but why show all lipid classes except cholesterol? And, for the left-hand panels in Fig. S1-2 those really should be leaflet specific - as both the membrane and protein are asymmetric.

The depletion/enrichment of Cholesterol is shown in Fig 2B and as are the Lipid Z-Density maps and contact occupancy structures a (in row 5 of Fig S2, labeled as CL in yellow). The Z-density maps are meant to provide an overall summary of lipid distribution. The contact occupancy structures showing the transverse views and intracellular/ extracellular views provide a better indication of the occupancy across the different leaflets.

In L237 for the comparison of Cav2.2 and Kv7.1 bound to PI(4,5)P2 structures: They do agree well with the PIP1 simulations but not as much for the main PIP2 binding site. If you look in the CG simulations, is there another (not the main) PIP2 binding site at that same location (which might also be stable in AA simulations)?

In some replicates of the CG simulations, we identify stable PIP1 binding via the other orientation (i.e. the one that overlaps with the Cav2.2 and Kv7.1 structures). Since we did not directly observe any PIP2 binding events from the other orientation, we did not run any backmapped atomistic simulations with PIP2 at this position. However, the binding site residues that the PIP1/2 headgroup binds to are the same regardless of which side PIP1/2 approaches from. We would expect that PIP2 bound from the alterative position is also stable.

Two references I want to put for consideration to the authors, for potential inclusion if the authors find their inclusion would strengthen the manuscript. This one gives a good demonstration of using the same PM mixture to define lipid protein fingerprints with Martini:

https://pubs.acs.org/doi/10.1021/acscentsci.8b00143.

And this one https://pubmed.ncbi.nlm.nih.gov/33836525/ shows how Nav1.4 function could also be affected by general changes in bilayer properties (in addition to the specific lipid interactions explored here).

We thank the reviewer for bringing to our attention these two relevant references that will help to respectively substantiate the use Martini to study membrane protein-lipid interactions, as well as, why Nav channels are interesting to study in the context of their membrane environment (and also the potential implications with drugs that can bind from within the membrane). We have added these citations to the introduction and discussion.

Minor comments and fixes:

L2, Title: A binding site for phosphoinositide modulation of voltage-gated sodium channels described by multiscale simulations

The title reads very strangely to me, should it be "A binding site for phosphoinositide" ; "modulation". We thank the reviewer for this comment - title has been updated to: A binding site for phosphoinositides described by multiscale simulations explains their modulation of voltage gated sodium channels.

L25, Abstract, "The phosphoinositide PI(4,5)P2 decreases Nav1.4 activity by increasing the difficulty of channel opening, accelerating fast activation and slowing recovery from fast inactivation." Assuming this is referring to results from Gada et al JGP, 2023 should this not be "accelerating fast inactivation"?

Corrected in the revised manuscript.

L71 maybe good to write the longer version of IFM on first use e.g. Ile-Phe-Met (IFM), as to not mistake it for some random three letter acronym.

Corrected in the revised manuscript.

L109, Fig.2. Maybe change the upper and lower leaflet to intracellular and cytoplasmic leaflets (or outer / inner). In D "(D) Distribution of PIP binding occupancies (left)" something missing can I assume, for/over all lipids exposed residues. Also, for D I am a little confused how occupancy is defined as the total occupancy per residue dose not add up to 100.

The figure has been updated with intracellular and cytoplasmic leaflet labels. The binding occupancy distribution boxplot shows binding occupancies for all lipid exposed residues. In our analysis, we define contact occupancy as the proportion of simulation time in which a lipid type is within 0.7 nm of a given residue. It is possible for more than one lipid to be within this cut in any given frame – that is, both a PIP and PE can be simultaneously bound.

L160 "occurring the identified site" in the

Corrected in the revised manuscript.

L170 "PIP3 (headgroup charge: -7e) has interacts similarly to PIP1," - remove has Corrected in the revised manuscript.

L194, "reducing system size" the size does not change, I am assuming you want to say reducing the number of particles?

Corrected in the revised manuscript.

L252, Fig.6 "(B) Occupancy of all PIPs (PIP1, PIP2, PIP3) at binding site residues in the three systems" A little confusing, initially was expecting 3x3 data points per residue, maybe change to, Combined occupancy of all PIPs...

Corrected in the revised manuscript.

L253, Fig.6 D, I don't really have a good suggestion for improvement here, so this is just a FYI that this panel was very confusing for me and took some time to figure out what is shown.

We have added to the caption of Fig. 6D to try to clarify this panel.

L257, Fig.6 (F) not in bold

Corrected in the revised manuscript.

L259 "PIP binding, we performed embedded three structures of Nav1.7" something missing?

Corrected in the revised manuscript.

L272, "In triplicate 50 us coarse-grained simulations" us instead of (micro_greek)s

Corrected in the revised manuscript.

L272, that paragraph how long/many simulations only reported for the inactivated Nav1.7 system not the Nav1.7-NavPas chimera, which I am assuming is the same?

Corrected in the revised manuscript.

L297, "marked by both shortened inactivation times", can I assume this is: shortened times to inactivation (i.e. to get inactivated not times in the inactivated states)?

Corrected in the revised manuscript.

L331, "are conserved in Nav1.1-1.9 (Fig. 5D)," Fig.5C Corrected in the revised manuscript.

L353, "channel opening []" [] maybe a missing reference?

Thank you for pointing out this oversight - Goldschen-Ohm et al. has been cited here.

L394, "The composition of the complex mammalian membrane is as reported in Ingólfsson, et al. (38)." Ref 38 is the "Computational lipidomics of the neuronal plasma membrane" which indeed uses the 63 component PM but the original reference for the average 63 lipid mixture PM is dx.doi.org/10.1021/ja507832e.

Corrected in the revised manuscript.

L404, "Additionally, a model Nav1.7 with all four VSDs in the deactivated state using Modeller (40)." Something missing, e.g. was also built and simulated for ...

Corrected in the revised manuscript.

Table S1 "Disease information", I am guessing this should be Disease information; mechanism? Of the x5 entries two have mechanism, one has "; unknown significance ", one has "; unknown" maybe clarify in title and make same if unknown.

Corrected in the revised manuscript.

Table S1 and S2 have different styles.

The tables have been amended to have the same style.

Fig. S3 "for all 12 lipid types in the mammalian membrane " there are many more lipid types in a typical PM (hundreds) and 63 in the PM mixture simulated here, so maybe write: 12 lipid classes?

Corrected in the revised manuscript.

Fig.S6 PIP headgroup, can I assume that is for the bound PIP only, please specify.

Only a single PIP at the identified binding site was backmapped into all cases of atomistic simulations. We have now clarified this point in the methods, results and the FigS6 caption.

Writing of PI(4,5)P2 and PI(4)P1 most of the time use 1 and 2 as subscripts but not always (at least not in SI), also the same with Nav vs Na_v (v subscript) and even NAV (in Table S1).

Subscripts have been implemented in the updated Supplementary Information (as well as within various figures and throughout the manuscript).

It's weird to me that people during the times of the trials thought that the animals/insects would just do what they were commanded to.

It's interesting that in some cases, aside from just standing trial like a person would, some animals were even dressed up in human clothing. I wonder if this was to put them at the same level as a person, or to make it seem like they have the same thinking process and awareness.

Sports being a part of the culture is fine. There are many sports that are a big part of many cultures, however, to compared to religion just isn't right in my opinion.

This is still mind-blowing to me that people can get this upset over sports. I love sports, I grew up watching and playing them. I remember being competitive and still am. I watch my kids play and sometimes things get heated, but you have to remind yourself it's just a game.

This paragraph talks about how social media, especially Instagram, might affect mental health, especially for teenage girls. It mentions different opinions, a study by Meta, anecdotes from cosmetic surgeons, and thoughts from comedian Bo Burnham. The passage acknowledges the complexity of measuring social media's impact and mentions a "digital detox." It doesn't label social media as entirely good or bad, aiming for a more nuanced view. I find it interesting, but the whole social media and mental health issue is really complicated.

Indeed, social media yields a dual impact on our lives, comprising both favorable and unfavorable consequences. On one hand, it serves as a conduit for global connectivity, fostering relationships across geographical boundaries. Conversely, it often instigates feelings of isolation and excessive mental stimulation. This paragraph underscores the exacerbating effect of social media on the existing challenges confronting youngsters. Acknowledging these hurdles is crucial, prompting efforts to navigate a harmonious equilibrium in our technological engagement.

I think Bo Burnham's reflections highlight the paradox of social media, which is a tool that simultaneously connects and isolates, empowers and objectifies. so it shows the need for a vital understanding of its impact, and the line between beneficial and harmful effects is often blurred. it becomes crucial to examine not just the immediate effects on individual mental health, but also the broader societal implications of our online interactions.

This survey is not comprehensive because there is evidence that various factors contribute to the decline in mental health, affecting both males and females. While social media is implicated, in 2019, there were additional influences affecting me personally. During COVID, I was just beginning to adapt to socializing, but everything I had learned was erased, and it seemed like others were also struggling to express themselves. It felt as if the world around me had changed. Moreover, it's not limited to just Generation Z; millennials are also facing challenges, and there are concerns that Generation Alpha may struggle as well.

The examination of social media's impact, particularly its detrimental effects on the mental health of teenage girls, highlights a critical area of concern within the digital age's societal framework. The revelation from Meta's internal study about Instagram underscores the ethical dilemma faced by social media corporations: the responsibility to safeguard the mental health of their users while balancing the inherent drive for engagement and growth. This conundrum is further complicated by the rising trend of individuals seeking to emulate digitally altered self-images, which distorts perceptions of body image and exacerbates mental health issues.

I think it's unfair to idealize both and there should be a sort of middle ground for things like this. It's always important to go outside because while you might be online a lot, it's not real, and it's important to appreciate the world as well as its inhabitants.

I think the discussion concerning social media and mental health is not about deciding between blatant rejection and unquestioning acceptance. It is about achieving a balanced relationship with digital technology, in which the advantages are maximized while the hazards are actively addressed through informed use, supporting communities, and responsible platform administration.

When you have an animated face, it conveys that you take interest in what you are saying, even if that may not be the case. It also makes it easier to connect with your audience. Jim Carey is a good exaggerated example of this. In a speech, it's easy to get so caught up in what you have to say. How you present yourself is just as important in getting your message across.

The persona seems to be trying to encourage herself to get out of her downtrodden state after breaking up with her lover, in order to boost her own self-esteem and not leave any trails of sadness for remembering her lover that she has lost. However, her encouragements seem to fail as the persona remembers all the memories she has had with her partner, hence she finds it a deep struggle to completely forget about this relationship. The rhyme of "echo" and "go" connects the two words together, placing more emphasis on these words. The persona seems to be using such high intensity of words to highlight that forgetting her partner is ultimately challenging due to how her partner will constantly be echoed in her mind. Whenever she tries to forget about her partner, his memories just seem to come back like an anchor that is stuck on the persona, showing how trapped the persona is from this already broken relationship, suggesting how much she has cared for this relationship which cannot be forgotten so easily. Seeing the persona desperately trying so hard to leave her miserable state but with no effect because of how precious her lover is is saddening for me to watch since I cannot help at all. I can reflect on the theme of self-encouragement, as sometimes when we are extremely grieved and saddened about something, the best source of medicine to cure ourselves would be to simply encourage ourselves to push out of our misery state, even if it is too difficult.

OMGG SHES A SOLDIER CU I WOULDVE FAINTED

WHY IS HE LIKE THIS

uhghghgh its comments like these man

@Suzanne check this out,

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Referee #1

Evidence, reproducibility and clarity

In this paper, Kerwin et al. investigate the role of the GGNBP2 in synaptic morphology and autophagy in motor neurons. Using Drosophila, the authors performed functional studies of GGNBP2, a putative nuclear protein which has been linked to ALS through GWAS. Through creating clean mutants using CRISPR, the authors found that the null mutants of the fly homolog of GGNBP2 (CG2182, a previously uncharacterized gene which they propose to name Ggnbp2) are viable and fertile, but exhibit motor defects in adult flies accompanied by synaptic phenotypes in the larval neuromuscular junction (NMJ). In addition, the authors show that overexpression of Ggnbp2 also cause behavioral and NMJ defects, which is significant for ALS studies since the variant associated with this condition seems to increase the levels of GGNBP2 based on eQTL studies. Interestingly, the human GGNBP2 was able to rescue the fly LOF mutant phenotypes, suggesting that they have conserved molecular functions. Surprisingly, while mammalian GGNBP2 has been suggested to function as a transcription factor and gain and loss of this gene seems to mildly alter the transcriptome in flies, the authors showed that majority of the endogenously expressed fly Ggnbp2 protein is found in the cytoplasm and that the predicted nuclear localization signal (NLS) is not required for its function in motor neurons. Finally, the authors performed some additional experiments to propose a functional link between this gene and autophagy, focusing on its potential regulation of PI(3)P and genetic interaction with a fly ortholog of TBK1, which have also been linked to ALS in human.

Overall, I feel this work addresses an important question in the field and the genetics experiments have been conducted with rigor. This study somewhat lacks mechanistic insights (e.g. how does Ggnbp2 regulate PI(3)P and motor neuron function?) but there are a number of novel findings (e.g. first generation and characterization of the null mutant of Ggnbp2 in flies, showing that it's predicted NLS is not important) that makes this paper provide value to the literature and community in its current form. While I have several major and minor issues that I would like to see addressed the authors, I would be generally in favor of this paper to be published in an appropriate journal that targets readers with interests in human neurological disorders and Drosophila biology.

