Author Response

eLife assessment

This manuscript provides useful information about the lipid metabolite 15d-PGJ2 as a potential regulator of myoblast senescence. The authors provide experimental evidence that 15d-PGJ2 inhibits myoblast proliferation and differentiation by binding and regulating HRas. However, the manuscript is incomplete in its current form, as it lacks robust support from the data regarding the main conclusions related to senescence and technical concerns related to the senescence models used in this study.

Authors Response- We ae grateful to the editors and the reviewers for their time and comments in sharpening the science and the writing of the manuscript. We have attached a detailed response to emphasize that the manuscript does include robust evidence regarding the claims, which could have been missed during the review process. We have provided a better context for these points now.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors show that upon treatment with Doxorubicin (Doxo), there is an increase in senescence and inflammatory markers in the muscles. They also show these genes get upregulated in C2C12 myoblasts when treated with conditioned media or 15d-PGJ2. 15dPGJ2 induces cell death in the myoblasts, decreases proliferation (measured by cell numbers), and decreases differentiation and fusion. 15d-PGJ2 modified Cys184 of HRas, which is required for its activation as indicated by the FRET analysis with RAF RBD. They also showed that 15d-PGJ2 activates ERK signaling, but not Akt signaling, through the electrophilic center. 15d-PGJ2 inhibits Golgi localization of HRAS (only WT, not C181 or C184 mutant). They also showed that expressing the WT HRas followed by 15d-PGJ2 treatment led to a decrease in the levels of MHC mRNA and protein, and this defect is dependent on C184. This is a well-written manuscript with interesting insights into the mechanism of action of 15d-PGJ2. However, some clarification and experiments will help the paper advance the field significantly.

Strengths:

The data clearly shows that 15d-PGJ2 has a negative role in the myoblast cells and that it leads to modification of HRas protein. Moreover, the induction of biosynthetic enzymes in the PGD2 pathway also supports the induction of 15d-PGJ2 in Doxorubicin-treated cells. Both conditioned media experiments and the 15d-PGJ2 experiments show that 15d-PGJ2 could be the active component secreted by the senescent myoblasts.

Weaknesses:

The genes that are upregulated in the muscles upon injection with Doxo are also markers for inflammation. Since Doxo is also known to induce systemic inflammation, it is important to delineate these two effects (inflammatory cells vs senescent cells). The expression of beta Gal and other markers of senescence in the tissue sections will help to delineate these.

As pointed out Doxo induces systemic inflammation along with inducing DNA damage-mediated senescence. Therefore, along with the inflammatory markers of the SASP (CXCL1/2, TNF1α, IL6, PTGS1/2, PTGDS) we also observed an increase in the mRNA levels of canonical markers of DNA damage-mediated senescence. We observed an increase in the mRNA levels of cell cycle and senescence associated proteins p16 and p21 (Fig. 1C). We also observed an increased nuclear accumulation of p21 (Fig. 1A) and increased levels of phosphorylated H2A.X in the nucleus (Fig. 1B). We will characterize other markers of senescence including senescence-associated β galactosidase in the revised manuscript.

In Figure 2, where the defect in the differentiation of myoblasts upon treatment with 15d-PGJ2 is shown, most of the cells die within 48 hours at higher concentrations, making it difficult to perform the experiments. This also shows that 15d-PGJ2 was toxic to these cells. Lower concentrations show a decrease in the differentiation based on the lower number of nuclei in fibers and low expression of MyoD, MyoG, and MHC. However, it is unclear if this is due to increased cell death or defective differentiation. It would be a lot more informative if the cell count, cell division, and cell death could be plotted for these concentrations of the drug during the experiment.

We only observed the death of cells at higher concentrations of 15d-PGJ2 (5 µM and 10 µM) (Fig. S2A), but not significantly at the 4 µM concentration used in Figure 2. This is the reason 4uM was used, and we should have clarified this. We will include viability data for the low concentration of 15d-PGJ2 (4 µM) in the revised manuscript.

Also, in the myoblast experiments, are the effects of treatment with Dox reversible?

The treatment with Doxorubicin is irreversible as the senescent phenotype was not reversed after withdrawal of Doxorubicin, even after 20 days.

In Figure 3, most of the experiments are done at a high concentration, which induces almost complete cell death within 48 hours.

Figure 3 is an acute experiment for only 1 hour, at which time no cell death was observed. Specifically, we measured the phosphorylation of Erk and Akt proteins after 1 hour of treatment with 15d-PGJ2 (10 µM) during which we did not observe any cell death.

Even at such a high concentration of 15dPGJ2, the increase in ERK phosphorylation is minimal.

We observe a ~30% increase in the phosphorylation of Erk proteins after treatment with 15d-PGJ¬2 in 0.2% serum medium compared to treatment with vehicle (DMSO). This is reproducible and significant.

The experiment Figure 4C shows that C181 and C84 mutants of the HRas show higher levels in Golgi compared with WT. However, this could very well be due to the defect in palmitoylation rather than the modification with 15d-PGJ2.

Our data does not suggest higher levels of C184S mutant in the Golgi compared with WT (Fig. S4A). We observed that the ratio of HRas levels in the Golgi to the HRas levels in the plasma membrane were similar in C2C12 cells expressing HRas C184S and HRas WT (Fig. S4A graph columns 1 and 5).

Though the authors allude to the possibility that intracellular redistribution of HRas by 15d-PGJ2 requires C181 palmitoylation, the direct influence of C184 modification on C181 palmitoylation is not shown. To have a meaningful conclusion, the authors need to compare the palmitoylation and modification with 15d-PGJ2.

Palmitoylation of HRas C181S is required for the localization of HRas at the plasma membrane. The inhibition of palmitoylation of C181, either by mutation (C181S) or treatment with protein palmitoyl transferase inhibitor (2-Bromopalmitate), results in the accumulation of HRas at Golgi(Rocks et al., 2005) (Fig. S4A). Modification of HRas at C184 by 15d-PGJ2 (Fig. 3A) could inhibit the palmitoylation of HRas at C181. However, our data does not support this hypothesis as modification of HRas WT by 15d-PGJ2 does not increase the level of HRas at the Golgi, like in the case of inhibition of cysteine palmitoylation due to C181S mutation.

To test if the inhibition of myoblast differentiation depends on HRas, they overexpressed the HRas and mutants in the C2C12 lines. However, this experiment does not take the endogenous HRAs into consideration, especially when interpreting the C184 mutant. An appropriate experiment to test this would be to knock down or knock out HRas (or make knock-in mutations of C184) and show that the effect of 15d-PGJ2 disappears.

Endogenous HRas (wild type) is present in the C2C12 cells overexpressing the EGFP-tagged HRas constructs. Therefore, we only observe a partial rescue in the differentiation after 15d-PGJ2 treatment in C2C12 cells expressing the C184S mutant (Fig. 4D and E). However, since HRas is expressed under high expression CMV promoter and in the absence of other regulatory elements, the overexpressed constructs do show a dominant effect over the endogenous HRas, showing cysteine mutant dependent inhibition of differentiation of myoblasts after treatment with 15d-PGJ2 (Fig. 4D and E).

Moreover, in this specific experiment, it is difficult to interpret without a control with no HRas construct and another without the 15d-PGJ2 treatment.

The mRNA levels of MyoD, MyoG, and MHC in C2C12 cells expressing HRas constructs after treatment with 15d-PGJ2 were normalized to the mRNA levels in C2C12 cells expressing corresponding constructs and were treated with vehicle (DMSO). mRNA levels in C2C12 cells treated with vehicle were not shown as they were normalized to 1. MHC protein levels in C2C12 cells expressing HRas constructs after 15d-PGJ2 treatment were normalized to that in C2C12 cells treated with vehicle (DMSO). Since the hypothesis to study the effect of HRas cysteine mutations on the differentiation of myoblasts after treatment with 15d-PGJ2, C2C12 cells expressing HRas WT serve as adequate control. Fig. 2 shows the effect of 15d-PGJ2 on muscle differentiation when HRas was not overexpressed.

Moreover, the overall study does not delineate the toxic effects of 15d-PGJ2 from its effect on the differentiation. The inhibition of differentiation in C212 cells after treatment with 15d-PGJ2 cannot be attributed to the general toxicity of 15d-PGJ2 in cells. We show that the inhibition of differentiation of myoblasts after 15d-PGJ2 depends on modification of HRas at C184 i.e. failure to modify HRas at C184 (Fig. 3A) and resultant activation (Fig. 3B) by 15d-PGJ2 rescues this inhibition of differentiation of C2C12 cells (Fig. 4D and E), dissecting the inhibition of differentiation of myoblasts by 15d-PGJ2 from general toxic effects of 15d-PGJ2 on cell physiology.

Please note that the effect of 15d-PGJ2 on cell physiology is context-specific. On one hand, 15d-PGJ2 has been shown to exert tumor-suppressor effects by inhibiting the proliferation of ovarian cancer cells and lung adenocarcinoma cells (de Jong et al., 2011; Slanovc et al., 2024), 15d-PGJ2 also exerts pro-carcinogenic effects by induction of epithelial to mesenchymal transition in breast cancer cells MCF7 and inhibition of tumor-suppressor protein p53 in MCF7 and PC-3 cells (Choi et al., 2020; Kim et al., 2010).

Reviewer #2 (Public Review):

Summary:

In this study, Swarang and colleagues identified the lipid metabolite 15d-PGJ2 as a potential component of senescent myoblasts. They proposed that 15d-PGJ2 inhibits myoblast proliferation and differentiation by binding and regulating HRas, suggesting its potential as a target for restoring muscle homeostasis post-chemotherapy.

Strengths:

The regulation of HRas by 15d-PGJ2 is well controlled.

Weaknesses:

The novelty of the study is compromised as the activation of PGD and 15d-PGJ2, as well as the regulation of HRas and cell proliferation, have been previously reported.

Literature does support this statement, and it is important to clarify this mis-impression for the field as whole

Let us clarify-

Covalent modification of HRas by 15d-PGJ2 has been reported only twice in the literature(Luis Oliva et al., 2003; Yamamoto et al., 2011) in fibroblasts and neurons respectively.

Interaction between HRas and 15d-PGJ2 in skeletal muscles has not been shown before, even though both HRas and 15d-PGJ2 are shown to be key regulators of muscle homeostasis.

Activation of HRas by 15d-PGJ2 was reported first by Luis Oliva et al (Luis Oliva et al., 2003). However, this study does not comment on the functional implications of activation of HRas signaling.

Recently, our lab contributed to a study where the functional implication of activation of HRas signaling due to covalent modification by 15d-PGJ2 was shown in the maintenance of senescence phenotype (Wiley et al., 2021).

15d-PGJ2 was shown to inhibit the differentiation of myoblasts by Hunter et al (Hunter et al., 2001). This study hypothesized that the inhibition of myoblast differentiation is via 15d-PGJ2 mediated activation of the PPARγ signaling, the study also showed inhibition of myoblast differentiation independent of PPARγ activity, suggesting the presence of other mechanisms.

This is the first study to show a molecular mechanism where activation of HRas signaling in skeletal myoblasts due to covalent modification by 15d-PGJ2 at C184 of HRas inhibits the differentiation of skeletal myoblasts.

Additionally, there are major technical concerns related to the senescence models, limiting data interpretation regarding the relevance to senescent cells.

Major concerns:

(1) The C2C12 cell line is not an ideal model for senescence study due to its immortalized nature and lack of normal p16 expression. A more suitable myoblasts model is recommended, with a more comprehensive characterization of senescence features.

C2C12 is a good model for DNA damage based senescence that is used in this manuscript. It is not a models for replicative senescence since it is immortalized. In this study we show that C2C12 cells undergo DNA damage mediated senescence after treatment with Doxo. We also observe similar phenotype in MCF7 breast cancer cells and IMR90 lung fibroblasts after treatment with Doxo (Data will be updated in the supplementary figure 1). Also, several reports in the literature have shown induction of senescence in C2C12 cells. Moiseeva et al 2023 show induction of senescence in C2C12 cells after etoposide mediated DNA damage. Moustogiannis et al 2021 show induction of replicative senescence in C2C12 cells.

(2) The source of increased PGD or its metabolites in the conditioned medium is unclear. Including other senescence models, such as replicative or oncogene-induced senescence, would strengthen the study.

Fig. 1E shows time dependent increase in the expression of PGD2 biosynthetic enzymes in senescent C2C12 cells. Fig. 1F shows increase in the levels of 15d-PGJ2 secreted by senescent C2C12 cells in the conditioned medium. This data shows that senescent C2C12 cells are the source of PGD and its metabolites in the conditioned medium.

Again, C2C12 is not suitable for replicative senescence due to its immortalized status.

We and others have shown that C2C12 cells undergo senescence, and this manuscript only used DNA damage induced senescence.

(3) In the in vivo part, it is unclear whether the increased expression of PTGS1, PTGS2, and PTGDS is due to senescence or other side effects of DOXO.

We concur that this is a limitation of this study and the subsequent work will demonstrate the origin of prostaglandin biosynthesis after treatment with Doxo in vivo.

(4) Figure 2A lacks an important control from non-senescent cells during the measurement of C2C12 differentiation in the presence of a conditioned medium.

Figure 2A tests the effect of prostaglandin PGD2 and its metabolites secreted by the senescent cells on the differentiation of myoblasts. Therefore, we inhibited the synthesis of PGD2 in senescent cells by treatment with AT-56, and then collected the conditioned medium. Conditioned medium collected from senescent C2C12 cells treated with vehicle (DMSO) served as a control for the experiment, whereas differentiation of C2C12 cells without any treatment serves as a positive control.

There is no explanation of how differentiation was quantified or how the fusion index was calculated.

The fusion index was calculated using a published myotube analyzer software (Noë et al., 2022). Appropriate info will be added to the materials and methods section in the revised manuscript.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The manuscript offers a commendable exploration into the relationship between plasma omega-6/omega-3 fatty acid ratios and mortality outcomes.

Strengths:

The chosen study design and analytical techniques align well with the research objectives, and the results resonate with existing literature.

Weaknesses:

Lack of information on the selection criteria for participants; 5. The analysis of individual PUFAs is not appropriate; The definition of comorbidities is vague; The rationale of conducting the mediation analysis of blood biomarkers is not given.

Thank you for your insightful feedback and for acknowledging the strengths of our manuscript, particularly regarding the alignment of our study design and analytical methods with our research objectives. Your recognition of how our results resonate with existing literature is greatly appreciated.

Addressing the concerns you've raised:

Selection Criteria for Participants: In the “Methods-Study population” section, we have outlined the exclusion criteria for participant selection. This information provides comprehensive insight into our methodology for selecting the study cohort.

Analysis of Individual PUFAs: We acknowledge your concern regarding the analysis of individual PUFAs due to their inter-correlations in plasma levels. However, the correlations between omega-3% and omega-6% (r = -0.12) and between DHA% and LA% (r = 0.03) are actually low. Because DHA is one of omega-3 PUFAs, we did not include PUFAs in the same model. Similar considerations apply to LA and omega-6. We believe that exploring the effects of individual fatty acids adds valuable depth to our research. Both DHA and LA have been included in the same model due to their low correlation, with careful adjustments for confounding factors to provide a nuanced understanding of their individual impacts on mortality.

Definition of Comorbidities: The definition of comorbidities, including hypertension, diabetes, and longstanding illness, is elaborated under the Methods section. These conditions were identified through self-reported data collected via the Assessment Centre Environment (ACE) touchscreen questionnaire, allowing us to capture a broad range of chronic conditions as reported by participants.

Rationale for Mediation Analysis: Initially, our approach to mediation analysis included various blood biomarkers available in the UK Biobank database to explore the potential underlying pathways. However, upon considering your feedback regarding the overlap of fatty acids with lipid classes or lipid particles in plasma, we have decided to remove these elements from our mediation analysis.

Reviewer #2 (Public Review):

Summary:

This study utilized a large sample from the UK Biobank which enhanced statistical robustness, employed a prospective design to establish clear temporal relationships, used objective biomarkers for assessing plasma omega-6/omega-3 ratio, and investigated various mortality causes including CVD and cancer for a holistic health understanding.

Strengths:

The authors used a large sample size, employed a prospective design, and investigated various mortality.

Weaknesses:

Analyzing n-3 and n-6 PUFAs separately might be less instructive. It might not be methodologically sound to treat TG, HDL, LDL, and apolipoproteins as mediators. It's imperative to exercise caution when drawing causal conclusions from the observed correlations. The manuscript might propose potential research trajectories.

We are grateful for your thoughtful analysis of our study's strengths and for your constructive feedback on areas for improvement.

Response to Weaknesses:

Analyzing n-3 and n-6 PUFAs Separately: We recognize the challenge in analyzing n-3 and n-6 PUFAs separately due to their correlations. However, the correlation between n-3% and n-6% in UK Biobank was actually relatively low (r = -0.12). We include them in one model to test if both are associated with the outcomes after controlling for the effects of the other. Indeed, both were negatively associated with the mortality outcomes in our analysis. We believe our supplemental analysis of n-3 and n-6 PUFAs provides useful information to the readers, in addition to our findings based on the n-6/n-3 ratio.

Mediation Analysis of TG, HDL, LDL, and Apolipoproteins: We appreciate your insight on the methodological considerations of treating these biomarkers as mediators. After careful review and in line with suggestions from another reviewer, we have removed these elements from our mediation analysis. This revision improves the net scientific rigor of our work, ensuring that our conclusions are drawn from the most robust and methodologically sound of our analyses.

Causal Conclusions from Correlations: We fully agree with the need for caution in interpreting correlations in observational studies. To this end, we have avoided implying causality in our manuscript. Terms suggesting causality, like "protective effects," have been replaced with "inverse associations" to more accurately represent our findings. This adjustment enhances the clarity and accuracy of our conclusions.

Proposing Future Research Trajectories: Recognizing the importance of advancing causal and mechanistic understanding in this field, we have called for future studies to further examine causality and characterize molecular mechanisms of the observed associations in our study.

Reviewer #3 (Public Review):

Summary:

The authors are trying to find out whether the levels of omega-6 and omega-3 fatty acids in the blood are linked to the likelihood of dying from anything, of dying from cancer and of dying from cardiovascular disease. They use a large dataset called UK Biobank where fatty acid levels were measured in blood at the start of the study and what happened to the participants over the following years (average of 12.7 years) was followed. They find that both omega-6 AND omega-3 fatty acids were linked with less likelihood of dying from anything, from cancer and from cardiovascular disease. The effects of omega-3s were stronger. They then made a ratio of omega-6 to omega-3 fatty acids and found that as that ratio increased risk of dying also increased,. This supports the idea that omega-3s have stronger effects than omega-6s.

Strengths:

This is a large study (over 85,000 participants) with a good follow up period (average 12.7 years). Using blood levels of fatty acids is superior to using estimated dietary intakes. The authors take account of many variables that could interfere with the findings (confounding variables) - they do this using statistical methods.

Weaknesses:

There are several omega-6 and omega-3 fatty acids - it is not clear which ones were actually measured in this study

Thank you for recognizing the strengths of our study, including the large sample size, the duration of follow-up, and our methodological approach to using blood levels of fatty acids and addressing potential confounders. Regarding the weakness you've highlighted, we understand the importance of specifying which omega-6 and omega-3 fatty acids were analyzed in our study. We have revised the Method section to provide detailed information about how the exposures were measured.

Recommendations for the author:

Reviewer #1 (Recommendations for the Authors):

To elevate the manuscript's scholarly rigor, I propose the following refinements:

(1) The manuscript lacks information on the selection criteria for participants and the representativeness of the UK Biobank cohort. It is important to provide details on how participants were selected and whether it is representative of the general population, which is crucial for assessing the generalizability of the findings.

We appreciate the opportunity to clarify the participant selection criteria and the representativeness of the UK Biobank cohort within our manuscript. In the “Methods-Study population” section, we delineated the exclusion criteria: "Participants with cancer (n=37,736) or CVD (n=100,972), those who withdrew from the study (n=879), and those with incomplete data on the plasma omega-6/omega-3 ratio (n=277,372) were excluded from this study, leaving 85,425 participants, 6,461 died during follow-up, including 2,794 from cancer and 1,668 from CVD." To further address representativeness, we performed a sensitivity analysis, examining the baseline characteristics of participants included in our study relative to those omitted due to lack of exposure information. This analysis, presented in Additional file 2: Table S13, indicates comparable baseline characteristics across both participant groups, bolstering confidence in the representativeness of our study sample with the general UK Biobank participants.

Regarding the UK Biobank's representativeness with the general population, we acknowledge that the cohort does not mirror the broader UK demographic in terms of socioeconomic and health profiles. Participants in the UK Biobank generally exhibit better health and higher socioeconomic status than the average UK resident, potentially influencing the disease prevalence and incidence rates. Nonetheless, the UK Biobank's extensive sample size and comprehensive exposure data enable the generation of valid estimates for exposure-disease associations. These estimates have been corroborated by findings from more demographically representative cohorts, as highlighted in the studies by Batty et al., and Fry et al..

We recognize the importance of this aspect and will incorporate a discussion on the implications of these factors for the generalizability of our findings in the “Discussion-Limitations” section of our manuscript. We are grateful for this insightful comment and believe that this addition will enhance the manuscript's contribution to the field.

Here is what we added in the “Discussion-Limitations” section of our manuscript: “Third, we acknowledged that the cohort did not mirror the broader UK demographic in terms of socioeconomic and health profiles. Participants in the UK Biobank generally exhibited better health and higher socioeconomic status than the average UK resident, potentially influencing the disease prevalence and incidence rates. Nonetheless, the UK Biobank's extensive sample size and comprehensive exposure data enable the generation of valid estimates for exposure-disease associations. These estimates have been corroborated by findings from more demographically representative cohorts47,48.”

References:

Batty, G. D., Gale, C. R., Kivimäki, M., et al. Comparison of risk factor associations in UK Biobank against representative, general population based studies with conventional response rates: prospective cohort study and individual participant meta-analysis. BMJ. 2020; 368: m131.

Fry A, Littlejohns TJ, Sudlow C, et al. Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population. Am J Epidemiol. 2017;186(9):1026–34.

(2) The study sample included different ancestries which may introduce confounding from genetic background. As over 90% of the participants were of European ancestry, I recommend excluding individuals of non-European ancestry in the main analysis.

Thank you for raising the concern regarding the inclusion of different ancestries in our study sample and the potential confounding. In our research, we have adhered to the widely accepted practice of including all participants in the study to ensure a comprehensive analysis. Recognizing the predominance of European ancestry within our cohort, which exceeds 90%, we have proactively incorporated ethnicity as a covariate in our statistical models to mitigate confounding influences.

We also considered the feasibility of conducting a stratified analysis for non-European participants. However, the small sample sizes of non-European subgroups do not provide sufficient statistical power to yield reliable or meaningful separate analyses. Consequently, to maintain the integrity and robustness of our findings, we opted to include all participants in the main analysis, adjusting for ethnicity to account for potential confounders.

(3) I noted that a large proportion of participants were excluded due to the lack of data on plasma PUFAs. Were the characteristics of these participants similar to the current analysis sample?

Thank you for raising this very important point. According to UK Biobank, “The EDTA plasma samples were picked randomly and are therefore representative of the 502,543 participants in the full cohort.” (As detailed in Julkunen et al.) Moreover, as noted in our reply to comment #1 above, we performed a sensitivity analysis, examining the baseline characteristics of participants included in our study relative to those omitted due to lack of exposure information.

The results of this analysis are detailed in Additional file 2: Table S13. They demonstrate that the baseline characteristics—such as age, gender, ethnicity, socioeconomic status, and lifestyle habits—are indeed similar between the two groups. This similarity supports the representativeness of our analysis sample and suggests that the exclusion of participants without plasma PUFA data does not introduce a bias that would undermine the validity of our study's findings.

References:

Julkunen H, Cichońska A, Tiainen M, et al. Atlas of plasma NMR biomarkers for health and disease in 118,461 individuals from the UK Biobank. Nat Commun. 2023 Feb 3;14(1):604. doi: 10.1038/s41467-023-36231-7.

(4) The methods section should include a detailed description of the measurement of plasma omega-6/omega-3 fatty acid ratio. It is important to provide information on the analytical techniques used and any quality control measures implemented to ensure the accuracy and reliability of the measurements. Importantly, were repeated measurements done?

Thank you for raising this important point. The details of the metabolomic profiling have been described in previous UK Biobank publications. In this revision, we added a brief description of the measurement process and provided references to previous publications.

Here is what we added in the “Methods- Ascertainment of exposure” section of our manuscript: “Metabolomic profiling of plasma samples was performed with high-throughput nuclear magnetic resonance (NMR) spectroscopy. At the time of this analysis (15 Mar 2023), UK Biobank released the Phase 1 metabolomic dataset, which covered a random selection of 118,461 plasma samples from the baseline recruitment. These samples were collected between 2007 and 2010 and had been stored in −80 °C freezers, while the NMR measurements took place between 2019 and 2020. Detailed descriptions could be found in previous publications about plasma sample preparation, NMR spectroscopy setup, quality control protocols, correction for sample dilution, verification with duplicate samples and internal controls, and comparisons with independent measurements from clinical chemistry assays20-22.”

(5) The analysis of individual PUFAs is not appropriate because plasma levels of these PUFAs, including n-3 PUFAs and n-6 PUFAs, EPA, DHA and AA, are usually correlated. It is hard to differentiate these correlated FAs in Cox model. Whereas the ratio of n-6/n-3 is indeed more comprehensive, and the current analysis demonstrated this ratio as a good marker of mortality. Therefore, the analyses of individual PUFAs can be removed and only focus on the ratio of n-6/n-3.

We resonate with the Reviewer regarding the importance of focusing on the ratio of n-6/n-3. Indeed, the ratio is our focus in this manuscript. We also acknowledge the Reviewer's concern regarding the inclusion of correlated covariates in one statistical model. In that specific analysis, the correlations between omega-3% and omega-6% (r = -0.12) and between DHA% and LA% (r = 0.03) are relatively low. Additionally, we also checked the model for multicollinearity and found that the variance inflation factors (VIFs) were within acceptable ranges. In the fully adjusted model that included omega-3% and omega-6%, all variables had VIFs below 1.13, with omega-3% at a VIF of 1.06 and omega-6% at a VIF of 1.12. Similarly, in the model including DHA% and LA%, all variables also exhibited VIFs under 1.13, with DHA% recording a VIF of 1.07 and LA% a VIF of 1.10. Because DHA is one of omega-3 PUFAs, we did not include them in the same model. We did not include LA and omega-6 in the same model, either. Because the ratio has two components and each component is the sum of multiple individual PUFAs, it is natural to ask which component is more important (e.g., omega-6 or omega-3?), which specific fatty acid is driving the effect of omega-3 PUFAs (e.g., ALA? Or the marine omega-3, EPA and DHA?). We received such feedback frequently when we presented our research previously. Therefore, as an effort to address them, we performed analysis of omega-3, omega-6, DHA, and LA. While we understand the complexities involved in differentiating the effects of individual fatty acids in a Cox model, we believe there is intrinsic value in exploring these relationships further. In our analysis, we have attempted to investigate the effects of individual PUFAs on mortality by including both DHA and LA within the same model due to their low correlation, making adjustments to account for confounding factors (As detailed in Additional file 2: Table S9). Our findings indicate significant inverse associations between both DHA and LA with all-cause, cancer, and cardiovascular disease (CVD) mortality. We agree with the Reviewer that the focus of our manuscript should be the ratio, but also hope the Reviewer will agree with us that keeping the results from individual PUFAs will provide additional useful information to the readers.

(6) The definition of comorbidities (including hypertension, diabetes, and longstanding illness) is vague. Please clarify what diseases longstanding illness includes.

We appreciate the request for clarification regarding the definition of comorbidities in our study, including the categorization of longstanding illness. The information regarding longstanding illnesses was obtained via the Assessment Centre Environment (ACE) touchscreen questionnaire. Participants were asked, "Do you have any long-standing illness, disability, or infirmity?" with the response options being “Yes,” “No,” “Do not know,” and “Prefer not to answer.” For the purposes of our analysis, participants who selected “Yes” were categorized as having a longstanding illness, while the remaining options were grouped as not having a longstanding illness.

This method of classification aligns with our detailed explanation in the “Methods-Ascertainment of covariates” section of the manuscript, where we state that “Comorbidities, including hypertension, diabetes, and longstanding illness, were self-reported at baseline. Longstanding illness refers to any long-standing illness, disability, or infirmity, without other specific information.” It is important to note that this approach is consistent with established precedents in the field. Specifically, the paper by Li et al. in the BMJ utilized a similar definition for comorbidities, reinforcing the validity of our methodology.

References:

Li ZH, Zhong WF, Liu S, et al. Associations of habitual fish oil supplementation with cardiovascular outcomes and all cause mortality: evidence from a large population based cohort study. BMJ. 2020 Mar 4;368:m456.

(7) The rationale of conducting the mediation analysis of blood biomarkers is not given. Since fatty acids can be formed as TG or bound with apolipoproteins in plasma, there is a large overlap of FAs with these biomarkers and thus it is not appropriate to analyze TG, HDL, LDL, and apolipoproteins as mediators.

We are grateful for the insightful feedback regarding the mediation analysis of blood biomarkers. Our mediation analysis aimed to explore the possible biomarkers and biological processes that explain the effects of PUFAs on mortality. Upon reflection, we recognize the complexities introduced by the inherent overlap of fatty acids with different lipid particles and lipid classes in plasma. Considering the potential confounding this overlap presents, and in agreement with your recommendation, we have decided to remove the mediation analyses involving cholesterol, TG, HDL-C, LDL-C, Lp(a), ApoA, and ApoB from our study. We appreciate your guidance on this matter and have updated our manuscript accordingly to reflect these changes.

Reviewer #2 (Recommendations for the Authors):

(1) Analyzing n-3 and n-6 PUFAs separately might be less instructive given the inherent correlations among plasma levels of n-3 PUFAs and n-6 PUFAs. Also, some important specific PUFAs, such as ALA, AA, EPA, etc. were not available in the UK Biobank data though the authors tried to analyze LA and DHA. The n-6/n-3 ratio, as evidenced by the current analysis, offers a more holistic perspective and might be a superior mortality marker. Thus, I recommend shifting the focus solely to this ratio.

Thank you for the thoughtful comment. Reviewer #1 raised a similar point (comment #5 above). We are glad that both reviewers recognized the importance of the omega-6/omega-3 ratio and agreed with us that the ratio should be the focus of the paper. Please also see our more detailed response above. Briefly, our manuscript centered on the ratio, while the supplemental analysis of omega-3%, omega-6%, DHA%, and LA% provided additional useful information. We included omega-3% and omega-6% in the same model because their correlation was relatively low (r = -0.12). We also checked the model for multicollinearity and found that the variance inflation factors (VIFs) for n-3 PUFAs and n-6 PUFAs were within acceptable ranges. In the fully adjusted model that included omega-3% and omega-6%, all variables had VIFs below 1.13, with omega-3% at a VIF of 1.06 and omega-6% at a VIF of 1.12. Similarly, in the model including DHA% and LA%, all variables also exhibited VIFs under 1.13, with DHA% recording a VIF of 1.07 and LA% a VIF of 1.10. Therefore, we decided to keep the content for omega-3 and omega-6 PUFAs. We hope that Reviewer will agree with us that this content only provides additional information to the readers.

(2) It might not be methodologically sound to treat TG, HDL, LDL, and apolipoproteins as mediators. Since the model included comorbidities as covariates, hypercholesteremia and hypertriglyceridemia seemed to have been adjusted in the analysis. Thus, further adjusting these blood biomarkers for mediation analysis which overlapped with comorbidities is redundant.

We appreciate your critical evaluation of our methodological approach. Your point is well-taken, especially in light of the fact that comorbidities such as hypercholesterolemia and hypertriglyceridemia have been accounted for as covariates in our model. This overlap, as you correctly identified, could indeed render the mediation analysis redundant. In concordance with your recommendation, and incorporating the comments of another reviewer, we have now omitted the mediation analysis involving these blood biomarkers from our study. We believe this adjustment strengthens the methodological soundness of our research and are thankful for your contribution to this refinement. We have updated our manuscript to reflect these changes and ensure our analysis remains robust and free from redundancy.

(3) It's imperative to exercise caution when drawing causal conclusions from the observed correlations. The inherent constraints of observational studies, coupled with potential residual confounding or reverse causality, should be acknowledged.

We concur with the caution against implying causality from correlations observed in our study. As such, we have carefully refrained from claiming any causal relationships within our paper. We acknowledge that the term "protective effects" could suggest a causal inference, and we have revised our language to describe these observations as "inverse associations" to more accurately reflect the nature of our findings.

We have also addressed the inherent limitations of observational research in the Discussion section under 'limitations' of our manuscript. There, we recognize that while we have accounted for many confounders, the possibility of residual confounding cannot be entirely excluded. We also agree that reverse causality is a concern in observational studies. To mitigate this, we performed a sensitivity analysis excluding participants who died within the first year of follow-up. The results from this analysis, which are provided in Additional file 2: Table S12, show consistency with our main findings, suggesting that the observed associations are less likely to be predominantly driven by reverse causation. We are grateful for your insights, which have guided us in strengthening our manuscript and ensuring that our conclusions are presented with the appropriate scientific rigor.

(4) To guide subsequent scholarly endeavors, the manuscript might propose potential research trajectories, such as spearheading randomized controlled trials to delve deeper into the causal nexus between plasma omega-6/omega-3 ratios and mortality outcomes or probing the mechanistic underpinnings of the observed correlations.

We agree that conducting randomized controlled trials could illuminate the potential causal relationships between plasma PUFA biomarkers and mortality outcomes. While the primary focus of our manuscript is to report on associations, we acknowledge the importance of causal analysis in advancing the field. In our secondary analysis, we touched upon mediation effects of blood biomarkers, which could serve as a preliminary step towards establishing causality. Although our current work did not delve deeply into causal mechanisms, the results we have presented may indeed stimulate further exploration. By reporting our mediation analysis results, we aim to provide a foundation that other researchers might build upon. We hope that our work will act as a catalyst for more in-depth studies, such as RCTs or mechanistic investigations, to pursue the questions we have begun to explore.

Following this recommendation, we have revised our Conclusion paragraph and added: “Our findings support the active management of a high circulating level of omega-3 fatty acids and a low omega-6/omega-3 ratio to prevent premature death. Future research is warranted to further test the causality, such as Mendelian randomization and randomized controlled trials. Mechanistic research, including comprehensive mediation analysis, in-depth experimental characterization in animal models or cell lines, and intervention studies, is also needed to unravel the molecular and physiological underpinnings.”

Reviewer #3 (Recommendations for the Authors):

(1) Line 32. Delete "a balanced" because a balanced o6:o3 cannot be defined.

Thank you for pointing out the issue with the term "a balanced". Most authors agree with your observation that defining what constitutes a 'balanced' ratio can be ambiguous and potentially misleading. One author, JTB, disagrees that “balance” as a concept is unacceptably ambiguous or misleading. In response, we have removed the words from our manuscript.

(2) In the abstract you should present the findings for omega-6 and omega-3 PUFAs first and then the findings for the ratio.

We appreciate your suggestion to present the findings for omega-6 and omega-3 PUFAs prior to those for the ratio in the abstract. As laid out in the Background section, the ratio was our primary exposure of interest. So, we organized our manuscript by centering on the ratio. We are glad that both Reviewer #1 and #2 expressed a particular interest in the ratio findings and urged us to keep the ratio as the focus. We believe that this emphasis reflects the novel aspects of our research and aligns with the thematic structure of our manuscript.

(3) Line 80. controversial should read uncertain.

Thank you for the suggestion. We have changed “controversial” to “uncertain”.

(4) It is unclear which fatty acids are included in total PUFAs, omega-6 PUFAs and omega-3 PUFAs. It is vital that this is specified.

Thank you very much for your suggestion. We agree that it is important to clarify the specific fatty acids included in the analysis. In the revised manuscript, we emphasized that we analyzed “total omega-6 PUFAs” and “total omega-3 PUFAs”, while “LA is one type of omega-6 PUFAs” and “DHA is one type of omega-3 PUFAs”. We also revised the Method section of “Ascertainment of exposure” to provide more information about how the exposures were measured. Here is what we added in the “Methods- Ascertainment of exposure” section of our manuscript: “Five PUFAs-related biomarkers were directly measured in absolute concentration units (mmol/L), including total PUFAs, total omega-3 PUFAs, total omega-6 PUFAs, docosahexaenoic acid (DHA), and linoleic acid (LA). Of note, DHA is one type of omega-3 PUFAs, and LA is one type of omega-6 PUFAs. Our primary exposure of interest, the omega-6/omega-3 ratio, was calculated based on their absolute concentrations. We also performed supplemental analysis for four exposures, the percentages of omega-3 PUFAs, omega-6 PUFAs, DHA, and LA in total fatty acids (omega-3%, omega-6%, DHA%, and LA%), which were calculated by dividing their absolute concentrations to that of total fatty acids.”

Author Response

Reviewer #1 (Public Review):

Weaknesses:

The signaling pathway upstream of Maf1 remains unknown. In eukaryotes, Maf1 is a negative regulator of RNA pol III and is regulated by external signals via the TORC pathway. Since TORC components are absent in the apicomplexan lineage, one central question that remains open is how Maf1 is regulated in P. falciparum. Magnesium is probably not the sole stimulus involved, as suggested by the observation that Ile deprivation also down-regulates RNA pol III activity.

We agree that there is still much to uncover relating to the PfMaf1 signaling pathway. While we still do not know each component, we have been able to link external factors (of course not limited to only magnesium) to the increased nuclear occupancy of PfMaf1. Other protein interactors that potentially regulate PfMaf1, while not confirmed, have been identified in plasma sample as candidates for future experiments to validate their potential involvement of RNA Pol III inhibition.

The study does not address why MgCl2 levels vary depending on the clinical state. It is unclear whether plasma magnesium is increased during asymptomatic malaria or decreased during symptomatic infection, as the study does not include control groups with non-infected individuals. Along the same line, MgCl2 supplementation in parasite cultures was done at 3mM, which is higher than the highest concentrations observed in clinical samples.

This reviewer raised a valid point. The plasma magnesium levels for the wet symptomatic samples (averaging [0.79mM]) were within the normal range of a healthy individual (between [0.75-0.95mM]) while the dry asymptomatic levels were above the normal range (averaging [1.13mM]). Ideally, we would have liked to have control uninfected plasma samples from individuals from The Gambia. Unfortunately, field studies and human volunteer studies do not always have all the ideal controls that in vitro studies have. We recognize that [3mM] is higher than the normal range for magnesium levels, which is why we included a revised Supplementary Figure 3A. This figure shows that magnesium concentrations as low as [1mM] (similar to the levels found in dry asymptomatic samples) reduced the expression of RNA Pol III-transcribed genes.

Although the study provides biochemical evidence of Maf1 accumulation in the parasite nuclear fraction upon magnesium addition, this is not fully supported by the immunofluorescence experiments.

We agree that the resolution of IFA images does not allow to support the WB data. We believe that the importance of the IFA Supplementary Figure is to show that PfMaf1 clusters together in foci, which has not been previously reported.

Reviewer #2 (Public Review):

Weaknesses:

However, most analyses are rather preliminary as only very few (3-5) candidate genes are analyzed by qPCR instead of carrying out comprehensive analyses with a large qPCR panel or RNA-seq experiments with GO term analyses. Data presentation lacks clarity, the number of biological replicates is rather low and the statistical analyses need to be largely revised. Although the in vivo data from wet (mildly symptomatic) and dry (asymptomatic) season parasites with different expression levels of Pol III-regulated genes, var genes, and MgCl2 are interesting, the link between the in vitro data and the in vivo virulence of P. falciparum, which is made in many sections of the manuscript, should be toned down. Especially since (i) the only endothelial receptor studied is CD36, which is associated with parasite binding during mild malaria, and (ii) several studies provide contradictory data on MgCl2 levels during malaria and in different disease states, which is not further discussed, but the authors mainly focused on this external stimulus in their experiments.

We agree that, ideally, we would have liked to do full RNA-seq on The Gambia samples. However, that was out of the scope of this project. The RNA samples were limited which is why we did not use more primers. We believe that an appropriate number of replicates was done for the experiments. The wet symptomatic samples from this study were from mildly symptomatic individuals, as stated in the manuscript. Therefore, CD36 was a relevant receptor to use for our studies.

We agree that the published studies about magnesium levels in infected individuals are not always consistent. What these studies do not consider is the time of year, whether the infection occurred during the dry or wet season. These studies were also done in different regions of the world using different technologies. For this reason, we only highlight the observed difference observed in our field study data from The Gambia.

Reviewer #3 (Public Review):

Weaknesses:

(1) The signals upstream of Maf1 remain rather a black box. 4 are tested - heat shock and low-glucose, which seem to suppress ALL transcription; low-Isoleucine and high magnesium, which suppress Pol3. Therefore the authors use Mg supplementation throughout as a 'starvation type' stimulus. They do not discuss why they didn't use amino acid limitation, which could be more easily rationalised physiologically. It may be for experimental simplicity (no need for dropout media) but this should be discussed, and ideally, sample experiments with low-IsoLeu should be done too, to see if the responses (e.g. cytoadhesion) are all the same.

We agree that deprivation of isoleucine would have been another experimental assay for our study, but it also would not have been as novel as magnesium. While understanding the exact mechanism or involvement of magnesium as a stress condition was not the scope of this manuscript, we believe that our data will be valuable into demonstrating that external stimuli act on P. falciparum virulence gene expression via RNA Pol III inhibition. Since we also had plasma level data for magnesium, and not isoleucine, we believed it made for a better external factor to use for our in vitro studies.

(2) The proteomics, conducted to seek partners of Maf1, is probably the weakest part. From Figure S3: the proteins highlighted in the text are clearly highly selected (as ones that might be relevant, e.g. phosphatases), but many others are more enriched. It would be good to see the whole list, and which GO terms actually came top in enrichment.

We apologize if the reviewer did not see the attached supplementary Co-IP MS data. The file includes all proteins found in each sample as well as GO term analysis. For the purpose of this work, we highlight proteins potentially involved in the canonical role of Maf1 that have been shown in model organisms to reversibly inhibit RNA Pol III (phosphatases, RNA Pol III subunits).

(3) Figure 3 shows the Maf1-low line has very poor growth after only 5 days but it is stated that no dead parasites are seen even after 8 cycles and the merozoites number is down only ~18 to 15... is this too small to account for such poor growth (~5-fold reduced in a single cycle, day 3-5)? It would additionally be interesting to see a cell-cycle length assessment and invasion assay, to see if Maf1-low parasites have further defects in growth.

We agree with the reviewer that the observed reduced merozoite numbers may not the only cause of the reduced growth rate. Other factors in the PfMaf1 knock-down line may contribute to the observed poor growth.

Author Response

Our answer to reviewer #1 comments:

We attempted to perform structural characterization of the ASK1 complex with TRX1, but were unable to prepare a sufficiently stable ASK1:TRX1 complex for cryo-EM analysis, probably due to their relatively weak interactions. Therefore, we subsequently decided to use HDX-MS to characterize the structural changes of ASK1 induced by interactions with TRX1.

Detailed information about cryo-EM data processing including 2D classification averages, local resolution of the EM map and FSC figure are shown in Supporting Information, Supplementary Table S1 and Figures S1-S3.

We fully agree with the reviewer that the presence of hydrogen bonding cannot be reliably described at this resolution. However, if there is a sufficient electron density in a given region and a corresponding hydrogen bond donor-acceptor pair in the model, this suggests the possible presence of such an interaction.

Our answer to reviewer #2 comments:

We are fully aware that the use of a C-terminally truncated construct limits this study due to the presumed role of the C-terminus in ASK1 dimerization. A C-terminally truncated construct consisting of TBD, CRR, and KD (residues 88-973) was used due to the low expression yield and solubility of full-length human ASK1.

Author Response

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

Thank you and the two reviewers for the thorough review of our manuscript. We found the reviewer’s comments highly valuable and addressed them by the following additional experiments and changes in the text and the figures:

(1) We measured the effect of ROCK MASO’s on the ROCK expression by immunostaining and observed a reduction in ROCK signal, supporting the downregulation of ROCK protein level under ROCK MASO’s (new Fig. S3).

(2) We measured the effect of lower concertation of ROCK inhibitor, Y27632 (10µM), and observe the same phenotypes of skeletal loss, skeletal reduction and ectopic branching in this concentration (Fig. 2, S4). Importantly, these phenotypes were not observed when directly inhibiting PKA and PKC, in whole sea urchin embryos (1) and in skeletogenic cell cultures (2), further supporting the specificity of ROCK inhibitor.

(3) We added a time course of Pl-ROCK expression and immunostaining of ROCK in the fertilized egg, that show that this gene is maternal and the protein is present in the egg Fig. 2SA-C.

(4) We recorded F-actin in ROCK MASO’s and demonstrate that it is still detected around the spicules and their tips, similarly to ROCK inhibited embryos (new Fig.S3).

(5) We revised the paper text and figures to provide a better description of our results, distinguish clearly between our data and our interpretations and emphasize the novelty of our findings.

This paper demonstrates that ROCK, F-actin polymerization and actomyosin contractility play critical roles in biomineral growth and in shaping biomineral morphology in the sea urchin embryo, and that ROCK activity affects skeletogenic gene expression. Our findings together with previous reports of the role of actomyosin in Eukaryotes biomineralization, suggest that this molecular machinery is a part of the common molecular tool-kit used in biomineralization. The identification of a common molecular mechanism within the diverse gene regulatory networks, organic scaffolds and minerals that Eukaryote use to build their biominerals will be of high interest to the field of biomineralization and evolutionary biology. Furthermore, our paper portrays the interplay between the cellular and the genetic machinery that drives morphogenesis. We believe it would be of great interest to the broad readership of eLife and particularly to the fields of biomineralization, cell, developmental and evolutionary biology.

Thank you very much for the helpful review of our paper.

Reviewer #1 (Public Review):

We thank the reviewer for the appreciation of our work the helpful comments that guided us to strengthen the experimental evidence for our conclusions and increase the paper’s clarity. Below are our responses to the specific comments:

Major comments

One MASO led to reduced skeleton formation while the other one additionally induced ectopic branching. How was the optimum concentration for the MASOs determined? Did the authors perform a dose-response curve? What is the reason for this difference? Which of the two MASOs can be validated by reduced ROCK protein abundance? Since the ROCK antibody works, I would like to see a control experiment on Rock protein abundance in control and ROCK MO injected larvae which is the gold-standard for validating the knock-down.

We tested several MASO concentrations to identify a concentration where the control embryos injected with Random MASO were overall healthy and ROCK MASO’s showed clear phenotypes.

To test the effect of ROCK MASO’s on ROCK protein levels we did immunostaining experiments that are now presented in new Fig. S3. We could not do Western blot for injected embryos since ROCK antibody requires thousands of embryos for Western blot, which is not feasible for injected embryos. Therefore, we tested the effect of the two translation ROCK MASO’s on ROCK abundance compared to uninjected and Random MASO injected embryos using immunostaining. We observed a reduction of ROCK signal, supporting the downregulation of ROCK protein level in these genetic perturbations (new Fig. S3).

L212 "Together, these measurements show that ROCK is not required for the uptake of calcium into cells." But what about trafficking and exocytosis? As mentioned earlier, I think this is a really important point that needs to be confirmed to understand the function of ROCK in controlling calcification. In their previous study (reference 45) the authors demonstrated that they have superior techniques in measuring vesicle dynamics in vivo. Here an acute treatment with the ROCK inhibitor would be sufficient to test if calcein-positive vesicle motion, including the observed reduction in velocity close to the tissue skeleton interface, is affected by the inhibitor.

We thank the reviewer for the appreciation of our previous work where we studied calcium vesicle dynamics in whole embryos (Winter et al, Plos Com Biol 2021). We agree with the reviewer that the best way to test directly the effect of ROCK on mineral deposition and vesicle kinetics is to observe it in live skeletogenic cells. However, in Winter et al 2021, we found that the skeleton (spicules) doesn’t grow when the embryos are immobilized in either control or treated embryos. We have to immobilize the embryos to record live timelapses of whole embryos. Hence, this means that we can not determine the role of ROCK or any other perturbation in vesicle trafficking and exocytosis based on experiments conducted in immobilized whole embryos, since skeletogenesis is arrested. We believe that we can do it in skeletogenic cell cultures and we are currently developing this assay for vesicle tracking, but this is beyond the scope of this current work.

Is there a colocalization of ROCK and f-actin in the tips of the spicules? This would support the mechano-sensing-hypothesis by ROCK.

Our studies show that F-actin is localized around the spicule cavity and in the cortex of the cells (Figs. 5 and 6) while ROCK is enriched in the skeletogenic cell bodies, with some localization near the skeletogenic cell membranes (Fig. 1). To directly address the reviewer question we immune-stained ROCK and F-actin in the same embryos, and showed that their sub-cellular localizations does not show a strong overlap (Fig. S3 Q-T). However, ROCK does not bind F-actin directly: ROCK activates another kinase, LimK that phosphorylates Cofilin that interacts with F-actin. Therefore, the fact that ROCK is not colocalized with F-actin does not support nor contradicts the possible role of ROCK in mechano-sensing.

L 283. "F-actin is enriched at the tips of the spicules independently of ROCK activity" The results of this paragraph clearly demonstrate that ROCK inhibition has no effect on the localization of f-actin at the tips of the growing spicules. In addition, the new cell culture experiments underline this observation. Still, the central question that remains is, what is the interaction between ROCK, f-actin, and the mineralization process, that leads to the observed deformations? What does the f-actin signal look like in a branched phenotype or in larvae that failed to develop a skeleton (inhibition from Y20)?

As we report in Fig. 6, and now on new Fig. S3, under ROCK late inhibition or in ROCK morphants, we still detect F-actin around the spicule and enriched at the tips. When ROCK is inhibited and the embryo fails to develop a skeleton, we observe Factin accumulation in the skeletogenic cells, but the F-actin is not organized (Fig. 5). As the spicule is absent in this condition, it is hard to conclude whether the effect on F-actin organization is direct or due to the absence of spicule in this condition. We stated that explicitly in the current version in the results, lines 324-326 and in the discussion, lines 405-408.

Immunohistochemical analyses on f-actin localization and abundance should be additionally performed with ROCK knock-down phenotypes to confirm the pharmacological inhibition.

We did that in our new Figure S3 and showed that ROCK morphant show the same F-actin localization at the tips like control and ROCK inhibited embryos.

L 365 "...supporting its role in mineral deposition..." "...Overall, our studies indicate that ROCK activity....is essential for the formation of the spicule cavity......which could be essential for mineral deposition..." I think the authors need to do a better job in clearly separating between the potential processes impacted by ROCK perturbation. Is it stabilization and mechano-sensing in the spicule tip or the intracellular trafficking and deposition of the ACC? If the dataset does not allow for a definite conclusion, I suggest clearly separating the different possibilities combined with thorough discussion-based findings from other mineralizing systems where the interaction between ROCK and F-actin has been described.

We thank the reviewer for this important comment. We believe that ROCK and the actomyosin are involved in both, mechano-sensing of the rigid biomineral and in the transport and exocytosis of mineral-bearing vesicles. In the current version we provide explicit explanations of these two hypotheses in the discussion section. The possible role in exocytosis and the experiments that are required to assess this role are described in lines 427-439, and the possible mechano-sensing role and effect on gene expression is described in lines 440-453.

Reviewer #1 (Recommendations For The Authors):

Minor comments

L185 "These SR-µCT measurements show that the rate of mineral deposition is significantly reduced under ROCK inhibition." To correctly support this statement I would suggest to calculate the real growth rates (µm3 time-1). For example, an increase in volume from 6,850 µm3 at 48 hpf to 14,673 µm3 at 72 hpf would result in a growth rate of 7823 µm3 24h-1.

We thank the reviewer for this suggestion. We calculated the rate of spicule growth as the reviewer suggested and we added this information in lines 218-221.

L343: "This implies that....within the skeletogenic lineage." This concluding sentence is very speculative and therefore misplaced in the results section.

We removed this sentence from the results section into the discussion, lines 443-445.

L382: "The participation of F-actin and ROCK in polarized tip-growth and vesicle exocytosis has been observed in both, animals and plants." L407-409: "...F-actin could be regulating the localized exocytosis of mineral-bearing vesicles...." I think this is exactly the core question that remains unresolved in this study. To reduce speculations I strongly recommend addressing the effect of ROCK inhibition on vesicle trafficking and exocytosis (Monitoring of calcein-positive Vesicles in PMCs).

We agree with the reviewer that this is a critical question that we would have address, but as we explained above, is beyond the scope of this study.

Figure 5: The values below the scale bars in the newly added figures U+V are extremely small. Also, the Legend for this figure sounds incorrect. Should read: "...and skeletogenic cell cultures that were treated with 30µM ROCK inhibitor that was added at 48hpf and recorded at 72hpf.

We increased the font near the scale bars and corrected the figure caption. Thanks for this and your other helpful comments!

Reviewer #2 (Public Review):

We thank the reviewer for raising the important issue of inhibitor concentration which led us to do additional experiments with lower concentration that were valuable and strengthen the manuscript. We also thank the reviewer for asking us to be clearer with the interpretation of the results. Below are our responses to the specific comments:

My concerns are the interpretation of the experiments. The main overriding concern is a possible over-interpretation of the role of ROCK. In the literature that ROCK participates in many biological processes with a major contribution to the actin cytoskeleton. And when a function is attributed to ROCK, it is usually based on the determination of a protein that is phosphorylated by this kinase. Here that is not the case. The observation here is in most cases stunted growth of the spicule skeleton and some mis-patterning occurs or there is an absence of skeleton if the inhibitor is added prior to initiation of skeletal growth. They state in the abstract that ROCK impairs the organization of F-actin around the spicules. The evidence for that as a direct role is absent.

We agree with the reviewer that since the spicule doesn’t form under ROCK continuous inhibition, it is unclear if the absence of F-actin around the spicule in this condition is a direct outcome of the lack of ROCK activation of F-actin polymerization, or an indirect outcome due to the lack of spicule to coat. We therefore deleted this line in the abstract and explicitly stated that we cannot conclude whether the impaired F-actin organization is directly due to ROCK effect on actin polymerization in the results, lines 324-326 and in the discussion, lines 405-408.

They use morpholino data and ROCK inhibitor data to draw their conclusion. My main concern is the concentration of the inhibitor used since at the high concentrations used, the inhibitor chosen is known to inhibit other kinases as well as ROCK (PKA and PKC). They indicate that this inhibition is specifically in the skeletogenic cells based on the isolation of skeletogenic cells in culture and spicule production either under control or ROCK inhibition and they observe the same - stunting and branching or absence of skeletons if treated before skeletogenesis commences. Again, however, the high concentrations are known to inhibit the other kinases.

In the previous version of the paper we used the range of 30-80µM Y-27632 to block ROCK activity. These concentrations are commonly used in mammalian systems and in Drosophila to block ROCK activity (3-8). The reviewer is correct stating that at high concentration, this inhibitor can block PKA and PKC. However, the affinity of the inhibitor for these kinases is more than 100 times lower than its affinity to ROCK as indicated by the biochemical Ki values reported in the manufactory datasheet: 0.14-0.22 μM for ROCK1, 0.3 μM for ROCK2, 25 μM for PKA and 26 μM for PKC.

Importantly, these Ki values are based on biochemistry assays where the activity of the inhibitor is tested in-vitro with the purified protein. Therefore, these concentrations are not relevant to cell or embryo cultures where the inhibitor has to penetrate the cells and affect ROCK activity in-vivo. Y-27632 activity was studied both in-vitro and in-vivo in Narumiya, Ishizaki and Ufhata, Methods in Enzymology 2000 (9). This paper reports similar concentrations to the ones indicated in the manufactory datasheet for the in-vitro experiments, but shows that 10µM concentration or higher are effective in cell cultures. We therefore tested the effect of 10µM Y-27632 added at 0hpf (continuous inhibition) and at 25hpf (late inhibition) and added this information to Figs. 2 and S3. Continuous inhibition at this concentration resulted with three major phenotypes: skeletal loss, spicule initiations and small spicules with ectopic branching. This result supports our conclusion that ROCK activity is necessary for spicule formation, elongation and prevention of branching. Late inhibition in this concentration resulted with the majority of the embryos developing branched spicules, which is very similar to the effect of MyoII inhibition with Blebbistatin. This result again, supports the inference that ROCK activity is required for normal skeletal growth and the prevention of ectopic branching. Importantly, there are two papers were PKA and PKC were directly inhibited in whole sea urchin embryos (1) and in skeletogenic cell cultures (2). In both assays, PKC inhibition resulted with mild reduction of spicule length while PKA inhibition did not affect skeletal formation. Neither skeletal loss nor ectopic branching were ever observed under PKC or PKA inhibition, supporting the specific inhibition of ROCK by Y-27362. Furthermore, both genetic and pharmacological perturbations of ROCK resulted with significant reduction of skeletal growth and with the enhancement of ectopic branching. Therefore, we believe we provide convincing evidence for the role of ROCK in spicule formation, growth and prevention of branching. We revised Fig. 2 and S3 to include the 10µM Y-27632 data and the text describing the inhibition to include the explanations and references we provided here.

They use blebbistatin and latrunculin and show that these known inhibitors of actin cytoskeleton lead to abnormal spiculogenesis, This coincidence is suggestive but is not proof that it is ROCK acts on the actomyosin cytoskeleton given the specificity concerns.

As stated above, we believe that in the current vesion we overcame the specificity concerns and provided solid evidence that ROCK activity is necessary for spicule formation, growth and prevention of branching. Furthermore, the skeletogenic phenotypes of late 10µM Y-27632 are highly similar to those of MyoII inhibition (Blebbistatin) while the phenotypes of higher concetrations resemble the inhibition of actin polymerization by Latrunculin. We agree with the reviewer that: “This coincidence is suggestive but is not proof that ROCK acts on the actomyosin cytoskeleton” and we revise the discussion paragraph to differentiate between our solid findings and our speculations (lines 421-426): “These correlative similarities between ROCK and the actomyosin perturbations lead us to the following speculations: the low dosage of late ROCK inhibition is perturbing mostly ROCK activation of MyoII contractility while the higher dosage affects factors that control actin polymerization (Fig. 8F). Further studies in higher temporal and spatial resolution of MyoIIP activity and F-actin structures in control and under ROCK inhibition will enable us to test this.”

Reviewer #2 (Recommendations For The Authors):

The following areas require attention:

(1) You begin and end the abstract with statements on evolution in which the actomyosin cytoskeleton is associated with skeletogenesis despite different GRNs, different contributing proteins, etc. You then move to ROCK and claim to reveal that ROCK is a central player in the process. As above, in the judgement of this reviewer, you fail to establish a direct role of ROCK to the actomyosin role in skeletogenesis. Sure, the ROCK inhibitors suggest that ROCK plays some kind of role in the process but you also indicate that ROCK could act on many processes, none of which you directly associate with the necessary activity of ROCK.

We agree that our paper provides correlative similarities between the phenotypes of ROCK and those of direct pertrubations of the actomyosin network, and lacks causal relationship. We made this point clear throughout the current version of the manuscript.

(2) In the abstract you report that ROCK inhibition impairs the actin cytoskeleton around the skeleton. In examining your images in Fig. 5 that is not the case. Based on Phalloidin staining, actin surrounds both the control and the ROCK-inhibited skeleton. The distribution of actin is the same in both cases. Myosin is also stained in this figure and it too shows similar staining both in experimental and control. So, to this reviewer, there is insufficient evidence to suggest that the actin cytoskeleton is impaired, and there is no evidence directly relating ROCK with that cytoskeleton. I'm not questioning the observation that inhibition of ROCK causes stunting and mispatterning of the skeleton. That you show and quantify well. The issue is the precise target of ROCK. Your data does not establish the specific cause. It could be the actin cytoskeleton but your experiments do not directly address that.

Fig. 5 shows a clear difference between F-actin in control and under ROCK inhibition. In control F-actin is enriched around the spicule and under ROCK inhibition the spicule doesn’t form and disorganized F-actin is accumulated in the skeletogenic cells. Yet, as we stated above – this is not a proof for the direct effect of ROCK on F-actin polymerization, and we explain it explicitly in the results, lines 324-326 and in the discussion, lines 405-408.

(3) In parts of the manuscript you use the term filopodia and in other parts I think you use pseudopodia to refer to the same structure. Since Ettensohn has provided the most evidence on the organization of the skeletogenic syncytia, I suggest you use the same term he used for those cellular extensions.

The filopodia and the pseudopodia are two distinct structures generated by the skeletogenic cells. The filopodia is the common cellular extension described in many cells, while the term “pseudopodia cable” describes the specific structure that forms between the skeletogenic cells in which the spicule cavity forms, in agreement with Prof. Ettensohn terminology.

(4) In trying to find relationships you cite a number of previous papers at the end of the introduction. I went back to those papers and they describe (from your work) calcium exocytosis, plus filopodia formation, plus planar cell polarity, plus CDC42, any one of which could involve an actin cytoskeleton. You even cite a paper saying that perturbations of ROCK prevent spicule formation. I went back to that paper and that isn't the case. You then summarize the Introduction by relating ROCK and the actin cytoskeleton, thereby raising reader expectation that the two will be connected. As above, in reality, your evidence here does not connect the two.

We thank the reviewer for giving us credit for all these works, but only the paper on vesicle kinetics is from our lab (winter et al 2021). As for Croce et al, 2006 that the reviewer refers to: in Fig. 9A, 75µM of Y-27632 is used to inhibit ROCK in the same sea urchin species that we use, and the phenotype is identical to what we observe – the skeletogenic cells are there, but the spicule is not formed. As mentioned above, in the current version we distinguished clearly between our solid findings and our interpretations.

(5) You emphasize in Fig. 1 the inhibition of ROCK in the presence of VEGFR inhibition. However, at no place in the manuscript do you say anything about how VEGFR is inhibited, when it is inhibited, or how you know it is inhibited. That oversight must be corrected. You mention axitinib but don't say anything about what it does. Some readers may know its activity but many will not.

We now indicate that we use Axitinib to block VEGFR in the results section (line 104) and in the methods section (lines 470-471).

(6) Fig. 2. The use of Y27632 as a selective inhibitor of ROCK. According to data sheets from the manufacturer, at the levels used in your experiments, 120 µm, 80 µm and 30 µm, those levels of inhibitor also inhibit the activity of PKA and PKC (both inhibited at around 25 µm). This is concerning because of the literature indicating that activation of the VEGFR operates through PKA. Inhibition of PKA, then, would inhibit the activity of VEGF signaling. Thus, the inhibitory effects of Y27632 may actually not be attributed specifically to ROCK. Furthermore, the heading of this section states that ROCK activity controls initiation, growth, and morphology of the spicule. Yet, even in high levels of inhibitor spicule production is initiated. Yes, the growth and the morphology are compromised, but the initiation doesn't seem to be.

The spicule fails to form under ROCK continuous inhibition in all concentrations (Fig. 2). Also, as we explained in details above, these Ki values are based on biochemical experiments with purified proteins and are not relevant to in-vivo use of the inhibitor. Yet, these Ki values demonstrate that the affinity of the inhibitor to ROCK is 100 higher than of its affinity to PKA and PKC. Specifically to the reviewer suggestion here: direct inhibition of PKA does not have skeletogenic phenotypes, not in whole embryos (1) and not in skeletogenic cell culture (2). Since we see the same skeletogenic phenotypes at low Y-27362 concentration and the genetic and pharmacological pertrubations of ROCK reconcile, we believe that these phenotypes can be atributed directly to ROCK.

(7) The synchrotron study is very nice with two points that should be addressed. Again, a high concentration of Y27632 was used giving a caveat on ROCK specificity. And second, the blue and green calcein pulses are very nice but the recent paper by the Bradham group should be cited.

We added a reference to Bradham recent paper on two calcein pulses (10).

(8) Fig. 5 is where an attempt is made to associate ROCK inhibition to alterations in actomyosin. Again, a high concentration of the inhibitor is used casting doubt on whether it specifically inhibits ROCK. However, even if the inhibition is specific to ROCK the images do not provide convincing evidence that ROCK activity normally is directed toward actomyosin. This is crucial to the manuscript.

As stated above, we addressed the specificity in this version and we modified the text to emphasize the correlation and not cuasation: Fig. 5 shows a clear difference between F-actin in control and under ROCK inhibition. In control F-actin is enriched around the spicule and under ROCK inhibition the spicule doesn’t form and disorganized F-actin is accumulated in the skeletogenic cells. Yet, as we stated above – this is not a proof for the direct effect of ROCK on F-actin polymerization, and we explain it explicitly in the results, lines 324-326 and in the discussion, lines 405-408.

(9) Again in Fig. 6 the inhibitor is used with the same concern about whether the effects noted are due to ROCK.

Fig. 6 is now Fig. 7 – the effect of ROCK on gene expression and as explained above, we addressed the specificity in this version.

(10) Lines 350-358. This interpretation falls apart without showing that the inhibitor is specific for ROCK as indicated above. Also, Fig. 5 is unconvincing in showing a difference in actin or myosin distribution in control vs ROCK inhibited embryos. Yes, the spicules are stunted, but whether actin or myosin have anything to do with that as a result of lack of ROCK activity is not demonstrated.

As stated above, we addressed the specificity in the revised version and we modified the text to emphasize the correlation and not cuasation: Fig. 5 shows a clear difference between F-actin in control and under ROCK inhibition. In control F-actin is enriched around the spicule and under ROCK inhibition the spicule doesn’t form and disorganized F-actin is accumulated in the skeletogenic cells. Yet, as we stated above – this is not a proof for the direct effect of ROCK on F-actin polymerization, and we explain it explicitly in the results, lines 324-326 and in the discussion, lines 405-408.

(11) Throughout, the manuscript spelling, grammar, and sentence structure will require extensive editing. The mistakes are numerous.

We did our best to correct the spelling and grammar. If we still missed some mistakes, we would be happy to further correct them.

References

(1) Mitsunaga K, Shinohara S, Yasumasu I. Probable Contribution of Protein Phosphorylation by Protein Kinase C to Spicule Formation in Sea Urchin Embryos: (sea urchin/protein kinase C/spicule formation/H-7/HA1004). Dev Growth Differ. 1990;32(3):335-42.

(2) Mitsunaga K, Shinohara S, Yasumasu I. Does Protein Phosphorylation by Protein Kinase C Support Pseudopodial Cable Growth in Cultured MicromereDerived Cells of the Sea Urchin, Hemicentrotus pulcherrimus?: (sea urchin/protein kinase C/spicule formation/phorbol ester/H-7). Dev Growth Differ. 1990;32(6):647-55.

(3) Su Y, Huang H, Luo T, Zheng Y, Fan J, Ren H, et al. Cell-in-cell structure mediates in-cell killing suppressed by CD44. Cell Discov. 2022;8(1):35.

(4) Kagawa H, Javali A, Khoei HH, Sommer TM, Sestini G, Novatchkova M, et al. Human blastoids model blastocyst development and implantation. Nature. 2022;601(7894):600-5.

(5) Canellas-Socias A, Cortina C, Hernando-Momblona X, Palomo-Ponce S, Mulholland EJ, Turon G, et al. Metastatic recurrence in colorectal cancer arises from residual EMP1(+) cells. Nature. 2022;611(7936):603-13.

(6) Becker KN, Pettee KM, Sugrue A, Reinard KA, Schroeder JL, Eisenmann KM. The Cytoskeleton Effectors Rho-Kinase (ROCK) and Mammalian DiaphanousRelated (mDia) Formin Have Dynamic Roles in Tumor Microtube Formation in Invasive Glioblastoma Cells. Cells. 2022;11(9).

(7) Segal D, Zaritsky A, Schejter ED, Shilo BZ. Feedback inhibition of actin on Rho mediates content release from large secretory vesicles. J Cell Biol. 2018;217(5):1815-26.

(8) Fischer RS, Gardel M, Ma X, Adelstein RS, Waterman CM. Local cortical tension by myosin II guides 3D endothelial cell branching. Curr Biol. 2009;19(3):2605.

(9) Narumiya S, Ishizaki T, Uehata M. Use and properties of ROCK-specific inhibitor Y-27632. Methods Enzymol. 2000;325:273-84.

