Author Response:

Reviewer #1 (Public Review):

Summary:

The authors aimed to identify potential biomarkers for acute myocardial infarction (AMI) through blood metabolomics and fecal microbiome analysis. They found that long chain fatty acids (LCFAs) could serve as biomarkers for AMI and demonstrated a correlation between LCFAs and the gut microbiome. Additionally, in silico molecular docking and in vitro thrombogenic assays showed that these LCFAs can induce platelet aggregation.

Strengths:

The study utilized a comprehensive approach combining blood metabolomics and fecal microbiome analysis.

The findings suggest a novel use of LCFAs as biomarkers for AMI.

The correlation between LCFAs and the gut microbiome is a significant contribution to understanding the interplay between gut health and heart disease.

The use of in silico and in vitro assays provides mechanistic insights into how LCFAs may influence platelet aggregation.

Weaknesses:

The evidence is incomplete as it does not definitively prove that gut dysbiosis contributes to fatty acid dysmetabolism.

We appreciate this reviewer’s insightful comment regarding the causal relationship between gut dysbiosis and fatty acid dysmetabolism. We acknowledge that our study primarily demonstrates a strong association rather than causation. While establishing causality was beyond the scope of the current study, we recognize the importance of addressing this point. In our revised manuscript, we will emphasize the observational nature of our findings and discuss the need for future research, including longitudinal studies and interventional trials, to explore the causal links between gut dysbiosis and fatty acid dysmetabolism. We believe that this clarification strengthens the interpretation of our results and aligns with the reviewer's concern.

The study primarily shows an association between the gut microbiome and fatty acid metabolism without establishing causation.

We agree with the reviewer that our study presents an association rather than definitive proof of causation between the gut microbiome and fatty acid metabolism. To address this, we plan to expand the discussion section to more clearly outline the limitations of our study in establishing causality. We will also propose future research directions, such as the use of animal models and longitudinal human studies, which could help elucidate the causal pathways. By clarifying this aspect, we aim to provide a more balanced perspective on our findings.

Reviewer #2 (Public Review):

Summary:

Fan et al. investigated the relationship between early acute myocardial infarction (eAMI) and disturbances in the gut microbiome using metabolomics and metagenomics analyses. They studied 30 eAMI patients and 26 healthy controls, finding elevated levels of long-chain fatty acids (LCFA) in the plasma of eAMI patients.

Strengths:

The research attributed a substantial portion of LCFA variance in eAMI to changes in the gut microbiome, as indicated by omics analyses. Computational profiling of gut bacteria suggested structural variations linked to LCFA variance. The authors also conducted molecular docking simulations and platelet assays, revealing that eAMI-associated LCFAs may enhance platelet aggregation.

Weaknesses:

The results should be validated using different assays, and animal models should be considered to explore the mechanisms of action.

We appreciate the reviewer’s suggestion to validate our findings using additional assays and animal models. We agree that further validation is crucial to confirm the robustness of our results and to explore the underlying mechanisms in greater detail. While our current study focused on human subjects and in vitro assays to establish initial findings, we acknowledge that additional experimental approaches are necessary. In the revised manuscript, we plan to include a discussion on the potential use of different assays (e.g., advanced metabolomics techniques, multi-omics integration) and animal models to validate and expand upon our findings. Moreover, we are planning to undertake these experiments in future studies to build upon the foundational work presented here.

We believe that our revised responses and the planned manuscript revisions will address the reviewers’ concerns effectively. We are confident that these changes will enhance the overall contribution of our study to the field. Thank you again for your valuable feedback.

Author response:

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

Public Reviews:

Joint Public Review: 

The molecular mechanisms that mediate the regulated exocytosis of neuropeptides and neurotrophins from neurons via large dense-core vesicles (LDCVs) are still incompletely understood. Motivated by their earlier discovery that the Rab3-RIM1 pathway is essential for neuronal LDCV exocytosis, the authors now examined the role of the Rab3 effector Rabphilin-3A in neuronal LDCV secretion. Based on multiple live and confocal imaging approaches, the authors provide evidence for a synaptic enrichment of Rabphilin-3A and for independent trafficking of Rabphilin-3A and LDCVs. Using an elegant NPY-pHluorin imaging approach, they show that genetic deletion of Rabphilin-3A causes an increase in electrically triggered LDCV fusion events and increased neurite length. Finally, knock-out-replacement studies, involving Rabphilin-3A mutants deficient in either Rab3- or SNAP25-binding, indicate that the synaptic enrichment of Rabphilin-3A depends on its Rab3 binding ability, while its ability to bind to SNAP25 is required for its effects on LDCV secretion and neurite development. The authors conclude that Rabphilin-3A negatively regulates LDCV exocytosis and propose that this mechanism also affects neurite growth, e.g. by limiting neurotrophin secretion. These are important findings that advance our mechanistic understanding of neuronal large dense-core vesicle (LDCV) secretion. 

The major strengths of the present paper are: 

(i) The use of a powerful Rabphilin-3A KO mouse model. 

(ii) Stringent lentiviral expression and rescue approaches as a strong genetic foundation of the study. 

(iii) An elegant FRAP imaging approach. 

(iv) A cutting-edge NPY-pHluorin-based imaging approach to detect LDCV fusion events. 

We thank the reviewers for their positive evaluation of our manuscript.

Weaknesses that somewhat limit the convincingness of the evidence provided and the corresponding conclusions include the following: 

(i) The limited resolution of the various imaging approaches introduces ambiguity to several parameters (e.g. LDCV counts, definition of synaptic localization, Rabphilin-3A-LDCV colocalization, subcellular and subsynaptic localization of expressed proteins, AZ proximity of Rabphilin-3A and LDCVs) and thereby limits the reliability of corresponding conclusions. Super-resolution approaches may be required here. 

We thank the reviewer for their constructive suggestion. We fully agree that super-resolution imaging would produce a more precise localization of RPH3A and co-localization with DCVs. We have now repeated our (co)-localization experiments with STED microscopy. We find that RPH3A colocalized with the pre-synaptic marker Synapsin1 and, to a lesser extent, with the post synaptic marker Homer and DCV marker chromogranin B (new Figure 1). This indicates that RPH3A is highly enriched in synapses, mostly the pre-synapse, and that RPH3A partly co-localizes with DCVs.  

(ii) The description of the experimental approaches lacks detail in several places, thus complicating a stringent assessment. 

We apologize for the lack of detail in explaining the experimental approaches. We have included a more detailed description in the revised manuscript. 

(iii) Further analyses of the LDCV secretion data (e.g. latency, release time course) would be important in order to help pinpoint the secretory step affected by Rabphilin-3A. 

We agree. To address this comment, we have now included the duration of the fusion events (new Figure S2D-F). The start time of the fusion events are shown in the cumulative plots in now Figure 3F and I. The kinetics are normal in the RPH3A KO neurons.

(iv) It remains unclear why a process that affects a general synaptic SNARE fusion protein - SNAP25 - would specifically affect LDCV but not synaptic vesicle fusion. 

We agree that we have not addressed this issue systematically enough in the original manuscript. We have now added a short discussion on this topic in the Discussion of the revised manuscript (p 15, line 380-386). In brief, we do not claim full selectivity for the DCV pathway. Some effects of RPH3A deficiency on the synaptic vesicle cycle have been observed. Furthermore, because DCVs typically do not mix in the synaptic vesicle cluster and fuse outside the active zone (and outside the synapse), DCVs might be more accessible to RPH3A regulation.

(v) The mechanistic links between Rabphilin-3A function, LDCV density in neurites, neurite outgrowth, and the proposed underlying mechanisms involving trophic factor release remain unclear. 

We agree that we have not addressed all these links systematically enough in the original manuscript, although we feel that we have at least postulated the best possible working model to link RPH3A function to DCV exocytosis/neurotrophic factor release and neurite outgrowth (p 15-16, line 396-400). Of course, a single study cannot support all these links with sufficient experimental evidence. We have now added a short text on what we can conclude exactly based on our experiments and how we see the links between RPH3A function, DCV exocytosis/neurotrophic factor release, neurite outgrowth and DCV density in neurites (p 13-14, line 317-325).

Reviewer #1 (Public Review): 

Summary:

The manuscript by Hoogstraaten et al. investigates the effect of constitutive Rabphilin 3A (RPH3A) ko on the exocytosis of dense core vesicles (DCV) in cultured mouse hippocampal neurons. Using mCherry- or pHluorin-tagged NPY expression and EGFP- or mCherry tagged RPHA3, the authors first analyse the colocalization of DCVs and RPH3A. Using FRAP, the authors next analyse the mobility of DCVs and RAB3A in neurites. The authors go on to determine the number of exocytotic events of DCVs in response to high-frequency electrical stimulation and find that RPH3A ko increases the number of exocytotic events by a factor 2-3, but not the fraction of released DCVs in a given cell (8x 50Hz stim). In contrast, the release fraction is also increased in RBP3A KOs when doubling the stimulation number (16x 50Hz). They further observe that RPH3A ko increases dendrite and axon length and the overall number of ChgrB-positive DCVs. However, the overall number of DCVs and dendritic length in ko cells directly correlate, indicating that the number of vesicles per dendritic length remains unaffected in the RPH3A KOs. Lentiviral co-expression of tetanus toxin (TeNT) showed a non-significant trend to reduce axon and dendrite length in RPH3a KOs. Finally, the authors use co-expression of RAB3A and SNAP25 constructs to show that RAB3A but not SNAP25 interaction is required to allow the exocytosis-enhancing effect in RPH3A KOs. 

While the authors' methodology is sound, the microscopy results are performed well and analyzed appropriately, but their results in larger parts do not sufficiently support their conclusions. Moreover, the experiments are not always described in sufficient detail (e.g. FRAP; DCV counts vs. neurite length) to fully understand their claims. 

Overall, I thus feel that the manuscript does not provide a sufficient advance in knowledge. 

Strengths: 

- The authors' methodology is sound, and the microscopy results are performed well and analyzed appropriately. 

- Figure 2: The exocytosis imaging is elegant and potentially very insightful. The effect in the RPH3A KOs is convincing. 

- Figure 4: the logic of this experiment is elegant. It shows that the increased number of DCV fusion events in RPH3A KOs is related to the interaction of RPH3A with RAB3A but not with SNAP25. 

We thank the reviewer for their positive evaluation of our manuscript.

Weaknesses: 

- The results in larger parts do not sufficiently support the conclusions. 

- The experiments are not always described in sufficient detail (e.g. FRAP; DCV counts vs. neurite length) to fully understand their claims. 

- Not of sufficient advance in knowledge for this journal 

- The significance of differences in control experiments WT vs. KO) varies between experiments shown in different figures. 

- Axons and dendrites were not analyzed separately in Figures 1 and 2. 

- The colocalization study in Figure 1 would require super-resolution microscopy. 

To address the reviewers’ comments, we have provided a more detailed explanation of our analysis (p 19-20, line 521-542). In addition, we have repeated our colocalization experiments using STED microscopy, see Joint Public Review item (i).  

Reviewer #2 (Public Review): 

Summary: 

Hoogstraaten et al investigated the involvement of rabphilin-3A RPH3A in DCV fusion in neurons during calcium-triggered exocytosis at the synapse and during neurite elongation. They suggest that RPH3A acts as an inhibitory factor for LDV fusion and this is mediated partially via its interaction with SNAP25 and not Rab3A/Rab27. It is a very elegant study although several questions remain to be clarified. 

Strengths: 

The authors use state-of-the-art techniques like tracking NPY-PHluorin exocytosis and FRAP experiments to quantify these processes providing novel insight into LDCs exocytosis and the involvement of RPH3A. 

We thank the reviewer for their positive evaluation of our manuscript.

Weaknesses: 

At the current state of the manuscript, further supportive experiments are necessary to fully support the authors' conclusions. 

We thank the reviewer for their comments and suggestions. We have performed additional experiments to support our conclusions, see Joint Public Review items (i) – (iv)

Reviewer #3 (Public Review): 

Summary: 

The molecular mechanism of regulated exocytosis has been extensively studied in the context of synaptic transmission. However, in addition to neurotransmitters, neurons also secrete neuropeptides and neurotrophins, which are stored in dense core vesicles (DCVs). These factors play a crucial role in cell survival, growth, and shaping the excitability of neurons. The mechanism of release for DCVs is similar, but not identical, to that used for SV exocytosis. This results in slow kinetic and low release probabilities for DCV compared to SV exocytosis. There is a limited understanding of the molecular mechanisms that underlie these differences. By investigating the role of rabphilin-3A (RPH3A), Hoogstraaten et al. uncovered for the first time a protein that inhibits DCV exocytosis in neurons. 

Strengths: 

In the current work, Hoogstraaten et al. investigate the function of rabphilin-3A (RPH3A) in DVC exocytosis. This RAB3 effector protein has been shown to possess a Ca2+ binding site and an independent SNAP25 binding site. Using colocalization analysis of confocal imaging the authors show that in hippocampal neurons RPH3A is enriched at pre- and post-synaptic sites and associates specifically with immobile DCVs. Using site-specific RPH3A mutants they found that the synaptic location was due to its RAB3 interaction site. They further could show that RPH3A inhibits DCV exocytosis due to its interaction with SNAP25. They came to that conclusion by comparing NPY-pHluorin release in WT and RPH3A KO cells and by performing rescue experiments with RPH3A mutants. Finally, the authors showed that by inhibiting stimulated DCV release, RPH3A controlled the axon and dendrite length possibly through the reduced release of neurotrophins. Thereby, they pinpoint how the proper regulation of DCV exocytosis affects neuron physiology. 

We thank the reviewer for their positive evaluation of our manuscript.

Weaknesses: 

Data context 

One of the findings is that RPH3A accumulates at synapses and is mainly associated with immobile DCVs.

However, Farina et al. (2015) showed that 66% of all DCVs are secreted at synapses and that these DCVs are immobile prior to secretion. To provide additional context to the data, it would be valuable to determine if RPH3A KO specifically enhances secretion at synapses. Additionally, the authors propose that RPH3A decreases DCV exocytosis by sequestering SNAP25 availability. At first glance, this hypothesis appears suitable. However, due to RPH3A synaptic localization, it should also limit SV exocytosis, which it does not. In this context, the only explanation for RPH3A's specific inhibition of DCV exocytosis is that RPH3A is located at a synapse site remote from the active zone, thus protecting the pool of SNAP25 involved in SV exocytosis from binding to RPH3A. This hypothesis could be tested using super-resolution microscopy. 

We thank the reviewer for their suggestion. We have now performed super resolution microscopy, see Joint Public Review item (i). However, these new data do not necessarily explain the stronger effect of RP3A deficiency on DCV exocytosis, relative to SV exocytosis. We have added a short discussion on this topic to the revised manuscript, see Joint Public Review item (iv).

Technical weakness 

One technical weakness of this work consists in the proper counting of labeled DCVs. This is significant since most findings in this manuscript rely on this analysis. Since the data was acquired with epi-fluorescence or confocal microscopy, it doesn't provide the resolution to visualize individual DCVs when they are clumped. The authors use a proxy to count the number of DCVs by measuring the total fluorescence of individual large spots and dividing it by the fluorescence intensity of discrete spots assuming that these correspond to individual DCVs. This is an appropriate method but it heavily depends on the assumption that all DCVs are loaded with the same amount of NPY-pHluorin or chromogranin B (ChgB). Due to the importance of this analysis for this manuscript, I suggest that the authors show that the number of DCVs per µm2 is indeed affected by RPH3A KO using super-resolution techniques such as dSTORM, STED, SIM, or SRRF. 

The reviewer is correct that this is a crucial issue, that we have not addressed optimally until now. We have previously devoted a large part of a previous manuscript to this issue, but have not referred to this previous work clearly enough. We have now clarified this (p 7, line 187-190). In brief, we have previously quantified the ratio between fluorescent intensity of ChgB and NPY-pHluorin in confocal microscopy over the number of dSTORM puncta in sparse areas of WT mouse hippocampal neurons (Persoon et al., 2018). This quantification yielded a unitary fluorescence intensity per vesicle that was very stable of different neurons. Although there might be some underestimation of the total number of DCVs when using confocal microscopy, the study of Persoon et al. (2018) has demonstrated that these parameters correlate well and that the estimations are accurate. Considering that the rF/F0 is similar in RPH3A WT and KO neurons (now Figure S2I), meaning that the intensity of NPY-pHluorin of one fusion event is comparable, we can presume that this correlation also applies for the RPH3A KO neurons.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

Major points: 

(1) The authors perform an extensive analysis regarding the colocalization of RPH3A and DCVs (Figure 1 upper part). This analysis is hampered by the fact that the recorded data has in relation to vesicle size limited resolution (> 1 µm) to allow making strong claims here. In my view, super-resolution microscopy would be required for the co-localization studies shown in Figure 1. 

We fully agree and have now performed super-resolution microscopy, see Joint Public Review item (i)

(2) The FRAP experiments (Figure 1 lower part) cannot be sufficiently understood from what is presented. The methods say that both laser channels were activated during bleaching but NPY-pHluorin is not bleached in Fig.1E. Explanation of the bleaching is not very circumspect. In 1D, it is rather EGFP-RPH3A that is entering the bleached area than the NPY vesicles. These experiments require a more careful explanation of methodology, observed results, and their interpretation. Overall, the observed effects in the original kymograph traces require a better explanation. 

We acknowledge that NPY-pHluorin in Figure 1E (now Figure 2C) is not completely bleached. NPY-pHluorin appeared to be more difficult to bleach than NPY-mCherry. However, it is important to clarify that we merely bleached the neurites to remove the stationary puncta and facilitate our analysis of DCV/RPH3A dynamics. This bleaching step does not affect the interpretation of our results. We apologize that this was not clearly stated in the text and have made the necessary adjustments in legend, results- and methods section, (p 6-7, line 162-163; p 5, line 140-142 and p 19, line 508-513). Additionally, we apologize for the accidental switch of the kymographs for NPY-mCherry and EGFP-RPH3A in Figure 1D (now Figure 2B, C). We greatly appreciate identifying this error.  

(3) Figure 1: The authors need to mention whether axons, dendrites, or both were analyzed throughout the different panels and how they were identified. Is it possible that axons were wrapping around dendrites in their cultures (compare e.g. Shimojo et al., 2015)? Given the limited spatial resolution and because of this wrapping, interpretation of results could be affected. 

We completely agree with the reviewer’s assessment and conclusion. We are unable to distinguish axons from dendrites using this experimental design. We have made sure to specify in the text that our observation that RPH3A does not co-travel with DCVs is true for both dendrites and axons, (p 5, line 150).

(4) Figure 2: The exocytosis imaging is elegant and potentially very insightful. The effect in the RPH3A KOs is convincing. However, the authors determine the efficacy of exocytosis from NPY-pHluorin unquenching of DCVs only. This is only one of several possible parameters to read out the efficiency of exocytosis. Kinetics like e.g. delay between stimulation and start of exocytosis events or release time course of NPY after DCV fusion were not determined. Such analysis could give a better insight into what process before or after the fusion of DCVs is affected by RPH3A ko. 

We fully agree with the reviewer. We have now included the duration of the fusion events (new Figure S2D-F). The start time of the fusion events are shown in the cumulative plots in now Figure 3F and I. The kinetics are normal in the RPH3A KO neurons.

Moreover, it needs to be mentioned whether 2C and D are from WT or ko cultures. It would be best to show representative examples from both genotypes. 

We have now adjusted this in the new figure (now Figure 3C, D).

The number of fusion events is much increased but the release fraction is not significantly changed. While this is consistent with results in Figure 4C it is at variance with 4F. This raises questions about the reliability of the effects in RPH3A KOs. 

The release fraction indicates the number of fusion events normalized to the total DCV pool. In Figure 4D, we observed a slightly bigger pool size, which explains the lack of significance when analyzing the released fraction. In Figure 4G, however, DCV pool sizes are similar between KO and WT, leading to a statistically significant effect on release fraction in KO neurons. Furthermore, Figures 4B and E distinctly show a substantial increase in fusion events in RPH3A KO neurons. This variability in pool size observed could potentially be attributed to variation in culture or inherent biological variability.

Given the increased number of ChgrB-positive DCVs in RPH3A KOs (shown in Figure 2) and that only the cumulative number of exocytosis events were analysed, how can the authors exclude that the RPH3A ko only affects vesicle number but not release, if the % change in released vesicles is not different to WT? Kinetics of release don't seem to be affected. Importantly, what was the density of NPY-pHluorin vesicles in WT vs. ko? 

In Figure 2 (now Figure 5) we show that RPH3A KO neurons are larger and contain more endogenous ChgB+ puncta than WT neurons. This increased number of ChgrB+ puncta scales with their size as puncta density is not increased. A previous study (Persoon et al., 2018) has demonstrated a strong correlation between DCV number and neuron size. Our data show that RPH3A deficiency increased DCV exocytosis, but the released fraction of vesicles depends on the total number of DCVs, which we determined during live recording by dequenching NPY-pHluorin using NH4+. Considering that this is an overexpression of a heterologous DCV-fusion reporter, and not endogenous staining of DCVs, as in the case of ChgrB+ puncta, some variability is not unexpected.

Also in these experiments, the question arises of whether the authors analyse axons, dendrites, or both throughout the different panels and how they were identified. 

In our experimental design we record all fusion events per cell, including both axons and dendrites but excluding the cell soma. We have clarified this in the method section, (p 19, line 508 and p 19, line 521-522).

(5) Figure 3: in D the authors show that ChgrB-pos. DCV density is slightly increased in KOs. How does this relate to the density of NPY-pHluorin DCVS in Figure 2? 

We do not observe a difference in NPY-pHluorin density (see Author response image 1). However, it is important to note that we relied on tracing neurites in live recording images to determine the neuronal size. In contrast, the ChgB density was based on dendritic length using MAP2 (post-hoc) staining was limited. In addition, Chgr+ puncta represent an endogenous DCV staining, NPY-pHluorin quantification is based on overexpression of a heterologous DCV-fusion reporter. These two factors likely contribute some variability.

Author response image 1.

The authors show a non-significant trend of TeNT coexpression to reduce axon and dendrite lengths in RPH3A KOs. While this trend is visible, I think one cannot draw conclusions from that when not reaching significance. The argument of the authors that the increased axon and dendrite lengths are created by growth factor peptide release from DCV during culture time is interesting. However, the fact that TeNT expression shows a trend toward reducing this effect on axons/dendrites is not sufficient to prove the release of such growth factors. 

We agree. We have toned down this speculation in the revised manuscript, (p 15-16, line 395-400).

Lastly, the authors don't provide insight into the mechanisms, of how RPH3A ko increases the number of DCVs per µm dendritic length in the neurons. In my view, there are too many loose ends in this story of how RPH3A ko first increases spontaneous release of DCVs and then enhances neurite growth and DCV density. Did the authors e.g. measure the spontaneous release of DCVs in their cultures? 

We measured spontaneous release of DCVs during the 30s baseline recording prior to stimulation. We observed no difference in spontaneous release between WT and KO neurons (now Figure S2H). However, baseline recording lasted only 30 seconds. It is possible that this was too short to detect subtle effects.

Other points: 

(1) Figure 4: the logic of this experiment is elegant. It shows that the increased number of DCV fusion events in RPH3A KOs is related to the interaction of RPH3A with RAB3A but not with SNAP25. As mentioned above, it is irritating that the reduction of fusion events in KOs and on the release fraction is sometimes reaching significance, but sometimes it does not. Likewise, the absence of significant effects on DCV numbers is not consistent with the results shown in Figures 3C and D. 

DCV numbers in Figure 3 (now Figure 5) are determined by staining for endogenous ChgB, whereas in Figure 4D and G DCV numbers are determined by overexpressing NPY-pHluorin and counting the dequenched puncta following a NH4+ puff.

(2) Figure 1B: truncation of the y-axis needs to be clearly indicated. 

We have replaced this figure with new Figure 1 and have indicated truncations of the y-axis when needed (new Figure 1E). 

(3) Page 10: "Given that neuropeptides are key modulators of adult neurogenesis (Mu et al., 2010), and that RPH3A depletion leads to increased DCV exocytosis, it is coherent that we observed longer neurites in RPH3A KO neurons." I cannot follow the argument of the authors here: what has neurogenesis to do with neurite length? 

We apologize for the confusion. We have clarified this in the revised text, (p 16, line 398-400).

Minor point: 

There are some typos in the manuscript. e.g., page 8: "... may partially dependent on regulated secretion...); page 6: "...to dequence all...". 

Thank you for noticing, we have corrected the typos.

Reviewer #2 (Recommendations For The Authors): 

(1) Supplementary Figure S1A, in my opinion, should be in Figure 1A as it illustrates all the constructs used in this study and helps the reader to follow it up. 

We thank the reviewer for their suggestion. However, we feel that with the adjustments we have made in Figure 1, the illustrations of the constructs fit better in Figure S1, since new Figure 1 shows the localization of endogenous RPH3A and not that of the constructs.  

(2) One of the conclusions of the manuscript is the synaptic localization of the different RPH3A mutants. The threshold for defining synaptic localization is not clear either from the images nor from the analysis: for example, the Menders coefficient for VGut1-Syn1 which is used as a positive control, ranges from 0.65-0.95 and that of RPH3A and Syn1 ranges from 0.5-0.95. These values should be compared to all mutants and the conclusions should be based on such comparison. 

We agree. We have now repeated our initial co-localization experiment with all the RPH3A mutants (now Figure S1D-F).  

(3) Strengthening this figure with STED/SIM/dSTORM microscopy can verify and add a new understanding of the subtle changes of RPH3A localization. 

We fully agree and have now added super-resolution microscopy data, see Joint Public Review item (i).

(4) As RAB3A/RAB27A (ΔRAB3A/RAB27A) loses the punctate distribution, please clarify how can it function at the synapse and not act as a KO. Is it sorted to the synapse and how does it is sorted to the synapse? 

We used lentiviral delivery to introduce our constructs, resulting in the overexpression of ΔRAB3A/RAB27A mutant RPH3A. This overexpression likely compensates for the loss of the punctate distribution of RPH3A, thereby maintaining its limiting effect on DCV exocytosis. It is plausible that under physiological conditions, the mislocalization of RPH3A would lead to increased exocytosis, similar to what we observed in the KO. 

(5) Is RPH3A expressed in both excitatory and inhibitory neurons? 

We agree this is an important question. Single cell RNA-seq already suggests the protein is expressed in both, but we nevertheless decided to test expression of RPH3A protein in excitatory and inhibitory neurons, using immunocytochemistry with VGAT and VGLUT as markers in hippocampal and striatal WT neurons. We found that RPH3A is expressed in both VGLUT+ hippocampal neurons and VGAT+ striatal neurons (new Figure S1A, B).  

(6) The differential use of ChgB and NPY as markers for DCVs should be clarified and compared as these are used at different stages of the manuscript. 

We have previously addressed the comparison between ChgB and NPY-pHluorin (Persoon et al., 2018). We made sure to indicate this more clearly throughout the manuscript to clarify the use of the two markers. 

(7) FRAP experiments- A graph describing NPY recovery should be added as a reference to 2H and discussed. 

We agree. We have made the necessary adjustments (new Figure 2G).

(8) Figure 2E shows some degree of "facilitation" between the 2 8x50 pulses RPH3A KO neurons. Can the author comment on that? What was the reason for using this dual stimulation protocol? 

There is indeed some facilitation between the two 8 x 50 pulses in KO neurons and to a lesser extent also in the WT neurons, which we have observed before in WT neurons (Baginska et al., 2023). Baginska et al. (2023) showed recently that different stimulation protocols can influence certain fusion dynamics, like the ratio of persistent and transient events and event duration. We used two different stimulation protocols to thoroughly investigate the effect of RPH3A on exocytosis, and assess the robustness of our findings regarding the number of fusion events. Fusion kinetics was similar in WT an KO neurons for both stimulation protocols (new Figure 2D-F).

(9) Figure 3 quantifies dendrites length and then moves to quantify both axon and dendrites for the Tetanus toxin experiment. What are the effects of KO on axon length? In the main figures, it is not mentioned but in S3 it seems not to be affected. How does it reconcile with the main conclusion on neurite length? 

Figure 3H (now Figure 6C) shows the effect of the KO on axon length: the axon length is increased in RPH3A KO neurons compared to WT, similar to dendrite length. Re-expressing RPH3A in KO neurons rescues axonal length to WT levels. In Figure S3, we observe a similar trend as in main Figure 3 (new Figure 6), yet this effect did not reach significance. Based on this, we concluded that neurite length is increased upon RPH3A depletion.

(10) For lay readers, please explain the total pool and how you measured it. However, see the next comment. 

We agree. We have now defined this better in the revised manuscript, (p 19, line 524-527 and p 20, line 535-539).

(11) It is a bit hard to understand if the total number of DCV was increased in the KO and if the pool size was increased and in which figure it is quantified. Some sentences like: "A trend towards a larger intracellular DCV pool in KO compared to WT neurons was observed" do not fit with "No difference in DCV pool size was observed between WT and KO neurons (Figure S2D)" or with "During stronger stimulation (16 bursts of 50 APs at 50 Hz), the total fusion and released fraction of DCVs were increased in KO neurons compared to WT". They are not directly supported, or not related to specific figures. Please indicate if the total DCVs pool, as measured by NH4, was increased and based on that, the fraction of the releasable DCVs following the long stimulation. From Figure 2H, the conclusion is an increase in fusion events. In general, NH4 is not quantified clearly- is it quantified in Figure S2C? And if it is a trend, how can it become significant in Figure 3? 

We agree there has been some inconsistency in the way we describe the data on the total number of DCVs. We have addressed this in the revised text to ensure better clarity. The total DCV pool measured by NPY-pHluorin was not significantly increased in KO neurons, we see a trend towards a bigger DCV pool in the 2x8 50 Hz stimulation paradigm (now Figure S2C), therefore the released fraction of vesicles is not increased in Figure 1G (now Figure 3G). The number of DCV in Figure 3 (now Figure 5) is based on endogenous ChgB staining and not overexpression like the DCV pool measured by NPY-pHluorin. In Figure 3 (now Figure 5) we show that RPH3A KO neurons have slightly more ChgB+ puncta compared to WT.

(12) In Figure 3, the quantification is not clear, discrete puncta are not visible but rather a smear of chromogranin staining. How was it quantified? An independent method to count DCV number, size, and distribution like EM is necessary to support and add further understanding. 

We acknowledge that discrete ChgB puncta are not completely visible in Figure 3 (now Figure 5). Besides the inherent limitation in resolution with confocal imaging, we believe that this is due to ChgB accumulation in the KO neurons, as shown in now Figure 5D. Nonetheless, to address this concern of the reviewer, we have selected other images that represent our dataset (now Figure 5A). Furthermore, the number of ChgB+ DCVs was calculated using SynD software (Schmitz et al., 2011; van de Bospoort et al., 2012) (see previous reply). EM would offer valuable independent confirmation on the total DCV number, size and distribution. However, with the current method we already know that vesicle numbers are at least similar. Does that justify the (major) investment in a quantitative EM study? Moreover, this issue does not affect the central message of the current study.

(13) Can the author discuss if the source of DCVs that are released at the synapse is similar or different from the source of DCVs fused while neurites elongate? 

With our current experimental design, we are unable to draw conclusions regarding this aspect. We are not sure how experiments to identify this source (probably the Golgi?) would be crucial to sustain the central message of our study.

(14) An interesting and related question: what are the expression levels of RPH3A during development and neuronal growth during the nervous system development? 

While we have not specifically examined the expression levels of RPH3A over development, public databases show that RPH3A expression increases over time in mice, consistent with other synaptic proteins (Blake et al., 2021; Baldarelli et al., 2021; Krupke et al., 2017). We have now added this to the revised manuscript (p 2, line 55-56).

(15) The conclusion from Figure 4 about the contribution of SNAP25 interaction to RPH3A inhibitory effect is not convincing. The data are scattered and in many neurons, high levels of fusion events were detected. Further or independent experiments are needed to support this conclusion. For example, is the interaction with SNAP25 important for its inhibitory activity in other DCV-releasing systems like adrenal medulla chromaffin cells? 

We agree that further studies in other DCV-releasing systems like chromaffin cells would provide valuable insight into the role of SNAP25 interaction in RPH3A’s inhibitory effect on exocytosis. However, we believe that starting new series of experiments in another model system is outside of the scope of our current study.

(16) Furthermore, the number of DCVs in the KO is similar in this experiment, raising some more questions about the quantification of the number of vesicles, that differ, in different sections of the manuscript (points # 10,11). 

The total DCV pool in the fusion experiments is measured by overexpression NPY-pHluorin, this cannot be directly compared to the number of endogenous ChgB+ DCV in Figure 3 (now Figure 5), see also item (11)

(17) The statement - "RPH3A is the only negative regulator of DCV" is not completely accurate as other DCV inhibitors like tomosyn were described before. 

We agree. By this statement, we intend to convey that RPH3A is the only negative regulator of DCVs without substantial impact on synaptic vesicle exocytosis, unlike Tomosyns. We have clarified this in the revised text, (p 15, line 366-367).

(18) The support for the effect of KO on the "clustering of DCVs" is not convincing. 

The intensity of endogenous ChgB puncta was decreased in RPH3A KO neurons (now Figure 5E). However, the peak intensity induced by single NPY-pHluorin labeled DCV fusion events (quanta) was unchanged (now Figure S2I). This indicates that the decrease in ChgB puncta intensity must be due to a reduced number of DCVs (quanta) in this specific location. We have interpreted that as ‘clustering’, or maybe ‘accumulation’. However, we only put forward this possibility. We are now more careful in our speculations within the text, (p 11 line 271-277).

(19) Final sentence: "where RPH3A binds available SNAP25, consequently restricting the assembly of SNARE complexes" should be either demonstrated or rephrased as no effect of trans or general SNARE complex formation is shown. 

We agree. We have made the necessary adjustments in the text, (p 15, line 387-389).   

(20) A scheme summarizing RPH3A's interaction with synaptic proteins and its effects on DCVs release, maybe even versus its effects on SVs release, should be considered as a figure or graphic abstract. 

We have included a working model in Figure 7.  

(21) Figure 4 logically should come after Figure 2 to summarize the fusion-related chapter before moving to neurite elongation. 

We have placed Figure 4 after Figure 2 (now Figure 3).

Reviewer #3 (Recommendations For The Authors): 

One important finding of this study is that RPH3A downregulates neuron size, possibly by inhibiting DCV release. Additionally, the authors demonstrated that the number of DCVs is directly proportional to the number of DCVs per µm2, and that RPH3A KO reduces DCV clustering. This conclusion was drawn by comparing ChgB with NPY-pHluorin loading of the DCVs. However, this comparison is not valid as ChgB is expressed at an endogenous level and NPY-pHluorin is over-expressed. In the KO situation where DCV exocytosis is enhanced, the available endogenous ChgB may be depleted faster than the overexpressed NPY-pHluorin. Hoogstraaten et al. should either perform a study in which ChgB is overexpressed to test whether the difference in DCV remains or at least provides an alternative interpretation of their data. 

We thank the reviewer for this comment. The reviewer challenges one or two conclusions in our original manuscript (It is not entirely clear to what exactly “This conclusion” refers): (a) “the number of DCVs is directly proportional to the number of DCVs per µm2”, and (b) “that RPH3A KO reduces DCV clustering”. The reviewer probably means that the number of DCVs per neuron is directly proportional to size of the neuron (a) and states this (these) conclusion(s) are “not valid as ChgB is expressed at an endogenous level and NPY-pHluorin is over-expressed” because “endogenous ChgB may be depleted faster than the overexpressed NPY-pHluorin”. We have three arguments to conclude that faster depletion of ChgB cannot affect these two conclusions: (1) DCVs bud off from the Golgi with newly synthesized (fresh) ChgB. Whether or not a larger fraction of DCVs is released does not influence this initial ChgB loading into DCVs (together with over-expressed NPY-pHluorin); (2) in hippocampal neurons merely 1-6% of the total DCV pool undergoes exocytosis (the current study and also extensively demonstrated in Persoon et al., 2018). RPH3A KO neurons release few percent more of the total DCV pool. Hence, “depletion of ChgB” is only marginally different between experimental groups; and (c) the proposed experiment overexpressing ChgB will not help scrutinize our current conclusions as ChgB overexpression is known to affect DCV biogenesis and the total DCV pool, most likely much more than a few percent more release by RPH3A deficiency.

Hoogstraaten et al. conducted a thorough analysis of the impact of RPH3A KO and its rescue using various mutants on dendrite and axon length (see Supplementary Figure 3). However, they did not test the effect of the ΔSNAP25 mutant. The authors demonstrated that this mutant is the least efficient in rescuing DCV exocytosis (Figure 4E). Hence the neurons expressing this mutant should have a similar size to the KO neurons. This finding would strongly support the argument that DCV exocytosis regulates neuron size. Otherwise, it would suggest that RPH3A may have a function in regulating exocytosis at the growth cones that is independent of SNAP25. Since the authors most probably have the data that allows them to measure the neuron size (acquired for Supplementary Figure 2), I suggest that they perform the required analysis. 

We agree this is important and performed new experiments to determine the dendrite length of RPH3A WT, KO and KO neurons expressing the ΔSNAP25 mutant. We observed that the dendrite length of RPH3A KO neurons expressing ΔSNAP25 mutant is indeed similar to KO neurons (new Figure S3C). Although not significant we observe a clear trend towards bigger neurons compared to WT.  This strengthens our conclusion that increased DCV exocytosis contributes to the observed increased neuronal size.

The authors displayed the result of DCV exocytosis in two ways. One is by showing the number of exocytosis events the other is to display the proportion of DCVs that were secreted. They do the latter by dividing the secreted DCV by the total number of DCVs. These are visualized at the end of the experiment through NH4+ application. While this method works well for synaptic secretion as the marker of SV is localized to the SV membrane and remains at the synapse upon SV exocytosis, it cannot be applied in the same manner when it is the DCV content that is labeled as it is released upon secretion. Hence, the total pool of vesicles should be the number of DCV counted upon NH4+ application in addition to those that are secreted. This way of analyzing the total pool of DCV might also explain the difference in this pool size between KO neurons stimulated two times with 8 stimuli instead of one time with 16 stimuli (Sup Fig 2 C and D). This is an important point as it affects the conclusions drawn from Figure 2. 

We thank the reviewed for this comment. We agree, and we have made the necessary adjustments throughout the manuscript. 

The kymogram of DCV exocytic events displayed in Figure 2D shows a majority of persistent (>20s long) events. This is strange as NPY-pHluori corresponds to the released cargo. Previous work using the same labeling and stimulation technique showed that content release occurs in less than 10s (Baginska et al. 2023). The authors should comment on that difference. 

In Baginska et al. (2023), the authors distinguished between persistent and transient events. The transient events are shorter than 10s for the 2x8 and 16x stimulation paradigms, whereas persistent events can last for more than 10s. In our study we did not make this distinction. However, in response to this reviewer, we have now quantified the fusion duration per cell. These new data show that the mean duration is similar between genotypes for both stimulation paradigms. We have added these new data (new Figure S2D-F).

In Figures 1D and E, some puncta in the kymogram appeared to persist after bleaching. This raises questions about the effectiveness of the bleaching procedure for the FRAP experiment. 

The reviewer is correct that NPY-pHluorin in Figure 1E (now Figure 2C) is not fully bleached. NPY-pHluorin was more resistant to bleaching than NPY-mCherry. However, we merely bleached the neurites to facilitate our analysis by reducing fluorescence of the stationary puncta without causing phototoxicity. Some remaining fluorescence after bleaching does not affect our conclusions in any way.

In the discussion, the paragraph titled "RPH3A does not travel with DCVs in hippocampal neurons" is quite confusing and would benefit from a streamlined explanation. 

We thank the reviewed for this comment. We made the necessary adjustments to make this paragraph clearer, (p 14, line 339-351).

First paragraph of page 8 "TeNT expression in KO neurons restored neurite length to WT levels. When compared to KO neurons without TeNT, neurite length was not significantly decreased but displayed a trend towards WT levels (Figure 3G, H)." These two sentences are confusing as they seem contradictory. 

We agree that this conclusion has been too strong. However, we do not see a contradiction. The significant effect between KO and control neurons on both axon and dendrite length is lost upon TeNT expression (which forms the basis for our conclusions cited by the reviewer, now Figure 6B, C). While the difference between KO neurons +/- TeNT did not reach statistical significance. The (strong) trend is clearly in the same direction. We have refined our original conclusion in the revised manuscript, (p 12, line 304-306).

The data availability statement is missing. 

We have added the data availability statement, (p 21, line 571-572).

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

O'Leary and colleagues present data identifying several procedures that alter discrimination between novel and familiar objects, including time, environmental enrichment, Rac-1, context reexposure, and brief reminders of the familiar object. This is complimented with an engram approach to quantify cells that are active during learning to examine how their activation is impacted following each of the above procedures at test. With this behavioral data, authors apply a modeling approach to understand the factors that contribute to good and poor object memory recall.

We thank the Reviewer for summarizing the scope and depth of our manuscript, and indeed for recognizing our efforts. We engage below with the Reviewer’s specific criticisms.

Strengths:

Authors systematically test several factors that contribute to poor discrimination between novel and familiar objects. These results are extremely interesting and outline essential boundaries of incidental, nonaversive memory.<br /> These results are further supported by engram-focused approaches to examine engram cells that are reactivated in states with poor and good object recognition recall.

We thank the Reviewer for these positive comments.

Weaknesses:

For the environmental enrichment, authors seem to suggest objects in the homecage are similar to (or reminiscent of) the familiar object. Thus, the effect of improved memory may not be related to enrichment per se as much as it may be related to the preservation of an object's memory through multiple retrievals, not the enriching experiences of the environment itself. This would be consistent with the brief retrieval figure. Authors should include a more thorough discussion of this.

This is one of the main issues highlighted by the Editor and the Reviewers. We agree that these results dove-tail with the reminder experiments. We have included additional discussion, see line 510-546.

Authors should justify the marginally increased number of engram cells in the non-enrichment group that did not show object discrimination at test, especially relative to other figures. More specific cell counting criteria may be helpful for this. For example, was the DG region counted for engram and cfos cells or only a subsection?

There was a marginal, but non-significant increase in the number of labelled cells within the standard housed mice in Figure 3f. The cell counting criteria was the same across experimental groups and conditions, where the entire dorsal and ventral blade of the dorsal DG was counted for each animal. This non-statistically significant variance may be due to surgical and viral spread difference between mice. We have clarified this in the manuscript, see line 229-232.

It is unclear why the authors chose a reactivation time point of 1hr prior to testing. While this may be outside of the effective time window for pharmacological interference with reconsolidation for most compounds, it is not necessarily outside of the structural and functional neuronal changes accompanied by reconsolidation-related manipulations.

A control experiment was performed to demonstrated that a brief reminder exposure of 5 mins on its own was insufficient to induce new learning that formed a lasting memory (Supplementary Figure S4a). Mice given only a brief acquisition period of 5 mins, exhibited no preference for the novel object when tested 1 hour after training, suggesting the absence of a lasting object memory (Supplementary Figure S4b & c). We therefore used the 1-hour time point for the brief reminder experiment in Figure 4a. We have clarified this within the manuscript and supplementary data see line 258-264.

Figure 5: Levels of exploration at test are inconsistent between manipulations. This is problematic, as context-only reexposures seem to increase exploration for objects overall in a manner that I'm unsure resembles 'forgetting'. Instead, cross-group comparisons would likely reveal increased exploration time for familiar and novel objects. While I understand 'forgetting' may be accompanied by greater exploration towards objects, this is inconsistent across and within the same figure. Further, this effect is within the period of time that rodents should show intact recognition. Instead, context-only exposures may form a competing (empty context) memory for the familiar object in that particular context.

The Reviewer raises an important question, and we agree with the Reviewer that there should be caution and qualification around interpreting these results as “forgetting”. Indeed, for the context-only rexposures, cross-group comparisons show increased exploration time for familiar and novel objects. As the mice exhibit relatively high exploration of both the novel and familiar objects. An alternative explanation would be that the mice have not truly forgotten the familiar object, but rather as the mouse has not seen the familiar object in the last 6 context only sessions, its reappearance makes it somewhat novel again. Therefore, this change in the object’s reappearance triggers the animal’s curiosity, and in turn drives exploration by the animal. In addition, the context-only exposures may form a competing memory for the familiar object in that particular context. We have highlighted this in the results and also included greater discussion. See lines 306-315.

I am concerned at the interpretation that a memory is 'forgotten' across figures, especially considering the brief reminder experiments. Typically, if a reminder session can trigger the original memory or there is rapid reacquisition, then this implies there is some savings for the original content of the memory. For instance, multiple context retrievals in the absence of an object reminder may be more consistent with procedures that create a distinct memory and subsequently recruit a distinct engram.

These findings raise an important question regarding the interpretation of ‘forgetting’. If a reminder trial or experience can trigger the original memory, or there is rapid reacquisition, then this would suggest there is a degree of savings for the original memory content (85, 86). Previous work has emphasized retrieval deficits as a key characteristic of memory impairment, supporting the idea that memory recall or accessibility may be driven by learning feedback from the environment (7, 8, 14–18). Within our behavioral paradigm, a lack of memory expression would still constitute forgetting due to the loss of learned behavioral response in the presence of natural retrieval cues. The changes in memory expression may therefore underlie the adaptive nature of forgetting. This is consistent with the idea that the engram is intact and available, but not accessible. Here we studied natural forgetting, and our data showing memory retrieval following optogenetic reactivation demonstrates that the original engram persists at a cellular level, otherwise activation of those cells would no longer trigger memory recall. We also agree with the reviewer that multiple context retrievals may indeed lead to the formation of a second distinct engram that competes with the original. Recent work suggests that retroactive interference emerges from the interplay of multiple engrams competing for accessibility (18). We have added clarification and included extra discission of this interpretation. See lines 589-598.

Authors state that spine density decreases over time. While that may be generally true, there is no evidence that mature mushroom spines are altered or that this is consistent across figures. Additionally, it's unclear if spine volume is consistently reduced in reactivated and non-reactivated engram cells across groups. This would provide evidence that there is a functionally distinct aspect of engram cells that is altered consistently in procedures resulting in poor recognition memory (e.g. increased spine density relative to spine density of non-reactivated engram cells and non-engram cells)

We thank the Reviewer for their helpful comments on explaining our engram dendritic spine data. We agree with the Reviewer that an analysis of the changes in spine type, as well as the difference between engram and non-engram spines as well and reactivation and non-reactivated engram spines would be interesting and may help to further illuminate the morphological changes of forgetting and memory retrieval. Indeed, future analysis could determine if spine density is reduced in reactivated and non-reactivated engram cells or indeed across engram non-engram cells within different learning conditions. This avenue of investigation could determine if there is a functionally distinct aspect of engram cells that are altered following forgetting (67). However, such analysis is beyond the scope of this study. We have highlighted this limitation and included its discussion. See lines 493-499.

Authors should discuss how the enrichment-neurogenesis results here are compatible with other neurogenesis work that supports forgetting.

We validated the effectiveness of the enrichment paradigm to enhance neural plasticity by measuring adult hippocampal neurogenesis. The hippocampus has been identified as one of the only regions where postnatal neurogenesis continues throughout life (75). Levels of adult hippocampal neurogenesis do not remain constant throughout life and can be altered by experience (41–43, 57).  In addition, adult born neurons have been shown to contribute to the process of forgetting (74, 78, 79). Although the contribution of adult born neurons to cognition and the memory engram is not fully understood (80, 81). Mishra et al, showed that immature neurons were actively recruited into the engram following a hippocampal-dependent task (67). Moreover, increasing the level of neurogenesis rescued memory deficits by restoring engram activity (67). Augmenting neurogenesis further rescued the deficits in spine density in both immature and mature engram neurons in a mouse model of Alzheimer’s disease (67). Whether neurogenesis alters spine density on differentially for reactivated or non-reactivation engrams cells remains to be investigated (67, 68). This avenue of research would help to illuminate the morphological changes following forgetting and provide evidence if there is a functionally distinct aspect of engram cells that is altered in forgetting (67, 68). Our engram labelling strategy which utilized c-fos-tTA transgenic mice combined with an AAV9-TRE-ChR2-eYFP virus does not necessarily label sufficient immature neurons. Future work could utilize a different engram preparation, such as a genetic labelling strategy (TRAP2) or using a different immediate early gene promoter such as Arc to investigate the contribution of new-born neurons to the engram ensemble. We have added additional discussion of how our work fits with previous literature investigating neurogenesis and forgetting. See lines 547-565.

Reviewer #2 (Public Review):

Summary:

The manuscript examines an important question about how an inaccessible, natural forgotten memory can be retrieved through engram ensemble reactivation. It uses a variety of strategies including optogenetics, behavioral and pharmacological interventions to modulate engram accessibility. The data characterize the time course of natural forgetting using an object recognition task, in which animals can retrieve 1 day and 1 week after learning, but not 2 weeks later. Forgetting is correlated with lower levels of cell reactivation (c-fos expression during learning compared to retrieval) and reduction in spine density and volume in the engram cells. Artificial activation of the original engram was sufficient to induce recall of the forgotten object memory while artificial inhibition of the engram cells precluded memory retrieval. Mice housed in an enriched environment had a slower rate of forgetting, and a brief reminder before the retrieval session promoted retrieval of a forgotten memory. Repeated reintroduction to the training context in the absence of objects accelerated forgetting. Additionally, activation of Rac1-mediated plasticity mechanisms enhanced forgetting, while its inhibition prolonged memory retrieval. The authors also reproduce the behavioral findings using a computational model inspired by Rescorla-Wagner model. In essence, the model proposes that forgetting is a form of adaptive learning that can be updated based on prediction error rules in which engram relevancy is altered in response to environmental feedback.

We thank the Reviewer for summarizing the scope and depth of our manuscript, and for recognizing our efforts. We engage below the Reviewer’s specific criticisms of our interpretations.

Strengths:

(1) The data presented in the current paper are consistent with the authors claim that seemingly forgotten engrams sometimes remain accessible. This suggests that retrieval deficits can lead to memory impairments rather than a loss of the original engram (at least in some cases).

We thank the Reviewer for their positive summary.

(2) The experimental procedures and statistics are appropriate, and the behavioral effects appear to be very robust. Several key effects are replicated multiple times in the manuscript.

We thank the Reviewer for their positive comments.

Weaknesses:

(1) My major issue with the paper is the forgetting model proposed in Figure 7. Prior work has shown that neutral stimuli become associated in a manner similar to conditioned and unconditioned stimuli. As a result, the Rescorla-Wagner model can be used to describe this learning (Todd & Homes, 2022). In the current experiments, the neutral context will become associated with the unpredicted objects during training (due to a positive prediction error). Consequently, the context will activate a memory for the objects during the test, which should facilitate performance. Conversely, any manipulation that degrades the association between the context and object should disrupt performance. An example of this can be found in Figure 5A. Exposing the mice to the context in the absence of the objects should violate their expectations and create a negative prediction error. According to the Rescorla-Wagner model, this error will create an inhibitory association between the context and the objects, which should make it harder for the former to activate a memory of the latter (Rescorla & Wagner, 1972). As a result, performance should be impaired, and this is what the authors find. However, if the cells encoding the context and objects were inhibited during the context-alone sessions (Figure 5D) then no prediction error should occur, and inhibitory associations would not be formed. As a result, performance should be intact, which is what the authors observe.

What about forgetting of the objects that occurs over time? Bouton and others have demonstrated that retrieval failure is often due to contextual changes that occur with the passage of time (Bouton, 1993; Rosas & Bouton, 1997, Bouton, Nelson & Rosas, 1999). That is, both internal (e.g. state of the animal) and external (e.g. testing room, chambers, experimenter) contextual cues change over time. This shift makes it difficult for the context to activate memories with which it was once associated (in the current paper, objects). To overcome this deficit, one can simply re-expose animals to the original context, which facilitates memory retrieval (Bouton, 1993). In Figure 2D, the authors do something similar. They activate the engram cells encoding the original context and objects, which enhances retrieval.

Therefore, the forgetting effects presented in the current paper can be explained by changes in the context and the associations it has formed with the objects (excitatory or inhibitory). The results are perfectly predicted by the Rescorla-Wagner model and the context-change findings of Bouton and others. As a result, the authors do not need to propose the existence of a new "forgetting" variable that is driven by negative prediction errors. This does not add anything novel to the paper as it is not necessary to explain the data (Figures 7 and 8).

We thank the reviewer for clearly explaining their concern about our model. We are very sorry that we did not sufficiently explain that our model is, in fact, based on the classic Rescorla-Wagner model. The key equation of the model that updates “engram strength”  is equivalent to the canonical Rescorla-Wagner model that is commonly used in research on reinforcement learning and decision-making (105). One potential minor difference is that we crucially assume different learning rates for positive and negative prediction errors. However, this variant of the Rescorla-Wagner model is common in the computational literature and is generally not regarded as a qualitatively different kind of model. In fact, it allows us to capture that establishing an object-context association (after a positive prediction error) is faster than the forgetting process (through negative errors).

The other equations that are explained in detail in the Methods are necessary to simulate exploration behavior and render the model suitable for model fitting. Concerning exploration behavior, we use the softmax function, which is commonly used in combination with the Rescorla-Wager model, in order to translate the learned quantity (in our case, engram strength) into behavior (here exploration). The other equations are necessary to fit the model to the data (learning rate α and behavioral variability in exploration behavior).

Therefore, we fully agree with the reviewer that the Rescorla-Wagner can explain our empirical results, in particular by assuming that the different manipulations affect the strength of object-context associations, which, in turn, governs forgetting as behaviorally observed. 

In our previous version of the manuscript, we only referred to the Rescorla-Wagner model directly in the Methods. But to make this important point clearer, we now refer to the origin of the model multiple times in the Results section as well. See lines 81, 386-393.

We also agree with the reviewer that the learning/forgetting process can be described in terms of changes in object-context associations (e.g., inhibitory associations after a negative prediction error). Therefore, we now explicitly refer to the relationship between updated object-context associations and forgetting and highlight that we believe that stronger associations signal higher engram “relevancy”. See lines 386-393.

We have extended Figure 7 (new panels a and b), where we illustrate the idea that (a) object-context associations govern forgetting and (b) show the key Rescorla-Wagner equation, including a simple explanation of the main terms (engram strength, prediction error, and learning rate). Finally, we have also extended our discussion of the model, where we now directly state that the Rescorla-Wagner model captures the key results of our experiments. See lines 573-580.

In order to further support a link between our empirical data and computational modeling, we also added extra experiments that showed the modulation of engram cells within the dentate gyrus can regulate these object-context associations. See Supplementary Figure 12a-f and lines 401-404.

To summarize our reply, we agree with the reviewer’s comment and hope that we have clarified the direct relationship to the Rescorla-Wagner model.

(2) I also have an issue with the conclusions drawn from the enriched environment experiment (Figure 3). The authors hypothesize that this manipulation alleviates forgetting because "Experiencing extra toys and objects during environmental enrichment that are reminiscent of the previously learned familiar object might help maintain or nudge mice to infer a higher engram relevancy that is more robust against forgetting.". This statement is completely speculative. A much simpler explanation (based on the existing literature) is that enrichment enhances synaptic plasticity, spine growth, etc., which in turn reduces forgetting. If the authors want to make their claim, then they need to test it experimentally. For example, the enriched environment could be filled with objects that are similar or dissimilar to those used in the memory experiments. If their hypothesis is correct, only the similar condition should prevent forgetting.

We thank the Reviewer for this alternative perspective on our findings. First of all, we agree that this statement is speculative. The effects of enrichment on neural plasticity are well established and it likely contributes to the enhanced memory recall. It is important to emphasize that this process of updating is not necessarily separate from enrichment-induced plasticity at an implementational level, but part of the learning experience within an environment containing multiple objects. The enrichment or, more generally, experience, may therefore enhance memory through the modification of activity of specific engram ensembles. The idea of enrichment facilitating memory updating is consistent with the results obtained by the reminder experiments and further supported by our analysis with the Rescorla-Wagner computational model, where experience updates the accessibility of existing memories, possibly through reactivation of the original engram ensemble.

We would like to further clarify that our explanation concerns the algorithmic level, in contrast to the neural level. Based on the computational analyses using the Rescorla-Wagner model and in line with the reviewer’s previous comment on the model, we believe that forgetting is governed by the strength of object-context associations (or engram relevancy). Our interpretation is that stronger associations signal that the memory or engram representation is important ("relevant") and should not be forgotten. Accordingly, due to a vast majority of experiences with extra cage objects in the enriched environment, mice might generally learn that such objects are common in their environment and potentially relevant in the future (i.e., the object-context association is strong, preventing forgetting). Our speculation of these results is to help unify our empirical data with the computational model.

We believe that the Reviewer's alternative explanation in terms of synaptic plasticity, spine growth is not mutually exclusive with the modelling work. It is possible that the computational mechanisms that we explore based on the Rescorla-Wagner model are neuronally related to the biological mechanisms that the reviewer suggests at the implementational level. Therefore, ultimately, the two perspectives might even complement each other. We have included additional discussion to clarify this point. See lines 510-546.

(3) It is well-known that updating can both weaken or strengthen memory. The authors suggest that memory is updated when animals are exposed to the context in the absence of the objects. If the engram is artificially inhibited (opto) during context-only re-exposures, memory cannot be updated. To further support this updating idea, it would be good to run experiments that investigate whether multiple short re-exposures to the training context (in the presence of the objects or during optogenetic activation of the engram) could prevent forgetting. It would also be good to know the levels of neuronal reactivation during multiple re-exposures to the context in the absence versus context in the presence of the objects.

We thank the Reviewer for their comments. We agree that additional experiments would be helpful to further support the idea of updating. We have performed additional experiments to test the idea that multiple short re-exposures to the training context, in the presence of objects prevents forgetting. In this paradigm, mice were repeatedly exposed to the original object pair (Supplementary Figure S5a). The results indicate that repeated reminder trials facilitate object memory recall (Supplementary Figure 5b&c). These data indicated that subsequent object reminders over time facilitates the transition of a forgotten memory to an accessible memory. See Supplementary Figure S5 and Lines 279-287.

(4) There are a number of studies that show boundary conditions for memory destabilization/reconsolidation. Is there any evidence that similar boundary conditions exist to make an inaccessible engram accessible?

The Reviewer asks an interesting question about boundary conditions and engram accessibility. Boundary conditions could indeed affect the degree of destabilization and reconsolidation, the salience or strength of the memory, as well as the timing of retrieval cues. Future models could focus on understanding the specific boundary conditions in which a memory becomes retrievable and the degree to which it is sufficiently destabilized and liable for updating and forgetting. We have included additional discussion on the potential role of boundary conditions for engram accessibility. See lines 661-666.

(5) More details about how the quantification of immunohistochemistry (c-fos, BrdU, DAPI) was performed should be provided (which software and parameters were used to consider a fos positive neurons, for example).

We have added additional information for the parameters of quantification of immunohistochemistry. See lines 796-809.

(6) Duration of the enrichment environment was not detailed.

We have highlighted the details for the environmental enrichment duration. See lines 756.

Reviewer #3 (Public Review):

Summary:

The manuscript by Ryan and colleagues uses a well-established object recognition task to examine memory retrieval and forgetting. They show that memory retrieval requires activation of the acquisition engram in the dentate gyrus and failure to do so leads to forgetting. Using a variety of clever behavioural methods, the authors show that memories can be maintained and retrieval slowed when animals are reared in environmental enrichment and that normally retrieved memories can be forgotten if exposed to the environment in which the expected objects are no longer presented. Using a series of neural methods, the authors also show that activation or inhibition of the acquisition engram is key to memory expression and that forgetting is due to Rac1.

We thank the Reviewer for summarizing the scope and depth of our manuscript, and indeed for recognizing our efforts. We engage below the Reviewer’s specific criticisms of our interpretations.

Strengths:

This is an exemplary examination of different conditions that affect successful retrieval vs forgetting of object memory. Furthermore, the computational modelling that captures in a formal way how certain parameters may influence memory provides an important and testable approach to understanding forgetting.

The use of the Rescorla-Wagner model in the context of object recognition and the idea of relevance being captured in negative prediction error are novel (but see below).

The use of gain and loss of function approaches are a considerable strength and the dissociable effects on behaviour eliminate the possibility of extraneous variables such as light artifacts as potential explanations for the effects.

We thank the Reviewer for their positive comments.

Weaknesses:

Knowing what process (object retrieval vs familiarity) governed the behavioural effect in the present investigation would have been of even greater significance.

The Reviewer touches on an important issue of the object recognition task. Understanding how experience alters object familiarity versus object retrieval and its impact on learning would help to develop better models of object memory and forgetting. We have added additional discussion. See lines 666-669.

The impact of the paper is somewhat limited by the use of only one sex.

We agree that using only male mice limits the impact of the paper. Indeed, the field of behavioural neuroscience is moving to include sex as a variable. Future experiments should include both male and female mice.

While relevance is an interesting concept that has been operationalized in the paper, it is unclear how distinct it is from extinction. Specifically, in the case where the animals are exposed to the context in the absence of the object, the paper currently expresses this as a process of relevance - the objects are no longer relevant in that context. Another way to think about this is in terms of extinction - the association between the context and the objects is reduced results in a disrupted ability of the context to activate the object engram.

We thank the reviewer for their insightful comment on the connection between engram relevance and memory extinction. Lacagnina et al., demonstrated that extinction training suppressed the reactivation of a fear engram, while activating a second putative extinction ensemble (59). In another study, these extinction engram cells and reward cells were shown to be functionally interchangeable (92). Moreover, in a study conducted by Lay et al., the balance between extinction and acquisition was disrupted by inhibiting the extinction recruited neurons in the BLA and CN (93). These results suggested that decision making after extinction can be governed by a balance between acquisition and extinction specific ensembles (93). Together, this may suggest that in the present study, when mice are repeatedly exposed to the training context, the association between the context and the objects is reduced, resulting in a disrupted ability of the context to activate the object engram. Therefore, memory relevance and extinction may operate similarly to effect engram accessibility, and in essence ‘forgetting’ of object memories may be due to neurobiological mechanisms similar to that of extinction learning (4). We have included additional discussion on the link between our results and the extinction literature. See lines 642-654.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Additional measures that may help interpretation of and clarify data are:

A minute-by-minute analysis for training and testing may provide insight about the learning rate and testing temporal dynamics that may shed light substantially on differential levels of exploration. This should be applied across figures and would support conclusions from models in Figures 7-8 as well.

Locomotion/distance travelled measures.

We have included additional analysis for a minute-by-minute analysis of training and testing of the object memory test at 24 hr, 2 weeks as well as under the standard housing and enrichment conditions. The results further support the initial finding that novel object recognition is increased in mice that recall the object at 24 hr. Similarly, mice housed in the enriched housing initially explore the novel object more compared to the familiar object. See Supplementary Figure 1 and 2, as well as lines 103-105 and 211-213.

The appropriate control for the context exposure figure would be to expose to a novel context in one group and the acquisition/testing context for the other.

We agree with the reviewer that an additional control of a novel context would further support our findings. Indeed, this line of investigate may dove-tail with the other reviewer comments on the role of competing engrams and interference. Future work could investigate the degree to which novel contexts and multiple memories can affect the rate of forgetting through engram updating. We have included additional discussion. See lines 643 and 655. However, in our experience it is necessary to pre-expose mice to different contexts before object exposure (e.g. Autore et al ’23), in order to form discriminate object/context associations. Establishing such a paradigm for this study would be at odds with the established paradigms and schedules in this current study. Moreover, the possibility that the effect of object displacement on forgetting requires the familiar context, or not, does not impact the main conclusions of this study. However, we agree that it is a point for expansion in the future.

A control virus+light group vs simply a no-light condition.

For optogenetic experiments. Control mice underwent the same surgery procedure with virus and optic fibre implantation. However, no light was delivered to excite or inhibit the respective opsin. Previous papers have shown laser light delivered to tissue expressing an AAV-TRE-EYFP lacking an light-opsin does cause cellular excitation. We have clarified this in the text. See lines 726-729.

Reviewer #2 (Recommendations For The Authors):

Minor details:

(1) In the pharmacological modification of Rac 1, please specify what percentage of DMSO was used to dissolve Rac1 inhibitor and correct the typo 'DSMO'

Rac1 inhibitor (Ehop016) was reconstituted and prepared in PBS with 1% Tween-80, 1% DMSO and 30% PEG. We have clarified this in the text and corrected the typo, thank you. See lines 767.

(2) In the penultimate paragraph there is a typo 'predication error'

This is now corrected. Thankyou.

Reviewer #3 (Recommendations For The Authors):

I was unable to find information on what the No Light group consisted of. Was there a control virus infused, were the animals implanted with optical fibres (in the presence or absence of a virus), were they surgical controls, etc?

For optogenetic experiments. No Light Control mice underwent the same surgery procedure with virus and optic fibre implantation. However, no light was delivered to excite or inhibit the respective opsin. We have clarified this in the text. See lines 726-729.

The discussion lacked specificity in places. For example, the idea of eluding to 'other variables' is somewhat vague (p. 21, middle paragraph). Some examples of what other variables could be relevant would be helpful in capturing what direction or relevance the model may have going forward.

We have expanded the discussion of other variables which might impact engram relevance and how the model might be developed moving forward. These may include, boundary conditions of destabilization and reconsolidation, the salience or strength of the memory as well as the timing of retrieval cues or updating experience. Future models could focus on understanding the specific boundary conditions in which a memory becomes retrievable and the degree to which it is sufficiently destabilized and liable for updating and forgetting. The role of perceptual learning on memory retrieval and forgetting may also be an avenue of future investigation. Understanding how experience alters object familiarity versus object retrieval and its impact on learning would also help to develop better models of object memory and forgetting. In the current study, only male mice were utilized. Therefore, future work could also include sex as a variable to fully elucidate the impact of experience on the processes of forgetting. See lines 642-669.

In the same paragraph (p. 21, middle paragraph) there is mention of multiple engrams and how they can compete. The authors reference Autore et al (2023), but I thought Lacagina did this really beautifully also in an experimental setting. This idea is also expressed in Lay et al. (2022). So additional references would further strengthen the authors argument here.

We thank the reviewer for the additional references for discussing engram competition. We have included these papers in the discission. See lines 642-654.

Relatedly, environmental enrichment was considered in terms of object relevance. I wonder if the authors may want to consider thinking about their results in terms of effects on perceptual learning.

Indeed, perceptual learning maybe playing a role in environmental enrichment. We have included additional discussion. See lines 666-669.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

The authors present a model for multisensory correlation detection that is based on the neurobiologically plausible Hassenstein Reichardt detector. It modifies their previously reported model (Parise & Ernst, 2016) in two ways: a bandpass (rather than lowpass) filter is initially applied and the filtered signals are then squared. The study shows that this model can account for synchrony judgement, temporal order judgement, etc in two new data sets (acquired in this study) and a range of previous data sets.

Strengths:

(1) The model goes beyond descriptive models such as cumulative Gaussians for TOJ and differences in cumulative Gaussians for SJ tasks by providing a mechanism that builds on the neurobiologically plausible Hassenstein-Reichardt detector.

(2) This modified model can account for results from two new experiments that focus on the detection of correlated transients and frequency doubling. The model also accounts for several behavioural results from experiments including stochastic sequences of A/V events and sine wave modulations.

Additional thoughts:

(1) The model introduces two changes: bandpass filtering and squaring of the inputs. The authors emphasize that these changes allow the model to focus selectively on transient rather than sustained channels. But shouldn't the two changes be introduced separately? Transients may also be detected for signed signals.

We updated the original model because our new psychophysical evidence demonstrates the fundamental role of unsigned transient for multisensory perception. While the original model received input from sustained unimodal channels (low-pass filters), the new version receives input from unsigned unimodal transient channels. Transient channels are normally modelled through bandpass filters (to remove the DC and high-frequency signal components) and squaring (to remove the sign). While these may appear as two separate changes in the model, they are, in fact, a single one: the substitution of sustained with unsigned transient channels (for a similar approach, see Stigliani et al. 2017, PNAS). Either change alone would not be sufficient to implement a transient channel that accounts for the present results.

That said, we were also concerned with introducing too many changes in the model at once. Indeed, we simply modelled the unimodal transient channels as a single band-pass filter followed by squaring. This is already a stripped-down version of the unsigned transient detectors proposed by Adelson and Bergen in their classic Motion Energy model. The original model consisted of two biphasic temporal filters 90 degrees out of phase (i.e., quadrature filters), whose output is later combined. While a simpler implementation of the transient channels was sufficient in the present study, the full model may be necessary for other classes of stimuli (including speech, Parise, 2024, BiorXiv). Therefore, for completeness, we now include in the Supplementary Information a formal description of the full model, and validate it by simulating our two novel psychophysical studies. See Supplementary Information “The quadrature MCD model” section and Supplementary Figure S8.

(2) Because the model is applied only to rather simple artificial signals, it remains unclear to what extent it can account for AV correlation detection for naturalistic signals. In particular, speech appears to rely on correlation detection of signed signals. Can this modified model account for SJ or TOJ judgments for naturalistic signals?

It can. In a recent series of studies we have demonstrated that a population of spatially-tuned MCD units can account for audiovisual correlation detection for naturalistic stimuli, including speech (e.g. the McGurk Illusion). Once again, unsigned transients were sufficient to replicate a variety of previous findings. We have now extended the discussion to cover this recent research: Parise, C. V. (2024). Spatiotemporal models for multisensory integration. bioRxiv, 2023-12.

Even Nidiffer et al. (2018) which is explicitly modelled by the authors report a significant difference in performance for correlated and anti-correlated signals. This seems to disagree with the results of study 1 reported in the current paper and the model's predictions. How can these contradicting results be explained? If the brain detects correlation on signed and unsigned signals, is a more complex mechanism needed to arbitrate between those two?

We believe the reviewer here refers to our Experiment 2 (where, like Nidiffer at al. (2018) we used periodic stimuli, not Experiment 1, which consists of step stimuli). We were also puzzled by the difference between our Experiment 2 and Nidiffer et al. (2018): we induced frequency doubling, Nidiffer did not. Based on quantitative simulations, we concluded that this difference could be attributed to the fact that while Nidiffer included on each trial an intensity ramp in their periodic audiovisual stimuli, we did not. As a result, when considering the ramp (unlike in Nidiffer’s analyses), all audiovisual signals used by Nidiffer were positively correlated (irrespective of frequency and phase offset), while our signals in Experiment 2 were sometimes correlated and other times not (depending on the phase offset). This important simulation is included in Supplementary Figure S7; we also have now updated the text to better highlight the role of the pedestal in determining the direction of the correlation.

(3) The number of parameters seems quite comparable for the authors' model and descriptive models (e.g. PSF models). This is because time constants require refitting (at least for some experimental data sets) and the correlation values need to be passed through a response mode (i.e. probit function) to account for behavioural data. It remains unclear how the brain adjusts the time constants to different sensory signals.

This is a deep question. For simplicity, here the temporal constants were fitted to the empirical psychometric functions. To avoid overfitting, whenever possible we fitted such parameters over some training datasets, while trying to predict others. However, in some cases, it was necessary to fit the temporal constants to specific datasets. This may suggest that the temporal tuning of those units is not crystalised to some pre-defined values, but is adjusted based on recent perceptual history (e.g., the sequence of trials and stimuli participants are exposed to during the various experiments).

For transparency, here we show how varying the tuning of the temporal constants of the filters affects the goodness of fit of our new psychophysical experiments (Supplementary Figure S8). As it can be readily appreciated, the relative temporal tuning of the unimodal transient detector was critical, though their absolute values could vary over a range of about 15 to over 100ms. The tuning of the low-pass filters of the correlation detector (not shown here) displayed much lower temporal sensitivity over a range between 0.1s to over 1s.

This simulation shows the impact of temporal tuning in our simulations, however, the question remains as to how such a tuning gets selected in the first place. An appealing explanation relies on natural scene statistics: units are temporally tuned to the most common audiovisual stimuli. Although our current empirical evidence does not allow us to quantitatively address this question, in previous simulations (see Parise & Ernst, 2016, Supplementary Figure 8), by analogy with visual motion adaptation, we show how the temporal constants of our model can dynamically adjust and adapt to recent perceptual history. We hope these new and previous simulations address the question about the nature of the temporal tuning of the MCD units.

(4) Fujisaki and Nishida (2005, 2006) proposed mechanisms for AV correlation detection based on the Hassenstein-Reichardt motion detector (though not formalized as a computational model).

This is correct, Fujisaki and Nishida (2005, 2007) also hypothesized that AV synchrony could be detected using a mechanism analogous to motion detection. Interestingly, however, they ruled out such a hypothesis, as their “data do not support the existence of specialized low-level audio-visual synchrony detectors”. Yet, along with our previous work (Parise & Ernst, 2016, where we explicitly modelled the experiments of Fujisaki and Nishida), the present simulations quantitatively demonstrate that a low-level AV synchrony detector is instead sufficient to account for audiovisual synchrony perception and correlation detection. We now credit Fujusaki and Nishida in the modelling section for proposing that AV synchrony can be detected by a cross-correlator.

Finally, we believe the reviewer is referring to the 2005 and 2007 studies of Fujisaki and Nishida (not 2006); here are the full references of the two articles we are referring to:

Fujisaki, W., & Nishida, S. Y. (2005). Temporal frequency characteristics of synchrony–asynchrony discrimination of audio-visual signals. Experimental Brain Research, 166, 455-464.

Fujisaki, W., & Nishida, S. Y. (2007). Feature-based processing of audio-visual synchrony perception revealed by random pulse trains. Vision Research, 47(8), 1075-1093.

Reviewer #2 (Public Review):

Summary:

This is an interesting and well-written manuscript that seeks to detail the performance of two human psychophysical experiments designed to look at the relative contributions of transient and sustained components of a multisensory (i.e., audiovisual) stimulus to their integration. The work is framed within the context of a model previously developed by the authors and is now somewhat revised to better incorporate the experimental findings. The major takeaway from the paper is that transient signals carry the vast majority of the information related to the integration of auditory and visual cues, and that the Multisensory Correlation Detector (MCD) model not only captures the results of the current study but is also highly effective in capturing the results of prior studies focused on temporal and causal judgments.

Strengths:

Overall the experimental design is sound and the analyses are well performed. The extension of the MCD model to better capture transients makes a great deal of sense in the current context, and it is very nice to see the model applied to a variety of previous studies.

Weaknesses:

My one major issue with the paper revolves around its significance. In the context of a temporal task(s), is it in any way surprising that the important information is carried by stimulus transients? Stated a bit differently, isn't all of the important information needed to solve the task embedded in the temporal dimension? I think the authors need to better address this issue to punch up the significance of their work.

In hindsight, it may appear unsurprising that transient signals carry most information for audiovisual integration. Yet, so somewhat unexpectedly, this has never been investigated using perhaps the most diagnostic psychophysical tools for perceived crossmodal timing; namely temporal order and simultaneity judgments–along with carefully designed experiments with quantitative predictions for the effect of either channel. The fact that the results conform to intuitive expectations further supports the value of the present work: grounding empirically with what is intuitively expected. This offers solid psychophysical evidence that one can build on for future advancements. Importantly, developing a model that builds on our new results and uses the same parameters to predict a variety of classic experiments in the field, further supports the current approach.

If “significance” is intended as shaking previous intuitions or theories, then no: this is not a significant contribution. If instead, by significance we intend to build a solid empirical and theoretical ground for future work, then we believe this study is not significant, it is foundational. We hope that this work's significance is better captured in our discussion.

On a side note, there is an intriguing factor around transient vs. sustained channels: what matters is the amount of change, not the absolute stimulus intensity. Previous studies, for example, have suggested a positive cross modal mapping between auditory loudness and visual lightness or brightness [Odegaard et al., 2004]. This study, conversely, challenges this view and demonstrates that what matters for multisensory integration in time is not the intensity of a stimulus, but changes thereof.

In a more minor comment, I think there also needs to be a bit more effort into articulating the biological plausibility/potential instantiations of this sustained versus transient dichotomy. As written, the paper suggests that these are different "channels" in sensory systems, when in reality many neurons (and neural circuits) carry both on the same lines.

The reviewer is right, in our original manuscript we glossed over this aspect. We have now expanded the introduction to discuss their anatomical basis. However, we are not assuming any strict dichotomy between transient and sustained channels; rather, our results and simulations demonstrate that transient information is sufficient to account for audiovisual temporal integration.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) Related to point 2 of the public review, can the authors provide additional results showing that the model can also account for naturalistic signals and more complex stochastic signals?

While working on this manuscript, we were also working in parallel on a project related to audiovisual integration of naturalistic signals. A pre-print is available online [Parise, 2024, BiorXiv], and the related study is now discussed in the conclusions.

(2) As noted in the public review, Fujisaki and Nishida (2005, 2006) already proposed mechanisms for AV correlation detection based on the Hassenstein-Reichardt motion detector. Their work should be referenced and discussed.

We have now acknowledged the contribution of Fujisaki and Nishida in the modelling section, when we first introduce the link between our model and the Hassenstein-Reichardt detectors.

(3) Experimental parameters: Was the phase shift manipulated in blocks? If yes, what about temporal recalibration?

To minimise the effect of temporal recalibration, the order of trials in our experiments was randomised. Nonetheless, we can directly assess potential short-term recalibration effects by plotting our psychophysical responses against both the current SOA, and that of the previous trials. The resulting (raw) psychometric surfaces below are averaged across observers (and conditions for Experiment 1). In all our experiments, responses are obviously dependent on the current SOA (x-axis). However, the SOA of the previous trials (y-axis) does not seem to meaningfully affect simultaneity and temporal order judgments. The psychometric curves above the heatmaps represent the average psychometric functions (marginalized over the SOA of the previous trial).

All in all, the present analyses demonstrate negligible temporal recalibration across trials, likely induced by a random sequence of lags or phase shifts. Therefore, when estimating the temporal constants of the model, it seems reasonable to ignore the potential effects of temporal recalibration. To avoid increasing the complexity of the present manuscript, we would prefer not to include the present analyses in the revised version.

Author response image 1.

Effect of previous trial. Psychometric surfaces for Experiments 1 and 2 plotted against the lag in the current vs. the previous trial. While psychophysical responses are strongly modulated by the lag in the last trial (horizontal axis), they are relatively unaffected by the lag in the previous trial (vertical axis).

(4) The model predicts no differences for experiment 1 and this is what is empirically observed. Can the authors support these null results with Bayes factors?

This is a good suggestion: we have now included a Bayesian repeated measures ANOVA to the analyses of Experiment 1. As expected, these analyses provide further, though mild evidence in support for the null hypothesis (See Table S2). For completeness, the new Bayesian analyses are presented alongside the previous frequentist ones in the revised manuscript.

Author response:

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

Reviewer #1 (Public Review):

Summary:

This important work advances our understanding of sperm motility regulation during fertilization by uncovering the midpiece/mitochondria contraction associated with motility cessation and structural changes in the midpiece actin network as its mode of action. The evidence supporting the conclusion is solid, with rigorous live cell imaging using state-of-the-art microscopy, although more functional analysis of the midpiece/mitochondria contraction would have further strengthened the study. The work will be of broad interest to cell biologists working on the cytoskeleton, mitochondria, cell fusion, and fertilization. Strengths: The authors demonstrate that structural changes in the flagellar midpiece F-actin network are concomitant to midpiece/mitochondrial contraction and motility arrest during sperm-egg fusion by rigorous live cell imaging using state-of-art microscopy.

Response P1.1: We thank the reviewer for her/his positive assessment of our manuscript.

Weaknesses:

Many interesting observations are listed as correlated or in time series but do not necessarily demonstrate the causality and it remains to be further tested whether the sperm undergoing midpiece contraction are those that fertilize or those that are not selected. Further elaboration of the function of the midpiece contraction associated with motility cessation (a major key discovery of the manuscript) would benefit from a more mechanistic study.

Response P1.2: We thank the reviewer for this point. We have toned down some of our statements since some of the observations are indeed temporal correlations. We will explore some of these possible connections in future experiments. In addition, we have now incorporated additional experiments and possible explanations about the function of the midpiece contraction.

Reviewer #2 (Public Review): 

(1) The authors used various microscopy techniques, including super-resolution microscopy, to observe the changes that occur in the midpiece of mouse sperm flagella. Previously, it was shown that actin filaments form a double helix in the midpiece. This study reveals that the structure of these actin filaments changes after the acrosome reaction and before sperm-egg fusion, resulting in a thinner midpiece. Furthermore, by combining midpiece structure observation with calcium imaging, the authors show that changes in intracellular calcium concentrations precede structural changes in the midpiece. The cessation of sperm motility by these changes may be important for fusion with the egg. Elucidation of the structural changes in the midpiece could lead to a better understanding of fertilization and the etiology of male infertility. The conclusions of this manuscript are largely supported by the data, but there are several areas for improvement in data analysis and interpretation. Please see the major points below.

Response P2.1: We thank the reviewer for the positive comments.

(2) It is unclear whether an increased FM4-64 signal in the midpiece precedes the arrest of sperm motility. in or This needs to be clarified to argue that structural changes in the midpiece cause sperm motility arrest. The authors should analyze changes in both motility and FM4-64 signal over time for individual sperm.

Response P2.2 : We have conducted single cell experiments tracking both FM4-64 and motility as the reviewer suggested (Supplementary Fig S1). We have observed that in all cases, cells gradually diminished the beating frequency and increased FM4-64 fluorescence in the midpiece until a complete motility arrest is observed. A representative example is shown in this Figure but we will reinforce this concept in the results section.

(3) It is possible that sperm stop moving because they die. Figure 1G shows that the FM464 signal is increased in the midpiece of immotile sperm, but it is necessary to show that the FM4-64 signal is increased in sperm that are not dead and retain plasma membrane integrity by checking sperm viability with propidium iodide or other means.

Response P2.3: This is a very good point. In our experiments, we always considered sperm that were motile to hypothesize about the relevance of this observation. We have two types of experiments: 

(1) Sperm-egg Fusion: In experiments where sperm and eggs were imaged to observe their fusion, sperm were initially moving and after fusion, the midpiece contraction (increase in FM4-64 fluorescence was observed) indicating that the change in the midpiece (that was observed consistently in all fusing cells analyzed), is part of the process. 

(2) Sperm that underwent acrosomal exocytosis (AE): we have observed two behaviours as shown in Figure 1: 

a) Sperm that underwent AE and they remain motile without midpiece contraction (they are alive for sure); 

b) Sperm that underwent AE and stopped moving with an increase in FM464 fluorescence. We propose that this contraction during AE is not desired because it will impede sperm from moving forward to the fertilization site when they are in the female reproductive tract. In this case, we acknowledge that the cessation of sperm motility may be attributed to cellular death, potentially correlating with the increased FM4-64 signal observed in the midpiece of immotile sperm that have undergone AE. To address this hypothesis, we conducted image-based flow cytometry experiments, which are well-suited for assessing cellular heterogeneity within large populations.

Author response image 1 illustrates the relationship between cell death and spontaneous AE in noncapacitated mouse sperm, where intact acrosomes are marked by EGFP. Cell death was evaluated using Sytox Blue staining, a dye that is impermeable to live cells and shows affinity for DNA. AE was assessed by the absence of EGFP in the acrosome. 

Author response image 1a indicates a lack of correlation between Sytox and EGFP fluorescence. Two populations of sperm with EGFP signals were found (EGFP+ and EGFP-), each showing a broad distribution of Sytox signal, enabling the distinction between cells that retain plasma membrane integrity (live sperm: Sytox-) and those with compromised membranes (dead cells: Sytox+). The observed bimodal distribution of EGFP signal, regardless of live versus dead cell populations, indicates that the fenestration of the plasma membrane known to occur during AE is a regulated process that does not necessarily compromise the overall plasma membrane integrity. 

These observations are reinforced by the single-cell examples in Author response image 1b, where we were able to identify sperm in four categories: live sperm with intact acrosome (EGFP+/Sytox-), live sperm with acrosomal exocytosis (EGFP-/Sytox-), dead sperm with intact acrosome (EGFP+/Sytox+), and dead sperm with AE (EGFP-/Sytox+). Note the case of AE (lacking EGFP signal) which bears an intact plasma membrane (lacking Sytox Blue signal). Author response image 2 shows single-cell examples of the four categories observed with confocal microscopy to reinforce the observations from Author response image 1a.

Author response image 1.

Fi. Image based flow cytometry analysis (ImageStream Merk II), of non-capacitated mouse sperm, showing the distribution of EGFP signal (acrosome integrity) against Sytox Blue staining (cell viability).  (A) The quadrants show: Sytox Blue + / EGFP low (17.6%), Sytox Blue + / EGFP high (40.1%), Sytox Blue - / EGFP high (20.2%), and Sytox Blue - / EGFP low (21.7%). Each quadrant indicates the percentage of the total sperm population exhibiting the corresponding staining pattern. Axes are presented in a log10 scale of arbitrary units of fluorescence.  (B) Representative single-cell images corresponding to the four categorized sperm populations from the flow cytometry analysis in panel (A). The top row displays sperm with compromised plasma membrane integrity (Sytox Blue +), showing low (left) and high (right) EGFP signals. The bottom row shows sperm with intact plasma membrane (Sytox Blue -), displaying high (left) and low (right) EGFP signal. It is worth noting that when analyzing the percentages in (A), we observed that the data also encompass a population of headless flagella, which was present in all observed categories. Therefore, the percentages should be interpreted with caution.

Author response image 2.

Confocal Microscopy Examples of AE and cell viability. The top row features sperm with compromised plasma membrane integrity (Sytox Blue +) and high EGFP expression; the second row displays sperm with compromised membrane and low EGFP expression; the third row illustrates sperm with intact membrane (Sytox Blue -) and high EGFP expression; the bottom row shows sperm with intact membrane and low EGFP expression. 

Author response images 3-5 provide insight into the relationship between FM4-64 and Sytox Blue fluorescence intensities in non-capacitated sperm (CTRL, Author response image 3), capacitated sperm and acrosome exocytosis events stimulated with 100 µM progesterone (PG, Author response image 4), and capacitated sperm stimulated with 20 µM ionomycin (IONO, Author response image 5). Two populations of sperm with Sytox Blue signals were clearly distinguished (Sytox+ and Sytox-), enabling the discernment between live and dead sperm. Interestingly, the upper right panels of Author response images 3A, 4A, and 5A (Sytox Blue+ / FM4-64 high) consistently show a positive correlation between FM4-64 and Sytox Blue. This observation aligns with the concern raised by Reviewer 2, suggesting that compromised membranes due to cell death provide more binding sites for FM4-64. 

Nonetheless, the lower panels of Author response images 3A, 4A and 5A (Sytox Blue-) show no correlation with FM4-64 fluorescence, indicating that this population can exhibit either low or high FM4-64 fluorescence. As expected, in stark contrast with the CTRL case, the stimulation of AE with PG or IONO in capacitated sperm increased the population of live sperm with high FM4-64 fluorescence (Sytox Blue+ / FM4-64 high: CTRL: 7.85%, PG: 8.73%, IONO: 13.5%). 

Single-cell examples are shown in Author response images 3B, 4B, and 5B, where the four categories are represented: dead sperm with low FM4-64 fluorescence (Sytox Blue+ / FM4-64 low), dead sperm with high FM4-64 fluorescence (Sytox Blue+ / FM4-64 high), live sperm with low FM4-64 fluorescence (Sytox Blue- / FM4-64 low), and live sperm with high FM4-64 fluorescence (Sytox Blue- / FM4-64 high). 

Author response image 3.

Relationship between cell death and FM4-64 fluorescence in capacitated sperm without inductor of RA. Image-based flow cytometry analysis of non-capacitated mouse sperm loaded with FM464 and Sytox Blue dyes, with one and two minutes of incubation time, respectively. (A) The quadrants show: Sytox Blue+ / FM4-64 low (13.3%), Sytox Blue+ / FM4-64 high (49.8%), Sytox Blue- / FM4-64 low (28.1%), and Sytox Blue- / FM4-64 high (7.85%). Each quadrant indicates the percentage of the total sperm population exhibiting the corresponding staining pattern. Axes are presented on a log10 scale of arbitrary units of fluorescence. (B) Representative single-cell images corresponding to the four categorized sperm populations from the flow cytometry analysis in panel (A).

Author response image 4.

Relationship between cell death and FM4-64 fluorescence capacitated sperm stimulated with progesterone. Image-based flow cytometry analysis of non-capacitated mouse sperm loaded with FM4-64 and Sytox Blue dyes, with one and two minutes of incubation time, respectively. (A) The quadrants show: Sytox Blue+ / FM4-64 low (9.04%), Sytox Blue+ / FM4-64 high (61.6%), Sytox Blue- / FM4-64 low (19.7%), and Sytox Blue- / FM4-64 high (8.73%). Each quadrant indicates the percentage of the total sperm population exhibiting the corresponding staining pattern. Axes are presented on a log10 scale of arbitrary units of fluorescence. (B) Representative single-cell images corresponding to the four categorized sperm populations from the flow cytometry analysis in panel (A)

Author response image 5.

Relationship between cell death and FM4-64 fluorescence capacitated sperm stimulated with ionomycin. Image-based flow cytometry analysis of non-capacitated mouse sperm loaded with FM464 and Sytox Blue dyes, with one and two minutes of incubation time, respectively. (A) The quadrants show: Sytox Blue+ / FM4-64 low (4.52%), Sytox Blue+ / FM4-64 high (60.6%), Sytox Blue- / FM4-64 low (20.5%), and Sytox Blue- / FM4-64 high (13.5%). Each quadrant indicates the percentage of the total sperm population exhibiting the corresponding staining pattern. Axes are presented on a log10 scale of arbitrary units of fluorescence. (B) Representative single-cell images corresponding to the four categorized sperm populations from the flow cytometry analysis in panel (A).

Based on the data presented in Author response images 1 to 6, we derive the following conclusions summarized below:

(1) There is no direct relationship between cell death (Sytox Blue-) and AE (EGFP) (Author response images 1 and 2).

(2) There is bistability in the FM4-64 fluorescent intensity. Before reaching a certain threshold, there is no correlation between FM4-64 and Sytox Blue signals, indicating no cell death. However, after crossing this threshold, the FM4-64 signal becomes correlated with Sytox Blue+ cells, indicating cell death (Author response images 4-6).

(3) The Sytox Blue- population of capacitated sperm is sensitive to AE stimulation with progesterone, leading to the expected increase in FM4-64 fluorescence.

Therefore, while the FM4-64 signal alone is not a definitive marker for either AE or cell death, it is crucial to use additional viability assessments, such as Sytox Blue, to accurately differentiate between live and dead sperm in studies of acrosome exocytosis and sperm motility. In the present work, we did not use a cell viability marker due to the complex multicolor, multidimensional fluorescence experiments. However, cell viability was always considered, as any imaged sperm was chosen based on motility, indicated by a beating flagellum. The determination of whether selected sperm die during or after AE remains to be elucidated. The results presented in Figure 2 and Supplementary S1 show examples of motile sperm that experience an increase in FM4-64 fluorescence.

All this information is added to the manuscript (Supplementary Figure 1D).

(4) It is unclear how the structural change in the midpiece causes the entire sperm flagellum, including the principal piece, to stop moving. It will be easier for readers to understand if the authors discuss possible mechanisms.

Response P2.4: As requested, we have incorporated a possible explanation in the discussion section (see line 644-656). We propose three possible hypotheses for the cessation of sperm motility, which can be attributed to the simultaneous occurrence of various events:

(1) Rapid increase in [Ca2+]i levels: A rapid increase in [Ca2+]i levels may trigger the activation of Ca2+ pumps within the flagellum. This process consumes local ATP levels, disrupting glycolysis and thereby depleting the energy required for motility.

(2) Reorganization of the actin cytoskeleton: Alterations in the actin cytoskeleton can lead to changes in the mechanical properties of the flagellum, impacting its ability to move effectively.

(3) Midpiece contraction: Contraction in the midpiece region can potentially interfere with mitochondrial function, impeding the energy production necessary for sustained motility.

(5) The mitochondrial sheath and cell membrane are very close together when observed by transmission electron microscopy. The image in Figure 9A with the large space between the plasma membrane and mitochondria is misleading and should be corrected. The authors state that the distance between the plasma membrane and mitochondria approaches about 100 nm after the acrosome reaction (Line 330 - Line 333), but this is a very long distance and large structural changes may occur in the midpiece. Was there any change in the mitochondria themselves when they were observed with the DsRed2 signal?

Response P2.5: The authors appreciate the reviewer’s observation regarding the need to correct the image in Figure 9A, as the original depiction conveys a misleading representation of the spatial relationship between the mitochondrial sheath and the plasma membrane. This figure has been corrected to accurately reflect a more realistic proximity, while keeping in mind that it is a cartoonish representation.

Regarding the comments about the distances mentioned between former lines 330 and 333, the measurement was not intended to describe the gap between the plasma membrane and the mitochondria but rather the distance between F-actin and the plasma membrane. 

Author response image 6 shows high-resolution scanning electron microscopy (SEM) of two sperm fixed with a protocol tailored to preserve plasma membranes (ref), where the insets clearly show the flagellate architecture in the midpiece with an intact plasma membrane covering the mitochondrial network. A non-capacitated sperm with an intact acrosome is shown in panel A, and a capacitated sperm that has experienced AE is shown in panel B.

Notably, the results depicted in Author response image 6 demonstrate that, irrespective of the AE status, the distance between the plasma membrane and mitochondria consistently remains less than 20 nm, thus confirming the close proximity of these structures in both physiological states. As Reviewer 2 pointed out, if there is no significant difference in the distance between the plasma membrane and mitochondria, then the observed structural changes in the actin network within the midpiece should somehow alter the actual deposition of mitochondria within the midpiece. Figure 5D-F shows that midpiece contraction is associated with a decrease in the helical pitch of the actin network; the distance between turns of the actin helix decreases from  l = 248  nm to  l = 159  nm. This implies a net change in the number of turns the helix makes per 1 µm, from 4 to 6 µm-1.

Author response image 6.

SEM image showing the proximity between plasma membrane and mitochondria. Scale bar 100 nm.

Additionally, a structural contraction can be observed in Figure 5D-F, where the radius of the helix decreases by about 50 nm. To clarify this point, we sought to measure the deposition of individual DsRed2 mitochondria using computational superresolution microscopy—FF-SRM (SRRF and MSSR), Structured Illumination Microscopy (SIM), or a combination of both (SIM + MSSR), in 2D. Author response image 7 shows that these three approaches allow the observation of individual DsRed mitochondria; however, the complexity of their 3D arrangement, combined with the limited space between mitochondria (as seen in Author response image 6), precludes a reliable estimation of mitochondrial organization within the midpiece. To overcome these challenges, we decided to study the midpiece architecture via SEM experiments on non-capacitated versus capacitated sperm stimulated with ionomycin to undergo the AE.

Author response image 7.

Organization of mitochondria observed via FF-SRM and SIM. Scale bar 2 µm. F.N: Fluorescence normalized. F: Frequency

Author response image 8 presents a single-cell comparison of the midpiece architecture in noncapacitated (NC) and acrosome-intact (AI) versus acrosome-reacted (AR) sperm, along with measurements of the midpiece diameter throughout its length. Notably, the diameter of the midpiece increases from the base of the head to more distal regions, ranging from 0.45 nm to 1.10 µm (as shown in Author response images 7 and 8). A significant correlation between the diameter of the flagellum and its curvature was observed (Author response image 9), suggesting a reorganization of the midpiece due to shearing forces. This is further exemplified in Author response images 8 and 9, which provide individual examples of this phenomenon.

Author response image 8.

Comparison of the midpiece architecture in acrosome-intact and acrosome-reacted sperm using scanning electron microscopy (SEM).

As expected, the overall diameter of the midpiece in AI sperm was larger than in AR sperm, with measurements of 0.731 ± 0.008 µm for AI and 0.694 ± 0.007 µm for AR (p = 0.013, Kruskal-Wallis test n > 100, N = 2), as shown in Author response image 10. Additionally, this Author response image 7 indicates that the reorganization of the midpiece architecture involves a change in the periodicity of the mitochondrial network, with frequencies shifting from fNC to fEA mitochondria per micron.  

Author response image 9.

Comparison of the midpiece architecture in acrosome-intact (A) and acrosome-reacted (B) sperm using scanning electron microscopy (SEM).

Collectively, the structural results presented in Figure 5 and Author response images 6 to 10 demonstrate that the AE involves a comprehensive reorganization of the midpiece, affecting its diameter, pitch, and the organization of both the actin and mitochondrial networks. All this information is now incorporated in the new version of the paper (Figure. 2F)

Author response image 10.

Quantification of the midpiece diameter of the sperm flagellum in acrosome-intact and acrosome-reacted sperm analyzed by scanning electron microscopy (SEM). Data is presented as mean ± SEM. Kruskal-Wallis test was employed,  p = 0.013 (AI n=85 , AR n=72).

(6) In the TG sperm used, the green fluorescence of the acrosome disappears when sperm die. Figure 1C should be analyzed only with live sperm by checking viability with propidium iodide or other means.

Response P2.6: We concur with Reviewer 2 that ideally, any experiment conducted for this study should include an intrinsic cell viability test. However, the current research employs a wide array of multidimensional imaging techniques that are not always compatible with, or might be suboptimal for, simultaneous viability assessments. In agreement with the reviewer's concerns, it is recognized that the data presented in Figure 1C may inherently be biased due to cell death. Nonetheless, Author response image 1 demonstrates that the relationship between AE and cell death is more complex than a straightforward all-or-nothing scenario. Specifically, Author response image 1C illustrates a case where the plasma membrane is compromised (Sytox Blue+) yet maintains acrosomal integrity (EGFP+). This observation contradicts Reviewer 1's assertion that "the green fluorescence of the acrosome disappears when sperm die," as discussed more comprehensively in response P2.3.

In light of these observations, we have meticulously revisited the entire manuscript to address and clarify potential biases in our results due to cell death. Consequently, Author response image 5 and its detailed description have been incorporated into the supplementary material of the manuscript to contribute to the transparency and reliability of our findings.

Reviewer #3 (Public Review):

(1) While progressive and also hyperactivated motility are required for sperm to reach the site of fertilization and to penetrate the oocyte's outer vestments, during fusion with the oocyte's plasma membrane it has been observed that sperm motility ceases. Identifying the underlying molecular mechanisms would provide novel insights into a crucial but mostly overlooked physiological change during the sperm's life cycle. In this publication, the authors aim to provide evidence that the helical actin structure surrounding the sperm mitochondria in the midpiece plays a role in regulating sperm motility, specifically the motility arrest during sperm fusion but also during earlier cessation of motility in a subpopulation of sperm post acrosomal exocytosis. The main observation the authors make is that in a subpopulation of sperm undergoing acrosomal exocytosis and sperm that fuse with the plasma membrane of the oocyte display a decrease in midpiece parameter due to a 200 nm shift of the plasma membrane towards the actin helix. The authors show the decrease in midpiece diameter via various microscopy techniques all based on membrane dyes, bright-field images and other orthogonal approaches like electron microscopy would confirm those observations if true but are missing. The lack of additional experimental evidence and the fact that the authors simultaneously observe an increase in membrane dye fluorescence suggests that the membrane dyes instead might be internalized and are now staining intracellular membranes, creating a false-positive result. The authors also propose that the midpiece diameter decrease is driven by changes in sperm intracellular Ca2+ and structural changes of the actin helix network. Important controls and additional experiments are needed to prove that the events observed by the authors are causally dependent and not simply a result of sperm cells dying.

Response P3.1: We appreciate the reviewer's observations and critiques. In response, we have expanded our experimental approach to include alternative methodologies such as mathematical modeling and electron microscopy, alongside further fluorescence microscopy studies. This diversified approach aims to mitigate potential interpretation artifacts and substantiate the validity of our observations regarding the contraction of the sperm midpiece. Additionally, we have implemented further control experiments to fortify the credibility and robustness of our findings, ensuring a more comprehensive and reliable set of results.