Major Points

Major #1: In Figs 5, 6 and S3, the authors demonstrated significant rescue of Ggnbp2 null phenotypes by overexpressing fly Ggnbp2 or human GGNBP2 protein using the GAL4/UAS system. However, data shown in Fig 3 and elsewhere reveals that overexpression of fly Ggnbp2 results in smaller bouton numbers and larger boutons. Regarding this...

1A: Does overexpression of human GGNBP2 in a wild-type background show similar NMJ and motor behavioral defects as fly Ggnbp2?

1B: It is quite surprising that the authors were able to rescue the null mutant NMJ phenotype using GAL4/UAS (in this case OK6-GAL4) system considering that overexpression of this protein seems to have a strong effect using this driver as well. Is this because they used the UAS-dGgpnb2::V5 as a heterozygous in FigS3, which is a condition in which the overexpression phenotype is not seen? If so, the genotype of FigS3 (and Fig3) should be matched with FigS4 (otherwise, it looks like the authors used homozygous of the UAS in FigS3).

Major #2: To assess adult fly locomotor performance, the authors employed the negative geotaxis assay to measure their climbing activity (Fig4). While the data show that the flies with LOF or GOF of Ggnbp2 have age-dependent defects, it is possible that the effect is developmental, especially for the overexpression paradigm. Considering that ALS is considered to be an adult onset neurodegenerative disease, it would be valuable if the authors can perform a conditional overexpression study of Ggnbp2[OE-EPgy2] using the Gal80[ts] system in which the fly Ggnbp2 that is overexpressed post-developmentally (i.e. overexpression of this protein induced only after eclosion) can also have an age-dependent motor defect. Considering that the authors do not perform any synaptic studies in adults (i.e. all NMJ experiments are performed in the larva), such experiments will increase the value of this work in the context of ALS research.

Major #3: The authors generated several of UAS-fly Ggnbp2 (V5 tagged with or without the NLS) and UAS-human GGNBP2 (Myc tagged). Regarding these...

3A: Other than in Fig6A, the authors do not show their expression pattern in motor neurons. It would appreciate if the authors can provide an immunostaining image of the all three proteins in the cell body and neurons of flies when expressed using OK6 or OK371. This way, the readers can appreciate whether the human and fly proteins behave similarly, and whether the deletion of the predicted NLS alters the subcellular localization of the protein. I acknowledge that it may be already difficult to observe the wild-type Ggnbp2 in the nucleus so one may not see a major difference but it would be important to document these.

3B: Considering that the Ggnbp2::V5 seems to show a punctate pattern, may be interesting to see if this signal overlaps with the Atg8a, PIP2 and 2xFYVE::GFP in the cell body or in the synapse.

Minor points

Minor #1: In line 62, "Given that 75% of genes..." needs a minor correction, as 77% is the the number of gene that is mentioned in the cited reference (77%). Perhaps the authors can say "Given that about 75% of genes...".

Minor #2: In line 266, the title of this section is "dGgnbp2 is linked to autophagy in motor neurons" but the author only shows data regarding the genetic interaction between Ggnbp2 and ik2 (official gene name is IKKε in FlyBase) in this section. Although IKKε and its mammalian homolog TBK1 is known to regulate autophagy, these are kinases that are involved in other processes (e.g. cell proliferation, cell death, cell polarity) so the title is a bit of an overstatement. Since the connection to autophagy is more directly shown in subsequent sections, I would recommend modifying the title of this section (e.g. "dGgnbp2 genetically interacts with IKKε, an ortholog of mammalian TBK1"". Also, note that IKKε is orthologous to both TBK1 and IKBKE so this may need to be clearly mentioned in the text.

Minor #3: In line 282, the loss-of-function (lof) allele for ik2[1] requires proper reference that experimentally showed that this is indeed a lof allele. Also, please change the 'ik2' nomenclature to 'IKKε' to match with the latest official gene name.

Minor #4: In FigS2, can the author show where the predicted NLS of the fly protein is that they deleted in Fig6E so the readers can see how conserved this region is between the fly and human proteins?

Minor #5: I personally feel that the section regarding "RNA-seq analysis of Ggnbp2" is a bit out of place. Currently, this follows the section that says Ggnbp2 is does not function in the nucleus, so it doesn't make much sense to perform RNA-seq experiment for something that you think primarily works in the cytoplasm if the goal of this assay was to find direct mechanistic targets. Perhaps the authors can consider showing moving this section to before the "dGgnbp2 functions in the cytoplasm of motor neurons" (and place Fig7 before Fig6) so you can use the fact that you didn't see much dramatic gene expression changes in the LOF/GOF mutants as a rationale of why you decided to question its nuclear requirements. Just a suggestion, but this may make your paper flow better.

Significance

Overall, I feel this work addresses an important question in the field and the genetics experiments have been conducted with rigor. This study somewhat lacks mechanistic insights (e.g. how does Ggnbp2 regulate PI(3)P and motor neuron function?) but there are a number of novel findings (e.g. first generation and characterization of the null mutant of Ggnbp2 in flies, showing that it's predicted NLS is not important) that makes this paper provide value to the literature and community in its current form. While I have several major and minor issues that I would like to see addressed the authors, I would be generally in favor of this paper to be published in an appropriate journal that targets readers with interests in human neurological disorders and Drosophila biology.

I never knew that CA varied in different aspects of our lives like that. I know that my anxiety acts up at job interviews, public speaking, and if I'm going somewhere alone where I don't other people who will be there. I wonder if that's just my anxiety, or if it's CA.

(#25)

*G2. (Savannah) Is the easiest way to determine if a fixed-effects or random-effects model is better for your data just running both models and seeing if there is a difference? For example, if both models yielded the same results, could we comfortably rely on the random effects model?

Response: You can eyeball it to see if they are different. If you need a test (i.e., to convince a reviewer), the Hausman test provides a formal evaluation of whether the FE and RE results are the same. (phtest in the plm library). https://libguides.princeton.edu/R-Panel#:~:text=If%20the%20p%2Dvalue%20is,to%20use%20fixed%20effects%20models. In the hybrid models in class H we will do both within (FE) and between (RE) at the same time.

*G3. (Delaney) ^ Yes can we talk more about using different models and how to know which one is the best? I think it's interesting that FE models are better almost all the time but we learn about other models too.

Response: With many problems, FE are definitely a step forward. However, when we consider multilevel models we will be dealing with a whole class of models that are built on a RE framework, so we will be considering lots of cases coming up where we use an RE approach. Also, we will cover hybrid models that use both RE and FE to look at within and between estimates.

Listen, Listen to me!

• The word intensity of “Listen to me!”, emphasises how much Bocchi wants others to listen to her, to understand her struggles and to be with her so that she can leave the lonely “blue planet” which she hates so much. The addition word “me” of the second plea conveys how Bocchi doesn’t want people’s attention to be at her “uncool shadow” or even her ”singing”, she wants it to be at her, her bottled up emotions. Bocchi wants to find a way to let it all go, she wants to vulnerable to others so that she doesn’t need to bear her burden of carrying these scary emotions around anymore. She wants to find someone she can trust to share this baggage with.

(I’m not sure if the following analysis should be allowed, but I saw this idea on a comment thread and felt that it should at least be shared)

• The original lyrics of “Listen, Listen to me” is 聞いて and 聴けよ respectively. The former is a verb that is used with a lower intensity, and the latter is of a higher intensity. So looking at these two phrases from the surface, the growing intensity in diction from one phrase to another symbolises Bocchi’s growing desperateness to be heard and to be noticed.

• But if we were to look deeper, the general use of the former phrase is to “listen to me like I’m white noise and only do it just because I exists, not because you want to listen to it”. This would then emphasises how Bocchi saw herself as a burden, someone that causes inconvenience to others. This conveys the overwhelming sense of self-doubt Bocchi feels to the extent that it overpowers Bocchi’s craving for human connection and attention, which like seen before, is written to be a a huge desire of Bocchi’s.

• But the second phrase is used in the Japanese language when a person genuinely wants to listen and pays attention. Bocchi choosing to use the latter phrase’s connotation emphasises how somehow, one way or another, she views herself as something more, something worth others to listen to. Previously, she always expected others to just listen to her with the former phrase since she thought that that was all she deserved. Thus, ordering the latter phrase second strongly suggest that something changed in her life, and the one other time she mentioned that something has changed is after she learnt her guitar. And based on the analysis in verse 2, we can’t conclude that this change in attitude both triggered by her being tired of constantly being unnoticed as well as her newfound pride in playing and expressing herself through guitar gave her the confidence she needs to think herself that she is worthy and deserving of other’s attention. I feel proud for Bocchi here because of how she is able to break free of the toxic mentality of thinking that she isn’t worth it. Previously, she only hoped that people with listen to her because she never really experienced it and thus never expected people to actually want to listen to her. But now, this change in connotation emphasises how she now thinks that she does deserve people’s attention, and that can be seen in the next chorus.

(I just realised all of these is technically the same thing oh what the heck)

• Theses lines could repeat the idea of the last quote, where this line is Bocchi’s assumption of what typical people worry about and that change is happening to their lives, making it more meaningful. Bocchi’s own thoughts than later states “Spring and fall, where did they go”? The juxtaposition here once again emphasises like last time that Bocchi doesn’t see the same change in her life that she sees in other’s. That life is just the same loop and there are no changing of season's in her life.

• However, this line could also suggest that Bocchi feels pressured by society’s expectations, and she feels a little dissatisfied that she didn’t really have the time to meet them. If taken that Bocchi wrote the the first half of this quote as a question asked by herself, then this could be the case. The word “wear” connotes that Bocchi is concerned about her how she looks to others both physically and not physically. This could be caused by the pressures she feel from having to meet society’s expectations of her. This is further developed later if the quote “spring and fall, where did they go?” Is read with a more betrayed tone. If this is the case, Bocchi is frustrated at time passing so quickly, which could also suggest that Bocchi feels that she doesn’t have enough time to meet society’s and her own expectations. I feel pity for Bocchi here because of how she feels pressured by society’s expectations and that her choice aren’t really ones that she wants to make, but rather choices she thinks she must make in order to maintain her physical image so she will fit into society better. I can reflect on the theme of expectations and how our expectations of ourselves are often there as a result of society's expectations of us. This can suggest how the pressure we exert on ourselves is unnecessary and even harmful to us just like how it makes Bocchi feel like an outcast here.

Main idea of verse 1

• Main ideas might contain context from anime just to have a more accurate representation of what the song means in context of the anime
• I think the main message of verse 1 is that Bocchi feels disconnected from society and has felt this way from young. She is unable to relate to society and feels pressured by what it expects of her. The last line of the verse can also suggest how time passes so quickly for her due to how boring and meaningless Bocchi finds her life to be everyday feels like the previous one.

Replacing an elixir, touched my chipped nail

• Looking at this quote literally, the elixir refers to a type of guitar string, so this quote literally is “The process of replacing a musical string is quite annoying”. With this context and the later quotes, it’s safe to assume that she is replacing a guitar string on a guitar. This phrase gives readers the impression that Bocchi dislikes maintaining her guitar and that Bocchi might even have regretted picking it up. The tactile imagery of “touched my chipped nail” conveyed Bocchi’s physical pain as she practised her guitar, mirroring the idea from the previous quote that she disliked it.

The 300mm body screams desperately - This personification emphasises strongly how it’s not just the guitar screaming out, but it’s also Bocchi who is playing it who is expressing herself and screaming out. This is a callback to the chorus where Bocchi repeatedly used to word “scream” as a way for her to let her feelings and emotions out and the word intensity in “desperately” once again shows a desire to be noticed, to have a connection with society and be a part of it.

For music, this is the earth - The “earth” is a metaphor for Bocchi’s own reality, her own world and that emphasises the significant impact music has on her and explains why she feels that its her only form of expression. To Bocchi, since she revealed before that she feels that she can freely “scream” and express herself or be vulnerable to it, and that to her it is everything, it emphasises how the first two line of the verses have a hidden meaning. The quotes “The process of replacing an elixir is quite annoying” and “I slightly touched my chipped nail” now emphasises that Bocchi herself did indeed feel pain and do find the process annoying, but she never resented or regretted it. Instead, considering how to her her guitar is everything, she’s going through all that physical pain willingly just to play the instrument. This sacrifice emphasises how much Bocchi relies on her guitar and the great significance it has in her life as it is her source of expressing herself and that its her method of escaping the real world into her “earth”. This is huge contrast compared to the pervious verses which stated how Bocchi doesn’t really feel much change in life. But Bocchi noticing her guitar and having a genuine relationship also shows how much the guitar has affected her in her life.

Main message of verse 2

• Verse 2 mainly talked about how much Bocchi loves playing her guitar, that despite the difficulties and pain it causes to her, she still plays it and the lyrics reveals that Bocchi does this as it is her way of expressing herself. In fact, it goes onto convey how the guitar is her entire world because it is the only object that she feels safe expressing herself with.