(10) Descoteaux AE, Zuch DT, Bradham CA. Polychrome labeling reveals skeletal triradiate and elongation dynamics and abnormalities in patterning cue-perturbed embryos. Dev Biol. 2023;498:1-13.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The OSCA/TMEM63 channels have recently been identified as mechanosensitive channels. In a previous study, the authors found that OSCA subtypes (1, 2, and 3) respond differently to stretch and poke stimuli. For example, OSCA1.2 is activated by both poke and stretch, while OSCA3.1, responds strongly to stretch but poorly to poke stimuli. In this study, the authors use cryo-EM, mutagenesis, and electrophysiology to dissect the mechanistic determinants that underlie the channels' ability to respond to poke and stretch stimuli.

The starting hypothesis of the study is that the mechanical activation of OSCA channels relies on the interactions between the protein and the lipid bilayer and that the differential responses to poke and stretch might stem from variations in the lipid-interacting regions of OSCA proteins. The authors specifically identify the amphipathic helix (AH), the fenestration, and the Beam Like Domain (BLD) as elements that might play a role in mechanosensing.

The strength of this paper lies in the technically sound data - the structural work and electrophysiology are both very well done. For example, the authors produce a high-resolution OSCA3.1 structure which will be a useful tool for many future studies. Also, the study identifies several interesting mutants that seemingly uncouple the OSCA1.2 poke and stretch responses. These might be valuable in future studies of OSCA mechanosensation.

However, the experimental approach employed by the authors to dissect the molecular mechanisms of poke and stretch falls short of enabling meaningful mechanistic conclusions. For example, we are left with several unanswered questions surrounding the role of AH and the fenestration lipids in mechanosensation: Is the AH really important for the poke response if mutating residues conserved between OSCA1.2 and OSCA3.1 disrupts the OSCA1.2 ability to respond to poke but mutating the OSCA1.2 AH to resemble that of OSCA3.1 results in no change to its "pokability"? Similar questions arise in response to the study of the fenestrationlining residues.

We thank the reviewer for their feedback. We believe that the different OSCA1.2 mutants on their own suggest an involvement of the AH and fenestration-lining residues in its mechanosensitive response. We attribute the inability to restore the poke response of OSCA3.1 with similar mutations to its inherent high threshold to this particular stimulus and perhaps other structural differences, or a combination of them, that we did not probe in this study. We agree more work is required in the field to address these remaining questions and further dissect the difference between poke and stretch responses.

Reviewer #2 (Public Review):

Summary:

Jojoa-Cruz et al. determined a high-resolution cryo-EM structure in the Arabidopsis thaliana (At) OSCA3.1 channel. Based on a structural comparison between OSCA3.1 and OSCA1.2 and the difference between these two paralogs in their mechanosensitivity to poking and membrane stretch, the authors performed structural-guided mutagenesis and tested the roles of three structural domains, including an amphipathic helix, a beam-like domain, and a lipid fenestration site at the pore domain, for mechanosensation of OSCA channels.

Strengths:

The authors successfully determined a structure of the AtOSCA3.1 channel reconstituted in lipid nanodiscs by cryo-EM to a high resolution of 2.6 Å. The high-resolution EM map enabled the authors to observe putative lipid EM densities at various sites where lipid molecules are associated with the channel. Overall, the structural data provides the information for comparison with other OSCA paralogs.

In addition, the authors identified OSCA1.2 mutants that exhibit differential responses to mechanical stimulation by poking and membrane stretch (i.e., impaired response to poke assay but intact response to membrane stretch). This interesting behavior will be useful for further study on differentiating the mechanisms of OSCA activation by distinct mechanical stimuli.

Major weakness:

The major weaknesses of this study are the mutagenesis design and the functional characterization of the three structural domains - an amphipathic helix (AH), a beam-like domain (BLD), and the fenestration site at the pore, in OSCA mechanosensation.

(1) First of all, it is confusing to the reviewer, whether the authors set out to test these structural domains as a direct sensor(s) of mechanical stimuli or as a coupling domain(s) for downstream channel opening and closing (gating). The data interpretations are vague in this regard as the authors tend to interpret the effects of mutations on the channel 'sensitivity' to different mechanical stimuli (poking or membrane stretch). The authors ought to dissect the molecular bases of sensing mechanical force and opening/closing (gating) the channel pore domain for the structural elements that they want to study.

We agree with the reviewer that our data are unable to distinguish the transduction of a mechanical stimulus and channel gating. We set up to determine whether these features were involved in the mechanosensitive response. However, as the reviewer points out, evaluating whether they work as direct sensors or coupling domains would require a more involved experimental design that lies beyond the scope of this work. Thus, we do not claim in our study whether these features act as direct sensors of mechanosensitive stimuli or as coupling domains, only their involvement.

Furthermore, the authors relied on the functional discrepancies between OSCA1.2 (sensitive to both membrane poking and stretch) and OSCA3.1 (little or weak sensitivity to poking but sensitive to membrane stretch). But the experimental data presented in the study are not clear to address the mechanisms of channel activation by poking vs. by stretch, and why the channels behave differently.

We had hoped that when we switched regions of the OSCA1.2 and OSCA3.1 channels we would abolish poke-induced responses in OSCA1.2 and confer poke-induced sensitivity to OSCA3.1. We agree with the reviewer that we were not able to pinpoint the reason or multiple reasons, as it could be a compounded effect of several differences, that caused OSCA3.1 higher threshold and thus we could not confer to it an OSCA1.2-like phenotype. Yet, we shed some light on some of the structural differences that appear to contribute to OSCA3.1 behavior, as mutagenesis of OSCA1.2 to resemble this channel led to OSCA3.1-like phenotype.

(2) The reviewer questions if the "apparent threshold" of poke-induced membrane displacement and the threshold of membrane stretch are good measures of the change in the channel sensitivity to the different mechanical stimuli.

The best way to determine an accurate measure of sensitivity to mechanical stimuli is stretch applied to a patch of membrane. There are more complicating factors that influence the determination of "apparent threshold" in the whole cell poking assay, including visualizing when the probe first hits the cell (very difficult to see). With that said, the stretch assay has its own issues such as the creep of the membrane into the pipette glass which we try to minimize with positive pressure between tests.

(3) Overall, the mutagenesis design in the various structural domains lacks logical coherence and the interpretation of the functional data is not sufficient to support the authors' hypothesis. Essentially the authors mutated several residues on the hotspot domains, observed some effects on the channel response to poking and membrane stretch, then interpreted the mutated residues/regions are critical for OSCA mechanosensation. Examples are as follows.

In the section "Mutation of key residues in the amphipathic helix", the authors mutated W75 and L80, which are located on the N- and C-terminal of the AH in OSCA1.2, and mutated Pro in the OSCA1.2 AH to Arg at the equivalent position in OSCA3.1 AH. W75 and L80 are conserved between OSCA 1.2 and OSCA3.1. Mutations of W75 and/or L80 impaired OSCA1.2 activation by poking, but not by membrane stretch. In comparison, the wildtype OSCA3.1 which contains W and L at the equivalent position of its AH exhibits little or weak response to poking. The loss of response to poking in the OSCA1.2 W/L mutants does not indicate their roles in pokinginduced activation.

Besides, the P2R mutation on OSCA1.2 AH showed no effect on the channel activation by poking, suggesting Arg in OSCA3.1 AH is not responsible for its weak response to poking. Together the mutagenesis of W75, L80, and P2R on OSCA1.2 AH does not support the hypothesis of the role of AH involved in OSCA mechanosensation.

Mutagenesis of OSCA1.2 in the amphipathic helix for residues W75 and L80 suggests a role of the helix in the poke response in OSCA1.2, regardless of OSCA3.1 having the same residues. Furthermore, the lack of alteration in the response for mutant P77R suggests that specific residues of the helix are involved in this response and is not a case where any mutation in the helix will lead to a loss of function.

OSCA3.1 WT exhibits a high-threshold response (near membrane rupture) in the poke assay without any mutations, and this could be due to other features, for example, the residues lining the membrane fenestration, as well as features not identified/probed in this study. We agree with the reviewer that the differences in the AH do not explain the different response to poke in OSCA1.2 and OSCA3.1, and we have added this statement explicitly in the discussion for clarification (line #251-252).

In the section "Replacing the OSCA3.1 BLD in OSCA1.2", the authors replaced the BLD in OSCA 1.2 with that from OSCA3.1, and only observed slightly stronger displacement by poking stimuli. The authors still suggest that BLD "appears to play a role" in the channel sensitivity to poke despite the evidence not being strong.

We agree with the reviewer that the experiments carried out show little difference between the response of OSCA1.2 WT and OSCA1.2 with OSCA3.1 BLD, and we have stated so (line #259: “Substituting the BLD of OSCA1.2 for that of OSCA3.1 had little effect on poke- or stretchactivated responses. Although these results suggest that the BLD may not be involved in modulating the MA response of OSCA1.2…”). However, the section of the discussion that the reviewer points out also considers evidence provided by recent reports from Zheng, et al. (Neuron, 2023) and Jojoa-Cruz, et al. (Structure, 2024) and we suggest an hypothesis to reconcile our findings with these new evidence.

OSCA1.2 has four Lys residues in TM4 and TM6b at the pore fenestration site, which were shown to interact with the lipid phosphate head group, whereas two of the equivalent residues in OSCA3.1 are Ile. In the section "Substitution of potential lipid-interacting lysine residues", the authors made K435I/K536I double mutant for OSCA1.2 to mimic OSCA3.1 and observed poor response to poking but an intact response to stretch. Did the authors mutate the Ile residues in OSCA3.1 to Lys, and did the mutation confer channel sensitivity to poking stimuli resembling OSCA1.2? The reviewer thinks it is necessary to perform such an experiment, to thoroughly suggest the importance of the four Lys residues in lipid interaction for channel mechanoactivation.

We thank the reviewer for this suggestion. We agree that the suggested experiments will further improve the quality of the results, but we are no longer able to perform such experiments.

Reviewer #3 (Public Review):

Summary:

Jojoa-Cruz et al provide a new structure of At-OSCA3.1. The structure of OSCA 3.1 is similar to previous OSCA cryo-em structures of both OSCA3.1 and other homologues validating the new structure. Using the novel structure of OSCA3.1 as a guide they created several point mutations to investigate two different mechanosensitive modalities: poking and stretching. To investigate the ability of OSCA channels to gate in response to poking they created point mutations in OSCA1.2 to reduce sensitivity to poking based on the differences between the OSCA1.2 and 3.1 structures. Their results suggest that two separate regions are responsible for gating in response to poking and stretching.

Strengths:

Through a detailed structure-based analysis, the authors identified structural differences between OSCA3.1 and OSCA1.2. These subtle structural changes identify regions in the amphipathic helix and near the pore that are essential for the gating of OSCA1.2 in response to poking and stretching. The use of point mutations to understand how these regions are involved in mechanosensation clearly shows the role of these residues in mechanosensation.

Weaknesses:

In general, the point mutations selected all show significant alterations to the inherent mechanosensitive regions. This often suggests that any mutation would disrupt the function of the region, additional mutations that are similar in function to the WT channel would support the claims in the manuscript. Mutations in the amphipathic helix at W75 and L80 show reduced gating in response to poking stimuli. The gating observed occurs at poking depths similar to cellular rupture, the similarity in depths suggests that these mutations could be a complete loss of function. For example, a mutation to L80I or L80Q would show that the addition of the negative charge is responsible for this disruption not just a change in the steric space of the residue in an essential region.

We thank the reviewer for this suggestion. We agree that the suggested experiments will further improve the quality of the results, but we are unable to perform such experiments due to the authors having moved on from the respective labs.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

I have several questions regarding some of the aspects of your study:

Mutation of the hydrophobic W75 and L80 in OSCA1.2 to charged residues significantly decreases the poke response in OSCA1.2 without affecting the stretch response. However, W75 and L80 are also present in OSCA3.1, which does not respond efficiently to poke. You conclude that these two residues are important for the poke response, but do not delve into why, if these residues are important, OSCA3.1 is not poke-sensitive.

In addition, mutation of the OSCA1.2 AH to resemble that of OSCA3.1 does not produce channels that are less poke-sensitive. Given the data presented, if AH were a universal "poke sensor", one could also expect WT OSCA3.1 to exhibit a robust poke response, like OSCA1.2. Here I think it would be important to explain in more detail how this data might fit together.

We thank the reviewer for bringing up this issue. We decided to test the importance of the AH due to the presence of similar structures in other mechanosensitive channels. Our data showed that single and double mutants of the AH of OSCA1.2 affected its poke response but not stretch. This supports the idea of the AH involvement in the poke response. Yet, we agree that the differences in the AH between OSCA1.2 and OSCA3.1 (P77R mutation) do not explain the higher threshold of OSCA3.1, we have explicitly added this in line #255. The particular OSCA3.1 phenotype may be due to other differences in the structure, for example, differences in the membrane fenestration area, or a combined effect of several differences, which we believe is more likely.

I also have some questions about the protein-lipid interactions in the fenestration. A lipid has been observed in this location in both OSCA1.2 and OSCA3.1 structures. Mutation of the two OSCA1.2 lysines to isoleucines results in channels that are resistant to poke which leads to the conclusion that the interactions between the fenestration lysines and lipids are important for the poke response.

Here, there are several questions that arise but are not answered:

It is not shown what happens when OSCA3.1 isoleucines are mutated to lysines - do these mutants result in poke-able channels? Is the OSCA3.1 mechanosensing altered?

We performed a preliminary test on OSCA3.1 I423K/I525K double mutant (n = 3). However, we did not see an increase in poke sensitivity. We attributed this to other unexplored differences in OSCA3.1 having an effect in channel mechanosensitivity.

It is implied that the poke response is predicated on the lysine-lipid interaction. However, lipid densities are present in both OSCA1.2 and OSCA3.1 structures, indicating that both fenestrations interact with lipids. How can we be certain that the mutation of lysine to isoleucine does not disrupt an inter-protein interaction rather than a protein-lipid one? For example, the K435I mutation might disrupt interactions with D523 or the backbone of G527?

The reviewer brings up a good point. We believe the phenotype seen is due to a different strength in the interaction between lipids and proteins, however, disrupted interaction with other residues is a valid alternative explanation. We agree that the suggested experiments will further clarify the results, but we are unable to perform such experiments due to the authors having moved on from the respective labs.

Similarly, the effects of single lysine-to-isoleucine (K435I or K536I) mutations are not explored.

The observed effect might be caused by only one of these substitutions.

We thank the reviewer for this suggestion. We agree that the suggested experiments will further improve the quality of the results, but we are unable to perform such experiments due to the authors having moved on from the respective labs.

I also wanted to take this opportunity to ask a couple of philosophical (?) questions about using a mammalian system to study ion channels that have evolved to function in plants. Your study highlights the intimate relationship between the lipid bilayer and protein function/mechanosensitivity. Plant cells contain high levels of sterols and cerebrosides that would significantly affect both cell stiffness and the specific interactions that can be formed between the protein and the lipid bilayer. I wonder if the properties of the lipid bilayer might shift the thresholds for poke and/or stretch stimuli and if structural elements that do not appear to have a major role in mechanosensation in a mammalian cell (e.g., BLD) might be very influential in a lipid environment that more closely resembles that of a plant?

Conversely, is it possible that OSCA channels are not poke-sensitive in plant cells? These questions are beyond the scope of your study, but they might be a nice addition to your discussion.

The reviewer poses a great question. Electrophysiological approaches for studying plant mechanosensitive channels suffer the limitation of not being able to fully reconstitute the environment of a plant cell. To be able to patch the cell, the cell wall needs to be disposed of, which eliminates the tension generated from this structure onto the membrane. In that sense, performing these assays in plant cells or another system would not give us a fully accurate picture of the physiological thresholds of these channels. Given this limitation, we performed our study with mammalian cells given our expertise with them. Like the reviewer, we are also intrigued by the effect of different membrane compositions on the behavior of OSCA channels and how these channels will behave under physiological conditions, but we agree with the reviewer that these questions are out of the scope of our work. To address this point, in line #294 we have added: “It is also important to note that the membrane of a plant cell contains a different lipid composition than that of HEK293 cells used in our assays, and thus these lipids, or the plant cell wall, may alter how these channels respond to physiological stimuli.”

Line 313 For structural studies, human codon-optimized OSCA3.1. Could you please clarify what this means?

We have changed the phrase to “For structural studies, the OSCA3.1 (UniProt ID: Q9C8G5) coding sequence was synthesized using optimized codons for expression in human cells and subsequently cloned into the pcDNA3.1 vector” in line #327 to clarify this sentence.

As a final comment, in the methods you use references to previously published work. I would strongly encourage you to replace these with experimental details.

We understand the reviewer’s argument. However, this article falls under eLIFE’s Research Advances and will be linked to the original published work to which we reference the method. As suggested in the guidelines for this type of article, we only described the methods that were different from the original paper.

Reviewer #2 (Recommendations For The Authors):

(1) In line 85, provide C-alpha r.m.s.d. values for the structural alignment among OSCA3.1, OSCA1.1, and OSCA1.2 protomers.

As requested, we have added the C-alpha RMSD in line #86.

(2) In line 90, should the figure reference to Fig. 1d be Fig. 1e?

We thank the reviewer for catching this error. We have corrected it in the manuscript.

(3) In lines 89-94, what putative lipid is it resolved in the OSCA3.1 pore? Can the authors assign the lipid identity? Is this the same or different from the lipids resolved in OSCA1.2, OSCA1.1, and TMEM63?

In the model, we have built the lipid as palmitic acid to represent a lipid tail, but the resolution in this area makes it difficult to ascertain the identity of said lipid, hence we cannot compare to lipids in other orthologs.

(4) In lines 115-121, the authors describe the presence of AHs and their functional roles in MscL and TMEM16. It will be more informative if the authors can add figures to show the structure of MscL and highlight the analogous AH. In addition, the current Supplementary Fig. 6 is not informative so it should be improved. It is not clear to the reviewer why that stretch of helix in TMEM16 is equivalent or analogous to the AH in OSCAs, either sequence alignment or a detailed structural alignment is helpful to address this point. Also, in lines 120-121, it says this helix in TMEM16 "does not present amphipathic properties", please show the sequence or amphipathicity of the helix.

We thank the reviewer for the feedback on this figure. Supplementary Fig. 6 has been thoroughly modified to address the reviewer’s concerns. We now include a panel showing the structure of MscL and its amphipathic helix. We have modified the alignment of OSCA3.1 to a TMEM16 homolog to make clearer the homologous positioning of the helices in question and zoom in to show their sequences.

(5) In discussion, lines 249-257, the authors referred to a recent study that suggested three evolutionarily coupled residue pairs located on BLD and TM6b. The authors speculate that the reason they did not observe a significant effect of channel response to poke/stretch stimuli in the BLD swapping between OSCA1.2 and 3.1 is due to the 2 of 3 salt bridges remaining for the residue pairs. To test the importance of these residue pairs and their coupling for channel gating, instead of swapping the entire BLD, can the authors systematically mutate the residue pairs, disrupt the salt-bridge interactions, and analyze the effect on channel response to mechanical force?

We thank the reviewer for this suggestion. We agree that the suggested experiments will further improve the quality of the results, but we are unable to perform such experiments due to the authors having moved on from the respective labs.

(6) The reviewer suggests the authors tone down the elaboration of polymodal activation of OSCA by membrane poking and stretch.

We believe the idea of polymodal activation is sufficiently toned down as we only postulate it as a possibility and following we give an alternative explanation based on methodological limitations: “Nonetheless, the discrepancy could be due to inherent methodological differences between these two assays, as whole-cell recordings during poking involve channels in inaccessible membranes (at the cell-substrate interface) and channel interactions with extracellular and intracellular components, while the stretch assay is limited to recording channels inside the patch.”

(7) In lines 81-83, the authors described the BLD as showing increased flexibility, and the EM map at this region is less well resolved for registry assignment. In the method for cryo-EM image processing and Supplementary Fig. 1, the authors only carried out 3D refinement and classification at the full channel level. Have the authors attempted to do focus refinement or classification at the BLD domain in order to improve the local resolution or to sort out conformational heterogeneity? The reviewer suggests doing so because the BLD domain is a hot spot that the authors have proposed to play an important role in OSCA mechanosensation. Conformational changes identified in this region might provide insights into its role in the channel function.

We thank the reviewer for this suggestion. We have performed focused classification on the BLD with and without surrounding regions and, in our hands, it did not improve the resolution or provide further insights.

Reviewer #3 (Recommendations For The Authors):

Here are a few specific minor corrections that should be addressed

(1) In lines 117-135, in the discussion of Figure 2, the data shows an apparent increase in the poking threshold to gate W75K/L80E. The substantial increase in the depth required to gate the channel suggests that these channels are less sensitive to poking. Would it be possible to compare the depth at which these two patches show activity and the depth at which the other 22 cells ruptured? Line 161 mentions that the rupture threshold of HEK cells is close to the gating of OSCA3.1 at 13.8 µm.

The distance just before the cell ruptured in 22 cells with no response was 12.5 +/- 2.5 um. The distance at which the cells ruptured was 0.5 um more (13 +/- 2.5 n=22). We have added this last value in line #137.

(2) Would it be possible in Figures 2 panels b and c, 3, and figure 4 to label the WT as WT OSCA1.2?

We thank the reviewer for pointing this out. We agree this modification will improve the clarity of the figures and have changed the figures to follow the reviewer’s suggestion.

(3) Can you provide a western blot of the mutations described in Figure 2? This would provide insight into the amount of protein at the cell surface and available to respond to poking, the stretch data shows that these channels are in the membrane but does not show if they are in the membrane in similar quantities.

We thank the reviewer for this suggestion. We agree that the suggested experiments will further improve the quality of the results, but we are unable to perform such experiments due to the authors having moved on from the respective labs.

(4) The functional differences between the two channels are projected to be tied to several distinct point mutations, however, the data could be strengthened by additional point mutations at all sites to show that the phenotypes are due to the mutations specifically not just any mutation in the region.

We thank the reviewer for this suggestion. We agree that the suggested experiments will further improve the quality of the results, but we are unable to perform such experiments due to the authors having moved on from the respective labs.

Author Response

Reviewer #1 (Public Review):

Summary:

This manuscript from Mukherjee et al examines potential connections between telomere length and tumor immune responses. This examination is based on the premise that telomeres and tumor immunity have each been shown to play separate, but important, roles in cancer progression and prognosis as well as prior correlative findings between telomere length and immunity. In keeping with a potential connection between telomere length and tumor immunity, the authors find that long telomere length is associated with reduced expression of the cytokine receptor IL1R1. Long telomere length is also associated with reduced TRF2 occupancy at the putative IL1R1 promoter. These observations lead the authors towards a model in which reduced telomere occupancy of TRF2 - due to telomere shortening - promotes IL1R1 transcription via recruitment of the p300 histone acetyltransferase. This model is based on earlier studies from this group (i.e. Mukherjee et al., 2019) which first proposed that telomere length can influence gene expression by enabling TRF2 binding and gene transactivation at telomere-distal sites. Further mechanistic work suggests that G-quadruplexes are important for TRF2 binding to IL1R1 promoter and that TRF2 acetylation is necessary for p300 recruitment. Complementary studies in human triple-negative breast cancer cells add potential clinical relevance but do not possess a direct connection to the proposed model. Overall, the article presents several interesting observations, but disconnection across central elements of the model and the marginal degree of the data leave open significant uncertainty regarding the conclusions.

Strengths:

Many of the key results are examined across multiple cell models.

The authors propose a highly innovative model to explain their results.

Weaknesses:

Although the authors attempt to replicate most key results across multiple models, the results are often marginal or appear to lack statistical significance. For example, the reduction in IL1R1 protein levels observed in HT1080 cells that possess long telomeres relative to HT1080 short telomere cells appears to be modest (Supplementary Figure 1I). Associated changes in IL1R1 mRNA levels are similarly modest.

Related to the point above, a lack of strong functional studies leaves an open question as to whether observed changes in IL1R1 expression across telomere short/long cancer cells are biologically meaningful.

Statistical significance is described sporadically throughout the paper. Most major trends hold, but the statistical significance of the results is often unclear. For example, Figure 1A uses a statistical test to show statistically significant increases in TRF2 occupancy at the IL1R1 promoter in short telomere HT1080 relative to long telomere HT1080. However, similar experiments (i.e. Figure 2B, Figure 4A - D) lack statistical tests.

TRF2 overexpression resulted in ~ 5-fold or more change in IL1R1 expression. Compared to this, telomere length-dependent alterations in IL1R1 expression, although about 2-fold, appear modest (~ 50% reduction in cells with long telomeres across different model systems used). Notably, this was consistent and significant across cell-based model systems and xenograft tumors (see Figure 1). Unlike TRF2 induction, telomere elongation or shortening vary within the permissible physiological limits of cells. This is likely to result in the observed variation in IL1R1 levels. For biological relevance, we further demonstrated that IL1 signalling in TNBC tissue and tumor organoids, and M2 macrophage infiltration, was significantly dependent on telomere length. Details of tests of significance were included in the individual figure legends. Based on the comment here we will expand on it in a dedicated paragraph in the methods section to make the information clearer for readers. We noticed that the stars (*) denoting statistical significance were omitted in some ChIP-experiment figures. This was likely an error during figure assembly for PDF conversion. We thank the reviewer for bringing this up; necessary changes will be made in the revised manuscript.

Reviewer #2 (Public Review):

This study highlights the role of telomeres in modulating IL-1 signaling and tumor immunity. The authors demonstrate a strong correlation between telomere length and IL-1 signaling by analyzing TNBC patient samples and tumor-derived organoids. Mechanistic insights revealed non-telomeric TRF2 binding at the IL-1R1. The observed effects on NF-kB signaling and subsequent alterations in cytokine expression contribute significantly to our understanding of the complex interplay between telomeres and the tumor microenvironment. Furthermore, the study reports that the length of telomeres and IL-1R1 expression is associated with TAM enrichment. However, the manuscript lacks in-depth mechanistic insights into how telomere length affects IL-1R1 expression. Overall, this work broadens our understanding of telomere biology.

The mechanism of how telomere length affects IL1R1 expression involves sequestration and reallocation of TRF2 between telomeres and gene promoters (in this case, the IL1R1 promoter). We have previously shown this across multiple genomic sites (Mukherjee et al, 2018; reviewed in J. Biol. Chem. 2020, Trends in Genetics 2023). We have described this in the manuscript along with references citing the previous works. A scheme explaining the model was provided as Additional Supplementary Figure 1, along with a description of the mechanistic model.

Figure 1-4 in main figures describe the molecular mechanism of telomere-dependent IL1R1 activation. This includes ChIP data for TRF2 on the IL1R1 promoter in long/short telomeres, as well as TRF2-mediated histone/p300 recruitment and IL1R1 gene expression. We further show how specific acetylation on TRF2 is crucial for TRF2-mediated IL1R1 regulation (Figure 5).

Reviewer #3 (Public Review):

Summary:

In this manuscript, entitled "Telomere length sensitive regulation of Interleukin Receptor 1 type 1 (IL1R1) by the shelterin protein TRF2 modulates immune signalling in the tumour microenvironment", Dr. Mukherjee and colleagues pointed out clarifying the extra-telomeric role of TRF2 in regulating IL1R1 expression with consequent impact on TAMs tumor-infiltration.

Strengths:

Upon careful manuscript evaluation, I feel that the presented story is undoubtedly well conceived. At the technical level, experiments have been properly performed and the obtained results support the authors' conclusions.

Weaknesses:

Unfortunately, the covered topic is not particularly novel. In detail, the TRF2 capability of binding extratelomeric foci in cells with short telomeres has been well demonstrated in a previous work published by the same research group. The capability of TRF2 to regulate gene expression is well-known, the capability of TRF2 to interact with p300 has been already demonstrated and, finally, the capability of TRF2 to regulate TAMs infiltration (that is the effective novelty of the manuscript) appears as an obvious consequence of IL1R1 modulation (this is probably due to the current manuscript organization).

Here we studied the TRF2-IL1R1 regulatory axis (not reported earlier by us or others) as a case of the telomere sequestration model that we described earlier (Mukherjee et al., 2018; reviewed in J. Biol. Chem. 2020, Trends in Genetics 2023). This manuscript demonstrates the effect of the TRF2-IL1R1 regulation on telomere-sensitive tumor macrophage recruitment. To the best of our knowledge, no previous study connects telomeres of tumor cells mechanistically to the tumor immune microenvironment. Here we focused on the IL1R1 promoter and provided mechanistic evidence for acetylated-TRF2 engaging the HAT p300 for epigenetically altering the promoter. This mechanism of TRF2 mediated activation has not been previously reported. Further, the function of a specific post translational modification (acetylation of the lysine residue 293K) of TRF2 in IL1R1 regulation is described for the first time. Additional experiments showed that TRF2-acetylation mutants, when targeted to the IL1R1 promoter, significantly alter the transcriptional state of the IL1R1 promoter. To our knowledge, the function of any TRF2 residue in transcriptional activation had not been previously described. Taken together, these demonstrate novel insights into the mechanism of TRF2-mediated gene regulation, that is telomere-sensitive, and affects the tumor-immune microenvironment. We are considering the suggestion to reorganize the manuscript to highlight the novel aspects of our work more convincingly.

Author Response

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

Recommendations for the authors

Reviewer #1 (Recommendations For The Authors):

(1) Please expand methods with additional details related to cell co-culture, such as cell numbers and duration.

We thank the reviewer for the careful reading and constructive suggestions and we are sorry to make you confused. We have added the experimental details (manuscript line 551-553) related to co-culture in the revised manuscript.

(2) Please unify the writing of the abbreviation of small extracellular vesicles in the text, figure, and caption.

Thank you for your comments. We have unified the abbreviation of extracellular vesicles to sEVs in the revised manuscript.

(3) The effects of components other than sEVs in mechanically stimulated osteocyte CM on the proliferation of NSCLC cells should be evaluated.

We evaluated the effects of SF, lEVs and sEVs in osteocyte CM on NSCLC cell proliferation under mechanical stimulation, and found that sEVs had the most obvious inhibition on NSCLC cell proliferation, as shown in the revised Supplemental Figure 4c, d.

(4) In addition to osteocytes and osteoblasts, the effects of other types of cells on the proliferation of NSCLC cells should be detected. It is recommended to add at least one type of cell from an infrequent metastatic site of NSCLC as a negative control.

We thank the reviewer for the suggestion. We added NCM460 cell line (derived from intestinal epithelium) as a negative control and found that NCM460 had no significant effect on NSCLC cell proliferation, as shown in Figure 1d. These experiments were conducted before our last submission.

(5) The bone microenvironment is complex. It is recommended to evaluate the effect of bone marrow-derived sEVs on NSCLC to validate whether the tumor suppressive effect of osteocyte sEVs is unique.

We thank the reviewer for the suggestion. We agree with the reviewer’s comments that the bone microenvironment is complex. We explored the effect of bone marrow-derived sEVs on NSCLC cell proliferation and found that bone marrow-derived sEVs promoted NSCLC cell proliferation, as shown in Supplemental Figure 2g, h in the revised manuscript.

(6) The description of exercise preconditioning is not clear enough. It is recommended to supplement the pattern diagram to improve readability. Exercise preconditioning should be further discussed by the Authors.

Thank you for your comments and we are sorry to make you confused. We have added the pattern diagram of the exercise preconditioning in Supplemental Figure 6a.

Reviewer #2 (Recommendations For The Authors):

(1) The histological images are analyzed in a qualitative manner, with no description of the methodology used. A quantitative assessment of the distance and level of Ki-67+ NSCLC cells needs to be performed in human and murine tissues. Because in bone metastases cancer cells are frequently mixed with bone marrow cells, the inclusion of a cell marker to identify NSCLC cells is needed for proper interpretation of the imaging data.

We thank the reviewer for the careful reading and constructive suggestions. We conducted the suggested quantitative assessment and descripted the methodology in the revised manuscript. The results showed that Ki-67 was lower in tumor cells adjacent to bone tissue than in the surrounding tumor cells (Figure 1a, b).

In order to effectively identify NSCLC cells in bone metastases, GFP-expressing NSCLC cells were used in the animal model. We have added the immunofluorescence analysis of GFP and CCND3 in Supplemental Figure 4e, 4g, 5 and 6b.

(2) The authors rely on KI-67 as a marker of proliferation. Yet, it is intriguing that some osteocytes, non-proliferating cells by definition, are often positive for this marker, which questions the specificity of the staining. The authors should provide the proper immunostaining controls to check for specificity and use additional markers of proliferation to confirm these results.

We thank the reviewer for the suggestions. Ki-67 staining was wildly used to determine the dormancy of tumor cells in previous studies [1-4]. To confirm the results of Ki-67 staining, we used cyclin D3 (CCND3) as an additional marker of proliferation as suggested by the reviewer. We added the immunofluorescence analysis of CCND3 in Supplemental Figure 4e, 4g, 5 and 6b, which is consistent with the result of the quantitative immunofluorescence analysis of KI-67.

(3) The lack of proper controls in the in vivo experiments makes the interpretation of the data difficult. For instance, in the preconditioning experiment, it is likely that the bone mass increases. thus, these mice start with high bone mass than the control mice. The lack of a proper control (naive mice exposed to moderate exercise) does not allow testing if the presence of cancer cells still promotes bone loss in this group. The authors need to include naive mice or analyze the bones from the non-injected contralateral legs.

We thank the reviewer for the thoughtful comments and we are sorry to make you confused. We absolutely agree with the reviewer that the bone mass increases after exercise preconditioning. Multiple tissues and organ systems are affected by exercise, initiating diverse homeostatic responses. Although exercise preconditioning effectively suppressed bone metastasis progression of NSCLC as mentioned in the previous manuscript, we cannot immediately conclude that it is completely dependent on osteocytes to function. The mechanism of exercise preconditioning in suppressing bone metastasis progression is complex which still need further exploration. The revised manuscript has expanded the discussion on this area (manuscript line 326-328).

(4)Further, validating the in vivo work with other osteocyte-like cells or primary osteocytes would have strengthened the results.

We thank the reviewer for the suggestion. We have conducted the experiments of co-culture of MLO-A5 (another type of osteogenic cell line) and NSCLC cells as shown in Supplemental Figure 1g. Not surprisingly, MLO-A5 cells also had an inhibitory effect on proliferation of NSCLC cells.

(5) The data on miRNA99b-3p on NSCLC in Supplementary Figure 3 is not convincing. The positive cells are difficult to see and most of the osteocyte lack nuclei. Better data, in humans and the mouse model, is needed to confirm that osteocytes produce miRNA99b-3p.

We thank the reviewer for the comments and we are sorry to make you confused. In this study, we used miRCURY LNA miRNA detection probes in ISH without staining the nuclei in the tissues, which method have been used in our previous studies with others [5-7]. Detailed experimental procedures for ISH of miRNA have been added in the revised manuscript (manuscript line 461-474).

(6) The authors do not provide a piece of data supporting that osteocytes are responsible for any of the effects seen by the interventions done in the in vivo models. Osteocytes, as well as other bone cells, can respond to mechanical stimulation and thus could virtually be responsible for the protective effects of mechanical loading or moderate exercise. In vivo experiments demonstrating a direct role of osteocytes-produced miRNA99b-3p are needed to support the notion that osteocytes maintain tumor dormancy in NSCLC bone metastasis.

We thank the reviewer for the thoughtful comments and suggestion. We constructed in vivo model by injecting with antagomir-NC and antagomir-99b-3p with mechanical loading [8]. The results showed that the injection of antagomiR-99b-3p could partially and effectively rescue the inhibitory effect on NSCLC cell proliferation (Figure 4i-k).

(7) Further, the authors solely rely on Ki-67 as a marker of dormancy. Completing this analysis with an assessment of a dormant gene expression signature or in vivo studies assessing tumor dormancy directly would be needed to confirm this notion.

We thank the reviewer for the suggestion. We conducted the suggested experiment by using CCND3 as an additional dormancy marker. We added the immunofluorescence analysis of CCND3 in Supplemental Figure 4e, 4g, 5 and 6b, which is consistent with the result of the quantitative immunofluorescence analysis of Ki-67.

References

[1] Guba M, Cernaianu G, Koehl G et al. A primary tumor promotes dormancy of solitary tumor cells before inhibiting angiogenesis. Cancer Res, 2001, 61: 5575-9.

[2] Bliss Sarah A, Sinha Garima, Sandiford Oleta A et al. Mesenchymal Stem Cell-Derived Exosomes Stimulate Cycling Quiescence and Early Breast Cancer Dormancy in Bone Marrow. Cancer Res, 2016, 76: 5832-5844.

[3] Correia Ana Luísa, Guimaraes Joao C, Auf der Maur Priska et al. Hepatic stellate cells suppress NK cell-sustained breast cancer dormancy. Nature, 2021, 594: 566-571.

[4] Hu Jing, Sánchez-Rivera Francisco J, Wang Zhenghan et al. STING inhibits the reactivation of dormant metastasis in lung adenocarcinoma. Nature, 2023, 616: 806-813.

[5] Song Qiancheng, Xu Yuanfei, Yang Cuilan et al. miR-483-5p promotes invasion and metastasis of lung adenocarcinoma by targeting RhoGDI1 and ALCAM. Cancer Res, 2014, 74: 3031-42.

[6] Carotenuto Pietro, Hedayat Somaieh, Fassan Matteo et al. Modulation of Biliary Cancer Chemo-Resistance Through MicroRNA-Mediated Rewiring of the Expansion of CD133+ Cells. Hepatology, 2020, 72: 982-996.

[7] Lv Yan, Wang Yin, Song Yu et al. LncRNA PINK1-AS promotes Gαi1-driven gastric cancer tumorigenesis by sponging microRNA-200a. Oncogene, 2021, 40: 3826-3844.

[8] Zhang Yun, Li Shuaijun, Jin Peisheng et al. Dual functions of microRNA-17 in maintaining cartilage homeostasis and protection against osteoarthritis. Nat Commun, 2022, 13: 2447.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

TRIP13/Pch2 is a conserved essential regulator of meiotic recombination from yeast to humans. In this manuscript, the authors generated TRIP13 null mice and Flag-tagged TRIP13 knock-in mice to study its role in meiosis. They demonstrate that TRIP13 regulates MORMA domain proteins and is essential for meiotic completion and fertility. The main impact of this manuscript is its clarification of the in vivo function of TRIP13 during mouse meiosis and its previously unrecognized role as a dose-sensitive regulator of meiosis.

Strengths:

Two previously reported Trip13 mutations in mice are both hypomorphic alleles with distinct phenotypes, precluding a conclusion on its function. This study for the first time generated the TRIP13 null mice, definitively revealing the function of TRIP13 in meiosis. The authors also show the novel localization of TRIP13 at SC and its independence from the axial element components. The finding of dose-sensitive regulation of meiosis by TRIP13 has implications in understanding human meiosis and disease phenotypes.

Weaknesses:

This manuscript would be more impactful if more mechanistic advancements could be made. For example, the authors could follow up with one of the new interactors identified by MS to offer new insight into the molecular function of TRIP13.

We agree that it would be interesting to follow up on new candidate interactors but think that it would be more feasible to follow up on them in future studies.

Reviewer #2 (Public Review):

Summary and Strengths:

In this manuscript, Chotiner and colleagues demonstrated the localization of TRIP13 and clarified the phenotypes of Trip13-null mice in mouse meiosis. The meiotic phenotypes of Trip13 have been well characterized using the hypomorph alleles in the literature. However, the null phenotypes have not been examined, and the localization of TRIP13 was not clearly demonstrated. The study fills these important knowledge gaps in the field. The demonstration of TRIP13 localization to SC in mice provides an explanation of how HOMRA domain proteins are evicted from SC in diverse organisms. This conclusion was confirmed in both IF and TRIP13-tagged Tg mice. Further, the phenotypes of Trip13-null mice are very clear. The manuscript is well crafted, and the discussion section is well organized and comprehends the topic in the field. All in all, the manuscript will provide important knowledge in the field of meiosis.

Weaknesses:

The heterozygous phenotypes demonstrate that TRIP13 is a dosage-sensitive regulator of meiosis. In relation to this conclusion, as summarized in the discussion section, other mutants defective in meiotic recombination showed dosage-sensitive phenotypes. However, the authors did not examine meiotic recombination in the Trip13-null mice.

Meiotic recombination was extensively characterized in Trip13 severe hypomorph mutants in two previous studies: gamma-H2AX, BLM, BRCA1, ATR, RPA, RAD51, DMC1, MLH1 (Li and Schimenti, 2007; Roig et al., 2010). All the meiotic defects in our Trip13-null mice were also present in Trip13 severe hypermorph mutants: meiotic arrest, defects in chromosomal synapsis, asynapsis at chromosomal ends, and accumulation of HORMAD1/2 on the SC axis. Therefore, the defects in meiotic recombination in Trip13-null mice are expected to be similar to those in Trip13 severe hypermorph mutants and thus we did not examine the proteins involved in meiotic recombination in the Trip13-null mutant.

Reviewer #3 (Public Review):

Summary:

The authors perform a thorough examination of the phenotypes of a newly generated Trip13 null allele in mice, noting defects in chromosome synapsis and impact on localization of other key proteins (namely HORMADs) on meiotic chromosomes. The vast majority of data confirms observations of several prior studies of Trip13 alleles (moderate and severe hypomorphs). The original or primary aims of the study aren't clear, but it can be assumed that the authors wanted to better study the role of this protein in evicting HORMADs upon synapsis by studying phenotypes of mutants and better characterizing TRIP13 localization data (which they find localizes to the central element of synapsed chromosomes using a new epitope-tagged allele). Their data confirm prior reports and are consistent with localization data of the orthologous Pch2 protein in many other organisms.

Strengths:

The quality of data is high. Probably the most important data the authors find is that TRIP13 is localized along the CE of synapsed chromosomes. However, this was not unexpected because PCH2 is also similarly localized. Also, the authors use a clear null (deletion allele), whereas prior studies used hypomorphs.

Weaknesses:

There is limited new data; most are confirmatory or expected (i.e., SC localization), and thus the impact of this report is not high. The claim that TRIP13 "functions as a dosage-sensitive regulator of meiosis" is exaggerated in my opinion. Indeed, the authors make the observation that hets have a phenotype, but numerous genes have haploinsufficient phenotypes. In my opinion, it is a leap to extrapolate this to infer that TRIP13 is a "regulator" of meiosis. What is the definition of a meiosis regulator? Is it at the apex of the meiosis process, or is it a crucial cog of any aspect of meiosis?

TRIP13 is not haploinsufficient, as Trip13 heterozygotes were still viable and fertile (albeit with defects in meiosis). TRIP13 is an ATPase and changes the conformation of meiosis-specific proteins such as HORMAD proteins. TRIP13 is essential for meiosis and its mutations cause defects in both meiotic recombination and chromosomal synapsis. Reviewer 1 stated that “TRIP13/Pch2 is a conserved essential regulator of meiotic recombination from yeast to humans”. Therefore, we feel that TRIP13 can be called a regulator of meiosis.

Reviewer #1 (Recommendations For The Authors):

A schematic illustration of SC structure, the components involved, and the main finding, would be helpful for readers to better understand the advancement made by this study.

We have now added a schematic illustration in a new panel - Figure 7C.

Fig. 1B, the stage with diplotene cells should be XII.

The pachytene cells (Pac) were mis-labelled as diplotene cells. Corrected.

Fig. 1C, color mislabeled.

Corrected.

Reviewer #2 (Recommendations For The Authors):

The manuscript will provide important knowledge in the field of meiosis. I support the publication of this study. I have some suggestions to improve and polish the manuscript.

Major points:

(1) The heterozygous phenotypes demonstrate that TRIP13 is a dosage-sensitive regulator of meiosis. In relation to this conclusion, as summarized in the discussion section, other mutants defective in meiotic recombination showed dosage-sensitive phenotypes. Given the function of HORMAD1 in meiotic recombination, it would be informative if the authors could examine how major makers of meiotic recombination behave in Trip13-null meiosis.

Please see our response to Weaknesses from Reviewer #2.

(2) Relating to the above point, the complete lack of synapsis on the sex chromosomes in the Trip13-null meiosis is impressive. This result raises a question as to whether the pathway to designate XY-obligatory crossover (which can be detected with large foci of ANKRD31 and MEI4/REC114 at PAR) is affected or not. It would be interesting to examine whether the ANKRD31 and MEI4/REC114 foci are present on PAR in Trip13-null meiosis.

We have performed immunofluorescent analysis of REC114 in spermatocytes. In Trip13-null pachytene-like spermatocytes, X and Y chromosomes are not synapsed. REC114 still formed one focus each on the unsynapsed X and Y chromosomes. We have added this new data in the Results as a new supplementary figure (Figure 4 -supplement 1).

(3) Figure 4 can be improved if there are quantified data for each phenotype. These phenotypes look nearly complete, but it would be informative to show the penetrance of these phenotypes.

Because some chromosomes have unsynapsed ends, resulting in two centromere or telomere foci, the total number of centromere or telomere foci is always higher in Trip13-null pachytene-like spermatocytes than wild type pachytene spermatocytes. Therefore, we did not count the foci of centromeres and telomeres. Consistently, the centromere and telomere markers localized as expected in both wild type and Trip13-null spermatocytes.

(4) I am not fully convinced by these photos: "synapsed sister chromatids (Figure 6B)" and "Sycp2-/- spermatocytes formed short stretches of synapsis (Figure 6C)". The authors may try confocal microscopy with super-resolution deconvolution as they did for other data.

These have been previously demonstrated. The “synapsed sister chromatids (Figure 6B)” were previously demonstrated by confocal microscopy with super-resolution deconvolution (Guan et al., 2020). The short stretches of synapsis in Sycp2-/- spermatocytes was previously demonstrated by electron microscopy (Tripartite SC structure) and SYCP1 immunofluorescence (Yang et al., 2006). We have revised the text by citing the previous evidence and the publications.

Minor points:

(1) Line 19-21: "Loss of TRIP13 leads to meiotic arrest and thus sterility in both sexes. Trip13-null meiocytes exhibit abnormal persistence of HORMAD1 and HOMRAD2 on synapsed SC". These findings confirm the previously reported phenotypes of the Trip13 hypomorph alleles. This information can be added to the abstract. Otherwise, it sounds like these are totally new findings, as written.

This information is now added to the abstract: “These findings confirm the previously reported phenotypes of the Trip13 hypomorph alleles.”

(2) The introduction section seems too long and contains unnecessary information. Some molecular details that are not touched in the result section can be deleted (e.g., Line 65-73).

We would like to keep the molecular details on the two conformation states, as it provides biochemical background on TRIP13-HORMAD interactions.

(3) Introduction, Line 92. A rationale can be added as to why the authors characterized the Trip13-null allele.

a rationale has been added as follows: “To determine the effect of complete loss of TRIP13, we characterized Trip13-null mice.”

(4) Line 205: Typo "TRRIP13". Corrected.

Reviewer #3 (Recommendations For The Authors):

Just a few recommendations:

(1) In my opinion, the title is an overreach. "Regulator" invokes other concepts such as transcription factors.

Please see our explanation in response to weaknesses from Reviewer #3.

(2) The first sentence of the results deals with TRIP13 expression in only 3 tissues. The authors might look at more comprehensive RNA-seq data from mice and humans.

We examined TRIP13 protein expression in 8 mouse tissues by WB and found that TRIP13 protein was abundant in testis but present at a very low level in ovary and liver (Figure 1A). We feel that readers can easily look up the relative transcript levels of Trip13 in more tissues from mice and humans from NCBI database under “Gene”.

(3) The null allele is semi-lethal. Is body size affected? Were the mice abnormal in any other ways, given that TRIP13 has been implicated in other diseases and processes, and is expressed in other tissues (TRIP13 stands for Thyroid receptor interacting protein).

The body weight of 2-3 month-old males was not significantly different between wild type (24.3±2.8 g, n=5) and Trip13 KO mice (22.8±1.7 g, n=5, p=0.3, Student’s t-Test). We have included the body weight information in the revised manuscript. We didn’t observe abnormal somatic defects in the viable Trip13-null mice, nor did the authors report any in the Trip13 hypomorph mutants in two previous studies (Li and Schimenti, 2007; Roig et al., 2010).

(4) Line 276 : It would be nice to elaborate on the "spatial explanation."

We meant that TRIP13 localizes to SC while HORMAD proteins are removed from SC upon chromosomal synapsis, thus providing a spatial explanation. However, we have now deleted “spatial”.

Author Response

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

Reviewer #1 (Public Review):

However, there are several concerns to be explained more in this study. In addition, some results should be revised and updated.

Thank you for your comments. The concerns were addressed by the description and experiment.

Some results were revised and updated accordingly.

Reviewer #2 (Public Review):

The minor weakness of the study is inconsistent use of terminology throughout the manuscript, occasional logic-jump in their flow, and missing detailed description in methodologies used either in the text or Materials and Methods section, which can be easily rectified.

Thank you for your review. We have revised the manuscript and corrected errors according to your comments.

Reviewer #3 (Public Review):

Importantly, besides the Miwi ubiquitination experiment which is performed in a heterologous and therefore may not be ideal for extracting conclusions, the possible involvement of ubiquitination was not shown for any other proteins that the authors found that interact with FBXO24. Could histones and transition proteins be targets of the proposed ubiquitin ligase activity of FBXO24, and in its absence, histone replacement is abrogated?

Thank you for your comments. The histones and transition proteins were not found in the immunoprecipitates of FBXO24, suggesting they are not the direct targets of FBXO24, shown in Figure S3G.

Miwi should be immunoprecipitated and Miwi ubiquitination should be detected (with WB or mass spec) in WT testis.

We agree with this suggestion. In the revision, the expression and ubiquitination of MIWI were detected in WT testis by the immunoprecipitation and ubiquitination assay, as shown in Figure 8H.

Therefore, the claim that FBXO24 is essential for piRNA biogenesis/production (lines 308, 314) is not appropriately supported.

We appreciate the comment. We have revised the description and modified the claim on page 11.

Reviewing Editor's note for revision

(1) As noted by all three reviewers, as currently written the rationale to focus on MIWI is not entirely clear. A transitional narrative to focus on MIWI needs to be provided as well as an explanation for how the absence of FBXO24 as an E3 ubiquitin ligase is responsible for the observed mRNA and protein differential expression.

We appreciate your comments. We have supplemented the transitional narrative by focusing on MIWI and explained mRNA and protein differential expression upon FBXO24 deletion, shown on Page 7 and Page 13, respectively.

(2) As it can be indirect, mass spec detection of MIWI in testis co-IP and MIWI ubiquitination should be detected (with WB or mass spec) in WT testis.

In the revision, the expression and ubiquitination of MIWI were detected in WT testis by the immunoprecipitation and ubiquitination assay, as shown in Figure 8H.

(3) Please tone down the claim that FBXO24 is essential for piRNA biogenesis/production as it requires further evidence.

We have revised the description and modified the claim on page 11.

(4) Ontology analysis of the genes with abnormally spliced mRNAs to provide an explanation for developmental defects.

In the revision, we have performed the ontology analysis and provided new data regarding the abnormally spliced genes, as shown in Figure S4D.

Reviewer #1 (Recommendations For The Authors):

Major comments

(1) The authors performed mainly with the WT (or knock-in) and Fbxo24-knockout mouse model. Do the heterozygous males and their sperm have any physiological defects like FBXO24-deficient mice?

This is a good question. We did the phenotype analysis and found that heterozygous males are all fertile, and their sperm do not have any physiological defects.

(2) Fbxo24-KO sperm carries swollen mitochondria. How do the mitochondria affect sperm function?

Thank you for raising this interesting question. Based on our data and published literature, the defective mitochondria were associated with energetic disturbances and reduced sperm motility, as shown on Page 12.

(3) TEM images show that Fbxo24-KO spermatids carry swollen mitochondria and enlarged chromatoid bodies. How the swollen mitochondria and enlarged chromatid are defective for sperm motility and flagellar development, requires more explanation. In addition, it is unclear how the enlarged diameter of the chromatoid body is critical for normal sperm development.

Thank you for your comments. The chromatoid bodies are considered to be engaged in mitochondrial sheath morphogenesis. Analysis of the chromatoid bodies' RNA content reveals enrichment of PIWI-interacting RNAs (piRNAs), further emphasizing the role of the chromatoid bodies in post-transcriptional regulation of spermatogenetic genes. We added this explanation on Page 12-13.

(4) The authors only show band images to compare the protein amounts between WT and KO sperm and round spermatids. As the blots for loading controls are not clear, the authors should quantify the protein levels and perform a statistical comparison.

We quantified the protein levels and performed a statistical comparison, as shown in Figure S3B.

(5) The authors show the defective sperm head structure from Fbxo24-KO sperm in Figure 5. However, the Fbxo24-KO sperm heads seem quite normal in Figure 3. How many sperm show defective sperm head structure? In addition, the authors observed altered histone-to-protamine conversion in sperm, but it is unclear whether the altered nuclear protein conversion causes morphological defects in the sperm head.

We appreciate the comments. In our study, we found over 80% of Fbxo24 KO sperm showed defective structure in the sperm head. Altered histone-to-protamine conversion caused the decondensed nucleus of Fbxo24 KO sperm. Notably, in many knockout mice studies, impaired chromatin condensation is frequently associated with abnormal sperm head morphology, as shown in reference 15 of Page 8.

(6) The authors compare the protein levels of RNF8, PHF7, TSSK6, which participate in nuclear protein replacement in sperm. However, considering the sperm is the endpoint for the nuclear protein conversion, it is unclear to compare the protein levels in mature sperm. The authors might want to compare the protein levels in developing germ cells.

Thank you for your comment. Yes, we actually detected the protein levels of RNF8, PHF7, and TSSK6 in the testes, not in sperm. We have corrected it in the Figure 5E. We apologize for our carelessness.

(7)This reviewer suggests describing more rationales for how the authors focus on the MIWI protein. Also, it is wondered whether MIWI is also detected from testis co-IP mass spectrometry.

We agree with this suggestion. Since MIWI was a core component of CB and also identified as an FBOX24 interacting partner from our immunoprecipitation-mass spectrometry (IP-MS) (Table S1), we focused on the examination of MIWI expression between WT and Fbxo24 KO testes. We have added this description in the revision (see lines 191-193 on page 7).

(8) The authors need to provide a more detailed explanation for how the altered piRNA production affects physiological defects in germ cell development. In addition, it will be good to describe more how the piRNAs affect a broad range of mRNA levels.

Thank you for your comments. The previously published studies have demonstrated that piRNAs could act as siRNAs to degrade specific mRNAs during male germ cell development and maturation. We have cited these studies on lines 369-372 of Page 13.

(9) The authors observed an altered splicing process in the absence of FBXO24. However, it is a little bit confusing how the altered splicing events affect developmental defects. Therefore, the authors should state which mRNAs have undergone abnormal splicing processes and provide ontology analysis for the genes.

We have performed the ontology analysis and showed the new data in Figure S4D.

Minor comments

(1) Figure 1A-C - Statistical comparison is missed. Numbers for biological replication should be described in corresponding legends.

Thank you for your careful review. We have provided the statistical comparison and the numbers for biological replication in the legends of Figure 1A-C.

(2) Figure 1E, F - Current images can't clearly resolve the nuclear localization of the FBXO24 testicular germ cells. To clarify the intracellular localization, the authors should provide images with higher resolution.

The resolution of Figure 1E, F was improved, as suggested. Thank you!

(3) Figure 1E, F - Scale bar information is missing.

The scale bars of Figure 1E, F were provided.

(4) It will be much better to show the predicted frameshift and early termination of the protein translation in Fbxo24-knockout mice.

The predicted frameshift of Fbxo24-knockout mice was added and shown in Figure S1B.

(5) It is required to provide primer information for qPCR.

The primer information for qPCR was provided, as shown in Table S7.

(6) The authors describe that Fbxo24-KO sperm show abrupt bending of the tail. However, the description is unclear and the sperm shown in Figure 3C seems quite normal. The authors should clarify the abnormal bending pattern of the tail and show quantified results.

Thank you for pointing out this issue. In Fbxo24 KO sperm, abnormal bending of the sperm tails mainly included neck bending and midpiece bending. We have shown them in Figure S3A.

(7) The authors mention that Fbxo24-KO sperm have swollen mitochondria at the midpiece, but this is also unclear. How many mitochondria are swollen in Fbxo24-KO sperm?

This is a good question. However, since it is very difficult to observe all of the mitochondria in each sperm using the electronic microscope, we could not quantify the swollen mitochondria in Fbxo24 KO sperm.

(8) Scale bar information is missed - Fig 3C insets, Fig 3D, Fig 3F insets, 4A insets, Figure 4C insets.

All the scale bars have been added.

(9) How many sperm have annulus defects? In Figure 3F, WT sperm does not have an annulus, which could be damaged during sample preparation. Is the annulus defects in Fbxo24-KO sperm consistent?

Thank you for asking these questions. Based on our results, about 30% of Fbxo24 KO sperm showed defective annulus structure. Since both TEM (Figure 3F) and SEM (Figure 3G) results clearly showed the defective annulus structure of Fbxo24 KO sperm, we believe the annulus defects are consistent and highly unlikely caused by sample preparation.

(10) A Cross-section image for the endpiece of Fbxo24-KO sperm is not suitable. There is a longitudinal column structure of the principal piece.

Thank you for your comments. It is difficult to observe a completely longitudinal structure of sperm tail under TEM. The cross-section of the endpiece and principal piece allowed us know the structure of the axoneme, ODFs and fibrous sheath (FS).

(11) The endpiece of Fbxo24-KO sperm seems to have a normal axoneme. Do all endpieces of Fbxo24KO sperm have normal axoneme? Also, the authors need to describe whether an axonemal structure is damaged and disrupted in all Fbxo24-KO sperm.

Our TEM data showed the axonemal structure was impaired in the endpiece of Fbxo24 KO sperm (See right panels of Figure 3H). Moreover, based on the ultrastructure analysis of TEM, we found over 90% of Fbxo24 sperm had a damaged axonemal structure.

(12) Reference blots in Fig 3I, 3J, 4E (left), 5C and 5E are quite faint. The authors should replace the blot images.

Thank you for pointing out this. We have rerun Western blot multiple times but could not obtain better images due to antibody sensitivity. However, we quantified the protein levels and performed a statistical comparison, as shown in Figure S3B, to establish a good readout from these images for the readers.

(13) Loading controls are required - 7D-H.

Done as suggested. Thanks!

(14) How do the authors measure the midpiece length? From where to where? This should be clarified.

Good question. We measured the midpiece length from the sperm neck to the sperm annulus by MitoTracker staining. We have clarified this on Page 16.

(15) How are the bands for Fbxo24 shifted during IP in Fig 7A?

The protein modification in the interaction may cause the band shift.

(16) There are several typos throughout the manuscript. Please check carefully and fix them.

Thank you for your careful review. We have corrected and fixed all the typos as far as we can.

Reviewer #2 (Recommendations For The Authors):

Major comments

(1) Please provide a schematic of HA-Fbxo24 knock-in construct and strategy together with knockout (Figure S2) or even separately early in Figure S1. The description of using the transgenic mouse is mentioned even earlier than the knockout but there are no citations or methods provided in the text other than that listed in Materials and Methods.

Thank you for your suggestion. As suggested, the schematic of the HA-Fbxo24 knock-in strategy has been supplemented in Figure S2A. The description of using the transgenic mouse has been added to the results, as shown on page 4 of lines 102-103.

Also, it is not clear to what extent the phenotypic and molecular characterization of HA-transgenic mice is performed. For example, Lines 134-139: The use of Fbxo24-HA labeled transgenic mice results in the rescue of spermatogenesis and fertility as shown in Figure 2F by measuring the litter size. It is not clear how this observation leads the author to state that this rescues defects in spermiogenesis. Please clarify how and what other measures are taken to support this conclusion. Is the observed infertility due to defects in spermatogenesis or spermiogenesis?

Thank you for your question. We crossed FBXO24-HATag males with FBXO24−/− females to obtain FBXO24−/−; FBXO24-HATag males. We examined the testes volume and histological morphology of FBXO24−/−; FBXO24-HATag males and found that they were similar to FBXO24+/−; FBXO24-HATag littermates, indicating that spermatogenesis was restored, as shown in Figure S2H.

(2) Line 107 vs Line 114: Please use the terminology spermatogenesis and spermiogenesis consistently throughout the text. Earlier in the introduction, the authors clearly defined that spermatogenesis involves three phases, with the third phase referred to as spermiogenesis. However, the author concludes in the first line that "FBXO24 plays a role during spermatogenesis" while summarizing at the end of the paragraph that this protein is "expressed in haploid spermatids specifically during spermiogenesis". Therefore, it is not clear whether the authors conclude that FBXO24 is important for all of spermatogenesis (line 107) or only for part of spermiogenesis (line 114). Another example is line 219 vs. 238: At this point in the manuscript, it is again unclear whether the authors want to study molecular changes during spermatogenesis or spermiogenesis upon FBXO24 depletion. Many examples of such cases throughout the text, and it is recommended to be consistent in using more restrictive terminology whenever applicable for a clear interpretation.

We thank you for your careful review. We have double-checked the terminology of spermatogenesis and spermiogenesis and made it consistent throughout the text of the revised manuscript.

(3) It is not clear how rampant/frequent the Fbxo24-knockout sperm show defects in head morphology based on Figures 3C, 3F, and 5A since it seems that there are some sperm showing relatively normallooking sperm heads. Please provide quantification.

We have performed the quantification and found that over 80% of Fbxo24 KO sperm showed defective structures in the sperm head.

(4) Figure 3B: The authors describe in the figure legend that 3 mice were analyzed in each group. The standard deviation for the WT analysis is missing, or if the author wanted to set the WT value to 100%, the bar and scale shown on the y-axis do not fit. The value for WT looks more like 95%.

We have indeed analyzed sperm motility based on the WT value set at 100% and have revised Figure 3B in the revision. We apologize for this oversight.

(5) Figure 3 B and C: It is not clear how the motility is measured. Is CASA used (not described in Methods). The conclusion about abnormal flagellar bending in KO spermatozoa cannot be drawn from the static microscopic images alone. Please provide more details of motility analysis together with videos of live cell imaging.

The sperm motility was measured manually using a hemocytometer, according to the reference.

We provided the details of sperm motility analysis in the Materials and Methods section on Page 16.

(6) Figure 3 I and J: These are one of a few figures that are not supported by statistical analysis. In particular, for 3I, GAPDH controls of WT and KO protein do not show equal loading, which could explain the lower expression of the KO protein. Please show normalized bar graphs with multiple biological replicates or at least show a representee technical replicat that shows equal loading of GAPDH to better support the conclusion.

Thank you for your suggestion. Statistical comparison of relative protein expression was supplemented, as shown in new Figure S3B.

(7) Line 184: It is not clear how the authors define a swollen mitochondrion? Are there any size criteria (roundness) that can be measured to distinguish between a swollen and a non-swollen mitochondrion? It is recommended to use another terminology as often 'swollen' implies there is a difference in osmolarity but there is no experiment to support this implication.

Thank you for your comment. We have changed the “swollen” to “vacuolar” in the revision, as shown on Page 7.

(8) Figure S4, without a bright field image, it is hard to see the purity and morphology of the isolated prep. Please provide the bright field images together or as overlaid images.

We agree with your comment. We have provided the overlaid images in new Figure S4A.

(9) There is a big logic jump in what prompts the authors to look MIWI protein level and link the observation to MIWI/piRNA pathway in both Introduction and Results while it is one of the main findings. It is recommended to provide a better rationale and logical flow in the text.

Thank you for your suggestion. We have added a sentence explaining why we wanted to focus on studying MIWI expression (see lines 190-193 on page 7).

Minor comments

(1) Please keep all the conventions of gene vs. protein nomenclature. For example, write the genes mentioned in the figures in italics with the first letter in Capital, as it is done in the main part. Proteins should be in ALL CAPITAL like FBXO24.

The names of gene and protein have been revised in the revision, as suggested.

(2) In the MM section, the name of the manufacturer and the location of the materials used are missing in several sections. Please go back through the MM section and add this information in the appropriate places.

Done as suggested. Thank you!

(3) On page 4, the authors mentioned that "Further qPCR analysis of developmental testes and purified testicular cells showed that FBXO24 mRNA was highly expressed in the round spermatids and elongating spermatids (Fig 1B-C)". Please include statistical analyses for Fig 1B-C as well as for Fig 1A to support the written statements.

Statistical comparison was supplemented, as shown in Figure 1. P-values are denoted in figures by *p < 0.05.

(4) Figure 3E: Please describe in more detail how the length of the midpiece was measured. Was it based on TEM images or based on fluorescent images using MitoTracker?

As we responded to Reviewer #1, we measured the midpiece length from the sperm neck to the sperm annulus by MitoTracker staining. We have clarified this in the Method and Material section on Page 16.

(5) Line 431: In the "Electron Microscopy" section of the MM part, the author should indicate the ascending ethanol series (%) used.

Done as suggested. Thank you!

(6) Line 432: The thickness of the sections prepared is missing, as well as an indication of the microtome used.

We have added thickness and the microtome in the Method and Material section on Page 16.

(7) Line 433: If the generated tiff files have been processed with Adobe Photoshop, this information is missing.

We have provided information on the usage of Adobe Photoshop for the generation of tiff files on Page 17.

(8) Lines 445, 452, 467: In some places in the paper, the temperature is written with a space between the number and {degree sign}C, and sometimes it is not. Please go through the paper and make it consistent. The usual spelling is 4{degree sign}C.

We have gone through the manuscript and checked all the spelling of temperature writing to make them consistent. Thank you for careful review.

(9) Line 469: The gel documentation system used is not mentioned.

Done as suggested. Thank you!

(10) Line 469: The 'TM' should be superscripted.

Done as suggested.

(11) Line 489: A space is missing between the changes and the parenthesis.

Done as suggested.

(12) Line 495-496: The authors write that the fractions enriched with round spermatids after sedimentation were collected manually. Was a determination of cell concentration - e.g., 2 x106 cells/ml -performed after collection of the cells? How were the cells stored until use? Please add the sedimentation time and used temperature.

Store the cell in the 1´ Krebs buffer on ice. The cell sediment was through a BSA density gradient for 1.5 h at 4°C. The cell concentration was determined after collection, as shown on Page 18.

(13) Line 505: spelling error. Instead of " manufacturer's procedure" it is written manufactures' instructions.

The spelling error was corrected.

(14) Line 520: Please write a short sentence on how the purification of the 16-40 nt long RNA was performed.

The length of 16–40 nt RNA was enriched by polyacrylamide gel electrophoresis. We added this information on Page 19 of line 531.

(15) Line 528: The version of the used GraphPad software is missing.

The version of GraphPad software was supplemented, as shown on Page 19.

(16) Line 677: For qPCR analyses, the number of mice analyzed (N) and a statistical evaluation are missing.

The statistical comparison and the numbers for biological replication were added, as shown on Page 26.

(17) Figure 3D: Please add a scale bar.

Done as suggested. Thanks!

(18) Line 371 and Line 377: Two times "in summary" is written. Please make one summary for the whole paper.

This sentence was revised, as shown in Page 13.

(19) Line 382: To be consistent in the whole paper, please write Figure 10 in bold letters.

Done as suggested.

(20) Please make the size and font of the references consistent with the main text.

Done as suggested. Thanks again for your careful review.

Reviewer #3 (Recommendations For The Authors):

I would like to see the description of the FBXO24 immunoprecipitation experiment performed in HEK293T cells. This somatic cell line does not normally express Miwi, so how Miwi was detected in FBXO24 mCherry IP beads? It is not mentioned if Miwi is expressed from a recombinant vector in this experiment. Similarly, I would like to see a better description of the experiment described in the same paragraph towards the end of it with the ubiquitin peptides, it is not clear.

Thank you for your comments. FBXO24-mCherry was expressed in HEK293T cells and the immunoprecipitates was incubated with the protein lysate of the testes (see lines 268-272 on Page 10). The description of the ubiquitin experiment was added as well, as shown in lines 283-286 on Page 10.

Line 263: I think the term ectopic here is not appropriate, a correction is needed.

We have changed “ectopic” to “increased” in the revision (see line 268 on Page 10).

I would like the authors to provide a tentative explanation or evidence of why FBXO24 KO males are completely sterile, even though there are still mature sperm produced with some motility. Since there are defects in nuclear condensation it will be very relevant to check DNA damage/fragmentation, which could contribute to the sterility phenotype.

This is a good suggestion. We reanalyzed the sperm DNA damage by TUNEL staining and shown the new data in Figure S3E-F.