First, we acknowledge the concerns raised by Reviewer 2 regarding the interpretation of the magnitude of the observed contraction of the sperm flagellum's midpiece (see response P2.5). Specifically, we believe that the assertion that "... there is a decrease in midpiece parameter due to a 200 nm shift of the plasma membrane towards the actin helix" stated by reviewer 3 needs careful examination. We recognize that the fluorescence microscopy data provided might not conclusively support such a substantial shift. Our live cell imaging and superresolution microscopy experiments indicate that there is a significant decrease in the diameter of the sperm flagellum associated with AE. This is supported by colocalization experiments where FM4-64-stained structures (fluorescing upon binding to membranes) are observed moving closer to Sir-Actinlabeled structures (binding to F-actin). Quantitatively, Figure S5 describes the spatial shift between FM4-64 and Sir-Actin signals, narrowing from a range of 140-210 nm to 50-110 nm (considering the 2nd and 3rd quartiles of the distributions). The mean separation distance between both signals changes from 180 nm in AI cells to 70 nm in AR cells, a net shift of 110 nm. This observation suggests caution regarding the claim of a "200 nm shift of the plasma membrane towards the actin cortex." 

Moreover, the concerns raised by Reviewer #3 about the potential internalization of membrane dyes, which might create a false-positive result by staining intracellular membranes, offer an alternative mechanism to explain a shift of up to 100 nm. This perspective is also supported by the critique from Reviewer #2 regarding the substantial distance (about 100 nm) between the plasma membrane and mitochondria post-acrosome reaction:  “The authors state that the distance between the plasma membrane and mitochondria approaches about 100 nm after the acrosome reaction (…), but this is a very long distance and large structural changes may occur in the midpiece”. These insights have prompted us to refine our methodology and interpretation of the data to ensure a more accurate representation of the underlying biological processes.

Author response image 11 shows a first principles approach in two spatial dimensions to explore three scenarios where a membrane dye, such as FM4-64, stains structures at and within the midpiece of a sperm flagellum, but yet does not result in a net change of diameter. Author response image 11A-C illustrates three theoretical arrangements of fluorescent dyes: Model 1 features two rigid, parallel structures that mimic the plasma membrane surrounding the midpiece of the flagellum. Model 2 builds on Model 1 by incorporating the possibility of dye internalization into structures located near the membrane, suggesting a slightly more complex interaction with nearby membranous intracellular structures. Model 3 represents an extreme scenario where the fluorescent dyes stain both the plasma membrane and internal structures, such as mitochondrial membranes, indicating extensive dye penetration and binding. Author response image 11D-F displays the convolution of the theoretical fluorescent signals from Models 1 to 3 with the theoretical point spread function (PSF) of a fluorescent microscope, represented by a Gaussian-like PSF with a sigma of 19 pixels (approximately 300 nm). This process simulates how each model's fluorescence would manifest under microscopic observation, showing subtle differences in the spatial distribution of fluorescence among the models. Author response image 11G-I reveals the superresolution images obtained through Mean Shift Super Resolution (MSSR) processing of the models depicted in Author response image 11D-F.

By analyzing the three scenarios, it becomes clear that the signals from Models 2 and 3 shift towards the center compared to Model 1, as depicted in Author response image 11J. This shift in fluorescence suggests that the internalization of the dye and its interaction with internal structures might significantly influence the perceived spatial distribution and intensity of fluorescence, thereby impacting the interpretation of structural changes within the midpiece. Consequently, the experimentally observed contraction of up to 100 nm in  could represent an actual contraction of the sperm flagellum's midpiece, a relocalization of the FM4-64 membrane dyes to internal structures, or a combination of both scenarios.

To discern between these possibilities, we implemented a scanning electron microscopy (SEM) approach. The findings presented in Figure 5 and Author response images 7 to 9 conclusively demonstrate that the AE involves a comprehensive reorganization of the midpiece. This reorganization affects its diameter, which changes by approximately 50 nm, as well as the pitch and the organization of both the actin and mitochondrial networks. This data corroborates the structural alterations observed and supports the validity of our interpretations regarding midpiece dynamics during the AE.

Author response image 11.

Modeling three scenarios of midpiece staining with membrane fluorescent dyes.

Secondly, we wish to clarify that in some of our experiments, we have utilized changes in the intensity of FM4-64 fluorescence as an indirect measure of midpiece contraction. This approach is supported by a linear inverse correlation between these variables, as illustrated in Figure S2D. It is important to note that this observation is correlative and indirect; therefore, our data does not directly substantiate the claim that "in a subpopulation of sperm undergoing AE and sperm that fuse with the plasma membrane of the oocyte, there is a decrease in midpiece parameter due to a 200 nm shift of the plasma membrane towards the actin helix". Specifically, we have not directly measured the distance between the plasma membrane and actin cortex in experiments involving gamete fusion.

All the concerns highlighted in this Response P1.1 have been addressed and incorporated into the manuscript. This addition aims to provide comprehensive insight into the experimental observations and methodologies used, ensuring that the data is transparent and accessible for thorough review and replication.

Editor Comment:

As the authors can see from the reviews, the reviewers had quite different degrees of enthusiasm, thus discussed extensively. The major points in consensus are summarized below and it is highly recommended that the authors consider their revisions.

(1) Causality of midpiece contraction with motility arrest is not conclusively supported by the current evidence. Time-resolved imaging of FM4-64 and motility is needed and the working model needs to be revised with two scenarios - whether the sperm contracting indicates a fertilizing sperm or sperm to be degenerated.

(2) The rationale for using FM4-64 as a plasma membrane marker is not clear as it is typically used as an endo-membrane marker, which is also related to the discrepancy of Fluo-4 signal diameter vs. FM4-64 (Figure 4E). The viability of sperm with increased FM4-64 needs to be demonstrated.

(3) The mechanism of midpiece contraction in motility cessation along the whole flagellum is not discussed.

(4) The use of an independent method to support the changes in midpiece diameter/structural changes such as DsRed (transgenic) or TEM.

(5) The claim of Ca2+ change needs to be toned down.

Response Editor: We thank the editor and the reviewers for their thorough and positive assessment of our work and the constructive feedback to further improve our manuscript. Please find below our responses to the reviewers’ comments. We have addressed all these points in the current version. Briefly,

(1) Time resolved images to show the correlation between FM4-64 fluorescence increase and the motility was incorporated

(2) The rationale for using FM4-64 was added.

(3) The mechanism of midpiece contraction was discussed in the paper

(4) An independent method was included to support our conclusions (SEM and other markers not based on membrane dyes)

(5) The results related to the calcium increase were toned down.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) To claim midpiece actin polymerization/re-organization is required for AE, demonstrating that AE does not occur in the presence of actin depolymerizing drugs (e.g., Latrunculin A, Cytochalasin D) would be necessary since the current data only shows the association/correlation. Was the block of AE by actin depolymerization observed?

Response R1.1: We agree with the reviewer but unfortunately, since actin polymerization and or depolymerization in the head are important for exocytosis, we cannot use this experimental approach to dissect both events. Addition of these inhibitors block the occurrence of AE (PMID: 12604633).

(2) Please provide the rationale for using FM4-64 to visualize the plasma membrane since it has been reported to selectively stain membranes of vacuolar organelles. What is the principle of increase of FM4-64 dye intensity, other than the correlation with midpiece contraction? For example, in lines 400-402: the authors mentioned that 'some acrosomereacted moving sperm within the perivitelline space had low FM4-64 fluorescence in the midpiece (Figure 6C). After 20 minutes, these sperm stopped moving and exhibited increased FM4-64 fluorescence, indicating midpiece contraction (Figure 6D).' While recognizing the increase of FM4-64 dye intensity can be an indicator of midpiece contraction, without knowing how and when the intensity of FM4-64 dye changes, it is hard to understand this observation. Please discuss.

Response R1.2: FM4-64 is an amphiphilic styryl fluorescent dye that preferentially binds to the phospholipid components of cell membranes, embedding itself in the lipid bilayer where it interacts with phospholipid head groups. Due to its amphiphilic nature, FM dyes primarily anchor to the outer leaflet of the bilayer, which restricts their internalization. It has been demonstrated that FM4-64 enters cells through endocytic pathways, making these dyes valuable tools for studying endocytosis.

Upon binding, FM4-64's fluorescence intensifies in a more hydrophobic environment that restricts molecular rotation, thus reducing non-radiative energy loss and enhancing fluorescence. These photophysical properties render FM dyes useful for observing membrane fusion events. When present in the extracellular medium, FM dyes rapidly reach a chemical equilibrium and label the plasma membrane in proportion to the availability of binding sites.

In wound healing studies, for instance, the fluorescence of FM4-64 is known to increase at the wound site. This increase is attributed to the repair mechanisms that promote the fusion of intracellular membranes at the site of the wound, leading to a rise in FM4-64 fluorescence. Similarly, an increase in FM4-64 fluorescence has been reported in the heads of both human and mouse sperm, coinciding with AE. In this scenario, the fusion between the plasma membrane and the acrosomal vesicle provides additional binding sites for FM4-64, thus increasing the total fluorescence observed in the head. This dynamic response of FM4-64 makes it an excellent marker for studying these cellular processes in real-time.

This study is the first to report an increase in FM4-64 fluorescence in the midpiece of the sperm flagellum. Figures 5 and Author response images 6 to 9 demonstrate that during the contraction of the sperm flagellum, structural rearrangements occur, including the compaction of the mitochondrial sheath and other membranous structures. Such contraction likely increases the local density of membrane lipids, thereby elevating the local concentration of FM4-64 and enhancing the probability of fluorescence emission. Additionally, changes in the microenvironment such as pH or ionic strength during contraction might further influence FM4-64’s fluorescence properties, as detailed by Smith et al. in the Journal of Membrane Biology (2010). The photophysical behavior of FM4-64, including changes in quantum yield due to tighter membrane packing or alterations in curvature or tension, may also contribute to the increased fluorescence observed. Notably, Figure S2 indicates that other fluorescent dyes like Memglow 700, Bodipy-GM, and FM1-43 also show a dramatic increase in their fluorescence during the midpiece contraction. Investigating whether the compaction of the plasma membrane or other mesoscale processes occur in the midpiece of the sperm flagellum could be a valuable area for future research. The use of fluorescent dyes such as LAURDAN or Nile Red might provide further insights into these membrane dynamics, offering a more comprehensive understanding of the biochemical and structural changes during sperm motility and gamete fusion events.

(3) As the volume of the whole midpiece stays the same while the diameter decreases along the whole midpiece (midpiece contraction), the authors need to describe what changes in the midpiece length they observe during the contraction. Was the length of the midpiece during the contraction measured and compared before and after contraction?

Response R1.3: As requested, we have measured the length of the midpiece in AI and AR sperm. As shown in Author response image 12 (For review purposes only), no statistically significant differences were observed. 

Author response image 12.

Midpiece length measured by the length of mitochondrial DsRed2 fluorescence in EGFP-DsRed2 sperm. Measurements were done before (acrosome-intact) and after (acrosome-reacted) acrosome exocytosis and midpiece contraction. Data is presented as the mean ± sem of 14 cells induced by 10 µM ionomycin. Paired t-test was performed, resulting in no statistical significance. 

(4) Most of all, it is not clear what the midpiece, thus mitochondria, contraction means in terms of sperm bioenergetics and motility cessation. Would the contraction induce mitochondrial depolarization or hyperpolarization, increase or decrease of ATP production/consumption? It will be great if this point is discussed. For example, an increase in mitochondrial Ca2+ is a good indicator of mitochondrial activity (ATP production).

Response R1.4: That is an excellent point. We have discussed this idea in the discussion (line 620-624). We are currently exploring this idea using different approaches because we also think that these changes in the midpiece may have an impact in the function of the mitochondria and perhaps, in their fate once they are incorporated in the egg after fertilization. 

(5) The authors claimed that Ca2+ signal propagates from head to tail, which is the opposite of the previous study (PMID: 17554080). Please clarify if it is a speculation. Otherwise, please support this claim with direct experimental evidence (e.g., high-speed calcium imaging of single cells).

Response R1.5: In that study, it was claimed that a [Ca2+]i  increase that propagates from the tail to the head occurs when CatSper is stimulated. They did not evaluate the occurrence of AE when monitoring calcium.

Our data is in agreement with our previous results (PMID: 26819478) that consistently indicated that only the[Ca2+]i  rise originating in the sperm head is able to promote AE. 

(6) Figure 4E: Please explain how come Fluo4 signal diameter can be smaller than FM4-64 dye if it stains plasma membrane (at 4' and 7').

Response R1.6: When colocalizing a diffraction-limited image (Fluo4) with a super-resolution image (FM4-64), discrepancies in signal sizes and locations can become apparent due to differences in resolution. The Fluo4 signal, being diffraction-limited, adheres to a resolution limit of approximately 200-300 nanometers under conventional light microscopy. This limitation causes the fluorescence signal to appear broader and less defined. Conversely, super-resolution microscopy techniques, such as SRRF (Super-Resolution Radial Fluctuations), achieve resolutions down to tens of nanometers, allowing FM4-64 to reveal finer details at the plasma membrane and display potentially smaller apparent sizes of stained structures. Although both dyes might localize to the same cellular regions, the higher resolution of the FM4-64 image allows it to show a more precise and smaller diameter of the midpiece of the flagellum compared to the broader, less defined signal of Fluo4. To address this, the legend of Figure 4E has been slightly modified to clarify that the FM4-64 image possesses greater resolution. 

(7) Figure 5D-G: the midpiece diameter of AR intact cells was shown ~ 0.8 um or more in Figure 2, while now the radius in Figure 5 is only 300 nm. Since the diameter of the whole midpiece is nearly uniform when the acrosome is intact, clarify how and what brings this difference and where the diameter/radius measurement is done in each figure.

Response R1.7: The difference resides in what is being measured. In Figure 2, the total diameter of the cell is measured, through the maximum peaks of FM4-64 fluorescence which is a probe against plasma membrane. As for Figure 5, the radius shown makes reference to the radius of the actin double helix within the midpiece. To that end, cells were fixed and stained with phalloidin, a F-actin probe.

Minor points

(8) Figure S1 title needs to be changed. The "Midpiece contraction" concept is not introduced when Figure S1 is referred to.

Response R1.8: This was corrected in the new version.

(9) Reference #19: the authors are duplicated.

Response R1.9: This was corrected in the new version.

(10) Line 315-318: sperm undergoing contraction -> sperm undergoing AR/AE?

Response R1.10: This was corrected in the new version.

(11) Line 3632 -> punctuation missing.

Response R1.11: Modified as requested.

(12) Movie S7: please add an arrow to indicate the spermatozoon of interest.

Response R1.12:  The arrow was added as suggested.

(13) Line 515: One result of this study was that the sperm flagellum folds back during fusion coincident with the decrease in the midpiece diameter. The authors did not provide an explanation for this observation. Please speculate the function of this folding for the fertilization process.

Response R1.13: As requested, this is now incorporated in the discussion. We speculate that the folding of the flagellum during fusion further facilitates sperm immobilization because it makes it more difficult for the flagellum to beat. Such processes can enhance stability and increase the probability of fusion success. Mechanistically, the folding may occur as a consequence of the deformation-induced stress that develops during the decrease of midpiece diameter. 

Reviewer #2 (Recommendations For The Authors):

(1) Figure 2C, D, E. Does "-1" on the X-axis mean one minute before induction? If so, the diameter is already smaller and FM4-64 fluorescence intensity is higher before the induction in the spontaneous group. Does the acrosome reaction already occur at "-1" in this group?

Response R2.1: Yes, “-1” means that the measurements of the diameter/FM4-64 fluorescence was done one minute before the induction. And it is correct that the diameter is smaller and FM464 fluorescence higher in the spontaneous group because these sperm underwent acrosome exocytosis before the induction, that is, spontaneously.

(2) Figure 3D. Purple dots are not shown in the graph on the right side.

Response R2.2: Modified as requested.

(3) Lines 404-406. "These results suggest that midpiece contraction and motility cessation occur only after acrosome-reacted sperm penetrate the zona pellucida". Since midpiece contraction and motility cessation also occur before the passage through the zona pellucida (Figure 9B), "only" should be deleted.

Response R2.3: Modified as requested.

Reviewer #3 (Recommendations For The Authors):

(1) Do the authors have a hypothesis as to why the observed decrease in midpiece parameter results in cessation of sperm motility? It would be beneficial for the manuscript to include a paragraph about potential mechanisms in the discussion.

Response R3.1: As requested, a potential mechanism has been proposed in the discussion section (line 644-656).

(2) Since the authors propose in Gervasi et al. 2018 that the actin helix might be responsible for the integrity of the mitochondrial sheath and the localization of the mitochondria, is it possible that the proposed change in plasma membrane diameter and actin helix remodeling for example alters the localization of the mitochondria? TEM should be able to reveal any associated structural changes. In its current state, the manuscript lacks experimental evidence supporting the author's claim that the "helical actin structure plays a role in the final stages of motility regulation". The authors should either include additional evidence supporting their hypothesis or tone down their conclusions in the introduction and discussion.

Response  R3.3: We agree with the reviewer. This is an excellent point. As suggested by this reviewer as well as the other reviewers, we have performed SEM to observe the changes in the midpiece observed after its contraction for two main reasons. First, to confirm this observation using a different approach that does not involve the use of membrane dyes. As shown in Author response image 6-10, we have observed that in addition to the midpiece diameter, there is a reorganization of the mitochondria sheet that is also suggested by the SIM experiments. These observations will be explored with more experiments to confirm the structural and functional changes that mitochondria undergo during the contraction. We are currently investigating this phenomenon, These results are now included in the new Figure  2F.

(3) In line 134: The authors write: 'Some of the acrosome reacted sperm moved normally, whereas the majority remained immotile". Do the authors mean that a proportion of the sperm was motile prior to acrosomal exocytosis and became immotile after, or were the sperm immotile to begin with? Please clarify.

Response R3.4: This statement is based on the quantification of the motile sperm after induction of AE within the AR population (Fig. 1C). 

(4) The authors do not provide any experimental evidence supporting the first scenario. In video 1 a lot of sperm do not seem to be moving to begin with, only a few sperm show clear beating in and out of the focal plane. The highlighted sperm that acrosome-reacted upon exposure to progesterone don't seem to be moving prior to the addition of progesterone. In contrast, the sperm that spontaneously acrosome react move the whole time. In video 1 this reviewer was not able to identify one sperm that stopped moving upon acrosomal exocytosis. Similarly in video 3, although the resolution of the video makes it difficult to distinguish motile from non-motile sperm. In video 2 the authors only show sperm that are already acrosome reacted. Please explain and provide additional evidence and statistical analysis supporting that sperm stop moving upon acrosomal exocytosis.

Response R3.5: In videos 1 and 3, the cells are attached to the glass with concanavalin-A, this lectin makes sperm immotile (if well attached) because both the head and tail stick to the glass. The observed motility of sperm in these videos is likely due to them not being properly attached to the glass, which is completely normal. On the contrary, in videos 2 and 4, sperm are attached to the glass with laminin. This is a glycoprotein that only binds the sperm to the glass through its head, that is why they move freely.

(5) Could the authors provide additional information about the FM4-64 fluorescent dye?

What is the mechanism, and how does it visualize structural changes at the flagellum? Since the whole head lights up, does that mean that the dye is internalized and now stains additional membranes, similar to during wound healing assays (PMID 20442251, 33667528). Or is that an imaging artifact? How do the authors explain the correlation between FM4-64 fluorescence increase in the midpiece and the observed change in diameter? Does FM4-64 have solvatochromatic properties?

Response R3.6: We appreciate the insightful queries posed by Reviewer 3, which echo the concerns initially brought forward by Reviewer 1. For a detailed explanation of the mechanism of FM4-64 dye, how we interpret  it, visualizes structural changes in the flagellum, and its behavior during cellular processes, please refer to our detailed response in Response R1.2. In brief, FM464 is a lipophilic styryl dye that preferentially binds to the outer leaflets of cellular membranes due to its amphiphilic nature. Upon binding, the dye becomes fluorescent, allowing for the visualization of membrane dynamics. The increase in fluorescence in the sperm head or midpiece likely results from the dye’s accumulation in areas where membrane restructuring occurs, such as during AE or in response to changes in the flagellum structure.

Regarding the specific questions about internalization and whether FM4-64 stains additional membranes similarly to what is observed in wound healing assays, it's important to note that FM4-64 can indeed be internalized through endocytosis and subsequently label internal vesicular structures. Additionally, FM4-64 may experience changes in its fluorescence as a result of fusion events that increase the lipid content of the plasma membrane, as observed in studies cited (PMID 20442251, 33667528). This characteristic makes FM4-64 valuable not only for outlining cell membranes but also for tracking the dynamics of both internal and external membrane systems, particularly during cellular events that involve significant membrane remodeling, such as wound healing or AE.

Concerning whether the increased fluorescence and observed changes in diameter are artifacts or reflect real biological processes, the correlation observed likely indicates actual changes in the midpiece architecture through molecular mechanisms that remain to be further elucidated. The data presented in Figures 5 and Author response images 6-10 support that this increase in fluorescence is not merely an artifact but a feature of how FM4-64 interacts with its environment. 

Finally, regarding the solvatochromatic properties of FM4-64, while the dye does show changes in its fluorescence intensity in different environments, its solvatochromatic properties are generally less pronounced than those of dyes specifically designed to be solvatochromatic. FM464's fluorescence changes are more a result of membrane interaction dynamics and dye concentration than of solvatochromatic shifts. 

(6) For the experiment summarized in Figure S1, did the authors detect sperm that acrosome-reacted upon exposure to progesterone and kept moving? This reviewer is wondering how the authors reliably measure FM4-64 fluorescence if the flagellum moves in and out of the focal plane. If the authors observe sperm that keep moving, what was the percentage within a sperm population and how did FM4-64 fluorescence change?

Response R3.6: We did identify sperm that underwent acrosome reaction upon exposure to progesterone and continued to exhibit movement. However, due to the issue raised by the reviewer regarding the flagellum going out of focus, we opted to quantify the percentage of sperm that were adhered to the slide (using laminin). This approach allows for the observation of flagellar position over time, facilitating an easy assessment of fluorescence changes. The percentage of sperm that maintained movement after AE is depicted in Figure 1C.

(7) In Figure S1B it doesn't look like the same sperm is shown in all channels or time points, the hook shown in the EGFP channel is not always pointing in the same direction. If FM4-64 is staining the plasma membrane, how do the authors explain that the flagellum seems to be more narrow in the FM4-64 channel than in the brightfield and DsRed2 channel?

Response 3.7: It is the same sperm, but due to technical limitations images were sequentially acquired. For example, for time 5 minutes after progesterone, all images in DIC were taken, then all images in the EGFP channel, then DsRed2* and finally FM4-64. The reason for this was to acquire images as fast as possible, particularly in DIC images which were then processed to get the beat frequency.

Regarding the flagellum that seems to be more narrow in the FM4-64 channel compared to the BF or DsRed2 channel, the explanation is related to the fact that intensity of the DsRed2 signal is stronger than the other two. This higher signal may have increased the amount of photons captured by the detector.

(8) Overall, it would be beneficial to include statistics on how many sperm within a population did change FM4-64 fluorescence during AE and how many did not, in addition to information about motility changes and viability. Did the authors exclude that the addition of FM4-64 causes cell death which could result in immotile sperm or that only dying sperm show an increase in FM4-64 fluorescence?

Response 3.8: The relationship between cell death and the increase in FM4-64 fluorescence is widely discussed in Response P2.3. In our experiments, we always considered sperm that were motile to hypothesize about the relevance of this observation. We have two types of experiments: 

(1) Sperm-egg Fusion: In experiments where sperm and eggs were imaged to observe their fusion, sperm were initially moving and after fusion, the midpiece contraction (increase in FM4-64 fluorescence was observed) indicating that the change in the midpiece (that was observed consistently in all fusing cells analyzed), is part of the process. 

(2) Sperm that underwent AE: we have observed two behaviours as shown in Figure 1: 

a) Sperm that underwent AE and they remain motile without midpiece contraction (they are alive for sure); 

b) Sperm that underwent AE and stopped moving with an increase in FM464 fluorescence. We propose that this contraction during AE is not desired because it will impede sperm from moving forward to the fertilization site when they are in the female reproductive tract. In this case, we acknowledge that the cessation of sperm motility may be attributed to cellular death, potentially correlating with the increased FM4-64 signal observed in the midpiece of immotile sperm that have undergone AE. To address this hypothesis, we conducted image-based flow cytometry experiments, which are well-suited for assessing cellular heterogeneity within large populations.

Regarding the relationship between the increase in FM4-64 and AE, we have always observed that AE is followed by an increase in FM4-64 in the head in mice (PMID: 26819478) as well as in human (PMID: 25100708) sperm. This was originally corroborated with the EGFP sperm. However, not all the cells that undergo AE increase the FM4-64 fluorescence in the midpiece.

(9) The authors report that a fraction of sperm undergoes AE without a change in FM4-64 fluorescence (Figure 1F). How does the [Ca2+]i change in those cells? Again statistics on the distribution of a certain pattern within a population in addition to showing individual examples would be very helpful.

Response 3.9: A recent work shows that an initial increase in [Ca2+]i  is required to induce changes in flagellar beating necessary for hyperactivation (Sánchez-Cárdenas et al., 2018). However, when [Ca2+]i  increases beyond a certain threshold, flagellar motility ceases. These conclusions are based on single-cell experiments in murine sperm with different concentrations of the Ca2+ ionophore, A23187. The authors reported that complete loss of motility was observed when using ionophore concentrations higher than 1 μM. In contrast, spermatozoa incubated with 0.5 μM A23187 remained motile throughout the experiment. Once the Ca2+ ionophore is removed, the sperm would reduce the concentration of this ion to levels compatible with motility and hyperactivation (Navarrete et al., 2016). However, some of the washed cells did not recover mobility in the recorded time window (Sánchez-Cárdenas et al., 2018). These results would indicate that due to the increase in [Ca2+]i  induced by the ionophore, irreversible changes occurred in the sperm flagellum that prevented recovery of mobility, even when the ionophore was not present in the recording medium. 

Taking into account our results, one possible scenario to explain this irreversible change would be the contraction of the midpiece. Our results demonstrate that the increase in [Ca2+]i observed in the midpiece (whether by induction with progesterone, ionomycin or occurring spontaneously) causes the contraction of this section of the flagellum and its subsequent immobilization. 

(10) While the authors results show that changes in [Ca2+]i correlate with the observed reduction of the midpiece diameter, they do not provide evidence that the structural changes are triggered by Ca2+i influx. It could just be a coincidence that both events spatially overlap and that they temporarily follow each other. The authors should either provide additional evidence or tone down their conclusion.

Response 3.10: We agree with the reviewer. As suggested, we have toned down our conclusion.

(11) Are the authors able to detect the changes in the midpiece diameter independent from FM4-64 or other plasma membrane dyes? An alternative explanation could be that the dyes are internalized due to cell death and instead of staining the plasma membrane they are now staining intracellular membranes, resulting in increased fluorescence and giving the illusion that the midpiece diameter decreased. How do the authors explain that the Bodipy-GM1 Signal directly overlaps with DsRed2 and SIR-actin, shouldn't there be some gap? Since the rest of the manuscript is based on that proposed decrease in midpiece diameter the authors should perform orthogonal experiments to confirm their observation.

Response 3.11: As requested by the reviewer, we have not used new methods to visualize the change in sperm diameter in the midpiece. In neither of them, a membrane dye was used. First, we have performed immunofluorescence to detect a membrane protein (GLUT3). Second, we have used scanning electron microscopy. The results are now incorporated in the new Figure 2FG. In both experiments, a change in the midpiece diameter was observed. Please, also visit responses P2.5 and Author response images 8 to 10.  

Regarding the overlap between the signal of Bodipy GM1 (membrane) and the fluorescence of DsRed2 (mitochondria) and Sir-Actin (F-actin), it is only observed in acrosomereacted sperm, not in acrosome-intact sperm (Figure S4). In our view, these structures become closed after midpiece contraction, and the resolution of the images is insufficient to distinguish them clearly. This issue is also evident in Figure 5B. Therefore, we conducted additional experiments using more powerful super-resolution techniques such as STORM (Figures 5D-F).

(12) The proposed gap of 200 nM between the actin helix and the plasma membrane, has been observed by TEM? Considering that the diameter of the mouse sperm midpiece is about 1 um, that is a lot of empty space which leaves only about 600 nm for the rest of the flagellum. The axoneme is 300 nm and there needs to be room for the ODFs and the mitochondria. Please explain.

Response 3.12: Unfortunately, the filament of polymerized actin cannot be observed by TEM. Furthermore, we were discouraged from trying other approaches, such as utilizing phalloidin gold, because for some reason, it does not work properly.

In our view, the 200 nm gap between the actin cytoskeleton and the plasma membrane is occupied by the mitochondria (that is the size that it is frequently reported based on TEM; see https://doi.org/10.1172/jci.insight.166869).

(13) The results provided by the authors do not convince this reviewer that the actin helix moves, either closer to the plasma membrane or toward the mitochondria, the observed differences are minor and not confirmed by statistical analysis.

Response 3.13: As requested, the title of that section was changed. Moreover, our conclusion is exactly as the reviewer is suggesting: “Since the results of the analysis of SiR-actin slopes were not conclusive, we studied the actin cytoskeleton structure in more detail”. This conclusion is based on the statistical analysis shown in Figure S5D-E.

(14) The fluorescence intensity of all plasma membrane dyes increases in all cells chosen by the authors for further analysis. Could the increase in SiR-Actin fluorescence be explained by a microscopy artifact instead of actin helix remodeling? Alternatively, can the authors exclude that the observed increase in SIR-Actin might be an artifact caused by the increase in FM4-64 fluorescence? Since the brightness in the head similarly increases to the fluorescence in the flagellum the staining pattern looks suspiciously similar. Did the authors perform single-stain controls?

Response 3.14: We had similar concerns when we were doing the experiments using SiR-actin. Although we have performed single stain controls to make sure that the actin helix remodelling occurs during the midpiece contraction, we have performed experiments using higher resolution techniques such as STORM using a different probe to stain actin (Phalloidin).

(15) Should actin cytoskeleton remodeling indeed result in a decrease of actin helix diameter, what do the authors propose is the underlying mechanism? Shouldn't that result in changes in mitochondrial structure or location and be visible by TEM? This reviewer is also wondering why the authors focus so much on the actin helix, while the plasma membrane based on the author's results is moving way more dramatically.

Response 3.15: This raises an intriguing point. Currently, we lack an understanding of the underlying mechanism driving actin remodeling, and we are eager to conduct further experiments to explore this aspect. For instance, we are investigating the potential role of Cofilin in remodeling the F-actin network. Initial experiments utilizing STORM imaging have revealed the localization of Cofilin in the midpiece region, where the actin helix is situated.

Regarding mitochondria, thus far, we have not uncovered any evidence suggesting that acrosome reaction or fusion with the egg induces a rearrangement of these organelles within the structure. The rationale for investigating polymerized actin in depth stems from the fact that, alongside the axoneme and other flagellar structures such as the outer dense fibers and fibrous sheet, these are the sole cytoskeletal components present in that particular tail region.

(14) The fact that the authors observe that most sperm passing through the zona pellucida, which requires motility, display high FM4-64 fluorescence, doesn't that contradict the authors' hypothesis that midpiece contraction and motility cessation are connected? Videos confirming sperm motility and information about pattern distribution within the observed sperm population in the perivitelline space should be provided.

Response 3.14: We believe it is a matter of time, as depicted in Figure 1D, our model shows that first the cells lose the acrosome, present motility and low FM4-64 fluorescence in the midpiece (pattern II) and after that, they lose motility and increase FM4-64 fluorescence in the midpiece (pattern III). That is why, we think that when sperm pass the zona pellucida they present pattern II and after some time they evolve into pattern III. 

(15) In the experiments summarized in Figure 8, did all sperm stop moving? Considering that 74 % of the observed sperm did not display midpiece contraction upon fusion, again doesn't that contradict the authors' hypothesis that the two events are interdependent? Similarly, in earlier experiments, not all acrosome-reacted sperm display a decrease in midpiece diameter or stop moving, questioning the significance of the event. If some sperm display a decrease in midpiece diameter and some don't, or undergo that change earlier or later, what is the underlying mechanism of regulation? The observed events could similarly be explained by sperm death: Sperm are dying × plasma membrane integrity changes and plasma membrane dyes get internalized × [Ca2+]i simultaneously increases due to cell death × sperm stop moving.

Response 3.15: The percentage of sperm that did not exhibit midpiece contraction in Fig.8B is 26%, not 74%, indicating that it does not contradict our hypothesis. However, this still represents a significant portion of sperm that remain unchanged in the midpiece, leaving room for various explanations. For instance, it's possible that: i) the change in fluorescence was not detected due to the event occurring after the recording concluded, or ii) in some instances, this alteration simply does not occur. Nevertheless, we did not track subsequent events in the oocyte, such as egg activation, to definitively ascertain the success of fusion. Incorporation of the dye only manifests the initiation of the process.

(16) The authors propose changes in Ca2+ as one potential mechanism to regulate midpiece contraction, however, the Ca2+ measurements during fusion are flawed, as the authors write in the discussion, by potential Ca2+ fluorophore dilution. Considering that the authors observe high Ca2+ in all sperm prior to fusion, could that be a measuring artifact? Were acrosome-intact sperm imaged with the same settings to confirm that sperm with low and high Ca2+ can be distinguished? Should [Ca2+]i changes indeed be involved in the regulation of motility cessation during fusion, could the authors speculate on how [Ca2+]i changes can simultaneously be involved in the regulation of sperm hyperactivation?

Response 3.16: We agree with the reviewer that our experiments using calcium probes are not conclusive for many technical problems. We have toned down our conclusions in the new version of the manuscript.

(17) 74: AE takes place for most cells in the upper segment of the oviduct, not all of them.

Please correct.

Response 3.17: Corrected in the new version.

(18) 88: Achieved through, or achieved by, please correct.

Response 3.18: Corrected in the new version.

(19) 243: Acrosomal exocytosis initiation by progesterone, please specify.

Response 3.19: Modified in the new version.

(20) 277: "The actin cytoskeleton approaches the plasma membrane during the contraction of the midpiece" is misleading. The author's results show the opposite.

Response 3.20: As suggested, this statement was modified.

(21) 298: Why do the authors find it surprising that the F-actin network was unchanged in acrosome-intact sperm that do not present a change in midpiece diameter?

Response 3.21: The reviewer is right. The sentence was modified.

(22) Figures 5D,F: The provided images do not support a shift in the actin helix diameter.

Response 3.22: The shift in the actin helix diameter is provided in Figure 5E and 5G.

(23) Figure S5C: The authors should show representative histograms of spontaneously-, progesterone induced-, and ionomycin-induced AE. Based on the quantification the SiRactin peaks don't seem to move when the AR is induced by progesterone.

Response 3.23: As requested, an ionomycin induced sperm is incorporated.

(24) 392: Which experimental evidence supports that statement?

Response 3.24: A reference was incorporated. 

Reference 13 is published, please update. Response 3.25: updated as requested.

Author response:

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

Reviewer #1 (Public Review):

Using the UK Biobank, this study assessed the value of nuclear magnetic resonance measured metabolites as predictors of progression to diabetes. The authors identified a panel of 9 circulating metabolites that improved the ability in risk prediction of progression from prediabetes to diabetes. In general, this is a well-performed study, and the findings may provide a new approach to identifying those at high risk of developing diabetes. I have some comments that may improve the importance of this study.

We deeply appreciate the reviewer's invaluable time dedicated to the review of this manuscript and the insightful comments to enhance its overall quality.

(1) It is unclear why the authors only considered the top 20 variables in the metabolite selection and why they did not set a wider threshold.

Thank you for the comment. We set the top 20 variables in the metabolite selection balancing the performance of the final diabetes risk prediction model and the clinical applicability due to measurement costs. We have added this explanation in the “Methods” section.

“We chose the intersection set of the top 20 most important variables selected by the three machine learning models, after balancing the performance of the final diabetes risk prediction model and the clinical applicability associated with measurement costs of metabolites.”

(2) The methods section would benefit from a more detailed exposition of how parameter tuning was conducted and the range of parameters explored during the training of the RSF model.

According to the reviewer’s suggestion, we have added a more detailed description of parameters tunning and the range of parameters explored during the training of the RSF model in the “Method S3” section in the Supplementary material.

“The RSF model was fitted using the “randomForestSRC” package and the grid search method was used for hyperparameter tuning. Specifically, the grid search method was used to tune hyperparameters among the RSF model, through minimizing out-of-sample or out-of-bag error1. Each tree in the RSF is constructed from a random sample of the data, typically a bootstrap sample or 63.2% of the sample size (as in the present study). Consequently, not all observations are used to construct each tree. The observations that are not used in the construction of a tree are referred to as out-of-bag observations. In an RSF model, each tree is built from a different sample of the original data, so each observation is “out-of-bag” for some of the trees. The prediction for an observation can then be obtained using only those trees for which the observation was not used for the construction. A classification for each observation is obtained in this way and the error rate can be estimated from these predictions. The resulting error rate is referred to as the out-of-bag error. Through calculating the out-of-bag error in each iteration, the best hyperparameters were finally determined.

The hyperparameters to be tuned and range of grid search in the present study were below: number of trees (50-1000, by 50), number of variables to possibly split at each node (3-6, by 1), and minimum size of terminal node (1-20, by 1)2.”

(3) It is hard to understand the meaning of the decision curve analysis and the clinical implications behind the net benefit, which are required to clarify the application values of models.

Thank you for the comment. We have added more description and discussion about the decision curve analysis in the “Methods” and “Discussion” sections.

“Furthermore, we used decision curve analysis (DCA) to assess the clinical usefulness of prediction model-based guidance for prediabetes management, which calculates a clinical “net benefit” for one or more prediction models in comparison to default strategies of treating all or no patients3.”

“Most importantly, a model with good discrimination does not necessarily have high clinical value. Hence, DCA was used to compare the clinical utility of the model before and after adding the metabolites, and this showed a higher net benefit for the latter than the basic model, suggesting the addition of the metabolites increased the clinical value of prediction, i.e., the potential benefit of guiding management in individuals with prediabetes3,4. These results provided novel evidence supporting the value of metabolic biomarkers in risk prediction and stratification for the progression from prediabetes to diabetes.”

(4) Notably, the NMR platform utilized within the UK Biobank primarily focused on lipid species. This limitation should be discussed in the manuscript to provide context for interpreting the results and acknowledge the potential bias from the measuring platform.

Thank you for the comment. We acknowledged this limitation that NMR platform within the UK Biobank primarily focused on lipid species and the potential bias from the measuring platform and have added this in “Discussion” section.

“Third, the Nightingale metabolomics platform primarily focused on lipids and lipoprotein sub-fractions, and thus the predictive value of other metabolites in the progression from prediabetes to diabetes warranted further research using an untargeted metabolomics approach.”

(5) The manuscript should explain the potential influence of non-fasting status on the findings, particularly concerning lipoprotein particles and composition. There should be a detailed discussion of how non-fasting status may impact the measurement and the findings.

According to the reviewer’s suggestion, we have added more details to explain the potential influence of non-fasting status on our findings in the “Discussion” section.

“Additionally, the use of non-fasting blood samples might increase inter-individual variation in metabolic biomarker concentrations, however, fasting duration has been reported to account for only a small proportion of variation in plasma metabolic biomarker concentrations5. Therefore, we believe the impact of non-fasting samples on our findings would be minor.”

(6) Cross-platform standardization is an issue in metabolism, and further descriptions of quality control are recommended.

Thank you for the comment. We have added more description of quality control in the “Method S1” section in the Supplementary material.

“Metabolic biomarker profiling by Nightingale Health’s NMR platform provides consistent results over time and across spectrometers. Furthermore, the sample preparation is minimal in the Nightingale Health’s metabolic biomarker platform, circumventing all extraction steps. These aspects result in highly repeatable biomarker measurements. Pre-specified quality metrics were agreed between UK Biobank and Nightingale Health to ensure consistent results across the samples, and pilot measurements were conducted. Nightingale Health performed real-time monitoring of the measurement consistency within and between spectrometers throughout the UK Biobank samples. Two control samples provided by Nightingale Health were included in each 96-well plate for tracking the consistency across multiple spectrometers. Furthermore, two blind duplicate samples provided by the UK Biobank were included in each well plate, with the position information unlocked only after results delivery. Coefficient of variation (CV) targets across the metabolic biomarker profile were pre-specified for both Nightingale Health’s internal control samples and UK Biobank’s blind duplicates. The targets were met for each consecutively measured batch of ~25,000 samples. For the majority of the metabolic biomarkers, the CVs were below 5% (https://biobank.ndph.ox.ac.uk/showcase/refer.cgi?id=3000). Further, the distributions of measured biomarkers from 5 sample batches indicated absence of batch effects (https://biobank.ctsu.ox.ac.uk/ukb/ukb/docs/nmrm_app1).”

Reviewer #2 (Public Review):<br /> Deciphering the metabolic alterations characterizing the prediabetes-diabetes spectrum could provide early time windows for targeted preventive measures to extend precision medicine while avoiding disproportionate healthcare costs. The authors identified a panel of 9 circulating metabolites combined with basic clinical variables that significantly improved the prediction from prediabetes to diabetes. These findings provided insights into the integration of these metabolites into clinical and public health practice. However, the interpretation of these findings should take account of the following limitations.

We appreciate the reviewer’s positive comments and encouragement.

(1) First, the causal relationship between identified metabolites and diabetes or prediabetes deserves to be further examined particularly when the prediabetic status was partially defined. Some metabolites might be the results of prediabetes rather than the casual factors for progression to diabetes.

Thank you for your insightful comments. We agree with you that the panel of metabolites in this study might not be the causal factor for progression from prediabetes to diabetes, which needs further validation in experimental studies. We have added this limitation in the “Discussion” section.

“Fifth, we could not draw any conclusion about the causality between the identified metabolites and the risk for progression to diabetes due to the observational nature, which remained to be validated in further experimental studies.”

(2) The blood samples were taken at random (not all in a non-fasting state) and so the findings were subjected to greater variability. This should be discussed in the limitations.

According to the reviewer’s suggestion, we have added more details to explain the potential influence of non-fasting status on our findings in the “Discussion” section.

“Additionally, the use of non-fasting blood samples might increase inter-individual variation in metabolic biomarker concentrations, however, fasting duration has been reported to account for only a small proportion of variation in plasma metabolic biomarker concentrations5. Therefore, we believe the impact of non-fasting samples on our findings would be minor.”

(3) The strength of NMR in metabolic profiling compared to other techniques (i.e., mass spectrometry [MS], another commonly used metabolic profiling method) could be added in the Discussion section.

According to the reviewer’s suggestion, we have added the strength of NMR in metabolic profiling compared to other techniques in the “Discussion” section.

“Circulating metabolites were quantified via NMR-based metabolome profiling within the UK Biobank, which offers metabolite qualification with relatively lower costs and better reproducibility6.”

(4) Fourth, the applied platform focuses mostly on lipid species which may be a limitation as well.

Thank you for the comment. We acknowledged this limitation that NMR platform within the UK Biobank primarily focused on lipid species and the potential bias from the measuring platform and have added this in the “Discussion” section.

“Third, the Nightingale metabolomics platform primarily focused on lipids and lipoprotein sub-fractions, and thus the predictive value of other metabolites in the progression from prediabetes to diabetes warranted further research using an untargeted metabolomics approach.”

(5) It is a very large group with pre-diabetes, but the results only apply to prediabetes and not to the general population. This should be clear, although the authors have also validated the predictive value of these metabolites in the general population.

Thank you for the comment. We agree with you that the results only apply to prediabetes and not to the general population, though they also showed potential predictive value among participants with normoglycemia. We have accordingly modified the relevant expressions in the “Conclusion” section to restrict these findings to participants with prediabetes.

“In this large prospective study among individuals with prediabetes, we detected a panel of circulating metabolites that were associated with an increased risk of progressing to diabetes.”

Recommendations for the Authors:

Thank you for providing the valuable feedback and the time you have dedicated to our work.

(1) In the first paragraph of the Discussion section, please include the specific names of the metabolites selected from machine learning methods.

Thank you for your comment and we have added accordingly in the first paragraph of the “Discussion” section.

“More importantly, our findings suggested that adding the selected metabolites (i.e., cholesteryl esters in large HDL, cholesteryl esters in medium VLDL, triglycerides in very large VLDL, average diameter for LDL particles, triglycerides in IDL, glycine, tyrosine, glucose, and docosahexaenoic acid) could significantly improve the risk prediction of progression from prediabetes to diabetes beyond the conventional clinical variables.”

(2) To enhance the readability and simplicity of the paper, the description of covariate collection in the methods section should be streamlined, with detailed information provided in the supplementary materials.

Thank you for your suggestion and we have moved details about covariates collection to the “Supplementary method S2” to enhance the readability and simplicity of the paper.

“Information on covariates was collected through a self-completed touchscreen questionnaire or verbal interview at baseline, including age, sex, ethnicity, Townsend deprivation index, household income, education, employment status, smoking status, moderate alcohol, physical activity, healthy diet score, healthy sleep score, family history of diabetes, history of cardiovascular disease (CVD), history of hypertension, history of dyslipidemia, history of chronic lung diseases (CLD), and history of cancer.

Physical measurements included systolic (SBP) and diastolic blood pressure (DBP), height, weight, waist circumference (WC), and hip circumference (HC). Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters (kg/m²). Missing covariates were imputed by the median value for continuous variables and a missing indicator for categorical variables. More details about covariates collection can be found in Method S2.”

3. Title for Table 2, using Cox proportional hazards prediction models is not common. You may consider the title "Performance of Cox proportional hazards regression models in prediction of progression of prediabetes to diabetes".

Thank you for your suggestion and we have revised it accordingly.

4. Figure 3, did the authors consider competing risk to compute cumulative incidence function?

Thank you for your comment. We did not consider competing risk from death when plotting the cumulative hazard curves. However, following your suggestion, we have included an additional cumulative hazard plot after considering the competing

References

(1) Janitza S, Hornung R. On the overestimation of random forest's out-of-bag error. PLoS One. 2018;13(8):e0201904.

(2) Tian D, Yan HJ, Huang H, et al. Machine Learning-Based Prognostic Model for Patients After Lung Transplantation. JAMA Netw Open. 2023;6(5):e2312022.

(3) Vickers AJ, van Calster B, Steyerberg EW. A simple, step-by-step guide to interpreting decision curve analysis. Diagn Progn Res. 2019;3:18.

(4) Li J, Xi F, Yu W, Sun C, Wang X. Real-Time Prediction of Sepsis in Critical Trauma Patients: Machine Learning-Based Modeling Study. JMIR Form Res. 2023;7:e42452.

(5) Li-Gao R, Hughes DA, le Cessie S, et al. Assessment of reproducibility and biological variability of fasting and postprandial plasma metabolite concentrations using 1H NMR spectroscopy. PLoS One. 2019;14(6):e0218549.

(6) Geng T-T, Chen J-X, Lu Q, et al. Nuclear Magnetic Resonance–Based Metabolomics and Risk of CKD. American Journal of Kidney Diseases. 2023.

Author response:

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

Reviewer #1 (Public Review):

Summary:

The study of human intelligence has been the focus of cognitive neuroscience research, and finding some objective behavioral or neural indicators of intelligence has been an ongoing problem for scientists for many years. Melnick et al, 2013 found for the first time that the phenomenon of spatial suppression in motion perception predicts an individual's IQ score. This is because IQ is likely associated with the ability to suppress irrelevant information. In this study, a high-resolution MRS approach was used to test this theory. In this paper, the phenomenon of spatial suppression in motion perception was found to be correlated with the visuo-spatial subtest of gF, while both variables were also correlated with the GABA concentration of MT+ in the human brain. In addition, there was no significant relationship with the excitatory transmitter Glu. At the same time, SI was also associated with MT+ and several frontal cortex FCs.

Strengths:

(1) 7T high-resolution MRS is used.

(2) This study combines the behavioral tests, MRS, and fMRI.

Weaknesses:

Major:

In Melnick (2013) IQ scores were measured by the full set of WAIS-III, including all subtests. However, this study only used visual spatial domain of gF. I wonder why only the visuo-spatial subtest was used not the full WAIS-III? I am wondering whether other subtests were conducted and, if so, please include the results as well to have comprehensive comparisons with Melnick (2013).

We thank the reviewer for pointing this out. The decision was informed by Melnick’s findings which indicated high correlations between Surround suppression (SI) and the Verbal Comprehension, Perceptual Reasoning, Working Memory, and Processing Speed Indexes, with correlation coefficients of 0.69, 0.47, 0.49, and 0.50, respectively. It is well-established that the hMT+ region of the brain is a sensory cortex involved in visual perception processing (3D perception). Furthermore, motion surround suppression (SI), a specific function of hMT+, aligns closely with this region's activities. Given this context, the Perception Reasoning sub-ability was deemed to have the clearest mechanism for further exploration. Consequently, we selected the most representative subtest of Perception Reasoning—the Block Design Test—which primarily assesses 3D visual intelligence.” For further clarification, due to these reasons, we conducted only the visuo-spatial subtest.

Minor:

Comments:

In the first revised version, we addressed the following recommendations in the 'Author response' file titled 'Recommendation for the authors.' It seems our response may not have reached you successfully. We would like to share and expand upon our response here:

(1) Table 1 and Table supplementary 1-3 contain many correlation results. But what are the main points of these values? Which values do the authors want to highlight? Why are only p-values shown with significance symbols in Table supplementary 2??

(1.1) What are the main points of these values?

Thank reviewer for pointing this out. These correlations represent the relationship between behavior task (SI/BDT) and resting-state functional connectivity. It indicates that left hMT+ is involved in the efficient information integration network when it comes to BDT task. In addition, left hMT+’s surround suppression is involved in several hMT+ - frontal connectivity. Furthermore, the overlap regions between two task indicates the underlying mechanism.

(1.2) Which values do the authors want to highlight?

Table 1 and Table Supplementary 1-3 present the preliminary analysis results for Table 2 and Table Supplementary 4-6. So, we generally report all value. Conversely, in the Table 2 and Table Supplementary 4-6, we highlight the value which support our main conclusion.

(1.3) Why are only p-values shown with significance symbols in Table Supplementary 2?

Thank you for pointing this out, it is a mistake. We have revised it and delete the significance symbols.

(2) Line 27, it is unclear to me what is "the canonical theory".

We thank reviewer for pointing this out. We have revised “the canonical theory" to “the prevailing opinion” (line 27)

(3) Throughout the paper, the authors use "MT+", I would suggest using "hMT+" to indicate the human MT complex, and to be consistent with the human fMRI literature.

We thank reviewer for pointing this out. We have revised them.

(4) At the beginning of the results section, I suggest including the total number of subjects. It is confusing what "31/36 in MT+, and 28/36 in V1" means.

We thank reviewer for pointing this out. We have included the total number of subjects in the beginning of result section. (line 110, line 128)

(5) Line 138, "This finding supports the hypothesis that motion perception is associated with neural activity in MT+ area". This sentence is strange because it is a well-established finding in numerous human fMRI papers. I think the authors should be more specific about what this finding implies.

We thank reviewer for pointing this out. We have revised it to:” This finding is in line with prior results, which indicates that motion perception is associated with neural activity in hMT+ area, but not in EVC (primarily in V1)” (lines 156-158)

(6) There are no unit labels for all x- and y-axies in Figure 1. I only see the unit for Conc is mmol per kg wet weight.

We thank reviewer for pointing this out. Figure 1 is a schematic and workflow chart, so labels for x- and y-axes are not needed. I believe this confusion might pertain to Figure 3. In Figures 3a and 3b, the MRS spectrum does not have a standard y-axis unit as it varies based on the individual physical conditions of the scanner; it is widely accepted that no y-axis unit is used. While the x-axis unit is ppm, which indicate the chemical shift of different metabolites. In Figure 3c, the BDT represents IQ scores, which do not have a standard unit. Similarly, in Figures 3d and 3e, the Suppression Index does not have a standard unit.

(7) Although the correlations are not significant in Figure Supplement 2&3, please also include the correlation line, 95% confidence interval, and report the r values and p values (i.e., similar format as in Figure 1C).

We thank reviewer for pointing this out. We have revised them and include the correlation line, 95% confidence interval, r values and p values.

(8) There is no need to separate different correlation figures into Figure Supplementary 1-4. They can be combined into the same figure.

We thank reviewer for the suggestion. However, each correlation figure in the supplementary figures has its own specific topic and conclusion. Please notes that in the revised version, we have added a figure showing the EVC (primarily in V1) MRS scanning ROI as Supplementary Figure 1. Therefore, the figures the reviewer is concerned about are Supplementary Figure 2-5. The correlation figures in Supplementary Figure 2 indicate that GABA in EVC (primarily in V1) does not show any correlation with BDT and SI, illustrating that inhibition in EVC (primarily in V1) is unrelated to both 3D visuo-spatial intelligence and motion suppression processing. The correlations in Supplementary Figure 3 indicate that the excitation mechanism, represented by Glutamate concentration, does not contribute to 3D visuo-spatial intelligence in either hMT+ or EVC (primarily in V1). Supplementary Figure 4 validates our MRS measurements. Supplementary Figure 5 addresses potential concerns regarding the impact of outliers on correlation significance. Even after excluding two “outliers” from Figures 3d and 3e, the correlation results remain stable.

(9) Line 213, as far as I know, the study (Melnick et al., 2013) is a psychophysical study and did not provide evidence that the spatial suppression effect is associated with MT+.

We thank reviewer for pointing this out. It was a mistake to use this reference, and we have revised it accordingly. (line 242)

(10) At the beginning of the results, I suggest providing more details about the motion discrimination tasks and the measurement of the BDT.

We thank reviewer for pointing this out. We have included some brief description of task in the beginning of result section. (lines 116-120)

(11) Please include the absolute duration thresholds of the small and large sizes of all subjects in Figure 1.

We thank reviewer for the suggestion. We have included these results in Figure 3.

(12) Figure 5 is too small. The items in plot a and b can be barely visible.

We thank reviewer for pointing this out. We increase the size and resolution of the Figure.

Reviewer #3 (Public Review):

(1) Throughout the manuscript, hMT+ connectivity with the frontal cortex has been treated as an a priori hypothesis/space. However, there is no such motivation or background literature mentioned in the Introduction. Can the authors clarify the necessity of functional connectivity? In other words, can BOLD activity of hMT+ in the localizer task substitute for functional connectivity between hMT+ and the frontal cortex?

(1.1) Throughout the manuscript, hMT+ connectivity with the frontal cortex has been treated as an a priori hypothesis/space. However, there is no such motivation or background literature mentioned in the Introduction. Can the authors clarify the necessity of functional connectivity?

We thank reviewer for pointing this out. We offered additional motivation and background literature in the introduction: “Frontal cortex is usually recognized as the cognitive core region (Duncan et al., 2000; Gray et al., 2003). Strong connectivity between the cognitive regions suggests a mechanism for large-scale information exchange and integration in the brain (Barbey, 2018; Cole et al., 2012).  Therefore, the potential conjunctive coding may overlap with the inhibition and/or excitation mechanism of hMT+. Taken together, we hypothesized that 3D visuo-spatial intelligence (as measured by BDT) might be predicted by the inhibitory and/or excitation mechanisms in hMT+ and the integrative functions connecting hMT+ with frontal cortex (Figure 1a).” (lines 67-74). Additionally, we have included a whole-brain analysis for validation. Functional connectivity reveals the information exchange relationships across regions, enhancing our understanding of how hMT+ and the frontal cortex collaborate when solving visual-spatial intelligence tasks.

(1.2) In other words, can BOLD activity of hMT+ in the localizer task substitute for functional connectivity between hMT+ and the frontal cortex?

We thank the reviewer for this question. The localizer task was used solely for defining the hMT+ MRS scanning area. Functional connectivity was measured using resting-state fMRI. Research has shown that resting-state functional connectivity between the frontal cortex and other ROIs can further reveal the neural mechanisms underlying intelligence tasks (Song et al., 2008).

(2) There is an obvious mismatch between the in-text description and the content of the figure:<br /> "In contrast, there was no correlation between BDT and GABA levels in V1 voxels (figure supplement 1a). Further, we show that SI significantly correlates with GABA levels in hMT+ voxels (r = 0.44, P = 0.01, n = 31, Figure 3d). In contrast, no significant correlation between SI and GABA concentrations in V1 voxels was observed (figure supplement 1b)."

We thank reviewer for pointing this out. We have revised it. The revised version is :” In contrast, there was no correlation between BDT and GABA levels in V1 voxels (figure supplement 2a). Further, we show that SI significantly correlates with GABA levels in hMT+ voxels (r = 0.44, P = 0.01, n = 31, Figure 3d). In contrast, no significant correlation between SI and GABA concentrations in V1 voxels was observed (figure supplement 2b).” (lines 151-156)

(3) The authors' response to my previous round of review indicated that the "V1 ROIs" covered a substantial amount of V3 (32%). Therefore, it would no longer be appropriate to call these "V1 ROIs". I'd suggest renaming them as "Early Visual Cortex (EVC) ROIs" to be more accurate. Can the authors justify why choosing the left hemisphere for visual intelligence task, which is typically believed to be right lateralized?

(3.1) The authors' response to my previous round of review indicated that the "V1 ROIs" covered a substantial amount of V3 (32%). Therefore, it would no longer be appropriate to call these "V1 ROIs". I'd suggest renaming them as "Early Visual Cortex (EVC) ROIs" to be more accurate.

We thank the reviewer for pointing this out. We have revised our description of the MRS scanning ROIs to Early Visual Cortex (EVC). Since the majority of our EVC ROIs are in V1 (around 70%) and almost no V2 was included, we decided to mark the EVC ROIs with the explanation "primarily in V1" for better clarification. This terminology has been widely used to better emphasize the V1-based experimental design.

(3.2) Can the authors justify why choosing the left hemisphere for visual intelligence task, which is typically believed to be right lateralized?

We thank the reviewer for pointing this out. The use of the left MT/V5 as a target was motivated by studies demonstrating that left MT+/V5 TMS is more effective at causing perceptual effects (Tadin et al., 2011). Therefore, we chose to use the left hMT+ as our MRS ROI and maintain consistency across different models' ROIs. Additionally, our results support the notion that the visual intelligence task is right lateralized in the frontal cortex. At the resting-fMRI level, we found that significant ROIs, where functional connectivity is highly correlated with BDT scores, are in the right frontal cortex (Figure 5a, b).

(4) "Small threshold" and "large threshold" are neither standard descriptions, and it is unclear what "small threshold" refers to in the following figure caption. Additionally, the unit (ms) is confusing. Does it refer to timing?<br /> "(f) Peason's correlation showing significant negative correlations between BDT and small threshold."

Thank you for pointing this out; we agree with your suggestion. We have revised the terms “small threshold” and “large threshold” to “duration threshold of small grating” and “duration threshold of large grating”, respectively. The unit (ms) refers to timing. The details are described in the methods section: “The duration was adaptively adjusted in each trial, and duration thresholds were estimated using a staircase procedure. Thresholds for large and small gratings were obtained from a 160-trial block that contained four interleaved 3-down/1-up staircases. For each participant, we computed the correct rate for different stimulus durations separately for each stimulus size. These values were then fitted to a cumulative Gaussian function, and the duration threshold corresponding to the 75% correct point on the psychometric function was estimated for each stimulus size”.

(5) In the response letter, the authors mentioned incorporating the neural efficiency hypothesis in the Introduction, but the revised Introduction does not contain such information.

We thank the reviewer for pointing this out. In our revised version, the second paragraph of the introduction addresses the neural efficiency hypothesis: “The “neuro-efficiency” hypothesis is one explanation for individual differences in gF (Haier et al., 1988). This hypothesis puts forward that the human brain’s ability to suppress irrelevant information leads to more efficient cognitive processing. Correspondingly, using a well-known visual motion paradigm (center-surround antagonism) (Liu et al., 2016; Tadin et al., 2003), Melnick et al found a strong link between suppression index (SI) of motion perception and the scores of the block design test (BDT, a subtest of the Wechsler Adult Intelligence Scale (WAIS), which measures the visuo-spatial component (3D domain) of gF (Melnick et al., 2013). Motion surround suppression (SI), a specific function of human extrastriate cortical region, middle temporal complex (hMT+), aligns closely with this region's activities (Gautama & Van Hulle, 2001). Furthermore, hMT+ is a sensory cortex involved in visual perception processing (3D domain) (Cumming & DeAngelis, 2001). These findings suggest that hMT+ potentially plays a significant role in 3D visuo-spatial intelligence by facilitating the efficient processing of 3D visual information and suppressing irrelevant information. However, more evidence is needed to uncover how the hMT+ functions as a core region for 3D visuo-spatial intelligence.” (lines 51-66)

Recommendations for the authors:

Reviewer #1 (Recommendations for The Authors):

In the Code availability, it states that "this paper does not report original code". It seems weird because at least the code to reproduce the figures from the data should be provided.

Thank you for pointing this out. Almost all figures were created using software such as DPABI, BrainNet, and GraphPad Prism 9.5, which are manually operated and do not require code adjustments. However, for the MRS fitting curve, we can provide our MATLAB code for redrawing the MRS fitting. The code has been uploaded to GitHub.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this work, Qiu and colleagues examined the effects of preovulatory (i.e., proestrous or late follicular phase) levels of circulating estradiol on multiple calcium and potassium channel conductances in arcuate nucleus kisspeptin neurons. Although these cells are strongly linked to a role as the "GnRH pulse generator," the goal here was to examine the physiological properties of these cells in a hormonal milieu mimicking late proestrus, the time of the preovulatory GnRH-LH surge. Computational modeling is used to manipulate multiple conductances simultaneously and support a role for certain calcium channels in facilitating a switch in firing mode from tonic to bursting. CRISPR knockdown of the TRPC5 channel reduced overall excitability, but this was only examined in cells from ovariectomized mice without estradiol treatment. The patch clamp experiments are comprehensive and overall solid but a direct demonstration of the role of these conductances in being necessary for surge generation (or at least having a direct physiological consequence on surge properties) is lacking, substantially reducing the impact of the findings.

Strengths:

(1) Examination of multiple types of calcium and potassium currents, both through electrophysiology and molecular biology.

(2) Focus on arcuate kisspeptin neurons during the surge is relatively conceptually novel as the anteroventral periventricular nucleus (AVPV) kisspeptin neurons have received much more attention as the "surge generator" population.

(3) The modeling studies allow for direct examination of manipulation of single and multiple conductances, whereas the electrophysiology studies necessarily require examination of each current in isolation. The construction of an arcuate kisspeptin neuron model promises to be of value to the reproductive neuroendocrinology field.

We thank the reviewer for recognizing our comprehensive examination of Kiss-ARH neurons through electrophysiological, molecular and computational modeling of their activity during the preovulatory surge, which as the reviewer pointed out is “conceptually novel.”  We  have bolstered our argument that Kiss1-ARH neurons transition from synchronized firing to burst firing with the E2-mediated regulation of channel expression with the addition of new experiments. We have addressed the recommendations as follows:

Weaknesses:

(1) The novelty of some of the experiments needs to be clarified. This reviewer's understanding is that prior experiments largely used a different OVX+E2 treatment paradigm mimicking periods of low estradiol levels, whereas the present work used a "high E2" treatment model. However, Figures 10C and D are repeated from a previous publication by the same group, according to the figure legend. Findings from "high" vs. "low" E2 treatment regimens should be labeled and clearly separated in the text. It would also help to have direct comparisons between results from low E2 and high E2 treatment conditions.

We have revised Figures 10C and 10D to include new findings (only) on Tac2 and Vglut2 expression in OVX and E2-treated Kiss1ARH.  Most importantly, our E2 treatment regime is clearly stated in the Methods and is exactly the same that was used previously (Qiu, eLife 2016 and Qiu, eLife 2018) for the induction of the LH surge in OVX mice (Bosch, Molecular and Cellular Endocrinology 2013) .

(2) In multiple places, links are made between the changes in conductances and the transition from peptidergic to glutamatergic neurotransmission. However, this relationship is never directly assessed. The data that come closest are the qPCR results showing reduced Tac2 and increased Vglut2 mRNA, but in the figure legend, it appears that these results are from a prior publication using a different E2 treatment regimen.

In the revised Figure 1, we have now included a clear depiction of the transition from synchronized firing driven by NKB signaling in OVX females to burst firing driven by glutamate in E2-treated females. All of the qPCR results in the revised manuscript are new.  We have used the same E2 treatment paradigm as previously published (Qiu, eLife 2018).

(3) Similarly, no recordings of arcuate-AVPV glutamatergic transmission are made so the statements that Kiss1ARH neurons facilitate the GnRH surge via this connection are still only conjecture and not supported by the present experiments.

Using a horizontal hypothalamic slice preparation, we have shown that Kiss1-ARH neurons excite GnRH neurons via Kiss1ARH glutaminergic input to Kiss1AvPV/Pen neurons (summarized in Fig. 12, Qiu, eLife 2016). We did not think that it was necessary to repeat these experiments for the current manuscript.

(4) Figure 1 is not described in the Results section and is only tenuously connected to the statement in the introduction in which it is cited. The relevance of panels C and D is not clear. In this regard, much is made of the burst firing pattern that arises after E2 treatment in the model, but this burst firing pattern is not demonstrated directly in the slice electrophysiology examples.

We have extensively revised Figure 1 to include new whole-cell, current clamp recordings that document burst firing  in  E2-treated, OVX females, which is now cited in the Results.

(5) In Figure 3, it would be preferable to see the raw values for R1 and R2 in each cell, to confirm that all cells were starting from a similar baseline. In addition, it is unclear why the data for TTA-P2 is not shown, or how many cells were recorded to provide this finding.

Before initiating photo-stimulation for each Kiss1-ARH neuron, we adjust the resting membrane potential to -70 mV, as noted  in each panel in Figure 3, through current injections. We have now included new findings on the effects of the T-channel blocker TTA-P2 on slow EPSP in the revised Figure 3. The number of cells tested with each calcium channel blocker is depicted in each of the bar graphs summarizing the effects of the blockers (Figure 3E).

(6) In Figure 5, panel C lists 11 cells in the E2 condition but panel E lists data from 37 cells. The reason for this discrepancy is not clear.

In Figure 5D, we measured the L-, N-, P/Q and R channel currents after pretreatment with TTA-P2 to block the T-type current, whereas in Figure 5C, we measured the total current without TTA-P2.

(7) In all histogram figures, it would be preferable to have the data for individual cells superimposed on the mean and SEM.

In the revised Figures we have included the individual data points for the individual neurons and animals (qPCR). 

(8) The CRISPR experiments were only performed in OVX mice, substantially limiting interpretation with respect to potential roles for TRPC5 in shaping arcuate kisspeptin neuron function during the preovulatory surge.

The TRPC5 channels are most  important for generating slow EPSPs when expression of NKB is high in the OVX state. Conversely, the glutamatergic response becomes more significant when the expression of NKB and TRPC5 channel are muted in the E2-treated state. Therefore, the CRISPR experiments were specifically conducted in OVX mice to maximize the effects.

(9) Furthermore, there are no demonstrations that the CRISPR manipulations impair or alter the LH surge.

In this manuscript, our focus is on the cellular electrophysiological activity of the Kiss1ARH neurons in OVX and E2-treated OVX females. Exploration of CRISPR manipulations related to the LH surge is certainly slated for future  experiments, but these in vivo experiments are  beyond the scope of these comprehensive cellular electrophysiological and molecular studies.

(10) The time of day of slice preparation and recording needs to be specified in the Methods.

We have provided the times of slice preparation and recordings in the revised Methods and Materials.

Reviewer #2 (Public Review):

Summary:

Kisspeptin neurons of the arcuate nucleus (ARC) are thought to be responsible for the pulsatile GnRH secretory pattern and to mediate feedback regulation of GnRH secretion by estradiol (E2). Evidence in the literature, including the work of the authors, indicates that ARC kisspeptin coordinate their activity through reciprocal synaptic interactions and the release of glutamate and of neuropeptide neurokinin B (NKB), which they co-express. The authors show here that E2 regulates the expression of genes encoding different voltage-dependent calcium channels, calcium-dependent potassium channels, and canonical transient receptor potential (TRPC5) channels and of the corresponding ionic currents in ARC kisspeptin neurons. Using computer simulations of the electrical activity of ARC kisspeptin neurons, the authors also provide evidence of what these changes translate into in terms of these cells' firing patterns. The experiments reveal that E2 upregulates various voltage-gated calcium currents as well as 2 subtypes of calcium-dependent potassium currents while decreasing TRPC5 expression (an ion channel downstream of NKB receptor activation), the slow excitatory synaptic potentials (slow EPSP) elicited in ARC kisspeptin neurons by NKB release and expression of the G protein-associated inward-rectifying potassium channel (GIRK). Based on these results, and on those of computer simulations, the authors propose that E2 promotes a functional transition of ARC kisspeptin neurons from neuropeptide-mediated sustained firing that supports coordinated activity for pulsatile GnRH secretion to a less intense firing in glutamatergic burst-like firing pattern that could favor glutamate release from ARC kisspeptin. The authors suggest that the latter might be important for the generation of the preovulatory surge in females.

Strengths:

The authors combined multiple approaches in vitro and in silico to gain insights into the impact of E2 on the electrical activity of ARC kisspeptin neurons. These include patch-clamp electrophysiology combined with selective optogenetic stimulation of ARC kisspeptin neurons, reverse transcriptase quantitative PCR, pharmacology, and CRIPR-Cas9-mediated knockdown of the Trpc5 gene. The addition of computer simulations for understanding the impact of E2 on the electrical activity of ARC kisspeptin cells is also a strength.

The authors add interesting information on the complement of ionic currents in ARC kisspeptin neurons and on their regulation by E2 to what was already known in the literature. Pharmacological and electrophysiological experiments appear of the highest standards. Robust statistical analyses are provided throughout, although some experiments (illustrated in Figures 7 and 8) do have rather low sample numbers.

The impact of E2 on calcium and potassium currents is compelling. Likewise, the results of Trpc5 gene knockdown do provide good evidence that the TRPC5 channel plays a key role in mediating the NKB-mediated slow EPSP. Surprisingly, this also revealed an unsuspected role for this channel in regulating the membrane potential and excitability of ARC kisspeptin neurons.

We thank the reviewer for recognizing that the “pharmacological and electrophysiological experiments appear of the highest standards” and “the addition of the computer modeling for understanding the impact of E2 on the electrical activity of ARC kisspeptin cells is also a strength.  However, we agree with the reviewer that we needed to provide a direct demonstration of “burst-like” firing of Kiss1-ARH neurons, which we have provided in Figure 1. We have addressed the other recommendations as follows:

Weaknesses:

The manuscript also has weaknesses that obscure some of the conclusions drawn by the authors.

One has to do with the fact that "burst-like" firing that the authors postulate ARC kisspeptin neurons transition to after E2 replacement is only seen in computer simulations, and not in slice patch-clamp recordings. A more direct demonstration of the existence of this firing pattern, and of its prominence over neuropeptide-dependent sustained firing under conditions of high E2 would make a more convincing case for the authors' hypothesis.

We have provided  a more direct demonstration of the existence of this firing pattern in the whole-cell current clamp experiments in the revised Figure 1.

In addition, and quite importantly, the authors compare here two conditions, OVX versus OVX replaced with high E2, that may not reflect the physiological conditions (the diestrous [low E2] and proestrous [high E2] stages of the estrous cycle) under which the proposed transition between neuropeptide-dependent sustained firing and less intense burst firing might take place. This is an important caveat to keep in mind when interpreting the authors' findings. Indeed, that E2 alters certain ionic currents when added back to OVX females, does not mean that the magnitude of these ionic currents will vary during the estrous cycle.

We have published that the magnitude of the slow EPSP, which is TRPC5 channel mediated, varies throughout the estrous cycle with the slow EPSP reaching a maximal amplitude during diestrus, which was significantly reduced during proestrus,  similar to that found in OVX compared to E2-treated, OVX females (Figure 2, Qiu, eLife 2016).  Moreover, TRPC5 channel mRNA expression,  similar to the peptides, is downregulated by an E2 treatment (Figure 10 this manuscript) that mimics proestrus levels of the steroid (Bosch et al., Mol Cell Endocrinology 2013). Furthermore, the magnitude of ionic currents is directly proportional to the number of ion channels expressed in the plasma membrane, which we have found correlates with mRNA expression. Therefore, it is likely that the magnitude of these ionic currents will vary during the estrous cycle.

Lastly, the results of some of the pharmacological and genetic experiments may be difficult to interpret as presented. For example, in Figure 3, although it is possible that blockade of individual calcium channel subtypes suppresses the slow EPSP through decreased calcium entry at the somato-dendritic compartment to sustain TRPC5 activation and the slow depolarization (as the authors imply), a reasonable alternative interpretation would be that at least some of the effects on the amplitude of the slow EPSP result from suppression of presynaptic calcium influx and, thus, decreased neurotransmitter and neuropeptide secretion. Along the same lines, in Figure 12, one possible interpretation of the observed smaller slow EPSPs seen in mice with mutant TRPC5 could be that at least some of the effect is due to decreased neurotransmitter and neuropeptide release due to the decreased excitability associated with TRPC5 knockdown.

The reviewer raises a good point, but our previous findings clearly demonstrated that chelating intracellular calcium with BAPTA in whole-cell current clamp recordings abolishes the slow EPSP and persistent firing (Qiu et al., J. Neurosci 2021), which we have noted is the  rationale for dissecting out the contribution of T, R, N, L and P/Q calcium channels to the slow EPSP in our current studies.  The revised Figure 3 also includes the effects of T-channel blocker.

However, to further bolster the argument for the post-synaptic contribution of the calcium channels to the slow EPSP  and eliminate the potential presynaptic effects of the calcium channel blockers on the postsynaptic slow EPSP amplitude, which may result from reduced presynaptic calcium influx and subsequently decreased neurotransmitter release, we have utilized an additional strategy. Specifically, we have measured the response to the externally administered TACR3 agonist senktide under conditions in which the extracellular calcium influx, as well as neurotransmitter and neuropeptide release, are blocked (revised Figure 3).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) The use of optogenetics in Figure 3 to trigger the slow EPSP could be better clarified in the text.

We have clarified in the Methods the optogenetic protocol for generating the slow EPSP, which we have published previously (Qiu et al., eLife 2016; eLife 2018, J. Neurosci 2021).

(2) The citation for Figure 4C in the text does not match what is shown in the figure.

Figure 4C has been removed in the revised manuscript.

(3) Figure 5 - it would be clearer to have panel D labeled as "model results" or similar to distinguish it from the slice recording data.

Panel D has been labeled as "Model results”.

(4) The text in lines 191-197 in the Results may be better suited to the Discussion.

We have modified the text in order to present the new findings without the discussion points.

(5) It is somewhat confusing to have figure panels cited out of order in the main text (e.g., 7H before 7G and 8H before 8G).

We have edited the text to report the findings in the proper order of the panels in Figures 7 and 8.

Reviewer #2 (Recommendations For The Authors):

- The observations that E2 treatment of OVX mice has an effect on the magnitude of a number of ionic currents does not necessarily mean that these changes will be seen during the estrous cycle, in response to fluctuations in circulating E2 concentrations. Experiments comparing either different estrous cycle stages or OVX mice treated with low or high E2 would be required to gain insight into this question. As such, the relevance of the authors' findings (however interesting these are as they stand) to any potential physiological endocrine/reproductive state transition is questionable, in the reviewer's opinion. The authors should acknowledge this important caveat and moderate the interpretations of their findings and the conclusions of their manuscript accordingly.

We have published that the magnitude of the slow EPSP, which is TRPC5 channel mediated, varies throughout the estrous cycle with the slow EPSP being large during diestrus and significantly reduced during proestrus,  similar to that found in OVX compared to E2-treated, OVX females (Figure 2, Qiu, eLife 2016).  Moreover, TRPC5 channel mRNA expression,  similar to the peptides, is downregulated by an E2 treatment (Figure 10 this manuscript) that mimics proestrus levels of the steroid (Bosch et al., Mol Cell Endocrinology 2013). Furthermore, the magnitude of ionic currents is directly proportional to the number of ion channels expressed in the plasma membrane, which we have found correlates with mRNA expression. Therefore, it is likely that the magnitude of these ionic currents will vary during the estrous cycle.

- The bursting firing pattern that the authors refer to and postulate will favor glutamate release under high E2 conditions is only seen in the computer simulations, not in patch-clamp recordings in brain slices (see also comment below). This substantially weakens some of the conclusions of the manuscript. Unless the authors can convincingly demonstrate a change in ARC kisspeptin firing pattern in response to increasing E2 using electrophysiology, these conclusions should be moderated.

We now include examples of burst firing activity under E2-treatment conditions in Figure 1 and have included summary figure (pie chart) documenting that a significant percentage of cells exhibit this activity with E2 treatment.  

Other comments:

- Title: "E2 elicits distinct firing patterns" is not shown in this work. As such, the title needs to be revised.

We now show these distinct firing patterns in Figure 1, so we think the wording in the title is an accurate reflection of our findings. 

- Abstract: some of the interpretations are overstated, in the reviewer's opinion.

Line 23, "... elevating the whole-cell calcium current and contributing to high-frequency firing" should be moderated, as what is shown by the authors is that blockade of calcium channel subtypes suppresses the slow EPSP and associated firing, the frequency of which is not reported (see also a later comment).

We now include examples of burst firing activity under E2-treatment conditions in Figure 1 and have modified the abstract to state “high frequency burst firing.”

Lines 26-28, that "mathematical modeling confirmed the importance of TRPC5 channels for initiating and sustaining synchronous firing, while GIRK channels, activated by Dyn binding to kappa opioid receptors, were responsible for repolarization" is simply not what the simulations show, in the reviewer's opinion. Indeed, there is no consideration of synchronous activity in the model, which simulates the firing of a single ARC kisspeptin neuron. Further, the model shows that TRPC5 can contribute to overall excitability (firing in response to current injection, Figure 12G) and that increasing TRPC5 conductance increases firing in response to NKB while this is decreased by adding GIRK conductance to the model (Figure 13A). Therefore, considerations of the importance of TRPC5 channels in initiating synchronous firing and the role of Dyn A-induced GIRK activity should not be included in the interpretations of the mathematical simulations.

The significance of synchronization lies in the fact that when neuronal networks synchronize, the behavior of each neuron within the network becomes identical. In such scenarios, the firing of a single neuron mirrors the activity of the entire neuronal network. Consequently, our model simulations, based on a single-cell neuronal model, can be utilized to make reliable inferences about synchronized neuronal activity.

Lines 31-33 (also lines 92-95), that "the transition to burst firing with high, preovulatory levels of E2 facilitates the GnRH surge through its glutamatergic synaptic connection to preoptic Kiss1 neurons" is not supported by the experiments (physiologic or computational) described in the manuscript, and is, therefore, only speculative. These statements should be removed throughout the manuscript.

Previously, we (Qiu et al., (eLife 2016) documented a direct glutamatergic projection from Kiss1-ARH neurons to Kiss1-AVPV/PeN neurons.  Moreover, Lin et al. (Frontiers Endocrinology 2021) demonstrated that low frequency stimulation of Kiss1-ARH:ChR2 neurons, that is known to only release glutamate, boosts the LH surge, and in a follow-up paper the O’Byrne lab blocked this stimulation with ionotropic glutamate antagonists (Shen et al., Frontiers in Endocrinology 2022).  We have included these references in the Introduction and Discussion, but we did not think that it was necessary to cite these papers in the Abstract.  However, we have re-worded this final statement in the Abstract to: “the transition to burst firing with high, preovulatory levels of E2 would facilitate the GnRH surge….” 

- Introduction: the usefulness of Figure 1 is questionable. From reading the figure legend, it is the reviewer's understanding that panels A and B are published elsewhere (there is no description of methods or results in the manuscript). Further, panels C and D are meant to illustrate that ARC kisspeptin neurons display different types of firing in OVX vs E2-treated OVX mice. The legend to C indicates that the trace illustrates "synchronous firing" but shows one cell (how can this be claimed as synchronous?) - the legend to D indicates that the trace "demonstrates" burst firing in ARC kisspeptin neurons. This part of the figure is, in the reviewer's opinion, misleading because these are only two examples (no quantifications or replicates are provided) obtained by stimulating firing in two different endocrine conditions by two different agonists. The "demonstration" of differential firing patterns would require a thorough examination of firing patterns in response to current injections (as in Figure 12 E-F) or in response to the two agonists, under the different hormonal conditions.

Figure 1 has now been completely revised to include new data documenting the different firing patterns.  The methods detailing these experiments can be found in the Material and Methods section.

The introduction presents a rather incomplete picture of what is known regarding how ARC kisspeptin neurons might coordinate their activity to drive episodic GnRH secretion, and it omits published work showing that blockade of glutamate receptors (in particular AMPA receptors) decreases ARC kisspeptin neuron coordinated activity in the brain slices and in vivo and suppresses pulsatile GnRH/LH secretion in mice.

If we are not mistaken, the reviewer is referring to fiber photometry recordings of GCaMP activity, which we cite in the Discussion.  However, for the Introduction we tried to “set the stage” for our studies on measuring the individual channels underlying the different firing patterns and how they are regulated by E2.

The introduction is also quite long with extensive descriptions of previous work by the authors and in other brain areas that would be better suited for the discussion.

Again, we are trying to rationalize why we focused on particular ion channels based on the literature.

- Results: lines 129-132 should be moderated, as whether calcium channels increase excitability or facilitate TRPC5 channel opening has not been directly assessed here.

High frequency optogenetic stimulation of Kiss1-ARH neurons and NKB through its cognate receptor (TACR3) activates TRPC 5 channels (Qiu et al., eLife 2016; J. Neurosci 2021). BAPTA prevents the opening of TRPC5 channels and abrogates the slow EPSP following high frequency stimulation.  Figure 3 documents that inhibition of voltage-activated calcium channels attenuates the slow EPSP, which results in a decrease in excitability.

Lines 145-146, one limitation of this experiment is that blockade of calcium channel subtypes will not only affect calcium entry and subsequent actions of calcium on TRPC5 channels but also impair the release of neurotransmitters and neuropeptides from kisspeptin neurons. The interpretation that "calcium channels contribute to maintaining the sustained depolarization underlying the slow EPSP" needs, therefore, to be moderated as it is not possible to extract the direct contribution of calcium channels to the activation of TRPC5 channels from these experiments.

We cited our previous findings documenting that chelating intracellular calcium with BAPTA abolishes the slow EPSP and persistent firing (Qiu et al., J Neurosci 2021).  However, to eliminate the potential effects of calcium channel blockers on the slow EPSP amplitude, which may result from reduced presynaptic calcium influx and subsequently decreased neurotransmitter and neuropeptide secretion, we adopted a different strategy by comparing responses between Senktide and Cd2+ plus Senktide. Our findings revealed that the non-selective Ca2+ channel blocker Cd2+ significantly inhibited Senk-induced inward current (Figures 3F-H).

Panel C should be removed from Figure 4, as it is published elsewhere.

Figure 4C has been removed.

Lines 168-169, "...E2 treatment led to a significant increase in the peak calcium current density in Kiss1ARH neurons, which was recapitulated as predicted by our computational modeling..." How did the model "predict" this increase in calcium current density? As no information is provided in the methods or supplementary information as to how the effect of E2 was integrated into the model, the authors will need to provide additional narration in the text to explain this statement. The "T-channel inflection" referred to in the figure legend will also need to be explained. Lastly, in Figure 5C, the current density unit should be pA/pF. 

We have added text in the supplementary information to explain how we used the qPCR and electrophysiological data to inform the model regarding the effect that E2 has on the various ionic currents and noted in the Figure 13 legend that the increase/decrease in the conductances is physiologically mediated by E2. We have eliminated the T-channel inflection point (Figure 5D) and corrected the current density label (Figure 5C).

Lines 198-199, please clarify "E2 does not modulate calcium channel kinetics directly but rather alters the mRNA expression to increase the conductance".

We have clarified that “that long-term E2 treatment does not modulate calcium channel kinetics but rather alters the mRNA expression to increase the calcium channel conductance” by referring to the specific figures (i.e., Figures 4, 6) in a previous sentence.

Figures 7 and 8 titles do not accurately reflect the contents: there is nothing about repolarization in the experiments illustrated in Figure 7 or Figure 8. The sample sizes (3 to 4 cells) are also quite small for these experiments.

We have modified the Figure titles per the reviewer’s comments and increased the cell numbers.

The title of Figure 9 also does not fully reflect the figure's contents. Although panel G does suggest that the M current contributes to regulating the membrane potential, the reviewer's reading of this figure panel is that the fractional contribution of the M current does not vary during a short burst of action potentials. The suggestion that "KCNQ channels play a key role in repolarizing Kiss1ARH neurons following burst firing" (line 272) and the statement that "our modeling predicted that M-current contributed to the repolarization following burst firing" (line 273) should be revised accordingly.

The point is that the M-current contributes, albeit a small fraction, to the repolarization during burst firing.

Line 288, please indicate what figure informs this statement.

We have revised the statement since the modeling (Figure 13) comes later in the Results.

Line 311-313, this sentence only superficially describes the simulation, in the reviewer's opinion. Does the model inform on how TRPC5 channels/currents do that? The supplementary information indicates that there is a tone of extracellular neurokinin B embedded in the model. This is important information that should be clearly stated in the manuscript. The authors should also consider discussing the influence of this neurokinin B tone on the contribution of TRPC5 to cell excitability. As a neurokinin B tone in the extracellular space will likely alter the firing of kisspeptin neurons in the model, readers will likely need more information about all this.

In our current ramp simulations of the model (Fig 12 G&H) there is no involvement of neurokinin B (i.e., the NKB parameter  is set to zero), and the effect on the rheobase is solely due to the decrease of the TRPC5 conductance.  In the model, TRPC5 channels are activated by intracellular calcium levels and are therefore contributing to cell excitability even in the absence of extracellular NKB. The NKB tone is used for the simulations presented in Figure 13 where we vary the TRPC5 conductance under saturating levels of extracellular NKB.

Lines 316-318 also read as quite superficial. More explanations of what is illustrated in Figure 13 are needed. In particular, it is unclear from the methods and supplementary information what the different ratios of conductances in OVX+E2 vs in OVX are and how they were varied in the model. Furthermore, it is unclear to the reviewer how the outcome of these simulations matches the authors' postulate that E2 enables a transition to a burst firing pattern that favors glutamate release. Looking at simulated firing in Figure 13B, E2 (by increasing calcium conductances) would tend to enable high-frequency firing within bursts (nearing 50 Hz by eye) and high burst rates (approximately 4 bursts per second), which the reviewer would argue might be expected to cause significant neuropeptide release in addition to that of glutamate.

We have added to the text: “Furthermore, the burst firing of the OVX+E2 parameterized model was supported by elevated h- and Ca 2+-currents (Figure 13B) as well as by the high conductance of Ca2+ channels relative to the conductance of TRPC5 channels (Figure 13C).” We have also provided in the Supplemental Information (Table of Model Parameters) the specific conductances in the OVX and OVX+E2 state and how they are varied to produce the model simulations.

Granted the high frequency firing during a burst could release peptide, but in the E2-treated, OVX females the expression of the peptides are at “rock bottom.”  Therefore, the sustained high frequency firing during the slow EPSP in the OVX state would generate maximum peptide release.

In Figure 13C, the reviewer is unclear on the ranges of TRPC5 conductances shown. The in vitro experiments suggest that E2 suppresses Trpc5 gene expression and might suppress TRPC5 currents. The ratio of gTRPC5(OVX+E2)/gTRPC5(OVX) should, thus, be <1.0. This is not represented in the parameter space provided, making the interpretation of this simulation difficult. Please clarify what the effect of decreasing gTRPC5 will be on firing patterns in the model.

Thank you for pointing this typographical error.  The ratio should be gTRPC5 (OVX)/TRPC5(OVX + E2) for the X-axis.

- Discussion: many statements and conclusions are overreaching and need to be revised; for example lines 320-322, 329-330, 335-338, 369, 371-373, 391-394, 463-464, and 489-494;

We have tempered these statements, so they are not “overreaching.”

Lines 489-494: the authors should integrate published observations that i) ablation of ARC kisspeptin neurons results in increased LH surges in mice and rats and that ii) optogenetic stimulation of ARC kisspeptin fibers in the POA is only effective at increasing LH secretion in a surge-like manner when done at high frequencies (20 Hz), in their discussion of the role of ARC kisspeptin neurons and their firing patterns in the preovulatory surge.

We have included the paper from the O’Byrne lab (Shen et al. Frontiers in Endocrinology 2022) in the Discussion. However, the Mittleman-Smith paper (Endocrinology, 2016) ablating KNDy neurons using NK3-saporin not only targeted KNDy neurons but other arcuate neurons that express NK3 receptors.  Therefore, we have not cited it in the Discussion.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

An online database called MRAD has been developed to identify the risk or protective factors for AD.

Strengths:

This study is a very intriguing study of great clinical and scientific significance that provided a thorough and comprehensive evaluation with regard to risk or protective factors for AD. It also provided physicians and scientists with a very convenient, free as well as user-friendly tool for further scientific investigation.

We thank the reviewer for the conclusion and positive comments.

Weaknesses:

(1) Comment: The paper mentions that the MRAD database currently contains data only from European populations, with no mention of data from other populations or ethnicities. Given potential differences in Alzheimer's Disease (AD) across different populations, the limitations of the data should be emphasized in the discussion, along with plans to expand the database to include data from more racial and geographic regions.

Thank you for your valuable comment. Further information regarding the limitations of populations is provided in the Conclusions section (page 19).

The newly added text describing the limitations of populations is as follows:

“However, in this study, since the GWAS datasets for both the exposure and the outcome traits (AD) selected were obtained from the public database (MRC IEU OpenGWAS), where the GWAS datasets for AD are only of European population, and since we use the TwoSampleMR, which requires that the populations for the exposure trait and the outcome trait be the same to satisfy the requirement for a control variable, this study currently has certain limitations in terms of population. We initiated a Mendelian randomization study on AD at clinical hospitals in China and are currently in the sample collection stage to address the limitations. In the future, we will integrate data from more populations and keep updating new progresses in AD research to explore its potential differences in different populations.”

(2) Comment: Sufficient information should be provided to clarify the data sources, sample selection, and quality control methods used in the MRAD database. Readers may expect more detailed information about the data to ensure data reliability, representativeness, and research applicability.

Thank you for your helpful suggestion. We appreciate you taking time and making effort in reviewing our manuscript and thank you for your insightful comments. We agree that adding more details is essential to make the manuscript more reliability, representativeness, and research applicability.

The newly added text describing more detailed information about the data is as follows:

(1) Sufficient information about data sources and sample selection (in the Data sources section of Methods section, page 8):

“Exposure traits

Inclusion criteria: datasets of the European population.

Exclusion criteria: (i) eQTL-related datasets; (ii) AD-related datasets.

“In this study, the GWAS datasets selected were derived from 42,335 GWAS datasets in the public database (MRC IEU OpenGWAS, https://gwas.mrcieu.ac.uk/). Based on the above inclusion and exclusion criteria, 19,942 eQTL-related datasets were excluded first, leaving 22,393 GWAS datasets. Next, the datasets with the European population were selected, and 18,117 GWAS datasets were obtained. Finally, 20 AD-related datasets were excluded; 18,097 GWAS datasets were obtained at the end as the exposure traits of this study (See Table S1 for basic information).

Outcome traits

Inclusion criteria: (i) datasets of patients with AD with complete information and clear data sources; (ii) datasets of the European population.

Exclusion criteria: (i) Number of SNPs <1 million; (ii) datasets with unspecified sex; (iii) datasets with a family history of AD; (iv) datasets with dementia.

Based on the above criteria, 16 GWAS datasets of outcome traits were selected from the MRC IEU OpenGWAS database, comprising datasets of AD from Alzheimer Disease Genetics Consortium (ADGC), Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE), The European Alzheimer’s Disease Initiative (EADI), and Genetic and Environmental Risk in AD/Defining Genetic, Polygenic and Environmental Risk for Alzheimer’s Disease Consortium (GERAD/PERADES) 2019 (ieu-b-2); AD from Benjamin Woolf 2022 (ieu-b-5067); AD from International Genomics of Alzheimer's Project (IGAP) 2013 (ieu-a-297) as the datasets of main outcome traits for AD, as well as 13 datasets from FinnGen biobank 2021 corresponding to various AD subtypes, referred to as AD-finn subtypes. (as shown in Figure 2).”

(2) Sufficient information about quality control methods (in the Statistical models for causal effect inference section of Methods section, page 9-10:

“A random-effects IVW model was used in this study as the major analysis method to uncover potential risk or protective factors for AD. The random-effects IVW model as the gold standard for MR studies, its principle is to calculate the inverse of the variance of each IV as its weight, assuming all IVs are valid. The regression does not include an intercept term, and the final result is the weighted average of the effect estimates from all IVs [34]. This model indicates that the true effect values may vary across different studies due to both sampling error and the heterogeneity of the true effect. The weight of each study is jointly determined by its inverse variance and the estimated heterogeneity variance. Thus, as long as there is no pleiotropy, even when there is significant heterogeneity (p < 0.05), this method remains the best MR model.

To assess the robustness of the IVW results, sensitivity analysis was performed using six additional models: (i) MR-Egger: MR-Egger’s biggest difference from IVW is that it considers the intercept term during regression to evaluate bias caused by horizontal pleiotropy. The intercept represents the magnitude of horizontal pleiotropy, with a value close to 0 indicating minimal pleiotropy. The primary purpose is to detect and correct for horizontal pleiotropy. Thus, when significant horizontal pleiotropy is observed (p < 0.05), this method is preferred [35,36]. (ii) Weighted median: The weighted median method is a technique for evaluating causal relationships using a majority of genetic variants (SNPs). If at least 50% of the SNPs are valid IVs, the median of the causal estimates will tend toward the true causal effect. This method provides an unbiased estimate (i.e., the “majority validity” assumption) [37]. (iii) Simple mode: Involves comparing the frequencies or proportions of genotypes or phenotypes between control and experimental groups. Moreover, it can illustrate whether the observed differences in genotypes or phenotypes between the two groups are statistically significant. (iv) Weighted mode: The weighted mode method is a technique for combining multiple Mendelian randomization estimates. This method assigns weights to the causal effect estimates of different genetic variants on the trait and then takes the weighted mode as the final estimate of the causal effect. In genetic variant estimates, the method can decrease bias caused by outliers. (v) Maximum likelihood: This method is used when it is known that a random sample follows a particular probability distribution; however, the specific parameters of that distribution remain unknown, and it involves conducting multiple experiments, observing the results, and using those results to infer the approximate values of the parameters [38]. (vi) Penalized weighted median: An enhanced version of the weighted median estimate that provides a consistent estimate of the causal effect. (vii) Heterogeneity and horizontal pleiotropy assessment use the heterogeneity tests [39] and Egger intercept tests [40], respectively.”

(3) Comment: While the authors mention that the MRAD database offers interactive visualization interfaces, the paper lacks detailed information on how to interpret and understand these visual results. Guidelines on effectively using these visualization tools to help researchers better comprehend the data are essential.

Thank you very much for your feedback, as we believe that our manuscript has been improved substantially as a result of your input.  Owing to space constraints, the MRAD database user guide is included in the Supplementary Material. Meanwhile, for better understanding, the subheading of the relevant content in the Supplementary Material has been revised to “MRAD User Guide” (see Supplementary Material for details, page 11). Furthermore, considering user-friendliness, the user guide has been integrated into the database and can be accessed directly from the homepage by clicking on the “User Guide” module.

(4) Comment: In the conclusion section of the paper, it is advisable to explicitly emphasize the practical applications and potential clinical significance of the MRAD database. The paper should articulate how MRAD can contribute to the early identification, diagnosis, prevention, and treatment of AD and its potential societal and clinical value more clearly.

Thank you for pointing this out. In the Discussion section of the revised manuscript, we have now added how MRAD can contribute to the early identification, diagnosis, prevention, and treatment of AD and its potential societal as well as clinical value. And we reorganized the structure of Discussion section to make the text easier to understand, which could be helpful to further clarify the significance of MRAD. (page 15)

The newly added text describing the practical applications and potential clinical significance of the MRAD database is as follows:

“(i) The current methods for identifying AD mainly rely on assessment scales, cerebrospinal fluid (CSF) examinations, and brain PET/MRI. However, assessment scales can be biased by factors such as the anxiety and nervousness of the subjects. CSF examinations require an invasive lumbar puncture, leading to low patient acceptance. PET/MRI scans are expensive and have limited equipment accessibility. These limitations restrict early AD identification. Thus, there is a pressing clinical need for readily available, time- and cost-effective, and accurate detection methods. In this study, the Medical laboratory science and Molecular trait used could be less expensive, faster to detect, easier to operate, and more accessible for widespread adoption. They hold great value for early AD identification and have the potential to become crucial tools for identifying AD in the future. (ii) Imaging acts as a powerful assistive tool for diagnosing Alzheimer’s disease. Traditional imaging examinations mainly depict changes in the brain’s macroscopic structure, while research on microstructural changes in disease-related areas is relatively limited. Studies have demonstrated that microstructural neurodegenerative processes are extensive and pronounced during AD progression. Our study results cover traditional macroscopic neuroimaging results and reveal numerous potential causal relationships between brain microstructure and AD. The combination of macroscopic and microstructural insights will provide more valuable information for clinical diagnosis. (iii) Clarifying patient’s disease, past history, and family history can aid in preventing AD at an early stage, and prevention of AD could be attained through monitoring anthropometric indicators, improving gut microbiota, and adjusting lifestyle traits. (iv) Currently, the development of new drugs for AD is mainly underscored by Aβ, Tau, and other inhibitors. Since 2000, global pharmaceutical companies have invested hundreds of billions of dollars in the development of new drugs for AD, and these drugs have not yielded successful results. AD drug development has thus been perceived as having the highest failure rate of all drug research, reaching 99.6%. Hence, further research on molecular traits to find new targets and develop new drugs for these targets will provide new pathways for AD treatment.”

(5) Comment: Grammar and Spelling Errors: There are several spelling and grammar errors in the paper. Referring to a scientific editing service is recommended.

We appreciate your comments and suggestions for improving our manuscript. We have now used a professional editing service offered by Taylor and Francis to revise the grammar and language, and we have obtained a certificate of proof, which is attached. Thank you for recognizing our research, we have tried our best to improve the quality of this paper to ensure that it meets the high standards required for publication in of journal elife.

Reviewer #2 (Public Review):

Summary:

This MR study by Zhao et al. provides a comprehensive hypothesis-free approach to identifying risk and protective factors causal to Alzheimer's Disease (AD).

Strengths:

The study employs a comprehensive, hypothesis-free approach, which is novel over traditional hypothesis-driven studies. Also, causal associations between risk/protective factors and AD were addressed using genetic instruments and analysis.

We greatly appreciate the positive feedback regarding the overall quality of our work.

Major comments:

(1) Comment: The authors used the inverse-variance weighted (IVW) model as the primary method and other MR methods (MR-Egger, weighted mean, etc.) for sensitivity analysis. However, each method has its own assumption, and IVW is only robust when pleiotropy and heterogeneity are not severe. Rather than using IVW imprudently across all associations, it would be more appropriate to choose the best MR method for each association based on heterogeneity/Egger intercept tests. This customized approach, based on tests of MR assumption violations, yields more stable and reliable results. For reference, please follow up on work by Milad et al. (EHJ - "Plasma lipids and risk of aortic valve stenosis: a Mendelian randomization study"). This study selected the best MR model for each association based on pleiotropy and heterogeneity tests. Given the large number of tests in this work, I suggest initially screening significant signals using IVW, as done, and then validating the results using multiple MR methods for those signals. It is common for MR estimates from different methods to vary significantly (with some being statistically significant and others not), and in such cases, the MR estimates from the best-fitted model should be trusted and highlighted.