Dizzying spiral

• The diction of “dizzying“ emphasises how Bocchi can’t see of feel things clearly. This could mean that Bocchi herself cannot find a direction in her life, an idea elaborated later on. It could also connote how Bocchi feels disconnected from her own sense of self in the midst of this “spiral”. While a bit of a stretch, Bocchi cannot feel or understand her own emotions in this spiral since she cannot see or feel clearly. This could symbolise how overwhelming her emotions and thoughts are to the extent that she is unable to control or even understand them.

• The visual imagery in “spiral” paints the picture of a never ending circular staircase. Bocchi wrote this as a metaphor for her life, emphasising how Bocchi views her own life as a never ending circle. This emphasises how much pain and pressure Bocchi is feeling under the pressures of life or the “information”. Circles are also perfectly smooth and has no edges, and Bocchi used this metaphor to convey her own dissatisfaction with her life. She thinks that her life has no bumps and edges or that there aren’t any perks to this. It’s just the same texture, mirroring the same idea in verse 1.

It won’t stop, my heart just wont stop

• The “it” and “heart” refers to Bocchi’s craving to play her guitar and the repetition of “stop” emphasises how much she needs some way to express herself in her life as “if” it stops, if she doesn’t plays her guitar, Bocchi will be unable to express herself and after spending all her life being trapped in the endless cycle of feeling alone, she just doesn’t want to relive that experience. She needs to let her bottle emotions and thoughts out and she feels so disconnected and trapped that the only way she feel that she can do that comfortably is with her guitar.

I’m singing just like the fool I am

• The diction of “fool” in this part of the chorus emphasises how Bocchi considers herself to be less than others, once again mirrors the idea that she is afraid she is inferior compared to others, This emphasis on how Bocchi feels useless and powerless also conveys how worthless and undeserving she feels, suggesting how unlikely she thinks others will notice her. This line explains why Bocchi, despite wanting social connections so much, still cannot let herself be vulnerable to others. It’s hard for her to believe that others have good intentions and that she can confide in them.

Main message of Chorus 2

• This chorus like before reiterates the idea in verse and pre-chorus 2, emphasising how to Bocchi, the guitar is giving her hope in her misery and suffering. It also heavily emphasises how inferior Bocchi feels compared to others, how she is afraid that they are against her and will stop her from being ”noticed” by others.

Author Response

The authors' responses to the public reviews can be found here

The following is the authors’ response to the most recent recommendations.

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

I appreciate the effort that the authors have put into this revised version of the manuscript. Before going into details, I would suggest that, in the future, the authors include enough information in their response to allow reviewers to follow the changes made. Not simply "Fixed", but instead "we have modified the description of these results and now state on lines XXX to XXX (revised text)".

We greatly apologize, we certainly did not wish to cause more work for the reviewer to find the necessary changes. We will list the line number and our changes in the following response.

The authors' response to my comments was confined to the minor points, with no attention to more important questions regarding speculations about mechanism which were (and still are) presented as factual conclusions. I do not consider the responses adequate.

We responded to each of your comments and where we disagree, we have explained in detail.

With respect to the meaning of "above" and "below" in the context of an intracellular organelle, I think that referring to up and down in a figure is fine, provided that the cytoplasmic and luminal sides are indicated in that figure. I think that labeling to that effect in each figure would be immensely helpful for the reader.

We agree with this point and have updated all the figures to include these labels.

The statement on lines 333-335 about non-competitive inhibition is a bit naïve. The only thing ruled out by this type of inhibition is that substrate and TBZ binding do not share the same binding process, in which case they would compete. It doesn't show that TBZ gets to its binding site from the lumen or from the bilayer, or by any other process that isn't shared with substrate. It also doesn't rule out kinetic effects, such as slow inhibitor dissociation, that result in non-competitive kinetics. Please rewrite this sentence to indicate that one explanation of the non-competitive nature of TBZ inhibition would be that TBZ diffuses into the vesicle and binds from the lumen. It's not the only explanation.

We have changed this sentence lines 334-336 to be more speculative and not include any statement about non-competitive inhibition. Please see, “Studies have proposed that TBZ first enters VMAT2 from the lumenal side, binding to a lumenal-open conformation.”

The revised version integrates the MD simulations into a plausible mechanism for luminal release of substrate. A key element in this mechanism is the protonation of D33, E312 and D399, which allows substrate to leave following water entry into the binding site. The acidic interior of synaptic vesicles should facilitate such protonation, but the fate of those protons needs to be considered. Are any of them predicted to dissociate prior to the return to a cytoplasm-facing conformation? If so, are all 3 released in that conformation? Postulating protonation events at one point in the reaction cycle requires some accounting for those protons - or at least recognition of the problem of reconciling their binding with the known stoichiometry of VMAT.

We completely agree with this point and while we cannot account for all protons with a single structure and simulation of neurotransmitter release, some discussion of the fate of the protons is warranted. We have included a highly speculative statement in the discussion on this point, see lines 462-465, “Given the known transport stoichiometry of two protons per neurotransmitter, we speculate that two protons may dissociate back into the lumen, perhaps driven by the formation of salt bridges between D33 and K138 or R189 and E312 for example in an cytosol-facing state.”

Reviewer #3 (Recommendations For The Authors):

On page 13, line 238, the statement "The protonation states of titratable residues D33, E312, D399, D426, K138 and R189, which are in close proximity to TBZ, also impact its binding stability (Table 4)" is misleading. Table 4 only shows that D426 is charged and what the pKa values are. This should be rephrased to separate out which residues are in close proximity from what is known about how their protonation states affect TBZ stability.

We agree with this statement and have rephrased this on line 290-294 on page 13 to read, “Several titratable residues, including D33, E312, D399, D426, K138, and R189, line the central cavity of VMAT2 and impact TBZ binding stability (Table 4). We found that maintaining an overall neutral charge within the TBZ binding pocket, as observed in system TBZ_1, most effectively preserves the TBZ-bound occluded state of VMAT2. Residues R189 and E312 in particular are within close proximity of TBZ and participate directly in binding.” We note that given the acidic pH of the vesicle lumen (5.5), it is likely all four residues may be protonated to a significant degree in this state.

Typos:

• luminal is another name for the drug generically known as phenobarbital, lumenal means in the lumen. (This typo seems to have crept into the published literature now too).

Thank you for pointing this out. Indeed, we had considered carefully whether to use ‘lumenal’ or ‘luminal’ in our revised text. In fact, both are used interchangeably throughout the scientific literature and luminal is the more commonly used term. Please also see: https://www.merriam-webster.com/medical/luminal we do agree that there may be confusion because ‘Luminal’ is a trademark of phenobarbital. Therefore, we have changed the text to read ‘lumenal’ throughout.

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

Reviewer #1 (Recommendations For The Authors):

I congratulate the authors on this study, which I enjoyed reading. Overall, the study reports a novel and exciting new structure for a member of the SLC18 family of vesicular monoamine transporters. Associated MD, binding and transport assays provide support for the hypothesis and firm up the modelled pose for the TBZ drug. The main strengths of the study largely sit with the structure, which, as the authors say, provides additional and essential insights above those available from AF2. The structures also reveal several potentially interesting observations concerning the mechanism of gating and proton-driven transport. The main weakness lies in the limited mutational data and studies into the role of pH in regulating ligand binding. As detailed below, my main comment would be to spend a little extra time expanding the mutational data (perhaps already done during the review?) to enable more evidence-based conclusions to be drawn.

We thank reviewer #1 for their helpful comments and suggestions. We agree that mutational analysis specifically of neurotransmitter transport would strengthen the mechanistic conclusions of the work. We also agree with reviewer #1 and #3 that the role of pH and the protonation state of charged residues was a weakness in the first version of the manuscript. Therefore, we have expanded our mutational and computational data as detailed below and we believe that this has further solidified our findings.

Specific comments & suggestions:

It is an interesting strategy to fuse the mVenus and anti-GFP nanobody to the N-/C-termini. The authors should also include in SI Fig. 1 a full model for the features observed in these maps and deposit this in the PDB.

Great point, we have made a main text panel describing the construct. Figure S1 includes a full description of the construct. The reviewer will note that the PDB entry contains the entire amino acid sequence of the construct and while the GFP and GFP-Nb cannot be well modeled into the density, we have included all of the relevant information for the reader.

Difficult to make out the ligand in Fig. 2b, I would suggest changing the color of the carbon atoms.

Fixed.

It is difficult to make out the side chains in ED Fig. 5d.

This is now its own supplemental figure and is presented larger.

ED Figures are called out of order in the manuscript. For example, in line 143 ED Fig.6 is called before ED Fig. 5d (line 152), and then ED 5d is called before ED 5a. This makes it rather confusing to follow the description, analysis, and data when reading the paper. Although there are other examples. I would suggest trying to order the figure callouts to flow with the narrative of the study.

Agreed. Fixed.

It wasn't clear to me what the result was produced by just imaging the ligand-free chimaera protein. It would be useful to say whether this resulted in low-resolution maps and whether the presence of the TBZ compound was essential for high-resolution structure determination.

The ligand is likely required for structure determination. We have not, however, made such a statement largely because we have yet to determine an apo reconstruction.

The role of E127 and W318 on EL1 in gating the luminal side of the transporter is very intriguing. As the authors suggest, this may represent an atypical gating mechanism for the MFS (line 182). I did wonder if the authors had considered providing more insight into this potentially novel mechanism. Additional experiments would be further mutations of W318 to F, Y, V, and I to see if they can identify a non-dead variant that could be analysed kinetically. They may have more luck with variants of E127, as they suggest this stabilises W318. If these side chains are important for gating and transport regulation, one might expect to see interesting effects on the transport kinetics.

This is a fantastic suggestion. We have done this, and we think that the reviewer will find the results to be quite interesting. Some VMAT2 sequences have an R or an H at position 318 while VPAT has an F at the equivalent position. We have made these mutants including the E127A mutant and analyzed them using TBZ binding and transport experiments. Interestingly the W318R, H, and F mutants preserve activity in varying degrees with the R mutant closely resembling wild type. W318A has no transport activity. Only the W318F mutant retains some TBZ binding. The E127A mutant also has little transport activity but nearly wild type like TBZ binding which we believe suggests a role for this residue also in stabilizing W318.

The authors identify an interesting polar network, which is described in detail and shown in Fig. 2d. However, the authors present no experimental data to shed further mechanistic insight into how these side chains contribute to monoamine transport or ligand binding. Additional experiments that would be helpful here might include repeating the binding and competition assays shown in Fig. 1c under different pH conditions for the WT and different mutations of this polar network. At present, this section of the manuscript is very descriptive without providing much novel insight into the mechanism of VMAT transport. I did wonder whether a similar analysis of pH effects on DTBZ binding might also provide insight into the role of E312 and the role of protons in the mechanism.

Thank you, we have addressed this point in several different ways. The first is that many of these residues have already been characterized in several earlier studies, see refs 31, 32, and 42 and we have incorporated this into our discussion where appropriate. With respect to E312, the reviewers’ comments are again very appropriate. We have addressed this using computational experiments exploring the protonation status of E312 and other residues as well as TBZ. Our simulations and Propka calculations clearly show that E312 must be protonated and TBZ must be deprotonated to maintain TBZ binding. We have also extended these computational studies toward understanding the protonation status of residues which orchestrate dopamine binding and release.

The authors then describe the binding pose for TBZ. This section also provides some biochemical characterisation of the binding site, in the form of the binding assay introduced in Fig. 1. However, the insights are again somewhat reduced as the mutants were chosen to show reduced binding. Could the authors return to this assay and try more conservative mutations of the key side chains to illuminate more detail? For example, does an R189K mutant still show binding but not transport? Similarly, what properties does an E312D have? The authors speculate that K138 might play a role in coupling ligand binding/transport to the protonation, possibly through an interaction with D426 and D33 (line 236). Given the presence of D33 in the polar network described previously, I was left wondering how this might occur. I feel that some of the experiments with pH and conservative mutants might shed some light on this important aspect. Please label the data points in Fig. 3d.

Indeed, alanine mutants at these positions while valuable do not provide the level of detailed insight into mechanism that we also would have liked to obtain. Thus, we have made more conservative and targeted mutants like the R189K mutant and various mutants at N34 for example and tested them in both transport and binding assays. We have also made a mutant at K138 and found that it is not transport competent or able to bind TBZ to a significant degree. With respect to labels and color codes, we have made the color codes consistent between the bar graphs and the curves. We have also labeled the data points in the figure legends.

The manuscript currently doesn't present a hypothesis for how TBZ induces the 'dead-end' complex compared to physiological ligands. Does the MD shed any light on this aspect of the study? If the authors place the physiological ligand in the same location as the TBZ and run the simulation for 500ns, what do they observe? 100ns is also a very short time window. I appreciate the comment about N34 in line 303, but is this really the answer? It would be very interesting to provide more evidence on this important aspect of VMAT pharmacology.

MD with a natural ligand (dopamine) provides substantial insight into why TBZ is a dead-end complex. Since water cannot penetrate into the binding site in the TBZ bound complex, this does not allow for substantial luminal release. In contrast, simulations conducted in the presence of DA bound to the occluded VMAT2 show the propensity of that structure to accommodate an influx of water molecules that promote the release of DA to the lumen. The new results are illustrated in Figure 5 (main text) as well as supplemental figure 8 panels d-h. The new simulations further emphasized the importance of the protonation state of acidic residues near the substrate-binding pocket.

Reviewer #2 (Recommendations For The Authors):

Line 68, "both sides of the membrane" -> "alternately to either side of the membrane".

Fixed. Thanks.