Line 213: There have been some conflicting reports about the role of RNF8 in spermiogenesis, but a recent report has shown that RNF8 is not involved in histone PTMs that mediate histone to protamine transition (Abe et al Biol Reprod 2021 https://doi.org/10.1093%2Fbiolre%2Fioab132).

Thank you for your comment. We have cited this critical reference and discussed it in Discussion section on Page 12.

Figure 7: I would like to see zoomed-out views of the affected exons, so that flanking unaffected exons can be used as a reference for unaffected splicing. Most of the genome browser views in this image only show affected exons and it is impossible to see if these alone are affected or if the reduced RNAseq coverage in those exons is a result of overall reduced mapped reads in these genes. Also, a fixed Y axis with the same max value should be shown for these genome browser snapshots so that the expression level is comparable between the two genotypes.

Thank you for your comments. Loading control of RT-PCR and scale range of Y axis were added in new Figure 7.

Minor corrections:

Line 70: correct "..functions as protein-protein interaction..".

Thank you for your careful review. We have corrected this sentence (see line 69 on Page 3).

Line 101: correct "..qPCR analysis of developmental testis..".

We have corrected this sentence (see line 100 on Page 4). Thanks again.

Line 116: correct "..results in detective..".

Corrected.

Line 186: correct ".. explored..".

Corrected.

Line 218: correct ".. gene expressions.

Corrected.

Line 221: correct "..genes significantly differentiated expressed".

Corrected.

Line 241: FBXO24 was shown earlier in both cytoplasm and nucleus.

We have changed “FBXO24 is mainly confined to the nucleus” to “FBXO24 expressed in the nucleus”, as shown in line 247 on Page 9.

Line 501-502: correct "..reverse transcriptional".

“reverse transcriptional” was changed into “reverse transcription”, showing in Page 18.

Line 686: correct ".. deficiency male..".

Corrected.

Line 769: correct "..Western blots were adopted..".

Corrected.

Line 784: correct "..WT tesis..".

Corrected.

I cannot understand exactly what is shown in Figure 9B. Some elements marked on the X-axis are single base locations (-2K, TSS, +2K) and others are stretches of sequences so they cannot be equivalent. Why there is only an intron shown? There should be a measure of normalized expression on the Y-axis.

Thank you for your questions. The X-axis means that genome segments were scaled to the same size and were calculated the signal abundance, which was analyzed by computeMatrix. Aim to know the piRNA source, piRNA was mapped to the gene body, including introns, CDS and UTRs. The value of the Y-axis is the normalized count.

Figure 6F is not needed.

Figure 6F was used to illustrate the number of different types of mRNA splicing upon FBXO24 deletion in the round spermatids. To better understand the splicing for the reader, we decided to keep it.

The last two paragraphs of the discussion seem to be redundant.

Thank you for pointing out this. We have revised the last two paragraphs of the discussion.

Author Response

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this study, Maestri et al. use an integrative framework to study the evolutionary history of coronaviruses. They find that coronaviruses arose recently rather than having undergone ancient codivergences with their mammalian hosts. Furthermore, recent host switching has occurred extensively, but typically between closely related species. Humans have acted as an intermediate host, especially between bats and other mammal species.

Strengths:

The study draws on a range of data sources to reconstruct the history of virus-host codivergence and host switching. The analyses include various tests of robustness and evaluations through simulation.

Weaknesses:

The analyses are limited to a single genetic marker (RdRp) from coronaviruses, but using other sections of the genome might lead to different conclusions. The genetic marker also lacks resolution for recent divergences, which precludes the detailed examination of recent host switches. Careful and detailed reconstruction of the timescale would be helpful for clarifying the evolutionary history of coronaviruses alongside their hosts.

The use of a single short genetic marker (the RdRp palmprint region) from coronaviruses is indeed a limitation. However, this marker is the one that is currently used for routinely delimiting operational taxonomic units in RNA viruses and reconstructing their evolutionary history (Edgar et al. 2022, see also the Serratus project; https://serratus.io/); therefore, we took the conscious decision early on to rely on this expertise. Unfortunately, this marker cannot provide robust timescale reconstructions for coronavirus evolution (previous estimates of coronavirus origin range from around 10 thousand years ago to 293 million years ago depending on modeling assumptions). Only future genomic work across Coronaviridae that will characterize multiple genetic regions with different evolutionary rates will allow us to precisely elucidate the timescale of the evolutionary history of coronaviruses alongside their hosts. In the meantime, we show here that, while the RdRp palmprint region cannot by itself resolve the precise timescale of coronavirus evolution, it strongly suggests, when used along with cophylogenetic approaches, a recent evolutionary origin in bats.

R. C. Edgar, et al., Petabase-scale sequence alignment catalyses viral discovery. Nature 602, 142–147 (2022).

Reviewer #2 (Public Review):

Summary:

In their study titled "Recent evolutionary origin and localized diversity hotspots of mammalian coronaviruses," authors Benoît Perez-Lamarque, Renan Maestri, Anna Zhukova, and Hélène Morlon investigate the complex evolutionary history of coronaviruses, particularly those affecting mammals, including humans. The study focuses on unraveling the evolutionary trajectory of these viruses, which have shown a high propensity for causing pandemics, as evidenced by the SARS-CoV2 outbreak.

The research addresses a significant gap in our understanding of the evolutionary dynamics of coronaviruses, particularly their history, patterns of host-to-host transmission, and geographical spread. These aspects are important for predicting and managing future pandemic scenarios.

Historically, studies have employed cophylogenetic tests to explore virus-host relationships within the Coronaviridae family, often suggesting a long history of virus-host codiversification spanning millions of years. However, the team led by Perez-Lamarque proposes a novel phylogenetic framework that contrasts this traditional view. Their approach, which involves adapting gene tree-species tree reconciliation, is designed to robustly test the validity of two competing scenarios: an ancient origination and codiversification versus a more recent emergence and diversification through host switching.

Upon applying this innovative framework to the study of coronaviruses and their mammalian hosts, the authors' findings challenge the prevailing notion of a deep evolutionary history. Instead, their results strongly support a scenario where coronaviruses have a more recent origin, likely in bat populations, followed by diversification predominantly through host-switching events. This diversification, interestingly, seems to occur preferentially within mammalian orders.

A critical aspect of their findings is the identification of hotspots of coronavirus diversity, particularly in East Asia and Europe. These regions align with the proposed scenario of a relatively recent origin and subsequent localized host-switching events. The study also highlights the rarity of spillovers from bats to other species, yet underscores the relatively higher likelihood of such spillovers occurring towards humans, suggesting a significant role for humans as an intermediate host in the evolutionary journey of these viruses.

The research also points out the high rates of host-switching within mammalian orders, including between humans, domesticated animals, and non-flying wild mammals.

In conclusion, the study by Perez-Lamarque and colleagues presents an important quantitative advance in our understanding of the evolutionary history of mammalian coronaviruses. It suggests that the long-held belief in extensive virus-host codiversification may have been substantially overestimated, paving the way for a reevaluation of how we understand, predict, and potentially control the spread of these viruses.

Strengths:

The study is conceptually robust, and its conclusions are convincing.

Weaknesses:

Despite the availability of a dated host tree the authors were only able to use the "undated" model in ALE, with the dated method (which only allows time-consistent transfers) failing on their dataset (possibly due to dataset size?). Further exploration of the question would be potentially valuable.

Our intuition is that ALE in its “dated” version did not necessarily fail on our dataset due to its size (ALE ran, but provided unrealistic parameter estimates and was not able to output possible reconciliations, as mentioned in our Material and Methods section). We think it most likely did not run because there is no pattern of codiversification: the coronavirus and mammal trees are so distinct that finding a reconciliation scenario between these trees with time-consistent transfers is very difficult and ALE fails at estimating an amalgamated likelihood for such an unlikely scenario. Following a suggestion from reviewer #3, we are going to try running the dated version of ALE independently on the alpha and beta-coronaviruses, resulting in smaller datasets. This will help us elucidate whether the dated version of ALE fails due to data size or the absence of a codiversification pattern.

Reviewer #3 (Public Review):

Summary:

This work uses tools and concepts from co-phylogenetic analyses to reconstruct the evolutionary and diversification history of coronaviruses in mammals. It concludes that cross-species transmissions from bats to humans are a relatively common event (compared to bats to other species). Across all mammals, the diversification history of coronaviruses suggests that there is potential for further evolutionary diversification.

Strengths:

The article uses an interesting approach based on jointly looking at the extant network of coronaviruses-mammals interactions, and the phylogenetic history of both these organisms. The authors do an impressive job of explaining the challenges of reconstructing evolutionary dynamics for RNA viruses, and this helps readers appraise the relevance of their approach.

Weaknesses:

I remain unconvinced by the argument that sampling does not introduce substantial biases in the analyses. As the authors highlight, incomplete knowledge of the extant interactions would lead to a biased reconstruction of the diversification history. In a recent paper (Poisot et al. 2023, Patterns), we look at sampling biases in the virome of mammals and suggest that is a fairly prominent issue, that is furthermore structured by taxonomy, space, and phylogenetic position. Case in point, even for betacoronaviruses, there have been many newly confirmed hosts in recent years. For organisms that have received less intense scrutiny, I think a thorough discussion of potential gaps in data would be required (see for example Cohen et al. 2022, Nat. Comms).

I was also surprised to see little discussion of the differences between alpha and beta coronaviruses - there is evidence that they may differ in their cross-species transmission (see Caraballo et al. 2022 Micr. Spectr.), which could call into question the relevance of treating all coronaviruses as a single, homogeneous group.

Some of the discussions in this paper also echo previous work by e.g. Geoghegan et al. (see 2017, PLOS Pathogens), which I was surprised to not see discussed, as it is a much earlier investigation of the relative frequencies of co-divergence and host switches for different viral families, with a deep discussion of how this may structure future evolutionary dynamics.

We totally agree that sampling biases in the virome of mammals is a prominent issue, which is why we conducted a series of sensitivity analyses to test their effect on our main conclusions. We thoroughly tested the effect of (i) the unequal sampling effort across mammalian species that have been screened and (ii) the unequal screening of mammalian species across the mammalian tree of life by subsampling the data to correct for the unequal sampling effort (see Supporting Information Text). In both cases, we still reported low support for a scenario of codiversification, the origin in bats in East Asia, the preferential host switches within mammalian orders, and the rare spillovers from bats to humans. The robustness of our findings to sampling biases may be explained by the fact that the cophylogenetic approach we used (ALE) explicitly accounts for undersampling by assuming that all host transfers involve unsampled intermediate hosts. To address the reviewer's comment, we will better underline the importance of sampling biases in our main text and include the suggested references. We will also better highlight our sensitivity analyses by moving them from the Supporting Information Text to the main text.

We agree that distinguishing between alpha and beta coronaviruses will provide useful additional insights; we are going to run separate cophylogenetic analyses for these two sub-clades. We will report the results of these additional analyses in the revised manuscript, and put them in context with the existing literature about the two sub-clades.

We were not aware of the work of Geoghegan et al. (see 2017, PLOS Pathogens), thank you for providing this reference that we will now discuss.

Author Response

Reviewer #1:

This manuscript presents an extremely exciting and very timely analysis of the role that the nucleosome acidic patch plays in SWR1-catalyzed histone exchange. Intriguingly, SWR1 loses activity almost completely if any of the acidic patches are absent. To my knowledge, this makes SWR1 the first remodeler with such a unique and pronounced requirement for the acidic patch. The authors demonstrate that SWR1 affinity is dramatically reduced if at least one of the acidic patches is absent, pointing to a key role of the acidic patch in SWR1 binding to the nucleosome. The authors also pinpoint a specific subunit - Swc5 - that can bind nucleosomes, engage the acidic patch, and obtain a cryo-EM structure of Swc5 bound to a nucleosome. They also identify a conserved arginine-rich motif in this subunit that is critical for nucleosome binding and histone exchange in vitro and for SWR1 function in vivo. The authors provide evidence that suggests a direct interaction between this motif and the acidic patch.

Strengths:

The manuscript is well-written and the experimental data are of outstanding quality and importance for the field. This manuscript significantly expands our understanding of the fundamentally important and complex process of H2A.Z deposition by SWR1 and would be of great interest to a broad readership.

We thank the reviewer for their enthusiastic and positive comments on our work.

Reviewer #2:

Summary:

In this study, Baier et al. investigated the mechanism by which SWR1C recognizes nucleosomal substrates for the deposition of H2A.Z. Their data convincingly demonstrate that the nucleosome's acidic patch plays a crucial role in the substrate recognition by SWR1C. The authors presented clear evidence showing that Swc5 is a pivotal subunit involved in the interaction between SWR1C and the acidic patch. They pared down the specific region within Swc5 responsible for this interaction. However, two central assertions of the paper are less convincing. First, the data supporting the claim that the insertion of one Z-B dimer into the canonical nucleosome can stimulate SWR1C to insert the second Z-B dimer is somewhat questionable (see below). Given that this claim contradicts previous observations made by other groups, this hypothesis needs further testing to eliminate potential artifacts. Secondly, the claim that SWR1C simultaneously recognizes the acidic patch on both sides of the nucleosome also needs further investigation, as the assay used to establish this claim lacks the sensitivity necessary to distinguish any difference between nucleosomal substrates containing one or two intact acidic patches.

Strengths:

As mentioned in the summary, the authors presented clear evidence demonstrating the role of Swc5 in recognition of the nucleosome acidic patch. The identification of the specific region in Swc5 responsible for this interaction is important.

We thank the reviewer for their careful critique of our work. Below we address each major concern.

Major comments:

(1) Figure 1B: It is unclear how much of the decrease in FRET is caused by the bleaching of fluorophores. The authors should include a negative control in which Z-B dimers are omitted from the reaction. In the absence of ZB dimers, SWR1C will not exchange histones. Therefore, any decrease in FRET should represent the bleaching of fluorophores on the nucleosomal substrate, allowing normalization of the FRET signal related to A-B eviction.

In this manuscript, as well as in our two previous publications (Singh et al., 2019; Fan et al.,2022), we have presented the results of no enzyme controls, +/- ZB dimers, no ATP controls, or AMP-PNP controls for our FRET-based, H2A.Z deposition assay (see also Figure S3). We do not observe significant levels of photobleaching in this assay, either during ensemble measurements or in an smFRET experiment. To aid the reader, we have added the AMP-PNP data for the experiment shown in Figure 1B. The results show there is less than a 10% decrease in FRET over 30’, and the signal from the double acidic patch disrupted nucleosome is identical to this negative control.

(2) Figure S3: The authors use the decrease in FRET signal as a metric of histone eviction. However, Figure S3 suggests that the FRET signal decrease could be due to DNA unwrapping. Histone exchange should not occur when SWR1C is incubated with AMP-PNP, as histone exchange requires ATP hydrolysis (10.7554/eLife.77352). And since the insertion of Z-B dimer and the eviction of A-B dimer are coupled, the decrease of FRET in the presence of AMP-PNP is unlikely due to histone eviction or exchange. Instead, the FRET decrease is likely due to DNA unwrapping (10.7554/eLife.77352). The authors should explicitly state what the loss of FRET means.

We agree with the reviewer, that loss of FRET can be due to DNA unwrapping from the nucleosome. We have previously demonstrated this activity by SWR1C in our smFRET study (Fan et al., 2022). However, DNA unwrapping is highly reversible and has a time duration of only 1-3 seconds. We and others have not observed stable unwrapping of nucleosomes by SWR1C, but rather the stable loss of FRET reports on dimer eviction. We assume the reviewer is concerned about the rather large decrease in FRET signal shown in the AMP-PNP controls for Figure S3, panels A and D. For the other 7 panels, the decrease in FRET with AMP-PNP are minimal. In fact, if we average all of the AMP-PNP data points, the rate of FRET loss is not statistically different from no enzyme control reactions (nucleosome plus ZB dimers).

Data for panels A and D used a 77NO nucleosomal substrate, with Cy3 labeling the linker distal dimer. This is our standard DNA fragment, and it was used in Figure 1B. The only difference between data sets is that the data shown in Fig 1B used nucleosome reconstituted with a Cy5-labelled histone octamer, rather than the hexasome assembly method used for Fig S3. Three points are important. First, for all of these substrates, we assembled 3 independent nucleosomes, and the results are highly reproducible. Two, we performed a total of 6 experiments for the 77NO-Cy5 substrates to ensure that the rates were accurate (+/-ATP). Third, and most important, we do not see this decrease in FRET signal in the absence of SWR1C (no enzyme control). This data was included in the data source file. Thus, it appears that there is significant SWR1C-induced nucleosome instability for these two hexasome-assembled substrates. We now note this in the legend to Figure S3. Key for this work, however, is that there is a large increase in the rate of FRET loss in the presence of ATP, and this rate is faster when a ZB dimer was present at the linker proximal location. In response to the last point, we state in the first paragraph of the results: “The dimer exchange activity of SWR1C is monitored by following the decrease in the 670 nm FRET signal due to eviction of the Cy5-labeled AB-Cy5 dimer (Figure 1A).”

(3) Related to point 2. One way to distinguish nucleosomal DNA unwrapping from histone dimer eviction is that unwrapping is reversible, whereas A-B eviction is not. Therefore, if the authors remove AMP-PNP from the reaction chamber and a FRET signal reappears, then the initial loss of FRET was due to reversible DNA unwrapping. However, if the removal of AMP-PNP did not regain FRET, it means that the loss of FRET was likely due to A-B eviction. The authors should perform an AMP-PNP and/or ATP removal experiment to make sure the interpretation of the data is correct.

See response to item 2 above

(4) The nature of the error bars in Figure 1C is undefined; therefore, the statistical significance of the data is not interpretable.

We apologize for not making this more explicit for each figure. The error bars report on 95% confidence intervals from at least 3 sets of experiments. This statement has been added to the legend.

(5) The authors claim that the SWR1C requires intact acidic patches on both sides of the nucleosomes to exchange histone. This claim was based on the experiment in Figure 1C where they showed mutation of one of two acidic patches in the nucleosomal substrate is sufficient to inhibit SWR1C-mediated histone exchange activity. However, one could argue that the sensitivity of this assay is too low to distinguish any difference between nucleosomes with one (i.e., AB/AB-apm) versus two mutated acidic patches (i.e., AB-apm/AB-apm). The lack of sensitivity of the eviction assay can be seen when Figure 1B is taken into consideration. In the gel-shift assay, the AB-apm/AB-apm nucleosome exhibited a 10% SWR1C-mediated histone exchange activity compared to WT. However, in the eviction assay, the single AB/AB-apm mutant has no detectable activity. Therefore, to test their hypothesis, the authors should use the more sensitive in-gel histone exchange assay to see if the single AB/AB-apm mutant is more or equally active compared to the double AB-apm/AB-apm mutant.

Our pincher model is based on three, independent sets of data, not just Figure 1C. First, as noted by the reviewer, we find that disruption of either acidic patch cripples the dimer exchange activity of SWR1C in the FRET-based assay. Whether the defect is identical to that of the double APM mutant nucleosome does not seem pertinent to the model. In a second set of assays, we used fluorescence polarization to quantify the binding affinity of SWR1C for wildtype nucleosomes, a double APM nucleosome, or each single APM nucleosome. Consistent with the pincher model, each single APM disruption decreases binding affinity at least 10-fold (below the sensitivity of the assay). Finally, we monitored the ability of different nucleosomes to stimulate the ATPase activity of SWR1C. Consistent with the pincher model, a single APM disruption was sufficient to eliminate nucleosome stimulation.

(6) The authors claim that the AZ nucleosome is a better substrate than the AA nucleosome. This is a surprising result as previous studies showed that the two insertion steps of the two Z-B dimers are not cooperative (10.7554/eLife.77352 and 10.1016/J.CELREP.2019.12.006). The authors' claim was based on the eviction assay shown in Fig 1C. However, I am not sure how much variation in the eviction assay is contributed by different preparations of nucleosomes. The authors should use the in-gel assay to independently test this hypothesis.

For all data shown in our manuscript, at least three different nucleosome preparations were used. The impact of a ZB dimer on the rates of dimer exchange was highly reproducible among different nucleosome preparations and experiments. We also see reproducible ZB stimulation for three different substrates – with ZB on the linker proximal side, the linker distal side, and on one side of a core particle. We do not believe that our data are inconsistent with previous studies. First, the previous work referenced by the reviewer performed dimer exchange reactions with a large excess of nucleosomes to SWR1C (catalytic conditions), whereas we used single turnover reactions. Secondly, our study is the first to use a homogenous, ZA heterotypic nucleosome as a substrate for SWR1C. All previous studies used a standard AA nucleosome, following the first and second rounds of dimer exchange that occur sequentially. And finally, we observe only a 20-30% increase in rate by a ZB dimer (e.g. 77N0 substrates), and such an increase was unlikely to have been detected by previous gel-based assays.

Minor comments:

(1) Abstract line 4: To say 'Numerous' studies have shown acidic patch impact chromatin remodeling enzymes activity may be too strong.

Removed

(2) Page 15, line 15: The authors claim that swc5∆ was inviable on formamide media. However, the data in Figure 8 shows cell growth in column 1 of swc5∆.

The term ‘inviable’ has been replaced with ‘poor’ or ‘slow growth’

(3) The authors should use standard yeast nomenclature when describing yeast genes and proteins. For example, for Figure 8 and legend, Swc5∆ was used to describe the yeast strain BY4741; MATa; his3Δ1; leu2Δ0; met15Δ0; ura3Δ0; YBR231c::kanMX4. Instead, the authors should describe the swc5∆ mutant strain as BY4741 MAT a his3∆1 leu2∆0 met15∆0 ura3∆0 swc5∆::kanMX4. Exogenous plasmid should also be indicated in italics and inside brackets, such as [SWC5-URA3] or [swc5(R219A)-URA3].

We apologize for missing this mistake in the Figure 8 legend. We had inadvertently copied this from the euroscarf entry and forgot to edit the entry. We decided not to add all the plasmid names to the figure, as it was too cluttered. We state in the figure legend that the panels show growth of swc5 deletion strains harboring the indicated swc5 alleles on CEN/ARS plasmids.

(4) According to Lin et al. 2017 NAR (doi: 10.1093/nar/gkx414), there is only one Swc5 subunit per SWR1C. Therefore, the pincher model proposed by the authors would suggest that there is a missing subunit that recognizes the second acidic patch. The authors should point out this fact in the discussion. However, as mentioned in Major comment 6, I am not sure if the pincer model is substantiated.

In our discussion, we had noted that the published cryoEM structure had suggested that the Swc2 subunit likely interacts with the acidic patch on the dimer that is not targeted for replacement, and we proposed that Swc5 interacts with the acidic patch on the exchanging H2A/H2B dimer. We have now made this more clear in the text.

Author Response

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

We wish to thank the reviewers for their helpful insightful comments. Their concerns were mainly related to the interpretation of the data, help in clarifying our statements and improving our discussion.

Reviewer #1 (Recommendations For The Authors):

This is a very interesting study It involves the utilization of hippocampal neuronal cultures from syntaxin 1 knock-out mice. These cultures serve as a platform for monitoring changes in synaptic transmission through electrophysiological recording of postsynaptic currents, upon lentiviral infection with various isoforms, chimeras, and point mutations of syntaxins.

The authors observe the following:

(1) Syntaxin2 restores neuronal viability and can partially rescue Ca2+-evoked release in syntaxin1 knock-out neurons that it is much slower (cumulative charge transfer differences) and with a clearly smaller RRP than when rescued with syntaxin1. In contrast, syntaxin2-mediated rescue leads to a high increase in spontaneous release (Figure 1). Convincingly, the authors conclude that syntaxin 1 is optimized for fast phasic release and for clamping of spontaneous release, in comparison with syntaxin2.

(2) The replacement of the SNARE domain (or its C-terminal part) of syntaxin1 by the SNARE domain of syntaxin2 (or its C-terminal part) rescues the fast kinetics, but not the amplitude, of Ca2+-evoked release. This is associated with a decrease in the size of the RRP and an increase in spontaneous release. The probability of vesicular release (PVR) is a little bit increased, which is intriguing because a little decrease would be expected instead according to the reduced RRP, indicating that an enhancement of Ca2-dependent fusion is occurring at the same time by unknown mechanisms as the authors properly point out. The replacement of the Analogous experiments in which the SNARE domain of syntaxin1 is replaced into syntaxin2, reveals the exitance of differential regulatory elements outside the SNARE domain.

(3) Different constructs of syntaxin 1 and syntaxin 2 display different expression levels. On the other hand, the expression levels of Munc-18 are associated with the characteristics of the transfected specific syntaxin construct. In any case, the electrophysiological phenotypes cannot be consistently explained by changes in Munc-18.

(4) Mutations in several residues of the outer surface of the C-terminal half of the syntaxin1 SNARE domain lead to alterations in the RRP and the frequency of spontaneous release, but the changes cannot attributed to a change in the net surface charge, because the alterations occur even in paired mutations in which electrical neutrality is conserved.

Comments:

(1) This is a comment regarding the interpretation of the results. In general, the decrease in the RRP size is associated with the increased frequency of spontaneous release due to unclamping. The authors claim that both phenomena seem to be independent of each other. In any case, how can the authors discard the possibility that the unclamping of spontaneous release leads to a decrease in the RRP size?

The main argument against the reduction of the RRP being caused by the observed increase in the mEPSC frequency is based on kinetics of refilling and depletion. The average time a vesicle fuses spontaneously after it becomes primed is 500 – 1000 seconds (spontaneous vesicle release rate – STX1 Figure 1, Figure 2 and Figure 3). The time it takes to refill the RRP after depletion is in the order of 3 seconds (Rosenmund and Stevens, 1996). Therefore, the refilling of the RRP is more than 100 times faster. Even when the spontaneous release would increase 5 fold, this would lead to less than 5 % of the steady state depletion of the RRP.

(2) The authors have analyzed the kinetics of mEPSCs and found differences (Fig2-Supp. Fig1; Fig2-Supp. Fig1). It would be interesting and pertinent to discuss these data in the context of potential phenotypes in the fusion pore kinetics involving syntaxin1 and syntaxin2 and their SNARE domains. Indeed, the figure will improve by including averaged traces of mEPSCs.

We thank the reviewer for the idea. Upon closer examination of the changes in mEPSC rise time and mEPSC decay time we noticed a minor slowing in the mEPSC rise time from 0.443ms (SEM0.0067) of STX1A to 0.535ms (SEM0.0151) for STX1A-2(SNARE) or 0.507ms (SEM0.01251) for STX1A-2(Cter), while the mEPSC half widths did not change significantly. It is possible that the measured change is related to the detection algorithm as mEPSC detection at elevated frequencies becomes more difficult due to increased overlap of event, and we therefore prefer to refrain from making any mechanistic claims.

Minor comments:

(1) Fig2 J; Fig 3 J. It is difficult to distinguish between different colors and implementing a legend within the graph will be very helpful.

(2) Fig3 H. Please change the color of the box plot for Stx1 A to improve the contrast with the individual data points.

(3) Page 6. Line 225. "Figure 2D and E" should be corrected to "Figure 2C and D"

(1) Colors were changed for clearer visualization. (2) Unfortunately, changing the color did not improve the contrast with the individual plots. However, the numerical data is all included in the data sheets of the corresponding figure. (3) The mistake was corrected.

Reviewer #2 (Recommendations For The Authors):

Line 135-136: Are cited numbers cited in the text mean and SEM? Please indicate.

Line 139 and Figure 1G: The difference between purple and blue was very hard to see on my hard copy.

Line 152: Reference to Figure 1L should probably be 1K.

Line 183: Reference to Figure 2C should probably be Figure 2F.

Line 225: Reference to Figure 2D and 2E should probably be 2C and 2D.

Line 239: Reference to Figure 3I should probably be 3H.

All typos were addressed and colors were changed for better visualization.

Line 210-211: Sentence ("One of the benefits..") is hard to understand.

Thank you for noticing this mistake, agreeably the the sentence did not add any important or new information and so it was deleted. Additionally, the message of the mentioned sentence was already clearly stated in lines 209-211.

Figure 4E-H misses data for STX2, for the figure to be arranged like Figure 5.

Given that STX1 is the endogenous syntaxin in hippocampal neurons, we use it at a control for all the analysis done in STX2 and STX2-chimera experimental groups, thus it is included in Figure 3 and 5.

It appears that the authors do not present or discuss the Western Blot in Fig. 4D. Are the quantitative results of the Western Blot consistent with or different from the quantification of the immunostainings (Fig. 4B-C)? A similar question for Figure 5D, which also seems not to be presented.

In terms of quantification, we have relied mainly on the ICC experiments because they test also for putative impairments in transport to the presynaptic compartment. Our WB data are overall consistent with the results, but were not used to quantitate expression of our syntaxin chimeras and mutations in the STX1-null hippocampal neuron model.

Figure 6F-G: The normalization of spontaneous vesicular release rates is not clear, because the vesicular release rates already contain a normalization (mEPSC rate divided by RRP size). Is a further normalization of the STX1A condition informative? The authors should consider presenting the release rates themselves. In any case, the normalization should be presented/explained, at least in the legends.

The reviewer is in principle correct. Due to the large number of experimental groups we had to perform recordings from multiple cultures, where not all experimental groups were present, while the WT STX1 was present as a consistent control. The reduce culture to culture variability, additional normalization to the WT control group was performed. However, we also included the raw data numerical values in the data-source sheets (Normalized and absolute), which produce a similar overall outcome.

References to Figure 7 subpanels (A, B, and C) are missing.

Thank you for the comment. We have integrated all panels into one for better representation and understanding since they are representative of one another.

Lines 330-339 and Figure 7 in Discussion: the authors discuss that adding the non-cognate STX2 SNARE-domain to syntaxin-1 might destabilize the primed state and decrease the fusion energy barrier (as indicated in Figure 7C). What is the evidence that the decrease in RRP size is not caused solely by the depletion of the pool due to the increased spontaneous fusion?

Please see the comments to major point 2 of reviewer 1.

Statistics: Missing is the number of observations (n) for all data. Even if all data points are displayed, this should be stated.

N numbers are included in the data sheets attached to each figure.

The statement (start of Discussion,) that the SNARE-domain of STX1 'plays a minimal role in the regulation for Ca2+-evoked release' is somewhat puzzling, since without the SNARE-domain in STX1 there would be no Ca2+-evoked release. I guess these statements (similar statements are found elsewhere) are due to the interesting finding that STX2 leads to a decrease in release kinetics, compared to STX1, and this is not (entirely) due to differences in the SNARE-domain. I would suggest rephrasing the finding in terms of release kinetics. Also, the statement in the last sentence of the Abstract is not clear.

Thank you for pointing this out and we agree that our experiments showed strong impact of the syntaxin isoform exchange on release kinetics and overall release output. A similar comment came also from reviewer #3 and so, we have addressed both comments as one.

Our confusing statement resulted from the order of the presented results and our summarizing remarks for each section. Our statement reflected our finding that mutating residues in the C-terminal part of the STX1 SNARE motif affected only spontaneous release and RRP size but not release efficacy. We now state (pg. 6 lines 231-233) that the data observed from the comparison of “the results obtained from the Ca2+-evoked release between STX1 and STX2 support major regulatory differences of the domains outside of the SNARE domain between isoforms”.

We have changed the abstract pg. 2 lines 55-56

We have changed the introduction pg. 3 lines 102-105 for a better contextualization.

We have changed the start of the discussion pg. 9 lines 250-252 for better contextualization.

Reviewer #3 (Recommendations For The Authors):

In this manuscript, Salazar-Lázaro et al. presented interesting data that C-terminal half of the Syx1 SNARE domain is responsible for clamping of spontaneous release, stabilizing RRP, and also Ca2+-evoked release. The authors routinely utilized the chimeric approach to replace the SNARE domain of Syx1 with its paralogue Syx2 and analyzed the neuronal activity through electrophysiology. The data are straightforward and fruitful. The conclusions are partly reasonable. One obvious drawback is that they did not explore the underlying mechanism. I think it is easy for the authors to carry out some simple assays to verify their hypothesis for the mechanism, instead of just talking about it in the discussion section. In all, I appreciate the data presented in the manuscript. If the authors could supply more data on the mechanisms, this would be important research in the field. Some critical comments are listed below:

We thank the reviewer for his/her comments and suggestions.

Major comments:

(1) In pg.3, lines 102-104, the authors stated that 'We found that the C-terminal half of the SNARE domain of STX1.. ..while it is minimally involved in the regulation of Ca2+-evoked release.' But in pg.5, lines 174-176, they wrote that 'Replacement of the full-SNARE domain (STX1A-2(SNARE)) or the C-terminal half (STX1A-2(Cter)) of the SNARE domain of STX1A with the same domain from STX2 resulted in a reduction in the EPSC amplitude (Figure 2B).' and in pg.5-6, lines 197-199, they wrote that 'Taken together our results suggest that the C-terminal half of the SNARE domain of STX1A is involved in the regulation of the efficacy of Ca2+-evoked release, the formation of the RRP and in the clamping of spontaneous release.' It puzzles me a lot as to what the authors are really trying to express for the relationship between C-half of the SNARE complex and Ca2+-evoked release (i.e., minimally involved or significantly participate in the process?). Please clarify and reorganize the contexts.

Please see our reply to the last comment of reviewer 2.

(2) Figure 1-figure supplement 1, the authors should analyze Syx1/VGlut1 level additionally. And, if possible, compare the difference between Syx1/VGlut1 and Syx2/VGlut1.

The levels of STX1/VGlut1 and STX2/VGlut1 were analyzed in detail in Figures 4 and 5.

The direct comparison between the expression levels of these two proteins is not possible since affinities of the antibodies to the target proteins are different and can induce potential biases. While this could be overcome by the use of a FLAG-tag to the syntaxin proteins, we have not utilized this approach in this publication. We in addition inferred sufficient and comparable expression of both syntaxins from their ability to rescue some of syntaxin1 loss of function phenotypes.

(3) Figure 2D only analyzed the EPSC half-width, could the author alternatively analyze the rise/decay time? Also, in Figure 3-figure supplement 1, does it refer to the kinetic parameters of Syx2-1A in Figure 3? It is very confused.

We have changed the text accordingly and each parameter is referenced to its corresponding figure for clarity. As for the decay and rise time of STX1 and STX1-chimeras, they are in Figure 2-figure supplement 1A and B.

(4) On pg.4, lines 151-152, 'Finally, no change was observed in the paired-pulse ratio (PPR) between STX1A and STX2 groups (Figure 1L).' does not contain any explanations and comments for this observation in the texts.

The small EPSC amplitudes and altered kinetics on the STX2 constricts (Figure 1 and Figure 3) have made it more difficult to quantitate paired pulse experiments. Therefore, we preferred not to overinterpret these measurements. The findings that the paired pulse data were not significantly different, fit with the vesicular release probability measurements which showed no major changes. We have made our statement on this basis.

(5) On pg.6, lines 235-236, the authors wrote that 'Additionally, we found that only STX2-1A(SNARE) and STX2-1A(Cter) could rescue the RRP to around double of what we measured from STX2 and STX2-1A(Nter) (figure 3F)'. However, in Figure 3F, the authors indicated 'n.s.' (p>0.05) for the differences between STX2 and STX2-1A(SNARE)/STX2-1A(Cter). It is perplexing how the authors interpret their data. Definitely, the p-value could not be arbitrarily used as a criterion of difference. An easier way is that indicating the exact p-values for each comparison (indicate in figure legends or list in tables).

We apologize for any confusion, and hope the modification gives more clarity in our interpretation. The calculated p-values are included in attached data source tables and hope this will provide clarity to our comparative analysis. We have changed the text in pg 7 lines 238-241 and are cautious to overinterpret these results and rely more on the data observed in STX1A-chimeras, which show significant changes in the RRP.

(6) I noticed that the authors preferred using 'xx% increase/decrease' or 'xx-fold increase/decrease' to interpret their inter-group data. I would doubt whether the interpretations are appropriate. First, it seems that most of the individual scatters from one set were not subject to Gaussian distribution; also, the authors utilized non-parameter tests to compare the differences. Second, the authors did not explicitly indicate the method to calculate the % or fold, e.g., by comparing mean value or median. I think it is a bad choice to use the median to calculate fold changes; meanwhile, the mean value would also be biased, given the fact that the data were not Gaussian-distributed. The authors should be cautious in interpreting their data.

We thank the reviewer for pointing the inaccuracy of our descriptions and have included the parameter used to calculated the percentage and fold increase/decrease in the materials and methods section. Specifically, the mean. Our intention is to plainly state the amount of change seen in a parameter based on the observed changes in the mean value. We agree with the reviewer that interpreting this could be problematic if we are speculating possible mechanisms. Further test should be conducted as to state whether similar increase/decrease changes in a parameter are due to the disturbance of the same mechanisms or different. E.g., we discussed whether the regulation of SYT1 might be or not be the mechanism affected in some of the chimeras that show an increase in the spontaneous release rate, for the release rate observed in some is massively higher than that seen in SYT1-KO (Bouazza-Arostegui et al., 2022). It is tempting to speculate that it could be due to other mechanisms based on the differences in the changes. For this reason, we have given an array of possible mechanisms affected when we manipulate the SNARE domain of STX1.

(7) The authors routinely analyzed the levels of Munc18-1 in neuronal lysates by WB and Munc18-1/VGlut1 by immunofluorescence in various Syx1 mutants. However, in my view, these assays were slightly indirect. It is evident that the SNARE domain of Syx1 participates in the binding to Munc18-1 according to the atomic structures (pdb entries: 3C98 and 7UDB). Meanwhile, Han et al. reported that K46E mutation (located in domain 1 of Munc18-1) strongly impairs Syx1 expression, Syx1-interaction, vesicle docking and secretion (Han et al., 2011, PMID: 21900502). Intriguingly, the residue K46 of Munc18-1, which is close to D231/R232 of Syx1, may have potential electrostatic contacts to D231 and R232 of Syx1. This is reminiscent of the possibility that Syx1D231/R232 and some Syx1-2 chimeras lost their normal function through their defective binding to Munc18-1.nmb, To better understand the underlying mechanism, the authors may need to carry out in vivo and/or in vitro binding analysis between syntaxin mutants/chimeras and Munc18-1. They also need to conduct more discussions about the issue.

We express our gratitude for the identification of a previously overlooked aspect in our investigation of the interplay between Munc18-1 and STX1. In response, we have incorporated additional discourse on this matter in pg11 lines 419-431.

Additionally, we appreciate the thoughtful suggestion regarding additional experiments to further explore the molecular relationship between Munc18-1 and STX1. We agree that co-immunoprecipitation experiments (either by using an antibody against Munc18-1 or STX1 and STX2) would offer greater insight into whether the binding of these proteins is affected in the isoform or the mutants. Notably, we performed immunoprecipitation experiments by using neuronal lysates of the corresponding groups and using STX1A and STX2 antibodies for the pull-downs. However, we were unable to co-IP Munc18-1 when doing so. Changing the conditions of the experiment did not yield better results and so these experiments remained inconclusive for the moment. For this reason, we included it as an open question and a potential concluding hypothesis of the molecular mechanism. However, Shi et al., 2021, have performed co-IP assays using Munc18-1-wt and a mutant form which affects the binding to the C-terminal half of the SNARE domain of STX, and STX1-wt and a STX mutants targeting some of our residues of interest and showed a decrease in the pulled-down levels of Munc18-1 using HeLa cells. We have made sure to mention the conclusion of this important publication in our discussion.

(8) The third possible mechanism (i.e., interaction with Syt1) proposed by the authors seems more reasonable. However, the discussions raised by the authors were not enough. For instance, plenty of literature has indicated that Syt1 may participate in synaptic vesicle priming through stabilizing partially or fully assembled SNARE complex (Li et al., 2017, PMID: 28860966; Bacaj et al., 2015, PMID: 26437117; Mohrmann et al., 2013, PMID: 24005294; Wang et al., 2011; PMID: 22184197; Liu et al., 2009, PMID: 19515907); complexins are also SNARE binding modules that regulate synaptic exocytosis. Lack of complexins could lead to unclasping of spontaneous fusion of synaptic vesicles, though it causes severe Ca2+-triggered release at the same time (Maximov et al., 2009, PMID: 19164751). Meanwhile, different domains of complexin may accomplish different steps of SV fusion, early research had indicated that the C-terminal sequence of complexin is selectively required for clamping of spontaneous fusion and priming but not for Ca2+-triggered release (Kaeser-Woo et al., 2012, PMID: 22357870). Likewise, if possible, the authors may need to carry out in vivo and/or in vitro binding analysis to confirm their hypothesis.

The exploration of complexin´s involvement was limited in our study primarily due to our methodological focus on comprehending molecular mechanisms concerning the sequence disparities between STX1 and STX2. Our laboratory has studied the role of Complexin extensively, and we certainly have had a possible involvement in mind. However, since the sites identified on syntaxin are either conserved between STX1 and STX2 or not close to the central or accessory helical domains of complexin, we did not perform experiments to test putative interactions, and we refrained from discussing complexin in this paper.

(9) Lastly, I would suspect that whether the defects of Syx2 and Syx1 chimeras were caused by the SNARE complex itself, from another point of view that is different from the hypothesis raised by the authors. Changing the outward residues (or we say the solvent-accessible residues) of the SNARE complex may affect the stability, assembly kinetics, and energetics (Wang and Ma, 2022, PMID: 35810329; Zorman et al., 2014, PMID: 25180101), especially for the C-terminal halves. Is this another possible mechanism through which the C-terminus of Syx1 might contribute to SV priming and clamping of spontaneous release? The authors should at least conduct some discussions about the point.

Thank you for this suggestion. We indeed assumed that since the hydrophobic layers of the SNARE domains that form the hydrophobic pocket of STX2 and STX1 are mainly conserved, that the intrinsic stability of the SNARE complex is largely unchanged. Additionally, Li et al., (2022) PMID: 35810329 examined the stability of the alfa-helix structure of the SNARE domain of SNAP25. And while they found no changes in the stability and formation of the alfa-helix when mutating outwards-facing residues for methodological purposes (bimane-tryptophan quenching), their study did not selectively explore the effect of mutations of outer-surface residues on the stability of the alfa-helix.

Zorman et al., (2014) PMID: 25180101, as noted by the reviewer, observed that changes in the sequence of the SNARE domain (by using SNARE proteins from different trafficking systems (neuron, GLUT4, yeast…) correlated with changes in the step-wise SNARE complex assembly. However, they also did not selectively mutate the outer solvent-accessible residues, hindering conclusive speculations in the contribution of said residues on the kinetics and energetics of assembly and intrinsic stability of the SNARE complex.

Upon petition of the reviewer, we have added this paragraph to discuss an additional mechanism:

“As a final remark, it is possible that the changes in the spontaneous release rate and the priming stability may stem from a reduced stability of the SNARE complex itself through putative interactions between outer surface residues. Studies of the kinetics of assembly of the SNARE complex which mutate solvent-accessible residues in the C-terminal half of the SNARE domain of SYB2 have shown reduction in the stability of the SNARE complex assembly and are correlated with impaired fusion (Jiao et al., 2018). However, STX1 mutations of outward residues were inconclusive and were always accompanied by hydrophobic layer mutations (Jiao et al., 2018), which affect the assembly kinetics and energetics of the SNARE complex (Ma et al., 2015). Single molecule optical-tweezer studies have focused on the impact of regulatory molecules on the stability of assembly such as Munc18-1 (Ma et al., 2015; Jiao et al., 2018) and complexin (Hao et al., 2023), or on the intrinsic stability of the hydrophobic layers in the step-wise assembly of the SNARE complex (Gao et al., 2012; Ma et al., 2015; Zhang et al., 2017). Although the conserved hydrophobic layers in the SNARE domains of STX1A and STX2 (Figure 1) suggest unchanged zippering and intrinsic stability of the complex, further studies addressing the contribution of surface residues on the stability of the alfa-helix structure of the SNARE domain of STX1 (Li et al., 2022) or the stability of the SNARE complex should be conducted.”

Minor comments:

(1) In pg.6, line 236, 'figure 3F', the initial 'f' should be uppercased.

(3) On pg.11, line 396, the section title 'The interaction of the C-terminus of de SNARE domain of STX1A with Munc18-1 in the stabilization of the primed pool of vesicles.' The word 'de' is confusing, please check.

(4) In pg.12, line 446, the section title, should 'though' be 'through'?

These comments have been acknowledged and changed. Thank you

(2) In pg.7, line 239, '..had an increased PVR (Figure 3G), no change in the release rate (Figure 3I)', should Figure 3I be Figure 3H? and line 240, 'and an increase in short-term depression during 10Hz train stimulation (Figure 3I)', should Figure 3I be Figure 3J? If so, Figure 3I will not be cited in the texts and lack adequate interpretations. Please check.

We apologize for the oversight in not referencing this specific subpanel of the figure and have incorporated the reference in the text. Additionally, our interpretation of this data is connected to the mechanisms that govern efficacy of Ca2+-evoked response, and its dependence on the integrity of the entire-SNARE domain. We wish to highlight the modifications made to the discussion on the regulation of the Ca2+-evoked response based on previous reviewer comment #1, and a similar comment from reviewer #2 (as stated previously).

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

We would like to thank the editors and reviewers who took their valuable time to evaluate the manuscript from various perspectives. We are delighted that our technique was found appealing to biologists and imaging technologists. However, we received several comments that the principles and effectiveness of our techniques are often vague and difficult to understand. They also pointed out that the explanations and representations for several figures were not appropriate. We will revise the manuscript to address these issues and make the manuscript more clear and rigorous.

Author Response

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

Reviewer 1

Comment 1.1: “Did the UKB or HCHS datasets have information on accurate markers of insulin resistance, such as HbA1c or HOMA-IR (if fasting glucose was not available)? Looking at that data would allow us to determine the contribution of insulin resistance to the observed cortical phenotype.”

Reply 1.1: We appreciate the insightful suggestion from the reviewer. In response, we incorporated the HbA1c into our analysis, enhancing its sensitivity to potential effects of insulin resistance. Subsequently, our analysis was reperformed, integrating HbA1c alongside non-fasting blood glucose in the PLS. This addition did not alter our main results, i.e., that of the PLS, virtual histology, and network contextualization analysis. Notably, as a result of the inclusion of HbA1c, the second latent variable now accounted for a greater shared variance (22.13%), with HbA1c showing the highest loading among MetS component variables. The manuscript has been thoroughly revised to incorporate these results.

Comments 1.2: “(Results, p.13, 291-292) "A correlation matrix relating all considered MetS component measures is displayed in supplementary figure S12. Please clarify in this figure labels whether this was non-fasting glucose. If this is non-fasting glucose, it is not a MetS-related risk factor. The reader might be misled into thinking that fasting-glucose has a weak correlation, while its contribution (and the effect of insulin resistance) was not studied here.”

“Table S8 and Table S9: Is the glucose metric here measured following fasting? If not, this should not be listed as a metabolic syndrome criterion. Or it should be specified that it isn't fasted glucose, otherwise, it sounds misleading.”

Reply 1.2: We thank the reviewer for bringing this ambiguity to our attention. The initial analysis included only non-fasting plasma glucose in the PLS, as fasting plasma glucose data was unavailable for UKB and HCHS participants. Following your suggestion in reply 1.1, we have now incorporated HbA1c, a more indicative marker of insulin resistance. We retained non-fasting blood glucose in our analysis, recognizing its relevance as a diagnostic variable for type 2 diabetes mellitus, although it is less informative than fasting plasma glucose, HbA1c, or HOMA-IR. This decision is substantiated by the significant correlation found between non-fasting plasma glucose and HbA1c in our sample (r=.49).

To enhance clarity, we have revised the methods section to explicitly mention that the study investigates non-fasting blood glucose. The revised sentence reads: “Here, we related regional cortical thickness and subcortical volumes to clinical measurements of MetS components, i.e., obesity (waist circumference, hip circumference, waist-hip ratio, body mass index), arterial hypertension (systolic blood pressure, diastolic blood pressure), dyslipidemia (high density lipoprotein, low density lipoprotein, total cholesterol, triglycerides) and insulin resistance (HbA1c, non-fasting blood glucose).”

Additionally, we have updated the caption of supplementary figure S13 (formerly supplementary figure S12) to clearly indicate the investigation of non-fasting plasma glucose. The table detailing diagnostic MetS criteria (supplementary table S2) has also been amended to clarify the absence of fasting plasma glucose data in our study and to indicate that only data on antidiabetic therapy and diagnosis of type 2 diabetes mellitus were used as criteria for insulin resistance in the case-control analysis.

Comment 1.3: “I do not understand how the authors can claim there is a deterministic relationship there if all the results are only correlational or comparative. Can the differences in functional connectivity and white matter fiber tracts observed not be caused by the changes in cortices they relate to? How can the authors be sure the network organisation is shaping the cortical effects and not the opposite (the cortical changes influence the network organisation)? This should be further discussed or explained.”

Reply 1.3: We agree with the reviewer's comment on the non-causative nature of our data and have accordingly revised the discussion section to reflect a more cautious interpretation of our findings. We have carefully reframed our language to avoid any implications of causality, ensuring the narrative aligns with the correlational nature of our data. Nevertheless, we believe that exploring causal interpretations can offer valuable clinical insights. Therefore, while moderating our language, we have maintained certain speculative discussions regarding potential causative pathomechanistic pathways.

Comment 1.4: “The hippocampus is also an area where changes have consistently been observed. Why did the authors limit their analysis to the cortex.”

Reply 1.4: We appreciate this reviewer comment. In response, we have added volumes of Melbourne Subcortical Atlas parcels (including the hippocampus) to the analysis. Corresponding results are now shown in figure 2. The subcortical bootstrap ratios indicated that higher MetS severity was related to lower volumes across all investigated subcortical structures.

Comment 1.5: “Which field ID of the UK biobank are the measures referring to? If possible, please specify the Field ID for each of the UKB metrics used in the study.”

Reply 1.5: We thank the reviewer for the recommendation. The Field IDs used in our study are now listed in supplementary figure S1.

Comment 1.6: “Several Figures were wrongly annotated, making it hard to follow the text.”

Reply 1.6: Thank you for bringing the annotation issues to our awareness. We have thoroughly edited all annotations which should now correctly reference the figure content.

Reviewer 2

Comment 2.1: “Do the authors have the chance to see how the pattern relates to changes in cognitive function in the UKBB and possibly HCHS? This could help to provide some evidence about the directionality of the effect.” Reply 2.1: Thank you for your suggestion. We acknowledge the potential value of investigating gray matter morphometric data alongside longitudinal information on cognitive function. Although we concur with the significance of this approach, we are constrained by the ongoing processing of the UKB's imaging follow-up data and the pending release of the HCHS follow-up data. Consequently, our current analysis cannot incorporate this aspect for now. We plan to explore the relationship between MetS, cognition and brain morphology using longitudinal data as soon as it becomes available.

Comment 2.2: “Also, you could project new data onto the component and establish a link with cognition in a third sample which would be even more convincing. I can offer LIFE-Adult study for this aim.”

Reply 2.2: We are grateful for your recommendation to enhance our study's robustness by including a third sample to establish a cognitive link. While we recognize the merit of such a sensitivity analysis, we believe that our current dataset, derived from two large, independent cohorts, is sufficiently comprehensive for the scope of our current analysis. However, we are open to considering this approach in future studies and appreciate your offer of the LIFE-Adult study. We would welcome further conversation with you regarding future joint projects.

Comment 2.3: “The sentences (p.17, ll.435 ff) seem to repeat: "Interestingly, we also observed a positive relationship between cortical thickness and MetS in the superior frontal, parietal and occipital lobe. Interpretation of this result is, however, less intuitive. We also noted a positive MetS-cortical thickness association in superior frontal, parietal and occipital lobes, a less intuitive finding that has been previously reported [60,61].”

Reply 2.3: Thank you for making us aware of this duplication. We have deleted the first part of the section. It now reads “We also noted a positive MetS-cortical thickness association in superior frontal, parietal and occipital lobes, a less intuitive finding that has been previously reported.”

Comment 2.4: “I would highly appreciate empirical evidence for the claim in ll. 442 "In support of this hypothesis, the determined cortical thickness abnormality pattern is consistent with the atrophy pattern found in vascular mild cognitive impairment and vascular dementia" Considering the previous reports about the co-localization of obesity-associated atrophy and AD neurodegeneration (Morys et al. 2023, DOI: 10.3233/JAD-220535), that most dementias are mixed and that MetS probably increases dementia risk through both AD and vascular mechanisms, I feel such "binary" claims on VaD/AD-related atrophy patterns should be backed up empirically.”

Reply 2.4: Thank you for highlighting the need for clarity in differentiating between vascular and Alzheimer's dementia. We recognize the intricate overlap in dementia pathologies. Acknowledging the prevalence of mixed dementia and the influence of MetS on both AD and vascular mechanisms, we realize our original statement might have implied a specificity to vascular dementia, which was not intended.

To address your concern, we have revised our statement to avoid an exclusive focus on vascular pathology, ensuring a more balanced representation of dementia types. Additionally, we have included Morys et al. 2023 as a reference. The section now reads: “In support of this hypothesis, the determined brain morphological abnormality pattern is consistent with the atrophy pattern found in vascular mild cognitive impairment, vascular dementia and Alzheimer’s dementia.”

Comment 2.5: “I wonder how specific the cell-type results are to this covariance pattern. Maybe patterns of CT (independent of MetS) show similar associations with one or more of the reported celltypes? Would it be possible to additionally show the association of the first three components of general cortical thickness variation with the cell type densities?”

Reply 2.5: Thank you for your query regarding the specificity of the cell-type results to the observed covariance pattern. To address this, we have conducted a virtual histology analysis of the first three latent variables of the main analysis PLS. The findings of this extended analysis have been detailed in the supplementary Figure S21. The imaging covariance profile of latent variable 2 was significantly associated with the density of excitatory neurons of subtype 3. The imaging covariance profile linked to latent variable 3 showed no significant association of cell type densities. Possibly, latent variable 3 represents only a noise component as it explained only 2.12% of shared variance. We hope this addition provides a clearer understanding of the specificity of our main results.

Comment 2.6: “I agree that this multivariate approach can contribute to a more holistic understanding, yet I would like to see the discussion expanded on how to move on from here. Should we target the MetS more comprehensively or would it be best to focus on obesity (being the strongest contributor and risk factor for other "downstream" conditions such as T2DM)? A holistic approach is somewhat at odds with the in-depth investigation of specific mechanisms.”

Reply 2.6: We value your suggestion to elaborate on the implications of our findings. Our study indicates that obesity may have the most pronounced impact on brain morphology among MetS components, suggesting it as a key contributor to the clinical-anatomical covariance pattern observed in our analysis. This highlights obesity as a primary target for future research and preventive strategies. However, we believe that our results warrant further validation, ideally through longitudinal studies, before drawing definitive clinical conclusions.

Additionally, our study endorses a comprehensive approach to MetS, highlighting the importance of considering the syndrome as a whole to gain broader insights. We want to clarify, however, that such an approach is meant to complement, rather than replace, the study of individual cardiometabolic risk factors. The broad perspective our study adopts is facilitated by its epidemiological nature, which may not be as applicable in experimental settings that are vital for deriving mechanistic disease insights.

To reflect these points, we have expanded the discussion in our manuscript to include a more detailed consideration of these implications and future research directions.

Comment 2.7: “Please report the number of missing variables.”

Reply 2.7: Thank you for your request to report the number of missing variables. We would like to direct your attention to table 1, where we have listed the number of available values for each variable in parentheses. To determine the number of missing variables, one can subtract these numbers from the total sample size.

Comment 2.8: “Was the pattern similar in pre-clinical (pre-diabetes, pre-hypertension) vs. clinical conditions?“

Reply 2.8: Thank you for your interest in the applicability of our findings across different MetS severity levels. Our analysis employs a continuous framework to encompass the entire range of vascular and cardiometabolic risks, including those only mildly affected by MetS. The linear relationship we observed between MetS severity and gray matter morphology patterns, as illustrated in Figure 2d, supports the interpretation that our findings apply to the entire spectrum of MetS severities.

Comment 2.9: “How did you deal with medication (anti-hypertensive, anti-diabetic, statins..)?”

Reply 2.9: Information on medication was considered for defining MetS for the case-control sensitivity analysis but was not included in the PLS. Detailed information can be found in table 1.

Comment 2.10: “It would be really interesting to determine the genetic variations associated with the latent component. Have you considered doing a GWAS on this, potentially in the CHARGE consortium or with UKBB as discovery and HCHS as replication sample?”

Reply 2.10: Thank you for your valuable suggestion regarding the implementation of a GWAS. We agree that incorporating a GWAS would provide significant insights, but we also recognize that it extends beyond the scope of our current analysis. However, we are actively planning a follow-up analysis. This subsequent analysis will encompass a comprehensive examination of both genetic variation and imaging findings in the context of MetS.

Comment 2.11: “Please provide more information on which data fields from UKBB were used exactly (e.g. in github repository).”

Reply 2.11: We appreciate your recommendation. The details regarding the Field IDs used in our study have been included as supplementary table S1.

Reviewer 3

Comments 3.1: “After a thorough review of the methods and results sections, I found no direct or strong evidence supporting the authors' claim that the identified latent variables were related to more severe MetS to worse cognitive performance. While a sub-group comparison was conducted, it did not adequately account for confounding factors such as educational level.”

“Page 18-19 lines 431-446: the fifth paragraph in the discussion section. - As previously mentioned in the "Weaknesses" section, this study did not conduct a direct association analysis between MetS and cognitive levels without considering subgroup comparisons. Hence, I recommend the content of this paragraph warrants careful reconsideration.”

Reply 3.1: We acknowledge the reviewer's constructive feedback regarding our analysis of cognitive data. We have performed a mediation analysis relating the subject-specific clinical PLS score of latent variable 1 representing MetS severity and cognitive test performances and testing for mediating effects of the imaging PLS score capturing the MetS-related brain morphological abnormalities. The imaging score was found to statistically mediate the relationship between the clinical PLS score and executive function and processing speed, memory, and reasoning test performance. These findings highlight brain structural differences as a relevant pathomechanistic correlate in the relationship of MetS and cognition. Corresponding information can now be found in figure 3, methods section 2.6.2, result section 3.3 and discussion section 4.2.

Moreover, we would like to apologize for any confusion caused by previous unclear presentation. Our study further incorporates association analyses between MetS, brain structure, and cognition using MetS components, regional brain morphological measures, and cognitive performance data in a PLS to investigate whether cognitive measures contribute to the latent variable. These analyses were separately performed on the UK Biobank and HCHS datasets, due to their distinct cognitive assessments. We adjusted for age, sex, and education in the subgroup analyses by removing their effects from the input variables. These relationships are detailed in supplementary figures S16b and S17b, with loadings close to zero for age, sex, and education, confirming effective deconfounding.

In sum, we greatly appreciate the suggestion to conduct a mediation analysis, which has substantially enhanced the strength and relevance of our analysis.

Comment 3.2: “I would suggest the authors provide a more comprehensive description of the metrics used to assess each MetS component, such as obesity (incorporating parameters like waist circumference, hip circumference, waist-hip ratio, and body mass index) and arterial hypertension (detailing metrics like systolic and diastolic blood pressure), etc.”

Reply 3.2: Thank you for your suggestion regarding a more detailed description of the metrics for assessing each component of MetS. We would like to point out that the specific metrics used, including those for obesity (such as waist circumference, hip circumference, waist-hip ratio, and body mass index) and arterial hypertension (including systolic and diastolic blood pressure), are comprehensively detailed in table 1 of our manuscript. We hope this table provides the clarity and specificity you are seeking regarding the MetS assessment metrics in our study.

Comment 3.3: “I recommend the inclusion of an additional, detailed flowchart to further illustrate the procedure of virtual histology analysis. This would enhance the clarity of the methodological approach and assist readers in better comprehending the analysis method.”

Reply 3.3: Thank you for your suggestion. Recognizing the challenges in visually representing many of our analysis steps, we have instead supplemented our manuscript with additional references. These references provide a clearer understanding of our virtual histology approach, particularly focusing on the processing of regional microarray expression data.

The corresponding sentence reads: “Further details on the processing steps covered by ABAnnotate can be found elsewhere (https://osf.io/gcxun) [42]”

Comment 3.4: “Why were both brain hemispheres used instead of solely utilizing the left hemisphere as the atlas, especially considering that the Allen Human Brain Atlas (AHBA) only includes gene data for the right hemisphere for two subjects?”

Reply 3.4: Thank you for your query regarding our decision to use both brain hemispheres instead of solely the left hemisphere, especially considering the Allen Human Brain Atlas (AHBA) predominantly featuring gene data from the left hemisphere. Given the AHBA's limited spatial coverage of expression data in the right hemisphere, our approach involved mirroring the existing tissue samples across the left-right hemisphere boundary using the abagen toolbox,1 a practice supported by findings that suggest minimal lateralization of microarray expression.2,3 Further details are provided in previous work employing ABAnnotate.4 These studies are now referenced in our methods section.

Comment 3.5: “The second latent variable was not further discussed. If this result is deemed significant, it warrants a more detailed discussion. "

Reply 3.5: Thank you for the suggestion. We have added a paragraph to the discussion that discusses the second latent variable in greater detail. It reads: “The second latent variable accounted for 22.33% of shared variance and linked higher insulin resistance and lower dyslipidemia to lower thickness and volume in lateral frontal, posterior temporal, parietal and occipital regions. The distinct covariance profile of this latent variable, compared to the first, likely indicates a separate pathomechanistic connection between MetS components and brain morphology. Given that HbA1c and blood glucose were the most significant contributors to this variable, insulin resistance might drive the observed clinicalanatomical relationship.”

Comment 3.6: “I suggest appending positive MetS effects after "..., insular, cingulate and temporal cortices;" for two reasons: a). The "positive MetS effects" might represent crucial findings that should not be omitted. b). Including both negative and positive effects ensures that subsequent references to "this pattern" are more precise.”

Reply 3.6: We concur with the notion that the positive MetS effects should be highlighted as well. We modified the first discussion paragraph now mentioning them.

Comment 3.7: “I would appreciate further clarification on this sentence and the use of the term "uniform" in this context. Does this suggest that despite the heterogeneity in the physiological and pathological characteristics of the various MetS components (e.g., obesity, hypertension), their impacts on cortical thickness manifest similarly? How is it that these diverse components lead to "uniform" effects on cortical thickness? Does this observation align with or deviate from previous findings in the literature?”

Reply 3.7: Thank you for highlighting the ambiguity in our previous explanation. We agree that the complexity of the relationship between MetS components and brain morphology requires clearer articulation. To address this, we have revised the relevant sentence for better clarity. It now reads: „This finding indicates a relatively uniform connection between MetS and brain morphology, implying that the associative effects of various MetS components on brain structure are comparatively similar, despite the distinct pathomechanisms each component entails.“

Comment 3.8: “Figure 1 does not have the labels "c)" and "d)". ”

Reply 3.8: Thank you. We have modified figure 1 and made sure that the caption correctly references its content.

Comment 3.10: “Incorrect figure/table citation:

• Page 18 line 418: "(figure 2b and 1c)" à (figure 2b and 2c).

• Page 18 line 419: "(supplementary figures S8 and S12-13)" à (supplementary figures S11 and S1516).

• In the supplementary material, "Text S5 - Case-control analysis" section contains several figure or table citation errors. Please take a moment to review and correct them.”

Reply 3.10: Thank you for bringing this to our attention. We have corrected the figure and table citation errors.

Comment 3.11: “Page 8 line 184: The more commonly used term is "insulin resistance" rather than "insuline resistance.”

Reply 3.11: We now use “insulin resistance” throughout the manuscript.

Comment 3.12: “Nevertheless, variations in gene sets may introduce a degree of heterogeneity in the results (Seidlitz, et al., 2020; Martins et al., 2021). Consequently, further validation or exploratory analyses utilizing different gene sets can yield more compelling results and conclusions.”

Reply 3.12: Thank you for your insightful comment regarding the potential heterogeneity introduced by variations in gene sets. We agree that exploring different gene sets could indeed enhance the robustness and generalizability of our findings. However, we think conducting a comprehensive methodological analysis of the available cell-type specific gene sets is a substantial effort and warrants its own investigation to thoroughly implement it and assess its implications. We also like to highlight that we are adhering to previous practices in our analysis setup.4,5

References

(1) Markello RD, Arnatkeviciute A, Poline JB, Fulcher BD, Fornito A, Misic B. Standardizing workflows in imaging transcriptomics with the abagen toolbox. Jbabdi S, Makin TR, Jbabdi S, Burt J, Hawrylycz MJ, eds. eLife. 2021;10:e72129. doi:10.7554/eLife.72129

(2) Hawrylycz MJ, Lein ES, Guillozet-Bongaarts AL, et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature. 2012;489(7416):391-399. doi:10.1038/nature11405

(3) Hawrylycz M, Miller JA, Menon V, et al. Canonical genetic signatures of the adult human brain. Nat Neurosci. 2015;18(12):1832-1844. doi:10.1038/nn.4171

(4) Lotter LD, Saberi A, Hansen JY, et al. Human cortex development is shaped by molecular and cellular brain systems. Published online May 5, 2023:2023.05.05.539537. doi:10.1101/2023.05.05.539537

(5) Lotter LD, Kohl SH, Gerloff C, et al. Revealing the neurobiology underlying interpersonal neural synchronization with multimodal data fusion. Neuroscience & Biobehavioral Reviews. 2023;146:105042. doi:10.1016/j.neubiorev.2023.105042

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.

Author Response

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

eLife assessment

This study presents a useful characterization of the biochemical consequences of a disease-associated point mutation in a nonmuscle actin. The study uses solid and well-characterized in vitro assays to explore function. In some cases the statistical analyses are inadequate and several important in vitro assays are not employed.

Public Reviews:

Reviewer #1 (Public Review):

Strengths:

The authors first perform several important controls to show that the expressed mutant actin is properly folded, and then show that the Arp2/3 complex behaves similarly with WT and mutant actin via a TIRF microscopy assay as well as a bulk pyrene-actin assay. A TIRF assay showed a small but significant reduction in the rate of elongation of the mutant actin suggesting only a mild polymerization defect.

Based on in silico analysis of the close location of the actin point mutation and bound cofilin, cofilin was chosen for further investigation. Faster de novo nucleation by cofilin was observed with mutant actin. In contrast, the mutant actin was more slowly severed. Both effects favor the retention of filamentous mutant actin. In solution, the effect of cofilin concentration and pH was assessed for both WT and mutant actin filaments, with a more limited repertoire of conditions in a TIRF assay that directly showed slower severing of mutant actin.

Lastly, the mutated residue in actin is predicted to interact with the cardiomyopathy loop in myosin and thus a standard in vitro motility assay with immobilized motors was used to show that non-muscle myosin 2A moved mutant actin more slowly, explained in part by a reduced affinity for the filament deduced from transient kinetic assays. By the same motility assay, myosin 5A also showed impaired interaction with the mutant filaments.

The Discussion is interesting and concludes that the mutant actin will co-exist with WT actin in filaments, and will contribute to altered actin dynamics and poor interaction with relevant myosin motors in the cellular context. While not an exhaustive list of possible defects, this is a solid start to understanding how this mutation might trigger a disease phenotype.

We thank the reviewer for the positive evaluation of our work.

Weaknesses:

• Potential assembly defects of the mutant actin could be more thoroughly investigated if the same experiment shown in Fig. 2 was repeated as a function of actin concentration, which would allow the rate of disassembly and the critical concentration to also be determined.

The polymerization rate of individual filaments observed in TIRFM experiments showed only minor changes, as did the bulk-polymerization rate of 2 µM actin in pyrene-actin based experiments. Therefore, we decided not to perform additional pyrene-actin based experiments, in which we titrate the actin concentration, as we expect only very small changes to the critical concentration. Instead, we focused on the disturbed interaction with ABPs, as we assume these defects to be more relevant in an in vivo context. Using pyrene-based bulkexperiments, we did determine the rate of dilution-induced depolymerization of mutant filaments and compare them with the values determined for wt (Figure 5A, Table 1).

• The more direct TIRF assay for cofilin severing was only performed at high cofilin concentration (100 nM). Lower concentrations of cofilin would also be informative, as well as directly examining by the TIRF assay the effect of cofilin on filaments composed of a 50:50 mixture of WT:mutant actin, the more relevant case for the cell.

The TIRF assay for cofilin severing was performed initially over the cofilin concentration range from 20 to 250 nM. The results obtained in the presence of 100 nM cofilin allow a particularly informative depiction of the differences observed with mutant and WT actin. This applies to the image series showing the changes in filament length, cofilin clusters, and filament number as well as to the graphs showing time dependent changes in the number of filaments and total actin fluorescence. We have not included the results for a 50:50 mixture of WT:mutant actin because its attenuating effect is documented in several other experiments in the manuscript.