Thank you for your professional comments. We agree that our description of the Statistical models for causal effect inference was not specific enough. Therefore, we have included a new text describing more details about each method’s assumption and supplied a predefined approach to select the best statistical estimation from these methods in the Statistical models for causal effect inference section of Methods section (page 9-10). However, we would like to clarify our analysis method. In this study, the main analysis method used is the IVW random effects model instead of the IVW fixed effects model. The IVW random effects model indicates that the true effect values of different studies may vary, including both sampling error and heterogeneity of the true effect. The weight of each study is jointly determined by its inverse variance and the estimated heterogeneity variance. Thus, as long as there is no pleiotropy, even when there is significant heterogeneity (p < 0.05), this method is still the best MR model. We would like to thank you again for your feedback, as we believe that our manuscript has been improved substantially as a result of your input.

The newly added text describing more details about each method’s assumption and the customized best-fitted model is as follows:

“Statistical models for causal effect inference

A random-effects IVW model was used in this study as the major analysis method to uncover potential risk or protective factors for AD. The random-effects IVW model as the gold standard for MR studies, its principle is to calculate the inverse of the variance of each IV as its weight, assuming all IVs are valid. The regression does not include an intercept term, and the final result is the weighted average of the effect estimates from all IVs [34]. This model indicates that the true effect values may vary across different studies due to both sampling error and the heterogeneity of the true effect. The weight of each study is jointly determined by its inverse variance and the estimated heterogeneity variance. Thus, as long as there is no pleiotropy, even when there is significant heterogeneity (p < 0.05), this method remains the best MR model.

To assess the robustness of the IVW results, sensitivity analysis was performed using six additional models: (i) MR-Egger: MR-Egger’s biggest difference from IVW is that it considers the intercept term during regression to evaluate bias caused by horizontal pleiotropy. The intercept represents the magnitude of horizontal pleiotropy, with a value close to 0 indicating minimal pleiotropy. The primary purpose is to detect and correct for horizontal pleiotropy. Thus, when significant horizontal pleiotropy is observed (p < 0.05), this method is preferred [35,36]. (ii) Weighted median: The weighted median method is a technique for evaluating causal relationships using a majority of genetic variants (SNPs). If at least 50% of the SNPs are valid IVs, the median of the causal estimates will tend toward the true causal effect. This method provides an unbiased estimate (i.e., the “majority validity” assumption) [37]. (iii) Simple mode: Involves comparing the frequencies or proportions of genotypes or phenotypes between control and experimental groups. Moreover, it can illustrate whether the observed differences in genotypes or phenotypes between the two groups are statistically significant. (iv) Weighted mode: The weighted mode method is a technique for combining multiple Mendelian randomization estimates. This method assigns weights to the causal effect estimates of different genetic variants on the trait and then takes the weighted mode as the final estimate of the causal effect. In genetic variant estimates, the method can decrease bias caused by outliers. (v) Maximum likelihood: This method is used when it is known that a random sample follows a particular probability distribution; however, the specific parameters of that distribution remain unknown, and it involves conducting multiple experiments, observing the results, and using those results to infer the approximate values of the parameters [38]. (vi) Penalized weighted median: An enhanced version of the weighted median estimate that provides a consistent estimate of the causal effect. (vii) Heterogeneity and horizontal pleiotropy assessment use the heterogeneity tests [39] and Egger intercept tests [40], respectively.”

(2) Comment: Lines 157-160 mentioned "But to date, AD has been reported as hypothesis-driven MR study based on a single factor, ignoring the potential role of a huge number of other risk factors. Also, due to the high degree of heterogeneity present in AD subtypes, which have different biological and genetic characteristics. Thus, the previous studies cannot offer a systematic and complete viewpoint.". This statement overlooks a similar study published in Molecular Psychiatry ("A Phenome-wide Association and Mendelian Randomization Study for Alzheimer's Disease: A Prospective Cohort Study of 502,493"), which rigorously assessed the effects of 4171 factors spanning 10 different categories on AD using observational analysis and MR. The authors should revise their statement on the novelty of their study type throughout the manuscript and discuss how their work differs from and potentially strengthens previous studies.

Thank you for directing us to this literature. We have read this article carefully. This study shares some similarities with our study but there are significant differences with regards to sample sources and research fields. The study, as mentioned by the reviewer, used the UKB database as its sample source, and analyzed the association between 10 categories (comprising 4,171 factors) and AD, which were sociodemographic, physical measures, lifestyle and environment, health conditions, mental health, medications and operations, cognitive function, sex-specific factors, employment, and early-life factors. However, the study revealed they are restricted by the available variables from the UKB database, which lead to variables such as air pollution, blood glucose measures and so on were not included. Conversely, our study used samples from the MRC IEU OpenGWAS database, the largest open GWAS database globally. Furthermore, our research focus differs, as we primarily investigate the causal relationship between the following 10 categories (comprising 18,097 traits) and AD, which were Disease, Medical laboratory science, Imaging, Anthropometric, Treatment, Molecular trait, Gut microbiota, Past history, Family history, and Lifestyle trait. Most importantly, we have established a database encompassing all MR analysis results, allowing researchers and clinicians worldwide to conveniently and rapidly retrieve AD-associated risk factors via an online open integrated platform (MRAD, https://gwasmrad.com/mrad/).We have now added a new text in the Background section (page 6-7) describing the differences and potential strengthens towards previous studies.

The newly added text describing the differences and novelty towards previous studies is as follows:

“Chen et al. [30] used MR analysis to reveal the causal relationship between AD and factors including sociodemographic and early life status. However, the study revealed they are restricted by the available variables from the UKB database, which lead to variables such as air pollution, blood glucose measures and so on were not included. And also, due to the high degree of heterogeneity present in AD subtypes, which have different biological and genetic characteristics. Thus, the previous studies cannot offer a systematic and complete viewpoint. Our study uses the MRC IEU OpenGWAS database as the sample source for MR analysis to address the aforementioned limitations. The MRC IEU OpenGWAS database, the largest open GWAS database globally, has compiled 42,335 GWAS summary datasets from sources such as the UK Biobank, FinnGen Biobank, and Biobank Japan. Analyzing large-scale datasets will break new ground for MR research on AD.

MR requires a combination of background knowledge in biology, computer science, software studies, and statistics, which often leads to a dilemma where biologists are not well-versed in computer and statistical fields, while computer science experts struggle to adopt a medical biology mindset. Consequently, the vast majority of available GWAS data have not been effectively utilized through MR. Therefore, the construction of a multi-level data platform specifically for AD based on MR analysis of massive GWAS data is of great strategic significance, and it will facilitate researchers and clinicians worldwide to conveniently and rapidly obtain risk factors that are causally associated with AD.”

Reference:

[30] Chen SD, Zhang W, Li YZ, et al. (2023). A Phenome-wide Association and Mendelian Randomization Study for Alzheimer's Disease: A Prospective Cohort Study of 502,493 Participants From the UK Biobank. Biol Psychiatry. 1;93(9):790-801.

(3) Comment: Given the large number of tests, the multiple testing issue is concerning. To mitigate potential false positives, I recommend employing the Bonferroni threshold or FDR. The authors should only interpret exposures that are significant at the Bonferroni threshold.

We sincerely appreciate the reviewer's feedback. Thank you for pointing this out. We have added the results of the Bonferroni correction to the Statistical models for the causal effect inference section of the Methods section (page 10) in response to the reviewer's feedback.

The newly added text describing Bonferroni threshold is as follows:

“The above analyses were performed using the TwoSampleMR[41] package in the R (version 4.1.2) software. Association of exposures with outcomes was assessed using odds ratio (OR) and 95% confidence interval (95% CI), with OR > 1 indicating a positive association (risk factor) and 0 < OR < 1 indicating a negative association (protective factor). Differences with a two-sided p < .05 were considered statistically significant. Furthermore, owing to the relatively large number of exposure and outcome traits included in this study, the multiple testing correction method Bonferroni correction was added to identify significant hits, threshold for Bonferroni-corrected was 0.05 divided by 289,552 tests (p <1.727e-07).”

(4) Comment: In the discussion, the authors should interpret or highlight exposures that remain significant after multiple testing corrections.

Thank you for your valuable comment. In response to reviewer feedback, we have put extra emphasis on the exposures that remained significant after multiple testing corrections in the Discussion section (page 17). We thank you again for your feedback, as we believe that our manuscript has been improved substantially as a result of your input.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) Comment: In this study, the authors used the inverse-variance weighted (IVW) model as the major analysis method to perform Mendelian randomization analysis to identify various classes of risk or protective factors for AD, early-onset AD, and late-onset AD. An online database called MRAD has been thereby developed with the assistance of Shiny package. This study is a very intriguing study of great clinical and scientific significance that provided a thorough and comprehensive evaluation with regard to risk or protective factors for AD. It also provided physicians and scientists with a very convenient, free as well as user-friendly tool for further scientific investigation.

I believe this manuscript is great research that is worth publishing with all the comments from the Public Review resolved.

We thank the reviewer for taking the time to read and provide valuable feedback on our manuscript, which allowed us to improve the overall quality of our research. All the comments from the Public Review have been rechecked, and appropriate changes have been made in accordance with the reviewers’ suggestions. Point-by-point responses to all the comments from the Public Review can be found in the above. If there are any further issues, please do not hesitate to let us know, so that we can ensure that our manuscript meets the high standards required for publication.

Reviewer #2 (Recommendations For The Authors):

(1) Comment: In the middle lower left section of the graphical abstract, the overlapping positive (N=63) and overlapping negative (N=16) do not sum to the overlapping number (N=80). Could you clarify if any have both positive and negative effects? Additionally, the font size inside the circular elements is too small to read.

We thank you for raising this issue. We have clarified this in the MRAD utility data mining section of Results section (page 12): A total of 63 exposure traits (risk factors) were positively associated with all the three main outcome traits, while 16 exposure traits (protective factors) were negatively associated with the three main outcome traits, with Ulcerative colitis (ebi-a-GCST000964) being negatively associated with the AD outcome traits of ieu-b-2 and ieu-a-297, and positively associated with the AD outcome traits of ieu-b-5067. Additionally, we apologize for the small, unreadable fonts in the graphical abstract figure. In response to reviewer feedback, we have increased the font size within the figure and enhanced the resolution to improve image readability (page 3).

(2) Comment: The x-axis label ("Alzheimer's disease outcome") should be more descriptive. If published GWAS results are used, indicate this as XXX et al. (2022). Also, specify the AD outcome for each category (e.g., AD, early-onset AD, late-onset AD). The y-axis labels should also be clarified; remove identification codes and retain only the exposure names. Apply the same improvements to Figures 2-8.

We appreciate your comments and suggestions for improving our manuscript.

(i) In response to reviewer feedback, information of published GWAS such as authors and year of publication have now been added to the x-axis labels, as demonstrated in Figure 4 (page 31).

(ii) The outcome IDs are unique. We used these IDs to represent the AD information on the x-axis to maintain a clean and clear figure. The corresponding details for each ID are explained in the Outcome traits section of the Methods section (page 8, as shown in Figure 2). AD_EO refers to early-onset AD, and AD_LO refers to late-onset AD, which are also specified in the Abbreviations (page 4).

(iii) We sincerely appreciate the reviewers’ meticulous feedback. While exposure IDs in this study are unique, exposure names are not. A single exposure name may correspond to multiple IDs, each with a potentially different source of information (e.g., author, year, population sample). We believe obtaining consistent results across multiple IDs further strengthens the reliability of our conclusions. Hence, for better clarity of specific exposure information, the exposure IDs have been retained.

(3) Comment: The results across Figures 1-8 are repetitive and not very informative. Consider other visualizations to condense the information into one or two figures. I would recommend using a Manhattan plot or PheWAS plot concept to effectively display many test results at once. Please display the Bonferroni threshold in the plot as a horizontal line to show which exposures are meaningful after adjusting multiple comparisons.

We appreciate this helpful suggestion. We have now condensed Figures 1–8 into a single figure (as shown in Figure 4). Additionally, we have now displayed the Bonferroni correction results in the sensitivity analysis results figures (as shown in Figure 5, Figure S1-S7).

(4) Comment: Consider placing Figure S1 as Figure 1, condensing Figures 1-8 into Figures 2 and 3, and placing the circular diagrams from Figure S6 as Figure 4.

We appreciate this valuable suggestion. The sequence of the figures has been adjusted.

(5) Comment: Create a main table summarizing robust and consistent exposures for AD that are significant at the Bonferroni threshold for readers. For each exposure, please include estimates from IVW, MR-Egger, weighted median, simple mode, weighted mode, maximum likelihood, and penalized weighted median, along with heterogeneity and horizontal pleiotropy tests. I would also highlight or bold estimates from the best-fit model/MR method to help readers identify the most reliable estimates when estimates from multiple methods are heterogeneous.

We appreciate this helpful suggestion. Owing to the excessive amount of information in the table, we have uploaded the table covering the aforementioned information according to the reviewer’s suggestion as supplementary materials (See Table S2). (i) The corresponding id.exposure that pass the Bonferroni threshold are reflected in red font. (ii) Furthermore, according to the customized best-fitted model (as mentioned in the Statistical models for causal effect inference section of Methods section), when there is no pleiotropy or when pleiotropy is not applicable (less than 3 SNPs), random-effects IVW model is the best model. These corresponding id.exposure are shown in red font with a yellow highlight. (iii) Moreover, according to the customized best-fitted model, when there is pleiotropy, MR-Egger is the best model. These corresponding id.exposure are shown in red font with a green highlight.

(6) Comment: Figures S4-S10: These figures are screenshots of web browsers and may not be worth showing. Consider using tools like Adobe AI or R ggplot to create more refined visualizations that are specific to the research question and improve the message of this work.

Thank you very much for your valuable suggestion in reviewing our manuscript. In this study, Figures S4-S10 are screenshots related to the user guide. We sincerely appreciate the reviewer’s feedback and have revised the subheading of this section to MRAD User Guide to clarify its purpose. Demonstrating both text and figures in this section, we aim to help users understand ways to operate MRAD more intuitively and easily.

(7) Comment: Additionally, please show upfront or highlight results from MR analyses based on R packages, as the author mentioned in the method section. Somehow it's difficult to find results from MR-Egger, weighted median, simple mode, weighted mode, maximum likelihood, and penalized weighted median, along with heterogeneity and horizontal pleiotropy tests in the supplementary materials. Apologies if I missed them. Please ensure these results are clearly presented.

We appreciate your comments and suggestions for improving our manuscript. Thank you for pointing this out. We have added the results of the sensitivity analysis based on R packages (as shown in Figure 5, Figure S1-S7, and Table S2).

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

I am not convinced how this study relates to HIV individual HFpEF, and the study design does not seem to be well thought out. 

This is an important point and we have modified the manuscript as mentioned below in our responses.

The connectivity of the study experiments is loose, and data analysis and conclusions are broadly overstated and misinterpreted.

We have modified the manuscript thoroughly so the data are interpret properly, and the conclusions are stated logically. 

For example the study lacks any measure of diastolic contractile function, and even if performed, the relevance of TNFa treatments to cells in vitro in these immature cell contexts would remain unclear. There is surprisingly no reported molecular analyses of potential mechanisms of the calcium transient changes. The study falls short in molecular detail and instead relies on drug treatments and responses that are hard to interpret with dosages that are not well justified and treatments that are numerous. Unclear what changes in calcium transients mean functionally without a comprehensive assessment of CM biomechanical contraction and relaxation measurements, and this would also require parallel molecular investigations of potential targets of any phenotypes observed.

As mentioned above, we have modified the manuscript so the data are interpret properly, and the conclusions are stated logically. In terms of mechanisms for the observed phenomenon, we agree that this was not the focus of studies, however, we have provided a paragraph in the discussion that covers this topic. Although Decay and downstroke time were utilized as surrogates of cardiomyocyte relaxation, direct biomechanical characterization of contraction was not conducted in this study. While cytosolic calcium concentration is a predominant factor to regulate the cell’s relaxation (Reference 52 in the manuscript), there are several mechanisms to modify the relationship, including the transition of sarcomere protein isoforms to pathogenic ones (Reference 53 in the manuscript) and the stimulation of β-adrenergic receptor on cardiomyocytes (Reference 54 in the manuscript). Since hiPSC-CMs utilized for each study is from iPS cells derived from a single donor, we believe that the patterns of sarcomere protein expression and the regulation of β-adrenergic receptor pathway should be consistent among samples, supporting their effects should be minimum in our system. We also did not elucidate molecular mechanisms underlying prolonged decay time induced by TNF-α and IFN-γ in this study. Lee et al. reported that 25 ng/ml TNFa treatment induced a longer decay portion of the calcium transient and a decreased sarcoplasmic ATPase (SERCA) expression in rabbit cardiomyocytes from pulmonary vein (Reference 55 in the manuscript), suggesting our observation in iPS-CM is also through decreased expression of SERCA though further studies remain conducted.

Calcium transient data need to be better illustrated such as with representative peak tracings. The data overall is with too few samples, particularly given the inherent heterogeneity of iPSCM studies. The iPS-CM system as a model for diastolic dysfunction remains unestablished.

We have now prepared several representative curves of calcium transient and their derivatives in Figure 4 D and E, H and I, and in Figure 1-figure supplement 1B. In terms of the way to collect Ca-transient data, each dot in the bar graphs represents the average of signals obtained from one well of the 96-well plates. About 75K cells were seeded in one well, and we believe that the number of cells integrated in the analyses should be sufficient for the statistical analyses. We modified our manuscript as this system does not quantifying diastolic function directly, but represents Ca measurements that indicate cardiomyocyte relaxation.

There are unclear dose choices for the various ART drugs tested, as well as the other drugs tested such as SGLT2i. Besides the observation that SLC5A2 (SGLT2 target) is not established to be expressed in adult mammalian cardiomyocytes. 

Thank you for the comment. The dose ranges of ART drugs were chosen to extend to 10fold above the IC50 concentrations and reflects the upper range of circulating drug concentration in patients receiving these medications (Reference 36-39 in the manuscript). For SGLT2 inhibitor concentration, we referred to a paper utilizing 1-10 μM dapagliflozin (PMID: 35818731). We conducted a preliminary study to test the effect of 1 and 10 μM of dapagliflozin on the Ca-transient of iPS-CMs, and we found that 1 μM of the drug treatment did not cause changes in Ca-transient. Marfella et al. reported that SLC5A2 (SGLT2) expresses in cardiomyocytes under diabetic condition (PMID 36096423). Since diabetes is associated with low grade systemic inflammation, HIV patients might also express SGLT2 in cardiomyocytes. Taken together, we believe that the dosages of the drugs used in our studies are relevant to the clinical therapeutical usages of the drugs.

HIV plasma samples were not tested for cytokine levels, but this could be done to assess the validity of the final experiments. It is unclear what is being tested with these experiments. 

This is a good point and we agree with the reviewer. However, we had limited amount of the patient serum and could not perform a comprehensive analysis of these samples. Nevertheless, we have added a section in the Discussion section providing some clinical relevance of our findings based on the papers that have assessed cytokine levels in the serum of HIV patients.

The choice of serum controls from a second institution (UCSF) opens up concerns over batch effects unrelated to differences in diastolic dysfunction. However, there were no differences with the Northwestern samples. It is unclear why this data is included as it does not add to the impact of the study. 

In our study, we utilized two sets of HIV patient serum samples from different institutions, supporting that our results can be reproduced. We believe that these results significantly augmented the rigor of our findings.

There are concerns about the quality of the iPS-CMs since there is no cell imaging or molecular analyses. Figure 5 Supplement 1 images are of low quality and low resolution to assess cell quality. Overall the iPS-CM QC data is extremely sparse 

We have now added the representative images of iPS-CMs to Figure 1- figure supplement 1A. Our group has used hiPS-CMs extensively in the past (PMID: 26439715). We also updated Fig 5 Supplement 1 with images with better resolution and added Fig 5 Supplement 2 with magnified images. 

Reviewer2 (Public Review):

However, there are some topics that are not well-connected, and the rationale and hypothesis are not clearly defined beforehand, such as mitochondrial membrane potential, mitochondrial ROS, and angiogenic potential. 

We modified the manuscript so the rationale and hypothesis of the study is clearly stated. 

As the hiPSC cardiomyocytes are treated with various reagents to measure diastolic dysfunction, it is important to confirm whether the treatment time and dose used were sufficient to exert a functional effect. Dose and time-dependent experiments are essential, or at least sufficient citations should be provided for selecting the dose for IFN and TNF. 

We used previous publications for the dosages of the drugs used in our paper (1-4). 

After IFN and TNF treatment, determining the expression levels of molecular markers of DD/HFpEF is crucial. Again, if sufficient evidence is available, it can be cited. 

We have included a section in the discussion to address this issue. Briefly, Lee et al. reported that 25 ng/ml TNFa induces a longer decay of calcium transient and a decrease in sarcoplasmic ATPase (SERCA) expression in rabbit cardiomyocytes from pulmonary vein (PMID 17383682). The prolonged Cadecay time in hiPS-CM with the drug administration may be due to a decrease in SERCA expression and impaired Ca-uptake into sarcoplasmic reticulum.

The Methods section describes TMRE colocalization and immunofluorescence, but no images are provided.

We have performed immunofluorescence of hiPSC-CM with TMRE for the quantification of mitochondrial membrane potential (MMP). 

The concentration of TNF and IFN in patients is critical, which was acknowledged and discussed as a limitation of the study by the authors. Authors should consider this aspect, and if not feasible, clinical reports should be cited to provide a rough estimation of their concentration. 

Thank you for this comment. A new section detailing the points brought up by the Reviewer is now added to discussion.

Recommendation for the authors:

Reviewer #1 (Recommendation for the authors):

I suggest a more comprehensive analysis of diastolic function including biomechanical studies of contraction and diastolic function. I suggest increasing the sample #'s, getting a better characterziation of the cardiomyocytes, their expression profiles, and maturation state. The team should dig more deeply into potential molecular mechanisms of the calcium transient changes. Are there changes in SERCA or other SR factors' phosphorylation state or other molecular explanations for the observed changes? I would remove the serum treatment experiments as they distract since they didn't show differences. These are a few of the suggestions I would have for the team.

Our system for measurement of Ca-transient unfortunately does not allow to obtain data on the cellular biomechanical property. We modified the manuscript so the results are not overstated and that the interpretation is correct. Since each dot in bar-graphs for Ca-transient data represents the average of signals generated from 75 K cells, we believe that the number of cells analyzed was sufficient for the analyses. Although it is not conclusive, previous reports suggested induction of SERCA2A expression by TNF-α treatment in isolated cardiomyocytes, suggesting that the mechanism underlying the prolonged calcium decay time in our model may be due to changes in SERCA levels. We included the data from human serum samples from HIV patients since they provide a platform to assess the effects of HIV patient serum on. We believe that these data convey a significant progress understanding the process of myocardial dysfunction in HIV patients.

References

Amirayan-Chevillard, N., Tissot-Dupont, H., Capo, C., Brunet, C., Dignat-George, F., Obadia, Y., Gallais, H., and Mege, J. L. (2000) Impact of highly active anti-retroviral therapy (HAART) on cytokine production and monocyte subsets in HIV-infected patients. Clinical and experimental immunology 120, 107-112

Fraietta, J. A., Mueller, Y. M., Yang, G., Boesteanu, A. C., Gracias, D. T., Do, D. H., Hope, J. L., Kathuria, N., McGettigan, S. E., Lewis, M. G., Giavedoni, L. D., Jacobson, J. M., and Katsikis, P. D. (2013) Type I interferon upregulates Bak and contributes to T cell loss during human immunodeficiency virus (HIV) infection. PLoS Pathog 9, e1003658

Lau, S. L., Yuen, M. L., Kou, C. Y., Au, K. W., Zhou, J., and Tsui, S. K. (2012) Interferons induce the expression of IFITM1 and IFITM3 and suppress the proliferation of rat neonatal cardiomyocytes. Journal of cellular biochemistry 113, 841-847

Stone, S. F., Price, P., Keane, N. M., Murray, R. J., and French, M. A. (2002) Levels of IL-6 and soluble IL-6 receptor are increased in HIV patients with a history of immune restoration disease after HAART. HIV Med 3, 21-27

Author response:

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

Reviewer #3:

Comments on current version:

As mentioned in my first review, this work is significantly underpowered for the following reasons: 1) n=4 for each treatment group.; 2) no randomization of the surgical sites receiving treatments; 3) implants surgically inserted without precision/guided surgery. The authors have not addressed these concerns.

On a minor note: not sure why the authors present a methodology to evaluate the dynamic bone formation (line 272) but do not present results (i.e. by means of histomorphometrical analyses) utilizing this methodology.

We sincerely appreciate your thorough review and valuable feedback. We have carefully considered your comments and would like to address them as follows:

As mentioned in my first review, this work is significantly underpowered for the following reasons:

(1) n=4 for each treatment group.;

We acknowledge your concern regarding the limited sample size (n=4 per group). While we understand this may affect statistical power, our choice was influenced by ethical considerations in animal experimentation and resource constraints. Increasing the sample size would undoubtedly strengthen the statistical power of our study. However, the logistical and ethical constraints associated with using a larger number of animals in such invasive procedures were significant limiting factors. Specifically, increasing the number of medium to large experimental animals could raise ethical issues, so we used the minimum number possible. Additionally, our study design was reviewed and approved by the animal IRB, which dictated the minimum number of animals we could use. Nevertheless, we conducted power analysis to ensure that our sample size, although limited, was sufficient to detect significant differences given the high variability typically observed in biological responses. The results obtained from our n=4 samples showed consistent trends and significant differences between groups, indicating the robustness of our findings. I will include this point in the limitations section of the discussion. Thank you.

(2) no randomization of the surgical sites receiving treatments;

Thank you for pointing out this issue. We agree that randomization is essential when considering individual differences and the anatomical variations of the jawbone, such as those found in humans. However, this study is an animal experiment where other conditions were controlled, and the interventions were applied after complete bone healing following tooth extraction. Therefore, the impact of randomization of surgical sites was likely minimal, and it is challenging to determine whether it significantly influenced the experimental results. Of course, twelve female OVX beagles were randomly designated into three groups. (Methods section, line 298) However regarding your concern, we would like to present the robustness of histological results from different surgical sites as shown below. Also we will include this point in the limitations section of the discussion.

Histologic analysis of the different surgical sites showed significant differences in bone formation and osseointegration among the three treatment groups: vehicle control, rhPTH(1-34), and dimeric Cys25PTH(1-34). Goldner trichrome staining (Figure A-C) showed enhanced bone formation in both the rhPTH(1-34) and dimeric Cys25PTH(1-34) groups compared to the vehicle control group. The rhPTH(1-34) group showed the most pronounced bone mass gain around the implant. Both treatment groups showed improved bone-to-implant contact compared to the control group, as indicated by the red arrows.

Masson trichrome staining (Figure D-F) further confirmed these results, showing an increase in bone matrix (blue staining) in the rhPTH(1-34) and dimeric Cys25PTH(1-34) groups, with the dimeric rhPTH(1-34) group showing the most extensive and dense bone formation.

TRAP staining (Figure G-I and G'-I') was used to assess osteoclast activity. Interestingly, both the rhPTH(1-34) and dimeric Cys25PTH(1-34) groups showed an increase in TRAP-positive cells compared to the vehicle control, suggesting enhanced bone remodeling activity. The highest number of TRAP-positive cells was observed in the rhPTH(1-34) group and the highest trabecular number, indicating the most active bone remodeling.

To summarize the results, histological analyses revealed that both rhPTH(1-34) and dimeric Cys25PTH(1-34) treatments significantly enhanced osseointegration and bone formation around titanium implants in a postmenopausal osteoporosis model compared to the control. The rhPTH(1-34) group demonstrated superior outcomes, exhibiting the most substantial increase in bone volume, bone-to-implant contact, and osteoclastic activity, indicating its greater efficacy in promoting bone regeneration and implant integration in this experimental context.

Author response image1.

Histological analysis using Goldner trichrome, Masson trichrome, and TRAP staining

(3) implants surgically inserted without precision/guided surgery. The authors have not addressed these concerns.

The primary purpose of precision guides is to prevent damage to various anatomical structures and to ensure perfect placement at the desired location. Even disregarding the potential inaccuracies of precision guides in actual clinical settings, the primary goal of this animal experiment was not to achieve perfect placement or prevent damage to anatomical structures. Instead, the objective was to histologically measure the integrity of the bone surrounding titanium fixture's platform after pharmacological intervention, ensuring it was fully seated in the alveolar bone. To this end, we secured sufficient visibility through periosteal dissection to confirm the perfect placement of the implant and adhered to the principle of maintaining sufficient mesiodistal distance between each fixture. Using such precision guides in this animal experiment, which is not an evaluation of 'implant precision guides,' could potentially introduce inaccuracies and contradict the experimental objectives. Furthermore, since this experiment was conducted on an edentulous ridge where all teeth had been extracted, achieving the same placement as in the presurgical simulation would be impossible, even with the use of precision guides. Thank you once again for your constructive feedback. We will include this point in the limitations section of the discussion.

On a minor note: not sure why the authors present a methodology to evaluate the dynamic bone formation (line 272) but do not present results (i.e. by means of histomorphometrical analyses) utilizing this methodology.

As the reviewer mentioned, we confirmed that the sentence was included in the Methods section despite the analysis not actually being performed. We sincerely apologize for this oversight and will make the necessary corrections immediately. Thank you very much for your keen observation.

Author response:

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

Reviewer #1 (Public Review):

Major comments: 

My main concern about the manuscript is the extent of both clinical and statistical heterogeneity, which complicates the interpretation of the results. I don't understand some of the antibiotic comparisons that are included in the systematic review. For instance the study by Paul et al (50), where vancomycin (as monotherapy) is compared to co-trimoxazole (as combination therapy). Emergence (or selection) of co-trimoxazole in S. aureus is in itself much more common than vancomycin resistance. It is logical and expected to have more resistance in the co-trimoxazole group compared to the vancomycin group, however, this difference is due to the drug itself and not due to co-trimoxazole being a combination therapy. It is therefore unfair to attribute the difference in resistance to combination therapy. Another example is the study by Walsh (71) where rifampin + novobiocin is compared to rifampin + co-trimoxazole. There is more emergence of resistance in the rifampin + co-trimoxazole group but this could be attributed to novobiocin being a different type of antibiotic than co-trimoxazole instead of the difference being attributed to combination therapy. To improve interpretation and reduce heterogeneity my suggestion would be to limit the primary analyses to regimens where the antibiotics compared are the same but in one group one or more antibiotic(s) are added (i.e. A versus A+B). The other analyses are problematic in their interpretation and should be clearly labeled as secondary and their interpretation discussed. 

Thank you for raising these important points and highlighting the need for clarification. We understand that the reviewer has concerns regarding the following points:

(1) The structure of presenting our analyses, i.e. main analyses and sub-group analyses and their corresponding discussion and interpretation

Our primary interest was whether combining antibiotics has an overarching effect on resistance and to identify factors that explain potential differences of the effect of combining antibiotic across pathogens/drugs. Therefore, pooling all studies, and thereby all combinations of antibiotics, is one of our main analyses. The decision to pool all studies that compare a lower number of antibiotics to a higher number of antibiotics was hence predefined in our previously published study protocol (PROSPERO CRD42020187257).

We indeed, find that heterogeneity is high in our statistical analyses. As planned in our study protocol, we did perform several prespecified sub-group analyses and added additional ones. We now emphasize that several sub-group analyses were performed to investigate heterogeneity (L 119ff): “The overall pooled estimates are based on studies that focus on various clinical conditions/pathogens and compare different antibiotics treatments. To explore the impact of these and other potential sources of heterogeneity on the resistance estimates we performed various sub-group analyses and metaregression.” 

The performed sub-group analyses specifically focused on specific pathogens/clinical conditions (figure 3) or explored heterogeneity due to different antibiotics in comparator arms – as suggested by the reviewer (figure 3B, SI section 6). We find that the heterogeneity remains high even if only resistances to antibiotics common to both arms are considered (SI section 6.1.8). With this analysis we excluded comparisons of different antibiotics (e.g., A vs B+C), such as those between vancomycin and cotrimoxazole named by the reviewer. While we aimed to explore heterogeneity and investigate potential factors affecting the effect of combining antibiotic on resistance, limitations arose due to limited evidence and the nature of data provided by the identified studies. Therefore, interpretability remains also limited for the subgroup analyses, which we highlight in the discussion. (L 186 ff: We accounted for many sources of heterogeneity using stratification and meta-regression, but analyses were limited by missing information and sparse data.) Further, specific subgroup analyses are discussed in more detail in the SI.

(2) Difference in resistance development due to the type of the antibiotics or due to combination therapy?

The reviewer raises an important point, which we also try to make: future studies should be systematically designed to compare antibiotic combination therapy, i.e. identical antibiotics in treatment arms should be used, except for additional antibiotics used in both treatment arms. We already mentioned this point in our discussion but highlight this now by emphasizing how many studies did not have identical antibiotics in their treatment arms. We write in L194ff: “19 (45%) of our included studies compared treatment arms with no antibiotics in common, and 22 studies (52%) had more than one antibiotic not identical in the treatment arms (table 1). To better evaluate the effect of combination therapy, especially more RCTs would be needed where the basic antibiotic treatment is consistent across both treatment arms, i.e. the antibiotics used in both treatment arms should be identical, except for the additional antibiotic added in the comparator arm (table 1).”

Furthermore, we investigated the importance of the type of antibiotics with several subgroup analyses (e.g. SI sections 6.1.8 and 6.1.10). We now further highlight the concern of the type of antibiotics in the result section of the main manuscript, where we discuss the sub-group analysis with no common antibiotics in the treatment arms 131 ff: “Furthermore, a lower number of antibiotics performed better than a higher number if the compared treatment arms had no antibiotics in common (pooled OR 4.73, 95% CI 2.14 – 10.42; I2\=37%, SI table S3), which could be due to different potencies or resistance prevalences of antibiotics as discussed in SI (SI section 6.1.10).” As mentioned above we also perform sub-group analyses, where only resistances of antibiotics common to both arms are considered (SI section 6.1.8). However, as discussed in the corresponding sections, the systematic assessment of antibiotic combination therapy remains challenging as not all resistances against antibiotics used in the arms were systematically measured and reported. Furthermore, the power of these sub-group analyses is naturally a concern, as they include fewer studies. 

Another concern is about the definition of acquisition of resistance, which is unclear to me. If for example meropenem is administered and the follow-up cultures show Enterococcus species (which is intrinsically resistant to meropenem), does this constitute acquisition of resistance? If so, it would be misleading to determine this as an acquisition of resistance, as many people are colonized with Enterococci and selection of Enterococci under therapy is very common. If this is not considered as the acquisition of resistance please include how the acquisition of resistance is defined per included study. Table S1 is not sufficiently clear because it often only contains how susceptibility testing was done but not which antibiotics were tested and how a strain was classified as resistant or susceptible. 

Thank you for pointing out this potential ambiguity. The definition of acquisition of resistance reads now (L 275 ff): “A patient was considered to have acquired resistance if, at the follow-up culture, a resistant bacterium (as defined by the study authors) was detected that was not present in the baseline culture.” We also changed the definition accordingly in the abstract (L 36 ff). We hope that the definition of acquisition is now clearer. Our definition of “acquisition of resistance” is agnostic to bacterial species and hence intrinsically resistant species, as the example raised by the reviewer, can be included if they were only detected during the follow-up culture by the studies. Generally, it was not always clear from the studies, which pathogens were screened for and whether the selection of intrinsically resistant bacteria was reported or not. Therefore, we rely on the studies' specifications of resistant and non-resistant without further distinction from our side, i.e. classifying data into intrinsic and non-intrinsic resistance. Overall, the outcome “acquisition of resistance” can be interpreted as a risk assessment for having any resistant bacterium during or after treatment. In contrast, the outcome “emergence of resistance” is more rigorous, demanding the same species to be detected as more resistant during or after treatment.

The information, which antibiotic susceptibility tests were performed in each individual study can be found in the main text in table 1. However, we agree that this information should be better linked and highlighted again in table S1. We therefore now refer to table 1 in the table description of table S1. L134 ff.: “See table 1 in the main text for which antibiotics the antibiotics tested and reported extractable resistance data”. Furthermore, we added the breakpoints for resistant and susceptible classification if specifically stated in the main text of the study. However, we did not do further research into old guidelines, manufactures manuals or study protocols in case the breakpoints are not specifically stated in the main text as the main goal of this table, in our opinion, is to show a justification, why the studies could be considered for a resistance outcome. We therefore decided against further breakpoint investigations for studies, where the breakpoint is not specifically stated in the main text. 

Line 85: "Even though within-patient antibiotic resistance development is rare, it may contribute to the emergence and spread of resistance." 

Depending on the bug-drug combination, there is great variation in the propensity to develop within-patient antibiotic resistance. For example: within-patient development of ciprofloxacin resistance in Pseudomonas is fairly common while within-patient development of methicillin resistance in S. aureus is rare. Based on these differences, large clinical heterogeneity is expected and it is questionable where these studies should be pooled. 

We agree that our formulation neglects differences in prevalence of within-host resistance emergence depending on bug-drug combinations. We changed our statement in L 86 to: “Within-patient antibiotic resistance development, even if rare, may contribute to the emergence and spread of resistance.”

Line 114: "The overall pooled OR for acquisition of resistance comparing a lower number of antibiotics versus a higher one was 1.23 (95% CI 0.68 - 2.25), with substantial heterogeneity between studies (I2=77.4%)" 

What consequential measures did the authors take after determining this high heterogeneity? Did they explore the source of this large heterogeneity? Considering this large heterogeneity, do the authors consider it appropriate to pool these studies?

Thank you for highlighting this lack of clarity. As mentioned above, we now highlight that we performed several subgroup analyses to investigate heterogeneity. (L 116ff): “The overall pooled estimates are based on studies that focus on various clinical conditions/pathogens and compare different antibiotics treatments. To explore the impact of these and other potential sources of heterogeneity on the resistance estimates we performed various subgroup analyses and meta-regression.” Nevertheless, these analyses faced limitations due to the scarcity of evidence and often still showed a high amount of heterogeneity. Given the lack of appropriate evidence, it is hard to identify the source of heterogeneity. The decision to pool all studies was pre-specified in our previously published study protocol (PROSPERO CRD42020187257) and was motivated by the question whether there is a general effect of combination therapy on resistance development or identify factors that explain potential differences of the effect of combination therapy across bug-drug combinations. Therefore, we think that the presentation of the overall pooled estimate is appropriate, as it was predefined, and potential heterogeneity is furthermore explored in the subgroup analyses. 

Reviewer #1 (Recommendations For The Authors): 

I want to congratulate the investigators for the rigorous approach followed and the - in my opinion - correct interpretation of the data and analysis. The disappointing outcome is independent of the quality of the approach used. Yet, the consequences of that outcome are rather limited, and will not be surprising for - at least - some in the field of antibiotic resistance. 

Thank you for your positive and differentiated feedback.

Reviewer #2 (Recommendations For The Authors): 

Line 93: "The screening of the citations of the 41 studies identified one additional eligible study, for a total of 42 studies". 

Why was this study missed in the search strategy? 

What is the definition of "quasi-RCTs"? Why were these included in the analysis? 

Thank you for pointing out this lack of clarity. The additional study, which was found through screening the references of included studies, was not identified with our search strategy as neither the abstract nor database specific identifiers provided any indications that resistance was measured in this study. We added an explanation in the supplementary materials L 792 ff. and refer to this explanation in the main manuscript (L 95). 

Quasi-randomized trials are trials that use allocation methods, which are not considered truly random. We added this specification in L 95. It now reads: “….two quasi-RCTs, where the allocation method used is not truly random” and in L 252 ff: “Studies were classified as quasi-RCTs if the allocation of participants to study arms was not truly random.” For instance, the study Macnab et al. (1994) assigned patients alternately to the treatment arms. Quasi-randomized controlled trials can lead to biases and especially old studies are more likely to have used quasi-random allocation methods. This can also be seen in our study, where the two quasi-randomized controlled trials were published in 1994 and 1997. The bias is considered in the risk of bias assessment and in our conducted sensitivity analysis regarding the impact of risk of bias on our estimates (supplementary information sections 3.0 and 4.2). Furthermore, one of the two previous conducted meta-analyses comparing beta-lactam monotherapy to beta-lactam and aminoglycoside, which assessed resistance development also included quasi-randomized controlled trials Paul et al 2014. Overall, while designing the study, we decided to include quasi-randomized controlled trials to increase statistical power as we expected that limited statistical power might be a concern and decided to assess potential biases in the risk of bias assessment.  

Line 100: "Consequently, most studies did not have the statistical power to detect a large effect on within-patient resistance development (figure 2 B, SI p 14).". 

Small studies actually have more power to detect large effects while smaller power to detect small effects. Please rephrase. 

Thank you for pointing out this lack of clarity. We rephrased the sentence in order to emphasize our point that the studies are underpowered even if we assume in our power analysis a large effect on resistance development between treatment arms. In this context “the small” studies include too few patients to detect a large difference in resistance development. As resistance development is a rare event, generally studies have to include a larger number of patients to estimate the effect of intervention. We rephrased the sentence in L 101ff to: “Consequently, most studies did not have the statistical power to detect differences in within-patient resistance development even if we assume that the effect on resistance development is large between treatment arms.”

Line 108: "... and prophylaxis for blood cancer patients with four studies (10%) respectively.". 

I would suggest using the medical term hematological malignancy patients. 

Thank you for the suggestion, we changed it as suggested to hematological malignancy patients, also accordingly in the figures, and table 1.

Line 117: "Since the results for the two resistance outcomes are comparable, our focus in the following is on the acquisition of resistance". 

The first OR is 1.23 and the second is 0.74, why do you consider these outcomes as comparable? 

Thank you for pointing out our unprecise formulation. Due to the lack of power the exact estimates need to be interpreted with care. Here, we wanted to make the point that qualitatively the results of both outcomes do not differ in the sense that our analysis shows no substantial difference between a higher and a lower number of antibiotics. We rephrased the sentence to be more precise (L 123ff): “The results for the two resistance outcomes are qualitatively comparable in the sense that individual estimates may differ, but show similar absence of evidence to support either the benefit, harm or equivalence of treating with a higher number of antibiotics. Therefore, our …”. More detailed discussion about differences in estimates can be found in the SI, when the estimates of emergence of resistance are presented (e.g. SI section 2.1).

Line 123: "Furthermore, a lower number of antibiotics performed better than a higher number if the compared treatment arms had no antibiotics in common (pooled OR 4.73, 95% CI 2.14 - 10.42; I 2 =37%, SI p 7).". 

How do you explain this? What does this mean? 

We now added a more detailed explanation in the supplement (L 376ff.): “The result that if the treatment arms had no antibiotics in common a lower number of antibiotics performed better than a higher number of antibiotics could be due to different potencies of antibiotics or resistance prevalences. Further, there could be a bias to combine less potent antibiotics or antibiotics with higher resistance prevalence to ensure treatment efficacy, which couldlead to higher chances to detect resistances in the treatment arm with higher number of antibiotics, e.g. by selecting pre-existing resistance due to antibiotic treatment (see also section 6.1.9).” We furthermore already specifically mention this point in the main manuscript and refer then to the detailed explanation in the SI (L134 ff, “which could be due to different potencies or resistance prevalences of antibiotics as discussed in SI (SI section 6.1.10)”)

Overall, we want to point out that these results need to be interpreted with caution as overall the statistical power is limited to confidently estimate the difference in effect of a higher and lower number of antibiotics.

Line 125: ". In contrast, when restricting the analysis to studies with at least one common antibiotic in the treatment arms are pooled there was little evidence of a difference (pooled OR 0.55, 95% CI 0.28 - 1.07". 

The difference was not statistically significant but there does seem to be an indication of a difference, please rephrase. 

We rephrased the sentence to (L135 ff.): “In contrast, when restricting the analysis to studies with at least one common antibiotic in the treatment arms we found no evidence of a difference, only a weak indication that a higher number of antibiotics performs better (pooled OR 0.55, 95% CI 0.28 – 1.07; I2 \=74%, figure 3B).” 

Line 190: "Similarly, today, relevant cohort studies could be analysed collaboratively using various modern statistical methods to address confounding by indication and other biases (66, 67)". 

However, residual confounding by indication is likely. Please also mention the disadvantages of observational studies compared to RCTs. 

We now highlight that causal inference with observational data comes with its own challenges and stress that randomized controlled trials are still considered the gold standard. L 204ff now reads: “However, even with appropriate causal inference methods, residual confounding cannot be excluded when using observational data (67). Therefore, will remain the gold standard to estimate causal relationships.”

Line 230: "Gram-negative bacteria have an outer membrane, which is absent in grampositive bacteria for instance, therefore intrinsic resistance against antibiotics can be observed in gram-negative bacteria (11)". 

Intrinsic resistance is not unique for Gram-negative bacteria but also exists for Grampositive bacteria. 

We agree with the reviewer that intrinsic resistance is not unique to gram-negative bacteria and refined our writing. We additionally added that differences between gram-negative and gram-positive bacteria are not only to be expected due to differing intrinsic resistances but also due to potential differences in the mechanistic interactions of antibiotics, i.e., synergy or antagonism. The paragraph reads now (SI L289): “The gram status of a bacterium may potentially determine how effective an antibiotic, or an antibiotic combination is. Differences between gram-negative and gram-positive bacteria such as distinct bacterial surface organisation can lead to specific intrinsic resistances of gram-negative and grampositive bacteria against antibiotics (55). These structural differences can lead to varying effects of antibiotic combinations between gram-negative and gram-positive bacteria (56).”

Author response:

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

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Line 127. Provide a few more words describing the voltage protocol. To the uninitiated, panels A and B will be difficult to understand. "The large negative step is used to first close all channels, then probe the activation function with a series of depolarizing steps to re-open them and obtain the max conductance from the peak tail current at -36 mV. "

We have revised the text as suggested (revision lines 127 to Line 131): “From a holding potential within the gK,L activation range (here –74 mV), the cell is hyperpolarized to –124 mV, negative to EK and the activation range, producing a large inward current through open gK,L channels that rapidly decays as the channels deactivate. We use the large transient inward current as a hallmark of gK,L. The hyperpolarization closes all channels, and then the activation function is probed with a series of depolarizing steps, obtaining the max conductance from the peak tail current at –44 mV (Fig. 1A).”

Incidentally, why does the peak tail current decay? 

We added this text to the figure legend to explain this: “For steps positive to the midpoint voltage, tail currents are very large. As a result, K+ accumulation in the calyceal cleft reduces driving force on K+, causing currents to decay rapidly, as seen in A (Lim et al., 2011).”

The decay of the peak tail current is a feature of gK,L (large K+ conductance) and the large enclosed synaptic cleft (which concentrates K+ that effluxes from the HC). See Govindaraju et al. (2023) and Lim et al. (2011) for modeling and experiments around this phenomenon.

Line 217-218. For some reason, I stumbled over this wording. Perhaps rearrange as "In type II HCs absence of Kv1.8 significantly increased Rin and tauRC. There was no effect on Vrest because the conductances to which Kv1.8 contributes, gA and gDR activate positive to the resting potential. (so which K conductances establish Vrest???). 

We kept our original wording because we wanted to discuss the baseline (Vrest) before describing responses to current injection.

Vrest is presumably maintained by ATP-dependent Na/K exchangers (ATP1a1), HCN, Kir, and mechanotransduction currents. Repolarization is achieved by delayed rectifier and A-type K+ conductances in type II HCs.

Figure 4, panel C - provides absolute membrane potential for voltage responses. Presumably, these were the most 'ringy' responses. Were they obtained at similar Vm in all cells (i.e., comparisons of Q values in lines 229-230). 

We added the absolute membrane potential scale. Type II HC protocols all started with 0 pA current injection at baseline, so they were at their natural Vrest, which did not differ by genotype or zone. Consistent with Q depending on expression of conductances that activate positive to Vrest, Q did not co-vary with Vrest (Pearson’s correlation coefficient = 0.08, p = 0.47, n= 85).

Lines 254. Staining is non-specific? Rather than non-selective? 

Yes, thanks - Corrected (Line 264).

Figure 6. Do you have a negative control image for Kv1.4 immuno? Is it surprising that this label is all over the cell, but Kv1.8 is restricted to the synaptic pole? 

We don’t have a null-animal control because this immunoreactivity was done in rat. While the cuticular plate staining was most likely nonspecific because we see that with many different antibodies, it’s harder to judge the background staining in the hair cell body layer. After feedback from the reviewers, we decided to pull the KV1.4 immunostaining from the paper because of the lack of null control, high background, and inability to reproduce these results in mouse tissue. In our hands, in mouse tissue, both mouse and rabbit anti-KV1.4 antibodies failed to localize to the hair cell membrane. Further optimization or another method could improve that, but for now the single-cell expression data (McInturff et al., 2018) remain the strongest evidence for KV1.4 expression in murine type II hair cells.

Lines 400-404. Whew, this is pretty cryptic. Expand a bit? 

We simplified this paragraph (revision lines 411-413): “We speculate that gA and gDR(KV1.8) have different subunit composition: gA may include heteromers of KV1.8 with other subunits that confer rapid inactivation, while gDR(KV1.8) may comprise homomeric KV1.8 channels, given that they do not have N-type inactivation .”

Line 428. 'importantly different ion channels'. I think I understand what is meant but perhaps say a bit more. 

Revised (Line 438): “biophysically distinct and functionally different ion channels”.

Random thought. In addition to impacting Rin and TauRC, do you think the more negative Vrest might also provide a selective advantage by increasing the driving force on K entry from endolymph? 

When the calyx is perfectly intact, gK,L is predicted to make Vrest less negative than the values we report in our paper, where we have disturbed the calyx to access the hair cell (–80, Govindaraju et al., 2023, vs. –87 mV, here). By enhancing K+ accumulation in the calyceal cleft, the intact calyx shifts EK—and Vrest—positively (Lim et al., 2011), so the effect on driving force may not be as drastic as what you are thinking.

Reviewer #2 (Recommendations For The Authors):

(1) Introduction: wouldn't the small initial paragraph stating the main conclusion of the study fit better at the end of the background section, instead of at the beginning? 

Thank you for this idea, we have tried that and settled on this direct approach to let people know in advance what the goals of the paper are.

(2) Pg.4: The following sentence is rather confusing "Between P5 and P10, we detected no evidence of a non-gK,L KV1.8-dependent.....". Also, Suppl. Fig 1A seems to show that between P5 and P10 hair cells can display a potassium current having either a hyperpolarised or depolarised Vhalf. Thus, I am not sure I understand the above statement. 

Thank you for pointing out unclear wording. We used the more common “delayed rectifier” term in our revision (Lines 144-147): “Between P5 and P10, some type I HCs have not yet acquired the physiologically defined conductance, gK,L.. N effects of KV1.8 deletion were seen in the delayed rectifier currents of immature type I HCs (Suppl. Fig. 1B), showing that they are not immature forms of the Kv1.8-dependent gK,L channels. ”

(3) For the reduced Cm of hair cells from Kv1.8 knockout mice, could another reason be simply the immature state of the hair cells (i.e. lack of normal growth), rather than less channels in the membrane? 

There were no other signs to suggest immaturity or abnormal growth in KV1.8–/– hair cells or mice. Importantly, type II HCs did not show the same Cm effect.

We further discussed the capacitance effect in lines 160-167: “Cm scales with surface area, but soma sizes were unchanged by deletion of KV1.8 (Suppl. Table 2). Instead, Cm may be higher in KV1.8+/+ cells because of gK,L for two reasons. First, highly expressed trans-membrane proteins (see discussion of gK,L channel density in Chen and Eatock, 2000) can affect membrane thickness (Mitra et al., 2004), which is inversely proportional to specific Cm. Second, gK,L could contaminate estimations of capacitive current, which is calculated from the decay time constant of transient current evoked by small voltage steps outside the operating range of any ion channels. gK,L has such a negative operating range that, even for Vm negative to –90 mV, some gK,L channels are voltage-sensitive and could add to capacitive current.”

(4) Methods: The electrophysiological part states that "For most recordings, we used .....". However, it is not clear what has been used for the other recordings.

Thanks for catching this error, a holdover from an earlier ms. version.  We have deleted “For most recordings” (revision line 466).

Also, please provide the sign for the calculated 4 mV liquid junction potential. 

Done (revision line 476).

Reviewer #3 (Recommendations For The Authors): 

(1) Some of the data in panels in Fig. 1 are hard to match up. The voltage protocols shown in A and B show steps from hyperpolarized values to -71mV (A) and -32 mV (B). However, the value from A doesn't seem to correspond with the activation curve in C.

Thank you for catching this.  We accidentally showed the control I-X curve from a different cell than that in A. We now show the G-V relation for the cell in A.

Also the Vhalf in D for -/- animals is ~-38 mV, which is similar to the most positive step shown in the protocol.

The most positive step in Figure 1B is actually –25 mV. The uneven tick labels might have been confusing, so we re-labeled them to be more conventional.

Were type I cells stepped to more positive potentials to test for the presence of voltage-activated currents at greater depolarizations? This is needed to support the statement on lines 147-148. 

We added “no additional K+ conductance activated up to +40 mV” (revision line 149-150).  Our standard voltage-clamp protocol iterates up to ~+40 mV in KV1.8–/– hair cells, but in Figure 1 we only showed steps up to –25 mV because K+ accumulation in the synaptic cleft with the calyx distorts the current waveform even for the small residual conductances of the knockouts. KV1.8–/– hair cells have a main KV conductance with a Vhalf of ~–38 mV, as shown in Figure 1, and we did not see an additional KV conductance that activated with a more positive Vhalf up to +40 mV.

(2) Line 151 states "While the cells of Kv1.8-/- appeared healthy..." how were epithelia assessed for health? Hair cells arise from support cells and it would be interesting to know if Kv1.8 absence influences supporting cells or neurons. 

We added our criteria for cell health to lines 477-479: “KV1.8–/– hair cells appeared healthy in that cells had resting potentials negative to –50 mV, cells lasted a long time (20-30 minutes) in ruptured patch recordings, membranes were not fragile, and extensive blebbing was not seen.”

Supporting cells were not routinely investigated. We characterized calyx electrical activity (passive membrane properties, voltage-gated currents, firing pattern) and didn’t detect differences between +/+, +/–, and –/– recordings (data not shown). KV1.8 was not detected in neural tissue (Lee et al., 2013). 

(3) Several different K+ channel subtypes were found to contribute to inner hair cell K+ conductances (Dierich et al. 2020) but few additional K+ channel subtypes are considered here in vestibular hair cells. Further comments on calcium-activated conductances (lines 310-317) would be helpful since apamin-sensitive SK conductances are reported in type II hair cells (Poppi et al. 2018) and large iberiotoxin-sensitive BK conductances in type I hair cells (Contini et al. 2020). Were iberiotoxin effects studied at a range of voltages and might calcium-dependent conductances contribute to the enhanced resonance responses shown in Fig. 4? 

We refer you to lines 310-317 in the original ms (lines 322-329 in the revised ms), where we explain possible reasons for not observing IK(Ca) in this study.

(4) Similar to GK,L erg (Kv11) channels show significant Cs+-permeability. Were experiments using Cs+ and/or Kv11 antagonists performed to test for Kv11? 

No. Hurley et al. (2006) used Kv11 antagonists to reveal Kv11 currents in rat utricular type I hair cells with perforated patch, which were also detected in rats with single-cell RT-PCR (Hurley et al. 2006) and in mice with single-cell RNAseq (McInturff et al., 2018).  They likely contribute to hair cell currents, alongside Kv7, Kv1.8, HCN1, and Kir. 

(5) Mechanosensitive ("MET") channels in hair cells are mentioned on lines 234 and 472 (towards the end of the Discussion), but a sentence or two describing the sensory function of hair cells in terms of MET channels and K+ fluxes would help in the Introduction too. 

Following this suggestion we have expanded the introduction with the following lines  (78-87): “Hair cells are known for their large outwardly rectifying K+ conductances, which repolarize membrane voltage following a mechanically evoked perturbation and in some cases contribute to sharp electrical tuning of the hair cell membrane.  Because gK,L is unusually large and unusually negatively activated, it strongly attenuates and speeds up the receptor potentials of type I HCs (Correia et al., 1996; Rüsch and Eatock, 1996b). In addition, gK,L augments a novel non-quantal transmission from type I hair cell to afferent calyx by providing open channels for K+ flow into the synaptic cleft (Contini et al., 2012, 2017, 2020; Govindaraju et al., 2023), increasing the speed and linearity of the transmitted signal (Songer and Eatock, 2013).”

(6) Lines 258-260 state that GKL does not inactivate, but previous literature has documented a slow type of inactivation in mouse crista and utricle type I hair cells (Lim et al. 2011, Rusch and Eatock 1996) which should be considered. 

Lim et al. (2011) concluded that K+ accumulation in the synaptic cleft can explain much of the apparent inactivation of gK,L. In our paper, we were referring to fast, N-type inactivation. We changed that line to be more specific; new revision lines 269-271: “KV1.8, like most KV1 subunits, does not show fast inactivation as a heterologously expressed homomer (Lang et al., 2000; Ranjan et al., 2019; Dierich et al., 2020), nor do the KV1.8-dependent channels in type I HCs, as we show, and in cochlear inner hair cells (Dierich et al., 2020).”

(7) Lines 320-321 Zonal differences in inward rectifier conductances were reported previously in bird hair cells (Masetto and Correia 1997) and should be referenced here.

Zonal differences were reported by Masetto and Correia for type II but not type I avian hair cells, which is why we emphasize that we found a zonal difference in I-H in type I hair cells. We added two citations to direct readers to type II hair cell results (lines 333-334): “The gK,L knockout allowed identification of zonal differences in IH and IKir in type I HCs, previously examined in type II HCs (Masetto and Correia, 1997; Levin and Holt, 2012).”

Also, Horwitz et al. (2011) showed HCN channels in utricles are needed for normal balance function, so please include this reference (see line 171). 

Done (line 184).

(8) Fig 6A. Shows Kv1.4 staining in rat utricle but procedures for rat experiments are not described. These should be added. Also, indicate striola or extrastriola regions (if known). 

We removed KV1.4 immunostaining from the paper, see above.

(9) Table 6, ZD7288 is listed -was this reagent used in experiments to block Gh? If not please omit. 

ZD7288 was used to block gH to produce a clean h-infinity curve in Figure 6, which is described in the legend.

(10) In supplementary Fig. 5A make clear if the currents are from XE991 subtraction. Also, is the G-V data for single cell or multiple cells in B? It appears to be from 1 cell but ages P11-505 are given in legend. 

The G-V curve in B is from XE991 subtraction, and average parameters in the figure caption are for all the KV1.8–/–  striolar type I hair cells where we observed this double Boltzmann tail G-V curve. I added detail to the figure caption to explain this better.

(11) Supplementary Fig. 6A claims a fast activation of inward rectifier K+ channels in type II but not type I cells-not clear what exactly is measured here.

We use “fast inward rectifier” to indicate the inward current that increases within the first 20 ms after hyperpolarization from rest (IKir, characterized in Levin & Holt, 2012) in contrast to HCN channels, which open over ~100 ms. We added panel C to show that the activation of IKir is visible in type II hair cells but not in the knockout type I hair cells that lack gK,L. IKir was a reliable cue to distinguish type I and type II hair cells in the knockout.

For our actual measurements in Fig 6B, we quantified the current flowing after 250 ms at –124 mV because we did not pharmacologically separate IKir and IH.

Could the XE991-sensitive current be activated and contributing?

The XE991-sensitive current could decay (rapidly) at the onset of the hyperpolarizing step, but was not contributing to our measurement of IKir­ and IH, made after 250 ms at –124 mV, at which point any low-voltage-activated (LVA) outward rectifiers have deactivated. Additionally, the LVA XE991-sensitive currents were rare (only detected in some striolar type I hair cells) and when present did not compete with fast IKir, which is only found in type II hair cells.

Also, did the inward rectifier conductances sustain any outward conductance at more depolarized voltage steps? 

For the KV1.8-null mice specifically, we cannot answer the question because we did not use specific blocking agents for inward rectifiers.  However, we expect that there would only be sustained outward IR currents at voltages between EK and ~-60 mV: the foot of IKir’s I-V relation according to published data from mouse utricular hair cells – e.g., Holt and Eatock 1995, Rusch and Eatock 1996, Rusch et al. 1998, Horwitz et al., 2011, etc.  Thus, any such current would be unlikely to contaminate the residual outward rectifiers in Kv1.8-null animals, which activate positive to ~-60 mV. 

(I-HCN is also not a problem, because it could only be outward positive to its reversal potential at ~-40 mV, which is significantly positive to its voltage activation range.)

Author response:

(1) Reviewer 1 suggested that we repeat the analyses in additional ROIs in the prefrontal cortex (PFC). We appreciate this suggestion and believe it will contribute to a comprehensive understanding of the current findings. These results will be included in the revision.

(2) Reviewer 1 suggested that we also examine results in motor-related ROIs to rule out influences from response planning. We would like to note that our experimental design makes it unlikely that response planning would have influenced our results, as participants were unable to plan their motor responses in advance due to randomized response mapping on a trial-by-trial basis. Nevertheless, we agree with the reviewer that showing results from motor-related ROIs is important, and will include these results in the revision.

(3) Reviewer 1 raised a question about the effect size of the results across different ROIs. In our manuscript, we tried to avoid direct comparisons of representational strength across ROIs, by focusing on the differences in representational strength between conditions within the same ROI. Nevertheless, we agree that clarifying this issue is important, which we will address in the revision.

(4) Reviewer 2 raised a concern about the similarity between the RNN and fMRI results. We acknowledge that the complexity of our results makes it challenging to replicate all fMRI findings within a single RNN (e.g., simulating three brain regions in a single network with distinct result patterns). Nonetheless, the current RNNs effectively captured our key fMRI findings, including increased stimulus representation in frontal cortex as well as the tradeoff in category representation with varying levels of flexible control. Reviewer 2 also made several suggestions in tweaking the RNN structure and in choosing alternative analysis methods. We are happy to carry out these points as we think they could potentially increase the alignment between the two modalities.

Author response:

We are grateful to the reviewers and editors for their insightful comments. All recognized that, while mutation recurrences have been used for inferring cancer drivers, our approach has the rigor of quantitative analysis. We would like to add that, without rigorously ruling out mutational hotspots, most CDNs have not been accepted as driver mutations.

This paper develops the theory stating that (i) recurrent point mutations are true Cancer Driving Nucleotides (CDNs); and (ii) non-recurrent mutations are unlikely to be CDNs. The reviewers question that, with the theory, we still have not discovered new driving mutations. This is done in the companion paper. Table 3 shows that, averaged across cancer types, the conventional method would identify 45 CDGs while the CDN method tallies 258 CDGs. The power of the CDN method in identifying new driver genes is evident.

The second question is "By this theory, will we be able discover most CDNs when the sample size increases from ~ 1000 to 10,000?" This is a question of forecast and can be partially answered using GENIE data. Fig. 7 of this study shows that, when n increases from ~ 1000 to ~ 9,000, the numbers of discovered CDNs increase by 3 – 5 fold, most of which come from the two-hit class, as expected.

Fig. 7 also addresses the queries whether we have used datasets other than TCGA. We indeed have used all public data, including GENIE, ICGC and other integrated resources such as COSMIC. For the main study, we rely on TCGA because it is unbiased for estimating the probability of CDN occurrences. In many datasets, the numerators are given but the denominators are not (the number of patients with the mutation / the total number of patients surveyed). 

The third question is about mutation recurrences among cancer types. As stated by one reviewer, "different cancer types have unique mutational landscapes". While this is true when the analysis is done at the whole-gene level, one gets a different picture at the nucleotide level where the resolution is much higher. The pan-cancer trend of point mutations is evident in Fig. 4 of the companion paper.

Again, we heartily appreciate the criticisms and suggestions of the reviewers and editors!

Author response:

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

Reviewer #1 (Public Review):

[...] Overall the manuscript is well written, and the successful generation of the new endogenous Cac tags (Td-Tomato, Halo) and CaBeta, stj, and stolid genes with V5 tags will be powerful reagents for the field to enable new studies on calcium channels in synaptic structure, function, and plasticity. There are also some interesting, though not entirely unexpected, findings regarding how Brp and homeostatic plasticity modulate calcium channel abundance. However, a major concern is that the conclusions about how "molecular and organization diversity generate functional synaptic heterogeneity" are not really supported by the data presented in this study. In particular, the key fact that frames this study is that Cac levels are similar at Ib and Is active zones, but that Pr is higher at Is over Ib (which was previously known). While Pr can be influenced by myriad processes, the authors should have first assessed presynaptic calcium influx - if they had, they would have better framed the key questions in this study. As the authors reference from previous studies, calcium influx is at least two-fold higher per active zone at Is over Ib, and the authors likely know that this difference is more than sufficient to explain the difference in Pr at Is over Ib. Hence, there is no reason to invoke differences in "molecular and organization diversity" to explain the difference in Pr, and the authors offer no data to support that the differences in active zone structure at Is vs Ib are necessary for the differences in Pr. Indeed, the real question the authors should have investigated is why there are such differences in presynaptic calcium influx at Is over Ib despite having similar levels/abundance of Cac. This seems the real question, and is all that is needed to explain the Pr differences shown in Fig. 1. The other changes in active zone structure and organization at Is vs Ib may very well contribute to additional differences in Pr, but the authors have not shown this in the present study, and rely on other studies (such as calcium-SV coupling at Is vs Ib) to support an argument that is not necessitated by their data. At the end of this manuscript, the authors have found an interesting possibility that Stj levels are reduced at Is vs Ib, that might perhaps contribute to the difference in calcium influx. However, at present this remains speculative.

Overall, the authors have generated powerful reagents for the field to study calcium channels and how they are regulated, but draw conclusions about active zone structure and organization contributing to functional heterogeneity that are not strongly supported by the data presented.

Reviewer 1 raises an interesting question that we agree will form the basis of important studies. Here, we set out to address a different question, which we will work to better frame. While we and others had previously found a strong correlation between calcium channel abundance and synaptic release probability (Pr (Akbergenova et al., 2018; Gratz et al., 2019; Holderith et al., 2012; Nakamura et al., 2015; Sheng et al., 2012)), more recent studies found that calcium channel abundance does not necessarily predict synaptic strength (Aldahabi et al., 2022; Rebola et al., 2019). Our study explores this paradox and presents findings that provide an explanation: calcium channel abundance predicts Pr among individual synapses of either low-Pr type-Ib or high-Pr type-Is inputs where modulating channel number tunes synaptic strength, but does not predict Pr between the two inputs, indicating an inputspecific role for calcium channel abundance in promoting synaptic strength. Thus, we propose that calcium channel abundance predictably modulates synaptic strength among individual synapses of a single input or synapse subtype, which share similar molecular and spatial organization, but not between distinct inputs where the underlying organization of active zones differs. Consistently, in the mouse, calcium channel abundance correlates strongly with release probability specifically when assessed among homogeneous populations of connections (Aldahabi et al., 2022; Holderith et al., 2012; Nakamura et al., 2015; Rebola et al., 2019; Sheng et al., 2012).

As Reviewer 1 notes, the two-fold difference in calcium influx at type-Is synapses is certainly an important difference underlying three-fold higher Pr. However, growing evidence indicates that calcium influx alone, like calcium channel abundance, does not reliably predict synaptic strength between inputs. For example, Rebola et al. (2019) compared cerebellar synapses formed by granule and stellate cells and found that lower Pr granule synapses exhibit both higher calcium channel abundance and calcium influx. In another example, Aldahabi et al. (2023) demonstrate that even when calcium influx is greater at high-Pr synapses, it does not necessarily explain differences in synaptic strength between inputs. Studying excitatory hippocampal CA1 synapses onto distinct interneuronal targets, they found that raising calcium entry at low-Pr inputs to high-Pr synapse levels is not sufficient to increase synaptic strength to high-Pr synapse levels. Similarly, at the Drosophila NMJ, the finding that type-Ib synapses exhibit loose calcium channel-synaptic vesicle coupling whereas type-Is synapses exhibit tight coupling suggests factors beyond calcium influx also contribute to differences in Pr between the two inputs (He et al., 2023). Consistently, a two-fold increase in external calcium does not induce a three-fold increase in release at low-Pr type-Ib synapses (He et al., 2023). Thus, upon finding that calcium channel abundance is similar at type-Ib and -Is synapses, we focused on identifying differences beyond calcium channel abundance and calcium influx that might contribute their distinct synaptic strengths. We agree that these studies, ours included, cannot definitively determine the contribution of identified organizational differences to distinct release probabilities because it is not currently possible to specifically alter subsynaptic organization, and will ensure that our language is tempered accordingly. However, in addition to the studies cited above and our findings, recent work demonstrating that homeostatic potentiation of neurotransmitter release is accompanied by greater spatial compaction of multiple active zone proteins (Dannhauser et al., 2022; Mrestani et al., 2021) and decreased calcium channel mobility (Ghelani et al., 2023) provide support for the interpretation that subsynaptic organization is a key parameter for modulating Pr.

Reviewer #2 (Public Review):

The authors aim to investigate how voltage-gated calcium channel number, organization, and subunit composition lead to changes in synaptic activity at tonic and phasic motor neuron terminals, or type Is and Ib motor neurons in Drosophila. These neuron subtypes generate widely different physiological outputs, and many investigations have sought to understand the molecular underpinnings responsible for these differences. Additionally, these authors explore not only static differences that exist during the third-instar larval stage of development but also use a pharmacological approach to induce homeostatic plasticity to explore how these neuronal subtypes dynamically change the structural composition and organization of key synaptic proteins contributing to physiological plasticity. The Drosophila neuromuscular junction (NMJ) is glutamatergic, the main excitatory neurotransmitter in the human brain, so these findings not only expand our understanding of the molecular and physiological mechanisms responsible for differences in motor neuron subtype activity but also contribute to our understanding of how the human brain and nervous system functions.

The authors employ state-of-the-art tools and techniques such as single-molecule localization microscopy 3D STORM and create several novel transgenic animals using CRISPR to expand the molecular tools available for exploration of synaptic biology that will be of wide interest to the field. Additionally, the authors use a robust set of experimental approaches from active zone level resolution functional imaging from live preparations to electrophysiology and immunohistochemical analyses to explore and test their hypotheses. All data appear to be robustly acquired and analyzed using appropriate methodology. The authors make important advancements to our understanding of how the different motor neuron subtypes, phasic and tonic-like, exhibit widely varying electrical output despite the neuromuscular junctions having similar ultrastructural composition in the proteins of interest, voltage gated calcium channel cacophony (cac) and the scaffold protein Bruchpilot (brp). The authors reveal the ratio of brp:cac appears to be a critical determinant of release probability (Pr), and in particular, the packing density of VGCCs and availability of brp. Importantly, the authors demonstrate a brp-dependent increase in VGCC density following acute philanthotoxin perfusion (glutamate receptor inhibitor). This VGCC increase appears to be largely responsible for the presynaptic homeostatic plasticity (PHP) observable at the Drosophila NMJ. Lastly, the authors created several novel CRISPRtagged transgenic lines to visualize the spatial localization of VGCC subunits in Drosophila. Two of these lines, CaBV5-C and stjV5-N, express in motor neurons and in the nervous system, localize at the NMJ, and most strikingly, strongly correlate with Pr at tonic and phasic-like terminals.

(1) The few limitations in this study could be addressed with some commentary, a few minor follow-up analyses, or experiments. The authors use a postsynaptically expressed calcium indicator (mhcGal4>UAS -GCaMP) to calculate Pr, yet do not explore the contribution that glutamate receptors, or other postsynaptic contributors (e.g. components of the postsynaptic density, PSD) may contribute. A previous publication exploring tonic vs phasic-like activity at the drosophila NMJ revealed a dynamic role for GluRII (Aponte-Santiago et al, 2020). Could the speed of GluR accumulation account for differences between neuron subtypes?

We did observe that GCaMP signals are higher at type Is synapses, where synapses tend to form later but GluRs accumulate more rapidly upon innervation (Aponte-Santiago et al., 2020). However, because we are using our GCaMP indicator as a plus/minus readout of synaptic vesicle release at mature synapses, we do not expect differences in GluR accumulation to have a significant effect on our measures. Consistently, the difference in Pr we observe between type-Ib and -Is inputs (Fig. 1C) is similar to that previously reported (He et al., 2023; Lu et al., 2016; Newman et al., 2022).

(2) The observation that calcium channel density and brp:cac ratio as a critical determinant of Pr is an important one. However, it is surprising that this was not observed in previous investigations of cac intensity (of which there are many). Is this purely a technical limitation of other investigations, or are other possibilities feasible? Additionally, regarding VGCC-SV coupling, the authors conclude that this packing density increases their proximity to SVs and contributes to the steeper relationship between VGCCs and Pr at phasic type Is. Is it possible that brp or other AZ components could account for these differences. The authors possess the tools to address this directly by labeling vesicles with JanellaFluor646; a stronger signal should be present at Is boutons. Additionally, many different studies have used transmission electron microscopy to explore SVs location to AZs (t-bars) at the Drosophila NMJ.

To date, the molecular underpinnings of heterogeneity in synaptic strength have primarily been investigated among individual type-Ib synapses. However, a recent study investigating differences between type-Ib and -Is synapses also found that the Cac:Brp ratio is higher at type-Is synapses (He et al., 2023).

At this point, we do not know which active zone components are responsible for the organizational (Figs. 1, 2) and coupling (now demonstrated by He et al., 2023) differences between type-Ib and -Is synapses or what establishes the differences in active zone protein levels we observe (Figs. 3,6), although Brp likely plays a local role. We find that Brp is required for dynamically regulating calcium channel levels during homeostatic plasticity and plays distinct roles at type-Ib and -Is synapses (Figs. 3, 4). Brp regulates a number of proteins critical for the distribution of docked synaptic vesicles near T bars of type Ib active zones, including Unc13 (Bohme et al., 2016). Extending these studies to type-Is synapses will be of great interest.

(3) In reference to the contradictory observations that VGCC intensity does not always correlate with, or determine Pr. Previous investigations have also observed other AZ proteins or interactors (e.g. synaptotagmin mutants) critically control release, even when the correlation between cac and release remains constant while Pr dramatically precipitates.

This is an important point as a number of molecular and organizational differences between high- and low-Pr synapses certainly contribute to baseline functional differences. The other proteins we (Figs. 3,6) and others (Dannhauser et al., 2022; Ehmann et al., 2014; He et al., 2023; Jetti et al., 2023; Mrestani et al., 2021; Newman et al., 2022) have investigated are less abundant and/or more densely organized at type-Is synapses. Investigating additional active zone proteins, including synaptic proteins, and determining how these factors combine to yield increased synaptic strength are important next steps.

(4) To confirm the observations that lower brp levels results in a significantly higher cac:brp ratio at phasic-like synapses by organizing VGCCs; this argument could be made stronger by analyzing their existing data. By selecting a population of AZs in Ib boutons that endogenously express normal cac and lower brp levels, the Pr from these should be higher than those from within that population, but comparable to Is Pr. I believe the authors should also be able to correlate the cac:brp ratio with Pr from their data set generally; to determine if a strong correlation exists beyond their observation for cac correlation.

We do not have simultaneous measures of Pr and Cac and Brp abundance. However, our findings suggest that distinct Cac:Brp ratios at type Ib and Is inputs reflect underlying organizational differences that contribute to distinct release probabilities between the two synaptic subtypes. In contrast, within either synaptic subtype, release probability is positively correlated with both Cac and Brp levels. Thus, the mechanisms driving functional differences between synaptic subtypes are distinct from those driving functional heterogeneity within a subtype, so we do not expect Cac:Brp ratio to correlate with Pr among individual type-Ib synapses. We will work to clarify this point in the revised text.

(5) For the philanthotoxin induced changes in cac and brp localization underlying PHP, why do the authors not show cac accumulation after PhTx on live dissected preparations (i.e. in real time)? This also be an excellent opportunity to validate their brp:cac theory. Do the authors observe a dynamic change in brp:cac after 1, or 5 minutes; do Is boutons potentiate stronger due to proportional increases in cac and brp? Also regarding PhTx-induced PHP, their observations that stj and α2δ-3 are more abundant at Is synapses, suggests that they may also play a role in PhTx induced changes in cac. If either/both are overexpressed during PhTx, brp should increase while cac remains constant. These accessory proteins may determine cac incorporation at AZs.

As we have previously followed Cac accumulation in live dissected preparations and found that levels increase proportionally across individual synapses (Gratz et al., 2019), we did not attempt to repeat these challenging experiments at smaller type-Is synapses. We will reanalyze our data to investigate Cac:Brp ratio at individual active zones post PhTx. However, as noted above, we do not expect changes in the Cac:Brp ratio to correlate with Pr among individual synapses of single inputs as this measure reflects organization differences between inputs and PhTx induces an increase in the abundance of both proteins at both inputs.

Determining the effect of PhTx on Stj levels at type-Ib and -Is active zones is an excellent idea and might provide insight into how lower Stj levels correlate with higher Pr at type-Is synapses. While prior studies have demonstrated critical roles for Stj in regulating Cac accumulation during development and in promoting presynaptic homeostatic potentiation (Cunningham et al., 2022; Dickman et al., 2008; Kurshan et al., 2009; Ly et al., 2008; Wang et al., 2016), its regulation during PHP has not been investigated.

Taken together this study generates important data-driven, conceptional, and theoretical advancements in our understanding of the molecular underpinnings of different motor neurons, and our understanding of synaptic biology generally. The data are robust, thoroughly analyzed, appropriately depicted. This study not only generates novel findings but also generated novel molecular tools which will aid future investigations and investigators progress in this field.

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Reviewer #1 (Recommendations For The Authors): 

Major points: 

(1) A central question regarding VGCC differences at Is vs Ib active zones is why is calcium influx higher at Is active zones compared to Ib. Ideally, the authors would have started this study by showing correlations between Cac abundance, presynaptic calcium influx, and Pr at Is vs Ib active zones. If they had, they would likely find that Cac abundance scales with calcium influx and Pr within Is vs Ib, but that calcium influx is over two-fold enhanced at Is over Ib when normalized to the same Cac abundance. This is more than sufficient to explain the Pr differences, so the rest of the study should have focused on revealing why influx is different at Is over Ib despite an apparently similar level of Cac abundance. Then the examination of CaBeta, Stj, etc could have been used to help explain this conundrum. 

A lesson might be gleaned in how to structure this narrative from the Rebola 2019 study, which the authors cite and discuss at length. Similar to the current study, that paper started with two synapses ("strong" vs "weak") and sought to explain why they were so different in synaptic strength. First, they examined presynaptic calcium influx, and surprisingly found that the strong synapse had reduced calcium influx compared to the weak. Then the rest of the paper sought to explain why synaptic strength (Pr) was higher at the strong synapse despite reduced calcium influx. The authors do not use this logical flow and narrative in the present study, despite the focus being on how Cav2 channels contribute to strong vs weak synapses - and the primary function of Cav2 channels is to pass calcium at active zones to drive vesicle fusion. 

Although the authors did not show that presynaptic calcium influx is higher at Is vs Ib active zones in the current manuscript, other studies have previously established that calcium influx is two-fold higher at Is active zones vs Ib (as the authors cite). Rather than focusing so much on Pr at Is vs Ib active zones, which as the authors know can be influenced by myriad differences, it seems the more relevant parameter to study is simply to address presynaptic calcium influx at Is vs Ib, which is the primary function of Cac. Put more simply, if Cac levels are the same at Is vs Ib active zones, why is calcium influx at least two-fold higher at Is? 

It would therefore seem crucial for the authors to determine presynaptic calcium influx levels (ideally at individual AZs) to really understand how Cac intensity levels correlate with calcium influx. The authors instead map Pr at individual AZs, but as the authors know there are many variables that influence whether a SV releases in addition to calcium influx. There are a number of options for this kind of imaging in Drosophila, including genetically encoded calcium indicators targeted to active zones. But since several studies have previously established that influx is higher at Is active zones over Ib, this may not be necessary. That being said, there is a lot of value in quantitatively analyzing Cac/Stj/CaBeta abundance, calcium influx, and Pr together at individual active zones.

We appreciate the perspective that we could have focused on why Ca2+ influx is 2x greater at type Is active zones, which we agree is an important and interesting question. However, growing evidence indicates that Ca2+ influx alone, like Ca2+ channel abundance, does not reliably predict synaptic strength between inputs. So, here we focused instead on how other differences between synapses influence Pr and contribute to synaptic heterogeneity between and/or among synapses formed by strong and weak inputs. We have changed our title and framing to better reflect this focus. 

As Reviewer 1 notes, Rebola et al. (2019) found that lower Pr granule synapses exhibit higher Ca2+ influx (and Ca2+ channel abundance). In another example, Aldahabi et al. (2022) demonstrated that even when Ca2+ influx is greater at high-Pr synapses, it does not necessarily explain differences in synaptic strength as raising Ca2+ entry at low-Pr synapses to high-Pr synapse levels was not sufficient to increase synaptic strength to high-Pr input levels. Similar findings have been reported at tonic and phasic synapses of the Crayfish NMJ (Msghina, 1999).

Several lines of evidence argue that factors beyond Ca2+ influx also play important roles in establishing distinct release properties at the Drosophila NMJ. A recent study using using a botulinum transgene to isolate type Ib and Is synapses for electrophysiological analysis found that increasing external [Ca2+] from physiological levels (1.8 mM) to 3 mM or even 6 mM does not result in a 3-fold increase in EPSCs or quantal content at type Ib synapses despite the prediction that the increase would be even greater given the power dependence of release on between Ca2+ concentration (He et al., 2023). The authors further found that type Ib synapses are more sensitive than type Is synapses to the slow Ca2+ chelator EGTA, indicating looser Ca2+ channel-SV coupling. 

Consistently, we find that although VGCC levels are similar at the two inputs, their density is greater at type Is active zones (Figs. 1 and 2). Our findings also reveal additional molecular differences that may contribute to the observed differences in neurotransmitter release properties between the two inputs, including lower levels of the active zone protein Brp (Fig 3) and the auxiliary subunit α2δ-3/Stj (Fig. 6) at high Pr type Is inputs. In contrast, levels of each of these proteins positively correlate with synaptic strength among active zones of a single input, whether low- or high-Pr (Figs. 1, 3, 6). Similarly, levels of each of these proteins increase during homeostatic potentiation of neurotransmitter release (Figs. 4 and 7). Thus, we propose that two broad mechanisms contribute to synaptic diversity in the nervous system: (1) spatial organization and relative molecular content establish distinct average basal release probabilities that differ between inputs and (2) among individual synapses of distinct inputs, coordinated modulation of Ca2+ channel and active zone protein abundance independently tunes Pr. These intersecting mechanisms provide a framework for understanding the extensive and dynamic synaptic diversity observed across nervous systems.

(2) In addition to key points made above, it seems the authors should at least consider (if not experimentally test) what other differences might contribute to the higher calcium influx at Is over Ib:  

- Distinct splice isoforms of Cac (and/or Stj/Cabeta): The recent RNAseq analysis of gene expression at Is vs Ib motor neurons from Troy Littleton's group may inform this consideration? 

- Stj reduction at Is: Do channel studies in heterologous systems give any insight into VGCC channel function with and without a2d-3? Do Cav2 channels without a2d pass more calcium? This would then offer an obvious solution to the key conundrum underlying this study. 

These are excellent questions that we are actively pursuing. While there is no evidence of differentially expressed splice isoforms of Stj or Ca-β in the recent RNA-seq data from Jetti et al., 2023, subtle changes in Cac isoform usage were observed that may contribute to differences in Ca2+ influx. In heterologous systems, α2δ expression generally increases Ca2+ channel membrane insertion and  Ca2+ currents. However, in vivo α2δ’s can also mediate extracellular interactions that may modulate channel function. We address these points in greater detail in the revised discussion.  

(3) Assess Stj and CaBeta levels at AZs after PhTx: The successful generation of endogenously tagged Stj and CaBeta enables some relatively easy experiments that would be of interest, similar to what the authors present for Cac. Does Brp similarly control Stj and CaBeta at Is vs Ib compared to what they show for Cac? In addition, does homeostatic plasticity similarly change Stj and CaBeta at Is vs Ib compared to what the authors have shown for Cac? i.e., do they both similarly increase in intensity, by the same amount, as Cac? 

We agree and have included an analysis of α2δ-3/Stj levels following PhTx exposure (Fig. 7A-C). We have also investigated the regulation of Stj during chronic presynaptic homeostatic potentiation (Fig. 7D-F). In both cases, StjV5-N levels significantly increase at type Ib and Is active zones, consistent with our finding that among AZs of either type Ib or Is inputs, Stj levels correlate with Cac abundance and, thus, Pr. Together with our and others’ findings, this suggests that coordinated increases Ca2+ channel, auxiliary subunit,  and active zone protein abundance positively tunes synaptic strength at diverse synaptic subtypes.

Minor points: 

(1) Including line numbers would make reviewing/commenting easier. 

We apologize for this oversight and have added line numbers to the revised manuscript.

(2) Fig. 2I: It is not apparent what the mean cluster density is between Ib vs Is (as it is in Fig. 2F-H graphs). The mean and error bars should be included in 2I as it is in 2G. Same with Fig. 3C. 

Thank you for pointing this out. We have added error bars to the paired analysis in 2I as well as in 3C and 1C.

(3) Fig. 4 - it might make more sense to normalize Brp and Cac intensity as a percentage of baseline (PhTx at Is or Ib) rather than normalizing everything to control Ib. 

We have revised the graphs as suggested in Figure 4 and throughout.

(4) Page 5 bottom - REFS missing after Fig. 1E. 

Thank you for catching this. We have fixed it.

Reviewer #2 (Recommendations For The Authors): 

This reader found differentiating between low Pr sites (deep purple) and cac measurements (black) difficult in Fig 1B. You may consider depicting this differently. 

Thank you for this feedback. We have changed the color scheme to improve readability.

I found it difficult to discern the difference between experiments Fig 1E and Fig 1J. Why are individual dots distributed differently? 

The individual data points are the same as in 1E and 1F, but we have removed the individual NMJ dimensionality to combine all Is and Ib data points together along with best fit lines for comparison of their slopes. We have added text to the revised manuscript to clarify this.

Results section, second paragraph, add references, remove 'REF': We next investigated the correlation between Pr and VGCC levels and found that at type Is inputs, single-AZ Cac intensity positively correlates with Pr (Fig. 1E; REFS). 

Thank you. We have corrected this error.

Author response:

Reviewer #1 (Public Review):

Greter et al. provide an interesting and creative use of lactulose as a "microbial metabolism" inducer, combined with tracking of H2 and other fermentation end products. The topic is timely and will likely be of broad interest to researchers studying nutrition, circadian rhythm, and gut microbiota. However, a couple of moderate to major concerns were noted that may impact the interpretation of the current data:

(1)  Much of the data relies on housing gnotobiotic mice in metabolic cages, but I couldn't find any details of methods to assess contamination during multiple days of housing outside of gnotobiotic isolators/cages. Given the complexity of the metabolic cage system used, sterility would likely be incredibly challenging to achieve. More details needed to be included about how potential contamination of the mice was assessed, ideally with 16S rRNA gene sequencing data of the endpoint samples and/or qPCR for total colonization levels relative to the more targeted data shown.

We thank the reviewer for pointing out that we have not made the experimental setup clear in the text. One of the unique features of our metabolic cage setup is that the mice do not need to be housed outside gnotobiotic isolators, but that the whole system is placed inside an isolator. We have developed and published this system recently (Hoces et al, PLOS Biol 2022), including extensive testing for sterility/gnotobiosis. We will improve clarity in a revised version.

Given that 16S sequencing of germ-free mice will typically produce false positive reads, we used Blautia pseudococcoides as an indicator strain for contaminations. This strain is present in our SPF mouse colony, forms spores that are highly resilient to decontamination measures, and has been the most likely contaminant in our gnotobiotic system. We have checked for presence of this strain in the cecum content of all our animals at the end of each experiment, and only included experiments which had a B. pseudococcoides signal below threshold level.

(2)  The language could be softened to provide a more nuanced discussion of the results. While lactulose does seem to induce microbial metabolism it also could have direct effects on the host due to its osmotic activity or other off-target effects. Thus, it seems more precise to just refer to lactulose specifically in the figure titles and relevant text. Additionally, the degree to which lactulose "disrupts the diurnal rhythm" isn't clear from the data shown, especially given that the markers of circadian rhythm rapidly recover from the perturbation. It is probably more precise to instead state that lactulose transiently induces fermentation during the light phase or something to that effect. The discussion could also be expanded to address what methods are available or could be developed to build upon the concepts here; for example, the use of genetic inducers of metabolism which may avoid the more complex responses to lactulose.

The point about language is well taken. We tried to make the argument that what we call disruption of the diurnal rhythm is acute, meaning that it is not disrupting the rhythm "chronically" (i.e., for longer), but that it recovers rapidly from this transient disruption. Given the confusion this wording is causing we are rephrasing this in a new version of the manuscript.

We also appreciate the mention of concepts from our study that can be built on in future studies, and we will add a paragraph on potential further research.

Despite these concerns, this was still an intriguing and valuable addition to the growing literature on the interface of the microbiome and circadian fields.

We thank the reviewer for all their encouraging and constructive remarks!

Reviewer #2 (Public Review):

Summary:

The authors aimed to investigate how microbial metabolites, such as hydrogen and short-chain fatty acids (SCFAs), influence feeding behavior and circadian gene expression in mice.

Specifically, they sought to understand these effects in different microbial environments, including a reduced community model (EAM), germ-free mice, and SPF mice. The study was designed to explore the broader relationship between the gut microbiome and host circadian rhythms, an area that is not well understood. Through their experiments, the authors hoped to elucidate how microbial metabolism could impact circadian clock genes and feeding patterns, potentially revealing new mechanisms of gut microbiome-host interactions.

Strengths:

The manuscript presents a well-executed investigation into the complex relationship between microbial metabolites and circadian rhythms, with a particular focus on feeding behavior and gene expression in different mouse models. One of the major strengths of the work lies in its innovative use of a reduced community model (EAM) to isolate and examine the effects of specific microbial metabolites, which provides valuable insights into how these metabolites might influence host behavior and circadian regulation. The study also contributes to the broader understanding of the gut microbiome's role in circadian biology, an area that remains poorly understood. The experiments are thoughtfully designed, with a clear rationale that ties together the gut microbiome, metabolic products, and host physiological responses. The authors successfully highlight an intriguing paradox: the significant influence of microbial metabolites in the EAM model versus the lack of effect in germ-free and SPF mice, which adds depth to the ongoing exploration of microbial-host interactions. Despite some methodological concerns, the manuscript offers compelling data and opens up new avenues for research in the field of microbiome and circadian biology.

We thank the reviewer for their encouraging remarks, specifically on the surprising findings that microbial metabolism seems to affect circadian clock gene expression and behavior differently in EAM and SPF mice.

Weaknesses:

The manuscript, while providing valuable insights, has several methodological weaknesses that impact the overall strength of the findings. First, the process for stool collection lacks clarity, raising concerns about potential biases, such as the risk of coprophagia, which could affect the dry-to-wet weight ratio analysis and compromise the validity of these measurements.

We thank the reviewer for pointing out that our description of the specific methods used for collecting feces were presented in a somewhat confusing manner. In short, dry and wet fecal weights were determined based on fecal pellets that were freshly produced and directly collected from restrained mice. To determine total fecal output over time, we collected all fecal pellets produced in a 5 hour window in a cage, determined their dry weight, and then used the water content determined for fresh feces to calculate wet weight. Using this method, we cannot account for potential differences in coprophagia between the groups. However, this is not likely to affect the dry-to-wet ratio of fecal output in our results.

Additionally, the use of the term "circadian" in some contexts appears inaccurate, as "diurnal" might be more appropriate, especially given the uncertainty regarding whether the observed microbiome fluctuations are truly circadian.

Similarly to our answer to reviewer 1 above, we appreciate this remark about imprecise language and have addressed this issue in the text. Indeed, we do not think the microbiota fluctuations are truly circadian, but likely a result of the entrainment through the host's food intake.

Another significant issue is the unexpected absence of an osmotic effect of lactulose in EAM mice, which contradicts the known properties of lactulose as an osmotic laxative. This finding requires further verification, including the use of a positive control, to ensure it is not artifactual.

This is a good point. We have used this lactulose dosage specifically to induce microbial metabolism without causing osmotic diarrhea, and went to some lengths do demonstrate this. In response to this comment (and one by reviewer 3 below about transit time), we are planning an experiment that will use a higher lactulose dose as a positive control.

The presentation of qRT-PCR data as log2-fold changes, with a mean denominator, could introduce bias by artificially reducing variability, potentially leading to spurious findings or increased risk of Type I error. This approach may explain the unexpected activation of both the positive and negative limbs of the circadian clock.

While we agree that our description of the qpcr method used for measuring circadian clock gene expression was lacking detail, we do not see how log2-fold changes (as opposed to, e.g., fold change) would lead to an increased risk of Type 1 error. We did not use a mean denominator for analyzing the data but used the house-keeping data for the same sample as denominator for the respective circadian clock genes. This will be described more clearly in a revised methods section.

Moreover, the lack of detailed information on the primers and housekeeping genes used in the experiments is concerning, particularly given the importance of using non-circadian housekeeping genes for accurate normalization.

We apologize for this omission, it seems like the resource table got lost in the submission, leading to missing information. It will be included in the revised manuscript.

The methods for measuring metabolic hormones, such as GLP-1 and GIP, are also not adequately described. If DPP-IV/protease inhibitor tubes were not used, the data could be unreliable due to the rapid degradation of these hormones by circulating proteases.

We thank the reviewer for spotting this mistake. We will add details of how GLP-1 and GIP were measured to the methods section. While we did not use DPP-IV/protease inhibitor tubes, we added the inhibitors to the syringes when sampling blood, leading to the same effect.

Finally, the manuscript does not address the collection of hormone levels during both fasting and fed phases, a critical aspect for interpreting the metabolic impact of microbial metabolites.

We agree that it will be interesting to measure hormone levels also in the fed phase, and we will include this data in a revised version of the manuscript. Even with that data, a more thorough examination of hormone levels over the diurnal cycle, as suggested by reviewer 3, might be relevant for a full-scale follow-up. Given our data, we of course cannot exclude that there may be time-point-specific differences and therefore have softened the language around this conclusion to state that hormone levels are not acutely changed after a lactulose intervention “at the time-points examined”.

These methodological concerns collectively weaken the robustness of the study's results and warrant careful reconsideration and clarification by the authors.

Because of these weaknesses, the authors have partially achieved their aims by providing novel insights into the relationship between microbial metabolites and host circadian rhythms. The data do suggest that microbial metabolites can significantly influence feeding behavior and circadian gene expression in specific contexts. However, the unexpected absence of an osmotic effect of lactulose, the potential biases introduced by the log2-fold change normalization in qRT- PCR data, and the lack of clarity in critical methodological details weaken the overall conclusions. While the study provides valuable contributions to understanding the gut microbiome's role in circadian biology, the methodological weaknesses prevent a full endorsement of the authors' conclusions. Addressing these issues would be necessary to strengthen the support for their findings and fully achieve the study's aims.

We thank the reviewer again for their careful and critical reading of our work, and for their constructive input. We hope that many of the concerns will be addressed by providing more methodological detail and additional experimental data in the revised version of our manuscript.

Despite the methodological concerns raised, this work has the potential to make a significant impact on the field of circadian biology and microbiome research. The study's exploration of the interaction between microbial metabolites and host circadian rhythms in different microbial environments opens new avenues for understanding the complex interplay between the gut microbiome and host physiology. This research contributes to the growing body of evidence that microbial metabolites play a crucial role in regulating host behaviors and physiological processes, including feeding and circadian gene expression.

We thank the reviewer for their encouraging remarks!

Reviewer #3 (Public Review):

Summary:

In the manuscript by Greter, et al., entitled "Acute targeted induction of gut-microbial metabolism affects host clock genes and nocturnal feeding" the authors are attempting to demonstrate that an acute exposure to a non-nutritive disaccharide (lactulose) promotes microbial metabolism that feeds back onto the host to impact circadian networks. The premise of the study is interesting and the authors have performed several thoughtful experiments to dissect these relationships, providing valuable insights for the field. However, the work presented does not necessarily support some of the conclusions that are drawn. For instance, lactulose is administered during the fasting period to mimic the impact of a feeding bout on the gut microbiota, but it would be important to perform this treatment during the fed state as well to show that the effects on food intake, etc. do not occur.

This is a good point, and we will include an experiment addressing this in a revised version of the manuscript.

To truly draw the conclusion that the current outcomes are directly connected to and mediated via an impact on the host circadian clock, it would be ideal to perform these studies in a circadian gene knock-out animal (i.e., Cry1 or Cry2 KO mice, or perhaps Bmal-VilCre tissue- specific KO mice). If the effects are lost in these animals, this would more concretely connect the current findings to the circadian clock gene network.

We agree that these would be interesting experiments to follow up on the question how the observed effects are actuated by host functions. However, they would require a large amount of preparatory work (including rederiving the KO mice to get them germ-free in our gnotobiotic facility), we argue that they are beyond the scope of this study.

Despite these reservations, the work is promising.

We thank the reviewer for their encouraging assessment.

Strengths:

Attempting to disentangle nutrient acquisition from microbial fermentation and its impact on diurnal dynamics of gut microbes on host circadian rhythms is an important step for providing insights into these host-microbe interactions.

The authors utilize a novel approach in leveraging lactulose coupled with germ-free animals and metabolic cages fitted with detectors that can measure microbial byproducts of fermentation, particularly hydrogen, in real-time.

The authors consider several interesting aspects of lactulose delivery, including how it shifts osmotic balance as well as provides calculations that attempt to explain the caloric contribution of fermentation to the animal in the context of reduced food intake. This provides interesting fundamental insights into the role of microbial outputs on host metabolism.

Thank you!

Weaknesses:

While the authors have done a large amount of work to examine the osmotic vs. metabolic influence of lactulose delivery, the authors have not accounted for the enlarged cecum and increased cecal surface area in germ-free mice. The authors could consider an additional control of cecectomy in germ-free mice.

We thank the reviewer for pointing out the potential effect of the anatomical differences of germ- free and conventionally colonized mice. We agree that when comparing germ-free mice to SPF mice, the enlarged cecum area in germ-free animals could lead to differences in water release or uptake. However, this is not the case in the gnotobiotic mice colonized with our minimal microbiota, which have comparable cecum sizes to germ-free mice, and thus comparing water transport over the cecum wall between those groups can be done without correcting for cecal surface areas. We will add information on cecum sizes in the different experimental groups to a revised version of the manuscript.

The authors have examined GI hormones as one possible mechanism for how food intake is altered by microbial fermentation of lactulose. However, the authors measure PYY and GLP-1 only at a single time point, stating that there are no differences between groups. Given the goal of the studies is to tie these findings back into circadian rhythms, it would be important to show if the diurnal patterns of these GI hormones are altered.

We fully agree that a deeper investigation of the diurnal fluctuations of hormone levels would be an interesting next step in studying whether perturbations in food intake can disturb these rhythms. Doing this for the whole rhythm would really require a full second study. For a revised version of this manuscript, we will add a second time-point of hormone measurements (during the fed phase) to this study. In addition, we will soften the statements made around these data to point out just that hormone level fluctuations could not be detected during specific time points after lactulose treatment, and therefore do not seem to explain the imminent behavioral changes.