Transmembrane proteins in intracellular organelles present unique issues of nomenclature. I suggest the authors refer to cytoplasmic and luminal faces of the protein (not intracellular or extracellular (line 124)) and adhere to these names to avoid confusion. This creates problems for loops called IL and EL, but they could be defined on first use.

We agree with this point and had initially gone with the conventional definitions used in the literature. We have now changed this throughout the text to be luminal and cytosolic.

Lines 135-6, are these residue numbers correct? The pdb file lists 126 as Asp and 333 as Ala.

Thank you. This is fixed.

ED Fig. 6 is not clear. A higher-resolution figure is needed.

We have updated this figure and hope that the reviewer will find it to be much clearer.

Lines 158-9, Is there any data to support effects on dynamics or folding? If not, please indicate that this is speculation.

Fixed.

Line 174, Should "I315" be "L315"?

Fixed.

Line 179, Please indicate what is meant by "inner" and "below" (also lines 183 and 258).

We have added Figure calls here where needed.

Line 192, S197 is listed as part of polar network 1, but not discussed further. Is it actually involved, or just in the neighborhood?

It is part of the network, but we did not discuss in further detail because we do not have data indicating its precise function and thus have left this as a description.

Line 199, E312, and N388 are fairly distant from each other. Do you want to clarify why they represent a network?

While they are not within hydrogen bonding distance, we nevertheless include them as part of the same network because they may come into closer proximity in a different conformational state.

Line 206, Protonation of all 3? VMAT2 doesn't transport 3 protons per cycle. Please clarify.

We believe that these residues may be protonated, but they may not necessarily all be involved in proton transport.

Line 219, Do you mean the aspartate unique to DAT, NET, and SERT? This is Gly in all the amino acid transporters in the NSS family. Please be specific.

Fixed. Thank you.

Line 224, "mutation of E312 to Q" or "mutation of Glu312 to Gln".

Fixed. Thank you.

Fig. 3d, Normally, one would expect full saturation curves for each mutant. How can a reader distinguish between low affinity or a decrease in the number of binding sites? Would full binding curves be prohibitive for the mutants because of the cost or availability of the ligand? These points should be addressed. A couple of the curves are not visible. Would an expanded scale inset show them more clearly? Also, would it be possible to include chemical structures for all ligands discussed?

Many if not most of these mutants bind TBZ with such low affinity that it is not possible to measure a full saturation curve either because of ligand availability (radioactive ligand concentration is only in µM) or due to technical issues with being able to measure such low affinity binding. We have changed the presentation of the curves and have split the gating and binding site mutants into their own figures. We feel this improves the readability of these curves. We have also included a table with the respective Kd values determined for each of the mutants where possible.

Line 235, The distances are long for a direct interaction between K138 and the TBZ methoxy groups. The unusual distances should be mentioned if an interaction is being proposed.

We do not think that K138 is directly involved in TBZ binding, however this was written in a confusing way and has been now changed.

Line 243, Please give a quantitative estimate of the affinity difference. "modestly" is vague.

It is an approximately 2-fold difference. Fixed in the text.

Line 248, 150 nM is, at best, a Kd, not an affinity.

Agreed, this is changed.

Reviewer #3 (Recommendations For The Authors):

The (3 x ~100ns-long) molecular dynamics simulations provided suggest some instability of the pose identified by cryo-EM. While it is not unreasonable that ligands shift around and adopt multiple conformations within a single binding site (in a reversible manner), the present results do raise questions about the assumptions made when starting the simulations, in particular (1) the protonation states of charged residues in the TBZ binding sites; (2) the parameters used for tetrabenazine; (3) the conformations of acidic side chains that are notoriously difficult to resolve in cryoEM maps; and (4) any contributions of the truncated regions truncated in the simulated structure, namely the cysteine cross-linked loop and the terminal domains. The authors should examine and/or discuss these contributions before attributing mechanistic insights into the newly observed binding orientation.

In order to estimate the effects of protonation states on TBZ binding, we now added three new systems with altered protonation on TBZ and binding pocket lining residues (see Table 3 in the revised vision); and for each system, we performed multiple MD runs to address the question and concerns raised by reviewer.

Regarding the protonation states: Propka3.0 was used to determine the protonation states, finding that E312 and D399 should be protonated. If I am not mistaken, this version of ProPka cannot account for non-protein ligands (https://github.com/jensengroup/propka). Given their proximity to the binding site, these protonation states will be critical factors for the stability of the simulations. The authors could test their assumption by repeating the calculations with Propka 3.1 or higher, to establish sensitivity to the ligand. Beyond this, showing the resultant hydrogen bond networks will help to reassure the reader that the dynamics in the lumenal gates do not arise from an artifact.

We thank the reviewer for suggestion of using higher version of Propka. We used the most recent Propka3.5 and carried out protonation calculations in the presence and absence of TBZ. The new calculations are presented in Table 4 and SI Figure 8c of the revised version.

It should be possible to assess whether waters penetrate the ligand binding site during the simulations if that is of concern.

We now added the number of waters within the ligand binding pockets for all MD simulations we performed, which are presented in Table 3 and Table 5 of the revised version.

Finally, I didn't fully understand the conclusion based on the simulations and the "binding affinity" calculations: do they imply that the pose identified in the EM map is not stable? What is the value of the binding affinity histogram?

We apologize for this confusion. For each MD snapshot, we calculated TBZ binding affinity using PRODIGY-LIG (Vangone et al., Bioinformatics 2019), which is a contact-based tool for computing ligand binding affinity. The binding affinity histogram shown in the original submission was the histogram of those binding affinities calculated for MD snapshots. In the revision, we replaced binding affinity histogram by time evolution of binding affinity changes (SI Fig 6c in the revision). The simulations confirmed that the pose identified in the EM map is stable, with a flattened binding affinity of -9.4 ± 0.3 kcal/mol in all three runs.

Recommendations regarding writing/presentation:

The authors use active tense terminology in attributing forces to elements of structure (cinching, packing tightly, locking). While appealing and commonplace in structural biology, this style frequently overstates the understanding obtained from a static structure and can give a rather misleading picture, so I encourage rephrasing.

We appreciate this point; the use of these words is not meant to overstate or provide a misleading picture but rather to aid the reader in mechanistic understanding of the proposed processes.

I would also recommend replacing the terms "above" and "below" for identifying aspects of the structure; the protein's location in the vesicular membrane makes these terms particularly difficult to follow.

These terms refer specifically to the Figures themselves which we have always oriented with the luminal side at the top of the page and the cytosolic on the bottom. We have indicated in Figure 1 the orientation of VMAT2. The Figures are the point of reference which we refer to, and the ‘above’ and ‘below’ terms have been used to assist the reader to make the manuscript easier for a more casual or non-expert reader to follow.

Minor corrections:

• the legend in Figure 2 lacks details, e.g. how many simulation frames are shown, how were the electrostatic maps calculated?

We revised Figure 2 and moved simulation frames to SI figure 6e. A total of 503 simulation frames are shown.

• how were the TBZ RMSDs calculated? using all atoms or just the non-hydrogen atoms?

For TBZ RMSDs, we used non-hydrogen atoms. This information is presented in the Methods section.

MD simulation snapshots and input files can be provided via zenodo or another website.

We will upload snapshots and input files to Zenodo upon acceptance of the manuscript.

Reviewing editor specific points:

Specific points

L.97: Remove "readily available"

Fixed.

L.99: The authors are not measuring competition binding. It is well known that reserpine and substrates inhibit TBZ binding only at concentrations 100 times higher than their respective KD and KM values. It is, therefore, surprising that the authors use this isotherm and refrain from commenting on the significance of the finding. Moreover, the presentation of results as "Normalized Counts" does not provide any information about the fraction of VMAT molecules binding the ligand. At least, the authors should provide the specific activity of the ligand, and the number of moles bound per mole of protein should be calculated.

The point was not to infer any details about the conformations that TBZ and reserpine bind but merely to point out that both constructs have a similar behavior with respect to their Ki for reserpine. We have added a sentence to say that reserpine binding stabilizes cytoplasmic-open so the reader is aware of the significance of this competition experiment.

L.102: The characterization of serotonin transport activity needs to be more satisfactory. The Km in rVMAT2 is 100-200 nM, so why are the experiments done at 1 and 10 micromolar? Is the Km of this construct very different? The results provided (counts per minute at the steady state) need to give more information.

The Km of human VMAT2 varies somewhat according to the source but has generally been reported to be between 0.6 to 1.4 µM for serotonin according to these references.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3019297/ https://www.cell.com/cell/pdf/0092-8674(92)90425-C.pdf https://www.pnas.org/doi/abs/10.1073/pnas.93.10.5166

Fig 1B could be more informative. I suggest adding a cartoon model with TMs labeled, similar to ED Fig6a.

This panel is to aid the reader in accessing the overall map quality and thus we do not wish to add additional labels/fits which would distract from that point. Instead, we have added overall views of the model in Figs 2,3.

L.179: The authors claim that the inner gate is located "below" (whatever this could mean) the TBZ ligand. In L.214, they claim that TBZ adopts a pose.....just "below" the location of the luminal gating residues. Please clarify and use appropriate terminology.

This refers to the position of these residues in the Figures themselves. We have added figure calls where appropriate here.

Fig. 4: The cartoon could be more informative.

We have added more information to the mechanism cartoon which is now Figure 6. This incorporates some of our new data and we believe it will be more informative.

L. 213: The paragraph describes residues involved in TBZ binding. Mutagenesis is used to validate the structural information. However, the results (ED fig. 5B) must be corrected for protein expression levels. In the Methods section, the authors state (L.444), "Mutants were evaluated similarly from cell lysates of transfected cells." Without normalization of protein expression levels, the results are meaningless even if they agree with predictions.

In fact, we have normalized the concentrations of protein in our binding experiments. This was noted in the methods section. And to account for these differences, experiments were conducted using 2.5 nM of VMAT2 protein as assessed by FSEC.

L.220: The referral to ED Fig.7 is not appropriate here. The figure shows docking-predicted poses of dopamine and serotonin.

Figure call has been changed.

L.226: The referral to Fig. 3b needs to be corrected. The figure shows TBZ and not the neurotransmitter.

This has been corrected.

L. 337: "The neurotransmitter substrate is bound at the central site." What do the authors mean in this cartoon? Do they have evidence for this? Tetrabenazine is not a substrate.

This cartoon drawing is meant to illustrate the elements of structure. Similar drawings are presented throughout the literature such as here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5940252/ Figure 3 and here: https://pubs.acs.org/doi/10.1021/acs.chemrev.0c00983 Figure 2.

The same compound is mentioned with different names: 3H-dihydrotetrabenazine and 3H-labeled DTBZ.

Fixed.

ED fig 1d is illegible.

The high-resolution figure is completely legible. We will provide this to the journal upon publication.

Figure 2d: A side view would be more visual.

We have updated this figure and believe that it is much easier to understand now.

L. 179: The inner gate is located 'below' the TBZ ligand

Please see above response, this refers to the figures themselves. The figures are our point of reference.

L. 213-215: Tetrabenazine binding site just 'below' the location of the luminal gating residues.

See above.

Throughout the paper, results are given as cpm or counts. The reader can only estimate the magnitude of the binding/transport by knowing the specific activity of the radiolabel. I recommend switching to nano/picomoles or supplying enough information to understand what the given cpm values could mean.

Binding experiments were done using scintillation proximity assays and therefore converting the CPMs to values in pmol of bound ligand is simply not possible. For the transport experiments (now Fig 1d) the point was to show that the wild type was similar in activity to the chimera. In our new transport experiments we have presented for the mutants, many experiments were combined together and therefore, we have normalized the counts to the relative activity of wild type VMAT2.

This line reflects the group mates' perspective that Ai's greatness and godly consistency as an idol will not change, as she will always continue to shine brightly, just as she always has. This evokes a sense of shock in me, as I would have never thought that someone could be so consistent with their performances, considering the potential exhaustion. I can relate this back to the theme of expectations, where Ai consistently has to deliver her best show to avoid falling short of her fans' and group mates’ ever-growing expectations.

A version of this could probably be said even today, except its not really half. It's more like, there are a bunch of different groups and individuals refusing to even try to get to know the other ones. I'm willing to bet there were more than just two sides even back then, because life never works on a binary. The issue is that most people adopt an "us vs. them" philosophy without truly under understanding who the "other side" really is.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The manuscript titled "Coevolution due to physical interactions is not a major driving force behind evolutionary rate covariation" by Little et al., explores the potential contribution of physical interaction between correlated evolutionary rates among gene pairs. The authors find that physical interaction is not the main driving of evolutionary rate covariation (ECR). This finding is similar to a previous report by Clark et al. (2012), Genome Research, wherein the authors stated that "direct physical interaction is not required to produce ERC." The previous study used 18 Saccharomycotina yeast species, whereas the present study used 332 Saccharomycotina yeast species and 11 outgroup taxa. As a result, the present study is better positioned to evaluate the interplay between physical interaction and ECR more robustly.

Strengths & Weaknesses:

Various analyses nicely support the authors' claims. Accordingly, I have only one significant comment and several minor comments that focus on wordsmithing - e.g., clarifying the interpretation of statistical results and requesting additional citations to support claims in the introduction.

We are pleased the reviewer found the analyses to support the claims. We have addressed comments related to clarifying interpretations as suggested in the Recommendations to the Authors. For example, we have added discussion and clarification on the other parameters that could affect the strength of ERC correlations.