• The more appropriate assay to determine the effect of the actin point mutation on class 5 myosin would be the inverted assay where myosin walks along single actin filaments adhered to a coverslip. This would allow an evaluation of class 5 myosin processivity on WT versus mutant actin that more closely reflects how Myo5 acts in cells, instead of the ensemble assay used appropriately for myosin 2.

Our results with Myo5A show a less productive interaction with mutant actin filaments as indicated by a 1.7-fold reduction in the average sliding velocity and an increase in the optimal Myo5A-HMM surface density from 770 to 3100 molecules per µm2. These results indicate a reduction in binding affinity and coupling efficiency, with a likely impact on processivity. We expect only a small incremental gain in knowledge about the extent of changes by performing additional experiments with an inverted assay geometry, given that under physiological conditions the motor properties of Myo5A and other cytoskeletal myosins are modulated by other factors such as the presence of tropomyosin isoforms and other actin binding proteins.

Reviewer #2 (Public Review):

Greve et al. investigated the effects of a disease-associated gamma-actin mutation (E334Q) on actin filament polymerization, association of selected actin-binding proteins, and myosin activity. Recombinant wildtype and mutant proteins expressed in sf9 cells were found to be folded and stable, and the presence of the mutation altered a number of activities. Given the location of the mutation, it is not surprising that there are changes in polymerization and interactions with actin binding proteins. Nevertheless, it is important to quantify the effects of the mutation to better understand disease etiology.

We thank the reviewer for the positive evaluation of our work.

Some weaknesses were identified in the paper as discussed below.

• Throughout the paper, the authors report average values and the standard-error-of-the-mean (SEM) for groups of three experiments. Reporting the SEM is not appropriate or useful for so few points, as it does not reflect the distribution of the data points. When only three points are available, it would be better to just show the three different points. Otherwise, plot the average and the range of the three points.

We have gone through the manuscript carefully to correct any errors in the statistics, as explained below.

Figure 1B, 5B, 5C, 5D, 8D, 9B, and 8 – figure supplement 2 all show the mean ± SD, as also correctly reported for Figure 8E and 8F in the figure legend. The statement, that these figures show the mean ± SEM was inaccurate. We corrected this mistake for all the listed figures. Furthermore, we now give the exact N for every experiment in the figure legend.

Figure 2C, 2E, 2F, 4B, 5A, 6B-E showed the mean ± SEM. As suggested by the reviewer, we corrected the figures to show the mean ± SD.

We still refer to the mean ± SEM in Figure 2B, where elongation rates for more than 100 filaments were recorded, and in Figure 8B, where sliding velocities for several thousand actin filaments were measured.

• The description and characterization of the recombinant actin is incomplete. Please show gels of purified proteins. This is especially important with this preparation since the chymotrypsin step could result in internally cleaved proteins and altered properties, as shown by Ceron et al (2022). The authors should also comment on N-terminal acetylation of actin.

We added an additional figure showing the purification strategy for the recombinant cytoskeletal γ –actin WT and p.E334Q protein with exemplary SDS-gels from different stages of purification (Figure 1 – figure supplement 1).

In a previous paper, we reported the mass spectrometric analysis of the post-translational modifications of recombinant human β- and γ-cytoskeletal actin produced in Sf-9 cells. (Müller et al., 2013, Plos One). Recombinant actin showing complete N-terminal processing resulting in cleavage of the initial methionine and acetylation of the following aspartate (β-actin) or glutamate (γ-actin) is the predominant species in the analyzed preparations (> 95 %). While the recombinant actin in the 2013 study was produced tag-free and purified by affinity chromatography using the column-immobilized actin-binding domain of gelsolin (G4-G6), we have no reason to assume that the purification strategy using the actin-thymosin-β4 changes the efficiency of the N-terminal processing in Sf-9 cells. This is supported by our, yet unpublished, mass-spectrometric studies on recombinant human α-cardiac actin purified using the actin- thymosin-β4 fusion construct, which revealed actin species with an acetylated aspartate-3. This N-terminal modification of α-cardiac actin is catalyzed by the same actinspecific acetyltransferase (NAA80) as the acetylation of asparate-2 or glutamate-2 in cytoskeletal actin isoforms (Varland et al., 2019, Trends in Biochemical Sciences). Furthermore, additional studies that used the actin-thymosin-β4 fusion construct for the production of recombinant human cytoskeletal actin isoforms in Pichia pastoris reported robust N-terminal acetylation, when the actin was co-produced with NAA80 (In contrast to Sf-9 cells, NAA80 is not endogenously expressed in Pichia pastoris) (Hatano et al., 2020, Journal of Cell Science).

We therefore, added the following statement to the manuscript:

“Purification of the fusion protein by immobilized metal affinity chromatography, followed by chymotrypsin–mediated cleavage of C–terminal linker and tag sequences, results in homogeneous protein without non–native residues and native N-terminal processing, which includes cleavage of the initial methionine and acetylation of the following glutamate. “

• The authors do not use the best technique to assess actin polymerization parameters. Although the TIRF assay is excellent for some measurements, it is not as good as the standard pyrene-actin assays that provide critical concentration, nucleation, and polymerization parameters. The authors use pyrene-actin in other parts of the paper, so it is not clear why they don't do the assays that are the standard in the actin field.

The polymerization rate of individual filaments observed in TIRFM experiments showed only minor changes, as did the bulk-polymerization rate of 2 µM actin in pyrene-actin based experiments. Therefore, we decided not to perform additional pyrene-actin based experiments, in which we titrate the actin concentration, as we expect only very small changes to the critical concentration. Instead, we focused on the disturbed interaction with ABPs, as we assume these defects to be more relevant in an in vivo context. Using pyrene-based bulkexperiments, we did determine the rate of dilution-induced depolymerization of mutant filaments and compare them with the values determined for WT (Figure 5A, Table 1).

• The authors' data suggest that, while the binding of cofilin-1 to both the WT and mutant actins remains similar, the major defect of the E334Q actin is that it is not as readily severed/disassembled by cofilin. What is missing is a direct measurement of the severing rate (number of breaks per second) as measured in TIRF.

The severing rate as measured in TIRF is dependent on a number of parameters in a nonlinear manner. Therefore, we opted to show the combination of images directly showing the progress of the reaction and graphs summarizing the concomitant changes in cofilin clusters, actin filaments, actin-related fluorescence intensity and cofilin-related fluorescence intensity.

• Figure 4 shows that the E334Q mutation increases rather than decreases the number of filaments that spontaneously assemble in the TIRF assay, but it is unclear how reduced severing would lead to increased filament numbers, rather, the opposite would be expected. A more straightforward approach would be to perform experiments where severing leads to more nuclei and therefore enhances the net bulk assembly rate.

Figure 4 shows polymerization experiments that were started from ATP-G-actin in the presence of cofilin-1. These experiments show clearly that, especially at the higher cofilin-1 concentration (100 nM), the filament number is strongly increased in experiments performed with mutant actin. Inspection of the corresponding videos of these TIRFM experiments suggest that the increased number of filaments must result from an increased number of de novo nucleation events and not primarily from a mutation-induced change in severing susceptibility. The observation of a cofilin-stimulated increase in the de novo nucleation efficiency of actin was initially described by Andrianantoandro & Pollard (2006, Molecular Cell) using TIRFMbased experiments and is thought to arise from the stabilization of thermodynamically unfavorable actin dimers and trimers by cofilin. While the exact role of this cofilin-mediated effect in vivo is not completely clear, it is thought to contribute to cofilin-meditated actin dynamics synergistically with cofilin-mediated severing. It is therefore necessary, to clearly distinguish between the two effects of cofilin in vitro: stimulation of de novo nucleation and stimulation of filament disassembly. Our data indicated that the E334Q mutation affects these two effects differentially, as we state in the abstract and in the discussion.

Abstract: “E334Q differentially affects cofilin-mediated actin dynamics by increasing the rate of cofilin-mediated de novo nucleation of actin filaments and decreasing the efficiency of cofilin-mediated filament severing.”

Discussion: “Cofilin-mediated severing and nucleation were previously proposed to synergistically contribute to global actin turnover in cells (Andrianantoandro & Pollard, 2006; Du & Frieden, 1998). Our results show that the mutation affects these different cofilin functions in actin dynamics in opposite ways. Cofilin-mediated filament nucleation is more efficient for p.E334Q monomers, while cofilin-mediated severing of filaments containing p.E334Q is significantly reduced. The interaction of both actin monomers and actin filaments with ADF/cofilin proteins involves several distinct overlapping reactions. In the case of actin filaments, cofilin binding is followed by structural modification of the filament, severing and depolymerizing the filament (De La Cruz & Sept, 2010). Cofilin binding to monomeric actin is followed by the closure of the nucleotide cleft and the formation of stabilized “long-pitch” actin dimers, which stimulate nucleation (Andrianantoandro & Pollard, 2006)”.

We interpret the reviewer's suggestion to mean that additional pyrene-actin-based bulk polymerization experiments should be performed to investigate the bulk-polymerization rate of ATP-G-actin in the presence of cofilin-1. In our understanding, these experiment would not provide additional value as 1) An observed increase of the bulk-polymerization rate cannot be directly correlated to a change of the efficiency of de novo nucleation or severing and 2) the effect of the mutation on cofilin-mediated filament disassembly was extensively analyzed in other experiments starting from preformed actin filaments. Moreover, our results are consistent with in silico modelling and normal mode analysis of the WT and mutant actin-cofilin complex.

• Figure 5 A: in the pyrene disassembly assay, where actin is diluted below its critical concentration, cofilin enhances the rate of depolymerization by generating more free ends. The E334Q mutation leads to decreased cofilin-induced severing and therefore lower depolymerization. While these data seem convincing, it would be better to present them as an XY plot and fit the data to lines for comparison of the slopes.

We now present the data as suggested by the reviewer. Furthermore, we determined the apparent second-order rate constant for cofilin-induced F-actin depolymerization (kc) to quantify the observed differences between WT, mutant and heterofilaments, as suggested by the reviewer.

The paragraph describing these results was changed accordingly:

“The observed rate constant values are linearly dependent on the concentration of cofilin–1 in the range 0–40 nM, with the slope corresponding to the apparent second– order rate constant (kC) for the cofilin-1 induced depolymerization of F–actin. In experiments performed with p.E334Q filaments, the value obtained for kC was 4.2-fold lower (0.81 × 10-4 ± 0.08 × 10-4 nM-1 s-1) compared to experiments with WT filaments (3.42 × 10-4 ± 0.22 × 10-4 nM-1 s-1). When heterofilaments were used, the effect of the mutation was reduced to a 2.2-fold difference compared to WT filaments (1.54 × 10-4 ± 0.11 × 10-4 nM-1 s-1).”

• Figure 5 B and C: the cosedimentation data do not seem to help elucidate the underlying mechanism. While the authors report statistical significance, differences are small, especially for gel densitometry measurements where the error is high, which suggests that there may be little biological significance. Importantly, example gels from these experiments should be shown, if not the complete set included in the supplement. In B, the higher cofilin concentrations would be expected to stabilize the filaments and thus the curve should be Ushaped.

We do not completely agree with the reviewer on this point. We think the co-sedimentation experiments are useful, as they show that cofilin-1 efficiently binds to mutant filaments, but is less efficient in stimulating disassembly in these endpoint-experiments. This information is not provided by the analysis of the effect of cofilin-1 on the bulk-depolymerization rate and adds to our understanding of the defect of the actin-cofilin interaction for the mutant.

While we agree with the reviewer on the point that co-sedimentation experiments must be repeated several times to produce reliable data, we cannot fully grasp the reasoning behind the statement “While the authors report statistical significance, differences are small, especially for gel densitometry measurements where the error is high, which suggests that there may be little biological significance.”. We interpret this statement as advice to be cautious when extrapolating the observed perturbances of cofilin-mediated actin dynamics in vitro to the in vivo context. We think we are cautious about this throughout the manuscript.

The author expects a U-shape curve, as high cofilin concentrations are reported to stabilize actin filaments by completely decorating the filament before severing-prone boundaries between cofilin-decorated and undecorated regions are generated. We have also performed these experiment with cytoskeletal β-actin and human cofilin-1 and never observed this U shape. This indicates that significant filament disassembly also happens at high cofilin concentrations, most likely directly after mixing of F-actin and cofilin. We cannot rule out that the incubation time plays an important role and that the U-shape only appears after longer incubation times. We also want to direct the reviewer to the publication “A Mechanism for Actin Filament Severing by Malaria Parasite Actin Depolymerizing Factor 1 via a Low Affinity Binding Interface” (Wong et al. 2013, JBC) in which comparable co-sedimentation experiments were performed (Figure 5E-G) with rabbit skeletal α-actin and human cofilin-1 and also no Ushaped curves were observed, even at higher molar excess of cofilin-1 compared to our experiments and with longer incubation times (1 hour vs. 10 minutes).

We now included an exemplary gel showing co-sedimentation experiments performed with WT, mutant actin and different concentrations of cofilin at pH 7.8 in the manuscript (Figure 5 – figure supplement 2)

• Figure 5 D: these data show that the binding of cofilin to WT and E334Q actin is approximately the same, with the mutant binding slightly more weakly. It would be clearer if the two plots were normalized to their respective plateaus since the difference in arbitrary units distracts from the conclusion of the figure. If the difference in the plateaus is meaningful, please explain.

As suggested by the reviewer, we normalized the data for a better understanding of the message conveyed.

• Figure 6: It is assumed that the authors are trying to show in this figure that cofilin binds both actins approximately the same but does not sever as readily for E334Q actin. The numerous parameters measured do not directly address what the authors are actually trying to show, which presumably is that the rate of severing is lower for E334Q than WT. It is therefore puzzling why no measurement of severing events per second per micron of actin in TIRF is made, which would give a more precise account of the underlying mechanism.

The severing rate as measured in TIRF is dependent on a number of parameters in a nonlinear manner. Therefore, we opted to show the combination of images directly showing the progress of the reaction and graphs summarizing the concomitant changes in cofilin clusters, actin filaments, actin-related fluorescence intensity and cofilin-related fluorescence intensity.

• Actin-activated steady-state ATPase data of the NM2A with mutant and WT actin would have been extremely useful and informative. The authors show the ability to make these types of measurements in the paper (NADH assay), and it is surprising that they are not included for assessing the myosin activity. It may be because of limited actin quantities. If this is the case, it should be indicated.

Indeed, the measurement of the steady-state actin-activated ATPase with recombinant cytoskeletal actin is very material-intensive and therefore costly, as a complete titration of actin is required for the generation of meaningful data. Since the vast majority of our assays involving a myosin family member were performed with NM2A-HMM, we decided to perform a full actin titration of the steady-state actin-activated ATPase of NM2A-HMM with WT and mutant filaments. The results of these experiments are now shown in Figure 8C. The panel showing the results used for determining the dissociation rate constants (k-A) for the interaction of NM2C-2R with p.E334Q or WT γ –actin in the absence of nucleotide was moved to the supplement (Figure 8 – figure supplement 2).

We added the following paragraph to the Material and Methods section concerning the Steady-State ATPase assay:

“For measurements of the basal and actin–activated NM2A–HMM ATPase, 0.5 µM MLCKtreated HMM was used. Phalloidin–stabilized WT or mutant F-actin was added over the range of 0–25 µM. The change in absorbance at 340 nm due to oxidation of NADH was recorded in a Multiskan FC Microplate Photometer (Thermo Fisher Scientific, Waltham, MA, USA). The data were fitted to the Michaelis-Menten equation to obtain values for the actin concentration at half-maximal activation of ATP-turnover (Kapp) and for the maximum ATP-turnover at saturated actin concentration (kcat).”

Furthermore, we added a description of the results of the experiments to the Results section of the manuscript:

“Using a NADH-coupled enzymatic assay, we determined the ability of p.E334Q and WT filaments to activate the ATPase of NM2A-HMM over the range of 0-25 µM F-actin (Figure 8C). While we observed no significant difference in Kapp, indicated by the actin concentration at half-maximal activation, in experiments with p.E334Q filaments (2.89 ± 0.49 µM) and WT filaments (3.20 ± 0.74 µM), we observed a 28% slower maximal ATP turnover at saturating actin concentration (kcat) with p.E334Q filaments (0.076 ± 0.005 s-1 vs. 0.097 ± 0.002 s-1).”

• (line 310) The authors state that they "noticed increased rapid dissociation and association events for E334Q filaments" in the motility assay. This observation motivates the authors to assess actin affinities of NM2A-HMM. Although differences in rigor and AM.ADP affinities are found between mutant and WT actins, the actin attachment lifetimes (many minutes) are unlikely to be related to the rapid association and dissociation event seen in the motility assay. Rather, this jiggling is more likely to be related to a lower duty ratio of the myosins, which appears to be the conclusion reached for the myosin-V data. These points should be clarified in the text.

We changed the text in accordance with the reviewer’ suggestion. It reads now: Cytoskeletal –actin filaments move with an average sliding velocity of 195.3 ± 5.0 nm s–1 on lawns of surface immobilized NM2A–HMM molecules (Figure 8A, B). For NM2A-HMM densities below about 10,000 molecules per μm2, the average sliding speed for cytoskeletal actin filaments drops steeply (Hundt et al, 2016). Filaments formed by p.E334Q actin move 5fold slower, resulting in an observed average sliding velocity of 39.1 ± 3.2 nm/s. Filaments copolymerized from a 1:1 mixture of WT and p.E334Q actin move with an average sliding velocity of 131.2 ± 10 nm s–1 (Figure 8A, B). When equal densities of surface-attached WT and mutant filaments were used, we observed that the number of rapid dissociation and association events increased markedly for p.E334Q filaments (Figure 8 – video supplement 7– 9).

Using a NADH-coupled enzymatic assay, we determined the ability of p.E334Q and WT filaments to activate the ATPase of NM2A-HMM over the range of 0-25 µM F-actin (Figure 8C). While we observed no significant difference in Kapp, indicated by the actin concentration at halfmaximal activation, in experiments with p.E334Q filaments (2.89 ± 0.49 µM) and WT filaments (3.20 ± 0.74 µM), we observed a 28% slower maximal ATP turnover at saturating actin concentration (kcat) with p.E334Q filaments (0.076 ± 0.005 s-1 vs. 0.097 ± 0.002 s-1). To investigate the impact of the mutation on actomyosin–affinity using transient–kinetic approaches, we determined the dissociation rate constants using a single–headed NM2A–2R construct (Figure 8D). …..

• (line 327) The authors report that the 1/K1 value is unchanged. There are no descriptions of this experiment in the paper. I am assuming the authors measured the ATP-induced dissociation of actomyosin and determined ATP affinity (K1) from this experiment. If this is the case, they should describe the experiment and show the data, provide a second-order rate constate for ATP binding, and report the max rate of dissociation (k2). This is a kinetic experiment done frequently by this group, so the absence of these details is surprising.

In the previous version of the manuscript, the method used to determine 1/K1 (ATP-induced dissociation of the actomyosin complex) was described in the Material and Methods paragraph “Transient kinetic analysis of the actomyosin complex” and the values obtained for 1/K1 were given in Table 1. We now included the experimental data as an additional figure in the manuscript (Figure 8 – figure supplement 3). Furthermore, we also give the maximal dissociation rate k+2 and the apparent second-order rate constant for ATP-binding (K1k+2) for the WT and mutant actomyosin complex in Table 1. Therefore, we changed the paragraph in the Results section concerning this experiment to:

“The apparent ATP–affinity (1/K1), the maximal dissociation rate of NM2A from F-actin in the presence of ATP (k+2), and the apparent second-order rate constant of ATP binding (K1k+2) showed no significant differences for complexes formed between NM2A and WT or p.E334Q filaments (Table 1, Figure 8 – figure supplement 3).”

and the section in the Material and Methods to:

“The apparent ATP–affinity of the actomyosin complex was determined by mixing the apyrase–treated, pyrene–labeled, phalloidin–stabilized actomyosin complex with increasing concentrations of ATP at the stopped–flow system. Fitting an exponential function to the individual transients yields the ATP–dependent dissociation rate of NM2A–2R from F–actin (kobs). The kobs–values were plotted against the corresponding ATP concentrations and a hyperbola was fitted to the data. The fit yields the apparent ATP–affinity (1/K1) of the actomyosin complex and the maximal dissociation rate k+2.

The apparent second–order rate constant for ATP binding (K1k+2) was determined by applying a linear fit to the data obtained at low ATP concentrations (0 – 25 µM).”

For a better understanding of the numerous rate and equilibrium constants, we have now included a figure showing the kinetic reaction scheme of the myosin ATPase cycle (Figure 8 – figure supplement 1).

Recommendations for the authors:

Reviewer #1:

• The subdomains of actin are mislabeled in Fig. 1A.

The labeling of the subdomains has been corrected.

• Additional experimental data addressing the 3 weaknesses noted in the public review would be informative but are not essential in my opinion. Examining the effect of cofilin on severing by the TIRF assay in more detail and using a processivity assay for myosin V (immobilized actin) would be the two aspects I would most value.

The TIRF assay for cofilin severing was performed initially over the cofilin concentration range from 20 to 250 nM. The results obtained in the presence of 100 nM cofilin allow a particularly informative depiction of the differences observed with mutant and WT actin. This applies to the image series showing the changes in filament length, cofilin clusters, and filament number as well as to the graphs showing time dependent changes in the number of filaments and total actin fluorescence. We have not included the results for a 50:50 mixture of WT:mutant actin because its attenuating effect is documented in several other experiments in the manuscript.

Our results with Myo5A show a less productive interaction with mutant actin filaments as indicated by a 1.7-fold reduction in the average sliding velocity and an increase in the optimal Myo5A-HMM surface density from 770 to 3100 molecules per µm2. These results indicate a reduction in binding affinity and coupling efficiency, with a likely impact on processivity. Given that Myo5A is only one of many cytoskeletal myosin motors and that the motor properties of all myosins are modulated by the presence of tropomyosin isoforms and other actin binding proteins, we expect only a small incremental gain in knowledge by performing additional experiments with an inverted assay geometry.

Reviewer #2:

• The authors should address the concerns regarding the statistical methodologies.

We have gone through the manuscript carefully to correct any errors in the statistics, as explained below.

Figure 1B, 5B, 5C, 5D, 8D, 9B, and 8 – figure supplement 2 all show the mean ± SD, as also correctly reported for Figure 8E and 8F in the figure legend. The statement, that these figures show the mean ± SEM was wrong and we corrected this mistake for all the listed figures. Furthermore, we now give the exact N for every experiment in the figure legend.

Figure 2C, 2E, 2F, 4B, 5A, 6B-E indeed showed the mean ± SEM. As the reviewer rightly points out, this is not the appropriate way to deal with such sample sizes. We therefore corrected the figures to show the mean ± SD.

We still refer to the mean ± SEM in Figure 2B, where elongation rates for more than 100 filaments were recorded, and in Figure 8B, where sliding velocities for several thousand actin filaments were measured.

• The authors should present the actin titration of the steady state ATPase activity for at least one of the myosins, or preferably all of them.

An actin titration of the steady state ATPase activity of NM-2A has been included in the revised version of the manuscript (Fig 8C).

• The authors should consider the use of pyrene-actin in measuring the assembly/disassembly of actin.

Values for the rate of actin assembly/disassembly measured with pyrene-actin are given in Table 1. Based on the small changes observed, we did not determine the critical actin concentration for the mutant construct.

Author Response

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

Reviewer #1 (Public Review):

We thank reviewer #1 for identifying the major caveats of the paper, and have split them out into separate comments below to address them.

Comment 1) The caveats are that ecosystem processes beyond water availability are not investigated although they are brought into play in the title and in the paper

Author response: We disagree that water availability is the only ecosystem process investigated in this study, as herbivory, plant mortality, and the maintenance of diversity in higher trophic levels are important processes within ecosystems. We have added text to the abstract and introduction clarifying that we consider these response measures to be ecosystem processes. Further language to this effect already exists in the abstract, methods, and discussion.

Comment 2) That herbivory beyond leaf damage was not reported (there might be none, the reader needs to be shown the evidence for this)

Author response: This is typically how herbivory is assessed in ecological studies, and our focus is on folivores. There may be additional herbivory in the form of fluid-sucking insects, shoot/root herbivory, etc., but these were not assessed. It would be interesting to assess these other forms of herbivory to see if they respond similarly with additional studies.

Comment 3) That herbivore diversity is defined by leaf damage (authors need to give evidence that this is a valid inference)

Author response: We thank reviewer #1 for pointing out the lack of written support for this claim. We have modified the methods (lines 138-139; 214-217) to clarify that this is a useful proxy for insect richness in the Piper system, and have added citations demonstrating it has been found to correlate well with insect richness in tropical forests.

Comment 4) That the plots were isolated from herbivores beyond their borders

Author response: This was not an assumption of the study. We have modified the methods (line 200) to make this clearer to the reader.

Comment 5) That the effects of extreme climate events were isolated to Peru

Author response: This was not an assumption of the study, rather it is an observation. While we consider it important to include observed climate differences between sites in the interpretation of our results, it was not necessary for there to be extreme climate events at other sites as we consider manipulated water availability to represent changes in precipitation that are expected to occur at these sites with climate change.

Comment 6) That intraspecific variation in the host plants needs to be explained and interpreted in more detail

Author response: We thank reviewer #1 for identifying that our current explanations needed development. We have modified the introduction to explore potential mechanisms relating intraspecific diversity to ecosystem function based on recent studies, and have modified the discussion to bring focus to why the effects of intraspecific differ from interspecific.

Reviewer #1 (Recommendations For The Authors):

Comment 1) Pare this material down to simpler results. The most significant to me is the intraspecific variation in damage. Were this broken out and reported in some detail it could be quite interesting. I find the results to be a confusing blizzard of multiple factors that differ among sites; after reading the paper twice I could not recall the takeaway lesson beyond that drought wrecks the diversity of herbivores and sometimes even kills the host plant.

Author response: We agree that the results are complicated given the variation in effects among sites, but this variation and complexity is important – and is in itself is one of the takeaway points. Unfortunately, nature is not simple. We have made several large edits to the results section, including the removal of methodological and otherwise redundant information, to hopefully bring the major takeaways into focus.

Reviewer #2 (Public Review):

Comment 1) This is an important and large experimental study examining the effects of plant species richness, plant genotypic richness, and soil water availability on herbivory patterns on Piper species in tropical forests.

A major strength is the size of the study and the fact that it tackled so many potentially important factors simultaneously. The authors examined both interspecific plant diversity and intraspecific plant diversity. They crossed that with a water availability treatment. And they repeated the experiment across five geographically separated sites.

The authors find that both water availability and plant diversity, intraspecific and interspecific, influence herbivore diversity and herbivory, but that the effects differ in important ways across sites. I found the study to be solid and the results to be very convincing. The results will help the field grapple with the importance of environmental change and biodiversity loss and how they structure communities and alter species interactions.

Author response: We thank reviewer #2 for their kind words.

Reviewer #2 (Recommendations For The Authors):

Comment 1) I was confused about why the authors measured species diversity/richness as a proportion of the species pool. This means that the metric of richness decreases if species are added to the species pool but not the plot/experiment. I think I understand it, but I suggest the authors explain this choice.

Author response: We thank reviewer #2 for pointing out that this was confusing. We have clarified the methods (lines 228-232) to explain that this choice was made to allow easier comparison between intra- and interspecific richness.

Comment 2) One of the stronger estimated relationships was a positive effect of plant species richness on insect richness. I found it a little hard to interpret this relationship. Is this just because there are host species specialists? So, with more host species there are more herbivore species? Or does insect richness increase multiplicatively with increasing plant species richness? One way to look for this would be for the authors to examine the relationship between plant species richness and the average number of herbivore damage types per plant species.

Author response: We agree that this is important for the reader to understand and have added text to the introduction and discussion sections explaining that this is the expectation based on theory and other empirical studies. We have additionally added text to the discussion (lines 386-388) pointing out that this pattern was not observed at all sites. While we agree that it would be interesting to explore if this effect was additive or multiplicative, we do not believe this is in the scope of the paper due to the methods used to measure insect richness.

Comment 3) Unless I missed it, some important information about the models was missing. E.g., what distributions were assumed for each of the variables? Any transformations?

Author response: We thank reviewer #2 for pointing this out, this information has been added to the methods (lines 272-274)

Comment 4) Why is there no model with water addition affecting insect richness directly but not percent herbivory directly?

Author response: While we originally decided to not include this model due to lack of theoretical support and low statistical performance, we have added references to this model (now model II) in the methods and results for consistency and to make model performance clearer to the reader. We have additionally moved supplemental table S1 to the main text to make the models and hypotheses tested by each model more accessible.

Comment 5) Fig. 2. What are the percentages above the figures? Maybe PD values?

Author response: These values are now clarified in the figure caption

Comment 6) L364 "can differ dramatically" This is vague and confusing. Differ in what way? From each other? Did the authors really expect plant richness to have the same effect on herbivory and plant survival? What would it mean anyway for plant richness to have the same effect on herbivory and plant survival?

Author response: We agree that the language here is confusing and thank reviewer #1 for drawing our attention to it. We have modified the discussion (lines 363-365) to clarify that the direction of effect of intraspecific richness can vary from the direction of effect of interspecific richness, rather than the effects on different response variables varying from each other.

Comment 7) L 375 "only meaningful differences" This statement feels a little overly strong. It seems like there is a good argument for this, but there could be other things going on.

Author response: We agree that the language here was unnecessarily strong, and have modified the discussion (lines 398-403) to focus on the lack of difference between methodologies at these two sites, and the observed differences in climate and community structure at each site.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

In this study, the authors aimed to investigate how cells respond to dynamic combinations of two stresses compared to dynamic inputs of a single stress. They applied the two stresses - carbon stress and hyperosmotic stress - either in or out of phase, adding and removing glucose and sorbitol.

Both a strength and a weakness, as well as the main discovery, is that the cells' hyperosmotic response strongly requires glucose. For in-phase stress, cells are exposed to hyperosmotic shock without glucose, limiting their ability to respond with the well-studied HOG pathway; for anti-phase stress, cells do have glucose when hyperosmotically shocked, but experience a hypo-osmotic shock when both glucose and sorbitol are simultaneously removed. Responding with the HOG pathway and so amassing intracellular glycerol amplifies the impact of this hypo-osmotic shock. Counterintuitively then, it is the presence of glucose rather than the stress of its absence that is deleterious for the cells.

The bulk of the paper supports these conclusions with clean, compelling time-lapse microscopy, including extensive analysis of gene deletions in the HOG network and measurements of both division and death rates. The methodology the authors develop is powerful and widely applicable.

Some discussion of the value of applying periodic inputs would be helpful. Cells are unlikely to have previously seen such inputs, and periodic stimuli may reveal behaviours that are rarely relevant to selection.

We thank the referee for his review. To answer the reviewer’s last comment, our main objective was not to study conditions that are ecologically relevant, but rather to perturb the system in an original way to reveal new mechanisms and properties of the system. The main advantage of periodic inputs over more complex or unpredictible types of temporal fluctuations is that they can be defined with few parameters that are easy to interpret and to integrate in biophysical models. For instance, by using periodic inputs we were able to investigate how changing the phasing of two stresses impacted fitness while keeping other parameters constant (the duration of each stress was kept constant). We added two sentences at the beginning of the discussion to highlight the value of using periodic inputs.

We do not fully agree with the reviewer’s statement that periodic stimuli may reveal behaviours that are rarely relevant to selection. Indeed, many parameters of natural environments are known to vary periodically, such as light, temperature, predation, tides. Even if the periodic stimuli we use are artificial, they can still be a valuable tool to reveal new molecular processes. For instance, null mutants have been invaluable to understand biological systems despite being unlikely to reveal behaviours relevant to selection.

The authors' findings demonstrate the tight links that can exist between metabolism and the ability to respond to stress. Their study appears to have parted somewhat from their original aim because of the HOG pathway's reliance on glucose. It would be interesting to see if the cells behaviour is simpler in periodically varying sorbitol and a stress where there is little known connection to the HOG network, such as nitrogen stress.

The use of periodic nitrogen stress is a very interesting suggestion from both reviewers. However, we think it represents a large amount of work that deserves its own study. In particular, it would require first identifying a relevant period at which nitrogen fluctuations have an impact on division rate similar to what we observed for glucose fluctuations before performing experiments in AS and IPS conditions.

Nitrogen starvation is known to induce filamentous growth via activation of components of the HOG pathway (Cullen and Sprague, 2012), with potential cross-talk between filamentous growth and hyperosmotic stress response. Therefore, periodic osmotic stress and periodic nitrogen starvation may interact in a complex way.

Reviewer #2 (Public Review):

The authors have used microfluidic channels to study the response of budding yeast to variable environments. Namely, they tested the ability of the cells to divide when the medium was repeatedly switched between two different conditions at various frequencies. They first characterized the response to changes in glucose availability or in the presence of hyper-osmotic stress via the addition of sorbitol to the medium. Subsequently, the two stresses were combined by applying the alternatively or simultaneously (in-phase). Interestingly, the observed that the in-phase stress pattern allowed more divisions and low levels of cell mortality compared to the alternating stresses where cells were dividing slowly and many cells died. A number mutants in the HOG pathway were tested in these conditions to evaluate their responses. Moreover, the activation of the MAPK Hog1 and the transcriptional induction of the hyper-osmotic stress promoter STL1 were quantified by fluorescence microscopy.

Overall, the manuscript is well structured and data are presented in a clear way. The time-lapse experiments were analyzed with high precision. The experiments confirm the importance of performing dynamic analysis of signal transduction pathways. While the experiments reveal some unexpected behavior, I find that the biological insights gained on this system remain relatively modest.

In the discussion section, the authors mention two important behaviors that their data unveil: resource allocation (between glycolysis and HOG-driven adaptation) and regulation of the HOG-pathway based on the presence of glucose. These behaviors had been already observed in other reports (Sharifan et al. 2015 or Shen et al. 2023, for instance). I find that this manuscript does not provide a lot of additional insights into these processes.

We thank the referee for his review. We agree with the reviewer that the interaction between glucose availability and osmotic stress response has been investigated in previous studies. However, this interaction was investigated using experimental procedures that differed from our approach in critical ways, and therefore the behaviors observed were not the same. In Sharifian et al. (2015), the authors identified a new negative feedback loop regulating Hog1 basal activity and described underlying molecular mechanisms. This feedback loop is unlikely to explain differences of cell fitness we observed in IPS and AS conditions, because 1) differences of division rate was still observed in hog1 mutant cells and 2) differences of death rate involve glycerol synthesis, which is independent of the feedback loop described in Sharifian et al. (2015). In Shen et al. (2023), the authors observed a stronger expression of Hog-responsive genes at lower glucose concentrations, which seems contradictory with our observation of very low pSTL1-GFP expression in absence of glucose. However, they did not use fluctuating conditions and they did not report expression of stress-response genes when glucose was totally depleted (the lower glucose concentration they used was 0.02%) as we did, which may explain the different outcomes. We added three sentences in the discussion to compare our findings to those of Shen et al. (2023).

One clear evidence that is presented, however, is the link between glycerol accumulation during the sorbitol treatment and the cell death phenotype upon starvation in alternating stress condition. However, no explanations or hypothesis are formulated to explain the mechanism of resource allocation between glycolysis and HOG response that could explain the poor growth in alternating stresses or the lack of adaptation of Hog1 activity in absence of glucose.

In the revised version of the manuscript, we included a new result section and a supplementary figure (Figure 4 – figure supplement 2) where we tested three hypotheses to explain the lower division rate observed in AS condition relative to IPS condition. We found no evidence supporting these hypotheses, and the mechanisms responsible for the reduced growth in AS condition therefore remains elusive.

Another key question is to what extent the findings presented here can be extended to other types of perturbations. Would the use of alternative C-source or nitrogen starvation change the observed behaviors in dynamic stresses? If other types of stresses are used, can we expect a similar growth pattern between alternating versus in-phase stresses?

As mentioned above in our response to the other reviewer, these are very interesting questions that we think go beyond the scope of our study due to the amount of work involved.

Recommendations for the authors:

Reviewer #1

My comments are only minor.<br /> - More paragraphs would improve legibility.

To improve legibility, we split the longer section of the Results in three paragraphs (page 12, section entitled “Osmoregulation is impaired under in-phase stresses but not under alternating stresses.” However, we kept it as one section with a single title for global coherency: each section of the results corresponds to one main figure and have one main conclusion.

• I found AS and IPS confusing because what becomes important is whether sorbitol appears with glucose or not. For me, an acronym that makes that co-occurrence clear would be better or even better still no acronyms at all.

We tried several alternative names for the two conditions in previous drafts of the manuscript. Based on colleagues feedback, AS and IPS acronyms appeared as a good compromise between concision and clarity. To avoid confusion, the two acronyms are precisely defined when they are first used in the Results section. We think it is more important to emphasize the co-occurrence (or not) of the two stresses, rather than the co-occurrence of glucose and sorbitol. Indeed, standard yeast medium contains glucose but no sorbitol, and therefore we defined the two periodic conditions based on differences from standard medium. Even though we avoided using acronyms as much as possible in the manuscript, the use of these two acronyms to refer to the dual fluctuations of the environment seemed essential for concision. Indeed, IPS and AS acronyms are used many times in the results (16 occurrences on page 12 alone), figures and figure legends.

• I would consider moving some of Fig S2 to the main text: it helps clarify where Fig 2 is coming from and is referenced multiple times.

We fully agree with the reviewer and we moved panels A-D from Figure S2 to the main Figure 2.

• On page 10, "constantly facing a single stress that changes over time" is confusing. Perhaps "repetitively facing a single stress" instead?

We agree this sentence could be wrongly interpreted the way it was written. We changed it to: “cells grow more slowly when facing periodic alternation of the two stresses (AS) than when facing periodic co-occurrence of these stresses (IPS)”.

• Is there any knowledge on how cells resist hyperosmotic stress in the absence of glucose? That would help explain the IPS results.

Based on comments from both reviewers, we surveyed the literature to flesh out the discussion of hypotheses that would help explain observed differences between AS and IPS conditions. We found few studies that investigated cell responses in the absence of glucose, and because of significant differences in the experimental approaches it remains difficult to explain our results from conclusions of these previous studies. For instance, Shen et al., 2023 described and modeled the hyperosmotic stress response at various glucose concentrations. They found that Hog1p relocation to the nucleus after hyperosmotic shock lasted longer at lower glucose concentration, which is consistent with our finding in absence of glucose. However, they did not include the absence of glucose in their experiments or periodic fluctuations of glucose concentration. In addition, their model ignores the impact of cell signaling processes involved in growth arrest in response to hyperosmotic stress or glucose depletion. It is therefore difficult to relate their conclusions to our results. We have developed the discussion of our study to include these hypotheses and to clarify what is explained or not in our IPS and AS results.

There is knowledge on activation of the hyperosmotic stress pathway in response to glucose fluctuations, but not about the response to hyperosmotic stress in absence of glucose.

• On page 11, Figure 5a should be Figure 4a.

Correct.

• I would explain the components of the HOG pathway in the caption of Fig 1 or in the text when you cite Fig 1a. They are described later, but an early overview would be useful.

To give more context, we added the following sentences to the caption of Figure 1: “Yeast cells maintain osmotic equilibrium by regulating the intracellular concentration of glycerol. Glycerol synthesis is regulated by the activity of the HOG MAP kinase cascade that acts both in the cytoplasm (fast response) and on the transcription of target genes in the nucleus (long-term response). For simplicity, we only represented on the figure genes and proteins involved in this study.”

• On page 16, I wasn't sure what "redirect metabolic fluxes against glycerol synthesis" meant.

For more clarity, we modified this sentence to: “Since glucose is a metabolic precursor of glycerol, the absence of glucose may prevent glycerol synthesis and thereby fast osmoregulation."

• For Fig 2, having a dot-dash and dash-dash lines rather than both dash-dash would be better.

We made the proposed change, assuming the reviewer was referring to the gray dashed lines and not the colored ones.

• In the caption of Fig 3, 2% glucose is 20 g/L.

We thank the reviewer for catching this typo.

• In the Materials and Methods Summary, adding how you estimated death rates would be helpful: they are not often reported.

The calculation of death rates was explained in the Methods section. For more clarity, we modified the names of the parameters in the equation to make more explicit which ones refer to cell death.

Reviewer #2 (Recommendations For The Authors):

In Figure 2, it would be interesting to show individual growth rates of the perturbations at various frequencies as shown in Figures 3 c and d.

We thank the reviewer for this suggestion. We added a new supplementary figure (Figure 2 – figure supplement 2) showing the temporal dynamics of division rates at three different frequencies of osmostress and glucose depletion. We did not include high frequencies (periods below 48 minutes) because the temporal resolution of image acquisition in our experiments (1 image every 6 minutes) was too low. Very interestingly, this new analysis suggests that the positive relationship between the frequency of glucose depletion and division rate is explained by a delay between glucose removal and growth arrest rather than a delay between glucose addition and growth recovery. We therefore added the following conclusion:

“Under periodic fluctuations of 2% glucose, the division rate was lower during half-periods without glucose than during half-periods with glucose (Figure 2 – figure supplement 2d-f), as expected. However, this difference depended on the frequency of glucose fluctuations: the average division rate during half-periods without glucose was higher at high frequency (small period) than at low frequency (large period) of fluctuations (Figure 2 – figure supplement 2d-f). Therefore, the effect of the frequency of glucose availability on the division rate in 2% glucose is likely due to a delay between glucose removal and growth arrest: cell proliferation never stops when the frequency of glucose depletion is too fast.”

According to Sharifan et al. 2015, I would have expected that Hog1 would not relocate in the nucleus in 0% glucose. I wonder if this is due to the use of sorbitol as a stressor or the presence of low levels of glucose in the medium. I would suggest performing some control experiments with NaCl as hyperosmotic agent and test the addition of 2-deoxy-glucose to completely block glycolysis.

After careful reading of Sharifian et al. 2015, we fail to understand why the reviewer think Hog1 would be expected to not relocate to the nucleus after hyperosmotic stress in 0% glucose. In this previous study, the authors never combined glucose depletion with a strong hyperosmotic stress as we did in our study. They report the results of independent experiments where cells were exposed either to a single pulse of hyperosmotic stress (0.4 M NaCl) or to transient glucose starvation, but they did not combine these two stimuli. In this context, it is difficult to compare their results with ours. The fact that Sharifian et al. 2015 did not observe Hog1 nuclear relocation in 0% glucose (consistent with our result in Figure 6 – figure supplement 1a, yellow curve) is not inconsistent with our observation of Hog1 nuclear enrichment in 0% glucose + 1M sorbitol. One potential discrepancy between the two studies is the fact that they observed a small transient peak of Hog1 nuclear localization just after glucose is added back to the medium, while we failed to observe this peak in similar conditions (yellow curve in Figure 6 – figure supplement 1a). However, this could be simply explained by the temporal resolution of our experimental system: we image cells once every 6 minutes and the peak lasts less than 2 minutes in Sharifian et al. 2015. We added a sentence to discuss this minor point in the Results: “Although previous studies observed small transient (less than two minutes) peaks of Hog1-GFP nuclear localization after glucose was added back to the medium following glucose depletion (Sharifian et al., 2015, Piao et al., 2013), the temporal resolution in our experiments (one image every 6 minutes) may have been too low to detect these peaks.”.

While we agree many additional experiments would be interesting, such as testing the effects of different stress factors or the non-metabolizable glucose analog 2-deoxy-D-glucose, we think this is beyond the scope of this study because such experiments are likely to open broad perspectives and to not be conclusive in a reasonable amount of time.

When discussing Figure 7, the authors write that the HOG pathway is "overactivated" or "hyperactivated". I would refrain from using these terms because as seen in Figure 6, the Hog1 activity pattern, if anything, decreases as the number of alternative pulses increases. The high level of pSTL1mCitrine measured is mostly due to the long half-life of the fluorescent protein.

We used the formulation “hyper-activation” of the HOG pathway because Mitchell et al. 2015 used it to refer to the same phenomenon in their seminal study. This "hyper-activation" refers to the fact that both the integral activation of Hog1p (sum of areas under Hog1 nuclear peaks) and the global activation of transcriptional targets is much higher during fast periodic hyperosmotic stress than during constant hyperosmotic stress. That being said, we understand the point made by the reviewer about the decreasing size of Hog1 peaks over time during repeated pulses of osmotic stress. Therefore, we slightly modified the text to refer to hyper-activation of pSTL1-mCitrine transcription or expression instead of hyper-activation of the HOG pathway. For coherency, we replaced all instances of “overactivation” by “hyper-activation”.

Last but not least, the high level of pSTL1-mCitrine is both due to the long half-life of the protein and to the fact that pSTL1 transcription is never turned off due to high Hog1p activity under fast periodic osmostress.

Minor comments:

In the main text, I think it might be more intuitive to refer to doubling time in hours instead of division rates in 1/min which are harder to interpret.

In an early draft of the manuscript, we made figures with either division rates or with doubling times (ln(2)/division rate) and we received mixed opinions from colleagues on what measure was more intuitive to interpret. Both measures are widely used in the literature, and we decided to use division rates in the final version of the figures because it was more directly related to population growth rate and to fitness. For instance, the population growth rate shown in Figure 5 is simply calculated by subtracting the death rate from the division rate. For coherency, we therefore reported division rates instead of doubling times in figures and results. However, to address the reviewer’s comment we included the doubling times (in addition to the division rates) when mentioning the most important results. For instance, page 12: “Strikingly, cells divided about twice as fast under IPS condition (1.67 x 10-3 division/min, corresponding to an average doubling time of 415 minutes) than under AS condition (9.4 x 10-4 division/min, corresponding to an average doubling time of 737 minutes)”.

I found various capitalized version of "HOG /Hog pathway"

We corrected this incoherency and used “HOG pathway” everywhere.

Page 11. Figure 5a should refer to Figure 4a I believe.

Correct.

The methods are generally very thorough and precise. The explanation about the calculation of the division rate seems incomplete. For completeness, it would be good to mention the brand and model of valves used. In addition, it would be interesting to have an idea of the number of cells and microcolonies tracked in the various growth experiments.

We are not sure why the reviewer found the explanation of the calculation of division rate incomplete. For more clarity, we modified the names of parameters in the equations to make them more explicit. We also added a reference to Supplementary File 1 that contains all R scripts used to calculate division rates and death rates. We included the brand and model of valves used, as requested. As for the number of cells tracked in the various experiments, we mentioned in the Methods: “we selected 25 positions (25 fields of view) of the motorized stage (Prior Scientific ProScan III) that captured 10 to 50 cells in each of the 25 growth chambers of the chip and were focused slightly below the median cell plane based on cell wall contrast.” To address the reviewer’s comment, we also included the range of number of tracked cells for each experiment in corresponding figure legends.

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)

Author Response

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

Reviewer #1 (Public Review):

Summary:

This study examines the role of host blood meal source, temperature, and photoperiod on the reproductive traits of Cx. quinquefasciatus, an important vector of numerous pathogens of medical importance. The host use pattern of Cx. quinquefasciatus is interesting in that it feeds on birds during spring and shifts to feeding on mammals towards fall. Various hypotheses have been proposed to explain the seasonal shift in host use in this species but have provided limited evidence. This study examines whether the shifting of host classes from birds to mammals towards autumn offers any reproductive advantages to Cx. quinquefasciatus in terms of enhanced fecundity, fertility, and hatchability of the offspring. The authors found no evidence of this, suggesting that alternate mechanisms may drive the seasonal shift in host use in Cx. quinquefasciatus.

Strengths:

Host blood meal source, temperature, and photoperiod were all examined together.

Weaknesses: The study was conducted in laboratory conditions with a local population of Cx. quinquefasciatus from Argentina. I'm not sure if there is any evidence for a seasonal shift in the host use pattern in Cx. quinquefasciatus populations from the southern latitudes.

We agree on the reviewers observation about the evidence on seasonal shift in the host use pattern in Cx. quinquefasciatus populations from southern latitudes. We include a paragraph in the Introduction section regarding this. Unfortunately, studies conducted in South America to understand host use by Culex mosquitoes are very limited, and there are virtually no studies on the seasonal feeding pattern. In Argentina, there is some evidence (Stein et al., 2013, Beranek, 2019) regarding the seasonal change in host use by Culex species, including Cx. quinquefasciatus, where the inclusion of mammals during the autumn has been observed. As part of a comprehensive study on characterising bridge vectors for SLE and WN viruses, our research group is currently working on the molecular identification of blood meals from engorged females to gain deeper insights into the seasonal feeding pattern of Culex mosquitoes. While the seasonal change in host use by Culex quinquefasciatus has not been reported in Argentina so far, there has been an observed increase in reported cases of SLE virus in humans between summer and fall (Spinsanti et al., 2008). It is based on this evidence that we hypothesise there is a seasonal change in host use by Cx. quinquefasciatus, similar to what occurs in the United States. This is also considering that both countries (Argentina and the United States) have regions with similar climatic conditions (temperate climates with thermal and hydrological seasonality). Since we work on the same species and in a similar temperate climate regimen, we assumed there is a seasonal shift in the host use by this mosquito species.

Reviewer #1 (Recommendations for the authors):

Abstract

Line 23: fed on two different hosts.

Accepted as suggested.

I think the concluding statement should be rewritten to say that immediate reproductive outcomes do not explain the shift in host use pattern of Cx. quinquefasciatus mosquitoes from birds to mammals towards autumn.

Accepted as suggested.

Introduction

No comments.

Materials and Methods

Please mention sample sizes in the text as well (n = ?) for each treatment.

Accepted as suggested.

Page 99: ......C. quinquefasciatus, since C. pipiens and its hybrids are present as well in Cordoba.

Accepted as suggested.

Results – Line 146: subsequently instead of posteriorly

Accepted all changes as suggested.

Line 148: were counted instead of was counted.

Accepted all changes as suggested.

Line 160: Subsequently instead of posteriorly

Accepted all changes as suggested.

Line 171: on fertility

Accepted all changes as suggested.

Line 174: there was an interaction effect on…

Accepted all changes as suggested.

Line 175: there were no differences in the number of eggs

Accepted all changes as suggested.

Discussion

I think the first paragraph in the discussion section is redundant and should be deleted.

The whole discussion was rewritten to be focused on our aims and results.

Line 282: this sentence needs to be rewritten.

Accepted as suggested.

Line 299: at 28{degree sign}C

Line 300: at 30{degree sign}C

Sorry, but we are not sure about your comment here. We checked. Temperatures are written as stated, 28°C and 30°C.

Line 363: I think the authors need to discuss more about the bigger question they were addressing. I think that the discussion section can be strengthened greatly by elaborating on whether there is evidence for a seasonal shift in host use pattern in Cx. quinquefasciatus in the southern latitudes. If yes, what alternate mechanisms they believe could be driving the seasonal change in host use in this species in the southern latitudes now that they show the 'deriving reproductive advantages' hypothesis to be not true for those populations.

Thanks for this observation. We agree and so the Discussion section was restructured to align it with our results, as suggested.

Reviewer #2 (Public Review):

Summary:

Conceptually, this study is interesting and is the first attempt to account for the potentially interactive effects of seasonality and blood source on mosquito fitness, which the authors frame as a possible explanation for previously observed host-switching of Culex quinquefasciatus from birds to mammals in the fall. The authors hypothesize that if changes in fitness by blood source change between seasons, higher fitness in birds in the summer and on mammals in the autumn could drive observed host switching. To test this, the authors fed individuals from a colony of Cx. quinquefasciatus on chickens (bird model) and mice (mammal model) and subjected each of these two groups to two different environmental conditions reflecting the high and low temperatures and photoperiod experienced in summer and autumn in Córdoba, Argentina (aka seasonality). They measured fecundity, fertility, and hatchability over two gonotrophic cycles. The authors then used a generalized linear mixed model to evaluate the impact of host species, seasonality, and gonotrophic cycle on fecundity and fertility and a null model analysis via data randomization for hatchability. The authors were trying to test their hypothesis by determining whether there was an interactive effect of season and host species on mosquito fitness. This is an interesting hypothesis; if it had been supported, it would provide support for a new mechanism driving host switching. While the authors did report an interactive impact of seasonality and host species, the directionality of the effect was the opposite of that hypothesized. While this finding is interesting and worth reporting, there are significant issues with the experimental design and the conclusions that are drawn from the results, which are described below. These issues should be addressed to make the findings trustworthy.

Strengths:

(1) Using a combination of laboratory feedings and incubators to simulate seasonal environmental conditions is a good, controlled way to assess the potentially interactive impact of host species and seasonality on the fitness of Culex quinquefasciatus in the lab.

(2) The driving hypothesis is an interesting and creative way to think about a potential driver of host switching observed in the field.

Weaknesses:

(1) There is no replication built into this study. Egg lay is a highly variable trait, even within treatments, so it is important to see replication of the effects of treatment across multiple discrete replicates. It is standard practice to replicate mosquito fitness experiments for this reason. Furthermore, the sample size was particularly small for some groups (e.g. 15 egg rafts for the second gonotrophic cycle of mice in the autumn, which was the only group for which a decrease in fecundity and fertility was detected between 1st and 2nd gonotrophic cycles). Replicates also allow investigators to change around other variables that might impact the results for unknown reasons; for example, the incubators used for fall/summer conditions can be swapped, ensuring that the observed effects are not artefacts of other differences between treatments. While most groups had robust sample sizes, I do not trust the replicability of the results without experimental replication within the study.

We agree egg lay is a variable trait and so we consider high numbers of mosquitoes and egg lay during experiments compared to our studies of the same topics. Evaluating variables such as fecundity, fertility, or other types of variables (collectively referred to as "life tables") is a challenging issue that depends on several intrinsic and extrinsic factors. Because all of this, in some experiments, sample sizes might not be very large, and in several articles, lower sample sizes could be found. For instance, in Richards et al. (2012), for Culex quinquefasciatus, during the second gonotrophic cycle, some experiments had 13 or even 6 egg rafts. For species like Aedes aegypti, the sample size for life table analysis is also usually small. As an example, Muttis et al. (2018) reported between 1 and 4 engorged females (without replicates). In addition, small sample size would be a problem if we would not have obtained any effect, which is not the case due to the fact that we were interested in finding an effect, regardless of the effect size. Because of this, we do find our sample sizes quite robust for our results.

Regarding the need to repeat the experiments in order to give more robustness to the study we also agree. However, after a review of the literature (articles cited in the original manuscript), it is apparent that similar experiments are not frequently repeated as such. Examples of this are the studies of Richards et al. (2012), Demirci et al. (2014) or Telang & Skinner (2019), which even they manipulate several cages at a time as “replicates”, they are not true replicates because they summarise and manipulate all data together, and do not repeat the experiment several times. We see these “replicates” as a way of getting a greater N.

As was stated by the reviewer, repetition is a resource and time-consuming activity that we are not able to do. Replicating the experiment poses a significant time and resources challenge. The original experiment took over three months to complete, and it is anticipated that a similar timeframe would be necessary for each replication (6 months in total considering two more replicates). Given our existing commitments and obligations, dedicating such an extensive period solely to this would impede progress on other crucial projects and responsibilities.

Given the limitations of resources and time and the infrequent use of experimental replication in this type of studies, we performed a simulation-based analysis via a Monte Carlo approach. This approach involved generating synthetic data that mimics the expected characteristics of the original experiment and subsequently subjecting it to the same analysis routine. The main goal of this simulation was to evaluate the potential spuriousness and randomness of the results that might arise due to the experimental conditions. So, evaluating the robustness and confidence of our results and data.

(2) Considering the hypothesis is driven by the host switching observed in the field, this phenomenon is discussed very little. I do not believe Cx. quinquefasciatus host switching has been observed in Argentina, only in the northern hemisphere, so it is possible that the species could have an entirely different ecology in Argentina. It would have been helpful to conduct a blood meal analysis prior to this experiment to determine whether using an Argentinian population was appropriate to assess this question. If the Argentinian populations don't experience host switching, then an Argentinian colony would not be the appropriate colony to use to assess this question. Given that this experiment has already been conducted with this population, this possibility should at least be acknowledged in the discussion. Or if a study showing host switching in Argentina has been conducted, it would be helpful to highlight this in the introduction and discussion.

Thanks for this observation. We agree. However, we conducted the experiment beside host use data from Argentina since we used the mosquito species, and the centre region of Argentina (Córdoba) has a similar temperate weather regimen that those observed in the east coast of US.

We are aware that few studies regarding host shifting in South America are available, some such that those conducted by Stein et al. (2013) and Beranek (2019) reported a moderate host switch for Culex quinquefasciatus in Argentina. We have already performed a study about seasonal host feeding patterns for this species. However, even though there are few studies regarding host shifting, our hypothesis is based mainly in the seasonality of human cases of WNV and SLEV, a pattern that has been demonstrated for our region, see for example the study of Spinsanti et al. (2008).

We include a new paragraph in the Introduction and Discussion sections. Please see answers Reviewer #1.

(3) The impacts of certain experimental design decisions are not acknowledged in the manuscript and warrant discussion. For example, the larvae were reared under the same conditions to ensure adults of similar sizes and development timing, but this also prevents mechanisms of action that could occur as a result of seasonality experienced by mothers, eggs, and larvae.

We understand the confusion that may have arisen due to a lack of further details in the methodology. If we are not mistaken, you are referring to our oversight regarding the consideration of carry-over effects of larvae rearing that could potentially impact reproductive traits. When investigating the effects of temperature or other environmental factors on reproductive traits, it is possible to acclimate either larvae or adults. This is due to the significant phenotypic plasticity that mosquitoes exhibit throughout their entire ontogenetic cycle. In our study, we followed an approach similar to that of other authors where the adults are exposed to experimental conditions (temperature and photoperiod). For a similar approach you can refer to the studies conducted by Ferguson et al. (2018) for Cx. pipiens, Garcia Garcia & Londoño Benavides (2007) for Cx. quinquefasciatus or Christiansen-Jucht et al. (2014, 2015) for Anopheles gambiae.

(4) There are aspects of the data analysis that are not fully explained and should be further clarified. For example, there is no explanation of how the levels of categorical variables were compared.

The methodology and statistical analysis were expanded for a better understanding.

(5) The results show the opposite trend as was predicted by the authors based on observed feeding switches from birds to mammals in the autumn. However, they only state this once at the end of the discussion and never address why they might have observed the opposite trend as was hypothesized.

The discussion was restructured to focus on our results and our model.

(6) Generally speaking, the discussion has information that isn't directly related to the results and/or is too detailed in certain parts. Meanwhile, it doesn't dig into the meaning of the results or the ways in which the experimental design could have influenced results.

As mentioned above, the discussion was restructured to reflect our findings. We also included the effect that our design might have influenced our results. However, as stated above we do not fully agree that the design is inadequate for our analysis, we performed standard protocols followed by other researchers and studies in this research field.

(7) Beyond the issue of lack of replication limiting trust in the conclusions in general, there is one conclusion reached at the end of the discussion that would not be supported, even if additional replicates are conducted. The results do not show that physiological changes in mosquitoes trigger the selection of new hosts. Host selection is never measured, so this claim cannot be made. The results don't even suggest that fitness might trigger selection because the results show that physiological changes are in the opposite direction as what would be hypothesized to produce observed host switches. Similarly, the last sentence of the abstract is not supported by the results.

We agree with this observation. However, we did not evaluate the impact of fitness on host selection in this study. Instead, we aimed to investigate the potential influence of seasonality on mosquito fitness as a potential trigger for a shift in host selection. We agree that we have incorrectly used the term “host selection” when we should actually be discussing “host use change”. Our results indicate a seasonal alteration in mosquito fitness in response to temperature and photoperiod changes. Building upon this observation, we re-discussed our hypothesis and theoretical model to explain this seasonal shift in host use.

(8) Throughout the manuscript, there are grammatical errors that make it difficult to understand certain sentences, especially for the results.

All English grammar and writing of the manuscript was revised and corrected to be easily understood.

This study is driven by an interesting question and has the potential to be a valuable contribution to the literature.

Reviewer #2 (Recommendations for The Authors):

I hope that the authors will consider the suggested revisions and experimental replication to improve the quality of the study and paper.

This study tests a very interesting hypothesis. I understand that additional replicates are difficult to conduct, but I do believe that fitness studies absolutely require experimental replicates. Unless you are able to replicate the observed effects, I personally would not trust the results of this study. I hope that you will consider conducting replicates so that this important question can be answered in a more robust manner. Below, I expand upon some additional points in the public review and also provide more specific suggestions. I provided some copy-editing feedback, but was not able to point out all grammatical mistakes. I suggest that you use ChatGPT to help you edit the English. For example, you can feed ChatGPT your MS and ask it to bold the grammatical errors or you can ask it to edit grammatical errors and bold the sections that were edited. I understand that writing in a second language is very difficult (from personal experience!), so I view ChatGPT as a great tool to help even the playing field for publishing. Below are line item suggestions. Apologies that wording is curt, I was trying to be efficient in writing.

20-21: I suggest that you emphasize that you are investigating the interactive effect.

Accepted as suggested.

22: they weren't "reared" (from larvae) in different conditions, they were "maintained" as adults

Accepted as suggested.

26-27: increased/decreased is a bit misleading since you did not evaluate these groups sequentially in time. It might be more accurate to describe it as less than/greater than. Also, if you say increased/decreased or less than/greater than, you should always say what you are comparing to. The same applies throughout the MS.

Accepted as suggested.

29-30: "finding the" is not correct here; could be "with the lowest..."

Accepted as suggested.

34-36: I do not think that your results suggest this, even if you were to replicate the results of this experiment. You haven't shown metabolic changes.

We understand the point. Accepted as suggested.

42-44: "one of the main responsible" should be "one of the main species responsible..."

Accepted as suggested.

48: I think that "host preference" is better than selection here; -philic denotes preference

Accepted as suggested.

50: "Moreover" isn't the correct transition word here

Accepted as suggested.

57: "could" isn't correct here; consider saying "... species sometimes feed primarily on mammal hosts, including humans, in certain situations."

Accepted as suggested.

58: Different isn't correct word here

Accepted as suggested.

60: delete "feeding"

Accepted as suggested.

66-68: I am not familiar with any blood meal analysis studies in the southern hemisphere that show host switching for Culex species between summer and autumn. If this hasn't been shown, then this critique of the host migration hypothesis doesn't make sense.

There are some studies pointing this out (Stein et al., 2013, Beranek 2019), and unpublished data from us). However, our hypothesis has supported by epidemiological data observed in human population which indicate a seasonal activity pattern. It was explained in depth in the Introduction section.

68: ensures is not the right word; I suggest "suggests"

Accepted as suggested.

68-70: this explanation isn't clear to me; please revise

It will be revised. Accepted as suggested.

70: change cares to care

Accepted as suggested.

76-77: can you explain how they were not supported by the data for the benefit of those who are not familiar with these papers please?

Accepted as suggested.

87-89: I suggest the following wording: "In the autumn, we expect a greater number of eggs (fecundity) and larvae (fertility) in mosquitoes after feeding on a mammal host compared to an avian host, and the opposite relationship in the summer."

Accepted as suggested.

99: edit for grammar

Accepted as suggested.

102: suggest: "...offered a blood meal from a restrained chicken twice a month"

Accepted as suggested.

107: powder

Accepted as suggested.

108: inbred? Is this the term you meant to use?

Changed as suggested.

109: "several" cannot be used to describe 20 generations; suggest using "over twenty generations"; also, it would be good to acknowledge in your discussion that lab adaptation could force evolution, especially since mosquitoes are kept at constant temperatures and fed with certain hosts (with easy access) in the lab. Also, it would be good to know when the experiments were conducted to know the lapse of time between the creation of the colony and the experiments.

Accepted as suggested.

110-111: Does humidity vary between summer and fall in Córdoba? If so, I suggest acknowledging in the discussion that if humidity differences are involved in a potential interaction between host species and seasonality, then this would not have been captured by your experimental design.

Several variables change during seasons. We were interested in capturing the effects of temperature and photoperiod, since humidity is a variable difficult to control.

113-116: I suggest combining into one sentence to make more concise.

Accepted as suggested.

135: You might be obscuring the true impact of seasonality by rearing the larvae under the same conditions. There may be signals that mothers/eggs/larvae receive that influence their behavior (e.g. I believe this is the case for diapause), so this limitation should also be acknowledged. I understand why you decided to do this to control for development time and size, but it is something that should be considered in the discussion.

As it was explained above, Cx. quinquefasciatus do not suffer diapause in our country. Maintaining mosquitoes from adults was an approach selected by us based on other studies.

138: edit: "with cotton pads soaked in... on plastic..."; what is plastic glass? Do you mean plastic dishes?

Accepted as suggested.

141: here and throughout paragraph, full should be "fully"

Accepted as suggested.

144: located should be "placed"

Accepted as suggested.

147: suggest editing to "at which point, they were fixed with 1 mL of 96% ethanol and the number of L1 larvae per raft was counted."

Accepted as suggested.

154-155: edit for grammar

Accepted as suggested.

157: Your GLM explanation doesn't say anything about how you made pairwise comparisons between your levels; did you use emmeans?

This revised version includes a more detailed methodology and statistical analysis. Accepted as suggested.

158-160: I don't understand why you took this approach - it seems strange to me to use this analysis, but I am not familiar with it, so it might be that I lack the knowledge to be able to adequately evaluate. Please provide more explanation so that readers can better understand this analysis. A citation for this kind of application of the analysis would be helpful.

It was changed to be in accordance with the remaining analyses.

173: replace neither with either

Accepted as suggested.

174: this applies throughout; edit to : "An interaction effect was observed..."

Accepted as suggested.

175: "it was not found" is grammatically incorrect; instead : "We did not find ..." or "no differences in... were detected", etc

Accepted as suggested.

183: "it was detected" is grammatically incorrect

Accepted as suggested.

185-186: "being this treatment... in terms of fitness": I do not understand what this means. Please rephrase

Accepted as suggested.

170-199: you should provide the effect sizes and p values in text and/or in the figure for the pairwise comparisons

Accepted as suggested.

193-196. These two sentences are confusing and I am not sure what you mean, especially in the first sentence.

It was rewritten. Accepted as suggested.

Figure 1: This figure is great and easy to read and interpret! Thank you for the comment! 218-219: it is important to state which mosquito species you are referring to here.

Accepted as suggested.

226-227: you definitely should acknowledge the small sample size here.

Considered.

227: "it was observed" should be "We observed" or "A greater hatching rate.... was observed."

Accepted as suggested.

228-229: is the result really comparable even though you took very different approaches to the analysis for these outcomes?

Changed to be comparable.

230-278: the discussion of these hypotheses is too long and detailed, especially since the comparison of mouse vs chicken wasn't your main question; you really wanted to understand this in the context of seasonality. I suggest cutting this down a lot and making room to dig into your results more, and also to discuss the potential impacts of your experimental design/limitations on the results.

Discussion was changed to focus on our results and model. Accepted as suggested.

281: Hoffman is an old citation; I suggest you cite a modern review.

Accepted as suggested. We deleted it due to the re-writing of the manuscript.

282: "It can be recognise".. I am not sure what you are trying to say here

Accepted as suggested.

1. After the first time you write a species name, you can abbreviate the genus in all future mentions unless it is at the beginning of a sentence.

Accepted as suggested.

303-305: Revise this sentence. E.g "Fewer studies are available regarding photoperiod and show mixed results; Mogi (1992) found that mid and long day lengths induced greater fecundity while Costanzo et al. (2015) did not find differences in fecundity by day length."

Accepted as suggested.

315-316: typically, unpublished data shouldn't be referenced; I'm not sure if eLife has a policy on this.

We will check this with eLife guidelines. However, since the lack of evidence on this pattern we consider important to include this unpublished data.

316: Aegypti should be lowercase

Accepted as suggested.

328-330: This sentence is redundant with the first sentence of the paragraph

Accepted as suggested.

321-336: You never reintroduced your hypothesis in your discussion. I suggest that you center your whole discussion more directly around the hypothesis that motivated the study. If you decide not to restructure your discussion, you should at least reintroduce your hypothesis here and discuss how your results do not support the hypothesis.

Accepted as suggested.

337-348: This paragraph is a bit confusing as you jump between fertility and hatchability

Accepted as suggested.

353: is viral transmission the right word to use here? I think you might mean bridge vector transmission to humans specifically?

Accepted as suggested.