Considerations of other factors, such as conjugated vs. deconjugated bile acids, microbial bile salt hydrolase activity, and bile acid resorption, might be an important consideration for how lactulose elicits more influence on ileal circadian clock genes relative to cecum and colon.

We absolutely agree that investigation of microbial bile acid modification and their metabolism by the host would be an interesting topic for a follow-up study.

Measurements of GI transit time (both whole gut and regional) would be an important for consideration for how lactulose might be impacting the ileum vs. cecum vs. colon.

This is also an interesting point, and we will add an assessment of transit time to a revised version of the manuscript.

Author response:

General comment:

"This important study examined neuronal activity in the dentate nucleus of the cerebellum when monkeys performed a difficult perceptual decision-making task. The authors provide convincing evidence that the cerebellum represents sensory, motor, and behavioral outcome signals that are sent to the attentional system, but further analysis focusing on the disparity of performance between animals would improve the quality of the paper. This paper is of great general interest in that it shows the involvement of the cerebellum in cognitive processes at the neuronal level."

We thank you for these general comments, and we agree with all of them. 

Public Reviews (Reviewer #1):

Summary:

Recordings were made from the dentate nucleus of two monkeys during a decision-making task. Correlates of stimulus position and stimulus information were found to varying degrees in the neuronal activities. 

We agree with this summary.

Strengths:

A difficult decision-making task was examined in two monkeys.

We agree with this statement.

Weaknesses:

One of the monkeys did not fully learn the task. The manuscript lacked a coherent hypothesis to be tested, and no attempt was made to consider the possibility that this part of the brain may have little to do with the task that was being studied. 

We understand these comments. It is correct that one of the monkeys did not fully learn the task, but it should be noted that both monkeys learned significantly above chance level, and we therefore find the recordings of both monkeys useful. We tested the hypothesis that neurons of the nucleus dentate can dynamically modulate their activity during a visual attention task, comprising not only sensorimotor but also cognitive attentional components. We agree that this hypothesis should be spelled out more explicitly in the introduction, which we will do in the revised version. We also appreciate the comment of this Reviewer that in our original submission we did not show our attempt to consider the possibility that this part of the brain may have little to do with the task that was being studied. We in fact did consider this possibility in that we applied muscimol to the dentate nucleus in one of the monkeys. The data of this one successful experiment show that the behaviour was reversibly affected in line with our hypothesis. Given that this only concerned one of the monkeys, we preferred not to present these data in the article. However, as the Reviewer correctly points out that this question remains hanging in the air, we will show them in our formal rebuttal letter. Please note that we decided to focus at the end of our research project on the tracing experiments, showing in both monkeys the connections of the dentate nucleus with the regions that are involved in attention. As a result, both monkeys have been sacrificed and we cannot expand upon our muscimol experiments anymore (which would have been useful indeed).

Last but not least, given the comments of the Reviewers, we will also add a Supplementary figure to Figure 2, in which we will present the data for both monkeys separately and provide our interpretation. This may help to strengthen our conclusions. 

Public Reviews (Reviewer #2):

The authors trained monkeys to discriminate peripheral visual cues and associate them with planning future saccades of an indicated direction. At the same time, the authors recorded single-unit neural activity in the cerebellar dentate nucleus. They demonstrated that substantial fractions of DN cells exhibited sustained modulation of spike rates spanning task epochs and carrying information about stimulus, response, and trial outcome. Finally, tracer injections demonstrated this region of the DN projects to a large number of targets including several known to interconnect the visual attention network. The data compellingly demonstrate the authors' central claims, and the analyses are well-suited to support the conclusions. Importantly, the study demonstrates that DN cells convey many motor and nonmotor variables related to task execution, event sequencing, visual attention, and arguably decision-making/working memory.

We thank the Reviewer for this positive and constructive feedback.

Author response:

We would like to thank the reviewers for their time and for their kind comments about our work. We expect that their comments will help us to improve the manuscript and so will plan the following experiments/revisions to address some of their comments:

Reviewer 1 (Public Review):

(1) The cutoffs the authors used to define "conditionally essential" mutants are not reported. The results also lack validation for lethality using a titratable system. It would be ideal to validate several genes in each dataset to determine cutoffs (i.e. 5-fold decrease in insertion mutants) for conditional lethality. It was not done (or described) here.

We will report the cutoffs used when we generate the revised manuscript. Our experiments identified hundreds of lethal combinations and we have six datasets, validation of several genes from each would require generation of at least 20 depletion strains and subsequent testing of each. Validation using a depletion system would therefore be a significant undertaking and is typically not the standard when using these approaches. However, should time permit then we will attempt a subset of these experiments.

(2) Also, two mutations that both make the cells sick could provide an additive effect (i.e. dapF and BamB), which doesn't necessarily mean the pathways are linked. The authors should revise their wording. They have not shown genetic linkage in some cases.

We will revise the text to address this.

(3) Mutations throughout the manuscript are not complemented. It would be ideal to add complementation data to show the gene-phenotype relationship is specific.

We thank the reviewers for highlighting this and will complete the complementation experiments.

(4) Also, I would argue the term "conditionally essential genes" should be replaced with "synthetically lethal". Strains were compared in the same conditions but with different genetic backgrounds.

We take the reviewers point and will revise the text accordingly.

Reviewer 2 (Public Review):

Weaknesses:

(1) An important control in any genetic interaction study is to do complementation tests to demonstrate that the phenotype observed is indeed due to the missing gene under analysis. Although the Keio library was designed to avoid polar effects, it is impossible to predict other undesirable effects of the deletions (hitting of a non-annotated sRNA or RNA stability effects, for example). Thus, before one can safely conclude that a proposed genetic interaction is real, complementation tests should be carried out. This seems particularly important in the case of a new and surprising interaction, such as that between bamB and DNA replication and repair genes.

We thank the reviewers for highlighting this and will complete the complementation experiments.

(2) Why not include the suppressor interactions in the work? There are probably plenty, and in principle, they should be as informative as the conditional essential (or synthetic lethal) ones. The only one highlighted in the paper is that between bamB and diaA, since it nicely fits with the synthetic lethal effects with initiation inhibitors seqA and hda. Even if the authors cannot make sense of the suppressor interactions, their inclusion in the paper should make the dataset richer and more valuable to the community.

These data are available in supplementary table 1. However, we appreciate this is not obvious and so will make a new supplementary table and include a brief description of the data for the revised paper.

(3) The enrichment analysis in Figure 2B deserves some clarification. What is the meaning of gene ratio? How can single genes of a pathway yield an enrichment signal? Why weren´t seqA and hda included in the DNA replication class in 2B?

We apologise for the confusion caused and will include a description of the analysis in the methods section.

(4) The writing puts too much emphasis on demonstrating that bam lipoproteins and chaperones are specialized instead of fully redundant. However, I have the impression this is a long-settled conclusion in the field, as the manuscript itself describes at several points when reviewing the literature.

We will revise the text to reduce this emphasis.

Reviewer #3 (Public Review):

In this work, Bryant, et al. investigate genetic interactions between non-essential members of the outer membrane protein biogenesis pathway and other genes in the genome using a transposon-directed insertion sequencing (TraDIS) approach in E. coli K-12. The authors identify interactions with other components of the envelope including LPS, peptidoglycan, and enterobacterial common antigen biogenesis, and they tie these interactions to specific members of the outer membrane biogenesis pathway. Although many of these interactions are known and have been previously investigated in the field, the study provides several synthetic phenotypes that could be useful for further investigations.

The strengths of the paper include their unbiased, TraDIS approach, and follow up on the interactions they observe. The interactions with genes of unknown function also are of interest as they may suggest experiments to find the functions of these genes. The largest weakness of this paper is the use of a gene deletion allele for bamB that is known to be polar leading to decreased expression of an essential gene. This largely invalidates all results related to DNA replication. In addition, it is a weakness that the paper does not adequately address its place in the field through discussion of existing results on the interactions they investigate.

We appreciate the reviewers’ comments and concerns about the bamB allele, and we will address these concerns by completing complementation experiments for the CRISPRi depletion experiments and the run-out assays. However, despite the statement that it is known to be polar, several previous studies have also used the bamB Keio library strain. Many of these studies transfer the allele to a clean background and use the derivative in which the cassette has been removed as we have done here (Cox et al., 2017, Gunasinghe et al., 2018, Psonis et al., 2019, Storek et al., 2019, Ranava et al. 2021, Steenhuis et al., 2021, Thewasano et al., 2023). Therefore, we feel somewhat justified in our choice of strain.

We are unable to find a reference for the Keio bamB strain causing polar effects and would have appreciated the reviewers’ guidance here. However, we believe the concern about polar effects stems from the observations of Ruiz et al., (2005), in which it was observed that a yfgL::ISE1 allele causes polar effects. This was hypothesised to be due to the ORF contained within the IS being transcribed in the opposite orientation to yfgL and the downstream der gene. They subsequently observed that a strain carrying a Tn5KAN-I-SceI insertion in yfgL (yfgL::kan) did not cause polar effects and this was hypothesised to be due to the kan cassette being co-oriented with yfgL. In addition, Charlson et al., 2006 generated a yfgL deletion by replacing the majority of the gene with a kan cassette in a manner similar to that of the Keio library that was subsequently flipped out. This study also found no evidence of polar effects on der. In theory, the strain used here, and in previous studies by other groups, should provide minimal disruption to transcription through generation of a mini-gene from the original bamB sequence to maintain operon expression. This is in contrast to the disruption caused by the yfgL::ISE1 allele.

While we do appreciate the concern, several pieces of evidence lend themselves to counter the statement that our strain choice largely invalidates the results. The der GTPase is essential, hence the concern about polar effects leading to the bamB phenotypes we see. However, depletion of der leads to cold sensitivity, whereas we find that the bamB strain used here actually performs better in colder temperatures. In addition, the der depletion is sensitive to doxycycline, whereas the bamB mutant has increased fitness in this condition (Fig 1) (Bharat and Brown, 2015, Hwang and Inouye, 2008). Hence, should the mutation lead to decreased expression of der then we would expect the bamB strain to phenocopy the der depletion, which it does not. Regardless of this information, we will still address these concerns by completing complementation experiments.

Author response:

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

Reviewer #1 (Public Review):

Weakness 1. Enhancing Reproducibility and Robustness: To enhance the reproducibility and robustness of the findings, it would be valuable for the authors to provide specific numbers of animals used in each experiment. Explicitly stating the penetrance of the rod-like neurocranial shape in dact1/2-/- animals would provide a clearer understanding of the consistency of this phenotype. 

In Fig. 3 and Fig. 4 animal numbers were added to the figure and figure legend (line 1111). In Fig. 5 animal numbers were added to the figure. We now state that dact1/2-/- animals exhibit the rod-like neurocranial shape that is completely penetrant (Line 260). 

Weakness 2. Strengthening Single-Cell Data Interpretation: To further validate the single-cell data and strengthen the interpretation of the gene expression patterns, I recommend the following: 

-Provide a more thorough explanation of the rationale for comparing dact1/2 double mutants with gpc4 mutants.

-Employ genotyping techniques after embryo collection to ensure the accuracy of animal selection based on phenotype and address the potential for contamination of wild-type "delayed" animals.

-Supplement the single-cell data with secondary validation using RNA in situ or immunohistochemistry techniques. 

An explanation of our rationale was added to the results section (Lines 391403) and a summary schematic was added to Figure 6 (panel A).

Genotyping of the embryos was not possible but quality control analysis by considering the top 2000 most variable genes across the dataset showed good clustering by genotype, indicating the reproducibility of individuals in each group (See Supplemental Fig. 4).

The gene expression profiles obtained in our single-cell data analysis for gpc4, dact1, and dact2 correlate closely with our in situ hybridization analyses. Further, our data is consistent with published zebrafish single-cell data. We validated our finding of increased capn8 expression in dact1/2 mutants by in situ hybridization. Therefore we are confident in the robustness of our single-cell data.  

Weakness 3. Directly Investigating Non-Cell-Autonomous Effects: To directly assess the proposed non-cell-autonomous role of dact1/2, I suggest conducting transplantation experiments to examine the ability of ectodermal/neural crest cells from dact1/2 double mutants to form wild-type-like neurocranium.  

The reviewer’s suggestion is an excellent experiment and something to consider for future work. Cell transplant experiments between animals of specific genotypes are challenging and require large numbers. It is not possible to determine the genotype of the donor and recipient embryos at the early timepoint of 1,000 cell stage where the transplants would have to be done in the zebrafish. So that each transplant will have to be carried out blind to genotype from a dact1+/-; dact2+/- or dact1-/-; dact2+/- intercross and then both animals have to be genotyped at a subsequent time point, and the phenotype of the transplant recipient be analyzed. While possible, this is a monumental undertaking and beyond the scope of the current study.

Weakness 4. Further Elucidating Calpain 8's Role: To strengthen the evidence supporting the critical role of Calpain 8, I recommend conducting overexpression experiments using a sensitized background to enhance the statistical significance of the findings. 

We thank the reviewer for their suggestion and have now performed capn8 overexpression experiments in embryos generated from dact1/2 double heterozygous breeding. We found a statistically significant effect of capn8 overexpression in the dact1+/-,dact2+/- fish (Lines 462-464 and Fig. 8C,D). 

Minor Comments:  

Comment: Creating the manuscript without numbered pages, lines, or figures makes orientation and referencing harder.  

Revised

Comment: Authors are inconsistent in the use of font and adverbs, which requires extra effort from the reader. ("wntIIf2 vs wnt11f2 vs wnt11f2l"; "dact1/2-/- vs dact1/dact2 -/-"; "whole-mount vs wholemount vs whole mount").  

Revised throughout.

Comment: Multiple sentences in the "Results" belong to the "Materials and Methods" or the "Discussion" section. 

We have worked to ensure that sentences are within the appropriate sections of the manuscript.

Comment: Abstract:

"wnt11f2l" should be "wnt11f2"  

Revised (Line 24).

Comment: Main text:

Page 5 - citation Waxman, Hocking et al. 2004 is used 3x without interruption any other citation. 

Revised (Line 112).

Page 9 - "dsh" mutant is mentioned once in the whole manuscript - is this a mistake?

Revised, Rewritten (Line 196).

Page 10 - Fig 2B does not show ISH.

Revised (Line 229).

Page 11 - "kyn" mutant is mentioned here for the first time but defined on page 15.

Revised (Line 245). Now first described on page 4.

Page 14 - "cranial CNN" should be CNCC.

Revised. (Line 334)

Page 16 - dact1/dact2/gpc4: Fig. 5C is used but it should be Fig 5E.

Revised. (Line 381)

Page 18 - dact1/2-/- or dact1-/-, dact2-/-. 

Revised. (Line 428)

Comment: Methods:

Page 24 - ZIRC () "dot" is missing. ChopChop ")" is missing. "located near the 5' end of the gene" - In the Supplementary Figure 1 looks like in the middle of the gene.

Revised. (Lines 600, 609, 611, respectively).

Page 25 - WISH -not used in the main text.

Revised. (Line 346).

Page 26 - 4% (v/v) formaldehyde; at 4C - 4{degree sign}C; 50% (v/v) ethanol; 3% (w/v) methylcellulose.

Revised. (Lines 659, 660, 662).

Page 27 - 0.1% (w/v) BSA. 

Revised. (Line 668).

Comment: Discussion:

The overall discussion requires more references and additional hypotheses. On page 20, when mentioning 'as single mutants develop normally,' does this refer to the entire animals or solely the craniofacial domain? Are these mutants viable? If they are, it's crucial to discuss this phenomenon in relation to prior morpholino studies and genetic compensation.

Observing how the authors interpret previously documented changes in nodal and shh signaling would be beneficial. While Smad1 is discussed, what about other downstream genes? Is shh signaling altered in the dact1/2 double mutants? 

We have revised the Discussion to include more references (Lines 473, 476, 483, 488, 491, 499, 501, 502, 510, 515, 529, 557, 558) and additional hypotheses (Lines 503-505, 511-519, 522-525). We have added more specific information regarding the single mutants (Lines 270-275, 480-493, Fig. S3). We have added discussion of other downstream genes, including smad1 (Lines 561-572) and shh (Lines 572-580).

Comment: Figures:

Appreciating differences between specimens when eyes were or were not removed is quite hard.

Yes this was an unfortunate oversight, however, the key phenotype is the EP shown in the dissections.

Fig 1. - wntIIf2 vs wnt11f2? C - Thisse 2001 - correct is Thisse et al. 2001.

Revised typo in Fig 1. (And Line 1083).

Fig 1E: These plots are hard to understand without previous and detailed knowledge. Authors should include at least some demarcations for the cephalic mesoderm, neural ectoderm, mesenchyme, and muscle. Missing color code.

We have moved this data to supplementary figure S1 and have added labels of the relevant cell types and have added the color code.

Comment:- Fig 2 - In the legend for C - "wildtype and dact2-/- mutant" and "dact1/2 mutant"; in the picture is dact1-/-, dact2-/-.

Revised (Line 1105).

Fig 2 - B - it is a mistake in 6th condition dact1: 2x +/+, heterozygote (+/-) is missing.

Revised Figure 2B.

Fig 4. - Typo in the legend: dact1/"t"2-/- .

Revised. (Line 1127).

Fig 8C - In my view, when the condition gfp mRNA says "0/197, " none of the animals show this phenotype. I assume the authors wanted to say that all the animals show this phenotype; therefore, "197/197" should be used.

We have removed this data from the figure as there were concerns by the reviewers regarding reproducibility. 

Fig S1 - Missing legend for the 28 + 250, 380 + 387 peaks? RT-qPCR - is not mentioned in the Materials and Methods. In D - ratio of 25% (legend), but 35% (graph).

Revised.(Line 1203, Line 625, Line 1213, respectively).

Fig S2 - The word "identified" - 2x in one sentence. 

Revised. (Line 1230).

Reviewer #2 (Public Review):

Weakness(1) While the qualitative data show altered morphologies in each mutant, quantifications of these phenotypes are lacking in several instances, making it difficult to gauge reproducibility and penetrance, as well as to assess the novel ANC forms described in certain mutants.  

In Fig. 3 and Fig. 4 animal numbers were added to the figure legend. In Fig. 5 animal numbers were added to the figure to demonstrate reproducibility. We now state that dact1/2-/- animals exhibit the rod-like neurocranial shape that is completely penetrant (Line 260). As the altered morphologies that we report are qualitatively significant from wildtype we did not find it necessary to make quantitative measurements. For experiments in which it was necessary to in-cross triple heterozygotes (Fig 3, Fig. 5), we dissected and visually analyzed the ANC of at least 3 compound mutant individuals. At least one individual was dissected for the previously published or described genotypes/phenotypes (i.e. wt, wntllf2-/-, dact1/2-/-, gpc4-/-, wls/-). We realize quantitative measurements may identify subtle differences between genotypes. However, the sheer number of embryos needed to generate these relatively rare combinatorial genotypes and the amount of genotyping required prevented quantitative analyses. 

Weakness 2) Germline mutations limit the authors' ability to study a gene's spatiotemporal functional requirement. They therefore cannot concretely attribute nor separate early-stage phenotypes (during gastrulation) to/from late-stage phenotypes (ANC morphological changes). 

We agree that we cannot concretely attribute nor separate early and latestage phenotypes. Conditional mutants to provide temporal or cell-specific analysis are beyond the scope of this work. Here we speculate based on evidence obtained by comparing and contrasting embryos with grossly similar early phenotypes and divergent late-stage phenotypes. We believe our findings contribute to the existing body of literature on zebrafish mutants with both early convergent extension defects and craniofacial abnormalities.   

Weakness (3) Given that dact1/2 can regulate both canonical and non-canonical wnt signaling, this study did not specifically test which of these pathways is altered in the dact1/2 mutants, and it is currently unclear whether disrupted canonical wnt signaling contributes to the craniofacial phenotypes, even though these phenotypes are typical non-canonical wnt phenotypes. 

Previous literature has attributed canonical wnt, non-canonical wnt, and nonwnt functions to dact, and each of these likely contributes to the dact mutant phenotype (Lines 87-89). We performed cursory analyses of tcf/lef:gfp expression in the dact mutants and did not find evidence to support further analysis of canonical wnt signaling in these fish. Single-cell RNAseq did not identify differential expression of any canonical or non-canonical wnt genes in the dact1/2 mutants.

Further research is needed to parse out the intracellular roles of dact1 and dact2 in response to wnt and tgf-beta signaling. Here we find that dact may also have a role in calcium signaling, and further experiments are needed to elaborate this role.      

Weakness (4) The use of single-cell RNA sequencing unveiled genes and processes that are uniquely altered in the dact1/2 mutants, but not in the gpc4 mutants during gastrulation. However, how these changes lead to the manifested ANC phenotype later during craniofacial development remains unclear. The authors showed that calpain 8 is significantly upregulated in the mutant, but the fact that only 1 out of 142 calpainoverexpressing animals phenocopied dact1/2 mutants indicates the complexity of the system. 

To further test whether capn8 overexpression may contribute to the ANC phenotype we performed overexpression experiments in the resultant embryos of dact1/dact2 double het incross. We found the addition of capn8 caused a small but statistically significant occurrence of the mutant phenotype in dact1/2 double heterozygotes (Fig.8D). We agree with the reviewer that our results indicate a complex system of dysregulation that leads to the mutant phenotype. We hypothesize that a combination of gene dysregulation may be required to recapitulate the mutant ANC phenotype. Further, as capn8 activity is regulated by calcium levels, overexpression of the mRNA alone likely has a small effect on the manifestation of the phenotype. 

Weakness (5) Craniofacial phenotypes observed in this study are attributed to convergent extension defects but convergent extension cell movement itself was not directly examined, leaving open if changes in other cellular processes, such as cell differentiation, proliferation, or oriented division, could cause distinct phenotypes between different mutants. 

Although convergent extension cell movements were not directly examined, our phenotypic analyses of the dact1/2 mutant are consistent with previous literature where axis extension anomalies were attributed to defects in convergent extension (Waxman 2004, Xing 2018, Topczewski 2001). We do not attribute the axis defect to differentiation differences as in situ analyses of established cell type markers show the existence of these cells, only displaced relative to wildtype (Figure 1). We agree that we cannot rule out a role for differences in apoptosis or proliferation however, we did not detect transcriptional differences in dact1/2 mutants that would indicate this in the single-cell RNAseq dataset. Defects in directed division are possible, but alone would not explain that dact1/2 mutant phenotype, particularly the widened dorsal axis (Figure 1).

Major comments:  

Comment (1) The author examined and showed convergent extension phenotype (CE) during body axis elongation in dact1/dact2-/- homozygous mutants. Given that dact2-/- single mutants also displayed shortened axis, the authors should either explain why they didn't analyze CE in dact2-/- (perhaps because that has been looked at in previously published dact2 morphants?) or additionally show whether CE phenotypes are present in dact1 and dact2 single mutants.  

The authors should quantify the CE phenotype in both dact2-/- single mutants and dact1/dact2-/- double mutants, and examine whether the CE phenotypes are exacerbated in the double mutants, which may lend support to the authors' idea that dact1 can contribute to CE. The authors stated in the discussion that they "posit that dact1 expression in the mesoderm is required for dorsal CE during gastrulation through its role in noncanonical Wnt/PCP signaling". However, no evidence was presented in the paper to show that dact1 influences CE during body axis elongation.  

Because any axis shortening in shortening in dact2-/- single mutants was overcome during the course of development and at 5 dpf there was no noticeable phenotype, we did not analyze the single mutants further.  

We have added data to demonstrate the resulting phenotype of each combinatorial genotype to provide a more clear and detailed description of the single and compound mutants (Fig. S3). 

Our hypothesis that dact1 may contribute to convergent extension is based on its apparent ability to compensate (either directly or indirectly) for dact2 loss in the dact2-/- single mutant. 

Comment (2) Except in Fig. 2, I could not find n numbers given in other experiments. It is therefore unclear if these mutant phenotypes were fully or partially penetrant. In general, there is also a lack of quantifications to help support the qualitative results. For example, in Fig. 4, n numbers should be given and cell movements and/or contributions to the ANC should be quantified to statistically demonstrate that the second stream of CNCC failed to contribute to the ANC.  

Similarly, while the fan-shaped and the rod-shaped ANCs are very distinct, the various rod-shaped ANCs need to be quantified (e.g. morphometry or measurements of morphological features) in order for the authors to claim that these are "novel ANC forms", such as in the dact1/2-/-, gpc4/dact1/2-/-, and wls/dact1/2-/- mutants (Fig. 5).  

We have added n numbers for each experiment and stated that the rod-like phenotype of the dact1/2-/- mutant was fully penetrant. 

Regarding CNCC experiments, we repeated the analysis on 3 individual controls and mutants and did not find evidence that CNCC migration was directly affected in the dact1/2 mutant. Rather, differences in ANC development are likely secondary to defects in floor plate and eye field morphometry. Therefore we did not do any further analyses of the CNCCs.

Regarding figure 5, we have added n numbers. We dissected and analyzed a minimum of three triple mutants (dact1/2-/-,gpc4-/- and dact1/2-/-,wls-/-) and numerous dact1/s double mutants and found that the triple mutant ANC phenotype was consistent and recognizably different enough from the dact1/2-/-, or gpc4 or wls single mutant that morphometry measurements were not needed. Further, the triple mutant phenotype (narrow and shortened) appears to be a simple combination of dact1/2 (narrow) and gpc4/wls (shortened) phenotypes. As we did not find evidence of genetic epistasis, we did not analyze the novel ANC forms further.

Comment (3): The authors have attributed the ANC phenotypes in dact1/2-/- to CE defects and altered noncanonical wnt signaling. However, no evidence was presented to support either. The authors can perhaps utilize diI labelling, photoconversionmediated lineage tracing, or live imaging to study cell movement in the ANC and compare that with the cell movement change in the gpc4-/- , and gpc4/dact1/2-/- mutants in order to first establish that dact1/2 affect CE and then examine how dact1/2 mutations can modulate the CE phenotypes in gpc4-/- mutants.  

Concurrently, given that dact1 and dact2 can affect (perhaps differentially) both canonical and non-canonical wnt signaling, the authors are encouraged to also test whether canonical wnt signaling is affected in the ANC or surrounding tissues, or at minimum, discuss the potential role/contribution of canonical wnt signaling in this context.  

Given the substantial body of research on the role of noncanonical wnt signaling and planar cell polarity pathway on convergent extension during axis formation (reviewed by Yang and Mlodzik 2015, Roszko et al., 2009) and the resulting phenotypes of various zebrafish mutants (i.e. Xing 2018, Topczewski 2001), including previous research on dact1 and 2 morphants (Waxman 2004), we did not find it necessary to analyze CE cell movements directly.  

Our finding that CNCC migration was not defective in the dact1/2 mutants and the knowledge that various zebrafish mutants with anterior patterning defects (slb, smo, cyc) have a similar craniofacial abnormality led us to conclude that the rod-like ANC in the dact1/2 mutant was secondary to an early patterning defect (abnormal eye field morphology). Therefore, testing dact1/2 and convergent extension or wnt signaling in the ANC itself was not an aim of this paper.  

Comment (4) The authors also have not ruled out other possibilities that could cause the dact1/2-/- ANC phenotype. For example, increased cell death or reduced proliferation in the ANC may result in the phenotype, and changes in cell fate specification or differentiation in the second CNCC stream may also result in their inability to contribute to the ANC. 

We agree that we cannot rule out whether cell death or proliferation is different in the dact1/2 mutant ANC. However, because we do not find the second CNCC stream within the ANC, this is the most likely explanation for the abnormal ANC shape. Because the first stream of CNCC are able to populate the ANC and differentiate normally, it is most likely that the inability of the second stream to populate the ANC is due to steric hindrance imposed by the abnormal cranial/eye field morphology. These hypotheses would need to be tested, ideally with an inducible dact1/2 mutant, however, this is beyond the scope of this paper.     

Comment (5) The last paragraph of the section "Genetic interaction of dact1/2 with Wnt regulators..." misuses terms and conflates phenotypes observed. For instance, the authors wrote "dact2 haploinsuffciency in the context of dact1-/-; gpc4-/- double mutant produced ANC in the opposite phenotypic spectrum of ANC morphology, appearing similar to the gpc4-/- mutant phenotype". However, if heterozygous dact2 is not modulating phenotypes in this genetic background, its function is not "haploinsuffcient". The authors then said, "These results show that dact1 and dact2 do not have redundant function during craniofacial morphogenesis, and that dact2 function is more indispensable than dact1". However this statement should be confined to the context of modulating gpc4 phenotypes, which is not clearly stated. 

Revised (Lines 380, 382).   

Comment (6) For the scRNA-seq analysis, the authors should show the population distribution in the UMAP for the 3 genotypes, even if there are no obvious changes. The authors are encouraged, although not required, to perform pseudotime or RNA velocity analysis to determine if differentiation trajectories are changed in the NC populations, in light of what they found in Fig. 4. The authors can also check the expression of reporter genes downstream of certain pathways, e.g. axin2 in canonical wnt signaling, to query if these signaling activities are changed (also related to point #3 above). 

We have added population distribution data for the 3 genotypes to Supplemental Figure 4. Although RNA velocity analysis would be an interesting additional analysis, we would hypothesize that the NC population is not driving the differences in phenotype. Rather these are likely changes in the anterior neural plate and mesoderm. 

Comment (7) While the phenotypic difference between gpc4-/- and dact1/2-/- are in the ANC at a later stage, ssRNA-seq was performed using younger embryos. The authors should better explain the rationale and discuss how transcriptomic differences in these younger embryos can explain later phenotypes. Importantly, dact1, dact2, and capn8 expression were not shown in and around the ANC during its development and this information is crucial for interpreting some of the results shown in this paper. For example, if dact1 and dact2 are expressed during ANC development, they may have specific functions during that stage. Alternatively, if dact1 and dact2 are not expressed when the second stream CNCCs are found to be outside the ANC, then the ANC phenotype may be due to dact1/2's functions at an earlier time point. The author's statement in the discussion that "embryonic fields determined during gastrulation effect the CNCC ability to contribute to the craniofacial skeleton" is currently speculative. 

We have reworded our rationale and hypothesis to increase clarity (Lines 391-405). We believe that the ANC phenotype of the dact1/2 mutants is secondary to defective CE and anterior axis lengthening, as has been reported for the slb mutant (Heisenberg 1997, 2000). We utilized the gpc4 mutant as a foil to the dact1/2 mutant, as the gpc4 mutant has defective CE and axis extension without the same craniofacial phenotype.

We have added dact1 and dact2 WISH of 24 and 48 hpf (Fig1. D,E) to show expression during ANC development. 

Comment (8) The functional testing of capn8 did not yield a result that would suggest a strong effect, as only 1 in 142 animals phenocopied dact1/2. Therefore, while the result is interesting, the authors should tone down its importance. Alternatively, the authors can try knocking down capn8 in the dact1/2 mutants to test how that affects the CE phenotype during axis elongation, as well as ANC morphogenesis. 

As overexpression of capn8 in wildtype animals did not result in a significant phenotype, we tested capn8 overexpression in compound dact1/2 mutants as these have a sensitized background. We found a small but statistically significant effect of exogenous capn8 in dact1+/-,dact2+/- animals. While the effect is not what one would expect comparing to Mendelian genetic ratios, the rod-like ANC phenotype is an extreme craniofacial dysmorphology not observed in wildtype or mRNA injected embryos hence significant. The experiment is limited by the available technology of over-expressing mRNA broadly without temporal or cell specificity control. It is possible that if capn8 over-expression was restricted to specific cells (floor plate, notochord or mesoderm) and at the optimal time period during gastrulation/segmentation that the aberrant ANC phenotype would be more robust. We agree with the reviewer that although the finding of a new role for capn8 during development is interesting, its importance in the context of dact should be toned down and we have altered the manuscript accordingly (Lines 455-467).  

Comment (9) A difference between the two images in Fig. 8B is hard to distinguish.

Consider showing flat-mount images. 

We have added flat-mount images to Fig. 8B

Minor comments:

Comment (1) wnt11f2 is spelled incorrectly in a couple of places, e.g. "wnt11f2l" in the abstract and "wntllf2" in the discussion. 

Revised throughout.

Comment (2) For Fig. 1D, the white dact1 and yellow dact2 are hard to distinguish in the merged image. Consider changing one of their colors to a different one and only merge dact1 and dact2 without irf6 to better show their complementarity.  

We agree with the reviewer that the expression patterns of dact1 and dact2 are difficult to distinguish in the merged image. We have added outlines of the cartilage elements to the images to facilitate comparisons of dact1 and dact2 expression (Fig 1F). 

Comment (3) For Fig. 1E, please label the clusters mentioned in the text so readers can better compare expressions in these cell populations.  

We have moved this data to supplementary figure S1 and have added labels.

Comment (4) The citing and labelling of certain figures can be more specific. For example, Fig. S1A, B, and Fig. S1C should be used instead of just Fig. S1 (under the section titled dact1 and dact2 contribute to axis extension...". Similarly, Fig. 4 can be better labeled with alphabets and cited at the relevant places in the text.  

We have modified the labeling of the figures according to the reviewer’s suggestion (Fig S2 (previously S1), Fig4) and have added reference to these labels in the text (Lines 202, 204, 212, 328, 334, 336). 

Comment (5) For Fig. 2B, the (+/+,-/-) on x-axis should be (+/-,-/-).  

Revised in Figure 2B.

Comment (6) Several figures are incorrectly cited. Fig. 2C is not cited, and the "Fig. 2C" and "Fig. 2D" cited in the text should be "Fig. 2D" and "Fig. 2E" respectively. Similarly, Fig. 5C and D are not cited in the text and the cited Fig. 5C should be 5E. The VC images in Fig. 5 are not talked about in the text. Finally, Fig. 7C was also not mentioned in the text.  

We have corrected the labeling and have added descriptions of each panel in the Results (Fig.2 Line 231, 237, 242, Fig 5 Line 373, 381, Fig 7 line 431). 

Comment (7) In the main text, it is indicated that zebrafish at 3ss were used for ssRNAseq, but in the figure legend, it says 4ss. 

Revised (Line 682)

Comment (8) No error bars in Fig. S1B and the difference between the black and grey shades in Fig. S1D is not explained.  

Error bars are not included in the graphs of qPCR results (now Fig S2C) as these are results of a pool of 8 embryos performed one time. We have added a legend to explain the gray vs. black bars (now Fig S2E). 

Reviewer #3 (Public Review):  

Weaknesses: The hypotheses are very poorly defined and misinterpret key previous findings surrounding the roles of wnt11 and gpc4, which results in a very confusing manuscript. Many of the results are not novel and focus on secondary defects. The most novel result of overexpressing calpain8 in dact1/2 mutants is preliminary and not convincing.  

We apologize for not presenting the question more clearly. The Introduction was revised with particular attention to distinguish this work using genetic germline mutants from prior morpholino studies. Please refer to pages 4-5, lines 106-121.

Weakness 1) One major problem throughout the paper is that the authors misrepresent the fact that wnt11f2 and gpc4 act in different cell populations at different times. Gastrulation defects in these mutants are not similar: wnt11 is required for anterior mesoderm CE during gastrulation but not during subsequent craniofacial development while gpc4 is required for posterior mesoderm CE and later craniofacial cartilage morphogenesis (LeClair et al., 2009). Overall, the non-overlapping functions of wnt11 and gpc4, both temporally and spatially, suggest that they are not part of the same pathway.  

We have reworded the text to add clarity. While the loss of wnt11 versus the loss of gpc4 may affect different cell populations, the overall effect is a shortened body axis. We stressed that it is this similar impaired axis elongation phenotype but discrepant ANC morphology phenotypes in the opposite ends of the ANC morphologic spectrum that is very interesting and leads us to investigate dact1/2 in the genetic contexts of wnt11f2 and gpc4.  Pls refer to page 4, lines 73-84. Further, the reviewer’s comment that wnt11 and gpc4 are spatially and temporally distinct is untested. We think the reviewer’s claim of gpc4 acting in the posterior mesoderm refers to its requirement in the tailbud (Marlow 2004). However this does not exclude gpc4 from acting elsewhere as well. Further experiments would be necessary. Both wnt11f2 and gpc4 regulate non-canonical wnt signaling and are coexpressed during some points of gastrulation and CF development (Gupta et al., 2013; Sisson 2015). This data supports the possibility of overlapping roles. 

Weakness 2) There are also serious problems surrounding attempts to relate single-cell data with the other data in the manuscript and many claims that lack validation. For example, in Fig 1 it is entirely unclear how the Daniocell scRNA-seq data have been used to compare dact1/2 with wnt11f2 or gpc4. With no labeling in panel 1E of this figure these comparisons are impossible to follow. Similarly, the comparisons between dact1/2 and gpc4 in scRNA-seq data in Fig. 6 as well as the choices of DEGs in dact1/2 or gpc4 mutants in Fig. 7 seem arbitrary and do not make a convincing case for any specific developmental hypothesis. Are dact1 and gpc4 or dact2 and wnt11 coexpressed in individual cells? Eyeballing similarity is not acceptable.  

We have moved the previously published Daniocell data to Figure S1 and have added labeling. These data are meant to complement and support the WISH results and demonstrate the utility of using available public Daniocell data. Please recommend how we can do this better or recommend how we can remediate this work with specific comment. 

Regarding our own scRNA-seq data, we have added rationale (line 391-403) and details of the results to increase clarity (Lines 419-436). We have added a panel to Figure 6 (panel A) to help illustrate or rationale for comparing dact1/2 to gpc4 mutants to wt. The DEGs displayed in Fig.7A are the top 50 most differentially expressed genes between dact1/2 mutants and WT (Figure 7 legend, line 422-424).   

We have looked at our scRNA-seq gene expression results for our clusters of interest (lateral plate mesoderm, paraxial mesoderm, and ectoderm). We find dact1, dact2, and gpc4 co-expression within these clusters. Knowing whether these genes are coexpressed within the same individual cell would require going back and analyzing the raw expression data. We do not find this to be necessary to support our conclusions. The expression pattern of wnt11f2 is irrelevant here.   

Weakness 3) Many of the results in the paper are not novel and either confirm previous findings, particularly Waxman et al (2004), or even contradict them without good evidence. The authors should make sure that dact2 loss-of-function is not compensated for by an increase in dact1 transcription or vice versa. Testing genetic interactions, including investigating the expression of wnt11f2 in dact1/2 mutants, dact1/2 expression in wnt11f2 mutants, or the ability of dact1/2 to rescue wnt11f2 loss of function would give this work a more novel, mechanistic angle.

We clarified here that the prior work carried out by Waxman using morppholinos, while acceptable at the time in 2004, does not meet the rigor of developmental studies today which is to generate germline mutants. The reviewer’s acceptance of the prior work at face value fails to take the limitation of prior work into account. Further, the prior paper from Waxman et al did not analyze craniofacial morphology other than eyeballing the shape of the head and eyes. Please compare the Waxman paper and this work figure for figure and the additional detail of this study should be clear. Again, this is by no means any criticism of prior work as the prior study suffered from the technological limitations of 2004, just as this study also is the best we can do using the tools we have today. Any discrepancies in results are likely due to differences in morpholino versus genetic disruption and most reviewers would favor the phenotype analysis from the germline genetic context. We have addressed these concerns as objectively as we can in the text (Lines 482-493). The fact that dact1/2 double mutants display a craniofacial phenotype while the single mutants do not, suggests compensation (Lines 503-505), but not necessarily at the mRNA expression level (Fig. S2C). 

This paper tests genetic interaction through phenotyping the wntll/dact1/dact2 mutant.

Our results support the previous literature that dact1/2 act downstream of wnt11 signaling. There is no evidence of cross-regulation of gene expression. We do not expect that changes in wnt11 or dact would result in expression changes in the others.

RNA-seq of the dact1/2 mutants did not show changes in wnt11 gene expression. Unless dact1 and/or dact2 mRNA are under expressed in the wnt11 mutant, we would not expect a rescue experiment to be informative. And as wnt11 is not a focus of this paper, we have not performed the experiment.  

Weakness 4) The identification of calpain 8 overexpression in Dact1/2 mutants is interesting, but getting 1/142 phenotypes from mRNA injections does not meet reproducibility standards.

As the occurrence of the mutant phenotype in wildtype animals with exogenous capn8 expression was below what would meet reproducibility standards, we performed an additional experiment where capn8 was overexpressed in embryos resulting from dact1/dact2 double heterozygotes incross (Fig. 8). We reasoned that an effect of capn8 overexpression may be more robust on a sensitized background. We found a statistically significant effect of capn8 in dact1/2 double heterozygotes, though the occurrence was still relatively rare (6/80). These data suggest dysregulation of capn8 contributes to the mutant ANC phenotype, though there are likely other factors involved. 

Comment: The manuscript title is not representative of the findings of this study.  

We revised the title to strictly describe that we generated and carried out genetic analysis in loss of function compound mutants (Genetic requirement) and that we found capn8 was important which modified this requirement.

Introduction: p.4:

Comment: Anterior neurocranium (ANC) - it has to be stated that this refers to the combined ethmoid plate and trabecular cartilages. 

Thank you, we agree that the ANC and ethmoid plate terminology has been confusing in the literature and we should endeavor to more clearly describe that the phenotypes in question are all in the ethmoid plate and the trabeculae are not affected. ANC has been replaced with ethmoid plate (EP) throughout the manuscript and figures. We also describe that all the observed phenotypes affect the ethmoid plate and not the trabeculae, (pages 13, Lines 265-267).

Comment: Transverse dimension is incorrect terminology - replace with medio-lateral.

Revised (Lines 69, 74).

Comment: Improper way of explaining the relationship between mutant and gene..."Another mutant knypek, later identified as gpc4..." a better  way to explain this would be that the knypek mutation was found to be a non-sense mutation in the gpc4 gene.  

Revised (Line 71)

Comment: "...the gpc4 mutant formed an ANC that is wider in the transverse dimension than the wildtype, in the opposite end of the ANC phenotypic spectrum compared to wnt11f2...These observations beg the question how defects in early patterning and convergent extension of the embryo may be associated with later craniofacial morphogenesis."

This statement is broadly representative of the general failure to distinguish primary from secondary defects in this manuscript. Focusing on secondary defects may be useful to understand the etiology of a human disease, but it is misleading to focus on secondary defects when studying gene function. The rod-like ethmoid of slb mutant results from a CE defect of anterior mesoderm during gastrulation(Heisenberg et al. 1997, 2000), while the wide ethmoid plate of kny mutants results from CE defects of cartilage precursors (Rochard et al., 2016). Based on this evidence, wnt11f2 and gpc4 act in different cell populations at different times.  

It is true that the slb mutant craniofacial phenotype has been stated as secondary to the CE defect during gastrulation and the kny phenotype as primary to chondrocyte CE defects in the ethmoid, however the direct experimental evidence to conclude only primary or only secondary effects does not yet exist. There is no experiment to our knowledge where wnt11f2 was found to not affect ethmoid chondrocytes directly. Likewise, there is no experiment having demonstrated that dysregulated CE in gpc4 mutants does not contribute to a secondary abnormality in the ethmoid. 

Here, we are analyzing the CE and craniofacial phenotypes of the dact1/2 mutants without any assumptions about primary or secondary effects and without drawing any conclusions about wnt11f2 or gpc4 cellular mechanisms.     

Comment: "The observation that wnt11f2 and gpc4 mutants share similar gastrulation and axis extension phenotypes but contrasting ANC morphologies supports a hypothesis that convergent extension mechanisms regulated by these Wnt pathway genes are specific to the temporal and spatial context during embryogenesis."

This sentence is quite vague and potentially misleading. The gastrulation defects of these 2 mutants are not similar - wnt11 is required for anterior mesoderm CE during gastrulation and has not been shown to be active during subsequent craniofacial development while gpc4 is required for posterior mesoderm CE and craniofacial cartilage morphogenesis (LeClair et al., 2009). Here again, the non-spatially overlapping functions of wnt11 and gpc4 suggest that are not part of the same pathway.  

Though the cells displaying defective CE in wnt11f2 and gpc4 mutants are different, the effects on the body axis are similar. The dact1/2 showed a similar axis extension defect (grossly) to these mutants. Our aim with the scRNA-seq experiment was to determine which cells and gene programs are disrupted in dact1/2 mutants. We found that some cell types and programs were disrupted similarly in dact1/2 mutants and gpc4 mutants, while other cells and programs were specific to dact1/2 versus gpc4 mutants. We can speculate that these that were specific to dact1/2 versus gpc4 may be attributed to CE in the anterior mesoderm, as is the case for wnt11. 

p.5

Comment: "We examined the connection between convergent extension governing gastrulation, body axis segmentation, and craniofacial morphogenesis." A statement focused on the mechanistic findings of this paper would be welcome here, instead of a claim for a "connection" that is vague and hard to find in the manuscript.  

We have rewritten this statement (Line 125).

p.7 Results:

Comment: It is unclear why Farrel et al., 2018 and Lange et al., 2023 are appropriate references for WISH. Please justify or edit.  

This was a mistake and has been edited (Page 9).

Comment: " Further, dact gene expression was distinct from wnt11f2." This statement is inaccurate in light of the data shown in Fig1A and the following statements - please edit to reflect the partially overlapping expression patterns.  

We have edited to clarify (Lines 142-143).

p.8

Comment: "...we examined dact1 and 2 expression in the developing orofacial tissues. We found that at 72hpf..." - expression at 72hpf is not relevant to craniofacial morphogenesis, which takes place between 48h-60hpf (Kimmel et al., 1998; Rochard et al., 2016; Le Pabic et al., 2014).  

We have included images and discussion of dact1 and dact2 expression at earlier time points that are important to craniofacial development (Lines 160-171)(Fig 1D,E). 

Comment: "This is in line with our prior finding of decreased dact2 expression in irf6 null embryos". - This statement is too vague. How are th.e two observations "in line".  

We have removed this statement from the manuscript.

Comment: Incomplete sentence (no verb) - "The differences in expression pattern between dact1 and dact2...".  

Revised (Line 172).

Comment: "During embryogenesis..." - Please label the named structures in Fig.1E.

Please be more precise with the described expression time. Also, it would be useful to integrate the scRNAseq data with the WISH data to create an overall picture instead of treating each dataset separately.  

We have moved the previously published Daniocell data to supplementary figure S1 and have labeled the key cell types. 

p.9

Comment: "The specificity of the gene disruption was demonstrated by phenotypic rescue with the injection of dact1 or dact2 mRNA (Fig. S1)." - please describe what is considered a phenotypic rescue.

-The body axis reduction of dact mutants needs to be documented in a figure. Head pictures are not sufficient. Is the head alone affected, or both the head and trunk/tail? Fig.2E suggests that both head and trunk/tail are affected - please include a live embryos picture at a later stage.  

We have added a description of how phenotypic rescue was determined (Line 208). We have added a figure with representative images of the whole body of dact1/2 mutants. Measurements of body length found a shortening in dact1/2 double mutants versus wildtype, however differences were not found to be significantly different by ANOVA (Fig. 3C, Fig. S3, Line 270-275).

p. 11

Comment: "These dact1-/-;dact2-/- CE phenotypes were similar to findings in other Wnt mutants, such as slb and kny (Heisenberg, Tada et al., 2000; Topczewski, Sepich et al., 2001)." The similarity between slb and kny phenotypes should be mentioned with caution as CE defects affect different regions in these 2 mutants. It is misleading to combine them into one phenotype category as wnt11 and gpc4 are most likely not acting in the same pathway based on these spatially distinct phenotypes.  

Here we are referring to the grossly similar axis extension defects in slb and kny mutants. We refer to these mutants to illustrate that dact1 and or 2 deficiency could affect axis extension through diverse mechanisms. We have added text for clarity (Lines 249-252).  

Comment: "No craniofacial phenotype was observed in dact1 or dact2 single mutants. However, in-crossing to generate [...] compound homozygotes resulted in dramatic craniofacial deformity."

This result is intriguing in light of (1) the similar craniofacial phenotype previously reported by Waxman et al (2004) using morpholino- based knock-down of dact2, and the phenomenon of genetic compensation demonstrated by Jakutis and Stainier 2001 (https://doi.org/10.1146/annurev-genet-071719-020342). The authors should make sure that dact2 loss-of-function is not compensated for by an increase in dact1 transcription, as such compensation could lead to inaccurate conclusions if ignored.  

We agree with the reviewer that genetic compensation of dact2 by dact1 likely explains the different result found in the dact2 morphant versus CRISPR mutant. We found increased dact1 mRNA expression in the dact2-/- mutant (Fig S2X) however a more thorough examination is required to draw a conclusion. Interestingly, we found that in wildtype embryos dact1 and dact2 expression patterns are distinct though with some overlap. It would be informative to investigate whether the dact1 expression pattern changes in dact2-/- mutants to account for dact2 loss.   

Comment: "Lineage tracing of NCC movements in dact1/2 mutants reveals ANC composition" - the title is misleading - ANC composition was previously investigated by lineage tracing (Eberhardt et al., 2006; Wada et al., 2005).  

This has been reworded (Line 292)

p.13

Comment: There is no frontonasal prominence in zebrafish.  

This is true, texts have been changed to frontal prominence.  (Lines 293,

299, 320)

Comment: The rationale for investigating NC migration in mutants where there is a gastrula-stage failure of head mesoderm convergent extension is unclear. The whole head is deformed even before neural crest cells migrate as the eye field does not get split in two (Heisenberg et al., 1997; 2000), suggesting that the rod-like ethmoid plate is a secondary defect of this gastrula-stage defect. In addition, neural crest migration and cartilage morphogenesis are different processes, with clear temporal and spatial distinctions.  

We carried out the lineage tracing experiment to determine which NC streams contributed to the aberrantly shaped EP, whether the anteromost NC stream frontal prominence, the second NC stream of maxillary prominence, or both.  We found that the anteromost NCC did contribute to the rod-like EP, which is different from when hedgehod signaling is disrupted,  So while it is possible that the gastrula-effect head mesoderm CE caused a secondary effect on NC migration, how the anterior NC stream and second NC stream are affected differently between dact1/2 and shh pathway is interesting.  We added discussion of this observation to the manuscript (page 23, Lines 514-520). 

p. 14-16

Comment: Based on the heavy suspicion that the rod-like ethmoid plate of the dact1/2 mutant results from a gastrulation defect, not a primary defect in later craniofacial morphogenesis, the prospect of crossing dact1/2 mutants with other wnt-pathway mutants for which craniofacial defects result from craniofacial morphogenetic defects is at the very least unlikely to generate any useful mechanistic information, and at most very likely to generate lots of confusion. Both predictions seem to take form here.  

However, the ethmoid plate phenotype observed in the gpc4-/-; dact1+/-; dact2-/- mutants (Fig. 5E) does suggest that gpc4 may interact with dact1/2 during gastrulation, but that is the case only if dact1+/-; dact2-/- mutants do not have an ethmoid cartilage defect, which I could not find in the manuscript. Please clarify.  

The perspective that the rod-like EP of the dact1/2 is due to gastrulation defect is being examined here. Why would other mutants such as wnt11f2 and gpc4 that have gastrulation CE defects have very different EP morphology, whether primary or secondary NCC effect?  Further dact1 and dact2 were reported as modifiers of Wnt signaling, so it is logical to genetically test the relationship between dact1, dact2, wnt11f2, gpc4 and wls. The experiment had to be done to investigate how these genetic combinations impact EP morphology. This study found that combined loss of dact1, dact2 and wls or gpc4 yielded new EP morphology different than those previously observed in either dact1/2, wls, gpc4, or any other mutant is important, suggesting that there are distinct roles for each of these genes contributing to facial morphology, that is not explained by CE defect alone.   

Comment: I encourage the authors to explore ways to test whether the rod-like ethmoid of dact1/2 mutants is more than a secondary effect of the CE failure of the head mesoderm during gastrulation. Without this evidence, the phenotypes of dact1/2 -gpc4 or - wls are not going to convince us that these factors actually interact.  

Actually, we find our results to support the hypothesis that the ethmoid of the dact1/2 mutants is a secondary effect of defective gastrulation and anterior extension of the body axis. However, our findings suggest (by contrasting to another mutant with impaired CE during gastrulation) that this CE defect alone cannot explain the dysmorphic ethmoid plate. Our single-cell RNA seq results and the discovery of dysregulated capn8 expression and proteolytic processes presents new wnt-regulated mechanisms for axis extension.    

p. 20 Discussion

Comment: "Here we show that dact1 and dact2 are required for axis extension during gastrulation and show a new example of CE defects during gastrulation associated with craniofacial defects."

Waxman et al. (2004) previously showed that dact2 is involved in CE during gastrulation.

Heisenberg et al. (1997, 2000), previously showed with the slb mutant how a CE defect during gastrulation causes a craniofacial defect.  

The Waxman paper using morpholino to disrupt dact2 is produced limited analysis of CE and no analysis of craniofacial morphogenesis. We generated genetic mutants here to validate the earlier morpholino results and to analyze the craniofacial phenotype in detail. We have removed the word “new” to make the statement more clear (Line 475).

Comment: "Our data supports the hypothesis that CE gastrulation defects are not causal to the craniofacial defect of medially displaced eyes and midfacial hypoplasia and that an additional morphological process is disrupted."

It is unclear to me how the authors reached this conclusion. I find the view that medially displaced eyes and midfacial hypoplasia are secondary to the CE gastrulation defects unchallenged by the data presented. 

This statement was removed and the discussion was reworded.

Comment: The discussion should include a detailed comparison of this study's findings with those of zebrafish morpholino studies.  

We have added more discussion to compare ours to the previous morpholino findings (Lines 476-484).

Comment: The discussion should try to reconcile the different expression patterns of dact1 and dact2, and the functional redundancy suggested by the absence of phenotype of single mutants. Genetic compensation should be considered (and perhaps tested).  

The different expression patterns of dact1 and dact2 along with our finding that dact1 and dact2 genetic deficiency differently affect the gpc4 mutant phenotype suggest that dact1 and dact2 are not functionally redundant during normal development. This is in line with the previously published data showing different phenotypes of dact1 or dact2 knockdown. However, our results that genetic ablation of both dact1 and dact2 are required for a mutant phenotype suggests that these genes can compensate upon loss of the other. This would suggest then that the expression pattern of dact1 would be changed in the dact2 mutant and visa versa. We find that this line of investigation would be interesting in future studies. We have addressed this in the Discussion (Lines 485498).

Comment: "Based on the data...Conversely, we propose...ascribed to wnt11f2 "

Functional data always prevail overexpression data for inferring functional requirements.  

This is true.

p.21

Comment: "Our results underscore the crucial roles of dact1 and dact2 in embryonic development, specifically in the connection between CE during gastrulation and ultimate craniofacial development."

How is this novel in light of previous studies, especially by Waxman et al. (2004) and Heisenberg et al. (1997, 2000). In this study, the authors fail to present compelling evidence that craniofacial defects are not secondary to the early gastrulation defects resulting from dact1/2 mutations.  p. 22

We have not claimed that the craniofacial defects are not secondary to the gastrulation defects. In fact, we state that there is a “connection”. Further, we do not claim that this is the first or only such finding. We believe our findings have validated the previous dact morpholino experiments and have contributed to the body of literature concerning wnt signaling during embryogenesis. 

Comment: The section on Smad1 discusses a result not reported in the results section. Any data discussed in the discussion section needs to be reported first in the results section.  

We have added a comment on the differential expression of smad1 to the results section (Lines 446-448).

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

In this manuscript entitled "Hexokinase regulates Mondo-mediated longevity via the PPP and organellar dynamics", Laboy and colleagues investigated upstream regulators of MML-1/Mondo, a key transcription factor that regulates aging and metabolism, using the nematode C. elegans and cultured mammalian cells. By performing a targeted RNAi screen for genes encoding enzymes in glucose metabolism, the authors found that two hexokinases, HXK-1 and HXK-2, regulate nuclear localization of MML-1 in C. elegans. The authors showed that knockdown of hxk-1 and hxk-2 suppressed longevity caused by germline-deficient glp-1 mutations. The authors demonstrated that genetic or pharmacological inhibition of hexokinases decreased nuclear localization of MML-1, via promoting mitochondrial β-oxidation of fatty acids. They found that genetic inhibition of hxk-2 changed the localization of MML-1 from the nucleus to mitochondria and lipid droplets by activating pentose phosphate pathway (PPP). The authors further showed that the inhibition of PPP increased the nuclear localization of mammalian MondoA in cultured human cells under starvation conditions, suggesting the underlying mechanism is evolutionarily conserved. This paper provides compelling evidence for the mechanisms by which novel upstream metabolic pathways regulate MML-1/Mondo, a key transcription factor for longevity and glucose homeostasis, through altering organelle communications, using two different experimental systems, C. elegans and mammalian cells. This paper will be of interest to a broad range of biologists who work on aging, metabolism, and transcriptional regulation. 

Reviewer #2 (Public Review):

Raymond Laboy et.al explored how transcriptional Mondo/Max-like complex (MML-1/MXL-2) is regulated by glucose metabolic signals using germ-line removal longevity model. They believed that MML-1/MXL-2 integrated multiple longevity pathways through nutrient sensing and therefore screened the glucose metabolic enzymes that regulated MML-1 nuclear localization. Hexokinase 1 and 2 were identified as the most vigorous regulators, which function through mitochondrial beta-oxidation and the pentose phosphate pathway (PPP), respectively. MML-1 localized to mitochondria associated with lipid droplets (LD), and MML-1 nuclear localization was correlated with LD size and metabolism. Their findings are interesting and may help us to further explore the mechanisms in multiple longevity models, however, the study is not complete and the working model remains obscure. For example, the exact metabolites that account for the direct regulation of MML-1 were not identified, and more detailed studies of the related cellular processes are needed. 

The identification of responsible metabolites is necessary since multiple pieces of evidence from the study suggests that lipid other than glucose metabolites may be more likely to be the direct regulator of MML-1 and HXK regulate MML-1 indirectly by affecting the lipid metabolism: 1) inhibiting the PPP is sufficient to rescue MML-1 function independent of G6P levels; 2) HXK-1 regulates MML-1 by increasing fatty acid beta-oxidation; 3) LD size correlates with MML-1 nuclear localization and LD metabolism can directly regulate MML-1. The identification of metabolites will be helpful for understanding the mechanism. 

Beta-oxidation and the PPP are involved in the regulation of MML-1 by HXK-1 and HXK-2, respectively. But how these two pathways participate in the regulation is not clear. Is it the beta-oxidation rate or the intermediate metabolites that matters? As for the PPP, it provides substrates for nucleotide synthesis and also its product NADPH is essential for redox balance. Is one of the metabolites or the NADPH levels involved in MML-1 regulation? More studies are needed to provide answers to these concerns. 

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Following are my comments that the authors may want to address to further improve this excellent paper.

Major comments 

(1) Although the authors provided evidence that hexokinases in glucose metabolism are associated with germline-deficient glp-1(-) mutants, they did not mention why they focused on glp-1(-) mutants rather than other longevity mutants. In their previous study (Nakamura et al., 2016), they showed that MML-1 is required for multiple longevity pathways in C. elegans, including reduced mitochondrial respiration and insulin/IGF-1 signaling. Please discuss why the authors focused on glp-1(-) mutants in this paper. It will be even better if the authors test the roles of hexokinases in some other longevity regimens. 

Many thanks for this astute comment. Previously we had shown that mml-1 is required for glp-1, daf-2, and isp-1 longevity, and Johnson et al. had shown a requirement for eat-2, hence the idea that MML-1 is a convergent transcription factor. We first focused on glp-1 because that was the starting point of our screen, and the result was clear and simple: hexokinases regulate MML‑1 nuclear localization and activity in glp-1 and are required for longevity. Naturally, the question arises: do hexokinases behave like MML-1 as convergent longevity regulators across pathways? To address this, we examined the interaction of hxk-1 and hxk-2 with isp-1, daf-2, and raga-1.  Specifically, we now show that:

A. Like glp-1(e2141) mutants, isp-1(qm150) mutants stimulate MML-1 nuclear localization, and the hexokinases are required for isp-1 longevity (Figure 1G-H).

B. daf-2(e1370) mutants do not further stimulate MML-1 nuclear localization beyond basal levels, yet MML-1 is strongly required for daf-2 longevity (Nakamura et al., 2016, Supplementary Figure 1L-M). However, the hexokinases are not required for daf-2 longevity (Supplementary Figure 1M), suggesting that the signaling pathway is wired differently in daf-2, and that other pathways regulate MML-1 activity.

C. raga-1(ok701) mutants stimulate MML-1 nuclear localization and mml-1 is required for raga-1 longevity, suggesting that MML-1 acts downstream of TORC1 signaling (Supplementary Figure 1N-O). However, hexokinases are not required for raga-1 longevity, suggesting that raga-1 acts downstream or parallel to hexokinase signaling (Supplementary Figure 1P).

D. We performed untargeted metabolomics in glp-1, daf-2, and mml-1 single and double mutants and observed that hexose phosphates, which have been shown to regulate MML-1 human homologs MondoA/ChREBP, were differentially regulated between mutants.

Author response image 1.

E. Altogether these experiments reveal that though MML-1 promotes longevity in most pathways, the hexokinases are only required in some (glp-1, isp-1), but not others (raga-1, daf-2). Furthermore, strong MML-1 nuclear localization is often but not always associated with longevity (e.g. daf-2), and the wiring of the signaling pathway is different for various longevity regimens. Consistently, mTOR and Insulin signaling are more functionally linked and therefore may show a more similar genetic profile. Differences in hexose phosphate between glp-1 and daf-2 could explain why MML-1 requires hexokinase function in glp-1 to promote longevity but not in daf-2. However, considerably more work is required to rigorously validate this hypothesis.

(2) In figure 5, the authors investigated whether the association between PPP and MML‑1/MondoA, tested in C. elegans, is conserved in mammals under starvation conditions. The authors should clarify why they tested the MondoA localization upon starvation in cultured human cells. This comment is related to my comment #1 as the authors could determine the roles of hexokinases under dietary restriction (DR)-conditions or in DR-mimetic in eat-2(-) mutants. 

In this case, the actual translatability to a worm longevity pathway was not our goal. Rather, we examined MondoA in cell culture under contrasting conditions of MondoA subcellular localization, where high glucose media had cytosolic/nuclear localization and starvation conditions cytosolic localization. We then showed that similar to our data in worms, PPP inhibition with 6-AN induced MondoA nuclear localization and activity. We now mention this rationale in the results section, lines 352-356.

(3) In figure 2, the authors showed that HXK-2 regulates mitochondrial localization of MML-1, and HXK-1 regulates nuclear localization of MML-1 through mitochondrial β-oxidation in glp‑1(-) mutants. Can the authors test whether mitochondrial β-oxidation affects the effects of hxk RNAi on longevity of glp-1(-) mutants? 

Excellent suggestion. We tried to test this idea and found that acs-2 RNAi alone abolished glp-1 longevity, making epistasis experiments difficult to interpret. This is consistent with published data showing that glp-1 longevity requires NHR-49, a transcription factor that regulates mitochondrial b‑oxidation, that drives acs-2 expression (Ratnappan et al., 2014). It could well be that b‑oxidation inhibition promotes MML-1 nuclear localization but abolishes lifespan extension because of epistatic effects on other transcription factors or processes. Further investigation would be required to elucidate the exact mechanism that goes beyond the scope of the paper.

(4) The authors showed that 2-deoxy-glucose, which decreases the activity of HXK, decreased the nuclear localization of MML-1, and this is consistent with their genetic data. Based on these data, 2-deoxy-glucose is expected to decrease longevity. Interestingly, however, 2-deoxy-glucose has been reported to increase lifespan by restricting glucose, whereas extra glucose intake decreases lifespan in C. elegans, shown by multiple research groups, including M. Ristow, C. Kenyon, and S.J.V. Lee labs. This is seemingly paradoxical and worth discussing with key references, especially because MondoA and Chrebp are known as glucose-responsive transcription factors. 

Thank you for this important comment. 2-DG has been shown to extend lifespan by suppressing glucose metabolism at concentrations ranging from 0.1 to 5 mM, higher concentrations ranging from 20 to 50 mM had the opposite effect decreasing lifespan (Schulz et al., 2007). The concentration we tested was 50 mM 2-DG and observed decreased MML-1 nuclear localization, which is consistent with the previous data showing decreased longevity. We now raise this point in the discussion suggesting that mild inhibition of glucose metabolism has beneficial effects on longevity, while strong suppression causes a shortening of the lifespan (lines 411-414).

Minor comments 

(1) The current Introduction does not include the explicit statement about that MML-1 and MondoA are homologs. Please clarify this as naive readers may be confused.

Thank you for pointing this out. We now say in the intro that MondoA and MML-1 are homologs (lines 59-60).

(2) In figure 1, the effects of hxk-3 on nuclear localization of MML-1 is small compared to those of hxk-1 and hxk-2. Please add speculation about why HXK-3 has different roles in nuclear localization of MML-1 compared to HXK-1 and HXK-2. 

According to GExplore 1.4 (Hutter & Suh, 2016), hxk-3 expression declines during larval development and is low expressed in the adult. Perhaps it has little effect in the young adult, and the other hexokinases suffice to support MML-1 nuclear localization. It also remains possible that hxk-3 is not required in glp-1, but required in other longevity pathways.

(3) The authors tested the effects of genetic inhibition of hxk-1 and hxk-2 on the regulation of MML-1 localization and lifespan of glp-1(-) mutants by using RNAi. I wonder whether the authors can perform the experiments with hxk-1 or hxk-2 loss (or reduction) of function mutants. If they cannot, please discuss the reason and the limitations of RNAi. 

This is an important point raised by the reviewer. We found that RNAi was most effective for phenotypes related to MML-1 nuclear localization and longevity, likely because it results in acute knockdown. We also showed that pharmacological inhibition of hexokinase function with 3BrP and 2‑DG (Supplementary Figure 1B and 1C) and the PPP with 6-AN (Figure 3B) had consistent results with our observation with RNAi.

We generated hexokinase KO mutants by deleting the coding sequence of each hexokinase by CRISPR/Cas9. First, we measured the expression of each hexokinase isozyme in each mutant. Notably, hxk-1(syb1271) null mutant had higher expression of hxk-2 and hxk-3, hxk-2(syb1261) did not significantly affect the expression of hxk-1 and hxk-3, and hxk-3(syb1267) had a mild increase in hxk-2 expression. We followed up on the hxk-1(syb1271) and hxk-2(syb1261) and crossed these mutants with our MML-1::GFP reporter. We observed a modest but significant reduction in MML-1 nuclear localization in both strains. The effect with RNAi is much stronger in comparison to the null mutants, potentially due to a compensatory upregulation of the other hexokinases in the mutants that we do not observe with RNAi (Supplementary Figure 1D-E). Another alternative is that there is a threshold in the effects of hexokinase function on MML-1 nuclear localization. We tried to generate a hxk-1; hxk-2 double mutant but it was lethal and therefore did not pursue this further.

Author response image 2.

(4) Please correct minor typos throughout the manuscript. Following are some examples. <br /> - On page 4, line 111, please correct "Supplementary Figure D-E" to "Supplementary Figure 1D-E". 

- On page 9, line 272, please correct "3A-B" to "4A-B". 

- On page 9, line 275, please correct "S4" to "4". 

- On page 10, line 309, please correct "4A" to "4B" 

Corrected.

(5) In Fig. 3E, please add the information about the scale bars in figure legends.

Corrected.

Reviewer #2 (Recommendations For The Authors):

Here are some detailed suggestions for the authors:

(1) Since MML-1/MXL-2 complex functions in multiple longevity models, e.g. DR, ILS, what are the roles of HXK-1 and HXK-2 in these models? 

We now show that although mml-1 is required in most longevity pathways, hxk-1 and hxk-2 are required in some pathways (glp-1, isp-1) but not others (daf-2, raga-1). See above for more details.

(2) As for the metabolites screening, the lipid metabolic genes can be included. Not only for the above reasons, also previous study had found that the mml-1 mRNA levels and MML-1 GFP nuclear localization were all increased in the glp-1 model, while mml-1 mRNA levels were unaffected by hxk knockdown, suggesting more pathways be involved. 

We agree with the reviewer that understanding what metabolites regulate MML-1 nuclear localization and activity is an important, yet challenging question. Our studies demonstrate a role of glucose metabolism, in particular, hexokinase in this process, consistent with hexose-p being activators of MondoA. Our data also suggest mechanisms beyond hexose-p regulate MML-1, since knockdown of the PPP components stimulates MML-1 even when hxk-2 is depleted and low G6P, and inhibition of the PPP with 6-AN stimulates MondoA nuclear localization under starvation conditions in mammalian cell culture. We tested redox regulation, nucleoside, and lipid metabolism as candidate processes (see below). Notably, our data suggest this other mechanism is tied to lipid metabolism through droplet size since various perturbations that impact LD size and number (atgl-1, dgat-2, tkt-1, Figure 4) affected MML-1 nuclear localization. It remains an open question whether MML-1 is regulated by other metabolites through a ligand-protein interaction or not. We cannot exclude that beyond lipid droplet regulation, specific lipids, other metabolites, or metabolic modules linked to the PPP might regulate MML-1 nuclear localization and activity.

We employed genetic manipulation and pharmacological inhibition to understand the upstream signals that regulate MML-1. These approaches will not be sufficient to determine whether other metabolite(s) are involved in MML-1/MondoA translocation to the nucleus through a direct interaction. Novel technologies that determine protein-metabolite interactions (e.g. MIDAS) will help us answer this question in future work, and go beyond the scope of this paper. As a compromise, we discuss possible metabolites that may orchestrate this based on our observations based on MML‑1 subcellular localization at LD/mitochondria (including PPP and TCA cycle intermediates).

(3) Line 238, it should be "NADPH". 

Corrected.

(4) RNAi targeting enzymes of different branches of PPP can be performed

In our initial screen, we examined the effect of various enzymes of the PPP on MML-1 nuclear localization (Figure 1A, Supplementary Table S1) and found that knockdown of enzymes in both the oxidative phase (PGDH/T25B9.9) and non-oxidative phase (transketolase/TKT-1) affect MML-1 nuclear localization. In line, 6-AN treatment, which affects the oxidative phase, also stimulated MML‑1 nuclear localization (Figure 3B). We also observed that knockdown of enzymes involved in ribose 5P conversion to ribose, ribose 1P, and phosphoribosyl pyrophosphate, an intermediate in nucleotide biosynthesis, decreased MML-1 nuclear localization (rpia-1, F07A11._5, _Y43F4B.5, _R151._2; Supplementary Table S1). Whether MML‑1/MondoA responds to nucleotide pool remains elusive.

(5) As for PPP, these are many possibilities that can be tested. For example, as PPP supplies NADPH for oxidative balance, does MML-1 respond to ROS? Also, it appears the genes in the non-oxidative arm of PPP regulate MML-1, so is nucleotide synthesis involved? 

Thank you for the suggestion. We tested other enzymes involved in NADPH production from the folate cycle and observed a mild but significant reduction of MML-1 nuclear localization upon dao-3i (Supplementary Table S1). Moreover, we tested whether MML-1 nuclear localization is responsive to ROS. While paraquat exposure induced oxidative stress by measuring the transcriptional reporter gst‑4p::GFP (Supplementary Figure 3A), paraquat exposure did not significantly affect MML-1 nuclear localization (Supplementary Figure 3B). Therefore we think it less likely that NADPH production acting through redox regulation is the main effect.

We also tried supplementation with some of the metabolite outputs of PPP including ribose, ribulose, and xylulose, as well as nucleosides (see below), but saw no effect on MML-1 nuclear localization. We agree that further studies are required to pinpoint whether there is another metabolic moiety regulating MML-1 at the protein-ligand level, but this goes beyond the scope of the current investigation.

Author response image 2.

Author response:

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

eLife assessment:

This important study reports the deep evolutionary conservation of a core genetic program regulating spermatogenesis in flies, mice, and humans. The data presented are supportive of the main conclusion and generally convincing. This work will be of interest to evolutionary and reproductive biologists.

The Authors would like to thank the Senior Editor and the two Reviewers for their positive assessment of our work, as well as for the helpful suggestions. Collectively, these suggestions provided insight that was instrumental in shaping the final version of the manuscript (see below for our point-by-point comments). The Authors believe that the refinements introduced to the final document clearly translate into an improved version of our work. Hence, we would like to thank all those involved in the peer review process for their encouraging words and constructive criticism.

Public Reviews: 

Reviewer #1 (Public Review):

Summary: 

By combining an analysis of the evolutionary age of the genes expressed in male germ cells, a study of genes associated with spermatocyte protein-protein interaction networks and functional experiments in Drosophila, Brattig-Correia and colleagues provide evidence for an ancient origin of the genetic program underlying metazoan spermatogenesis. This leads to identifying a relatively small core set of functional interactions between deeply conserved gene expression regulators, whose impairment is then shown to be associated with cases of human male infertility.

Strengths: 

In my opinion, the work is important for three different reasons. First, it shows that, even though reproductive genes can evolve rapidly and male germ cells display a significant level of transcriptional noise, it is still possible to obtain convincing evidence that a conserved core of functionally interacting genes lies at the basis of the male germ transcriptome. Second, it reports an experimental strategy that could also be applied to gene networks involved in different biological problems. Third, the authors make a compelling case that, due to its effects on human spermatogenesis, disruption of the male germ cell orthoBackbone can be exploited to identify new genetic causes of infertility.

We thank the Reviewer for their positive assessment. Indeed, it was our main objective to convincingly demonstrate these three points.

Weaknesses: 

The main strength of the general approach followed by the authors is, inevitably, also a weakness. This is because a study rooted in comparative biology is unlikely to identify newly emerged genes that may adopt key roles in processes such as species-specific gamete recognition. Additionally, using a TPM >1 threshold for protein-coding transcripts may exclude genes, such as those encoding proteins required for gamete fusion, which are thought to be expressed at a very low level. Although these considerations raise the possibility that the chosen approach may miss information that, depending on the species, could be potentially highly functionally important, this by no means reduces its value in identifying genes belonging to the conserved genetic program of spermatogenesis.

The Authors acknowledge the points raised by the Reviewer as inevitable trade-offs of the focus of our study (to uncover the deeply conserved genetic basis of spermatogenesis). Certainly, our pipeline could, in the future, be adapted to look for newly emerged genes or to employ different minimum expression cut-offs. To this end, we made all computational data and custom scripts easily available to the community. We would, nevertheless, kindly emphasize the challenge associated with the use of less restrictive TPM cut-offs, given the substantial level of transcriptional noise associated with this cell type. An abridged version of this discussion can be found in lines 512-515 of the manuscript.

Reviewer #2 (Public Review):

Summary: 

This is a tour de force study that aims to understand the genetic basis of male germ cell development across three animal species (human, mouse, and flies) by performing a genetic program conservation analysis (using phylostratigraphy and network science) with a special emphasis on genes that peak or decline during mitosis-to-meiosis. This analysis, in agreement with previous findings, reveals that several genes active during and before meiosis are deeply conserved across species, suggesting ancient regulatory mechanisms. To identify critical genes in germ cell development, the investigators integrated clinical genetics data, performing gene knockdown and knockout experiments in both mice and flies. Specifically, over 900 conserved genes were investigated in flies, with three of these genes further studied in mice. Of the 900 genes in flies, ~250 RNAi knockdowns had fertility phenotypes. The fertility phenotypes for the fly data can be viewed using the following browser link:https://pages.igc.pt/meionav. The scope of target gene validation is impressive. Below are a few minor comments.

We thank the Reviewer for their positive appraisal of our work.

(1) In Supplemental Figure 2, it is notable that enterocyte transcriptomes are predominantly composed of younger genes, contrasting with the genetic age profile observed in brain and muscle cells. This difference is an intriguing observation and it would be curious to hear the author's comments.

Indeed, this is an intriguing observation for which we can only provide a speculative answer. Enterocytes are specialized to absorb nutrients, hence their genetic program is finely tuned to maximize uptake under specific dietary conditions. In this regard, we can posit that variations in nutrient preference/availability in the course of each species’ evolutionary history (associated with habitat, environmental and/or behavioral changes) may have exerted a selective pressure for the emergence of new genes that could provide enterocytes with more efficient uptake capabilities under new circumstances. The application of evolutionary thinking to the rapidly expanding field of nutrigenomics could shed light on this possibility.

(2) Regarding the document, the figures provided only include supplemental data; none of the main text figures are in the full PDF. 

We thank the Reviewer for this helpful comment. We will ensure that the three main figures are correctly formatted in the final version of the manuscript.

(3) Lastly, it would be great to section and stain mouse testis to classify the different stages of arrest during meiosis for each of the mouse mutants in order to compare more precisely to flies.

We agree with the Reviewer that adding more mouse data would further improve what can already be considered an extensive body of experimental work. Given the costs associated with the generation of such data (in terms of resources and otherwise), the Authors believe such a study would be best suited to a follow-up manuscript.

This paper serves as a vital resource, emphasizing that only through the analysis of hundreds of genes can we prioritize essential genes for germ cell development. its remarkable that about 60% of conserved genes have no apparent phenotype during germ cell development.

Once again, we thank the Reviewer for their positive assessment of our work. Clarifying the degree of functional redundancy in an essential biological process such as male gametogenesis represents an exciting (and experimentally complex) future challenge.

Strengths:

The high-throughput screening was conducted on a conserved network of 920 genes expressed during the mitosis-to-meiosis transition. Approximately 250 of these genes were associated with fertility phenotypes. Notably, mutations in 5 of the 250 genes have been identified in human male infertility patients. Furthermore, 3 of these genes were modeled in mice, where they were also linked to infertility.

This study establishes a crucial groundwork for future investigations into germ cell development genes, aiming to delineate their essential roles and functions.

The Authors thank the Reviewer for emphasizing the potential usefulness of our results to the community, as that was one of the main motivations behind this project.

Weaknesses: 

The fertility phenotyping in this study is limited, yet dissecting the mechanistic roles of these proteins falls beyond its scope. Nevertheless, this work serves as an invaluable resource for further exploration of specific genes of interest.

Please see the previous point.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

Although the manuscript already includes a significant amount of data, there are two aspects that the authors may consider exploring: 

(1) I understand that the choice of species whose gene expression was analyzed in the study was largely influenced by the quality of the corresponding genome annotations. However, since in evolutionary terms humans and mice are much closer to each other than Drosophila (as also shown in Figure 1c and Supplementary Figure 1), I found the statement "three evolutionarily distant gonochoric species" partially questionable. Have the authors considered adding an additional established animal model, such as for example zebrafish, to provide further coverage of the evolutionary space? Or, alternatively, could a posteriori analysis of the transcriptome of such an additional species be used to cross-validate their findings? The authors touch upon this point in the Discussion, but I wonder if they actually tried something in this direction, or simply decided that the currently available expression data from other organisms was too poor to be used for this purpose.

We thank the Reviewer for bringing up this point, as it echoes one of our main concerns in terms of our approach (as discussed in lines 487-492). Indeed, when we were designing our study, we extensively discussed whether zebrafish and C. elegans datasets should be included, as high-quality expression and phenotypical data were available for both species. We ended up not including them for one main reason: the sexual system of these species deviates from that of humans, mice and fruit flies (all gonochoric species). More specifically, C. elegans are hermaphrodites and although zebrafish is a gonochoric species at the adult stage, they start their lifecycle as juvenile hermaphrodites (they first develop juvenile ovaries that later degenerate into a testis in males). Since it is largely unknown to what extent the transcriptome of male germ cells from these species deviates from the gonochoric program (by retaining oogenesis-related characteristics, for example), we decided to avoid possible confounding effects by excluding the two species. Undoubtedly, as more transcriptomic data from non-model organisms become available, these (and other) questions can be extensively revisited as our pipeline was designed to easily accommodate new data.

(2) Although the use of the STRING database is a sensible choice given the general purpose of this work, in my experience the reliability of its individual interactions can vary significantly. I wonder if the authors have considered exploiting AlphaFold-Multimer as a parallel approach to estimate what proportion of the 79 functional interactions that they identified may reflect direct protein-protein contacts.

We thank the Reviewer for this question and suggestion, as we were also concerned about STRING's reliability for individual interactions. For that
reason, we only utilized protein-protein interactions with a STRING combined confidence score ≥0.5
(corresponding to the estimated likelihood of a given association being
true), as described in more detail in the "Protein-protein interaction
(PPI) network construction" subsection. In addition, to make sure we were not biasing results towards conserved genes (which could arguably be overrepresented in STRING) we pursued a random rewiring test of degree
centrality and page rank, as detailed in section "Deeply conserved genes
are central components of the male germ cell transcriptome". We very much like the suggestion of using AlphaFold-Multimer to estimate the proportion of
direct protein-protein contacts for the 79 core interactions, but given
the already quite complex analytical pipeline of the present work, we will leave such analysis for a follow-up study. The final version of the manuscript now contains a reference to such an approach (lines 499-502).

Finally, probably because my primary focus is not on gene regulation, I must say that I found the manuscript somewhat heavy to read. The integration of various data types and analyses, while enriching, also complicates the ability to clearly recall the main conclusions of each result section by the time one reaches the summary at the beginning of the Discussion. Given the relative brevity of the latter, expanding it to both reiterate what these conclusions are and illustrate how all the components converge to support the central message of the study would, in my opinion, benefit a general readership.  

We thank the Reviewer for their fresh perspective on our document and for this most welcome suggestion. The final version of the manuscript now includes a longer discussion, containing an initial paragraph (lines 467-479) that summarizes our main findings and how they converge into a coherent body of work.

Additionally, on a minor note, I suggest that the concept of phylostratigraphy be briefly explained when first mentioned in the Introduction, rather than later in the manuscript. This early clarification would aid comprehension for readers unfamiliar with the term. 

To safeguard the flow of the manuscript, we have slightly tweaked the introduction section to avoid the use of highly specific terminology (such as phylostratigraphy) this early in the text. We replaced it with “comparison of genome sequences” (line 85). Phylostratigraphy is later explained in full detail in the corresponding section of the manuscript. We thank the Reviewer for this helpful suggestion.

Reviewer #2 (Recommendations For The Authors): 

Major concern - the absence of main text figures.

We thank the Reviewer for this helpful comment. We will ensure that the three main figures are correctly formatted in the final version of the manuscript.

Typos throughout - this will need your attention. 

The Authors thank the Reviewer for the thorough and attentive assessment of our work. We have carefully revised the text to ensure a pleasant reading experience free of typographical errors.

Author response:

We want to thank the reviewers for their constructive feedback.

General

The recall values of our method range between 78.6% for all urine cases to 83.3% for feces (and not between 70-80%, as stated by reviewer #2), with a mean precision of 85.6%. This is rather similar to other machine learning-based methods commonly used for the analysis of complicated behavioral readouts. For example, in the paper presenting DeepSqueak for analysis of mouse ultrasonic vocalizations (Coffey et al. DeepSqueak: a deep learning-based system for detection and analysis of ultrasonic vocalizations. Neuropsychopharmacol. 44, 859–868 (2019). https://doi.org/10.1038/s41386-018-0303-6), the recall values reported for both DeepSqueak, Mupet and Ultravox (Fig. 2c, f) are very similar to our method.

We have analyzed and reported all the types of errors made by our methods, which are mostly technical. For example, depositions that overlap the mouse blob for too long till getting cold will be associated with the mouse and therefore will not be detected (“miss” events). These technical errors are not supposed to create a bias for a specific biological condition and, hence, shouldn’t interfere with the use of our method. A video showing all of the mistakes made by our algorithm on the test set was submitted (Figure 2-video 1).

Below we will to relate to specific points and describe our plan to revise the manuscript accordingly.

Detection accuracy

a. It should be noted that when large urine spots are considered, our algorithm got 100% correct classification (Figure 2, supplement 1, panel b). However, small urine deposits are very similar to feces in their appearance in the thermal picture. In fact,  if the feces are not shifted, discrimination can be quite challenging even for human annotators. To demonstrate the accuracy of the proposed method relative to human annotators, we plan to compare its results with the accuracy of a second human annotator.

b. As part of the revision, we plan to test general machine learning-based object detectors such as faster-RCNN or YOLO (as suggested by Reviewer 2) and compare them with our method.

c. To check if our method may introduce bias to the results, we plan to check if the errors are distributed evenly across time, space, and genders.

Design choices

(A) The preliminary detection algorithm has several significant parameters. These are:

a. Minimal temperature rise for detection: 1.1°C rise during 5 sec.

b. Size limits of the detection: 2 - 900 pixels.

c. Minimal cooldown during 40 sec: 1.1°C and at least half the rise.

d. Minimal time between detections in the same location: 30 sec.

We chose to use low thresholds for the preliminary detection to allow detection of very small urinations and to minimize the number of “miss” events, relying on the classifier to robustly reject false alarms. Indeed, we achieved a low rate of miss events: 5 miss events for the entire test set (1 miss event per ~90 minutes of video). We attribute these 5 “miss” events to partial occlusion of the detection by the mouse.

To adjust the preliminary detection parameters to a new environment, one will need to calibrate these parameters in their own setup. Mainly, the size of the detection depends on the resolution of the video, and the cooldown rate might be affected by the material of the floor, as well as the room temperature.

We plan to explore the robustness of these parameters in our setup and report the influence on the accuracy of the preliminary algorithm.

(B) We chose to feed the classifier with 71 seconds of videos (11 seconds before the event and 60 seconds after it) as we wanted the classifier to be able to capture the moment of the deposition, the cooldown process, as well as urine smearing or feces shifting which might give an additional clue for the classification. In the revised paper we plan to report accuracy when using a shorter video for classification.

Generability

a. In the revised version, we plan to report the accuracy of the method used on a different strain of mice (C57), with a different arena color (white arena instead of black).

Statistics

a. In the revised paper, we will explain why we chose each time window for analysis. Also, we will report statistics for different time windows, as suggested by Reviewer 3.

b. Unlike reviewer #2, we don’t think that the small difference in recall rate between urine and feces (78.6% vs. 83.3%, respectively) creates a bias between them. Moreover, we don’t compare the urine rate to the feces rate.

c. In the revised manuscript we will explicitly report the precision scores, although they also appear in our manuscript in Fig. 2- Supplement 1b.

Author response:

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

Reviewer 1:

• Although ROC AUC is a widely used metric. Other metrics such as precision, recall, sensitivity, and specificity are not reported in this work. The last two metrics would help readers understand the model’s potential implications in the context of clinical research.

In response to this comment and related ones by Reviewer 2, we have overhauled how we evaluate our models. In the revised version, we have removed Micro ROC-AUC, as this evaluation metric is hard to interpret in the recommender system setting. Instead, the updated version fully focuses on two metrics: ROC-AUC and Precision at 1 of the negative class, both computed per spectrum and then averaged (equivalent to the instance-wise metrics in the previous version of the manuscript). We believe these metrics best reflect the use-case of AMR recommenders. In addition, we have kept (drug-)macro ROC-AUC as a complementary evaluation metric. As the ROC-AUC can be decomposed into sensitivity and specificity (at different prediction probability thresholds), we have added a ROC curve where sensitivity and specificity are indicated in Figure 8 (Appendices).

• The authors did not hypothesize or describe in any way what an acceptable performance of their recommender system should be in order to be adopted by clinicians.

In Section 4.3, we have extended our experiments to include a baseline that represents a “simulated expert”. In short, given a species, an expert can already make some best guesses as to what drugs will be effective or not. To simulate this, we count resistance frequencies per species and per drug in the training set, and use this as predictions of a “simulated expert”.

We now mention in our manuscript that any performance above this level results in a real-world information gain for clinical diagnostic labs.

• Related to the previous comment, this work would strongly benefit from the inclusion of 1-2 real-life applications of their method that could showcase the benefits of their strategy for designing antibiotic treatment in a clinical setting.

While we think this would be valuable to try out, we are an in silico research lab, and the study we propose is an initial proof-of-concept focusing on the methodology. Because of this, we feel a real-life application of the model is out-of-scope for the present study.

• The authors do not offer information about the model features associated with resistance. This information may offer insights about mechanisms of antimicrobial resistance and how conserved they are across species.

In general, MALDI-TOF mass spectra are somewhat hard to interpret. Because of a limited body of work analyzing resistance mechanisms with MALDI-TOF MS, it is hard to link peaks back to specific pathways. For this reason, we have chosen to forego such an analysis. After all, as far as we know, typical MALDI-TOF MS manufacturers’ software for bacterial identification also does not provide interpretability results or insights into peaks, but merely gives an identification and confidence score.

However, we do feel that the whole topic revolving around “the degree of biological insight a data modality might give versus actual performance and usability” merits further discussion. We have ultimately decided not to include a segment in our discussion section as it is hard to discuss this matter concisely.

• Comparison of AUC values across models lacks information regarding statistical significance. Without this information it is hard for a reader to figure out which differences are marginal and which ones are meaningful (for example, it is unclear if a difference in average AUC of 0.02 is significant). This applied to Figure 2, Figure 3, and Table 2 (and the associated supplementary figures).

To make trends a bit more clear and easier to discern, in our revised manuscript, all models are run for 5 replicates (as opposed to 3 in the previous version).

There is an ongoing debate in the ML community whether statistical tests are useful for comparing machine learning models. A simple argument against them is that model runs are typically not independent from each other, as they are all trained on the same data. The assumptions of traditional statistical tests are therefore violated (t-test, Wilcoxon test, etc.). With such tests statistical significance of the smallest differences can simply be achieved by increasing the number of replicates (i.e. training the same models more times).

More complicated but more appropriate statistical tests also exist, such as the 5x2 cross-validated t-test of Dietterich: “Approximate statistical tests for comparing supervised classification learning algorithms”, Neural computation 1998. However, these tests are typically not considered in deep learning, because only 10% of the data can be used for training, which is practically not desirable. The Friedman test of Demšar "On the appropriateness of statistical tests in machine learning." Workshop on Evaluation Methods for Machine Learning in conjunction with ICML. 2008., in combination with posthoc pairwise tests, is still frequently used in machine learning, but that test is only applicable in studies where many datasets are tested.

For those reasons, most deep learning papers that only analyse a few datasets typically do not consider any statistical tests. For the same reasons, we are also not convinced of the added value of statistical tests in our study.

• One key claim of this work was that their single recommender system outperformed specialist (single species-antibiotic) models. However, in its current status, it is not possible to determine that in fact that is the case (see comment above). Moreover, comparisons to species-level models (that combine all data and antibiotic susceptibility profiles for a given species) would help to illustrate the putative advantages of the dual branch neural network model over species-based models. This analysis will also inform the species (and perhaps datasets) for which specialist models would be useful to consider.

We thank the reviewer for this excellent suggestion. In our new manuscript, we have dedicated an entire section of experiments to testing such species-specific recommender models (Section 4.2). We find that species-specific recommender systems generally outperform the models trained globally across all species. As a result, our manuscript has been majorly reworked.

• Taking into account that the clustering of spectra embeddings seemed to be species-driven (Figure 4), one may hypothesize that there is limited transfer of information between species, and therefore the neural network model may be working as an ensemble of species models. Thus, this work would deeply benefit from a comparison between the authors' general model and an ensemble model in which the species is first identified and then the relevant species recommender is applied. If authors had identified cases to illustrate how data from one species positively influence the results for another species, they should include some of those examples.

See the answer to the remark above.

• The authors should check that all abbreviations are properly introduced in the text so readers understand exactly what they mean. For example, the Prec@1 metric is a little confusing.

See the answer to a remark above for how we have overhauled our evaluation metrics in the revised version. In addition, in the revised version, we have bundled our explanations on evaluation metrics together in Section 3.2. We feel that having these explanations in a separate section will improve overall comprehensibility of the manuscript.

• The authors should include information about statistical significance in figures and tables that compare performance across models.

See answer above.

• An extra panel showing species labels would help readers understand Figure 11.

We have tried to play around with including species labels in these plots, but could not make it work without overcrowding the figure. Instead, we have added a reminder in the caption that readers should refer back to an earlier figure for species labels.

• The authors initially stated that molecular structure information is not informative. However, in a second analysis, the authors stated that molecular structures are useful for less common drugs. Please explain in more detail with specific examples what you mean.

In the previous version of our manuscript, we found that one-hot embedding-based models were superior to structure-based drug embedders for general performance. The latter however, delivered better transfer learning performance.

In our new experiments however, we perform early stopping on “spectrum-macro” ROC-AUC (as opposed to micro ROC-AUC in the previous version). As a consequence, our results are different. In the new version of our manuscript, Morgan Fingerprints-based drug embedders generally outperform others both “in general” and for transfer learning. Hence, our previously conflicting statements are not applicable to our new results.

• The authors may want to consider adding a few sentences that summarize the 'Related work' section into the introduction, and converting the 'Related work' section into an appendix.

While we acknowledge that such a section is uncommon in biology, in machine learning research, a “related work” section is very common. As this research lies on the intersection of the two, we have decided to keep the section as such.

Reviewer 2:

• Are the specialist models re-trained on the whole set of spectra? It was shown by Weis et al. that pooling spectra from different species hinders performance. It would then be better to compare directly to the models developed by Weis et al, using their splitting logic since it could be that the decay in performance from specialists comes from the pooling. See the section "Species-stratified learning yields superior predictions" in https://doi.org/10.1038/s41591-021-01619-9.

We train our “specialist” (or now-called “species-drug classifiers”) just as described in Weis et al.: All labels for a drug are taken, and then subsetted for a single species. We have clarified this a bit better in our new manuscript. The text now reads:

“Previous studies have studied AMR prediction in specific species-drug combinations. For this reason, it is useful to compare how the dual-branch setup weighs up against training separate models for separate species and drugs. In Weis et al. (2020b), for example, binary AMR classifiers are trained for the following three combinations: (1) E. coli with Ceftriaxone, (2) K. pneumoniae with Ceftriaxone, and (3) S. aureus with Oxacillin. Here, such "species-drug-specific classifiers" are trained for the 200 most-common combinations of species and drugs in the training dataset.

• Going back to Weis et al. a high variance in performance between species/drug pairs was observed. The metrics in Table 2 do not offer any measurement of variance or statistical testing. Indeed, some values are quite close e.g. Macro AUROC of Specialist MLP-XL vs One-hot M.

See our answer to a remark of Reviewer 1 for our viewpoint on statistical significance testing in machine learning.

• Since this is a recommendation task, why were no recommendation system metrics used, e.g. mAP@K, mRR, and so (apart from precision@1 for the negative class)? Additionally, since there is a high label imbalance in this task (~80% negatives) a simple model would achieve a very high precision@1.

See the answer to a remark above for how we have overhauled our evaluation metrics in the revised version. In addition, in choosing our metrics, we wanted metrics that are both (1) appropriate (i.e. recommender system metrics), but also (2) easy to interpret for clinicians. For this reason, we have not included metrics such as mAP@K or mRR. We feel that “spectrum-macro” ROC-AUC and precision@1 cover a sufficiently broad evaluation set of metrics but are easy enough to interpret.

• A highly similar approach was recently published (https://doi.org/10.1093/bioinformatics/btad717). Since it is quite close to the publication date of this paper, it could be discussed as concurrent work.

We thank the reviewer for bringing our attention to this study. We have added a paragraph in our revised version discussing this paper as concurrent work.

• It is difficult to observe a general trend from Figure 2. A statistical test would be advised here.

See our answer to a remark of Reviewer 1 for our viewpoint on statistical significance testing in machine learning.

• Figure 5. UMAPs generally don't lead to robust quantitative conclusions. However, the analysis of the embedding space is indeed interesting. Here I would recommend some quantitative measures directly using embedding distances to accompany the UMAP visualizations. E.g. clustering coefficients, distribution of pairwise distances, etc.

In accordance with this recommendation, we have computed many statistics on the MALDI-TOF spectra embedding spaces. However, we could not come up with any statistic that illuminated us more than the visualization itself. For this reason, we have kept this section as is, and let the figure speak for itself.

• Weis et al. also perform a transfer learning analysis. How does the transfer learning capacity of the proposed models differ from those in Weis et al?

Weis et al. perform experiments towards “transferability”, not actual transfer learning. In essence, they use a model trained on data from one diagnostic lab towards prediction on data from another. However, they do not conduct experiments to learn how much data such a pre-trained classifier needs to fine-tune it for adequate performance on the new diagnostic lab, as we do. The end of Section 4.4 discusses how our proposed models specifically shine in transfer learning. The paragraph reads:

“Lowering the amount of data required is paramount to expedite the uptake of AMR models in clinical diagnostics. The transfer learning qualities of dual-branch models may be ascribed to multiple properties. First of all, since different hospitals use much of the same drugs, transferred drug embedders allow for expressively representing drugs out of the box. Secondly, owing to multi-task learning, even with a limited number of spectra, a considerable fine-tuning dataset may be obtained, as all available data is "thrown on one pile".”

Author response:

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

Reviewer #1 (Recommendations for the authors):

In the revision the authors addressed all the points from this reviewer and most from other reviewers. The method is now described practically and in detail. The only thing this reviewer still misses is number of subtomograms for each structure. How many subtomograms did the authors extract by Dynamo from how many rootlets? How many out of them were valid in K-mean classification and used for sub-averages? Was the subaverage used for training by TomoSeg or each subtomograms belonging to the class? By clarifying it, this work will be referred by those who would take the same approach for other biological structures. 

We now added the particle numbers of all structures to the corresponding text, figure legends and methods and elaborate on this below. We also clarify how we trained the TomoSeg network.

Particle numbers:

We extracted 591,453 subtomograms from 14 tomograms. This initial set was rigorously cleaned with Zcleaning, reducing it to 358,863 particles. Further cross-correlation and cluster cleaning yielded a final set of 180,252 particles. 

This refined set was used for the structures presented in Figures 3E, F and S5A, B, as well as for the classification shown in Figure S5C. Of the classified particles, 34,490 particles contributed to classaverage 5 in Figure 3G and S5D, E. The detailed particle distribution of this classification is added as a supplementary table: 

We further clarified the numbers in the results, method, and supplementary material section:

Results:

Page 7: “Figure 3. … (E) The initial average after alignment of 180,252 particles with a wide spherical alignment mask. (F) The initial average of particles aligned with a narrower cylindrical mask. (G) A class average of 34,490 particles, aligned and classified with a narrow mask.”

Page 7/8: “We manually defined the D1-bands as surfaces in Dynamo (Castaño-Díez et al, 2017) and then approximated the number of filaments per surface area. We extracted 591,453 subtomograms from 14 tomograms, approximately four times as many subtomograms as the expected number of filaments. This initial set was rigorously cleaned to discard particles that did not have a filament in their center or had distorted striations, reducing it to 358,863 particles. Further cross-correlation and cluster cleaning yielded a final set of 180,252 particles.”

Page 8: “We directly unbinned the data to a pixel size of 5.55 Å/pixel and used the rigorously cleaned set of 180,252 particles.”

Page 8: “The resulting class averages contained a twist along the filament length in classes 2, 3 and 4 and most prominently in class 5. These four classes contain 72.29% of the particles, highlighting the prevalence of the twist-feature (Fig S5C, Table S2). Class 5 contained 19.27% of the data, i.e. 34,490 particles, and revealed the twist is formed by a filament of 2 nm thick by 5 nm wide with a helical groove along its length (Fig 3G).”

Methods: 

Page 13: “Surface triangulation was set to result in 591,453 extraction coordinates approximately 4 times the number of expected filaments.”

Page 13: “Particles with no filament in their center, or particles that originated from regions in the rootlet with distorted striations (at the edge of a grid hole) were discarded, resulting in a particle set of 358,863 particles. Cluster- and careful per-tomogram cross-correlation cleaning were applied to remove particle duplicates, remaining particles with no filaments, and particles with disordered D-bands. This resulted in a final cleaned particle dataset of 180,252 particles.”

Page 13: “For the final subtomogram class-average that contained the twist, the cleaned particle dataset motl with 180,252 particles was converted to a STAR file compatible with RELION 4.0 Alpha (Zivanov et al, 2022).”