Reviewer #2 (Public Review):

Summary:

The authors address an important outstanding question: what forces are the primary drivers of evolutionary rate covariation? Exploration of this topic is important because it is currently difficult to interpret the functional/mechanistic implications of evolutionary covariation. These analyses also speak to the predictive power (and limits) of evolutionary rate covariation. This study reinforces the existing paradigm that covariation is driven by a varied/mixed set of interaction types that all fall under the umbrella explanation of 'co-functional interactions'.

Strengths:

Very smart experimental design that leverages individual protein domains for increased resolution.

Weaknesses:

Nuanced and sometimes inconclusive results that are difficult to capture in a short title/abstract statement.

We appreciate the reviewer’s acknowledgement of the experimental design. We have addressed the nuance of the results by changing the title and clarifying other statements throughout the manuscript as suggested in the reviewer’s recommendations. We have also addressed reviewer comments asking for further explanation on using Fisher transformations when normalizing the Pearson correlations for branch counts.

Reviewer #3 (Public Review):

Summary:

The paper makes a convincing argument that physical interactions of proteins do not cause substantial evolutionary co-variation.

Strengths:

The presented analyses are reasonable and look correct and the conclusions make sense.

Weaknesses:

The overall problem of the analysis is that nobody who has followed the literature on evolutionary rate variation over the last 20 years would think that physical interactions are a major cause of evolutionary rate variation. First, there have been probably hundreds of studies showing that gene expression level is the primary driver of evolutionary rate variation (see, for example, [1]). The present study doesn't mention this once. People can argue the causes or the strength of the effect, but entirely ignoring this body of literature is a serious lack of scholarship. Second, interacting proteins will likely be co-expressed, so the obvious null hypothesis would be to ask whether their observed rates are higher or lower than expected given their respective gene expression levels. Third, protein-protein interfaces exert a relatively weak selection pressure so I wouldn't expect them to play much role in the overall evolutionary rate of a protein.

We thank the reviewer for their comments and suggestions. A point to immediately clarify is that the methods studied in this manuscript deal with rate variation of individual proteins over time, and if that variation correlates with that of another protein.. The numerous studies the reviewer refers to deal with explaining the differences in average rate between proteins. These are different sources of variation. It has not, to our knowledge, been shown that variation in the expression level of a single protein over time is responsible for its variation in evolutionary rate over time, let alone to a degree that allows its variation to correlate with that of a functionally related protein. That question interests us, but it is not the focus of this study.

In our study, we sought to test for a contribution of physical interaction to the correlation of evolutionary rate changes as they vary over time, i.e. between branches. We made many changes to clarify this distinction in our revisions.

We agree that the manuscript would be more clear to define the forces proposed to lead to difference in rate in general, which includes expression levels. We had generally considered expression level as one of the many potential non-physical forces, but failed to make that explicit and instead focused on selection pressure. In our revision we describe expression level as another potential driver of evolutionary rate variation over time. References to previous literature have been made in the introduction. We also added a more explicit explanation of the rate covariation over time that we are measuring in contrast with the association between expression level and rate differences between proteins that was studied in previous literature.

On point 3, the authors seem confused though, as they claim a co-evolving interface would evolve faster than the rest of the protein (Figure 1, caption). Instead, the observation is they evolve slower (see, for example, [2]). This makes sense: A binding interface adds additional constraint that reduces the rate at which mutations accumulate. However, the effect is rather weak.

The values in Fig 1B are a measure of correlation, specifically a Fisher transformed correlation coefficient. They are not evolutionary rates, so they are not reflecting faster or slower evolution, rather more or less covariation of evolutionary rates over time. We are not predicting that physically interacting interfaces evolve faster than the rest of the protein, but rather that if physical interaction drives covariation in evolutionary rates over time, their correlation would be stronger between pairs of physically interacting domains. In response, we have used clearer language in the figure caption and reorganized labels in Figure 1B to clearly show that the values are correlations. Revised Figure 1 Legend:

“Overview of experimental schema and hypotheses. Proteins that share functional/physical relationships have similar relative rates of evolution across the phylogeny, as shown in (A) with SMC5 and SMC6. The color scale along the bottom indicates the relative evolutionary rate (RER) of the specific protein for that species compared to the genome-wide average. A higher (red) RER indicates that the protein is evolving at a faster rate than the genome average for that branch. Conversely, a lower (blue) RER indicates that protein is evolving at a slower rate than the genome average. The ERC (right) is a Pearson correlation of the RERs for each shared branch of the gene pair. (B) Suppose the correlation in relative evolutionary rates between two proteins is due to compensatory coevolution and physical interactions. In that case, the correlation of their rates (ie. ERC value) would be higher for just the amino acids in the physically interacting domain. (C) Outline of experimental design. Created with Biorender.com”

All in all, I'm fine with the analysis the authors perform, and I think the conclusions make sense, but the authors have to put some serious effort into reading the relevant literature and then reassess whether they are actually asking a meaningful question and, if so, whether they're doing the best analysis they could do or whether alternative hypotheses or analyses would make more sense.

[1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4523088/

[2] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4854464/

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Major comments

(1) Numerous parameters influence ECR calculation. The authors note that their use of a large dataset of budding yeast provides sufficient statistical power to calculate ECR. I agree with that. However, a discussion of other parameters needs to be improved, especially when comparing the present study to others like Kann et al., Hakes et al., and Jothi et al.. For example, what is the evolutionary breadth and depth used in the Kann, Hakes, Jothi and other studies? How does that compare to the present study? Budding yeast evolve rapidly with gene presence/absence polymorphisms observed in genes otherwise considered universally conserved. Is there any reason to expect different results in a younger, slower-evolving clade such as mammals? There is potential to acknowledge and discuss other parameters that may influence ECR, such as codon optimization and gene/complex "essentiality," among others.

More discussion of these parameters is a good idea. We have added the number and phylogeny of species used in the previous studies in the discussion paragraph starting with “Previous studies attributed varying degrees of evolutionary rate covariation signal to physical interactions between proteins.” We also like the idea of studying the effect of younger and more slowly evolving clades as opposed to the contrary, but currently we lack the required number of datasets to do this.

We have also added more discussion and clarification of potential non-physical forces leading to ERC correlations in the introduction.

Minor comments

(1) It would be good to add a citation to the second sentence of the first paragraph, which reads, "It has been observed that some genes have rates that covary with those of other genes and that they tend to be functionally related."

Added citation to Clark et al. 2012

(2) In the last sentence of the first paragraph of the introduction, ERC is discussed in the context of only amino acid divergence, however, there is no reason that DNA sequences can't be used, especially if ERC is being calculated among species that are less ancient than, for example, Saccharomycotina yeasts. Thus, it may be more accurate to suggest that ERC measures how correlated branch-specific rates of sequence divergence are with those of another gene.

Nice suggestion to generalize. We have made this change.

(3) ERC was not calculated in reference #2. For the sentence "Protein pairs that have high ERC values (i.e., high rate covariation) are often found to participate in shared cellular functions, such as in a metabolic pathway2 or meiosis3 or being in a protein complex together," I think more appropriate citations (including inspiring work by the corresponding author) would be

a) Coevolution of Interacting Fertilization Proteins (https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1000570)

b) Evolutionary rate covariation analysis of E-cadherin identifies Raskol as a regulator of cell adhesion and actin dynamics in Drosophila (https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1007720)

c) An orthologous gene coevolution network provides insight into eukaryotic cellular and genomic structure and function (https://www.science.org/doi/10.1126/sciadv.abn0105)

d) PhyKIT: a broadly applicable UNIX shell toolkit for processing and analyzing phylogenomic data (https://academic.oup.com/bioinformatics/article/37/16/2325/6131675)

Thank you for pointing out these works. We agree that there are more appropriate citations and we have referenced your suggested b-d.

(4) The dataset of 343 yeast species also includes outgroup taxa. Therefore, indicating that 332 species are Saccharomycotina yeast and 11 are closely related outgroup taxa may be more accurate.

Thank you for the suggestion, the following sentence has been added, citing the Shen et. al 2018 paper that the dataset was derived from:

“To investigate the discrepancy between contributions to ERC signal from co-function and physical interaction, we used a dataset of 343 evolutionarily distant yeast species. 332 of the species are Saccharomycotina with 11 closely related outgroup species providing as much evolutionary divergence as humans to roundworms3”

(5) Are there statistics/figures to support the claim that "Almost all complexes and pathways had mean ERC values significantly greater than a null distribution consisting of random protein pairs"?

This is shown in supplementary figure 1. A reference to this figure was added as well as quantification within the text.

(6)Similar to the previous comment, can quantitative values be added to the statement "While protein complexes appear to have higher mean ERC scores than the pathways..."?

The median of the mean ERC scores for protein complexes is 5.366 while the median for the mean ERC score in pathways is 4.597. This quantification has been included in the text: “While protein complexes have higher mean ERC scores (median 5.366) than the pathways (median 4.597), the members of a given complex are also co-functional, making interpretation of the relative contribution of physical interactions to the average ERC score difficult”

These quantifications are were also added to the figure caption for figure 2A

(7) A semantic point: In the sentence "The lack of significance in the global permutation test shows that the...", I recommend saying that the analysis suggests, not shows, because there is potential for a type II error.

Good suggestion, we have made this change.

(8) The authors suggest that shared evolutionary pressures, "and hence shared levels of constraint," drive signatures of coevolution. The manuscript does not delve into selection measures (e.g., dN/dS). Perhaps it would be more representative to remove any implication of selection.

We have added better language to clarify that discussion of selection is purely a hypothesis and that selection is not probed in our analyses.

“Previous work finds evidence that relaxation of selective constraint can lead to drastic rate variation and hence covariation6. Rather, the greater and consistent contribution comes from non-physical interaction drivers that could include variation in essentiality, expression level, codon adaptation, and network connectivity. These non-physical forces would be under shared selective pressures and hence shared levels of constraint, the result of which was elevated ERC between non-interacting proteins, as visible in our study of genetic pathways that do not physically interact (Figure 2).”

Reviewer #2 (Recommendations For The Authors):

Major comments:

-Title: In my opinion, the title of the manuscript is a somewhat misleading summary of the results of this paper. In the majority of the analyses in this paper, physical interactions do account for a significantly outsized portion of the ERC signature. The current title downplays the consistent (although sometimes small effect-sized) result that physically interacting domains do show higher ERC than non-physically interacting domains by every statistical measure employed in this paper to compare physical vs non-physical interactions. The authors' interpretation of their results within the manuscript body is that the effect of physical interactions is an inconsistent, weak, and non-generalizable driver of ERC. I generally agree with the authors' interpretations, but the nuance of these interpretations is lost in the title of the paper. I would suggest rewording the title to try to capture the nuance or at least be subjectively accurate. For example, stating that "...physical interactions are not the sole driving force.." is inarguably accurate based on these results.

As an alternative title, I would suggest focusing on an important takeaway from the paper: ERC is a reliable predictor of co-functional interactions but not necessarily physical interactions. I agree with the statement that "there is not a strong enough signal to confidently call an interaction physical or not and would be of little value to an experimentalist wanting to infer interacting domains" and I think that a title that emphasizes this idea would be more accurate and impactful.

Great suggestion. We agree that the current title is downplaying the minutiae of the method and the signal we capture with it, we have used your suggested title.

There are an outsized number of complexes that had ROC-AUCs greater than 0.5 which is why we performed the permutation tests to determine how significant each of the individual ROC-AUCs were given the differing number of protein/domain pairs in each complex. Between the statistical methods used only 3 of the 17 complexes ranked physical interactions significantly higher than non-physically interacting domains in every analysis. Even among the 3 that were statistically significant some of the physically interacting domains still fell among the bottom portion of the ERC scores for that complex (Figure 5: MCM and CUL8 complexes) This is why we concluded that physical interactions are not the sole driving force of the signal captured by ERC.

-Abstract: related to my preceding comment, the word "negligible" in the abstract is misleading. If physical interactions were truly entirely negligible, the comparisons of physically interacting vs non-physically interacting domains would yield 0.5. Instead, these comparisons always yielded results greater than 0.5. Consider rewording.

Thank you for the suggestion this phrasing has been changed to “Therefore, we conclude that coevolution due to physical interaction is weak, but present in the signal captured by ERC”

We agree that “negligible” may be too strong of a word, however, the comparisons do not always yield results greater than 0.5.

5 of the 17 complexes do not reach the 0.5 threshold for the initial ROC analysis and even among those that do, only 4 had significantly high ROC-AUCs. You are correct that the signal is not completely negligible which is why we continued by determining if the physical interaction was driving high ERC only within proteins (Figure 5)

-Figure 3: I think there may be an error in the domain labeling in Figure 3. The comparison between OKP1_2 and AME1_3 is the highest ERC value in the matrix. From the complex structure, it appears that OKP1_2 and AME1_3 are two helix domains that appear to physically interact. However, in the ERC matrix, they are not shaded to indicate they are a physical interaction pair. Please double-check that the interacting domains are properly annotated, since mis-annotation would have a large impact on the interpretation of this figure with respect to the overall question the paper addresses.

Thank you for catching this - fixed.