357: you say "neither" but never define which traits you are referring to

Accepted as suggested.

361: I suggest "two variables previously analyzed separately..."

Accepted as suggested.

General: There is no statement about the availability of data; it is eLife policy to require all data to be publicly available. Also, it would be helpful to share your code to help understand how you conducted pairwise comparisons, etc.

In the submission it was not mentioned anything about data availability. However, all data and scripts will be uploaded with the VOR if it is required.

Recommendations for the authors:

I found your study interesting and potentially promising. However, there are some fundamental problems with the study design and the hypothesis, including:

<(1) Seasonality simulation - Seasonality is strongly associated with time, so it is unusual to simulate seasonal factors without accounting for time. The actual factors associated with seasonal change in reproductive output may be neither a difference in host blood meal nor temperature and photoperiod. It is therefore, odd to reduce seasonality to a difference in photoperiod and temperature in summer and autumn without even mentioning the time of year when the experiment was carried (except for the mention of February as the time the stock samples were collected from the wild).

The temperature and photoperiod settings are established according to a representative day in both autumn and summer. To determine these settings, we utilized climate data spanning a 3-year period (2020-2022), encompassing the most frequently occurring temperatures and day lengths. The weather conditions remained notably consistent throughout this time frame, which is why the specific year was not mentioned. Moreover, including the year in laboratory experiment details is uncommon, as evident in various papers. This practice can be corroborated by referring to multiple sources (cited in the original manuscript). We mention this in the new version.

(2) Hypothesis - While the hypothesis alludes to the 'reason' for seasonal host shift, the prediction is on the outcome of the interaction between blood meal type and season.

It might be nicer to frame your hypothesis to be consistent with the aim, which is, testing the partial contributions of blood meal type, versus photoperiod and temperature to seasonal change in the reproductive output of Culex quinquefasciatus. A hypothesis like that can be accompanied by alternative predictions according to the expected individual and interactive effects of both factors.

It was rewritten in the revised version to be consistent with our predictions and findings.

Blood meal type, temperature, and photoperiod are all components of seasonality, so the strength of the study is its potential to decouple the effect of blood meal type from that of temperature and photoperiod on the seasonal reproductive output of Culex quinquefasciatus by comparing the two blood meal types under simulated summer and winter conditions. Ideally, this should have been over a natural summer and winter because a natural time difference captures the effect of other seasonal factors other than temperature and photoperiod.

Furthermore, the hypothesis stemmed from field observations, while the study itself was conducted under laboratory conditions using a local population of Culex quinquefasciatus from Argentina. It remains uncertain whether there is supporting evidence for a seasonal shift in host usage in Culex quinquefasciatus from the stock population. Discussing the field observations within the stock population would provide valuable insights.

It was considered in the new version.

Author Response

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

eLife assessment

This valuable study seeks to disentangle the different selective forces shaping the evolutionary dynamics of transposable elements (TEs) in the wild grass Brachypodium distachyon. Using haplotype-length metrics, and genetic and environmental differentiation tests, the authors present in large parts convincing evidence that positive selection on TE polymorphisms is rare, and that the distribution of TE ages points to purifying selection being the main force acting on TE evolution in this species. A caveat of this study, as of other studies that seek to assess TE insertion polymorphisms with short reads, is that the rates of false negatives and false positives are difficult to estimate, which may have major effects on the interpretation. This study will be relevant for anyone interested in the role of TEs in evolution and adaptation.

Thank you for considering our manuscript for publication in eLife. We appreciate the constructive comments and suggestions of the reviewers. We have addressed the raised issues by the reviewers. Below, we provide a more detailed response to each of the reviewer comments.

Public Reviews:

Reviewer #1:

The study presented in this manuscript presents very convincing evidence that purifying selection is the main force shaping the landscape of TE polymorphisms in B. distachyon, with only a few putatively adaptive variants detected, even though most conclusions are based on the 10% of polymorphisms contributed by retrotransposons. That first conclusion is not novel, however, as it had already been clearly established in natural A. thaliana strains (Baduel et al. Genome Biol 2021) and in experimental D. simulans lines (Langmüller et al. NAR 2023), two studies that the authors do not mention, or improperly mention. In contrast to the conclusions reached in A. thaliana, however, Horvath et al. report here a seemingly deleterious effect of TE insertions even very far away from genes (>5kb), a striking observation for a genome of relatively similar size. If confirmed, as a caveat of this study is the lack of benchmarking of the TE polymorphisms calls by a pipeline known for a high rate of false positives (see detailed Private Recommendations #1), this set of observations would make an important addition to the knowledge of TE dynamics in the wild and questioning our understanding of the main molecular mechanisms through which TEs can impact fitness.

Thank you for your positive evaluation of our paper. We have now adjusted the manuscript to include the mentioned studies (Line 330-333) and to address the issue of false positive and false negative calls. The detailed responses to all the raised points are below.

Reviewer #2:

Summary:

Transposable elements are known to have a strong potential to generate diversity and impact gene regulation, and they are thought to play an important role in plant adaptation to changing environments. Nevertheless, very few studies have performed genome-wide analyses to understand the global effect of selection on TEs in natural populations. Horvath et al. used available whole-genome re-sequencing data from a representative panel of B. distachyon accessions to detect TE insertion polymorphisms (TIPs) and estimate their time of origin. Using a thorough combination of population genomics approaches, the authors demonstrate that only a small amount of the TE polymorphisms are targeted by positive selection or potentially involved in adaptation. By comparing the age-adjusted population frequencies of TE polymorphisms and neutral SNPs, the authors found that retrotransposons are affected by purifying selection independently of their distance to genes. Finally, using forward simulations they were able to quantify the strength of selection acting on TE polymorphisms, finding that retrotransposons are mainly under moderate purifying selection, with only a minority of the insertions evolving neutrally.

Strengths:

Horvath et al., use a convincing set of strategies, and their conclusions are well supported by the data. I think that incorporating polymorphism's age into the analysis of purifying selection is an interesting way to reduce the possible bias introduced by the fact that SNPs and TEs polymorphisms do not occur at the same pace. The fact that TE polymorphisms far from genes are also under purifying selection is an interesting result that reinforces the idea that the trans-regulatory effect of TE insertions might not be a rare phenomenon, a matter that may be demonstrated in future studies.

Weaknesses:

TEs from different classes and orders strongly differ in multiple features such as size, the potential impact of close genes upon insertion, insertion/elimination ratio (ie, MITE/TIR excision, solo-LTR formation), or insertion preference. Given such diversity, it is expected that their survival rates on the genome and the strength of selection acting on them could be different. The authors differentiate DNA transposons and retrotransposons in some of the analyses, the specificities of the most abundant plant TE types (ie, LTR/Gypsy, LTR/Copia, MITE DNA transposons) are not considered.

The authors used a short-read-based approach to detect TIPs and TAPs. It is known that detecting TE polymorphisms is challenging and can lead to false negatives, depending on the method used and the sequencing coverage. The methodology used here (TEPID) has been previously applied to other species, but it is unclear if the sensitivity of the TIP/TAP caller is equivalent to that of the SNP caller and how these potential differences may affect the results.

Thank you for your positive evaluation of our paper. We have now adjusted the manuscript and the discussion to include the mentioned points on the different TE superfamilies and the reliability of the TE calls. The detailed responses to all the raised points are below.

Private Recommendations:

Reviewer #1:

(1) TE polymorphisms (presence and absence variants) were called from short-read sequencing data using a pipeline (TEPID, Stuart et al. eLife 2016) that is known to have a low specificity as well as a low sensitivity in its detection of presence variants (Baduel et al. MIMB 2021). An assessment of the rate of false positives and false negatives in the data presented in this study and how it varies across TE superfamilies is therefore of crucial importance as it may bias all downstream analyses, especially if it impacts the identification of polymorphisms contributed by retrotransposons, as these are the basis of most conclusions of the manuscript. Nonetheless, the fact that the PCA of the polymorphisms contributed by DNA transposons is less able to distinguish genetic clades than with those contributed by retrotransposons, suggests the issue of false positives is most preeminent for DNA transposons. However, high rates of false positives may explain why no significant increase in TE frequency is detected within selective sweep regions, a result that runs against the expectation of hitch-hiking of neutral or weakly deleterious polymorphisms which the authors claim is the category of many TE polymorphisms. Furthermore, given that the reference genome belongs to the B_east clade, and the TEPID is better at calling absence than presence it may bias analyses in this clade (where clade-specific insertions will take the form of absence in other clades which are well detected) compared to other clades (where clade-specific insertions will be presence polymorphisms and may be missed). A benchmark of TE polymorphism calls could be done by de novo assembling one genome from each clade or by cross-checking at least the presence variant calls from TEPID with those made with another of the many TE calling pipelines available.

We agree with this issue raised by both reviewers regarding the effects of false negative and false positive TE calls. We also think that some reasonable follow-ups should be done to check the potential impact of the false negative and false positive TE calls on the presented results, without turning the manuscript in a method comparison paper as this is not the main goal of this study. Therefore, we generated a subsample of our dataset that included only accession with an average genome wide mapping coverages of at least 20x, as the false negative TE call rate is correlated with the mapping coverage and a high mapping coverage is expected to lead to a reduction in the false negative TE call rates. We then used this subsample to check if our results would change if our dataset had a lower false negative TE call rate. However, reducing the rate of false negative calls through the use of only higher coverage samples did not change our results and interpretations.

Re-running the ANCOVA analyses revealed similar results regarding the accumulation of TEs in selective sweep regions. This was added to the main text Line 143-148: “Similar results were obtained when investigating the number of fixed TE polymorphisms (Additional file 2: Table S1) and the allele frequency of TE polymorphisms (Additional file 2: Table S2) in high iHS regions using a subset of our dataset with an expected lower false negative TE call rate, that only included samples with a genome-wide mapping coverage of at least 20x (see Discussion and Materials and Methods for more details).” and in Additional file 2: Table S1 and S2.

Further, we re-ran the age-adjusted SFS based on this subset of our dataset and found that the results and conclusions from the age-adjusted SFS were not only driven by false negative TE calls. This was also included in the text Line 338-349: “One caveat of the approach used in this study is that TE calling pipelines based on short-reads tend to have higher false positive and false negative call rates than SNP calling pipelines, which is also the case for the TEPID TE calling pipeline used here [57, 59]. A high false negative TE calling rate however might bias our TE frequency estimates toward lower frequencies, which could drive the observed patterns in the age-adjusted SFS. To assess if the false negative TE calling rate in our study substantially affected our results, we re-run the age-adjusted SFS on a subset of our dataset only including samples with a genome-wide mapping coverage of at least 20x, as higher mapping coverages are expected to reduce the false negative call rate [27, 59]. Using the TE allele frequencies estimated based on this subset of our data to estimate  frequency revealed similar results of the age-adjusted SFS based on the whole dataset (Additional file 1: Fig. S9), indicating that our observation of retrotransposons evolving under purifying selection is not solely driven by a high false negative TE calling rate.” and in Additional file 1: Fig. S9.

The details of this analyses have been added to the materials and methods Line 493-498: “Mapping coverage is known to influence false discovery rate [27, 59]. To investigate the impact of false positive and false negative TE calls on our results, we down sampled the TE dataset to only include TEs that have been called in samples that had at least an average mapping coverage of 20x. The allele frequencies of TEs present in our high coverage dataset was recalculated only considering samples with at least an average mapping coverage of 20x. This second TE dataset was then used to check if using a dataset with a higher mapping coverage and presumably a lower false TE calling rate impacted our results.”

(2) If confirmed, the observation that retrotransposons located more than 5kb away from genes appear to be also affected by purifying selection (L209) is indeed surprising. The authors should add a comparison with SNPs at the same distance from genes to strengthen the claim and make sure it is not the result of mapping artifacts, such as alignment quality dropping far away from genes.

We added a comparison of the age-adjusted SFS of SNPs and retrotransposons more than 5 kb away from genes to evaluate if the observed shape of the age-adjusted SFS of retrotransposons more than 5 kb away from genes were due to artefacts. The results are included on line 383-389: “Finally, we tested whether TE polymorphisms located more than 5 kb away from genes are evolving under purifying selection could be due to mapping or other artefacts by comparing the shape of the age-adjusted SFS of retrotransposons and SNPs more than 5 kb away from genes. However, the age-adjusted SFS of SNPs 5 kb away from genes differs from the one of retrotransposons (Additional file 1: Fig. S10), indicating that the shape of the age-adjusted SFS of retrotransposons more than 5 kb away from genes is not likely to be the result of artefacts in regions of the genome far away from genes.” and Additional file 1: Fig. S10.

(3) The authors' claim that most TE polymorphisms are under weak to moderate purifying selection (L273) relies on the comparison of the age of polymorphisms in the oldest age bin with forward simulations. However, the conclusions from these comparisons cannot be extrapolated to the fitness effects of all TE polymorphisms as variants in the oldest age bin are de facto a biased sample of the variants of a category, a point the authors highlight.

We adjusted the mentioned paragraph to better highlight this point. Line 390-397: “To further ascertain the strength of purifying selection, we used forward simulation and showed that simulations assuming a moderately weak selection pressure (S = -5 or S = -8) against TE polymorphisms best fitted our observed data. In theory, no TE polymorphisms under strong purifying selection should be present in a natural population, as such mutations are expected to be quickly lost, especially in a predominantly selfing species where most loci are expected to be homozygous. Therefore, it is not surprising that TE polymorphisms which persist in B. distachyon are under weak to moderate selection, as also shown, for example, for the L1 retrotransposons in humans [27] or the BS retrotransposon family in Drosophila melanogaster [62].”

L220-228 for high-effect SNPs. Indeed, the most deleterious TE polymorphisms would be purged very quickly and never contribute to variants in the oldest age bin. Unless new arguments can be made to support this claim, this conclusion should be rephrased to claim instead that even the oldest TE polymorphisms are still mostly non-neutral and under weak to moderate purifying.

This has been adjusted. Line 231-232: “. Hence, even the oldest retrotransposon polymorphisms seem to be mostly non-neutral and are affected by purifying selection.”

L214: replace smaller with more negative for clarity.

Done.

L233: Given the discussion L220-228, the oldest age bin seems to be biased in its composition and thus not useful for comparisons. The sentence should therefore be rephrased to reflect that DNA transposon polymorphisms appear to be actually less deleterious than high-effect SNPs in S9A and B based on the penultimate age bin.

This has been fixed.

Reviewer #2:

• I wonder if false negative detection could artificially increase the evidence for purifying selection by increasing the amount of low-frequency variants. This could be easily checked if long-read data or genome assembly is available for any of the samples in the collection, by comparing the TIP/TAP prediction with the actual sequence.

We agree with this point from the reviewers that false negative calls can lead to misinterpretations of the observed low-frequencies of the TEs. (But see response to the first comment of reviewer #1). Unfortunately, long-read data from the sample used here are not available to estimate false negative call rates. However, to check if the observed results are manly driven by high false negative rates, we re-run the age-adjusted SFS based on samples with at least 20x mapping coverage, which should result in the reduction the false negative TE calling rate. The results and conclusions from this second analyses were included in the text Line 338-349: “One caveat of the approach used in this study is that TE calling pipelines based on short-reads tend to have higher false positive and false negative call rates than SNP calling pipelines, which is also the case for the TEPID TE calling pipeline used here [57, 59]. A high false negative TE calling rate however might bias our TE frequency estimates toward lower frequencies, which could drive the observed patterns in the age-adjusted SFS. To assess if the false negative TE calling rate in our study substantially affected our results, we re-run the age-adjusted SFS on a subset of our dataset only including samples with a genome-wide mapping coverage of at least 20x, as higher mapping coverages are expected to reduce the false negative call rate [27, 59]. Using the TE allele frequencies estimated based on this subset of our data to estimate  frequency revealed similar results of the age-adjusted SFS based on the whole dataset (Additional file 1: Fig. S9), indicating that our observation of retrotransposons evolving under purifying selection is not solely driven by a high false negative TE calling rate.” and in Additional file 1: Fig. S9.

• Supplementary Figure S1. DNA transposons are much worse at separating the samples in comparison to LTR-retrotransposons. Doesn´t this suggest that these two classes have very different dynamics in the population and maybe different intensities of the selection forces acting on them? Could this profile be explained as DNA transposons being older and likely more fixed in all the clades, whereas retrotransposons are more recent and more specific to some populations? Another possibility might be that some B. distachyon DNA transposons had an unusually high excision rate. In any case, in my opinion, this reinforces the need to study the different TE orders in more detail.

Indeed, different TE orders and superfamilies can have different excision rates, age distributions and be under different selective regimes. To investigate the possibility that different TE orders are affected by very different selective regimes, we split our TE dataset into the four different TE types: Copia, Ty3, Helitron and MITE. We than re-run the age-adjusted SFS analyses and added our results to the text Line 422-430: “To further examine our conclusion on purifying selection, we investigated the selective regime affecting different retrotransposons and DNA-transposons superfamilies. Thereby, we generated age-adjusted SFS for the four most common TE superfamilies Copia, Ty3 (also known under the name Gypsy, but we will avoid using this name because of its problematic nature see [71]), Helitron and MITE and found similar deviations of the  frequency from 0 in the four investigated TE superfamilies (Additional file 1: Fig. S12–S15). These results indicate that our conclusion on the broad effect of purifying selection is not driven by a single TE superfamily but is at least common among the four most numerous TE superfamilies.” and in Additional file 1: Fig. S12- S15.

• Line 112: "most TE polymorphisms in our dataset were young and only a few were very old". Does this change substantially among TE orders/superfamilies?

Indeed, there are some differences in the age distribution of the TEs depending on the superfamilies, However, the differences are no substantial as the age bins in the age-adjusted SFS of the different TE superfamilies are fairly similar. See Additional file 1: Fig. S12-S15.

• Figure 2. Is difficult to read, especially lower panels. I think the grey border of the boxplots makes visualization difficult.

The gray borders have been removed.

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.

Author Response

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

eLife assessment

This is an important study that provides new insights into the development and function of medullary thymus epithelial cells (mTEC). The authors provide compelling evidence to support their claims as to the differentiation and lineage outcomes of CCL21+ mTEC progenitors, which further our understanding of how central tolerance of T cells is enforced within the thymus.

Public Reviews:

Reviewer #1 (Public Review):

The work by Ohigashi and colleagues addresses the developmental and lineage relationship of a newly characterized thymus epithelial cell (TEC) progenitor subset. The authors take advantage of an elegant and powerful set of experimental approaches to demonstrate that CCL21-expressing TECs appear early in thymus organogenesis and that these cells, which are centrally located, go on to give rise to medullary (m)TECs. What makes the findings intriguing is that these CCL21-expressing mTECs are a distinct subset, which do not express RANK or AIRE, and transcriptomic and lineage tracing approaches point to these cells as potential mTEC progenitor-like cells. Of note, using in vitro and in vivo precursor-product cell transfer experiments, the authors show that this subset has a developmental potential to give rise to AIRE+ self-antigen-displaying mTECs, revealing that CCL21-expressing mTECs can give rise to distinct mTEC subsets. This functional duality provides an attractive rationale for the necessary function of mTECs, which is to attract CCR7+ thymocytes that have just undergone positive selection in the thymus cortex to enter the medulla to undergo tolerance-induction against self-antigen-displaying mTECs. Overall, the work is well supported and offers new insights into the diverse functions of the medullary compartment, and how two distinct subsets of mTECs can achieve it.

Reviewer #2 (Public Review):

Summary:

The authors set out to discover a developmental pathway leading to functionally diverse mTEC subsets. They show that Ccl21 is expressed early during thymus ontogeny in the medullary area. Fate-mapping gives evidence for the Ccl21 positive history of Aire positive mTECs as well as of thymic tuft cells and postnatally of a certain percentage of cTECs. Therefore, the differentiation potential of Ccl21+ TECs is tested in reaggregate thymus experiments - using embryonic or postnatal Ccl21+ TECs. From these experiments, the authors conclude that at least embryonic mTECs in large part pass through a Ccl21 positive stage prior to differentiation towards an Aire expressing or tuft cell stage.

The authors are using Ccl21a as a marker for a bipotent progenitor that is detectable in the embryonic thymus and is still present at the adult stage mainly giving rise to mTECs. The choice of this marker gene is very interesting since Ccl21 expression can directly be linked to an important aspect in thymus biology: the expression of Ccl21 by cells in the thymic medulla allows trafficking of T cells into the medulla in order to undergo T cell selection.

Making use of the Ccl21 detection, the authors can nicely show that cells actively expressing Ccl21 are localized throughout the medulla at an embryonic stage but also in adult thymus tissue. This suggests, that this progenitor is not accumulating at a specific area inside the medulla. This is a new finding.

Moreover, the finding that a Ccl21+ progenitor population plays a functional role in thymocyte trafficking towards the medulla has not been described. Thus, Ccl21 expression may be used to localize a late bipotent progenitor in the thymic lobes.

In addition, in Fig.8, the authors provide evidence that these progenitor cells have the potential to self-maintain as well as to differentiate in reaggregate experiments at E17 (not at 4 weeks of age). The first point is of great interest and importance since these cells in theory can be of therapeutic use.

Overall assessment:

The authors highlight a developmental pathway starting from a Ccl21-expressing TEC progenitor that contributes to a functionally diverse mTEC repertoire. This is a welcome addition to current knowledge of TEC differentiation.

Reviewer #3 (Public Review):

In this manuscript, the authors define the developmental trajectory resulting in a diverse mTEC compartment. Using a variety of approaches, including a novel CCL21-fate mapping model, data is presented to argue that embryonic CCL21-expressing thymocyte attracting mTECs naturally convert to into self-antigen displaying mTEC subsets, including Aire+ mTECs and thymic tuft cells. Perhaps somewhat surprisingly, a large fraction of cTECs were also marked for having expressed CCL21, suggesting that there exists some conversion of mTEC (progenitors) into cTEC, a developmentally interesting observation that could be followed up later. Overall, the experimental setup, writing, and conclusions, are all outstanding.

Provisional author response

We thank the editors and the reviewers for their supportive comments on our manuscript. We will revise the manuscript according to their helpful recommendations.

Author response to recommendations

We thank the editors and the reviewers for their supportive comments on our manuscript. We also thank the three reviewers for their helpful recommendations. We have revised the manuscript accordingly, as detailed below.

Reviewer #1 (Recommendations For The Authors):

There are several unanswered questions, which the authors themselves acknowledge, a principal one being whether CCL21+ mTECs represent a progenitor for yet another distinct subset of cortical (c)TECs, or whether they represent an intermediary or unique population of mTECs derived from a bipotent (cTEC/mTEC) progenitor. These questions will need to be addressed in future work as they go beyond the initial characterization of this intriguing mTEC subset.

Indeed, our findings reported in this manuscript have stimulated many interesting questions, including those pointed out by the reviewer. We would like to address them one by one in our future work.

The presence of GFP+ cTECs, which are lineage-traced as having expressed CCL21, begs the question as to whether these cells are generated as a consequence of later steps in mTEC differentiation or derived from earlier bipotent cells, which again the authors point out. The authors could discuss this further or perhaps experimentally address this by using a model system whereby mTEC differentiation is absent or halted (e.g., Relb ko, or TCRa/TCRd ko) and test whether GFP+ cTECs are still present.

According to the suggestion, we have revised the manuscript by adding a statement that it is interesting to examine whether GFP+ cTEC development in Ccl21a-Cre x CAG-loxP-EGFP mice is mediated through RelB-dependent mTEC developmental progression or developing thymocyte-dependent mTEC-nurturing ‘crosstalk’ signals.

Reviewer #2 (Recommendations For The Authors):

Even though the manuscript highlights the functional aspect of a postnatal bipotent progenitor, there are several aspects that need further discussion.

(1) The title is somewhat misleading since the identified TEC subset can not only be detected in embryonic, but also in postnatal thymus. Only the RTOC experiments indicate a higher developmental potential of TECs isolated from embryos, but this might as well be due to experimental difficulties as discussed in the text. Furthermore, Ccl21+ TECs are shown to differentiate postnatally into mTECs and cTECs, therefore this subset presumably belongs to a bipotent progenitor population described earlier (their ref. 22, 39).

We are fully aware of previous studies showing that mTEC progenitors include cells that transcribe Ccl21a, and have cited them in the manuscript. The manuscript title describes our finding that thymocyte-attracting CCL21-expressing functional mTECs isolated from embryonic thymus show the capability to give rise to self-antigen-displaying mTECs. We thank the reviewer for further pointing out the possibility that postnatal CCLl21+ TECs include cells that retain the capability to differentiate into mTECs and cTECs.

(2) In the introduction the authors claim that the "developmental progression of the self-antigen-displaying mTEC subset occurs in a single stream as mTEClow progenitors -> mTEChigh Aire-expressing cells -> mTEClow mimetic cells." line 79. So far it only could be shown that some mimetic cell types undergo an Aire+ stage; whether this is true for all mimetic cells remains to be shown. Therefore, this statement should be toned down.

Following the suggestion, this sentence has been toned down in the revised manuscript.

(3) In line 86, the reference to another paper, describing Ccl21a expression in a postnatal mTEC biased progenitor should be added: Nusser et al. Nature. 2022 PMID: 35614226, in which the developmental potential of the Ccl21 positive so-called postnatal progenitor is analysed by barcoding and results give evidence for differentiation into mature mTECs (see lines 94-96).

As suggested, the Introduction of the revised manuscript now cites Nusser, et al. study showing that postnatal mTEC-biased progenitors include cells that transcribe Ccl21a.

(4) Have a look at Extended Data Figure 2b of PMID: 35614226, wherein the population-specific gene expression pattern of the progenitor population at different time points is depicted. Ccl21a belongs to a group of genes, which identifies the postnatal progenitor, and indicates that its functionality and/or developmental potential is age-dependent. Therefore, it would be important to specify the age of the analysed mice throughout the text of the results part instead of describing them as "postnatal" only.

As recommended, mouse age has been added to the revised manuscript and figures.

(5) Line 113: "embryonic" needs to be replaced since the results of Fig. 1 are referring to 5-week-old mice.

The manuscript has been revised per the reviewer’s suggestion.

(6) Referring to Fig. 3g, line 173: It is interesting to see that, at 3 weeks of age, 95% of mTECs have a Ccl21-history but only approx. 70% of cTECs. Therefore, the earliest progenitor giving rise to the first cTECs might still be productive and feed into the cTEC lineage. This reporter would allow for the analysis of progenitor activity over time. The same could be done for mTECs since at E15 the tdTomato signal is still low compared to the assigned medullary area in Fig. 2c in order to detect when the Ccl21-expressing progenitor becomes the main source of mTECs. The finding in Fig. 4e (line196) also argues for the timed replacement of cTECs by a progenitor which locates to the medulla, thus, leading to a decline in Ccl21-history signal towards the subcapsular region at 2 weeks of age. This should be better explained/discussed.

We appreciate the work of Nusser, et al. showing that postnatal mTEC-biased, but not embryonic cTEC-biased, TEC progenitors include cells that transcribe a detectable amount of Ccl21a (cited in the Introduction as ref. 23). It is important to clarify whether and how those postnatal TEC progenitors (23) overlap with the embryonic and postnatal CCL21-protein-expressing mTECs reported in this study. It is also interesting to shed light on how Ccl21a+ progenitors contribute to cTECs and mTECs over the ontogeny and whether the enrichment of Ccl21a+ progenitor-derived cTECs in the perimedullary area reflects a temporal replacement of cTECs derived from Ccl21a+ progenitors localized in the medulla. We would like to clarify these issues in our future work. The revised manuscript includes a discussion of these issues.

(7) Line 304 and 355: Note that the "unstable" age-dependent gene expression profiles were already reported in Nusser et al. Nature. 2022. Not only Ccl21 expression, but other progenitor-specific genes also change their expression levels with age. The entirety of changes in gene expression during aging likely impacts the developmental potential of progenitor populations. These changes might be reflected in the negative results of the RTOC experiment using TECs of 4-week-old mice. The manuscript would benefit from a discussion in light of this "unstable" age-dependent gene expression.

It is interesting to point out that the age-dependent difference in gene expression profiles, which was reported in TEC progenitors by Nusser, et al. (23), is also detected in CCL21-expressing mTECs in this study. Similarly to the recommendation no. 6 by reviewer 2, and as described in the revised manuscript, it is interesting to clarify whether and how embryonic and postnatal CCL21-expressing mTECs overlap with the previously reported TEC progenitors.

(8) Line 321: as discussed above, the exact time point should be added to the text since the proportion of cTECs derived from a Ccl21+ progenitor is associated with a certain time point, "2/3 of cTECs" refers to 3 weeks of age.

The manuscript has been revised following the reviewer’s suggestion.

Reviewer #3 (Recommendations For The Authors):

The one question I have, which may be more of a curiosity of this reviewer than a requirement for the manuscript, is whether thymocytes themselves are required for the conversion/maturation of attracting TECs to mTECs? For example, in CD3e-/- (or Rag-/-) mice, are mTECs arrested at the thymocyte attracting stage, or is the conversion process 'pre-programed'? In the same vein, do cTECs (or the immature cTECs) maintain CCL21 expression in the absence of mature thymocytes? These are not critical studies but are fairly straightforward (effort- and time-wise) that would aid in placing this process in the overall scope of thymus development.

We previously showed that Aire+ mTECs are detectable in the thymus of RAG2-deficient mice, in which thymocyte development is arrested beyond the CD4/CD8 double-negative 3 stage (Hikosaka, et al. 2006; PMID: 18799150). In another work, we also showed that Aire+ mTECs and CCL21+ mTECs are detectable in the thymus of TCR-alpha-KO mice, which lack mature CD4/CD8 single-positive TCR-alpha/beta-expressing thymocytes (Lkhagvasuren, et al. 2013; PMID: 23585674). These results indicate that thymocyte maturation beyond the Rag-dependent stage is not essential for the development of Aire+ mTECs. Nonetheless, we agree with the reviewer pointing out that it is important to clarify how developing thymocytes contribute to the growth and differentiation of diverse TEC subpopulations, including GFP+ cTEC development in Ccl21a-Cre x CAG-loxP-EGFP mice. The revised manuscript includes a discussion of these issues.

Author Response

We thank eLife Senior Editor and reviewers for the comprehensive evaluation and constructive comment on our manuscript. We are grateful that all 3 reviewers recognize the value of the large pharmacological and proteomics screen of 51 cancer cell lines in relation to vitamin C IC50 values. As reviewer 1 points out, our findings are of interest as high dose vitamin C is in clinical trials. Most importantly, we show that all 51 cell lines tested can be killed at a dose range that is achievable by intravenous administration in the clinic. These pharmacological findings underscore high-dose vitamin C as a potent anti-cancer agent. Moreover, we provide an elaborate description of functional terms associated with the vitamin C IC50 values in the different cell panels (Figs 1-5) and the common denominators across panels (Figs 6, 7 and 8), thereby enhancing our biological insights of sensitivity to vitamin C treatment. This study indeed is of descriptive nature and our large scale pharmacological and proteomics scale dataset should be seen as a resource for further research. The raw and processed data will be available in the ProteomeXchange repository (accession number and reviewer password were provided before) and the resubmission will include all processed proteome and phosphoproteome data as a supplementary file.

It is beyond the scope of our study to do mechanistic studies with knock-downs to see if we can further sensitize cancer cell lines that are less sensitive. We do not call these cell lines resistant as cell growth can be inhibited at a clinically achievable dose.

In our detailed rebuttal we will follow up on the suggestion of reviewer 1 to put our data also in the context of NCI-60 growth inhibition data for other cytotoxic agents. This will expand our comparative analysis to cisplatin in the lung cancer panel (Fig 5A) where we show that vitamin C IC50 values and cisplatin IC50 values are not one-on-one correlated as one of the most cisplatin resistant NSCLC cell lines in our panel was very sensitive to high dose vitamin C. Furthermore, we will clarify method details and annotate mutational status in our panels and explore potential genomic associations to high-dose vitamin C sensitivity as presented in previous studies (e.g. mutant BRAF and/or KRAS tumors, https://doi.org/10.1126/science.aaa5004).

Finally, we will critically read the manuscript and add references where needed.

Author Response

Reviewer #1 (Public Review):

Summary:

Heer and Sheffield used 2 photon imaging to dissect the functional contributions of convergent dopamine and noradrenaline inputs to the dorsal hippocampus CA1 in head-restrained mice running down a virtual linear path. Mice were trained to collect water rewards at the end of the track and on test days, calcium activity was recorded from dopamine (DA) axons originating in the ventral tegmental area (VTA, n=7) and noradrenaline axons from the locus coeruleus (LC, n=87) under several conditions. When mice ran laps in a familiar environment, VTA DA axons exhibited ramping activity along the track that correlated with distance to reward and velocity to some extent, while LC input activity remained constant across the track, but correlated invariantly with velocity and time to motion onset. A subset of recordings taken when the reward was removed showed diminished ramping activity in VTA DA axons, but no changes in the LC axons, confirming that DA axon activity is locked to reward availability. When mice were subsequently introduced to a new environment, the ramping to reward activity in the DA axons disappeared, while LC axons showed a dramatic increase in activity lasting 90 s (6 laps) following the environment switch. In the final analysis, the authors sought to disentangle LC axon activity induced by novelty vs. behavioral changes induced by novelty by removing periods in which animals were immobile and established that the activity observed in the first 2 laps reflected novelty-induced signal in LC axons.

Strengths:

The results presented in this manuscript provide insights into the specific contributions of catecholaminergic input to the dorsal hippocampus CA1 during spatial navigation in a rewarded virtual environment, offering a detailed analysis of the resolution of single axons. The data analysis is thorough and possible confounding variables and data interpretation are carefully considered.

Weaknesses:

Aspects of the methodology, data analysis, and interpretation diminish the overall significance of the findings, as detailed below.

The LC axonal recordings are well-powered, but the DA axonal recordings are severely underpowered, with recordings taken from a mere 7 axons (compared to 87 LC axons). Additionally, 2 different calcium indicators with differential kinetics and sensitivity to calcium changes (GCaMP6S and GCaMP7b) were used (n=3, n=4 respectively) and the data pooled. This makes it very challenging to draw any valid conclusions from the data, particularly in the novelty experiment. The surprising lack of novelty-induced DA axon activity may be a false negative. Indeed, at least 1 axon (axon 2) appears to be showing a novelty-induced rise in activity in Figure 3C. Changes in activity in 4/7 axons are also referred to as a 'majority' occurrence in the manuscript, which again is not an accurate representation of the observed data.

The reviewer points out a weakness in the analysis of VTA axons in our dataset. The relatively low n (currently 7) comes from the fact that VTA axons in the CA1 region of the hippocampus are very sparse and very difficult to record from (due to their sparsity and the low level of baseline fluorescence inherent in long range axon segments). This is the reason they have not been recorded from in any other lab outside of our lab. LC axons, on the other hand, are more abundant in CA1. In the paper when comparing VTA versus LC axons we deal with the mismatch in n by downsampling the LC axons to match the VTA axons and repeated this 1000 times to create a distribution. However, because the VTA axon n is relatively low, it is possible that we have not sampled the VTA axon population sufficiently and therefore have a biased population in our dataset. The issue is that it takes months for the baseline expression of GCaMP to reach sufficient levels to be able to record from VTA axons, and it is typical to find only a single axon in a FOV per animal. There are additional reasons why mice and/or axon recordings do not reach criteria and cannot be included in the dataset (these exclusion criteria are reported in the Methods section). For instance, out of the 54 DAT-Cre mice injected, images were never conducted in 36 for lack of expression or because mice failed to reach behavioral criteria. Another 11 mice were excluded for heat bubbles that developed during imaging, z-drift of the FOV, or bleaching of the GCaMP signal.

However, we do have n=2 additional VTA axon recordings that we will add to the dataset to bring the n up from 7 to 9. We plan on re-analyzing the data with n=9 VTA axons and making comparisons to down-sampled LC axons as described above. This boost in n will increase the power of our VTA axon analysis. To more formally test whether this is sufficient for statistical tests, we plan to utilize the G*power power-analysis tool to compute statistical power for each of the different tests we use. We will report this in the next version of the paper. However, the n=2 additional axons were nor recorded in the novel environment, so the next version will remain at n=7 for the novel environment analysis. We agree with the reviewer that the lack of the novelty induced DA axon activity may be a false negative, and so we will adjust the description of our results and discussion accordingly.

During the data collection of VTA axon activity we tried two variants of GCaMP: 6s and 7b, to see if one would increase the success rate of finding and recording from VTA axons. Given the long time-course of these experiments and the low yield in success, we pooled the GCaMP variants together to increase statistical power. Because the 2 additional VTA DA axons that were recorded from expressed GCaMP6s, the next version of the paper will have n=5 GCaMP6s, and n=4 GCaMP7b VTA DA axons, which will allow us to compare the activity of the two sensors in the familiar environment. The reviewer correctly pointed out that the sensors themselves could confound our results, and so they should not be pooled unless we can show they do not produce different signals in the axons. We will make this comparison and report the findings in the next version of the paper. If we find no significant differences, we will pool the data. If differences are detected, we will keep these axons separate for subsequent analysis and comparisons to LC axons.

The authors conducted analysis on recording data exclusively from periods of running in the novelty experiment to isolate the effects of novelty from novelty-induced changes in behavior. However, if the goal is to distinguish between changes in locus coeruleus (LC) axon activity induced by novelty and those induced by motion, analyzing LC axon activity during periods of immobility would enhance the robustness of the results.

This is indeed true, and this suggested analysis could further support our conclusions regarding the LC novelty signal. For the next version of the paper, we will use the periods of immobility to analyze and isolate any novelty induced activity in LC axons. However, following exposure to the novel environment, mice spend much less time immobile, therefore there may not be sufficient periods of immobility close in time to the exposure to the novel environment (which is when the novelty signal occurs). We plan to analyze mouse behavior during the early exposure to the novel environment for immobility and check whether we have enough of this behavior to perform the suggested analysis.

The authors attribute the ramping activity of the DA axons to the encoding of the animals' position relative to reward. However, given the extensive data implicating the dorsal CA1 in timing, and the remarkable periodicity of the behavior, the fact that DA axons could be signalling temporal information should be considered.

This is a very good point. We agree that the VTA DA axons could be signaling temporal information, as we have previously shown that these axons also exhibit ramping activity when you average their activity by time to reward (Krishnan et. al., 2022). We will conduct this analysis on this dataset. We have not, however, conducted any experiments designed to separate out time from distance, such as the experiments conducted in Kim et. al., 2020. Therefore, we cannot determine whether this is due to proximity in space to reward or time to reward. We will clarify in our text that by proximity, we mean either place or time, and cannot conclude which feature of the experience drives the VTA axon signal.

Krishnan, L.S., Heer, C., Cherian, C., Sheffield, M.E. Reward expectation extinction restructures and degrades CA1 spatial maps through loss of a dopaminergic reward proximity signal. Nat Commun 13, 6662 (2022).

Kim, HyungGoo R., Athar N. Malik, John G. Mikhael, Pol Bech, Iku Tsutsui-Kimura, Fangmiao Sun, Yajun Zhang, et al. A Unified Framework for Dopamine Signals across Timescales. Cell 183, no. 6 (2020).

The authors should explain and justify the use of a longer linear track (3m, as opposed to 2m in the DAT-cre mice) in the LC axon recording experiments.

LC axon activity was recorded on a 3m track to match the track length from an experiment we recently published (Dong et al., 2021) in which mice were exposed to a novel 3m track while populations of CA1 pyramidal cells were recorded. In that paper we described the time course of place field formation on the novel track. We wanted to test if LC axons signaled novelty (as we hypothesized) and whether the time course of LC axon activity matched the time course of place field formation. We briefly discuss this in the Discussion section of this paper and hypothesize that LC axons in CA1 could open a window of plasticity in which new place fields can form.

VTA axons were recorded on a 2m track (same VR tracks as LC axons were recorded on) to match another recent paper from our lab in which reward expectation was manipulated (Krishnan et al, 2022). In that study CA1 populations of pyramidal cells were recorded during the reward expectation experiment. To match the experience during recordings of VTA axons in CA1 to test how reward expectation may influence axon signaling along the track, we also used a 2m track. The idea was to check how VTA dopaminergic inputs to CA1 may influence CA1 population dynamics along the track.

Although the tracks were identical for LC and VTA recordings for both the familiar and novel tracks in terms of visual cues and design, the track lengths are different (simply modulated by gain control of the rotary encoder). To account for this we normalized the lengths for our comparison analysis. This normalization allows for a direct comparison of the patterns of activity across the two types of axons, controlling for the potential confound introduced by the different track lengths. By adjusting the data to a common scale, we could assess the relative changes in activity levels at matched spatial bins, ensuring that any observed differences or similarities are due to the intrinsic properties of the axons rather than differences in track lengths. However, the different lengths do make the animal’s experience slightly different. This is somewhat offset by the observations in our study that none of the LC or VTA axon signals would be expected to be majorly influenced by variations in track length. For instance, LC axons are associated with velocity and a pre-motion initiation signal, neither of which would be influenced by track length. VTA axons are also associated with velocity, which would not influence a direct comparison to LC axon velocity signals as mice reach maximal velocity very rapidly along the track. VTA axons do ramp up in activity as they approach the reward zone, and this signal could be modulated by track length (or maybe not if the signal is encoding time to reward rather than distance). However, LC axons show no ramping to reward signals, so a comparison across axons recorded on different track lengths for this analysis is justified.

However, to add rigor to comparisons of axon dynamics recorded along 2m and 3m tracks, we plan to plot axon activity of both sets of axons by time to reward, and actual (un-normalized) distance from reward.

Krishnan, L.S., Heer, C., Cherian, C., Sheffield, M.E. Reward expectation extinction restructures and degrades CA1 spatial maps through loss of a dopaminergic reward proximity signal. Nat Commun 13, 6662 (2022).

Dong, C., Madar, A. D. & Sheffield, M.E. Distinct place cell dynamics in CA1 and CA3 encode experience in new environments. Nat Commun 12, 2977 (2021).

Reviewer #2 (Public Review):

Summary:

The authors used 2-photon Ca2+-imaging to study the activity of ventral tegmental area (VTA) and locus coeruleus (LC) axons in the CA1 region of the dorsal hippocampus in head-fixed male mice moving on linear paths in virtual reality (VR) environments.

The main findings were as follows:

• In a familiar environment, the activity of both VTA axons and LC axons increased with the mice's running speed on the Styrofoam wheel, with which they could move along a linear track through a VR environment.
• VTA, but not LC, axons showed marked reward position-related activity, showing a ramping-up of activity when mice approached a learned reward position.
• In contrast, the activity of LC axons ramped up before the initiation of movement on the Styrofoam wheel.
• In addition, exposure to a novel VR environment increased LC axon activity, but not VTA axon activity.

Overall, the study shows that the activity of catecholaminergic axons from VTA and LC to dorsal hippocampal CA1 can partly reflect distinct environmental, behavioral, and cognitive factors. Whereas both VTA and LC activity reflected running speed, VTA, but not LC axon activity reflected the approach of a learned reward, and LC, but not VTA, axon activity reflected initiation of running and novelty of the VR environment.

I have no specific expertise with respect to 2-photon imaging, so cannot evaluate the validity of the specific methods used to collect and analyse 2-photon calcium imaging data of axonal activity.

Strengths:

(1) Using a state-of-the-art approach to record separately the activity of VTA and LC axons with high temporal resolution in awake mice moving through virtual environments, the authors provide convincing evidence that the activity of VTA and LC axons projecting to dorsal CA1 reflect partly distinct environmental, behavioral and cognitive factors.

(2) The study will help a) to interpret previous findings on how hippocampal dopamine and norepinephrine or selective manipulations of hippocampal LC or VTA inputs modulate behavior and b) to generate specific hypotheses on the impact of selective manipulations of hippocampal LC or VTA inputs on behavior.

Weaknesses:

(1)The findings are correlational and do not allow strong conclusions on how VTA or LC inputs to dorsal CA1 affect cognition and behavior. However, as indicated above under Strengths, the findings will aid the interpretation of previous findings and help to generate new hypotheses as to how VTA or LC inputs to dorsal CA1 affect distinct cognitive and behavioral functions.

(2) Some aspects of the methodology would benefit from clarification.<br /> First, to help others to better scrutinize, evaluate, and potentially to reproduce the research, the authors may wish to check if their reporting follows the ARRIVE (Animal Research: Reporting of In Vivo Experiments) guidelines for the full and transparent reporting of research involving animals (https://arriveguidelines.org/). For example, I think it would be important to include a sample size justification (e.g., based on previous studies, considerations of statistical power, practical considerations, or a combination of these factors). The authors should also include the provenance of the mice. Moreover, although I am not an expert in 2-photon imaging, I think it would be useful to provide a clearer description of exclusion criteria for imaging data.

We thank the reviewer for helping us formalize the scientific rigor of our study. There are ten ARRIVE Guidelines and we have addressed most of them in our study already. However, there is an opportunity to add detail. We have listed below all ten points and how we have or will address each one.

(1) Experimental design - we go into great depth explaining the experimental set-up, how we used the autofluorescent blebs as imaging controls, how we controlled for different sample sizes between the two populations, and the statistical tests used for comparisons. We also carefully accounted for animal behavior when quantifying and describing axon dynamics both in the familiar and novel environments.

(2)Sample size - We state both the number of ROIs and mice for each analysis. Wherever we state how many axons had a certain kind of activity, we will also state the number of mice we saw this activity in. For the next version of the paper, we plan to conduct a power analysis using G*power to assess the power of our sample sizes for statistical analysis.

(3) Inclusion/exclusion criteria - Out of the 36 NET-Cre mice injected, 15 were never recorded for either failing to reach behavioral criteria, or a lack of visible expression in axons. Out of the 54 DAT-Cre mice injected, images were never conducted in 36 for lack of expression or failing to reach behavioral criteria. Out of the remaining 21 NET-CRE, 5 were excluded for heat bubbles, z-drift, or bleaching, while 11 DAT-Cre were excluded for the same reasons. This was determined by visually assessing imaging sessions, followed by using the registration metrics output by suite2p. This registration metric conducted a PCA on the motion-corrected ROIs and plotted the first PC. If the PC drifted largely, to the point where no activity was apparent, the video was excluded from analysis.

(4) Randomization - Already included in the paper is a description of random down sampling of LC axons to make statistical comparisons with VTA axons. LC axons were selected pseudo-randomly (only one axon per imaging session) to match VTA sampling statistics. This randomization was repeated 1000 times and comparisons were made against this random distribution.

(5) Blinding-masking - no blinding/masking was conducted as no treatments were given that would require this. We will include this statement in the next version.

(6) Outcomes - We defined all outcomes measured, such as those related to animal behavior and related axon signaling.

(7) Statistical methods - None of the reviewers had any issues regarding our description of statistical methods, which we described in detail in this version of the paper.

(8) Experimental animals - We described that DAT- Cre mice were obtained through JAX labs, and NET-Cre mice were obtained from the Tonegawa lab (Wagatsuma et al. 2017)

(9) Experimental procedure - Already listed in detail in Methods section.

(10) Results - Rigorously described in detail for behaviors and related axon dynamics.

Wagatsuma, Akiko, Teruhiro Okuyama, Chen Sun, Lillian M. Smith, Kuniya Abe, and Susumu Tonegawa. “Locus Coeruleus Input to Hippocampal CA3 Drives Single-Trial Learning of a Novel Context.” Proceedings of the National Academy of Sciences 115, no. 2 (January 9, 2018): E310–16. https://doi.org/10.1073/pnas.1714082115.

Second, why were different linear tracks used for studies of VTA and LC axon activity (from line 362)? Could this potentially contribute to the partly distinct activity correlates that were found for VTA and LC axons?

A detailed response to this is written above for a similar comment from reviewer 1.

Third, the authors seem to have used two different criteria for defining immobility. Immobility was defined as moving at <5 cm/s for the behavioral analysis in Figure 3a, but as <0.2 cm/s for the imaging data analysis in Figure 4 (see legends to these figures and also see Methods, from line 447, line 469, line 498)? I do not understand why, and it would be good if the authors explained this.

This is an error leftover from before we converted velocity from rotational units of the treadmill to cm/s. This will be corrected in the next version of the paper.

(3) In the Results section (from line 182) the authors convincingly addressed the possibility that less time spent immobile in the novel environment may have contributed to the novelty-induced increase of LC axon activity in dorsal CA1 (Figure 4). In addition, initially (for the first 2-4 laps), the mice also ran more slowly in the novel environment (Figure 3aIII, top panel). Given that LC and VTA axon activity were both increasing with velocity (Figure 1F), reduced velocity in the novel environment may have reduced LC and VTA axon activity, but this possibility was not addressed. Reduced LC axon activity in the novel environment could have blunted the noveltyinduced increase. More importantly, any potential novelty-induced increase in VTA axon activity could have been masked by decreases in VTA axon activity due to reduced velocity. The latter may help to explain the discrepancy between the present study and previous findings that VTA neuron firing was increased by novelty (see Discussion, from line 243). It may be useful for the authors to address these possibilities based on their data in the Results section, or to consider them in their Discussion.

This is a great point. The decreased velocity in the novel environment could lead to a diminished novelty response in LC axons. We will add a discussion point on this in the next version. This could also be the case for VTA axons, so will add a discussion point that the lack of novelty signaling seen in VTA axons could be due to reduced velocity masking this signal.

(4) Sensory properties of the water reward, which the mice may be able to detect, could account for reward-related activity of VTA axons (instead of an expectation of reward). Do the authors have evidence that this is not the case? Occasional probe trials, intermixed with rewarded trials, could be used to test for this possibility.

Mice receive their water reward through a waterspout that is immobile and positioned directly in front of their mouth (which is also immobile as they are head fixed) and water delivery is triggered by a solenoid when the mice reach the end of the virtual track. Therefore, because the waterspout remains in the same place relative to the mouse, and the water reward is not delivered until they reach the end of the virtual track, there is nothing for the mice to detect. We will update the paper to make this clearer.

Additionally, on the initial laps with no reward, the ramping activity is still present (Krishnan et al, 2022) indicating this activity is not directly related to the presence/absence of water but is instead caused by reward expectation.

Reviewer #3 (Public Review):

Summary:

Heer and Sheffield provide a well-written manuscript that clearly articulates the theoretical motivation to investigate specific catecholaminergic projections to dorsal CA1 of the hippocampus during a reward-based behavior. Using 2-photon calcium imaging in two groups of cre transgenic mice, the authors examine the activity of VTA-CA1 dopamine and LC-CA1 noradrenergic axons during reward seeking in a linear track virtual reality (VR) task. The authors provide a descriptive account of VTA and LC activities during walking, approach to reward, and environment change. Their results demonstrate LC-CA1 axons are activated by walking onset, modulated by walking velocity, and heighten their activity during environment change. In contrast, VTA-CA1 axons were most activated during the approach to reward locations. Together the authors provide a functional dissociation between these catecholamine projections to CA1. A major strength of their approach is the methodological rigor of 2-photon recording, data processing, and analysis approaches. These important systems neuroscience studies provide solid evidence that will contribute to the broader field of learning and memory. The conclusions of this manuscript are mostly well supported by the data, but some additional analysis and/or experiments may be required to fully support the author's conclusions.

Weaknesses:

(1) During teleportation between familiar to novel environments the authors report a decrease in the freezing ratio when combining the mice in the two experimental groups (Figure 3aiii). A major conclusion from the manuscript is the difference in VTA and LC activity following environment change, given VTA and LC activity were recorded in separate groups of mice, did the authors observe a similar significant reduction in freezing ratio when analyzing the behavior in LC and VTA groups separately?

In response to this comment, we will analyze the freezing ratios in DAT-Cre and NET-Cre mice separately. However, other members of the lab have seen the same result in other mouse strains (See Dong et al. 2021), so we do not expect to see a difference (but it is certainly worth checking).

(2) The authors satisfactorily apply control analyses to account for the unequal axon numbers recorded in the LC and VTA groups (e.g. Figure 1). However, given the heterogeneity of responses observed in Figures 3c, 4b and the relatively low number of VTA axons recorded (compared to LC), there are some possible limitations to the author's conclusions. A conclusion that LC-CA1 axons, as a general principle, heighten their activity during novel environment presentation, would require this activity profile to be observed in some of the axons recorded in most all LC-CA1 mice.

We agree with the reviewer’s point here. To help avoid this problem, when downsampling LC axons to compare to VTA axons, we matched the sampling statistics of the VTA axons/mice (i.e. only one LC axon was taken from each mouse to match the VTA dataset).

However, in the next version of the paper we will also report the number of mice that we see a significant novel response in. We will also add the number of mice with significant activity for each of the measures in the familiar environment (e.g. how many mice had axons positively correlated with velocity).

Additionally, if the general conclusion is that VTA-CA1 axons ramp activity during the approach to reward, it would be expected that this activity profile was recorded in the axons of most all VTA-CA1 mice. Can the authors include an analysis to demonstrate that each LC-CA1 mouse contained axons that were activated during novel environments and that each VTA-CA1 mouse contained axons that ramped during the approach to reward?

As stated above, we will add the number of mice that had each activity type we reported here.

(3) A primary claim is that LC axons projecting to CA1 become activated during novel VR environment presentation. However, the experimental design did not control for the presentation of a familiar environment. As I understand, the presentation order of environments was always familiar, then novel. For this reason, it is unknown whether LC axons are responding to novel environments or environmental change. Did the authors re-present the familiar environment after the novel environment while recording LC-CA1 activity?

This is an important point to address. While we never varied the presentation order of the familiar vs novel environments, we did record the activity of LC axons in some of the mice in a dark environment (no VR cues) prior to exposure to the familiar environment. We will look at these axons to address whether they respond to initial exposure to the familiar environment. This will allow us to check whether they are responding to environmental change or novelty. We will add this analysis to the next version of the paper.

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.

References:

Chang YC, Huang AM, Kuo YM, Wang ST, Chang YY, Huang CC. Febrile seizures impair memory and cAMP response-element binding protein activation. Ann Neurol. 2003 Dec;54(6):706-18. doi: 10.1002/ana.10789. PMID: 14681880.

D'Agosta R, Prizon T, Zacharias LR, Marques DB, Leite JP, Ruggiero RN. Alterations in hippocampal-prefrontal cortex connectivity are associated with working memory impairments in rats subjected to early-life status epilepticus. In: NEWROSCIENCE INTERNATIONAL SYMPOSIUM, 2023, Ribeirão Preto. Poster.

Hajipour S, Khombi Shooshtari M, Farbood Y, Ali Mard S, Sarkaki A, Moradi Chameh H, Sistani Karampour N, Ghafouri S. Fingolimod Administration Following Hypoxia Induced Neonatal Seizure Can Restore Impaired Long-term Potentiation and Memory Performance in Adult Rats. Neuroscience. 2023 May 21;519:107-119. doi: 10.1016/j.neuroscience.2023.03.023. Epub 2023 Mar 28. PMID: 36990271.

Karnam HB, Zhou JL, Huang LT, Zhao Q, Shatskikh T, Holmes GL. Early life seizures cause long-standing impairment of the hippocampal map. Exp Neurol. 2009 Jun;217(2):378-87. doi: 10.1016/j.expneurol.2009.03.028. Epub 2009 Apr 2. PMID: 19345685; PMCID: PMC2791529.

Karnam HB, Zhao Q, Shatskikh T, Holmes GL. Effect of age on cognitive sequelae following early life seizures in rats. Epilepsy Res. 2009 Aug;85(2-3):221-30. doi: 10.1016/j.eplepsyres.2009.03.008. Epub 2009 Apr 22. PMID: 19395239; PMCID: PMC2795326.

Kubová H, Mareš P. Are morphologic and functional consequences of status epilepticus in infant rats progressive? Neuroscience. 2013 Apr 3;235:232-49. doi: 10.1016/j.neuroscience.2012.12.055. Epub 2013 Jan 7. PMID: 23305765.

Kloc ML, Marchand DH, Holmes GL, Pressman RD, Barry JM. Cognitive impairment following experimental febrile seizures is determined by sex and seizure duration. Epilepsy Behav. 2022 Jan;126:108430. doi: 10.1016/j.yebeh.2021.108430. Epub 2021 Dec 10. PMID: 34902661; PMCID: PMC8748413.

Kubová H, Mares P, Suchomelová L, Brozek G, Druga R, Pitkänen A. Status epilepticus in immature rats leads to behavioural and cognitive impairment and epileptogenesis. Eur J Neurosci. 2004 Jun;19(12):3255-65. doi: 10.1111/j.0953-816X.2004.03410.x. PMID: 15217382.

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.

Marques DB, Rossignoli MT, Mesquita BDA, Prizon T, Zacharias LR, Ruggiero RN and Leite JP. Decoding fear or safety and approach or avoidance by brain-wide network dynamics abbreviated. bioRxiv. 2022b https://doi.org/10.1101/2022.10.13.511989.

Mikulecká A, Druga R, Stuchlík A, Mareš P, Kubová H. Comorbidities of early-onset temporal epilepsy: Cognitive, social, emotional, and morphologic dimensions. Exp Neurol. 2019 Oct;320:113005. doi: 10.1016/j.expneurol.2019.113005. Epub 2019 Jul 3. PMID: 31278943.

Najafian SA, Farbood Y, Sarkaki A, Ghafouri S. FTY720 administration following hypoxia-induced neonatal seizure reverse cognitive impairments and severity of seizures in male and female adult rats: The role of inflammation. Neurosci Lett. 2021 Mar 23;748:135675. doi: 10.1016/j.neulet.2021.135675. Epub 2021 Jan 28. PMID: 33516800.

Ruggiero RN, Rossignoli MT, Marques DB, de Sousa BM, Romcy-Pereira RN, Lopes-Aguiar C and Leite JP. Neuromodulation of Hippocampal-Prefrontal Cortical Synaptic Plasticity and Functional Connectivity: Implications for Neuropsychiatric Disorders. Frontiers in Cellular Neuroscience. 2021 15 (October): 1–23. https://doi.org/10.3389/fncel.2021.732360.

Sorg AL, von Kries R, Borggraefe I. Cognitive disorders in childhood epilepsy: a comparative longitudinal study using administrative healthcare data. J Neurol. 2022 Jul;269(7):3789-3799. doi: 10.1007/s00415-022-11008-y. Epub 2022 Feb 15. PMID: 35166927; PMCID: PMC9217877.

Stafstrom CE, Chronopoulos A, Thurber S, Thompson JL, Holmes GL. Age-dependent cognitive and behavioral deficits after kainic acid seizures. Epilepsia. 1993 May-Jun;34(3):420-32. doi: 10.1111/j.1528-1157.1993.tb02582.x. PMID: 8504777.

Author Response

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

Reviewer #1

This is a short but important study. Basically, the authors show that α-synuclein overexpression's negative impact on synaptic vesicle recycling is mediated by its interaction with E-domain containing synapsins. This finding is highly relevant for synuclein function as well as for the pathophysiology of synucleinopathies. While the data is clear, functional analysis is somewhat incomplete.

(1) The authors should present a clearer dissociation of endocytosis and exocytosis under the various conditions they study. They should quantify the rate of rise and decay of pHluorin signals. 2. In addition, I strongly recommend a few additional experiments with and without a vATPase inhibitor such as bafilomycin to estimate the relative effects on exo- vs. endocytosis. As the authors are aware bafilomycin will mask the re-acidification /endocytosis component, thus revealing pure exocytosis and thus enabling quantification of endocytosis with minimal contamination from exocytosis.

In the revised version, we analyzed and quantified exocytosis and endocytosis separately, with bafilomycin experiments, as the reviewer suggested (new data, Fig. 1- Fig. Supp. 1A-B). Overexpression of human alpha-synuclein only attenuated exocytosis in neurons that also expressed synapsins (WT neurons and synapsin TKO neurons transduced with synapsin Ia). In parallel, we also examined endocytosis by calculating the time-constant of the decay in the fluorescence of sypHy during the endocytotic phase (Fig. 1- Fig. Supp. 1C-E). Previous studies have shown that after brief stimulus-trains – like those used in our study (20Hz/300AP) – most endocytosis occurs after the cessation of stimulation 1. Expression of human alpha-synuclein did not alter the endocytosis time-constant in any of our experiments. To summarize, the interaction of alpha-synuclein with the synapsin E domain was required for alpha-synuclein induced attenuation of exocytosis, but not endocytosis.

Reviewer #2

...The paper will be improved significantly if additional experiments are added to expand and provide a more mechanistic understanding of the effect of α-syn and the intricate interplay between synapsin, α-syn, and the SV. For an enthusiastic reader, the manuscript as it looks now with only 3 figures, ends prematurely. Some of the experiments above or others could complement, expand and strengthen the current manuscript, moving it from a short communication describing the phenomenon to a coherent textbook topic. Nevertheless, this work provides new and exciting evidence for the regulation of neurotransmitter release and its regulation by synapsin and α-syn.

(1) Did the authors try to attach E-domain for example to synapsin Ib and restore α-syn inhibition with synapsin Ib-E?

This is an interesting idea, but in previous studies, we found that synapsin Ib does not associate with synaptic vesicles2, so it will not be present at the right location to be able to restore alpha-synuclein induced synaptic attenuation. We have also seen that this mis-localization alters synaptic properties (unpublished).

(2) Was the expression level of Synapsin-IaScrE examined and compared to WT Synapsin-Ia in Fig 3?

Yes, this data is now shown in Fig. 3-Fig. Supp. 1.

(3) Were SVs dispersed in α-syn overexpression as predicted?

We interpret the reviewer’s question and reasoning as follows. If alpha-synuclein binds to the E-domain of synapsin, a prediction in the alpha-synuclein over-expression scenario is that the overabundance of alpha-synuclein molecules would bind to and sequester the E-domain synapsins away from synaptic vesicles. In the absence of E-domain synapsins, the synaptic-vesicle clustering effects of synapsins would be lost, and there would be dispersion of synaptic vesicles. We tested this prediction, which is now shown in an additional figure (new data, Fig. 4). Indeed, the AAV-mediated over-expression of alpha-synuclein leads to a dispersion of synaptic vesicles, and this dispersion is dependent on synapsins Ia and Ib, but not IIa and IIb (please see Fig. 4D-E in the revised manuscript). Appropriate text is also added, starting with “Previous studies have shown that loss of all synapsins...” presents this data and interprets it.

(4) How does this study coincide with the effects of α-syn on fusion pore and endocytosis? This should be at least discussed. It is also possible that the effects of α-syn on endocytosis might affect the results as if endocytosis is affected, SVs number and distribution will be also affected.

It is difficult to reconcile our data with the idea that alpha-synuclein facilitates fusion-pore opening, as proposed by the Edwards lab 3. In fact, its difficult to reconcile this concept with their own previous data, showing that alpha-synuclein over-expression attenuates SV-recycling 4. As mentioned above, modulation of endocytosis does not seem to be a major factor in our experiments, though this does not rule out a physiologic role for alpha-synuclein in endocytosis, since all our experiments are based on over-expression paradigms. Future experiments looking at phenotypes after acute alpha-synuclein knockdown may provide more clarity. In any case, there are many purported roles of alpha-synuclein, and this is now mentioned in the last paragraph (starting with Additionally, -syn has been implicated…”

(5) What happened after stimulation when synapsin is detached from SV, does α-syn continues to be linked to it?

The fate of alpha-synuclein after stimulation is unclear in our experiments. Previous experiments suggest that while both synapsin and alpha-synuclein detach from the SV cluster during stimulation, synapsin returns to synapses while alpha-synuclein does not 5. However, our more recent experiments (unpublished) suggest that the activity-induced dispersion of alpha-synuclein might be phosphorylation-dependent, and that over-expression of alpha-synuclein may not be the best setting to evaluate protein dispersion. We hope to answer this question more rigorously using alpha-synuclein knock-in constructs.

(6) The experiment with E-domain fused to syPhy assumes that α-syn will still be bound to the SV. So how does α-syn inhibit ST?

The goal of this experiment was to force the synapsin E-domain to be in a location where it would normally be present – i.e. surface of the synaptic vesicle – by tagging it to sypHy (sypHy-E), and ask if this forced-retention would be sufficient to reinstate the alpha-synuclein mediated attenuation of SV-recycling (as shown in Fig. 3F, it does). Please note that the sypHy-E in these experiments does target to the synapses (new data, Fig. 3-Fig. Supp. 2D). In this context, we are not sure what the reviewer means by “So how does a-syn inhibit synaptic transmission?” We don’t think that alpha-synuclein needs to unbind from the SVs in order to inhibit synaptic transmission. Overall, we think that alpha-synuclein needs to cooperate with synapsins to perform its function, but as mentioned above and in the manuscript, the precise role of alpha-synuclein in this process is still unclear.

(7) An interesting experiment will be the expression of the isolated E-domain and examining blockage of α-syn inhibition and disruption of synapsin- α-syn interaction. Have the authors examined it as was done in other models?

We did do the experiment where we only over-expressed the isolated synapsin E-domain in neurons. We were thinking that perhaps the E-domain would have a dominant-negative effect on SV-clustering, as it did in the lamprey and other model-systems, where the E-peptide was directly injected into the axon. However, we found that in cultured hippocampal neurons, the over-expressed E-domain behaves like a soluble protein and is not enriched in synapses (see new data, Fig. 3-Fig. Supp. 2B). Also, the over-expressed E-domain cannot reinstate the synaptic attenuation induced by alpha-synuclein (new data, Fig. 3-Fig. Supp. 2C), likely because the E-domain does not target to synapses. Actually, this is why we did the syPhy-E domain experiment in the first place, to ensure that the E-domain was in the right location to have an effect.

(8) A schematic model/scheme providing a mechanistic view of the interplay between the proteins is essential and can improve the paper.

The only model we can confidently make right now would be stick-figures showing the site where alpha-synuclein C-terminus binds to synapsin, which is obviously not very insightful. As noted above (and in the revised version), several different functions have been attributed to alpha-synuclein, and the precise role of alpha-synuclein/synapsin interactions in regulating the SV-cycle is unclear. We hope to create a better model after getting some more data from us and our colleagues working on this challenging problem.

References

(1) Kononenko NL & Haucke V. (2015) Molecular mechanisms of presynaptic membrane retrieval and synaptic vesicle reformation. Neuron 85, 484-496.

(2) Gitler D, Xu Y, Kao H-T, Lin D, Lim S, Feng J, Greengard P & Augustine GJ. (2004) Molecular Determinants of Synapsin Targeting to Presynaptic Terminals. J. Neurosci. 24, 3711-3720.

(3) Logan T, Bendor J, Toupin C, Thorn K & Edwards RH. (2017) α-Synuclein promotes dilation of the exocytotic fusion pore. Nat Neurosci 20, 681-689.

(4) Nemani VM, Lu W, Berge V, Nakamura K, Onoa B, Lee MK, Chaudhry FA, Nicoll RA & Edwards RH. (2010) Increased expression of alpha-synuclein reduces neurotransmitter release by inhibiting synaptic vesicle reclustering after endocytosis. Neuron 65, 66-79.

(5) Fortin DL, Nemani VM, Voglmaier SM, Anthony MD, Ryan TA & Edwards RH. (2005) Neural activity controls the synaptic accumulation of alpha-synuclein. J Neurosci 25, 10913-10921.

Author Response

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

Reviewer 1: I would have preferred to see more figures with brain images showing the cellular abundance maps and the atrophy maps. Without being able to see these figures, it's difficult for the reader to 1) validate the atrophy patterns or 2) gain intuition about how the cellular abundance maps vary across the brain. The images in Figure 1C give a small preview, but I'd like to see these maps in their entirety on the brain surface or axial image slices.

(1) We added brain surface visualization plots of the voxel-wise cellular abundance maps to Figure 1 (lateral, dorsal, and ventral views of both hemispheres). To illustrate how their spatial distributions are associated with brain tissue damage, in Figure 2, we have also added brain surface visualizations of regional values from the atrophy t-statistic maps for the thirteen neurodegenerative conditions and the cell-type map most strongly associated with each condition. These plots allow us to observe variability across the cell-type density and atrophy maps, as well as to visually validate and compare how the patterns vary across the brain.

Reviewer 1: FTD is an umbrella category for a family of distinct clinical syndromes with different atrophy patterns. It doesn't seem a good idea to take the average of all subjects in this group to form a single atrophy map. Instead, different average maps for each syndrome should be provided.