Supplementary material: 

Page 17: “Table S1. Particle distribution of RELION 4.0 Alpha classification with alignment.”

Page 22: “Figure S5: (C) Class averages of a classification with alignment of particles from Fig S5A. Their particle distribution is shown in Table S2.”

For the initial classification, to identify a homogeneous subset, we used the original set of 591,453 picked particles (Fig S5A). The class distribution for this set is added as a supplementary table.

We further clarified this in the results, methods and supplementary material:

Results:

Page 8: “To ask if there were any recurring arrangements of neighboring filaments in the data that could allow us to average a homogeneous subset, we resorted to classification of the original set of 591,453 particles (Fig S5A, Table S1).”

Methods:

Page 13: “Prior to classification in subTOM, alignments with limited X/Y/Z shifts and increasingly finer in-plane rotations were performed on the original dataset with 591,453 particles.”

Supplementary material:

Page 17: “Table S2. Particle distribution of subTOM classification for particle heterogeneity.”

Page 22: “Figure S5: … The surfaces of a cross-section through the filament classes are shown in orange. The particle distribution is provided in Table S1. (B) …”

TomoSeg network training

The subtomograms and the class averages presented at the end of the manuscript were not used as input for training the TomoSeg network. TomoSeg training requires positive and negative sets of segmented 2D regions of interest within tomogram slices. These areas were selected and segmented within the Eman2 TomoSeg GUI, iteratively increasing the size of the training sets until satisfactory performance was achieved. 

We have clarified the TomoSeg training process in the methods section to avoid confusion:

Methods: 

Page 13: “The tomograms were then preprocessed in EMAN2.2 for training of the TomoSeg CNN (Chen et al, 2017). Here, the features (filaments, D-bands, A-bands, gold fiducials, actin, membranes, membrane-associated densities and ice contaminations) were individually trained for each tomogram. This involved manually tracing a training set of 10-20 positive and 100-150 negative boxed areas per feature. We iteratively expanded and curated the training set until the segmentations were accurate, as recommended in the software manuals. Segmented maps were allowed to compete for the assignment of pixels in the tomograms, cleaned up in Amira (Thermo Fisher Scientific) and converted to object files.”

Author response:

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

Reviewer #1:

(1) One issue that needs to be considered is the nomenclature of the enhancer. The authors have presented data to show this enhancer controls the expression of Ctnnb1 in the stomach, intestine, and colon tissues. However, the name proposed by the authors, ieCtnnb1 (intestinal enhancer of Ctnnb1), doesn't represent its functions. It might be more appropriate to call it a different name, such as gieCtnnb1 (gastrointestinal enhancer of Ctnnb1).

We thank the reviewer for the insightful suggestion and agree that wholemount reporter assays indicated ieCtnnb1 and ieCTNNB1 indeed display activity in the stomach. However, in current study, we focused on the cellular distribution and the function in intestinal epithelia. After careful consideration, we reasoned that the current designation, ieCtnnb1, would be more appropriately represent its expression pattern and functions based on provided evidence. We hope the reviewer could understand our reasoning.  

(2) The writing of this manuscript can be improved in a few places. 

a) The definitions or full names for the abbreviations of some terms, e.g., Ctnnb1, ieCtnnb1, in both abstract and main text, are needed when they first appear. Specifically, Line 108 should be moved to Lines 26 and 95. Lines 125126 are redundant. ieCtnnb1 in Line 130 needs to be defined.

We appreciate the suggestion. In the revision, we have included the definition of Ctnnb1 and the full name of ieCtnnb1 when they first appear in the abstract and the main text. Lines 125-126 were deleted in the revision.

b) Line 192-194, the description of the result needs to be rewritten to reflect

the higher expression of LacZ transcript in eGFP+ cells. 

We would like to emphasize that the key point of this part is that the enhancer activity of ieCtnnb1 is present in both Lgr5-eGFP+ and Lgr5-eGFP- cells. This was validated by single-cell sequencing, which revealed the presence of LacZ transcripts in the Paneth cells. Moreover, we could not confidently conclude that eGFP+ cells have higher expression levels of LacZ, as these measurements were obtained from separate, semi-quantitative RTqPCR experiments.

c)  More details are needed for how the data using human tumor samples were generated and how they were analyzed. 

We thank the suggestion. In the revision, we have provided additional details regarding the data and subsequent analyses of human CRC samples as follows: “We previously conducted paired analyses of chromatin immunoprecipitation sequencing (ChIP-seq) for H3K27ac and H3K4me3, alongside RNA-seq on 68 CRC samples and their adjacent normal (native) tissue (Li et al., 2021).  In the current study, we performed analyses for the enrichment of H3K27ac and H3K4me3 at ieCTNNB1 and CTNNB1 promoter regions, as well as the expression levels of CTNNB1, followed by combined analyses (Figure. 5A, Figure 5 - figure supplement 1).”

d) The genomic structures from multiple species are presented at the bottom of Figure 1a. However, the description and explanation are lacking in both the main text and the figure legend.

We apologize for not presenting clearly. We have added related description in the legend of Figure 1A as “The sequence conservation of the indicated species is shown at the bottom as vertical lines”. We also added an explanation in lines 162-163 of the main text: “Notably, unlike neCtnnb1, the primary sequence of ieCtnnb1 is not conserved among vertebrates (Figure 1A, bottom)”.

Reviewer #2:

(1) One of the main issues emerging during reading concerns the interpretation of the consequence of deleting the ieCtnnb1 enhancer. The authors write on line 235 that the deletion of ieCtnnb1 "undermined" Wnt signaling in the intestinal epithelium. This feels too strong, as the status of the pathway is only mildly affected, testified by the observation that mice with homozygous deletion on ieCtnnb1 are alive and well. The enhancer likely "only" drives higher Ctnnb1 expression, and it does not affect Wnt signaling by other mechanisms. The reduction of Wnt target gene expression upon its deletion is easily interpreted as the consequence of reduced β-catenin. Also the title, in my opinion, allows this ambiguity to stick in readers' minds. In other words, the authors present no evidence that the ieCtnnb1 enhancer controls Wnt signaling dosage via any mechanism other than its upregulation of Ctnnb1 expression in the intestinal epithelium. Reduced Ctnnb1, in turn, could explain the observed reduction of Wnt signaling output and the interesting downstream physiological consequences. Unless the authors think otherwise, I suggest they clarify this throughout the text, including necessary modifications to the title.

We greatly appreciate the reviewer’s important comments and suggestion. We agree that ieCtnnb1’s direct effect on the canonical Wnt signaling is to regulate the transcription of Ctnnb1 in the intestinal epithelia. Therefore, knockout of ieCtnnb1 leads to compromised expression of Ctnnb1 and, consequently, reduced Wnt signaling.  The term “undermined” is indeed too strong and has been revised to “compromised” in the revision (line 237). Similar revisions have been made throughout the manuscript. Particularly, the title was changed into “A Ctnnb1 enhancer transcriptionally regulates Wnt signaling dosage to balance homeostasis and tumorigenesis of intestinal epithelia”. However, as we state in the following point, decreased levels of β-catenin on ieCtnnb1 loss could lead to indirect effect, including the reduced expression of Bambi, which might cause a more significant decrease of nuclear β-catenin.

(2) It is unclear how the reduction of Ctnnb1 mRNA caused by deletion of ieCtnnb1 in mice could lead to a preferential decrease of nuclear more than membranous β-catenin (Fig. 1K and L). This might reflect a general cell autonomous reduction in Wnt signaling activation; yet, it is not clear how this could occur. Do the authors have any explanations for this?

It's a very important question. We observed that in inCtnnb1 knockout epithelia, the expression of Bambi (BMP and activin membrane-bound inhibitor) was significantly downregulated. Since BAMBI has been reported to stabilize β-catenin and facilitate its nuclear translocation, it is likely that the reduced level of BAMBI resulting from the loss of ieCtnnb1 further decreased nuclear βcatenin. In the revision, the expression change of Bambi has been added in Figure 1M. Moreover, the related content was extensively discussed with proper citations: “We noticed that after knocking out ieCtnnb1, the level of βcatenin in the nuclei of small intestinal crypt cells of Ctnnb1Δi.enh mice decreased more significantly compared to that in the cytoplasm (49.5% vs. 29.8%). Although the loss of ieCtnnb1 should not directly lead to reduced nuclear translocation of β-catenin, RNA-seq results showed that the loss of ieCtnnb1 causes a reduction in the expression of Bambi (BMP and activin membranebound inhibitor), a target gene in the canonical Wnt signaling pathway (Figure 1M). BAMBI promotes the binding of Frizzled to Dishevelled, thereby stabilizing β-catenin and facilitating its nuclear translocation (Lin et al., 2008; Liu et al., 2014; Mai et al., 2014; Zhang et al., 2015). Thus, it is likely that the decreased level of BAMBI resulting from the loss of ieCtnnb1 further reduced nuclear βcatenin”. 

(3) In Figure 1 K-L the authors show β-catenin protein level. Why not show its mRNA?

The mRNA levels of Ctnnb1 in small and large intestinal crypts were shown in Figure 1I and 1J, demonstrating reduced expression of Ctnnb1 upon ieCtnnb1 knockout. We hope the reviewer understands that it is unnecessary to measure the nuclear and cytosolic levels of Ctnnb1 transcripts, as the total mRNA level generally reflects the protein level. 

(4) Concerning the GSEA of Figure 1 that includes the Wnt pathway components: a) it would be interesting to see which components and to what extent is their expression affected; b) why should the expression of Wnt components that are not Wnt target genes be affected in the first place? It is odd to see this described uncritically and used to support the idea of downregulated Wnt signaling.

We appreciate the suggestion and apologize for any lack of clarity. The affected components of the Wnt signaling pathway and the extent of their changes are summarized in Figure 1 – figure supplement 3. Additionally, we have provided explanations for their downregulation. For instance, the reduced expression of Wnt3 and Wnt2b ligands in ieCtnnb1-KO crypts may be attributed to the decreased numbers of Paneth cells.  

(5) In lines 251-252 the authors refer to "certain technical issues" in the isolation of cell type from the intestinal epithelium. Why this part should be obscure in the characterization of a tissue for which there are several established protocols of isolation and analysis is not clear. I would rather describe what these issues have been and how they protocol of isolation and analysis is not clear. I would rather describe what these issues have been and how they might have affected the data presented.

We thank the reviewer for pointing this out. The single-cell preparation and sequencing of small intestinal cryptal epithelial cells were carried out largely according to reported protocols with slight modification. The enrichment of live crypt epithelial cells (EpCAM+DAPI-) by flow cytometry and cell filtering after single-cell sequencing were appropriate (Figure 2 – figure supplement 1A1C). We would like to emphasize a few points: 1) Unlike other protocols, we did not exclude immune cells, erythrocytes, or endothelial cells using negative sorting antibodies. 2) When defining cell populations, we focused exclusively on epithelial cell types and did not consider other cell types, such as immune cells. As a result, the so-called “undefined” cells include a mixture of nonepithelial cells. Indeed, markers for erythrocytes (AY036118/Erf1, PMID:12894589) and immune cells (Gm42418 and Lars2, PMID:30940803, PMID: 35659337) were the top three enriched genes in the “undefined” cluster (Figure 2 – figure supplement 1D). 3) Nonetheless, the overall findings remain robust, as key observations such as the loss of Paneth cells and reduced cell proliferation were validated through histological studies. This information has been incorporated into the revised manuscript with related references cited (lines 254-259). 

(6) It is interesting that human SNPs exist that seem to fall within the ieCTNNB1 enhancer and affect the gastrointestinal expression of CTNNB1. Could the author report or investigate whether this SNP is present in human populations that have been considered in large-scale studies for colorectal cancer susceptibility? It seems to me a rather obvious next step of extreme importance to be ignored.

(7) From Figure 5A a reader could conclude that colorectal tumor cells have a higher expression of CTNNB1 mRNA than in normal epithelium. This is the first time I have seen this observation which somewhat undermines our general understanding of Wnt-induced carcinogenesis exclusively initiated by APC mutations whereby it is β-catenin's protein level, not expression of its mRNA, of crucial importance. I find this to be potentially the most interesting observation of the current study, which could be linked to the activity of the enhancer discovered, and I suggest the authors elaborate more on this and perhaps consider it for future experimental follow-ups.

We appreciate the comments and suggestions.  We therefore added related content in the revision (lines 470-475): “Importantly, ieCTNNB1 displayed higher enhancer activity in most CRC samples collected in the study. Moreover, the SNP rs15981379 (C>T) within ieCTNNB1 is associated with the expression of CTNNB1 in the GI tract. Future population studies could investigate how the enhancer activity of ieCTNNB1 and this particular SNP are associated with CRC susceptibility and prognosis”.

(8) I am surprised that the authors, who seem to have dedicated lots of resources to this study, are satisfied by analyzing their ChIP experiments with qPCR rather than sequencing (Figure 6). ChIP-seq would produce a more reliable profile of the HNF4a and CREB1 binding sites on these loci and in other control regions, lending credibility to the whole experiment and binding site identification. Sequencing would also take care of the two following conceptual problems in primer design. 

First: while the strategy to divide enhancer and promoter in 6 regions to improve the resolution of their finding is commendable, I wonder how the difference in signal reflects primers' efficiency rather than HNF4/CREB1 exact positioning. The possibility of distinguishing between regions 2 and 3, for example, in a ChIP-qPCR experiment, also depends on the average DNA fragment length after sonication, a parameter that is not specified here. 

Second: what are the primers designed to detect the ieCtnnb1 enhancer amplifying in the yellow-columns samples of Figure 6G? In this sample, the enhancer is deleted, and no amplification should be possible, yet it seems that a value is obtained and set to 1 as a reference value.

This is indeed a crucial point, and we fully agree with the reviewer that “ChIP-seq would produce a more reliable profile of the HNF4a and CREB1 binding sites on these loci and in other control regions”. However, we believe that our current ChIP-qPCR experiments have adequately addressed the potential concerns raised by the reviewers. (1) We have ensured that the DNA fragment length after sonication falls within the range of 200 bp to 500 bp, with an average length of approximately 300 bp (Author response image 1A). We have stated the point in the revised methods section (line 633). (2) We have randomly inspected 14 out of 26 primer sets used in Figure 6 and its supplemental figure (Author response image 1B-E), confirming that all primer sets demonstrate equal amplification efficiency (ranging from 90% to 110%). This information has also been included in the revised methods section (line 650). (3) Figures 6G and 6H show reduced enrichment of HNF4𝛼 (6G) and p-S133-CREB1 (6H) at the Ctnnb1 promoter in ieCtnnb1 knockout ApcMin/+ tumor tissues. The ChIP-qPCR primers used were positioned at the Ctnnb1 promoter, not at ieCtnnb1, with IgG control enrichment serving as the reference values on the Y-axes. 

Author response image 1.

(A) Agarose gel electrophoresis of sonicated DNA. (B-E) Tests of amplification efficiency for primer sets used in ChIP-qPCR.

(9) The ChIP-qPCR showing preferential binding of pS133-CREB1 in small intestinal crypts and CHT15 cells (line 393) should be shown. 

The ChIP-qPCR results demonstrating preferential binding of p-S133-

CREB1 over CREB1 have been added in revised Figure 6C, 6D and Figure 6 – Supplement 1C.

(10) It is not entirely clear what the blue tracks represent at the bottom of Figures 6C-D and Figure 6 - Figure Supplement 1C-D. The ChIP-seq profiles of both CREB1 and HNF4a shown in Figures 6A and Figure 6 - Figure Supplement 1A do not seem to match. Taking HNF4a, for example from Figure 6 - Figure Supplement 1A it seems to bind on the Ctnnb1 promoter, while in Figure 6 - Figure Supplement 1D the peaks are within the first intron. I realize this might all be a problem with a different scale across figure panels, but I suggest producing a cleared figure.

We apologize for the confusion. We have revised Figure 6C-6D, Figure 6 - figure supplement 1C-D, and the corresponding legends to enhance clarity. (1) The top panels of Figures 6C and 6D respectively highlight shaded regions of ieCTNNB1 (pink) and the CTNNB1 promoter (grey) in Figure 6A, emphasizing the enrichment of p-S133-CREB1.  (2) The top panels of Figure 6 – figure supplement 1C and 1D respectively highlight shaded regions of ieCtnnb1 (pink) and the Ctnnb1 promoter (grey) in Figure 6A – figure supplement 1A, emphasizing the enrichment of HNF4α. (3) Because Figures 6C-6D and Figure 6 - figure supplement 1C-1D respectively correspond to human and mouse genomes, the positions of peaks and scales differ.  

(11) In the intro the authors refer to "TCF-4". I suggest they use the more recent unambiguous nomenclature for this family of transcription factors and call it TCF7L2.

TCF-4 has been changed into TCF7L2 in the revision (line 81)

(12) In lines 121-122, the authors write "Although numerous putative enhancers...only a fraction of them were functionally annotated". To what study/studies are the authors referring? Please provide references.

References were added in the revision (line 124)

(13) In some parts the authors use strong words that should in my opinion be attenuated. Examples are: (i) at line 224, "maintains" would be better substituted with "contribute", as in the absence of ieCtnnb1, Ctnnb1 is still abundantly expressed; (ii) at line 266 "compromised" when the proliferative capacity of CFCs and TACs seems to be only mildly reduced; (iii) at line 286 "disrupts", the genes are simply downregulated.

We thank these great suggestions. 1) On lines 224-225, the sentence was revised to: “These data suggest that ieCtnnb1 plays a specific role in regulating the transcription of Ctnnb1 in intestinal epithelia”. 2) On line 271, “compromised” were replaced with “mildly reduced”. 3) In ieCtnnb1 knockout epithelial cells of small intestine, genes related to secretory functions were decreased, while genes related to absorptive functions were increased. Therefore, the term 'disrupts' is more appropriate than 'downregulates'. 

Reviewer #3:

Line 81, c-Myc should be human MYC (italics) to agree with the other human gene names in this sentence. 

c-Myc has been changed into MYC in the revision (line 82)

Line 215, wildtype should be wild-type. 

“wildtype” has been changed into “wild-type” in the revision (line 215)

Line 224, Elimination of the enhancer did not abolish expression of Ctnnb1; therefore, it would be better to say that it "helps to maintain Ctnnb1 transcription" 

The sentence was changed into “These data suggest that ieCtnnb1 plays a specific role in regulating the transcription of Ctnnb1 in intestinal epithelia” in revision (lines 224-225)

Line 228, perhaps "to activate transcription" is meant. 

“active” has been changed into “activate” in the revision (line 228)

Line 235, consider "reduced" instead of "undermined". 

“undermined” has been replaced with “compromised” in the revision (line 237)

Line 262, "em" dashes should be a both ends of this insertion. 

Line 298, "dysfunctional" would be better.

Line 356, "samples were". 

Line 481, 12-hr (add hyphen). 

All above points have been optimized according to the reviewer’s suggestion.

Line 712, Is "poly-N" meant? 

“Poly-N” indicates undetected bases during sequencing. This explanation was added in the revision (lines 759-760).

Figure 1K, the GAPDH signal is not visible and that panel is unnecessary as there is an H3 control.   

Figure 1K and 1L respectively show levels of nuclear and cytoplasmic βcatenin. GAPDH and H3 were used as internal references for the cytoplasmic and nuclear fractions, respectively, confirming both robust fractionation and equal loading.

Author response:

We are grateful to the reviewers and editors for their insightful comments. All recognized that, while mutation recurrences have been used for inferring cancer drivers, our approach has the rigor of quantitative analysis. We would like to add that, without rigorously ruling out mutational hotspots, most CDNs have not been accepted as driver mutations.

This paper develops the theory stating that i) recurrent point mutations are true Cancer Driving Nucleotides (CDNs); and ii) non-recurrent mutations are unlikely to be CDNs. The reviewers question that, with the theory, we still have not discovered new driving mutations. This is done in the companion paper. Table 3 shows that, averaged across cancer types, the conventional method would identify 45 CDGs while the CDN method tallies 258 CDGs. The power of the CDN method in identifying new driver genes is evident.

The second question is "By this theory, will we be able discover most CDNs when the sample size increases from ~ 1000 to 10,000?"  This is a question of forecast and can be partially answered using GENIE data. Fig. 7 of this study shows that, when n increases from ~ 1000 to ~ 9,000, the numbers of discovered CDNs increase by 3 – 5 fold, most of which come from the two-hit class, as expected.

Fig. 7 also addresses the queries whether we have used datasets other than TCGA. We indeed have used all public data, including GENIE, ICGC and other integrated resources such as COSMIC. For the main study, we rely on TCGA because it is unbiased for estimating the probability of CDN occurrences. In many datasets, the numerators are given but the denominators are not (the number of patients with the mutation / the total number of patients surveyed). 

The third question is about mutation recurrences among cancer types. As stated by one reviewer, "different cancer types have unique mutational landscapes". While this is true when the analysis is done at the whole-gene level, one gets a different picture at the nucleotide level where the resolution is much higher. The pan-cancer trend of point mutations is evident in Fig. 4 of the companion paper.

Again, we heartily appreciate the criticisms and suggestions of the reviewers and editors!

Author response:

We are grateful for the reviewers' acknowledgment of the originality of our manuscript and its potential importance in cancer treatment. We appreciate the reviewers' critiques on certain conclusions and thank them for their thorough feedback on the manuscript. In the revised version, we will provide a more detailed clarification of the previous data and methods, bolster the existing data, and present additional evidence in support of our hypothesis. Please find below our replies to particular concerns.

In brief, to address the comments from Reviewer 1, we will make the following revisions in the manuscript:

(1) To discuss the issues regarding the specificity of ATP5⍺ CAT-tailing, we will provide new patient-derived cell lines and tumor samples and investigate the CAT-tail modifications of nuclear genome-encoded mitochondrial proteins and changes in RQC proteins within them. We will endeavor to explore the nature of NEMF modifications in GSC cells (Fig. S1A).

(2) To enhance the quality of image data, we will substitute some images (such as Fig. 1E and 3A) with higher quality images.

(3) To further understand the influence of NEMF on cancer, the effects of NEMF overexpression in GSC cells will be evaluated through testing (e.g., Fig. 3D).

(4) To further explore changes in apoptosis, we will employ additional methods to detect apoptosis, including Annexin-PI FACS assays, caspase cleavage analysis, assessing BAX-BCL2 ratios, and monitoring cytochrome c release.

(5) To further confirm the effectiveness of the CAT-tailing-mitochondria mechanism in in vivo tumor models, we will utilize a Drosophila model to study the impact of the RQC pathway and CAT-tailing mechanism on tumor proliferation in vivo. The overactivation of the Notch signaling pathway in Drosophila can stimulate malignant proliferation of neural stem cells (NSCs) through both canonical (c-Myc mediated pathway) and non-canonical (PINK1-mitochondrial-mTORC2 pathway) pathways, leading to the development of a tumor-like phenotype in the larval brain. A recent publication in PNAS Nexus (Khaket et al., PNAS Nexus, 2024) discusses the impact of the RQC pathway on c-Myc. It is possible for us to analyze the alterations in CAT-tailing on mitochondrial proteins and mitochondrial membrane potential in this Notch model and study how the RQC pathway regulates them. Moreover, tumor implantation experiments will be carried out using immunodeficient mice. Our goal is to conduct a comparative analysis of the growth of control and NEMF KD glioblastoma cell lines in animal models, alongside performing essential biochemical analyses.

Reference:

Khaket, T. P., et al. (2024). Ribosome stalling during c-myc translation presents actionable cancer cell vulnerability. PNAS nexus, 3(8), pgae321.

To address the comments from Reviewer 2, we will make the following revisions in the manuscript:

(1) The concerns raised by the reviewer regarding the authenticity of the ATP5a CAT-tail modification are duly noted. Critical control experiments will be incorporated into our study, including NEMF knockout (or NFACT domain mutants) and cycloheximide treatment, alongside other methodologies. The results of these experiments will include placements such as Fig. 1B, 1C, S3A, and S3B to improve comprehension of the CAT-tail modification on ATP5⍺.

(2) We thank the reviewer for reminding us to consider the differences between the artificial tail and the endogenous CAT-tail. A recently published study (Khan et al., 2024) provides a thorough analysis of the components of the CAT-tail. Our approach to addressing this issue involves emphasizing the use of the artificial CAT-tail sequence and adopting a more measured tone in the revised version. Additionally, we will induce the endogenous ATP5⍺-CAT-tail by express ATP5⍺-K20-non-stop in cells to validate their function in glioblastoma cells.

(3) Moreover, we aim to examine the impact of different amino acid compositions in the ATP5⍺ c-terminus extension, such as the poly (Gly-Ser) repeats noted by the reviewer, on both mitochondrial function and glioblastoma biology in our revision. By comparing the results obtained from ATP5⍺-CAT-tails with different compositions, it is anticipated that more definitive conclusions can be drawn.

(4) Additional minor revisions will be implemented to the text in accordance with the feedback given by the reviewer.

Reference:

Khan, D., Vinayak, A. A., Sitron, C. S., & Brandman, O. (2024). Mechanochemical forces regulate the composition and function of CAT tails. bioRxiv, 2024-08.

Author response:

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

Public Reviews:

Reviewer #1:

Summary:

The current manuscript uses electron spin resonance spectroscopy to understand how the dynamic behavior and conformational heterogeneity of the LPS transport system change during substrate transport and in response to the membrane, bound nucleotide (or transition state analog), and accessory subunits. The study builds on prior structural studies to expand our molecular understanding of this highly significant bacterial transport system. 

Strengths 

This series of well-designed and well-executed experiments provides new mechanistic insights into the dynamic behavior of the LPS transport system. Notable new insights provided by this study include its indication of the spatial organization of the LptC domain, which was poorly resolved in structures, and how the LptC domain modulates the dynamic behavior of the gate through which lipids access the binding site. In addition, a mass spectrometry approach designed to examine LPS binding at different stages in the nucleotide-dependent conformational cycle provides insight into the order of operations of LPS binding and transport. 

We thank the reviewer for the very positive comments and highlighting the important findings from our study.

Reviewer #2 (Public Review):

Lipopolysaccharide (LPS) is a major component of the outer membrane of Gram-negative bacteria and plays a critical role in bacterial virulence. The LPS export mechanism is a potential target for new antibiotics. Inhibiting this process can render bacteria more susceptible to the host immune system or other antibacterial agents. Given the rise of antibiotic-resistant bacteria, novel targets are urgently needed. The seven LPS transport (Lpt) proteins, A-G, move LPS from the inner to the outer membrane. This study investigated the conformational changes in the LptB2FG-LptC complex using site-directed spin labeling (SDSL) electron paramagnetic resonance (EPR) spectroscopy, revealing how ATP binding and hydrolysis affect the LptF βjellyroll domain and lateral gates. The findings highlight the role of LptC in regulating LPS entry, ensuring efficient and unidirectional transport across the periplasm. 

The β-jellyrolls are not fully resolved in the vanadate-trapped structure of LptB2FG and LptB2FGC. Therefore, the current study provides valuable information on the functional dynamics of these periplasmic domains, their interactions, and their roles in the unidirectional transport of LPS. Additionally, the dynamic perspective of the lateral gates in LptFG in the presence and absence of LptC is another strength of this study. Moreover, at least in detergent samples, more comprehensive intermediates of the ATP turnover cycle are studied than in the available structures, providing crucial missing mechanistic details. 

We thank the reviewer for highlighting our major findings!

Other major strengths of the study include high-quality DEER distance measurements in both detergent and proteoliposomes, the latter providing valuable dynamics information in the lipid environment. However, lipid composition is not mentioned. The proteoliposome study is crucial since the previous structural study (Li, Orlando & Liao 2019) was done in rather small-diameter nanodiscs, which might affect the overall dynamics of the complex. It would have been beneficial if the investigators had reconstituted the complex in lipid nanodiscs with the same composition as proteoliposomes. The mixed lipid/detergent micelles provide an alternative. It seems the ATPase activity of the protein complex is much lower in detergent compared with lipid nanodiscs (Li, Orlando & Liao 2019). In the current study, ATPase activity in proteoliposomes is not provided. Also, the reviewer assumes cysteine-less (CL) constructs of the complex components were utilized. The ATPase assay on CL complex is not presented. Additionally, from previous structural studies and the mass spectrometry data presented here, LPS co-purifies and is already bound to the complex, thus the Apo state may represent the LPS-bound state without nucleotides. 

The liposomes are made from E. coli polar lipid extract, which we added to the Materials and Methods part now. We could not yet perform the investigations in nanodiscs, which is one of our aims for future. The ATPase activity is lower in micelles and the reviewer is correct in that we did not perform/compare ATPase activity in proteoliposomes. The data denoted as wild-type (WT, Figure S4) corresponds to the cysteine-less (CL) variant, which is now corrected in the supporting information. As the reviewer commented, the mass spectrometry data reveal bound LPS in the apo-state. However, as seen from our results, ADP-Mg2+ state is similar to the apo state, thus in the cellular environment LPS may bind to this state as well.

The selection of sites to probe lateral gate 2, which forms the main LPS entry site, may pose an issue. Although the authors provide justification based on the available structures, one site (position 325 in LptF) is located on a flexible loop, and position 52 in LptG is on the neighboring transmembrane helix, separated by a potentially flexible loop from the gating TM1. These labeling sites could exhibit significant local dynamics, resulting in a broader distribution of distances and potentially masking the gating-related conformational changes. 

Position 52 in LptG is located at the beginning of the neighboring transmembrane helix. As we have discussed in the manuscript, position 325 in LptF is located on a short loop connected to TM5. In the structures, this loop shows a very similar orientation (Figure S6). Further, the observed heterogeneity for the lateral gate-2 is considerably modulated into distinct conformation(s) upon LptC binding (Figure 6D-E). This would not be the case if this loop possesses any independent flexibility. Confirming these observations, the room temperature continuous wave ESR spectra revealed the least flexibility for this spin pair (Figure S5, S7). In view of the reasons and observations detailed above, we conclude that the local flexibility at the labelled sites might not make any significant contribution for the broad distribution observed at this gate in LptB2FG (Figure 4). 

Reviewer #3 (Public Review):

Summary: 

The manuscript by Dajka and co-workers reports the application of a biophysical approach to analyse the dynamics of the LptB2FG-C ABC transporter, involved in LPS transport across the cell envelope in Escherichia coli. LptB2FG-C belongs to a new class of ABC transporters (type VI) and is essential and conserved in several Gram-negative pathogens. Since LPS is the major component of the outer membrane of the Gram-negative cell and is responsible for the low permeability of this membrane to several antibiotics, a deep understanding of the mechanism and function of the LptB2FG-C transporter is crucial for the development of new drugs targeting Gram-negative pathogens. 

Several structural studies have been published so far on the LptB2FG-C transporter, disclosing important aspects of the transport mechanism; nevertheless, lack of resolution of some regions of the individual proteins as well as the dynamic nature of the transport mechanism per se (e.g. the insertion and removal of the TM helix of LptC from the TMDs of the transporter during the LPS transport cycle) has greatly limited the understanding of the mechanism that couples ATP binding and hydrolysis with LPS transport. This knowledge gap could be filled by applying an approach that allows the analysis of dynamic processes. The DEER/PELDOR technique applied in this work fits well with this requirement. 

Strengths: 

In this study, the authors provide some new pieces of information on the LptB2FG-C function and the role of LptC in the transporter. Notably, they show that: 

- There is high heterogeneity in the conformational states of the entry gate of LPS in the transporter (gate-2) that are reduced by the insertion of LptC, and the heterogeneity observed is not altered by ATP binding or hydrolysis (as expected since LPS entry is ATP-independent). 

- ATP binding induces an allosteric opening of LptF β-jellyroll domain that allows for LPS passage to the β-jellyroll of LptC, which is stably associated with the β-jellyroll of LptF throughout the cycle. 

- The β-jellyroll of LptG is highly flexible, indicating an involvement in the LPS transport cycle. 

The manuscript is timely and overall clear. 

We thank the reviewer for the positive comments and highlighting our findings and the strength of DEER/PELDOR spectroscopy for characterizing the dynamics aspect of the LPS transport system.

Weaknesses:

I list my concerns below and provide suggestions that, in my opinion, should be addressed to reinforce the findings of this study. 

(1) Protein complex controls: the authors assess the ATPase activity of the spin-labelled variants of their protein complexes to rule out the possibility that engineering the proteins to enable spin labelling could affect their functionality (Figure S4). It has been reported that the association of LptC to LptB2FG complex inhibits its ATPase activity. However, in the ATPase assay data shown in Figure S4, the inhibitory effect of the LptC TM is not visible (please compare LptB2FG F-A45C G-I335C and F-L325C G-A52C with and without LptC). This can lead to suspect that the regulatory function of LptC is missing in the LptC-containing complexes used in this work. I suggest the authors include wt LptB2FGC in the assay to compare the ATPase activity of this complex with wt LptB2FG. The published inhibitory effect of TM LptC has been observed in proteoliposomes. Since it is not clear from the paper if the ATPase assay in Figure 4 has been conducted in DDM or proteoliposomes, the lack of inhibitory effect could be due to the assay conditions. A comparative test could answer this question. 

We could not observe the inhibitory effect of LptC on the ATPase activity of LptB2FG. As the reviewer pointed out, the primary reason is that we performed the assays in detergent micelles and not in proteoliposomes. For this reason, a comparison of the activity between (cysteine-less) LptB2FG and LptB2FG-C as the reviewer suggested would not be informative. As this information is not directly relevant for our current interpretations, we plan to perform those experiments in liposomes in the near future.

(2) Figure 2: NBD closure upon ATP binding to LptB2FG is convincingly demonstrated both in DDM micelles and proteoliposomes, validating the experimental system. However, since under physiological conditions, ATP binding should take place before the displacement of the TM of LptC (Wilson and Ruiz, Mol microbiol 2022), I suggest the authors carry out the experiments with LptC-containing complexes to investigate conformational changes (if any) that are triggered when ATP binding occurs before the TM displacement.  

We thank the reviewer for the suggestion. These experiments are in our to do list and would be performed in the near future.

(3) Proteoliposomes: in the experiments shown in Figures 3 and 4, unlike those in Figure 2, measurements in proteoliposomes give different results from the experiments in DDM, showing higher heterogeneity. Could this be related to the presence (or absence) of LPS in liposomes? It is not mentioned in the materials and methods section whether LPS is present. Could the authors please discuss this? 

We thank the reviewer for bringing out this interesting point. The liposomes are made from E. coli polar lipid extract. In the polar lipid extract, phosphatidylethanolamine (PE) is the predominant lipid component with minor amounts of phosphatidylglycerol (PG) and cardiolipin. Thus, the differences in the heterogeneity we observed in proteoliposomes might not be due to the presence of LPS. We added a short description on this aspect in the ‘Discussion’ part.

(4) The authors show large conformational heterogeneity in gate-2 (using the spin-labelled pair F-L325R1-G-A52R1) and suggest that deviation from the corresponding simulations could be due to the need for enhanced dynamics to allow for gate interaction with LPS or LptC. The effect of LptC is probed in the experiments shown in Figure 6, but I suggest the authors add LPS to the complexes to evaluate the possible stabilizing effect of LPS on the conformations shown in Figure 4. 

This indeed is an important experiment, which we plan to do in the near future.

(5) Figure 6: the measurement of lateral gate 1 and 2 dynamics in the LptC-containing complexes clearly supports the hypothesis, proposed based on the available structures, that TM LptC dissociates from LptB2FG upon ATP binding. However, direct evidence of this movement is still missing. Would it be possible to monitor the dynamics of the TM LptC by directly labelling this protein domain? This would give a conclusive demonstration of the displacement during the ATPase cycle. 

Yes, it should be possible to label LptC and monitor its position with respect to LptF or LptG. These experiments are in progress in our laboratory. 

(6) LPS release assay: Figure 6 panels H-I-J show the MS spectra relative to LPS-bound and free proteins obtained from wt LptB2FG upon ATP binding and ATP hydrolysis conditions. From these spectra the authors conclude that LPS is completely released only upon ATP hydrolysis. However, the current model predicts that LPS release into the Lpt bridge made by LptC-A-D is triggered by ATP binding. For this reason, I suggest the authors assess LPS release also from the LptB2FGC complex where, in the absence of LptA, LPS would be expected to be mostly retained by the complex under the same conditions. 

These indeed are exciting experiments. LPS binding and release by LptB2FGC is in progress in our laboratories.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Page 2 typo: apo-sate should be apo-state 

Thank you! We corrected the typo.

Can the authors clarify whether LPS is co-purified with the protein? Does it remain bound throughout the liposome reconstitution process? 

Our mass spectrometry data show that LPS is co-purified with LptB2FG in micelles. However, we cannot yet verify the presence of bound LPS after reconstitution into proteoliposomes. We added a sentence in the last paragraph before Discussion as ‘Thus, LPS is co-purified with LptB2FG in micelles.’

Reviewer #2 (Recommendations for The Authors): 

Several points require clarification: 

(1) The reviewer would have benefited from access to the raw DEER traces. For instance, in Figure 4, the change in the raw data appears subtle. The differences between the Apo and vanadate-trapped states in b-DDM might be related to a lower signal-to-noise ratio in the Apo state. 

We would be happy to share the raw DEER data upon request. The analysis is performed with the primary data, which also takes into account of the noise level for the calculating the confidence interval. Therefore, the distances with the 95% confidence interval are reliable to the extent as they are presented.  

(2) The panel labels in Figures 2-4 do not match the legends. 

Thank you! We corrected them.

(3) In Figure 2G, the authors state, "Overall, the ATP-induced closure as observed in micelles (and the structures) is maintained in the native-like lipid bilayers for the NBDs." This statement is technically incorrect since the vanadate-trapped state is not equivalent to the ATP+EDTA "ATP binding" state, which was not tested in proteoliposomes (PLS). The authors should have tested this condition for a few mutants in proteoliposomes. They should revise the manuscript to reflect this or provide evidence that the ATP+EDTA state is similar to the vanadate-trapped state in PLS. 

We corrected the sentence as ‘Overall, the nucleotide-induced closure as observed in micelles (and the structures) is maintained in the native-like lipid bilayers for the NBDs.’

(4) The mutant F-L325R1_G-A52R1 is not optimal for probing gate 2. Specifically, position 325 in LptF is highly flexible, as indicated by the very broad distance distributions in Figure 4, and may hinder probing the associated conformational changes in this gate. Comparing the cryo-EM structures of this loop under different conditions (Figure S6) does not provide solid evidence for the lack of flexibility. 

Position 52 in LptG is located at the beginning of the neighboring transmembrane helix. As we have discussed in the manuscript, position 325 in LptF is located on a short loop connected to TM5. In the structures, this loop shows a very similar orientation (Figure S6). Further, the observed heterogeneity for the lateral gate-2 is considerably modulated into distinct conformation(s) upon LptC binding (Figure 6D-E). This would not be the case if this loop possesses any independent flexibility. Confirming these observations, the room temperature continuous wave ESR spectra revealed the least flexibility for this spin pair (Figure S5, S7). In view of the reasons and observations detailed above, we conclude that the local flexibility of the labelled sites might not make any significant contribution for the broad distribution observed at this gate in LptB2FG (Figure 4). 

(5) Regarding Figure 4B, the authors state, "In the vanadate-trapped and ATP samples, the major population is centered at 2 nm (which corresponds to the simulation on the vanadate trapped structure)". While the shift to shorter distances aligns with the structures, the average distance from the simulation is around 3 nm and does not correspond closely to the DEER distances of 2 nm. 

Thank you for noting this point. We corrected the sentence as ‘In the vanadate-trapped and ATP samples, the major population is centred at 2 nm (which is closer to the simulation on the vanadate-trapped structure).’

(6) Regarding Figure 4D, the authors state, "Unlike the lateral gate-1 (and the NBDs), ADP-Mg2+ also induced a similar shift in the distance distribution." The reviewer believes that even without interaction with LptC, an equilibrium exists between two states in gate-2, and ATP binding or vanadate-trapping shifts the equilibrium to a shorter-distance population. Additionally, if the signal-to-noise ratio of the Apo state were similar to that of the ADP-Mg2+ state, similar distance distributions would have been observed for the Apo state. 

We thank the reviewer for bringing out this excellent point. We thoroughly modified the corresponding section as ‘ADP-Mg2+ also gave a broad distribution comparable to the apo-state. Thus, in the apo-state this gate appears to exist in an equilibrium between the two conformations observed from the corresponding structures. ATP binding or vanadate-trapping shifts the equilibrium towards the collapsed conformation.’

(7) Defining the conformational dynamics of the b-jellyroll domains is one of the major strengths of this study. The LptF and LptG b-jellyroll domains exhibit high flexibility in detergent micelles. Unfortunately, none of the experiments were repeated in proteoliposomes to determine if this flexibility persists in a lipid environment. 

As it is conceivable, it is truly beyond the scope of the current study to repeat all the measurements in liposomes. Currently we are extending those investigations to liposomes and would be able to provide more insights in the near future.

(8) Regarding Figure 6G, the authors claim, "Distances corresponding to the apo state are present possibly due to an incomplete vanadate trapping for this sample." It is unlikely that vanadate trapping would be incomplete for just one sample. A repeat experiment is recommended. 

We will update on this point is due time.

(9) Regarding the structural dynamics of the lateral gates, detergent micelles, and liposomes are vastly different environments. It is challenging to reach a consensus model based on data mostly derived from detergent micelles and only a few from proteoliposomes. 

The observations in PLS are qualitatively similar to the micellar sample for the investigated positions (please see the first paragraph in “Discussion”). Further, our observations are in agreement with previous structural and biochemical data and further extent the mechanism in a coherent manner. 

Reviewer #3 (Recommendations For The Authors):

Minor comments 

(1) Figure legends: There are several mismatches between panel nomenclature and the corresponding descriptions in the legends. Please check the correspondence between panel identification and descriptions throughout the manuscript (for example, F-G and H-J in Figure 2; and I and H in Figure 3). 

Thank you! We corrected them.

- Figure 6 legend: asterisk is in panel D and not C. 

Corrected

- Panels E and F are not mentioned. Moreover, the spectra for vanadate trapped conformation of LptF219-LptC104 have not been given a letter. 

Corrected

- A description of the different colors in the "Distance r" axis should be added to figure 2, 3, and 4 legends. 

Corrected

- Please indicate the meaning of the black arrows in figure legends. 

Corrected

(2) To improve data comprehension by the readers, the authors should indicate the relative spinlabelled pairs on the top of Figure 2, 3, and 4, as done for Figures 5 and 6. 

Done

(3) Reference 56 is cited incorrectly in the reference list and refers to a study employing reconstituted LptB2FG complexes rather than isolated β-jellyroll domains. 

Corrected

(4) Figure 3: How do the authors explain the evidence that ATP binding influences gate 1 conformational flexibility only in DDM micelles with respect of PLS? Is this something related to the release of LPS from the complex in different environments? 

We do not know whether this difference is related to LPS release. Therefore, we generally interpreted as an effect of the membrane environment.

(5) The initial sentence of the discussion looks somewhat incomplete, please correct it. 

Done

(6) To improve the readability of the paper, it could be useful to better focus the topic of the headings of the result paragraphs concerning the analysis of the individual lateral gates (for example, by indicating the name of the gate in the headings).

Done

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

In this study, the authors used a stopped-flow method to investigate the kinetics of substrate translocation through the channel in hexameric ClpB, an ATP-dependent bacterial protein disaggregase. They engineered a series of polypeptides with the N-terminal RepA ClpB-targeting sequence followed by a variable number of folded titin domains. The authors detected translocation of the substrate polypeptides by observing the enhancement of fluorescence from a probe located at the substrate's C-terminus. The total time of the substrates' translocation correlated with their lengths, which allowed the authors to determine the number of residues translocated by ClpB per unit time.

Strengths:

This study confirms a previously proposed model of processive translocation of polypeptides through the channel in ClpB. The novelty of this work is in the clever design of a series of kinetic experiments with an engineered substrate that includes stably folded domains. This approach produced a quantitative description of the reaction rates and kinetic step sizes. Another valuable aspect is that the method can be used for other translocases from the AAA+ family to characterize their mechanism of substrate processing.

Weaknesses:

The main limitation of the study is in using a single non-physiological substrate of ClpB, which does not replicate the physical properties of the aggregated cellular proteins and includes a non-physiological ClpB-targeting sequence. Another limitation is in the use of ATPgammaS to stimulate the substrate processing. It is not clear how relevant the results are to the ClpB function in living cells with ATP as the source of energy, a multitude of various aggregated substrates without targeting sequences that need ClpB's assistance, and in the presence of the co-chaperones.

Indeed, we agree that our RepA-Titinx substrates are not aggregates but are model, soluble, substrates used to reveal information about enzyme catalyzed protein unfolding and translocation.  Our substrates are similar to RepA-GFP and GFP-SsrA used by multiple labs including Wickner, Horwich, Sauer, Baker, Shorter, Bukua, to name only a few.  The fact that “this is what everyone does” does not make the substrates physiological or the most ideal. However, this is the technology we currently have until we and others develop something better. In the meantime, we contend that  the results presented here do advance our knowledge on enzyme catalyzed protein unfolding

Part of what this manuscript seeks to accomplish is presenting the development of a single-turnover experiment that reports on processive protein unfolding by AAA+ molecular motors, in this case, ClpB.  Importantly, we are treating translocation on an unfolded polypeptide chain and protein unfolding of stably folded proteins as two distinct reactions catalyzed by ClpB. If these functions are used to disrupt protein aggregates, in vivo, then this remains to be seen.

We contend that processive ClpB catalyzed protein unfolding has not been rigorously demonstrated prior to our results presented here.  Avellaneda et al mechanically unfolded their substrate before loading ClpB (Avellaneda, Franke, Sunderlikova et al. 2020).  Thus, their experiment represents valuable observations reflecting polypeptide translocation on a pre-unfolded protein.  Our previous work using single-turnover stopped-flow experiments employed unstructured synthetic polypeptides and therefore reflects polypeptide translocation and not protein unfolding (Li, Weaver, Lin et al. 2015).  Weibezahn et al used unstructured substrates in their study with ClpB (BAP/ClpP), and thus their results represent translocation of a pre-unfolded polypeptide and not enzyme catalyzed protein unfolding (Weibezahn, Tessarz, Schlieker et al. 2004). 

Many studies have reported the use of  GFP with tags or RepA-GFP and used the loss of GFP fluorescence to conclude protein unfolding.  However, such results do not reveal if ClpB processively and fully translocates the substrate through its axial channel.  One cannot rule out, even when trapping with “GroEL trap”, the possibility that ClpB only needs to disrupt some of the fold in GFP before cooperative unfolding occurs leading to loss of fluorescence.  Once the cooperative collapse of the structure occurs and fluorescence is lost it has not been shown that ClpB will continue to translocate on the newly unfolded chain or dissociate. In fact, the Bukau group showed that folded YFP remained intact after luciferase was unfolded (Haslberger, Zdanowicz, Brand et al. 2008).  Our approach, reported here, yields signal upon arrival of the motor at the c-terminus or within the PIFE distance thus we can be certain that the motor does arrive at the c-terminus after unfolding up to three tandem repeats of the Titin I27 domain.

ATPgS is a non-physiological nucleotide analog.  However, ClpB has been shown to exhibit curious behavior in its presence that we and others, as the reviewer acknowledges, do not fully understand (Doyle, Shorter, Zolkiewski et al. 2007).  Some of the experiments reported here are seeking to better understand that fact.  Here we have shown that ATPgS alone will support processive protein unfolding. With this assay in hand, we are now seeking to go forward and address many of the points raised by this reviewer. 

The authors do not attempt to correlate the kinetic step sizes detected during substrate translocation and unfolding with the substrate's structure, which should be possible, given how extensively the stability and unfolding of the titin I27 domain were studied before. Also, since the substrate contains up to three I27 domains separated with unstructured linkers, it is not clear why all the translocation steps are assumed to occur with the same rate constant.

We assume that all protein unfolding steps occur with the same rate constant, ku.  We conclude that we are not detecting the translocation rate constant, kt, as our results support a model where kt is much faster than ku.  We do think it makes sense that the same slow step occurs between each cycle of protein unfolding.

We have added a discussion relating our observations to mechanical unfolding of tandem repeats of Titin I27 from AFM experiments  (Oberhauser, Hansma, Carrion-Vazquez and Fernandez 2001). Most interestingly, they report unfolding of Titin I27 in 22 nm steps.  Using 0.34 nm per amino acids this yields ~65 amino acids per unfolding step, which is comparable to our kinetic step-size of 57 – 58 amino acids per step.

Some conclusions presented in the manuscript are speculative:

The notion that the emission from Alexa Fluor 555 is enhanced when ClpB approaches the substrate's C-terminus needs to be supported experimentally. Also, evidence that ATPgammaS without ATP can provide sufficient energy for substrate translocation and unfolding is missing in the paper.

In our previous work we have used fluorescently labeled 50 amino acid peptides as substrates to examine ClpB binding (Li, Lin and Lucius 2015, Li, Weaver, Lin et al. 2015).  In that work we have used fluorescein, which exhibits quenching upon ClpB binding.  We have added a control experiment where we have attached alexa fluor 555 to the 50 amino acid substrate so we can be assured the ClpB binds close to the fluorophore.  As seen in supplemental Fig. 1 A  upon titration with ClpB, in the presence of ATPγS, we observe an increase in fluorescence from AF555, consistent with PIFE.  Supplemental Fig. 1 B shows the relative fluorescence enhancement at the peak max increases up to ~ 0.2 or a 20 % increase in fluorescence, due to PIFE, upon ClpB binding.   

Further, peak time is our hypothesized measure of ClpB’s arrival at the dye. Our results indicate that the peak time linearly increases as a function of an increase in the number of folded TitinI27 repeats in the substrates which also supports the PIFE hypothesis. Finally, others have shown that AF555 exhibits PIFE and we have added those references.

The evidence that ATPγS alone can support translocation is shown in Fig. 2 and supplemental Figure 1.  Fig. 2 and supplemental Figure 1 are two different mixing strategies where we use only ATPgS and no ATP at all.  In both cases the time courses are consistent with processive protein unfolding by ClpB with only ATPγS.

Reviewer #2 (Public Review):

Summary:

The current work by Banwait et al. reports a fluorescence-based single turnover method based on protein-induced fluorescence enhancement (PIFE) to show that ClpB is a processive motor. The paper is a crucial finding as there has been ambiguity on whether ClpB is a processive or non-processive motor. Optical tweezers-based single-molecule studies have shown that ClpB is a processive motor, whereas previous studies from the same group hypothesized it to be a non-processive motor. As co-chaperones are needed for the motor activity of the ClpB, to isolate the activity of ClpB, they have used a 1:1 ratio ATP and ATPgS, where the enzyme is active even in the absence of its co-chaperones, as previously observed. A sequential mixing stop-flow protocol was developed, and the unfolding and translocation of RepA-TitinX, X = 1,2,3 repeats was monitored by measuring the fluorescence intensity with the time of Alexa F555 which was labelled at the C-terminal Cysteine. The observations were a lag time, followed by a gradual increase in fluorescence due to PIFE, and then a decrease in fluorescence plausibly due to the dissociation from the substrate allowing it to refold. The authors observed that the peak time depends on the substrate length, indicating the processive nature of ClpB. In addition, the lag and peak times depend on the pre-incubation time with ATPgS, indicating that the enzyme translocates on the substrates even with just ATPgS without the addition of ATP, which is plausible due to the slow hydrolysis of ATPgS. From the plot of substrate length vs peak time, the authors calculated the rate of unfolding and translocation to be ~0.1 aas-1 in the presence of ~1 mM ATPgS and increases to 1 aas-1 in the presence of 1:1 ATP and ATPgS. The authors have further performed experiments at 3:1 ATP and ATPgS concentrations and observed ~5 times increase in the translocation rates as expected due to faster hydrolysis of ATP by ClpB and reconfirming that processivity is majorly ATP driven. Further, the authors model their results to multiple sequential unfolding steps, determining the rate of unfolding and the number of amino acids unfolded during each step. Overall, the study uses a novel method to reconfirm the processive nature of ClpB.

Strengths:

(1) Previous studies on understanding the processivity of ClpB have primarily focused on unfolded or disordered proteins; this study paves new insights into our understanding of the processing of folded proteins by ClpB. They have cleverly used RepA as a recognition sequence to understand the unfolding of titin-I27 folded domains.

(2) The method developed can be applied to many disaggregating enzymes and has broader significance.

(3) The data from various experiments are consistent with each other, indicating the reproducibility of the data. For example, the rate of translocation in the presence of ATPgS, ~0.1 aas-1 from the single mixing experiment and double mixing experiment are very similar.

(4) The study convincingly shows that ClpB is a processive motor, which has long been debated, describing its activity in the presence of only ATPgS and a mixture of ATP and ATPgS.

(5) The discussion part has been written in a way that describes many previous experiments from various groups supporting the processive nature of the enzyme and supports their current study.

Weaknesses:

(1) The authors model that the enzyme unfolds the protein sequentially around 60 aa each time through multiple steps and translocates rapidly. This contradicts our knowledge of protein unfolding, which is generally cooperative, particularly for titinI27, which is reported to unfold cooperatively or utmost through one intermediate during enzymatic unfolding by ClpX and ClpA.

We do not think this represents a contradiction.  In fact, our observations are in good agreement with mechanical unfolding of tandem repeats of Titin I27 using AFM experiments (Oberhauser, Hansma, Carrion-Vazquez and Fernandez 2001).  They showed that tandem repeats of TitinI27 unfolded in steps of ~22 nm.  Dividing 22 nm by 0.34 nm/Amino Acid gives ~65 amino acids per unfolding event.  This implies that, under force, ~65 amino acids of folded structure unfolds in a single step.  This number is in excellent agreement with our kinetic step-size of 65 AA/step. 

Importantly, the experiments cited by the reviewer on ClpA and ClpX are actually with ClpAP and ClpXP.  We assert that this is an important distinction as we have shown that ClpA employs a different mechanism than ClpAP (Rajendar and Lucius 2010, Miller, Lin, Li and Lucius 2013, Miller and Lucius 2014).  Thus, ClpA and ClpAP should be treated as different enzymes but, without question, ClpB and ClpA are different enzymes.

(2) It is also important to note that the unfolding of titinI27 from the N-terminus (as done in this study) has been reported to be very fast and cannot be the rate-limiting step as reported earlier(Olivares et al, PNAS, 2017). This contradicts the current model where unfolding is the rate-limiting step, and the translocation is assumed to be many orders faster than unfolding.

Most importantly, the Olivares paper is examining ClpXP and ClpAP catalyzed protein unfolding and translocation and not ClpB.  These are different enzymes.  Additionally, we have shown that ClpAP and ClpA translocate unfolded polypeptides with different rates, rate constants, and kinetic step-sizes indicating that ClpP allosterically impacts the mechanism employed by ClpA to the extent that even ClpA and ClpAP should be considered different enzymes (Rajendar and Lucius 2010, Miller, Lin, Li and Lucius 2013).  We would further assert that there is no reason to assume ClpAP and ClpXP would catalyze protein unfolding using the same mechanism as ClpB as we do not think it should be assumed ClpA and ClpX use the same mechanism as ClpAP and ClpXP, respectively. 

The Olivares et al paper reports a dwell time preceding protein unfolding of ~0.9 and ~0.8 s for ClpXP and ClpAP, respectively.   The inverse of this can be taken as the rate constant for protein unfolding and would yield a rate constant of ~1.2 s-1, which is in good agreement with our observed rate constant of 0.9 – 4.3 s-1 depending on the ATP:ATPγS mixing ratio.  For ClpB, we propose that the slow unfolding is then followed by rapid translocation on the unfolded chain where translocation by ClpB must be much faster than for ClpAP and ClpXP.  We think this is a reasonable interpretation of our results and not a contradiction of the results in Olivares et al. Moreover, this is completely consistent with the mechanistic differences that we have reported, using the same single-turnover stopped flow approach on the same unfolded polypeptide chains with ClpB, ClpA, and ClpAP (Rajendar and Lucius 2010, Miller, Lin, Li and Lucius 2013, Miller and Lucius 2014, Li, Weaver, Lin et al. 2015).

(3) The model assumes the same time constant for all the unfolding steps irrespective of the secondary structural interactions.

Yes, we contend that this is a good assumption because it represents repetition of protein unfolding catalyzed by ClpB upon encountering the same repeating structural elements, i.e. Beta sheets. 

(4) Unlike other single-molecule optical tweezer-based assays, the study cannot distinguish the unfolding and translocation events and assumes that unfolding is the rate-limiting step.

Although we cannot, directly, distinguish between protein unfolding and translocation we have logically concluded that protein unfolding is likely rate limiting. This is because the large kinetic step-size represents the collapse of ~60 amino acids of structure between two rate-limiting steps, which we interpret to represent cooperative protein unfolding induced by ClpB.  It is not an assumption it is our current best interpretation of the observations that we are now seeking to further test. 

Reviewer #3 (Public Review):

Summary:

The authors have devised an elegant stopped-flow fluorescence approach to probe the mechanism of action of the Hsp100 protein unfoldase ClpB on an unfolded substrate (RepA) coupled to 1-3 repeats of a folded titin domain. They provide useful new insight into the kinetics of ClpB action. The results support their conclusions for the model setup used.

Strengths:

The stopped-flow fluorescence method with a variable delay after mixing the reactants is informative, as is the use of variable numbers of folded domains to probe the unfolding steps.

Weaknesses:

The setup does not reflect the physiological setting for ClpB action. A mixture of ATP and ATPgammaS is used to activate ClpB without the need for its co-chaperones, Hsp70. Hsp40 and an Hsp70 nucleotide exchange factor. This nucleotide strategy was discovered by Doyle et al (2007) but the mechanism of action is not fully understood. Other authors have used different approaches. As mentioned by the authors, Weibezahn et al used a construct coupled to the ClpA protease to demonstrate translocation. Avellaneda et al used a mutant (Y503D) in the coiled-coil regulatory domain to bypass the Hsp70 system. These differences complicate comparisons of rates and step sizes with previous work. It is unclear which results, if any, reflect the in vivo action of ClpB on the disassembly of aggregates.

We agree with the reviewer, there are several strategies that have been employed to bypass the need for Hsp70/40 or KJE to simplify in vitro experiments.  Here we have developed a first of its kind transient state kinetics approach that can be used to examine processive protein unfolding.  We now seek to go forward with examining the mechanisms of hyperactive mutants, like Y503D, and add the co-chaperones so that we can address the limitations articulated by the reviewer.   In fact we already began adding DnaK to the reaction and found that DnaK induced ClpB to release the polypeptide chain (Durie, Duran and Lucius 2018).  However, the sequential mixing strategy developed here was needed to go forward with examining the impact of co-chaperones. 

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Line 1: I recommend changing the title of the paper to remove the terms that are not clearly defined in the text: "robust" and "processive". What are the Authors' criteria for describing a molecular machine as "robust" vs. "not robust"? A definition of processivity is given in equation 2, but its value for ClpB is not reported in the text, and the criteria for classifying a machine as "processive" vs. "non-processive" are not included. Besides, the Authors have previously reported that ClpB is non-processive (Biochem. J., 2015), so it is now clear that a more nuanced terminology should be applied to this protein. Also, Escherichia coli should be fully spelled out in the title.

The title has been changed.  We have removed “robust” as we agree with the reviewer, there is no way to quantify “robust”.  However, we have kept “processive” and have added to the discussion a calculation of processivity since we can quantify processivity.  Importantly, the unstructured substrates used in our previous studies represent translocation and not protein unfolding.  here, on folded substrates, we detect rate-limiting protein unfolding followed by rapid translocation.  Thus, we report a lower bound on protein unfolding processivity of 362 amino acids. 

Line 20: The comment about mitochondrial SKD3 should be removed. SKD3, like ClpB, belongs to the AAA+ family, and it is simply a coincidence that the original study that discovered SKD3 termed it an Hsp100 homolog. The similarity between SKD3 and ClpB is limited to the AAA+ module, so there are many other metazoan ATPases, besides SKD3, that could be called homologs of ClpB, including mitochondrial ClpX, ER-localized torsins, p97, etc.

Removed.

Lines 133-139. Contrary to what the authors state, it is not clear that the "lag-phase" becomes significantly shorter for subsequent mixing experiments (Figure 1E) perhaps except for the last one (2070 s). It is clear, however, that the emission enhancement becomes stronger for later mixes. This effect should be discussed and explained, as it suggests that the pre-equilibrations shorter than ~2000 sec do not produce saturation of ClpB binding to the substrate.

We have added supplemental figure 2, which represents a zoom into the lag region.  This better illustrates what we were seeing but did not clearly show to the reader.  In addition, we address all three changes in the time courses, i.e. extend of lag, change in peak position, and the change in peak height. 

Line 175. The hydrolysis rate of ATPgammaS in the presence of ClpB should be measured and compared to the hydrolysis rate with ATP/ATPgammaS to check if the ratio of those rates agrees with the ratio of the translocation rates. These experiments should be performed with and without the RepA-titin substrate, which could reveal an important linkage between the ATPase engine and substrate translocation. These experiments are essential to support the claim of substrate translocation and unfolding with ATPgammaS as the sole energy source.

The time courses shown in figure 2 and supplemental Figure 1 are collected with only ATPgS and no ATP.  The time courses show a clear increase in lag and appearance of a peak with increasing number of tandem repeats of titin domains.  We do not see an alternate explanation for this observation other than ATPγS supports ClpB catalyzed protein unfolding and translocation.  What is the reviewers alternate explanation for these observations?

We agree with the reviewer that the linkage of ATP hydrolysis to protein unfolding and translocation is essential and we are seeking to acquire this knowledge.  However, a simple comparison of the ratio of rates is not adequate. We contend that a complete mechanistic study of ATP turnover by ClpB is required to properly address this linkage and such a study is too substantial to be included here but is currently underway. 

All that said, the statement on line 175 was removed since we do not report any ATPase measurements in this paper.

Line 199: It is an over-simplification to state that "1:1 mix of ATP to ATPgammaS replaces the need for co-chaperones". This sentence should be corrected or removed. The ClpB co-chaperones (DnaK, DnaJ, GrpE) play a major role in targeting ClpB to its aggregated substrates in cells and in regulating the ClpB activity through interactions with its middle domain. ATPgammaS does not replace the co-chaperones; it is a chemical probe that modifies the mechanism of ClpB in a way that is not entirely understood.

We agree with the reviewer.  The sentence has been modified to point out that the mix of ATP and ATPγS activates ClpB.

Figure 3B, Supplementary Figure 5A. The solid lines from the model fit cannot be distinguished from the data points. Please modify the figures' format to clearly show the fits and the data points.

Done.

Lines 326, 329. It is not clear why the authors mention a lack of covalent modification of substrates by ClpB. AAA+ ATPases do not produce covalent modifications of their substrates.

The issue of covalent modification was presented in the introduction lines 55 – 60 pointing out that much of what we have learned about protein unfolding and translocation catalyzed by ClpA and ClpX is from the observations of proteolytic degradation catalyzed by the associated protease ClpP.  However, this approach is not possible for ClpB/Hsp104 as these motors do not associate with a protease unless they have been artificially engineered to do so. 

Lines 396-399. I am puzzled why the authors try to correlate the size of the detected kinetic step with the length of the ClpB channel instead of the size characteristics of the substrate.

We are attempting to discuss/rationalize the observed large kinetic step-size which, in part, is defined by the structural properties of the enzyme as well as the size characteristics of the substrate.  We have attempted to clarify this and better discuss the properties of the substrate as well as ClpB.

As I mentioned in the Public Review, it is essential to demonstrate that the emission increase used as the only readout of the ClpB position along the substrate is indeed caused by the proximity of ClpB to the fluorophore. One way to accomplish that would be to place the fluorophore upstream from the first I27 domain and determine if the "lag phase" in the emission enhancement disappears.

Alexa Fluor 555 is well established to exhibit PIFE.  However, as in the response to the public review, we have included an appropriate control showing this in supplemental Fig. 1.

Finally, the authors repetitively place their results in opposition to the study of Weibezahn et al. published in 2004 which first demonstrated substrate translocation by engineering a peptidase-associated variant of ClpB. It should be noted that the field of protein disaggregases has moved since the time of that publication from the initial "from-start-to-end" translocation model to a more nuanced picture of partial translocation of polypeptide loops with possible substrate slipping through the ClpB channel and a dynamic assembly of ClpB hexamers with possible subunit exchange, all of which may affect the kinetics in a complex way. However, the present study confirmed the "start-to-end" translocation model, albeit for a non-physiological ClpB substrate, and that is the take-home message, which should be included in the text.

It is not clear to us that the field has “moved on” since Weibezahn et al 2004.  Their engineered construct that they term “BAP” with ClpP is still used in the field despite us reporting that proteolytic degradation is observed in the absence of ATP with that system  (Li, Weaver, Lin et al. 2015) and should, therefore, not be used to conclude processive energy driven translocation. The “partial translocation” by ClpB is also grounded in observations of partial degradation catalyzed by ClpP with BAP from the same group (Haslberger, Zdanowicz, Brand et al. 2008). It is not clear to us that the idea of subunit exchange leading to the possibility of assembly around internal sequences is being considered.  We do agree that this is an important mechanistic possibility that needs further interrogation. We agree with the reviewer, all these factors are confounding and lead to a more nuanced view of the mechanism.

All that said, we have removed some of the opposition in the discussion.

Reviewer #2 (Recommendations For The Authors):

(1) It is assumed that the lag phase will be much longer than the phase in which we see a gradual increase in fluorescence, as the effect of PIFE is significant only when the enzyme is very close to the fluorophore. Particularly for RepA-titin3, the enzyme has to translocate many tens of nm before it is closer to the C-terminus fluorophore. However, in all cases, the lag time is lower or similar to the gradual increase phase (for example, Figure 3B). Could the authors explain this?

The extent of the lag, or time zero until the signal starts to increase, is interpreted to indicate the time the motor moves from it’s initial binding site until it gets close enough to the fluorophore that PIFE starts to occur.  In our analysis we apply signal change to the last intermediate and dissociation or release of unfolded RepA-TitinX.  The increase in PIFE is not “all or nothing”.  Rather, it is starting to increase gradually.  Further, because these are ensemble measurements, and each molecule will exhibit variability in rate there is increased breadth of the peak due to ensemble averaging. 

(2) Although the reason for differences in the peak position (for example, Figure 1E, 2B) is apparent, the reason for variations in the relative intensities has to be given or speculated.

We have addressed the reason for the different peak heights in the revised manuscript.  It is the consequence of the fact that each substrate has slightly different fluorescent labeling efficiencies.  Thus, for each sample there is a mix of labeled and unlabeled substrates both of which will bind to ClpB but the unlabeled ClpB bound substrates do not contribute to the fluorescence signal, but will represent a binding competitor.  Thus, for low labeling efficiency there is a lower concentration of ClpB bound to fluorescent RepA-Titinx and for higher labeling efficiency there is higher concentration of ClpB bound to RepA-Titinx leading to an increased peak height.  RepA-Titin2 has the highest labeling efficiency and thus the largest peak height.

Reviewer #3 (Recommendations For The Authors):

The authors should make it clear that they and previous authors have used different constructs or conditions to bypass the physiological regulation of ClpB action by Hsp70 and its co-factors as mentioned above. In particular, the construct used by Avellaneda et al should be explained when they challenge the findings of those authors.

Minor points:

The lines fitting the experimental points are difficult or impossible to see in Figures 2B, 3B, and s5B.

Fixed

Typo bottom of p6 - "averge"

Fixed

Avellaneda, M. J., K. B. Franke, V. Sunderlikova, B. Bukau, A. Mogk and S. J. Tans (2020). "Processive extrusion of polypeptide loops by a Hsp100 disaggregase." Nature.

Doyle, S. M., J. Shorter, M. Zolkiewski, J. R. Hoskins, S. Lindquist and S. Wickner (2007). "Asymmetric deceleration of ClpB or Hsp104 ATPase activity unleashes protein-remodeling activity." Nature structural & molecular biology 14(2): 114-122.

Durie, C. L., E. C. Duran and A. L. Lucius (2018). "Escherichia coli DnaK Allosterically Modulates ClpB between High- and Low-Peptide Affinity States." Biochemistry 57(26): 3665-3675.

Haslberger, T., A. Zdanowicz, I. Brand, J. Kirstein, K. Turgay, A. Mogk and B. Bukau (2008). "Protein disaggregation by the AAA+ chaperone ClpB involves partial threading of looped polypeptide segments." Nat Struct Mol Biol 15(6): 641-650.

Li, T., J. Lin and A. L. Lucius (2015). "Examination of polypeptide substrate specificity for Escherichia coli ClpB." Proteins 83(1): 117-134.

Li, T., C. L. Weaver, J. Lin, E. C. Duran, J. M. Miller and A. L. Lucius (2015). "Escherichia coli ClpB is a non-processive polypeptide translocase." Biochem J 470(1): 39-52.

Miller, J. M., J. Lin, T. Li and A. L. Lucius (2013). "E. coli ClpA Catalyzed Polypeptide Translocation is Allosterically Controlled by the Protease ClpP." Journal of Molecular Biology 425(15): 2795-2812.

Miller, J. M. and A. L. Lucius (2014). "ATP-gamma-S Competes with ATP for Binding at Domain 1 but not Domain 2 during ClpA Catalyzed Polypeptide Translocation." Biophys Chem 185: 58-69.

Oberhauser, A. F., P. K. Hansma, M. Carrion-Vazquez and J. M. Fernandez (2001). "Stepwise unfolding of titin under force-clamp atomic force microscopy." Proc Natl Acad Sci U S A 98(2): 468-472.

Rajendar, B. and A. L. Lucius (2010). "Molecular mechanism of polypeptide translocation catalyzed by the Escherichia coli ClpA protein translocase." J Mol Biol 399(5): 665-679.

Weibezahn, J., P. Tessarz, C. Schlieker, R. Zahn, Z. Maglica, S. Lee, H. Zentgraf, E. U. Weber-Ban, D. A. Dougan, F. T. Tsai, A. Mogk and B. Bukau (2004). "Thermotolerance requires refolding of aggregated proteins by substrate translocation through the central pore of ClpB." Cell 119(5): 653-665.

Author response:

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

We would like to thank the reviewers and editor for their helpful comments. We have addressed their concerns as detailed below.

It would have been nice to have included a bona-fide SIRT2 target as a control throughout the study.

We agree that including a bona-fide SIRT2 target as a control is important for validating our results. Previous data from our work has shown that SIRT2 demyristoylates ARF6. Thus, we have included a blot in Figure S15 demonstrating that SIRT2 knockdown results in increased myristoylation of ARF6. This serves as a control to confirm the activity and role of SIRT2 in our study.

Did the authors also consider investigating SIRT1 in their assays? SIRT1 activates ACSS2 while SIRT2 leads to degradation of ACSS2. They should at least discuss these seemingly opposing roles of SIRT1 and SIRT2 in the regulation of ACSS2 and acetate metabolism in more depth particularly as it concerns situations (i.e., diseases, pathologies) where either SIRT1, SIRT2, or both sirtuins, are active. This would enhance the significance of the findings to the broader research community.

The study by Hallows et al. showed increased SIRT1 deacetylate K661 of ACSS2 and increase its catalytic activity. Subsequently, a follow-up investigation unveiled the role of the circadian clock in modulating intracellular acetyl-CoA levels through SIRT1-catalyzed K661 deacetylation of. Conversely, our research elucidates a contrasting mechanism wherein SIRT2 inhibits ACSS2 by deacetylating K271 under conditions of nutrient stress. The dual regulation of ACSS2 by SIRT1 through the circadian clock and SIRT2 under nutrient stress underscores the intricate and multifaceted nature of regulatory mechanisms involved in lipid metabolism. These findings underscore the versatility of lysine acetylation in modulating cellular metabolic pathways.

Collectively, these studies contribute to a better understanding of how SIRT1 and SIRT2 regulate ACSS2 activity in various metabolic contexts, thereby enhancing our knowledge of acetate metabolism and its implications in health and disease.

We have included such discussion of the manuscript.

In Figure 3, the authors should consider immunoblotting for endogenous ACSS2 throughout the differentiation and lipogenesis study since the total ACSS2 levels is the crucial aspect to affecting acetate-dependent promotion of lipogenesis in adipocytes, and to confirm TM-dependent stabilization of ACSS2 in that assay.

We have updated Figure 3 to include immunoblotting for endogenous ACSS2 levels. Additionally, we have confirmed the TM-dependent stabilization of ACSS2, which is now shown in Figure S12.

Do the authors have any data proving the K271 mutants of ACSS2 are still functional? Or that K271 ACSS2 protein is folded correctly?

To assess the functionality of the mutants, we isolated Flag-tagged wildtype, K271R, and K271Q ACSS2 proteins from SIRT2 knockdown HEK293T cells. Subsequently, we examined acetyl-CoA formation from acetate and CoA using high-performance liquid chromatography (HPLC). Our findings indicate that while the wildtype ACSS2 exhibits slightly higher activity compared to the K271R and K271Q mutants, but all variants remain functional (Figure S13).

Nearly all experiments are performed in a single cell line. Authors should test whether SIRT2 regulates ACSS2 acetylation in at least 1 or 2 more cell lines. Does SIRT2 regulate ACSS2 acetylation in 3T3-L1 preadipocytes?

Experiments showing that endogenous ACSS2 levels change in EBSS and nutrient-deprived media were repeated in A549 cells (Figure S5). However, due to the poor transfection efficiency of A549 cells, we were unable to obtain acetylation data. Similarly, conducting acetylation experiments in 3T3-L1 preadipocytes is challenging due to poor transfection efficiency.

The article does not explicitly address whether the absence of amino acids impacts the acetylation and subsequent degradation of ACSS2 by activating SIRT2. If so, one would expect the level of ACSS2 acetylation or ACSS2 expression under amino acid deprivation to be lower than that under normal conditions, as depicted in Fig. 1C and Fig. S3.

The experiments shown in Fig. 1C and Fig. S3 were using overexpressed Flag-tagged ACSS2 and we actually adjust the amount of DNA used to have similar Flag-ACSS2 levels.

To address the comment raised by the reviewer, we added Figure S14, which shows that endogenous ACSS2 acetylation is decreased under amino acid deprivation in SIRT2 control KD cells, indicating that the absence of amino acids impacts ACSS2 acetylation. The decreased expression of ACSS2 under amino acid deprivation is also addressed in Figure S6.

Several reviewers noted discrepancies between what is occurring to basal levels of ACSS2 vs in SIRT2 KD conditions. Fig. 2H shows higher basal level of acetylated ACSS2 in K271R mutant compared to wildtype (input may be an issue). If Fig. 2H is a critical piece of data, authors are recommended to show this using FLAP-IP & then Ac-K.

The increased stability of the K271R mutant compared to the wildtype (WT) results in higher protein levels, which results in the different input levels. However, this does not affect the conclusion that K271 is the acetylation site as the quantification result shows that K271R mutant has lower acetylation level and is not regulated by SIRT2 (Figure S16).

Regarding the basal levels of ACSS2 in control and SIRT2 KD conditions, it was because the experiments in question were using overexpressed Flag-tagged ACSS2 and we actually adjust the amount of DNA used to have similar Flag-ACSS2 levels. To address the concern, we monitored endogenous ACSS2 protein and acetylation levels and the results are shown in Figure S14.

Also, in Fig 2I there is no difference in basal ubiquitination between WT and K271R mutant. Related, based on model you would expect that overexpression of ACSS2-K271R mutant compared to wildtype would be at higher levels. In many figures authors do not see this (Fig. 2I, 3A, 3B). This needs to be explained.

This is related to some previous comments. In these experiments, we actually adjusted the DNA used in the transfection to obtain equal protein levels so that we can quantify other things (acetylation or ubiquitination levels). As stated in the manuscript regarding Figures 3A and 3B, "To ensure comparable expression levels at the beginning, we adjusted the amount of transfected DNA for both wild-type and the K271R mutant ACSS2." This approach allowed us to accurately compare the ubiquitination status between the wildtype and K271R mutant ACSS2 variants.

Data showing role of ACSS2-K271 mutant in lipid accumulation requires clarification. Based on model overexpression of ACSS2-K271 mutant should by itself cause increased lipid accumulation compared to wildtype.

This is indeed the case and we have added this in the revised manuscript “Consistent with our above observation that ACSS2 K271R mutant is more stable than the WT, expressing the K271R mutant lead to more lipid droplets than expressing the WT ACSS2 (Figure S12).”

Loading controls are notably absent at certain instances, such as IPs in Fig. 1A, 1C, and the IP in Fig. 2H. Such controls are required to interpret potential changes in acetylation.

For this experiment, we employed an approach where we overexpressed Flag-tagged wild-type (WT) and mutant forms of ACSS2. We conducted an immunoprecipitation (IP) targeting acetyl-lysine residues to enrich lysine-acetylated proteins, followed by immunoblotting for the Flag tag to specifically detect ACSS2 acetylation levels. To ensure the reliability of our results, we included a Flag blot to confirm equal expression levels of ectopically expressed ACSS2 across our samples before IP. Given the nature of our experimental design and the specific aim of investigating ACSS2 acetylation, we believe that additional loading controls beyond the input Flag blot are not required for the interpretation of our results. The inclusion of the input Flag blot serves as a control for protein expression levels, which is crucial for accurate assessment of ACSS2 acetylation status.

While CHX treatment is known to inhibit protein synthesis, it appears contradictory that CHX treatment in Fig. 2C seemingly leads to ACSS2 accumulation in SIRT2 knockdown HEK293T cells. This discrepancy requires clarification.

We conducted quantitative analysis of the immunoblot with replicates to ensure the reliability of our findings. Our analysis indicates that the protein level of ACSS2 remains relatively stable over the time course of CHX treatment. The observed slight increase at the 8-hour time point can be attributed to inherent experimental variability, as evidenced by the presence of large error bars in the graph. We have included a graph in Figure S7 to show that there is no significant change in the level of ACSS2 in the SIRT2 HEK293T cells.

In Fig. 2F-H, the authors argue that SIRT2 deacetylates ACSS2 to facilitate its ubiquitination and subsequent proteasomal degradation. However, these results are depicted under normal conditions, whereas findings in Fig. 1 suggest that SIRT2 deacetylates ACSS2 exclusively under nutrient stress. An explanation for this inconsistency is warranted.

These experiments were done in amino acid deprived (EBSS) media. We have corrected this in the manuscript.

Line 160 authors conclude "amino acid limitation..deacetylates K271"..but this was not directly demonstrated. Authors should add this data or change conclusion.

Addressed in response to some of the comments above.

Figures 1A and 1B, acetylation quantification, not clear if it is relative to the Flag tag or actin.

Acetylation quantification is relative to Flag tag. This is clarified in the figure legend.

Methods section lacking details & not well referenced (how did authors express wildtype & mutant in 3T3-L1 cells?) 

ACSS2 wildtype and K271R mutant Flag-tagged expression plasmids were transfected into ACSS2 knockdown 3T3-L1 cells using PEI transfection reagent following the manufacturer’s protocol. The pCMV-Tag4a empty vector was used as the negative control. Differentiation of 3T3L1 cell lines were done according to manufacturer’s protocol (DIF001-1KT, Sigma Aldrich) 24 hours after transfection. This has been included in the methods.

In Figure 3A, is the actin blot from the same immunoblots above it? Reviewers recommend the authors upload original immunoblot.

This experiment was repeated, and the blot has been replaced.

Author response:

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

Thank you for your time and consideration on our submission. We also thank the reviewers for their consideration and helpful comments.  We have revised the introduction, results, and discussion sections of the revised manuscript in accordance with the reviewers’ suggestions, which have enhanced the clarity of our work. Specifically, we have clarified that the aim of the study is to report newly discovered sperm behaviours inside the uterus via high resolution deep tissue live imaging, and to stimulate further studies and discussion in the field of postcopulatory sexual selection in mice based on our observations. To the best of our knowledge, many of the specific sperm behaviours described in our manuscript are being reported for the first time, proven through direct observation inside the living reproductive tract.

We have also restructured our manuscript and moved our hypothetical interpretations based on our experimental observations to the discussion section. We hope that these revisions have clarified our claims and that our revised manuscript effectively communicates the importance of our findings and its values in prompting new questions and insight that encourage further studies. We believe that our work clearly demonstrates the importance of sperm/reproductive tract interaction, which cannot be adequately studied in artificial environments, and may become an important guideline for designing future experiments and studies.

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

The authors want to determine the role of the sperm hook of the house mouse sperm in movement through the uterus. The authors are trying to distinguish between two hypotheses put forward by others on the role of the sperm hook: (1) the sperm cooperation hypothesis (the sperm hook helps to form sperm trains) vs (2) the migration hypothesis (that the sperm hook is needed for sperm movement through the uterus). They use transgenic lines with fluorescent labels to sperm proteins, and they cross these males to C57BL/6 females in pathogen-free conditions. They use 2-photon microscopy on ex vivo uteri within 3 hours of mating and the appearance of a copulation plug. There are a total of 10 post-mating uteri that were imaged with 3 different males. They provide 10 supplementary movies that form the basis for some of the quantitative analysis in the main body figures. Their data suggest that the role of the sperm hook is to facilitate movement along the uterine wall. 

We thank the reviewer for summarizing our work and the critical review of our paper. As summarized, the sperm hook has been primarily associated with the sperm cooperation (sperm hook) hypothesis and the migration hypothesis. However, we would like to emphasize that the aim of our work is not to cross check between the two hypotheses. Our aim was not to disprove either hypothesis, but rather to develop an experimental platform that enables detailed observation of sperm migration dynamics within the live reproductive tract. 

Through live imaging, we observed both the formation of sperm trains as well as interaction between the sperm and female reproductive tract epithelium. However, in our observations, we could not find advantage in terms of faster movement for the rarely observed sperm trains. While these events were infrequent in our experiments, we are not asserting that the sperm train hypothesis is invalid but rather reporting our observations as is. 

The main findings of our work lie in the newly observed dynamic behaviours of mouse sperm interacting with the female reproductive tract epithelium. Specifically, tapping and associated guided movement along the uterus wall, anchoring and related resistance to internal fluid flow and migration through the utero-tubal junction, and self-organized behaviour while clinging onto the colliculus tubarius. We have extensively revised the manuscript structure to clarify our findings.

Strengths: 

Ex vivo live imaging of fluorescently labeled sperm with 2-photon microscopy is a powerful tool for studying the behavior of sperm. 

Weaknesses: 

The paper is descriptive and the data are correlations. 

The data are not properly described in the figure legends. 

When statistical analyses are performed, the authors do not comment on the trend that sperm from the three males behave differently from each other. This weakens confidence in the results. For example, in Figure 1 the sperm from male 3613 (blue squares) look different from male 838 (red circles), but all of these data are considered together. The authors should comment on why sperm across males are considered together when the individual data points appear to be different across males. 

Thank you for your comments and suggestions. We have revisited all figure legends and made the necessary amendments (shown in the red-lined manuscript). Please note that, for a better flow of the paper, the previous Figure 1 has been changed to Figure 2 in the revised manuscript.

Regarding the analysis using different males, we would like to explain the statistics used. We used generalized linear mixed models to test the effect of the Angle and Distance to the wall on the migration kinetic parameters. The advantage of the generalized linear mixed models is that they consider individual variations in the data as an error term, thereby controlling such individual variations. 

There are two main factors contributing to individual variations. One is, as you pointed out, the difference in sperm from different males. However, we used genetically similar mice, so genetical variations must be minimal. Nonetheless, there must be individual differences that caused variations including age, stress level as well as body conditions. As these factors cannot be controlled, we used the mixed model approach where individual variations are grouped within the individual. This approach enabled us to test the effect of each explanatory variable (Angle and Distance) within an individual. 

The second factor that could cause variations is the female oestrous status. To avoid artifacts that could influence sperm behaviour, we did not use any invasive methods, such as hormone injections, to control or induce female oestrus. We controlled for this possible effect by including the mating date as a random effect. Since each female was used only once, the mating date reflects the variation caused by each female.

To provide further verification that the variation between individual males do not affect our results, we conducted analysis per individual male and mating dates (per each female). As clearly shown, sperm data points from individual males or female also show consistent clear correlations with the distance from the uterus wall. As pointed out, while the mean sperm speed could be different between individuals, they are not the topic we are interested in here. Our interest here is the effect of the distance between sperm and the uterine wall. Additionally, the variation between males is not always larger than those effect of the day (female), which in total suggest that integrating male variation is not essential. We have added this information to Supplementary Figure (Fig. S3) of the revised supplementary materials.

Moving forward, we can also consider the same analysis for the effects of the distance from wall on sperm SWR and LIN (linearity of forward progression) where no statistical significance was found. As see in the following figures, no statistically significant effect of the distance to wall on SWR and LIN are seen in that the regression lines drawn for each male and mating dates.

In summary, the statistical approach we used here has successfully reflected variations in sperm kinetics from different males as well as the variance from different females. We hope that our explanations and additional analysis answer your concerns. 

Movies S8-S10 are single data points and no statistical analyses are performed. Therefore, it is unclear how penetrant the sperm movements are. 

With respect to Movie S8, Figure 4A and B (Figure 5A and B in the current revised manuscript) depict the trajectories of accumulated spermatozoa (sperm trains) in the female uterus, as shown in Movie S8. We have added this information to the revised figure legend (L 293) for clarity. We could not observe sperm trains that moved faster than single sperms during over 100 hours of observation and collection of over 10TB of images. The three sperm trains presented in Fig. 5B were the sperm trains that moved in the head-forward direction. Most other identifiable trains, or clusters, did not move or could not move forward as their heads were entangled randomly. Although we of course agree that a statistical test for Movie S8 (also Fig. 5B) would be great, due to the small number of sperm trains we found, we could not perform meaningful statistical tests. Instead, we provided all data in the box plots in Fig. 5C so that readers can evaluate and understand our points. We believe that this is a more neutral way of presenting our data rather than providing statistical significance.

Regarding Movies S9 and S10, we are not entirely sure whether we understood your comments clearly. It would be very helpful if you could point out more specifically to the manuscript with line numbers as we would like to address your concerns and suggestions, and we believe that your input will improve our manuscript. We did not describe the penetration of sperm in these movies. Movies S9 and S10 are newly found sperm behaviours inside the UTJ and Isthmus. We observed that sperm beating is influenced by the width of luminal space as well as internal flow as see in Movies S9 and S10. As our animal model only expresses red fluorescence in the midpiece, accurate beating frequency measurement cannot be performed. However, we can clearly observe that beating is not continuous and almost results in a halt with respect to reproductive tract variations. We revised our description about the findings about beating speed changes in the revised manuscript (LL 305-335).  

Movies S1B - did the authors also track the movement of sperm located in the middle of the uterus (not close to the wall)? Without this measurement, they can't be certain that sperm close to the uterus wall travels faster. 

We revised the new Movie S1B to include videos that were used for the sperm migration kinetics analysis in Figure 2 (previously Figure 1). As you can see in the movies, the graph, and statistical analysis, there is a clear trend showing spermatozoa migration is slower as a function of distance from the uterus wall. Regarding your comment with respect to the middle of the uterus (not close to the wall), we have added another movie (Movie S1C) that was acquired at different depths from the wall (going towards the centre of the uterus). As clearly seen in Movie S1c, when imaging deeper into the uterus, there are an increasing number of inactive or slow-moving spermatozoa. Since the diameter of the uterus is easily over 2mm, we currently do not have optical access to exactly the centre of the uterus, but for all depths that are observable, spermatozoa near the wall were clearly faster.

Movie S5A - is of lower magnitude (200 um scale bar) while the others have 50 and 20 uM scale bars. Individual sperm movement can be observed in the 20 uM (Movie 5SC). If the authors went to prove that there is no upsucking movement of sperm by the uterine contractions, they need to provide a high magnification image. 

The main focus of video S5A, is the intramural UTJ where spermatozoa are located in rows within narrow luminal space (see Author response image 1). When there is up-suck like sperm passive carriage, there must be sperm movement from the uterus to intramural UTJ as in Author response image 1 left. However, there is no such sperm movement could be seen in our observations, as shown in Movie 5A. Importantly, as you can see in Movie 5A, indicated by an arrow from 5 sec to 6 sec, some spermatozoa are moving downward (see also Author response image 1 right). This is the opposite direction of movement with respect to possible up-suck like sperm carriage. 

Genetical evidence also support up-suck like passive sperm carriage is not the case for sperm migration from the uterus to UTJ. If environmental up-suck like passive transfer plays an important role, it is unlikely that genetically modified spermatozoa cannot pass the entrance of the intramural UTJ (Nakanishi et al., 2004, Biol. Reprod.; Li et al., 2013, J. Mol. Cell Biol.; Larasati et al., 2020, Biol. Reprod.; Qu et al., 2021, Protein Cell). 

Author response image 1.

The left image represents what is expected when up-suck like passive sperm carriage occurs. The right image represents what is actually experimentally observed in the intramural UTJ (see Movie S5A). The direction of the arrowheads indicates the direction of sperm movement.

Movie S8 - if the authors want to make the case that clustered sperm do not move faster than unclustered sperm, then they need to show Movie S8 at higher magnification. They also need to quantify these data. 

We understand your concern. As shown in Figure 5B, we included all sperm kinetics data of each sperm train and unlinked spermatozoon around the trains as individual dots. The only analysis we did not conduct was a statistical test with the data as it could be erroneous due to the large sample size difference (3 trains vs 181 unlinked spermatozoa). As the medians of the four sperm kinetic parameters are similar except SWR, we concluded that they are not necessarily faster than unlinked single spermatozoa. Since there is no known advantage to spermatozoa (including sperm trains) with intermediate moving speeds for sperm competition – for example in IVF, success fertilization rate is high when faster and active spermatozoa with normal shape are selected (Vaughan & Sakkas, 2019, Biol. Reprod.) – it is questionable whether there can be an advantage to the formation of sperm trains whose speed is not faster than unlinked spermatozoa in our data.

However, we do not agree with your comment regarding the need for higher magnification. Measurement of the sperm migration speeds (kinetic parameters) does not require measurement of exact tail movements in this study. Only sperm heads were tracked to measure their trajectory and such tracking was better done at low mag. For example, measuring the speed of a car does not need higher magnifications to visualize the rotation of the wheels. Additionally, including the effect of observation magnification on the sperm kinetic parameters for all 4 GLMM models for Figure 2 (Table S3) does not change the result, which shows that magnification is not a factor that influences our analysis. 

Movie S9C - what is the evidence that these sperm are dead or damaged? 

Thank you for your valid comment. We tracked sperm movements for at least 10 minutes and such entangled spermatozoa in the UTJ never became re-active. As you can see in the new Movie S9b, entangled spermatozoa were also acrosome re-acted (green acrosome head is gone) while active spermatozoa are responding to peristaltic movement by exhibiting movements within the same video. However, as you pointed out, we did not measure their viability with appropriate dyes. Although we also considered about extracting these spermatozoa and performing viability tests, we could not come up with a way to specifically extract the exact spermatozoa that were imaged. Considering your comments, we changed the term damaged or dead to inactive in the revised manuscript (LL 313-316, Legend Figure 6D. LL 380-384).

Movie S10 - both slow- and fast-moving sperm are seen throughout the course of the movie, which does not support the authors' conclusion that sperm tails beat faster over time. 

There must have been a misunderstanding. We did not indicate that sperm beating got faster over time anywhere in the main manuscript, including the figure legend and related movie captions. As correctly pointed out, the sperm beating speed changes over time (not getting faster over time) and shows a correlation with internal fluid flow and width of luminal space (LL 320-332). Please let us know if you meant something else. 

Reviewer #2 (Public Review): 

Summary: 

The specific objective of this study was to determine the role of the large apical hook on the head of mouse sperm (Mus musculus) in sperm migration through the female reproductive tract. The authors used a custom-built two-photon microscope system to obtain digital videos of sperm moving within the female reproductive tract. They used sperm from genetically modified male mice that produce fluorescence in the sperm head and flagellar midpiece to enable visualization of sperm moving within the tract. Based on various observations, the authors concluded that the hook serves to facilitate sperm migration by hooking sperm onto the lining of the female reproductive tract, rather than by hooking sperm together to form a sperm train that would move them more quickly through the tract. The images and videos are excellent and inspirational to researchers in the field of mammalian sperm migration, but interpretations of the behaviors are highly speculative and not supported by controlled experimentation. 

Thank you for your critical review and valuable comments on our manuscript. As pointed out, some of our findings and suggestions were largely observation based. However, to the best of our knowledge, many of our observations are novel, particularly in the context of live imaging inside the female uterus and reproductive tract. We believe these observations open doors to many questions and follow up studies that can be envisioned based on our findings, which is what drives science forward. 

That being said, we entirely agree that many follow up experiments need to be designed and performed, especially to validate the exact molecular mechanisms of the observed dynamics. We acknowledge that it is unfortunate we currently lack the proper molecular experimental toolsets to perform further tests. We have removed much of the hypothetical discussions from the results section and moved them to the discussion section. We hope that our revision more clearly defines the observed experimental data and our interpretations.

Strengths: 

The microscope system developed by the authors could be of interest to others investigating sperm migration. 

The new behaviors shown in the images and videos could be of interest to others in the field, in terms of stimulating the development of new hypotheses to investigate. 

Weaknesses: 

The authors stated several hypotheses about the functions of the sperm behaviors they saw, but the hypotheses were not clearly stated or tested experimentally. 

The hypothesis statements were weakened by the use of hedge words, such as "may". 

We appreciate your helpful comments and have revised our hypotheses and suggestions accordingly. We have removed instances of “may” or revised it to be more direct. We have also moved most of our interpretations and hypotheses from the results to the discussion section. 

It is important to note that experimental approaches to test what we suggested from our findings in the current ex-vivo observation platform are not trivial and require extensive investigation of several unknown factors of the female reproductive tract. For instance, obtaining detailed information on the chemical characteristics and fluid dynamics in the female reproductive tract is essential to build a microfluidic channel that accurately resembles the uterus and oviduct, replicating what we found in an extracted living entire organ. This poses a significant challenge and requires collaborative expertise from many labs, which we hope to build in the near future. 

Furthermore, our biggest concern is that, even if we were to construct the appropriate microfluidic channel to test sperm migration, it is very likely that the sperm behaviours that we observed under natural conditions may not be replicated in artificial environments. This raises questions about whether in-silico or in-vitro findings can truly resemble what we reported here using the ex-vivo observation inside a living organ.

To share our experience related to this difficulty, at the initial stage of our study, we attempted sperm injection combined with fluorescent beads to visualize the fluid flow, as well as dyeing the female reproductive tract and spermatozoa after mating. However, none of these resulted in meaningful results. Another potential approach to perform similar research regarding our claims is using genetical engineering to indirectly confirm the influence of the sperm hook morphology on sperm behaviour. However, such an approach lacks a mechanical demonstration about how the sperm hook interacts with the female reproductive tract. 

It is unfortunate that the sperm behaviours that we found and reported here are considered as highly speculative. The main findings of our work lie in the newly observed dynamic behaviours of mouse sperm interacting with the female reproductive tract epithelium. Specifically, these behaviours include tapping and associated guided movement along the uterus wall, anchoring and related resistance to internal fluid flow and migration through the utero-tubal junction, and self-organized behaviour while clinging onto the colliculus tubarius. 

We have extensively revised the manuscript structure to clarify our findings and integrated our points in the introduction. Although we understand our following hypotheses may be considered speculative and the causative relationship between the sperm hook and its role in sperm migration requires further experimental approaches, we believe that the image-based observation of dynamic behaviours of spermatozoa are solid. We believe our findings will facilitate further studies and discussion in the field of studies on postcopulatory sexual selection in rodents.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

The manuscript is written for an expert in a fairly small field. I recommend that the authors rewrite the manuscript to make it more accessible to people outside of the field. These suggestions include 

(1) Provide a diagram of the female reproductive tract in Figure 1. 

a. Indicate where sperm enter the tract and the location of the oocyte they are trying to reach. 

b. Label all areas of the uterus that are mentioned in this study and be consistent about the label. 

(2) All movies should have a diagram of the location of the uterus that is being imaged. 

Thank you for the great suggestion. We have added a diagram of the female reproductive tract in the revised Figure 1A. In response to your comments 1a and b, we have indicated such information by including eggs in the ampulla and arrows that indicate sperm migration direction. We have also labelled the name of the specific areas that were studied in the manuscript.

We are unsure how to integrate the diagram in all movies without reframing the videos, which could cause serious corruption of the files. More importantly, we think that adding the same diagram to all movies may complicate the visuals and disrupt indications and subject in the movie. Instead, we have referred to the common diagram (Figure 1A) in each movie caption, specifying where the video was taken. Thank you for the suggestion. With this information, we hope readers can now more easily understand where we made the observations. 

(3) The major questions in the field need to be better described in the introduction. 

Thank you for your valuable suggestions and specific comments which have greatly helped improve our manuscript. We have revised our introduction and discussion sections by adding more literature reviews and integrating studies across a wider range of the postcopulatory sexual selection, as per your suggestion (LL 34-57, LL 385-398).

(4) The major question that the authors are trying to address should be described in the introduction. 

Thank you for the helpful suggestion. We have clarified in the introduction that our aim was to contribute to the field of postcopulatory sexual selection in rodents by advancing methodological progress and to stimulate discussion and future research on the function of the sperm hook in murine rodents (LL 76-94) based on our observations.

(5) A discussion of the sperm hook should be provided. How many species have this structure (or similar structure)? 

We have integrated your point into the revised discussion section. Essentially, most murine rodent species have sperm hooks (while their exact shapes differ). However, as there are over 500 species and not all of them have been tested, we do not know exactly how many of them have this structure. Therefore, we included paper references that examined species variations in sperm hook characteristics and their possible correlation with sperm competition (LL 385417) in the discussion. Additionally, we also included papers by Breed (2004) and by Roldan et al (1992) that investigated murine rodents with a sperm hook in the introduction section as well (LL 58-61).  

(6) The figure legends must describe everything in the figure or movie. 

Thank you for the helpful suggestion. We previously thought that our figure legends may be too long. We have included further information in the figure legends and movie captions. We have also revised the movies by adding some clips following our revision (Movie S1).

Reviewer #2 (Recommendations For The Authors): 

Here are some specific concerns I had about the clarity of approach to experiments and interpretations of results. 

In the Introduction, the authors stated that the study was intended to determine the function of the hooks on the mouse sperm heads. However, in the Results section, the authors did not explain the rationale for the first set of experiments with respect to the overall objective of the study. In this experiment, the authors measured the velocities of sperm swimming in the uterus and found that the sperm moved faster when closer to the uterine wall (VCL, VSL). They concluded that migration along the uterine wall "may" be an efficient strategy for reaching the entrance to the uterotubal junction (UTJ) and did not explain how this related to the function of the hooks. 

Thank you for your critical comment and guidance. We have changed the order of Figure 1 and Figure 2 and revised the result section to integrate your points. At the initial stage of the study, we expected to find evidence of the function of sperm trains in aiding sperm migration in the female uterus (which has not been observed in the live uterus; previous works were done invitro with extracted sperm from epididymis or uterus after mating). However, what we found was something unexpected: dynamic sperm hook related movements facilitating sperm migration inside the female uterus by playing a mechanical role in sperm interaction with the uterine wall. These results that were presented in the previous Figure 2 has been reorganized as the new Figure 1.

Based on this observation, our research later moved to clarify whether such sperm-epithelium interaction indeed helps sperm migration. This led us to measure sperm kinetics in relation to their distance and angle to the uterine wall. We have revised our introduction and result parts by integrating these points. We hope that our revision will answer your questions. We have also reduced the use of ‘may’ or ‘can’ in the results section. In the revised manuscript, we have moved such hypotheses to the discussion section and focused on what we observed in the results section.

The authors proposed that the sperm hook "may" play a crucial role in determining the direction of migration. When sperm encountered a uterine wall, significantly more changed migration direction toward the pro-hook direction than toward the anti-hook direction. In Figure 2B, sperm behavior is not visually understandable nor clearly explained. 