Minor comments:

-Methods: "The full ERC pipeline can be found at (Github)." Provide github URL here? Thanks for the catch, fixed

-Discussion: "Evidence for physical coevolution however was tempered by a global permutation test, which did not reach significance, indicating that this inference is sensitive to approach and further underlines the relatively weak contribution of physical coevolution." The word "relatively" may not be a good choice of words. In comparison to what? As is, the phrasing could be interpreted as implying "in comparison to non-physical interactions". This would not be accurate, because the results show that in general, physical interactions are a stronger contributor to ERC (consistent trend but varied significance, depending on methodology) than non-physical interactions.

Thank you for your help with clarification. The word relatively was removed.

However, we do not agree that in general physical interactions are a stronger contributor to ERC than non-physical interactions (such as gene expression, codon adaptation, etc.). In all of our statistical tests a maximum of 5 of the 17 complexes ranked physical interactions significantly higher than non-physical interactions. While the ROC-AUC is greater than 0.5 for 12 of the 17 complexes only 4 of those were significant.

-I have not seen Fisher-transformed correlation coefficients used in the context of ERC. I understand that it's helpful in normalizing the results so that they are comparable between ERC comparisons with differing numbers of overlapping branches (i.e. points on a linear correlation plot). A reference of where the authors got this idea or a little more verbiage to describe the rationale would be helpful. On a related note, I would expect that using linear correlation p-value instead of R-squared would account for differences in overlapping branches, eliminating the need to apply fisher-transformation. It would be helpful for the authors to outline their rationale for using a correlation coefficient rather than a P-value.

We agree that this method could be made clearer. We made a methodological choice to use Fisher transformation over linear correlation p-value. Both methods should achieve the same end result by taking the number of branches into consideration. We have added additional explanation to the results section “Both protein pathways and complexes have elevated ERC”:

“ERC was calculated for all pairs of the 12,552 genes. For each pair the correlation is Fisher transformed to normalize for the number of shared branches that contribute to the correlation. This normalization is necessary to reduce false positives that have high correlation solely due to a small number of data points. This normalization also allows for direct comparison of ERC between gene pairs that have differing numbers of branches contributing to the score.”

We also added additional explanation in the methods section including the formula used to calculate the Fisher transformation

-Did the authors use Pearson or Spearman correlation coefficient?

Pearson. We clarified this in the methods section, “Calculating evolutionary rate covariation” : “Evolutionary rate covariation is calculated by correlating relative evolutionary rates (RERs) between two gene trees using a Pearson correlation.”

-Did the authors explore ERC between domains within a single protein? Do domains within a protein exhibit ERC? I would expect that they do. If they do, this could likely be attributed to linkage/genetic hitchhiking, representing a new angle/factor beyond physical interaction that could lead to ERC. This is just an idea for a future analysis, not necessarily a request within the scope of the present paper.

We did calculate the ERC between domains of a single protein but did not include them in the analysis since they didn’t address the specific question we posed. As expected they are highly correlated, and past unpublished studies in the lab do find a very weak, but detectable genome-wide, signature of rate covariation between neighboring colinear genes on a chromosome. That signal was however so weak as to be eclipsed by true functional relationships, when present.

Reviewer #3 (Recommendations For The Authors):

Please read the literature and revise accordingly.

We understand the confusion surrounding previous literature on the relationship between expression levels and evolutionary rates when comparing between different proteins. Those studies clearly showed how expression level is highly predictive of a given protein’s average evolutionary rate. However, we are studying the change in evolutionary rate over branches for single proteins. This is inherently different because we’re following rate fluctuations in the same protein over time. To our knowledge it has not yet been shown that expression level commonly varies enough over time to produce large rate variations over time in the same protein, and if it is responsible for the correlations of rate we observe between co-functional proteins. It is however reasonable to expect that what governs between-protein differences in rate could also contribute to between-branch differences (over time for a single protein). In fact, our earlier study approached this (Clark et al. Genome Research 2012). We expect expression level could influence rate over time and lump its effect together with general non-physical forces, such as selection pressures. We recognize we could do better in defining more of the non-physical forces and the past literature. We added the following section to the introduction and many other clarifying statements throughout the manuscript:

“For the purposes of this study, the forces that contribute to correlated evolutionary rates are grouped into two bins, physical and non-physical. The physical force is coevolution occurring at physical interaction interfaces. Non-physical forces include gene co-expression, codon adaptation, selective pressures, and gene essentiality. There is a well accepted negative relationship between gene expression and rate of protein evolution where genes that are highly expressed generally have slower rates of evolution14,15. However, Cope et al.16 found that there is a weak relationship between both gene expression and the number of interactions a protein has with the coevolution of expression level. Conversely, they found a strong relationship between proteins that physically interact and the coevolution of gene expression. These findings illuminate the difference between the strong relationship of gene expression level on the average evolutionary rate of a protein and the weak contribution of gene expression level to correlated evolutionary rates of proteins across branches. The finding that physically interacting proteins have strong expression level coevolution brings to question how much coevolution of physically interacting proteins contributes to overall covariation in protein evolutionary rates.”

This is probably just a throw away line but I think it's interesting that they use an example from Rime of the ancient Mariner, a horrific cautionary tale here. also, Wordsworth had nothing to do with making of the poem. It was just published in a book with some of his work.

Reviewer #1 (Public Review):

Summary:<br /> The authors utilize fluid-structure interaction analyses to simulation fluid flow within and around the Cambrian cnidarian Quadrapyrgites to reconstruct feeding/respiration dynamics. Based on vorticity and velocity flow patterns, the authors suggest that the polyp expansion and contraction ultimately develop vortices around the organism that are like what modern jellyfish employ for movement and feeding. Lastly, the authors suggest that this behavior is likely a prerequisite transitional form to swimming medusae.

Strengths:<br /> While fluid-structure-interaction analyses are common in engineering, physics, and biomedical fields, they are underutilized in the biological and paleobiological sciences. Zhang et al. provide a strong approach to integrating active feeding dynamics into fluid flow simulations of ancient life. Based on their data, it is entirely likely the described vortices would have been produced by benthic cnidarians feeding/respiring under similar mechanisms. However, some of the broader conclusions require additional justification.

Weaknesses:

1. The claim that olivooid-type feeding was most likely a prerequisite transitional form to jet-propelled swimming needs much more support or needs to be tailored to olivooids. This suggests that such behavior is absent (or must be convergent) before olivooids, which is at odds with the increasing quantities of pelagic life (whose modes of swimming are admittedly unconstrained) documented from Cambrian and Neoproterozoic deposits. Even among just medusozoans, ancestral state reconstruction suggests that they would have been swimming during the Neoproterozoic (Kayal et al., 2018; BMC Evolutionary Biology) with no knowledge of the mechanics due to absent preservation.<br /> 2. While the lack of ambient flow made these simulations computationally easier, these organisms likely did not live in stagnant waters even within the benthic boundary layer. The absence of ambient unidirectional laminar current or oscillating current (such as would be found naturally) biases the results.<br /> 3. There is no explanation for how this work could be a breakthrough in simulation gregarious feeding as is stated in the manuscript.

Despite these weaknesses the authors dynamic fluid simulations convincingly reconstruct the feeding/respiration dynamics of the Cambrian Quadrapyrgites, though the large claims of transitionary stages for this behavior are not adequately justified. Regardless, the approach the authors use will be informative for future studies attempting to simulate similar feeding and respiration dynamics.

The following text is directly in response to the revised version of the manuscript.<br /> Dynamic simulations of feeding and respiration of the early Cambrian periderm-bearing cnidarian polyps

Revision 1

I think this manuscript has been improved by the authors, and I appreciate their time and effort in considering my earlier comments. While most of my line by line comments have been incorporated, I do feel that some of my larger points have been insufficiently addressed. Those are repeated with additional clarifications below.

Original comment: The claim that olivooid-type feeding was most likely a prerequisite transitional form to jet-propelled swimming needs much more support or needs to be tailored to olivooids. This suggests that such behavior is absent (or must be convergent) before olivooids, which is at odds with the increasing quantities of pelagic life (whose modes of swimming are admittedly unconstrained) documented from Cambrian and Neoproterozoic deposits. Even among just medusozoans, ancestral state reconstruction suggests that they would have been swimming during the Neoproterozoic (Kayal et al., 2018; BMC Evolutionary Biology) with no knowledge of the mechanics due to absent preservation.

Author response: Thanks for your suggestions. Yes, we agree with you that the ancestral swimming medusae may appear before the early Cambrian, even at the Neoproterozoic deposits. However, discussions on the affinities of Ediacaran cnidarians are severely limited because of the lack of information concerning their soft anatomy. So, it is hard to detect the mechanics due to absent preservation. Olivooids found from the basal Cambrian Kuanchuanpu Formation can be reasonably considered as cnidarians based on their radial symmetry, external features, and especially the internal anatomies (Bengtson and Yue 1997; Dong et al. 2013; 2016; Han et al. 2013; 2016; Liu et al. 2014; Wang et al. 2017; 2020; 2022). The valid simulation experiment here was based on the soft tissue preserved in olivooids.

Reviewer response: This response does not sufficiently address my earlier comment. While the authors are correct that individual Ediacaran affinities are an area of active research and that Olivooids can reasonably be considered cnidarians, this doesn't address the actual critique in my comment. Most (not all) Ediacaran soft-bodied fossils are considered to have been benthic, but pelagic cnidarian life is widely acknowledged to at least be present during later White Sea and Nama assemblages (and earlier depending on molecular clock interpretations). The authors have certainly provided support for the mechanics of this type of feeding being co-opted for eventual jet-propulsion swimming in Olivooids. They have not provided sufficient justifications within the manuscript for this to be broadened beyond this group.

Original comment: There is no explanation for how this work could be a breakthrough in simulation gregarious feeding as is stated in the manuscript.

Author response: Thanks for your suggestion. We revised the section "Perspectives for future work and improvements" (lines 396-404 in our revised version of MS). Conducting simulations of gregarious active feeding behavior generally need to model multi (or clustered) organisms, which is beyond the present computational capability. However, exploiting the simulation result and thus building a simplified model can be possible to realize that, as we may apply an inlet or outlet boundary condition to the peridermal aperture of Quadrapyrgites with corresponding exhale or inhale flow velocity profiles collected in this work. By doing this we can obtain a simplified version of an active feeding Quadrapyrgites model without using computational expensive moving mesh feature. Such a model can be used solely or in cluster to investigate gregarious feeding behavior incorporated with ambient current. Those above are explicit explanations for how this work could be a "breakthrough" in simulation gregarious feeding. However, we modified the corresponding description in section "Perspectives for future work and improvements" to make it more appropriate.

Reviewer response: I think I understand where the authors are trying to take this next step. If the authors were to follow up on this study with the proposed implementation of inhalant/exhalent velocities profiles (or more preferably velocity/pressure fields), then that study would be a breakthrough in simulating such gregarious feeding. Based on what has been done within the present study, I think the term "breakthrough" is instead overly emphatic.<br /> An additional note on this. The authors are correct that incorporating additional models could be used to simulation a population (as has been successfully done for several Ediacaran taxa despite computational limitations), but it's not the only way. The authors might explore using periodic boundary conditions on the external faces of the flow domain. This could require only a single Olivooid model to assess gregarious impacts - see the abundant literature of modeling flow through solar array fields.

Original comment: L446: two layers of hexahedral elements is a very low number for meshing boundary layer flow

Author response: Many thanks for your question. We agree that an appropriate hexahedral elements mesh for boundary layer is essential to recover boundary flow, especially in cases where turbulence model incorporated with wall function is adopted such as the standard k-epsilon model. In this case, the boundary flow is not the main point since the velocity profile was collected above periderm aperture rather than near no-slip wall region. What else, we do not need drag (related to sheer stress and pressure difference) computations in this case, which requires a more accurate flow velocity reconstruction near no-slip walls as what previous palaeontological CFD simulations have done. Thus, we think two layers of hexahedral elements are enough. What else, hexahedral elements added to periderm aperture domain, as illustrated in figure below, can let the velocity near wall vary smoothly and thus can benefit the convergency of simulations.

Reviewer response: As the authors point out in the main text, these organisms are small (millimeters in scale) and certainly lived within the boundary layer range of the ocean. While the boundary layer is not the main point, it still needs to be accurately resolved as it should certainly affect the flow further towards the far field at this scale. I'm not suggesting the authors need to perfectly resolve the boundary layer or focus on using turbulence models more tailored to boundary layer flows (such as k-w), but the flow field still needs sufficient realism for a boundary bounded flow. The authors really should consider quantitatively assessing the number of hexahedral elements within their mesh refinement study.

This is so interesting when thinking about how we've been considering conscience. "I think, therefore I am". Who is to determine how an animal feels when we cant really know what they're thinking. It's unfair to say that animals don't have emotions just because we aren't capable of understanding them.

I wonder what old theory-laden stuff I could find...

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

The manuscript by Zhu and colleagues aimed to clarify the importance of isoform diversity of PCDHg in establishing cortical synapse specificity. The authors optimized 5' single-cell sequencing to detect cPCDHg isoforms and showed that the pyramidal cells express distinct combinations of PCDHg isoforms. Then, the authors conducted patch-clamp recordings from cortical neurons whose PCDHg diversity was disrupted. In the elegant experiment in Figure 3, the authors demonstrated that the neurons expressing the same sets of cPCDHg isoforms are less likely to form synapses with each other, suggesting that identical cPCDHg isoforms may have a repulsive effect on synapse formation. Importantly, this phenomenon was dependent on the similarity of the isoforms present in neurons but not on the amount of proteins expressed.