(2) Considering the heterogeneity of clinical FTD syndromes, we addressed the reviewers' concerns about using the averaged atrophy map across all patients with an FTD diagnosis. As suggested, we accessed different atrophy maps for each major variant of clinical FTD, including behavioral FTD (n = 70), as well as the semantic (n = 36) and nonfluent variants of primary progressive aphasia (n = 30). These maps are based on data from the participants from the same dataset of the Frontotemporal Lobar Degeneration Neuroimaging Initiative (FTLDNI) that we originally used. Similar to our previous results using the atrophy map averaged over all FTD patients, the analysis showed significant associations of atrophy patterns with cell type densities in all three major variants (see Figure 3A). Notably, these new findings offer insights into specific differences in spatial vulnerability of different cell-types across the variants of FTD, each characterized by unique symptoms, clinical manifestations, and atrophy patterns. In response to these additions, we have updated all figures, results, and interpretations accordingly.

Reviewer 2: In the abstract, the list of neurodegenerative disorders should be edited: frontotemporal dementia is an umbrella clinical syndrome, not a neurodegenerative disorder. Frontotemporal lobar degeneration (FTLD) is a neurodegenerative disorder, and many tauopathies are FTLDs. While the authors grab their definitional classes from various sources (i.e., published cohort, and other studies), the reader fatigues to understand the population that is being assessed.

(3) To address potential confusion arising from the inclusion of atrophy maps from FTLD patients across two different studies, stratified based on both clinical and pathological criteria, we added clarifications regarding the assessed population and the used definitions. We used the term FTD when addressing the clinical syndromes, and the term FTLD was employed when referencing the histologically confirmed neurodegenerative pathologies. In addition, we added details on the diagnostic criteria employed for participant recruitment in the FTLDNI cohort, which data we used for atrophy maps in clinical subtypes of FTD. Lastly, throughout the text and within the figures, we systematically refined the nomenclature for FTLD pathological types, categorizing them based on their known definitions used in literature and type of proteinaceous inclusions (FTLD- 3-repeat and 4-repeat tauopathies and FTLD-TDP types A and C).

Reviewer 1: The results section contains perhaps too much interpretation. While the information that's provided serves as an interesting review (e.g., the discussion of the blood-brain barrier), the discussion may be a better place for this.

(4) We removed sentences with excessive interpretation but insisted on including those outlining the fundamental functions of cell types and their literature-based relevance to neurodegenerative diseases in the Results section, clarifying the significance of our findings to the readers.

Reviewer 2: The authors based their methodology on the use of a deconvolutional cell classifier; however, do not extensively recognize that their data on gene expression are based on normal brain levels rather than on diseased ones.

(5) We acknowledged that the gene expression data is based on normal human brain levels in figure titles and all sections of the paper (Introduction, Results, Discussion, Methods) to remind the readers that the analysis shows how changes in gray matter tissue in diseased brains correlates with healthy reference levels of cellular density.

Reviewer 2: More information in the text needs to be provided regarding the method used to infer gene expression levels at non-sampled brain locations. The reader should not be forced to read reference 40 or investigate the methods section. Figure 1 schematics do not sufficiently explain the used method.

(6) We added clarifications/references about the used Gaussian progress regression for imputing gene expression (Results and figure titles).

Reviewer 2: Also, while predicted levels are uniquely based on patterns of brain atrophy, it is not possible to know whether this strategy is generalizable to all diseases (for instance, it is known that pure DLB, PD and ALS are not associated with extensive brain atrophy), or even adequately comparable between subtypes of diseases within the same class (e.g., different forms of FTLD). The authors do not acknowledge that only data based on true neuropathological assessment may prove whether their findings are true.

(7) Although diagnoses of most dementia conditions used in our study were histologically confirmed, we added acknowledgement about the importance of neuropathological assessment (Discussion section).

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

Abercrombie ED, Keller RW, Zigmond MJ. 1988. Characterization of hippocampal norepinephrine release as measured by microdialysis perfusion: Pharmacological and behavioral studies. Neuroscience 27:897–904. doi:10.1016/0306-4522(88)90192-3

Aston-Jones G, Bloom FE. 1981a. Nonrepinephrine-containing locus coeruleus neurons in behaving rats exhibit pronounced responses to non-noxious environmental stimuli. Journal of Neuroscience 1:887–900. doi:10.1523/JNEUROSCI.01-08-00887.1981

Aston-Jones G, Bloom FE. 1981b. Activity of norepinephrine-containing locus coeruleus neurons in behaving rats anticipates fluctuations in the sleep-waking cycle. J Neurosci 1:876–886. doi:10.1523/JNEUROSCI.01-08-00876.1981

Aston-Jones G, Cohen JD. 2005. AN INTEGRATIVE THEORY OF LOCUS COERULEUSNOREPINEPHRINE FUNCTION: Adaptive Gain and Optimal Performance. Annual Review of Neuroscience 28:403–450. doi:10.1146/annurev.neuro.28.061604.135709

Chen T-W, Wardill TJ, Sun Y, Pulver SR, Renninger SL, Baohan A, Schreiter ER, Kerr RA, Orger MB, Jayaraman V, Looger LL, Svoboda K, Kim DS. 2013. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499:295–300. doi:10.1038/nature12354

Chowdhury N, Quinn JJ, Fanselow MS. 2005. Dorsal hippocampus involvement in trace fear conditioning with long, but not short, trace intervals in mice. Behavioral Neuroscience 119:1396–1402. doi:http://dx.doi.org/10.1037/0735-7044.119.5.1396

Eshel N, Tian J, Bukwich M, Uchida N. 2016. Dopamine neurons share common response function for reward prediction error. Nat Neurosci 19:479–486. doi:10.1038/nn.4239

Feng J, Zhang C, Lischinsky JE, Jing M, Zhou J, Wang H, Zhang Y, Dong A, Wu Z, Wu H, Chen W, Zhang P, Zou J, Hires SA, Zhu JJ, Cui G, Lin D, Du J, Li Y. 2019. A Genetically Encoded Fluorescent Sensor for Rapid and Specific In Vivo Detection of Norepinephrine. Neuron 102:745-761.e8. doi:10.1016/j.neuron.2019.02.037

Frey U, Schroeder H, Matthies H. 1990. Dopaminergic antagonists prevent long-term maintenance of posttetanic LTP in the CA1 region of rat hippocampal slices. Brain Research 522:69–75. doi:10.1016/0006-8993(90)91578-5

Huang YY, Kandel ER. 1995. D1/D5 receptor agonists induce a protein synthesis-dependent late potentiation in the CA1 region of the hippocampus. Proceedings of the National Academy of Sciences 92:2446–2450. doi:10.1073/pnas.92.7.2446

Kitamura T, Pignatelli M, Suh J, Kohara K, Yoshiki A, Abe K, Tonegawa S. 2014. Island Cells Control Temporal Association Memory. Science 343:896–901. doi:10.1126/science.1244634

Matsumoto M, Hikosaka O. 2009. Two types of dopamine neuron distinctly convey positive and negative motivational signals. Nature 459:837–841. doi:10.1038/nature08028

O’Carroll CM, Martin SJ, Sandin J, Frenguelli BG, Morris RGM. 2006. Dopaminergic modulation of the persistence of one-trial hippocampus-dependent memory. Learning & memory 13:760–769.

Pan W-X, Schmidt R, Wickens JR, Hyland BI. 2005. Dopamine Cells Respond to Predicted Events during Classical Conditioning: Evidence for Eligibility Traces in the Reward-Learning Network. J Neurosci 25:6235–6242. doi:10.1523/JNEUROSCI.1478-05.2005

Schultz W. 1998. Predictive Reward Signal of Dopamine Neurons. Journal of Neurophysiology 80:1–27. doi:10.1152/jn.1998.80.1.1

Sun F, Zhou J, Dai B, Qian T, Zeng J, Li X, Zhuo Y, Zhang Y, Wang Y, Qian C, Tan K, Feng J, Dong H, Lin D, Cui G, Li Y. 2020. Next-generation GRAB sensors for monitoring dopaminergic activity in vivo. Nat Methods 17:1156–1166. doi:10.1038/s41592-02000981-9

Wagatsuma A, Okuyama T, Sun C, Smith LM, Abe K, Tonegawa S. 2018. Locus coeruleus input to hippocampal CA3 drives single-trial learning of a novel context. Proceedings of the National Academy of Sciences 115:E310–E316. doi:10.1073/pnas.1714082115

Wilmot JH, Puhger K, Wiltgen BJ. 2019. Acute Disruption of the Dorsal Hippocampus Impairs the Encoding and Retrieval of Trace Fear Memories. Frontiers in Behavioral Neuroscience 13. doi:10.3389/fnbeh.2019.00116

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.

Author Response

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

Reviewer #1, in both the public review and recommendations to authors, raises the important question of generalizability of the new technique to other brain areas, to analysis with sorters other than Kilosort, and in the absence of reference data. Specifically, how can experimenters working in brain areas other than visual cortex understand if the tracking is functioning, and set the parameters in the tracking pipeline.

We agree that generalizability of the tracking procedure is a serious issue, especially with respect to other brain areas with varying degrees of measured waveform preservation over time. As the number of potential recording conditions is combinatorial to experimentally test, we instead address these issues in the manuscript by providing a general prescription for interpreting the distribution of vertical distances of matched pairs that can be used for data from any recording using any spike-sorter (Methods section 4.2, Supplement section 8.4, figure S9, paragraphs 7-10 of the Discussion section). This extension of the method allows users to estimate the matching success in the context of their own data, even in the absence of reference data. To address the concern of overfitting, we have also added discussion covering adjustment of the two parameters in the procedure (the relative weight of waveform distance vs. physical distance, and the threshold for accepting matches as real) to the Discussion section.

Reviewer #2 suggested clarification of the following points in the public review. We answer those here and have also clarified these points in the main text where appropriate.

(1) What is the purpose of testing the drift correction with imposed drift (Figure 2, page 6 in the original manuscript), and how the value was chosen?

To test the ability of EMD to detect substantial drift, we need examples that resemble experimental data, including error in fit unit positions and units with no correct matches. We chose to create these examples by taking waveform and position sets from real data with modest drift, and adding a fixed shift to one dataset. The value of 12 um in the figure is arbitrary, simply an example in the range of real drift. These tests allow us to demonstrate the success of EMD for detection of drift in real data.

(2) How is performance affected by using a different weighting of the 2 measures (physical distance and waveform distance) in the EMD?

Recovery rate (number of reference units successfully matched in EMD) vs weighting of the waveform distance is shown in Supplement section 8.10. Recovery rate increases with low values of waveform weighting, leveling off at a value of 1500. We selected that inflection point for the analysis in this paper, to avoid coincidental matching of physically distant units with similar waveforms.

(3) Should the intervals measured in the survival plot in Figure 5 be identical for the three different classes of tracked neurons?

The plot includes all chains of tracked neurons, which can start on arbitrary days in the set of all recordings (see the definition of chains in section 2.4). As a result, the gaps between days, which determine where there is a point on the plot, can be different for the three sets of neurons (reference, putative, and mixed). We have added a comment to the Figure 5 caption to ensure this is clear.

(4) Would other metrics of the similarity of visual responses work better?

The similarity metric we use was adopted from the original paper using this data (reference 7). We chose to use the same metric both to take advantage of the original authors’ expertise about the data and allow for reasonable comparison of the new technique to theirs. It is correct that this similarity metric alone does not allow for unique matching (see Discussion and Supplement section 8.2). However, the agreement of EMD with reference pairs determined from the combination of position and visual response similarity is very high, suggesting there are few incorrect reference pairs. Any incorrect reference pairs cause an underestimate of the tracking accuracy.

(5) Add a definition of ROC.

Added this definition to the text.

Reviewer #1 Recommendation to authors:

The main text needs proofreading.

We agree that the manuscript needed more thorough proofreading, and we have made corrections of typos and minor language errors throughout.

Additional comment from the authors:

Since the posting of this manuscript, another method for tracking neurons has been introduced:

Enny H. van Beest, Célian Bimbard, Julie M. J. Fabre, Flóra Takács, Philip Coen, Anna Lebedeva, Kenneth Harris, Matteo Carandini, Tracking neurons across days with high-density probes, bioRxiv 2023.10.12.562040; doi: https://doi.org/10.1101/2023.10.12.562040

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors compared four types of hiPSCs and four types of hESCs at the proteome level to elucidate the differences between hiPSCs and hESCs. Semi-quantitative calculations of protein copy numbers revealed increased protein content in iPSCs. Particularly in iPSCs, proteins related to mitochondrial and cytoplasmic were suggested to reflect the state of the original differentiated cells to some extent. However, the most important result of this study is the calculation of the protein copy numbers per cell, and the validity of this result is problematic. In addition, several experiments need to be improved, such as using cells of different genders (iPSC: female, ESC: male) in mitochondrial metabolism experiments.

Strengths:

The focus on the number of copies of proteins is exciting and appreciated if the estimated calculation result is correct and biologically reproducible.

Weaknesses:

The proteome results in this study were likely obtained by simply looking at differences between clones, and the proteome data need to be validated. First, there were only a few clones for comparison, and the gender and number of cells did not match between ESCs and iPSCs. Second, no data show the accuracy of the protein copy number per cell obtained by the proteome data.

We agree with the reviewer in their assessment that more independent stem cell clones and an equal gender balance would be preferable. We will mention these considerations as limitations of our study and encourage a larger-scale follow-up.

Regarding the estimated copy numbers, we would like to highlight that they have been extensively in the field, with direct validation of the differences in copy numbers with orthogonal methods like FACS2-4,7,10. Furthermore, the original paper directly compared the copy numbers estimated using the “proteomic ruler” to spike-in protein epitope signature tags and found remarkable concordance. This was performed with a much older generation mass spectrometer with reduced peptide coverage, and the author predicted that higher coverage would increase the quantitative performance.

Reviewer #2 (Public Review):

Summary:

Pluripotent stem cells are powerful tools for understanding development, differentiation, and disease modeling. The capacity of stem cells to differentiate into various cell types holds great promise for therapeutic applications. However, ethical concerns restrict the use of human embryonic stem cells (hESCs). Consequently, induced human pluripotent stem cells (ihPSCs) offer an attractive alternative for modeling rare diseases, drug screening, and regenerative medicine.

A comprehensive understanding of ihPSCs is crucial to establish their similarities and differences compared to hESCs.

This work demonstrates systematic differences in the reprogramming of nuclear and non-nuclear proteomes in ihPSCs.

We thank the reviewer for the positive assessment.

Strengths:

The authors employed quantitative mass spectrometry to compare protein expression differences between independently derived ihPSC and hESC cell lines. Qualitatively, protein expression profiles in ihPSC and hESC were found to be very similar. However, when comparing protein concentration at a cellular level, it became evident that ihPSCs express higher levels of proteins in the cytoplasm, mitochondria, and plasma membrane, while the expression of nuclear proteins is similar between ihPSCs and hESCs. A higher expression of proteins in ihPSCs was verified by an independent approach, and flow cytometry confirmed that ihPSCs had larger cell sizes than hESCs. The differences in protein expression were reflected in functional distinctions. For instance, the higher expression of mitochondrial metabolic enzymes, glutamine transporters, and lipid biosynthesis enzymes in ihPSCs was associated with enhanced mitochondrial potential, increased ability to uptake glutamine, and increased ability to form lipid droplets.

Weaknesses:

While this finding is intriguing and interesting, the study falls short of explaining the mechanistic reasons for the observed quantitative proteome differences. It remains unclear whether the increased expression of proteins in ihPSCs is due to enhanced transcription of the genes encoding this group of proteins or due to other reasons, for example, differences in mRNA translation efficiency. Another unresolved question pertains to how the cell type origin influences ihPSC proteomes. For instance, whether ihPSCs derived from fibroblasts, lymphocytes, and other cell types all exhibit differences in their cell size and increased expression of cytoplasmic and mitochondrial proteins. Analyzing ihPSCs derived from different cell types and by different investigators would be necessary to address these questions.

We agree with the Reviewer that our study does not provide a mechanistic reason for the quantitative differences between the two cell types. However, we will include an expanded section in the discussion where we discuss the potential causes.<br /> We also agree studying hiPSCs reprogrammed from different cell types, such as blood lymphocytes, would be of great interest and will include a section about this within the discussion to encourage further research into the area.

Reviewer #3 (Public Review):

Summary:

In this study, Brenes and colleagues carried out proteomic analysis of several human induced pluripotent (hiPSC) and human embryonic stem cell (hESC) lines. The authors found quantitative differences in the expression of several groups of cytoplasmic and mitochondrial proteins. Overall, hiPSC expressed higher levels of proteins such as glutamine transporters, mitochondrial metabolism proteins, and proteins related to lipid synthesis. Based on the protein expression differences, the authors propose that hiPSC lines differ from hESC in their growth and metabolism.

Strengths:

The number of generated hiPSC and hESC lines continues to grow, but potential differences between hiPSC and hESC lines remain to be quantified and explained. This study is a promising step forward in understanding of the differences between different hiPSC and hESC lines.

Weaknesses:

It is unclear whether changes in protein levels relate to any phenotypic features of cell lines used. For example, the authors highlight that increased protein expression in hiPSC lines is consistent with the requirement to sustain high growth rates, but there is no data to demonstrate whether hiPSC lines used indeed have higher growth rates.

We respectfully disagree with the reviewer on this point. Our data shows that hESCs and hiPSCs show significant differences in protein mass and cell size, validated by the EZQ assay and FACS, while having no significant differences in their cell cycle profiles. Thus increased size and protein content would require higher growth rates to sustain the increased mass, which is what we show.

The authors claim that the cell cycle of the lines is unchanged. However, no details of the method for assessing the cell cycle were included so it is difficult to appreciate if this assessment was appropriately carried out and controlled for.<br /> We apologise for this omission; the details will be included in the revised version of the document.

Details and characterisation of iPSC and ESC lines used in this study were overall lacking. The lines used are merely listed in methods, but no references are included for published lines, how lines were obtained, what passage they were used at, their karyotype status, etc. For details of basic characterisation, the authors should refer to the ISSC Standards for the use of human stem cells in research. In particular, the authors should consider whether any of the changes they see may be attributed to copy number variants in different lines.

We agree with the reviewer on this. The hiPSC lines were generated by the HipSci consortium in the Wellcome Sanger Centre as described in the flagship HipSci paper13. We cite the flagship paper which specifies in great detail the reprogramming protocols and quality control measures, including looking at copy number variations13. However, we agree that we did not make this information easily accessible for readers. We also believe it is relevant to also explicitly include this information on our manuscript instead of expecting readers to look at the flagship paper. These details will be added to the revised version.

The expression data for markers of undifferentiated state in Figure 1a would ideally be shown by immunocytochemistry or flow cytometry as it is impossible to tell whether cultures are heterogeneous for marker expression.

We agree with the reviewer on this. FACS is indeed much more quantitative and a better method to study heterogeneity. However, we did not have protocols to study these markers using FACS.

TEM analysis should ideally be quantified.

We agree with the reviewer that it would be nice to have a quantitative measure.

All figure legends should explicitly state what graphs are representing (e.g. average/mean; how many replicates (biological or technical), which lines)? Some data is included in Methods (e.g. glutamine uptake), but not for all of the data (e.g. TEM).

We agree with the reviewer completely. These points will be remediated in the revised version of the manuscript.

Validation experiments were performed typically on one or two cell lines, but the lines used were not consistent (e.g. wibj_2 versus H1 for respirometry and wibj_2, oaqd_3 versus SA121 and SA181 for glutamine uptake). Can the authors explain how the lines were chosen?

We will include these details within the updated manuscript.

The authors should acknowledge the need for further functional validation of the results related to immunosuppressive proteins.

We agree with the reviewer and will add a clear sentence in the discussion making this point explicitly.

Differences in H1 histone abundance were highlighted. Can the authors speculate as to the meaning of these differences?

Regarding H1 histones, our study of the literature as well as interaction with chromatin and histone experts both within our institute and externally have not shed light into what the differences could imply. We think this is an interesting result that merits further study, but we don’t have a clear hypothesis on the consequences.

In summary, we thank the reviewers for their comments and will prepare a revised version that addresses their suggestions.

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.

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.

Author Response

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

We thank the reviewers for their valuable feedback which has improved this work greatly from its original form, and are elated to have such glowing reviews of the revised work published alongside the revised preprint. Reviewer 3 raises some final salient points, which deserve a brief address here.

Teeth: We thank the reviewer for clarifying their points. We do make the assumption that the ecological parameter space of toothed and beaked organisms will be comparable. Both are governed by the same set of physical principles and have the jaw bone as the most likely point of failure (teeth are harder than bone, and keratinous rhamphothecae are malleable and can be regrown with relative ease when deformed). Differences in stress/strain distribution between toothed and beaked organisms will occur but are already accounted for in our methods as we model both the teeth and rhamphotheca and will observe these different effects. We have added an explicit statement of this hypothesis to the Methods section of the manuscript.

Cranial kinesis: In our opinion, it is a safe assumption that the lower jaws of extant birds and enantiornithines are comparable. We do not see why the acquisition of kinesis in the upper jaw would generally affect the functional role of or constraints on the lower jaw. One possibility we discussed is that a quickly-moving kinetic premaxilla could let the lower jaw move a shorter distance during effective prey capture and lower the selection for speed (i.e. allow jaw-closing MA to remain higher). While we have added this possibility to our call for the investigation of cranial kinesis, we consider it too speculative to begin altering interpretations of fossil taxa. All raw measurement data remains available so that, if evidence is found for cranial kinesis having predictable effects on our measured parameters, future researchers can re-analyse our data and update any ecological predictions accordingly.

Organization: To our knowledge eLife format incorporates what one would think of as a Conclusions section into the Discussion. Our Discussion section currently contains 18 subheadings which should guide a reader to any specific topic of interest. The Discussion also progresses from a more narrow to broad focus which we and several colleagues find intuitive.

We thank all three reviewers once again for their feedback that has improved this work and their kind words throughout the process.

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

We thank all three reviewers for their detailed reviews, and generally agree with their feedback. To accompany the reviewed preprint of this manuscript, we wished to respond to comments from the reviewers so that they (and the public) will know what we are planning to incorporate in the revised manuscript we are currently preparing. If there are any comments on our plans in the meantime, please let us know.

• Reviewer 1, on concerns regarding identification of ontogenetic stage and comparison of taxa from different ontogenetic stages: It is fair to say that enantiornithine ontogeny is still poorly understood, though we believe all current evidence points to each specimen used in this study to being adequately mature for comparison to the extant birds used in the study. Stages of skeletal fusion are the standard method of assessing enantiornithine ontogeny (Hu and O'Connor 2017), and our comparison of histological work (Atterholt, Poust et al. 2021) to skeletal stages in Table S4 suggests a transition from juvenile to subadult in stage 0 or 1 and from subadult to adult within stage 3. Thus, the specimens we quantitatively examine in this study, all at stages 2 or 3 (Figure S10), are advanced subadults or adults. It is well-known that many living animals considered “adults” would be considered subadults or even juveniles to a palaeontologist (Hone, Farke et al. 2016). So, even if some individuals in this study are not fully skeletally mature, they should have obtained the morphology which they would possess for most of their lives and thus the morphology which undergoes selective pressure. We will add this context to the “Bohaiornithid Ontogeny” section and thank the reviewer for seeking more detail for this point.

• Reviewer 2, on need of a context figure: We have an artistic life reconstruction of a bohaiornithid in preparation, and can include that in the revised manuscript as a figure.

• Reviewer 2, on raptor claw categories: We explain these categories in-depth in a previous work (Miller, Pittman et al. 2023). However, we will now add a short summary of that explanation to this work so that this manuscript will become self-contained in this regard. In short, the “large raptor” category includes extant birds with records of regularly taking prey which cannot be encircled with the pes, while birds in the “small raptor” have no such records. As Reviewer 2 points out this does often follow phylogenetic lines, but not always. E.g. most owls specialise in taking small prey, but the great horned owl Bubo virginianus regularly takes mammals and birds larger than its pes (Artuso, Houston et al. 2020); and conversely we can only find reports of the common black hawk Buteogallus anthracinus taking prey samll enough for the pes to encircle (Schnell 2020) despite other accipiters frequently taking large prey. In both cases these taxa plot in PCA nearer to other large or small raptors (respectively) than to their phylogenetic relatives.

• Reviewer 3, on teeth vs beaks: We are not aware of any foods which are exclusive to toothed or beaked animals. There are some aspects of extant bird biology that may affect the way a certain diet may need to be adapted to which we do comment on, e.g. discussion of alternatives to the crop and ventriculus for processing plant matter in the Bohaiornithid Ecology and Evolution section. For functional studies, e.g. FEA, we have included the rhamphotheca in toothless models which serves the same role as teeth, to be a feeding surface. It should not matter, in theory, if the feeding surface is hard or soft as mechanical failure occurs in high stress/strain states regardless of the medium. If having teeth necessarily increases or decreses overall stress/strain relative to a beak (and from our work this does not appear to be the case), this would in turn necessarily limit dietary options. So, all models in our work should be directly comparable.

As an additional note on this topic, we address tooth shape in bohaiornithids at the end of the Bohaiornithid Ecology and Evolution section. We specifically note that their tooth shape is likley controlled by phylogeny in the current version, though we will add a note in the upcoming version that the morphospace of bohaiorntihid teeth overlaps that of many other clades with purportedly diverse diets, which is consistent with a hypothesis of diverse diets within the clade.

• Reviewer 3, on cranial kinesis: Our FE models should be unaffected by cranial kinesis, as these are two-dimensional and model the akinetic lower jaw only. Some mediolateral kinesis may be relevant in the mandible in the form of “wishboning” in different taxa, but its prevalence in extant birds is currently unknown. The preservation of enantiornithines (two-dimensionally and typically in lateral view) limits the ability to capture any mediolateral function regardless.

Our models of mechanical advantage do not account for any cranial kinesis. This is a necessary simplifcation. The nature of cranial kinesis in extant birds, and the role that it plays in feeding, is poorly understood. Cranial kinesis will increase gape, but we don’t yet know how/if it affects jaw closing force and speed (moreover, given the variation in quadrate and hinge morphology present in extant birds, this is also something that is likely to be highly diverse). We have therefore modelled the extant birds’ jaw closing systems as having one, akinetic out lever (the jaw joint to the bite point), to match the situation in our fossil taxa. This is a common simplification that has been used previously with success (Corbin, Lowenberger et al. 2015, Olsen 2017). However, we acknowledge that this simplification may introduce some error. Unfortunately, until the mechanics of cranial kinesis – and the variation in the anatomy and performance of kinetic structures in extant birds – are better understood, we cannot determine exactly what that error looks like. We therefore have greater confidence in the inter-species comparability this conservative, akinetic approach (in other words, we may not be making assumptions that are 100% accurate, but we are at least making the same assumption across all taxa, so it should be comparable in its error). We will add a section in the Mechanical Advantage and Functional Indices discussion calling for further research into the mechanics of cranial kinesis so future mechanical advantage work in birds can take this matter into account.

• Reviewer 3, on skull reconstruction: This issue is partly addressed in the Bohaiornithid Skull Reconstruction section, though we agree that adding more mentions of it in the MA and FEA Discussion sections and the Bohaiornithid Ecology and Evolution sections will benefit the manuscript. Most notably Shenqiornis and Sulcavis have similar ecological interpretations, but much of the Shenqiornis skull reconstruction uses Sulcavis bones. Longusunguis is the only other taxon which takes more than two bones from a different taxon, and in this case all but the quadrate are not used in any quanitative measurements. We have ensured that the skull reconstructions presented in Figure 2 show what portions of the skull come from what specimen so that as new material is discovered and phylogenetic relationships are updated it will be clear to future readers which parts of reconstructions will need to be updated.

• Reviewer 3, on data availability: All data including FEA models and raw measurement data are included in the same repository as the scripts, which we will make clear in the manuscript. Good catch on the data link being dead, we will publish it now.

As a final note, it was brought to our attention by another colleague that the original manuscript’s ancestral state reconstrction lacked an outgroup. An updated reconstruction using Sapeornis as an outgroup will be included in the revised manuscript. The addition of the outgroup does not change any conclusions of the manuscript.

We once again thank our reviewers for their valuable feedback and will submit a revised version of this manuscript for publication shortly. Please let us know if you have any additional comments after reading our response that we can take onboard in our revision.

References

Artuso, C., C. S. Houston, D. G. Smith and C. Rohner (2020). Great Horned Owl (Bubo virginianus), version 1.0. Birds of the World. A. F. Poole. Ithaca, NY, USA, Cornell Lab of Ornithology.

Atterholt, J., A. W. Poust, G. M. Erickson and J. K. O'Connor (2021). "Intraskeletal osteohistovariability reveals complex growth strategies in a Late Cretaceous enantiornithine." Frontiers in Earth Science 9: 640220.

Corbin, C. E., L. K. Lowenberger and B. L. Gray (2015). "Linkage and trade‐off in trophic morphology and behavioural performance of birds." Functional ecology 29(6): 808-815.

Hone, D. W. E., A. A. Farke and M. J. Wedel (2016). "Ontogeny and the fossil record: what, if anything, is an adult dinosaur?" Biology letters 12(2): 20150947.

Hu, H. and J. K. O'Connor (2017). "First species of Enantiornithes from Sihedang elucidates skeletal development in Early Cretaceous enantiornithines." Journal of Systematic Palaeontology 15(11): 909-926.

Miller, C. V., M. Pittman, X. Wang, X. Zheng and J. A. Bright (2023). "Quantitative investigation of Mesozoic toothed birds (Pengornithidae) diet reveals earliest evidence of macrocarnivory in birds." iScience 26(3): 106211.

Olsen, A. M. (2017). "Feeding ecology is the primary driver of beak shape diversification in waterfowl." Functional Ecology 31(10): 1985-1995.

Schnell, J. H. (2020). Common Black Hawk (Buteogallus anthracinus), version 1.0. Birds of the World. A. F. Poole and F. B. Gill. Ithaca, NY, USA, Cornell Lab of Ornithology.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The current work by Kulich et al. examines the dynamic relocalization of NGR1 (LAZY2) a member of the LAZY protein family which is key for auxin redistribution during gravitropic responses. After gravistimulation of the triple mutant ngr123 (lazy234), the PIN3 activating kinase D6PK is not polarized in the columella cells.

Strengths:

The authors show a thorough characterization of NGR1 relocalization dynamics after gravistimulation.

Weaknesses:

Genetically the relocalization of D6PK depends on the LAZY protein family, but some essential details are missing in this study. On the one hand, NGR1-GFP does not associate with the BFA compartments and maintains its association with the PM and amyloplasts. On the other hand, D6PK relies on GNOM, via vesicle trafficking sensitive to BFA, suggesting that D6PK follows a different relocalization route than NGR1 which is BFA-insensitive. Based on these observations, D6PK relocalization requires the LAZY proteins, but D6PK and NGR1 relocalize through independent routes. How can this be interpreted or reconciled?

Response: Since we demonstrated that D6PK does not relocalize in the absence of NGR proteins, we conclude that NGR1 acts upstream of D6PK. The molecular mechanism driving this interaction is not fully understood; however, it is evident that NGR1 triggers the mobilization of D6PK. Despite previous investigations into D6PK mobility, the underlying mechanisms remain elusive. Notably, despite its sensitivity to BFA, D6PK does not localize to BFA bodies and does not undergo conventional endocytosis (https://doi.org/10.1016/j.devcel.2014.05.006). We fully acknowledge the importance and interest in gaining a better understanding of these processes, and it will be a focal point of our future research.

Two other works (now published) provide valuable and fundamental findings related to the mechanism examined in the current manuscript and display complementary and similar results to the ones shown in the current manuscript. Given the similarities in the examined mechanisms, these preprints should be referenced, recognized, and discussed in the manuscript under review. It is assumed that the three projects were independently developed, but the results of these previous works should be addressed and taken into account at least during the discussion and when drawing any conclusions. This does not mean that this work is less relevant. On the contrary, some of the observations that seem to be redundant are more solid, and firm conclusions can now be drawn from them.

Response: We have included and discussed these works in the revised discussion

Reviewer #2 (Public Review):

Summary:

This manuscript addresses what rapid molecular events underly the earliest responses after gravity-sensing via the sedimentation of starch-enriched amyloplasts in columella cells of the plant root cap. The LAZY or NEGATIVE GRAVITROPIC RESPONSE OF ROOTS (NGR) protein family is involved in this process and localizes to both the amyloplast and to the plasma membrane (PM) of columella cells.

The current manuscript complements and extends Nishimura et al., Science, 2023. Kulich and colleagues describe the role of the LZY2 protein, also called NGR1, during this process, imaging its fast relocation and addressing additional novel points such as molecular mechanisms underlying NGR1 plasma membrane association as well as revealing the requirement of NGR1/LZY2, 3,4 for the polar localization of the AGCVIII D6 protein kinase at the PM of columella cells, in which NGR1/LZY2 acts redundantly with LZY3 and LZY4.

The authors initially monitored relocalization of functional NGR1-GFP in columella cells of the ngr1 ngr2 ngr3 triple mutant after 180-degree reorientation of the roots. Within 10 -15 min NGR1-GFP signal disappeared from the upper PM after reorientation and reappeared at the lower PM of the reoriented cells in close proximity to the sedimented amyloplasts. Reorientation of NGR1-GFP occurred substantially faster than PIN3-GFP reorientation, at about the same time or slightly later than a rise in a calcium sensor (GCaMP3) just preceding a change in D2-Venus auxin sensor alterations. Reorientation of NGR1-GFP proved to be fast and not dependent on a brefeldin A-sensitive ARF GEF-mediated vesicle trafficking, unlike the trafficking of PIN proteins, like PIN3, or the AGCVIII D6 protein kinase. Strikingly, the PM association of NGR1-GFP was highly sensitive to pharmacological interference with sterol composition or concentration and phosphatidylinositol (4)kinase inhibition as well as dithiothreitol (DTT) treatment interfering with thioester bond formation e.g. during S-acylation. Indeed, combined mutation of a palmitoylation site and polybasic regions of NRG1 abolished its PM but not its amyloplast localization and rendered the protein non-functional during the gravitropic response, suggesting NRG1 PM localization is essential for the gravitropic response. Targeting the protein to the PM via an artificially introduced N-terminal myristoylation and an ROP2-derived polybasic region and geranylgeranylation site partially restored its functionality in the gravitropic response.

Strengths:

This timely work should be of broad interest to plant, cell and developmental biologists across the field as gravity sensing and signaling may well be of general interest. The point that NGR1 is rapidly responsive to gravistimulation, polarizes at the PM in the vicinity to amyloplast and that this is required for repolarization of D6 protein kinase, prior to PIN relocation is really compelling. The manuscript is generally well-written and accessible to a general readership. The figures are clear and of high quality, and the methods are sufficiently explained for reproduction of the experiments.

Weaknesses:

Statistical analysis has been performed for some figures but is lacking for most of the quantitative analyses in the figure legends.

Response: We added this information to the figure legends

The title claims a bit more than what is actually shown in the manuscript: While auxin response reporter alterations are monitored, "rapid redirection of auxin fluxes" are not really directly addressed and, while D6PK can activate PIN proteins in other contexts, it is not explicitly shown in the manuscript that PIN3 is a target in the context of columella cells in vivo. A title such as "Rapid redirection of D6 protein kinase during Arabidopsis root gravitropism relies on plasma membrane translocation of NGR proteins" would reflect the results better.

Response: We modified the title to Rapid translocation of NGR proteins driving polarization of PIN-activating D6 protein kinase during root gravitropism

Fig. 4: The point that D6PK is transcytosed cannot be made here based on the data of these authors. They should have used a photoswitchable version of NGR1 to show that the same molecules observed at the upper PM are translocated to the lower PM. Nishimura and colleagues actually did that for NGR4. However, this is a lot of work and maybe for NGR1 that fusion would have too low fluorescence intensity (as it was the case for NGR3). So, I think a rewording would be sufficient such as NGR-dependent reorientation of D6PK plasma membrane localization" as this does not say, from where it comes to the lower PM. Theoretically, the signal could also be amyloplast-derived or newly synthesized (or just folded) NGR1-GFP.

Response: We fully agree and rephrased the text using translocation instead of transcytosis

The authors make a model in which D6PK AGCVIII kinase-dependent on NGRs activates PIN3 to drive auxin fluxes. However, alterations in auxin responses are observed prior to PIN3 reorientation. They should explain this discrepancy better and clearly describe that this is a working hypothesis for the future rather than explicitly proven, yet.

Reviewer #3 (Public Review):

The mechanism controlling plant gravity sensing has fascinated researchers for centuries. It has been clear for at least the past decade that starch-filled plastids (termed statoliths) in specialised gravity-sensing columella cells sense changes in root orientation, triggering an asymmetric auxin gradient that alters root growth direction. Nevertheless, exactly how statolith movement triggers PIN auxin efflux carrier activation and auxin gradient formation has remained unclear until very recently. A series of new papers (in Science and Cell) and this manuscript report how LAZY proteins (also referred to as NEGATIVE GRAVITROPIC 50 RESPONSE OF ROOTS; NGR) play a pivotal role in regulating root gravitropism. In terms of their overall significance, their collective findings provide seminal insights into the very earliest steps for how plant roots sense gravity which are arguably the most important papers about root gravitropism in the past decade.

In the current manuscript, Kulich et al initially report (through creating a functional NGR1-GFP reporter) that "NGR1-GFP displayed a highly specific columella expression, which was most prominent at the PM and the statolith periphery." Is NGR1-GFP expressed in shoot tissues? If yes, is it in starch sheath (the gravity-sensing equivalent of root columella cells)? The authors also note "NGR1-GFP signal from the PM was not evenly distributed, but rather polarized to the lower side of the columella cells in the vicinity of the sedimented statoliths (Fig. 1A)." and (when overexpressing NGR-GFP) "chloroplasts in the vicinity of the PM strongly correlated with NGR1 accumulating at the PM nearby, similar to the scenario in columella" suggesting that NGR1 does not require additional tissue-specific factors (i.e. trafficking proteins or lipids) to assist in its intracellular movement from plastid to PM.

Response: Yes, NGR1, also called LAZY2 is expressed in the inner hypocotyl tissues, according to https://doi.org/10.1104/pp.17.00942. Unfortunately, we saw very little signal with our NGR-GFP construct, possibly due to NGR1-GFP weak signal and/or NGR1 being expressed only exclusively in the inner tissues.

Next, the authors study the spatiotemporal dynamics of NGR1-GFP re-localisation with other early gravitropic signals and/or components Calcium, auxin, and PIN3. The temporal data presented in Figure 1 illustrates how the GCaMP calcium reporter (in panel E) revealed "the first signaling event in the root gravitropic bending is the statolith removal from the top membrane, rather than its arrival at the bottom" It appeared that the auxin DII-VENUS reporter was also changing rapidly (panel G) - was this detectable BEFORE statolith re-sedimentation?

Response: In our data (Figure 1G), we observe that the increase in signal at the top side begins prior to starch sedimentation, in contrast to the bottom side, where the decrease starts only after starch grains land on the bottom membrane. While this observation aligns with our hypothesis and other data, we refrained from commenting on it due to the small differences between the first 2-3 timepoints, which are obscured by noise. This phenomenon arises because the DII response relies on protein degradation and is relatively slow. Hence, for rapid tracking of the auxin response, we utilized auxin-induced calcium as a proxy, with NPA treatment serving as a negative control.

Please can the authors explain their NPA result in Fig 1E? Why would treatment with the auxin transport inhibitor NPA block Ca signalling (unless the latter was dependent on the former)?

Response: Auxin induces rapid calcium transients (e.g., http://dx.doi.org/10.1016/j.cub.2015.10.025). Consequently, when auxin reaches the bottom elongation zone approximately 5-6 minutes after rotation, we observe an increased GCaMP signal at this location. Notably, when we inhibit PIN function using NPA, the GCaMP signal persists, but the difference between the top and bottom diminishes. This validates that the calcium transients at the bottom side can be interpreted as monitoring increase in auxin accumulation as a result of auxin transport.

They go on to note "This initial auxin asymmetry is mediated by PIN-dependent auxin transport, despite visible polarization of PIN3 can be detected only later" which suggests that PIN activity was being modified prior to PIN polarisation.

In contrast to other proteins involved in gravity response like RLDs and PINs, NGR1 localization and gravity-induced polarization does not undergo BFA-sensitive endocytic recycling by ARF-GEF GNOM. This makes sense given NGR1 is initially targeted to plastids, THEN the PM. Does NGR1 contain a cleavable plastid targeting signal? The authors go on to elegantly demonstrate that NGR1 PM targeting relies on palmitoylation through imaging and mutagenesis-based transgenic ngr rescue assays.

Response: Yes, there is weakly conserved plastid targeting signal on NGR1. Although we also started researching in this direction, we quickly realized, that two other groups showed very comprehensive data regarding NGR plastid localization.

Finally, the authors demonstrate that gravitropic-induced auxin gradient formation is initially dependent on PIN3 auxin efflux activation (prior to PIN3 re-localisation). This early PIN3 activation process is dependent on NGR1 re-targeting D6PK (a PIN3 activating kinase). This elegant molecular mechanism integrates all the regulatory components described in the paper into a comprehensive root gravity sensing model.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Minor comments:

Line 83: This construct fully rescued the agravitropic bending phenotype of the ngr1/2/3 triple mutant (see further).

What does it mean the see further in this context?

Response: It is a reference to the second part of the manuscript (Fig. 3, Supplementary Fig S3, Fig S4), where we extensively address the complementation with wild type and point mutated versions of NGR. There we show that the construct we are using is functional. This does not prove, but strongly imply that the GFP signal we obtain is relevant. We updated the text to point this out.

Line 101: Timing of events during the gravitropic response

When describing the equipment employed and the rotation applied to the samples, "the vertical stage microscope and minimized the time required for rotating the sample. 180{degree sign} rotation..."

The authors mentioned a travel time of 5 minutes first and later of 15 minutes for the relocalization of NGR1. Are these two different experiments? Were there two different rotation angles or degrees applied? Could the authors please rephrase this part of the description to answer these questions and help the reader understand how the assay performed?

Response: We added this explanation to the text.

Figure 1 E, F, and G.

Could the authors please provide pictures and/or videos for the PIN3 localization dynamics, intracellular calcium transients, and auxin reporter DII-Venus? In other words, show the complementing images for Figure 1E, 1F, and 1G as the authors did for Figure 2D where authors presented the pictures and the corresponding quantification plots.

Response: We wanted to avoid overcrowding the figure, but we would also love to show the videos. Therefore, we did additional supplementary movie 3, where we put all the additional observations.

Line 194: This implies the existence of posttranslational modifications such as S-acylation to associate with PM.

Why is this specific modification suggested/examined and no other modification? What is the criteria to select this kind of modification? Based on what premises? Could the authors elaborate on that? Could the authors please include references?

Response: Thank you for this comment. We of course first checked the prediction tools which have shown very strongly conserved S-acylation side. We now clarified this in the text and added other modifications as an example. Later on, we rule out myristoylation (that happens on the glycins) and prenylation (it happens only at the C-terminus CAAX box).

Line 255: NGR1 PM localization is synergistically mediated by polybasic regions and a palmitoylation site

Similarly to the previous commentary, How and why are these regions examined/analyzed? Likewise, why is the palmitoylation site selected? Please provide some background, criteria, and references.

Response: Here, we clearly state that the prediction of the palmitoylation site is made based on the GPS lipid prediction tool.

As for the polybasic region, these can be seen upon manual inspection of the primary protein sequence. We simply looked at the protein and saw it there. We rephrased the text so that it is more clear.

Reviewer #2 (Recommendations For The Authors):

Please, proofread the manuscript for style and minor language errors.

Statistical analysis has been performed for some figures but is lacking for most of the quantitative analyses in the figure legends. Where it has been performed it is not given what "n" number of roots, cells, or plasma membranes were analyzed NGR1-GFP and no information is given whether the data is derived from a representative experiment or several or pooled data from several experiments. This certainly requires revision in Fig. 1D-G, Fig. 2B-D, Fig. S2 B,E, Fig. 3B,D, F-H, Fig. S.3 B,D, Fig. S. 4 ,E-H, Fig. 4 D.

Response: Thank you, we added this information to the figure legends.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary

This fascinating paper by M. Alfatah et al. describes work to uncover novel genes affecting lifespan in the budding yeast S. cerevisiae, eventually identifying and further characterizing a gene, YBR238C, now named AAG1 by the authors. The authors began by considering published gene sets pulled from the Saccharomyces genome database that described increases or decreases in either chronological lifespan or replicative lifespan in yeast. They also began with gene sets known to be downregulated upon treatment with the lifespan-extending TOR inhibitor rapamycin.

YBR283C was unique in being largely uncharacterized, downregulated upon rapamycin treatment, and linked to both increased replicative lifespan and increased chronological lifespan upon deletion.

The authors show that YBR283C may act to negatively regulate mitochondrial function, in ways that are both dependent on and independent of the stressresponsive transcription factor Hap4, largely by looking at relative expression levels of relevant mitochondrial genes.

In a hard-to-fully interpret but well-documented series of experiments the authors note that the two paralogues YBR283C and RMD9 (which have ~66% similarity) (a) have opposite effects when acting alone, and (b) appear to interact in that some phenotypes of ybr283c are dependent on RMD9.

A particularly interesting finding in light of the current literature and of the authors' strategy in identifying YBR283C is that changes in electron transport chain genes upon rapamycin treatment appear to be affected via YBR283C.

Based on a series of experiments the authors move to conclude the existence of "a feedback loop between TORC1 and mitochondria (the TORC1-Mitochondria-TORC1 (TOMITO) signaling process) that regulates cellular aging processes."

Strengths

Overall, this study describes a great deal of new data from a large number of experiments, that shed light on the potential specific roles of YBR238C and its paralog RMD9 in aging in yeast, and also underscore the potential of an approach looking for "dark matter" such as uncharacterized genes when seining the increasing deluge of published datasets for new hypotheses to test. This work when revised will become a valuable addition to the field.

Weaknesses

A paralog of YBR283C, RMD9, also exists in the yeast genome. While the authors indicate that part of their interest in YBR283C lies in its uncharacterized nature, its paralogue, RMD9, is not uncharacterized but is named due to its phenotype of Required for Meiotic nuclear Division, which is not mentioned or discussed anywhere in the manuscript currently.

In the context of the current work, in addition to the cited Hillen, H.S et al. and Nouet C. et al, the authors might be very interested in the 2007 Genetics paper "Translation initiation in Saccharomyces cerevisiae mitochondria: functional interactions among mitochondrial ribosomal protein Rsm28p, initiation factor 2, methionyl-tRNAformyltransferase and novel protein Rmd9p" (PMID: 17194786), which does not appear to be cited or discussed in the current version of the manuscript.

Thank you for your thorough and insightful review of our manuscript. We value your positive feedback and recognition of the strengths in our study. Your constructive comments have been carefully considered, leading to the inclusion of RMD9, identified as 'Required for Meiotic Nuclear Division,' and the addition of the relevant reference (PMID: 12586695) in the revised manuscript. This information has been incorporated into the second paragraph of the "The YBR238C paralogue RMD9 deletion decreases the lifespan of cells" results section.

Furthermore, we appreciate the reviewer's suggestion to include the 2007 Genetics paper on translation initiation in Saccharomyces cerevisiae mitochondria (PMID: 17194786). This citation has been integrated into our revised manuscript.

We believe that these revisions significantly strengthen the manuscript and address the concerns raised by Reviewer #1. We thank the reviewer for their time and valuable input.

Reviewer #2 (Public Review):

The effectors of cellular aging in yeast have not been fully elucidated. To address this, the authors curated gene expression studies to link genes influenced by rapamycin - a well-known mediator of longevity across model systems - to genes known to affect chronological and replicative lifespan (RLS) in yeast. Through their analyses, they find one gene, ybr238c, whose deletion increases both CLS and RLS upon deletion and that is downregulated by rapamycin. Curiously, despite these selection criteria, the authors only use CLS as a proxy for cellular aging throughout their study and do not explore the effects of ybr238c deletion on RLS. This does not diminish their conclusions, but given the importance of this phenotype in their selection criteria, it is surprising that the authors did not choose to test both types of aging throughout their study.

Nonetheless, the authors demonstrate that deletion of ybr238c increases CLS across multiple yeast strains and through multiple assays. The authors also test the effects of YBR238C overexpression on lifespan and find the opposite effect, with overexpression yeast showing decreased survival relative to wild-type cells, consistent with "accelerated aging" as the authors propose. The authors also note that ybr238c has a paralog, rmd9, whose deletion decreases CLS and seems to be epistatic to ybr238c, as a double ybr238c/rmd9 mutant has decreased CLS relative to a wild-type strain.

Collectively, the data presented by the authors convincingly demonstrate that ybr238c influences lifespan in a manner that is distinct from (and likely opposite to) rmd9. However, the authors then link the increased CLS in Δybr238c yeast to mitochondrial function using only a handful of assays that do not directly test mitochondrial function. These include total cellular ATP levels, levels of reactive oxygen species, and the transcript levels of select nuclear-encoded mitochondrial genes. Yeast is well established to generate ATP through non-mitochondrial pathways such as glycolysis in fermentive conditions. While it is possible that the ATP levels assayed in the manuscript were tested in stationary phase, which would more likely reflect "mitochondrial function," the methods nor the figure legends contain these details, which are critical for the interpretation of these data. Similarly, ROS can be generated through non-mitochondrial pathways, and the transcription of nuclear-encoded mitochondrial genes is an indirect measure of mitochondrial function at best. Thus, the authors' proposed connection of ybr238c to mitochondrial function is correlative and should be substantiated with assays that more closely align with organellar function, such as respirometry or assaying the activity of oxidiative phosphorylation complexes. Finally, the authors attempt to tie the phenotypes of mitochondrial dysfunction caused by the deletion of ybr238c to TORC1 signaling, as the gene is influenced by rapamycin. However, the presentation of the data, such as reporting ATP levels as relative percentages or failing to perform appropriate statistical comparisons between conditions in which the authors derive conclusions, renders the data difficult to interpret. As such, this manuscript establishes that ybr238c is rapamycin responsive and influences CLS, but its influence on mitochondrial activity and ties to TORC1 signaling remain speculative.

We would like to express our gratitude to Reviewer #2 for the thoughtful feedback on our manuscript. We have carefully considered your comments and have made comprehensive revisions to address the concerns raised.

We appreciate the suggestion to investigate the role of YBR238C in replicative lifespan (RLS). However, we want to bring to your attention that four previous studies (references 7, 39, 40, and 41) have already identified the involvement of YBR238C in the RLS phenotype. Given the existing body of literature on this aspect, we chose not to duplicate these efforts in our study.

Instead, we focused our efforts on validating the role of YBR238C in chronological lifespan (CLS) phenotype, a finding reported in only one genome-wide study (reference 38). To enhance the comprehensiveness of our study, we performed analyses on different phenotypes, including mitochondria activity and oxidative stress, under both logarithmic-phase (condition for RLS) and stationary phase (condition for CLS). We now clearly indicate the logarithmic-phase/stationary phase conditions in the figure legends of the manuscript, specifying whether the conditions are relevant to RLS or CLS. Additional results of the new experiments have been included in the revised manuscript as supplementary figures (S3E-S3I).

To address concerns about the indirect nature of our mitochondrial function assays, we have performed relative mitochondria content (S3F), quantification of ROS levels from fermentative to stationary phase conditions (S3G), and assessment in respiratory glycerol medium (S3H), which provides a more direct insight into mitochondrial biology. Additionally, we have investigated the resistance of ybr238c∆ cells to H2O2 toxicity and found them to be more resistant compared to wild-type cells.

We believe these revisions strengthen the scientific rigor and clarity of our study. We sincerely appreciate the guidance from Reviewer #2, and we hope these modifications address the concerns raised effectively.

Reviewer #3 (Public Review):

Summary: The study by Alfatah et al. presented a role for YBR238C in mediating lifespan through improved mitochondrial function in a TOR1-dependent metabolic pathway. The authors used a dataset comparison approach to identify genes positively modulating yeast chronological (CLS) and Replicative (RLS) lifespan when deleted, and their expression is reduced under Rapamycin treatment condition. This approach revealed an unknown, mitochondria-localized yeast gene YBR238C, and through mechanistic studies, they identified its paralogous gene RMD9 regulating lifespan in an antagonistic effect.

Strengths:

Findings have valuable implications for understanding the YBR238C-mediated, mitochondrial-dependent yeast lifespan regulation, and the interplay between two paralogous genes in the regulation of mitochondrial function represents an inserting case for gene evolution.

Weaknesses:

Overall, the implication/findings of this study are restricted only to the yeast model since these two genes do not have any homology in higher eukaryotes. The primary methods must be carefully designed by considering two different metabolic states: respiration-associated with CLS and fermentation-associated with RLS in a single comparative approach. Yeast CLS and RLS are two completely different processes. It is already known that most gene-regulating CLS is not associated with RLS or vice versa. The method section is poorly written and missing important information. The experimental approaches are poorly designed, and variability across the datasets (e.g., media condition "YPD," "SC" etc.) and their experimental conditions are not well described/considered; thus, presented data are not conclusive, which decreases the overall rigor of the study.

We sincerely appreciate your thorough review of our manuscript and your insightful comments. We acknowledge the limitation of our study being yeast-specific due to the absence of homologous genes in higher eukaryotes. However, we would like to highlight the significance of our findings in revealing a feedback loop between mitochondrial function and TORC1 signaling (TORC1-Mitochondria-TORC1 or TOMITO signaling process) in cellular lifespan regulation.

Our interpretation of the experimental results is grounded in recent literature. Two studies (references 62 and 63) support our findings by demonstrating TORC1 activation after mitochondrial electron transport chain dysfunction and the delay in brain pathology progression upon TORC1 inhibition, respectively. These studies, discussed in our manuscript, reinforce the relevance of our work in a broader biological context.

We recognize the importance of carefully designing our primary methods to account for the different metabolic states associated with cellular processes, such as respiration in cellular lifespan (CLS) and fermentation in replicative lifespan (RLS). We want to bring to your attention that four previous studies (references 7, 39, 40, and 41) have already identified the involvement of YBR238C in the RLS phenotype. To avoid duplicating these efforts, we have chosen not to reiterate these findings in our study. However, we have clarified the logarithmic-phase/stationary phase conditions in the figure legends, specifying their metabolic states relevance to RLS or CLS. Additionally, we have included new supplementary figures (S3E-S3I) to provide further details on the new experiments conducted.

We appreciate your feedback regarding the clarity and completeness of our method section. In the revised manuscript, we have invested additional effort to enhance the clarity of the method section, providing a more detailed account of the experimental procedures, including the missing information you identified.

We believe these revisions strengthen the scientific rigor and clarity of our study. We sincerely appreciate the guidance from Reviewer #3, and we hope these modifications address the concerns raised effectively.

Reviewer #1 (Recommendations For The Authors):

Thank you for your detailed review and valuable recommendations. We have carefully addressed each of your comments in the revised manuscript. The specific changes made include:

(1) "TORC1 positively regulates aging, and its inhibition increases lifespan in various eukaryotic organisms including yeast and mammalian 13,26,27,29,30." Here I would suggest replacing "mammalian" with "mammals".

We have amended the sentence as recommended.

(2) "Next, we experimentally tested whether the transcriptome longevity signatures are associated with enhanced mitochondrial metabolism, whether the cellular energy level has gone up and cellular stress responses are induced with a switch to oxidative metabolism 47,48." Here I would replace "transcriptome longevity signatures is" with "transcriptome longevity signatures are".

We have amended the sentence as recommended.

(3) "Thus, HAP4-independent mechanism does exist through which YBR238C also affects cellular aging (Figure 3I)." I would replace "Thus, HAP4-independent" with "Thus, a HAP4-independent".

We have amended the sentence as recommended.

(4) "We examined other mitochondrial dysfunctional conditions to confirm that suppressive effect of rapamycin is not only specific to YBR238C-OE." I would change "that suppressive effect" to "that the suppressive effect".

We have amended the sentence as recommended.

(5) "Understanding the mechanism of aging will also require to understand the role of many genes of yet unknown function as YBR238C at the beginning of this work." I would switch "require to understand" to "require understanding".

We have amended the sentence as recommended.

(6) "The gene lists that modulate cellular lifespan in aging model organism yeast Saccharomyces cerevisiae were extracted from database SGD 22 and GenAge 23 (as of 8th November 2022)" "yeast" should not be italicized.

Corrected.

(7) Figure 1, panels C and D, ybr238c should be italicized.

Corrected.

(8) Figure 2B, top left-most (oxidative phosphorylation) network. I might consider repositioning some labels to make them more readable if possible.

Thank you for your feedback. The figure labels in Figure 2B are default from Metascape analysis, so repositioning isn't feasible. However, we have indicated in the figure legends that the full set of genes for functional enrichment analysis and the MCODE complex is available in Additional File 3.

(9) Figure 4E, rmd9, pet100, and cox6 should be italicized.

Corrected.

(10) Figure 5C, rmd9 and rmd9 ybr238c should be italicized. Corrected.

Reviewer #2 (Recommendations For The Authors):

Thank you for your detailed review and valuable recommendations. We have carefully addressed each of your comments in the revised manuscript. The specific changes made include:

(1) The presentation of data as heatmaps (Figures 1F, 3D, 4C, 4G, 5B, 5H, 5L, 6K) obfuscates the quantitative nature of the data. These data would be much stronger if presented as bar graphs with appropriate statistical analysis. If the authors prefer the visual of the heat map, there should be some statistical analysis performed to accompany these figures. This is particularly important for Figure 3D, in which the authors state "We found that HAP4 deletion significantly decrease the ETC complex I-V genes' expression" (bottom of page 8). As no statistical analyses were performed, the authors should refrain from using such language as it is unsupported by the data as analyzed.

Thank you for your insightful comments and suggestions regarding the presentation of our data. We appreciate the attention you have given to Figures 1F, 3D, 4C, 4G, 5B, 5H, 5L, and 6K.

In response to your feedback, we have carefully re-evaluated our approach. Considering the large volume of data associated with our lifespan analysis at different time points, we initially chose to visualize it using heatmaps to comprehensively capture the complexity of the results. However, we have now incorporated quantification information into the heatmaps.

For Figure 3D, which addresses the impact of HAP4 deletion on the expression of ETC complex I-V genes, we have replaced the heatmap with a bar graph. This modification allows for a clearer representation of the quantitative nature of the data. Moreover, we have conducted thorough statistical analyses comparing data between ybr238c∆ and ybr238c∆ hap4∆ to support the statements made in the text. The results of these analyses are now included in the revised figure. Moreover, we also replaced the Figure 6K heatmap with a bar graph.

We believe that these changes enhance the interpretability and robustness of our findings. We are grateful for your guidance, and we are confident that these adjustments will strengthen the overall quality of our manuscript.

(2) The presentation of ATP data, given its importance in supporting the core conclusions of this manuscript, is poor. The conditions under which yeast was collected are not reported, making these data impossible to interpret; total cellular ATP levels would be significantly altered and influenced by separate pathways in fermentive versus stationary phases. Minimally, the authors should describe the conditions of yeast growth (e.g., age, culture media) in which these measurements were made. The presentation of relative ATP percentages is problematic, particularly with measurements that deviate so far from wild-type ATP levels in conditions such as those in Figure 6A, in which the authors report that rapamycin induces a 1200% increase in cellular ATP. Previous papers have established that ATP levels in yeast hover around 4 mM and are stable through the cell cycle and across nutrient conditions (PMID: 30858198, 35438635). Given this, the reported ATP levels would be expected to be near 48 mM, which is strongly outside of the typically accepted values of 1-10 mM for this metabolite. Without understanding the contexts in which these measurements are made, as well as the absolute values for these measurements (which would be easily achievable through the use of a standard curve of ATP), these data are uninterpretable. Furthermore, it seems unlikely that yeast would be able to accommodate shifts of ATP levels that span an order of magnitude without dire cellular consequences, particularly during rapamycin treatment.

We appreciate the valuable feedback from the reviewer regarding the importance of providing detailed information on yeast growth conditions for interpreting ATP data. In response to this suggestion, we have enhanced the figure legends associated with the relevant figures to include a comprehensive description of the yeast growth conditions. This now specifies the age of the culture, culture media composition, and other pertinent parameters.

In addressing the concern raised about the rapamycin-induced ATP increase, we have carefully re-examined our experimental procedures. We performed additional experiments and confirmed the consistency of our findings in logarithmic-treated cultures. The results remain in alignment with our initial observations, reinforcing the reliability and reproducibility of our data.

(3) As stated above, the inference of mitochondrial function from cellular ATP levels, cellular ROS levels, and gene expression of a handful of nuclear-encoded genes is not sound. The authors should include further experimentation as evidence of mitochondrial functionality, such as respirometry or metabolic flux experiments.

Thank you for your constructive feedback on our manuscript. We appreciate your careful consideration of our work. In response to your concerns regarding the indirect nature of our mitochondrial function assays, we have implemented the following changes: We have incorporated additional assays to provide a more direct insight into mitochondrial biology. Specifically, we performed relative mitochondria content analysis (S3F) and quantified ROS levels under fermentative to stationary phase conditions (S3G). These assays offer a more direct and comprehensive assessment of mitochondrial function. Furthermore, we conducted experiments in respiratory glycerol medium (S3H) to complement our previous findings.

To further support our claims, we investigated the resistance of ybr238c∆ cells to H2O2 toxicity. Our results demonstrate that these cells exhibit increased resistance compared to wild-type cells. This additional evidence strengthens the link between mitochondrial function and cellular response to oxidative stress.

We believe these adjustments address your concerns and significantly enhance the robustness of our study. We hope you find these modifications satisfactory. We are grateful for your valuable input, which has undoubtedly improved the clarity and reliability of our findings.

(4) Multiple gene expression analyses are performed on n=2 measurements, and this should be bolstered by further replicates. Many bar graphs do not have accompanying statistics; these should be added. Some statistical tests are performed across inappropriate comparisons, such as Figure 3G, in which expression levels of mitochondrial genes in both deletion and overexpression strains should be compared to a wild-type control rather than to each other.

Thank you for your thorough review and constructive feedback on our manuscript. We appreciate your careful examination of our work. In response to your comments, we have made the following revisions to address your concerns: The multiple gene expression analysis in our study focused specifically on ETC genes. It is important to note that ETC genes themselves represent multiple replicates within the ybr238c deletion and overexpression cells, as illustrated in Figures 4D, 4G, and 6B.

We acknowledge and appreciate your observation regarding Figure 3G. To address this concern, we have revised the statistical comparisons. The expression levels of mitochondrial genes in the overexpression strain are now appropriately compared to a wild-type control. This correction has been applied in the figure that correctly corresponds to text in the manuscript.

(5) Figure 2B is uninterpretable as it stands, as most gene symbols are obscured.

We appreciate the reviewer's attention to Figure 2B and the feedback provided. Regarding the gene labels in Figure 2B, we would like to clarify that these labels are default outputs from the Metascape analysis, and unfortunately, repositioning them within the current figure layout isn't feasible without compromising the integrity of the information.

However, we have taken the reviewer's concern seriously and have made efforts to address the interpretability issue. To provide readers with access to the full set of genes for functional enrichment analysis and the MCODE complex, we have included this information in Additional File 3. The figure legends have been updated accordingly to guide readers to refer to Additional File 3 for a more detailed examination of the gene symbols and their annotations.

We hope that this solution addresses the concern raised by the reviewer.

(6) The conclusions to be drawn from Figure 3A are not clear, and this figure is cited only once in the text along with two other figures (page 8).

Thank you for your valuable feedback. We have carefully considered your comments and made revisions to improve the clarity of the conclusions drawn from Figure 3A.

(7) Figure 6K reports a range of 100-200% cell survival - how does a cell have 200% survival? Isn't survival binary (i.e., you survive or you are dead)? Perhaps this is meant to be relative to another condition; this should be more clearly stated in the figure, or the axis should be normalized to a maximum of 100% survival.

Thank you for your guidance and valuable feedback. Based on your recommendation, we have made significant changes to Figure 6K in the revised manuscript. Specifically, we replaced the heatmap with a bar graph to enhance clarity. Additionally, we would like to highlight that cell survival of combined treated cells is measured relative to the control treatment, which is considered 100% survival. This aims to provide a more accurate and comprehensible representation of the data. We believe these modifications contribute to a clearer presentation of our findings.

(8) The authors state that "TORC1 inhibition in yeast and human cells with mitochondrial dysfunction suppresses their accelerated aging." No studies of aging were done in human cells; survival in response to mitochondrial toxins does not reveal aging phenotypes. To state such is a substantial overstatement and should be amended to perhaps "cellular survival" rather than directly linked to aging.

We appreciate the careful review of our manuscript and the constructive feedback provided by the reviewer. In response to the concern raised regarding the statement about TORC1 inhibition and accelerated aging in human cells, we have revised the relevant passage as follows: "In turn, TORC1 inhibition in yeast and human cells with mitochondrial dysfunction enhances their cellular survival." We believe that this modification accurately reflects the outcomes of our experiments and addresses the concern raised by the reviewer. We would like to express our gratitude for the valuable feedback, which has contributed to the improvement of our manuscript. Thank you for your thoughtful consideration.

Reviewer #3 (Recommendations For The Authors):

Thank you for your detailed review and valuable recommendations. We have carefully addressed each of your comments in the revised manuscript. The specific changes made include:

The authors should have attempted to fully characterize the RLS and CLS phenotype of strains lacking the YBR238C and RMD9 gene, the single most important gene identified in this study. Before further characterization, its association with aging must be tested to replicate findings from the literature. Although Figure 3 shows partially characterized CLS in SC medium, different media conditions could be tested, and the full spectrum of CLS lifespan curves should be represented. RLS phenotypes of these cells were not analyzed throughout the study.

We appreciate the suggestion to investigate the role of YBR238C in both Replicative Lifespan (RLS) and Chronological Lifespan (CLS). However, it's essential to note that the involvement of YBR238C in the RLS phenotype has been previously documented in four studies (references 7, 39, 40, and 41). Considering the established literature on this matter, we chose not to duplicate these efforts in our study.

Our primary focus was on confirming the role of YBR238C in the chronological lifespan (CLS) phenotype, as indicated by a genome-wide study (reference 43). Accordingly, we also conducted an analysis of the role of RMD9 in CLS. The methods and figure legends explicitly state that CLS experiments for prototrophic CEN.PK113-7D strains were conducted in synthetic defined (SD) medium containing 6.7 g/L yeast nitrogen base with ammonium sulfate without amino acids and 2% glucose. For auxotrophic BY4743 strains, SD medium was supplemented with histidine (40 mg/L), leucine (160 mg/L), and uracil (40 mg/L).

It is important to clarify that SC medium was not used for CLS analysis. Instead, we employed SD medium, recommended for CLS analysis (reference 15; PMID: 22768836). The CLS experiments were conducted using three different methods, providing a comprehensive representation of the entire CLS lifespan (Figures 1C, 1D, 1E, and 1F).

While we did not present the Replicative Lifespan (RLS) phenotype explicitly, we performed experiments such as mitochondrial activity and ROS production under both CLS and RLS conditions. These additional analyses contribute valuable insights into the broader implications of YBR238C and RMD9 on cellular function.

We believe that these clarifications and the inclusion of additional experimental details enhance the robustness and validity of our findings. We hope these explanations address the concerns raised by the reviewer and contribute to the overall improvement of our manuscript.

In addition, authors include RNAseq data from Rapamycin-treated cells to identify differentially expressed genes. Notably, genes with decreased expression were used to compare KO strains' lifespan phenotype. Additional RNAseq analyses were performed on individual KO cells. The methodology section needs to be better written with information on which media and metabolic state that these cells are collected after treatment with rapamycin. If the cells are collected during logarithmic growth, the data can be compared with RLS aging gene sets only. A separate experiment has to be performed on stationary cells (respiratory) to collect RNAseq data after rapamycin treatment, then can be compared to the CLS aging gene set.

Thank you for your insightful comments and considerations regarding our methodology for obtaining Rapamycin response genes (RRGs). We appreciate the opportunity to address your concerns and provide further clarification on our experimental approach.

As mentioned in our manuscript, we obtained RRGs by treating logarithmic cells with 50 nM Rapamycin for 1 hour, and the details have been included in supplementary Figure S1C legends. Our primary objective was to compare these RRGs with agingassociated genes that modulate both Replicative Lifespan (RLS) and Chronological Lifespan (CLS). We acknowledge the significance of this comparison and believe that our approach, treating logarithmic cells, is suitable for achieving this goal.