Thank you for the helpful comments. We have removed “may” and “might” to make our claim clearer and more concise. We have also revised the previous Figure 2B by combining it with the previous Figure 2C (they have been combined into Figure 1C now). We have also revised Figure 1B by increasing the line thickness of the sperm trajectory of the pro-wall-hook direction and added the anti-wall-hook trajectory. We hope that these revisions make the figure easier to understand.

In Figure 2E, are the authors showing that the tip of the hook is caught between two epithelial cells? Please clarify the meaning of this figure. 

Please clarify the difference between "tapping" and "anchoring". 

Thank you for the detailed comments. As you pointed out, we currently have no evidence whether sperm can be caught in epithelia inter-cellular gaps. We have revised this source of confusion by removing the gap in the revised figure (Figure 1E). We have also included the definition of anchoring (LL 142-143) and tapping (LL 128-130). Anchoring facilitates the attachment of sperm to the uterine epithelia. Such anchoring also involves the catching of the sperm head in the inter-mucosal fold or gap, particularly at the entrance of the intramural UTJ at the end of the uterus. Tapping is the interaction between the head hook and epithelia in which the sperm hook is tapping (or patting) on the surface. Sperm tapping can be a byproduct that results from flagella beating when spermatozoa migrate toward the pro-wall-hook direction along the uterine wall (epithelia) or can play some role in sperm migration. As we currently cannot draw a conclusion, we did not integrate the possible function of the tapping in the manuscript.

The authors proposed that opposite sliding of neighboring mucosal folds lining the UTJ would cause small openings to form, through which only perhaps one sperm at a time could enter and pass through the UTJ into the uterus. This hypothesis was not actually tested. 

Imaging inside deep tissue is challenging due to light scattering as it penetrates through biological tissue. While this is also true for the uterus, the intramural UTJ is especially difficult to image because the UTJ consists of several thick muscle and cell layers (see Movie S5A). Another challenge is that the peristaltic movement of the UTJ results in constant movement, making continuous tracking of single sperms while passing through the entirety of the UTJ impossible in our current experiments. We have moved this hypothesis to the discussion section and restated that this is a pure hypothetical model (LL 399-406). We hope that our model encourages the community in designing or establishing an improved ex-vivo observation system that may be able to test this hypothetical model in the near future.

Next, the authors hypothesized that sperm that encounter the small openings in the UTJ may then be guided onward and the hooks could prevent backward slipping. This was also not tested. 

As you’ve noted, the function of the sperm hook that aids in sliding and preventing backward slipping could not be tested directly in our ex-vivo observation platform that relies on natural movement of the living organ. However, we believe that these limitations also highlight the importance of continued research and the development of more advanced methodologies in this field.

We would also like to note that we provide direct observations of spermatozoa resisting internal flow due to reproductive tract contractions in Movie S3A, B as well as Movie S5B. We referred to these movies and pointed out the role of anchoring (sperm attachment) in preventing sperm from being squeezing out (LL 140-149, LL 224-241). Unfortunately, we cannot conceive of how this behaviour can be tested additionally in any uterus-resembling microfluidic device or ex-vivo systems. In line with your suggestion, we have rewritten the related result section and moved our related discussions in the result part to the discussion section (LL 224-241, LL 399-417). 

The authors observed that large numbers of uterine sperm are attached to the entrance of the UTJ. Some sperm clustered and synchronized their flagellar beating. The authors speculated that this behavior served to push sperm in clusters onward through the UTJ. 

We would like to note that we did not speculate that sperm clustering and their synchronization could serve to push spermatozoa in a cluster to move onward through the UTJ. We only pointed out our observation in recorded videos, that generative flow from the clustered spermatozoa pushed away other spermatozoa as seen in Movie S7 (LL 261-264). Although such sperm cooperation is possible (blocking passage of later sperm), we cannot draw that conclusion from our observation. The possibility you pointed out (pushing sperm onward through the UTJ) was suggested by Qu et al in 2021 [Cooperation-based sperm clusters mediate sperm oviduct entry and fertilization, Protein & Cell] based on their observations on cleared dead reproductive tracts.

The authors found only a few sperm trains in the uterus, UTJ, and oviduct, so they could not measure sufficient numbers of samples to test whether sperm trains swim faster than single sperm. Without sufficient data, they concluded that the "sperm trains did not move faster than unlinked single spermatozoa." 

We would like to take this opportunity to clarify our claims. We do not claim that our current experiments can give the final verdict on whether the sperm train hypothesis for faster swimming is correct or not. The phrase “sperm trains did not move faster” was not intended to mean that the sperm train hypothesis is invalid.  We did not draw a conclusion but dryly described the experimental data that we observed (LL 279-286).  We would once again like to emphasize that the main claim of our manuscript is not to rule out the sperm train hypothesis, but to present the various dynamic interactions of the sperm head with the female reproductive tract. To make the statement more balanced, we revised the sentence as “observed sperm trains did not move faster or slower than unlinked single spermatozoa” (LL 281-282).

The authors hypothesized that the dense sperm clusters at the entrance into the UTJ could prevent the rival's sperm from entering the UTJ (due to plugging entrance and/or creating an outward flow to sweep back the rival's sperm), but they did not test it. 

We agree that we were not able to test such possible function of the sperm cluster at UTJ entrance. Following your concerns, we revised the result part (LL 256-264) by removing most of our discussions related to the observed phenomena. We also integrated some interpretation rather to the discussion section (LL 421-437) and suggested that future works using appropriate microfluidic channel designs or sequential double mating experiments may be performed for additional tests (LL 443-447). However, we would like to point out that Movie S7C clearly shows surrounding sperms that are swept away from the sperm clusters. Since the sperm density is high, this is almost equivalent to a particle image velocimetry experiment, and we can clearly see the effect of the outward flow generated by the sperm clusters.

Author response:

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

This valuable study combines multidisciplinary approaches to examine the role of insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) as a potential novel host dependency factor for Zika virus. The main claims are partially supported by the data, but remain incomplete. The evidence would be strengthened by improving the immunofluorescence analyses, addressing the role of IGF2BP2 in "milder" infections, and elucidating the role of IGF2BP2 in the biogenesis of the viral replication organelle. With the experimental evidence strengthened, this work will be of interest to virologists working on flaviviruses.

We thank the reviewers for their feedback and constructive suggestions. In this revised version of the manuscript, we have addressed the reviewer’s comments to the best of our ability as detailed below. We believe that the newly incorporated data strengthens our study and conclusions. We hope that this revised manuscript will satisfy the reviewers and will be of high interest to flavivirologists.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This study investigated the co-option of IGF2BP2, an RNA-binding protein by ZIKV proteins. Designed experiments evaluated if IFG2BP2 co-localized to sites of viral RNA replication, interacted with ZIKV proteins, and how ZIKV infection changed the IGF2BP2 interactome.

Strengths:

The authors have used multiple interdisciplinary techniques to address several questions regarding the interaction of ZIKV proteins and IGF2BP2.

The findings could be exciting, specifically regarding how ZIKV infection alters the interactome of IGF2BP2.

We thank the reviewer for acknowledging the multidisciplinary approach of our study and its exciting potential.

Weaknesses:

Significant concerns regarding the current state of the figures, descriptions in the figure legends, and the quality of the immunofluorescence and electron microscopy exist.

In this new version of the manuscript, we have improved the quality of the microscopy data and included the requested information in the figure legends as described below in the Recommendations section.

Reviewer #2 (Public Review):

Clément Mazeaud et al. identified the insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) as a proviral cellular protein that regulates Zika virus RNA replication by modulating the biogenesis of virus-induced replication organelles.

The absence of IGF2BP2 specifically dampens ZIKV replication without having a major impact on DENV replication. The authors show that ZIKV infection changes IGF2BP2 cellular distribution, which relocates to the perinuclear viral replication compartment. These assays were conducted by infecting cells with an MOI of 10 for 48 hours. Considering the ZIKV life cycle, it is noteworthy that at this time there may be a cytopathic effect. One point of concern arises regarding how the authors can ascertain that the observed change in localization is a consequence of the infection rather than of the cytopathic effect. To address this concern, shorter infection periods (e.g., 24 hours post-infection) or additional controls, such as assessing cellular proteins that do not change their localization or infecting with another flavivirus lacking the IGF2BP2 effect, could be incorporated into their experiments.

We thank the reviewer for these relevant comments regarding the specificity of IGF2BP2 relocalization to the ZIKV replication compartment.

It is noteworthy that we chose the 2-day post-infection time point for our analyses because it corresponds to the peak of replication with much more titers produced compared to those at 24 hours post-infection (generally ~106 PFU/mL vs. ~104 PFU/mL). Consistently, the abundance of viral replication factories is more obvious at this time-point. A MOI of 5-10 was chosen to maximize the % of infected cells. That said, as suggested by the reviewer, we have analyzed the distribution of IGF2BP2 in ZIKV-infected cells at one-day post-infection, and we provide evidence in Figure S1 that IGF2BP2 relocalizes to the dsRNA-containing compartment at this time point.

Importantly, we now show in Figure S5 that in contrast to IGF2BP2, other host RNA-binding proteins such as LARP1 and DDX5 do not accumulate to ZIKV replication compartment at 2 days post-infection. LARP1 actually seems to be excluded from it while DDX5 remains nuclear. Of note, consistent with the ZIKV-induced decrease in expression observed in western blots (Fig 4A), the intensity of DDX5 signal decreases in infected cells. Altogether, this demonstrates that the IGF2BP2 relocalization phenotype is specific and is not due to ZIKV-induced cell death.

By performing co-immunoprecipitation assays on mock and infected cells that express HAtagged IGF2BP2, the authors propose that the observed change in IGF2BP2 localization results from its recruitment to the replication compartment by the viral NS5 polymerase and associated with the viral RNA. Given that both IGF2BP2 and NS5 are RNA-binding proteins, it is plausible that their interaction is mediated indirectly through the RNA molecule. Notably, the authors do not address the treatment of lysates with RNase before the IP assay, leaving open the possibility of this indirect interaction between IGF2BP2 and NS5.

We agree with the hypothesis of the reviewer. As suggested, we have performed coimmunoprecipitation assays following RNase A treatment of the cell lysates. As shown in new Fig S6, the abundance of ZIKV NS5 co-immunoprecipitating with IGF2BP2-HA is drastically decreased upon RNase A treatment compared to the untreated condition. This demonstrates that the IGF2BP2/NS5 interaction is mostly RNA-dependent, which is not surprising as RNA is often a structural component of ribonucleoprotein complexes. Of note, the same is observed with ATL2. This new set of data allows us to refine our model of Figure 11 and the discussion as they strongly suggest that the direct binding of IGF2BP2 to viral RNA (evidenced in vitro; Fig 5D) is required for subsequent association with NS5 and ER-shaping protein ATL2. This is in line with the fact that viral RNA is a co-factor in the biogenesis of ER-derived ZIKV vesicle packets (PMID: 32640225). However, we cannot exclude a contribution of cellular RNA in these processes as discussed.   

In in vitro binding assays, the authors demonstrate that the RNA-recognition motifs of the IGF2BP2 protein specifically bind to the 3' nontranslated region (NTR) of the ZIKV genome, excluding binding to the 5' NTR. However, they cannot rule out the possibility of this host protein associating with other regions of the viral genome. Using a reporter ZIKV subgenomic replicon system in IGF2BP2 knock-down cells, they additionally demonstrate that IGF2BP2 enhances viral genome replication. Despite its proviral function, the authors note that the "overexpression of IGF2BP2 had no impact on total vRNA levels." However, the authors do not delve into a discussion of this latter statement.

We agree with the reviewer’s comments. We now mention in the discussion that we cannot exclude the possibility that IGF2BP2 associates with RNA motifs within the coding region of the viral genomic RNA, especially considering that it contains N6A-methylated sequences (PMID: 27773535; 27773536; 29373715). Moreover, we discuss the observation that IGF2BP2 overexpression has no impact on vRNA levels (as well as titers). We believe that this is because endogenous IGF2BP2 is highly expressed in cancer cells such as the Huh7.5 and JEG-3 cells used here and is presumably not limiting for viral replication in our system (PMID: 38320625; 35111811; 34309973; 35023719; 37088822; 33224879; 35915142).

In this study, the authors extend their findings by illustrating that ZIKV infection triggers a remodeling of IGF2BP2 ribonucleoprotein complex. They initially evaluate the impact of ZIKV infection on IGF2BP2's interaction with its endogenous mRNA ligands. Their results reveal that viral infection alters the binding of specific mRNA ligands, yet the physiological consequences of this loss of binding in the cell remain unexplored. 

We acknowledge that it would be of interest to further study the physiological relevance of the modulation of IGF2BP2 ribo-interactome. Since we have focused here on the role of IGF2BP2 in viral replication, we feel that this will be the focus of future studies notably involving a larger omic-centered approach to identify the most impacted IGF2BP2 mRNA ligands. Of note, Gokhale and colleagues have already reported that CIRBP, TNRC6A and PUM2 proteins regulates the replication of Flaviviridae (PMID: 31810760).

Additionally, the authors demonstrate that ZIKV infection modifies the IGF2BP2 interactome. Through proteomic assays, they identified 62 altered partners of IGF2BP2 following ZIKV infection, with proteins associated with mRNA splicing and ribosome biogenesis being the most represented. In particular, the authors focused their research on the heightened interaction between IGF2BP2 and Atlastin 2, an ER-shaping protein reported to be involved in flavivirus vesicle packet formation. The validation of this interaction by Western blot assays prompted an analysis of the effect of ZIKV on organelle biogenesis using a newly described replication-independent vesicle packet induction system. Consequently, the authors demonstrate that IGF2BP2 plays a regulatory role in the biogenesis of ZIKV replication organelles.

Based on these findings and previously published data, the authors propose a model outlining the role of IGF2BP2 in ZIKV infectious cycle, detailing the changes in IGF2BP2 interactions with both cellular and viral proteins and RNAs that occur during viral infection.

The conclusions drawn in this paper are generally well substantiated by the data.

We thank the reviewers for this encouraging general comments on our study.

However, it is worth noting that the majority of infections were conducted at a high MOI for 48 hours, spanning more than one infectious cycle. To enhance the robustness of their findings and mitigate potential cell stress, it would be valuable to observe these effects at shorter time intervals, such as 24 hours post-infection.

As explained above, IGF2BP2 relocalization to the (dsRNA-enriched) replication compartment was also observed in ZIKV infected cells at one day post-infection.

Furthermore, the assertion regarding the association of IGF2BP2 with NS5 could be strengthened through additional immunoprecipitation (IP) assays. These assays, performed in the presence of RNAse treatment, would help exclude the possibility of an indirect interaction between IGF2BP2 and NS5 (both RNA-binding proteins) through viral RNA, thus providing more confidence in the observed association.

See above for our answer and the description of the new data of Fig. S7.

Reviewer #3 (Public Review):

Summary:

The manuscript by Mazeaud and colleagues pursued a small-scale screen of a targeted RNAi library to identify novel players involved in Zika (ZIKV) and dengue (DENV) virus replication. Loss-of-function of IGF2BP2 resulted in reduced titers for ZIKV of the Asian and African lineages in hepatic Huh7.5 cells, but not for either of the four DENV serotypes nor West Nile virus (WNV). The phenotype was further confirmed in two additional cell lines and using a ZIKV reporter virus. In addition, using immunoprecipitation assays the interaction between IGF2BP2 and ZIKV NS5 protein and RNA genome was detected. The work addressed the role of IGF2BP2 in the infected cell combining confocal microscopy imaging, and proteomic analysis. The approach indicated an altered distribution of IGF2BP2 in infected cells and changes in the protein interactome including disrupted association with partner mRNAs and modulation of the abundance of a specific set of protein partners in IGF2BP2 immunoprecipitated ribonucleoprotein (RNP) complexes. Finally, based on the changes in IGF2BP2 interactome and specifically the increment in the abundance of Atlastin 2, the biogenesis of ZIKV replication organelles (vRO) is investigated using a genetic system that allows virus replication-independent assembly of vRO. Electron microscopy showed that knockdown of IGF2BP2 expression reduced the number of cells with vRO.

Strengths:

The role of IGF2BP2 as a proviral factor for ZIKV replication is novel. The study follows a logical flow of experiments that altogether support the assembly of a specialized RNP complex containing IGF2BP2 and ZIKV NS5 and RNA genome.

We thank the reviewer for their positive feedback on our study and its novelty.

Weaknesses:

The statistical analysis should clearly indicate the number of biological replicates of experiments to support statistical significance.

This information has been included in all figure legends.

The claim that IGF2BP2 knockdown impairs de novo viral organelle biogenesis and viral RNA synthesis is built upon data that show a reduction in RNA synthesis <0.5-fold as assessed using a reporter replicon, thus suggesting a limited impact of the knockdown on RNA replication.

We agree that a 50% decrease in the replication of our reporter replicon might be considered mild. However, we want to pinpoint that in an infectious set-up, the phenotypes were higher as demonstrated by an 80% decrease in viral particle production even when IGF2BP2 levels were never depleted more that 80% compared to endogenous levels. Moreover, our findings were validated through the analysis of de novo vRO biogenesis by electron microscopy in a replication-independent set-up. Together, these experiments provide compelling evidence for a role for IGF2BP2 in the early stages of viral genome replication.

Validation of IGF2BP2 partners that are modulated upon ZIKV infection (i.e. virus yield in knocked down cells) can be relevant especially for partners such as Atlastin 2, as the hypothesis of a role for IGF2BP2 RNP in vRO biogenesis is based on the observed increase in the abundance of Atlastin 2 in the RNP complex preciìtated from infected cells.

First, we would like to emphasize that the proviral role of ATL2 in flavivirus replication, including links to vRO biogenesis, was already reported in two independent studies notably by one of the co-authors (PMID: 31636417; 31534046). Therefore, we have chosen to discuss these previous studies in the manuscript rather than repeating published experiments.  Second, we agree that it would be interesting to further interrogate the role of modulated IGF2BP2 protein partners in ZIKV replication. However, these experiments would constitute a new project per se involving fastidious RNAi-based phenotypic screening and subsequent functional characterization of the identified hits. Therefore, this will be the focus of follow-up studies.  

Recommendations for the Authors:

Reviewer #1 (Recommendations For The Authors):

All IFAs claimed that showing co-localization is minimal, this needs to be addressed.

We have performed colocalization analyses for relevant images in the revised manuscript (see below and Figs. 4B, 5A, S4A-C and S5A-D. Although this quantification increases confidence in our analysis, we were still cautious in our conclusions, stating that colocalization was partial and that IGF2BP2 accumulates in the replication compartment.

Western blots and IPs need to be quantified.

As requested, we have included WB quantification in Figs. 2A, 4A, 4D, 8B-D, S6C and S7D.

Figure 1: What is the strain background for the ZIKV reporter virus?

As indicated in the legend of Figure 1E of the primary submission, the Rluc-expressing ZIKV reporter virus (ZIKV-R2A) was based on the FSS13025 isolate (Asian lineage)(PMID: 27198478). To clarify this, we have also indicated the strain background in the main text of the Results and Material & Methods sections.

Figure 2A: If shGF2BP2 reduces viral titer, the NS3 should show a reduction in 2A, but it doesn't.

We agree with the reviewer. Although NS3 seems not to be decreased upon IGF2BP2 knockdown in the experiment initially shown in Figure 2A, it should be noted that our homemade rat anti-NS3 antibody is highly sensitive, leading to signal saturation that makes it challenging to distinguish changes in NS3 expression without diluting substantially the lysate sample before the PAGE-SDS. The initial reason for including Fig 2A was not to make a statement about viral protein expression but to validate IGF2BP2 knock-down efficiency. Conclusions about NS3 levels in the initial figure are further complicated by the high MOI of ZIKV was used in Huh7.5 cells which are not quantitative for viral replication measurements. To address this issue, we assessed the impact of IGF2BP2 knockdown on viral protein abundance (as a read-out of overall viral replication) with a lower MOI of ZIKV. The results of the repeat experiment (seen in the new Fig. 2A) show that IGF2BP2 knockdown leads to a decrease in the abundance of NS4A, NS5 and NS3, which is consistent with the titer decrease phenotypes.

Figure S3: The re-localization claimed is minimal and does not show overlap with NS3. The dsRNA is difficult to see here. Suggest improving the immunofluorescence images and reducing the claim for "strong" co-option of RNP complexes.

In addition to replication complexes, NS3 labels convoluted membranes which are devoid of dsRNA and IGF2BP2 and surround the cage-like replication compartment as large puncta (PMID: 27545046; 33432690; 28249158). The signal overlap is more obvious between IGF2BP2 and NS3/dsRNA-containing areas, which is reflected by the Mander’s coefficients that have been included in the revised version (Fig. S5C-D). We have also adjusted the text to conclude that the colocalization was partial and that IGF2BP2 accumulated in the replication compartment. We acknowledge that the dsRNA signal is weak, and we have updated the images (and others, when relevant) to better visualize this viral component. Moreover, we have rephrased the sentence to remove the word “strongly”.

Figure 4A: Western blot needs quantification.

This is now included in the figure.

Figure 4B: As in many of the IFAs, the co-localization is only partial. Additionally, the dsRNA is not visible. So the images need to be improved. The colocalization should be quantified across the cell diameter.

We changed the color and intensity of the dsRNA staining to make it more visible. Mander’s colocalization coefficients have been determined and included in Figures 4B and S5C-D.

Figure 4C: It is difficult to understand what the +/- is on the blots for the cell extracts and the anti-HA IP samples. It is not described in the figure legend or the text.

As already indicated on the right of the panel, the +/- indicates whether or not IGF2BP2-HA was overexpressed in the cells. In the revised version, this is clarified in the figure legend.

Figure 5A: Once again similar to other IFAs, the co-localization is only minimal and thus difficult to claim as "co-localization" is actually happening. It would be good to either improve the images or discuss this observation in the text and reduce the claim of colocalization. Specifically, since the two proteins might be co-localizing in specific regions which would make it a very interesting observation. Also, quantification of co-localizing regions would be beneficial.

We have included the requested colocalization analysis. We have been cautious to indicate that colocalization was only partial. It is noteworthy that, despite many efforts in the optimization of the cell permeabilization procedure, we noticed that the FISH probes were not very efficient in accessing the perinuclear area of the infected cells, where replication complexes accumulate. In that respect, it is likely that this imaging approach “miss” some of the IGF2BP2/vRNA complexes and that the determined colocalization factor is underestimated. This explains why the confirmation of the vRNA/IGF2BP2 complex with a biochemical approach (Fig. 5B) was very relevant.

Figure 5D: It is unclear what the blue squares represent. Clearer figure legends and text would be beneficial.

As stated in the initial figure, the blue squares indicate values obtained with the ZIKV 5’ UTR probe while the green circles involve a 3’ UTR probe. We have further emphasized this information in the figure legend to make it clearer.

Figure 6B. The graph is missing the data and X-axis label for shIGF2BP2.

We had initially omitted the values of the conditions with shIGF2BP2 and the replicationdead GAA replicon, since this viral system does not allow accumulation of viral genomes or proteins and was not relevant at the 48h time point. We thought that the inclusion of the shNT/GAA condition was enough an internal negative control of viral replication since values for shIGF2BP2/GAA did not exceed background. Nevertheless, we have now included this condition in the revised figure.

Figure 7D: It is unclear what the -/+ signs are in the cell extracts and the IP blots. Specifically, since there is an NS5 signal in the (-) lanes.

As explained above, the +/- indicates whether IGF2BP2-HA was overexpressed. The meaning of these symbols is now further clarified in the figure legend.

Figure 8C: The circles with the different colors are not clearly described. What does it mean?

As indicated in the figure (left part), the red and green circles identify the partners of the STRING network whose association with IGF2BP2 is decreased and increased during infection, respectively. We have included this information in the figure legend.

Figure 9: The electron microscopy to quantify vesicles should be carried out using whole-cell tomography in order to get the most accurate quantification of the vesicles following different treatments. This is because if you only look at one cell profile (slice), the number of vesicles might be less in that profile and more in another below or above it. It is unclear how many cell profiles were used for the quantification and how the calculations were carried out.

We agree with the reviewer that ideally, one should perform 3D electron tomography to precisely assess the morphology of VPs. Regardless the fact that we do not possess the imaging infrastructure to perform that type of analysis, such an approach would represent a tremendous amount of work if one would like to process at least 200-400 vesicles from > 50 cells and their whole cytoplasm (as we did). Despite not having 3D images, this number of data points is sufficient to see general changes in viral replication vesicle morphology, especially considering that Huh7-Lunet cells are relatively flat cells. (PMID: 32640225; 36700643; 34696522; 31636417). Furthermore, since IGF2BP2 knockdown decreases the abundance of VPs and does not impact their diameter, we believe that the addition of sophisticated 3D analysis would not bring any new and relevant information and that the TEM data stand by themselves for the conclusion we made. A more refined morphological analysis to determine how IGF2BP2 is structurally involved in virus-mediated membrane reorganization could be the focus of a future study.

We feel that we have already provided sufficient information about the quantification in the Material & Methods section of the first version of the manuscript: “Quantification was performed by systematically surveying cells and evaluating the presence of VPs. Only cells with >2 VPs were considered as positive. For each condition, >50 cells were surveyed over 4 biological replicas. All observed VPs were imaged, and VP diameters were determined using ImageJ by measuring the distance across two axes and averaging”.

Reviewer #2 (Recommendations For The Authors):

The inclusion of a control in the knock-down and infection assays with the reporter virus could enhance the validity of the findings. Introducing STAT2 knockdown, a recognized antiviral protein for ZIKV, as a control would provide a valuable benchmark to evaluate the extent of viral enhancement in the experiments. This additional control not only supports the proposed function of LARP1 in virus assembly/release but also strengthens the overall interpretation of the results.

We agree that adding a positive control could have been relevant for assessing the extent of replication modulation, especially for increases such as that observed with shLARP1. However, finding such control proteins in our system was a challenge. Indeed, STAT2 would not have been a good control for these experiments since we used Huh7.5 cells for the RNAi mini-screening, which do not express a functional RIG-I protein, and generally do not produce type I and III interferons. Thus, STAT2 knockdown is not expected to result in an increase in replication. That said, we feel that it was unnecessary to include a control for replication inhibition here given that only a few statistically reliable candidates we obtained. Instead, we have opted for an extensive secondary validation approach by assessing the proviral role of IGF2BP2 for multiple viruses - DENV1-2-3-4, WNV and SARS-CoV-2, and 3 ZIKV strains in three relevant cell types.

Additionally, in Figure S4, the authors employ an antibody against NS5 that specifically recognizes ZIKV NS5 but not DENV NS5. Given the objective of highlighting distinctions between these two viruses, it is advisable to use an antibody that detects DENV NS5 as well. This approach would contribute to a more comprehensive comparison, ensuring a balanced representation of both viruses in the experimental analysis.

We thank the reviewer for this relevant suggestion. We have repeated the coimmunoprecipitation assays using antibodies specific to DENV NS5 (Aithor response image 1). While we specifically pulled down ZIKV NS5 with IGF2BP2-HA as expected, this was not the case for DENV NS5 when using extracts from DENV-infected cells despite our multiple attempts. Indeed, the amount of pulled-down DENV NS5 with IGF2BP2-HA was always comparable to that in the negative control (“empty” pWPI lentivirus-transduced cells, “-“ condition), which corresponds to non-specific binding to the HA-resin. Thus, while the antibody was very efficient at detecting DENV NS5 in the cell extracts, no specific binding between DENV NS5 and IGF2BP2-HA could be evidenced. Consistent with our different replication phenotypes between DENV and ZIKV, this strongly supports that the NS5/IGF2BP2 interaction is specific to ZIKV. The specificity of the IGF2BP2 interaction with ZIKV NS5 compared to DENV NS5 is discussed in the updated manuscript.

Author response image 1.

DENV NS5 is not specifically co-immunoprecipitated with IGF2BP2-HA in contrast to ZIKV NS5. Huh7.5 cells stably expressing IGF2BP2-HA (+) and control cells (-) were infected with ZIKV H/PF/2013 at a MOI of 10 or left uninfected. Two days later, cell extracts were prepared and subjected to RNase A treatment (+) or not (-) before anti-HA immunoprecipitations. The resulting complexes were analyzed by western blotting for their abundance in the indicated proteins.

Reviewer #3 (Recommendations For The Authors):

(1) Statistical analysis. Please clearly indicate what columns and error bars represent for bar graphs such as those presented in Figures 1A-D and F, Figures 2B-C, and bottom panels in DE, Figure 3, Figure 5B, Figure 6B-C, and Figures 9B-D and F. For instance, the mean of n independent experiments and standard deviation.

Information about the number of replicates, error bars, and statistical tests has been added for all figures in the legends. 

(2) What is the scale in the Y-axis of Figure 2C? As shown, it is difficult to know what is the virus titer in knocked-down cells. Please use a linear scale or a log scale.

This is a linear scale of viral titers, which we have modified to make it clearer for the reader.

(3) Throughout the manuscript (e.g. Figures 1, 2, and 3) the fold reduction in titer is presented instead of the actual virus titers. I suggest showing the titer as it may be much more informative for the reader.

We prefer showing the data as fold reduction as they better reflect the IGF2BP2 knockdowninduced phenotypes across the independent biological replicates. Indeed, from one experiment to another, the reference titers in the control condition sometimes varies because of the cell passage or the lentiviral transduction efficiency for instance, especially when low multiplicities of infection are used. However, the reduction phenotype in foldchange observed upon IGF2BP2 knockdown was always consistent regardless of the titer value.  Of note, all considered experiments had reference titers above 105 PFU/mL.

(4) Is it possible to perform a colocalization analysis of confocal images showing overlapping signals?

This has been done and the results of these analyses are included in the updated figures 4B, 5A, S4 and S5.

(5)  Assessing the effect of Atlastin2 knockdown in virus yield and showing coimmunoprecipitation of Atlastin 2 with NS5 can add relevant information.

As mentioned in the discussion and above, ATL2 was already reported to be required for DENV and ZIKV replication in two independent studies (including one by one of the coauthors)(PMID: 31636417; 31534046). We have not tested whether ATL2 associates with NS5. However, new Fig. S7 of the revised manuscript shows that IGF2BP2/ATL2 is RNAdependent. This suggests that, as initially depicted in our model, IGF2BP2 associates with the ER (and thus, ATL2) after its binding to the viral RNA. Further interrogation into the role of atlastins in the flavivirus replication cycle is the focus of another ongoing IGF2BP2-unrelated study from one of the co-authors which will be reported elsewhere.

Author response:

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

eLife assessment

The manuscript reports useful findings by resolving the crystal structure of Sedoheptulose-1,7-Bisphosphatase (SBPase) from the green algae Chlamydomonas reinhardtii, which is involved in the Calvin cycle. The data presented are solid based on validated methodologies, which help in understanding the structure and function of this enzyme.

We thank the editors for this positive assessment.

Public Reviews:

Reviewer #1 (Public Review):

In this study, Le Moigne and coworkers shed light on the structural details of the Sedoheptulose-1,7-Bisphosphatase (SBPase) from the green algae Chlamydomonas reinhardtii. The SBPase is part of the Calvin cycle and catalyzes the dephosphorylation of sedoheptulose-1,7-bisphosphate (SBP), which is a crucial step in the regeneration of ribulose-1,5-bisphosphate (RuBP), the substrate for Rubisco. The authors determine the crystal structure of the CrSBPase in an oxidized state. Based on this structure, potential active site residues and sites of post-translational modifications are identified. Furthermore, the authors determine the CrSBPase structure in a reduced state revealing the disruption of a disulfide bond in close proximity to the dimer interface. The authors then use molecular dynamics (MD) to gain insights into the redox-controlled dynamics of the CrSBPase and investigate the oligomerization of the protein using small-angle X-ray scattering (SAXS) and size-exclusion chromatography. Despite the difference in oligomerization, disruption of this disulfide bond did not impact the activity of CrSBPase, suggesting additional thiol-dependent regulatory mechanisms modulating the activity of the CrSBPase.

We thank reviewer 1 for his/her careful reading of our manuscript.

The authors provide interesting new findings on a redox-mechanism that modulates the oligomeric behavior of the SBPase, however without investigating this potential mechanism in more detail. The conclusions of this manuscript are mostly supported by the data, but they should be more carefully evaluated in respect to what is known from other systems as e.g. the moss Physcomitrella patens. This is especially of interest, as SBPase was previously reported to be dimeric, whereas for FBPase a dimer/tetramer equilibrium has been observed.

We thank reviewer 1 for his/her comments on the novel or confirmatory character of our structure-function analysis onCrSBPase. We address the questions of oligomeric states later in this response.

(1) Given that PpSBPase has been already characterized in detail, the authors should provide a more rigorous comparison to the existing data on SBPases. This includes a more conclusive structural comparison but also the enzymatic assays should be compared to the findings from P. patens. Do the authors observe differences between the moss and the chlorophyte systems, maybe even in regard to the oligomerization of the SBPase?

Indeed, a previous study conducted by one of the authors of the current manuscript (Stéphane D. Lemaire) and collaborators determined the structure and regulatory properties of SBPase from the moss Physcomitrella patens (Gütle et al. 2018 https://doi.org/10.1073/pnas.1606241113). We added a clearer reference to this earlier work. The differences that we observed regarding the oligomeric states of SBPase from Chlamydomonas reinhardtii principally stem from our analytical method in vitro through size-exclusion chromatography, in comparison with crystal packing analysis in the reference study. We detailed PpSBPase/CrSBPase oligoimeric state comparison in the paragraph 'Oligomeric states of CrSBPase'. Besides, the asymmetric unit of our CrSBPase crystal structure is also a homodimer, similarly to PpSBPase, and we suggest that PpSBPase is also likely to adopt several oligomeric states in vitro. If this were confirmed by experiments, SBPase in several organisms would behave analogously to FBPase regarding the dimer/tetramer equilibrium.

In paragraph 'Crystal structure of CrSBPase' we added a comparison by alignment of our CrSBPase crystal structure to the previously reported _Pp_SBPase crystal structure, stating that with RMSD=0.478 Å the proteins are essentially identical.

In paragraph 'CrSBPase enzymatic activity' we compared the value we obtained for enzyme specific activity to those previously published on other SBPase from Chlamydomonas or the land plant Spinacia oleracea, highlighting the similarity of results in three different systems and teams (Seuter et al. 2002 https://doi.org/10.1023/A:1019297521424 and Tamoi et al. 2005 DOI: 10.1271/bbb.69.848).

(2) The authors should include the control experiments (untreated SBPase) and the assays performed with mutant versions of the SBPase, which are currently only mentioned in the text or not shown at all.

We add supplementary figure 14 in order to illustrate that since SBPase C115S or C120S mutants are still activated by reducing agent, the disulfide bridge between cysteines 115 and 120 is not the single control over SBPase activity but rather a control over the oligomeric exchange of the enzyme indirectly contributing to redox activation of the active site.

(3) The representation of the structure in figures (especially Figures 1 and 3) should be adjusted to match the author's statements. In Figure 1, the angle from which the structure is displayed changes over the entire figure making it difficult to follow especially as a non-structural biologist. Furthermore, important aspects of the structure mentioned in the text are not labeled and should be highlighted, by e.g. a close-up. Same holds true for Figure 3 that currently mostly shows redundant information.

We thank reviewer 1 for his/her advise on how to improve Figure 1. We drew new images for the complete figure, hopefully providing more consistent and clearer visual support to our text. For simplicity, protein is now always represented centered around its active site in the same orientation. We represent co-crystallized water in all projections as a guide to the eye.

Figure 3 and supplementary figure 3 were switched in order to better represent the experimental evidence provided by the resolution of SBPase structure under reducing conditions, i.e., the increase in local disorder around C115-C120 pair of cysteines in the 113-130 stretch forming a redox-conditionally dynamic loop and β-hairpin motif.

(4) The authors state that mutation of C115 and C120 to serine destabilize the dimer formation, while more tetramer and monomer is formed. As the tetramer is essentially a dimer of dimers, the authors should elaborate how this might work mechanistically. In my opinion, dimer formation is a prerequisite for tetramer formation and the two mutations rather stabilize the tetramer instead of destabilizing the dimer.

Time-dependent dynamic character of SBPase oligomer exchange is not resolved by the current study because we essentially combined size-exclusion chromatography (SEC) and X-ray crystallography to define quaternary structures at equilibrium. Overall, homodimer is the dominant state of wild-type SBPase by abundance in the purified recombinant form and by forming the constitutive asymmetric unit in all crystal packings. Dimer is indeed present in the tetramer state, a dimer of dimers, as pertinently stated by reviewer 1.

This being recognized, we tried to explain the systematic co-elution of the principal dimeric form with an additional species of smaller size on SEC (supplementary figure 1, right-side shoulder of the peak), at the apparent mass of a monomer. When solving the crystal structures of SBPase we realized that the dimer interface is contributed by residues 113-130 forming a loop and β-hairpin motif. Notably, in this loop cysteine 115 (C115) maps at bonding distance of 3.9 Å of side chain of arginine 220 (R220) from dimer partner subunit. In loop 113-120, cysteine pair C115 and C120 are subject to redox switching between disulfide (closed) and dithiol (open) conformations, as shown in our structures 7B2O and 7ZUV, respectively. Given that the reduction of C115-C120 disulfide bridge correlates with a higher flexibility of this motif that contributes to dimer interface (figure S3), we hypothesized that reduction of SBPase would destabilize dimer state to the benefit of transitory monomer state, and indeed point mutagenesis of C115S or C120S caused a large modification of oligomer equilibrium in favour of the monomer (figure S1C).

Mechanistically, we suggest two scenarios for the tetramer formation: either monomers first interact as in the crystallographic dimer before pairing such dimers into tetramers (as proposed by reviewer 1), or monomers start tetramerization by favoring the alternative subunit interface (figure 5B, between cyan and magenta chains) before stabilizing the crystallographic homodimer interface. In this latter case, monomerization would be necessary to efficiently re-arrange SBPase dimers into tetramers.

In physiological conditions the re-arrangement switch would be controlled by C115-C120 reduction through ferredoxin-thioredoxin redox cascade. Structural studies in dynamic conditions like native mass spectroscopy/photometry would be necessary to solve this speculation unambiguously although at this stage of our investigation there seem little doubt to us that C115-C120 disulfide-dithiol exchange is essential to control a dimer/monomer balance in first instance.

Reviewer #2 (Public Review):

The central theme of the manuscript is to report on the structure of SBPase - an enzyme central to the photosynthetic Calvin-Benson-Bassham cycle. The authors claim that the structure is first of its kind from a chlorophyte Chlamydomonas reinhardtii, a model unicellular green microalga. The authors use a number of methods like protein expression, purification, enzymatic assays, SAXS, molecular dynamics simulations and xray crystallography to resolve a 3.09 A crystal structure of the oxidized and partially reduced state. The results are supported by the claims made in the manuscript. One of the main weakness of the work is the lack of wider discussion presented in the manuscript. While the structure is the first from a chlorophyte, it is not unique. Several structures of SBPase are available. As the manuscript currently reads, the wider context of SBPase structures available and comparisons between them is missing from the manuscript. Another important point is that the reported structure of crSBPase is 0.453A away from the alphafold model. Though fleetingly mentioned in the methods section, it should be discussed to place it in the wider context.

We thank reviewer 2 for his/her assessment of our manuscript. In response to his/her suggestion to better compare our SBPase structure from the model microalga Chlamydomonas reinhardtii to that of the ortholog from Physcomitrium patens previously reported by an author of this manuscript (Stéphane D. Lemaire) and collaborators (Gütle et al. 2018), we wish to point out that paragraph 3 of the introduction was dedicated to this reference along with a mention to related Thermosynechococcus elongatus dual function fructose-1,6-bisphosphatase sedoheptulose-1,7-bisphosphatase (F/SBPase). We nevertheless follow his/her suggestion to better detail comparison between chloroplastic SBPase structures in the first result section 'Crystal structure of CrSBPase', consistently with response 1 to reviewer 1 (see above).

Regarding the integration of AlphaFold (AF) computational models in a general discussion about SBPase molecular structure, we wish to point out that our initial 7B2O crystallographic model of CrSBPase was deposited in PDB on 2020-11-27 before AlphaFold2 was available for the scientific community (Jumper et al. publication date is 15 July 2021).

AF2 entry AF-P46284-F1-model_v4 from AlphaFold Protein Structure Database aligns with our crystal structure 7B2O chain E with RMSD = 0.434 Å, showing excellent agreement between experiment and prediction at the level of protein main chain. It must still be pointed out that it is the AF2 model which is at 0.434 Å away from the experiment, and not the opposite. Exceptions of alignments are in local differences in several loops conformations and in the length of secondary structure elements. Many amino acid residues side chains adopt distinct orientations between the computational model and the experimental structure.

AF3 was recently communicated (Abramson et al. 2024) along with its online prediction server hosted at https://golgi.sandbox.google.com. CrSBPase model from AF3 align to our crystal structure 7B2O chain A with RMSD = 0.489 Å showing again their strong similarity and with a smaller discrepancy between AF2 and AF3 of RMSD = 0.216 Å. The only significant deviations between 7B2O and AF3 are in the orientation of several side chains and notably on the conformation of region 114-131 that contain the redox sensor motif.

We added the last two paragraphs to the revised version of the manuscript, after the results section presenting our crystallographic work.

Recommendations for the authors:

We made all recommended modifications as detail below.

Reviewer #1 (Recommendations For The Authors):

I have outlined a number of minor points below.

We addressed all minor points listed.

Line 220: The asymmetric unit only contains three dimers. The dimer of dimer or tetramer can only be reconstituted by displaying the symmetry mates.

We corrected our sentence for 'The asymmetric unit is composed of six polypeptide chains packing as three dimers'.

I also suggest that the authors separate the description of the asymmetric unit content from the modeled water molecules and rephrase e.g. „..and four water molecules could be modeled."

We rephrased as suggested.

I appreciate that the authors uploaded the structure in advance of this article, which allowed to evaluate the quality of the structure. Although this does not add valuable information, I have identified several unmodeled blobs, which possibly also account for waters.

Unmodeled blobs were tentatively assigned to water but had to be removed during later refinements. We used Coot Validate tools 'Unmodelled blobs' and 'Check/Delete water' to progress towards the current optimal refinement statistics. We admit that the resolution of the crystallographic dataset (3.09 Å) is limiting to reliably model mobile or less resolved elements like water molecules. Overall, we estimate that the functional elements of the structure are modeled to the best of our knowledge and with minimal subjectivity.

Line 222: Please write 309 instead of spelling the number.

We corrected for 309 instead of spelling the number.

Line 223: The structure representation in Figure 1A/B has to be improved. The authors might consider labeling the two domains & color them in two colors instead of the rainbow color coding. Furthermore, the 90{degree sign} rotation does not add much information. Here, turning the model in a different direction that allows to see the central b-sheet of domain 2 might be better suited. Furthermore, instead of describing b-strands first, followed by a-helices, I suggest describing which secondary structure elements form the two domains.

We improved Figure 1A as suggested while keeping Figure 2B with 90° rotation as rainbow color gradient in order to display with clarity the secondary structure content and connectivity. The orientation was tilted to better display the central β-sheet. This new version of Figure 1A/B should facilitate the text description of SBPase architecture that we amended as suggested.

Line 229: The information on A113-120 should be depicted in a closeup in Figure 1A.

We made a close-up view of sequence 113-120 as added figures 1C-D and modified the rest of the figure and legend accordingly.

Line 234: Please provide an r.m.s.d here.

We now provide r.m.s.d. for all structural alignments.

Line 242: Please introduce the domain labeling in Fig 1C to make it easier to track the exact region within SBP here. Is the residue numbering according to SBP or the human FBP?

Modified version of figure 1 now shows SBPase in the same orientation for panels A, E, F, G, H for simplicity. Domains labeling is indicated in panel A with NTD/CTD distinct colors as suggested. We explicited the position of W401 on all panels as a guide to the eye. We indicated in figure legend that residue numbering is according to Chlamydomonas SBPase Uniprot entry P46284.

Line 244: Is Figure 1D in the same orientation as C? I suggest making the surface transparent and showing the cartoon below, which will allow to easier see the solvent accessibility of the residues. Also, clearly label W401 (although it's the only water shown/modeled in this region).

We modified figure 1 to show all equivalent panels (ie. A-E-F-G-H) with the same orientation. In this new form we think that solvent accessibility and the relative position of significant residues is easier to interpret for the reader. W401 is consistently labeled throughout figure 1 panels.

Line 263: Please provide a close-up of the C222 and C231 including measured distance. It's clearly not visible from this view. It might even be helpful to provide close-ups of all cysteine residues that are mentioned in the text.

In the modified version of figure 1 we estimate that C222 and C231 are more easily visible. We added a close-up view of C22-C231 environment in a new supplementary figure 2. Since we do not explore further the functional relevance of this redox pair we chose not include C222-C231 close-up view in main figure 1. We added legends and modified supplementary figures numbering accordingly.

Line 276: As already mentioned earlier, none of the panels in Figure 1 provide a close-up of this loop. This should be added.

This loop is now displayed as a close-up view in panels C and D of main figure 1.

Line 284: It is difficult to follow the relative positions of the potential modification sites if the model is always depicted from a different angle in Figure 1. The authors might want to change this across Figure 1 or show the rotation angle.

This problem was addressed in the revised figure 1, panels A-E-F-G-H are in the same orientation now. Panel B was kept at a rotation of 90° with corresponding annotation.

Line 290: Please label W401. Also stick to one nomenclature (W or H20).

We labeled W401 and kept nomenclature consistent throughout the manuscript.

For comparative reasons, a full kinetic measurement (determination of Km and kcat) of the SBPase would also be helpful here.

We resolved to avoid a full kinetic measurement of CrSBPase because we could neither identify a reliable chemical provider nor synthesize ourselves the physiological substrate sedoheptulose-1,7-bisphosphate (SBP) and only characterized the reaction with fructose-1,6-bisphosphate. However, in the revised form of the manuscript we added in main text paragraph 'CrSBPase enzymatic activity' the kinetic constants from the previous reference study conducted on spinach SBPase (Cadet and Meunier, Biochem. J. 1988) with KMSBP\=0.05 mM and kcatSBP\=81 sec-1 of fully active enzyme with SBP as a substrate. For comparison, the authors of this study report that activity of SBPase on FBP is in the same range but lower, with KMFBP\=0.38 mM and kcatFBP\=21 sec-1. We also added a comparison of specific activities of our CrSBPase and spinach SBPase in the main text, showing that our enzyme behaves as previously reported ortholog from land plant.

Line 303: How much MgSO4 was used for the experiment shown in Figure 2A?

10 mM of MgS04 was used for experiment shown in Figure 2A. We added this information in the figure legend. We also added in the legend that 10 mM DTT is present in the experiment of Figure 2B and that 10 mM of MgSO4 and 1 mM of DTT are present in the experiment of Figure 2C.

Line 321: In my opinion it is not necessary to show the regions of all molecules here. I was rather expecting a superposition of the two structures (oxidized and reduced) with a close-up of the respective disulfide in the two states.

We agree that the initial version of Figure 3 panels showing side-by-side all conformational variants of the redox motif appear redundant. We switched initial Figure 3 to supplementary data and replaced it with the crystallographic b-factor mapping of the redox motif, in the variable conditions resolved by the crystals. We would like to stress that all these conformations were experimentally determined through X-ray crystallography, whether of the crystal of pure inactive enzyme that proved to be oxidized on the redox motif, or of the equivalent crystals submitted to activating treatment by the chemical reductant TCEP. As an attempt to clarification we added visual boxes to better appreciate this reduction-induced conformational plasticity that we interpreted as a local conditional disorder.

Line 331: Could the authors provide movies of the MD simulation? Otherwise, interpretation of the MD simulation results might be difficult for non-experts.

We added two movies of 20-µsec MD simulations as supplementary data to help non-expert readers.

Line 343: It might be helpful to label the structure elements in Figure 4 accordingly (e.g. residues, etc.)

We added secondary structure labeling in Figure 4.

Line 381: Should be changed to Figure 5A.

We changed reference to figure 6 that is a renumbering of figure 5 with changes included from suggestions below. Figure 6 now includes chromatograms of recombinant SBPase in panel A and chromatogram and western blot analysis of Chlamydomonas extracts in panel B.

Line 383: See above, figure 5B. Which structure is shown in the figure? 7zuv or 7b2o? Maybe include both structures in the figure in a side-by-side view. The authors might also want to include the SEC chromatograms in the main figure. Especially the purification from Chlamydomonas is helpful to estimate whether post-translational modifications have an impact on the oligomerization. This should also be mentioned in the text.

7b2o and 7zuv are illustrated side-by-side in panels A and B of figure 5. This was indicated in the figure legend, we now added the information on the figure. As suggested above we included chromatograms initially presented as supplementary material in a new main figure 6, panel A for recombinant proteins and panel B for proteins extracted from Chlamydomonas. Initial figures 5D-E, showing surface conservation of the dimeric SBPase, is moved to supplementary figure 5.

Line 385: I don't find the cultivation of Chlamydomonas in the method section. It should be added.

We added a methods paragraph dedicated to « Cultivation of Chlamydomonas for native SBPase analysis ».

Line 390-392: This information is not really helpful. Concentrated purified proteins might precipitate after a week storage without physiologically relevant effects being the reason.

We agree that the observation of a precipitate building up in vitro after a week of storage bears no particular physiological implications. We rather intended to report that an aggregated form of purified protein can be turned to droplets under the redox conditions that activate the enzyme. We reformulated these lines for clarification.

Line 397: I would appreciate having the SEC-chromatograms of the mutants also in the main figure.

Size-exclusion chromatograms that were initially in supplementary figures are now shown in main text figure 6 panel A, with the profiles WT and mutants aligned.

Line 402: Where are these data shown? They should be included in Figure 5.

We added a figure to present these data, not shown in the initial version of the manuscript. We preferred to place it as supplementary material because C115S and C120S mutant catalytic activity is essentially the same as WT and do not reveal a direct mechanistic effect of C115-C120 reduction over the catalytic pocket.

Line 427: Did the authors look into a possible cooperativity of their SBPase?

We did not observe direct positive cooperativity that could be ascribed to allostery in our enzymatic assays. It was previously reported for spinach SBPase that SBP saturation functions were hyperbolic with no evidence of homotropic interactions in the enzyme oligomer (Cadet and Meunier Biochem J. 1988 253, 249-254). The authors of this kinetic study however present a clear sigmoid response of SBPase to Mg2+ concentration, suggestive of an activating cross-talk between active sites in the oligomer. We consider this hypothesis of interest and wish we could further investigate allosteric conformational changes when SBP physiological substrate would be available.

Line 428-434: I don't really understand how the proteome mapping fits in here. Do the authors speculate that SBPase is recruited by some of the identified enzymes or directly interacts with them or that rather the spatial distribution optimizes the reaction kinetics?

We indeed want to correlate our in vitro observations of CrSBPase conditions of activity to those recently published by the group of Dr. Martin Jonikas in a physiological, in vivo setup of Chlamydomonas reinhardtii (Wang, Patena et al. Cell 2023 186, 3499–3518). We have no experimental evidence demonstrating the first suggestion that SBPase is recruited or directly interacts with partner enzymes but we privilege the second suggestion that local spatial distribution in the chloroplast stroma optimizes enzyme reaction kinetic thanks to Calvin-Benson-Bassham enzymes proximity. We rephrased these lines to clarify our hypothesis and express its speculative character.

Reviewer #2 (Recommendations For The Authors):

To make the manuscript stronger, the authors are recommended to do the following:

We followed given recommendations.

(1) include a wider discussion on the other SBPase structures that are available. A detailed comparison should be made between the oxidized and reduced structures present in the PDB with the structures that are being reported in the manuscript.

Consistently with reviewer #1 suggestion, and as detailed in response to public review above, we followed the recommendation to better report previous structural studies of SBPase in the results section. We also added comparisons with computational models from AlphaFold2 and AlphaFold3.

(2) The authors mention co-operativity between the subunits. With excellent sampling from molecular dynamics simulations, the authors should demonstrate co-operativity between the subunits.

Our molecular dynamic (MD) simulations span 20 µsec of SBPase in the dimeric state, starting from the experimental structures determined by XRC. In the considered time window, the only significant events that we observed are the local reorganization of the LBH motif that is a prerequisite for dimer rearrangement. We infer that local disorder contributes a separation of the pair of subunits in order to later allow for the building of the active homotetramer, at longer time scales that are outside the capacities used in this work. Moreover, demonstrating cooperativity with MD simulations would require more than a single event to ensure that results are significant, and performing series of 20µs-MD of SBPase is also outside the available capacities.

Author response:

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

eLife assessment

This study provides a useful strategy for treating mouse cutaneous squamous cell carcinoma (mCSCC) with serum derived from mCSCC-exposed mice. The exploration of serum-derived antibodies as a potential therapy for curing cancer is particularly promising but the study provides inadequate evidence for specific effects of mCSCC-binding serum antibodies. This study will be of interest to scientists seeking a novel immunotherapic strategy in cancer therapy.

Joint Public Review:

Summary:

This study presents an immunotherapeutic strategy for treating mouse cutaneous squamous cell carcinoma (mCSCC) using serum from mice inoculated with mCSCC. The author hypothesizes that antibodies in the generated serum could aid the immune system in tumor volume reduction. The study results showed a reduction in tumor volume and altered expression of several cancer markers (p53, Bcl-xL, NF-κB, Bax) suggesting the potential effectiveness of this approach.

Strengths:

The approach shows potential effect on preventing tumor progression, from both the tumor size and the cancer biomarker expression levels bringing attention to the potential role of antibodies and B cell responses in cancer therapy.

We greatly appreciate your positive feedback on our study.

Weaknesses:

These are some of the specific things that the author could consider to strengthen the evidence supporting the claims in their study.

(1) The study fails to provide evidence of the specific effect of mCSCC-antibodies on mCSCC. The study utilized serum which also contains many immune response factors like cytokines that could contribute to tumor reduction. There is no information on serum centrifugation conditions, which makes it unclear whether immune components like antigen-specific T cells, activated NK cells, or other immune cells were removed from the serum. The study does not provide evidence of neutralizing antibodies through isolation, analysis of B cell responses, or efficacy testing against specific cancer epitopes. To affirm the specific antibodies' role in the observed immune response, isolating antibodies rather than employing whole serum could provide more conclusive evidence. Purifying the serum to isolate mCSCC-binding antibodies, such as through protein A purification, and ELISA would have been more useful to quantify the immune response. It would be interesting to investigate the types of epitopes targeted following direct tumor cell injection. A more thorough characterization of the antibodies, including B cell isolation and/or hybridoma techniques, would strengthen the claim.

I am deeply appreciative of the reviewer's highly professional comments. Tumor development involves the coexistence of cancer cells at different developmental stages, each harboring a variety of known and unknown mutated proteins. These mutated proteins expose multiple known and unknown epitopes, each capable of stimulating the production of corresponding antibodies in healthy mice. Identifying all these antibodies presents a significant challenge. Current research methodologies, such as ELISA, WB, and ChIP, can only identify known antibodies based on existing antigens. A prerequisite for using these techniques is that both antigens and antibodies are identified. At present, there is no technology available to identify antibodies produced by an unknown mutated protein and epitope. However, I find the reviewer's comments insightful. Perhaps we can initially identify some known mCSCC-antibodies on mCSCC. However, studying the specific effect of these known mCSCC-antibodies on mCSCC is uncertain because we believe that tumor shrinkage results from the combined action of both known and unknown antibodies.

We concur with the reviewer's observations regarding the use of serum, which is rich in immune response factors such as cytokines that could potentially contribute to tumor reduction. In our future research, we plan to systematically analyze the individual roles of these antibodies and cytokines in tumor reduction. In 1973, Nature published a report indicating that serum demonstrated promising results in tumor treatment (Immunotherapy of Cancer with Antibody in Rats. Nature 243, 492 (1973). https://doi.org/10.1038/243492b0). Since then, there have been scarcely any reports on serum therapy for tumors. The primary focus of our study is to evaluate the efficacy of serum therapy in treating tumors. We hypothesize that antibodies and cytokines form a complex interactive network, working in synergy to reduce tumors. Consequently, we believe that studying these antibodies and cytokines in isolation may not yield effective results.

In this study, the methodology section outlines the process of serum preparation. It is important to note that serum is devoid of blood cells. I hypothesized that whole blood might have superior therapeutic effects compared to serum. This is because antibodies could potentially synergize with immune cells (including T cells, B cells, and NK cells), thereby enhancing the effectiveness of the treatment. As previously discussed, these antibodies, cytokines, and immune cells form a complex interactive network aimed at tumor reduction. Consequently, there are numerous factors that could influence the experimental outcomes, which presents a challenge for analyzing the results. Furthermore, the implementation of whole blood transfusion therapy introduces additional considerations, such as potential side effects and reactions associated with blood transfusions.

We thank the reviewers for their suggestion to purify the serum in order to isolate mCSCC-binding antibodies. As we previously mentioned, separating a large number of both known and unknown serum antibodies presents a significant technical challenge. We are eager to discuss and consider suggestions from the reviewers regarding methods to identify a large variety and number of unknown antibodies on cells. Perhaps, as the reviewer suggested, we could begin with known antibodies and employ Protein A purification technology to purify these antibodies and subsequently detect immune responses. We could also categorize the types of epitopes targeted, direct tumor cell injection, to study the epitopes of these types in further studies. The suggestion to study the response of B cells is valuable, and we plan to conduct comprehensive research on the response and status of B cells in our future studies.  

The purification of antibodies to enhance the specificity of their effectiveness against tumors is a critical aspect of our study. However, we would like to address some concerns raised. (1) The separation of all antibodies and cytokines presents a significant technical challenge. Particularly, there is a risk of overlooking antibodies that are present in low concentrations but play crucial roles. (2) What concerns us is that studying the composition separately would lose the overall effectiveness of the study. Our primary concern is that studying these components in isolation could compromise the holistic understanding of the study. This is akin to current research on traditional medicine, where the separation and individual study of compounds often result in a loss of overall therapeutic efficacy. For instance, consider a scenario where 100 antibodies collectively work to shrink a tumor. These antibodies interact with 20 cytokines, forming a complex network that enhances the cytokines' activity against tumor cells. Furthermore, many important antibodies and cytokines are currently unknown. Studying these antibodies in isolation could potentially result in the loss of this therapeutic effect. Therefore, in the discussion section, we have emphasized that our study considers a tumor mass, including tumor cells at various stages of development, as a single entity. As a practicing clinician, my primary focus is on the therapeutic outcomes in tumor treatments, despite the mechanisms of serum therapy remaining largely elusive, liking a black box.

(2) In the study design, the control group does not account for the potential immunostimulatory effects of serum injection itself. A better control would be tumor-bearing mice receiving serum from healthy non-mCSCC-exposed mice. Additionally, employing a completely random process for allocating the treatment groups would be preferable. Also, the study does not explain why intravenous injection of tumor cells would produce superior antibodies compared to those naturally generated in mCSCC-bearing mice.

I concur with the reviewer's perspective that using serum from healthy, non-mCSCC exposed mice as a control could potentially improve our study. Initially, our primary concern was to minimize harm to the mice and avoid excessive blood reactions, which led us to exclude the use of serum from healthy, non-mCSCC exposed mice in our control group. The main objective of our study was to investigate tumor shrinkage through serum treatment, specifically serum-derived antibodies. We anticipated that tumor-bearing mice receiving serum from healthy, non-mCSCC exposed mice would exhibit a response to the injected serum, which would manifest as a blood reaction. However, we did not expect this to result in a tumor treatment effect. If it turns out that normal serum (from healthy, non-mCSCC-exposed mice) possesses tumor-reducing properties, it would indeed be a novel discovery. We appreciate the reviewer's insightful suggestion and will consider incorporating it into our future research.

We concur with the reviewer's observations that the use of a completely random process for assigning treatment groups would be more desirable. Indeed, the complete randomization of the entire process further underscores the efficacy and universality of serum therapy. In this study, we utilized paired mice to mitigate the risk of cross-infection and adverse reactions associated with blood transfusions. We deeply value the reviewer's expert feedback.  

Lastly, the reason why tumor cells, when intravenously injected, produce antibodies superior to those naturally generated in mCSCC-bearing mice, is due to the following reasons. As tumor cells grow, they produce a variety of mutated proteins to adapt to the immune microenvironment and evade the immune system of mCSCC-bearing mice. However, these tumor cells with mutated proteins are exceptionally sensitive and recognizable to healthy mice. This recognition triggers an immune response in healthy mice, leading to the production of specific therapeutic antibodies. This simultaneous production of diverse and abundant antibodies is only achievable by living organisms.

(3) In Figure 2B, it would be more helpful if the author could provide raw data/figures of the tumor than just the bar graph. Similarly in Figure 3, the author should show individual data points in addition to the error bar to visualize the actual distribution.

Raw data (numerical values) have been incorporated into Figures 2B and 3, but the data is placed in the table below the graph. If placed above the error bar, it requires a small font and may not be clear.

(4) The author mentioned that different stages of tumor cells have different surface biomarkers. Therefore, experimenting with injecting tumor cells at various stages could reveal the most immunogenic stage. Such an approach would allow for a comparative analysis of immune responses elicited by tumor cells at different stages of development.

Yes, throughout the course of tumor development, tumor cells at various stages will exhibit distinct markers or possess different mutated proteins. The concept of segregating tumor cells from different stages and independently comparing their immune responses is indeed commendable. Future research could involve isolating cells that express identical biomarkers at each stage for a comparative analysis of the immune responses triggered by the tumor cells. However, this approach diverges from the original intent of this study.

Most tumor cells exist within the same developmental stage. However, this does not imply that all tumor cells within the tumor mass are at the same stage. For instance, a stage III liver cancer tumor may contain both stage I and stage IV tumor cells. Moreover, due to the complexity of tumor development, not all tumor cell surface markers are identical, even for tumors at the same stage. For instance, 20 major proteins and 100 minor proteins are implicated in tumor formation. In fact, random mutations in just 5 of these major proteins and 10 minor proteins can instigate the development of tumors. This implies that the protein pattern (tumor cell surface markers) associated with each individual's tumor is unique. While studying tumor cells at different stages separately allows for the observation of the immune response of tumor cells at each stage, it lacks a comprehensive research and treatment effect. For this reason, the design of this study treats a tumor mass as a whole, encompassing both the primary stage tumor cells and those not in that stage. These tumor cells are then injected to produce corresponding therapeutic antibodies. Furthermore, if tumor cells from only one stage are isolated and specific antibodies are produced against these cells, it could lead to immune escape of tumor cells at other stages, preventing the tumor from shrinking. Therefore, our approach aims to address this issue by considering the tumor mass as a whole.

(5) In the abstract the author mentioned that using mCSCC is a proof-of-concept for this potential cancer treatment strategy. The discussion session should extend to how this strategy might apply to other cancer types beyond carcinoma.

We have incorporated an additional paragraph in the discussion section where we delve into the concepts and experimental principles underpinning this study. This, we believe, addresses the reviewer's query regarding the applicability of our study's methodology to other types of tumors. The process for other tumors also involves isolating cells from the tumor, stimulating therapeutic antibody production in healthy mice using these cells, and ultimately reintroducing these antibodies into mice with tumors to facilitate tumor elimination

Recommendations For The Authors:

The author is encouraged to refine the study's design in future studies considering the weaknesses highlighted above, summarize the results more effectively, and seek opportunities to expand on this promising idea and enhance the research's impact and applicability.

We greatly appreciate the valuable suggestions provided by the editor and reviewers. These insights will certainly be addressed in our future research endeavors.

Suggestions for title modification:

Following the scope of the study, the term 'specific homologous neutralizing-antibodies' may be misleading as neutralizing antibodies typically refer to antibodies preventing viral cell entry. In cancer therapy, 'neutralization' is not a relevant concept, as cancer cells do not infect host cells. Using whole tumor cells as immunogens diverges from the specificity of traditional vaccination approaches that utilize well-defined proteins or antigens. Furthermore, the term "homologous" suggests a precision in targeting that is not demonstrated by reintroducing serum without isolating its specific components. Therapeutic effects should not be attributed to "neutralizing antibodies" without isolating or characterizing the antibody response or verifying their efficacy against specific cancer epitopes. Additionally, it is suggested that you indicate the biological system that your study utilised in the title. More so, this approach is not entirely novel, as seen with the use of adjuvants in some flu vaccines, or in Moderna's cancer vaccine mRNA-4157, which encodes up to 34 patient-specific tumor neoantigens. You can consider the title below or a variant of the same.

Suggested title: Generating serum-based antibodies from tumor-exposed mice: a potential strategy in cutaneous squamous cell carcinoma treatment

I concur with your suggestion and have modified the title to " Generating serum-based antibodies from tumor-exposed mice: a new potential strategy for cutaneous squamous cell carcinoma treatment ". I believe this research remains some new, hence the addition of the word "new". Furthermore, the term "novel" in the paper has been either removed or substituted.

Moreover, I propose that this study shares similarities with Moderna's cancer vaccine mRNA-415, albeit with certain differences. Moderna's cancer vaccine mRNA-415 encodes 34 recognized neoantigens to stimulate an immune response by eliciting specific T cell responses. This is similar to the strategy of some companies developing a protein set for diagnosing lung cancer, liver cancer, among others. Without a doubt, these methods have improved the effectiveness of tumor diagnosis and treatment. However, I think that these methods currently face challenges in completely eradicating tumors because they perceive tumors as a static process and cells that express certain mutated proteins in a fixed manner. I believe that small molecule antibodies, cytokines, and immune cells present in serum that are difficult to detect, have low concentrations, or are unknown are essential for maintaining the expression of important mutant proteins and the escape of tumor cells. This is also the primary reason why tumors are difficult to treat and prone to recurrence at present.

From my perspective, different tumors, as well as different stages of the same tumor, express varying mutated proteins or surface markers. Targeting some may result in others escaping or even creating a more conducive growth environment for those that do escape. Our study adopts a comprehensive view of a tumor block, encompassing tumor cells at different stages and tumor cells at the same stage but expressing different biomarkers. This approach generates a multitude of known and unknown antibodies that work in concert with cytokines and immune cells. While our method may not be capable of generating all mutated proteins and epitope antibodies due to the weakness of some antigens (epitopes of mutated proteins), it can still be effective. As long as the number of tumor cells is reduced below a certain threshold following multiple rounds of treatment with various antibodies produced at different stages, these cancer cells can be eradicated by the body's immune system. This is a process that is real-time and dynamic. Undoubtedly, if it becomes evident that alterations in a set of proteins can bolster the immune system and eradicate tumor cells, then the implications are significant. The immunotherapy proteins, which have demonstrated positive therapeutic effects, developed by certain companies are also predicated on this very principle.

Finally, I greatly appreciate your suggestions, which will be considered and gradually addressed in future research.

Author response:

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

Reviewer#1:

Comment #1: It is unclear how the fraction of NK cell populations is quantified in the spatial-seq datasets. Figures display spatial data with expression scores, but the method for calculating the score and determining NK cell presence in tumor tissue is ambiguous. Clarification is needed on whether the identification relied solely on visual inspection or if quantitative analyses using other criteria were conducted.

Thank you for your questions. We removed the background and made the accordingly modifications according to your demand. We used the AddModuleScore function in Seurat to quantify the main immune subpopulations in spatial-seq using the gene sets identified in single-cell-seq. Additionally, the tumor and non-tumor region was identified by immunohistochemistry as well as cell clusters in spatial-seq, it is rough that we can't quantify the NK cell presence in each region precisely. The consolation is that the differences of NK cell presence in tumor and non-tumor region is observable by visual inspection. The methodology has been supplemented in the revised manuscript (line 190-193).

Comment #2: The authors do not provide a clear definition of "resting" NK cells. It remains unclear whether they refer to a senescent state or a non-matured NK cell population. Furthermore, the criteria used to define resting and activated cells based on the expression of KIR2DL4, GPR183, GRP171, CD69, IFNG, GZMK, TTC38, CD160, and PLEKNF1 in Figure 4 are not well-defined. The expression patterns of these genes in Figure 4D are not distinct, and it is unclear which combination of genes was used to classify the populations. Clarification is needed on whether the presence of GZMK alone defines resting NK cells, or if the presence of any of the described genes (GZMK, TTC38, or CD160) is sufficient. Additionally, the method used for this classification, whether visual or algorithm-based, should be described.

Thank you for your question. The resting and activated NK cells was defined by the preferential expression of the described resting genes (AZU, BPI, CAMP, CD160,CD2, CDHR1, CEACAM8, DEFA4, ELANE, GFI1, GZMK, KLRC4, MGAM, MS4A3, NME8, PLEKHF1, TEP1, TRBC1, TTC38, ZNF135) and activated NK genes (APOBEC3G, APOL6, CCL4, CCND2, CD69, CDK6, CSF2, DPP4, FASLG, GPR171, GPR18, GRAP2, IFNG, KIR2DL4, KIR2DS4, LTA, LTB, NCR3, OSM, PTGER2, SOCS1, TNFSF14) in CIBERSORT. Actually, these marker genes were not specifically expressed in a single NK cells subset. On the other hand, combined with further flow cytometric analysis verification, the resting NK cell tend to be a decidual-like NK cells and tumor- infiltrated NK cells with higher expression of CD9, CD49a and PD-1.

Comment #3: Criteria used to define high or low NK cell presence/infiltration in Figure 5 are not described in the main text or figure legend. Since, the claim that the presence of the resting or activated NK cells predicts cancer prognosis is based on this figure, this needs to be clearly described.

Thank you for your questions. The activated and resting NK cell percentage in TCGA and GSE29623 was determined by CIBERSORT. Additionally, the infiltration of activated and resting NK cell was also determined by the AddModuleScore function using the gene sets of activated and resting NK cell identified in single-cell-seq, the differences of activated and resting NK cell presence in tumor and non-tumor region is also determined by visual inspection. We have amended in the main text and figure legend in the revised manuscript.

Comment #4: The absence of FMO controls for KIR2DL4 or GZMK and the lack of increase in GZMK expression during co-culture with tumour lines raises concerns since GZMK was used as a defining feature of resting NK cells.

Thank you for your questions. We did a new batch of flow experiments and FMO controls of all the markers used in the experiments were set up to define the precise positive gate locations.

Author response image 1.

The positive gate locations of CD56, GZMK, KIR2DL4, CD9, CD49a, PD-1 defined according to the FMO control.

Comment #5: All the co-cultures were performed with tumour cell line only and no healthy cells, such as human foreskin fibroblasts, were used as control. In the absence of a non-tumour cell line, it is very difficult to draw any conclusions. Furthermore, to claim that resting or activated NK cells are responsible for tumour migration or proliferation, it is important to at least isolate resting and activated NK cells ex vivo and culture with tumour lines, instead of NK cell lines.

Thank you for your questions. According to your suggestion, NK cells were co-cultured with human foreskin fibroblasts, the phenotype was identified by Flow cytometry. When co-cultured with HFF in direct contact (CN group), NK cells were also tending towards tissue infiltration state (high expression of CD9). However, the domestication effect is significantly reduced compared to co-culturing with tumor cells. Additionally, unlike supernatant of CNS group (NK and HCT were in contact) from NK and HCT co-culture system could significantly increase the migration of fresh HCT, fresh HCT underwent a limited increase (no statistical significance was found) in migration when cultured in the supernatant from the co-culture system in which NK and HFF were in contact (CNS group), but not when co-cultures were performed in the cell supernatant (SNS group) and fresh medium (MNS group). Finally, we tried to isolate resting and activated NK cells from fresh colon cancer surgical specimen. Unfortunately, the NK cells were too few to perform further functional experiments such as migration and proliferation.

Author response image 2.

Phenotype switch of NK cells in different co-cultured system and the corresponding NK cell-mediated effect on cell migration of fresh colon cancer cell (HCT-116).

A-B: NK cells underwent phenotype switch (high expression of CD9) when cocultured with HCT and HFF, the phenotype switch was more obvious when co-cultured with HCT. CN: NK cells cocultured with HCT/HFF; SN: NK cells cocultured with supernatant of HCT/HFF; MN: NK cells cocultured in fresh medium. C-E: Transwell assay showed the only tumor co-cultured NK mediated the inductive effect on cell migration of colon cancer cell (HCT-116). CNS: Colon cancer cells were cultured in the supernatant from co-culture system that NK and HCT/HFF were cultured in direct contact; SNS: Colon cancer cells were cultured in the supernatant from co-culture system that NK cocultured with supernatant of HCT/HFF; MNS: Colon cancer cells were cultured in the fresh medium.

Comment #6: It seems that flow cytometric analyses and GZMK and KIR2DL4 staining were performed without cell permeabilization. Could authors confirm if this is accurate, or if they performed intracellular staining instead?

Thank you for your questions. For GZMK, which known as the secretory protein, flow cytometric analyses were performed both with (Fig.3) and without cell fixation and permeabilization, no significant differences were found among each group. The difference is that GZMK was nearly all negative without fixation and permeabilization while it is all positive with fixation and permeabilization. Conditions of flow cytometry analyses for GZMK may need further optimization or GZMK may not be a suitable flow cytometric marker for resting NK cells. On the other hand, for membrane protein such as CD56, CD9, CD49a, KIR2DL4, PD-1, staining was performed without cell permeabilization.

Author response image 3.

Phenotype switch (CD56+, GZMK+) of NK cells was analyzed by FACS after fixation and permeabilization in different co-cultured groups. CN: NK cells cocultured with colon cancer cells; SN: NK cells cocultured with supernatant of cancer cells; MN: NK cells cocultured in fresh medium.

Comment #7: The identity of the published datasets used for analysis is not provided, and references are not cited in the results section.

Thank you for your questions. We are sorry for the neglect of our previous work. We have added the information in the revised manuscript (section of Materials and Methods) (Line 123-128).

Comment #8: References are difficult to locate, as the main text follows APA style while the reference section is organized numerically with no clear order.

Thank you for your questions. We have modified the format of the references in the revised manuscript.

Comment #9: Figure 3 shows volcano plots showing DEG genes between tumor and healthy tissue NK cells are not described clearly, and authors did not discuss the significance of these genes, highlighted in the plot.

Thank you for your questions. Volcano plots of Figure 3 showed the DEGs between colon cancer with metastasis and without metastasis in TCGA database. We focused on the genes which were enriched in the pathway of “Natural killer cell mediated cytotoxicity” and found nearly all the genes enriched in the pathway were down-regulated in the colon cancer with metastasis. We have modified the description in the result section and added the description of importance of these genes in the discussion section in the revise manuscript (Line 322-326).

Comment #10: The meaning of "M0" and "M1" in Figures 5A and 5B is unclear and should be defined in the text.

Thank you for your questions. "M0" and "M1" in Figure 5A and 5B means “colon cancer without metastasis” and “colon cancer with metastasis”, respectively. We have modified in the revise manuscript (Line 350-354).

Comment #11: Terms such as "dynamic remodelling of NK cells" and "landscape of NK cells" are used without explanation, necessitating clarification of their meaning.

Thank you for your questions. We have modified in the revise manuscript (Line 331-334).

Comment #12: In vitro assays are described vaguely, making it difficult for readers to understand. More clarity is needed in describing these assays.

Thank you for your questions. We have added clarification in the revise manuscript (Line 205-211).

Reviewer #2:

Comment #1: This manuscript investigates the role of the abundant NK cells that are observed in colon cancer liver metastasis using sequencing and spatial approaches in an effort to clarify the pro and anti-tumorigenic properties of NK cells. This descriptive study characterises different categories of NK cells in tumor and tumor-adjacent tissues and some correlations. An attempt has been made using pseudotime trajectory analysis but no models around how these NK cells might be regulated are provided.

Thank you for your questions. The single-cell sequencing data enrolled in this study are CD45 positive immune cells and do not involve tumor cells, cellular communication analysis between NK cells and tumor cells cannot be conducted. The change process of NK can only be predicted through pseudotime trajectory analysis. Our hypothesis is that tumor cells domesticate NK cells into a tumor- infiltrated NK cells through direct contact, and flow cytometry experiments have also confirmed that tumor cells can only have such domestication through direct contact with NK cells (with prominent high expression of CD9). However, the detailed mechanism remained unclear.

Comment #2: A small number of patients are analyzed in this study. The descriptive gene markers, while interesting, need to be further validated to understand how strong this analysis might be and its potential application.

Thank you for your questions. The sample size included in this study is indeed a bit small, which is also a limitation of our study. However, this is the only large sample single-cell sequencing dataset could be found that includes primary colon cancer tissues, paired paratumor normal colon tissues, paired liver metastatic cancer tissue, and paired paratumor normal liver tissues. We will expand the sample size to further verify the current conclusion in subsequent experiments. In addition, the marker genes of different NK groups used in this study refer to the CIBERSORT's classification of activated NK cells and resting NK cells, which is a widely recognized indicator. We will verify the expression and clinical application value of the screened genes in tissues in subsequent studies.

Comment #3: Figure 1C and other figures throughout the paper. It is not clear how marker genes were selected.

Thank you for your questions. The marker genes displayed in the Figure.3C were the highly variable genes of each cell group as well as the marker genes of each immune cells, such as T cells (CD3D, CD3E), NK cells (NKG7, KLRD1), monocytes (LYZ, S100A8, S100A9), B cells (CD79A), plasma cells (JCHAIN, IGHA1, IGHA2), Neutrophils (CXCL8, FCGR3B).

Comment #4: Figure 1E. P and T have not been defined. Lines should not connect the datasets as they are independent assessments.

Thank you for your questions. P and T means paratumor normal tissues and tumor tissues, respectively. Which have been added in the caption of Figure 1E. Additionally, the single cell sequencing samples included in the study were paired, with primary colon cancer tissues, paired normal tissues adjacent to colon cancer, paired liver metastatic cancer tissue, and paired normal liver tissues from 20 colon cancer patients with liver metastasis, paired test analysis was thus performed.

Comment #5: Figure 2C. It is unclear what ST-P1 means. This is not a particularly informative figure.

Thank you for your questions. We are sorry that it was our annotation error. Actually, it is the spatial transcriptome of the primary colon cancer tissue and liver metastasis tissue of four patients. We have made the modifications in the revised manuscript.

Comment #6: Multiple figures - abbreviations are used but not provided in the legend. They occur in the text but are not directly related to the figures where they are used to label axes or groups.

Thank you for your questions. We have rechecked and made corresponding modifications in the revised manuscript.

Comment #6: Patients: it is not clear what other drugs patients have been exposed to or basic data (sex, age, underlying conditions etc)

Thank you for your questions. The baseline data of the patient of SC dataset and ST dataset were showed in the Table.1 and Table.2 followed, respectively. They were not presented before as no patients characteristics related analysis was performed in the current study.

Author response table 1.

The baseline data of patient from single cell sequencing database.

Author response table 2.

The baseline data of patient from spatial transcriptome database.

Author response:

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

Reviewer #1 (Public Review): 

In the manuscript "Mechanistic target of rapamycin (mTOR) pathway in Sertoli cells regulates age-dependent changes in sperm DNA methylation", the authors proposed to test if the balance of mTOR complexes in Sertoli cells may play a significant role in age-dependent changes in the sperm epigenome. The paper could be of interest and has a good scientific aim but there are too many drawbacks that hamper the initial enthusiasm. All sections need extensive revision. The paper is mostly descriptive without a mechanistic-orientated explanation for the observed results. 

Comments on revised version: 

I am not sure that the authors have made an attempt to clearly answer the reviewers comments that aimed to improve the quality of the manuscript. It stands as mostly descriptive and with limited interest as it is. 

We are thankful to the reviewer for agreeing to review our revised manuscript. Unfortunately, we completely disagree with the evaluation provided by the reviewer. Research on sperm DNA methylation experienced a significant rise of interest in the current century and by now more than 2000 papers have been published. Although it was demonstrated that the sperm DNA methylome may be affected by almost every factor analyzed, no study was published to identify molecular mechanisms that may link these factors with the sperm epigenome. Our study is the FIRST to identify such a mechanism (mTOR complexes balance in Sewrtoli cells). More so, we demonstrated experimentally that manipulations of this mechanism allow regulation of the rates of epigenetic aging of sperm in both directions (accelerate aging or rejuvenate). Thus, our study provides a mechanistic background for the development of therapeutic interventions that may target sperm epigenome.

We acknowledge that our study does not provide the full cascade of events linking the balance of mTOR complexes in Sertoli cells with the sperm DNA methylome. It suggests, however, the most plausible event next in a cascade (BTB permeability changes). Our group is working on this question now and we hope to provide the answer soon in a separate study. Even after that, we will be far from understanding the complete chain of molecular events that link mTOR and sperm methylome. It may take many years and significant effort of many research groups to dissect the whole cascade. It is worth mentioning that understanding of a complete cascade involved in pathology is not needed to develop efficient therapies if the critical nodes are known. For many common drugs (e.g. metformin) we do not know the full chain of molecular mechanisms but use them successfully.

Thus, we believe that our study is mechanistic as it identified a critical mechanism manipulation of which allows experimental aging and rejuvenation of the sperm methylome. Additionally, it generates new mechanistic questions and hypotheses to be answered in the future.

Reviewer #3 (Public Review): 

Summary and Strength: 

The manuscript by Amir et al. describes that Sertoli-specific inactivation of the mTORC1 and mTORC2 complex by KO of either Raptor or Rictor, respectively, resulted in progressive changes in blood-testis-barrier (BTB) function, testis weight, and sperm parameters, including counts, morphology, mtDNA content and sperm DNA methylation. 

The described studies are based on the hypothesis that a decline of BTB function with increasing chronological age of a male contributes to the DNA methylation changes that are known to occur in sperm DNA of old males when compared to sperm DNA from isogenic young males. In order to demonstrate the relevance of a functioning BTB for the maintenance of sperm methylation patterns, the authors generated mice with genetically disrupted mTORC2 complex or mTORC1 complex in Sertoli cells and determined sperm methylation patterns in comparison to isogenic wild-type males. In line with previously published scientific literature (e.g. Mok et al., 2013; Dong et al, 2015; and others), the manuscript corroborates that a Sertoli-cell specific deletion of mTORC2 caused a loss of BTB function and a progressive spermatogenic defect. The authors further show that sperm DNA is differentially methylated (DMRs) as a consequence of either a mTORC2 disruption (associated with a loss of BTB function) or following a mTORC1 disruption (BTB function either increased or not leaky) when compared to their isogenic age-matched wt controls. Those DMRs overlap partially with changes in sperm DNA methylation that were found when comparing sperm from 8-week males with sperm isolated from 22-week-old male mice. 

The authors interpret the observed changes as representative of the sperm DNA methylation changes that occur during normal chronological aging of the male. For an aged control group, the authors use sperm DNA of 22-week-old wild-type mates from the mTORC2 and mTORC2 KO breeding and compare the sperm methylation patterns found in sperm from those 22-week males to 8-week young males, that are intended to represent an old and a young cohort, respectively. DNA methylation analysis indicates that a disruption of mTORC2 (& decrease of BTB function) results in increased DNA methylation of sperm DNA, while a disruption of mTORC1 (and proposed increase of BTB tightness, not shown in the manuscript, though) resulted in increased hypomethylation. 

Weaknesses: 

While the hypothesis and experimental system are interesting and the data demonstrating the relevance of the mTORC2 complex for BTB function is convincing, several open questions limit the evidence that supports the hypothesis that the sperm DNA methylation changes seen in old males are caused by BTB failure following an imbalance of mTOR signaling complexes. The major critique points are the lack of a chronologically old group and the choice of 8 weeks & 22 weeks age of age: 

- Data illustrating the degree of BTB decline and sperm DNA methylation changes from chronologically "old" male mice is missing. 22-week-old mice are not considered old but are of good and mature breeding age, equivalent to humans in their mid-late twenties. (In the manuscript, the 22-week-old wildtype mice show no evidence of BTB breakdown (Figure 3), so why are their sperm used to represent "aged" sperm? 

- Adding a group of "old" wild-type mice of 12-14 months of age, which is closer to the end of effective reproduction in mice, more equivalent to 45-59 year-old humans) could be used to illustrate that (a) aging causes a marked decrease in BTB function at this time in mouse life, and that this BTB breakdown chronologically aligns with the age-associated DNA hypermethylation seen in old sperm. Age-matched "old" mTORC1 KO, with a (supposedly) tighter BTB barrier, could then be expected to have a sperm DMA methylation profile closer to that of younger wild-type animals. Such data are currently missing. While the progressive testicular decline observed in the mTORC1 KO (Fig.5) could make it difficult to obtain the appropriately aged mTORC1 KO tissues, it is completely feasible to obtain data from chronologically old wild-type males. (The progressive testicular decline further raises the question of what additional defects the KO causes, and how such additional defects would influence the sperm DNA methylation profile.) The addition of data from an old group to the currently included groups could strengthen the interpretation that the observations in the BTB-defective mTORC2 KO mice are modelling an age-related testicular decline, provided that the DMRs seen in the chronologically old group significantly overlap with the BTB-defective changes. 

- In the current form, the described differences in sperm DNA methylation are based on comparisons between pubertal mice (8 weeks) and mature but not old adult males (22 weeks), while a chronologically "old" group is missing from the data sets and comparisons. Thus, it appears that the described sperm methylation changes reflect developmental changes associated with normal maturation and not necessarily declining sperm quality due to aging. (Sperm obtained from 8-week-old mice likely were generated, at least in part, during the 1st wave of spermatogenesis, which is known to differ from the continuously proceeding spermatogenesis during the remained of the mature life. During the 1st wave of spermatogenesis, Sertoli cells are known to undergo gene expression changes which could contribute to varying degrees of BTB function, and thus have effects on the sperm DNA methylation profiles of such 1st wave sperm.) 

- It is unclear why the aging-related DMRs between the 8 and 22-week-old wild-type mice vary so dramatically between the two wild-type groups derived from the mTORC1 and the mTORC2 breeding (Fig. S4). If the main difference was due to mTORC1 or mTORC2 activity, both wildtype groups should behave very similarly. Changes seen in a truly "old" mouse (e.g. 20 weeks to 56 weeks), changes in "young mTORC1" and in "old mTORC2" are missing.

How do those numbers and profiles compare to the shown samples? 

Comments on latest version: 

The rebuttal letter and public response indicate the authors' reluctance to consider the limitations of their study, i.e. having chosen chronologically young animals to demonstrate a sperm aging effect and indicate that they are not willing to include adequate controls. 

Since there is no evidence that mice at this young age have a deteriorating blood-testis-barrier (indeed, normal intact BTB is clearly visible in the figures included in this study from animals of the relevant age group), the whole central hypothesis that the study is built upon (i.e. that increasing age causes deteriorating BTB integrity which in turn causes age-related changes in sperm DNA methylation), appears irrelevant or invalid. 

The authors' claim that age-related DNA methylation changes in sperm occur in linear fashion and that the changes are somewhat proportional with chronological age is in stark contrast of the claim that a decline of the BTB in old animals is causative for age-related sperm epigenetic changes, putting the relevance of the whole study in question. 

We are thankful to the reviewer for agreeing to review our revised manuscript. We disagree with the evaluation provided by the reviewer, however.

First, the reviewer misinterpreted the hypothesis of the study, although it is formulated in the last sentence of the Introduction:  “ … we hypothesized that the balance of mTOR complexes in Sertoli cells may also play a significant role in age-dependent changes in the sperm epigenome.” Instead, the reviewer assigned a different hypothesis to our study (that BTB integrity changes are responsible for age-dependent changes in sperm DNA methylation) and criticized us for not providing clear testing of this hypothesis.

To clarify, we believe that our study provides high-quality testing of OUR hypothesis as we demonstrated experimentally that manipulations of mTOR complexes balance in Sertoli allow acceleration and deceleration of epigenetic aging of sperm. Additionally, our study generated a hypothesis that BTB permeability may mediate the effects of the mTOR pathway on sperm methylome. This second hypothesis is to be tested in the future research.

We also disagree with the reviewer's interpretation of the aging process as an abrupt transition from a young, healthy, and undamaged state to an old, moribund, and damaged state. The whole body of biogerontological knowledge suggests instead steady accumulation of damage over lasting periods of time. For example, this understanding of steady change at the molecular level allowed the development and successful use of epigenetic clock and other molecular clock models, including several variants of sperm epigenetic clocks. These models clearly demonstrate linear or semi-linear accumulation in DNA-methylation changes in various tissues and biological species across the whole lifespan. It is reasonable to assume that BTB permeability decreases with age steadily as well and that in younger animals this decrease may be not easily detected by the existing analytical methods. Experimental data showing the dynamics of the BTB deterioration over age do not exist to our knowledge although it was demonstrated that older animals have loose BTB as compared with young. We agree with the reviewer that future studies testing the role of BTB deterioration for sperm methylome aging will need to provide such evidence. It was not the subject of the current study, however.

---

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

Reviewer #1 (Public Review):

In the manuscript "Mechanistic target of rapamycin (mTOR) pathway in Sertoli cells regulates age-dependent changes in sperm DNA methylation", the authors proposed to test if the balance of mTOR complexes in Sertoli cells may play a significant role in age-dependent changes in the sperm epigenome. The paper could be of interest and has a good scientific aim but there are too many drawbacks that hamper the initial enthusiasm. All sections need extensive revision. The paper is mostly descriptive without a mechanistic-orientated explanation for the observed results.

Specific comments:

(1) The abstract is poorly written. There is a lot of unnecessary introduction that does not provide a rationale for the work. It is not possible to understand the experimental approach or the major data just by reading the abstract. It does not clearly represent the work.

- We have added details of experimental design and results to the abstract and reduced the introductory part of the abstract.

(2) The introduction is somewhat vague and does not provide a clear rationale for the hypothesis. There should be more focus more on the role of mTOR in Sertoli cells that goes far beyond BTB. That will give more focus on mTOR. Then it is important to focus on BTB and mTOR: what is known? What is the gap and how can it be solved? Several relevant references are missed concerning mTOR and Sertoli cells.

- The goal of this study was not to explore all potential roles of mTOR pathway in Sertoli cells, but to test if shifts in the balance of mTOR complexes regulate (accelerate/decelerate) epigenetic aging of sperm. As such, we disagree with the reviewer and consider that the current Introduction provides a focused rational for the study.

(3) The Material and Methods section needs improvement. There is much important information missing. For instance: how many animals were used per group and how was the breeding done? At what age? Statistical analysis should be explained in detail.

- The number of animals was clearly stated in the original manuscript. We have added details of breeding and statistical analysis. 

(4) The results description could be improved. It is vague without highlighting how much difference was detected. The results should be numerically described when possible and the differences should be highlighted. A 10% difference may be significant but not biologically relevant. To correctly evaluate the differences it is important to describe them with some degree of detail.

- For all DNA methylation experiments we provide numerical characteristics of methylation changes, including numbers of DMRs, % change, significance, correlation coefficients. We believe that only age- and genotype-associated changes in reproductive parameters were not characterized in our manuscript in detail. We have added Table 1 to provide these numbers.

(5) There is no discussion of the data. The authors just summarize their findings without a comprehensive analysis of the literature and how the effects can be mediated. mTOR interacts with different pathways (mTORC1 and mTORC2 are even mediators of distinct pathways). This would be very relevant to discuss. In addition, there are many study limitations not discussed. There is no clear mechanistic explanation of the way by which the mTOR pathway in Sertoli cells regulates age-dependent changes in sperm DNA methylation. The paper seems preliminary.

- We have added an additional paragraph to the discussion to highlight a potential molecular mechanism that links mTOR pathway with the sperm epigenome.

(6) Figure 1 is too simple and does not provide any schematic support for the text.

- We disagree with the reviewer and believe that the figure represents a good visualization of our hypothesis useful for the perception of the study.

(7) Figure 2 lacks some detail. For instance, how many animals were used for each step?

- Numbers of animals are provided in the text of the paper.

(8) Taking into consideration the roles of mTOR on sperm, particularly mTORC1, it is not clear whether there were any differences in sperm motility.

- We did not assess sperm motility in this study. 

Reviewer #2 (Public Review):

In this study, the authors hypothesized that the balance of mTOR complexes in Sertoli cells may also play a significant role in age-dependent changes in the sperm epigenome. To test this hypothesis, the authors use transgenic mice with manipulated activity of mTOR complexes in Sertoli cells. These results suggest that the mTOR pathway in Sertoli cells may be used as a novel target of therapeutic interventions to rejuvenate the sperm epigenome in advanced-age fathers.

The authors attempt to demonstrate that the balance of mTOR complexes in Sertoli cells regulates the rate of sperm epigenetic aging. The authors have effectively met their research objectives, and their conclusions are supported by the data presented.

- We are very thankful for the positive evaluation of our study.

Reviewer #3 (Public Review):

Summary and Strength:

The manuscript by Amir et al. describes that Sertoli-specific inactivation of the mTORC1 and mTORC2 complex by KO of either Raptor or Rictor, respectively, resulted in progressive changes in blood-testis-barrier (BTB) function, testis weight, and sperm parameters, including counts, morphology, mtDNA content and sperm DNA methylation.

The described studies are based on the hypothesis that a decline of BTB function with increasing chronological age of a male contributes to the DNA methylation changes that are known to occur in sperm DNA of old males when compared to sperm DNA from isogenic young males. In order to demonstrate the relevance of a functioning BTB for the maintenance of sperm methylation patterns, the authors generated mice with genetically disrupted mTORC2 complex or mTORC1 complex in Sertoli cells and determined sperm methylation patterns in comparison to isogenic wild-type males. In line with previously published scientific literature (e.g. Mok et al., 2013; Dong et al, 2015; and others), the manuscript corroborates that a Sertoli-cell specific deletion of mTORC2 caused a loss of BTB function and a progressive spermatogenic defect. The authors further show that sperm DNA is differentially methylated (DMRs) as a consequence of either a mTORC2 disruption (associated with a loss of BTB function) or following a mTORC1 disruption (BTB function either increased or not leaky) when compared to their isogenic age-matched wt controls. Those DMRs overlap partially with changes in sperm DNA methylation that were found when comparing sperm from 8-week males with sperm isolated from 22-week-old male mice.

The authors interpret the observed changes as representative of the sperm DNA methylation changes that occur during normal chronological aging of the male. For an aged control group, the authors use sperm DNA of 22-week-old wild-type mates from the mTORC2 and mTORC2 KO breeding and compare the sperm methylation patterns found in sperm from those 22-week males to 8-week young males, that are intended to represent an old and a young cohort, respectively. DNA methylation analysis indicates that a disruption of mTORC2 (& decrease of BTB function) results in increased DNA methylation of sperm DNA, while a disruption of mTORC1 (and proposed increase of BTB tightness, not shown in the manuscript, though) resulted in increased hypomethylation.

Weaknesses:

While the hypothesis and experimental system are interesting and the data demonstrating the relevance of the mTORC2 complex for BTB function is convincing, several open questions limit the evidence that supports the hypothesis that the sperm DNA methylation changes seen in old males are caused by BTB failure following an imbalance of mTOR signaling complexes. The major critique points are the lack of a chronologically old group and the choice of 8 weeks & 22 weeks age of age:

- Data illustrating the degree of BTB decline and sperm DNA methylation changes from chronologically "old" male mice is missing. 22-week-old mice are not considered old but are of good and mature breeding age, equivalent to humans in their mid-late twenties. (In the manuscript, the 22-week-old wildtype mice show no evidence of BTB breakdown (Figure 3), so why are their sperm used to represent "aged" sperm?

- Adding a group of "old" wild-type mice of 12-14 months of age, which is closer to the end of effective reproduction in mice, more equivalent to 45-59 year-old humans) could be used to illustrate that (a) aging causes a marked decrease in BTB function at this time in mouse life, and that this BTB breakdown chronologically aligns with the age-associated

DNA hypermethylation seen in old sperm. Age-matched "old" mTORC1 KO, with a (supposedly) tighter BTB barrier, could then be expected to have a sperm DMA methylation profile closer to that of younger wild-type animals. Such data are currently missing. While the progressive testicular decline observed in the mTORC1 KO (Fig.5) could make it difficult to obtain the appropriately aged mTORC1 KO tissues, it is completely feasible to obtain data from chronologically old wild-type males. (The progressive testicular decline further raises the question of what additional defects the KO causes, and how such additional defects would influence the sperm DNA methylation profile.) The addition of data from an old group to the currently included groups could strengthen the interpretation that the observations in the BTB-defective mTORC2 KO mice are modelling an age-related testicular decline, provided that the DMRs seen in the chronologically old group significantly overlap with the BTB-defective changes.

- In the current form, the described differences in sperm DNA methylation are based on comparisons between pubertal mice (8 weeks) and mature but not old adult males (22 weeks), while a chronologically "old" group is missing from the data sets and comparisons. Thus, it appears that the described sperm methylation changes reflect developmental changes associated with normal maturation and not necessarily declining sperm quality due to aging. (Sperm obtained from 8-week-old mice likely were generated, at least in part, during the 1st wave of spermatogenesis, which is known to differ from the continuously proceeding spermatogenesis during the remained of the mature life. During the 1st wave of spermatogenesis, Sertoli cells are known to undergo gene expression changes which could contribute to varying degrees of BTB function, and thus have effects on the sperm DNA methylation profiles of such 1st wave sperm.)

- It is unclear why the aging-related DMRs between the 8 and 22-week-old wild-type mice vary so dramatically between the two wild-type groups derived from the mTORC1 and the mTORC2 breeding (Fig. S4). If the main difference was due to mTORC1 or mTORC2 activity, both wildtype groups should behave very similarly. Changes seen in a truly "old" mouse (e.g. 20 weeks to 56 weeks), changes in "young mTORC1" and in "old mTORC2" are missing. How do those numbers and profiles compare to the shown samples?

Some general comments regarding the chosen age of animals:

- As mentioned, sperm from 8-week-old mice represent many sperm that were produced in the 1st wave of spermatogenesis; 22-week-old mice are not considered chronologically old mice, but mature and "relatively" young animals. 18-24 month-old mice are considered to be equivalent to 56-69 year-old humans, and might be more suitable to detect aging effects. "Old mice" for study purposes should be at least 12-14 months of age, ideally >18 months of age. 22 weeks (5 months of age) are mice at good breeding age, but still considered mature adults, not old males, and therefore are not expected to show typical aging health problems (like declining fertility).

Even the cited reference (Flurkey et al. 2007) defines that "... mice used a reference group for "young mice" should be at least 3 months of age (~ 13 weeks), i.e. fully sexually mature. The authors specifically state: " The young adult group should be at least 3 months old because, although mice are sexually mature by 35 days, relatively rapid maturational growth continues for most biologic processes and structures until about 3 months. The upper age range for the young adult group is typically about 6 months. ... For the middleaged group, 10 months is typically the lower limit.... The upper age limit for the middleaged group is typically 14-15 months, because at this age, most biomarkers still have not changed to their full extent, and some have not yet started changing. For the old group, the lower age limit is 18 months because age-related change for almost all biomarkers of aging can be detected by then. The upper limit is 22-26 months, depending on the genotype." According to this reference, mice up to 6 months of age are generally considered "mature adults" (equivalent to humans 20-30 yrs), mice of 10-14 month are "middle-aged adults" (equivalent to ~38-47 human years) and 18-24 month mice are "old" (equivalent to human of 56-69 yrs.).

Going on these commonly used age ranges, it is unclear why the authors used 8-week-old mice (generally considered pubertal to late adolescent age) as young mice and 5-month-old mice as "old mice".

Differences seen between these cohorts most likely do not reflect aging, but more likely reflect changes associated with normal developmental maturation, since testis and epididymides continue to grow until about 10-11 weeks of age.

- The DMRs identified between 8 and 22-week-old animals could represent DMRs that are dependent on developmental maturation more than being changed in an "age-dependent" manner (in the sense of increased chronological age). This interpretation is congruent with the fact that those DMRs are enriched for developmental categories.