One of the major concerns in an earlier version was whether PCDHg isoforms, which are expressed at a much lower level than C-type isoforms, have true physiological significance. The authors conducted additional experiments to address this point by using PCDHg cKO and provided convincing data supporting their conclusion. The results from PCDHg C4 overexpression, showing no impact on synaptic connectivity, further clarified the importance of isoforms. I have no further concerns, however, I would like to point out that the evidence for the necessity of the PCDHg isoform is still lacking because most experiments were done by overexpression. It would be helpful for the readers if the authors could add this point to the discussion.

Thank you for the positive feedback on our work. We have now incorporated a discussion of the limitations associated with overexpression.

Reviewer #2 (Public Review):

This short manuscript by Zhu et al. describes an investigation into the role of gamma protocadherins in synaptic connectivity in the mouse cerebral cortex. First, the authors conduct a single-cell RNA-seq survey of postnatal day 11 mouse cortical neurons, using an adapted 10X Genomics method to capture the 5' sequences that are necessary to identify individual gamma protocadherin isoforms (all 22 transcripts share the same three 3' "constant" exons, so standard 3'biased methods can't distinguish them). This method adaptation is an advance for examining individual gamma transcripts, and it is helpful to publish the method, the characterization of which is improved in this revised manuscript. The results largely confirm what was known from other approaches, which is that a few of the 19 A and B subtype gamma protocadherins are expressed in an apparently stochastic and combinatorial fashion in each cortical neuron, while the 3 C subtype genes are expressed ubiquitously. Second, using elegant paired electrophysiological recordings, the authors show that in gamma protocadherin cortical slices, the likelihood of two neurons on layers 2/3 being synaptically connected is increased. That suggests that gamma protocadherins generally inhibit synaptic connectivity in the cortex; again, this has been reported previously using morphological assays, but it is important to see it confirmed here with physiology. Finally, the authors use an impressive sequential in utero electroporation method to provide evidence that the degree of isoform matching between two neurons negatively regulates their reciprocal synaptic connectivity. These are difficult experiments to do, and while some caveats remain, the main result is consistent. Strengths include the impressive methodology and improved demonstration of the previously-reported finding that gamma protocadherins work via homophilic matching to put a brake on synapse formation in the cortex. Weaknesses include the writing, which even in the revision fails to completely put the new results in context with prior work, which together has largely shown similar results; a still-incomplete characterization of a new alpha protocadherin KO mouse (a minor point but it should still be addressed); and a lack of demonstration of protein levels in electroporated brains. Because of the unique organization and expression pattern of the gamma protocadherins, it is unlikely that these results will be directly applicable to the broader understanding of the role of cell adhesion molecules in synapse development. However, the methodology, which is now better described, should be applicable more broadly and the improved demonstration of the role of gamma protocadherin's negative role in cortical synaptogenesis is helpful.

Thank you for the positive comments on our work. We have taken your suggestion into account and expanded our discussion to contextualize our research within the broader field of PCDH. Additionally, we have included more data to further illustrate the decrease in αPCDH expression in Pcdha conditional knockout mice. Your feedback has been invaluable in enhancing our manuscript.

Reviewer #3 (Public Review):

In this study, Zhu and authors investigate the expression and function of the clustered Protocadherins (cPcdhs) in synaptic connectivity in the mouse cortex. The cPcdhs encode a large family of cadherin-related transmembrane molecules hypothesized to regulate synaptic specificity through combinatorial expression and homophilic binding between neurons expressing matching cPcdh isoforms. But the evidence for combinatorial expression has been limited to a few cell types, and causal functions between cPcdh diversity and wiring specificity have been difficult to test experimentally. This study addresses two important but technically challenging questions in the mouse cortex: 1) Do single neurons in the cortex express different cPcdh isoform combinations? and 2) Does Pcdh isoform diversity or particular combinations among pyramidal neurons influence their connectivity patterns? Focusing on the Pcdh-gamma subcluster of 22 isoforms, the group performed 5'end-directed single-cell RNA sequencing from dissociated postnatal (P11) cortex. To address the functional role of Pcdhg diversity in cortical connectivity, they asked whether the Pcdhgs and isoform matching influence the likelihood of synaptic pairing between 2 nearby pyramidal neurons. They performed simultaneous whole-cell recordings of 6 pyramidal neurons in cortical slices, and measured paired connections by evoked monosynaptic responses. In these experiments, they measured synaptic connectivity between pyramidal neurons lacking the Pcdhgs, or overexpressing dissimilar or matching sets of Pcdhg isoforms introduced by electroporation of plasmids encoding Pcdhg cDNAs.

Overall, the study applies elegant methods that demonstrate that single cortical neurons express different combinations of Pcdh-gamma isoforms, including the upper layer Pyramidal cells that are assayed in paired recordings. The electrophysiology data demonstrate that nearby Pyramidal neurons lacking the entire Pcdhg cluster are more likely to be synaptically connected compared to the control neurons, and that overexpression of matching isoforms between pairs decreases the likelihood to be synaptically connected. These are important and compelling findings that advance the idea that the Pcdhgs are important for cortical synaptic connectivity, and that the repertoire of isoforms expressed by neurons influence their connectivity patterns potentially through a self/nonself discrimination mechanism. However, the findings are limited to probability in connectivity and do they do not support the authors' conclusions that Pcdhg isoforms regulate synaptic specificity, 'by preventing synapse formation with specific cells' or to 'unwanted partners'. Characterizations of the cellular basis of these defects are needed to determine whether they are secondary to other roles in cell positioning, axon/dendrite branching and synaptic pruning, and overall synaptic formation. Claims that Pcdh-alpha and Pcdhg C-type isoforms are not functionally required are premature, due to limitations of the experiments. Moreover, claims that 'similarity level of γPCDH isoforms between neurons regulate the synaptic formation' are not supported due to weak statistical analyses presented in Fig4. The overstatements should be corrected. There was also missed opportunity to clearly discuss these results in the context of other published work, including recent publications focused on the cortex.

Thank you for your feedback on the strengths and weaknesses of our work. In terms of the cellular basis of affected synaptic connectivity caused by γ-PCDH isoforms, we have compared the probability of connectivity for neuronal pairs with similar range of distance. Our findings indicate that the manipulation primarily affects pairs within the 50-150 micrometer range, suggesting that cell positioning might be a critical factor for the impact of γ-PCDH on synapse formation. However, we acknowledge that we couldn't definitively determine whether the negative effect on synaptic connectivity stems directly from impaired synapse formation or indirectly from synaptic pruning or the influence of PCDHγ on axon/dendritic branching. We've added these limitations to our discussion to provide a more comprehensive view of our research. Furthermore, we've adjusted our statements to better reflect the significance of our findings. Your feedback has been instrumental in improving the clarity and depth of our manuscript.

Strengths:

• The 5' end sequencing with a Pcdhg-amplified library is a technical feat and addresses the pitfall of conventional scRNA-Seq methods due to the identical 3'sequences shared by all Pcdhg isoform and the low abundance of the variable exons. New figures with annotated cell types confirm that several pyramidal and inhibitory cortical subpopulations were captured.

Statistical assessment of co-occurrence of isoform expression within clusters is also a strength.

• By establishing the combinatorial expression of Pcdhgs by maturing pyramidal cells, the study further substantiates the 'single neuron combinatorial code for cPcdhs' model. Although combinatorial expression is not universal (ie. serotonergic neurons), there was limited evidence. The findings that individual pyramidal neurons express ~1-3 variable Pcdhg transcripts plus the Ctype transcripts aligns with single RT-PCR studies of single Purkinje cells (Esumi et al 2005; Toyoda et al 2014). They differ from the findings by Lv et al 2022, where C-type expression was lower among pyramidal neurons. OSNs also do not substantially express C-type isoforms (Mountoufaris et al 2017; Kiefer et al 2023). Differences, and the advantages of the 5'end -directed sequencing (vs. SmartSeq) could be raised in the discussion.

• Simultaneous whole-cell recordings and pairwise comparisons of pyramidal neurons is a technically outstanding approach. They assess the effects of Pcdhg OE isoform on the probability of paired connections.

• The connectivity assay between nearby pairs proved to be sensitive to quantify differences in probability in Pcdhg-cKO and overexpression mutants. The comparisons of connectivity across vertical vs lateral arrangement are also strengths. Overexpressing identical Pcdhg isoform (whether 1 or 6) reduces the probability of connectivity, but there are caveats to the interpretations (see below).

Weaknesses:

n earby pairs but are not sufficient evidence for synapse specificity. The cPcdhs play multiple roles in neurite arborization, synaptic density, and cell positioning. Kostadinov 2015 also showed that starburst cells lacking the Pcdhgs maintained increased % connectivity at maturity, suggesting a lack of refinement in the absence of Pcdhgs. The known roles raise questions on how these manipulations might have primary effects in these processes and then subsequently impact the probability of connectivity. Investigations of morphological aspects of pyramidal development would strengthen the study and potentially refine the findings. The authors should more clearly relate their findings to the body of cPcdh studies in the discussion.

Previous studies revealed the adverse effects of γ-PCDHs on dendritic spines, demonstrating that their absence results in increased dendritic spines density, while overexpression leads to a reduction. In our study, we consistently observed that γ-PCDHs exert a negative influence on synaptic connectivity. This consistency strengthens the overall body of evidence in support of the role of γ-PCDHs in synaptic connectivity and dendritic spine regulation. While we have previously mentioned this point in our discussion to highlight the concordance between our findings and prior research, your input is greatly appreciated in reinforcing the scientific context of our work.

• Pcdhg cKO-dependent effects on connectivity occur between closely spaced soma (50-100um - Figure 2E), highlighting the importance of spatial arrangement to connectivity (also noted by Tarusawa 2016). Was distance considered for the overexpression (OE) assays, and did the authors note changes in cell distribution which might diminish the connectivity? Recent work by Lv et al 2022 reported that manipulating Pcdhgs influences the dispersion of clonally-related pyramidal neurons, which also impacts the likelihood of connections. Overexpression of Pcdhgc3 increased cell dispersion and decreased the rate of connectivity between pairs. Though these papers are mentioned, they should be discussed in more detail and related to this work.

Our data indicated that variable γ-PCDH isoforms primarily influence synaptic connectivity in neuronal pairs within the 50-150 micrometer range. Notably, as the distance between neurons increases, we observed a corresponding reduction in synaptic connectivity, as illustrated in Figure 2E. We have also included additional discussion regarding potential variances among different C-type isoforms.

• Though the authors added suggested citations and improved the contextualization of the study, several statements do not accurately represent the cited literature. It is at the expense of crystalizing the novelty and importance of this present work. For instance, Garrett et al 2012 PMID: 22542181 was the first to describe roles for Pcdhgs in cortical pyramidal cells and dendrite arborization, and that pyramidal cell migration and survival are intact. Line 52 cited Wang et al 2002, but this was limited to gross inspection. Garrett et al is the correct citation for: 'The absence of γ-PCDH does not cause general abnormality in the development of the cerebral cortex, such as cell differentiation, migration, and survival (Wang et al., 2002).' Second, single cell cPcdh diversity is introduced very generally, as though all neuron types are expected to show combinatorial variable expression with ubiquitous C-Type expression. But those initial studies were limited to Purkinje cells (Esumi 2005 and Toyoda 2014). Profiling of serotonergic neurons and OSN reveals different patterns (citations needed for Chen 2017 PMID: 28450636; Mountofaris et al PMID: 2845063; Canzio 2023 PMID: 37347873), raising the idea that cPcdh diversity and ubiquitous Ctype expression is not universal. Thus, the authors missed the opportunity to emphasize the gap regarding cPcdh diversity in the cortex.

We would like to extend our gratitude to the reviewer for pointing out the citation related to the roles of γ-PCDHs in the neocortex. After a thorough review of both papers, Wang et al., 2002 and Garrett et al., 2012, we concur that it would be more appropriate to cite both of these papers here. Your suggestion to underscore the diverse expression patterns of γPCDHs in neocortical neurons is well-received, and we have integrated this aspect of our findings with previous observations into a new paragraph within the discussion section. Your insights have greatly enriched the depth of our paper, and we genuinely appreciate your contribution.

• They have not shown rigorously and statistically that the rate of connectivity changes with% isoform matching. In Figure 4D, comparisons of % isoform matching in OE assays show a single statistical comparison between the control and 100% groups, but not between the 0%, 11% and 33% groups. Is there a significant difference between the other groups? Significant differences are claimed in the results section, but statistical tests are not provided. The regression analysis in 4E suggests a correlation between % isoform similarity and connectivity probability, but this is not sound as it is based on a mere 4 data points from 4D. The authors previously explained that they cannot evaluate the variance in these recordings as they must pool data together. However, there should be some treatment of variability, especially given the low baseline rate of connectivity. Or at the very least, they should acknowledge the limitations that prevent them from assessing this relationship. Claims in lines 230+ are not supported: ' Overall, our findings demonstrate a negative correlation between the probability of forming synaptic connections and the similarity level of γPCDH isoforms expressed in neuron pairs (Fig. 4E)".

We employed a bootstrap method to estimate the potential variance in the analysis presented in Fig. 4E. It's important to note that due to methodological limitations, a comprehensive assessment of variance based solely on recordings from a single animal is challenging. As such, we have adjusted our claims to be more aligned with our observations.