It is important to note that the use of a higher concentration of Rapamycin for treatment renders the cells less efficient in terms of growth, resulting in a very low optical density (OD) at 72 hours, as illustrated in Figure 6H. Unfortunately, due to this limitation in growth efficiency, obtaining Rapamycin response genes at the stationary phase was not feasible in our experimental setup.

As the experimental conditions vary among the reports and the gene expression signature significantly changes under different metabolic conditions, the media condition that samples are collected for RNAseq analyses should match the media condition that the lifespans of those KO strains are tested. However, more information needs to be detailed on these methodologies. For example, the transcriptomic signature of the YBR238C KO strain should be done under both fermentative and respiratory conditions to understand the true gene expression signature associated with CLS and RLS. Throughout the manuscript, these two metabolic conditions and associated lifespan types (CLS vs. RLS) are not differentiated and treated as the same, probably causing the biggest confounding effect that resulted in the identification of a single yeast-specific gene.

We obtained the transcriptomic signature of the YBR238C KO strain from logarithmic phase cultures. This consistency was maintained to align with the Rapamycin Response Genes (RRGs) obtained from logarithmic cells treated with rapamycin. Detailed methodology and metabolic status information is provided in the method section and relevant figure legends.

To broaden the scope of our study, we conducted analyses on various phenotypes, including mitochondrial activity and oxidative stress, under both logarithmic phase (relevant to Replicative Lifespan, RLS) and stationary phase (relevant to Chronological Lifespan, CLS). We have now explicitly indicated the logarithmic phase/stationary phase conditions in the figure legends of the manuscript, specifying their relevance to RLS or CLS.

Results from these additional experiments have been incorporated into the revised manuscript as supplementary figures (S3E-S3I). We believe that these clarifications and the inclusion of additional experimental details enhance the robustness and validity of our findings. We trust that these explanations effectively address the concerns raised by the reviewer and contribute to the overall improvement of our manuscript.

YBR238C gene KO effect on mitochondrial function missing comprehensive characterization. Whether the improved mito function caused by increased mtDNA copy number and/or increased mitochondrial number could be easily tested by analyzing normalizing RNAseq reads from mtDNA genes to reads from nucDNA genes. Data could be further combined with western blot specific to mito membrane proteins to analyze mito copy number.

Thank you for your insightful comments and suggestions. Following your recommendation, we conducted an assessment of relative mitochondrial content (see Figure S3F) and observed significantly higher mtDNA content in the ybr238c∆ compared to the wild type (see Figure S3F). Additionally, we have incorporated the methodology for mitochondrial DNA copy number analysis in the methods section.

The two paralogous gene interaction is an interesting observation. However, in yeast, it is known that deletion of one of the paralogous genes causes copy number amplification of the certain chromosome that the other paralogous gene is located, causing aneuploid chromosome. Many of the observed phenotypes can be associated with increased chromosome copy number and should be carefully tested. However, the authors did not consider this important point. Simply, using RNA seq data normalized read/per chromosome could be plotted to analyze the karyotype of YBR238C and RMD9 KO cells.

We appreciate your thoughtful consideration of our work and the suggestion to investigate chromosome copy number variations. While we did not directly test the chromosome copy, we want to highlight that our study extensively explores the impact of YBR238C on cellular lifespan through an RMD9-dependent mechanism (Figure 5). Deletion of YBR238C increases, whereas overexpression of YBR238C decreases the expression of its paralog, RMD9 (Figure 5F). Furthermore, this phenotype is associated with the lifespan of YBR238C-deleted and overexpressed cells. In our study, we have thoroughly investigated this aspect.

Author Response

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

We appreciate the care and the detail shown by the Reviewers. Their comments have made our article more focused and more accessible to a general audience.

We would like to begin with a comment about the last sentence of the “eLife assessment”. The evolution of metamorphosis in insects was a major triumph in animal evolution that subsequently impacted almost every aspect of plant and animal evolution in the terrestrial and freshwater aquatic biospheres. Unlike the metamorphoses of most other groups, whose evolutions are lost in time, insect evolution arose relatively recently (~400 mya) and insect orders have branched off at various points in this evolution and have persisted to modern times. Although these “relic” groups also have undergone millions of years of evolution and specialization, they still provide us with windows into how this progression may have come about. The study of these groups provides a unique opportunity to explore the mechanisms that underlie major life history shifts and should be of interest to anyone interested in evolution – not just entomologists.

Reviewer #1 (Public Review):

Summary:

This paper provides strong evidence for the roles of JH in an ametabolous insect species. In particular, it demonstrates that:

• JH shifts embryogenesis from a growth mode to a differentiation mode and is responsible for terminal differentiation during embryogenesis. This, and other JH roles, are first suggested as correlations, based on the timing of JH peaks, but then experimentally demonstrated using JH antagonists and rescue thereof with JH mimic. This is a robust approach and the experimental results are very convincing.

• JH redirects ecdysone-induced molting to direct formation of a more mature cuticle

• Kr-h1 is downstream of JH in Thermobia, as it is in other insects, and is a likely mediator of many JH effects

• The results support the proposed model that an ancestral role of JH in promoting and maintaining differentiation was coopted during insect radiations to drive the evolution of metamorphosis. However, alternate evolutionary scenarios should also be considered.

Strengths:

Overall, this is a beautiful, in-depth student. The paper is well-written and clear. The background places the work in a broad context and shows its importance in understanding fundamental questions about insect biology. The researchers are leaders in the field, and a strength of this manuscript is their use of a variety of different approaches (enzymatic assays, gene expression, agonists & antagonists, analysis of morphology using different types of microscopy and detection, and more) to attack their research questions. The experimental data is clearly presented and carefully executed with appropriate controls and attention to detail. The 'multi-pronged' approach provides support for the conclusions from different angles, strengthening conclusions. In sum, the data presented are convincing and the conclusions about experimental outcomes are well-justified based on the results obtained.

Weaknesses:

This paper provides more detail than is likely needed for readers outside the field but also provides sufficient depth for those in the field. This is both a strength and a weakness. I would suggest the authors shorten some aspects of their text to make it more accessible to a broader audience. In particular, the discussion is very long and accompanied by two model figures. The discussion could be tightened up and much of the text used for a separate review article (perhaps along with Figure 11) that would bring more attention to the proposed evolution of JH roles.

We appreciate the comments about the strengths and weaknesses of the paper. To deal with the weaknesses, we have condensed some of the Results to make them less cumbersome and the Discussion has been completely revised, keeping a sharp focus on the actions of JH in Thermobia embryos and how these actions relate to the status quo functions of JH in insects with metamorphosis. As part of the revision of the Discussion, we have replaced Figures 10 and 11.

Reviewer #1 (Recommendations For The Authors):

In keeping with my public review, this paper is very strong and I have very few suggestions for improvement. They are:

(1) Thermobia are extant insects and are not ancestral insects. It is likely that they retain features found in an insect ancestor. However, these insects have been evolving for a very long time, and for any one feature, many changes may have occurred, both gain and loss of gene function and morphology. Further, even for morphological features present in an extant species that are the same as an ancestor, genetic pathways regulating this feature may have changed over time (see for examples papers from the Haag and Pick labs). Although I realize this is a small, possibly almost semantic point, I feel it is important to be precise here. For example, in the title, "before" is speculative as there could have been a different role in the ancestor with the role in embryogenesis arising in lineages leading to Thermobia; similarly in the abstract, "this ancestral role of JH' is an overstatement since we cannot actually measure the ancestral role.

Since the title has already been cited in a Perspectives review, we decided to keep the title as is.

(2) I don't understand the results in Met and myo in Fig. 3B. Perhaps include them in the explanation of Fig.3 and not after the description of Fig. 4 and explain them in more detail (or perhaps not include them at all?). I don't really understand the statistical analysis of these panels either.

We have revised the figure legends to explain the statistics.

(3) Another point regarding language - talking about the embryo being "able" to go through a developmental stage implies decision-making. I would suggest dropping that wording (e.g, in the description of Fig. 5C). Similarly, in explaining Fig. 6B, it would be more correct to say "JH treatment no longer inhibited" than as written "could no longer inhibit" (implying 'no matter how hard it tried, it still couldn't do it')

We have removed the “can’t” wording. Figure 6 has been revised

Reviewer #2 (Public Review):

The authors have studied in detail the embryogenesis of the ametabolan insect Thermobia domestica. They have also measured the levels of the two most important hormones in insect development: juvenile hormone (JH) and ecdysteroids. The work then focuses on JH, whose occurrence concentrates in the final part (between 70 and 100%) of embryo development. Then, the authors used a precocene compound (7-ethoxyprecocene, or 7EP) to destroy the JH producing tissues in the embryo of the firebrat T. domestica, which allowed to unveil that this hormone is critically involved in the last steps of embryogenesis. The 7EP-treated embryos failed to resorb the extraembryonic fluid and did not hatch. More detailed observations showed that processes like the maturational growth of the eye, the lengthening of the foregut and posterior displacement of the midgut, and the detachment of the E2 cuticle, were impaired after the 7EP treatment. Importantly, a treatment with a JH mimic subsequent to the 7EP treatment restored the correct maturation of both the eye and the gut. It is worth noting that the timing of JH mimic application was essential for correcting the defects triggered by the treatment with 7EP.

This is a relevant result in itself since the role of JH in insect embryogenesis is a controversial topic. It seems to have an important role in hemimetabolan embryogenesis, but not so much in holometabolans. Intriguingly, it appears important for hatching, an observation made in hemimetabolan and in holometabolan embryos. Knowing that this role was already present in ametabolans is relevant from an evolutionary point of view, and knowing exactly why embryos do not hatch in the absence of JH, is relevant from the point of view of developmental biology.

The unique and intriguing aspect of juvenile hormone is its status quo action in the control of metamorphosis. Our reason for dealing with an insect group that branched off from the line of insects that eventually evolved metamorphosis, was to gain insight into the ancestral functions of this hormone. Our data from Thermobia as well as that from grasshoppers and crickets indicate that the developmental actions of JH were originally confined to embryogenesis where it promoted the terminal differentiation of the embryo. Its actions in promoting differentiation also included suppressing morphogenesis. This latter function was not pronounced during embryogenesis because JH only appeared after morphogenesis was essentially completed. However, it was a preadaptation that proved useful in more derived insects that delayed aspects of morphogenesis into the postembryonic realm. JH was then used postembryonically to inhibit morphogenesis until late in juvenile growth when JH disappears, and this inhibition is released.

Then, the authors describe a series of experiments applying the JH mimic in early embryogenesis, before the natural peak of JH occurs, and its effects on embryo development. Observations were made under different doses of JHm, and under different temporal windows of treatment. Higher doses triggered more severe effects, as expected, and different windows of application produced different effects. The most used combination was 1 ng JHm applied 1.5 days AEL, checking the effects 3 days later. Of note, 1.5 days AEL is about 15% embryonic development, whereas the natural peak of JH occurs around 85% embryonic development. In general, the ectopic application of JHm triggered a diversity of effects, generally leading to an arrest of development. Intriguingly, however, a number of embryos treated with 1 ng of JHm at 1.5 days AEL showed a precocious formation of myofibrils in the longitudinal muscles. Also, a number of embryos treated in the same way showed enhanced chitin deposition in the E1 procuticle and showed an advancement of at least a day in the deposition of the E2 cuticle.

While the experiments and observations are done with great care and are very exhaustive, I am not sure that the results reveal genuine JH functions. The effects triggered by a significant pulse of ectopic JHm when the embryo is 15% of the development will depend on the context: the transcriptome existing at that time, especially the cocktail of transcription factors. This explains why different application times produce different effects. This also explains why the timing of JHm application was essential for correcting the effects of 7EP treatment. In this reasoning, we must consider that the context at 85% development, when the JH peaks in natural conditions and plays its genuine functions, must be very different from the context at 15% development, when the JHm was applied in most of the experiments. In summary, I believe that the observations after the application of JHm reveal effects of the ectopic JHm, but not necessarily functions of the JH. If so, then the subsequent inferences made from the premise that these ectopic treatments with JHm revealed JH functions are uncertain and should be interpreted with caution.

We disagree with the reviewer. An analogous situation would be in exploring gene function in which both gain-of-function and loss-of-function experiments often provide complementary insights into how a gene functions. We see JH effects only when its receptor, Met, is present and JH can induce its main effector protein, Kr-h1. The latter gives us confidence that we are looking at bona fide JH effects. We have also kept in mind, though, that the nature of the responding tissues is changing through time. Nevertheless, we see a consistent pattern of responses in the embryo and these can be related to its postembryonic effects in metamorphic insects.

Those inferences affect not only the "JH and the progressive nature of embryonic molts" section, but also, the "Modifications in JH function during the evolution of hemimetabolous and holometabolous life histories" section, and the entire "Discussion". In addition to inferences built on uncertain functions, the sections mentioned, especially the Discussion, I think suffer from too many poorly justified speculations. I love speculation in science, it is necessary and fruitful. But it must be practiced within limits of reasonableness, especially when expressed in a formal journal.

We have tried to dial back the speculation.

Finally, In the section "Modifications in JH function during the evolution of hemimetabolous and holometabolous life", it is not clear the bridge that connects the observations on the embryo of Thermobia and the evolution of modified life cycles, hemimetabolan and holometabolan.

Our Figure 12 should put this into perspective.

Reviewer #2 (Recommendations For The Authors):

Main points

(1) Please, reduce the level of overinterpretation of ectopic treatment experiments with JHm, since the resulting observations represent effects, but not necessarily functions of JH.

We have revised this section to indicate that the “effects” of ectopic treatments provide insights into the function of JH. Using a genetic analogy, both “loss-of-function” and “gain-of-function” experiments provide insights into a given gene. (see response to Public Comments)

(2) Especially in the sections "JH and the progressive nature of embryonic molts" and "Modifications in JH function during the evolution of hemimetabolous and holometabolous life histories", and the entire "Discussion", please keep the level of speculation within reasonable limits, avoiding especially the inference of conclusions on the basis of speculation, itself based on previous speculation.

We have toned down some of the speculation and provided reasons why it is worth suggesting.

(3) Please revisit the argued roles of myoglianin in the story, in light of its effects as an inhibitor of JH production, repressing the expression of JHAMT, as has been reliably demonstrated in hemimetabolan species (DOI: 10.1073/pnas.1600612113 and DOI: 10.1096/ fj.201801511R).

Our appreciation to the reviewer. We are more explicit about the relationship between JH and myo.

Minor points

(4) Please keep the consistency of the scientific binomial nomenclature for the species mentioned. For example, read "Manduca sexta" (in italics) at the first mention, and then "M. sexta" (in italics) in successive mentions (instead of reading "Manduca" on page 17, and then "Manduca sexta" on page 18, for example). The same for "Drosophila" ("Drosophila melanogaster" first, and then "D. melanogaster"), "Thermobia" ("Thermobia domestica" first, and then "T. domestica"), etc. In the figure legends, I recommend using the complete name: Thermobia domestica, in the main heading.

Where there is no possibility of confusion, we intend to use Thermobia, rather than T. domestica, etc. We think that it is easier for a non-specialist to read and it is commonly done in endocrine papers.

(5) There is no purpose in evolution and biological processes. Thus, I suggest avoiding expressions that have a teleological aftertaste. For example (capitals are mine), on p. 3 "appears to have been extended into postembryonic life where it acts TO antagonize morphogenic and allow the maintenance of a juvenile state".

We have tried to avoid teleological wording.

(6) The title "The embryonic role of juvenile hormone in the firebrat, Thermobia domestica, reveals its function before its involvement in metamorphosis" contains a redundancy ("role" and "function"), and an apparent obviousness ("before its involvement in metamorphosis"). I suggest a more straightforward title. Something like "Juvenile hormone plays developmental functions in the embryo of the firebrat Thermobia domestica, which predate its status quo action in metamorphosis".

As noted above, we are retaining the title since it has already been cited.

(7) Page 2. "The transition from larva to adult then occurred through a transitional stage, the pupa, thereby providing the three-part life history diagnostic of the "complete metamorphosis" exhibited by holometabolous insects (reviews: Jindra, 2019; Truman & Riddiford, 2002, 2019)". I suggest adding the reference ISBN: 9780128130209 9 7 8 - 0 - 1 2 - 8 1 3 0 2 0 - 9, as the most comprehensive and recent review on complete metamorphosis.

Done

(8) Page 3. "These severe developmental effects suggest that the developmental role of JH in insects was initially CONFINED to the embryonic domain" (capitals are mine). This appears contradictory with the observations of Watson, 1967, on the relationships between the apparition of scales and JH, mentioned shortly before by the authors.

This is explained in the Discussion. Although JH can suppress scale appearance in the J4 stage, we have not been able to show that scales appearance is caused by changes in the juvenile JH titer.

(9) Page 4. "we measured JH III levels during Thermobia embryogenesis at daily intervals starting at 5 d AEL". Why not before, like in the case of ecdysteroids? The authors might perhaps argue that the levels of Kr-h1 expression are consistently low from the very beginning, according to Fernandez-Nicolas et al, 2022 (reference cited later in the manuscript).

(10) Page 4. "Ecdysteroid titers through embryogenesis and the early juvenile instars were measured using the enzyme immunoassay method (Porcheron et al., 1989) that is optimized for detecting 20-hydroxyecdysone (20E)". The antibody generated by Porcheron (and now sold by Cayman) recognizes ecdysone and 20-hydroxyecdysone alike. But that's not relevant here. I would refer to "ecdysteroids" when mentioning measurements. Also in figure 2B (and "juvenile hormone III" without the formula, in Panel A, for harmonization). And I would not expand on specifications, like those at the beginning of page 5, or towards the end of page

We thank the reviewer for this important correction.

(12) ("the fact that we detected only a slight rise in ecdysteroids at this time (Fig 2B) is likely due to the assay that we used being designed to detect 20E rather than ecdysone").

Omitted.

(11) Page 5. "Low levels of Kr-h1 transcripts were present at 12 hr after egg deposition, but then were not detected until about 6 d AEL when JH-III first appeared". There is a very precise Kr-h1 pattern in Fernandez-Nicolas et al. 2023 (reference mentioned later in the manuscript).

(12) Page 5. "notably myoglianin (myo), have become prominent as agents that promote the competence and execution of metamorphosis in holometabolous and hemimetabolous insects (He et al., 2020; Awasaki et al., 2011)". See my note 3 above.

The myoglianin issue has been revised.

(13) Page 5. "a drug that suppresses JH production". Rather, "a drug that destroys the JH producing tissues". Why the way, do the authors know when the CA are formed in T. domestica embryo development?

We prefer to keep our original wording. There have been some cases in which precocene has blocked JH production but did not kill the CA cells. We do not have observations that show that 7EP kills the CA cells in Thermobia embryos.

(14) Page 5. "subsequent treatment with a JHm". I would say here that the JHm is pyriproxyfen, not on page 6 or page 7. Thus, to be consistent, after the first mention of "pyriproxyfen (JHm)" on page 5, I'd consistently use the abbreviation "JHm".

(15) Page 9. "Limb loss in such embryos was often STOCHASTIC, i.e., in a given embryo some limbs were completely lost while others were maintained in a reduced state" (capitals are mine). The meaning of "stochastic" is random, involving a random variable; it is a concept usually associated to probability theory and related fields. I suggest using the less specialized word "variable", since to ascertain that the values are really stochastic would require specific mathematical approaches.

We are still using stochastic because the loss is random.

(16) Page 10. "9E). Indeed, the JH treatment redirects the molt to be more like that to the J2 stage, rather than to the E2 (= J1) stage". Probably too assertive given the evidence available (see my points 1 and 2 above).

We do not see a problem with our conclusion. In response to the JHm treatment, the embryo produced a smooth, rather than a “pebbly” cuticle, failed to make the J1-specific egg tooth, and attempted to make cuticular lenses (a J2 feature). This ability of premature JH exposure to cause embryos to “skip” a stage is also seen in locusts (Truman & Riddiford, 1999) and crickets (Erezyilmaz et al., 2004). The JHm treatment resulted in the production of smooth cuticle, lack of a hatching tooth, and an attempt to make cuticular lenses.

(17) Page 11. "early JHM treatment", read "early JHm treatment".

Corrected

(18) Page 11. "likely. A target of JH, and likely Kr-h1, in Thermobia is myoglianin...". Please see my notes 1, 2, and especially 3, above.

This has been revised

(19) Page 13. "the locust, Locusta americana (Aboulafia-Baginshy et al.,1984)". Please read "the locust, Locusta migratoria (Aboulafia-Baginshy et al.,1984)".

Corrected

(20) Page 13 "Acheta domesticus" three times. The correct name now is "Acheta domestica", after harmonizing the declension of the specific name with the generic one. See additionally my note 4 above.

Acheta domesticus has been used in hundreds (thousands?) of papers since it was originally named by Linnaeus. We will continue to use it.

(21) Page 15, "(also called the vermiform larva (Bernays, 1971) redirects embryonic development to form an embryo with proportions, cuticular pigmentation, cuticular sculpturing and bristles characteristic of a nymph, while pronymph modifications, such as the cuticular surface sculpturing (Bernays, 1971)". The reference "Bernays, 1971" is indeed "Bergot et al., 1971".

There was a mistake in the references. The Bernays reference was omitted from the revised Discussion

(22) Page 16. "Since JH also induces Kr-h1 in embryos of many insects, including Thermobia". I'm not sure that this has been studied in many insects. In any case, any reference would be useful.

(23) Page 17. "Tribolium casteneum". Please read "Tribolium castaneum".

Changed

(24) Page 17. "...results in a permanent larva that continues to molt well after it has surpassed its critical weight (He et al., 2019)". The paper of He et al., 2019 is preceded by two key papers that previously demonstrate (and in hemimetabolan insects) that myoglianin is a determining factor in the preparation for metamorphosis: DOI: 10.1073/pnas.1600612113 and DOI: 10.1096/ fj.201801511R). See my note 3 above.

Corrected in revision

(25) Page 18. "These persisting embryonic primordia join the wing primordia in delaying their morphogenesis into postembryonic life". This reader does not understand this sentence.

Made clearer in the revision.

(26) Page 18. "is first possible in the commercial silkworm (Daimon et al., 2015)". Please mention the scientific Latin name of the species, Bombyx mori.

(27) Page 19. "The functioning of farnesol derivatives in growth versus differentiation control extends deep into the eukaryotes.../... this capacity was eventually exploited by the insects to provide the hormonal system that regulates their metamorphosis". This information appears quite out of place.

We have retained this point.

(28) Page 21. Heading "Hormones". I suggest using the heading "Bioactive compounds", as neither pyriproxyfen nor 7-ethoxyprecocene are hormones.

Done

(29) Page 29, legend of figure 1. "Photomicrographs" is somewhat redundant. The technical word is "micrographs". "Thermobia domestica" appears in the explanation of panel C, but this is not necessary, as the name appears in the main heading of the legend.

Done

(30) Page 30, legend of figure 2. Panel B, see my comment 10 above. Why embryonic age is expressed in % embryo development in panel C (and in days in panels A and B)?

All have been converted to days AEL

(31) Page 35, legend of figure 5. "Photomicrograph" see my note 28 above.

Done

(32) Page 40, figure 10. In panel A, the indication of the properties of JH is misleading. The arrow going to promoting differentiation and maturation is OK, but the repression sign that indicates suppression of morphogenetic growth and cell determination seems to suggest that JH has retroactive effects. In panel B, I suggest to label "Flies" instead of "Higher Diptera", which is an old-fashioned term. In any case, see my general comments 1 and 2, above, about speculation.

Figure has been completely revised

(33) Figure 11. See my general comments 1 and 2, above, about speculation.

Figure has been revised

Reviewer #3 (Public Review):

Summary:

In this manuscript, the authors use inhibitors and mimetics of juvenile hormone (JH) to demonstrate that JH has a key role in late embryonic development in Thermobia, specifically in gut and eye development but also resorption of the extraembryonic fluid and hatching. They then exogenously apply JH early in development (when it is not normally present) to examine the biological effects of JH at these stages. This causes a plethora of defects including developmental arrest, deposition of chitin, limb development, and enhanced muscle differentiation. The authors interpret these early effects on development as JH being important for the shift from morphogenetic growth to differentiation - a role that they speculate may have facilitated the evolution of metamorphosis (hemi- and holo-metaboly). This paper will be of interest to insect evo-devo researchers, particularly those with interests in the evolution of metamorphosis.

Strengths:

The experiments are generally conducted very well with appropriate controls and the authors have included a very detailed analysis of the phenotypes.

The manuscript significantly advances our understanding of Thermobia development and the role of JH in Thermobia development.

The authors interpret this data to present some hypotheses regarding the role of JH in the evolution of metamorphosis, some aspects of which can be addressed by future studies.

Weaknesses:

The results are based on using inhibitors and mimetics of JH and there was no attempt to discern immediate effects of JH from downstream effects. The authors show, for instance, that the transcription of myoglianin is responsive to JH levels, it would have been interesting to see if any of the phenotypic effects are due to myoglianin upregulation/suppression (using RNAi for example). These kinds of experiments will be necessary to fully work out if and how the JH regulatory network has been co-opted into metamorphosis.

We agree completely and should be a feature of future work.

The results generally support the authors' conclusions. However, the discussion contains a lot of speculation and some far-reaching conclusions are made about the role of JH and how it became co-opted into controlling metamorphosis. There are some interesting hypotheses presented and the author's speculations are consistent with the data presented. However, it is difficult to make evolutionary inferences from a single data point as although Thermobia is a basally branching insect, the lineage giving rise to Thermobia diverged from the lineages giving rise to the holo- and hemimetabolous insects approx.. 400 mya and it is possible that the effects of JH seen in Thermobia reflect lineage-specific effects rather than the 'ancestral state'. The authors ignore the possibility that there has been substantial rewiring of the networks that are JH responsive across these 400 my. I would encourage the authors to temper some of the discussion of these hypotheses and include some of the limitations of their inferences regarding the role of JH in the evolution of metamorphosis in their discussion.

We have tried to be less all-encompassing in the Discussion. The strongest comparisons can be made between ametabolous and hemimetabolous insects and we have focused most of the Discussion on the role of JH in that transition. We still include some discussion of holometabolous insects because the ancestral embryonic functions of JH may be somehow related to the unusual reappearance of JH in the prepupal period. We have reduced this discussion to only a few sentences.

Reviewer #3 (Recommendations For The Authors):

(1) The overall manuscript is very long (especially the discussion), and the main messages of the manuscript get lost in some of the details. I would suggest that the authors move some of the results to the supplementary material (e.g. it might be possible to put a lot of the detail of Thermobia embryogenesis into the supplementary text if the authors feel it is appropriate). The discussion contains a lot of speculation and I suggest the authors make this more concise. One example: At the moment there is a large section on the modification in JH function during the evolution of holo and hemi-metabolous life history strategies. There are some interesting ideas in this section and the authors do a good job of integrating their findings with the literature - but I would encourage the authors to limit the bulk of their discussion to the specific things that their results demonstrate. E.g. The first half of p17 contains too much detail, and the focus should be on the relationship with Thermobia (as at the bottom of p17).

Section has been revised and is more focused

(2) I would also suggest a thorough proofread of the manuscript, I have highlighted some of the errors/points of confusion that I found in the list below - but this list is unlikely to be exhaustive . We appreciate catching the errors. Hopefully the final version is better proofed.

(3) It might be me, but I found the wording in the second half of the abstract a bit confusing. Particularly the statement about the redeployment of morphogen systems - could this be stated more clearly?

Abstract has been revised.

(4) Introduction

a. "powered flight" rather than 'power flight'

Done

b. 'brought about a hemimetabolous lifecycle' implies causality which hasn't been shown and directionality to evolution - suggest 'facilitated the evolution of a hemi...". Similar comment for 'subsequent step to complete metamorphosis'.

c. Bottom of p2 - unclear whether you are referring to hemi- holo- or both

d. Suggest removing sentence beginning "besides its effects..." as the relevance of the role of JH in caste isn't clear.

Kept sentence but removed initial clause

e. State that Thermoia is a Zygentoma.

Done

f. Throughout - full species names on first usage only, T. domestica on subsequent usages.

We will continue to use genus names for the reason given above.

Gene names e.g. kr-h1 in italics.

g. 'antagonise morphogens"? rather than 'antagonise morphoentic'.

Done

(5) Results

a. Unclear why drawings are provided rather than embryonic images in Fig. 1A

We think that the points can be made better with diagrams.

b. Top of p4, is 'slot' the correct word?

Corrected

c. Unclear why the measurements of JHIII weren't measured before 5 days AEL, especially given that many of the manipulative experiments are at earlier time points than this. I appreciate that, based on kr-h1, levels that JHIII is also likely to be low.

d. Reference for the late embryonic peak of 20E being responsible for the J2 cuticle?

Clarified that this is an assumption

e. Clarify "some endocrine related transcripts" why were these ones in particular picked? Kr-h1 is a good transcriptional proxy for JH and Met is the JH-receptor, why myoglianin and not some of the other transcriptional proxies of neuroendocrine signalling?

Hopefully, the choice is clearer.

f. Fig 2C rather than % embryo development for the gene expression data please represent this in days (to be consistent with your other figures).

It is now consistent with other parts of figure.

g. In Fig. 3 the authors do t-tests, because there are three groups there needs to be some correction for multiple testing (e.g. Bonferroni) can the authors add this to the relevant methods section?

We think that pair-wise comparisons are appropriate.

h. Fig. 3 legend: you note that you treat stage 2 juveniles with 7EP - I couldn't tell what AEL this corresponded to.

This is after hatching so AEL does not apply.

i. Top of p7 'deformities' rather than 'derangements'?

Done

j. Regarding the dosage effects of embryonic abnormalities - it would be good to include these in the supp material, as it convinces the reader that the effects you have seen aren't just due to toxicity.

It is not clear what the objection is.

k. Bottom of p7 'problematic' not 'problematical'

Done

l. P8 Why are the clusters of Its important? - provide a bit more interpretation for the reader here.

This is clear in the revised version.

m. P9 Why is the modulation of transcription of kr-h1, met, and myo important in this context

Explained

n. P9 'fig. 7F'? there is no Fig. 5F

Thanks for catching the typo.

o. Fig. 7B add to the legend which treatment the dark and light points correspond to.

We think it is obvious from the labeling on Fig 7B.

(6) Discussion:

a. What do we know about how terminal differentiation is controlled in non-insect arthropods? Most of the discussion is focused on insects (which makes sense as JH is an insect-specific molecule), but if the authors are arguing the ancestral role of JH it would be useful to know how their findings relate to non-insect arthropods.

We have not been able to find any information about systemic signals being involved in non-insect arthropods.

b. There is no Fig. 5E (are they referring to 7E?)

Yes, it should have been Fig. 7E.

c. Is myoglianin a direct target of JH in other species?

Other reports are in postembryonic stages and show that myoglianin suppresses JH production. Our paper is the first examination in embryos and we find that the opposite is true – i.e., that JH treatment suppresses myoglianin production. We suspect that these two signaling systems are mutually inhibitory. It would be interesting to see whether treatment of a post-critical weight larva with JH (which would induce a supernumerary larval molt) would also suppress myoglianin production (as we see in Thermobia embryos).

d. P12 What is the evidence that JH interacts with the first 20E peak to alter the embryonic cuticle?

We are not sure what the issue is. The experimental fact is that treatment with JH before the E1 ecdysteroid peak causes the production of an altered E1 cuticle. We are faced with the question of why is this molt sensitive to JH when the latter will not appear until 3 or 4 days later? A possible answer is that the ecdysone response pathway has a component that has inherent JH sensitivity. The mosquito data suggest that Taiman provides another link between JH and ecdysone action

e. Top of p13 - this paragraph can be cut down substantially. Although this is evidence that JH can alter ecdysteriods - it is in a species that is 400 my derived from the target species. Is it likely to be the exact same mechanism? I would encourage the authors to distil and retain the most important points.

This paragraph has been shortened and focused.

f. Bottom of p13 - what does this study add to this knowledge?

The response of Thermobia embryos to JH treatment is qualitatively the same as seen in other short germband embryos. This similarity supports the assumption that the same responses would have been seen in their last common ancestor.

g. P19 the last paragraph in the conclusions is really peripherally relevant to the paper and is a bit of a stretch, I would encourage the authors to leave this section out.

We agree that it is a stretch. JH and its precursor MF are the only sesquiterpene hormones. How did they come about to acquire this function? We think it is worth pointing out the farnesol metabolites have been associated with promoting differentiation in various eukaryotes. An ancient feature of these molecules in promoting (maintaining?) differentiation may have been exploited by the insects to develop a unique class of hormones. It is worth putting the idea out to be considered.

h. P19 "conclusions" rather than 'concluding speculations'.

Changed as suggested.

Methods:

It is standard practice to include at least two genes as reference genes for RT-qPCR analysis (https://doi.org/10.1186/gb-2002-3-7-research0034, https://doi.org/10.1373/clinchem.2008.112797) If there are large-scale differences in the tissues being compared (e.g. as there are here during development) then more than two reference genes may be required and a reference gene study (such as https://doi.org/10.3390%2Fgenes12010021) is appropriate. Have the authors confirmed that rp49 is stably expressed during the stages of Thermobia development that they assay here?

We have explained our choice in the Methods.

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.

Author Response

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

We thank the editors and reviewers for their tremendously helpful comments. We outline below changes we have made to the manuscript in response to each point. These include new analyses and a substantial rewrite to address the concerns about lack of clarity.

We believe the revisions strengthen the evidence for our conclusion that grid fields can be either anchored to or independent from a task reference frame, and that anchoring is selectively associated with successful path integration-dependent behaviour. Our additional analyses of non-grid cells indicate that while some are coherent with the grid population, many are not, suggesting cell populations within the MEC may implement grid-dependent and grid-independent computations in parallel.

We hope the reviewers will agree that our novel experimental strategy complements and avoids limitations of perturbation-based approaches, and by providing evidence to dissociate the two major hypotheses for whether and when grid cells contribute to behaviour our results are likely to have a substantial impact on the field.

Public Reviews:

Reviewer #1 (Public Review):

In this study, Clark et. al. uncovered an association between the positional encoding of grid cell activity with good performance in spatial navigation tasks that requires path integration, highlighting the contribution of grid firing to behaviour… The conclusions of this paper are mostly well supported by data, the finding about the association between grid cell encoding and behaviour in spatial memory tasks is important. However, some aspects of the analysis need to be clarified or extended.

Thankyou for the overview and constructive comments.

(1) While the current dataset aims to demonstrate a "correlation" between grid cell encoding and task performance, the other variables that could confound this correlation should be carefully examined.

(1.1) The exact breakdown of the fraction of beaconed/non-beaconed/probe trials is never shown. if the session makeup has a significant effect on the coding scheme or other results, this variable should be accounted for.

The lack of information about the trial organisation was a substantial oversight in our preparation of the first version of the manuscript. Session make up can not account for effects on grid stability and its relationship to behavioural outcome but this was not made at all clear.

In all sessions trial types were varied in a fixed repeating sequence. Therefore, continuous blocks of trials on which grid firing is anchored (or independent from) the track can not be explained by the mouse experiencing a particular trial type. We have revised the manuscript to make this clearer, e.g. p 5, ‘These switches could not be explained by variation between trials in the availability of cues or rewards, as these were interleaved in blocks that repeated throughout a session (see Methods), whereas periods in which grid cell activity was in a given mode extended across the repeating blocks (e.g. Figures 3D,E, 4A, 5E,F).’ and methods p 12, ‘Trials were delivered in repeating blocks throughout a recording session…’

(1.2) The manuscript did not provide information about whether individual mice experienced sessions with different combinations of the three trial types, and whether they show different preferences in position or distance encoding even in comparable sessions. This leads to the question of whether different behaviour and activity encoding were dominated by experimental or natural differences between individual mice. Presenting the data per mouse will be helpful.

As we note above, because trial types were interleaved in a fixed sequence, experience of a particular trial type can not account for switching between task-anchored and taskindependent firing modes. This was insufficiently clear in the first version of the manuscript.

We varied the proportions of trials of a particular type between sessions with the aim of maximising the number of non-beaconed and probe trials. This was necessary because we find that if we introduce too high a proportion of these trials early in training then mice appear to ‘lose interest’ in the task and their performance drops off. We therefore used an approach in which we increased the proportions of non-beaconed and probe trials over training days as mice became familiar with the task. This is now described in the methods (p 12).

Because the decision for when to vary the proportion of trial types was based on the previous day’s performance, the experimental design was not optimised for addressing the reviewer’s question about dissociating experimental from natural differences in mice. To provide some initial insight we have analysed the relationship between task anchored coding and proportion of beaconed trials in a session (Figure 3, Figure Supplement 7). While on average there is a higher proportion of trials in which grid fields are task-anchored in sessions with more beaconed trials, this effect is small and most of the variance is independent from the proportion of beaconed trials.

(1.3) Related to the above point, in Figure 5, the mice appeared to behave worse in probe trials than non-beaconed trials. If the mouse did not know if a trial is a probe or a non-beacon trial, they should behave equivalently until the reward location and thus should stop an equal amount. If this difference is because multiple probe trials are placed consecutively, did the mouse learn that it will not get a reward and then stop trying to get rewards? Did this affect switching between position and distance coding?

Thankyou for flagging this. This reflected an inconsistency arising from the way we detected stops that we have now corrected. Briefly, the temporal resolution of the processed location data against which the stop detection threshold was applied was insufficiently high. As a result, stops in the non-beaconed group were picked up, as they tended to be longer because mice remained still to consume rewards, whereas some stops in the probe group were missed because they were relatively short. We have corrected this by repeating the analyses on raw position data at the highest temporal resolution available. This analysis is now clearly described in the Methods (see p13 “A stop was registered in Blender3D if the speed of the mouse dropped below 4.7 cm/s. Speed was calculated on a rolling basis from the previous 100 ms at a rate of 60 Hz.”).

(1.4) It is not shown how the behaviours (e.g., running speed away from the reward zone, licking for reward) in beaconed/non-beaconed/probe trials were different and whether the difference in behaviours led to the different encoding schemes.

Because trial types were interleaved and repeated with a period less than the length of typical trial sequences during which grid cell activity remained either task-anchored or taskindependent, differences between trial types are unlikely to explain use of the different coding schemes. Hopefully, this is clarified by the comments above.

To further describe the relationship between behavioural outcomes, trial types and grid anchoring, we now also show running speed as a function of location for each combination of trial types and trial outcomes (Figure 6, Figure Supplement 1). This illustrates and replicates our previous findings (Tennant et al. 2018) that running speed profiles are similar for a given trial outcome regardless of trial type (Figure 6, Figure Supplement 1A), and further further shows that the behavioural profile for a given trial outcome and trial-type does not differ when grid cells are in task-anchored and task-independent modes (Figure 6, Figure Supplement 1B). This further argues against the possibility that difference in behaviours leads to the different encoding schemes.

(2) Regarding the behaviour and activity encoding on a trial-by-trial basis, did the behavioural change occur first, or did the encoding switch occur first, or did they happen within the same trial? This analysis will potentially determine whether the encoding is causal for the behaviour, or the other way around.

This is a good question but our experimental design lacks sufficient statistical power to address the timing of mode switches within a trial. This is because mode switching is relatively infrequent (so the n for switching is low) and only a subset of trials are uncued (making the relevant n even lower), while at a trial level the behavioural outcome is variable (increasing the required n for adequate power).

(3) The author determined that the grid cell coding schemes were limited to distance encoding and position encoding. However, there could be other schemes, such as switching between different position encodings (with clear spatial fields but at different locations), as indicated by Low et. al., 2021, and switching between different distant encodings (with different distance periods). If these other schemes indeed existed in the data, they might contribute to the variation of the behaviours.

Switching between position encoding schemes appears to be rare within our dataset and unlikely to contribute to variation in behaviour. In most sessions we did not observe switching between grid phases / position encodings (e.g. Figures 2A-B, 3B-E, 4A, 5C-D, F). In one session we found switching between different phases when grid cells were taskanchored. Because the grid period was unchanged, the spatial periodograms remained similar. We report this example in the revised manuscript (Figure 5E).

(4) The percentage of neurons categorised in each coding scheme was similar between nongrid and grid cells. This implies that non-grid cells might switch coding schemes in sync with grid cells, which would mean the whole MEC network was switching between distance and position coding. This raises the question of whether the grid cell coding scheme was important per se, or just the MEC network coding scheme.

We very much appreciate this suggestion. We note first that while the proportion of taskanchored grid and non-grid cells is similar, task-independent periodic firing of non-grid cells is much rarer than for grid cells (Figure 2E), suggesting a dissociation between the populations. To further address the question we have included additional analyses of nongrid cells (Figure 3, Figure Supplement 5). This shows that while some non-grid cells have anchoring that switches coherently with simultaneously recorded grid cells, others do not. Figures 4 and 5 now show examples of non-grid cell activity recorded simultaneously with grid cells.

Together, our data suggest that the MEC implements multiple coding schemes: one that is associated with the grid network and includes some non-grid cells; and one (or more) that can be independent from the grid network. This dissociation adds to the insights into MEC function that are provided by our study and is now highlighted in the abstract and discussion.

(5) In Figure 2 there are several cell examples that are categorised as distance or position coding but have a high fraction of the other coding scheme on a per-trial basis. Given this variation, the full session data in F should be interpreted carefully, since this included all cells and not just "stable" coding cells. It will be cleaner to show the activity comparison only between the stable cells.

We have now included examples in Figure 2A-C where the grid mode is stable throughout a session. As the view of activity at a session level is important, we have not updated Figure 2F, but have clarified the terminology to now clearly refer to classification at either season or trial levels. In addition, we have repeated the analyses shown in Figure 2F but after grouping cells according to whether their firing has a single mode on >85% of the trials (Figure 3 Figure Supplement 4). This analysis supports similar conclusions to those of Figure 2F.

(6) The manuscript is not well written. Throughout the manuscript, there are many unexplained concepts (especially in the introduction) and methods, mis-referenced figures, and unclear labels.

We very much appreciate the feedback and have substantially rewritten the manuscript. We have paid particular attention to explaining key concepts in the introduction and have carefully checked the figures. We welcome further feedback on whether this is now clearer.

Reviewer #2 (Public Review):

Clark and Nolan's study aims to test whether the stability of grid cell firing fields is associated with better spatial behaviour performance on a virtual task… This study is very timely as there is a pressing need to identify/delimitate the contribution of grid cells to spatial behaviours. More studies in which grid cell activity can be associated with navigational abilities are needed.

Thank you for the supportive comments and highlighting the importance of the question.

The link proposed by Clark and Nolan between "virtual position" coding by grid cells and navigational performance is a significant step toward better understanding how grid cell activity might support behaviour. It should be noted that the study by Clark and Nolan is correlative. Therefore, the effect of selective manipulations of grid cell activity on the virtual task will be needed to evaluate whether the activity of grid cells is causally linked to the behavioural performance on this task. In a previous study by the same research group, it was shown that inactivating the synaptic output of stellate cells of the medial entorhinal cortex affected mice's performance of the same virtual task (Tennant et al., 2018). Although this manipulation likely affects non-grid cells, it is still one of the most selective manipulations of grid cells that are currently available.

Again, thank you for the supportive comments. We recognise the previous version of the manuscript did not sufficiently clarify the motivation for our approach, or the benefits of capitalising on behavioural variable variability as a complementary strategy to perturbation approaches. We now make this clearer in the revised introduction (p 2, paragraphs 2 and 3).

When interpreting the "position" and "distance" firing mode of grid cells, it is important to appreciate that the "position" code likely involves estimating distance. The visual cues on the virtual track appear to provide mainly optic flow to the animal. Thus, the animal has to estimate its position on the virtual track by estimating the distance run from the beginning of the track (or any other point in the virtual world).

We appreciate the ambiguity here was confusing. We have re-named the groups to ‘taskanchored’, corresponding to when grid cells encode position on the track (as well as distance as the reviewer correctly points out), and ‘task-independent’, corresponding to the group we previously referred to as distance encoding.

It is also interesting to consider how grid cells could remain anchored to virtual cues. Recent work shows that grid cell activity spans the surface of a torus (Gardner et al., 2022). A run on the track can be mapped to a trajectory on the torus. Assuming that grid cell activity is updated primarily from self-motion cues on the track and that the grid cell period is unlikely to be an integer of the virtual track length, having stable firing fields on the virtual track likely requires a resetting mechanism taking place on each trial. The resetting means that a specific virtual track position is mapped to a constant position on the torus. Thus, the "virtual position" mode of grid cells may involve 1) a trial-by-trial resetting process anchoring the grid pattern to the virtual cues and 2) a path integration mechanism. Just like the "virtual position" mode of grid cell activity, successful behavioural performance on non-beaconed trials requires the animal to anchor its spatial behaviour to VR cues.

Reviewer #3 (Public Review):

This study addresses the major question of 'whether and when grid cells contribute to behaviour'. There is no doubt that this is a very important question. My major concern is that I'm not convinced that this study gives a significant contribution to this question, although this study is well-performed and potentially interesting. This is mainly due to the fact that the relation between grid cell properties and behaviour is exclusively correlative and entirely based on single cell activity, although the introduction mentions quite often the grid cell network properties and dynamics. In general, this study gives the impression that grid cells exclusively support the cognitive processes involved in this task. This problem is in part related to the text.

Thank you for the comments. We recognise now that the previous text was insufficiently clear. We have modified the introduction to clarify the value of an approach that takes advantage of behavioural variability. Importantly, this approach is complementary to perturbation strategies we and others have used previously. In particular it addresses critical limitations of perturbation strategies which can be confounded by off-target effects and possible adaptation, both of which are extremely difficult to fully rule out. We hope that with this additional clarification it is now clear that as for any important question multiple and complementary testing strategies are required to make progres, and second, that our study makes a new and important contribution by introducing a novel experimental approach and by following this up with careful analyses that clearly distinguish competing hypotheses.

However, it would be interesting to look at the population level (even beyond grid cells) to test whether at the network level, the link between behavioural performance and neural activity is more straightforward compared to the single-cell level. This approach could reconcile the present results with those obtained in their previous study following MEC inactivation.

We’re unclear here about what the reviewer means by ‘more straightforward’ as clear relationships between activity of single grid cells and populations of grid cells are well established (Gardner et al., 2021; Waaga et al., 2021; Yoon et al., 2013).

To give a clearer indication of the corresponding population level representations, as mentioned in response to Reviewer #1, we now include additional data showing many simultaneously recorded neurons, and analyses of non-grid as well as grid cells (Figures 4, 5, Figure 5 Figure Supplement 2).

To reconcile results with our previous study of MEC inactivation we have paid additional attention to the roles of non-grid cells (following suggestions by Reviewer #1). We show that while some non-grid cells show transitions between task-anchored and task-independent firing that are coherent with the grid population, many others have more stable firing that is independent of grid representations. This is consistent with the idea that the MEC supports localised behaviour in the cued and uncued versions of the task (Tennant et al., 2018), and suggests that while grid cells preferentially contribute when cues are absent, non-grid cells could also support the cued version. We make this additional implication clear in the revised abstract and discussion.

The authors used a statistical method based on the computation of the frequency spectrum of the spatial periodicity of the neural firing to classify grid cells as 'position-coding' (with fields anchored to the virtual track) and 'distance-coding' (with fields repeating at regular intervals across trials). This is an interesting approach that has nonetheless the default to be based exclusively on autocorrelograms. It would be interesting to compare with a different method based on the similarities between raw maps.

While our main analyses use a periodogram-based method to identify when grid cells are / are not anchored to the task environment, we validate these analyses by examination of the rate maps in each condition (Figures 2-4). For example, when grid cells are task-anchored, according to the periodogram analysis, the rate maps clearly show spatially aligned peaks, whereas when grid cells are not anchored the peaks in their rate maps are not aligned (Figure 2A vs 2B; Figure 3B-E; Figure 4C). We provide further validation by showing that spatial information (in the track reference frame) is substantially higher when grid cell activity is task-anchored vs task-independent (Figures 2F, 3G, 4F and Figure 3 Figure Supplement 4).

To further address this point we have carried out additional complementary analyses in which we identify task anchored vs task independent modes using a template matching method applied to the raw rate maps (Figure 6, Figure Supplement 2). These analyses support similar conclusions to our periodogram-based analyses.

Beyond this minor point, cell categorization is performed using all trial types.

Each trial type (i.e. beacon or non-beacon) is supposed to force mice to use different strategies and should induce different spatial representations within the entorhinal-hippocampal circuit (and not only in the grid cell system). In that context, since all trials are mixed, it is difficult to extrapolate general information.

We recognise that the description of the task design was insufficiently clear but are unsure why ‘it is difficult to extrapolate general information’. Before addressing this point, we should first be clear that mice are not ‘forced’ to adopt any particular strategy. Rather, on uncued trials a path integration strategy is the most efficient way to solve the task. However, mice could instead use a less efficient strategy, for example by stopping at short intervals they still obtain rewards. Detailed behavioural analyses indicate that such random stopping strategies are used by naive mice, while with training mice learn to use spatial stopping strategies (Tennant et al. 2018).

In terms of ‘extracting general information’ from the task, the following findings lead to general predictions: 1) Grid cells can exist in either task-anchored or task-independent periodic firing modes; 2) These modes can be stable across a session, but often modeswitching occurs within a session; 3) While some non-grid cells show task-independent periodic firing, this is much less common than for grid cells, which suggests a model in which many non-grid MEC neurons operate independently from the grid network; 4) When a marker cue is available mice locate a reward equally well when grid cells are in taskanchored versus task-independent modes, which argues against theories in which grid cells are a key part of a general system for localisation; 5) When markers cues are absent taskanchored grid firing is associated with successful reward localisation, which corroborates a key prediction of theories in which grid cells contribute to path integration.

In revising the manuscript we have attempted to improve the writing to make these advances clearer, and have clarified methodological details that made interpretation more challenging than it should have been. For example, as noted in our response to Reviewer #1, we have included additional details to clarify the organisation of trials and relationships between trials, behavioural outcomes and neural codes observed.

On page 5 the authors state that 'Since only position representations should reliably predict the reward location, ..., we reasoned that the presence of positional coding could be used to assess whether grid firing contributes to the ongoing behaviour'. I do not agree with this statement. First of all, position coding should be more informative only in a cue-guided trial. Second, distance coding could be as informative as position coding since at the network level may provide information relevant to the task (such as distance from the reward).

Again, this point perhaps reflects a lack of clarity on our part in writing the manuscript. When grid cells are anchored to the track reference frame (now called ‘tasked anchored’, previously ‘position encoding’), then the location of the rate peaks in grid firing is reliable from trial to trial. This is the case whether or not the trial is cued. When grid cells are independent of the track reference frame (now called ‘task independent’, previously ‘distance encoding’), then the location of the firing rate peaks vary from trial to trial. In the latter case, position can not be read out directly from trial to trial.

In principle, in the task-independent mode track position could be calculated by storing the grid network configuration at the start of the track, which would differ on each trial, and then implementing a mechanism to readout relative distance as mice move along the track. However, if mice do use this computation we would expect them to do so equally well on cued and uncued trials. By contrast, our results clearly show a dissociation between trial types in the relationship between grid firing and behavioural outcome. We highlight and discuss this possibility in the revised manuscript (p 10, ‘Alternatively, mice could in principle estimate track location with a system that utilises information about distance travelled obtained from task-independent grid representations’).

Third, position-coding is interpreted as more relevant because it predominates in correct trials. However, this does not imply that this coding scheme is indeed used to perform correct trials.

We have revised the manuscript to clarify our goal of distinguishing major hypotheses for the roles of grid cells in behaviour (Introduction, ‘On the one hand, theoretical arguments that grid cell populations can generate high capacity codes imply that they could in principle contribute to all spatial behaviours (Fiete et al., 2008; Mathis et al., 2012; Sreenivasan and Fiete, 2011). On the other hand, if the behavioural importance of grid cells follows from their hypothesised ability to generate position representations by integrating self-motion signals (McNaughton et al., 2006), then their behavioural roles may be restricted to tasks that involve path integration strategies.’

By showing that performance on cued trials is similar regardless of whether grid cells are task-anchored or not, we provide strong evidence against the idea that grid firing is in general necessary for location-based behaviours. By showing that task anchoring is associated with successful localisation when cues are absent we corroborate a key prediction of hypothesised roles for grid cells in path integration-dependent behaviour. Therefore, we substantially reduce the space of behaviours to which grid cells might contribute. Importantly, this space is much larger for the MEC, which is required for cued and uncued versions of the task. We have revised the introduction and discussion to make these points clearer.

While we believe our results add a key piece of evidence to the puzzle of when and where grid cells contribute to behaviour, we agree that further work will be required to develop and test more refined hypotheses. Alternative models also remain plausible, for example perhaps the behaviourally relevant computations are implemented elsewhere in the brain with grid anchoring to the track as an indirect consequence. Nevertheless, explanations of this kind are more difficult to reconcile with evidence that inactivation of stellate cells in the MEC impairs learning of the task, and other manipulations that modify grid firing impair performance on similar tasks. We now discuss these possibilities (discussion p 10, ‘mice could in principle estimate track location with a system that utilises information about distance travelled obtained from task-independent grid representations’).

It could be more informative to push forward the correlative analysis by looking at whether behavioural performance can be predicted by the coding scheme on a trial-by-trial basis.

The previous version of the manuscript showed these analyses (now in Figure 6). Thus, task anchored grid firing predicts more successful performance on uncued trials at the session level (Figure 6A-B) and at the trial level (Figure 6C-D).

Reviewer #1 (Recommendations For The Authors):

(1) The author particularly mentioned that the 1D tracks are different from the "cue-rich environments that are typically used to study grid cells". It is not clear what conclusions would hold for a cue-rich environment or a track, which may require relatively less path integration compared to the cue-sparse environment. This point should be discussed.

This is an important point that we did not pay sufficient attention to in the previous version of the manuscript. Our finding of successful localisation in the cued environment when grid cells are not task anchored implies that grid anchoring is not required to solve cued tasks. The implication here is that cue rich environments may then not be the most suitable for investigation of grid roles in behaviour as non-grid mechanisms may suffice, although this does not rule out the possibility that anchored grid codes may play important roles in learning about cue rich environments. We now address this point in the discussion (p 10, ‘An implication of this result is that cue rich tracks often used to investigate grid activity patterns may not engage behaviours that require anchored grid firing.’).

(2) It would be good to see the statistics for the number of different cells (stable position or distance encoding, and unstable cells) identified per mouse/session and the number of grid cells per session.

These are now added to Supplemental Data 2 and will also be accessible through code and datasets that we will make available alongside the version of record.

(3) Figure 2F: any explanation about why AG cells had high spatial information?

Previously the calculation used bits per spike and as aperiodic cells have low firing rates the spatial information was high. We have replaced this with bits per second, which provides a more intuitive measure and no longer implies high spatial information. We have amended this in the methods (p 15, ‘Spatial information was calculated in bits per second…’).

(4) The following methods sections should provide additional details:

(4.1) Details of the training protocol are largely left to reference papers. The reference papers give a general outline of the training protocol, but the details are not completely comparable given the single experiment performed on these mice. More details should be given on training stages and experience at the time of the experiment.

The task is more clearly described in the introduction (p 3), and additional details of the training protocol are now provided in the methods (p 12-13).

(4.2) The methods reference mean speed across sessions, but it is not clear where this was used.

This was very poor wording. We have now changed this to ‘For each session the mean speed was calculated for each trial outcome’.

(4.3) The calculation of the spatial autocorrelogram on a per-trial basis should be more explicitly stated. Is it the average of each 10 cm increment with the centre trial?

We have added additional information to the methods (p 16-18).

(4.4) 1D field detection is not sufficiently explained in Figure 1/S2. This information should also appear in the methods section.

This is now clarified on page 16 in section ‘Analysis of neural activity and behaviour during the location memory task’.

(5) The data in Figure 4A and B only shows speed vs. location for one example mouse. The combined per mouse or per session data should also be shown.

This is now shown in Figure 5A and Figure 5, Figure Supplemental 2

(6) Figure 5 is somewhat confusing. Why are A/B by session and C/D by trial? The methods imply that A/B are originally averaged by cell, but that duplicate cells in the same session are excluded because behaviour versus session type is identical. This method should be valid if all grid cells within a session are all "stable". This is likely given the synchrony of code-switching between grid cells, but not all co-active grid cells behaved identically.

It is understandable that C/D are performed by trial, but it should be made clear that it is not a comparable analysis to A/B. It is unclear what N refers to in C. The figure says by trial, but the legend says the error bar is by cell. If data is calculated by trial and then averaged by cell, this should be more clearly stated.

In Figure 6A/B (previously Figure 5A/B) we focus our analysis on sessions in which the mode of grid firing, either task-anchored or task-independent, was relatively stable on a trialto-trial basis (see Figure 3F for definitions). This enables us to then compare behaviour averaged across each session, with sessions categorised as task-anchored and task independent. This analysis has the advantage that it focuses on large blocks of time (whole sessions) in which the mode of grid firing is unambiguous, but the disadvantage is that it excludes many sessions in which grid firing switches between task-anchored and taskindependent modes.

Figure 6C/D (previously Figure 5C/D) addresses this limitation by carrying out similar analyses with behaviour sorted into task-anchored versus task-independent groups at the level of trials. A potential limitation for this analysis is that grid firing is somewhat variable on a trial-by-trial basis and so some trials may be mis-classified. We don’t expect this to lead to systematic bias, but it may make the data more noisy. Nevertheless, these analyses are important to include as they allow assessment of whether conclusions from 6A/B hold when all sessions are considered.

We have added additional clarification of the rationale for these analyses to the main text (p7-8, ‘’We addressed this by using additional trial-level comparisons’). We have also added clarification in the methods section for categorisation of task-anchored versus taskindependent trials when multiple grid cells were recorded simultaneously (p 17, ‘When assigning a common classification across a group of cells recorded simultaneously...’) and an explanation for the N in the figure legend. We also clarify that the analyses use a nested random effects design to account for dependencies at the levels of sessions and mice (methods, p 20, ‘Random effects had a nested structure to account for animals and sessions…’) .

(7) Panels E and F of Figure 5 are not explained in the main text.

This is now corrected (see p8, ‘Additional analyses…’).

(8) Figure 5: Since stable grid cells and all grid cells are shown, it will be better to show unstable cells, which can be compared with grid cells.

Given that the rationale for differences between Figure 6A/B and C/D (previously Figure 5AD) were not previously clear, the reason for focussing on stable grid cells here was likely also not clear (see point 6 above). We don’t show unstable grid cells in Figure 6A-B as the behaviour averaged at the level of a session would be a mix of trials when they are taskanchored and when they are task-independent. Therefore, the analysis would not test predictions about the relationship between task-anchored vs task-independent modes and behaviour. We hope this is now clear in the manuscript given the revisions introduced to address point 6 above.

(9) The methods describing the statistics for these experiments are also confusing. The methods section should be written more clearly, and it should be made clear in the text or figure legend whether this data is the "original" data or is processed in relation to the model, such as excluding duplicate grid cells within a session. The figure legend should also state that a GLMM was used to calculate the statistics.

We have revised the methods section with the goal of improving clarity, adding detail and removing ambiguity. This includes updates of the methods for the GLMM analysis, which are referred to within the Figure 6 legend. A clear definition of a stable session is now also added to the Figure 6 legend.

Reviewer #2 (Recommendations For The Authors):

When grid fields are anchored to the virtual world (position mode), there is probably small trialto-trial variability in the firing location of the firing fields. Is this trial-to-trial variability related to the variability in the stop location? This would provide a more direct link between path integration in grid cell networks and behaviour that depends on path integration.

When attempting to address this we find that the firing of individual grid cells is too variable to allow sufficiently precise decoding of their fields at a single trial level. This is expected given the Poisson statistics of spike generation and previous evaluations of grid coding (e.g. (Stemmler et al., 2015)).

The conclusion of the abstract is: "Our results suggest that positional anchoring of grid firing enhances the performance of tasks that require path integration." This statement is slightly confusing. The task requires 1) anchoring the behaviour to the visual cues presented at the start of the trial and 2) path integration from thereon to identify the rewarded location. The performance is higher when grid cells anchor to the visual cues presented at the start of the trial. What the results show is that the anchoring of grid firing fields to visual landmarks enhances the performance of tasks that require path integration from visual landmarks (i.e. grid cells being anchored to the reference frame that is behaviorally relevant).

To try to more clearly explain the logic and conclusion we have rewritten the abstract, including the final sentence.

Similar comment for the title of Figure 5: "Positional grid coding is not required for cued spatial localisation but promotes path integration-dependent localisation." Positional coding means that grid cells are anchored to the behaviorally relevant reference frame.

To address the lack of clarity we have modified the little of Figure 6 (previously Figure 5) to read ‘Anchoring of grid firing to the task reference frame promotes localisation by path integration but is not required for cued localisation’.

In Figure 1, there is a wide range of beaconed (40-80%) and non-beaconed (10-60%) trials given. It is not 100% clear whether these refer to the percentage of trials of a given type within the recording sessions. Was the proportion of non-beaconed trials manipulated? If so, was the likelihood of position and distance coding changing according to the percentage of nonbeaconed trials?

The ranges given refer to proportions across different behavioural sessions. Within any given behavioural session the proportion was constant. We now make this clear in the figure legend and in the results and methods sections.

We did not manipulate proportions of trial types during a session. Manipulations betweens sessions were carried out with the goal of maximising the numbers of uncued trials that the mice would carry out (see response to public comments above). While the effect of trial-type at the session level is not relevant to the hypotheses we aim to test here, we have included an additional analysis of the relationship between task anchoring and the proportions of trial types in a session (Figure 3, Figure Supplement 7)(also discussed above). As disentangling the effects of learning and motivation will be complex and likely require new experimental designs we have not drawn strong conclusions or pursued the analysis further..

I was not convinced that the labels "position" and "distance" were appropriate for the two grid cell firing modes. My understanding is that the "position" code also requires the grid cell network to estimate distance. It seems that the main difference between the "position" and "distance" modes is that when in the "position" mode, the activity on the torus is reset to a constant toroidal location when the animal reaches a clearly identifiable location on the virtual track. In the "distance" mode, this resetting does not take place.

As previously mentioned, we agree these terms weren’t the best and have since relabelled these as “task-anchored” and “task-independent”.

There are a few sections in the manuscript that implicitly suggest that a causal link between grid cell activity and behaviour was demonstrated. For instance: "It has been challenging to directly test whether and when grid cells contribute to behaviour.": The assumption here is that the manuscript overcomes this challenge, but the study is correlative.

We have modified the wording to be clear that we are introducing new tests of predictions made by hypotheses about causal relationships between grid coding and behaviour (introduction, p 1-2). We also clarify that our results argue against the hypothesis that grid cells provide a general coded for behaviour, but corroborate predictions of hypotheses in which they are specifically important for path integration (discussion, p 10).

We have modified the title abstract and main text to try to treat claims about causality with care. We now more thoroughly introduce and contrast the approach we report here with previous experiments that use perturbations (introduction, p2). While it is tempting to make stronger claims for causality with these approaches, there are also logical limitations with perturbation-based approaches, for example the challenges of fully excluding off target effects and adaptation. We now explain how these strategies are complementary. Our view is that both strategies will be required to develop strong arguments for whether and when grid cells contribute to behaviour. From this perspective, it is encouraging that our conclusions are in agreement with what are probably the most specific perturbations of grid cells reported to date (Gil et al. 2017), while perturbations that more generally affect MEC function appear to impair cued and path integration-dependent behaviours (Tennant et al. 2018). We now discuss these points more clearly (introduction, p 2).

I am slightly confused by the references to the panels in Figure 4.

"In some sessions, localization of the reward occurred almost exclusively when grid cells were anchored to position and not when they encoded distance (Figure 4C). Figure 4C only shows position coding.

"In other sessions, animals localised the reward when grid firing was anchored to position or distance, but overall performance was improved on positional trials (Figure 4D-E)." The reference should probably point to Figure 4E-F or just to 4E.

"In a few sessions, we observed spatial stopping behaviour comparable to cued trials, even when grid firing almost exclusively encoded distance rather than position (Figure 4F)." From Figure 4F, it seems that the performance on non-beaconed trials is better during "position" coding.

We have now updated Figure 5 (Figure 4 in the original manuscript) and references to the Figure in the text. Now Figure 5 shows the activity of cells recorded in stable and unstable task-anchored and task-independent sessions (see Figure 5C-F).

Minor issues:

Is this correct: (Figure 4A and Figure 4, Figure Supplement 1).

This has been corrected.

Figure 4B: There could be an additional label for position and distance.

Figure 4B from the original manuscript has now been removed.

Figure 4C-F. The panels on the right side should be explained in the Figure Legend.

Legends for Figure 5C-F (previously Figure 4C-F) have now been updated.

Reviewer #3 (Recommendations For The Authors):

Specific questions :

(1) Position coding reflects a coding scheme in which fields are spaced by a fixed distance; previous studies have shown that a virtual track grid map is a slice of the 2D classic grid. In that case, the fields are still anchored to the track but would produce a completely different map. Did the authors check whether it is the case at least for some cells? If not, what could explain such a major difference?

Το avoid confusion we now use the term ‘task-anchored’ rather than ‘position coding’ (see comments above). We should further clarify that our conclusions rest on whether or not the grid fields are anchored to the track. Task anchored firing does not require that grid fields maintain their spacing from 2D environments, only that fields are at the same track position on each trial. Thus, whether the spacing of the fields corresponds to a slice through a 2D grid makes no difference to the hypotheses we test here.

We agree that the relationship between 1D and 2D field organisation could be an interesting future direction, for example anchoring could involve resetting the grid phase while maintaining a stable period, or it could be achieved through local distortions in the grid period. However, since these outcomes would not help distinguish the hypotheses we test here we have not included analyses to address them.

(2) Previous studies have highlighted the role of grid cells in goal coding. Here there is an explicit reward in a particular area. Are there any grid modifications around this area? This question is not addressed in this study.

Again, we note that the hypotheses we test here relate to the firing mode of grid cells - taskanchored or task-independent - and interpretation of our results is independent from the specific pattern of grid fields on the track. This question nevertheless leads to an interesting prediction that if grid fields cluster in the goal area then this clustering should be apparent in the task-anchored but not the task-independent firing mode.

We test this by considering the average distribution of firing fields across all grid cells in each firing mode (Reviewer Figure 1). We find that when grid firing is task-anchored there is a clear peak around the reward zone, which is consistent with previous work by Butler et al. and Boccara et al. Consistent with our other prediction, this peak is reduced when grid cells are in the task-independent mode.

Author response image 1.

Plot shows the grid field distribution during stable grid cell session (> 85 % task-anchored or task-independent) (A) or during task-anchored and task-independent trials (B). Shaded regions in A and B represent standard error of the mean measured across sessions and epochs respectively.

(3) The behavioural procedure during recording is not fully explained. Do trial types alternate within the same session by blocks? How many trials are within a block? Is there any relation between trial alternation and the switch in the coding scheme observed in a large subset of the grid cells?

We agree this wasn’t sufficiently clear in the previous version of the manuscript. Trial types were interleaved in a fixed order within each session. We have updated the results and methods sections to provide details (see responses above).

(4) From the examples in Figure 2 it seems that firing fields tend to shift toward the start position. Is it the case in all cells? Could this reflect some reorganisation at the network level with cells signalling the starting as time progresses?

This is inconsistent between cells. To make this variability clear we have included additional examples of spiking profiles from different grid cells (Figure 2 - 5). Because quantification of the phenomena would not, so far as we can tell, help distinguish our core hypotheses we have not included further analyses here.

(5) Are grid cells with different coding properties recorded in different parts of the MEC? Are there any differences between these cell categories in the 2D map?

The recordings we made are from the dorsal region of the MEC (stated at the start of the results section). We don’t have data to speak to other parts of the MEC.

Minor:

There are very few grid cell examples that repeat in the different figures. I would suggest showing more examples both in the main text and supplementary material.

We have now provided multiple additional examples in Figures 2, 4 and 5. Grid cell examples repeat in the main figures twice, in both cases only when showing additional examples are shown from the same recording session (Figure 2A example #1 with Figure 5C, Figure 3E with Figure 4A). Further similar repeats are found in the supplemental figures (Figure 3D with Figure 5, Figure Supplement 2A, Figure 3C with Figure 5, Figure Supplement 2F).

Fig1 A-B shows the predictions in a 1D track based on distance or position coding. The A inset represents the modification of field distribution from a 2D arena to a 1D track, as performed in this study. The inset B is misleading since it represents the modifications expected from a circular track to a 1D track as in Jacob et al 2019, that is not what the authors studied. It would be better to present either the predictions based on the present study or the prediction based on previous studies. In that case, they should mention the possibility that the 1D map is a slice of the 2D map.