- We are thankful to the reviewer for a detailed explanation of their disagreement with the ages of mice used in this study. In short, the reviewer suggests that our older group (22 weeks) is not old enough to represent aged animals and our young group (8 weeks) may still have spermatozoa from the first wave of spermatogenesis, and as such the observed differences between the 2 ages cannot be considered as aging-related but rather may represent different stages of maturation of the reproductive system. At the first glance this criticism looks valid. 

However, to design our experiments we used our data that was not included to this manuscript initially. These data demonstrated that age dependent changes in sperm DNA are linearly or semi linearly associated with age in the age range from 56 to 334 days. Thus, within this interval any 2 ages, distant enough to register the difference in DNA methylation, can be used to assess age dependent changes in DNA methylation and changes in the rates of epigenetic aging of sperm in response to genetic manipulations. We have added these results now, - see “Identification of agedependent patterns in sperm DNA methylation” section in Material and Methods and “Patterns of age-dependent changes in sperm DNA methylation” in Results. We also consider that the reviewer’s suggestion that sperm from 8-week-old mice represents the first wave of spermatogenesis does not have ground. Indeed, C57BL/6 mice first have fertile sperm in cauda epididymis at 37 days of age [1], 19 days earlier than the age of 56 days (8 weeks) at which sperm was collected in our study in the youngest group of mice. Given that young C57BL/6 mice ejaculate spontaneously around 3 times per 5 days [2], 8 weeks old mice have ejaculated > 10 times since the first wave of spermatogenesis before the sperm was collected for our study, making negligibly small the chances of survival of any first wave sperm in their cauda epididymides to the age of 8 weeks. We have added this information to the text.

(1) Mochida, K.; Hasegawa, A.; Ogonuki, N.; Inoue, K.; Ogura, A. Early Production of Offspring by in Vitro Fertilization Using First-Wave Spermatozoa from Prepubertal Male Mice. J. Reprod. Dev. 2019, 65, 467–473, doi:10.1262/jrd.2019-042.

(2) Huber, M.H.; Bronson, F.H.; Desjardins, C. Sexual Activity of Aged Male Mice: Correlation with Level of Arousal, Physical Endurance, Pathological Status, and Ejaculatory Capacity. Biol. Reprod. 1980, 23, 305–316, doi:10.1095/biolreprod23.2.305.

Author response:

We thank the editors and reviewers for their enthusiasm for this work and helpful suggestions. In summary, the reviewers provided suggestions for additional discussion items and clarifications for the text and figures, especially in relation to the cryo-EM structures and suppressor screen sections of the manuscript. We will consider each of these and make edits as needed. In particular, reviewers asked for further details about the structural model in addition to analysis of our new structure with respect to previously reported intron lariat spliceosome (ILS) complexes. For the latter point, we present additional evidence for the correct assignment of Yju2 in the S. cerevisiae ILS structure and note that docking of the 3’ splice site is not observed in any ILS structure from yeast, worms, or humans. This is consistent with our proposed mechanism. We will clarify these points in the text as well highlight some caveats of prior studies of the ILS complex. We feel that these changes will add additional nuance to the manuscript as well as clarify the findings and their context and significance for the reader.

Author response:

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This study sought to reveal the potential roles of m6A RNA methylation in gene dosage regulatory mechanisms, particularly in the context of aneuploid genomes in Drosophila. Specifically, this work looked at the relationships between the expression of m6A regulatory factors, RNA methylation status, classical and inverse dosage effects, and dosage compensation. Using RNA sequencing and m6A mapping experiments, an in-depth analysis was performed to reveal changes in m6A status and expression changes across multiple aneuploid Drosophila models. The authors propose that m6A methylation regulates MOF and, in turn, deposition of H4K16Ac, critical regulators of gene dosage in the context of genomic imbalance.

Strengths:

This study seeks to address an interesting question with respect to gene dosage regulation and the possible roles of m6A in that process. Previous work has linked m6A to X-inactivation in humans through the Xist lncRNA, and to the regulation of the Sxl in flies. This study seeks to broaden that understanding beyond these specific contexts to more broadly understand how m6A impacts imbalanced genomes in other contexts.

Weaknesses:

The methods being used particularly for analysis of m6A at both the bulk and transcript-specific level are not sufficiently specific or quantitative to be able to confidently draw the conclusions the authors seek to make. MeRIP m6A mapping experiments can be very valuable, but differential methylation is difficult to assess when changes are small (as they often are, in this study but also m6A studies more broadly). For instance, based on the data presented and the methods described, it is not clear that the statement that "expression levels at m6A sites in aneuploidies are significantly higher than that in wildtype" is supported. MeRIP experiments are not quantitative, and since there are far fewer peaks in aneuploidies, it stands to reason that more antibody binding sites may be available to enrich those fewer peaks to a larger extent. But based on the data as presented (figure 2D) this conclusion was drawn from RPKM in IP samples, which may not fully account for changing transcript abundances in absolute (expression level changes) and relative (proportion of transcripts in input RNA sample) terms.

Methylated RNA immunoprecipitation followed by sequencing (MeRIP-seq) is a commonly used strategy of genome-wide mapping of m6A modification. This method uses anti-m6A antibody to immunoprecipitate RNA fragments, which results in selective enrichment of methylated RNA. Then the RNA fragments were subjected to deep sequencing, and the regions enriched in the immunoprecipitate relative to input samples are identified as m6A peaks using the peak calling algorithm. We identified m6A peaks in different samples by the exomePeak2 program and determined common m6A peaks for each genotype based on the intersection of biological replicates. Figure 2D shows the RPM values of m6A peaks in MeRIP samples for each genotype, indicating that the levels of reads in the m6A peak regions were significantly higher in the aneuploid IP samples than in wildtypes. When the enrichment of IP samples relative to Input samples (RPM.IP/RPM.Input) was taken into account, the statistics for all three aneuploidies were still significantly higher than those of the wildtypes (Mann Whitney U test p-values < 0.001). This analysis is not about changes in the abundance of transcripts, but from the MeRIP perspective, showing that there are relatively more m6A-modified reads mapped to the m6A peaks in aneuploidies than that in wildtypes. In addition, we have added the results of IP/Input in the main text, and revised the description in the manuscript to make it more precise to reduce possible misunderstandings.

The bulk-level m6A measurements as performed here also cannot effectively support these conclusions, as they are measured in total RNA. The focus of the work is mRNA m6A regulators, but m6A levels measured from total RNA samples will not reflect mRNA m6A levels as there are other abundance RNAs that contain m6A (including rRNA). As a result, conclusions about mRNA m6A levels from these measurements are not supported.

According to some published articles, m6A levels of purified mRNA or total RNA can be detected by different methods (such as mass spectrometry, 2D thin-layer chromatography, etc.) in Drosophila cells or tissues [1-3].

Here, we used the EpiQuik m6A RNA Methylation Quantification Kit (Colorimetric) (Epigentek, NY, USA, Cat # P-9005), which is suitable for detecting m6A methylation status directly using total RNA isolated from any species such as mammals, plants, fungi, bacteria, and viruses. This kit has previously been used by researchers to detect the m6A/A ratio in total RNA [4, 5] or purified mRNA [6] from different species.

In order to compare the m6A levels between the total RNA and mRNA, it was shown that the enrichment of mRNA from total RNA using Dynabeads™mRNA Purification Kit (Invitrogen Cat # 61006) did not show any significantly differences comparing with the results of total RNA (Figure 1). That’s the reason why most of the results of m6A levels in the manuscript were detected in total RNA.

Author response image 1.

The m6A levels of total RNA and mRNA

As suggested, we will try to extract and purify mRNA from different genotypes to verify our conclusion based on the m6A levels of total RNA if necessary. In addition, m6A modification in other types of RNA other than mRNA (e.g., lncRNA, rRNA) is not necessarily meaningless. We will also add discussions of this issue in the manuscript.

(1) Lence T, et al. (2016) m6A modulates neuronal functions and sex determination in Drosophila. Nature 540(7632):242-247.

(2) Haussmann IU, et al. (2016) m(6)A potentiates Sxl alternative pre-mRNA splicing for robust Drosophila sex determination. Nature 540(7632):301-304.

(3) Kan L, et al. (2017) The m(6)A pathway facilitates sex determination in Drosophila. Nat Commun 8:15737.

(4) Zhu C, et al. (2023) RNA Methylome Reveals the m(6)A-mediated Regulation of Flavor Metabolites in Tea Leaves under Solar-withering. Genomics Proteomics Bioinformatics 21(4):769-787.

(5) Song H, et al. (2021) METTL3-mediated m(6)A RNA methylation promotes the anti-tumour immunity of natural killer cells. Nat Commun 12(1):5522.

(6) Yin H, et al. (2021) RNA m6A methylation orchestrates cancer growth and metastasis via macrophage reprogramming. Nat Commun 12(1):1394.

Reviewer #2 (Public Review):

Summary:

The authors have tested the effects of partial- or whole-chromosome aneuploidy on the m6A RNA modification in Drosophila. The data reveal that overall m6A levels trend up but that the number of sites found by meRIP-seq trend down, which seems to suggest that aneuploidy causes a subset of sites to become hyper-methylated. Subsequent bioinformatic analysis of other published datasets establish correlations between the activity of the H4K16 acetyltransferase dosage compensation complex (DCC) and the expression of m6A components and m6A abundance, suggesting that DCC and m6A can act in a feedback loop on each other. Overall, this paper uses bioinformatic trends to generate a candidate model of feedback between DCC and m6A. It would be improved by functional studies that validate the effect in vivo.

Strengths:

• Thorough bioinformatic analysis of their data.

• Incorporation of other published datasets that enhance scope and rigor.

• Finds trends that suggest that a chromosome counting mechanism can control m6A, as fits with pub data that the Sxl mRNA is m6A modified in XX females and not XY males.

• Suggests this counting mechanism may be due to the effect of chromatin-dependent effects on the expression of m6A components.

Weaknesses:

• The linkage between H4K16 machinery and m6A is indirect and based on bioinformatic trends with little follow-up to test the mechanistic bases of these trends.

We found a set of ChIP-seq data (GSE109901) of H4K16ac in female and male Drosophila larvae from the public database, and analyzed whether H4K16ac is directly associated with m6A regulator genes. ChIP-seq is a standard method to study transcription factor binding and histone modification by using efficient and specific antibodies for immunoprecipitation. The results showed that there were H4K16ac peaks at the 5' region in gene of m6A reader Ythdc1 in both males and females. In addition, most of the genome sites where the other m6A regulator genes located are acetylated at H4K16 in both sexes, except that Ime4 shows sexual dimorphism and only contains H4K16ac peak in females. These results indicate that the m6A regulator gene itself is acetylated at H4K16, so there is a direct relationship between H4K16ac and m6A regulators. We have added these contents to the text.

Besides the above conclusion from the seq data, we are also going to do some experiments to test the linkage between H4K16 and m6A in the next, such as how about the m6A levels when MOF is over expressed with the increased levels of H4K16Ac, the H4K16 levels when YT521B is knocked down or over expressed and the relative expression levels of important regulatory genes in there.

• The paper lacks sufficient in vivo validation of the effects of DCC alleles on m6A and vice versa. For example, Is the Ythdc1 genomic locus a direct target of the DCC component Msl-2 ? (see Figure 7).

In order to study whether Ythdc1 genomic locus is a direct target of DCC component, we first analyzed a published MSL2 ChIP-seq data of Drosophila (GSE58768). Since MSL2 is only expressed in males under normal conditions, this set of data is from male Drosophila. According to the results, the majority (99.1%) of MSL2 peaks are located on the X chromosome, while the MSL2 peaks on other chromosomes are few. This is consistent with the fact that MSL2 is enriched on the X chromosome in male Drosophila [1, 2]. Ythdc1 gene is located on chromosome 3L, and there is no MSL2 peak near it. Similarly, other m6A regulator genes are not X-linked, and there is no MSL2 peak. Then we analyzed the MOF ChIP-seq data (GSE58768) of male Drosophila. It was found that 61.6% of MOF peaks were located on the X chromosome, which was also expected [3, 4]. Although there are more MOF peaks on autosomes than MSL2 peaks, MOF peaks are absent on m6A regulator genes on autosomes. Therefore, at present, there is no evidence that the gene locus of m6A regulators are the direct targets of DCC component MSL2 and MOF, which may be due to the fact that most MSL2 and MOF are tethered to the X chromosome by MSL complex under physiological conditions. Whether there are other direct or indirect interactions between Ythdc1 and MSL2 is an issue worthy of further study in the future.

(1) Bashaw GJ & Baker BS (1995) The msl-2 dosage compensation gene of Drosophila encodes a putative DNA-binding protein whose expression is sex specifically regulated by Sex-lethal. Development 121(10):3245-3258.

(2) Kelley RL, et al. (1995) Expression of msl-2 causes assembly of dosage compensation regulators on the X chromosomes and female lethality in Drosophila. Cell 81(6):867-877.

(3) Kind J, et al. (2008) Genome-wide analysis reveals MOF as a key regulator of dosage compensation and gene expression in Drosophila. Cell 133(5):813-828.

(4) Conrad T, et al. (2012) The MOF chromobarrel domain controls genome-wide H4K16 acetylation and spreading of the MSL complex. Dev Cell 22(3):610-624.

Quite a bit of technical detail is omitted from the main text, making it difficult for the reader to interpret outcomes.

(1) Please add the tissues to the labels in Figure 1D.

Figure 1D shows the subcellular localization of FISH probe signals in Drosophila embryos. Arrowheads indicate the foci of probe signals. The corresponding tissue types are (1) blastoderm nuclei; (2) yolk plasm and pole cells; (3) brain and midgut; (4) salivary gland and midgut; (5) blastoderm nuclei and yolk cortex; (6) blastoderm nuclei and pole cells; (7) blastoderm nuclei and yolk cortex; (8) germ band. We have added these to the manuscript.

(2) In the main text, please provide detail on the source tissues used for meRIP; was it whole larvae? adult heads? Most published datasets are from S2 cells or adult heads and comparing m6A across tissues and developmental stages could introduce quite a bit of variability, even in wt samples. This issue seems to be what the authors discuss in lines 197-199.

In this article, the material used to perform MeRIP-seq was the whole third instar larvae. Because trisomy 2L and metafemale Drosophila died before developing into adults, it was not possible to use the heads of adults for MeRIP-seq detection of aneuploidy. For other experiments described here, the m6A abundance was measured using whole larvae or adult heads; material used for RT-qPCR analysis was whole larvae, larval brains, or adult heads; Drosophila embryos at different developmental stages were used for fluorescence in situ hybridization (FISH) experiments. We provide a detailed description of the experimental material for each assay in the manuscript.

(3) In the main text, please identify the technique used to measure "total m6A/A" in Fig 2A. I assume it is mass spec.

We used the EpiQuik m6A RNA Methylation Quantification Kit (Colorimetric) (Epigentek, NY, USA, Cat # P-9005) to measure the m6A/A ratio in RNA samples. This kit is commercially available for quantification of m6A RNA methylation, which used colorimetric assay with easy-to-follow steps for convenience and speed, and is suitable for detecting m6A methylation status directly using total RNA isolated from any species such as mammals, plants, fungi, bacteria, and viruses.

(4) Line 190-191: the text describes annotating m6A sites by "nearest gene" which is confusing. The sites are mapped in RNAs, so the authors must unambiguously know the identity of the gene/transcript, right?

When the m6A peaks were annotated using the R package ChIPseeker, it will include two items: "genomic annotation" and "nearest gene annotation". "Genomic annotation" tells us which genomic features the peak is annotated to, such as 5’UTR, 3’UTR, exon, etc. "Nearest gene annotation" indicates which specific gene/transcript the peak is matched to. We modified the description in the main text to make it easier to understand.

Author response:

We would like to thank all reviewers for their valuable comments that help us to improve our manuscript. We will make the following modifications in the revised manuscript:

(1) To reduce the complexity of the experiments we carried out, we will summarize trimeric G proteins in Ciona in the first paragraph of the Result section and explain how we focused on Gas and Gaq in the initial phase of this study.

(2) As the reviewer 1 suggested, the polymodal roles of papilla neurons are interesting. We will add a discussion regarding this aspect. The sentences will be like the following:

“The recent study (Hoyer et al., 2024) provided several lines of evidence suggesting that papilla neurons can serve as the sensors of several chemicals in addition to the mechanical stimuli. This finding and our model seem mutually related because these chemicals could modify Ca2+ and cAMP signaling. The use of G protein signaling may allow Ciona to reflect various environmental stimuli to initiate metamorphosis in the appropriate situation, both mechanically and chemically.”

(3) As both reviewers suggested, imaging cAMP on the backgrounds of some G protein knockdowns and pharmacological treatments is important, and we will carry out some of these experiments.

(4) According to reviewer 2's comment, we will carefully modify the text about interpreting the results so that the descriptions suitably reflect the results.

Author response:

Response to reviewers (Public review):

We thank all the three reviewers for their opinion on our work on Candida albicans β-1,6-glucan, which highlights the importance of this cell wall component in the biology of fungi. Here are our responses to their comments for public reviews:

(1) Indeed, the data presented for immunological studies is preliminary. It has been acknowledged by the reviewers that our analysis providing insights into the biosynthetic pathways involved in comprehensive in dealing with organization and dynamics of the β-1,6-glucan polymer in relation with other cell wall components and environmental conditions (temperature, stress, nutrient availability, etc.). However, we anticipated that there would be immediate curiosity as to what the immunological contribution of β-1,6 glucan and we therefore felt we needed to initiative these studies and include them. We therefore performed immunological studies to assess whether β-1,6-glucans act as a pathogen-associated molecular pattern (PAMP), and if so, what its immunostimulatory potential is. Our data clearly suggest that β-1,6-glucan is a PAMP, and consequently lead to several questions: (a) what are the host immune receptors involved in the recognition of this polysaccharide, and thereby the downstream signaling pathways, (b) how is β-1,6-glucan differentially recognized by the host when C. albicans switches from a commensal to an opportunistic pathogen, and (c) how does the host environment impact the exposure of this polysaccharide on the fungal surface. We believe addressing these questions is beyond the scope of the present manuscript and aim to present new data in future manuscript. Nonetheless, in the revised manuscript, suggest approaches that we can take to identify the receptor that could be involved in the recognition of β-1,6-glucan. Moreover, we have modified the discussion presenting it based on the data rather than being descriptive.    

(2) It will be interesting to assess the organization of β-1,6-glucan and other cell wall components in the opaque cells. It is documented that the opaque cells are induced at acidic pH and in the presence of N-acetylglucosamine and CO2. Our data shows that pH has an impact on β-1,6-glucan, which suggests that there will be differential organization of this polysaccharide in the cell wall of opaque cells. As suggested by the reviewer, we will include analysis of opaque cells (and other C. albicans cell types) in future studies.

With the exception of these major new avenues for this research, our revision can address each of the comments provided by the reviewers.

Author response:

Reviewer #1 (Public Review): 

Summary: 

In this study, Masroor Ahmad Paddar and his/her colleagues explore the noncanonical roles of ATG5 and membrane ATG8ylation in regulating retromer assembly and function. They begin by examining the interactomes of ATG5 and expand the scope of these effects to include homeostatic responses to membrane stress and damage. 

Strengths: 

This study provides novel insights into the noncanonical function of ATG8ylation in endosomal cargo sorting process. 

Weaknesses: 

The direct mechanism by which ATG8ylation regulates the retromer remains unsolved. 

We agree with the reviewer.  We do however show how at least one aspect of ATG8ylation contributes to the proper retromer function, which occurs via lysosomal membrane maintenance and repair. Understanding the more direct effects on retromer will require a separate study. We will emphasize this in the revised manuscript and point out the limitations of the present work.

Reviewer #2 (Public Review): 

Summary:

Padder et al. demonstrate that ATG5 mediates lysosomal repair via the recruitment of the retromer components during LLOMe-induced lysosomal damage and that mAtg8-ylation contributes to retromer-dependent cargo sorting of GLUT1. Although previous studies have suggested that during glucose withdrawal, classical autophagy contributes to retromer-dependent GLUT1 surface trafficking via interactions between LC3A and TBC1D5, the experiments here demonstrate that during basal conditions or lysosomal damage, ATGs that are not involved in mATG8ylation, such as FIP200, are not functionally required for retromer-dependent sorting of GLUT1. Overall, these studies suggest a unique role for ATG5 in the control of retromer function, and that conjugation of ATG8 to single membranes (CASM) is a partial contributor to these phenotypes. 

Strengths: 

(1) Overall, these studies suggest a unique non-autophagic role for ATG5 in the control of retromer function. They also demonstrate that conjugation of ATG8 to single membranes (CASM) is a partial contributor to these phenotypes. Overall, these data point to a new role for ATG5 and CASM-dependent mATG8ylation in lysosomal membrane repair and trafficking. 

(2) Although the studies are overall supportive of the proposed model that the retromer is controlled by CASM-dependent mATG8-ylaytion, it is noteworthy that previous studies of GLUT1 trafficking during glucose withdrawal (Roy et al. Mol Cell, PMID: 28602638) were predominantly conducted in cells lacking ATG5 or ATG7, which would not be able to discriminate between a CASM-dependent vs. canonical autophagy-dependent pathway in the control of GLUT1 sorting. Is the lack of GLUT1 mis-sorting to lysosomes observed in FIP200 and ATG13KO cells also observed during glucose withdrawal? Notably, deficiencies in glycolysis and glucose-dependent growth have been reported in FIP200 deficient fibroblasts (Wei et al. G&D, PMID: 21764854) so there may be differences in regulation dependent on the stress imposed on a cell. 

We thank the reviewer on the overall assessment of the strengths of the study.

We have discussed in the manuscript the elegant study by Roy et al., PMID 28602683. To accommodate reviewer’s comment, we will additionally emphasize in the text that our study is focused on basal conditions and conditions that perturb endolysosomal compartments. We agree with the reviewer that under metabolic stress conditions (such as glucose limitation) more complex pathways may be engaged and will acknowledge that in the discussion.

Weaknesses: 

(1) Additional controls are needed to clarify the role of CASM in the control of retromer function. Because the manuscript proposes both CASM-dependent and independent pathways in the ATG5 mediated regulation of the retromer, it is important to provide robust evidence that CASM is required for retromer-dependent GLUT1 sorting to the plasma membrane vs. lysosome. The experiments with monsensin in Fig. 7C-E are consistent with but not unequivocally corroborative of a role for CASM.

We fully agree with the reviewer. In fact, our data with bafilomycin A1 treatment causing GLUT1 miss-sorting (manuscript line 317) show that it is the perturbance of lysosomes  and not CASM per se that leads to mis-sorting of GLUT1 (Fig. 7D,E). Note that it has been shown (PMIDs: 28296541, 25484071 and 37796195) that although bafilomycin A1 deacidifies lysosomes it does not induce but instead inhibits CASM. This is because bafilomycin A1 cases dissociation of V1 and V0 sectors of V-ATPase, unlike other CASM-inducing agents which promote V1 V0 association. Complementing this, our data with ATG2AB DKO and ESCRT VPS37A KO (Fig. 8A-F) indicate that the repair of lysosomes is important to keep the retromer machinery functional (as illustrated in Fig. 8G). This may be one of the effector mechanisms downstream of membrane atg8ylation in general and hence also downstream of CASM. We will revise Fig. 7 title to read “Lysosomal damage causes GLUT1 mis-sorting” and will explain these relationships in the text.

Based on the results shown with ATG16KO in Fig 4A-D, rescue experiments of these 16KO cells with WT vs. C-terminal WD40 mutant versions of ATG16 will specifically assess the requirement for CASM and potentially provide more rigorous support for the conclusions drawn. 

We will carry out the experiment proposed by the reviewer for the planned revision.

(2) Also, the role of TBC1D5 should be further clarified. In Fig S7, are there any changes in the interactions between TBC1D5 and VPS35 in response to LLOMe or other agents utilized to induce CASM?

We thank the reviewer for pointing this out. We do have data with VPS35 in co-IPs shown in Fig. S7.  There is no change in the amounts of VPS35 or TBC1D5 in GFP-LC3A co-IPs. We will include a graph with quantification in the revised manuscript and emphasize this point.

Does TBC1D5 loss-of-function modulate the numbers of GLUT1 and Gal3 puncta observed in ATG5 deficient cells in response to LLOMe? 

We agree that TBC1D5 is an interesting aspect. However, because TBC1D5 does not change its interactions in the experiments in our study, we consider this topic (i.e. whether TBC1D5 phenocopies VPS35 and ATG5 KOs in its effects on Gal3) to be beyond the scope of the present work. We underscore that LLOMe (lysosomal damage) mis-sorts GLUT1 even without any genetic intervention (e.g., in WT cells in the absence of ATG5 KO; Fig. 7). Thus, in our opinion the effects of TBC1D5 inactivation may be a moot point.

(3) Finally, the studies here are motivated by experiments in Fig. S1 (as well as other studies from the Deretic and Stallings labs) suggesting unique autophagy-independent functions for ATG5 in myeloid cells and neutrophils in susceptibility to Mycobacterium tuberculosis infection. However, it is curious that no attempt is made to relate the mechanistic data regarding the retromer or GLUT1 receptor mis-sorting back to the infectious models. Do myeloid cells or neutrophils lacking ATG5 have deficiencies in glucose uptake or GLUT1 cell surface levels? 

Reviewer’s point is well taken. Glucose uptake, its metabolism, and diabetes underly resurgence in TB in certain populations and are important factors in a range of other diseases. This was alluded to in our discussion (lines 461-469). However, these are complex topics for future studies. We will expand this section of the discussion.

Reviewer #3 (Public Review): 

In this manuscript, Padder et al. used APEX2 proximity labeling to find an interaction between ATG5 and the core components of the Retromer complex, VPS26, VPS29, and VPS35. Further studies revealed that ATG5 KO inhibited the trafficking of GLUT1 to the plasma membrane. They also found that other autophagy genes involved in membrane atg8ylation affected GLUT1 sorting. However, knocking out other essential autophagy genes such as ATG13 and FIP200 did not affect GLUT1 sorting. These findings suggest that ATG5 participates in the function of the Retromer in a noncanonical autophagy manner. Overall, the methods and techniques employed by the authors largely support their conclusions. These findings are intriguing and significant, enriching our understanding of the non-autophagic functions of autophagy proteins and the sorting of GLUT1. Nevertheless, there are several issues that the authors need to address to further clarify their conclusions. 

(1) The authors confirmed the interaction between Atg5 and the Retromer complex through Co-IP experiments. Is the interaction between Atg5 and the Retromer direct? If it is direct, which Retromer complex protein regulates the interaction with Atg5? Additionally, does ATG5 K130R mutant enhance its interaction with the Retromer? 

AlphaFold modeling in the initial submission of our study to eLife (absent from the current version) suggested the possibility of a direct interaction between ATG5 and VPS35 with ATG12—ATG5 complex facing outwards, in which case K130R would not matter. However, mutational experiments in putative contact residues did not alter association in co-IPs. So either ATG5 interacts with other retromer subunits or more likely is in a larger protein complex containing retromer. It will take a separate study to dissect associations and find direct interaction partners. We can provide our data on the currently available modeling and mutational analyses in a full point-for-point rebuttal but believe that since they are inconclusive, they should not be included in the study.

(2) To more directly elucidate how ATG5 regulates Retromer function by interacting with the Retromer and participates in the trafficking of GLUT1 to the plasma membrane, the authors should identify which region or crucial amino acid residues of ATG5 regulate its interaction with the Retromer. Additionally, they should test whether mutations in ATG5 that disrupt its interaction with the Retromer affect Retromer function (such as participating in the trafficking of GLUT1 to the plasma membrane) and whether they affect Atg8ylation. They also need to assess whether these mutations influence canonical autophagy and lysosomal sensitivity to damage. 

Please see the response to point 1.

We thank the editors and reviewers for their assessment, constructive criticisms and recommendations.

Author response:

Public Reviews:

Reviewer #1 (Public Review): 

By mapping H3K4me2 in mouse oocytes and pre-implantation embryos, the authors aim to elucidate how this histone modification is erased and re-established during the parental-to-zygotic transition, as well as how the reprogramming of H3K4me2 regulates gene expression and facilitates zygotic genome activation.

Employing an improved CUT&RUN approach, the authors successfully generated H3K4me2 profiling data from a limited number of embryos. While the profiling experiments are very well executed, several weaknesses, particularly in data analysis, are apparent:

(1) The study emphasizes H3K4me2, which often serves as a precursor to H3K4me3, a well-studied modification during early development. Analyzing the new H3K4me2 dataset alongside published H3K4me3 data is crucial for comprehensively understanding epigenetic reprogramming post-fertilization and the interplay between histone modifications. However, the current analysis is preliminary and lacks depth.

Thank you very much for your valuable suggestions. The data of histone H3K4me3 in humans and mice has been published，and our previous data revealed the unique pattern of H3K4me3 during early human embryos and oocytes (Xia et al., 2019). So, this study mainly focuses on the localization of H3K4me2 in mouse oocytes and preimplantation embryos, how it is erased and re-established during mammalian parental-to-zygote transition, and its function. The combined analysis of H3K4me2 and H3K4me3 is not our main work, but it is not ruled out that there may be new discoveries between these two histones. Previously, our data tended to show that the H3K4me2 not only acts as a precursor of H3K4me3, but also plays its role independently.

(2) Tranylcypromine (TCP) is known as an irreversible inhibitor of monoamine oxidase and LSD1. While the authors suggest TCP inhibits the expression of LSD2, this assertion is questionable. Given TCP's potential non-specific effects in cells, conclusions related to the experiments using TCP should be made with caution.

Thank you for pointing this out, and we thank the reviewer again for the important suggestion. We found that the previous study indicated that TCP was a non-reversible inhibitor of LSD1 and LSD2, but according to our data, the content of LSD1 was very low in the early stages of mouse embryos, which mainly inhibited the function of LSD2. (Binda et al., 2010; Fang et al., 2010 )

(3) Some batches of H3K4me2 antibody are known to cross-react with H3K4me3. Has the H3K4me2 antibody used in CUT&RUN been tested for such cross-reactivity? Heatmaps in the figures indeed show similar distribution for H3K4me2 and H3K4me3, further raising concerns about antibody specificity.

We thank the reviewer for the insightful comments. The H3K4me2 antibody was purchased from Millipore (cat. 07030). Figure 2A shows the specific enrichment area of H3K4me2 in promoter and distal region. Some batches of H3K4me2 antibody are known to cross-react with H3K4me3, but the H3K4me2 antibody we used in our CUT&RUN seems to have Low cross-reactivity.

(4) Certain statements lack supporting references or figures (examples on page 9 can be found on line 245, line 254, and line 258).

Thank you for pointing this out, and we will add references to support the statement in the paper as suggested.

(5) Extensive language editing is recommended to clarify ambiguous sentences. Additionally, caution should be taken to avoid overstatement - most analyses in this study only suggest correlation rather than causality.

Thank you for your kind comments. We will revise the expression in the manuscript later.

Reviewer #2 (Public Review):

Chong Wang et al. investigated the role of H3K4me2 during the reprogramming processes in mouse preimplantation embryos. The authors show that H3K4me2 is erased from GV to MII oocytes and re-established in the late 2-cell stage by performing Cut & Run H3K4me2 and immunofluorescence staining. Erasure and re-establishment of H3K4me2 have not been studied well, and profiling of H3K4me2 in germ cells and preimplantation embryos is valuable to understanding the reprogramming process and epigenetic inheritance.

(1) The authors claim that the Cut & Run worked for MII oocytes, zygotes, and the 2-cell embryos. However, it is unclear if H3K4me2 is erased during the stage or if the Cut & Run did not work for these samples. To support the hypothesis of the erasure of H3K4me2, the authors conducted immunofluorescence staining, and H3k4me2 was undetected in the MII oocyte, PN5, and 2-cell stage. However, the published papers showed strong staining of H3K4me2 at the zygote stage and 2-cell stage ((Ancelin et al., 2016; Shao et al., 2014)). The authors need to cite these papers and discuss the contradictory findings.

The authors used 165 MII oocytes and 190 GV oocytes for the Cut & Run. The amount of DNA in MII oocytes is halved because of the emission of the first polar body. Would it be a reason that H3K4me2 has fewer H3K4me2 peaks in MII oocytes than GV oocytes?

First of all, thank you for your valuable advice. The published papers showed strong staining of H3K4me2 at the zygote stage and 2-cell stage, which is interesting. I think we may have used different parameters in the confocal laser shooting process(Ancelin et al., 2016). We used the same parameter to continuously shoot the blastocyst stage from the GV stage. If we only shot the fertilized egg and the 2-cell stage, I think we may also see weak fluorescence at the 2-cell stage under different parameters. We will refer to this reference and discuss it in the resubmitted version.

Moreover, you mentioned the H3K4me2 has fewer H3K4me2 peaks in MII oocytes than GV oocytes, because the MII expelled the polar body. There is no problem with this logic. However, the first polar body expelled from the MII stage is still in the zona pellucida, and we also collected the polar body in the CUT&RUN experiment; Therefore, compared to GV, the DNA content of MII samples is not halved. After further discussion, we believe that the reduction of H3K4me2 peaks in MII stage compared with GV stage may be closely related to oocyte maturation. It is the specific modification of histones in different forms at different times that affects the chromatin structure change appropriately with the different stages of meiosis. At present, it has been confirmed that H3K4me3 gradually decreases from GV to MII stage during the maturation of human oocytes. H3K27me3 did not change from GV to MII stage.

In Figure 3C, 98% (13,183/13,428) of H3K4me2 marked genes in GV oocytes overlap with those in the 4-cell stage. Furthermore, 92% (14,049/15,112) of H3K4me2 marked genes in sperm overlap with those in the 4-cell stage. Therefore, most regions maintain germ line-derived H3K4me2 in the 4-cell stage. The authors need to clarify which regions of germ line-derived H3K4me2 are maintained or erased in preimplantation embryos. Additionally, it would be interesting to investigate which regions show the parental allele-specific H3K4me2 in preimplantation embryos since the authors used hybrid preimplantation embryos (B6 x DBA).

Thank you very much for your suggestion. Further analysis of which regions show the parental allele-specific H3K4me2 in preimplantation embryos will make the study more interesting. We will discuss this in depth in resubmitted vision.

(2) The authors claim that Kdm1a is rarely expressed during mouse embryonic development (Figure 4A). However, the published paper showed that KDM1a is present in the zygote and 2-cell stage using immunostaining and western blotting ((Ancelin et al., 2016)). Additionally, this paper showed that depletion of maternal KDM1A protein results in developmental arrest at the two-cell stage, and therefore, KDM1a is functionally important in early development. The authors should have cited the paper and described the role of KDM1a in early embryos.

In the analysis of this experiment, we believe that in the early embryonic development of mice, the expression of KDM1A is lower than that of KDM1B, which is relative. Similarly, the transcriptome data we cite also show that KDM1A is expressed at elevated levels during oocyte maturation and fertilization compared to immature oocytes. In addition, the effects of loss of maternal KDM1a on embryonic development were not discussed. We believe that the absence of maternal KDM1b blocks embryonic development, and we will cite and discus the references later.

(3) The authors used the published RNA data set and interpreted that KDM1B (LSD2) was highly expressed at the MII stage (Figure S3A). However, the heat map shows that KDM1B expression is high in growing oocytes but not at 8w_oocytes and MII oocytes. The authors need to interpret the data accurately.

After re-checking the data, we found that there was a problem with the normalization method of our heat map, and we will re-make the heatmap and submit it in the modified version. With reference to Figure 4A, the content of Kdm1b is indeed higher than that of Kdm1a.

(4) All embryos in the TCP group were arrested at the four-cell stage. Embryos generated from KDM1b KO females can survive until E10.5 (Ciccone et al., 2009); therefore, TCP-treated embryos show a more severe phenotype than oocyte-derived KDM1b deleted embryos. Depletion of maternal KDM1A protein results in developmental arrest at the two-cell stage ((Ancelin et al., 2016)). The authors need to examine whether TCP treatment affects KDM1a expression. Western blotting would be recommended to quantify the expression of KDM1A and KDM1B in the TCP-treated embryos.

We will further dig the transcriptome data to confirm the specificity of TCP to KDM1b. In addition, the intervention of TCP on the whole fertilized egg in this study increased the H3K4me2 content, and the embryo development retarding effect was more significant than that obtained by crossing with normal paternal lines after knocking down KDM1B from the mother.

(5) H3K4me2 is increased dramatically in the TCP-treated embryos in Figure 4 (the intensity is 1,000 times more than the control). However, the Cut & Run H3K4me2 shows that the H3K4me2 signal is increased in 251 genes and decreased in 194 genes in the TCP-treated embryos (Fold changes > 2, P < 0.01). The authors need to explain why the gain of H3K4me2 is less evident in the Cut & Run data set than in the immunofluorescence result.

Thanks a lot for your question. In the experimental group, the fluorescence value of H3K4me2 in IF was increased by 1000 times (Figure 4E), and the expression of H3K4Me2-related genes in CR was up-regulated and down-regulated for a total of 445 changes (Figure 6A). In our opinion, as a semi-quantitative analysis, immunofluorescence cannot be compared with the quantitative analysis method of CR because of the different analysis models and threshold Settings.

References

Ancelin, K., ne Syx, L., Borensztein, M., mie Ranisavljevic, N., Vassilev, I., Briseñ o-Roa, L., Liu, T., Metzger, E., Servant, N., Barillot, E., Chen, C.-J., Schü le, R., & Heard, E. (2016). Maternal LSD1/KDM1A is an essential regulator of chromatin and transcription landscapes during zygotic genome activation. https://doi.org/10.7554/eLife.08851.001

Ciccone, D. N., Su, H., Hevi, S., Gay, F., Lei, H., Bajko, J., Xu, G., Li, E., & Chen, T. (2009). KDM1B is a histone H3K4 demethylase required to establish maternal genomic imprints. Nature, 461(7262), 415-418. https://doi.org/10.1038/nature08315

Shao, G. B., Chen, J. C., Zhang, L. P., Huang, P., Lu, H. Y., Jin, J., Gong, A. H., & Sang, J. R. (2014). Dynamic patterns of histone H3 lysine 4 methyltransferases and demethylases during mouse preimplantation development. In Vitro Cellular and Developmental Biology - Animal, 50(7), 603-613. https://doi.org/10.1007/s11626-014-9741-6

References

Xia W, Xu J, Yu G, Yao G, Xu K, Ma X, Zhang N, Liu B, Li T, Lin Z, Chen X, Li L, Wang Q, Shi D, Shi S, Zhang Y, Song W, Jin H, Hu L, Bu Z, Wang Y, Na J, Xie W, Sun YP. Resetting histone modifications during human parental-to-zygotic transition. Science. 2019 Jul 26;365(6451):353-360. doi: 10.1126/science.aaw5118. Epub 2019 Jul 4. PMID: 31273069.

Binda C, Valente S, Romanenghi M, Pilotto S, Cirilli R, Karytinos A, Ciossani G, Botrugno OA, Forneris F, Tardugno M, Edmondson DE, Minucci S, Mattevi A, Mai A. Biochemical, structural, and biological evaluation of tranylcypromine derivatives as inhibitors of histone demethylases LSD1 and LSD2. J Am Chem Soc. 2010 May 19;132(19):6827-33.

Fang R, Barbera AJ, Xu Y, Rutenberg M, Leonor T, Bi Q, Lan F, Mei P, Yuan GC, Lian C, Peng J, Cheng D, Sui G, Kaiser UB, Shi Y, Shi YG. Human LSD2/KDM1b/AOF1 regulates gene transcription by modulating intragenic H3K4me2 methylation. Mol Cell. 2010 Jul 30;39(2):222-33. doi: 10.1016/j.molcel.2010.07.008. PMID: 20670891; PMCID: PMC3518444.

Ancelin K, Syx L, Borensztein M, Ranisavljevic N, Vassilev I, Briseño-Roa L, Liu T, Metzger E, Servant N, Barillot E, Chen CJ, Schüle R, Heard E. Maternal LSD1/KDM1A is an essential regulator of chromatin and transcription landscapes during zygotic genome activation. Elife. 2016 Feb 2;5:e08851. doi: 10.7554/eLife.08851. PMID: 26836306; PMCID: PMC4829419.

Reviewer #3 (Public Review):

Summary:

This study explores the dynamic reprogramming of histone modification H3K4me2 during the early stages of mammalian embryogenesis. Utilizing the advanced CUT&RUN technique coupled with high-throughput sequencing, the authors investigate the erasure and re-establishment of H3K4me2 in mouse germinal vesicle (GV) oocytes, metaphase II (MII) oocytes, and early embryos.

Strengths:

The findings provide valuable insights into the temporal and spatial dynamics of H3K4me2 and its potential role in zygotic genome activation (ZGA).

Weaknesses:

The study primarily remains descriptive at this point. It would be advantageous to conduct further comprehensive functional validation and mechanistic exploration.

Key areas for improvement include enhancing the innovation and novelty of the study, providing robust functional validation, establishing a clear model for H3K4me2's role, and addressing technical and presentation issues. The text would benefit from the introduction of a novel conceptual framework or model that provides a clear explanation of the functional consequences and molecular mechanisms underlying H3K4me2 reprogramming in the transition from parental to early embryonic development.

While the findings are significant, the current manuscript falls short in several critical areas. Addressing major and minor issues will significantly strengthen the study's contribution to the field of epigenetic reprogramming and embryonic development.

Author response:

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

Reviewer #1 (Public Review):

The authors did a great job addressing the weaknesses I raised in the previous round of review, except on the generalizability of the current result in the larger context of multi-attribute decision-making. It is not really a weakness of the manuscript but more of a limitation of the studied topic, so I want to keep this comment for public readers.

The reward magnitude and probability information are displayed using rectangular bars of different colors and orientations. Would that bias subjects to choose an additive rule instead of the multiplicative rule? Also, could the conclusion be extended to other decision contexts such as quality and price, where a multiplicative rule is hard to formulate?

We thank the reviewer for the comment. With regards whether the current type of stimuli may have biased participants to use an additive rule rather, we believe many other forms of stimuli for representing choice attributes would be equally likely to cause a similar bias. This is because the additive strategy is an inherently simplistic and natural way to integrate different pieces of non-interacting information. More importantly, even though it is easy to employ an additive strategy, most participants still demonstrated some levels of employing the multiplicative rule. However, it would indeed be interesting for future studies to explore whether the current composite model remains dominant in situations where the optimal solutions require an additive or subtractive rule, such as those concerning quality and price.

“The same would apply even with a different choice of cues as long as the information is conveyed by two independent visual features.”

“While the additive strategy is a natural and simple approach for integrating non-interacting pieces of information, to some extent, participants also used the multiplicative strategy that was optimal in the current experiment. A general question for such composite models is whether people mix two strategies in a consistent manner on every trial or whether there is some form of probabilistic selection occurring between the two strategies on each trial such that only one strategy is used on any given trial while, on average, one strategy is more probable than the other. It would also be interesting to examine whether a composite model is appropriate in contexts where the optimal solution is additive or subtractive, such as those concerning quality and price.”

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

Reviewer #1 (Public Review):

Summary:

The current study provided a follow-up analysis using published datasets focused on the individual variability of both the distraction effect (size and direction) and the attribute integration style, as well as the association between the two. The authors tried to answer the question of whether the multiplicative attribute integration style concurs with a more pronounced and positively oriented distraction effect.

Strengths:

The analysis extensively examined the impacts of various factors on decision accuracy, with a particular focus on using two-option trials as control trials, following the approach established by Cao & Tsetsos (2022). The statistical significance results were clearly reported.

The authors meticulously conducted supplementary examinations, incorporating the additional term HV+LV into GLM3. Furthermore, they replaced the utility function from the expected value model with values from the composite model.

We thank the reviewer for the positive response and are pleased that the reviewer found our report interesting.

Reviewer #1 Comment 1

Weaknesses:

There are several weaknesses in terms of theoretical arguments and statistical analyses.

First, the manuscript suggests in the abstract and at the beginning of the introduction that the study reconciled the "different claims" about "whether distraction effect operates at the level of options' component attributes rather than at the level of their overall value" (see line 13-14), but the analysis conducted was not for that purpose. Integrating choice attributes in either an additive or multiplicative way only reflects individual differences in combining attributes into the overall value. The authors seemed to assume that the multiplicative way generated the overall value ("Individuals who tended to use a multiplicative approach, and hence focused on overall value", line 20-21), but such implicit assumption is at odds with the statement in line 77-79 that people may use a simpler additive rule to combine attributes, which means overall value can come from the additive rule.

We thank the reviewer for the comment. We have made adjustments to the manuscript to ensure that the message delivered within this manuscript is consistent. Within this manuscript, our primary focus is on the different methods of value integration in which the overall value is computed (i.e., additive, multiplicative, or both), rather than the interaction at the individual level of attributes. However, we do not exclude the possibility that the distractor effect may occur at multiple levels. Nevertheless, in light of the reviewer’s comment, we agree that we should focus the argument on whether distractors facilitate or impair decision making and downplay the separate argument about the level at which distractor effects operate. We have now revised the abstract:

“It is widely agreed that people make irrational decisions in the presence of irrelevant distractor options. However, there is little consensus on whether decision making is facilitated or impaired by the presence of a highly rewarding distractor or whether the distraction effect operates at the level of options’ component attributes rather than at the level of their overall value. To reconcile different claims, we argue that it is important to incorporate consideration of the diversity of people’s ways of decision making. We focus on a recent debate over whether people combine choice attributes in an additive or multiplicative way. Employing a multi-laboratory dataset investigating the same decision making paradigm, we demonstrated that people used a mix of both approaches and the extent to which approach was used varied across individuals. Critically, we identified that this variability was correlated with the effect of the distractor on decision making. Individuals who tended to use a multiplicative approach to compute value, showed a positive distractor effect. In contrast, in individuals who tended to use an additive approach, a negative distractor effect (divisive normalisation) was prominent. These findings suggest that the distractor effect is related to how value is constructed, which in turn may be influenced by task and subject specificities. Our work concurs with recent behavioural and neuroscience findings that multiple distractor effects co-exist.” (Lines 12-26)

Furthermore, we acknowledge that the current description of the additive rule could be interpreted in several ways. The current additive utility model described as:

where  is the options’ utility,  is the reward magnitude,  is the probability, and  is the magnitude/probability weighing ratio . If we perform comparison between values according to this model (i.e., HV against LV), we would arrive at the following comparison:

If we rearrange (1), we will arrive at:

While equations (1) and (2) are mathematically equivalent, equation (1) illustrates the interpretation where the comparison of the utilities occurs after value integration and forming an overall value. On the other hand, equation (2) can be broadly interpreted as the comparison of individual attributes in the absence of an overall value estimate for each option. Nonetheless, while we do not exclude the possibility that the distractor effect may occur at multiple levels, we have made modifications to the main manuscript employ more consistently a terminology referring to different methods of value estimation while recognizing that our empirical results are compatible with both interpretations.

Reviewer #1 Comment 2

The second weakness is sort of related but is more about the lack of coherent conceptual understanding of the "additive rule", or "distractor effect operates at the attribute level". In an assertive tone (lines 77-80), the manuscript suggests that a weighted sum integration procedure of implementing an "additive rule" is equal to assuming that people compare pairs of attributes separately, without integration. But they are mechanistically distinct. The additive rule (implemented using the weighted sum rule to combine probability and magnitude within each option and then applying the softmax function) assumes value exists before comparing options. In contrast, if people compare pairs of attributes separately, preference forms based on the within-attribute comparisons. Mathematically these two might be equivalent only if no extra mechanisms (such as inhibition, fluctuating attention, evidence accumulation, etc) are included in the within-attribute comparison process, which is hardly true in the three-option decision.

We thank the reviewer for the comment. As described in our response to Reviewer #1 Comment 1, we are aware and acknowledge that there may be multiple possible interpretations of the additive rule. We also agree with the reviewer that there may be additional mechanisms that are involved in three- or even two- option decisions, but these would require additional studies to tease apart. Another motivation for the approach used here, which does not explicitly model the extra mechanisms the reviewer refers to was due to the intention of addressing and integrating findings from previous studies using the same dataset [i.e. (Cao & Tsetsos, 2022; Chau et al., 2020)]. Lastly, regardless of the mechanistic interpretation, our results show a systematic difference in the process of value estimation. Modifications to the manuscript text have been made consistent with our motivation (please refer to the reply and the textual changes proposed in response to the reviewer’s previous comment: Reviewer #1 Comment 1).

Reviewer #1 Comment 3

Could the authors comment on the generalizability of the current result? The reward magnitude and probability information are displayed using rectangular bars of different colors and orientations. Would that bias subjects to choose an additive rule instead of the multiplicative rule? Also, could the conclusion be extended to other decision contexts such as quality and price, whether a multiplicative rule is hard to formulate?

We thank the reviewer for the comment. We agree with the observation that the stimulus space, with colour linearly correlated with magnitude, and orientation linearly correlated with probability, may bias subjects towards an additive rule. But that’s indeed the point: in order to maximise reward, subjects should have focused on the outcome space without being driven by the stimulus space. In practice, people are more or less successful in such endeavour. Nevertheless, we argue that the specific choice of visual stimuli we used is no more biased towards additive space than any other. In fact, as long as two or more pieces of information are provided for each option, as opposed to a single cue whose value was previously learned, there will always be a bias towards an additive heuristic (a linear combination), regardless of whether the cues are shapes, colours, graphs, numbers, words.

As the reviewer suggested, the dataset analyzed in the current manuscript suggests that the participants were leaning towards the additive rule. Although there was a general tendency using the additive rule while choosing between the rectangular bars, we can still observe a spread of individuals using either, or both, additive and multiplicative rules, suggesting that there was indeed diversity in participants’ decision making strategies in our data.

In previous studies, it was observed that human and non-human individuals used a mix of multiplicative and additive rules when they were tested on experimental paradigms different from ours (Bongioanni et al., 2021; Farashahi et al., 2019; Scholl et al., 2014). It was also observed that positive and negative distractor effects can be both present in the same data set when human and non-human individuals made decisions about food and social partner (Chang et al., 2019; Louie et al., 2013). It was less clear in the past whether the precise way a distractor affects decision making (i.e., positive/negative distractor effect) is related to the use of decision strategy (i.e., multiplicative/additive rules) and this is exactly what we are trying to address in this manuscript. A follow-up study looking at neural data (such as functional magnetic resonance imaging data) could provide a better understanding of the mechanistic nature of the relationship between distractor effects and decision strategy that we identified here.

We agree with the reviewer that it is true that a multiplicative strategy may not be applicable to some decision contexts. Here it is important to look at the structure of the optimal solution (the one maximizing value in the long run). Factors modulating value (such as probability and temporal delay) require a non-linear (e.g., multiplicative solution), while factors of the cost-benefit form (such as effort and price) require a linear solution (e.g., subtraction). In the latter scenario the additive heuristic would coincide with the optimal solution, and the effect addressed in this study may not be revealed. Nonetheless, the present data supports the notion of distinct neural mechanisms at least for probabilistic decision-making, and is likely applicable to decision-making in general.

Our findings, in conjunction with the literature, also suggest that a positive distractor effect could be a general phenomenon in decision mechanisms that involve the medial prefrontal cortex. For example, it has been shown that the positive distractor effect is related to a decision mechanism linked to medial prefrontal cortex [especially the ventromedial prefrontal cortex (Chau et al., 2014; Noonan et al., 2017)]. It is also known a similar brain region is involved not only when individuals are combining information using a multiplicative strategy (Bongioanni et al., 2021), but also when they are combining information to evaluate new experience or generalize information (Baram et al., 2021; Barron et al., 2013; Park et al., 2021). We have now revised the Discussion to explain this:

“In contrast, the positive distractor effect is mediated by the mPFC (Chau et al., 2014; Fouragnan et al., 2019). Interestingly, the same or adjacent, interconnected mPFC regions have also been linked to the mechanisms by which representational elements are integrated into new representations (Barron et al., 2013; Klein-Flügge et al., 2022; Law et al., 2023; Papageorgiou et al., 2017; Schwartenbeck et al., 2023). In a number of situations, such as multi-attribute decision making, understanding social relations, and abstract knowledge, the mPFC achieves this by using a spatial map representation characterised by a grid-like response (Constantinescu et al., 2016; Bongioanni et al., 2021; Park et al., 2021) and disrupting mPFC leads to the evaluation of composite choice options as linear functions of their components (Bongioanni et al., 2021). These observations suggest a potential link between positive distractor effects and mechanisms for evaluating multiple component options and this is consistent with the across-participant correlation that we observed between the strength of the positive distractor effect and the strength of non-additive (i.e., multiplicative) evaluation of the composite stimuli we used in the current task. Hence, one direction for model development may involve incorporating the ideas that people vary in their ways of combining choice attributes and each way is susceptible to different types of distractor effect.” (Lines 260-274)

Reviewer #1 Comment 4

The authors did careful analyses on quantifying the "distractor effect". While I fully agree that it is important to use the matched two-option trials and examine the interaction terms (DV-HV)T as a control, the interpretation of the results becomes tricky when looking at the effects in each trial type. Figure 2c shows a positive DV-HV effect in two-option trials whereas the DV-HV effect was not significantly stronger in three-option trials. Further in Figure 5b,c, in the Multiplicative group, the effect of DV-HV was absent in the two-option trials and present in the three-option trials. In the Additive group, however, the effect of DV-HV was significantly positive in the two-option trials but was significantly lowered in the three-option trials. Hence, it seems the different distractor effects were driven by the different effects of DV-HV in the two-option trials, rather than the three-option trials?

We thank the reviewer for the comment. While it may be a bit more difficult to interpret, the current method of examining the (DV−HV)T term rather than (DV−HV) term was used because it was the approach used in a previous study (Cao & Tsetsos, 2022).

During the design of the original experiments, trials were generated pseudo-randomly until the DV was sufficiently decorrelated from HV−LV. While this method allows for better group-level examination of behaviour, Cao and Tsetsos were concerned that this approach may have introduced unintended confounding covariations to some trials. In theory, one of the unintended covariations could occur between the DV and specific sets of reward magnitude and probability of the HV and LV. The covariation between parameters can lead to an observable positive distractor effect in the DV−HV as a consequence of the attraction effect or an unintended byproduct of using an additive method of integrating attributes [for further elaboration, please refer to Figure 1 in (Cao & Tsetsos, 2022)]. While it may have some limitations, the approach suggested by Cao and Tsetsos has the advantage of leveraging the DV−HV term to absorb any variance contributed by possible confounding factors such that true distractor effects, if any, can be detected using the (DV−HV)T term.

Reviewer #1 Comment 5

Note that the pattern described above was different in Supplementary Figure 2, where the effect of DV-HV on the two-option trials was negative for both Multiplicative and Additive groups. I would suggest considering using Supplementary Figure 2 as the main result instead of Figure 5, as it does not rely on multiplicative EV to measure the distraction effect, and it shows the same direction of DV-HV effect on two-option trials, providing a better basis to interpret the (DV-HV)T effect.

We thank the reviewer for the comments and suggestion. However, as mentioned in the response to Reviewer #1 Comment 4, the current method of analysis adopted in the manuscript and the interpretation of only (DV−HV)T is aimed to address the possibility that the (DV−HV) term may be capturing some confounding effects due to covariation. Given that the debate that is addressed specifically concerns the (DV−HV)T term, we elected to display Figure 5 within the main text and keep the results of the regression after replacing the utility function with the composite model as Supplementary Figure 5 (previously labelled as Supplementary Figure 2).

Reviewer #2 (Public Review):

This paper addresses the empirical demonstration of "distractor effects" in multi-attribute decision-making. It continues a debate in the literature on the presence (or not) of these effects, which domains they arise in, and their heterogeneity across subjects. The domain of the study is a particular type of multi-attribute decision-making: choices over risky lotteries. The paper reports a re-analysis of lottery data from multiple experiments run previously by the authors and other laboratories involved in the debate.

Methodologically, the analysis assumes a number of simple forms for how attributes are aggregated (adaptively, multiplicatively, or both) and then applies a "reduced form" logistic regression to the choices with a number of interaction terms intended to control for various features of the choice set. One of these interactions, modulated by ternary/binary treatment, is interpreted as a "distractor effect."

The claimed contribution of the re-analysis is to demonstrate a correlation in the strength/sign of this treatment effect with another estimated parameter: the relative mixture of additive/multiplicative preferences.

We thank the reviewer for the positive response and are pleased that the reviewer found our report interesting.

Reviewer #2 Comment 1

Major Issues

(1) How to Interpret GLM 1 and 2

This paper, and others before it, have used a binary logistic regression with a number of interaction terms to attempt to control for various features of the choice set and how they influence choice. It is important to recognize that this modelling approach is not derived from a theoretical claim about the form of the computational model that guides decision-making in this task, nor an explicit test for a distractor effect. This can be seen most clearly in the equations after line 321 and its corresponding log-likelihood after 354, which contain no parameter or test for "distractor effects". Rather the computational model assumes a binary choice probability and then shoehorns the test for distractor effects via a binary/ternary treatment interaction in a separate regression (GLM 1 and 2). This approach has already led to multiple misinterpretations in the literature (see Cao & Tsetsos, 2022; Webb et al., 2020). One of these misinterpretations occurred in the datasets the authors studied, in which the lottery stimuli contained a confound with the interaction that Chau et al., (2014) were interpreting as a distractor effect (GLM 1). Cao & Tsetsos (2022) demonstrated that the interaction was significant in binary choice data from the study, therefore it can not be caused by a third alternative. This paper attempts to address this issue with a further interaction with the binary/ternary treatment (GLM 2). Therefore the difference in the interaction across the two conditions is claimed to now be the distractor effect. The validity of this claim brings us to what exactly is meant by a "distractor effect."

The paper begins by noting that "Rationally, choices ought to be unaffected by distractors" (line 33). This is not true. There are many normative models that allow for the value of alternatives (even low-valued "distractors") to influence choices, including a simple random utility model. Since Luce (1959), it has been known that the axiom of "Independence of Irrelevant Alternatives" (that the probability ratio between any two alternatives does not depend on a third) is an extremely strong axiom, and only a sufficiency axiom for a random utility representation (Block and Marschak, 1959). It is not a necessary condition of a utility representation, and if this is our definition of rational (which is highly debatable), not necessary for it either. Countless empirical studies have demonstrated that IIA is falsified, and a large number of models can address it, including a simple random utility model with independent normal errors (i.e. a multivariate Probit model). In fact, it is only the multinomial Logit model that imposes IIA. It is also why so much attention is paid to the asymmetric dominance effect, which is a violation of a necessary condition for random utility (the Regularity axiom).

So what do the authors even mean by a "distractor effect." It is true that the form of IIA violations (i.e. their path through the probability simplex as the low-option varies) tells us something about the computational model underlying choice (after all, different models will predict different patterns). However we do not know how the interaction terms in the binary logit regression relate to the pattern of the violations because there is no formal theory that relates them. Any test for relative value coding is a joint test of the computational model and the form of the stochastic component (Webb et al, 2020). These interaction terms may simply be picking up substitution patterns that can be easily reconciled with some form of random utility. While we can not check all forms of random utility in these datasets (because the class of such models is large), this paper doesn't even rule any of these models out.

We thank the reviewer for the comment. In this study, one objective is to address an issue raised by Cao and Tsetsos (2022), suggesting that the distractor effect claimed in the Chau et al. (2014) study was potentially confounded by unintended correlation introduced between the distractor and the chooseable options. They suggested that this could be tested by analyzing the control binary trials and the experimental ternary trials in a single model (i.e., GLM2) and introducing an interaction term (DV−HV)T. The interaction term can partial out any unintended confound and test the distractor effect that was present specifically in the experimental ternary trials. We adopted these procedures in our current studies and employed the interaction term to test the distractor effects. The results showed that overall there was no significant distractor effect in the group. We agree with the reviewer’s comment that if we were only analysing the ternary trials, a multinomial probit model would be suitable because it allows noise correlation between the choices. Alternatively, had a multinomial logistic model been applied, a Hausman-McFadden Test could be run to test whether the data violates the assumption of independence of irrelevant alternatives (IIA). However, in our case, a binomial model is preferred over a multinomial model because of: (1) the inclusion of the binary trials, and (2) the small number of trials in which the distractor was chosen (the median was 4% of all ternary trials).

However, another main objective of this study is to consider the possibility that the precise distractor effect may vary across individuals. This is exactly why we employed the composite model to estimate individual’s decision making strategy and investigated how that varied with the precise way the distractor influenced decision making.

In addition, we think that the reviewer here is raising a profound point and one with which we are in sympathy; it is true that random noise utility models can predict deviations from the IIA axiom. Central to these approaches is the notion that the representations of the values of choice options are noisy. Thus, when the representation is accessed, it might have a certain value on average but this value might vary from occasion to occasion as if each sample were being drawn from a distribution. As a consequence, the value of a distractor that is “drawn” during a decision between two other options may be larger than the distractor’s average value and may even have a value that is larger than the value drawn from the less valuable choice option’s distribution on the current trial. On such a trial it may become especially clear that the better of the two options has a higher value than the alternative choice option. Our understanding is that Webb, Louie and colleagues (Louie et al., 2013; Webb et al., 2020) suggest an explanation approximately along these lines when they reported a negative distractor effect during some decisions, i.e., they follow the predictions of divisive normalization suggesting that decisions become more random as the distractor’s value is greater.

An alternative approach, however, assumes that rather than noise in the representation of the option itself, there is noise in the comparison process when the two options are compared. This is exemplified in many influential decision making models including evidence accumulation models such as drift diffusion models (Shadlen & Shohamy, 2016) and recurrent neural network models of decision making (Wang, 2008). It is this latter type of model that we have used in our previous investigations (Chau et al., 2020; Kohl et al., 2023). However, these two approaches are linked both in their theoretical origin and in the predictions that they make in many situations (Shadlen & Shohamy, 2016). We therefore clarify that this is the case in the revised manuscript as follows:

“In the current study and in previous work we have used or made reference to models of decision making that assume that a noisy process of choice comparison occurs such as recurrent neural networks and drift diffusion models (Shadlen & Shohamy, 2016; Wang, 2008). Under this approach, positive distractor effects are predicted when the comparison process becomes more accurate because of an impact on the noisy process of choice comparison (Chau et al., 2020; Kohl et al., 2023). However, it is worth noting that another class of models might assume that a choice representation itself is inherently noisy. According to this approach, on any given decision a sample is drawn from a distribution of value estimates in a noisy representation of the option. Thus, when the representation is accessed, it might have a certain value on average but this value might vary from occasion to occasion. As a consequence, the value of a distractor that is “drawn” during decision between two other options may be larger than the distractor’s average value and may even have a value that is larger than the value drawn from the less valuable choice option’s distribution on the current trial. On such a trial it may become especially clear that the better of the two options has a higher value than the alternative choice option. Louie and colleagues (Louie et al., 2013) suggest an explanation approximately along these lines when they reported a positive distractor effect during some decisions. Such different approaches share theoretical origins (Shadlen & Shohamy, 2016) and make related predictions about the impact of distractors on decision making.” (Lines 297-313)

Reviewer #2 Comment 2

(2) How to Interpret the Composite (Mixture) model?

On the other side of the correlation are the results from the mixture model for how decision-makers aggregate attributes. The authors report that most subjects are best represented by a mixture of additive and multiplicative aggregation models. The authors justify this with the proposal that these values are computed in different brain regions and then aggregated (which is reasonable, though raises the question of "where" if not the mPFC). However, an equally reasonable interpretation is that the improved fit of the mixture model simply reflects a misspecification of two extreme aggregation processes (additive and EV), so the log-likelihood is maximized at some point in between them.

One possibility is a model with utility curvature. How much of this result is just due to curvature in valuation? There are many reasonable theories for why we should expect curvature in utility for human subjects (for example, limited perception: Robson, 2001, Khaw, Li Woodford, 2019; Netzer et al., 2022) and of course many empirical demonstrations of risk aversion for small stakes lotteries. The mixture model, on the other hand, has parametric flexibility.

There is also a large literature on testing expected utility jointly with stochastic choice, and the impact of these assumptions on parameter interpretation (Loomes & Sugden, 1998; Apesteguia & Ballester, 2018; Webb, 2019). This relates back to the point above: the mixture may reflect the joint assumption of how choice departs from deterministic EV.

We thank the reviewer for the comment. They are indeed right to mention the vast literature on curvature in subjective valuation; however it is important to stress that the predictions of the additive model with linear basis functions are quite distinct for the predictions of a multiplicative model with non-linear basis functions. We have tested the possibility that participants’ behaviour was better explained by the latter and we showed that this was not the case. Specifically, we have added and performed model fitting on an additional model with utility curvature based on prospect theory (Kahneman & Tversky, 1979) with the weighted probability function suggested by (Prelec, 1998):

where  and  represent the reward magnitude and probability (both rescaled to the interval between 0 and 1), respectively.  is the weighted magnitude and  is the weighted probability, while  and  are the corresponding distortion parameters. This prospect theory (PT) model is included along with the four previous models (please refer to Figure 3) in a Bayesian model comparison. Results indicate that the composite model remains the best account of participants’ choice behaviour (exceedance probability = 1.000, estimated model frequency = 0.720). We have now included these results in the main text and Supplementary Figure 2:

“Supplementary Figure 2 reports an additional Bayesian model comparison performed while including a model with nonlinear utility functions based on Prospect Theory (Kahneman & Tversky, 1979) with the Prelec formula for probability (Prelec, 1998). Consistent with the above finding, the composite model provides the best account of participants’ choice behaviour (exceedance probability = 1.000, estimated model frequency = 0.720).” (Lines 193-198)

Reviewer #2 Comment 3

3) So then how should we interpret the correlation that the authors report?

On one side we have the impact of the binary/ternary treatment which demonstrates some impact of the low value alternative on a binary choice probability. This may reflect some deep flaws in existing theories of choice, or it may simply reflect some departure from purely deterministic expected value maximization that existing theories can address. We have no theory to connect it to, so we cannot tell. On the other side of the correlation, we have a mixture between additive and multiplicative preferences over risk. This result may reflect two distinct neural processes at work, or it may simply reflect a misspecification of the manner in which humans perceive and aggregate attributes of a lottery (or even just the stimuli in this experiment) by these two extreme candidates (additive vs. EV). Again, this would entail some departure from purely deterministic expected value maximization that existing theories can address.

It is entirely possible that the authors are reporting a result that points to the more exciting of these two possibilities. But it is also possible (and perhaps more likely) that the correlation is more mundane. The paper does not guide us to theories that predict such a correlation, nor reject any existing ones. In my opinion, we should be striving for theoretically-driven analyses of datasets, where the interpretation of results is clearer.

We thank the reviewer for their clear comments. Based on our responses to the previous comments it should be apparent that our results are consistent with several existing theories of choice, so we are not claiming that there are deep flaws in them, but distinct neural processes (additive and multiplicative) are revealed, and this does not reflect a misspecification in the modelling. We have revised our manuscript in the light of the reviewer’s comments in the hope of clarifying the theoretical background which informed both our data analysis and our data interpretation.

First, we note that there are theoretical reasons to expect a third option might impact on choice valuation. There is a large body of work suggesting that a third option may have an impact on the values of two other options (indeed Reviewer #2 refers to some of this work in their Reviewer #2 Comment 1), but the body of theoretical work originates partly in neuroscience and not just in behavioural economics. In many sensory systems, neural activity changes with the intensity of the stimuli that are sensed. Divisive normalization in sensory systems, however, describes the way in which such neural responses are altered also as a function of other adjacent stimuli (Carandini & Heeger, 2012; Glimcher, 2022; Louie et al., 2011, 2013). The phenomenon has been observed at neural and behavioural levels as a function not just of the physical intensity of the other stimuli but as a function of their associated value (Glimcher, 2014, 2022; Louie et al., 2011, 2015; Noonan et al., 2017; Webb et al., 2020).

Analogously there is an emerging body of work on the combinatorial processes that describe how multiple representational elements are integrated into new representations (Barron et al., 2013; Papageorgiou et al., 2017; Schwartenbeck et al., 2023). These studies have originated in neuroscience, just as was the case with divisive normalization, but they may have implications for understanding behaviour. For example, they might be linked to behavioural observations that the values assigned to bundles of goods are not necessarily the sum of the values of the individual goods (Hsee, 1998; List, 2002). One neuroscience fact that we know about such processes is that, at an anatomical level, they are linked to the medial frontal cortex (Barron et al., 2013; Fellows, 2006; Hunt et al., 2012; Papageorgiou et al., 2017; Schwartenbeck et al., 2023). A second neuroscientific fact that we know about medial frontal cortex is that it is linked to any positive effects that distractors might have on decision making (Chau et al., 2014; Noonan et al., 2017). Therefore, we might make use of these neuroscientific facts and theories to predict a correlation between positive distractor effects and non-additive mechanisms for determining the integrated value of multi-component choices. This is precisely what we did; we predicted the correlation on the basis of this body of work and when we tested to see if it was present, we found that indeed it was. It may be the case that other behavioural economics theories offer little explanation of the associations and correlations that we find. However, we emphasize that this association is predicted by neuroscientific theory and in the revised manuscript we have attempted to clarify this in the Introduction and Discussion sections:

“Given the overlap in neuroanatomical bases underlying the different methods of value estimation and the types of distractor effects, we further explored the relationship. Critically, those who employed a more multiplicative style of integrating choice attributes also showed stronger positive distractor effects, whereas those who employed a more additive style showed negative distractor effects. These findings concur with neural data demonstrating that the medial prefrontal cortex (mPFC) computes the overall values of choices in ways that go beyond simply adding their components together, and is the neural site at which positive distractor effects emerge (Barron et al., 2013; Bongioanni et al., 2021; Chau et al., 2014; Fouragnan et al., 2019; Noonan et al., 2017; Papageorgiou et al., 2017), while divisive normalization was previously identified in the posterior parietal cortex (PPC) (Chau et al., 2014; Louie et al., 2011).” (Lines 109-119)

“At the neuroanatomical level, the negative distractor effect is mediated by the PPC, where signal modulation described by divisive normalization has been previously identified (Chau et al., 2014; Louie et al., 2011). The same region is also crucial for perceptual decision making processes (Shadlen & Shohamy, 2016). The additive heuristics for combining choice attributes are closer to a perceptual evaluation because distances in this subjective value space correspond linearly to differences in physical attributes of the stimuli, whereas normative (multiplicative) value has a non-linear relation with them (cf. Figure 1c). It is well understood that many sensory mechanisms, such as in primates’ visual systems or fruit flies’ olfactory systems, are subject to divisive normalization (Carandini & Heeger, 2012). Hence, the additive heuristics that are more closely based on sensory mechanisms could also be subject to divisive normalization, leading to negative distractor effects in decision making.

In contrast, the positive distractor effect is mediated by the mPFC (Chau et al., 2014; Fouragnan et al., 2019). Interestingly, the same or adjacent, interconnected mPFC regions have also been linked to the mechanisms by which representational elements are integrated into new representations (Barron et al., 2013; Klein-Flügge et al., 2022; Law et al., 2023; Papageorgiou et al., 2017; Schwartenbeck et al., 2023). In a number of situations, such as multi-attribute decision making, understanding social relations, and abstract knowledge, the mPFC achieves this by using a spatial map representation characterised by a grid-like response (Constantinescu et al., 2016; Bongioanni et al., 2021; Park et al., 2021) and disrupting mPFC leads to the evaluation of composite choice options as linear functions of their components (Bongioanni et al., 2021). These observations suggest a potential link between positive distractor effects and mechanisms for evaluating multiple component options and this is consistent with the across-participant correlation that we observed between the strength of the positive distractor effect and the strength of non-additive (i.e., multiplicative) evaluation of the composite stimuli we used in the current task. Hence, one direction for model development may involve incorporating the ideas that people vary in their ways of combining choice attributes and each way is susceptible to different types of distractor effect.” (Lines 250-274)

Reviewer #2 Comment 4

(4) Finally, the results from these experiments might not have external validity for two reasons. First, the normative criterion for multi-attribute decision-making differs depending on whether the attributes are lotteries or not (i.e. multiplicative vs additive). Whether it does so for humans is a matter of debate. Therefore if the result is unique to lotteries, it might not be robust for multi-attribute choice more generally. The paper largely glosses over this difference and mixes literature from both domains. Second, the lottery information was presented visually and there is literature suggesting this form of presentation might differ from numerical attributes. Which is more ecologically valid is also a matter of debate.

We thank the reviewer for the comment. Indeed, they are right that the correlation we find between value estimation style and distractor effects may not be detected in all contexts of human behaviour. What the reviewer suggests goes along the same lines as our response to Reviewer #1 Comment 3, multi-attribute value estimation may have different structure: in some cases, the optimal solution may require a non-linear (e.g., multiplicative) response as in probabilistic or delayed decisions, but other cases (e.g., when estimating the value of a snack based on its taste, size, healthiness, price) a linear integration would suffice. In the latter kind of scenarios, both the optimal and the heuristic solutions may be additive and people’s value estimation “style” may not be teased apart. However, if different neural mechanisms associated with difference estimation processes are observed in certain scenarios, it suggests that these mechanisms are always present, even in scenarios where they do not alter the predictions. Probabilistic decision-making is also pervasive in many aspects of daily life and not just limited to the case of lotteries.

While behaviour has been found to differ depending on whether lottery information is presented graphically or numerically, there is insufficient evidence to suggest biases towards additive or multiplicative evaluation, or towards positive or negative distractor effects. As such, we may expect that the correlation that we reveal in this paper, grounded in distinct neural mechanisms, would still hold even under different circumstances.

Taking previous literature as examples, similar patterns of behaviour have been observed in humans when making decisions during trinary choice tasks. In a study conducted by Louie and colleagues (Louie et al., 2013; Webb et al., 2020), human participants performed a snack choice task where their behaviour could be modelled by divisive normalization with biphasic response (i.e., both positive and negative distractor effects). While these two studies only use a single numerical value of price for behavioural modelling, these prices should originate from an internal computation of various attributes related to each snack that are not purely related to lotteries. Expanding towards the social domain, studies of trinary decision making have considered face attractiveness and averageness (Furl, 2016), desirability of hiring (Chang et al., 2019), as well as desirability of candidates during voting (Chang et al., 2019). These choices involve considering various attributes unrelated to lotteries or numbers and yet, still display a combination of positive distractor and negative distractor (i.e. divisive normalization) effects, as in the current study. In particular, the experiments carried out by Chang and colleagues (Chang et al., 2019) involved decisions in a social context that resemble real-world situations. These findings suggests that both types of distractor effects can co-exist in other value based decision making tasks (Li et al., 2018; Louie et al., 2013) as well as decision making tasks in social contexts (Chang et al., 2019; Furl, 2016).

Reviewer #2 Comment 5

Minor Issues:

The definition of EV as a normative choice baseline is problematic. The analysis requires that EV is the normative choice model (this is why the HV-LV gap is analyzed and the distractor effect defined in relation to it). But if the binary/ternary interaction effect can be accounted for by curvature of a value function, this should also change the definition of which lottery is HV or LV for that subject!

We thank the reviewer for the comment. While the initial part of the paper discussed results that were defined by the EV model, the results shown in Supplementary Figure 2 were generated by replacing the utility function based on values obtained by using the composite model. Here, we have also redefined the definition of HV or LV for each subject depending on the updated value generated by the composite model prior to the regression.

References

Apesteguia, J. & Ballester, M. Monotone stochastic choice models: The case of risk and time preferences. Journal of Political Economy (2018).

Block, H. D. & Marschak, J. Random Orderings and Stochastic Theories of Responses. Cowles Foundation Discussion Papers (1959).

Khaw, M. W., Li, Z. & Woodford, M. Cognitive Imprecision and Small-Stakes Risk Aversion. Rev. Econ. Stud. 88, 1979-2013 (2020).

Loomes, G. & Sugden, R. Testing Different Stochastic Specificationsof Risky Choice. Economica 65, 581-598 (1998).

Luce, R. D. Indvidual Choice Behaviour. (John Wiley and Sons, Inc., 1959).

Netzer, N., Robson, A. J., Steiner, J. & Kocourek, P. Endogenous Risk Attitudes. SSRN Electron. J. (2022) doi:10.2139/ssrn.4024773.

Robson, A. J. Why would nature give individuals utility functions? Journal of Political Economy 109, 900-914 (2001).

Webb, R. The (Neural) Dynamics of Stochastic Choice. Manage Sci 65, 230-255 (2019).

Reviewer #3 (Public Review):

Summary:

The way an unavailable (distractor) alternative impacts decision quality is of great theoretical importance. Previous work, led by some of the authors of this study, had converged on a nuanced conclusion wherein the distractor can both improve (positive distractor effect) and reduce (negative distractor effect) decision quality, contingent upon the difficulty of the decision problem. In very recent work, Cao and Tsetsos (2022) reanalyzed all relevant previous datasets and showed that once distractor trials are referenced to binary trials (in which the distractor alternative is not shown to participants), distractor effects are absent. Cao and Tsetsos further showed that human participants heavily relied on additive (and not multiplicative) integration of rewards and probabilities.

The present study by Wong et al. puts forward a novel thesis according to which interindividual differences in the way of combining reward attributes underlie the absence of detectable distractor effect at the group level. They re-analysed the 144 human participants and classified participants into a "multiplicative integration" group and an "additive integration" group based on a model parameter, the "integration coefficient", that interpolates between the multiplicative utility and the additive utility in a mixture model. They report that participants in the "multiplicative" group show a negative distractor effect while participants in the "additive" group show a positive distractor effect. These findings are extensively discussed in relation to the potential underlying neural mechanisms.

Strengths:

- The study is forward-looking, integrating previous findings well, and offering a novel proposal on how different integration strategies can lead to different choice biases.

- The authors did an excellent job of connecting their thesis with previous neural findings. This is a very encompassing perspective that is likely to motivate new studies towards a better understanding of how humans and other animals integrate information in decisions under risk and uncertainty.

- Despite that some aspects of the paper are very technical, methodological details are well explained and the paper is very well written.

We thank the reviewer for the positive response and are pleased that the reviewer found our report interesting.

Reviewer #3 Comment 1

Weaknesses:

The authors quantify the distractor variable as "DV - HV", i.e., the relative distractor variable. Do the conclusions hold when the distractor is quantified in absolute terms (as "DV", see also Cao & Tsetsos, 2023)? Similarly, the authors show in Suppl. Figure 1 that the inclusion of a HV + LV regressor does not alter their conclusions. However, the (HV + LV)*T regressor was not included in this analysis. Does including this interaction term alter the conclusions considering there is a high correlation between (HV + LV)*T and (DV - HV)*T? More generally, it will be valuable if the authors assess and discuss the robustness of their findings across different ways of quantifying the distractor effect.

We thank the reviewer for the comment. In the original manuscript we had already demonstrated that the distractor effect was related to the integration coefficient using a number of complementary analyses. They include Figure 5 based on GLM2, Supplementary Figure 3 based on GLM3 (i.e., adding the HV+LV term to GLM2), and Supplementary Figure 4 based on GLM2 but applying the utility estimate from the composite model instead of expected value (EV). These three sets of analyses produced comparable results. The reason why we elected not to include the (HV+LV)T term in GLM3 (Supplementary Figure 3) was due to the collinearity between the regressors in the GLM. If this term is included in GLM3, the variance inflation factor (VIF) would exceed an acceptable level of 4 for some regressors. In particular, the VIF for the (HV+LV) and (HV+LV)T regressors is 5.420, while the VIF for (DV−HV) and (DV−HV)T is 4.723.

Here, however, we consider the additional analysis suggested by the reviewer and test whether similar results are obtained. We constructed GLM4 including the (HV+LV)T term but replacing the relative distractor value (DV-HV) with the absolute distractor value (DV) in the main term and its interactions, as follows:

GLM4:

A significant negative (DV)T effect was found for the additive group [t(72)=−2.0253, p=0.0465] while the multiplicative group had a positive trend despite not reaching significance. Between the two groups, the (DV)T term was significantly different [t(142)=2.0434, p=0.0429]. While these findings suggest that the current conclusions could be partially replicated, simply replacing the relative distractor value with the absolute value in the previous analyses resulted in non-significant findings. Taking these results together with the main findings, it is possible to conclude that the positive distractor effect is better captured using the relative DV-HV term rather than the absolute DV term. This would be consistent with the way in which option values are envisaged to interact with one another in the mutual inhibition model (Chau et al., 2014, 2020) that generates the positive distractor effect. The model suggests that evidence is accumulated as the difference between the excitatory input from the option (e.g. the HV option) and the pooled inhibition contributed partly by the distractor. We have now included these results in the manuscript:

“Finally, we performed three additional analyses that revealed comparable results to those shown in Figure 5. In the first analysis, reported in Supplementary Figure 3, we added an  term to the GLM, because this term was included in some analyses of a previous study that used the same dataset (Chau et al., 2020). In the second analysis, we added an  term to the GLM. We noticed that this change led to inflation of the collinearity between the regressors and so we also replaced the (DV−HV) term by the DV term to mitigate the collinearity (Supplementary Figure 4). In the third analyses, reported in Supplementary Figure 5, we replaced the utility terms of GLM2. Since the above analyses involved using HV, LV, and DV values defined by the normative Expected Value model, here, we re-defined the values using the composite model prior to applying GLM2. Overall, in the Multiplicative Group a significant positive distractor effect was found in Supplementary Figures 3 and 4. In the Additive Group a significant negative distractor effect was found in Supplementary Figures 3 and 5. Crucially, all three analyses consistently showed that the distractor effects were significantly different between the Multiplicative Group and the Additive Group.” (Lines 225-237)

Reviewer #3 Comment 2

The central finding of this study is that participants who integrate reward attributes multiplicatively show a positive distractor effect while participants who integrate additively show a negative distractor effect. This is a very interesting and intriguing observation. However, there is no explanation as to why the integration strategy covaries with the direction of the distractor effect. It is unlikely that the mixture model generates any distractor effect as it combines two "context-independent" models (additive utility and expected value) and is fit to the binary-choice trials. The authors can verify this point by quantifying the distractor effect in the mixture model. If that is the case, it will be important to highlight that the composite model is not explanatory; and defer a mechanistic explanation of this covariation pattern to future studies.

We thank the reviewer for the comment. Indeed, the main purpose of applying the mixture model was to identify the way each participants combined attributes and, as the reviewer pointed out, the mixture model per se is context independent. While we acknowledge that the mixture model is not a mechanistic explanation, there is a theoretical basis for the observation that these two factors are linked.

Firstly, studies that have examined the processes involved when humans combine and integrate different elements to form new representations (Barron et al., 2013; Papageorgiou et al., 2017; Schwartenbeck et al., 2023) have implicated the medial frontal cortex as a crucial region (Barron et al., 2013; Fellows, 2006; Hunt et al., 2012; Papageorgiou et al., 2017; Schwartenbeck et al., 2023). Meanwhile, previous studies have also identified that positive distractor effects are linked to the medial frontal cortex (Chau et al., 2014; Noonan et al., 2017). Therefore, the current study utilized these two facts to establish the basis for a correlation between positive distractor effects and non-additive mechanisms for determining the integrated value of multi-component choices. Nevertheless, we agree with the reviewer that it will be an important future direction to look at how the covariation pattern emerges in a computational model. We have revised the manuscript in an attempt to address this issue.

“At the neuroanatomical level, the negative distractor effect is mediated by the PPC, where signal modulation described by divisive normalization has been previously identified (Chau et al., 2014; Louie et al., 2011). The same region is also crucial for perceptual decision making processes (Shadlen & Shohamy, 2016). The additive heuristics for combining choice attributes are closer to a perceptual evaluation because distances in this subjective value space correspond linearly to differences in physical attributes of the stimuli, whereas normative (multiplicative) value has a non-linear relation with them (cf. Figure 1c). It is well understood that many sensory mechanisms, such as in primates’ visual systems or fruit flies’ olfactory systems, are subject to divisive normalization (Carandini & Heeger, 2012). Hence, the additive heuristics that are more closely based on sensory mechanisms could also be subject to divisive normalization, leading to negative distractor effects in decision making.

In contrast, the positive distractor effect is mediated by the mPFC (Chau et al., 2014; Fouragnan et al., 2019). Interestingly, the same or adjacent, interconnected mPFC regions have also been linked to the mechanisms by which representational elements are integrated into new representations (Barron et al., 2013; Klein-Flügge et al., 2022; Law et al., 2023; Papageorgiou et al., 2017; Schwartenbeck et al., 2023). In a number of situations, such as multi-attribute decision making, understanding social relations, and abstract knowledge, the mPFC achieves this by using a spatial map representation characterised by a grid-like response (Constantinescu et al., 2016; Bongioanni et al., 2021; Park et al., 2021) and disrupting mPFC leads to the evaluation of composite choice options as linear functions of their components (Bongioanni et al., 2021). These observations suggest a potential link between positive distractor effects and mechanisms for evaluating multiple component options and this is consistent with the across-participant correlation that we observed between the strength of the positive distractor effect and the strength of non-additive (i.e., multiplicative) evaluation of the composite stimuli we used in the current task. Hence, one direction for model development may involve incorporating the ideas that people vary in their ways of combining choice attributes and each way is susceptible to different types of distractor effect.” (Lines 250-274)

Reviewer #3 Comment 3

-  Correction for multiple comparisons (e.g., Bonferroni-Holm) was not applied to the regression results. Is the "negative distractor effect in the Additive Group" (Fig. 5c) still significant after such correction? Although this does not affect the stark difference between the distractor effects in the two groups (Fig. 5a), the classification of the distractor effect in each group is important (i.e., should future modelling work try to capture both a negative and a positive effect in the two integration groups? Or just a null and a positive effect?).

We thank the reviewer for the comment. We have performed Bonferroni-Holm correction and as the reviewer surmised, the negative distractor effect in the additive group becomes non-significant. However, we have to emphasize that our major claim is that there was a covariation between decision strategy (of combining attributes) and distractor effect (as seen in Figure 4). That analysis does not imply multiple comparisons. The analysis in Figure 5 that splits participants into two groups was mainly designed to illustrate the effects for an easier understanding by a more general audience. In many cases, the precise ways in which participants are divided into subgroups can have a major impact on whether each individual group’s effects are significant or not. It may be possible to identify an optimal way of grouping, but we refrained from taking such a trial-and-error approach, especially for the analysis in Figure 5 that simply supplements the point made in Figure 4. The key notion we would like the readers to take away is that there is a spectrum of distractor effects (ranging from negative to positive) that will vary depending on how the choice attributes were integrated.

Reviewer #1 (Recommendations For The Authors):

Reviewer #1 Recommendations 1

Enhancements are necessary for the quality of the scientific writing. Several sentences have been written in a negligent manner and warrant revision to ensure a higher level of rigor. Moreover, a number of sentences lack appropriate citations, including but not restricted to:

- Line 39-41.

- Line 349-350 (also please clarify what it means by parameter estimate" is very accurate: correlation?).

We thank the reviewer for the comment. We have made revisions to various parts of the manuscript to address the reviewer’s concerns.

“Intriguingly, most investigations have considered the interaction between distractors and chooseable options either at the level of their overall utility or at the level of their component attributes, but not both (Chau et al., 2014, 2020; Gluth et al., 2018).” (Lines 40-42)

“Additional simulations have shown that the fitted parameters can be recovered with high accuracy (i.e., with a high correlation between generative and recovered parameters).” (Lines 414-416)

Reviewer #1 Recommendations 2

Some other minor suggestions:

- Correlative vs. Causality: the manuscript exhibits a lack of attentiveness in drawing causal conclusions from correlative evidence (manuscript title, Line 91, Line 153-155).

- When displaying effect size on accuracy, there is no need to show the significance of intercept (Figure 2,5, & supplementary figures).

- Adding some figure titles on Figure 2 so it is clear what each panel stands for.

- In Figure 3, the dots falling on zero values are not easily seen. Maybe increasing the dot size a little?

- Line 298: binomial linking function (instead of binomial distribution).

- Line 100: composite, not compositive.

- Line 138-139: please improve the sentence, if it's consistent with previous findings, what's the point of "surprisingly"?

We thank the reviewer for the suggestions. We have made revisions to the title and various parts of the manuscript to address the reviewer’s concerns.

- Correlative vs. Causality: the manuscript exhibits a lack of attentiveness in drawing causal conclusions from correlative evidence (manuscript title, Line 91, Line 153-155).

We have now revised the manuscript:

“Distractor effects in decision making are related to the individual’s style of integrating choice attributes” (title of the manuscript)

“More particularly, we consider whether individual differences in combination styles could be related to different forms of distractor effect.” (Lines 99-100)

“While these results may seem to suggest that a distractor effect was not present at an overall group level, we argue that the precise way in which a distractor affects decision making is related to how individuals integrate the attributes.” (Lines 164-167)

- When displaying effect size on accuracy, there is no need to show the significance of intercept (Figure 2,5, & supplementary figures).

We have also modified all Figures to remove the intercept.

- Adding some figure titles on Figure 2 so it is clear what each panel stands for.

We have added titles accordingly.

- In Figure 3, the dots falling on zero values are not easily seen. Maybe increasing the dot size a little?

In conjunction with addressing Reviewer #3 Recommendation 6, we have adapted the violin plots into histograms for a better representation of the values.

- Line 298: binomial linking function (instead of binomial distribution).

- Line 100: composite, not compositive.

- Line 138-139: please improve the sentence, if it's consistent with previous findings, what's the point of "surprisingly"?

We have made revisions accordingly.

Reviewer #2 (Recommendations For The Authors):

Reviewer #2 Recommendations 1

Line 294. The definition of DV, HV, LV is not sufficient. Presumably, these are the U from the following sections? Or just EV? But this is not explicitly stated, rather they are vaguely referred to as values." The computational modelling section refers to them as utilities. Are these the same thing?

We thank the reviewer for the suggestion. We have clarified that the exact method for calculating each of the values and updated the section accordingly.

“where HV, LV, and DV refer to the values of the chooseable higher value option, chooseable lower value option, and distractor, respectively. Here, values (except those in Supplementary Figure 5) are defined as Expected Value (EV), calculated by multiplying magnitude and probability of reward.” (Lines 348-350)

Reviewer #2 Recommendations 2

The analysis drops trials in which the distractor was chosen. These trials are informative about the presence (or not) of relative valuation or other factors because they make such choices more (or less) likely. Ignoring them is another example of the analysis being misspecified.

We thank the reviewer for the suggestion and this is related to Major Issue 1 raised by the same reviewer. In brief, we adopted the same methods implemented by Cao and Tsetsos (Cao and Tsetsos, 2022) and that constrained us to applying a binomial model. Please refer to our reply to Major Issue 1 for more details.

Reviewer #2 Recommendations 3

Some questions and suggestions on statistics and computational modeling:

Have the authors looked at potential collinearity between the regressors in each of the GLMs?

We thank the reviewer for the comment. For each of the following GLMs, the average variance inflation factor (VIF) has been calculated as follows:

GLM2 using the Expected Value model:

Author response table 1.

GLM2 after replacing the utility function based on the normative Expected Value model with values obtained by using the composite model:

Author response table 2.

GLM3:

Author response table 3.

As indicated in the average VIF values calculated, none of them exceed 4, suggesting that the estimated coefficients were not inflated due to collinearity between the regressor in each of the GLMs.

Reviewer #2 Recommendations 4

- Correlation results in Figure 4. What is the regression line displayed on this plot? I suspect the regression line came from Pearson's correlation, which would be inconsistent with the Spearman's correlation reported in the text. A reasonable way would be to transform both x and y axes to the ranked data. However, I wonder why it makes sense to use ranked data for testing the correlation in this case. Those are both scalar values. Also, did the authors assess the influence of the zero integration coefficient on the correlation result? Importantly, did the authors redo the correlation plot after defining the utility function by the composite models?

We thank the reviewer for the suggestion. The plotted line in Figure 4 was based on the Pearson’s correlation and we have modified the text to also report the Pearson’s correlation result as well.

If we were to exclude the 32 participants with integration coefficients smaller than 1×10-6 from the analysis, we still observe a significant positive Pearson’s correlation [r(110)=0.202, p=0.0330].

Author response image 1.

Figure 4 after excluding 32 participants with integration coefficients smaller than 1×10-6.

“As such, we proceeded to explore how the distractor effect (i.e., the effect of (DV−HV)T obtained from GLM2; Figure 2c) was related to the integration coefficient (η) of the optimal model via a Pearson’s correlation (Figure 4). As expected, a significant positive correlation was observed [r(142)=0.282, p=0.000631]. We noticed that there were 32 participants with integration coefficients that were close to zero (below 1×10-6). The correlation remained significant even after removing these participants [r(110)=0.202, p=0.0330].” (Lines 207-212)

The last question relates to results already included in Supplementary Figure 5, in which the analyses were conducted using the utility function of the composite model. We notice that although there was a difference in integration coefficient between the multiplicative and additive groups, a correlational analysis did not generate significant results [r(142)=0.124, p=0.138]. It is possible that the relationship became less linear after applying the composite model utility function. However, it is noticeable that in a series of complementary analyses (Figure 5: r(142)=0.282, p=0.000631; Supplementary Figure 3: r(142)=0.278, p=0.000746) comparable results were obtained.

Reviewer #2 Recommendations 5

- From lines 163-165, were the models tested on only the three-option trials or both two and three-opinion trials? It is ambiguous from the description here. It might be worth checking the model comparison based on different trial types, and the current model fitting results do not tell an absolute sense of the goodness of fit. I would suggest including the correctly predicted trial proportions in each trial type from different models.

We thank the reviewer for the suggestion. We have only modeled the two-option trials and the key reason for this is because the two-option trials can arguably provide a better estimate of participants’ style of integrating attributes as they are independent of any distractor effects. This was also the same reason why Cao and Tsetsos applied the same approach when they were re-analyzing our data (Cao and Tsetsos, 2022). We have clarified the statement accordingly.

“We fitted these models exclusively to the Two-Option Trial data and not the Distractor Trial data, such that the fitting (especially that of the integration coefficient) was independent of any distractor effects, and tested which model best describes participants’ choice behaviours.” (Lines 175-178)

Reviewer #2 Recommendations 6

- Along with displaying the marginal distributions of each parameter estimate, a correlation plot of these model parameters might be useful, given that some model parameters are multiplied in the value functions.

We thank the reviewer for the suggestion. We have also generated the correlation plot of the model parameters. The Pearson’s correlation between the magnitude/probability weighting and integration coefficient was significant [r(142)=−0.259, p=0.00170]. The Pearson’s correlation between the inverse temperature and integration coefficient was not significant [r(142)=−0.0301, p=0.721]. The Pearson’s correlation between the inverse temperature and magnitude/probability weighting was not significant [r(142)=−0.0715, p=0.394].

“Our finding that the average integration coefficient  was 0.325 coincides with previous evidence that people were biased towards using an additive, rather than a multiplicative rule. However, it also shows rather than being fully additive ( =0) or multiplicative ( =1), people’s choice behaviour is best described as a mixture of both. Supplementary Figure 1 shows the relationships between all the fitted parameters.” (Lines 189-193)

Reviewer #2 Recommendations 7

Have the authors tried any functional transformations on amounts or probabilities before applying the weighted sum? The two attributes are on entirely different scales and thus may not be directly summed together.

We thank the reviewer for the comment. Amounts and probabilities were indeed both rescaled to the 0-1 interval before being summed, as explained in the methods (Line XXX). Additionally, we have now added and performed model fitting on an additional model with utility curvature based on the prospect theory (Kahneman & Tversky, 1979) and a weighted probability function (Prelec, 1998):

where  and  represent the reward magnitude and probability (both rescaled to the interval between 0 and 1), respectively.  is the weighted magnitude and  is the weighted probability, while  and  are the corresponding distortion parameters. This prospect theory (PT) model was included along with the four previous models (please refer to Figure 3) in a Bayesian model comparison. Results indicate that the composite model remains as the best account of participants’ choice behaviour (exceedance probability = 1.000, estimated model frequency = 0.720).

“Supplementary Figure 2 reports an additional Bayesian model comparison performed while including a model with nonlinear utility functions based on Prospect Theory (Kahneman & Tversky, 1979) with the Prelec formula for probability (Prelec, 1998). Consistent with the above finding, the composite model provides the best account of participants’ choice behaviour (exceedance probability = 1.000, estimated model frequency = 0.720).” (Lines 193-198)

Reviewer #3 (Recommendations For The Authors):

Reviewer #3 Recommendations 1

- In the Introduction (around line 48), the authors make the case that distractor effects can co-exist in different parts of the decision space, citing Chau et al. (2020). However, if the distractor effect is calculated relative to the binary baseline this is no longer the case.

- Relating to the above point, it might be useful for the authors to make a distinction between effects being non-monotonic across the decision space (within individuals) and effects varying across individuals due to different strategies adopted. These two scenarios are conceptually distinct.

We thank the reviewer for the comment. Indeed, the ideas that distractor effects may vary across decision space and across different individuals are slightly different concepts. We have now revised the manuscript to clarify this:

“However, as has been argued in other contexts, just because one type of distractor effect is present does not preclude another type from existing (Chau et al., 2020; Kohl et al., 2023). Each type of distractor effect can dominate depending on the dynamics between the distractor and the chooseable options. Moreover, the fact that people have diverse ways of making decisions is often overlooked. Therefore, not only may the type of distractor effect that predominates vary as a function of the relative position of the options in the decision space, but also as a function of each individual’s style of decision making.” (Lines 48-54)

Reviewer #3 Recommendations 2

- The idea of mixture models/strategies has strong backing from other Cognitive Science domains and will appeal to most readers. It would be very valuable if the authors could further discuss the potential level at which their composite model might operate. Are the additive and EV quantities computed and weighted (as per the integration coefficient) within a trial giving rise to a composite decision variable? Or does the integration coefficient reflect a probabilistic (perhaps competitive) selection of one strategy on a given trial? Perhaps extant neural data can shed light on this question.

We thank the reviewer for the comment. The idea is related to whether the observed mixture in integration models derives from value being actually computed in a mixed way within each trial, or each trial involves a probabilistic selection between the additive and multiplicative strategies. We agree that this is an interesting question and to address it would require the use of some independent continuous measures to estimate the subjective values in quantitative terms (instead of using the categorical choice data). This could be done by collecting pupil size data or functional magnetic resonance imaging data, as the reviewer has pointed out. Although the empirical work is beyond the scope of the current behavioural study, it is worth bringing up this point in the Discussion:

“The current finding involves the use of a composite model that arbitrates between the additive and multiplicative strategies. A general question for such composite models is whether people mix two strategies in a consistent manner on every trial or whether there is some form of probabilistic selection occurring between the two strategies on each trial such that only one strategy is used on any given trial while, on average, one strategy is more probable than the other. To test which is the case requires an independent estimation of subjective values in quantitative terms, such as by pupillometry or functional neuroimaging. Further understanding of this problem will also provide important insight into the precise way in which distractor effects operate at the single-trial level.” (Lines 275-282)

Reviewer #3 Recommendations 3

Line 80 "compare pairs of attributes separately, without integration". This additive rule (or the within-attribute comparison) implies integration, it is just not multiplicative integration.

We thank the reviewer for the comment. We have made adjustments to the manuscript to ensure that the message delivered within this manuscript is consistent.

“For clarity, we stress that the same mathematical formula for additive value can be interpreted as meaning that 1) subjects first estimate the value of each option in an additive way (value integration) and then compare the options, or 2) subjects compare the two magnitudes and separately compare the two probabilities without integrating dimensions into overall values. On the other hand, the mathematical formula for multiplicative value is only compatible with the first interpretation. In this paper we focus on attribute combination styles (multiplicative vs additive) and do not make claims on the order of the operations. More particularly, we consider whether individual differences in combination styles could be related to different forms of distractor effect.” (Lines 92-100)

Reviewer #3 Recommendations 4

- Not clear why the header in line 122 is phrased as a question.