• Figure 4 provides connectivity probability, but this result might be affected by overall synapse density. Did connection probability change with directionality (e.g between red to green cells, or green to red cells).

As suggested by the reviewer, we have conducted an analysis to assess the directionality of connections under different conditions. This analysis involved comparing the directionalities of connections following the overexpression of six variable isoforms, as depicted in Fig. 3E. Upon examining 33 connected OE-Ctrl pairs following the electroporation of these 6 isoforms, we observed 3 pairs with bidirectional connections, 19 pairs with connections from OE to Ctrl, and 11 with connections from Ctrl to OE. To assess the statistical significance of these observations, we applied a Chi-square test. The results from this analysis indicated that there was no significant difference in the directionality of connections. These findings offer further support for the idea that overexpressing multiple γ-PCDH isoforms within a single neuron might not be sufficient to alter its connections with other neurons.

• Generally, the statistical approaches were not sufficiently described in the methods nor in the figure legends, making it difficult to assess the findings. They do not report on how they calculated FDR for connectivity data, when this is typically used for larger multivariate datasets.

We employed the False Discovery Rate (FDR) correction, specifically the BenjaminiHochberg method, to determine which values remained statistically significant. This method is widely accepted and involves inputting all the p-values and the total number, 'n.' Additional details about this correction are now provided in the Method section for clarity.

• The possibility that the OE effects are driven by total Pcdhg levels, rather isoform matching, should be examined. As shown by qRT-PCR in Fig. 3, expression of individual isoforms can vary. It is reasonable that protein levels cannot be measured by IHC, although epitope tags could be considered as C-terminal tagging of cPcdhs preserves the function in mice (see Lefebvre 2008). Quantification of constant Pcdhg RNA levels by qRT-PCR or sc-RT-PCR would directly address the potential caveat that OE levels vary with isoform combinations.

Through a series of multiple whole-cell recordings, we examined neuronal pairs within the 0% group, where both neurons exhibited overexpression of different combinations of γPCDH isoforms. What we discovered is that the connectivity level within pairs of neurons where both neurons overexpressed γ-PCDH isoforms, pairs with only one neuron overexpressing these isoforms, and pairs with two control neurons (lacking overexpression) was remarkably similar. However, as we incrementally raised the similarity level between the recorded neurons by increasing the overlap in the combinatorial expression of γ-PCDH isoforms, we observed a gradual decrease in the connectivity probability between these neurons. Notably, the connectivity probability reached its minimum when the recorded cells had the exact same combinatorial expression of γ-PCDH isoforms at the 100% similarity level. These findings suggest that the similarity level between neurons, rather than the absolute expression level of γ-PCDH isoforms, plays a critical role in affecting synapse formation.

-A caveat for the relative plasmid expression quantifications in Figure 3-S1 is that IHC was used to amplify the RFP-tagged isoform, and thus does not likely preserve the relationship between quantities and detection.

We attempted to enhance the mNeongreen signal, known for its exceptional signal-tonoise ratio, by utilizing the 32f6-100 antibody from Chromotek. However, our observations did not reveal any additional cells through immunostaining compared to the images obtained solely based on the mNeongreen signal. This indicates that the application of the available antibody did not yield a significant improvement in cell detection.<br /> It's important to emphasize that if the RFP signal is overvalued, it would result in an increase in both the "red only" and "red in total" categories. However, it's worth noting that the "red only" category is more sensitive to the outcome than the "red in total" category. Therefore, an overvaluation of the RFP signal would lead to an underestimation of the total estimated plasmid content in electroporated neurons. Consequently, this would result in a lower estimate for the proportion of co-expression cells rather than a higher estimate. We have updated the calculation method in the "Estimating the numbers of overexpressed γPCDH isoform" section to reflect these considerations.

• Figure 1 didn't change in response to reviews to improve clarity. New panels relating to the scRNASeq analyses were added to supplementary data but many are central and should be included in Figure 1 (ie. S1-Fig6D). In the Results, the authors state that neuronal subpopulations generally show a combinatorial expression of some variable RNA isoforms and near ubiquitous C-type expression. But they only show data for the Layer 2/3 neuron-specific cluster in S1-Fig-6D, and so it is not clear if this pattern applies to other clusters. Fig. S1-5 show a low number of expressed isoforms per cell, but specific descriptions on whether these include C-type isoforms would be helpful. Figure 1F showing isoform profile in all neurons is not particularly meaningful. There is a lot of interest in neuron-type specific differences in cPcdh diversity, and the authors could highlight their data from S1-5 accordingly.

In addition to the layer 2/3 cluster, we observed a diverse combinatorial expression of various variable γ-PCDH isoforms alongside nearly ubiquitous C-type expression in all other clusters of cells. We have now explicitly mentioned this observation in the main text. To underscore this point further, we have included a new figure, Fig. 1-S6, which provides information on the similarity analysis for all other clusters. It's important to note that the data in previous Fig. S1-5 (now renumbered as S1-7) were solely related to "variable" isoforms. We apologize for any confusion and have made this clarification by including it in the title of the figure.

• The concept of co-occurrence and results should be explained within the results section, to more clearly relate this concept to data and interpretations. Explanations are now found in the methods, but this did not improve the clarity of this otherwise very interesting aspect of the study.

Thanks for your suggestion. We have incorporated some of the explanations from the methods section into the main text t, mainly for the concept of “co-occurence”.

• The claim that C-type Pcdhgs do not functionally influence connectivity is premature. Tests were limited to PcdhgC4, which has unique properties compared to the other 2 C-type isoforms (Garrett et al 2019 PMID: 31877124; Mancia et al PMID: 36778455). The text should be corrected to limit the conclusion to PcdhgC4, and not generally to C-type. The authors should test PcdhgC3 and PcdhgC5 isoforms.

We have changed the claim for PcdhgC4, but not generally for C-type to better reflect our observation.

• The group generated a novel conditional Pcdh-alpha mouse allele using CRISPR methods, and state that there were no changes in synaptic connectivity in these Pcdh-alpha mutants. But this claim is premature. The Southern blots validate the targeting of the allele. But further validations are required to establish that this floxed allele can be efficiently recombined, disrupting Pcdha protein levels and function. Pcdha alleles have been validated by western blots and by demonstration of the prominent serotonergic axonal phenotype of Pcdha-KO (ie. Chen 2017 PMID: 28450636; IngEsteves 2018 PMID: 29439167).

We have obtained a new set of qRT-PCR data that confirms the decreased expression of α-PCDH in Pcdha CKO mice. These data have been integrated into Figure 2-S2D.

• The Discussion would be strengthened by a deeper discussion of the findings to other cPcdh roles and studies, and of the limitations of the study. The idea that the Pcdhgs are influencing the rate of connectivity through a repulsion mechanism or synaptic formation (ie through negative interactions with synaptic organizers such as Nlgn - Molumby 2018, Steffen 2022) could be presented in a model, and supported by other literature.

I would like to express my sincere appreciation to the reviewer for their invaluable comments and suggestions, which have led to extended discussions within our work. We have incorporated these suggestions into our paper to establish stronger connections between our observations and prior research findings.

Reviewer #1 (Recommendations For The Authors):

1) In Figure S6, the authors measured Euclidean distance from the single cell data to take account of the isoform expression levels in explaining diversity. However, it is hard to interpret the data without any control. The authors could measure the same value from a shuffled /randomized dataset for comparison (similarly to Fig 1F).

We understand the reviewer's concern about the significance of the Euclidean distance analysis without an appropriate control. The inclusion of the Euclidean distance metric was initially a response to suggestions from other reviewers who recommended incorporating diverse methods for analyzing expression patterns among neurons.

In response to your valuable feedback, we have taken measures to address these concerns. We have introduced shuffled data for comparison, thus enhancing the meaningful context for interpreting the results derived from the Euclidean distance analysis.

2) The authors need to clarify which cortical regions were used for electrophysiological experiments.

Apologies for any confusion. To clarify, all recordings were conducted on neurons located in layer 2/3 of the neocortex without further discrimination. We have reinstated this information in both the main text and the methods section to ensure its clarity.

Reviewer #2 (Recommendations For The Authors):

There are still some issues that must be addressed.

1) The references to gamma protocadherin repulsion are not correct in context. A repulsive role of homophilic interaction has been inferred from certain knockout phenotypes in a subset of neurons (not in cortical neurons). However, repulsion has never been shown to follow gamma protocadherin engagement. The authors present no new evidence that their results are attributable to cellular repulsion at nascent synaptic contacts. The mechanism is unknown. The references to repulsion to explain their results should make it clear that this is one possible explanation, but it is not shown. Also some references in the text are not correct. For example, line 63/64: the results of Molumby and Steffen are not involving homophilic adhesion or repulsion, but rather a cis interaction with neuroligins. Those papers should not be discussed as involving repulsion as in the reference to Lefebvre 2012. Also line 268/269 "Together with previous findings (Molumby,,,Tarusawa), our observations solidify repulsion effect of g-PCDH on synapse formation. . .". This is not the case. Neither Molumby nor Tarusawa demonstrated any such repulsion.

Thank you to the reviewer for pointing out the errors in our citations. We have made the necessary corrections to the citations and have also refined the descriptions of our observations to improve clarity and accuracy.

2) The discussion of the results when C4 is overexpressed must also be greatly toned down. C4 is a strange C-type protein--it cannot get to the cell surface alone but relies on other cPCDHs for this, and its primary role is in preventing cell death. It is odd that the authors used this isoform to represent C-types. They should have used C3, which two recent papers showed have specific roles at some synapses (Meltzer et al 2023, Ginty lab) and in dendrite branching (Steffen et al 2023, Weiner lab) , or C5. It is entirely possible that just C4 has no role in synaptic matching--but C3 and C5 might. They should not conclude that the C-types have no such role and only A and B types do. That must be toned down (e.g., line 198/199, line 281).

We acknowledge that using C4 to represent all three C-types (C3, C4, and C5) is not accurate. We have now modified the statement in the main text to rectify this.

3) For the citation of Pcdhg flox/flox mice (line 126), Prasad et al., Development, 2008, Weiner lab, should also be cited as it fully characterized that line that was also used in Lefebvre et al 2008. They were co-published.

Thank you for highlighting the missing citation, and we have now included it in the relevant section.

4) the Pcdh alpha KO Mouse characterization is still insufficient. The authors must show that alpha expression is gone following introduction of Cre, either by RT-PCR using alpha constant domain primers, or an alpha antibody on Western. blot. The southern and off-target sequencing do not confirm that all alpha gene expression is gone.

Thank you for your feedback. We have conducted the qRT-PCR analysis as per your suggestion. The results clearly indicate a substantial reduction in α-PCDH expression within the neocortex of Pcdha cKO mice. We have thoughtfully incorporated this data into the manuscript, and it is visually represented in the new panel of Figure 2-S2D. Your valuable input has contributed to enhancing the quality of our work, and we sincerely appreciate the opportunity to address this important aspect.

5) I do not understand something in Figure 4-S1A. Why with 0% matching is synaptic connectivity so low? This is not the same as in Figure 3E. This has to be explained because it does suggest that overexpression of ANY isoforms can inhibit synapse formation, which is consistent with Molumby 2017, even though this paper says it is not just the levels but the isoform specificity.

The panel of Fig.4-S1A illustrates the connection rate between neurons with the same color (icons in upper left), representing cells that express the same combination of γ-PCDHs (100% of similarity). The X-axis (0%, 11%, 33%, and 100%) reflects the similarity level between the 2 populations of cells (GFP and RFP).

6) There are still issues with the English grammar in the paper. It is not too bad in the main text but someone should re-edit it. However, the figure legends are indeed much worse and truly must be edited professionally before they are acceptable.

We apologize for our English writings in the paper. We have now polished most part of the manuscript, especially the parts for figure legends.

Reviewer #3 (Recommendations For The Authors):

• This study has many strengths and innovative findings. Most comments above included suggestions to strengthen the paper. The overall message that Pcdhgs influence the rate of synaptic connectivity between nearby cells is compelling. How this Pcdhg-isoform-dependent process could influence synaptic specificity can be explored in a model in the discussion. But this study did not test a role in 'synaptic specificity'; this term should be removed from the title and line 81 in the intro.

Thank you for your invaluable comments aimed at improving our paper. Regarding the title, we believe that "synaptic connectivity" might be a more suitable choice than "synaptic specificity." However, we're open to considering other alternatives as well.

• The manuscript and overall quality of the science will be improved by removing those sections that are not adequately investigated (ie.Pcdh-a cKO; PcdhgC4 is assessed but findings can't be extended to other C-type isoforms) and by outlining limitations of the study.

We have modified the related claim mentioned in the main text.

• The studies negatively correlating between isoform matching and connectivity are not robust. Additional approaches are needed if the authors want to make this claim.

In Figure 4E, we have implemented a bootstrapping method. Bootstrapping is a statistical technique falling under the broader category of resampling methods. It involves random sampling from the observed data with replacement, enabling the calculation of standard errors, confidence intervals, and supporting hypothesis testing.

• Statistical approaches should be described in methods, figure legends.

More information about statistical approaches has been added in the figure legends.

• The discussion should elaborate on the limitations of the study, and relate to other studies, including Lv et al 2022.

We have added more discussion to relate our observations to previous findings.