The goal of Figure 1A-B is to illustrate predictions (right) based on conclusions from previous studies (left). Figure 1A shows predicted 1D track firing given anchoring to the environment typically observed in grid cell studies in 2D arenas. Figure 1B shows predicted 1D track firing given the firing shifting firing patterns observed by Jacob et al. in a circular 2D track. To improve clarity, we have modified the legend to make clear that the schematics to the right are predictions given the previous evidence summarised to the left. As we outline above, the critical prediction relates to whether the representations anchor to the track. Whether the 1D representation is a perfect slice isn’t relevant to the hypotheses tested and so isn’t included in the schematic (see comments above).

Author Response

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

eLife assessment

This study is valuable as it sheds light on the pivotal role played by alterations in glycan metabolism within chondrocytes in the onset of cartilage degeneration and early onset of osteoarthritis (OA) through the process of hypertrophic differentiation of chondrocytes, giving insights into the identification of nascent markers for early-stage OA. Although the methods, data, and analyses broadly support the claims, the data shown by the authors are incomplete because the mechanism by which cartilage degeneration induced by changes in glycometabolism occurs has not been fully elucidated. The authors' deductions stand to gain further credence through undertaking additional experiments aimed at analyzing the mechanisms underlying the changes in glycometabolism in cartilage, such as the meticulous identification of the target glycan molecules bearing core fucose and analysis of endochondral ossification in cartilage-specific Fut8 KO mice.

We wish to express our strong appreciation to the Reviewer for his or her insightful comments on our paper. We feel the comments have helped us significantly improve the paper. In particular, we wish to acknowledge the Reviewer’s highly valuable comments on the effect of Fut8 on endochondral ossification.

Reviewer #1 (Public Review): :<br /> Summary:

This study is valuable in that it may lead to the discovery of future OA markers, etc., in that changes in glycan metabolism in chondrocytes are involved in the initiation of cartilage degeneration and early OA via hypertrophic differentiation of chondrocytes. However, more robust results would be obtained by analyzing the mechanisms and pathways by which changes in glycosylation lead to cartilage degeneration.

Strengths:

This study is important because it indicates that glycan metabolism may be associated with pre-OA and may lead to the elucidation of the cause and diagnosis of pre-OA.

We thank reviewer #1 for their interest in our work and their overall positive report.

Weaknesses:

More robust results would be obtained by analyzing the mechanism by which cartilage degeneration induced by changes in glycometabolism occurs.

To understand the mechanisms of cartilage degeneration induced by changes in glycometabolism, we attempted additional experiments using rescue experiments with external administration of TGF-β. We had shown that the addition of mannosidase to an organ culture system of normal wild-type mouse cartilage increased TGF-β gene expression from 6 hours (Fig. 3E) and that TGF-β expression was even suppressed in chondrocytes from Fut8 cKO mice (Fig. 4D). In addition to these results, an early OA model in which mannosidase is added to the cartilage was used to test the effect of exogenous TGF-β. As a result, under TGF-β treated conditions, no degenerative changes occurred when high-mannose type N-glycans were trimmed, and proteoglycan leakage during the recovery period was significantly reduced. This was considered to be a very useful finding and it was decided to include the experimental results in Figure 4F, rather than making them supplement data.

Reviewer #2 (Public Review):

Summary:

This paper consists of mostly descriptive data, judged from alpha-mannosidase-treated samples, in which they found an increase in core fucose, a product of Fut 8.

Strengths:

This paper is interesting in the clinical field, but unfortunately, the data is mostly descriptive and does not have a significant impact on the scientific community in general.

We thank reviewer #2 for their interest in our work and their overall positive report. In response to your comment about our attempts to show that glycan changes occur at the precursor stage of cartilage substrate degeneration and that this glycosylation is also what triggers substrate degeneration, we would like to add that reversing cartilage substrate degeneration is a very ambitious challenge. We are currently in the preparatory stages of characterizing the appropriate glycan-substrate relationships to 'rescue' cartilage tissue from degeneration, and we hope to use this approach to provide information on the pre-developmental stages of OA.

Weaknesses:

If core fucose is increased, at least the target glycan molecules of core fucose should be evaluated. They also found an increase in NO, suggesting that inflammatory processes also play an important role in OA in addition to glycan changes.

As the increase in NO was observed in the organ culture system and cartilage is a tissue without vascular invasion, we thought that the involvement of immune cells could be excluded. On the other hand, our research group has reported that chondrocytes themselves have inflammatory circuits (Ota et al., Arthritis Rheum. 2019. DOI:10.1002/art.41182), but as we did not find increased expression of NF-κB, an indicator of inflammatory amplifier activation, we concluded that inflammation was not involved in this study.

It has already been reported that core fucose is decreased by administration of alpha-mannosidase inhibitors. Therefore, it is expected that alpha-mannosidase administration increases core fucose.

The report by Toegel et al. that the synthesis of complex-type N-glycans (Man2a1, Mgat2) is predicted in human OA chondrocytes along with the expression of Fut8 also led to the expectation that administration of α-mannosidase would increase core fucose. However, there was no conclusive evidence that administration of α-mannosidase increased core fucose; in 1987, Vignon et al performed an enzyme assay on experimental OA cartilage (rabbit ACLT model) and showed that mannosidase was very high in operated joints and that its activity increased and decreased with the severity of fibrosis in the cartilage. The results suggest that glycoprotein hexose degradation is an early transient event in the enzymatic process of cartilage destruction. These findings led to the conception of a novel 'pre-OA model' in which mannosidase is added to the joint. The present study is valuable in its demonstration that glycometabolism is a driver of degeneration.

(see manuscript REF. 25, 9)

Toegel et al., Arthritis Res. Ther. 2013. DOI:10.1186/ar4330

Vignon et al., Clin Rheumatol. 1987. DOI:10.1007/BF02201026

Reviewer #3 (Public Review):

Summary:

In the manuscript "Articular cartilage corefucosylation regulates tissue resilience in osteoarthritis", the authors investigate the glycan structural changes in the context of pre-OA conditions. By mainly conducting animal experiments and glycomic analysis, this study clarified the molecular mechanism of N-glycan core fucosylation and Fut8 expression in the extracellular matrix resilience and unrecoverable cartilage degeneration. Lastly, a comprehensive glycan analysis of human OA cartilage verified the hypothesis.

Strengths:

Generally, this manuscript is well structured with rigorous logic and clear language. This study is valuable and important in the early diagnosis of OA patients in the clinic, which is a great challenge nowadays.

We thank reviewer #3 for their interest in our work and their mainly positive report. This is precisely the purpose of our study, as we are primarily interested in the detection of conditions prior to the onset of OA.

Weaknesses:

I recommend minor revisions:

(1) I would suggest the authors prepare an illustrative scheme for the whole study, to explain the complex mechanism and also to summarize the results.

We would like to thank the reviewer for this comment and have created a new Figure 7 for the overall study scheme.

We included the following statement in the opening discussion part:

"The objective of this work was to provide novel and translational insights into pathogenesis of OA associated with changes in glycan structure. A graphical abstract summarizing our findings is shown in Fig. 7." (line199-201, p9)

(2) Including but not limited to Figures 2A-C, Figures 3A and C, Figure 4B, and Figures 5A and D. The texts in the above images are too small to read, I would suggest the authors remake these images.

The font size of the figures has been reviewed and revised throughout.

(3) The paper is generally readable, but the language could be polished a bit. Several writing errors should be realized during the careful check.

Thanks to your suggestion, I have noticed several writing errors. In addition, we have had the manuscript rewritten by an experienced scientific editor, who has improved the grammar and stylistic expression of the paper.

(4) As several species and OA models were conducted in this study, it would be better if the authors could note the reason behind their choice for it.

The authors agree with the reviewer's argument that since several species and OA models were performed in this study, it would be better to note the reason for their choice.

We first attempted to inject mannosidase into rabbits, matching the animal species to a previous paper showing that N-glycans are altered prior to degeneration of the cartilage matrix. Next, we checked whether similar changes occur in mouse cartilage after mannosidase treatment, assuming that we would verify this in genetically engineered mice. We then used the integrated glycome in human cartilage to see if the corefucosylation phenomenon detected was conserved across species.

For the modeling of OA in Fut8 cKO mice, the instability-induced OA model and the age-associated OA model were adapted. The former emphasizes mechanical stress factors in OA, the latter aging factors. OA is a multifactorial disease. Therefore, we thought it was appropriate to validate both aspects of OA.

We included the following statements in each Methods part:

"We injected mannosidase into rabbit knee joints in accordance with a previous paper showing that N-type glycans are altered prior to cartilage matrix degeneration." (line289-290, p12)

"Organ culture experiments in mice were established to study the effects of mannosidase on articular cartilage without immunoreaction and in anticipation of later candidate gene research using transgenic mice." (line326-328, p14)

"To determine whether the glycosylation detected is conserved across species, we analyzed the total glycome in human cartilage." (line407-408, p17)

We included the following statements in the Discussion part:

"For the modeling of OA in Fut8 cKO mice, the instability-induced OA model and the age-associated OA model were adapted. The former emphasizes mechanical stress factors in OA, the latter aging factors. OA is a multifactorial disease. Therefore, we thought it was appropriate to validate both aspects of OA." (line254-257, p11)

Reviewer #1 (Recommendations For The Authors):

(1) The cited literature states that core fucosylation by FUT8 has a chondroprotective effect via the TGF-β pathway and that the loss of these chondroprotective effects in Fut8 led to cartilage degeneration, but these need to be proven by experiment.

We agree that corefucosylation and the TGF-β signaling pathway are important lines of investigation. We have now acknowledged this and added in the revised manuscript that additional experiments have shown that TGF-β restores the protective effects of Fut8 cKO cartilage by external administration.

We included the following statements in the Results part:

"To evaluate whether TGF-β1 decreases cartilage degeneration after mannosidase stimulation, TGF-β1 was exogenously added to Col2-Fut8−/− cartilage in the presence of α-mannosidase stimulation for 24 h. The samples treated with TGF-β1 leaked significantly less PG following mannosidase stimulation compared to samples not treated with TGF-β1 (Fig. 4F)." (line143-147, p6-7)

We included the following statements in the Discussion part:

"Here, the exogenous addition of TGF-β1 rescued them from cartilage degeneration." (line274-275, p12)

(2) There are skeletal differences in cartilage-specific Fut8 KO mice compared to WT, and the effect of Fut8 on endochondral ossification should also be analyzed.

We agree that Fut8 is associated with various endochondral ossification processes (for example by the TGF-β signaling pathway). Moreover, we would like to thank the reviewer for the proposed experiment.

The growth curve was normal at birth, with differences beginning around weaning (~3 w for mice). Therefore, we evaluated the epiphyseal line of 4-week-old mice stained with toluidine, type 10 collagen, and proliferating cell nuclear antigen. This is similar to the epiphyseal growth plate phenotype of Smad3ex8/ex8 mice by Yang et al. and is consistent with the finding that Smad3 deficiency does not affect chondrogenesis during developmental stages, but the hypertrophic zone is increased in 3-4 week-old Smad3 KO mice. Chondrocytes in Fut8 cKO mice were suppressed of Tgf-β expression (Fig. 4D), suggesting that inhibition of TGF-β signaling, which is suppressive for late hypertrophic chondrocyte differentiation, led to the increased height of the hypertrophic zone.

The results suggested that the growth plate of Fut8 cKO mice had an enlarged hypertrophic layer and decreased primary trabecular bone. Because these results have important implications for the content of the paper, we have included the staining results in Figure 5 and added a graph quantitatively assessing the extent of the hypertrophic zone as supplementary Figure S6.

We included the following statement in the Results part:

"To assess the role of FUT8 in endochondral ossification, we performed an epiphyseal plate analysis of 4-week-old Col2-Fut8−/− mice. This uncovered a significant enlargement of the zone of hypertrophic chondrocytes in the growth plates of the long bones of Col2-Fut8−/− mice compared to controls (Fig. 5C, S6 Figure)." (line154-158, p7)

We included the following statement in the Discussion part:

"The high-mannose/corefucosylation relationship estimated function to maintain formed cartilage. In endochondral ossification, the Fut8 cKO growth plate had an enlarged hypertrophic zone and reduced primary spongiosa because it is involved in the next process of cartilage replacement into bone rather than the process of cartilage formation." (line214-217, p9)

Literature mentioned above (not included in manuscript):

Yang X, et al. TGF-beta/Smad3 signals repress chondrocyte hypertrophic differentiation and are required for maintaining articular cartilage. J Cell Biol. 2001;153(1):35–46.

(3) The DMM model analysis is performed with n=5 for each group. Please consider if the sample size is sufficient.

In the literature, the sample sizes for DMM models have varied in previous studies (Doyran et al., n=5; Liao et al., n=6-7; Ouhaddi et al., n=8). Therefore, we performed a preliminary test of the DMM in WT and Flox mice with n=3 each and a power analysis with the outcome set to the OARSI score at 8 weeks. This resulted in n=4. The sample size for this study was increased to n=5 to account for attrition. The summed OARSI score of the WT in this study was comparable to that of Ouhaddi et al. and the model was judged to be working accurately. The summed OARSI score of the WT in this study was comparable to that of Ouhaddi et al. and the model was judged to be working accurately. The summed OARSI score of the WT in this study was comparable to that of Ouhaddi et al. and the model was judged to be working accurately.

Literature mentioned above (not included in manuscript):

(1) Doyran B, Tong W, Li Q, Jia H, Zhang X, Chen C, et al. Nanoindentation modulus of murine cartilage: a sensitive indicator of the initiation and progression of post-traumatic osteoarthritis. Osteoarthr Cartil. 2017;25(1):108–17.

(2) Liao L, Zhang S, Gu J, Takarada T, Yoneda Y, Huang J, et al. Deletion of Runx2 in Articular Chondrocytes Decelerates the Progression of DMM-Induced Osteoarthritis in Adult Mice. Sci Rep. 2017 24;7(1):2371.

(3) Ouhaddi Y, Nebbaki SS, Habouri L, Afif H, Lussier B, Kapoor M, et al. Exacerbation of Aging-Associated and Instability-Induced Murine Osteoarthritis With Deletion of D Prostanoid Receptor 1, a Prostaglandin D2 Receptor. Arthritis Rheum. 2017;69(9):1784–95.

Reviewer #2 (Recommendations For The Authors):

This paper is suitable for publication in clinical Journals related to osteoarthritis and cartilage.

Identification of core fucosylated glycans from chondrocytes is essential for this type of paper.

We mentioned that we had identified similar corefucosylated glycans in isolated mouse chondrocytes from the cartilage (line117-118, p5), but we have now also added the following to the subtitle of the Results section to avoid any potential confusion: "Corefucosylated N-glycan was formed in resilient cartilage and its isolated chondrocyte" (line109, p5)

Thank you again for your comments on our paper. We trust that the revised manuscript is suitable for publication.

Author Response

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

eLife assessment

This manuscript describes fundamental single-molecule correlative force and fluorescence microscopy experiments to visualize the 1D diffusion dynamics and long-range nucleosome sliding activity of the yeast chromatin remodelers, RSC and ISW2. Compelling evidence shows that both remodelers exhibit 1D diffusion on bare DNA but utilize different mechanisms, with RSC primarily hopping and ISW2 mainly sliding on DNA. These results will be of interest to researchers working on chromatin remodeling.

Reviewer #1 (Public Review):

Single-molecule visualization of chromatin remodelers on long chromatin templates-a long sought-after goal-is still in its infancy. This work describes the behaviors of two remodelers RSC and ISW2, from SWI/SNF and ISWI families respectively, with well-conducted experiments and rigorous quantitative analysis, thus representing a significant advance in the field of chromatin biology and biophysics. Overall, the conclusions are supported by the data and the manuscript is clearly written. However, there are a few occasions where the strength of the conclusion suffers from low statistics. Some of the statements are too strong given the evidence presented.

We thank the reviewer for the thorough and considerate review of our manuscript. We have increased the statistics when possible and have toned down the conclusions wherever further experimentation to improve statistics could not be done expeditiously.

Specific Comments:

(1) It is confusing what is the difference between the "non-diffusive" behavior of the remodeler upon nucleosome encounter and the nucleosome-translocating behavior in the presence of ATP. For example, in Figure 3F, readers can see a bit of nucleosome translocation in the first segment. Is the lower half-life of "non-diffusive" ISW2 with ATP on a nucleosome array because it is spending more time translocating nucleosomes? The solid and dashed green lines in Figure 3F and 3G are not explained. It is also not explained why Figure 3H and 3I are fit by double exponentials.

We thank the reviewer for calling upon us to clarify these points. In both the case of translocation and stable non-translocating colocalization, the chromatin remodeler is marked as “non-diffusive” because the molecule is not moving quickly enough to be detected by our rolling-window (20 frames considered) diffusion coefficient analysis. We have updated the text to point out the translocation that is occurring in the panels indicated and noted that this type of motion is not detected by our automated analysis. Thus, translocation events were manually segmented for analysis from kymographs; a note of this was added to the results section (Results section # 1; Paragraph # 2).

To address the question of whether the half-life of “non-diffusive” ISW2 with ATP on the nucleosome array is because of increased time spent in translocation, we have computed the percentage of “non-diffusive” time spent translocating in the presence of ATP for both remodelers; for ISW2, 14% of “non-diffusive” times are translocation whereas for RSC, 28% of “non-diffusive” times are translocation. Given that these percentages are not negligible, the reviewer helped identify an important parameter that better describes the effects of ATP hydrolysis on nucleosome binding for ISW2. In addition, we computed and compared the half-life of translocation times for both remodelers to the “non-diffusive” times and found that RSC translocates with a half-life of 20 s (similar to the half-life of “non-diffusion”) whereas ISW2 translocates with a half-life of 17 s (longer than the half-life of “non-diffusion”). We believe that this new information improves understanding of the role of ATP hydrolysis in turning over ISW2-nucleosome binding interactions, which result in the shorter “non-diffusive” lifetime as well as the shorter and more rarely observed ISW2 translocation events. We have updated the text to include these observations and our interpretation (Results section # 3; Paragraph # 3). As was already included in the text (Results section # 3; Final Paragraph), we speculate that this behavior may be due to a hydrolysis-dependent turnover of the ISW2-nucleosome bound state and refer the reader to Tim Richmond’s 2004 EMBO paper titled “Reaction cycle of the yeast Isw2 chromatin remodeling complex” in which bulk experiments show that ATP hydrolysis affects ISW2-nucleosome bound lifetimes.

We thank the reviewer for also pointing out where details were missing from the figure legend and results section regarding Figure 3. We have added a description of the dashed and solid lines to the figure legend (Figure 3; Legend). We have also described why Figures 3H and I are fit to double exponentials to the results section (Results section # 3; Paragraph # 2).

(2) What is the fraction of 1D vs. 3D nucleosome encountered by the remodelers? This is an important parameter to compare between RSC and ISW2.

We thank the reviewer for raising this point. We agree that this is an important parameter to compare between RSC and ISW2; knowledge of this parameter would enable quantitative predictions to be made from our data regarding target localization efficiency increases owed to 1D scanning for each remodeler. We regretfully could not quantify this due to technical limitations of our measurements. A note about this limitation along with an explanation for why we were unable to quantify this parameter have been added to the main text (Results section # 3; end of Paragraph # 1).

(3) A major conclusion stated repeatedly in the manuscript is that nucleosome translocation by a remodeler is terminated by a downstream nucleosome. But this is based on a total of 4 events. The problem of dye photobleaching was mentioned, which is a bit surprising considering that the green excitation was already pulsed. The authors should try to get more events by lowering the laser power or toning down the conclusion that translocation termination is prominently due to blockage by a downstream nucleosome. Quantifying the translocation distances before termination, in addition to the durations (Figure 4G and 4H), would also be helpful.

We thank the reviewer for these observations and feedback. We agree that only 4 observations of direct visualization of remodeler translocation termination by a downstream nucleosome is a small n-value, and have chosen to omit presentation of these rare events in the manuscript.

(4) The claim on nucleosome translocation directionality is also based on a small number of events, particularly for RSC. 6/9 is hardly over 50% if one considers the Poisson counting error (RSC was also found to switch directions.) If the authors would like to make a firm statement to support the "push-pull" model, they should obtain more events.

We thank the reviewer for this critique and agree with the reviewer’s concern. In addition to adding data from two additional experimental replicates of RSC nucleosome translocation (which had the smaller n-value), we have also re-evaluated all events containing translocation for additional evidence in support or against the “push-pull” model. Previously we were only considering events where 1D diffusion on DNA leads immediately to translocation. Now we add the following categories to the count: (1) events where translocation terminates with the remodeler dissociating from the nucleosome and performing a 1D diffusive search, (2) events where 1D diffusion on DNA leads to association with a nucleosome and after a paused colocalization we observe translocation, and (3) the inverse scenario of (2) (see schematics in Figure 5 – figure supplement 1). These new results, detailed below, are now included in place of the older results in (Results Section # 5; Paragraph # 2). Furthermore, we toned down our argument and clarified that a larger n-value would be needed to be definitive, especially since we observe RSC switching directions, as the reviewer points out.

By aggregating in new RSC data and using only events where 1D diffusion leads immediately to translocation, we observe 10/12 events in support of the “push” model. If we include these other categories in addition to aggregating the previous data with the new data, a total of 20/25 events are in support of the “push” model. For RSC, the breakdown in the other categories was as follows: (1) 7/10 events, (2) 1/1 events with a paused time of 5 seconds, and (3) 2/2 events with a paused time of 36 and 50 seconds.

For ISW2, we had previously reported 12/13 events where 1D search lead immediately to translocation. After combing through the data a second time, we decided to omit two events which were less clear; Now we report 10/11 events in support of the “pull” model from this initial category. If we include these other categories in addition to the original, a total of 19/21 events are in support of the “pull” model. For ISW2, the breakdown in the other categories was as follows: (1) 4/4 events, (2) 4/4 events with pause times of 44, 27, 29, and 8 seconds, (3) 1/2 events with paused times of 5 and 19 seconds.

(5) At 5 pN of tether tension, the outer wrap of nucleosomes is destabilized, which could impact nucleosome translocation dynamics. Additionally, a low buffer flow was kept on during data acquisition, which could bias remodeler diffusion behavior. The authors should rule out or at a minimum discuss these possibilities.

We thank the reviewer for raising the important point regarding outer wrap destabilization of the nucleosome occurring at 5pN of tension. We have added an additional section to the discussion that reviews the literature on tension effects on nucleosome stability as well as what is currently known of the effects of tension on remodeler translocation on DNA (Discussion Paragraph # 3). While we cannot exclude the possibility that the 5pN of tension used in this study is a causative factor of the observed fast speed or high processivity nucleosome translocation that we report, we believe that with the modifications made to the text to emphasize to the reader of these possibilities, the reader can draw informed conclusions on the significance of our findings. The topic of force effects on remodeling outcomes is an interesting subject for the future.

We apologize that the experimental details on buffer flow used during imaging was unclear in our initial submission; we do not have buffer flowing during imaging, rather the buffer containing protein is flowed over the DNA at low pressure just prior to imaging. The flow is completely stopped before the DNA or nucleosome array is stretched to 5pN of tension for imaging (See Methods section: Single Molecule Tracking and Analysis).

Reviewer 1 (Recommendations For The Authors):

(1) The figure panels could be better arranged to focus on the main messages of the paper.

(i) Figure 3C-E should go to a supplemental figure.

We thank the reviewer for this helpful suggestion. As recommended, we moved Figure 3C to the supplemental figure as this panel did not pertain to the main message of the paper.

(ii) Figure 4 could be split into two figures, one characterizing processive nucleosome translocation (4C, D, G, H, I, J, K, and relevant panels in S4), and the other showing the differential directionality of each remodeler (4E, F, L, and relevant panels in S4).

We thank the reviewer for their suggestions that help better organize our presentation of the data. As the reviewer suggests, we split figure 4 into two figures: figure 4 which now focuses on translocation characterization and figure 5 which now focuses on the differential directionality of each remodeler.

(iii) The nucleotide condition should be clearly indicated in the figures or legends. For example, it is unclear if the data in Figure 2 were generated with or without ATP.

We thank the reviewer for taking note of this. We have added clear indications of the nucleotide condition to figures where this is relevant, including in Figure 2 as indicated.

(iv) There are many cartoon panels, and some are redundant (e.g., Figure 1A and 1B, Figure 3A and 3B).

We thank the reviewer for bringing up this point. We agree that some cartoons are redundant. We have eliminated Figure panel 1B and Figure panel 3A of the original figures from the new figures.

(2) The last paragraph of the Results section should be moved to Discussion. This paper did not directly address the effects of RSC/ISW2 on NDR length.

We thank the reviewer for this suggestion. We agree and have moved the last paragraph of the Results section to the Discussion..

(3) There are some typos in the text. For example, "Of the two main types of 1D diffusion, hopping and sliding" is not a complete sentence.

We thank the reviewer for catching this typo and bringing our attention to others. Upon a more careful proofreading of the text and figures we have caught and amended this and other typos.

(4) What are the green lines in Figure S1F?

We thank the reviewer for asking this question. The green lines were meant emphasize how the percentage of traces in the majority high diffusion category increases for RSC but not for ISW2 in response to increases in the KCl concentration. Since this was confusing, we removed these green lines.

Reviewer # 2 (Public Review):

Summary:

The authors use a dual optical trap instrument combined with 2-color fluorescence imaging to analyze the diffusion of RSC and ISW2 on DNA, both in the presence and absence of nucleosomes, as well as long-range nucleosome sliding by these remodelers. This allowed them to demonstrate that both enzymes can participate in 1D diffusion along DNA for rather long ranges, with ISW2 predominantly tracking the DNA strand, while RSC diffusion involves hopping. In an elegant two-color assay, the authors were able to analyze interactions of diffusing remodeler molecules, both of the same or different types, observing their collisions, co-diffusion, and bypassing. The authors demonstrate that nucleosomes act as barriers for remodeler diffusion, either repelling or sequestering them upon collision. In the presence of ATP, they observed surprisingly processive unidirectional nucleosome sliding with a strong bias in the direction opposite to where the remodeler approached the nucleosome from for ISW2. These results have fundamentally important implications for the mechanism of nucleosome positioning at promoters in vivo, will be of great interest to the scientific community, and will undoubtedly spark exciting future research.

Strengths:

The mechanism of target search for chromatin-interacting protein machines is a 'hot' topic, and this manuscript provides extremely important and timely new information about how RSC and ISW2 find the nucleosomes they slide. Intriguingly, although both remodelers analyzed in this study can diffuse along DNA, the diffusion mechanisms are substantially different, with extremely interesting mechanistic implications.

The strong directional preference in nucleosome sliding by ISW2 dictated by the direction it approaches the nucleosomes from during 1D sliding on DNA is a very intriguing result with interesting implications for the regulation of nucleosome organization around promoters. It will be of great interest to the scientific community and will undoubtedly inspire future research.

Relatively little is known about nucleosome sliding at longer ranges (>100bp), and this manuscript provides a unique view into such sliding and also establishes a versatile methodology for future studies.

Weaknesses:

All measurements were conducted at 5pN tension, which induces unwrapping of the outer DNA gyre from nucleosomes. This could potentially represent a limitation for experiments involving nucleosomes, since partial nucleosome unwrapping could affect the behavior of remodelers, especially their sliding of nucleosomes.

We thank the reviewer for succinctly summarizing the strengths and weaknesses of our study. We have changed the Discussion to better review the literature on the effects of 5pN of tension on nucleosome wrapping and have more clearly presented the limitations of our studying owing to our conducting measurements at 5pN of tension. In doing so, we have tried to emphasize the strengths of our study identified by the reviewer and better inform the reader of the weaknesses.

Reviewer #2 (Recommendations For The Authors):

Although not required, nucleosome sliding data under lower tensions (e.g., <=2pN) could be a valuable addition to the manuscript. Indeed, to my knowledge, there is no data on force-dependent rates of nucleosome sliding, so a conclusive demonstration of changes in remodeling rate with tension would be an exciting new result and might be discussed in the context of a potential tension in chromatin. If such experiments cannot readily be added, the authors could alternatively discuss this potential limitation in more detail.

We thank the reviewer for this suggestion. We agree that adding data at lower tensions (<= 2pN) would have been valuable. Due to time constraints, this will be the subject for the future. We agree that knowledge of the effects of tension would be especially interesting in light of the possibility that tension on chromatin in cells may be affecting remodeler function. We have added a discussion of this potential significance of future work to the discussion (Discussion Section; Paragraph # 3). We have also elaborated on the potential limitation of only conducting measurements at 5pN to the discussion (Discussion Section; Paragraph # 3), as the reviewer recommends.

The quantitative implications of the proposed mechanism for targeting ISW2 and RSC towards +1 and -1 nucleosomes are highly interesting. To further strengthen the mechanistic implications, the authors could consider quantitatively analyzing how the observed 1D diffusion would affect the probabilities of binding to +1 and -1 versus to other nucleosomes.

We thank the reviewer for their thoughtful suggestion. While we would have liked to present a final quantitative model that integrates the experimental parameters on 1D diffusion that we present in this study with the parameters extracted from live cell single particle tracking studies, there are key parameters for model building that are missing from our study, due to technical limitations. Namely, we were not able to quantify the fraction of 1D vs 3D nucleosome encounters by remodelers, because the majority of the protein that we image has been bound before the start of imaging; very few proteins bind the nucleosome arrays after the start of imaging as the protein concentration in the imaging chamber is very low. This makes observing binding directly to a nucleosome a very rare event, especially due to the sparse density of nucleosomes (~10) on the array (~50,000 kb).

The low-diffusion state is intriguing - could the authors speculate about the nature of this state?

We thank the reviewer for the question. We had added some speculation about the nature of the low-diffusion state to the results section (Results Section # 1; Paragraph 2). One thought that we have is that this may be due to more stable interactions made between remodelers and free DNA when they become trapped in a conformation state that binds more tightly to DNA. Conformational changes may result in different scanning speeds for chromatin remodelers; e.g. SWR1 was shown to scan DNA quicker when bound to ATP (Carcamo, C. et al. eLife 2022). Another possibility is that certain sequences due to their intrinsic curvature, for instance, or their AT-content may trap the remodeler which may make more contacts with the DNA at these sites.

Minor points:

Information on the labeling efficiencies for the remodelers would be helpful.

We thank the reviewer for pointing this out. We assessed labeling saturation by running gels of remodeler labeling with increasing molar ratios of dye to protein and did not observe increased labeling efficiency above the molar ratio used for proteins imaged in our study (see added Figure 1 – figure supplement 1, panel A). From this, we assessed that we have high protein labeling efficiency. We could not assess the labeling efficiency using the standard absorbance method as the extinction coefficient for JFX650 was measured with 1% v/v TFA (PMCID: PMC8154212) which is not compatible for use in assessing our protein labeling efficiency in an aqueous buffer.

How were the experimental conditions adjusted for two-color diffusion experiments in order to optimize the probability of observing two remodeler molecules with different labels at the same time.

We thank the reviewer for this clarifying question. To image both remodelers on the same DNA, we combined the remodelers using the same concentrations that produced single molecule densities when the remodelers were imaged separately. We have clarified this point in the Methods section: “Bimolecular Remodeler-Remodeler Imaging and Interaction Analysis”.

The authors should check the figures for consistency of labeling and provide definitions for abbreviations used in them (e.g. CDF and PDF).

We thank the reviewer for catching inconsistencies in labeling in our figures. We have updated the figures such that there is consistent labeling throughout. We have also provided definitions for abbreviations such as Cumulative Distribution Function (CDF) and Probability Distribution Function (PDF) in the figure legends where applicable.

In the section "Remodeler-remodeler collisions during 1D search" (4th line from the end) reference to Fig3D seems to be out of place.

We thank the reviewer for catching this typo. We have reworded this section such that each figure panel can be discussed sequentially, eliminating this out of place reference to Fig 3D.

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).

Author Response

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

eLife assessment

This landmark study sheds light on a long-standing puzzle of Protein kinase A activation in Trypanosoma. Extensive experimental work provides compelling evidence for the conclusions of the manuscript. It represents a significant advancement in our understanding of the molecular mechanism of Cyclic Nucleotide Binding domains and will be of interest to researchers with interest in kinases and mechanistic studies.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Cyclic Nucleotide Binding (CNB) domains are pervasive structural components involved in signaling pathways across eukaryotes and prokaryotes. Despite their similar structures, CNB domains exhibit distinct ligand-sensing capabilities. The manuscript offers a thorough and convincing investigation that clarifies numerous puzzling aspects of nucleotide binding in Trypanosoma.

Strengths:

One of the strengths of this study is its multifaceted methodology, which includes a range of techniques including crystallography, ITC (Isothermal Titration Calorimetry), fluorimetry, CD (Circular Dichroism) spectroscopy, mass spectrometry, and computational analysis. This interdisciplinary approach not only enhances the depth of the investigation but also offers a robust cross-validation of the results.

Weaknesses:

None noticed.

Reviewer #2 (Public Review):

Summary:

This manuscript clearly shows that Trypanosoma PKA is controlled by nucleoside analogues rather than cyclic nucleotides, which are the primary allosteric effectors of human PKA and PKG. The authors demonstrate that the inosine, guanosine, and adenosine nucleosides bind with high affinity and activate PKA in the tropical pathogens T. brucei, T. cruzi and Leishmania. The underlying determinants of nucleoside binding and selectivity are dissected by solving the crystal structure of T. cruzi PKAR(200-503) and T. brucei PKAR(199-499) bound to inosine at 1.4 Å and 2.1 Å resolution and through comparative mutational analyses. Of particular interest is the identification of a minimal subset of 2-3 residues that controls nucleoside vs. cyclic nucleotide specificity.

Strengths:

The significance of this study lies not only in the structure-activity relationships revealed for important targets in several parasite pathogens but also in the understanding of CNB's evolutionary role.

Weaknesses:

The main missing piece is the model for activation of the kinetoplastid PKA which remains speculative in the absence of a structure for the trypanosomatid PKA holoenzyme complex. However, this appears to be beyond the scope of this manuscript, which is already quite dense.

We fully agree that insight into the activation mechanism and its possible deviation from the mammalian paradigm requires a holoenzyme structure revealing the details of R-C interaction. We have attempted Cryo-EM from LEXSY-produced holoenzyme, yet upscaling the purification procedures described in this manuscript have repeatedly failed in spite of numerous protocol changes and optimizations. Much more work is required to achieve this.

Reviewer #2 (Recommendations For The Authors):

Some minor points to consider for enhancing the impact of this interesting manuscript:

(1) The nucleoside affinities measured are mainly for the regulatory subunits unbound to the kinase domain. How would nucleoside affinities change when the regulatory subunits are bound to the kinase domain, which is presumably the case under resting conditions? An estimation of this change in affinity is important because it more closely relates to the variations in cellular nucleoside concentrations needed for activation.

This is an important question and we have given an indirect answer in the manuscript, but not very explicit. The EC50 values for kinase activation of the purified holoenzyme complexes are very similar or almost identical to the kD values measured by ITC with free regulatory subunits. By inference, the binding kD for the holoenzyme and for the free R-subunit cannot be very different. In addition, we have recently determined the EC50 for PKA activation in vivo in trypanosomes using a bioluminescence complementation reporter assay. The values fit perfectly to the values obtained with purified holoenzyme (Wu et al. in preparation). A sentence in Results (lines 201-203) has been added.

(2) The authors should point out that a major implication of nucleoside vs. cyclic nucleotide activation is in terms of signal termination. If phosphodiesterases (PDEs) are responsible for cAMP/cGMP signal termination, what terminates nucleoside-dependent signaling? Although the answer to this question may not be known at this stage, it is important to highlight this critical implication of the authors' study.

The mechanism of signal termination is indeed unknown so far. We speculate that some enzymes of the purine salvage pathways are differentially localized in subcellular compartments and thereby able to establish microdomains that enable nucleoside signaling. In addition, PKA subunit phosphorylations/dephosphorylations and/or protein turnover may also regulate signal termination. As an example, free PKAC1 is rapidly degraded upon depletion of the PKAR subunit by RNAi. We have now mentioned signal termination in Discussion and have revised the last part of Discussion (lines 567-602). A possible approach to monitor compartmentalized signaling would be using the FluoSTEPs technology (Tenner et al., Sci. Adv. 2021; 7: eabe4091), but adapting this to the trypanosome system will not be a short-term task.

Author Response

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

We highly thank the editor and reviewers for their time and insightful comments and suggestions. We have made revisions by performing additional experiments and analysis, and clarified the items based on the suggestions.

Reviewer #1 (Public Review):

Summary of Author's Objectives:

The authors aimed to explore JMJD6's role in MYC-driven neuroblastoma, particularly in the interplay between pre-mRNA splicing and cancer metabolism, and to investigate the potential for targeting this pathway.

Strengths:

(1) The study employs a diverse range of experimental techniques, including molecular biology assays, next-generation sequencing, interactome profiling, and metabolic analysis. Moreover, the authors specifically focused on gained chromosome 17q in neuroblastoma, in combination with analyzing cancer dependency genes screened with Crispr/Cas9 library, analyzing the association of gene expression with prognosis of neuroblastoma patients with large clinical cohort. This comprehensive approach strengthens the credibility of the findings. The identification of the link between JMJD6-mediated premRNA splicing and metabolic reprogramming in MYC-driven cancer cells is innovative.

(2) The authors effectively integrate data from multiple sources, such as gene expression analysis, RNA splicing analysis, JMJD6 interactome assay, and metabolic profiling. This holistic approach provides a more complete understanding of JMJD6's role.

(3) The identification of JMJD6 as a potential therapeutic target and its correlation with the response to indisulam have significant clinical implications, addressing an unmet need in cancer treatment.

Weaknesses:

(1) The manuscript contains complex technical details and terminology that may pose challenges for readers without a deep background in molecular biology and cancer research. Providing simplified explanations or additional context would enhance accessibility.

We have provided simplified explanations for some terminology.

(2) It would be beneficial to explore whether treatment with JMJD6 inhibitors, both in vitro and in vivo, can effectively target the enhanced pre-mRNA splicing of metabolic genes in MYC-driven cancer cells.

Unfortunately, there is no potent and selective JMJD6 inhibitors available.

Reviewer #3 (Public Review):

Summary:

Jablonowski and colleagues studied key characteristics of MYC-driven cancers: dysregulated pre-mRNA splicing and altered metabolism. This is an important field of study as it remains largely unclear as to how these processes are coordinated in response to malignant transformation and how they are exploitable for future treatments. In the present study, the authors attempt to show that Jumonji Domain Containing 6, Arginine Demethylase And Lysine Hydroxylase (JMJD6) plays a central role in connecting pre-mRNA splicing and metabolism in MYC-driven neuroblastoma. JMJD6 collaborates with the MYC protein in driving cellular transformation by physically interacting with RNA-binding proteins involved in pre-mRNA splicing and protein regulation. In cell line experiments, JMJD6 affected the alternative splicing of two forms of glutaminase (GLS), an essential enzyme in the glutaminolysis process within the central carbon metabolism of neuroblastoma cells. Additionally, the study provides in vitro (and in silico) evidence for JMJD6 being associated with the anti-proliferation effects of a compound called indisulam, which degrades the splicing factor RBM39, known to interact with JMJD6.

Overall, the findings presented by Jabolonowski et al. begin to illuminate a cancer-promoting metabolic, and potentially, a protein synthesis suppression program that may be linked to alternative pre-mRNA splicing through the action of JMJD6 - downstream of MYC. This discovery can provide further evidence for considering JMJD6 as a potential therapeutic target for the treatment of MYC-driven cancers.

Strengths:

Alternative Splicing Induced by JMJD6 Knockdown: the study presents evidence for the role of JMJD6 in alternative splicing in neuroblastoma cells. Specifically, the RNA immunoprecipitation experiments demonstrated a significant shid from the GAC to the KGA GLS isoform upon JMJD6 knockdown. Moreover, a significant correlation between JMJD6 levels and GAC/KGA isoform expression was identified in two distinct neuroblastoma cohorts. This suggests a causative link between JMJD6 activity and isoform prevalence.

Physical Interaction of JMJD6 in Neuroblastoma Cells: The paper provides preliminary insight into the physical interactome of JMJD6 in neuroblastoma cells. This offers a potential mechanistic avenue for the observed effects on metabolism and protein synthesis and could be exploited for a deeper investigation into the exact nature, and implications of neuroblastoma-specific JMJD6 protein-protein interactions.

Weaknesses:

There are several areas that would benefit from improvements with regard to the current data supporting the claims of the paper (i.e., the conclusion presented in Figure 8).

Neuroblastoma Modelling Strategy: The study heavily relies on cell lines without incorporating patient derived cells/biomaterials. Using databases to fill gaps in the experimental design can only fortify the observations to a certain extent. A critical oversight is the absence of non-cancerous control cells in many figures, and the rationale for selecting specific cell lines for assays/approaches remains somewhat unclear. A foundational control for such experiments should involve the non-transformed neural crest cell line, which the authors have readily available. Are the observed splicing and metabolic effects of JMJD6 specific to neuroblastoma? Is there a neuroblastoma-specific JMJD6 interactome? Is MYC function essential?

In Vivo Modelling: The inclusion of a genetic mouse model combined with an inducible JMJD6 knockdown, would enhance the study by allowing examination of JMJD6's role during both tumor initiation and growth in vivo. For instance, the TH-MYCN mice overexpressing MYCN in neural crest cells, could be a promising choice.

Dependence on Colony Formation Assay: The study leans on 2D and semi-quantitative colony formation assays to assess malignant growth. To validate the link between the mechanistic insights discussed (e.g., reduced protein synthesis) and JMJD6-mediated malignant growth as a potential therapeutic target, evidence from in vivo or representative 3D models would be crucial.

Data Presentation and Rigor: The presented data is predominantly qualitative and necessitates quantification. For instance, Western blots should be quantified. The RNAseq, metabolism, and pulldown data should be transparently and numerically presented. The figure legends seem elusive and their lack of transparency (oden with regards to biological repeats, error bars, cell line used etc.) is concerning. Adequate citation and identification of all data sources, including online resources, are imperative. The manuscript would also benefit from a more rigorous depiction and quantification of RNA interference of both stable and transient knockdowns with quantitative validation at mRNA and protein levels.

Novelty Concerns: The emphasis on JMJD6 as a novel neuroblastoma target is contingent on the new mechanistic revelations about the JMJD6-centered link between splicing, metabolism, and protein synthesis. Given that JMJD6 has been previously linked to neuroblastoma biology, the rationale (particularly in Figure 1) for concentrating on JMJD6 may stem more from bias rather than data-driven reasoning.

Depth of Mechanistic Investigation: Current evidence lacks depth in key areas such as JMJD6-RNA binding. A more thorough approach would involve pinpointing specific JMJD6 binding sites on endogenous RNAs using techniques such as cross-linking and immunoprecipitation, paired with complementary proximity-based methodologies. Regarding the presented metabolism data, diving deeper into metabolic flux via isotope labeling experiments could shed light on dynamic processes like TCA and glutaminolysis. As it stands, the 'pathway cartoon' in Figure 6d appears overly qualitative.

Response: We agree with this reviewer that more in-depth studies are needed to understand the biological functions of JMJD6 in neuroblastoma. We have included one paragraph “limitation of the study” to point out that additional work needs to be done to address the comments from this reviewer.

We have also added details in figure legend to increase rigor.

Reviewer #1 (Recommendations For The Authors):

In this study, Jablonowski and colleagues identify the link between JMJD6-mediated pre-mRNA splicing and metabolic reprogramming in cancer cells, with implications for therapeutic response to splicing inhibitors. I have reviewed your manuscript and found it quite promising. However, there are some specific points that require further clarification and additional experiments. Please consider the following comments:

Major concerns:

(1) Regarding Figure 1d and e: to enhance the robustness of your findings, it would be beneficial to include additional datasets, such as the Kocak-649 dataset. It is important to narrow down the analysis to high-risk patient groups when examining survival rates, specifically to investigate whether the elevated expression of the 114 gene signature correlates with poor survival within this subgroup. Additionally, please consider conducting a more detailed breakdown of the subsets depicted in Fig. 1b to explore the association between their expression levels and patient survival rates.

Response: We have included the Kocak-649 datasets as Supplemental Figure 1. We have further analyzed the 114 gene signature in low-risk and high-risk patients, respectively, as Supplemental Figure 2.

(2) Fig. 2b: Similar to the previous comment, it would strengthen your findings to include survival rate analysis in more datasets, particularly in high-risk patient groups.

Response: We have further analyzed the association of JMJD6 with survival in low-risk and high-risk patients, respectively, as Supplemental Figure 3. Regardless of the risk factors, high expression of JMJD6 was associated with a poor outcome.

(3) In reference to Fig. S1D, please clarify the time point under investigation. It looks like siRNAs were utilized in this study. Ensure consistency between the siRNA # mentioned in the methods section and what is presented in Fig. S1d.

Response: We have clarified the time point under investigation in Fig. S1D (now as Fig. S4D). We have corrected the siRNA# on the method section.

Additionally, it would be beneficial to include data on knockdown efficacy and consider incorporating western blot results, similar to those presented in Fig. 2c.

Response: These experiments were performed as shown in Figure 4C. We assumed the knockdown efficiency was comparable.

Furthermore, I recommend analyzing the RNA-seq data from JMJD6-depleted BE(2)C cells to identify any alterations in the expression of neuronal differentiation signature genes, with the aim of exploring potential associations with changes in cell morphology showed in Fig. S1D.

Response: We have analyzed the data and indeed like this reviewer expected, we do see the upregulation of neuronal differentiation pathways. We have included the data as Fig. S7B.

(4) Fig. 4g: Confirm whether the data is related to GAC, and if so, where is the data for KGA?

Response: We apologize for this. KGA data was missed when we assembled the figure. We have added back as Figure 4H.

(5) In relation to Fig. 4, I suggest conducting experiments to individually silence GAC and KGA, if feasible (for instance, by targeting their 3'-UTRs). This would allow for a more in-depth investigation into whether GAC and KGA play essential roles in NB cell proliferation.

Response: As this reviewer suggested, we have performed the experiments to knock down GAC and KGA in BE2C cells, and we found that both isoforms seemed to be important for cell survival. We have included the data as Figure 5G-I. Additionally, we have also performed RNA-seq to understand the differential functions of GAC and KGA in neuroblastoma cells when they were overexpressed separately. We have included the data as Figure 5E,F, and Supplemental Figure 9.

(6) Fig. 5c: Could this protein synthesis reduction be attributed to an artificial overexpression of JMJD6? It would be interesting to investigate whether the genetic silencing of JMJD6 has an impact on total protein synthesis.

Response: This is a great question but could be very challenging to have a definitive answer. Since cells are not happy with knockdown of JMJD6, we may have a secondary effect resulting from activation of cell death. While we have successfully generated single cell JMJD6 CRISPR KO clones, the cells are not happy either. In the future, we may generate dTAG knockin cell line which will allow us to induce an acute protein degradation, and then we can assess if JMJD6 loss will consequently impact total protein synthesis.

(7) Fig. S7: the authors have shown that knocking down of JMJD6 in NB cells reduced cell proliferation (Fig. 2c-e). Please clarify how you obtained sufficient cells ader CRISPR knockout of JMJD6 clones and whether the cells remained healthy. It would be helpful to provide cell images.

Response: We harvested cells at different time points in Fig 2C-E, and we have added the information in Figure legends. Cells were not happy ader JMJD6 KD or KO. We therefore harvest cells for Western blot at an early time point while stained cells for survival effect at a late time point.

(8) Fig. 7f: Address the paradox where JMJD-knockdown cells grow slower (Fig. 2c-e), but these JMJD-KO4E5 cells grow at a similar rate compared to SKNAS-WT in the DMSO treatment group. Clarify whether this aligns with the results observed with shRNA results shown in Fig. 2c-e.

Response: The JMJD6 KO cells grew much slower than the wild-type cells. In these experiments, we intentionally seeded a lot more cells for JMJD6 KO clone so that we can have a comparable comparison for the cells with DMSO treatment.

Minor concerns:

(1) Fig. 2c: Please specify the time point for Fig. 2c to provide a clearer context for readers.

We have added the information.

(2) In Line 204, it is stated that 'Supplementary Table 3,' which describes the 'Correlation of JMJD6 KO and its co-dependency genes,' can actually be found in 'Supplementary Table 4.' Please clarify this discrepancy.

We apologize for this. We probably accidentally uploaded the duplicates. We have uploaded the new table in our revision.

(3) Line 207: The order of figures should be clarified. Fig. 3c should be mentioned before Fig. 3b in the text.

Yes, we did.

(4) In Line 216, it is mentioned that 'Supplementary Table 4,' which describes 'Differentially expressed genes by JMJD6 KD,' can actually be found in 'Supplementary Table 3.' Please provide clarification for this discrepancy.

We have corrected this.

(5) Line 244-247: Please provide clarification of this section to ensure readers can fully understand your point.

We have rephrased the sentence.

(6) Line 1048: Confirm whether Fig. 2c represents siRNA or shRNA, as the label in the graph does not match the figure legends.

Sorry for this. We have corrected.

(7) Line 1161: Provide clarification regarding the use of Image J from k, and in Line 1162, specify the source of Image J from l.

We apologized for the confusion of our description. We meant “Image J” sodware. We have corrected in Figure legend.

Reviewer #2 (Recommendations For The Authors):

Suggestions to authors:

Line 39 - suggest introducing JMJD6.

Response: We have added the full name of JMJD6.

Line 47 - suggest slightly rephrasing 'metabolic program that is coupled with...'.

We have made a slight change by changing “coupled” to “associate”.

Line 85 - please delete/replace 'exceptional'; proofread for inadequate use of ambiguous wording.

We have changed it as “significant”.

Line 141 - please concisely define 'high risk'.

We have defined it with a citation (line 142-146).

Line 143 - please concisely define 'event free'.

We have defined the event free and overall survival precisely (line 149, 150).

Line 153 - provide an adequate citation for 'cBioportal'.

We have added the citation (line166).

Line 161 - please state the utilized cell lines.

We have referenced to Materials and Methods (line 175).

Line 166 - please note that 'morphological changes' of a cell do not suffice to determine 'stemness', please rephrase.

We agreed and changed it to “regulate cellular differentiation” (line 181).

Line 182 - provide a quantifiable measure for color change and or remove observation from the narrative.

We have removed “indicative of acidic pH change” (line 198).

Line 185 - the statement commencing with 'It is believed...' requires referencing.

We have added references (line 200).

Line 187 - please provide an adequate citation for the 'JoMa1' neural crest-derived cells (J. Maurer and colleagues?).

We have added the reference (line 201).

Line 203 - please provide an adequate citation for 'DepMap'.

There is no citation specifically for DepMap and that’s why we can only provide the DepMap link.

Line 234 - please provide an adequate citation for 'two algorithms'.

We have provided the reference (line 265).

Line 265 - please provide a rationale for the choice of the three tested cell lines.

We have added definition by saying C-MYC overexpressed SKNAS, BE2C and SIMA with MYCN amplification (line 302, 303).

Line 279 - suggest rephrasing 'gaining more ATPs'.

We have removed these words as we do not have direct evidence to show ATP production (line 320).

Line 342 - suggest rephrasing 'are in the only gene signature'.

We have rephrased by saying “lysine demethylase (HDM) genes, including JMJD6, are present in the most significantly enriched gene signature in indisulam-sensitive cells” (line 416-416).

Line 424 - please state the source or all cell lines (commercial provider?).

We have added the source of cell lines.

Lines 438 to 442 - are STR and mycoplasma profiling data adequately presented in the manuscript?

We routinely test STR and mycoplasma for all cell lines cultured in hood in our Department every month.

Lines 520 onwards - is the JMJD6 knockout generation data (e.g., cell viability upon knockout) adequately presented in the manuscript? Why does the study depend on transient transfection of siRNAs for obtaining mechanistic results?

We created stable JMJD6 KO clones by selecting single cell with complete knockout. Cells are not happy ader KO. siRNA knockdown is a method for relatively acute depletion of JMJD6, which is easy and fast, and may be more reliable to assess the direct effect of JMJD6.

Figures: please provide adequate axis-labeling for all graphs (e.g., FIg2 b, and e).

We have added the axis labeling.

Discussion line 370 - what is meant by 'too harsh' - please use unambiguous phrasing to highlight limitations.

We have changed to “stringent”.

Please provide a study limitation paragraph.

We have added one limitation paragraph.

Limitation of the study

Our study focused on the understanding of JMJD6 function in neuroblastoma cell lines. In the future, we will consolidate our study by expanding our models to patient-derived xenograds, organoids, and neuroblastoma genetic models, in comparison with non-cancerous cells. Although we have identified a conserved interactome of JMJD6 in neuroblastoma cells, it remains to be determined whether it is neuroblastoma-specific and essential to MYC-driven cancers. The genome-wide RNA binding by JMJD6 in cancer cells and normal cells coupled with isotope labeling to dissect the metabolic effect of JMJD6 will enhance our understanding of the biological functions of JMJD6, awaiting future studies. Inability to target the enhanced pre-mRNA splicing of metabolic genes in MYC-driven cancer cells by pharmacologic inhibition of JMJD6 is another limitation, due to lack of selective and potent JMJD6 inhibitors.

Additional editing and proof-reading of the manuscript's narrative, figures, legends, and methods is highly recommended.

We have gone through the whole MS to have proof-reading.

Author Response

We are grateful for the reviewers' appreciation of our work and for their constructive feedback. We will address their comments through a revised version of the manuscript.

Reviewer #1 (Public Review):

This study by Paoli et al. used a resonant scanning multiphoton microscope to examine olfactory representation in the projection neurons (PNs) of the honeybee with improved temporal resolution. PNs were classified into 9 groups based on their response patterns. Authors found that excitatory repose in the PNs precedes the inhibitory responses for ~40ms, and ~50% of PN responses contain inhibitory components. They built the neural circuit model of the mushroom body (MB) with evolutionally conserved features such as sparse representation, global inhibition, and a plasticity rule. This MB model fed with the experimental data could reproduce a number of phenomena observed in experiments using bees and other insects, including dynamical representations of odor onset and offset by different populations of Kenyon cells, prolonged representations of after-smell, different levels of odor- specificity for early/delay conditioning, and shift of behavioral timing in delay conditioning. The trace conditioning was not modeled and tested experimentally. Also, the experimental result itself is largely confirmatory to preceding studies using other organisms. Nonetheless, the experimental data and the model provide a solid basis for future studies.

We thank the reviewer for summarizing the value of our study and recognizing its generality and significance. As suggested, in a revised version of the manuscript, we will discuss the implication of our approach for the context of trace conditioning. The model we presented hinges on the learning-induced plasticity of KC-to-MBON synapses recruited during the learning window (i.e., the simulated US arrival). In the case of trace conditioning, the model predicts that the time of the behavioral response time should match the expected US arrival. Contrary to this prediction, preliminary analyses on empirical measurements of PER latency upon trace conditioning indicate this is not the case. In a revised version of the manuscript, we will discuss the differences between the predictions of the model and the experimental observations in a trace conditioning paradigm.

Reviewer #2 (Public Review):

The study presented by Paoli et al. explores temporal aspects of neuronal encoding of odors and their perception, using bees as a general model for insects. The neuronal encoding of the presence of an odor is not a static representation; rather, its neuronal representation is partly encoded by the temporal order in which parallel olfactory pathways participate and are combined. This aspect is not novel, and its relevance in odor encoding and recognition has been discussed for more than the past 20 years.

The temporal richness of the olfactory code and its significance have traditionally been driven by results obtained based on electrophysiological methods with temporal resolution, allowing the identification and timing of the action potentials in the different populations of neurons whose combination encodes the identity of an odor. On the other hand, optophysiological methods that enable spatial resolution and cell identification in odor coding lack the temporal resolution to appreciate the intricacies of olfactory code dynamics.

(1) In this context, the main merit of Paoli et al.'s work is achieving an optical recording that allows for spatial registration of olfactory codes with greater temporal detail than the classical method and, at the same time, with greater sensitivity to measure inhibitions as part of the olfactory code.

The work clearly demonstrates how the onset and offset of odor stimulation triggers a dynamic code at the level of the first interneurons of the olfactory system that changes at every moment as a natural consequence of the local inhibitory interactions within the first olfactory neuropil, the antennal lobe. This gives rise to the interesting theory that each combination of activated neurons along this temporal sequence corresponds to the perception of a different odor. The extent to which the corresponding postsynaptic layers integrate this temporal information to drive the perception of an odor, or whether this sequence is, in a sense, a journey through different perceptions, is challenging to address experimentally.

In their work, the authors propose a computational approach and olfactory learning experiments in bees to address these questions and evaluate whether the sequence of combinations drives a sequence of different perceptions. In my view, it is a highly inspiring piece of work that still leaves several questions unanswered.

We thank the reviewer for considering that our work has an inspiring nature. Below we have tried to answer the questions raised by the following comments, and we will include part of these answers in the revised version of our manuscript.

(2) In my opinion, the detailed temporal profile of the response of projection neurons and their respective probabilities of occurrence provide valuable information for understanding odor coding at the level of neurons transferring information from the antennal lobes to the mushroom bodies. An analysis of these probabilities in each animal, rather than in the population of animals that were measured, would aid in better comprehending the encoding function of such temporal profiles. Being able to identify the involved glomeruli and understanding the extent to which the sequence of patterns and inhibitions is conserved for each odor across different animals, as it is well known for the initial excitatory burst of activity observed in previous studies without the fine temporal detail, would also be highly significant.

We thank the reviewer for recognizing the relevance of the findings in understanding the logic of olfactory coding. We agree about the importance of establishing if the different glomerular response profiles are evenly distributed across individuals or have individual biases. In the revised version of the manuscript, we will provide data on the distribution of response profiles for each animal and for different olfactory stimuli. Also, we fully agree on the importance of assessing to what extent such response profiles - largely determined by the local network of AL interneurons - are glomerulus-specific and conserved across individuals.

In my view, the computational approach serves as a useful tool to inspire future experiments; however, it appears somewhat simplistic in tackling the complexity of the subject. One question that I believe the researchers do not address is to what extent the inhibitions recorded in the projection neurons are integrated by the Kenyon cells and are functional for generating odor-specific patterns at that level.

The model we proposed represents, indeed, a simplification of olfactory signal processing throughout the honey bee olfactory circuit. Still, it shows that simple but realistic rules can be sufficient to grasp some fundamental aspects of olfactory coding. However, we agree with the reviewer and believe that such a minimalistic model can provide a basis for designing future experiments in which complexity can be increased by adding relevant features, such as the learning-induced plasticity of PN-to-KC synapses or the divergence of multiple PNs from the same glomerulus to different KCs

Concerning the reviewer's question on the involvement of inhibitory inputs in generating odor-specific patterns at the level of the KCs, the short answer is yes, they contribute to the summed input of a target KC, thus to the odor representation. In designing the model, we considered that a given glomerulus provides maximal input at maximal excitation and minimal input (=0 input) at maximal inhibition. For this reason, an inhibited glomerulus contributes less (to KC action potential probability) than a glomerulus showing baseline activity. This, in turn, contributes less than an excited glomerulus. From the modeling point of view, normalizing the signal between 0 and 1 (i.e., setting minimal inhibition to 0 and maximal excitation to 1) would yield a similar result as with the current approach, where values range from -25% to +30% F/F. We implement the model's description to clarify this point.

Lastly, the behavioral result indicating a difference in conditioned response latency after early or delayed learning protocol is interesting. However, it does not align with the expected time for the neuronal representation that was theoretically rewarded in the delayed protocol. This final result does not support the authors' interpretation regarding the existence of a smell and an after-smell as separate percepts that can serve as conditioned stimuli.

Considering that our odor stimulus lasted 5 seconds, glomerular activity is highly variable at odor onset (i.e., within the first 1s) because of short excitatory response profiles and the delayed and slower onset of inhibitory responses. After the initial phase, the neural representation of the stimulus becomes more stable. Consequently, a neural signature learned in the case of delay conditioning, i.e., with the US appearing towards the end of the olfactory stimulation (t = 4 - 5s), may present itself much earlier (t = 1.5s), triggering a behavioral response that largely anticipates the expected US arrival time.

In the model, we observe an early decrease in action potential probability even in the case of delay conditioning. This occurs because the synapses recruited during the last second of olfactory stimulation (within the learning window during which CS and US overlap) become inactive. Because odorant-induced activity recruits highly overlapping synaptic populations between 1.5 and 5 s from the onset, a learning-induced inactivation of part of these synapses will result in a reduced action-potential probability in the modeled MBON. Importantly, this event will not be governed by time but by the appearance of the learned synaptic configuration.

We will add a new section to the revised version of the manuscript to clarify this concept and perform further analyses to characterize the contribution of different response types to the modeled response latency.

Author Response

Reviewer #1 (Public Review):

Strengths:

• The paper is clearly written, and all the conclusions stem from a set of 3 principles: circular topology, rotational symmetry, and noise minimization. The derivations are sound and such rigor by itself is commendable.

• The authors provide a compelling argument on why evolution might have picked an eight-column circuit for path-integration, which is a great example of how theory can inform our thinking about the organization of neural systems for a specific purpose.

• The authors provide a self-consistency argument on how cosine-like activity supports cosine-like connectivity with a simple Hebbian rule. However, their framework doesn't answer the question of how this system integrates angular velocity with the correct gain in the absence of allothetic cues to produce a heading estimate (more on that on point 3 below).

Weaknesses:

• The authors make simplifying assumptions to arrive at the cosine activity/cosine connectivity circuit. Among those are the linear activation function, and cosine driving activity u. The authors provide justification for the linearization in methods 3.1, however, this ignores the well-established fact that bump amplitude is modulated by angular velocity in the fly head direction system (Turner-Evans et al 2017). In such a case, nonlinearities in the activation function cannot be ignored and would introduce harmonics in the activity.

We thank the reviewer for pointing out this omission. We added a paragraph at the end of section 4.1 clarifying that transient non-linearity, for instance when the circuit is actively receiving external input, is compatible with our work because we only need linearity in the line attractor, but not outside (lines 407-419).

“In more intuitive terms, the neurons have a saturating nonlinear activation function where they modulate their gain based on the total activity in the network. If the activity in the network is above the desired level, r, the gain is reduced and the activity decreases, and when the activity of the network is less than desired level, both the gain and the activity increase. Note that in this scenario transient deviations from the line attractor, which would induce nonlinear behaviour in the circuit dynamics, are tolerable. External inputs, u(t), could transiently modify the shape of the activity, producing activity shapes deviating from what the linear model can accommodate. For example, the shape of the bump attractor could be modified through nonlinearities while the insect attains high angular velocity (Turner-Evans et al., 2017).

Such nonlinear dynamics do not conflict with the theory developed here, which only requires linearity when the activity is projected onto the circular line attractor. In our framework, the linearity of integration at the circular line attractor is not a computational assumption, but rather it emerges from the principle of symmetry.”

Furthermore, even though activity has been reported to be cosine-like, in fact in the fruit fly it takes the form of a somewhat concentrated activity bump (~80-100 degrees, Seelig & Jayaraman 2015; Turner-Evans et al 2017), and one has to take into account the smoothing effect of calcium dynamics too which might make the bump appear more cosine-like. So in general, it would be nice to see how the conclusions extend if the driving activity is more square-like, which would also introduce further harmonics.

We added a cautionary comment on the sinusoidal activity (lines 222-226).

“We note, however, that data from the fruit fly shows a more concentrated activity bump than what would be expected from a perfect sinusoidal profile (Seelig and Jayaraman, 2015; Turner-Evans et al., 2017), and that calcium imaging (which was used to measure the activity) can introduce biases in the activity measurements (Siegle et al., 2021; Huang et al., 2021). Thus the sinusoidal activity we model is an approximation of the true biological process rather than a perfect description.”

Overall, it would be interesting to see whether, despite the harmonics introduced by these two factors interacting in the learning rule, Oja's rule can still pick up the "base" frequency and produce sinusoidal weights (as mentioned in methods 3.8). At this point, the examples shown in Figure 5 (tabula rasa and slightly perturbed weights) are quite simple. Such a demonstration would greatly enhance the generality of the results.

We also extended the self-consistency framework from Oja’s rule to the non-linear case, and found that while Oja’s rule with non-linear neurons would not give pure harmonics, the secondary harmonics will remain small. We added a sentence explaining this in the main text (section 2.4, lines 309-312) and a methods section to develop the self-consistency framework for the case of non-linear activations (section 4.7.2).

“For neurons with a nonlinear activation function, secondary harmonics would emerge, but would remain small under mild assumptions, as shown in Section 4.7.2. Oja’s rule will still cause the weights to converge to approximately sinusoidal connectivity.”

• The match of the theoretical prediction of cosine-like connectivity profiles with the connectivity data is somewhat lacking. In the locust the fit is almost perfect, however, the low net path count combined with the lack of knowledge about synaptic strengths makes this a motivating example in my opinion. In the fruit fly, the fit is not as good, and the function-fitting comparison (Methods Figure 6) is not as convincing. First, some function choices clearly are not a good fit (f1+2, f2). Second, the profile seems to be better fit by a Gaussian or other localized function, however the extra parameter of the Gaussian results in the worst AIC and AICc. To better get at the question of whether the shape of the connectivity profile matches a cosine or a Gaussian, the authors could try for example to fix the width of the Gaussian (e.g. to the variance of the best-fit cosine, which seems to match the data very well even though it wasn't itself fit), and then fit the two other parameters to the data. In that case, no AIC or AICc is needed. And then do the same for a circular distribution, e.g. von Mises.

We also included the fit with von Mises and Gaussian with the width parameters fixed to match the cosine as the reviewer suggested. We found that even though these two distributions fit the data better, the difference is very small (2%), probably due to the high variability of the fruit fly connectome data. We also changed the wording and state that the theory is compatible with experimental data.

In the Methods 4.6 (lines 568-585), we wrote

“As a complementary approach to evaluate the shape of the distribution, we first fit the Gaussian and von Mises distributions to the best fit f = 1 curve. We then freeze the width parameters of the distributions (σ_g for the Gaussian and κ_v for the von Mises) and only optimise the amplitude and vertical offset parameters (β and γ) to fit the data. This approach limits the number of free parameters for the Gaussian and von Mises distributions to two, to match the sinusoid. The results are shown in Methods Fig. 6 and Table 5. Both the fixed-width Gaussian and von Mises distributions are a slightly better fit to the data than the sinusoid, but the differences between the three curves are very small.

In simplifying the fruit fly connectome data, we assumed all synapses of different types were of equal weight, as no data to the contrary were available. Different synapse types having different strengths could introduce nonlinear distortions between our net synaptic path count and the true synaptic strength, which could in turn make the data a better or worse fit for a sinusoidal compared to a Gaussian profile. As such, we don’t consider the only 2% relative differences between the f = 1 sinusoid and fixed-width Gaussian and von Mises distributions to be conclusive.

Overall, we find that the cosine weights that emerge from our derivations are a very close match for the locust, but less precise for the fly, where other functions fit slightly better. Given the limitations in using the currently available data to provide an exact estimate of synaptic strength (for the locust), and due to the high variability of the synaptic count (for the fruit fly), we consider that our theory is compatible with the observed data.”

In addition, the theoretical prediction of cosine-like connectivity is not clearly stated in the abstract, introduction, or discussion. As a prediction, I believe it should be center forward, as it might be revisited again in the future in lieu of e.g. new experimental data.

We added the explicit prediction in the abstract and the introduction (lines 52-53).

• I find the authors' claim that Oja's rule suffices to learn the insect head direction circuit (l. 273-5) somewhat misleading/vague. The authors seem to not be learning angular integration here at all. First, it is unclear to me what is the form of u(t). Is it the desired activity in the network at time t given angular velocity? This is different than modelling a population of PEN neurons jointly tuned to head direction and angular velocity, and learning weights so as to integrate angular velocity with the correct gain (Vafidis et al 2022). The learning rule here establishes a self-consistency between sinusoidal weights and activity, however, it does not learn the weights from PEN to EPG neurons so as to perform angular integration. Similar simple Hebbian rules have been used before to learn angular integration (Stringer et al 2002), however, they failed to learn the correct gain. Therefore, the authors should limit the statement that their simpler learning rule is enough to learn the circuit (l. 273-5), making sure to outline differences with the current literature (Vafidis et al 2022).