We thank the reviewer for the suggestion. We have modified the header to the following:

“The distractor effect was absent on average” (Line 129)

Reviewer #3 Recommendations 5

- The discussion and integration of key neural findings with the current thesis are outstanding. It might help the readers if certain statements such as "the distractor effect is mediated by the PPC" (line 229) were further unpacked.

We thank the reviewer for the suggestion. We have made modifications to the original passage to further elaborate the statement.

“At the neuroanatomical level, the negative distractor effect is mediated by the PPC, where signal modulation described by divisive normalization has been previously identified (Chau et al., 2014; Louie et al., 2011). The same region is also crucial for perceptual decision making processes (Shadlen & Shohamy, 2016).” (Lines 250-253)

Reviewer #3 Recommendations 6

- In Fig. 3c, there seem to be many participants having the integration coefficient close to 0 but the present violin plot doesn't seem to best reflect this highly skewed distribution. A histogram would be perhaps better here.

We thank the reviewer for the suggestion. We have modified the descriptive plots to use histograms instead of violin plots.

“Figures 3c, d and e show the fitted parameters of the composite model: , the integration coefficient determining the relative weighting of the additive and multiplicative value ( , ); , the magnitude/probability weighing ratio ( , ); and , the inverse temperature ( , ). Our finding that the average integration coefficient  was 0.325 coincides with previous evidence that people were biased towards using an additive, rather than a multiplicative rule.” (Lines 186-191)

Author response:

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

Reviewer #1 (Public Review):

We thank the reviewer for his careful reading, which enabled us to improve the quality of this manuscript. We have addressed some major criticisms, and in particular, we have now included the characterization of the impact of BMP2 on other lines as well as the study of the impact of reversion of the H3.3K27M mutation (Figure 3 - figure supplement 1C-D). This control, judiciously proposed by the reviewer, seems more relevant than using mutant H3.1K27M / ACVR1 lines, given the possibility of BMP2 action via other receptors.

---

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

Reviewer #1

Summary:

Mutational analysis of diffuse midline glioma (DMG) found that ACVR1 mutations, which up-regulate the BMP signaling pathway are found in most H3.1K27M, but not H3.3K27M DMG cases. In this manuscript, Huchede et al attempted to determine whether the BMP signaling pathway has any role in H3.3K27M DMG tumors. They found that the BMP signaling is activated to a similar level in H3.3K27M DMG cells with wild-type ACVR1 compared to ACVR1 DMG cells, likely due to the expression of BMP7 or BMP2. They went on to test whether cells treated with BMP7 or BMP2 treatments affected the gene expression and cell fitness of tumor cells with H3.3K27M mutation. They concluded that BMP2/7 synergizes with H3.3K27M to induce a transcriptomic rewiring associated with a quiescent but invasive cell state. The major issue for this conclusion is that the authors did not use the right models/controls to obtain results to support this conclusion as detailed below. Therefore, in order to strengthen the conclusion, the authors need to address the major concerns below.

Strength:

This paper addresses an important question in the DMG field.

Major concerns/weakness:

(1) All the results in Fig. 2 utilized two glioma lines SF188 and Res259. The authors should repeat all these experiments in a couple of H3.3K27M DMG lines by deleting the H3.3K27M mutation first.

We thank the referee for his/her comments that have helped us to strengthen our conclusions. Although we were rather interested in studying how the BMP pathway can participate in installing a particular cell state at the time of expression of the K27M mutation, we have now included the characterization of the native H3.3K27M BT245 and SU-DIPGXIII cell lines, and their counterparts in which the mutation was reverted by CRISPRCas9 (Harutyunyan et al., 2019). As shown in Figure 3-figure supplement D, the growth arrest induced by BMP2 seems indeed to be specific of the K27M epigenetic context, which could also be required to settle a positive regulation loop to activate the BMP pathway, as mentioned in the Discussion.

(2) Fig. 3. The experiments of BMP2 treatment should be repeated in other H3.3K27M DMG lines using H3.1K27M ACVR1 mutant tumor lines as controls.

The use of mutant ACVR1 lines is interesting, but their control status seems questionable, as the addition of BMPs could have a cumulative effect on the effect of the mutation, notably by activating other receptors in the pathway. But we have now included 3 different cell lines (HSJD-DIPG-014, BT245 and SU-DIPGXIII), and observed similar impact of BMP2 with growth arrest as a readout (Figure 3-figure supplement C-D)

Minor concerns

Fig.2A. BMP2 expression increased in H3.3K27M SF188 cells. Therefore, the statement "whereas BMP2 and BMP4 expressions are not significantly modified (Figure 2A and Figure 2-figure supplement A-B)" is not accurate.

The referee is absolutely right, and we have corrected this statement.

Reviewer #2 (Public Review):

The manuscript by Huchede et al investigates the BMP pathway in H3K27M-mutant gliomas carrying or not activating mutations in ALK2 (ACVR1). Their results in cell lines and in datasets acquired from the literature on patient tumors indicate that the BMP signaling pathway is activated at similar levels between ACVR1 wild-type and mutant tumors. The group further identifies BMP2 and BMP7 as possibly the main activators of the pathway in cells. They then show that BMP2 and 7 crosstalk with the H3 mutation and synergize to induce transcriptomic rewiring leading to an invasive cell state.

The paper is well-written and easy to follow with a robust experimental plan and datasets supporting the claims. While previous work (acknowledged by the authors) indicated activation of BMP in H3K27M tumors, wild type for the ACVR1 mutation this paper is a nice addition and provides further mechanistic cues as to the importance of the BMP pathway and specific members in these deadly brain cancers. The effect of these BMPs in quiescence and invasion is of particular interest.

We thank the referee for his/her supportive comments.

A few suggestions to clarify the message are provided below 1- In thalamic diffuse midline gliomas, the BMP pathway should not be activated as it is in the pons. The authors should identify thalamic tumors in the datasets they explored and patients-derived cell lines from thalamic tumors available to investigate whether this pathway is active across all H3.3K27M mutants in the brain midline or specifically in tumors from the pons.

The inter-patient variability observed in the level of activation of the BMP pathway may indeed be due, at least in part, to different tumor locations. However, we failed to find this information in the publicly available datasets that we used. We however included this element in the Discussion part.

(2) There are ~20% H3.3K27M tumors that carry an ACVR1 mutation and similar numbers of H3.1K27M that are wild type for this gene. Can the authors identify these outliers in their datasets and assess the activation of BMP2 and 7 or other BMP pathway members in this context?

We have now included the outliers present in our datasets in the legends of Figure 1B and Figure 1-figure supplement B and F. From the few samples available to document these outliers in the cohorts that we used, we have not observed major differences regarding the expression levels of BMP2/7 or BMP pathway members and have discussed the fact that it may result from the establishment in all cases of a feedback loop of activation.

In all this is an interesting paper that provides meaningful data to pursue clinical targeting of the BMP pathway, which would be a nice addition to the field.

We thank the reviewer for his/her supportive comments.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

The study by Vengayil et al. presented a role for Ubp3 for mediating inorganic phosphate (Pi) compartmentalization in cytosol and mitochondria, which regulates metabolic flux between cytosolic glycolysis and mitochondrial processes. Although the exact function of increased Pi in mitochondria is not investigated, findings have valuable implications for understanding the metabolic interplay between glycolysis and respiration under glucose-rich conditions. They showed that UBP3 KO cells regulated decreased glycolytic flux by reducing the key Pi-dependent-glycolytic enzyme abundances, consequently increasing Pi compartmentalization to mitochondria. Increased mitochondria Pi increases oxygen consumption and mitochondrial membrane potential, indicative of increased oxidative phosphorylation. In conclusion, the authors reported that the Pi utilization by cytosolic glycolytic enzymes is a key process for mitochondrial repression under glucose conditions.

Comments on revised version:

This reviewer appreciates the author's responses addressing some of the concerns.

(1) However, the concern of reproducibility and experimental methods applied to the study is still valid, particularly considering that many conclusions were drawn from western blot analysis. The authors used separate gel loading controls for western blot analysis, which is not a valid method. Considering loading and other errors/discrepancies during the transfer phase of the assay, the direct control should be analyzing the membrane after transfer or using an internal control antibody on the same membrane. None of the western blots are indicated with marker sizes, and it isn't very clear how many repeats there are and whether those repeats are biological or technical repeats.

We thank the reviewer for raising this concern. This point requires detailed clarification regarding two key points: the first one regarding the use of Coomassie stained gels over internal ‘housekeeping gene’ antibodies, and the second one regarding the challenges in performing controls for western blots In case of high abundance proteins such as glycolytic enzymes.

(1) In our western blots, we have used Coomassie stained gel as a loading control for all our western blots. This is performed by cutting one half of the gel and using it for transfer followed by blotting and using the other half for Coomassie staining. I.e. This is not two separate gels that are loaded, but the same gel. Practically, this is no different from cutting a membrane to blot with different antibodies. This method is of course valid method for normalizing western blot data, and is used by multiple studies, for the reasons mentioned below. The historical use of a ‘house-keeping’ gene as a loading control for western blotting assumes that the protein levels of these does not change under different conditions. However, this approach has multiple, severe limitations (since a ‘housekeeping gene’ is entirely contextual, and indeed), and therefore it is correct to use total protein as a loading control. This is indeed recommended for use by multiple studies (Collins et al., 2015). Coomassie staining for total protein is far more reliable than using house-keeping genes as a loading control in western blots (Welinder and Ekblad, 2011). A notable example would be GAPDH itself, which is widely used as a loading control in many studies. As is clear from our data in this manuscript, GAPDH levels itself decrease in ubp3Δ cells. Had we used GAPDH as a loading control, we wouldn’t have identified the decrease in glycolytic enzymes in ubp3Δ cells, and this story would have met with a tragic fate very early on in its inception. We have in fact be very careful with these quantitations, and even before loading samples on gels, they are first normalized using a standard protein estimation assay (Bradford), followed by normalized loading, followed by cutting the gel into two parts - one for coomassie staining and protein normalization, and the other for the western blot for the respective proteins. However, in point (2) below, we clarify on why sometimes we have to load a separate gel with normalized protein, which should resolve this point.

(2) Glycolytic enzymes are highly abundant proteins and to achieve a signal in the linear range of western blot, the protein extracts have to be diluted (up to 25 or 50 times). As discussed under point 1, an internal control ‘housekeeping gene’ antibody is not a reliable method to use as loading control. Even if we want to use an antibody for an internal protein as a control, there are not many proteins that are as abundant as metabolic enzymes and because of this simple reason, the sample dilution results in these proteins not getting detected in the western blot since the signal will be below the limit of detection. This leaves using a separate gel loading control as the only easy to perform, reliable option.

We would like to further highlight the fact that the changes in metabolic enzymes and ETC proteins that we observe in the ubp3 mutant by western blot, were also independently observed by large scale untargeted quantitative proteomics study by  (Isasa et al., 2015), which we cite extensively in this manuscript. Since an entirelyindependent study, using a completely different (untargeted) method has also shown very similar  changes in proteins that we observe (mitochondrial, and glycolytic enzymes), there should be no room for doubt regarding the altered glycolytic enzyme and ETC protein  levels that we discover in this study.

None of the western blots are indicated with marker sizes

We have clearly indicated the marker sizes in all our western blots. Separately, raw images of the blots and Coomassie stained gels have been provided with the manuscript raw data, and is therefore easily available for any interested reader.

It isn't very clear how many repeats there are and whether those repeats are biological or technical repeats.

We have already clearly indicated the details of each blot in the figure legends. For example “A representative blot (out of three biological replicates, n=3) and their quantifications are shown. Data represent mean ± SD.” We kindly request the reviewer to thoroughly go through the figure legends for details regarding the western blots, or any other data. We hope this addresses all the reviewer concerns regarding the credibility of our western blot results and the method of using Coomassie stained gels as loading controls in this study.

(2) Concern regarding citing the Ouyang et al. paper is still valid. This paper is an essential implication in phosphate metabolism and is directly related to some of the findings associated with mitochondrial function, along with conflicting results, which should be discussed in the discussion section. As a reviewer, I do not request citing any paper from the authors in general; however, considering some of the conflicting results here, citing and discussing paper from Ouyang et al. will improve the interoperation/value of their findings.

As mentioned in detail in our previous response  letter, we do not believe that the study from Ouyang et al., present ‘conflicting results’ of any kind. Nevertheless, in response to the reviewer's suggestion, we have revised the discussion section of our manuscript and added a few points that  incorporate the insights from Ouyang et al. These are in the discussion section (“It is important to highlight that our experiments, whether involving Pi supplementation or Pi limitations, maintain the cellular Pi concentration within the millimolar range and are conducted within a short timeframe (~ 1 hour). This differs significantly from Pi starvation studies, where cells are subjected to prolonged and complete Pi deprivation, triggering extensive metabolic adjustments to sustain available Pi pools, such as an increase in mitochondrial membrane potential, independent of respiration”). We trust that this modification will enhance the interested readers' understanding of our study's overarching conclusions.

Reviewer #2 (Public Review):

Summary:

Cells cultured in high glucose tend to repress mitochondrial biogenesis and activity, a prevailing phenotype type called Crabree effect that observed in different cell types and cancer. Many signaling pathways have been put forward to explain this effect. Vengayil et al proposed a new mechanism involved in Ubp3/Ubp10 and phosphate that controls the glucose repression of mitochondria. The central hypothesis is that ∆ubp3 shift the glycolysis to trehalose synthesis, therefore lead to the increase of Pi availability in the cytosol, then mitochondrial received more Pi and therefore the glucose repression is reduced.

Strengths:

The strength is that the authors used an array of different assays to test their hypothesis. Most assays were well designed and controlled.

Weaknesses:

I think the main conclusions are not strongly supported by the current dataset. Here are my comments on authors' response and model.

(1) The authors addressed some of my concerns related to ∆ubp3. But based on the results they observed and discussed, the ∆ubp3 redirect some glycolytic flux to gluconeogenesis while the 0.1% glucose in WT does not. Similarly, the shift of glycolysis to trehalose synthesis is also not relevant to the WT cells cultured in low glucose situation. This should be discussed in the manuscript to make sure readers are not misled to think ∆ubp3 mimic low glucose. It is likely that ∆ubp3 induce proteostasis stress, which is known to activate respiration and trehalose synthesis.

But based on the results they observed and discussed, the ∆ubp3 redirect some glycolytic flux to gluconeogenesis while the 0.1% glucose in WT does not. Similarly, the shift of glycolysis to trehalose synthesis is also not relevant to the WT cells cultured in low glucose situation.

We would like to clarify that we do not observe a redirection of glycolytic flux to gluconeogenesis in ubp3 mutant. What we observe is a rewiring of glycolytic flux into increased trehalose synthesis and PPP, and decreased glycolysis. Also, the shift of glycolysis to trehalose synthesis is relevant to WT cells cultured in low glucose. It is a well-known fact that the trehalose synthesis increases with decrease in media glucose. In case of 0.1% glucose, this increase in trehalose is not due to an increase in gluconeogenesis (since the pathways utilizing alternate carbon sources still remain repressed  in 0.1% glucose (Yin et al., 2003)), but by the increase in glycolytic flux towards trehalose. This is also supported by increase in Tps2 protein levels upon decreasing glucose concentration (Shen et al., 2023). We will also note that there are very few studies that actually estimate gluconeogenic flux in cess (and they only rely on steady state measurements). Estimating gluconeogenic flux appropriately is challenging in itself (eg. see Niphadkar et al 2024). 

In case of glucose concentrations lower than 0.1%, the shift to trehalose synthesis might not be as relevant. We observe that the glycolysis defective mutant tdh2tdh3 cells does not show an increase in trehalose synthesis (Figure 3-figure supplement 1E). However, in this context, the decrease in the rate of GAPDH catalyzed reaction alone appears to be sufficient to increase the Pi levels (Figure 3F) even without an increase in trehalose. Therefore, there might be differences in the relative contributions of these two arms towards Pi balance, based on whether it is low glucose in the environment, or a mutant such as ubp3Δ that modulates glycolytic flux. In ubp3Δ cells, the combination of low rate of GAPDH catalyzed reaction and high trehalose will happen (based on how glycolytic flux is modulated), vs only the low rate of the GAPDH catalyzed reaction in tdh2tdh3 cells. As an end point the increase in Pi happens in both cases, but this happens via slightly differing outcomes. Also note: in terms of free Pi sources a low-glucose condition (with low glycolytic rate) is very different from a no-glucose, respiratory condition (where cells perform very high gluconeogenesis, at a rate that is an order of magnitude higher than in low glucose). In respiration-reliant conditions such as in ethanol, cells switch to high gluconeogenesis, where there is a large increase in trehalose synthesis as a default (eg see Varahan et al 2019). In this condition, trehalose synthesis could become a major source for Pi (eg see Gupta 2021). This could also support the increased mitochondrial respiration. In an ethanol-only medium, the directionality of the GAPDH reaction is itself reversed (i.e. G-1,3-BP → G-3-P). Therefore, this reaction now becomes an added source of Pi, instead of a net consumer of Pi (see illustration in Figure 3G). Therefore, a very reasonable inference is that a combination of increased trehalose and increased 1,3 BPG to G3P conversion can become a Pi source, supporting increased mitochondrial respiration in a non-glucose, respiratory medium.

We have now clarified these points in the discussion section in the updated version of our manuscript. Lines xxx. We hope that this updated discussion section satisfies the reviewer’s concern regarding how relevant the increase in trehalose synthesis is for altered Pi balance and increased mitochondrial respiration in WT cells.

It is likely that ∆ubp3 induce proteostasis stress, which is known to activate respiration and trehalose synthesis.

Apart from some general changes in metabolism, there are no reports whatsoever that suggest that general proteostasis stress can results in an extensive, precise metabolic rewiring - where there is an increased in respiration, mitochondrial de-repression, precise decrease in two limiting glycolytic enzyme levels, and a precise reduction in glycolytic flux, as observed in the ubp3 mutant. If this was the case, deletion of any deubiquitinase should result in an increase in trehalose and respiration which clearly does not happen (as is already clear from the large screen shown in Figure 1)

However, in response to this query, we performed experiments to assess the extent of proteostasis stress in ubp3 mutants. For this, we have now estimated the changes in global ubiquitination in WT vs ubp3 mutant, and compared this with conditions of moderate proteostasis stress (mild heat shock at 42C/~1hr). These data are now included in the revised manuscript as Figure 1- figure supplement 1J. Notably, our analysis reveals only very minor  alteration in global ubiquitination levels in ubp3 mutants compared to WT cells. This is in very stark contrast to  limited heat stress, where a clear increase in global ubiquitination can be easily observed. Given these data, we can conclude that there is no significant general proteostatic stress in ubp3 mutants, that could induce substantial metabolic rewiring of such precise nature.

(2) Pi flux: it is known that vacuole can compensate the reduction of Pi in the cytosol. The paper they cited in the response, especially the Van Heerden et al., 2014 showed that the pulse addition of glucose caused transient Pi reduction and then it came back to normal level after 10min or so. If the authors mean the transient change of glycolysis and respiration, they should point that out clearly in the abstract and introduction. If the authors are trying to put out a general model, then the model must be reconsidered.

In Van Heerden et al., the pulse addition of glucose causes transient Pi reduction due to rapid Pi consumption in glycolysis. The phosphate levels came back to normal level because of the glucose flux into trehalose synthesis releasing free Pi. This is the entire crux of the study and this is the reason why tps2 mutants which cannot synthesize trehalose exhibit a growth defect and have decreased Pi levels. As explained in detail in our early response, the cellular Pi levels are maintained by a relative balance of reactions that consume and release Pi and therefore a change in this balance can change Pi as well. Indeed, if this were not the case, the tps2 mutants would simply maintain the Pi levels similar to WT cells by increasing Pi transport from the medium, which is clearly not the case (eg see Gupta 2021).

The cytosol has ~50mM Pi (van Eunen et al., 2010 FEBSJ), while only 1-2mM of glycolysis metabolites, not sure why partial reduction of several glycolysis enzymes will cause significant changes in cytosolic Pi level and make Pi the limiting factor for mitochondrial respiration. In response to this comment, the authors explained the metabolic flux that the rapid, continuous glycolysis will drain the Pi pool even each glycolytic metabolite is only 1-2mM. However, the metabolic flux both consume and release Pi, that's why there is such measurement of overall free Pi concentration amid the active metabolism. One possibility is that the observed cytosolic Pi level changes was caused by the measurement fluctuation.

The measurement fluctuations that we mentioned in our previous response letter was in case of cells grown in high and low glucose, where there are multiple factors such as mitochondrial amount which complicates the Pi measurements. In case of ubp3 mutants which have a similar amount of total mitochondria as that of WT cells, there is minimal fluctuation for Pi measurement. We have done extensive standardization of mitochondrial isolation and Pi measurement in the isolated mitochondria (as explained in detail in the manuscript) to minimize any such fluctuations. 

However, the metabolic flux both consume and release Pi, that's why there is such measurement of overall free Pi concentration amid the active metabolism

The reviewer is correct in pointing out that metabolic flux consume and release Pi. However, in glucose grown yeast cells, the rate of glycolysis which is a Pi consuming reaction is higher than any other metabolic pathway. In fact, the glycolytic rate in glucose-grown S. cerevisiae is one of the highest ever observed in any living system. A decrease in glycolysis and an increase in trehalose therefore shifts the balance in Pi utilization and results in increased free Pi in ubp3 cells. For a more detailed theoretical reasoning on the consumption and production of Pi, see Gupta 2021.

Importantly, the authors measured Pi inside mito for ethanol and glucose, but not the cytosolic Pi, which is the key hypothesis in their model. The model here is that the glycolysis competes with mito for free cytosolic Pi, so it needs to inhibit glycolysis to free up cytosolic Pi for mitochondrial import to increase respiration. I don't see measurement of cytosolic Pi upon different conditions, only the total Pi or mito Pi. The fact is that in Fig.3C they saw WT+Pi in the medium increase total free Pi more than the ∆ubc3, while WT decrease mito Pi compared to WT control and ∆ubc3 and therefore decrease basal OCR upon Pi supplement. A simple math of Pitotal = Pi cyto + Pi mito tells us that if WT has more Pitotal (Fig.3C) but less Pi mito (fig.5 supp 1C), then it has higher Pi cyto. This is contradictory to what the authors tried to rationalize. Furthermore, as I pointed out previously, the isolated mitochondria can import more Pi when supplemented, so if there is indeed higher Picyto, then the mito in WT should import more Pi. So, to address these contradictory points, the authors must measure Pi in the cytosol, which is a critical experiment not done for their model. For example, they hypothesized that adding 2-DG, or ∆ubp3, suppress glycolysis and thus increase the supply of cytosolic Pi for mito to import, but no cytosolic Pi was measured (need absolute value, not the relative fold changes). It is also important to specific how the experiments are done, was the measurement done shortly after adding 2-DG. Given that the cells response to glucose changes/pulses differently in transient vs stable state, the authors are encouraged to specify that.

(1) Importantly, the authors measured Pi inside mito for ethanol and glucose, but not the cytosolic Pi, which is the key hypothesis in their model. The model here is that the glycolysis competes with mito for free cytosolic Pi, so it needs to inhibit glycolysis to free up cytosolic Pi for mitochondrial import to increase respiration. I don't see measurement of cytosolic Pi upon different conditions, only the total Pi or mito Pi.

As clearly described in the manuscript, the key hypothesis that emerges is the role of the availability/accessibility of Pi for the mitochondria, in the context of activity. As discussed in detail in the discussion section, this can come from a combination of available Pi pools in the cytosol and increased transport of this Pi to the mitochondria. While it is true that the decreased glycolysis in ubp3 mutants frees up available Pi pools in the cytosol, measurement of cytosolic Pi in these mutants growing in log phase might not necessarily show an increased cytosolic Pi, if the Pi is being actively transported the the mitochondria at a rate higher that the WT, as indicated by the ~6 fold increase in mitochondrial Pi in ubp3 cells. This would require tools such as intracellular fluorescence based-Pi sensors that could accurately capture temporal changes in cytosolic and mitochondrial Pi following glycolytic inhibition. However, these tools are not available till date for use in yeast and measuring cytosolic Pi following glycolytic inhibition over time using colorimetric Pi assays are extremely difficult.  

However, the reviewer does correctly state that we had not included measurement of cytosolic Pi. Since the mitochondrial Pi estimate was itself a very challenging (and critical) experiment we had originally thought that data was sufficient. We have therefore now performed a series of new experiments, where we first enrich the cytosolic fraction (without mitochondrial contamination), and estimated cytosolic Pi amounts in WT and ubp3 cells. Our Pi measurements indicate a cytosolic Pi concentration in the range of ~35 mM, which is similar to the earlier reported values in yeast. We further observe that the cytosolic Pi is about ~25% lower in ubp3 mutants (~25-27 mM) compared to WT cells (Figure 4B). As mentioned earlier, this would be consistent with higher transport of Pi from the cytosol to the mitochondria in these cells. Effectively, ubp3 cells have a total increase in cellular Pi, and with a Pi pool distribution such that there is increased Pi availability in mitochondria (Figure 4B). This further substantiates this hypothesis of an increased Pi allocation to mitochondria in ubp3 mutants. The reason for increased rate of Pi transport to mitochondria is not immediately clear, but could also come from changes in cytosolic pH - a possibility that we suggest in our discussion, and is discussed in a later section of this response letter as well.   

(2) The fact is that in Fig.3C they saw WT+Pi in the medium increase total free Pi more than the ∆ubc3, while WT decrease mito Pi compared to WT control and ∆ubc3 and therefore decrease basal OCR upon Pi supplement. A simple math of Pitotal = Pi cyto + Pi mito tells us that if WT has more Pitotal (Fig.3C) but less Pi mito (fig.5 supp 1C), then it has higher Pi cyto. This is contradictory to what the authors tried to rationalize. Furthermore, as I pointed out previously, the isolated mitochondria can import more Pi when supplemented, so if there is indeed higher Picyto, then the mito in WT should import more Pi.

a) “The fact is that in Fig.3C they saw WT+Pi in the medium increase total free Pi more than the ∆ubc3, while WT decrease mito Pi compared to WT control and ∆ubc3 and therefore decrease basal OCR upon Pi supplement. A simple math of Pitotal = Pi cyto + Pi mito tells us that if WT has more Pitotal (Fig.3C) but less Pi mito (fig.5 supp 1C), then it has higher Pi cyto.”

In WT cells supplemented with external Pi (WT+Pi), there is an increased total Pi, but a decreased mitochondrial Pi. As discussed in the discussion section in the manuscript, this could be due to the supplemented Pi not being transported to mitochondria. The reviewer is correct in pointing out that as per simple math this should mean that the cytosolic Pi in WT+Pi should be high. We have now assessed cytosolic Pi upon external Pi supplementation, and this is exactly what we observe in our cytosolic Pi measurements now included in the revised manuscript (Figure 5-figure supplement 5C). There is a higher cytosolic Pi in WT+Pi (~52 mM) compared to WT cells (~35 mM) and ubp3 cells (~27 mM). We have now pointed this out in the discussion section in the revised manuscript “Notably, this increased respiration does not happen upon direct Pi supplementation to highly glycolytic WT cells, where the Pi accumulates in cytosol, without increasing mitochondrial Pi (Figure 5-figure supplement 1C).” We hope that these new data completely addresses the reviewer’s concern regarding the Pi allocations in case of WT+Pi cells.

b) This is contradictory to what the authors tried to rationalize. Furthermore, as I pointed out previously, the isolated mitochondria can import more Pi when supplemented, so if there is indeed higher Picyto, then the mito in WT should import more Pi.

We would like to clarify that the Pi measurements in WT+Pi absolutely do not contradict our hypothesis. Furthermore, nowhere do we claim that an increase in cytosolic Pi will increase mitochondrial Pi!! On the contrary, we explain in detail that supplementing Pi to WT cells (which increases cytosolic Pi) will not increase respiration if the increased Pi is not being transported to mitochondria. This is exactly what happens in WT+Pi, where Pi accumulates in the cytosol but does not result in increased mitochondrial Pi. The reviewer argues that if there is higher cyto Pi, mitochondria should import more Pi. This is true in case of transport via diffusion where the external concentration dictates the direction of metabolite transport, but is fundamentally wrong in case of transport of metabolites where active transporters and additional regulators are involved. This is the entire basis of the idea of metabolic compartmentalisation where  cells maintain pools of metabolites in different organelles which regulate the cellular metabolic state. A well-studied example is pyruvate, whose cytosolic concentration is high in glycolytic cells, but it's transport to mitochondria is reduced in glycolysis to maintain cytosolic fermentation. As discussed in the manuscript, a logical explanation for Pi supplementation not increasing respiration and mitochondria Pi is that there might be mechanisms in highly glycolytic cells that restrict the transport of Pi to mitochondria, thereby compartmentalizing Pi in the cytosol. One such possible mechanism is pH (discussed in a later section) and it is possible that there are other mechanisms involved. 

In case of isolated mitochondria, Pi supplementation results in an increased respiration simply because it is an in vitro set up where we supplement metabolites such as pyruvate, malate and ADP along with phosphate to ensure that mitochondria is actively respiring and in this case Pi will be consumed since it is being used for ATP synthesis. This is entirely different from an in vivo scenario where cells are glycolytic, and mechanisms to prevent mitochondrial transport of metabolites such as pyruvate and phosphate are active. 

c) It is also important to specific how the experiments are done, was the measurement done shortly after adding 2-DG?

Cells were treated with 2-DG for one hour and respiration was measured. We have mentioned these details clearly in the figure legends and methods.  

d) The most likely model to me is that, which is also the consensus in the field, is that no matter 2-DG or ∆ubp3, the cells re-wiring metabolism in both cytosol and mitochondria, and it is the total network shift that cause the mitochondrial respiration increase, which requires the increase of mito import of Pi, ADP, O2, and substrates, but not caused/controlled by the Pi that singled out by the authors in their model.

The aim of our study is only to highlight the importance of mitochondrial Pi availability as a critical factor in controlling mitochondrial respiration. Of course this would require sufficient other factors such as ADP, substrates and oxygen. It cannot be otherwise. However, as we point out in the discussion, a major limiting factor might be Pi availability. While the altered glycolysis in ubp3 mutants might control availability of other factors such as pyruvate and ADP, this is not the focus of our study. We would also like to point out that prior studies show that even though cytosolic ADP decreases in the presence of glucose, this does  not limit mitochondrial ADP uptake, or decrease respiration, due to the very high affinity of the mitochondrial ADP transporter. This is discussed in our discussion section as well. Further we show that the levels of ETC proteins can be altered by changing Pi levels, which places Pi as a major regulator of respiration. We would like to point out once again that studies in other systems have also highlighted a major role of mitochondrial Pi availability in controlling respiration. These references are included in our manuscript (Scheibye-Knudsen et al., 2009, Seifer et al., 2015). This includes a recent study in T cells that clearly shows increased mitochondrial respiration upon overexpressing mitochondrial Pi transporter SLC25A3 alone (Wu et al., 2023). Our manuscript now in fact provides a contextual explanation of these diverse observations from other cellular systems where mitochondrial Pi transport appears to regulate respiration.

(3) The explanation that cytosolic pH reduction upon glucose depletion/2DG is a mistake. There are a lot of data in the literature showing the opposite. If the authors do think this is true, then need to show the data. Again, it is important to distinguish transient vs stable state for pH changes.

We observe that directly supplementing Pi to WT cells growing in high glucose does not result in higher mitochondrial Pi or increased respiration. However, supplementing Pi to WT cells increases mitochondrial respiration in the presence of glycolytic inhibitor 2-DG. We therefore merely suggest that cytosolic pH could be an additional regulator of mitochondrial Pi transport, since this will be consistent with the differences in mitochondrial Pi transport in highly glycolytic cells, and cells with decreased glycolysis ( such as 2-DG addition and ubp3 mutant). This is because in mitochondria, Pi is co-transported along with protons. Therefore, changes in cytosolic pH (which changes the proton gradient) will control the mitochondrial Pi transport (Hamel et al., 2004).  The glycolytic rate is itself a major factor that controls cytosolic pH. The cytosolic pH in highly glycolytic cells is maintained ~7, and decreasing glycolysis results in cytosolic acidification (Orij et al., 2011). Therefore, under conditions of decreased glycolysis (such as loss of Ubp3), cytosolic pH becomes acidic. Since mitochondrial Pi transport depends on the proton gradient, a low cytosolic pH would favour mitochondrial Pi transport. Therefore, under conditions of decreased glycolysis (2DG treatment, or loss of Ubp3), where cytosolic pH would be acidic, increasing cytosolic Pi might indirectly increase mitochondria Pi transport, thereby leading to increased respiration. But we certainly do leave alternate interpretations to the imagination of any reader, and are indeed open to them. These are all exciting future directions this study will enable a contextual interpretation of.

The explanation that cytosolic pH reduction upon glucose depletion/2DG is a mistake.

We have cited two independent studies which suggest that cytosolic pH decreases upon a decrease in glycolysis (Orij et al.,2011 ,Dechant et al., 2010). This control of cytosolic pH by the glycolytic rate has been extensively shown using glycolytic mutants, cells in low glucose and cells grown in the presence of glycolytic inhibitors. According to the reviewer, this is a mistake and

there are a lot of data in the literature showing the opposite.

In our literature review we did not come across any relevant studies that actually show the opposite. If the  reviewer still thinks this is a mistake, the reviewer is welcome to include some of the relevant literature that clearly shows the opposite in the comments, with actual measurements of cytosolic pH. Additionally,  the possible role of cytosolic pH in this context does not affect the conclusions of our study, and we only include this as a possibility in the discussion. Therefore, this is obviously well beyond the scope of experiments in our current study, and considering the extensive data from multiple studies that shows that cytosolic pH decreases under low glycolysis, there is no relevance  to including experiments to address the same in this study. We leave this as a point for an interested reader to think about, and it certainly can nucleate new directions of future study.

Author response:

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

Summary of the changes

Changes in the manuscript were made to clarify some ambiguities raised by the reviewers and to improve the report following their recommendations. A summary of the main changes is listed below:

- The title was changed to better reflect the results of this study - Re-training the model on log transformed FACS scores.

- Testing the specificity of the FEPS to facial expression of pain within this experimental setup by comparing it to the activation maps obtained from the Warm stimulation condition.

- Testing for sensitization/habituation of the behavioral measures (FACS scores and pain ratings).

- Adding a section in the discussion to better address the limitations of this study and provide potential directions for future studies.

Other changes target areas where the original manuscript may have been ambiguous or lacked precision. To address these concerns, additional details have been incorporated, and certain terms have been revised to ensure a more precise and transparent presentation of the information.

Public Reviews:

Reviewer #1 (Public Review):

Picard et al. report a novel neural signature of facial expressions of pain. In other words, they provide evidence that a specific set of brain activations, as measured by means of functional magnetic resonance imaging (fMRI), can tell us when someone is expressing pain via a concerted activation of distinctive facial muscles. They demonstrate that this signature provides a better characterization of this pain behaviour when compared with other signatures of pain reported by past research. The Facial Expression of Pain Signature (FEPS) thus enriches this collection and, if further validated, may allow scientists to identify the neural structures subserving important non-verbal pain behaviour. I have, however, some reservations about the strength of the evidence, relating to insufficient characterization of the underlying processes involved.

We are thankful for the summary of our work. We are hopeful that the modifications made in the latest version effectively address these concerns. The changes are outlined in the summary above, and detailed in the following point-by-point response.

Strengths:

The study relies on a robust machine-learning approach, able to capitalise on the multivariate nature of the fMRI data, an approach pioneered in the field of pain by one of the authors (Dr. Tor Wager). This paper extends Wager's and other colleagues' work attempting to identify specific combinations of brain structures subserving different aspects of the pain experience while examining the extent of similarity/dissimilarity with the other signatures. In doing so, the study provides further methodological insight into fine-grained network characterization that may inspire future work beyond this specific field.

We are thankful for the positive comments.

Weaknesses:

The main weakness concerns the lack of a targeted experimental design aimed to dissect the shared variance explained by activations both specific to facial expressions and to pain reports. In particular, I believe that two elements would have significantly increased the robustness of the findings:

(1) Control conditions for both the facial expressions and the sensory input. An efficient signature should not be predictive of neutral and emotional facial expressions (e.g., disgust) other than pain expressions, as well as it should not be predictive of sensations originating from innocuous warm stimulation or other unpleasant but non-painful stimulation.

We do recognize the lack of specificity testing for the FEPS, especially towards negative emotional facial expressions. This would be relevant to test given the behavioural overlap between the facial expressions of pain and disgust, fear, anger, and sadness (Kunz et al., 2013; Williams, 2003). The experimental design used in this study did not include other negative states. However, we fully support the necessity of collecting data throughout those conditions, and we believe that the present study highlights the importance of such a demonstration. Future research should involve recording facial expressions while exposing participants to stimuli that elicit a range of negative emotions but, to our knowledge, such combination of fMRI and behavioural data is currently unavailable. As raised by the reviewer, this approach would allow us to assess the specificity of the FEPS to the facial expression evoked by pain compared to different affective states. We would like to emphasise that specificity and generalizability testing is a massive amount of work, requiring multiple studies to address comprehensively. A Limitations paragraph addressing this research direction has been added to the Discussion. A conclusion was added to the abstract as follows: “Future studies should explore other pain-relevant manifestations and assess the specificity of the FEPS against other types of aversive or emotional states.”

(2) Graded intensity of the sensory stimulation: different intensities of the thermal stimulation would have caused a graded facial expression (from neutral to pain) and graded verbal reports (from no pain to strong pain), thus offering a sensitive characterisation of the signal associated with this condition (and the warm control condition).

However, these conditions are missing from the current design, and therefore we cannot make a strong conclusion about the generalisability of the signature (regardless of whether it can predict better than other signatures - which may/may not suffer from similar or other methodological issues - another potential interesting scientific question!). The authors seem to work on the assumption that the trials where warm stimulation was delivered are of no use. I beg to disagree. As per my previous comment, warm trials (and associated neutral expressions) could be incorporated into the statistical model to increase the classification sensitivity and precision of the FEPS decoding.

The experience of pain can fluctuate for a fixed intensity or after controlling statistically for the intensity of the stimulation (Woo et al., 2017). Consistent with this, the current study focused on spontaneous facial expression in response to noxious thermal stimuli delivered at a constant intensity that produced moderate to strong pain in every participant. As the reviewer points out, this does not allow us to characterise and compare the stimulus-response function of facial expression and pain ratings. The advantage of the approach adopted is to maximise the number of trials where facial expression is more likely to occur, while ensuring that changes in facial expression and pain ratings are not confounded with changes in stimulus intensity. The manuscript has been revised to clarify that point. However, we do agree that it would be interesting to conduct more studies focusing on facial expression in response to a range of stimulus intensities. This discussion has been added to the Limitations paragraph.

Furthermore, following the reviewer’s suggestion, we performed complementary analyses on the warm trials in the proposed revisions. The dot product (FEPS scores) between the FEPS and the activation maps associated with the warm condition was computed. A linear mixed model was conducted to investigate the association between FEPS scores and the experimental condition (warm vs pain). The trials in the pain condition were divided into two conditions: null FACS scores (painful trials with no facial response; FACS scores = 0) and non-null FACS scores (painful trials with a facial response; FACS > 0). The details of this analysis have been added to the manuscript (see Response of the FEPS to pain and warm section in the Methods; lines 427 to 439) as well as the corresponding results (see Results and Discussion; lines 138 to 158). The FEPS scores were larger in the pain condition where a facial response was expressed, compared to both the pain condition without facial expression and the warm condition. These results confirmed the sensitivity of the FEPS to facial expression of pain.

Reviewer #2 (Public Review):

Summary:

The objective of this study was to further our understanding of the brain mechanisms associated with facial expressions of pain. To achieve this, participants' facial expressions and brain activity were recorded while they received noxious heat stimulation. The authors then used a decoding approach to predict facial expressions from functional magnetic resonance imaging (fMRI) data. They found a distinctive brain signature for pain facial expressions. This signature had minimal overlap with brain signatures reflecting other components of pain phenomenology, such as signatures reflecting subjective pain intensity or negative effects.

We appreciate this concise and accurate summary of our study.

Strength:

The manuscript is clearly written. The authors used a rigorous approach involving multivariate brain decoding to predict the occurrence and intensity of pain facial expressions during noxious heat stimulation. The analyses seem solid and well-conducted. I think that this is an important study of fundamental and clinical relevance.

Weaknesses:

Despite those major strengths, I felt that the authors did not suffciently explain their own interpretation of the significance of the findings. What does it mean, according to them, that the brain signature associated with facial expressions of pain shows a minimal overlap with other pain-related brain signatures?

We express our sincere gratitude for the valuable insights and constructive comments on the strengths and weaknesses of the current study. We thank reviewer 2 for the encouragement to reinforce our interpretation of the significance of the findings, while acknowledging the limitations raised by the three reviewers.

A few questions also arose during my reading.

Question 1: Is the FEPS really specific to pain expressions? Is it possible that the signature includes a facial expression signal that would be shared with facial expressions of other emotions, especially since it involves socio-affective regulation processes? Perhaps this question should be discussed as a limit of the study?

We acknowledge this limitation as outlined in response to Reviewer #1. We have incorporated a Limitations paragraph to provide a more in-depth discussion of this limitation and to explore potential future avenues (lines 225 to 268). Again, please note that the demonstration of specificity is an incremental process that requires a systematic comparison with other conditions where facial expressions are produced without pain. A concluding sentence was added to the abstract to encourage specificity testing in future studies. as indicated above.

Question 2: All AUs are combined together in a composite score for the regression. Given that the authors have other work showing that different AUs may be associated with different components of pain (affective vs. sensory), is it possible that combining all AUs together has decreased the correlation with other pain signatures? Or that the FEPS actually reflects multiple independent signatures?

The question raised is consistent with the work of Kunz, Lautenbacher, LeBlanc and Rainville (2012), and Kunz, Chen and Rainville (2020). In the current study, the pain-relevant action units were combined in order to increase the number of trials where a facial response to pain was expressed, thus enhancing the robustness of our analyses. Given the limited sample size, our current dataset is unfortunately insufficient to perform such analysis as there would not be enough trials to look at the action units separately or in subgroups. While the approach of combining the different AUs has proven to be valid and useful, we recognize the value of investigating potential independent signatures associated with the different AUs within the FEPS, and examining whether those signatures can lead to more similar patterns compared to previously developed pain signatures. This discussion has been included in the Limitations paragraph in the Discussion (lines 225 to 268).

Question 3: Is facial expressivity constant throughout the experiment? Is it possible that the expressivity changes between the beginning and the end of the experiment? For instance, if there is a habituation, or if the participant is less surprised by the pain, or in contrast if they get tired by the end of the experiment and do not inhibit their expression as much as they did at the beginning. If facial expressivity changes, this could perhaps affect the correlation with the pain ratings and/or with the brain signatures; perhaps time (trial number) could be added as one of the variables in the model to address this question.

The concern raised by the reviewer is legitimate. We conducted a mixed-effects model to assess the impact of successive trials and runs on facial expressivity. Results indicate that the FACS scores did not change significantly throughout the experiment, suggesting no notable effect of habituation or sensitization on the facial expressivity in our study. Details about the analysis and the results have been added to the Facial Expression section in the Methods (lines 335 to 346).

Reviewer #3 (Public Review):

In this manuscript, Picard et al. propose a Facial Expression Pain Signature (FEPS) as a distinctive marker of pain processing in the brain. Specifically, they attempt to use functional magnetic resonance imaging (fMRI) data to predict facial expressions associated with painful heat stimulation. The main strengths of the manuscript are that it is built on an extensive foundation of work from the research group, and that experience can be observed in the analysis of fMRI data and the development of the machine learning model. Additionally, it provides a comparative account of the similarities of the FEPS with other proposed pain signatures. The main weaknesses of the manuscript are the absence of a proper control condition to assess the specificity of the facial pain expressions, a few relevant omissions in the methodology regarding the original analysis of the data and its purpose, and a biased interpretation of the results.

I believe that the authors partially succeed in their aims, as described in the introduction, which are to assess the association between pain facial expression and existing pain-relevant brain signatures, and to develop a predictive brain activation model of the facial responses to painful thermal stimulation. However, I believe that there is a clear difference between those aims and the claim of the title, and that the interpretation of the results needs to be more rigorous.

We wish to express our appreciation for the insightful and constructive critique provided. The limitation pertaining to the absence of specificity testing had been addressed in response to Reviewer #1, and it has been incorporated into the manuscript (lines 251 to 258).

The commentary made by Reviewer #3 has drawn our attention to a critical concern, namely the potential misalignment between the study findings and our original title. Consequently, we have changed the title to “A distributed brain response predicting the facial expression of acute nociceptive pain”. We also revised the interpretation of the results in the discussion section and we have added a section on limitations.

Recommendations for the Authors:

Reviewer #1 (Recommendations For The Authors):

I hope the following comments will be useful to improve the manuscript.

Abstract

I felt the abstract could be more clear in terms of experimental or scientific questions, hypotheses/expectations, and findings. I also feel the abstract should briefly support the conclusive claim ("is better than...": how better? Or according to what criterion? This may be more relevant than the final conclusive general sentence that does not specifically address the significance of the findings).

The abstract was revised to reinforce the functional perspective adopted to interpret brain activity produced by noxious stimuli and predicting various pain-relevant manifestations. We also mention explicitly the other pain-relevant signatures against which the FEPS is compared in this report, and we added a concluding sentence highlighting the importance of assessing the specificity of the FEPS in future studies.

Introduction - background and rationale

I would postpone the discussion around pain signature and anticipate the one about the brain mechanisms of facial expressions of pain. This will allow you to reinforce the logical flow of rationale, literature gap/question, why the problem is important, and study aims. Only then go for a review of relevant literature on signatures before providing a more specific final paragraph about the study-specific questions, expectations, and implementation. At the moment this is limited to a single very descriptive short paragraph at the end of the intro.

The introduction was structured to guide the readers through a comprehensive understanding of different pain neurosignatures. The introduction aimed to establish a robust rationale for the subsequent analyses detailed in the results section. Indeed, the presentation of that literature ensured that the discussion around pain signatures is contextualised within a broader continuous framework. We acknowledge the reviewer’s comment on the limited description of the brain mechanisms of facial expression of pain. However, this was addressed in several previous reports of our laboratory (Kunz et al. 2011; Vachon-Presseau et al. 2016; Kunz, Chen, and Rainville 2020). We have added some more details about the brain mechanisms of facial expression, and highlighted those references in the first paragraph of the introduction.

Methods and Results

(1) Was there any indication of power based on the previous work or the other signature papers? If yes, how that would inform the present analysis?

The NPS was trained on 20 participants that experienced 12 trials at each of four different intensities. The assessment of the effect sizes was performed on the Neurological Pain Signature in Han et al. (2022). That study revealed a moderate effect size for predicting between-subject pain reports, and a large one for predicting within-subject pain reports. We trained our model on 34 participants that underwent 16 trials. We expected our results to show a smaller effect size as the current experimental design only allowed us to examine spontaneous changes in the facial expression, as noted in the comments made by Reviewer #1. However, the best way to calculate the unbiased effect size of the results presented in the current study would be to test the unchanged model on new independent datasets (see Reddan, Lindquist, and Wager, 2017). Unfortunately, such datasets do not currently exist.

(2) I would clarify to the reader what is meant by normal range of thermal pain and why is this relevant. Also, I did not find data about this assessment nor about the assessment of facial expressiveness (or reference to where it can be found).

We changed this formulation to “All participants included in this study had normal thermal pain sensitivity” and we added a few references. By targeting a healthy population with normal thermal pain sensitivity, our study sought to identify a predictive brain pattern related to facial expression evoked by typical responses to pain that could eventually be generalised to other individuals from the same population. Details about the assessment of facial expressiveness have been added in the appropriate section in the Methods.

(3) That pain ratings are only weakly associated with facial responses is, in its own right, an interesting finding, as a naïve reader would expect the two to be highly positively correlated. I'd suggest discussing this aspect (in reference to previous research) as it is interesting on both theoretical and empirical grounds.

The likelihood and the strength of pain facial expression generally increase with pain ratings in response to acute noxious stimuli of increasing physical intensities, thereby leading to a positive association between the two responses that is driven by the stimulus. However, the poor correlation or the dissociation between facial pain expression and pain rating is a very well known phenomenon that can be demonstrated easily using experimental methods where the stimulus intensity is held constant and spontaneous fluctuations are observed in both facial expression and pain ratings. This result was not discussed in the current manuscript as it was already addressed in the work of Kunz et al. (2011) and Kunz, Karos and Vervoot (2018). We added the references to these studies in the revised manuscript (lines 330 to 334).

(4) It may be worth having CIs throughout the whole set of analyses.

Thanks for the suggestions, this was an oversight. The confidence intervals have been added in the manuscript where applicable.

(5) I would clarify if there are two measures of the brain signature: dot-product and activation map. Relatedly, I cannot find where the authors explained what "FEPS pattern expression scores". Can the authors please clarify?

The clarification has been added in the manuscript (lines 413 to 414).

(6) There seems to be the assumption that the relationship between pain-relevant brain signatures and facial expressions of pain would be parametric and linear. However, this might not hold true. Did the authors test these assumptions?

We indeed decided to use a linear regression technique (i.e. LASSO regression) to model the association between the brain activity and the facial expression of pain. The algorithm choice was mainly based on the simplicity and the interpretability of that approach, and our limited number of observations. The choice was also coherent with previous studies in the domain (e.g. Wager et al., 2011; Wager et al., 2013; Krishnan et al. 2016; Woo et al., 2017). Using a linear model, we were able to predict above chance level the facial expression evoked by pain using the fMRI activation. However, it is legitimate to think that more complex non linear models can better capture the brain patterns predictive of that behavioural manifestation of pain.

(7) Did the authors assess whether the FACS were better to be transformed/normalised? More generally, I would report any data assessment/transformation that has not been reported.

Thank you for this highly relevant suggestion. FACS scores were indeed not normally distributed and the analyses were conducted again to predict the log transformed FACS scores. This transformation was effective to normalize the distribution (skewness = 0.75, kurtosis = -0.84). The predictive model was confirmed on transformed data.

(8) Page 12: I am not clear on whether all the signatures are included in the same model (like a multiple regression) or if separate regressions are calculated per signature. The authors seem to imply that several regressions have been computed (possibly one per comparison with each signature?).

The correlation between the FACS scores and the pain-related signatures was computed separately for each signature. This information has been clarified.

(9) MVPA: See my main comment about warm trials and experimental/statistical design. For example, the LASSO regression model for the pain trials could be compared with a model using warm trials besides (or instead of) the unfitted model. Otherwise, add the warm trials as another predictor or within the subject level in a dummy fixed factor comprising pain and warm trials.

The inclusion of warm trials in the model training would be inconsistent with the goal of the main analysis to predict the facial expression of pain when a noxious pain stimulus is presented. Secondary analyses were conducted to compare the response of the FEPS to the warm trials compared to noxious pain trials. The dot product between the FEPS and the activation maps (FEPS scores) associated with the warm condition was computed. A linear mixed model was conducted to investigate the association between FEPS scores and the experimental condition (warm vs pain). Additional contrasts compared the warm trials with the pain trials with and without pain facial expression. The details of this analysis have been added to the manuscript (see Response of the FEPS to pain and warm in the Methods) as well as the corresponding results (see Results and Discussion).

(10) I would clarify for the reader why the separate M1 analysis has been run. Although obvious, I feel the reader would benefit from the specific hypothesis about this control analysis being spelled out together with the other statistical hypotheses within the statistical design in a more streamlined manner.

We extended the discussion on the rationale of that analysis and its interpretation taking into account the most recent results using the log transformed FACS scores (lines 125 to 133).

(11) The mixed model aimed to assess the relationship between pain ratings FEPS scores and facial scores is a crucial finding. I believe it speaks to the importance of a more complete design, which I already highlighted. I have a couple of technical questions: did the authors assess random slopes too? And, what was the strategy used to determine the random effects structure?

The linear mixed model considered the participants as a random effect, with random intercepts, considering the grouping structure in our data (i.e., each participant completed multiple trials). The reported results in the original manuscript were considering fixed slopes. However, following the reviewer’s comment, we re-computed the mixed linear models allowing the slopes to vary according to the intensity ratings. The results were changed in the manuscript to represent the output of those models.

(12) The text from lines 63 to 67 could go in the methods.

We decided to include those lines within the Result and Discussion section to give the reader more specification about the FACS scores, as this term is subsequently referenced in the following part of the Results and Discussion section. We are concerned that putting this information only in the Methods section would disrupt the reading.

Reviewer #2 (Recommendations For The Authors):

p. 4-5. When you report the positive weight clusters, you follow up with a sentence specifying which cognitive processes those brain regions are typically associated with. However, when you report the negative weight clusters, you do not specify the cognitive processes typically associated with those brain areas. I think that providing that information would be helpful to the readers.

Thanks for noticing this omission. The information has been added in the most recent version of the manuscript (lines 119 to 121).

p. 9. You specify that the degree of expressiveness of participants was evaluated. How did you evaluate expressiveness? Did you use this variable in your analyses? Were participants excluded based on their degree of expressiveness?

Details about the assessment of facial expressiveness have been added in the appropriate section in the Methods (lines 285 to 289).

p. 10. You explain that two certified FACS-coders evaluated the video recordings to rate the frequency of AUs. Could you please provide more details about the frequency measure? I think that there are different ways in which this could have been done. For instance, were the videos decomposed into frames, and then the frequency measured by summing the number of frames in which the AU occurred? Or was it "expression-based", so one occurrence of an AU (frequency of 1) would correspond to the whole period between its activation onset and offset? Both ways have pros and cons. For example, if the frequency represents the number of frames, then it controls for the total duration of the AU activation within a trial (pro); but if there were multiple activations/deactivations of the AU within one trial, this will not be controlled for (con). And vice-versa with the second way of calculating frequency.

Details about the frequency scores have been added to the manuscript (lines 315 to 319).

p. 11. When you explained how you calculated the association between the facial expression of pain and pain-related brain signatures, I felt that there was some information missing. Did you use the thresholded maps (available in the published articles), or did you somehow have access to the complete, voxel-by-voxel, raw regression coefficient maps?

The unthresholded maps were used. The information has been clarified in the latest version of the manuscript, as well as the details about the availability of the maps (see Data Availability section at the end of the manuscript).

Reviewer #3 (Recommendations For The Authors):

Format

The authors will notice that many observations about the manuscript are related to missing information and a lack of graphical representations. I believe the topic and the content of the manuscript are too complex to condense into a short report.

Title

The claim of the title is simply not substantiated by the content of the manuscript. Demonstrating that the FEPS is a distinctive (i.e., specific) marker of pain processing requires a substantially different experimental design, with more rigorous controls and a broader set of painful stimulations. The manuscript would benefit from a more accurate title.

We agree that the title could better align with our findings. We modified the title accordingly : “A distributed brain response predicting the facial expression of acute nociceptive pain”.

Abstract

I find it puzzling that the authors claim that there is limited knowledge of the neural correlates of facial expression of pain given what they describe in the first paragraph of the introduction. Besides, they propose to reanalyze a dataset that has been extensively described in Kunz et al. (2011), which is unlikely to provide any new significant information.

We respectfully disagree with that comment. We considered that three articles (i.e., Kunz et al., 2011; Vachon-presseau et al., 2016; Kunz, Chen and Rainville, 2020) on the topic do constitute limited knowledge, especially if we compare it to the very large body of literature on the neural correlates associated with pain ratings. Except for these three studies, all the other citations pertain to behavioral studies on facial expression of pain, and do not examine the brain activity related to it. Furthermore, we believe that the complementary nature of the analyses performed in Kunz et al. (2011) and in this manuscript offers new insights into our understanding of facial expression in the context of pain. Indeed, the multivariate approach used in this study addresses some limitations present in Kunz et al. (2011) univariate analyses, mainly that it provides a quantifiable way to compare the similarity between different predictive patterns (Reddan and Wager, 2017). We submit that the assessment of the FEPS against several other pain-relevant signatures provides new and important information.

Furthermore, the abstract does not clearly state the aim, and the first line of the results does not match what the authors claim in the preceding line. The take-home message (last sentence) introduces the concept of a biomarker, which, as stated before, cannot be validated with the current data/experimental design. To put it in plain words, a given facial expression (or a composite score derived from a combination of expressions) cannot be a specific biomarker for pain, because a person can always mimic the same expression without feeling pain. Whether a given facial expression can be predicted from brain activity is a different issue, and whether that prediction can differentiate between painful and non-painful origins of the facial expression is another different issue. Unfortunately, neither of those issues can be tested with the current data/experimental design. The abstract would improve if the authors would circumscribe to what they actually tested, which is accurately described in the last sentence of the Introduction.

The abstract was revised accordingly. The term ‘biomarker’ was used in accordance with preceding studies in the field (see Reddan and Wager, 2017; Lee et al., 2021). Please note that we applied the same reasoning to fluctuations in pain expression as previous studies have applied to pain ratings. Of course, we can not dismiss the possibility of someone mimicking facial expressions. Similar reasoning applies to subjective reports, as individuals can intentionally overestimate their pain experience conveyed through verbal reports. This is another case of specificity testing that cannot be addressed in the present study (see new conclusion of the abstract and discussion of limitations). The challenge of pain assessment is a classical problem within both the scientific and the clinical literature. Here, we suggest that the consideration of multiple manifestations of pain is necessary to address this challenge and will provide a more comprehensive portrait of pain-related brain function.

Introduction

I believe that the Introduction would benefit from a strict definition of what is a marker/biomarker/neuromarkers (all those terms are used in the manuscript) and what are its desirable features (validity, reliability, specificity, etc.). I also believe that the Introduction (and the rest of the text) would benefit from a critical assessment of the term "signature". The Introduction describes four existing "signatures", all of them differing in the experimental condition in which acute nociceptive pain is studied, and proposes a fifth one. Keeping with the analogy, I'm wondering whether they should be called (pain) "signatures" if there is a different one for each experimental acute pain condition, and they are so dissimilar between them when they are tested on the same condition (this dataset).

The last part of that comment raises fundamental methodological potential limitations that should be addressed in more depth in another article. That point goes beyond the scope of a research article. Regarding the stability aspect of the signatures, most of the signatures have not been studied extensively. It is thus difficult to currently assess their reliability. However, Han et al. (2022) showed high within-individual test-retest reliability for the NPS across eight different studies. Given that pain is a multidimensional experience, it is not surprising to find different patterns of activation predictive of different aspects or dimensions of the pain experience (see Čeko et al., 2022 for a similar discussion applied to negative affect).

The authors state that "As an automatic behavioral manifestation, pain facial expression might be an indicator of activity in nociceptive systems, perceptual and evaluative processes, or general negative affect." Doesn't it reflect all three of them? (and instead of or?) Why "might"?

The original sentence has been modified as follows: “As an automatic behavioral manifestation, pain facial expression is considered to be an indicator of activity in nociceptive systems, and to reflect perceptual and affective-evaluative processes” (lines 65 to 67).

Methods

The pain scale should be described. Kunz et al. used a 0-100 scale, where 50 was the pain threshold. This is crucial to interpret the 75-80/100 score for the painful thermal intensity.

The description of the pain scale has been added to the manuscript (lines 299 to 300).

Ratings for warm and painful temperatures should be reported (ideally plotted with individual-trial/subject data). In the same line of reasoning, FACS scores should be reported as well (ideally plotted with individual-trial/subject data). It would be interesting to explore the across-trial variability of pain ratings and FACS scores. That is, do people keep giving the same ratings and making the same facial expression after 16 trials? How much variability is between trials and between subjects?

The point raised in that comment was already addressed in response to a comment made by Reviewer #1 (also see the new Figures S2 and S4; see also lines 335 to 346).

How come only painful trials are analyzed? What if the FEPS signature was the same for warm and painful stimulation, thus reflecting the settings (fMRI experiment, stimulation, etc.) rather than the brain response to the stimuli?

The point raised in that comment was already addressed in response to a comment made by Reviewer #1. There was no pain expression in the warm trials and the FEPS shows no response to warm trials. This is now illustrated in the new Figure S4B (see also lines 138 to 158).

The authors propose to predict the trial-by-trial FACS composite score from the pain ratings using a LMM. However, it is interesting that they aim for an almost constant within- and between-subject pain score (75-80/100) as stated in the Methods. This should theoretically render the linear model invalid since its first (and main) assumption would be that FACS should vary linearly with the pain score. Even if patients were not aware that the temperatures were constant across trials, the variation in pain scores should be explained by random noise for a constant stimulation intensity.

Reviewer #3 raises an important point that we need to clarify. Contrary to the expectation that FACS responses should be strongly correlated to pain ratings, we posited that these response channels depend at least in part on separate brain networks that may be differentially sensitive to a variety of modulatory mechanisms (attention, emotion, expectancy, motor priming, social context, etc.). This implies that part of the variance in FACS is independent from pain ratings. We, therefore, consider what Reviewer #3 refers to as random noise to be relevant and meaningful fluctuations reflecting endogenous processes influencing one’s experience of pain and differentially affecting various output responses.

I noticed that fMRI data was analyzed with SPM5 in the original paper (Kunz et al., 2011) and with SPM8 in this manuscript. Was fMRI data re-processed for this manuscript? Were there any differences between the original analysis and this one that might induce changes in the interpretation of results?

The data were indeed re-processed using SPM8, which was the most recent version available when we started the analyses reported here. We used trial-by-trial activation maps for MVPA, which differs from what was used in the previous study (contrast maps at the level of the conditions, not the trials). We have no reason to believe that the different versions will change the message of this manuscript since those versions do not differ significantly in terms of the fMRI preprocessing pipeline (see SPM8 release notes; https://www.fil.ion.ucl.ac.uk/spm/software/spm8/). Furthermore, the aim of this present study is not to compare the different analysis parameters implemented in SPM5 vs SPM8.

What is the rationale for including PVP in the comparison among signatures? The experimental settings in which it was devised are distant from those described here.

The inclusion of the PVP was aimed at enhancing our comparative analysis with the FEPS, as we sought to investigate the potential functional meaning of the FEPS. The PVP was developed to capture the aversive value of pain, a dimension that is conceptually proximal to the interpretation of the facial expression as a manifestation of the affective response to nociceptive pain.

The LASSO-PCR approach is, in my opinion, not a procedure for (brain) decoding in this context. It is accurately described in the section title as a method for multivariate pattern analysis, or as a variable selection and regularization method for a prediction model. Here, brain activity in specific areas related to pain processing can hardly be described as "encoded", and the method just helps select those activations relevant for explaining a certain outcome (in this case, facial expressions).

We understand the point made by reviewer #3. The term brain decoding was changed for multivariate pattern analysis in the latest version of the manuscript.

Details are missing with regards to the dataset split into training, validation, and testing.

Details about the training and testing procedure were added in the manuscript (lines 383 to 385).

This might just be ignorance from me, so I apologize in advance, but what are "contrast" fMRI images? They are mentioned three times in the text but not really described. Are they the "Pain > Warm" contrasts from the original paper?

We apologize for any confusion caused by the use of the term “contrast images” which suggests a direct comparison between two experimental conditions. We have replaced “contrast images” with “activation maps” to provide a more accurate description of the nature of the data used in the multivariate pattern analysis (lines 388 to 389).

In the "Facial expression" section, the authors run an LMM to test the association between pain ratings (response variable) and facial responses (explanatory variable). If I understand correctly, in the "Multivariate pattern analysis" section they test the association between facial composite scores (response variable) and pain ratings (explanatory variable), but they obtain different results.

The analyses were recomputed on the log transformed data, as mentioned previously in the response to reviewers 1-2. The first model (in the “Facial expression” section) used the log transformed FACS scores as a dependent variable, the pain ratings as the fixed effect, and the participants as the random effect. The results of that analysis suggested that the transformed facial expression scores were not significantly associated with the pain ratings (p = .07). The second model uses both the FEPS pattern expression scores and pain ratings as fixed effects to predict facial responses. This analysis showed the significant contribution of the FEPS to the prediction of FACS scores (p < .001) and no significant effect of the pain ratings. However, a significant interaction was found (p = .03) suggesting that the prediction of the pain facial expression by the FEPS may vary with pain ratings (i.e. moderator effect). Those results have been clarified in the “Multivariate pattern analysis” section in the Methods (lines 416 to 426).

In this same section, what are "FEPS pattern expression scores"? They are used three times in the text, but I could not find their description.

The FEPS pattern expression scores correspond to the dot product between the trial-by-trial activation maps and the unthresholded FEPS signature. This information has been added to the manuscript (lines 413 to 414).

It would not be far-fetched to hypothesize that FACS scores could be predicted using solely activity from the motor cortex. The authors attempted to do this, but only with information from M1. Why did they not use the entire motor cortex, or better, regions of the motor cortex directly linked with the AUs described in the manuscript?

The selection of the primary motor area (M1) was based on the results found in Kunz et al. (2011). In this study, M1 showed the strongest correlation with facial expression of pain. There are numerous possibilities of combinations of multiple brain regions considering a variety of criteria based on distributed networks involved in motor, affective, or pain-related processes. We limited our exploration to the region with the strongest hypothesis due to practical feasibility concerns.

Results and Discussion

As a general recommendation, results should present individual data whenever possible. For example, the association between signatures and facial expression should be plotted using scatterplots.

We have added figures showing individual data when it was applicable (Figure S2; Figure S4).

The authors state that the LASSO-PCR model accounts for the facial responses to pain. I believe this is an overstatement, considering:

- A Pearson's r of 0.49 is usually considered low/weak correlation (moderate at best). In the same line, an R2 of 0.17 means that only 17% of the variance is explained by the model.

More nuanced interpretation of the results has been added to the discussion. A section has been added to highlight the limitations of the study.

- Figure 1 needs to display individual subject data and the ideal regression line.

The model was trained using a k-fold cross-validation procedure. The regression lines thus represent the model’s prediction for each one of the 10 folds (i.e. each fold is trained and tested on a different subset of the data). A scatter plot including the ideal regression line computed across all trials and subjects was added in supplementary material to illustrate the relation between the FACS scores and the FEPS pattern expression scores (Figure S4).

- Looking at Figure 1, it is clear that the model has an intercept different from zero. This means that when the FACS score was zero (i.e., volunteers did not make any distinguishable facial expression), the model predicted a score larger than zero. This is not discussed in the manuscript, and in simple terms, it means that there are brain activation patterns when no discernible facial expression is being made by the volunteers. In the original paper by Kunz et al., two groups of subjects were categorized, and one of them was a facially low- or non-expressive group (n=13). This fact is not even mentioned in the manuscript.

The categorization in the previous report (Kunz et al., 2012) was based on a pre-experimental session. All subjects were included in the current analysis. This is now indicated in the Methods (lines 287 to 289).

- On the other end of the range in Figure 1, differences between the FACS scores near the maximum range (40) are underestimated by 23 to 33 points! I guess that the RMSE is smaller (6-7 points), because many FACS scores are concentrated on the low end of the scale.

This is a very interesting comment. A section discussing the limits of the model to predict the lower and higher FACS scores has been added in the manuscript (lines 232 to 250).

It is of course acceptable to interpret the low similarity between signatures as a sign that each signature describes a different mechanism related to pain processing. However, I believe that a complete discussion should contemplate other competing hypotheses. Considering that all signatures were developed using a similar painful thermal stimulation protocol, it is reasonable to expect larger similarities between signatures. The fact that they are so dissimilar could be a reflection of model overfit, i.e., all these signatures are just fitted to these particular experimental protocols and data, and do not generalize to brain mechanisms of pain processing.

We appreciate the pertinent observation. We have included a limitations section in which we discussed, among other considerations, the possible overfitting of models and the necessity of pursuing generalizability studies (lines 225 to 268).

Author response:

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

eLife assessment

This is an important study on the regulation of chlorophyll biosynthesis in rice embryos. It provides insights into the genetic and molecular interactions that underlie chlorophyll accumulation, highlighting the inhibition of OsGLK1 by OsNF-YB7 and the broader implications for understanding chloroplast development and seed maturation in angiosperms. The results presented, including mutation analysis, gene expression profiles, and protein interaction studies, provide convincing evidence for the function of OsNF-YB7 as a repressor in the chlorophyll biosynthesis pathway.

Thank you very much for your positive assessment of our manuscript. We have carefully revised the manuscript according to the reviewers’ valuable suggestions and comments. For more details, please see the point-to-point response to the reviewers below.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This manuscript investigates the regulation of chlorophyll biosynthesis in rice embryos, focusing on the role of OsNF-YB7. The rigorous experimental approach, combining genetic, biochemical, and molecular analyses, provides a robust foundation for these findings. The research achieves its objectives, offering new insights into chlorophyll biosynthesis regulation, with the results convincingly supporting the authors' conclusions.

Strengths:

The major strengths include the detailed experimental design and the findings regarding OsNF-YB7's inhibitory role.

Weaknesses:

However, the manuscript's discussion on the practical implications for agriculture and the evolutionary analysis of regulatory mechanisms could be expanded.

Thank you for your insightful comments and suggestions. In the revised manuscript, we discussed the potential application of the chlorophyllous embryo (please see line 270-274). The presence of chlorophyll in the embryo facilitates photosynthesis at early developmental stages, potentially leading to improved seedling growth and vigor (Smolikova and Medvedev, 2016). In crops such as soybean and canola, green embryo is considered as a valuable trait due to its association with enhanced photosynthetic capacity, which consequently promotes fatty acid biosynthesis (Ruuska et al., 2004). However, chlorophyll degradation must be carefully managed during seed maturation to avoid negative effects on seed viability and meal quality (Chung et al., 2006). Interestingly, the green embryo of lotus (Nelumbo nucifera) is widely used as a food ingredient in Asian, Australia, and North America. It is employed in herbal medicine to treat nervous disorders, insomnia, and other conditions (Zhu et al., 2017; Ha et al., 2022), highlighting the significant potential value of the green embryo.

In many chloroembryophytes, such as Arabidopsis, the embryo occupies a large proportion of the seed. From an evolutionary perspective, the presence of chlorophyll in the embryo may promote adaptation in such chloroembryophytes because more reserves can be accumulated in the seed through active photosynthesis, better supporting the embryo development and subsequent seedling growth (Sela et al., 2020). On the other hand, some leucoembryophytes, such as rice, have persistent endosperm rich in storage reserves to nourish embryo development (Liu et al., 2022). Gaining the ability to accumulate chlorophyll in the embryo is unnecessary for such species. In agreement with this hypothesis, cholorophyllous embryos are more prevalent in non-endospermous seeds (Dahlgren, 1980). However, we would like to emphasize that the evolutionary force driving the divergence of chloroembryophytes and leucoembryophytes is currently almost completely unknown and deserves in-depth investigation in the future. We discussed the possible evolution of the ability to accumulate chlorophyll in the embryo, please find the details in Line 276-295.

Reviewer #2 (Public Review):

Summary:

The authors set out to establish the role of the rice LEC1 homolog OsNF-YB7 in embryo development, especially as it pertains to the development of photosynthetic capacity, with chlorophyll production as a primary focus.

Strengths:

The results are well-supported and each approach used complements each other. There are no major questions left unanswered and the central hypothesis is addressed in every figure.

Weaknesses:

There are a handful of sections that could use clarifying for readers, but overall this is a solidly composed manuscript.

The authors clearly achieved their aims; the results compellingly establish a disparity between how this system operates in rice and Arabidopsis. Conclusions are thoroughly supported by the provided data and interpretations. This work will force a reconsideration of the value of Arabidopsis as a model organism for embryo chlorophyll biosynthesis and possibly photosynthesis during embryo maturation more broadly, as rice is a major crop organism and it very clearly does not follow the Arabidopsis model. It will thus be useful to carry out similar tests in other organisms rather than relying on Arabidopsis and attempting to more fully establish the regulatory mechanism in rice.

Thank you very much for your positive comments. We have carefully revised the manuscript according to your and the other reviewers’ comments and suggestions. Particularly, we emphasized the necessary to carry out similar tests in other organisms rather than relying on Arabidopsis to better understand the regulatory mechanism in rice.

Reviewer #3 (Public Review):

Summary:

In this study, the authors set out to understand the mechanisms behind chlorophyll biosynthesis in rice, focusing in particular on the role of OsNF-YB7, an ortholog of Arabidopsis LEC1, which is a positive regulator of chlorophyll (Chl) biosynthesis in Arabidopsis. They showed that OsNF-YB7 loss-of-function mutants in rice have chlorophyll-rich embryos, in contrast to Arabidopsis LEC1 loss-of-function mutants. This contrasting phenotype led the authors to carry out extensive molecular studies on OsNF-YB7, including in vitro and in vivo protein interaction studies, gene expression profiling, and protein-DNA interaction assays. The evidence provided well supported the core arguments of the authors, emphasising that OsNF-YB7 is a negative regulator of Chl biosynthesis in rice embryos by mediating the expression of OsGLK1, a transcription factor that regulates downstream Chl biosynthesis genes. In addition, they showed that OsNF-YB7 interacts with OsGLK1 to negatively regulate the expression of OsGLK1, demonstrating the broad involvement of OsNF-YB7 in rice Chl biosynthetic pathways.

Strengths:

This study clearly demonstrated how OsNF-YB7 regulates its downstream pathways using several in vitro and in vivo approaches. For example, gene expression analysis of OsNF-YB7 loss-of-function and gain-of-function mutants revealed the expression of selected downstream chl biosynthetic genes. This was further validated by EMSA on the gel. The authors also confirmed this using luciferase assays in rice protoplasts. These approaches were used again to show how the interaction of OsNF-YB7 and OsGLK1 regulates downstream genes. The main idea of this study is very well supported by the results and data.

Weaknesses:

From an evolutionary perspective, it is interesting to see how two similar genes have come to play opposite roles in Arabidopsis and rice. It would have been more interesting if the authors had carried out a cross-species analysis of AtLEC1 and OsNF-YB7. For example, overexpressing AtLEC1 in an osnf-yb7 mutant to see if the phenotype is restored or enhanced. Such an approach would help us understand how two similar proteins can play opposite roles in the same mechanism within their respective plant species.

We appreciate your insightful comments and suggestions. It is a very interesting question whether AtLEC1 can fully restore osnf-yb7, given the possible functional divergence between the genes in terms of regulation of chlorophyll biosynthesis in the embryo. We have previously expressed OsNF-YB7 in the lec1-1 background in Arabidopsis, driven by the native promoter of LEC1 (Niu et al., 2021). We found that OsNF-YB7 could almost completely rescue the embryo defects in Arabidopsis, indicating that OsNF-YB7 plays a resemble role in rice as the LEC1 does in Arabidopsis (Niu et al., 2021). We sought to determine whether AtLEC1 can complement the chlorophyll defect in osnf-yb7. However, given the fact that osnf-yb7 shows severe callus induction defect, which is not surprising, because many studies have shown that LEC1 is indispensable for somatic embryo development in various plant species, we are struggling to obtain the genetic materials for analysis. We have to transform OsNF-YB7pro::AtLEC1 into the WT background first, and then cross the transformant with the osnf-yb7 mutant. This is a time-consuming process in rice, but hopefully we will able to isolate a line expressing OsNF-YB7pro::AtLEC1 in the osnf-yb7 background from the resulting segregating population.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

A minor comment regarding the chlorophyll contents quantification in the study. Line 87: "The results showed that WT had an achlorophyllous embryo throughout embryonic development,...." In the TEM result, chloroplast was not observed in the WT embryo sections, indicating a lack of chlorophyll-containing structures, contrary to what was found in the osnf-yb7 embryos where chloroplasts were observed.

The authors stated that the embryo morphologies and Chl autofluorescence data showed that WT had an achlorophyllous embryo throughout embryonic development. However, the quantification of Chl levels in Figure 1D and Figure 4C showed that WT does produce some chlorophylls, albeit at lower levels than osnf-yb7 or OSGLK-OX embryos (WT values in the two figures are slightly different). This discrepancy warrants clarification to ensure consistency and accuracy in the manuscript's findings.

We re-evaluated the Chl content in the embryos of WT and OsGLK1-OX mature seeds. The result confirmed our previous finding that WT embryos produce a small amount of chlorophyll (please see the updated Fig. 4C). Notably, we observed that the dark-grown etiolated plants still have measurable chlorophyll content as reported in many studies (for example, Wang et al., 2017; Yoo et al., 2019), suggesting that there is potential bias in measuring chlorophyll content using an absorbance-based approach. We assume this possibly explains the concern you have raised.

Reviewer #2 (Recommendations For The Authors):

Mild editing for grammar is needed throughout, e.g. line 73, "It is still a mysterious why plant species".

We have carefully edited the grammar.

As a minor point, the placement of figure panels, such as in Figure 1, is not always intuitive.

Thank you for your suggestion. This figure has been revised as suggested. Please see the updated Fig. 1.

What is the significance of the two GFP mutants in Figures 2C and 2D? Is one of those the mislabeled Flag mutant?

The lines showed in Fig. 2C and D were not mislabeled. They were two independent transgenic events, both of which showed that OsNF-YB7 inhibited the expression of OsPORA and OsLHCB4 in rice. The transgenic lines overexpressing OsNF-YB7 tagging with the 3× Flag (NF-YB7-Flag) were also used for this experiment. In agreement, OsPORA and OsLHCB4 were significantly downregulated in the three independent NF-YB7-Flag lines (Fig. S4C), confirming the results showed in Fig. 2C and D.

In Figures 2G and 2H, what is that enormous band at the bottom of the gel?

The bands at the bottom of the gel were free probes. We indicated this in the revised figure.

Not until the Materials and Methods section did I realize that any of this study was being done in tobacco; the Introduction implies it's rice vs. Arabidopsis and it might be a good idea to mention the organism of study somewhere before Figure 6.

We apologize for any confusion caused by our previous writing. While the majority of this study was performed with rice plants or protoplasts, the split complementary LUC assays and BiFC assays were performed with tobacco. We have specified these in the revised manuscript as suggested.

Reviewer #3 (Recommendations For The Authors):

It would be nice if the author could show what the phenotype is in AtLEC1 OX in osnf-yb7 and also OsNF-YB7 OX in atlec1 mutants.

Thank you for your suggestion. We have previously expressed OsNF-YB7 in the lec1-1 background of Arabidopsis, driven by the native promoter of Arabidopsis LEC1 (Niu et al., 2021). Since OsNF-YB7 could rescue the embryo morphogenesis defects in Arabidopsis (Niu et al., 2021), we assumed that OsNF-YB7 plays a similar role in rice as the LEC1 does in Arabidopsis. However, it remains unknown whether expression of LEC1 in osnf-yb7 may restore the chlorophyllous embryo phenotype in rice. As the generation of genetic material is time-consuming, and especially given the fact that osnf-yb7 has a severe callus induction defect, we are struggling to obtain the complementary line for analysis. We have to transform OsNF-YB7pro::AtLEC1 in a WT background first, and then cross the transformant with the osnf-yb7 mutant. Hopefully, we will be able to isolate a line expressing OsNF-YB7pro::AtLEC1 in osnf-yb7 background, from the derived segregating population. We discussed the reviewer’s concern in the revised manuscript, please see Line 369-376.

Line 46, I think it is vague to mention that 'Like most plant species'. Some species might have different copy numbers, for example, a single GLK in liverwort M. polymorpha.

The statement has been revised. Please see Line 46.

Figures 2F and 5B, why was only one promoter region used for OsLHCB4? It would be better to have more regions like OsPORA.

Thank you for your comments. Here, we have examined more promoter regions (P1, P2 and P3) in the revised manuscript as suggested, among which, the previously selected promoter region (P3) contains both the G-box and CCAATC motifs that can be potentially recognized by GLK1. Consistent to our previous report, the results showed that OsNF-YB7 (left) and OsGLK1 (right) were associated with the P3 region, but showed no significant differences in the other probes. Please see the results in Fig. 2F and Fig. 5B of the revised manuscript.

Legend of Figures 2G, H, OsPORA (I), and OsLHCB (J) should be (G) and (H) respectively.

Corrected.

References

Chung, D.W., Pruzinska, A., Hortensteiner, S., and Ort, D.R. (2006). The role of pheophorbide a oxygenase expression and activity in the canola green seed problem. Plant Physiol 142, 88-97.

Ha, T., Kim, M.S., Kang, B., Kim, K., Hong, S.S., Kang, T., Woo, J., Han, K., Oh, U., Choi, C.W., and Hong, G.S. (2022). Lotus Seed Green Embryo Extract and a Purified Glycosyloxyflavone Constituent, Narcissoside, Activate TRPV1 Channels in Dorsal Root Ganglion Sensory Neurons. J Agric Food Chem 70, 3969-3978.

Liu, J., Wu, M.W., and Liu, C.M. (2022). Cereal Endosperms: Development and Storage Product Accumulation. Annu Rev Plant Biol 73, 255-291.

Niu, B., Zhang, Z., Zhang, J., Zhou, Y., and Chen, C. (2021). The rice LEC1-like transcription factor OsNF-YB9 interacts with SPK, an endosperm-specific sucrose synthase protein kinase, and functions in seed development. Plant J 106, 1233-1246.

Ruuska, S.A., Schwender, J., and Ohlrogge, J.B. (2004). The capacity of green oilseeds to utilize photosynthesis to drive biosynthetic processes. Plant Physiol 136, 2700-2709.

Sela, A., Piskurewicz, U., Megies, C., Mene-Saffrane, L., Finazzi, G., and Lopez-Molina, L. (2020). Embryonic Photosynthesis Affects Post-Germination Plant Growth. Plant Physiol 182, 2166-2181.

Smolikova, G.N., and Medvedev, S.S. (2016). Photosynthesis in the seeds of chloroembryophytes. Russ J Plant Physl+ 63, 1-12.

Wang, Z., Hong, X., Hu, K., Wang, Y., Wang, X., Du, S., Li, Y., Hu, D., Cheng, K., An, B., and Li, Y. (2017). Impaired Magnesium Protoporphyrin IX Methyltransferase (ChlM) Impedes Chlorophyll Synthesis and Plant Growth in Rice. Front Plant Sci 8, 1694.

Yoo, C.Y., Pasoreck, E.K., Wang, H., Cao, J., Blaha, G.M., Weigel, D., and Chen, M. (2019). Phytochrome activates the plastid-encoded RNA polymerase for chloroplast biogenesis via nucleus-to-plastid signaling. Nat Commun 10, 2629.

Zhu, M., Liu, T., Zhang, C., and Guo, M. (2017). Flavonoids of Lotus (Nelumbo nucifera) Seed Embryos and Their Antioxidant Potential. J Food Sci 82, 1834-1841.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

As a reviewer for this manuscript, I recognize its significant contribution to understanding the immune response to saprophytic Leptospira exposure and its implications for leptospirosis prevention strategies. The study is well-conceived, addressing an innovative hypothesis with potentially high impact. However, to fully realize its contribution to the field, the manuscript would benefit greatly from a more detailed elucidation of immune mechanisms at play, including specific cytokine profiles, antigen specificity of the antibody responses, and long-term immunity. Additionally, expanding on the methodological details, such as immunophenotyping panels, qPCR normalization methods, and the rationale behind animal model choice, would enhance the manuscript's clarity and reproducibility. Implementing functional assays to characterize effector T-cell responses and possibly investigating the microbiota's role could offer novel insights into the protective immunity mechanisms. These revisions would not only bolster the current findings but also provide a more comprehensive understanding of the potential for saprophytic Leptospira exposure in leptospirosis vaccine development. Given these considerations, I believe that after substantial revisions, this manuscript could represent a valuable addition to the literature and potentially inform future research and vaccine strategy development in the field of infectious diseases.

Reviewer #2 (Public Review):

Summary:

The authors try to achieve a method of protection against pathogenic strains using saprophytic species. It is undeniable that the saprophytic species, despite not causing the disease, activates an immune response. However, based on these results, using the saprophytic species does not significantly impact the animal's infection by a virulent species.

Strengths:

Exposure to the saprophytic strain before the virulent strain reduces animal weight loss, reduces tissue kidney damage, and increases cellular response in mice.

Weaknesses:

Even after the challenge with the saprophyte strain, kidney colonization and the release of bacteria through urine continue. Moreover, the authors need to determine the impact on survival if the experiment ends on the 15th.

Reviewer #3 (Public Review):

Summary:

Kundu et al. investigated the effects of pre-exposure to a non-pathogenic Leptospira strain in the prevention of severe disease following subsequent infection by a pathogenic strain. They utilized a single or double exposure method to the non-pathogen prior to challenge with a pathogenic strain. They found that prior exposure to a non-pathogen prevented many of the disease manifestations of the pathogen. Bacteria, however, were able to disseminate, colonize the kidneys, and be shed in the urine. This is an important foundational work to describe a novel method of vaccination against leptospirosis. Numerous studies have attempted to use recombinant proteins to vaccinate against leptospirosis, with limited success. The authors provide a new approach that takes advantage of the homology between a non-pathogen and a pathogen to provide heterologous protection. This will provide a new direction in which we can approach creating vaccines against this re-emerging disease.

Strengths:

The major strength of this paper is that it is one of the first studies utilizing a live non-pathogenic strain of Leptospira to immunize against severe disease associated with leptospirosis. They utilize two independent experiments (a single and double vaccination) to define this strategy. This represents a very interesting and novel approach to vaccine development. This is of clear importance to the field.

The authors use a variety of experiments to show the protection imparted by pre-exposure to the non-pathogen. They look at disease manifestations such as death and weight loss. They define the ability of Leptospira to disseminate and colonize the kidney. They show the effects infection has on kidney architecture and a marker of fibrosis. They also begin to define the immune response in both of these exposure methods. This provides evidence of the numerous advantages this vaccination strategy may have. Thus, this study provides an important foundation for future studies utilizing this method to protect against leptospirosis.

Weaknesses:

Although they provide some evidence of the utility of pretreatment with a non-pathogen, there are some areas in which the paper needs to be clarified and expanded.

The authors draw their conclusions based on the data presented. However, they state the graphs only represent one of two independent experiments. Each experiment utilized 3-4 mice per group. In order to be confident in the conclusions, a power analysis needs to be done to show that there is sufficient power with 3-4 mice per group. In addition, it would be important to show both experiments in one graph which would inherently increase the power by doubling the group size, while also providing evidence that this is a reproducible phenotype between experiments. Overall, this weakens the strength of the conclusions drawn and would require additional statistical analysis or additional replicates to provide confidence in these conclusions.

A direct comparison between single and double exposure to the non-pathogen is not able to be determined. The ages of mice infected were different between the single (8 weeks) and double (10 weeks) exposure methods, thus the phenotypes associated with LIC infection are different at these two ages. The authors state that this is expected, but do not provide a reasoning for this drastic difference in phenotypes. It is therefore difficult to compare the two exposure methods, and thus determine if one approach provides advantages over the other. An experiment directly comparing the two exposure methods while infecting mice at the same age would be of great relevance to and strengthen this work.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Major Comments

(1) Elucidation of Immune Mechanisms: The manuscript intriguingly suggests that exposure to saprophytic Leptospira primes the host for a Th1-biased immune response, contributing to survival and mitigation of disease severity upon subsequent pathogenic challenge. However, the underlying mechanisms remain broadly defined. A more detailed investigation into the cytokine profiles, particularly the levels of IFN-γ, IL-12, and other Th1-associated cytokines, could clarify the mechanism of Th1 bias. Moreover, exploring the role of antigen-presenting cells (APCs) in priming T cells towards a Th1 phenotype would add valuable insights.

In this study we continue to elucidate the immune mechanisms engaged by pathogenic and non-pathogenic Leptospira as a follow up to our previous work (Shetty et al, 2021 PMID: 34249775, and Kundu et al 2022 PMID 35392072). We, and others, have shown that saprophytic L. biflexa and pathogenic L. interrogans induce major chemo-cytokines associated with Th1 biased immune responses (Shetty et al. 2021; Cagliero et al. 2022; Krangvichian et al. 2023) and engage myeloid immune cells such as macrophages and dendritic cells. The role of antigen presenting cells such as dendritic cells in priming T cells and activating adaptive response is a separate question and can be addressed in the future. To further address this question, a recent mechanistic study (Krangvichian et al. 2023) showed that non-pathogenic leptospires (L. biflexa) promote MoDC maturation and stimulate the proliferation of IFN-γ-producing CD4+ T cells and potentially elicit a Th1-type response in mice, which also supports our current claim and it is referenced in our manuscript.

(2) Quantitative Analysis of Kidney Colonization: The manuscript reports that pre-exposure to L. biflexa did not prevent the colonization of kidneys by L. interrogans but led to a more regulated immune response and reduced fibrosis. A more nuanced quantification of bacterial loads in the kidneys, using techniques such as CFU counting or more sensitive qPCR methods, could provide a clearer picture of how saprophytic exposure affects the ability of pathogenic Leptospira to establish infection. Additionally, a time-course study showing the kinetics of bacterial colonization and clearance post-infection would be informative.

We are currently validating digital PCR to use in the future and plan to do time course studies.

(3) Characterization of B Cell and T Cell Responses: While the manuscript mentions increased B cell frequencies and effector T helper cell responses, specifics regarding the nature of these responses are lacking. For instance, detailing the isotype and specificity of antibodies produced, the proliferation rates of specific B and T cell subsets, and their functional capabilities (e.g., cytotoxicity, help for B cells) would significantly enrich the understanding of the immune response elicited by pre-exposure to saprophytic Leptospira.

Indeed, additional experiments need to be conducted to flush out the immune responses engaged after pre-exposure to saprophytic Leptospira followed by LIC challenge.

(4) Comparative Analysis with Other Models of Pre-exposure: The study primarily focuses on pre-exposure to a live saprophytic Leptospira. Including a comparison with pre-exposure to killed saprophytic bacteria, or even to other non-pathogenic microbes, could help discern whether the observed protective effect is unique to live saprophytic Leptospira exposure or if it represents a more general phenomenon of trained immunity.

Regarding the use of other non-pathogenic microbes, our lab has shown in the past that oral use of probiotic strain Lactobacillus plantarum (Potula et al 2017) also reduces the severity of Leptospirosis by recruiting myeloid cells. Thus, there may be a general phenomenon of trained immunity involved. We added this to the discussion.

(5) Assessment of Long-term Immunity: The study provides valuable insights into the short-term outcomes following saprophytic Leptospira exposure and subsequent pathogenic challenge. Extending these observations to assess long-term immunity, including memory B and T cell responses several months post-infection, would be crucial for understanding the potential of saprophytic Leptospira exposure in providing lasting protection against leptospirosis.

Long term immunity is a complex and separate question that we plan to address later.

Minor Comments

(1) Technical Specifics of Flow Cytometry Analysis: The manuscript could benefit from including more details on the flow cytometry gating strategy and the specific markers used to identify different immune cell subsets. This addition would aid in the reproducibility of the results and allow for a clearer interpretation of the immune profiling data.

We included the technical specifics of the flow-cytometry analysis in the materials and methods section. The gating strategy (Fig S1) and the specific markers (TableS1) used to identify different immune cell subsets were incorporated in the supplementary datasheet. The cell specific markers were incorporated in the figures (Fig 5 and 6) under each representative cell subset which facilitates clarity and reproducibility of immune profiling.

(2) Statistical Methodology for IgG Subtyping: The analysis of IgG subtypes in response to Leptospira exposure is intriguing but would be strengthened by specifying the statistical tests used to compare IgG1, IgG2a, and IgG3 levels between groups. Additionally, discussing the biological significance of the observed differences in IgG subtype levels would provide a more comprehensive understanding of the immune response.

We applied the ordinary One-way ANOVA test to compare the IgG subtypes between groups followed by a Tukey’s multiple comparison correction analysis (included in the figure legend of Fig 4). We addressed the biological relevance of the observed differences in IgG subtype levels in the discussion section.

(3) Details on Animal Welfare and Ethical Approval: While the manuscript mentions compliance with institutional animal care and use committee protocols, providing the specific ethical guidelines followed, such as the 3Rs (Replacement, Reduction, Refinement), would reinforce the commitment to ethical research practices.

This is addressed in our institutional IACUC which is approved and listed in Methods.

(4) Clarification of Figure Legends: Some figure legends are brief and could be expanded to more thoroughly describe what the figures show, including details on what specific data points, error bars, and statistical symbols represent.

We updated and expanded the figure legends (Fig 1-4).

(5) Revision of Introduction and Background: The introduction provides a good overview of leptospirosis and the rationale behind the study. However, it could be further improved by briefly summarizing current challenges in vaccine development against leptospirosis and how understanding the immune response to saprophytic Leptospira could address these challenges.

We revised the introduction keeping this comment in mind.

Reviewer #2 (Recommendations For The Authors):

- Perform the same challenge experiment with a hamster.

We clarified throughout the manuscript that all the work was done using the C3H-HeJ mouse model which was developed in our lab for the purpose of measuring differences in sublethal and lethal LIC infections. We leave the experiments using hamster to the investigators that have thoroughly validated the hamster model of lethal Leptospira infection.

- Review the written part where it is understood that the challenge with saprophyte strain before virulence prevents the disease.

We reviewed the manuscript to be understood that inoculation of mice with a saprophyte Leptospira before pathogenic challenge prevents severe leptospirosis and promotes kidney homeostasis and increased shedding of Leptospira in urine which is interesting. The last 2 sentences of the abstract read: “Thus, mice exposed to live saprophytic Leptospira before facing a pathogenic serovar may withstand infection with far better outcomes. Furthermore, a status of homeostasis may have been reached after kidney colonization that helps LIC complete its enzootic cycle.”

Reviewer #3 (Recommendations For The Authors):

(1) Line 83: The authors refer to the classification of Leptospira by old nomenclature. The bacteria are now categorized into clades P1, P2, S1 and S2. See Vincent et al. Revisiting the taxonomy and evolution of pathogenicity of the genus Leptospira through the prism of genomics. PLoS Negl Trop Dis. 2019 May 23;13(5):e0007270. doi: 10.1371/journal.pntd.0007270. PMID: 31120895; PMCID: PMC6532842.

We have included the categories (S1 for L. biflexa and P1+ for L. interrogans) in introduction and methods but we did not update the figures because we want to be specific about the species used in these experiments. We also include a few sentences on evolution of Leptospira species in discussion and reference Thibeaux 2018, Vincent 2019 and Giraud-Gatineau, 2024.

(2) Line 133: Please remove the extra line to be consistent with the rest of the method section format.

We addressed all formatting issues.

(3) Line 137: Are these primers specific to pathogenic L. interrogans? Or do they cross react with L. biflexa? If not specific, how long does L. biflexa stick around after infection?

The primers are specific to the genus Leptospira. Surdel et al. in 2022 used 16s rRNA target sequence to amplify L. biflexa Patoc in mice at 6 hours post infection. We did not detect any positive sample for L. biflexa with the 16s rRNA primer set because we do our analysis 30 days and 45 days post inoculation with L. biflexa. We clarified this issue in methods and results.

(4) Statistical analysis:

(a) Some of your graphs have more than 4 points on them (such as Figure 4), while the legend still reads "represents one of two independent experiments". Are these actually combined replicates in the same graph? Combining them would provide strength to your conclusions throughout your manuscript and may provide stronger power for comparisons. If they are not included, why are they not included together? Please clarify what is included in each graph, and why the two experiments were not included together.

We updated the legends with the total number of mice used in the experiment represented in the figure. Figures 1, 2, 4 and S2 contain the combined results from two independent experiments. Figures 3, 5 and 6 represent data from one of two independent experiments. For Fig 3 it would be redundant to show HE images of two experiments. Regarding Figs 5 and 6, the flow-cytometry equipment acquires data at different voltage every single time and biological samples vary between experiments even if all the markers and procedures are the same. So, we reproduce the experiment and show results from one experiment after confirming that the trend between individual experiments are the same.

(b) If ANOVA was used, were all columns compared to each other? Why in some graphs are "ns" labeled only for certain comparisons? I would suggest removing the "ns" comparisons and only highlighting the significant differences.

We have incorporated the comparison analysis between control (PBS) versus the PBS-LIC, LB versus LB-LIC and PBS-LIC versus LB-LIC in both the studies although we have compared significance between all groups.

(5) Line 165: Bacteria were not plated, extract was plated. Perhaps you mean "extract corresponding to 107-108 bacteria"?

We addressed it as follows: “Nunc MaxiSorp flat-bottom 96 well plates (eBioscience, San Diego, CA) were coated with extracts prepared from 107-108 bacteria per well and incubated at 4℃ overnight” …

(6) Line 260: The authors claim that "Exposure to non-pathogenic L. biflexa before pathogenic L. interrogans challenge provided a significant immune cell boost with an increase in overall B and helper T cell frequencies..." However, in Figure 5A, the number of B cells in both the PBS2LIC2 and the LB2LIC2 are not significantly different. Thus, the claim is not supported by the evidence provided. It appears that infection with LIC led to similar increases in B cells regardless of pretreatment.

We rephrased that title to reflect the finding that increased differences were measured in effector Helper T cells between PBS2LIC2 and LB2LIC2 (Figs 5D and 6B, 6C) and we re-wrote this section for clarity.

(7) Lines 314-315: The authors claim that it protected against kidney fibrosis, however, the data only supports that only a single exposure to LB reduced levels of a marker associated with kidney fibrosis. Fibrosis was never directly measured.

Indeed, we didn’t do Mason’s Trichrome stain to get supporting data for kidney fibrosis and only measured a fibrosis marker ColA1. We toned down this section: “ …. it may confer protection against kidney fibrosis.”

(8) Line 317: Authors state that pre-exposure induced higher antibodies in serum, however, this was never shown. Only an increase in IgG2a was shown. Please word this statement to make it clear total antibodies were never measured.

We did measure total anti-Leptospira interrogans IgM and IgG antibodies. We added the following sentence to description of these results: “In both experiments, total IgM and IgG were significantly increased in PBS-LIC and LB-LIC when compared to the respective controls, but not between PBS-LIC and LB-LIC.  Regarding IgG isotypes, IgG1…”

(9) Line 323: The authors state that the exposure "induced antibody responses that provided heterologous protection." There is no evidence that the protection is due to the antibody response in these experiments. In fact, they also showed that it induced increased T cell responses.

We toned down this statement: “In our study, exposure to a saprophytic Leptospira induced antibody responses that may provide heterologous protection against the pathogenic strain of Leptospira.”

(10) Line 328: The authors us the term "stark difference", however, only slight differences are seen.

We toned down that statement as follows:  “Differences in antibody titer among the L. interrogans infected….”

(11) Line 490: reword this sentence to provide clarity and easier to read: "inoculated once with 10^8 L. biflexa at 6 weeks and they were challenged with 10^8 L. interrogans SEROVAR Copenhageni FioCruz (LIC) at 8 weeks."

We revised the sentence.

(12) Figure 1 and 2: Quantifying bacteria in culture after infection is not meaningful, as there are numerous factors that can affect the replication in culture after infection, such as how the organ perhaps was cut before placing it in culture. The comparisons in Figure 2E and F therefore are not interpretable. I would suggest presenting this data as Culture Positive or Culture Negative.

We added these data to the figure under DFM (dark field microscopy).

(13) Figure 3A: H&E staining often leads to different qualities of stains. But is there a better image that can be chosen for the PBS1LIC1 that provides a better comparison with the other images chosen? This is not worth repeating the experiment to get one, just make the figure look better if you have one available.

We screened the images again but the one incorporated in the figure3A for PBS1LIC1 is the best.

(14) Figure 3D: I agree that the PBS-LIC treatment is significant, but please include P value, as it looks very similar to the LB-LIC group. The two LIC groups are not significantly different, so the conclusion would be pre-exposure does not mitigate renal fibrosis marker ColA in the double-exposure study.

We included the p-values in this figure. The two LIC groups are significantly different (ColA1) in the single exposure experiment, and the in double exposure we don’t expect to be able to measure ColA1 differences because the mice are older (10 wk) when we do the LIC challenge.